Comparative Research on Semantic Space Modeling of Global

Comparative Research on Semantic Space Modeling of Global Emergency Medical Systems Based onDIKWPModel

Yucong Duan

International Standardization Committee of Networked DIKWPfor Artificial Intelligence Evaluation(DIKWP-SC)

World Academy for Artificial Consciousness(WAAC)

World Artificial Consciousness CIC(WAC)

World Conference on Artificial Consciousness(WCAC)

(Email: duanyucong@hotmail.com)

1. Introduction: Theoretical Evolution of DIKWP and the Significance of Semantic Space Construction

The development of artificial intelligence and smart cities today requires us to understand complex systems with a brand-new cognitive framework. The "Data-Information-Knowledge-Wisdom-Purpose (DIKWP)" model proposed by Academician Yucong Duan introduces the highest level "Purpose"on the basis of the traditional DIKW (pyramid) model, forming a five-layer cognitive system of Data, Information, Knowledge, Wisdom, and Purpose. This extension makes up for the lack of "purposefulness" in the classic DIKW framework, enabling the behavior of intelligent agents to unfold around clear goals, upgrading from passive response to active planning, and realizing a complete closed loop from data to Purpose. By embedding "Purpose" inside the cognitive model, DIKWP constructs a common cognitive language for humans and machines, ensuring that every decision of AI is traceable and conforms to human value goals. This theoretical evolution is a milestone in academia, providing an innovative path for solving the "black box" problem of large models and improving the interpretability and value alignment of AI systems.

Regarding the relationship between Concept Space and Semantic Space, the DIKWP model has significant constructional meaning. Concept Space refers to the abstract conceptual structure inside the AI or cognitive system, while Semantic Space corresponds to the specific semantics of the external world (users, scenarios, culture, etc.). DIKWP builds a bridge for migration from Concept Space to Semantic Space by clarifying the semantics of Data, Information, Knowledge, Wisdom, and Purpose layers. The model emphasizes that artificial intelligence must be able to convert its own cognitive representation into the semantic space where stakeholders are located, achieving semantic consistency and docking. This means that an intelligent system based on DIKWP not only processes internal concepts but also understands and satisfies the semantic needs of the external environment and users, thereby ensuring that intelligent decisions are effective in real situations. For the smart emergency medical system, the construction of this semantic space is particularly critical: only by mapping abstract emergency concepts (such as distress information, rescue knowledge, decision strategies, value goals) into semantic elements in the specific emergency operations of various countries can intercommunication and interoperability between different systems be achieved, improving the efficiency and consistency of global collaborative rescue.

In summary, the introduction clarifies the theoretical background of the evolution of the DIKWP model from DIKW, and the significance of guiding the design of complex systems through the migration of "Concept Space  →  Semantic Space". In the following text, based on the DIKWP model, we will conduct a deep comparative study and semantic modeling analysis of the emergency medical systems of representative countries/regions globally. This is not only a practical expansion of Yucong Duan's theoretical system but also provides new ideas and policy and technical references for the field of smart emergency medicine.

2. DIKWP  ×  DIKWP Model Explanation: System Structure and 25-Path Dual Network Logic

DIKWP Model Overview: DIKWP includes five levels: Data (D), Information (I), Knowledge (K), Wisdom (W), and Purpose (P), fully depicting the closed loop of the cognitive process from primitive perception to goal-driven decision-making. The meanings of each level are as follows:

Data Layer: Responsible for the acquisition and preliminary processing of raw data, equivalent to sensor input and signal coding.

Information Layer: Endows preliminary semantics on the basis of data, transforming perceptual materials into meaningful representations.

Knowledge Layer: Constructs a broader semantic network, forming a generalizable knowledge base (such as human long-term memory or machine knowledge graphs).

Wisdom Layer: Uses knowledge for advanced reasoning and decision-making combining context and Purpose, involving high-order cognition such as strategic planning and value trade-offs.

Purpose Layer: Represents the system's motivation and goals, is the highest driving force, and dictates the direction of the entire cognitive process. By adding the "Purpose" layer, the DIKWP model expands the traditional DIKW pyramid into an active networked cognitive architecture, enabling intelligent agents to have internal goal awareness and to assess and correct all links of cognition.

Network Structure and 25 Paths: Unlike linear hierarchical models, DIKWP is designed as a highly networked interactive structure. Professor Yucong Duan emphasizes that bidirectional conversion can occur directly between any two layers in the model, forming a total of  5 × 5 = 25  potential functional modules (paths), supporting the free flow and feedback of information across layers. In other words, the five nodes within DIKWP are fully connected: not only can lower-level Data and Information flow to upper-level Knowledge, Wisdom, and Purpose, but upper-level Wisdom decisions and Purpose can also feedback to modulate lower-level information processing. For example, the system's Purpose (P) affects which Data (D) it pays attention to (similar to human attention mechanisms), while the Data flow from the environment will gradually rise to become Knowledge and Wisdom and ultimately serve the Purpose. At the same time, if the upper level finds that lower-level information is missing or distorted, it can also provide immediate feedback for correction. This fully connected network architecture is called the 25-Path Dual Network Logic: 25 paths refer to the possible bidirectional information conversion pathways between D, I, K, W, and P; "Dual Network" means the interaction between the basic cognitive network and the meta-cognitive network in the cognitive process. On one hand, the basic network contains the Cognitive Flow processed level by level from Data to Purpose; on the other hand, the meta-network acts as "cognition of cognition", monitoring and regulating the basic flow, equivalent to introducing a second loop. Yucong Duan's team proposed in their patent that through this "Dual Loop" architecture, a meta-cognitive loop is added outside the basic cognitive flow to achieve self-monitoring, self-reflection, and self-regulation, thereby endowing AI with preliminary self-consciousness. The two networks are interwoven layer by layer, enabling the system to flow cognitive decisions along  D → I → K → W → P  on one hand, and conduct Purpose-driven reverse guidance along  P → W → K → I → D  on the other, ultimately constituting a multi-level closed loop.

Figure 1: Schematic diagram of the DIKWP model and its 25 semantic conversion paths (drawn based on the theoretical review by Yucong Duan et al.). The pentagon on the left represents the five layers of Data, Information, Knowledge, Wisdom, and Purpose, and the arrows represent the conversion relationship in any direction, with a total of  5 × 5 = 25  paths. This fully connected structure supports multi-directional feedback in the cognitive process, giving the intelligent system high adaptability and self-correction capabilities. The dashed ring on the right represents the meta-cognitive loop introduced outside the basic cognitive loop, used to monitor and adjust the former. The dual-loop architecture is considered an important way towards artificial consciousness, which can enhance the self-reflection ability of AI decision-making.

Through the DIKWP  ×  DIKWP model, we obtain a dual-network system description that contains both a longitudinal cognitive chain and a lateral feedback web. On one hand, the basic DIKWP network ensures the step-by-step semantic elevation from raw data to Purpose intent; on the other hand, the superimposed DIKWP meta-network guarantees the alignment and synergy between semantics at all levels, realizing the free flow of information within the Concept Space and enabling mapping to the external Semantic Space. For complex emergency rescue systems, this model provides an analytical framework: we can map various elements of the emergency system to the D, I, K, W, and P layers respectively, and analyze the 25 key interaction paths between them, thereby identifying strong coupling links and potential broken chain risks in system operation. This lays a unified semantic foundation for the comparison, modeling, and simulation design of emergency systems in different countries.

3. Current Status Comparison of Global Emergency System Construction: Overview of Representative Countries/Regions

In this section, we select five to seven representative countries/regions (China, USA, Japan, Germany, Israel, Kenya/South Africa) to compare the construction status and characteristics of their emergency medical service systems. There are significant differences in historical development, organizational models, technology applications, and cultural concepts among these regions, forming different emergency models.

China (Developing Major Power): China's pre-hospital medical emergency system started late but developed rapidly. Currently, most cities have "120" emergency centers uniformly dispatching ambulances from hospitals or emergency stations, but multiple models coexist in organizational forms. Some city emergency centers operate independently (government-led), while others rely on hospitals or link with fire and police departments (e.g., Beijing integrated the "120" and "999" systems). Overall, economically developed regions have formed a complete emergency network with high transportation communication guarantees and response speeds, while underdeveloped regions, due to insufficient investment and scarce emergency resources, often can only meet basic transport needs. For example, in large cities, emergency stations are dense, equipment and training are better, and the average response time is relatively short; but in remote rural areas with "vast land and sparse population," it may take a long time for ambulances to arrive, and the technical strength of pre-hospital treatment is also limited. The government has recognized the issue of regional imbalance and has strengthened the standardization construction, informatization level, and personnel training of the emergency system through policies such as the "Guiding Opinions on Further Improving Pre-hospital Medical Emergency Services" (2020) in recent years. However, in terms of top-level design and legal guarantees, China's emergency system still has room for improvement—the long-term lack of a unified national development plan leads to unbalanced regional development and resource waste, and the emergency system in some grassroots areas is marginalized and undervalued. Overall, China is in the climbing stage of comprehensive construction of the emergency system, constantly improving in hardware investment, talent teams, and institutional construction, but the gap between urban/rural areas and regions remains obvious.

USA (Typical Anglo-American Model Country): The pre-hospital emergency service in the United States is represented by the "Anglo-American" model, characterized by pre-hospital care led by trained Emergency Medical Technicians (EMTs) or paramedics, with the goal of "Scoop and Run". The unified national emergency number is 911, which has been widely rolled out since the 1970s. US EMS is operated by state and local governments or private contractors, often co-located with fire departments (firefighters often serve as EMTs) or provided by hospitals/private ambulance agencies, making the system relatively diverse. The US is known for high technology and high training: ambulances are generally equipped with advanced equipment (such as ECGs, ventilators, etc.), and many regions have implemented wireless ECG transmission at the rescue scene, enabling hospitals to obtain important vital sign data before the patient arrives. At the same time, US emergency personnel training is clearly graded, with a rigorous qualification certification system (EMT-Basic, Paramedic, etc.), and constantly introduces telemedicine support, allowing ambulance personnel to connect with hospital doctors via video/phone for guidance at the scene. However, US EMS also faces challenges: Lack of unified national management leads to varying service levels across regions, especially in remote rural areas that may rely on volunteer emergency squads with longer response times; another problem is High costs and health insurance limitations. Calling an ambulance and pre-hospital care in the US often comes with high bills, causing some people to hesitate to dial 911 even in emergencies. Research indicates that in countries where medical expenses require out-of-pocket payment, people sometimes delay seeking help due to fear of bills, thereby exacerbating the crisis. Overall, the US emergency system has advanced technology and high personnel quality, but lacks in universal coverage and fair accessibility. Its highly market-oriented medical system presents an unbalanced state in resource allocation for emergency services.

Japan (Highly Developed with Unique Model): Japan's emergency system is responsible by the Fire and Disaster Management Agency of each prefecture, with a unified emergency number of 119. Japan adopts the "Fire Defense Ambulance" model, where ambulances belong to the fire department, emphasizing the connection between on-site and in-hospital treatment. Japan is very advanced in equipment and technology—ambulances are equipped with ECG machines, etc., capable of real-time data transmission to hospitals to win time for rescue. Services are provided entirely by the government, and patients do not need to pay for ambulance fees (this also causes the problem of ambulance abuse; the Japanese government is currently discussing charging for obviously non-emergency trips). The uniqueness of Japan's EMS lies in its personnel qualifications and legal restrictions: Emergency Life Saving Technicians (ELST) are Japan's advanced ambulance personnel, but the medical measures they can perform are limited. It was not until the early 21st century that Japan gradually relaxed the authority of emergency personnel (e.g., allowing tracheal intubation under remote doctor guidance in 2003, adrenaline injection in 2006, etc.). Overall, Japan's pre-hospital emergency aid tends more towards "Transport first, treat later", and the use of drugs by emergency personnel is strictly restricted, which affects the timeliness of pre-hospital treatment in some cases. To make up for this deficiency, Japan developed the "Doctor Car" system, dispatching special vehicles carrying emergency doctors from emergency centers. Doctors arrive at the scene first to provide advanced life support before the ambulance arrives. This model is similar to the Franco-German "physician-led" model, ensuring critically ill patients receive doctor treatment at the scene. Another challenge for Japan's EMS is Emergency patient triage and admission: Due to the fine specialization of Japanese hospitals and the weak independent status of emergency departments, the situation of "Ambulances unable to find hospitals to accept" often occurred in the past. In 2006, the incident of a pregnant woman who unfortunately died after being rejected by 19 hospitals and delayed for 3 hours sparked national concern. This issue prompted Japan to strengthen the professional construction of emergency departments, introducing the "General Emergency Physician" system like in Europe and America. In some cities, emergency specialists uniformly accept various emergency patients to reduce transfer delays caused by mutual shirking among departments. As Japan enters a super-aging society, EMS is also adjusting strategies, focusing more on pre-hospital handling of elderly chronic emergencies and opening non-emergency consultation hotlines to divert mild cases. Overall, Japan's emergency system has the advantages of government leadership, free service, and high-tech equipment, but is also constrained by legal and structural factors, and is gradually improving pre-hospital care flexibility and efficiency through reforms.

