Data-model knowledge combined driving method for multi-region energy interconnection

Abstract

The invention discloses a data-model knowledge combined driving method for multi-region energy interconnection, and mainly relates to advanced technical means such as a digital twinborn technology, a reinforcement learning algorithm and an intelligent operation and maintenance technology. Therefore, efficient management and optimization of multi-region energy interconnection are realized. The core of the method lies in that the operation of the energy system is intelligently monitored, predicted and optimized through real-time data collection and analysis in combination with an advanced mathematical model and algorithm, so that the energy utilization efficiency is improved, the operation cost is reduced, and the stability and reliability of the system are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-regional energy interconnection, and in particular to a data-model knowledge joint driving method for multi-regional energy interconnection. Background Art

[0002] With the continuous growth of global energy demand and the increasing diversification of energy structure, multi-regional energy interconnection has become a key way to improve energy efficiency, ensure energy security and promote sustainable development. The "data-model knowledge" joint driving method, as a product of the deep integration of modern information technology and energy system, provides strong technical support for multi-regional energy interconnection.

[0003] We intelligently monitor, predict and optimize the operation of energy systems through real-time data collection and analysis combined with advanced mathematical models and algorithms, thereby improving energy efficiency, reducing operating costs, and enhancing system stability and reliability. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a "data-model knowledge" joint driving method for multi-regional energy interconnection.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A "data-model knowledge" joint driving method for multi-regional energy interconnection, including the "data-model knowledge" joint driving method for multi-regional energy interconnection mainly involves the use of advanced technical means such as digital twin technology, reinforcement learning algorithm, and intelligent operation and maintenance technology to achieve efficient management and optimization of multi-regional energy interconnection. The core of this method is to intelligently monitor, predict and optimize the operation of the energy system through real-time data collection and analysis, combined with advanced mathematical models and algorithms, so as to improve energy utilization efficiency, reduce operating costs, and enhance system stability and reliability.

[0007] S1 data collection and preprocessing:

[0008] ① Data collection: Collect real-time operation data, historical data and environmental data (such as meteorological data) from multi-regional energy systems (including power systems, natural gas systems, thermal systems, etc.).

[0009] ②Data preprocessing: Clean, convert, and standardize the collected data to ensure data quality and facilitate subsequent analysis and modeling.

[0010] S2 model construction and optimization:

[0011] ① Physical model: Based on the physical characteristics and operating laws of the energy system, a physical model of the multi-regional energy system is constructed, including power transmission model, heat transmission model, natural gas transmission model, etc.

[0012] ②Data-driven model: Use machine learning, deep learning and other technologies to build prediction models and optimization models based on historical data and real-time data to improve system prediction accuracy and scheduling efficiency.

[0013] ③Knowledge integration: Integrate expert knowledge, policy regulations, industry norms, etc. into the model building process to enhance the practicality and reliability of the model.

[0014] S3 Knowledge Representation and Reasoning:

[0015] ① Knowledge base construction: Establish a multi-regional energy system knowledge base, including energy policies, technical standards, system operation rules, etc.

[0016] ②Knowledge reasoning: Based on the knowledge base, combined with real-time data and model prediction results, knowledge reasoning is performed to provide decision support for system optimization and scheduling.

[0017] S4 Decision support and optimal scheduling:

[0018] ① Decision support: Based on the model prediction results and knowledge reasoning results, provide decision support information to system managers, including energy allocation plans, fault warnings, energy-saving suggestions, etc.

[0019] ②Optimization and scheduling: Based on multi-objective optimization algorithms (such as genetic algorithms, particle swarm algorithms, etc.), combined with real-time data and prediction models, the optimal scheduling of multi-regional energy systems can be achieved to improve energy utilization efficiency and reduce operating costs.

[0020] Preferably, the key technologies are:

[0021] S1 Data Fusion and Mining

[0022] ① Use data fusion technology to integrate multi-source heterogeneous data into a unified data format to facilitate subsequent analysis and modeling.

