Abstract Analysis of Energy Facilities Based on Hierarchical
通用人工智能AGI测评DIKWP实验室
Abstract Analysis of Energy Facilities Based on Hierarchical DIKWP
International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation(DIKWP-SC)
World Artificial Consciousness CIC(WAC)
World Conference on Artificial Consciousness(WCAC)
(Email: duanyucong@hotmail.com)
The world is entering the era of artificial intelligence (AI), and new energy infrastructure is undergoing profound transformations. Critical systems such as power grids, energy dispatch, and communication networks are becoming increasingly digitalized and intelligent, making large-scale data-driven automated decision-making a reality. However, in these complex systems, relying solely on traditional data-driven algorithms often fails to ensure global consistency and goal-oriented optimization. How to enable AI to better understand the semantics, objectives, and rules of energy systems has become an urgent issue.
To address this, we introduce the semantic mathematical framework DIKWP proposed by Professor Yucong Duan as the theoretical foundation ((PDF) DIKWP White-Box Evaluation and LLM Black-Box Benchmark Capability Mapping Meta-Analysis (DIKWP Artificial Consciousness International Team - In-Depth Research Release)) ((PDF) DIKWP White-Box Evaluation and LLM Black-Box Benchmark Capability Mapping Meta-Analysis (DIKWP Artificial Consciousness International Team - In-Depth Research Release)). DIKWP stands for "Data, Information, Knowledge, Wisdom, Purpose/Protocol," a five-layer semantic system. Building upon the classic DIKW (pyramid model), it adds the highest level of "Purpose/Intent," emphasizing the consideration of subjective intentions and goals in cognitive and decision-making processes ((PDF) DIKWP White-Box Evaluation and LLM Black-Box Benchmark Capability Mapping Meta-Analysis (DIKWP Artificial Consciousness International Team - In-Depth Research Release)) ((PDF) DIKWP White-Box Evaluation and LLM Black-Box Benchmark Capability Mapping Meta-Analysis (DIKWP Artificial Consciousness International Team - In-Depth Research Release)). This model abstracts the essence of the universe into five fundamental elements and their interactions, providing a formal framework to describe the complete evolution from raw data to purposeful decision-making. In short, DIKWP outlines a semantic path starting from "Data," progressing through "Information, Knowledge, Wisdom," and ultimately serving a specific "Purpose," achieving global consistency through closed-loop feedback.
This report aims to explore the reconstruction path of new energy infrastructure in the AI era, grounded in the core mathematical semantics of DIKWP. We will first outline the key concepts and characteristics of the DIKWP theory, then illustrate how to extend this model to energy infrastructure domains such as power grids, energy dispatch, and communication networks, using examples of DIKWP applications in the medical field. Next, we will delve into how to integrate energy data, dispatch protocols, AI control systems, and the DIKWP mapping model, envisioning potential architectural designs and operational logic for multi-domain DIKWP interactions (i.e., "DIKWP × DIKWP") in future smart cities and smart grids. The report will systematically present this reconstruction path through accessible language, rigorous reasoning, and visual aids such as analogies, tables, and process modeling, aiming to provide forward-thinking guidance for researchers and the general public.
Theoretical Foundation of DIKWP Semantic Mathematics
The DIKWP semantic model decomposes the cognitive process into five hierarchically abstracted levels, each representing:
Data (D, Data): Objective raw facts and observations, the most basic symbolic or signal inputs. Examples include sensor readings and log records.
Information (I, Information): Processed and contextualized data. Information reveals the specific states or details conveyed by data, such as compiling raw data into reports, metrics, or alerts.
Knowledge (K, Knowledge): Patterns, regularities, and causal relationships derived from information, often existing as rules, models, or experiences. Knowledge reflects domain understanding, such as professional manuals, operational guidelines, or theoretical formulas.
Wisdom (W, Wisdom): The ability to make comprehensive trade-offs and creative decisions based on knowledge. Wisdom represents high-level judgment, enabling the evaluation of pros and cons of different solutions in complex scenarios to make informed choices.
Purpose/Protocol (P, Purpose/Protocol): The highest-level goals, intentions, or rule constraints that guide the direction of the cognitive process. It embodies the ultimate objectives or principles the system seeks to achieve, such as reducing carbon emissions or ensuring safe and stable operation.
These five elements do not exist in isolation but form a closed-loop semantic mapping system. Within this system, interactions, compensations, validations, transformations, and path optimizations occur among the levels, ensuring the consistency of the entire cognitive framework:
Interaction: Elements at different levels interact, with lower levels providing foundations for higher levels, and higher levels guiding lower ones. For example, data generates knowledge through information processing, while knowledge guides new data collection.