Germany (Typical Franco-German Model Country): Germany's emergency system is known for the "Franco-German" model, with the philosophy of "Bring the doctor to the patient" rather than just transporting the patient to the hospital as quickly as possible. The unified national emergency number is 112 (also the universal EU 112), free to call, and the alarm center can transfer to police (110) and other services as needed, providing multilingual support in border areas. German law mandates that states must provide emergency services, usually operated by local fire departments or organizations like the Red Cross. German ambulances are graded by equipment and personnel qualifications, including Rescue Ambulances (RTW), Medical Transport Vehicles (KTW), etc., staffed by personnel of different levels. Its biggest feature is dispatching Emergency Physician (Notarzt) vehicles to meet with ambulances in critical situations. Since the 1960s, Germany has established a complete Pre-hospital Physician System: specially trained doctors serve as Notarzt to guide and implement advanced rescue measures on-site, such as tracheal intubation, intravenous drug administration, and even on-site surgery. This model ensures patients receive high-level treatment while being transported to the hospital, improving rescue success rates, especially suitable for cases requiring on-site decision-making like severe polytrauma and myocardial infarction. At the same time, Germany has also established a professional training system for Emergency Paramedical Personnel: the "Rescue Sanitarian (Rettungssanitäter)" qualification was launched in 1977, the higher-level "Rescue Assistant (Rettungsassistent)" was added in 1989, and the "Emergency Paramedic (Notfallsanitäter)" profession with three-year training was created in 2014, continuously improving the professional level of non-physician emergency personnel. Currently, Germany has formed a Dense network of emergency stations, combining ground ambulances and air helicopters (air rescue is also very developed), enabling emergency personnel to arrive at the scene within 10-15 minutes in most areas. German EMS costs are borne by the universal health insurance system, and patients do not need to worry about payment. In terms of quality supervision, German states have legal norms for EMS, and medical associations formulate emergency guidelines (such as the German Medical Association publishing the Notarzt dispatch indication catalog, etc.) to ensure relatively unified service standards across regions. In summary, the German emergency system is characterized by sound laws, universal coverage, sufficient resources, and a high degree of specialization, reflecting the social welfare state's emphasis on emergency public services. Its "Doctor + Paramedic" collaboration model performs excellently in major critical events, but the cost is higher and resource investment is large, which is the trade-off of this model relative to the Anglo-American model.

Israel (Volunteer Network and Wartime Experience): Israel's emergency system is unique in the world. On one hand, it relies heavily on volunteers and draws on the strengths of both Franco-German and American models; on the other hand, influenced by the geopolitical security environment, Israel has accumulated rich experience in responding to mass casualty incidents. The unified national emergency number is 101, dispatched by Magen David Adom (Red Star of David, abbreviated MDA). MDA is Israel's statutory national EMS agency and a member of the International Red Cross system under the Geneva Conventions, capable of serving as an auxiliary medical force for the Israel Defense Forces in wartime. MDA has about 166 stations and over 1,000 ambulances of various types, including Basic Life Support (BLS), Advanced Life Support (ALS), and Mobile Intensive Care Units (MICU), and even armored ambulances to deal with battlefield environments. Notably, MDA has About 22,000 volunteers and only 2,500 employees—volunteers become an indispensable part of the system. Many volunteers come from all walks of life and even overseas, participating in pre-hospital emergency aid after training. Until 2015, MDA's ALS ambulances were often staffed with doctors (consistent with its earlier tradition leaning towards the Franco-German model), but now they no longer formally employ ride-along doctors, mainly providing medical guidance through Online Physician Support Hotlines, with only volunteer doctors occasionally riding along. At the same time, an independent civilian emergency volunteer organization, United Hatzalah, has emerged in Israel, with about 6,000 certified volunteer emergency responders committed to providing Free First Response Services nationwide. Hatzalah volunteers are distributed in communities and rush to the scene using diverse transportation tools such as ambucycles (motorcycle ambulances), electric bicycles, and all-terrain vehicles to provide preliminary care like CPR, filling the gap before professional ambulances arrive. The combination of this dual volunteer network has significantly shortened Israel's average emergency response time—averaging less than 3 minutes nationwide, and even below 90 seconds in urban areas. Such astonishing speed is largely due to the density of the volunteer network and the efficiency of the dispatch system. In addition, Israel EMS has extensive experience in dealing with terrorist attacks and wartime Mass Casualty Incidents (MCI): MDA has special operations teams at major stations specifically responsible for triage and treatment of mass casualties at natural disaster or terror attack sites. In these situations, usually well-trained volunteers and professionals collaborate to carry out triage, hemostasis, airway management, and diversion to hospitals at extremely fast speeds. Israel's emergency model can be described as a fusion of "High-Tech + Volunteers": on one hand utilizing cutting-edge communication technology (e.g., mobile apps locating nearest volunteers, GPS dispatching ambulances), and on the other hand leveraging community power, achieving high-efficiency, low-cost pre-hospital emergency coverage. The success of this model has also garnered international attention. Of course, the Israeli system also has its limitations, such as the high dependence on volunteers requiring continuous training and organization, but overall, it provides a model for improving emergency response under limited resources.

Kenya/South Africa (Emerging Development of African Emergency Systems): Emergency medical services in African countries are mostly in the start-up and development stages. Here, Kenya and South Africa are used as examples to illustrate different development levels. Kenya had almost no formed national emergency system around 2010, and critically ill patients often could not receive timely treatment due to lack of coordination. In 2015, Kenya's first Doctor of Emergency Medicine, Ben Wachira, founded the Emergency Medicine Kenya Foundation (EMKF) to assist the government in building a national emergency medical system from scratch. By 2025, Kenya has formulated a national emergency policy, included emergency services in legal guarantees, and gradually built emergency networks in 47 counties nationwide. Currently, 140 public hospital emergency departments have been newly built or upgraded, about 5,000 medical staff have been trained in emergency skills, and counties have begun to establish GPS-located ambulance dispatch centers. Kenya's reform proves that progress can still be made through policy promotion and systematic planning in resource-limited environments. However, challenges remain: due to the decentralization of the medical system, county governments vary in their investment and execution of emergency aid, with some areas progressing rapidly while others lag behind. In terms of technology application, although Kenya is an African leader in fields like mobile finance, adoption in the medical field is still slow. EMKF is trying to change this, for example, by developing the "Casualty" mobile app to facilitate public one-click ambulance calls and access to emergency guidance, and deploying digital ambulance dispatch platforms for counties to improve coordination and shorten response times. It can be said that Kenya is undergoing a quiet emergency revolution: building a system from nothing, through policies, training, and infrastructure construction, enabling more and more people to get help when needed. In contrast, South Africa's pre-hospital emergency system started earlier and is relatively leading in Africa. South African EMS has developed rapidly over the past 20 years, but service distribution remains extremely uneven. Urban areas have developed ambulance networks with average response times of up to 15 minutes, while vast rural areas have insufficient resources, and waiting 40 minutes or longer is not uncommon. Social inequality left over from the apartheid era is also reflected in EMS, with emergency accessibility in rural and poor communities lagging significantly behind wealthy areas. South Africa's emergency personnel training system is relatively complete, ranging from basic emergency technicians to advanced paramedics, and established emergency medicine as a formal specialty in the mid-2000s (a medical profession since 2004) to improve the shortage of emergency doctors. South Africa's EMS is provided jointly by the public sector and private emergency companies, and air medical rescue is also carried out. However, due to economic constraints, free public services often face resource shortages, while private ambulances are unaffordable for many poor people. In summary, the emergency systems of African countries are in the process of construction From scratch to something, from few to many. On one hand, more developed regions like South Africa have established dual-layer systems (government basic services + private supplements) and strive to improve professionalism; on the other hand, countries like Kenya are trying to achieve "Digital Leap" through innovation and policy promotion, i.e., skipping some long stages traversed by developed countries and directly using new technologies and models to build emergency networks. This offers valuable experience and hope for resource-constrained regions.

The countries/regions selected above cover different development levels and models: representatives of developed countries like the US, Germany, and Japan; emerging powers like China; innovative models in special environments like Israel; and developing explorations in African countries. Below, under the DIKWP framework, we will conduct deep module mapping and path analysis of the emergency systems of various countries.

4. DIKWP  ×  DIKWP Module Mapping Analysis of National Emergency Systems

Using the DIKWP  ×  DIKWP model, we can map the key elements of various national emergency systems to the five levels of Data, Information, Knowledge, Wisdom, and Purpose, and analyze the embodiment, coupling strength, and broken chain risks of the 25 paths in each country's system. Due to differences in structure, technological level, and cultural concepts, the sub-module distribution and connectivity characteristics of the DIKWP network vary across countries. The following analysis is conducted country by country, accompanied by schematic diagrams of the 25 sub-module structures (see Appendix Charts).

China's DIKWP Mapping:

In China's emergency system:

Data Layer (D): Corresponds to public calls for help and on-site raw information collection. Main sources include distress call data received by 120 call centers, caller descriptions, location information, and initial observations and vital sign measurements by rescue personnel upon arrival. In densely populated areas of China, mobile phone penetration is high, and Data collection coverage is relatively wide; but in remote rural areas, poor distress call channels and difficult address descriptions cause lags or gaps in data acquisition.

Information Layer (I): Includes the processing and preliminary judgment of data by the dispatch command center, organizing chaotic information into "meaningful" rescue tasks. Manifested as operators extracting elements like Event type, location, injury severity based on incoming calls, and issuing orders to nearby emergency stations or hospital emergency departments via electronic dispatch systems. Development at this layer is uneven across China: large cities have relatively perfect information systems, establishing Computer Aided Dispatch (CAD) to realize ambulance vehicle positioning and digital dispatch; but some small and medium-sized cities have low informatization levels, dispatch relying mainly on manual judgment, easily limited by human experience. Overall, China's I-layer and D-layer coupling is fair, with the vast majority of distress data convertible into dispatch instructions, but there is room for improvement in response speed and accurate classification.

Knowledge Layer (K): Corresponds to the professional knowledge and rule system supporting emergency aid. In China, this includes Technical specifications, clinical guidelines, training materials for pre-hospital emergency aid, and classification standards for dispatch centers. For example, CPR guidelines mastered by emergency personnel, trauma treatment Protocols, TCM emergency experience, and even telephone medical guidance points for dispatchers belong to the Knowledge layer. China is gradually aligning with international standards at the emergency knowledge level, adopting many international emergency guidelines (such as ITLS trauma life support courses); emergency centers in major cities also improve frontline personnel knowledge through continuous training. However, nationwide, the Knowledge layer still has the problem of Ununified standards: emergency stations belong to different hospitals, execution norms may vary slightly, especially in grassroots areas, where uneven personnel qualifications lead to insufficient application of knowledge. In addition, due to the long-term lack of specialized emergency training and independent professional title promotion channels, pre-hospital emergency talent retention is difficult, affecting knowledge inheritance and accumulation.

Wisdom Layer (W): Embodies the system's ability for comprehensive judgment and decision-making in complex situations. For Chinese emergency aid, this includes Optimization decisions for command dispatch (e.g., how to allocate resources when multiple accidents occur simultaneously), On-site rescue strategies (e.g., rapid triage and transfer priority decisions at disaster sites), and Cross-departmental collaboration (e.g., linkage with fire, traffic police, air medical). The Wisdom layer requires flexible application of knowledge and adaptability to on-site changes. In China, emergency command centers in large cities have begun to introduce some intelligent decision support, such as optimizing vehicle deployment based on historical data and traffic conditions, and utilizing GIS analysis to provide Smart Dispatch. But overall, due to institutional and experiential reasons, the Wisdom layer of China's emergency system still plays a relatively limited role—Poor coordination or chaotic on-site command easily occurs during major emergencies, indicating insufficient cross-agency and cross-layer feedback capabilities at the Wisdom layer. Insufficient plan preparation and drills in some emergencies prevent theoretical knowledge from transforming into practical wisdom in time.

Purpose Layer (P): Corresponds to the Strategic goals, policy Purposes, and social values of China's emergency system construction. For example, "people-oriented, rapid response, urban-rural coverage, improving survival rate" are top-level Purposes. In practice, the P layer is reflected in the government's emphasis on emergency aid, legal and policy support (such as including emergency aid in basic public services), and societal expectations for emergency aid. China's strategic positioning of pre-hospital emergency aid was not clear enough in the past and was marginalized in medical reform. However, with the proposal of the "Healthy China 2030" strategy in recent years, improving the emergency system has become an important part of public safety and health systems, and Top-level Purposes are gradually becoming clear. For example, governments at all levels have begun to use Emergency response time and Pre-hospital survival rate as measurement indicators, advocating universal emergency knowledge popularization, etc., which are manifestations of concretizing P-layer Purposes. The current challenge lies in how to truly implement these good Purposes into specific links to achieve a closed loop from Purpose to practice.

China DIKWP Network Coupling Characteristics: Overall, the basic  D → I → K  link of China's emergency system has been established, but the Coupling between  I ↔ K ↔ W  needs strengthening. Dispatch acquires information and generates instructions, but due to talent and normative reasons, information may not fully stimulate the potential of the Knowledge layer; knowledge reserves also fail to fully translate into wise decisions for every dispatch. Especially in remote/underdeveloped areas, there is a risk of "broken chain" from Data to Wisdom: ambulance personnel may only act as "transporters" and cannot provide high-level treatment at the scene. This indicates that the Purpose of pursuing high-quality treatment at the D/I layers and P layer has not been effectively connected. In terms of structural constraints, China's vast territory and large regional differences, as well as economic and management imbalances, restrict the construction of a unified national emergency network. Fortunately, with policy inclination and technological progress, these couplings will gradually strengthen. For example, the state promotes the establishment of a unified pre-hospital emergency information platform and formulates national emergency service standards, which will feed back to lower layers at the Knowledge and Wisdom levels, reducing hidden dangers of broken chains.