[0023] ② Apply data mining technology to extract valuable information from massive data and provide data support for model building and decision support.

[0024] S2 Modeling and Simulation

[0025] ① Use system simulation technology to simulate multi-regional energy systems to verify the effectiveness and accuracy of the model.

[0026] ② Introduce theories such as multi-agent systems (MAS) and complex networks to build a dynamic simulation model of a multi-regional energy system and simulate the operating status of the system under different scenarios.

[0027] S3 Knowledge Representation and Reasoning

[0028] ① Use knowledge graphs, ontology and other technologies to structure the knowledge of multi-regional energy systems to facilitate knowledge storage and query.

[0029] ② Based on rule reasoning, case reasoning and other methods, combined with real-time data and model prediction results, knowledge reasoning is performed to provide a decision-making basis for system optimization and scheduling.

[0030] S4 Collaborative Optimization and Scheduling

[0031] ① Introduce a multi-objective optimization algorithm to comprehensively consider multiple objectives such as economic costs, environmental benefits, and energy security to achieve coordinated optimization of multi-regional energy systems.

[0032] ② Apply intelligent scheduling technology to achieve intelligent scheduling of multi-regional energy systems based on real-time data and prediction models, and improve the flexibility and reliability of system operation.

[0033] Preferably, the implementation steps are:

[0034] S1 Demand analysis: Clarify the specific needs of the multi-regional energy interconnection system, including data collection needs, model building needs, knowledge representation needs, etc.

[0035] S2 Data collection and preprocessing: Collect data as needed, and perform cleaning, conversion, and standardization.

[0036] S3 model building and optimization: Build models based on physical and data characteristics, and optimize models based on expert knowledge.

[0037] S4 Knowledge base construction and reasoning: Establish a knowledge base and perform knowledge reasoning to provide decision support for system optimization scheduling.

[0038] S5 Decision support and optimal scheduling: Develop energy allocation plans and optimal scheduling strategies based on model prediction results and knowledge reasoning results.

[0039] S6 System Implementation and Evaluation: Apply the optimized scheduling strategy to the actual system, evaluate the effect, and iteratively optimize the method based on the evaluation results.

[0040] Preferably, the project preparation and demand analysis:

[0041] Form a project team: Form a project team with multidisciplinary backgrounds including energy experts, data scientists, system engineers, model developers, etc.

[0042] Clarify project goals: Determine specific goals for multi-regional energy interconnection, such as improving energy efficiency, reducing operating costs, and enhancing system stability.

[0043] Demand analysis: Analyze the status quo, demand and interconnection bottlenecks of each regional energy system in detail, identify key data points, model requirements and key points of knowledge base construction.

[0044] Preferably, the data collection and preprocessing are:

[0045] Data collection plan design: Develop a data collection plan, including data source selection, collection frequency, data format, etc., to ensure the legality of data collection and privacy protection.

[0046] Data collection implementation: Use sensors, smart meters, data interfaces, etc. to collect real-time and historical data from multi-regional energy systems.

[0047] Data preprocessing: Clean the collected data, remove duplications, errors and outliers, convert and standardize the data to ensure consistency and comparability.

[0048] Preferably, the model is constructed and optimized:

[0049] Physical model construction: Based on the physical characteristics and operating laws of the energy system, physical models of each regional energy system are constructed, and different regional models are integrated to form a physical model framework for multi-regional energy interconnection.

[0050] Data-driven model development: Use machine learning, deep learning and other technologies to develop prediction models, optimization models, etc., train models based on historical data and real-time data, and improve prediction accuracy and scheduling efficiency.

[0051] Model fusion and optimization: Integrate the physical model with the data-driven model to form a comprehensive model, and continuously optimize and adjust the model based on actual operation data feedback.

[0052] Preferably, the knowledge base construction and reasoning:

[0053] Knowledge base framework design: Determine the structure and content scope of the knowledge base, including energy policies, technical standards, system operation rules, etc.