Compensation: Insufficient information at one level can be compensated by feedback from other levels. If data is insufficient to support a decision, higher-level wisdom can guide the acquisition of new data or supplementary knowledge to refine the decision basis.
Validation: Each step of elevation requires verification to ensure semantic consistency and correctness ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report) ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report). For instance, the accuracy of information extraction can be validated by the knowledge layer, while the rationality of knowledge application can be scrutinized by the wisdom layer. Ultimately, decisions must align with the purpose layer for calibration.
Transformation: This refers to the step-by-step elevation from data to information, information to knowledge, knowledge to wisdom, and wisdom to purpose ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report). For example, sensor data is computed into situational information, which is analyzed to form knowledge, synthesized into wisdom for judgment, and finally implemented as concrete plans to achieve the purpose.
Path Optimization: DIKWP forms a directed closed-loop topological structure ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report) (as shown in the figure), where all possible reasoning paths converge within this loop to prevent cognitive deviation. The system can continuously optimize the path from D to P based on feedback, making it more efficient and accurate in achieving goals.
(Image) Figure 1: The directed closed-loop topology of the five-layer DIKWP elements. Data (D) is elevated semantically through Information (I), Knowledge (K), and Wisdom (W) to ultimately serve the realization of Purpose (P). Conversely, Purpose/Intent guides the acquisition and interpretation of lower-level data, forming a closed loop.
Through these mechanisms, the DIKWP model ensures global semantic consistency and closure ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report) ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report). No matter how complex or multi-step the cognitive process is, the conclusions drawn remain constrained within the predefined semantic space, avoiding contradictions or deviations from the original intent. For example, a decision chain might operate as follows: initial raw data is reasoned into new knowledge, which, combined with wisdom-level judgment, is used to fulfill a purpose. This purpose then triggers further data collection, initiating the next cycle ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report). This iterative process ensures that AI systems remain stable and reliable when handling complex reasoning tasks, always operating within the established semantic framework.
Notably, the DIKWP model has been extended in recent research to the form of "DIKWP × DIKWP," which can be understood as the Cartesian product-like combination of the type-level DIKWP framework and the instance-level DIKWP instance space ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report). In other words, there is both an abstract five-element structure and specific five-element instances in application scenarios, with the two multiplying to form a semantic operational universe. In this universe, every concrete system follows the same DIKWP paradigm for information processing and decision-making while operating on its own domain-specific instances. This enables unified modeling across domains and levels—preserving commonality while accommodating individuality. In the later discussion on smart cities, we will further elaborate on how DIKWP × DIKWP leverages this combination of types and instances.
In summary, DIKWP semantic mathematics provides AI with a rigorous and comprehensive cognitive architecture. It not only defines the evolutionary path from data to wisdom and then to purpose but also standardizes how each layer interacts and validates, laying the theoretical foundation for intelligent decision-making in complex systems. In the next section, we will use examples from the medical industry to intuitively demonstrate the application of the DIKWP model in real-world scenarios, thereby drawing insights for the energy sector.
Example of DIKWP Application in the Medical Field
To understand how the DIKWP model can be implemented in real-world systems, let’s first examine a case study from the medical industry. The medical field involves diverse and highly specialized data types, making it an ideal scenario for the DIKWP model to demonstrate its value. Researchers have attempted to map the diagnostic and treatment processes in hospitals to the five layers of DIKWP, constructing a DIKWP framework for medical AI ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report):
Data (D): Raw information from patients, such as symptom descriptions, vital sign monitoring data, and lab test reports. These represent the most basic medical data.
Information (I): Organized and preliminarily interpreted patient data, such as medical history summaries and aggregated examination results.
Knowledge (K): Medical domain knowledge and treatment protocols, including textbook knowledge, clinical guidelines, and case experience. This knowledge can be used to infer causes and evaluate treatment options.
Wisdom (W): The clinical experience and comprehensive judgment of doctors. In complex cases, doctors must integrate multidisciplinary knowledge and weigh options based on the patient’s specific condition—this represents the "wisdom" layer in medical decision-making.
Purpose (P): The goals or intentions guiding clinical decisions, such as curing diseases, alleviating pain, or improving quality of life. Different patients may have different priorities, but these all serve as the ultimate intent of decision-making.
The above mapping shows that the key elements of medical AI processing align one-to-one with the five layers of DIKWP, making DIKWP a naturally suitable framework for medical AI ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report). In other words, we can use a unified perspective to examine the flow of information in hospitals: data collected from patients is interpreted into information, supported by medical knowledge for diagnostic reasoning, integrated with doctors' wisdom to make decisions, and ultimately aimed at achieving the purpose of healing and saving lives.