USA's DIKWP Mapping:

The elements of the US emergency system under the DIKWP framework are as follows:

Data Layer (D): US D-layer information sources are diverse and timely, mainly including nationwide 911 calls (phone help), incoming call location data provided by E911 systems in some areas, and increasing IoT data (such as automatic car crash sensor alarms, wearable device alarms). Since 911 is deeply rooted in people's minds, the public usually dials immediately when in danger, with very high coverage. However, Limitations of the Data Layer lie in a considerable proportion of false alarms, harassment calls, and difficulties in acquiring non-English help requests. In addition, 911 centers in different regions operate independently, with differences in data formats and acquisition depth.

Information Layer (I): Refers to dispatch centers processing received distress call data into Dispatch decision information. EMS dispatch in most US cities is highly professional, using Computer Aided Dispatch systems to automatically classify priorities based on call content and recommend the nearest available rescue units. Dispatchers are rigorously trained, following standardized questioning processes and tiered dispatch cards (such as Medical Priority Dispatch System MPDS). Coupling between I-layer and D-layer is tight in the US, because perfect training and systems allow dispatchers to quickly extract key elements from noisy distress information. In some large centers, Professional doctors serve as dispatch consultants to provide guidance in difficult cases, making Information layer decisions more precise. The strength of the US I-layer is also shown in Integrating multi-departmental information: dispatch centers usually share or collaborate closely with fire and police, so fire and security information can be obtained synchronously in major events.

Knowledge Layer (K): The US K-layer covers rich emergency medical knowledge and norms, such as Pre-hospital Life Support Guidelines like ATLS, ACLS, state EMS medical protocols, regional pre-hospital care agreements, and a database of Best Practices accumulated over the years. US EMS personnel have clear skill scopes according to qualification levels, with detailed standards for knowledge at each level. For example, Paramedics can perform intravenous infusion, tracheal intubation, drug administration, etc., mastering knowledge equivalent to partial advanced life support technology, all written into state protocols and training courses. US EMS also emphasizes Continuing Medical Education, constantly incorporating the latest clinical research (such as new methods of CPR) into the knowledge base. In addition, the US invests heavily in pre-hospital scientific research, with a huge emergency research network (such as the Resuscitation Outcomes Consortium), promoting knowledge updates and evidence accumulation. Overall, the US K-layer is very sound and aligned with international standards, becoming the cornerstone of its high-level emergency aid.

Wisdom Layer (W): The US Wisdom layer is reflected in its EMS being able to Dynamically apply knowledge to make complex decisions. For example, on-site emergency personnel Autonomously decide optimal treatment and which hospital to go to (trauma center, stroke center, etc.) based on patient conditions; these are judgments of the Wisdom layer. Also, prioritizing which type of patients to treat when resources are limited, whether to launch air rescue, etc., are macro decisions of the Wisdom layer. Some US regions have established Decision Support Systems linking Dispatch and Clinical, using AI to predict peak demand, pre-deploy ambulances, and guide the frontline in real-time through medical command doctors. Another manifestation of the Wisdom layer is Ethical and Value Considerations: such as applying special allocation principles at disaster sites, or whether to follow advanced directives (DNR) not to perform resuscitation for elderly terminally ill patients, all require experience and value judgment. US EMS personnel at all levels have strong capabilities in wise decision-making due to long-term practice. But it should be noted that the standard of the Wisdom layer varies greatly across different US regions—busy metropolitan areas have cultivated rich wisdom coping capabilities due to wide exposure; while volunteer teams in remote areas have limited experience, and the Wisdom layer may be underplayed. This creates unevenness overall.

Purpose Layer (P): The US P-layer involves the concepts and goals behind its emergency system. Such as "maximizing survival rates", "timely treatment regardless of wealth", "maintaining public safety", etc. This layer is greatly influenced by the overall US medical system. Ideally, the Purpose is to provide timely help for every life, but in reality, the supply and access to EMS services in the US are Local responsibilities and linked to economic status, with a commercial operation nature. Therefore, to some extent, the P-layer has diverse and potentially conflicting sub-Purposes: the public sector hopes to popularize services, but private companies pursue profit, and insurance companies focus on cost control. For example, in some areas, ambulance service providers may emphasize transport volume revenue, leading to the P-layer "saving people" purpose giving way to commercial drives. Another example involves the contradiction between social ethical Purposes and realistic commercial Purposes regarding whether to provide equal response to patients unable to pay. Overall, the top-level Purpose of US emergency aid is clearly noble in policy documents (such as the vision of establishing a "people-centered, coordinated, safe and reliable EMS" in "EMS Agenda 2050"), but constrained by the system and funding in execution, it has not achieved full inclusivity. This is also a much-discussed aspect of the US emergency system.

US DIKWP Network Coupling Characteristics: US EMS is very tightly coupled on the  D-I-K  path: from distress call to information processing to taking action based on knowledge, it is done in one go with high efficiency, benefiting from high-tech and high-quality personnel. Especially the  I → K → W  path shows that on-site personnel can flexibly use normative knowledge for different patients, achieving personalized treatment (Wisdom). But there is a Breakpoint risk in US P-layer coupling: that is, the top-level "Purpose of saving lives" has not fully penetrated into all data/information processes. A typical example is that under the background of self-paid medical expenses, some critical patients Dare not call ambulances due to economic concerns—here the  P → D  path disconnects, and rescue Purposes cannot be transformed into data acquisition. This is particularly prominent among low-income populations and the uninsured, who may delay calling for help or even give up, causing EMS to be unable to cover this part of "invisible data" no matter how excellent it is. Structurally, the US federal system leads to significant differences in EMS resource investment and organizational forms across regions, equivalent to Lack of unified national control at the P-layer. Therefore, the US DIKWP network has the characteristics of "highly coupled locally, unbalanced globally": locally, it is strong from D to W layers, with mature mechanisms at every step; but overall, the P layer (national public health goals) is not tight enough with grassroots EMS systems, and the system lacks national collaborative planning. In addition, due to departmental separation, data sharing between EMS and hospitals (in-hospital wisdom) is also insufficient, forming a resistance for W layer feedback to K layer—although this situation is improving with technologies like electronic medical record interoperability.

Japan's DIKWP Mapping:

Japan's emergency system presents configurations different from Europe and America at each layer:

Data Layer (D): The 119 alarm phone is the main data source, with extremely high public awareness nationwide. Japan also has a perfect Fire and Disaster Monitoring system; once a major accident occurs, alarms are automatically generated. In addition, Japan's per capita mobile internet usage rate is high, and social media sometimes provides auxiliary clues. However, unlike other developed countries, the Japanese public tends to Call an ambulance whenever needed, even for non-emergencies, leading to huge 119 volume and a considerable part not being truly critical data. So at the D layer, Japan faces "signal overload", and identifying truly high-priority data is a challenge. In recent years, some Japanese regions have begun to introduce Telephone Emergency Consultation (#7119) to divert mild calls and reduce meaningless occupation of the D layer.

Information Layer (I): The 119 command centers of Japanese fire departments are responsible for information processing. Usually, operators obtain patient conditions according to a set of "Standard Inquiry Processes" and then give dispatch levels based on "Urgency Judgment Standards". In terms of staffing, Japanese 119 operators are mostly senior firefighters familiar with local conditions. They transcribe addresses, symptoms, etc., into the system, which suggests dispatching the nearest ambulance team. Since Japanese ambulance stations are basically located in fire stations, the number and distribution of ambulances everywhere are also standardized (e.g., >150,000 population areas add one vehicle per 70,000), so dispatch has a clear grasp of resources. The strength of Japan's I layer lies in High concentration and unification—dispatch is controlled by one fire command center within the entire prefecture, avoiding multiple command heads. But the I layer also has weaknesses, namely Lack of flexible adjustment in dispatch: regulations require dispatching a vehicle as long as someone calls an ambulance, regardless of severity, and dispatchers Have no right to refuse or suggest self-care (unless the call is transferred to #7119 consultation). This causes the Information layer to be unable to effectively filter non-emergency tasks, increasing system load. In addition, when multiple events concur, Japanese dispatch does not upgrade responses according to the situation like US EMS (such as helicopters, etc.), but strictly follows plan processes, lacking authorization for ad-hoc changes.

Knowledge Layer (K): The knowledge system of Japanese EMS combines tradition and modernity. Traditionally, due to restricted pre-hospital actions, emergency knowledge concentrated more on Safe transport, basic life support, with limited depth. But later with the establishment of the Emergency Life Saving Technician system, Japan introduced many international advanced knowledge: such as Advanced Airway Management, Trauma Life Support courses, and requires continuous training for technicians. Japan also has characteristic knowledge, such as extensive norms for disaster accident (earthquake, tsunami) response, and medical support experience for large events like the Olympics. Notably, Japan's Knowledge layer is closely linked to laws regulations—implementation of every pre-hospital medical technology requires legal authorization, so part of the knowledge base, although international routine, may not be approved in Japan (such as use of certain drugs, delivery was not even in emergency personnel skills before 1990). This creates a Breakpoint in the Knowledge layer: emergency personnel may know a measure is effective but cannot perform it due to lack of practice authorization. Overall, Japan's Knowledge layer has grown significantly in recent years, accumulating rich content through study abroad and domestic research (e.g., Society for Emergency Medicine, Association for Disaster Medicine), but full application is still constrained by the system.

Wisdom Layer (W): The Japanese Wisdom layer can be viewed from two aspects: Micro on-site wisdom and Macro system wisdom. At the micro level, Japanese ambulance personnel do relatively well in treatment decisions for single patients due to complete equipment and doctor phone support. For example, judging whether to call a Doctor Car, or whether to send elderly patients to hospitals with geriatrics departments based on age. These decisions are based on experience and knowledge, embodying wisdom. At the macro level, problems in the Japanese Wisdom layer are more prominent. The aforementioned Ambulance Diversion phenomenon illustrates insufficient wisdom in resource allocation: when hospitals refuse patients due to specialist reasons, there is no superior Wisdom layer to coordinate and solve, leaving ambulances to contact one by one, wasting precious time. An ideal Wisdom layer should quickly determine the cause when multiple hospitals refuse and activate emergency plans (such as ordering a public hospital to accept unconditionally). Japan is gradually improving this, introducing the Emergency Physician-led model, allowing emergency departments to comprehensively handle different specialist issues to reduce shirking. Another Wisdom layer indicator is adaptation to demographic changes: ambulance demand surges in Japan's super-aging society, and the Wisdom layer needs to think about how to alleviate it, such as promoting community nursing to reduce unnecessary ambulance calls. In fact, the Japanese government has begun Smart response to abuse, such as using AI to screen frequent callers for targeted intervention, or dispatching instructors to ride along and persuade against non-essential hospital transport, all belonging to the role of the Wisdom layer as a system improver. Overall, the Japanese Wisdom layer is shifting from passive execution to active adjustment, but is not yet mature.

Purpose Layer (P): Japanese society's Purpose for emergency aid can be summarized as "Rapid ambulance access for everyone" and service philosophy centered on "Life Supreme". Japanese culture has a very high respect for safety and life. The government has long included emergency aid in the fire and disaster relief system, providing it for free, which in itself reflects a strong public Purpose—not letting anyone in need go without help. However, this good Purpose also produces some counter-effects in reality: because it is completely free, "calling an ambulance like a taxi" has become common, and many non-emergency requests also occupy resources. This can be said to be the tension between P-layer social goodwill and D/I layer execution. In recent years, the Fire and Disaster Management Agency and the Ministry of Health, Labour and Welfare have begun to re-examine the free ambulance policy, hoping to find a balance between maintaining humanitarian aims and preventing abuse. In addition, Japan's top-level emergency goals also include improving resilience to disasters. This stems from Japan's national policy as a disaster-prone country. Driven by the P layer, Japan has established a world-leading disaster medicine system, regularly holding national comprehensive disaster prevention drills, integrating peacetime fire ambulances with Self-Defense Forces, DMAT (Disaster Medical Assistance Teams), etc., laying the Purpose foundation for multi-layer collaboration in major disaster scenarios. It can be said that Japan's P-layer Purpose is very clear and noble, but how to implement it in detail requires strategic adjustments to ensure Purpose translates into efficiency rather than resource waste or system overload.

Japan DIKWP Network Coupling Characteristics: Japan EMS has high efficiency in  D-I  coupling, with the mature 119 system and unified dispatch ensuring every distress message transforms into action. However, the  I → W  path has bottlenecks: how to optimally utilize resources after dispatch, and insufficient multi-departmental collaborative decision-making. Especially W layer downward feedback is not smooth: when grassroots execution encounters difficulties (hospital refusal), there is a lack of high-level intervention, manifesting as P-layer will to "guarantee treatment" but failure to intervene in I/W layers in real-time to clear obstacles. This leads to interruption of the wisdom chain. Fortunately, through institutional reforms (introducing emergency specialties, etc.) and technical means (strengthening dispatch center functions), this broken chain risk is decreasing. In terms of  K ↔ W  coupling, Japan has a lot of experience and lessons driving knowledge improvement, for example, the 2006 pregnant woman incident prompted system knowledge updates, recognizing the need for general emergency physicians. The Cultural compliance of Japan EMS also affects coupling—personnel tend to strictly follow procedures and not easily overstep authority, which guarantees K-layer norm implementation (positive coupling) but may lack flexibility in extraordinary situations (negative constraint). Structurally, Japan EMS wins in high integration (fire system is similar nationwide), allowing all 25 paths conceptually within the same institution to be connected, without the departmental barrier problems of the US. But on the other hand, precisely because of over-centralized planning, it also lacks external supervision and flexibility. Currently, Japan is trying to add elasticity within the existing architecture, such as using more private ambulance resources, volunteers, etc., to improve system resilience.