[0054] Knowledge collection and organization: Collect relevant knowledge from authoritative institutions, professional websites, industry reports and other channels, organize, classify and label the knowledge for subsequent query and use.

[0055] Knowledge reasoning engine development: Based on rule reasoning, case reasoning and other methods, develop a knowledge reasoning engine, combine real-time data and model prediction results, and perform knowledge reasoning and decision support.

[0056] Preferably, the system is pre-tested for integration:

[0057] System integration design: Design integration solutions for multi-regional energy interconnection systems, including data interfaces, model calls, knowledge reasoning, etc.

[0058] System development and deployment: Based on the design plan, develop a multi-regional energy interconnection system and deploy the system to each regional energy system to ensure system interconnection.

[0059] System testing: Conduct unit testing, integration testing, and system testing to ensure the stability and reliability of the system, and make necessary adjustments and optimizations based on the test results.

[0060] Preferably, its operation maintenance and iterative optimization:

[0061] System operation monitoring: Real-time monitoring of the operating status of multi-regional energy interconnection systems to promptly detect and handle system anomalies and failures.

[0062] Data analysis and evaluation: Conduct in-depth analysis of system operation data, evaluate system performance and optimization effects, and propose improvement suggestions and optimization plans based on the evaluation results.

[0063] Iterative optimization: Based on the evaluation results and new changes in requirements, the system is iteratively optimized to continuously optimize data collection, model building, knowledge base, system integration and other aspects.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] The present invention uses real-time data collection and analysis combined with advanced mathematical models and algorithms to intelligently monitor, predict and optimize the operation of the energy system, thereby improving energy utilization efficiency, reducing operating costs, and enhancing system stability and reliability. It can achieve efficient interconnection, optimized scheduling and intelligent management of multi-regional energy systems, and promote the sustainable development of the energy industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a flowchart of a "data-model knowledge" joint driving method for multi-regional energy interconnection proposed in the present invention.

[0067] Figure 2 This is a block diagram of the project preparation and requirements analysis in the present invention.

[0068] Figure 3 It is a block diagram of data collection and preprocessing in the present invention.

[0069] Figure 4 A block diagram of the model construction and optimization in the present invention.

[0070] Figure 5 A block diagram of knowledge base construction and reasoning in the present invention.

[0071] Figure 6 It is a block diagram of system integration and testing in the present invention.

[0072] Figure 7 A block diagram of operation maintenance and iterative optimization in the present invention. DETAILED DESCRIPTION

[0073] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0074] Reference Figure 1-4 , a "data-model knowledge" joint driving method for multi-regional energy interconnection, including the "data-model knowledge" joint driving method for multi-regional energy interconnection mainly involves the use of advanced technical means such as digital twin technology, reinforcement learning algorithm, and intelligent operation and maintenance technology to achieve efficient management and optimization of multi-regional energy interconnection. The core of this method is to intelligently monitor, predict and optimize the operation of the energy system through real-time data collection and analysis, combined with advanced mathematical models and algorithms, so as to improve energy utilization efficiency, reduce operating costs, and enhance system stability and reliability.

[0075] S1 data collection and preprocessing:

[0076] ① Data collection: Collect real-time operation data, historical data and environmental data (such as meteorological data) from multi-regional energy systems (including power systems, natural gas systems, thermal systems, etc.).

[0077] ②Data preprocessing: Clean, convert, and standardize the collected data to ensure data quality and facilitate subsequent analysis and modeling.

[0078] S2 model construction and optimization:

[0079] ① Physical model: Based on the physical characteristics and operating laws of the energy system, a physical model of the multi-regional energy system is constructed, including power transmission model, heat transmission model, natural gas transmission model, etc.

[0080] ②Data-driven model: Use machine learning, deep learning and other technologies to build prediction models and optimization models based on historical data and real-time data to improve system prediction accuracy and scheduling efficiency.

[0081] ③Knowledge integration: Integrate expert knowledge, policy regulations, industry norms, etc. into the model building process to enhance the practicality and reliability of the model.