Based on this mapping, the construction of medical AI systems can follow a progressive, layered approach ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report) ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report):
Building Medical Knowledge Bases and Semantic Models: First, establish ontologies and knowledge graphs for the medical domain to store vast amounts of medical knowledge and experience ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report). For example, a medical knowledge graph containing concepts and relationships for diseases, symptoms, medications, and tests lays the foundation for the data/information layers of the DIKWP framework ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report). This stage essentially builds the base for the K layer and part of the W layer, enabling the AI to "know" the facts and rules of the medical field.
Developing a DIKWP Medical Cognitive Engine: Implement a "cognitive engine" in the AI system to transform raw patient data step-by-step into knowledge, wisdom, and ultimately decision recommendations ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report). Specific components include:
Natural Language Processing Module: Parses inputs from doctors and patients (e.g., complaints, consultation dialogues) into structured medical information (realizing the D→I transformation).
Diagnostic Reasoning Module: Uses extracted information and the knowledge base to infer possible diagnoses and treatment plans (the I→K→W reasoning chain).
Decision-Making Module: Integrates the patient’s personal preferences and overall medical goals to select the optimal solution from available options (W→P decision).
Through these modules, the AI essentially covers the entire process from data to purpose, with each step corresponding to a transformation in the DIKWP model ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report). For example, the AI can interpret "fever and cough" (data) as "suspected lung infection" (information), further use the knowledge base to deduce "possible diagnoses of pneumonia or bronchitis, recommending chest imaging" (knowledge + wisdom), and finally consider the patient’s desire for quick recovery (purpose) to choose "immediate antibiotics and imaging" (decision).
Human-AI Collaborative Iterative Training: Before deployment, medical AI requires extensive refinement. The advantage of the DIKWP framework lies in its interpretability and corrigibility. Whenever the AI provides a diagnostic reasoning chain, human doctors can review the output at each layer:
Check if data extraction is accurate (D layer).
Verify if the referenced medical knowledge is appropriate (I/K layer).
Assess whether the comprehensive judgment at the wisdom level is reasonable (W layer).
Confirm if the final decision aligns with medical ethics and patient preferences (P layer).
If an issue is identified at any step, doctors can provide targeted feedback to guide adjustments in the corresponding layer’s parameters or rules. For example, if the AI overlooks a critical symptom leading to misdiagnosis, the doctor can highlight the need to prioritize that symptom during D→I conversion. Such granular feedback makes the AI’s learning process more efficient, transparent, and precise compared to adjusting the model solely based on final outcomes. After multiple rounds of human-AI co-training, the AI’s diagnostic performance gradually improves.
Deployment and System Integration: Once the prototype system is sufficiently reliable, it can be gradually deployed in real medical environments. Initial pilots may focus on vertical scenarios, such as chatbots for chronic disease management or clinical decision support systems (CDSS) assisting physicians ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report) ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report). As the technology matures, DIKWP medical AI can be further integrated into hospital information systems (HIS), connecting data sources like electronic health records to enable hospital-wide intelligent analysis from data→knowledge→wisdom ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report). Simultaneously, patient-facing DIKWP-based health management apps can help individuals interpret their health data and receive personalized advice ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report). Ultimately, the vision for the medical field is a smart healthcare network centered on the DIKWP framework, connecting data flows among patients, doctors, and hospitals to enable real-time knowledge generation and guide clinical practice ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report).
Through this approach, a medical AI system structured around DIKWP takes shape. It not only provides diagnostic conclusions but also reveals the reasoning behind them, making it more trustworthy for medical professionals ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report) ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report). Such a system promises several benefits, such as ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report) ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report):
Improved Diagnostic Quality: With support from the knowledge and wisdom layers, AI can offer more comprehensive and evidence-based diagnostic suggestions, reducing misdiagnoses ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report). For instance, when dealing with complex cases, AI can integrate multidisciplinary knowledge for holistic judgment, acting as a highly knowledgeable assistant to doctors ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report).
Reduced Burden on Doctors: AI can automate tedious data organization and preliminary reasoning, allowing doctors to focus on critical decisions and patient communication ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report). In scenarios like chronic disease management or follow-up consultations, DIKWP AI may independently handle most professional dialogues, only involving human doctors when empathetic interaction is needed, thereby significantly improving healthcare efficiency and accessibility ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report).
Personalized Medicine: Since the DIKWP framework explicitly includes the purpose/intent layer, it inherently supports individualized decision-making. Each patient’s life goals and risk preferences differ, and AI can model these factors at the purpose layer, incorporating them into wisdom-level decisions to align with patient values. This embodies patient-centered care and enhances acceptance of treatment plans.