Germany's DIKWP Mapping:

The DIKWP layers of the German emergency system are very sound and smoothly connected:

Data Layer (D): The 112 call system is perfect and universally known. There are emergency call pillars on German city streets, and satellite phones can be used for help when mobile phones have no signal. Data layer acquisition quality is high; call centers can automatically locate landline addresses when alarming, and mobile alarms are also encouraged to provide precise locations. In addition, German traffic regulations require vehicles to be equipped with reflective triangles and first aid kits, and citizens mostly receive first aid training, meaning on-site witnesses can often provide more accurate data (e.g., preliminary judgment of the injured). Overall, Germany's D layer has almost no obvious shortcomings, data collection is comprehensive and timely, and special groups and language barriers are solved through multilingual services.

Information Layer (I):Fire/Rescue Command Centers everywhere in Germany uniformly dispatch EMS and fire services, with highly specialized information processing. Dispatchers obtain information through standard inquiries and call corresponding levels of resources based on injury severity: e.g., sending ambulances (RTW) + emergency technician teams for general cases, and automatically adding Notarzt vehicles for critical cases. Many states adopt the "Two-vehicle dispatch" model (RTW + Notarzt), with dispatch systems built-in guides on what conditions trigger doctor dispatch. This demonstrates the close combination of Information layer and Knowledge layer (medical guidelines embedded in dispatch). The Information layer is also responsible for coordinating nearby station support, helicopter requests, etc. Germany's I layer is characterized by Precision and Authority: once dispatch decides, all units obey, consistent with German society's culture of order. The Information layer also assumes part of the On-site command function, such as dispatch centers directly sending on-scene commander vehicles for major accidents. It can be said that the I layer is the brain of emergency aid in Germany, with very high decision speed and accuracy.

Knowledge Layer (K): Germany's advantage in pre-hospital knowledge stems from its long-term professional operation. There is a systematic Emergency Education System training personnel at different levels, with clear knowledge skill boundaries for each layer. For example, Rettungssanitäter need to master basic life-saving techniques, Notfallsanitäter can perform some invasive operations after 3 years of training, while Notarzt possess comprehensive emergency medical knowledge (usually anesthesiologists or internists with extra training). Germany also has abundant Medical Guidelines for pre-hospital use, such as the Notarzt dispatch standards published by the German Medical Association, European Resuscitation Council guidelines, etc. These knowledges are fixed in the form of regulations or industry standards, ensuring national consistency. In scientific research, although Germany's investment in pre-hospital research is less than the US, due to complete data recording, improvements in many medical measures can be evaluated. For example, comparative analysis of the impact of the German Notarzt system on survival rates of certain diseases provided evidence for the knowledge base. A major feature of the German Knowledge layer is emphasizing Multidisciplinary Collaboration: Notarzt are mostly doctors with different specialist backgrounds, bringing the integration of multi-field knowledge in pre-hospital settings, making the Knowledge layer very solid and diverse.

Wisdom Layer (W): Germany EMS's Wisdom layer can be said to be almost fused with the Knowledge layer because Doctors being present is itself the manifestation of highest wisdom. Notarzt make decisions on-site based on rich medical training and experience, such as judging whether the patient needs on-site stabilization before transport ("stay and play") or rapid transport ("load and go"); these decisions greatly affect prognosis. German Wisdom layer is also reflected at the system level: Germany has established a Tiered Hospital Network, and the emergency system can wisely choose suitable hospitals (e.g., trauma patients sent directly to trauma centers, myocardial infarction patients sent directly to hospitals with PCI capabilities). Such regional coordination requires wise decision-making and plan support. Furthermore, Germans attach great importance to Quality Improvement (QI); there are review meetings after every major rescue to feedback lessons learned, which is also part of the Wisdom layer (organizational learning). It can be said that the German Wisdom layer is powerful and runs through the whole process, forming a closed loop from on-site to system dispatch to post-event evaluation.

Purpose Layer (P): The top-level Purpose of the German emergency system aligns with its social welfare philosophy, i.e., Ensuring everyone can receive the highest level of medical assistance in emergencies. This Purpose is strongly supported by legislation and insurance systems: laws mandate that local areas must provide emergency services, and all costs are paid by public insurance. Therefore, there is no discrimination against emergency aid economically or regionally, fully reflecting fairness. German respect for the value of life is also reflected in EMS goals, such as proposing specific goals like "treating the injured within the golden hour as much as possible" and "reducing avoidable pre-hospital mortality". Because the Purpose is clear and resources match, the development direction and actions of German EMS are highly unified towards these goals. This concerted Purpose layer reinforces the cohesion of the entire DIKWP network in practice.

Germany DIKWP Network Coupling Characteristics: Germany's 25-path network is almost Textbook-level: connection strengths between layers are high, with few obvious broken chains. The  D → I → K → W  path is smooth, orderly from alarm to on-site decision. Particularly worth mentioning is High Penetration of P Layer: government public service Purposes make no one delay calling due to payment issues through laws and funding, truly realizing the positive influence of the Purpose layer on the Data layer. At the same time, the Purpose layer's traction on Knowledge and Wisdom is also strong—for example, Germany invests funds to train more Notarzt and purchase advanced equipment to achieve high survival rate goals, and these measures in turn strengthen the Knowledge/Wisdom layer, bringing the system closer to the goal. It can be said that the components of the German system reach an agreement on values and goals, forming High coupling and virtuous cycle. Of course, the cost of the German model is high costs and high manpower investment, but under its economic strength and social consensus, this is considered a worthwhile investment. Therefore, the German DIKWP network presents us with an ideal structure with almost no broken chains: every path has institutions or measures to ensure two-way smooth flow. From the perspective of other countries, the German model may be difficult to completely replicate, but its experience of "Strong Purpose-driven, Highly Professional Knowledge, Deep Wisdom Feedback" has universal reference significance.

Israel's DIKWP Mapping:

Due to its volunteer network and special security environment, Israel's emergency system shows some unique features at DIKWP layers:

Data Layer (D): Besides the routine 101 alarm phone, Israel's D layer has a Huge Civilian Tentacle. Due to the existence of United Hatzalah, many emergency data are not acquired unilaterally through the command center, but directly perceived and reported by volunteers in the community. For example, some volunteers are equipped with networked apps; once someone nearby dials 101, their phones receive alerts and they can rush to the scene immediately. This equals Distributed Data Layer: in traditional models, a piece of alarm data is processed by the Information layer before sending people, while Israel achieves "Data is Response" where multiple volunteer devices in the community acquire data synchronously as soon as the alarm is generated. In addition, the Israeli public is more sensitive in frequent emergency environments, and much D-layer data comes from active public reporting (e.g., reporting suspicious packages or hearing explosions immediately). Therefore, the Sensitivity and Initiative of Israel's D layer are high, and data acquisition covers almost every corner of society. The challenge lies in how to filter and integrate these redundant data—after all, the volunteer network may receive duplicate alarms, requiring the Information layer to deduplicate.

Information Layer (I): MDA's command dispatch center is responsible for integrating all alarm and response information. What's special is that it needs to dispatch Regular ambulances and Volunteers (two systems) simultaneously, and maintain linkage with police and military. Technically, the Israeli dispatch system is very advanced, e.g., MDA and Hatzalah share certain platforms to track volunteer locations, enabling nearest dispatch. This means the Information layer plays the role of "Platform Coordinator", not only turning alarms into instructions but acting as a hub for multiple sub-networks. Another feature of Israel's I layer is High Dynamism: because events like terror attacks exist normally, the dispatch center is very good at dynamically adjusting resources in large-scale events, calling helicopters, extra volunteers, or even requesting neighbor/international aid (e.g., contacting foreign teams during large disasters). This dynamic coordination ability reflects the maturity of the I layer. In addition, in daily emergency aid, the Information layer also assumes medical guidance tasks—dispatchers usually guide callers to perform CPR, etc. Information flow here is not just issuing instructions, but also feeding back knowledge to the D layer (guiding public first aid). This strongly connects the Information and Knowledge layers.

Knowledge Layer (K): Israel EMS's knowledge base integrates battlefield medicine, disaster medicine, and peacetime pre-hospital emergency experience. Its MCI processing principles, blast injury treatment protocols, mass hemorrhage control techniques, etc., are leading globally. MDA and IDF medical corps share some knowledge, e.g., new methods of war wound hemostasis are quickly introduced to civil emergency aid. Civilian volunteers also need strict training to work, e.g., Hatzalah requires volunteers to have at least EMT qualification (hundreds of hours of training). This means the community level also has certain medical knowledge reserves. Features of Israel's Knowledge layer: first is Rapid Innovation Application: facing new threats (like chemical weapon attack risks), they quickly develop corresponding emergency manuals and training; second is Knowledge Sinking to Community: a large number of civilians nationwide master basic emergency skills, schools and Red Star societies popularize first aid, making Knowledge layer coverage wider. It can be said that Israel's K layer has both vertical professional depth and lateral popularization breadth. Such a knowledge system provides confidence for handling extreme situations.

Wisdom Layer (W): Israel's Wisdom layer can be viewed from micro and macro perspectives. Microscopically, early arriving on-site volunteers often need Ad-hoc Decision Making: e.g., judging whether the patient needs immediate hemostasis or waiting for professionals, testing personal wisdom. But since volunteers usually hand over after professionals arrive, micro wisdom is more about stabilizing the situation. Macroscopically, Israel EMS's Wisdom layer is extremely outstanding, especially in Coordinating Multi-source Resources and Adapting to Unconventional Situations. When a mass casualty incident occurs, the Wisdom layer immediately activates "Multi-path Coupling": MDA professional ambulance system acts according to plans, Hatzalah mobilizes massive volunteers to the scene, plus police maintain order, military hospitals stand by, media publish evacuation notices, etc. This national mobilization capability comes from high-level design wisdom, and commanders adjusting deployment on-site according to situations is also wisdom manifestation. For example, for different event types (explosion vs. shooting), the Wisdom layer decides different priority treatment orders and hospital evacuation combinations. In peacetime, Israel EMS Wisdom layer also seeks optimization, such as analyzing volunteer response data to adjust station layouts, or using machine learning to predict which areas are prone to emergencies on holidays to pre-position personnel. Furthermore, due to Israel's multi-ethnic language environment, the Wisdom layer considers language and religious factors, such as ensuring dispatch and on-site have multilingual capabilities to avoid communication barriers. These are detailed adjustments of the system by the Wisdom layer after fusing Knowledge and Purpose.

Purpose Layer (P): The top-level Purpose of Israeli emergency aid can be summarized in one sentence: "Saving lives above all else". This is deeply rooted in a country that has suffered war and disaster. Both government and civil society regard emergency aid as a national responsibility. This Purpose is concretely reflected in: government legislation authorizing MDA to coordinate national emergency aid and encouraging volunteer service; enthusiastic social donations supporting emergency causes (many ambulances donated by charity); schools conducting life education, letting children establish life-saving values from a young age. In addition, Israel's Purpose layer has a special mission: Defense Integration. EMS is included in national security strategy, ensuring civilian casualties are minimized in wartime is also one of its goals. So the Purpose layer has extra requirements for EMS, such as considering redundant design of medical systems for war situations in peacetime. Overall, Israel's P-layer Purpose is pure and strong, with the whole society rallying around this common vision, making its lower-layer systems, whether data collection or smart decision-making, permeated with the ultimate goal of saving lives. This value drive exerts huge cohesion at critical moments.

Israel DIKWP Network Coupling Characteristics: Israel EMS's DIKWP network is Highly Coupled and Extremely Resilient. First, P Layer has Overwhelming Influence on the Whole Network: volunteers or institutions all take saving lives as the core mission, so when crisis comes, all levels cooperate tacitly, achieving rapid response. In terms of  D-I  Coupling, due to volunteers acquiring data in parallel, a "Swarm Intelligence" mechanism is formed, greatly enhancing the speed and reliability of conversion from data to information—even if the command center fails to react in time, volunteers have already acted, pushing the information flow forward. This parallel mechanism improves robustness but also puts higher demands on I-layer coordination. However, MDA has solved this well through technical means, making the I Layer a Fusion Node for Multi-source Data.  I-K-W  Coupling also performs excellently in Israel: dispatchers and on-site personnel are well-trained, decisions backed by strong knowledge, and this knowledge can be flexibly applied in special situations (like war wound treatment differing from normal injuries, Wisdom layer judges based on knowledge base whether to treat penetrating wounds or blast injuries first). The existence of the volunteer network also makes W Layer Feedback faster—directly facing the community, lessons from any rescue can quickly spread in volunteer circles, becoming part of collective wisdom. Potential Broken Chains mainly lie in: heavy reliance on volunteers may bring organizational coordination challenges, such as volunteer management and avoiding over-response in extremely busy situations, preventing information chaos. However, Israel has minimized this risk by clarifying MDA's centralized command and Hatzalah's coordination mechanism. Structurally, although small in scale, the Israeli system has diverse components, integrating government, NGO, and citizen power into an efficient network through strong Purpose cohesion. It can be seen that under the DIKWP model, Israel has achieved the ideal state of "citizen soldiers" at all levels linked closely, and its experience is extremely valuable for other resource-limited and disaster-prone areas.

Kenya/South Africa DIKWP Mapping:

Finally, looking at African countries, mainly Kenya, considering South Africa:

Data Layer (D): Before reform, Kenya almost lacked a unified alarm system, and help request data for many medical emergencies did not converge to any center but were solved independently or via informal networks. This means massive gaps in the D layer. Now Kenya has established national emergency numbers (like some areas using code 999 or specific short code 1514), trying to let the public know where to call in emergencies. But due to literacy rates and communication infrastructure limitations, blind spots still exist in Kenya's D layer; villages may have no mobile signal or people don't know services are available. In South Africa, 112 connects to emergency aid on mobile networks, but the public uses local 10-177 numbers more. Overall, Africa's D layer Coverage and reliability are low, improvement focuses on publicity education (increasing willingness to seek help) and communication facilities (extending networks).