[0082] S3 Knowledge Representation and Reasoning:

[0083] ① Knowledge base construction: Establish a multi-regional energy system knowledge base, including energy policies, technical standards, system operation rules, etc.

[0084] ②Knowledge reasoning: Based on the knowledge base, combined with real-time data and model prediction results, knowledge reasoning is performed to provide decision support for system optimization and scheduling.

[0085] S4 Decision support and optimal scheduling:

[0086] ① Decision support: Based on the model prediction results and knowledge reasoning results, provide decision support information to system managers, including energy allocation plans, fault warnings, energy-saving suggestions, etc.

[0087] ②Optimization and scheduling: Based on multi-objective optimization algorithms (such as genetic algorithms, particle swarm algorithms, etc.), combined with real-time data and prediction models, the optimal scheduling of multi-regional energy systems can be achieved to improve energy utilization efficiency and reduce operating costs.

[0088] Its key technologies are:

[0089] S1 Data Fusion and Mining

[0090] ① Use data fusion technology to integrate multi-source heterogeneous data into a unified data format to facilitate subsequent analysis and modeling.

[0091] ② Apply data mining technology to extract valuable information from massive data and provide data support for model building and decision support.

[0092] S2 Modeling and Simulation

[0093] ① Use system simulation technology to simulate multi-regional energy systems to verify the effectiveness and accuracy of the model.

[0094] ② Introduce theories such as multi-agent systems (MAS) and complex networks to build a dynamic simulation model of a multi-regional energy system and simulate the operating status of the system under different scenarios.

[0095] S3 Knowledge Representation and Reasoning

[0096] ① Use knowledge graphs, ontology and other technologies to structure the knowledge of multi-regional energy systems to facilitate knowledge storage and query.

[0097] ② Based on rule reasoning, case reasoning and other methods, combined with real-time data and model prediction results, knowledge reasoning is performed to provide a decision-making basis for system optimization and scheduling.

[0098] S4 Collaborative Optimization and Scheduling

[0099] ① Introduce a multi-objective optimization algorithm to comprehensively consider multiple objectives such as economic costs, environmental benefits, and energy security to achieve coordinated optimization of multi-regional energy systems.

[0100] ② Apply intelligent scheduling technology to achieve intelligent scheduling of multi-regional energy systems based on real-time data and prediction models, and improve the flexibility and reliability of system operation.

[0101] The implementation steps are:

[0102] S1 Demand analysis: Clarify the specific needs of the multi-regional energy interconnection system, including data collection needs, model building needs, knowledge representation needs, etc.

[0103] S2 Data collection and preprocessing: Collect data as needed, and perform cleaning, conversion, and standardization.

[0104] S3 model building and optimization: Build models based on physical and data characteristics, and optimize models based on expert knowledge.

[0105] S4 Knowledge base construction and reasoning: Establish a knowledge base and perform knowledge reasoning to provide decision support for system optimization scheduling.

[0106] S5 Decision support and optimal scheduling: Develop energy allocation plans and optimal scheduling strategies based on model prediction results and knowledge reasoning results.

[0107] S6 System Implementation and Evaluation: Apply the optimized scheduling strategy to the actual system, evaluate the effect, and iteratively optimize the method based on the evaluation results.

[0108] Project preparation and demand analysis:

[0109] Form a project team: Form a project team with multidisciplinary backgrounds including energy experts, data scientists, system engineers, model developers, etc.

[0110] Clarify project goals: Determine specific goals for multi-regional energy interconnection, such as improving energy efficiency, reducing operating costs, and enhancing system stability.

[0111] Demand analysis: Analyze the status quo, demand and interconnection bottlenecks of each regional energy system in detail, identify key data points, model requirements and key points of knowledge base construction.

[0112] Data collection and preprocessing:

[0113] Data collection plan design: Develop a data collection plan, including data source selection, collection frequency, data format, etc., to ensure the legality of data collection and privacy protection.

[0114] Data collection implementation: Use sensors, smart meters, data interfaces, etc. to collect real-time and historical data from multi-regional energy systems.