Of course, deploying DIKWP systems in medicine faces challenges, such as protecting medical data privacy, difficulties in cross-hospital data sharing, the high cost of building and updating knowledge bases, and defining legal accountability for AI decisions ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Through Cognitive Limits Research Report). Nonetheless, the exploration of DIKWP in healthcare provides a template: transparently decomposing AI decision-making into data-information-knowledge-wisdom-purpose chains and continuously refining them with human expert collaboration. This concept can readily be extended to other complex domains, such as new energy infrastructure, which we will now explore with a focus on power systems.
DIKWP Mapping in Power Grids and Energy Dispatch
Power systems are critical infrastructure that underpins national economies and livelihoods, encompassing generation, transmission, distribution, and consumption, with complex real-time dispatch and stringent safety requirements. Under traditional architectures, grid operation and management largely rely on human interpretation of data and experience-based dispatch decisions. Today, with the widespread deployment of sensors and smart devices, power grids generate vast amounts of energy data, while various intelligent dispatch algorithms and autonomous control systems continue to emerge. However, achieving a truly smart grid still requires a top-level architecture capable of integrating data, knowledge, and objectives. The DIKWP model provides an ideal theoretical blueprint for this purpose. Below, we attempt to map power grid and energy dispatch scenarios to the five layers of DIKWP and explore how to reconstruct power infrastructure accordingly.
DIKWP Hierarchy Mapping in Power Systems
First, let’s define the corresponding elements of DIKWP in power systems:
Data (D): Raw operational data from the power grid. Examples include:
Real-time monitoring data: Voltage, current, frequency, power readings from sensors, and equipment status data such as transformer temperature and switch states.
Historical operational data: Past load curves, generation output records, and electricity market prices.
External related data: Weather information (affecting solar/wind generation and load demand) and market trading data.
These data sources are diverse in format but collectively form the factual foundation of power system operations.
Information (I): Processed and calculated grid status information and event alerts derived from raw data. Examples include:
Situational awareness information: Grid-wide operational conditions (e.g., high-load lines or low-voltage areas) obtained through state estimation algorithms.
Forecast information: Short-term load forecasts and renewable energy generation predictions based on historical data analysis.
Alarm information: Fault alerts or safety warnings (e.g., overcurrent alarms on a transmission line) generated from anomaly detection.
This layer refines and consolidates massive data, enabling dispatchers or AI to grasp the overall situation and key issues.
Knowledge (K): Extensive domain-specific knowledge related to power system planning and operation, including:
Power engineering knowledge: Theoretical principles such as circuit laws, grid protection mechanisms, and stability analysis methods.
Dispatch protocols and operational rules: Regulations and safety guidelines (e.g., N-1 contingency criteria) formulated by grid operators, often distilled from years of experience.
Optimization control models: Dispatch optimization models (e.g., power flow calculations, reactive power optimization, and load distribution algorithms) serve as tools in the knowledge layer.
Historical case studies: Records of past major incidents and their resolutions, as well as exemplary dispatch schemes, provide reference for similar scenarios.
This knowledge typically exists in documents, manuals, expert system rules, or mathematical models, forming the "brain trust" of grid operations. In recent years, the power industry has also begun constructing power grid knowledge graphs to organize equipment, events, and protocols in a semantically linked manner for machine comprehension and application (Analysis of Key Technologies for Artificial Intelligence Applied to Power Grid Dispatch and Control). A robust knowledge layer is essential for enabling AI to "understand" power systems.
Wisdom (W): High-level comprehensive judgment and decision-making tailored to specific scenarios. In grid operations, this manifests as:
Dispatcher’s holistic decisions: Experienced dispatchers weigh factors like load demand, generation costs, safety margins, and policy objectives to adjust generation plans or reconfigure power flows. Such decisions often require balancing multiple objectives (e.g., safety vs. economy) and anticipating potential outcomes, embodying wisdom-level judgment.
AI-driven optimization: With AI, the wisdom layer can be implemented via optimization algorithms or reinforcement learning agents. These leverage knowledge-layer models to search for optimal dispatch solutions while adhering to safety constraints. For example, AI at this layer might balance goals like reducing losses, balancing loads, and maximizing renewable energy integration to produce a compromise control strategy.
Multi-scheme evaluation: Another function of the wisdom layer is evaluating and selecting among feasible solutions. If the knowledge layer generates multiple candidate plans (e.g., different generator combinations), the wisdom layer assesses their merits and selects the best based on higher-level goals and experience.
This layer emphasizes adaptability and "human-like" flexibility in decision-making rather than rigid rule-following.