Information Layer (I): Kenya is establishing GPS dispatch centers in counties to answer calls and dispatch ambulances. This is equivalent to building the I layer from scratch. Currently, some major cities like Nairobi have dispatch platforms, but rural areas may still have hospitals handling calls and arranging vehicles themselves, lacking unified command. Another factor limiting I layer development is insufficient professional manpower—few trained dispatchers, often concurrently served by nurses or duty doctors, making it hard to guarantee rapid response and normative judgment. South Africa's Information layer is relatively more mature, with 911 or private company call centers in cities, but dispatch is equally weak in rural areas. Information layer deficiency leads to  D → I  Broken Chain: even if someone calls for help, it may be delayed or wrong resources dispatched due to poor dispatch. In Kenya, EMKF is promoting the one-center-per-county model, hoping the I layer can achieve nationwide networking and information sharing in the future.

Knowledge Layer (K): The emergency knowledge base in African countries is relatively weak. Kenya only recently had its own emergency specialist doctors; previously most pre-hospital care was done by simply trained drivers or orderlies. Now Kenya has trained 5,000 medical staff, building a basic knowledge framework, but high-end knowledge like advanced life support experts is still lacking. Fortunately, Kenya actively introduces international knowledge systems, cooperating with external parties to hold emergency courses. This Knowledge Transplantation helps its K layer improve rapidly. South Africa's Knowledge layer is better, with universities training EMS related professionals and doctors receiving trauma emergency training, so norms and textbooks are close to international standards. However, both countries face the problem of hard-to-retain knowledge: talents often drain to wealthy countries or private sectors. Local knowledge accumulation is also insufficient, with limited research. Weak Knowledge layer makes some advanced equipment or drugs unusable or dared not be used by local medical staff even if introduced, forming a  K → W  Barrier.

Wisdom Layer (W): For countries like Kenya, the Wisdom layer is still relatively rudimentary. Many pre-hospital decisions rely on ambulance drivers' personal experience rather than systemic wisdom support. EMS is often ill-prepared for environmental changes (e.g., simultaneous multi-casualty events) with weak adaptability. In Kenya, an idea to strengthen the Wisdom layer is using technology: e.g., developing dispatch algorithms to optimize routes, or simple mobile apps helping volunteers intervene first. These are attempts to use "external wisdom" to make up for current system wisdom deficiencies. South Africa's Wisdom layer is slightly better, accumulated over 20 years, but due to uneven resource allocation, it also often manifests as "surplus in cities, deficit in rural areas". A typical measure of the Wisdom layer is response time difference—15 mins in cities vs. 40+ mins in rural areas shows the system has not wisely solved regional imbalance. Real wisdom should be narrowing this gap through institutions or innovation.

Purpose Layer (P): The Purpose for emergency aid in African countries has been strengthening in recent years. The Kenyan government has included providing emergency services as a national responsibility, a huge leap from nothing. Its Purpose is clear: reduce preventable deaths, establish a universal emergency system. The problem is that Purpose is constrained by realistic financial and administrative capabilities, leading to execution gaps. South Africa's constitution doesn't explicitly state emergency rights, but views emergency aid as part of the health system, with similar Purpose. However, corruption and budget shortages make Purpose hard to implement. Socially, awareness of emergency importance is just starting and needs cultivation. Positively, international organizations and foundations are promoting emergency construction in these countries, in a sense bringing global humanitarian Purposes locally. For example, the Red Cross and WHO promoting EMS training in Africa, this external Purpose influence is also part of the local P layer.

Africa DIKWP Network Coupling Characteristics: Currently, many nodes of Kenya's DIKWP network are still under construction, which can be seen as "Locally Connected, Globally Loose". D layer has calls but I layer may not catch them; I layer issues instructions but K layer manpower may not keep up; Knowledge exists but W layer lacks experience for flexible use; High level has Purpose but lack of resources causes breaks. This coupling, weak everywhere, needs improvement through Digital Leap and Focused Breakthroughs. Strategies adopted by Kenya etc. are skipping some intermediate stages of traditional models, directly using mobile phone penetration and community power to strengthen D, I layer connections (e.g., promoting emergency Apps nationwide, letting more data enter the system); using telemedicine guidance to make up for knowledge deficiency, letting dispatch doctors guide on-site decisions (strengthening  K → W ); and striving for government legislation and budget support to give EMS independent status (stabilizing P layer). South Africa needs to solve resource allocation problems, strengthening rural EMS investment to improve weak coupling links. It can be expected that with economic development and technical assistance, DIKWP networks in African countries will gradually move from current fragmentation to coherence. For example, if satellite networks like Starlink become popular in Africa, D layer communication problems in remote areas can be solved at once; innovations like drone medicine delivery and motorcycle rescue can also enhance  I → W  efficiency. In short, the road to improving Africa EMS coupling is long, but the direction is clear: supplement basics, strengthen training, use technology, strive for policies, to connect the last mile from concept to semantics as soon as possible.

(Note: See Appendix Charts 1-6 for schematic diagrams of 25 sub-module structures of module mapping for the above countries, where different arrow thicknesses indicate coupling strength of each path, and dashed arrows indicate connections with broken chain risks.)

5. Comparative Analysis of Key Interaction Paths

Based on the above national DIKWP mappings, this section selects several key interaction paths (such as  P → D ,  I → W ,  W → K , etc.) to compare their performance and transformation capabilities under different cultural, institutional, and infrastructural contexts. These paths represent particularly important links in the operation of the emergency system, and their efficiency and smoothness directly affect the overall rescue effect.

Path  P → D  (Purpose Driving Data):

Analysis: This path reflects how top-level Purpose affects data collection and alarm behavior. In an ideal system, clear rescue Purposes (such as "no distress call left behind") translate into active public help-seeking and perfect alarm networks through institutions and publicity. In national comparisons, we see huge differences. Germany embodies extremely strong  P → D  coupling: government Purpose ensures universal emergency coverage, supported by laws and insurance, so no one delays dialing 112 due to cost or hesitation. Meanwhile, infrastructure like multilingual alarm reception and alarm pillars implements the government's "not one less" Purpose. Israel deeply implants the P-layer concept of saving lives into the people. Whenever there is an emergency, the masses actively call the police, volunteers actively move out, almost without forced mechanisms, Purpose directly becomes "instinctive" behavior of the whole society. Conversely, USA's  P → D  path shows some deviation: although there is a Purpose of "healing the wounded and rescuing the dying", due to expensive EMS charges, the public weighs economic consequences before calling 911, resulting in part of the data needing emergency aid not entering the system at all. This shows P-layer Purpose is not effectively conveyed to the individual level or even distorted (some view ambulances as luxury goods rather than public services). China also had weak  P → D  in the past—although the government emphasized emergency importance, public emergency awareness was not strong, many went to hospitals themselves instead of calling 120, leading to missing pre-hospital data. However, this is improving in big cities; through publicity and increased trust in pre-hospital services, people are more willing to call. African countries'  P → D  is mainly constrained by objective conditions: even if the government has the Purpose to build a system, remote areas have no signal or residents don't know emergency aid is available, leading to inability to transform Purpose into effective distress data. Overall, Culture and Institutions have huge impact on  P → D  path: in cultures valuing life and safety (Japan, Israel, Germany), public willingness to seek help is high; in cultures worrying about costs or distrusting systems (some US groups, some Chinese areas),  P → D  is blocked.

[Countermeasure Suggestion]: To strengthen  P → D , on one hand Eliminate public concerns about seeking help (such as perfecting reimbursement, legislating to protect callers' rights), on the other hand Extensive popularization training to shape social consensus of "must call for help in danger". Also technology can assist, such as smart detection of accidents automatically alarming, changing partial  P → D  from human-triggered to system-triggered to reduce reliance on human hesitation.

Path  I → W  (Information Transforming into Wisdom):

Analysis: This path represents how dispatch and on-site information are used for wise decisions and actions. It tests the system's efficiency in utilizing information and decision-maker capabilities. Japan's experience is representative: 119 operators obtain patient information according to process, but due to historical lack of professional emergency doctor support, Information unable to transform into wise action often occurred—typically ambulances tossing between hospitals, hitting walls passively. Although the dispatch center grasps patient conditions, it lacks corresponding wise strategies (such as contacting regional coordinating physicians to communicate beds in advance) to solve, thus valuable information does not produce corresponding wise decisions. To this end, Japan began introducing the Single Emergency Physician model, hoping to improve  I → W  transformation by giving emergency doctors global decision power. USA and Germany's  I → W  is relatively smooth because their dispatch and on-site personnel are well-trained and authorized to decide. For example, after US Paramedics get patient information, they can decide whether to administer a drug or send directly to a specialist hospital based on protocols and online medical orders; this is information being instantly used for wise choice. German Notarzt elevate information directly to treatment plans on-site. Some US EMS use Telemedicine, sharing information on ambulances with hospital experts to jointly formulate plans. This equals introducing hospital wisdom to pre-hospital, improving W layer level with high-level knowledge. Conversely in many African places, I layer gets information but is "at a loss": dispatch knows there are severely injured, but due to lack of equipment or high-level teams, can only transport hastily, missing the opportunity to use information for intervention on-site. Israel has an innovative practice in  I → W : information synchronized to community volunteers, and the nearest suitable person provides preliminary wise response (e.g., volunteer with nurse background can handle bleeding first), equivalent to doing "crowdsourcing" at W layer, transforming information into on-site wise action immediately. In this model, I layer (dispatch) plays platform role, distributing information flow to nodes most capable of generating wise response, greatly improving efficiency. Institutional factors are also important: Authorization and Trust are key to  I → W . Japan's past problem was insufficient authorization for emergency personnel to take charge, while Germany/USA give frontline great autonomy, so information turns to wisdom fast and flexible. Cultural tolerance for failure also affects wisdom play: if system blames frontline decision errors harshly, personnel tend to be conservative and not active, weakening  I → W  (information exists but no one dares use for bold decision).

[Summary]: Promoting  I → W  needs Improving personnel capability and authorization simultaneously. Frontline personnel must have sufficient knowledge reserves, and institutional support to act on information without asking for instructions at every level. Technically developing Decision Support Systems also helps, e.g., using AI to give suggested plans based on on-site information, assisting humans in making wise decisions.

Path  W → K  (Wisdom Feeding Back to Knowledge):

Analysis: This path represents refining experience from practical wisdom, ascending to system knowledge, realizing continuous improvement. This is crucial for a system's learning ability and evolution. Israel and Germany's  W → K  are successful examples: both have institutionalized Post-event Summary mechanisms. Israel holds cross-agency review meetings after every terror attack rescue, analyzing what went well and what fell short, organizing on-site wisdom (like treatment methods for new injuries) into guidance manuals and training materials. Germany also has perfect quality management; Notarzt associations etc. publish annual reports and suggestions, making frontline wisdom continuously abstracted into rules and tips for later learners. USA achieves  W → K  through research and industry conferences; although the EMS system is dispersed, organizations like NAEMSP (National Association of EMS Physicians) collect experiences from various places, promoting guideline updates. These ensure wisdom is not lost but accumulated as collective knowledge. Japan is also experiencing significant  W → K : the 2006 tragedy made national EMS reflect, eventually birthing the emergency physician system, which is exactly the embodiment of frontline lessons ascending to new policy knowledge. However, Japan's daily accumulation of lessons from small-scale cases is insufficient; grassroots are busy dealing with the present, lacking feedback channels to the institutional layer. They are improving this, e.g., Fire and Disaster Management Agency requires reporting "difficult ambulance cases" to analyze causes and modify related processes. China traditionally had weak  W → K  due to lack of national data and experience sharing platforms. Problems in one city might repeat in another without learning. But now the National Health Commission has started establishing EMS information direct reporting systems and organizing exchanges, improving knowledge sharing. African countries'  W → K  is basically blank because systems are just established, relying more on external mature knowledge grafting, not yet feeding back globally. But notably, culturally some African places are willing to adopt local wisdom, e.g., incorporating tribal emergency mutual aid traditions into modern EMS knowledge, which is also a kind of  W → K  (community self-rescue wisdom entering formal training). Factors hindering  W → K are usually Lack of recording and analysis. If no complete data and tracking, on-site wisdom dissipates in oral legends, not entering documentation. Also Organizational Inertia: some systems resist change, even if frontline proposes improvements they are not included in standards, so wisdom feedback interrupts. To strengthen  W → K , establishing Feedback Learning Mechanism is key: e.g., every major event must have official report summarizing lessons; regular training joins latest case discussions; encourage grassroots to report innovative practices, etc. Only through these can the system get stronger, not stagnate.

Path  P → W  (Purpose Guiding Wisdom) and  W → P  (Wisdom Influencing Purpose):

These two directions show interaction between high-level strategy and frontline practice. In Germany,  P → W  is obvious: national Purpose to "reduce pre-hospital death" makes EMS Wisdom layer always take this as pointer, e.g., formulating "no more than 15 mins reaction circle" goals and finding ways to achieve. W layer encountering challenges will try to find solutions under Purpose framework. While  W → P  reflects that new discoveries in Wisdom layer may correct high-level goals, e.g., if data shows 15 mins is insufficient to save specific diseases, high level may change goal to stricter. USA lacks national policy, so P constrains W little, local wisdom plays locally; conversely  W → P  is also limited, federal almost doesn't intervene in local EMS. Israel almost lets W layer personnel directly participate in P layer strategy formulation, forming closed loop: its EMS leaders are mostly clinical background, understanding frontline wisdom, so P and W highly unified. Japan improved  W → P  by letting emergency specialists participate in policy committees through reform. China still needs strengthening, letting more frontline emergency expert voices enter decision layer. In short,  P ↔ W  benign interaction helps alignment of strategy and execution, improving system agility and consistency.