[0115] Data preprocessing: Clean the collected data, remove duplications, errors and outliers, convert and standardize the data to ensure consistency and comparability.

[0116] Its model construction and optimization:

[0117] Physical model construction: Based on the physical characteristics and operating laws of the energy system, physical models of each regional energy system are constructed, and different regional models are integrated to form a physical model framework for multi-regional energy interconnection.

[0118] Data-driven model development: Use machine learning, deep learning and other technologies to develop prediction models, optimization models, etc., train models based on historical data and real-time data, and improve prediction accuracy and scheduling efficiency.

[0119] Model fusion and optimization: Integrate the physical model with the data-driven model to form a comprehensive model, and continuously optimize and adjust the model based on actual operation data feedback.

[0120] Its knowledge base construction and reasoning:

[0121] Knowledge base framework design: Determine the structure and content scope of the knowledge base, including energy policies, technical standards, system operation rules, etc.

[0122] Knowledge collection and organization: Collect relevant knowledge from authoritative institutions, professional websites, industry reports and other channels, organize, classify and label the knowledge for subsequent query and use.

[0123] Knowledge reasoning engine development: Based on rule reasoning, case reasoning and other methods, develop a knowledge reasoning engine, combine real-time data and model prediction results, and perform knowledge reasoning and decision support.

[0124] Its system integration pre-test:

[0125] System integration design: Design integration solutions for multi-regional energy interconnection systems, including data interfaces, model calls, knowledge reasoning, etc.

[0126] System development and deployment: Based on the design plan, develop a multi-regional energy interconnection system and deploy the system to each regional energy system to ensure system interconnection.

[0127] System testing: Conduct unit testing, integration testing, and system testing to ensure the stability and reliability of the system, and make necessary adjustments and optimizations based on the test results.

[0128] Its operation maintenance and iterative optimization:

[0129] System operation monitoring: Real-time monitoring of the operating status of multi-regional energy interconnection systems to promptly detect and handle system anomalies and failures.

[0130] Data analysis and evaluation: Conduct in-depth analysis of system operation data, evaluate system performance and optimization effects, and propose improvement suggestions and optimization plans based on the evaluation results.

[0131] Iterative optimization: Based on the evaluation results and new changes in requirements, the system is iteratively optimized to continuously optimize data collection, model building, knowledge base, system integration and other aspects.

[0132] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A data-model knowledge joint driving method for multi-regional energy interconnection, characterized by: The data-model knowledge joint driving method for multi-regional energy interconnection mainly involves the use of digital twin technology, reinforcement learning algorithm, and intelligent operation and maintenance technology to achieve efficient management and optimization of multi-regional energy interconnection. The core of this method is to intelligently monitor, predict and optimize the operation of the energy system through real-time data collection and analysis, combined with advanced mathematical models and algorithms, so as to improve energy utilization efficiency, reduce operating costs, and enhance system stability and reliability. The method includes the following steps: S1 data collection and preprocessing: ① Data collection: collect real-time operation data, historical data and environmental data from multi-regional energy systems; ②Data preprocessing: clean, convert and standardize the collected data to ensure data quality and facilitate subsequent analysis and modeling; S2 model construction and optimization: ① Physical model: Based on the physical characteristics and operating laws of the energy system, a physical model of the multi-regional energy system is constructed, including power transmission model, heat transmission model, and natural gas transmission model; ②Data-driven model: Using machine learning and deep learning technologies, we build prediction models and optimization models based on historical data and real-time data to improve system prediction accuracy and scheduling efficiency; ③ Knowledge integration: Integrate expert knowledge, policy regulations, and industry norms into the model building process to enhance the practicality and reliability of the model; S3 Knowledge Representation and Reasoning: ① Knowledge base construction: Establish a multi-regional energy system knowledge base, including energy policies, technical standards, and system operation rules; ②Knowledge reasoning: Based on the knowledge base, combined with real-time data and model prediction results, knowledge reasoning is performed to provide decision support for system optimization and scheduling; S4 Decision support and optimized scheduling: ① Decision support: Based on the model prediction results and knowledge reasoning results, it provides decision support information to system managers, including energy allocation plans, fault warnings, and energy-saving suggestions; ②Optimized scheduling: Based on multi-objective optimization algorithms, combined with real-time data and prediction models, optimized scheduling of multi-regional energy systems can be achieved to improve energy utilization efficiency and reduce operating costs.