Purpose/Protocol (P): The ultimate goals, constraints, and policy intentions guiding power system operations. This layer includes:
Power supply reliability: Ensuring uninterrupted electricity supply and avoiding large-scale blackouts, the grid’s foremost intent.
Economic dispatch objectives: Minimizing generation costs, improving energy efficiency, and meeting market revenue targets.
Environmental and sustainability goals: Increasing clean energy integration, reducing carbon emissions, and aligning with national "dual-carbon" policies.
Safety standards and protocols: Mandatory industry regulations and laws (e.g., grid safety standards and dispatch command procedures) that reflect managerial or regulatory intent.
In short, the P layer defines "what the power system operates for." Whether short-term supply-demand balance or long-term strategic objectives, this layer ensures AI decisions align with unified benchmarks rather than narrowly optimizing a single metric.
Integration in Smart Grid Architecture
Now, we see that the five DIKWP elements in power systems are clearly defined, with each layer rich in content. In a smart grid architecture:
Data continuously flows in from sensors and IoT devices.
Information is refined by energy management systems (EMS) and advanced metering infrastructure.
Knowledge resides in dispatch protocols and models.
Wisdom is embodied in the decision-making processes of control centers.
Purpose is dictated by utility mandates and societal needs.
This layered description parallels the medical case study, demonstrating DIKWP’s universality.
Synergy with Communication Networks
Notably, modern smart grids are deeply integrated with communication networks ("Big Talk on Power Knowledge Graphs" - Polar Star Power News). Data collection and control at all grid levels rely on stable, high-speed communication systems. Thus, in the DIKWP model for smart grids, communication networks are not standalone but deeply embedded, ensuring data transmission (D layer) and information aggregation (I layer).
We can view the power grid and its communication network as a larger DIKWP instance:
The communication network ensures efficient data delivery and executes control commands (a "Protocol" layer function).
Conversely, grid operations’ wisdom-layer decisions impose requirements on communications (e.g., low latency, high reliability).
This synergy between power and communication reflects the DIKWP×DIKWP concept—where frameworks from both fields interact through aligned inputs/outputs, forming a more complex semantic network.
Envisioning a DIKWP-Based AI System for Power Grids
After establishing the mapping, we can design a DIKWP-based intelligent control system for power grids and outline its implementation path. The overarching vision is to make AI the "brain" of the power system, with its cognition and decision-making strictly following DIKWP logic, continuously optimized under human expert supervision. Below is the step-by-step reconstruction path:
1. Constructing a Power Domain Knowledge Graph and Model Library (K Layer)
Similar to the medical case where a medical knowledge base was first established, the power sector must also organize its "knowledge assets." This includes:
Power equipment and topology ontology: Building a model that includes grid topology, device specifications, and connection relationships, enabling AI to "understand" grid components and their interactions.
Operational rules and experience database: Digitizing existing dispatch protocols, safety regulations, emergency plans, and historical incident records into a semantic knowledge base. For example, constructing a "Power Grid Fault Handling Knowledge Graph" that links fault types, symptoms, possible causes, and mitigation measures (Analysis of Key Technologies for Artificial Intelligence Applied to Power Grid Dispatch and Control).
Simulation and optimization model library: Aggregating power system simulation models (e.g., load forecasting, power flow calculations, stability analysis) into AI-callable modules. When evaluating a solution, the wisdom-layer AI can simulate outcomes using these models.
Expert experience encoding: Translating senior dispatchers' tacit knowledge into rules or algorithms. For instance, "which units to prioritize during peak load periods" can be formalized into rules for the knowledge base.
This step requires collaboration between power experts and AI engineers. Fortunately, industry efforts are already underway, such as research on grid security situational awareness knowledge graphs (Application and Prospects of Multimodal Knowledge Graphs in Power Inspection). The output of this phase will support the K layer (knowledge) and part of the W layer (wisdom) in the DIKWP framework, equipping AI with fundamental power system expertise.
2. Building the Energy DIKWP Cognitive Engine
This engine serves as the "central brain" of the power system, automating the flow from D to P. It consists of several core modules:
Data Integration and Parsing Module (D→I): Collects real-time data from monitoring points, performs cleaning and consolidation, and converts it into grid-wide situational information. For example:
State estimation algorithms aggregate substation data to output power flow distribution and reserve capacity.
Weather forecasts are translated into solar generation predictions.
This step compresses noisy, massive raw data into actionable status information.
Intelligent Diagnosis and Reasoning Module (I→K→W):
Anomaly detection: Uses the knowledge base to identify violations (e.g., a line at 90% load) or faults (e.g., sensor malfunctions).
Fault reasoning: Leverages the fault knowledge graph to infer root causes and impact scope (Analysis of Key Technologies for Artificial Intelligence Applied to Power Grid Dispatch and Control).