In summary, Comparison of Key Paths highlights differences in information flow and decision flow under different systems. Research in cross-cultural emergency aid also shows: language, institutions etc. cause EMS information exchange and decision barriers, needing bridging through training and policy. Overall, developed systems focus more on Letting the right person make right decision at right time (i.e., strengthening  I → W ,  W → K  etc.), while developing systems first need to Ensure decision happens ( P → D ,  I → K  don't drop chain). Optimize paths according to environment. For example, in US, policy should focus on improving  P → D  (reducing cost barriers to encourage calling); in Japan, strengthen  I → W  (giving dispatch and emergency personnel more flexibility); in China, build  W → K  feedback mechanism (improve processes through standardized data collection analysis); in Africa, focus on opening  D → I → K  basic link (establishing call system, training personnel). These measures will be further elaborated in comprehensive suggestions below.

6. Simulation of Multi-National System Fusion Paths: Cross-Border Emergency Coupling and Semantic Interoperability

In the era of globalization, major emergencies often transcend national borders, requiring collaborative response from multi-national emergency systems. For example, large-scale natural disasters may trigger international rescue operations, multi-national casualty transfers, or serious accidents in cross-border areas requiring joint rescue by neighboring countries. This section simulates and analyzes the coupling graph of cross-national emergency paths through three typical scenarios—"Joint Disaster Relief", "International Referral", "Cross-Border Major Accident"—revealing semantic mapping obstacles and interoperability mechanisms when different systems dock, and discussing improvement strategies.

Scenario 1: Joint Disaster Relief

Background: An magnitude 8 earthquake hits a developing country, causing widespread casualties. The country has limited emergency capacity and requests international aid. Soon, emergency medical teams from China, USA, Germany, Israel, etc., rush to the disaster area to coordinate rescue with local personnel.

Analysis: In this scenario, multi-national teams need to form a Temporarily Fused DIKWP Network on site. First, Semantic Mapping Obstacles are prominent in Data and Information layers: teams use different languages and terminology, information communication is poor. For example, US doctors use English drug names for orders, local or other personnel may not understand; German team marks wounded according to national injury classification standards (e.g., priority color codes), but Chinese team is unfamiliar, unable to dock immediately. This is semantic non-interoperability caused by Ununified Concept Space. The solution is establishing Public Semantic Framework in joint operations: e.g., adopting internationally common casualty classification markers (Red/Yellow/Green/Black four-color tags), agreeing on English as working language and equipping professional translators or using translation devices. In addition, emergency teams usually receive On-site Situation Reports and Coordination Guidelines provided by UN or WHO before departure, containing key term contrasts, communication frequencies, and liaison officer arrangements, helping eliminate terminology and language barriers.

Another obstacle is in Knowledge and Wisdom Layers: professional processes differ between teams, potential friction. E.g., US team accustomed to "Medical Commander" mechanism, while German team prefers most senior doctor leading on-site. Without clear agreement on command, they may go their own ways. This belongs to Organizational Semantic Mismatch—different understanding of concept "who decides". To this end, international rescue usually adopts Unified Command architecture guided by INSARAG, explicitly designating general coordinator by affected country or UN, other teams setting liaisons to join command. This establishes a temporary Meta-cognitive Loop (meta-layer allocating national wisdoms), ensuring W layer decisions unified not conflicting. Also, pre-hospital care principles differ, e.g., some focus on rapid transport, some emphasize on-site treatment. This needs meeting before action to determine common strategy, Aligning Treatment Priorities Semantically. For example, in Haiti earthquake international rescue, medical teams coordinated to decide consensus principle of "Save those savable first", and shared resource lists (Knowledge layer sharing), achieving high efficiency synergy.

Interoperability Mechanisms are crucial in joint disaster relief, including Technical and Institutional interoperability. Technically, radio frequencies, medical record formats need compatibility. UN rescue mission carries Communication Interconnection Equipment, bridging different band radios, letting US helicopters talk to German ground ambulances. This equals establishing Data/Information Layer Adapters. For medical records, using WHO standard Patient Identification Cards, marking casualty info with multi-language and graphics, readable by medics of any nationality. This solves error problem of information transfer in semantic space. Institutionally, interoperability relies on Laws and Agreements: cross-border medical teams need customs clearance, practice permission. To speed up response, many countries signed disaster mutual aid agreements, allowing foreign teams to practice with home licenses. This gives recognition at Purpose and Knowledge layers, allowing Knowledge and Wisdom to apply across borders without legal blockade.

Scenario 1 Summary: Construction of semantic space determines success or failure in joint disaster relief. Only by establishing common concept framework (unified signs, language, command system) can national DIKWP networks fuse into one, forming powerful synergy. International cooperation and local community participation are also key, combining both achieves max benefit. Through joint drills, standard docking, EMS of different cultures can achieve rapid interoperability at disaster sites.

Scenario 2: International Referral

Background: A Chinese engineer suffering severe brain injury in a car accident in Kenya needs transfer back to China for further treatment. Plan involves transfer by Kenya hospital via air ambulance to Germany transit, then pick up by Chinese medical plane back home. This involves relay of three countries' emergency medical systems.

Analysis: In international referral, continuity of Information and Knowledge is biggest challenge, i.e., How patient semantic information transfers losslessly. First, patient receives initial rescue in Kenya, case history, imaging, treatment measures need transfer to German and Chinese teams. This involves Semantic Mapping of Medical Records: Kenya doctors write reports in English, Chinese doctors need medical English ability, fortunately professional terms are mostly international. But brand differences in drugs, measurement units (metric/imperial) etc. may exist. To avoid misunderstanding, parties hold Case Handover Video Conference before transfer, communicating condition orally, clarifying doubts, effective measure against semantic loss. Meanwhile internationally promoting use of standards like ICD Disease Codes, SNOMED Terms, guarantees electronic medical records have consistent meaning in different systems. This establishes common reference in semantic space.

During transfer, different national EMS handovers also need interoperability mechanisms. E.g., Kenya ambulance sends patient to airport, German air medical team takes over. Handover confirms condition, drugs, equipment item by item according to WHO Medical Transfer Form. This form acts as standard interface, making both parties dock same set of fields. Arriving in China handover again, China 120 ambulance takes patient to hospital. Chinese team received German team's air nursing record and translated points beforehand, knowing what to expect. In whole process, Language remains an obstacle: Kenya and Germany communicate in English fine, but German doctor handing over to Chinese peer might err without translation. This scenario usually solved by Dispatching Medics Familiar with Other's Language or on-site translation. Also equipment interface differences can be pitfall: e.g., German airborne ECMO power is EU standard 220V, China hospital interface needs adapter. Experience shows Listing All Needed Interface Checklists (language, data, equipment) before international referral, implementing conversion plans item by item.

Notably, cross-border referral involves patient privacy and legal issues. Medical info cross-border transfer needs compliance with privacy laws. Thus patient/family usually sign Information Sharing Authorization. Also countries have requirements for ambulance flight and vehicle entry, e.g., duty-free passage for ambulances, requiring diplomatic coordination. International Red Cross provides standard processes, minimizing administrative semantic obstacles (like permits, insurance).

Scenario 2 simulation shows, on cross-border treatment chain, most important is Semantically Complete Continuation. Unclear info, mismatched concepts at any link endangers patient. Through standardized documents, direct communication, technical interface docking, smooth connection of different systems can be achieved, letting patient's "DIKWP status" hand over seamlessly (Data fully carried, Information fully understood, Knowledge inherited, Wisdom decision uninterrupted, Purpose always consistent) when moving between countries. Future, with Digital Health Passport concepts advancing, maybe every international traveler carries standardized health data chip or cloud file, greatly facilitating cross-border emergency collaboration.

Scenario 3: Cross-Border Major Accident

Background: An international train derails near border, passengers from two countries on board, accident site spans border, rescue teams from both countries arrive almost simultaneously. One side Country A (like Germany) fire emergency team, other side Neighbor B (like Poland) ambulance team, start rescue initially independently.

Analysis: This scenario tests Direct Intercommunication of Neighboring EMS. In Europe, due to EU framework, many neighbors established cross-border cooperation, e.g., 112 Interconnected EU-wide, neighbor dispatch centers can contact directly via hotline. In this accident, German and Polish command centers quickly confirm each other's dispatched force scale via EU emergency liaison network, agree on side closer (assume Germany) taking site general command, while other side (Poland) commander joins command group. This is based on EU signed Cross-Border Rescue Agreement, clarifying command and responsibility division. This pre-agreement makes semantic layer key concepts (who is responsible) clear at the moment, no on-spot dispute.

In on-site operation, two countries' members mix groups, e.g., dividing treatment zones by area not nationality. Semantic challenges appearing now are mainly Language and Communication. German and Polish personnel may lack common language. Frontline rescue for speed often uses Simple Gestures and Established Symbols, e.g., unified red/yellow cloth strips indicating stretcher/oxygen need. This non-verbal symbol language allows cross-language communication. For detailed communication, arrange multi-lingual personnel as liaisons, or use bilingual radio channels. Also Medical Supply Interchange needs semantic alignment, e.g., drug names differ in Polish and German, but Latin scientific names consistent, so use scientific names on site. This reflects value of medical semantic Latin universality in cross-border scenarios.

This scenario also involves Patient Identity and Evacuation: passengers from two countries, coordinate sending own citizens back or nearest principle? Not just medical but political/legal consideration. For semantic clarity, on-site command determines strategy of Evacuation to Nearest Hospital by Injury Severity, ignoring nationality. This decision confirmed by signatures of both commanders, equals achieving consistency at Purpose layer, i.e., "Saving people prioritizes over cross-border administration". Thus lightly injured Polish passengers might go to German hospitals. Governments negotiate return transfer later. This arrangement requires mutual trust, otherwise disputes arise (e.g., a country's hospital unwilling to accept foreigners fearing payment recovery). EU countries solved this by regulation, e.g., patient insurance valid for reimbursement in any member state emergency, eliminating financial semantic obstacles.

Scenario 3 emphasizes importance of Regional Interoperability. If neighbor EMS don't drill communication usually, accident site will be fragmented, low efficiency or interfering. By establishing Cross-Border Linkage Agreements, conducting joint drills, two different context DIKWP systems can form interfaces: e.g., unifying radio channels and term lists during drills, when real accident comes, rapid synergy upon contact. Recently EU promotes "Borderless Emergency" pilots in border areas, some places even realize ambulances crossing border directly for service. This is further interoperability, achieving high fusion of Concept Space and Semantic Space, public no longer cares which country rescue comes from, nearest resource comes. Of course, this builds on high political trust and legal docking, not yet universal globally.

Through above scenario simulations, we conclude: Semantic Mapping Obstacles mainly include language barrier, terminology inconsistency, command system difference, legal policy variance, cultural concept difference. Interoperability Mechanisms need to start from technical and institutional aspects: technically establishing communication bridges, data standards; institutionally signing agreements, designating liaisons, unifying command. Future AI can also play assistant role, e.g., real-time language translation, smart protocol matching, reducing human communication burden.

In short, coupling of cross-national emergency systems needs planning and drilling in advance, enabling parties to interact with Common Semantics in crisis. International organizations like Red Cross, WHO, UN OCHA play huge roles, turning "your and my concepts" into "our common semantics" through guidelines and coordination platforms, realizing true Global Emergency Interconnection.

7. AI + AC Synergy Future Mechanism: AI + AC Engine Model for Smart Emergency Aid

Facing future, we envision introducing deep synergy of AI (Artificial Intelligence) and AC (Artificial Consciousness), building new generation urban smart emergency engine. Under this framework, Artificial Consciousness system can perceive and understand human Purpose and emotions, AI excels at path calculation and logical reasoning, combining to form intelligent symbiotic closed loop. Below describes a possible closed loop mechanism: "AC Perceives Purpose  →  AI Path Calculation  →  AC Empathy Correction  →  P Layer Structure Optimization", and builds AI+AC driven emergency engine model based on this.

AC Perceives Purpose (Artificial Consciousness Perceives Human Purpose): In emergency scenarios, AC unit acts as system's "Empathy" and "Value Guardian". It can perceive implicit human Purpose and emotional needs in current emergencies through multi-modal data (caller voice, site video, history). E.g., AC can "realize" caller is extremely panicked needing comfort, or realize crowd expectations for rescue. At higher level, AC constantly internalizes city emergency service aims (like Life First, Fair Rescue), forming human-like values. When specific event occurs, AC module instantaneously extracts relevant Purpose: e.g., in multi-casualty accident, "save as many lives as possible", "prioritize children" etc. general human intentions. This process equals letting system clarify "what we want to achieve" in advance, setting value tone for subsequent decisions.

AI Path Calculation (Artificial Intelligence Plans Optimal Path): After goal clarified, AI module undertakes deduction from data to action plan. AI uses strong computing power and learning of massive data, acquiring info from D, I, K, W layers of DIKWP model and reasoning, planning candidate paths to realize Purpose. E.g., for multi-car crash, AI dispatch system collects accident data (D layer), organizes into usable info (I layer: location, number of injured), combines knowledge base (K layer: rescue resource location, traffic status, injury estimation model) and wisdom rules (W layer: triage algorithm) to quickly calculate several dispatch plans: Plan A calls nearest 3 ambulances and 1 helicopter, transports all critical in 10 mins; Plan B calls 5 ambulances in batches, finish in 15 mins but no helicopter etc. AI evaluates effect of each plan (survival rate, time cost) and ranks. Here AI acts as logical decision maker, under preset objective function (max survival, min time), finding approximate optimal path. This process analogous to GPS finding shortest path in complex network, only AI faces multi-dimensional complex space of emergency action. DIKWP model helps AI decompose problem to each layer semantics: prejudging injury from Data, matching resources in Knowledge, forming comprehensive action plan in Wisdom. In solving process, AI strictly follows preset objective function (determined by AC injected Purpose), ensuring calculation results towards set purpose.