Optimization proposal generation: Calls upon models to compute dispatch alternatives (e.g., generation schedules or load management plans), each tagged with expected outcomes (cost, risk, etc.).
Decision Selection Module (W→P):
Evaluates proposals against top-level objectives (e.g., "reliability-first" vs. "cost-minimization") and selects the optimal action. For example:
During emergencies, prioritizes fault isolation over economic dispatch.
In normal operations, balances cost and emissions based on policy goals.
This engine ensures the AI’s reasoning is transparent and traceable: every step—from raw data to dispatch commands—is semantically labeled, allowing dispatchers to audit "why the AI did this." If unexpected results occur, the faulty layer can be quickly pinpointed.
3. Iterative Training and Dispatcher Validation
Like in healthcare, human experts play a critical role in training power AI:
Offline simulation: The AI processes historical or synthetic scenarios, generating decision chains for dispatchers to review. Experts validate:
Data integration accuracy (D layer).
Reasoning logic (I→K transitions).
Trade-off alignment with safety/economy principles (W layer).
Targeted feedback: If the AI suggests load shedding for a fault but experts propose network reconfiguration instead, the AI is adjusted to prioritize "reliability maximization" in future decisions.
This layer-by-layer calibration is safer and more efficient than black-box machine learning, especially for rare events (e.g., blackouts) where data is scarce.
Once the AI performs satisfactorily, it can be deployed incrementally—first as a decision-support tool, with humans retaining final authority. This builds trust while allowing further refinements.
4. Phased Deployment and Integration
Implementation should progress from niche applications to full autonomy:
Localized optimization: Start with DIKWP AI for substation inspections, renewable farm controls, etc. Proven performance in these areas establishes credibility.
Dispatch decision support: Expand to control centers, where AI advises on generation schedules and security analyses. Dispatchers reference AI suggestions (with rationale) to adjust plans.
Grid-wide autonomous control: Ultimately, the DIKWP AI integrates with Energy Management Systems (EMS) and Distributed Energy Management Systems (DEMS), directly executing routine dispatch (e.g., via Automatic Generation Control). Data and commands flow through a unified communication network, closing the D→I→K→W→P loop.
Expected Benefits and Challenges
A mature DIKWP-driven grid AI promises:
Higher reliability/safety: 24/7 monitoring and proactive risk mitigation (e.g., preemptively rerouting power to avoid overloads) (Analysis of Key Technologies...).
Optimized dispatch: Balancing cost, emissions, and renewables integration unlocks hidden efficiencies (e.g., reducing transformer losses at night).
Reduced human workload: AI handles routine data monitoring, freeing dispatchers for strategic tasks.
Transparent, auditable decisions: Full traceability aids post-incident analysis and regulatory compliance.
Self-evolution: Continuous learning from new scenarios (e.g., novel faults) enhances future performance.
Data silos and privacy: Grid data is fragmented across entities, complicating integration.
Knowledge maintenance: Keeping digitalized rules/models up-to-date demands sustained effort.
Trust barriers: Full autonomy in safety-critical scenarios requires prolonged validation.
Cybersecurity risks: AI adoption may expand attack surfaces.
Applying the DIKWP model to power grids enables a paradigm shift from "experience-driven" to "semantics-driven" operations. AI evolves from a mere optimizer to a "digital dispatcher" imbued with industry wisdom and purpose. This reconstruction will empower the stable, efficient, and sustainable operation of future power systems.
Application of the DIKWP Model in Communication Networks
The smart grid discussed earlier inherently incorporates communication networks. As the "lifeblood" of energy systems, the intelligent management of communication networks can similarly benefit from the DIKWP model. In this section, we briefly extend the DIKWP mapping approach to general communication networks (e.g., telecom networks, internet data networks) to demonstrate the model's cross-domain applicability.
Communication networks, as infrastructure for information transmission, face challenges in optimizing resource utilization and ensuring service quality. For example:
In carrier backbone networks, how to dynamically adjust routing and bandwidth allocation based on traffic fluctuations to avoid congestion?
In 5G cellular networks, how to intelligently allocate spectrum resources and power control to simultaneously meet diverse user demands for speed and latency?
These problems all require AI-assisted decision-making. Below, we analyze them through the DIKWP lens:
The vast real-time data generated by network operations, including:
Link traffic statistics, switch node forwarding logs, packet loss and latency measurements, and terminal signal strength.
Configuration data (device parameters) and topology data (network structure diagrams).
In communications, the D layer represents the aggregation of network telemetry and logs.
Network state information derived from processed data, such as:
Topology views: Real-time network connectivity maps.
Performance metrics: Key KPIs (e.g., link utilization, end-to-end latency, server CPU load) for health monitoring.
Alerts: Rule-triggered events (e.g., fiber cuts, high packet loss).
Predictive insights: Traffic forecasts and user behavior patterns based on historical data.
This layer semantically refines raw data into observable, analyzable network status.
Domain expertise and strategies, including:
Protocols: TCP/IP, BGP, 5G NR, etc., which encode behavioral rules.
Optimization strategies: Congestion control algorithms, traffic engineering (TE) policies.
Troubleshooting guides: Manuals documenting fault resolution steps (e.g., "timeout retransmissions may indicate X; remedy Y").
Resource allocation models: Formulas for user handovers or spectrum scheduling.
Modern networks increasingly use knowledge graphs to organize these elements, enabling AI-driven reasoning.
High-level decision-making in network control:
Dynamic routing: SDN controllers adjust paths based on multi-factor权衡 (load, priority, latency).
Self-optimization: SON (Self-Organizing Networks) features like automatic base station power adjustment.
Multi-objective trade-offs: Balancing low latency for critical services with energy savings.
Strategy evolution: Adapting algorithms to new service demands.
The W layer grants networks autonomous intelligence, akin to human expert oversight.
Top-level intents governing network behavior:
Service quality: SLA guarantees (e.g., bandwidth, latency).
Business policies: Prioritizing premium users or emergency communications.
Efficiency goals: Minimizing energy use, maximizing resource utilization.
Security/compliance: Regulatory requirements (e.g., net neutrality) and attack prevention.
The P layer defines the "value function" for optimization. For instance, during major events, "ensuring live-stream stability" may override other metrics.
Architecture of an Intelligent Communication Network
Similar to the smart grid:
Data collection: Probes and telemetry protocols feed D-layer data to the AI engine.
Information layer: Real-time state assessment (e.g., digital twin visualization) and traffic forecasting.
Knowledge layer: Protocol libraries and rule bases (e.g., "if congestion → reroute").
Wisdom layer: The "brain" that synthesizes inputs to reconfigure networks (e.g., SDN routing, edge computing resource allocation).
Purpose layer: Admin-defined optimization priorities (e.g., latency vs. throughput), adjustable via dashboards or business policies.
Development and Deployment
Like grid AI, communication network AI requires iterative human-AI collaboration:
Engineers validate AI-proposed solutions, refining knowledge rules or objectives as needed.
Gradual deployment starts with localized optimizations (e.g., automated fault detection), scales to network-wide autonomy (self-healing, energy-saving modes).
A DIKWP-powered network delivers:
Enhanced user experience: Proactive congestion mitigation, fewer outages.
Lower OPEX: Reduced manual troubleshooting.
Agility: Rapid adaptation to business needs.
The semantic closed-loop of DIKWP ensures decisions are data-driven yet goal-anchored, avoiding reactive suboptimization.
Cross-Domain Universality
Both power grids and communication networks exemplify how DIKWP provides a unified paradigm for infrastructure intelligence. Whether managing electrons or bits, the core chain remains:
Data → Information → Knowledge → Wisdom → Purpose.
In the next section, we expand this vision to smart cities, where multiple DIKWP systems intertwine (DIKWP×DIKWP), forging a new infrastructure architecture.
DIKWP×DIKWP Architecture for Smart Cities
Future smart cities are envisioned as organic entities: energy, transportation, communications, healthcare, public safety, and other subsystems must collaborate and evolve together to sustain the efficient operation of this "superorganism." Achieving such synergy requires more than isolated digital systems—it demands an intelligent cross-system neural hub. The DIKWP×DIKWP semantic model offers an inspiring framework for smart city design: a unified semantic coordinate system that connects AI systems across domains, ensuring consistency and closed-loop interactions for data, knowledge, and objectives.
Multi-Domain DIKWP Interaction
Imagine a smart city where AI systems for energy grids, traffic management, healthcare, and environmental monitoring are each built on the DIKWP framework. While these systems optimize locally within their domains, how can they collaborate across domains? DIKWP×DIKWP suggests that individual DIKWP instances can form a higher-level DIKWP structure:
Aggregated raw data from all sectors (power load, traffic flow, ER admissions, air pollution indices, etc.).
While isolated data may seem trivial, cross-domain correlation reveals comprehensive urban insights.
City-Level Information (I):
Fused and analyzed data generates key operational insights.
Example: "Today’s record-high power demand in industrial zones" + "20% rise in morning traffic congestion" → factory activity impacts traffic.
Integrates weather forecasts, energy/public health data → "Heatwave will spike AC demand and heatstroke risks."
City-Level Knowledge (K):
Cross-domain knowledge bases and models:
Traffic-environment models: Vehicle emissions vs. air quality.