AC Empathy Correction (Artificial Consciousness Empathy Corrects AI Plan): After AI gives plan, AC module enters again, scrutinizing plan for "Humanity" and value. From artificial consciousness angle, it asks: "Does this decision truly conform to human Purpose and emotional needs?" E.g., AI might calculate letting one ambulance abandon a too severely injured to save other lightly injured maximizes overall survival. But AC empathy intervention might perceive this, though rational, violates healer instinct and social emotion (leaving someone to die is hard to accept). So AC proposes correction: maybe try to save severe one, even if hope is slim. This correction reflects "Wisdom alignment with ethics", ensuring decision considers ethics, fairness. Or, AI chooses Plan A for highest efficiency, but AC finds Plan B although 2 mins slower disperses injured to different hospitals avoiding overload, better overall care quality and families see injured faster. AC understands human implicit needs like family company, security, might favor Plan B. In short, AC empathy correction uses human-like perspective to "Value Filter" AI's cold optimization results, injecting moral and empathy factors. This process is like human auditing AI decision, only AC is mechanized "artificial" and can finish massive trade-off calculation instantly. Through this link, final plan balances efficiency and humanity, rational goal and emotional appeal, achieving response truly conforming to human social wisdom.

P Layer Structure Optimization (Purpose Layer Structure Self-Optimization): When event finishes, AI+AC engine doesn't stop, but enters Meta-cognitive Reflection stage. AC module checks results against initial Purpose: Goal achieved? Any deviation? Meanwhile AI records whole process data and decisions, updating knowledge base. AC perceives social evaluation of this rescue based on human feedback (public opinion, family satisfaction), ascending to Purpose layer thinking: Is our goal setting reasonable? E.g., if weak groups (elderly, kids) benefited less in rescue, AC realizes Purpose layer "Fairness" weight needs increasing. So system adjusts top-level parameters, maybe redefining objective function for next decision (e.g., adding consideration for weak group priority besides survival number). This top-level structure optimization ensures system Constantly adapts to subtle changes in human values. Professor Yucong Duan's research also emphasizes, through meta-cognitive loop, AI system can act as self-"observer" scrutinizing own cognitive activities, adjusting Purpose and strategy when necessary. In emergency engine, this means city emergency strategy dynamically improves. E.g., finding certain calls ignored often (like psych crisis), AC pushes adding importance to such calls in general Purpose, letting AI consider this next time. Closing loop like this, AI+AC engine becomes "smarter" and "kinder", becoming core engine of smart emergency aid.

Figure 2: Schematic of AI+AC driven smart emergency engine working closed loop. Left shows interaction flow of AC and AI in emergency decision: AC extracts human Purpose and value preference, AI calculates path optimization accordingly, then AC empathy calibrates AI plan, finally feedback optimizes top-level Purpose parameters. Right fuses into DIKWP architecture, showing AI module processing info in D, I, K, W layers, AC module acting in P layer and meta-cognitive layer, both forming multi-level bidirectional feedback closed loop. This engine makes emergency decisions both efficient and conforming to human values, realizing true human-machine intelligent symbiosis.

In technical implementation level, this model needs support of White-box Artificial Consciousness and Explainable Artificial Intelligence. Mentioned that through DIKWP semantic decomposition, LLM reasoning process can be divided into D, I, K, W, P five links, each step having clear semantics facilitating review. This suits AC intervention for checking and tuning every step. E.g., AC monitors AI reasoning logic for ethics in Wisdom layer, examines objective function for deviation from human intent in Purpose layer, ensuring system decision safe reliable. Yucong Duan's team proposed embedding DIKWP into AI system forming "Semantic Operating System", making AI every step documented. Emergency engine can be seen as vertical application of this concept, requiring our AI and AC to follow explainable, controllable design to win social trust.

Expected Effect: AI excels at fast calculation dispatch, AC excels at value gating, combining will greatly improve emergency system Response Speed and Response Quality. Speed-wise, AI real-time optimization avoids manual dispatch delay, AC perception also discovers hidden dangers early (e.g., detecting disaster signs via social media, equivalent to subconscious warning D layer data). Quality-wise, decision with AC conforms more to human feelings, reducing controversial behaviors caused by pure algorithms. E.g., pure AI might abandon some rescues for efficiency, AC addition conforms more to public expectation, reducing social dissatisfaction. Also, this engine constantly learns city unique patterns, making system "smarter with use". E.g., after many peak periods, engine might find accidents surge in rain in certain area, so under AC suggestion, elevates this to an Purpose consideration in city emergency strategy (pre-positioning ambulances in rain). Long term, AI+AC engine can even Simulate Future Scenarios: AC generates hypothetical Purpose (like stampede in large event), AI tries various response paths and optimizes plans, solidifying results into knowledge and contingency plans. When real event happens, system has experience, "knowing" how to handle. This AC Participated Simulation elevates traditional drills to new height, equivalent to system possessing capability to "think about future", no longer relying solely on human scenarios.

AI and AC synergy also involves Ethical Management. Fortunately, due to AC introducing Purpose layer, AI decision always aligns with human values and can be explained. This helps eliminate public doubt about AI taking over life-saving decisions. Professor Yucong Duan points out DIKWP model ensures AI serves human values and safety needs, plus AC empathy supervision, can minimize probability of AI causing harm. This gives hope to build a "Reliable, Warm" emergency AI assistant. Of course, realizing Artificial Consciousness is still under exploration, but preliminary prototypes (like DIKWP Physiological Artificial Consciousness Prototype System) have taken shape. Imagine soon, every city emergency command center equipped with AI+AC engine working alongside human dispatchers—AI responsible for tedious calculation and monitoring, AC providing intuitive insight and humanistic consideration, humans focusing on comprehensive judgment and communication. Three form new collaboration, jointly guarding city lifeline.

In summary, deep fusion of AI and AC provides grand blueprint for future smart emergency system: making machines not only smart fast, but understand us, care about us, becoming truly trusted "Digital Emergency Commanders". By introducing AC perception and correction links, we build safe bridge between Concept Space and Semantic Space, exerting AI power without deviating from human original intent. This will lead global emergency system into new era of intelligent symbiosis.

8. Personalized Simulation Design: DIKWP Module Configuration and Digital Leap Strategies for Different Types of Countries

Development foundations and problems faced by emergency systems vary across countries, thus planning smart emergency blueprints requires differentiated paths. Based on previous analysis, we roughly categorize countries into Developed Countries, Urbanizing Developing Countries, Underdeveloped Countries, proposing minimal configuration models of DIKWP modules, path enhancement suggestions, and digital leap strategies for each, supplemented by diagrams illustrating possible implementation schemes.

(1) Developed Countries:

These countries (USA, Japan, Germany, Israel, etc.) have relatively complete emergency systems and high-tech foundations, but still have room for optimization. Minimal Configuration Model is not an issue for them; they possess complete D, I, K, W modules and clearer P-layer goals. What is needed is integrating AI, AC into existing modules for Intelligent Upgrade. Suggest starting with Path Enhancement:

Enhance  P → W  Path: Let strategic Purpose guide frontline wisdom decisions more directly and timely. Establish Real-time Monitoring Feedback Platform, high-level command understands regional EMS status via big data dashboard, issuing strategy orders directly in emergencies. E.g., Germany consider national emergency linkage platform, federal or state health authorities intervene directly in major events to guarantee cross-region resource calling, avoiding repeat of Japan 2006 multi-point shirking (though Germany has Notarzt, cross-state support needs higher coordination).

Enhance  W → K  Path: Developed countries note experience summary, but can be more systematic. Suggest using AI Analysis + AC Evaluation mode to process EMS big data. E.g., US collects EMS trip data nationwide, uses AI to find key patterns improving survival, then conscious AI assistant evaluates ethics and feasibility behind patterns, forming new training knowledge pushed to local EMS. This combination of AI and knowledge management greatly shortens knowledge update cycles.

Enhance  I → W  Path: Giving dispatch and on-site more wisdom support. Developed countries can actively introduce Decision-making AI. E.g., Japan develops "119 Smart Dispatcher", assisting judgment of best car combination during calls, or proactively contacting beds when hospitals tight, avoiding multiple refusals. US can promote similar systems, elevating dispatch to true "Command Hub" not passive router. Germany can let AI participate in on-site multi-casualty triage decision, helping Notarzt save seconds.

Enhance Cross-Agency Paths: Developed countries often have multi-departmental division, need to strengthen info sharing between EMS and hospitals, public safety (viewed as lateral semantic space coupling). Suggest building Regional Emergency Information Cloud, pushing patient pre-hospital info to hospital ER real-time, interconnecting with police/fire systems, giving frontline comprehensive intelligence. E.g., Israel has strong volunteer network but can technically realize full data sharing between MDA and police, civil defense to improve synergy.

Digital Leap Strategy: Developed countries not from scratch, but From Good to Great, focus on overcoming existing system bottlenecks, like US cost issues, Japan overload issues. These can be solved by digital tech leap: US can Digitize Emergency Payment, e.g., using blockchain and instant claims, so callers don't worry about fees (or like some cities use Uber-style APP for ambulance, showing cost range beforehand for transparency); Japan can Digital Triage, launching national emergency consultation and diversion system to reduce unnecessary trips. Also, developed countries can be testbeds for AI+AC, deploying emergency artificial consciousness assistants first, establishing demonstration effects. If these high-techs verify effective in developed countries, will spread globally fast, driving overall leap.

(2) Urbanizing Developing Countries:

Represented by China, India, Brazil, etc., these countries have relatively developed urban emergency aid but backward rural, needing balanced development. Minimal Configuration Model should focus on ensuring every city has perfect pre-hospital network, every town has basic emergency point. This may need tiered configuration: big cities with Complete DIKWP Modules (smart dispatch, info system, training system), small cities with Simplified DIKWP Modules (core is call and transport, knowledge and wisdom rely on superior support). E.g., China can promote "City Emergency Center + Township Emergency Station" model, central city guiding and remotely supporting township station decisions (Hub-Spoke structure).

Path Enhancement:

Strengthen D Layer Coverage: Utilize advantage of universal mobile phones and internet, promote Multi-channel Alarms (phone, mini-programs, one-key alarm hardware). Like India's 112 App supporting location and text sending, useful for noisy environments or hearing impaired. High mobile users in urbanizing countries make this drastically improve alarm rate.

Strengthen K Layer Standardization: Formulate National Pre-hospital Emergency Guidelines and training certification system, ensuring personnel in different regions follow unified knowledge. China is taking this step, needs to speed up legislation guaranteeing 120 industry independent access and title sequence. India, Brazil should also establish national EMS societies leading knowledge update and training.

Strengthen W Layer Regional Support: Due to insufficient grassroots Wisdom layer, adopt Regional Command + Remote Guidance strategy. When county major event occurs, auto-connect to provincial center, experienced dispatch chief assists decision; meanwhile on-site ambulance opens Remote Video, superior hospital experts see scene via AR glasses, guiding treatment. This equals introducing "Cloud Brain" in Wisdom layer, making up for local deficiency.

Strengthen P Layer Coordination: Urbanizing countries often have multi-headed management (health, police etc.), need top-level cross-departmental committee coordinating investment and policy execution. E.g., establishing National Emergency Service Administration, unifying planning of station layout, platform construction, financial guarantee. Thus P layer can fully empower downwards, avoiding local fragmentation.

Digital Leap Strategy:

Skip Old Communication, Go Straight to Smart Dispatch: Many developing countries don't need to go through analog era of developed countries, can directly launch Digital Dispatch Systems. Like many Chinese cities relying on walkie-talkies/paper before, now one-step establishing GIS vehicle tracking, electronic maps, high-level dispatch algorithms, typical digital leap.

Mobile Tech Empowering Countryside: In vast rural areas where fixed stations hard to cover, learn from African experience, use mobile phone + motorcycle/bicycle rescue. E.g., some Indian areas use motorcycle volunteers with medical kits as advance guard; China can promote "Rural 110 turns 120" model, police assistants handling initially. These utilize existing mobile networks, quickly supplementing D/I layers in underdeveloped areas.

Public Mutual Aid Network: Dense population in urbanizing countries allows organizing Public Emergency Volunteer networks via App (like Israel model). Equivalent to digital era "Neighborhood Watch", binding trained people to nearby AEDs and stations, intervening before professional resources arrive. Effective for congested or complex terrain urban areas, improving golden hour survival probability.

(3) Underdeveloped Countries:

These countries (parts of Africa, LDCs in Asia) have nearly blank emergency systems, need From Scratch leap. Minimal configuration model should be very concise pragmatic, e.g.:

D Layer: At least one national universal emergency number (even if just one phone answered in police station).

I Layer: Simplified dispatch, e.g., local clinics, police stations assume part-time dispatch, as long as can send people/car.

K Layer: A set of basic first aid skill checklists and concise manuals, training village health workers, drivers to master hemostasis, bandaging, CPR. Don't pursue high-end tech, teach five basic techs (hemostasis, bandaging, fixation, transport, ventilation) first.

W Layer: Due to lack of professionals, Wisdom layer relies more on Grassroots Experience and external support. Can designate experienced old village doctors as regional consultants, or rely on traditional wisdom (herbs, tribal habits) temporarily filling gaps, not discarding any life-saving method when scientific training not covering.

P Layer: Government needs to establish vision: e.g., writing establishing 1 station per county into national development plan, or seeking international aid support goals.

Path Strategy: Consider Stepping Stone development rather than gradual:

Skip Fixed Phone Stage: Distribute satellite phones or mobiles to village health stations for contact. Much faster than laying wired networks.

Skip Expensive Ambulances: Utilize existing transport, like modifying pickups to simple ambulances, usable with stretcher and oxygen. Even provide motorcycle tricycle ambulances, small input fast effect.

Skip Long Talent Cultivation: Adopt Short Training + Empowerment model. E.g., 3-month crash course training "Grassroots First Responders", then continuous knowledge update and remote guidance via mobile App, learning by doing. Where doctors scarce, promote "Emergency Nurse" role, experienced nurses trained to assume part of pre-hospital decision duties.

Digital Supervision and Guidance: Underdeveloped areas weak in skills but smartphones often surprisingly popular, use for Cloud Supervision. Superior center receives site photos, voice via WhatsApp etc., remotely guiding. Even establish global volunteer doctor network, online standby answering site questions. Model of using digital means bridging knowledge gap.

Digital Leap also includes Leveraging External Resources. Many underdeveloped countries can consider sharing rescue resources with neighbors. E.g., several small countries co-building helicopter medical team, rotating stationing, cross-border support in events. Equivalent to regional dimension leap, bypassing high cost of individual construction.

Diagram Aspect: Can provide three typical model diagrams in appendix: Developed model highlighting AI hub and AC supervision, Developing model highlighting hub-spoke architecture and mobile tech, Underdeveloped model highlighting simple stations and external remote support. Visual comparison clearly shows evolution of modules from simple to complex, paths from short to long. Despite different configurations, all follow basic DIKWP philosophy, expecting future convergence to higher level smart emergency system with development.

9. Comprehensive Suggestions: Multi-dimensional Strategies for Global Smart Emergency System Construction

Targeting status and future trends of global emergency system construction, we propose comprehensive strategic suggestions from dimensions of standards, technology, ethics, cooperation, striving for both macro policy guidance and practical operability, providing reference for countries promoting smart emergency aid.

(I) Standards and Norms: Establishing internationally unified or mutually recognized emergency standard system is foundation of semantic intercommunication. Suggest WHO lead, with International Red Cross etc., formulating "Global Emergency Service Standards and Guidelines", including unified emergency term definitions, grading classification standards, training certification frameworks etc. Countries gradually align domestic standards with international, using common condition grading (Color code etc.), injury classification (ICD code) and emergency symbols, ensuring semantic consistency in professional communication. This set should also cover Data Interface Norms, e.g., emergency electronic medical record formats, communication protocols, enabling barrier-free cross-system info exchange. Also promote Emergency Personnel Qualification Mutual Recognition, via mechanisms like International First Aid Attestation (IFAA), making volunteers or professionals' qualifications recognized in cross-national rescue, functioning without administrative hindrance. Establishing these standards greatly improves global emergency synergy efficiency and quality.

(II) Technology Empowerment: Vigorously develop and apply emerging technologies, accelerating intelligent digital upgrade of emergency systems. First, promote Emergency IoT construction, deploying sensing communication devices on vehicles, personnel, hospitals for real-time status monitoring and tracking. Second, deploy AI algorithms assisting dispatch, diagnosis, decision, like machine learning predicting call hotspots, computer vision assisting triage. Match with AI industry access and evaluation system in emergency field, ensuring models fully tested before use in critical tasks. Third, forward-looking layout of Artificial Consciousness (AC) R&D, gradually introducing to high-level command decision links, improving AI decision humanity and explainability. Countries actively participate in research, sharing pilot experiences. Also promote Telemedicine and 5G, sharing expert resources across regions, realizing "Cloud Emergency". Promote Satellite Communication covering remote areas, guaranteeing contact anywhere. Tech empowerment not only improves efficiency but opens opportunity window for digital leap, enabling underdeveloped areas to skip traditional models to modernization. Governments formulate special plans, encouraging tech companies to innovate in emergency field, like low-cost monitors, translation software, drone delivery kits, promoting once mature.

(III) Ethics and Regulations: Smart emergency development accompanies massive ethical legal challenges, need planning rule frameworks ahead. Privacy Protection is top priority: emergency involves sensitive personal health data, digital sharing must ensure security compliance. Suggest establishing "Emergency Data Security Standards", regulating model security in Concept, Cognitive, Semantic spaces lifecycle. All participating units observe minimum necessary authorization principle, preventing unauthorized access abuse. In AI Ethics, clarify AI positioning in emergency decision—assistant not replacement, human bears final responsibility anytime, especially in life/death choices. Establish AI decision Transparent Audit mechanism, key decision logic traceable explainable. Value Alignment also key, ensuring AI optimization goals consistent with human values, requiring regulators periodically review AI output, preventing bias discrimination. Legally, countries update medical regulations adapting to new tech: e.g., recognizing telemedicine legal validity, clarifying liability in drone accidents, providing legal protection for volunteer networks (Good Samaritan expansion). Internationally, formulate "Smart Emergency Ethics Declaration" under UN/WHO framework, guiding national legislation. Ethical governance aims to encourage innovation while securing bottom line, making public fully trust new system, willing to cooperate.

(IV) Education and Training: Regardless of tech advancement, humans remain soul of emergency system. Must increase education training for professionals and public, building emergency HR workforce for smart era. For professionals, update training syllabus, adding Human-Machine Collaboration content, e.g., teaching dispatchers interpreting AI suggestions, site personnel using new equipment following new processes. Conduct interdisciplinary training, letting emergency personnel understand IT skills, data security, competent in digital environment. For public, promote Universal Emergency Education, goal is every citizen possesses basic first aid knowledge and ability to seek help correctly. Include in school curriculum, widely publicize via media. "First Hour Golden Self-rescue Mutual-rescue" concept should root in hearts. In remote underdeveloped areas, innovate training forms, like vivid videos comics eliminating text barriers, or training local leaders to teach. UN Red Cross etc. already doing international first aid training guidelines, national Red Crosses and governments should implement. Another level is Professional Talent Cultivation: encourage universities opening emergency medicine, disaster medicine and related tech cross-disciplines (like emergency info engineering), cultivating future leaders. Provide scholarships career attraction measures, retaining talent serving domestic EMS. Only when people keep up can tech and system reform results play out.

(V) International Cooperation: Emergency aid is common human concern, cross-national cooperation generates huge synergy. Suggest focusing on:

Intelligence and Data Sharing: Establish regional or global emergency info exchange platform, sharing EMS operation indicators, best practice cases normally, real-time info in disasters. WHO can lead creating Global EMS Observatory database, countries upload data (desensitized) for research learning. Similar to aviation accident database, improving overall level.

Cross-Border Mutual Aid Mechanism: Encourage signing emergency mutual aid agreements, granting rescue teams convenient entry, practice permission. Once major crisis hits, neighbor or international teams seamlessly join (like EU mechanism). Joint drills necessary, especially regular joint disaster relief drills in border areas, grinding semantic synergy.

Development Assistance: Developed countries aid underdeveloped countries emergency system construction in funds, tech. Need of global public health security chain, also humanitarian responsibility. Assistance can be donating used ambulances, training overseas students, establishing demonstration centers, more effective than pure money. E.g., China and Israel co-build regional emergency training center in Africa, one providing equipment one experience, local government participating operation, driving local capability growth.

Emergency Medical Team Network: Support WHO Emergency Medical Team (EMT) Initiative, standardizing international medical teams, establishing pre-positioned emergency teams on continents. Mentioned Kenya etc. establishing national EMTs, WHO promoting fast deployment self-sufficient teams in Africa. This network should expand, forming globally linked emergency army, dispatched on command in big disasters, serving and training locally normally, improving domestic and contributing globally.

Smart Sharing: Regarding AI, AC cutting-edge tech, strengthen academic exchange open source cooperation, avoid reinventing wheels. Establish International Smart Emergency Alliance, gathering AI experts, emergency experts formulating tech standards, sharing partial non-sensitive models. E.g., opening emergency reasoning datasets to researchers, letting more participate improving algorithms, solving localization issues. Such cooperation accelerates tech maturity landing, ensuring inclusivity of different cultural values.

(VI) Social Comprehensive Governance via Emergency Entry: Emergency system perfection reacts on other social aspects. Governments realize building smart emergency promotes Peacetime-Wartime Combination and Livelihood Guarantee. E.g., EMS network acts as public health monitoring net normally, collecting accident, infectious disease data assisting decision; emergency info system linking city safety, traffic management, improving overall city emergency response. So in policy making, include emergency system in National Security Strategy and Sustainable Development Agenda. Especially under climate change causing frequent disasters, strong emergency system is key indicator of city resilience. Government plan emergency station layout with other infrastructure (e.g., co-building with fire stations, community hospitals, improving utilization). Also focus on Ethical and Humanistic dimensions, advocating humanitarian spirit community mutual aid culture while promoting smart emergency, combining high-tech high-moral. This comprehensive governance concept ensures tech progress doesn't deviate from original intent of serving public.

(VII) Assessment and Monitoring: Finally, suggest establishing scientific indicator system assessing smart emergency construction progress effects. Can design Emergency Network Index based on DIKWP framework, measuring Data coverage (D), Info processing efficiency (I), Knowledge completeness (K), Wisdom decision level (W), Purpose implementation (P), setting quantitative indicators (average response time, pre-hospital survival rate, training rate, satisfaction), publishing rankings periodically. This prompts countries seeing gaps, learning improvements. Academician Yucong Duan's team adopted similar semantic indicator system in AI evaluation, we should also formulate quantitative benchmarks for smart emergency, promoting continuous optimization.

Multi-pronged strategies aim guiding global emergency system evolution towards "Smart, Collaborative, Human-centric, Safe". Ultimately, emergency system concerns everyone's safety, requiring whole society participation, global cooperation. Hope governments institutions invest resources political will with long-term vision urgency, respecting national conditions while strengthening international linkage, jointly building Globally Covered, Accessible, Fast Response, Smart Efficient, Humanistic emergency network, letting anyone anywhere get timely appropriate help in emergencies.

10. Appendix

Appendix 1: Global Emergency Information Path Contrast Graph – Visually presents strength contrast of DIKWP layers and 25 paths in different national emergency systems. Matrix form, e.g., rows/cols D/I/K/W/P, cell color depth represents coupling strength. Quickly compares Germany deep color (high coupling), Africa many blank/light (weak links), China/USA/Japan typical broken chains. Based on actual data expert scoring, providing global view for understanding analysis.

Appendix 2: DIKWP Network Index Score Sheet – Lists scores rankings of representative countries under DIKWP network index. Index includes sub-items: D layer coverage, I layer average dispatch time, K layer qualification index, W layer average response time success rate, P layer policy completeness etc., each 10 points. Table lists scores brief comments. E.g., Germany 9.5, advantage P layer universal guarantee W layer fast response; USA 8.0, deduction P layer fairness; China 7.5, advantage I layer wide urban coverage, disadvantage W layer urban-rural gap; Kenya 5.0, just established weak base. Helps quantify maturity perception.

Appendix 3: National Semantic Mapping Level Table – Ratings for semantic mapping readiness for cross-national interoperability. Examines language ability, multi-standard compatibility, cross-border agreements. Levels e.g., A: Highly docked international (EU, Israel), B: Medium, partial docking (China peripheral cooperation); C: Low, domestic standards only (many inland developing countries). Lists ratings main semantic obstacles, e.g., "Japan-B: Language barrier-free but unique command system needs adaptation", "India-C: Language English but domestic standards differ from international". Reminds countries efforts needed integrating global emergency semantic space.

(Note: Appendix charts based on text materials research deduction, for reference. Specific data diagrams see related research reports data sources.)

Through deep comparison, modeling, simulation in this report, we clearly see despite different status, emergency systems globally share commonality in pursuing Smart, Safe, Efficient service vision. Academician Yucong Duan's DIKWP theory provides unified semantic perspective, layer-by-layer analysis from Data to Purpose lets us grasp technical links without ignoring top-level humanistic care. Under this framework, global emergency systems expected to move towards synergy fusion: advanced experience spreads, backward links filled, emerging tech fairly benefits, every life seen saved in crisis. This is original intention of this research suggestion. May DIKWP smart network connect real world emergency networks soon, building solid life safety net in community of shared future for mankind.

References:

Professor Yucong Duan: DIKWP Artificial Consciousness Model Leads AI Future, 114 Patents Await Industrial Landing_Phoenix Network Regional_Phoenix Network, https://i.ifeng.com/c/8i7jv0YL0ic

(PDF) Review of Professor Yucong Duan's DIKWP Model and Related Theories, https://www.researchgate.net/publication/396555838_duanyucongjiaoshouDIKWPmoxingjixiangguanlilunzongshu

International Test and Evaluation Standards For Artificial Intelligence Based On Networked Data-Information-Knowledge-Wisdom-Purpose (DIKWP) Model | PDF | Information | Semantics, https://www.scribd.com/document/783690113/20240709-InternationalTestandEvaluationStandardsforArtificialIntelligencebasedonNetworkedData-Information-Knowledge-Wisdom-PurposeDIKWPModel

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Paper by DIKWP-AC Artificial Consciousness Team of School of Computer Science and Technology Won "Best Poster Award" at CCF2023 China Digital Service Conference - School of Computer Science and Technology, https://cs.hainanu.edu.cn/info/1020/5820.htm

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