Energy-economy models: Industrial production vs. power demand.
Emergency protocols: Coordinated responses for blackouts, disasters.
Enables context-aware decisions. Example: Traffic AI adjusts signals preemptively if the energy AI warns of grid instability.
Cross-system decision-making that balances trade-offs.
Example: To alleviate power shortages, the energy AI proposes staggered power use. The city-level W-layer evaluates impacts:
Prioritizes hospitals and subways over office buildings.
Coordinates with traffic AI to promote off-peak commuting.
Implemented via multi-agent collaboration or unified optimization models.
Top-level intents like "safety, economic growth, sustainability, livability."
Translated into measurable goals (emission cuts, GDP targets, emergency response times).
Ensures alignment across sectors. Example: "Net-zero" goals drive energy (renewables), traffic (EVs), and industry (efficiency) policies.
This forms a city-wide DIKWP loop: IoT/sensor data (D) → fused insights (I) → cross-domain knowledge (K) → AI-assisted decisions (W) → serving civic goals (P). Like interconnected brain regions, sector-specific AIs collaborate under a shared semantic framework—the essence of DIKWP×DIKWP ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Cognitive Limits).
Architecture and Operational Logic
Key enablers for this vision:
Unified Data & Model Sharing Platform:
A city-wide digital twin with standardized semantic tagging (e.g., distributed knowledge graphs).
Enables "knowledge sharing" while preserving data privacy.
Cross-Domain Semantic Protocols:
Standardized messaging for AI-to-AI communication.
Example: Energy AI alerts traffic AI of impending blackouts via structured DIKWP-formatted messages (data fields, intent references).
Hierarchical Autonomy + Central Coordination:
Daily operations: Sector AIs self-optimize (local DIKWP loops).
Crisis/events: Central "city brain" AI orchestrates cross-sector responses (global W-layer override).
Human-AI Co-Decision & Feedback Learning:
Humans refine AI proposals based on socio-political factors (e.g., rejecting factory closures to protect jobs).
Feedback trains AI to incorporate broader intents (e.g., "optimize for employment + emissions").
Once operational, this architecture enables:
Proactive governance: Predicting cascading risks (e.g., industrial power surges → grid/traffic stress) and preempting them.
Resource hyper-efficiency: Coordinated energy, water, and traffic management eliminates waste (e.g., dimming streetlights during low-demand nights).
Resilience: Rapid adaptation to crises (e.g., pandemic-mode reprioritization of medical supply chains).
Implementation hurdles include:
Standardization: Aligning sector-specific data formats and protocols.
Security: Zero-trust architectures to prevent cross-system breaches.
Yet, early steps are underway—city data platforms, smart grid-traffic integrations, and digital government initiatives lay the groundwork. In 5–10 years, DIKWP-driven smart cities could emerge as self-regulating, adaptive ecosystems.
This report leverages Prof. Yucong Duan’s DIKWP semantic mathematics to chart a path for AI-era infrastructure redesign. From healthcare to energy grids, communications, and smart cities, DIKWP provides a unified cognitive lens, mapping system complexity into Data-Information-Knowledge-Wisdom-Purpose layers with closed-loop consistency.
For researchers, DIKWP’s rigor and extensibility offer fertile ground—integrating with knowledge graphs, LLMs, and reinforcement learning to advance AI cognition ((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Cognitive Limits). For the public, it promises cities that "think": resilient, efficient, and human-centric.
Challenges remain—technical, organizational, and ethical—but incremental adoption (e.g., grid assistants, hospital AI) can validate the approach. As Prof. Duan’s work shows, DIKWP is helping AI transcend cognitive limits ((PDF) DIKWP White-Box Evaluation and LLM Black-Box Benchmark Capability Mapping Meta-Analysis), paving the way for Artificial General Intelligence (AGI)-ready systems.
In sum, infrastructure redesign in the AI age demands principled frameworks. DIKWP provides the compass—ensuring every step from data to purpose is visible, interpretable, and optimizable. The future is a DIKWP-powered, semantically interconnected world, where infrastructure doesn’t just function but understands why.
((PDF) DIKWP White-Box Evaluation and LLM Black-Box Benchmark Capability Mapping Meta-Analysis)
((PDF) DIKWP×DIKWP Semantic Mathematics Helps Large Models Break Cognitive Limits)
玩透DeepSeek：认知解构+技术解析+实践落地
人工意识概论：以DIKWP模型剖析智能差异，借“BUG”理论揭示意识局限
人工智能通识 2025新版 段玉聪 朱绵茂 编著 党建读物出版社
邮箱|duanyucong@hotmail.com