2. According to claim 1, a data-model knowledge joint driving method for multi-regional energy interconnection is characterized in that The key technologies are: S1 Data Fusion and Mining ①Use data fusion technology to integrate multi-source heterogeneous data into a unified data format to facilitate subsequent analysis and modeling; ② Apply data mining technology to extract valuable information from massive data and provide data support for model building and decision support; S2 Modeling and Simulation ①Use system simulation technology to simulate multi-regional energy systems and verify the effectiveness and accuracy of the model; ② Introduce multi-agent system and complex network theory to build a dynamic simulation model of multi-regional energy system and simulate the operating status of the system in different scenarios; S3 Knowledge Representation and Reasoning ① Using knowledge graph and ontology technology to structure the knowledge of multi-regional energy systems to facilitate knowledge storage and query; ② Based on rule reasoning and case reasoning methods, combined with real-time data and model prediction results, knowledge reasoning is performed to provide a decision-making basis for system optimization and scheduling; S4 Collaborative Optimization and Scheduling ① Introducing a multi-objective optimization algorithm to comprehensively consider multiple objectives including economic cost, environmental benefit, and energy security, and to achieve coordinated optimization of multi-regional energy systems; ② Apply intelligent scheduling technology to achieve intelligent scheduling of multi-regional energy systems based on real-time data and prediction models, and improve the flexibility and reliability of system operation.

3. A data-model knowledge joint driving method for multi-regional energy interconnection according to claim 1, characterized in that The implementation steps are: S1 Demand analysis: clarify the specific requirements of the multi-regional energy interconnection system, including data collection requirements, model building requirements, and knowledge representation requirements; S2 Data collection and preprocessing: collect data according to requirements, and perform cleaning, conversion, and standardization; S3 model building and optimization: Build models based on physical and data characteristics, and optimize models based on expert knowledge; S4 Knowledge base construction and reasoning: Establish a knowledge base and perform knowledge reasoning to provide decision support for system optimization scheduling; S5 Decision support and optimal scheduling: Formulate energy allocation plans and optimal scheduling strategies based on model prediction results and knowledge reasoning results; S6 System Implementation and Evaluation: Apply the optimized scheduling strategy to the actual system, evaluate the effect, and iteratively optimize the method based on the evaluation results.

4. A data-model knowledge joint driving method for multi-regional energy interconnection according to claim 3, characterized in that Project preparation and demand analysis: Establish a project team: Establish a multidisciplinary project team including energy experts, data scientists, system engineers, and model developers; Clarify project goals: Determine specific goals for multi-regional energy interconnection, such as improving energy efficiency, reducing operating costs, and enhancing system stability; Demand analysis: Analyze the status quo, demand and interconnection bottlenecks of each regional energy system in detail, identify key data points, model requirements and key points of knowledge base construction.

5. A data-model knowledge joint driving method for multi-regional energy interconnection according to claim 3, characterized in that Data collection and preprocessing: Data collection plan design: Develop a data collection plan, including data source selection, collection frequency, and data format, to ensure the legality of data collection and privacy protection; Data collection implementation: using sensors, smart meters, and data interfaces to collect real-time and historical data from multi-regional energy systems; Data preprocessing: Clean the collected data, remove duplications, errors and outliers, convert and standardize the data to ensure consistency and comparability.

6. A data-model knowledge joint driving method for multi-regional energy interconnection according to claim 3, characterized in that Its model construction and optimization: Physical model construction: Based on the physical characteristics and operating laws of the energy system, physical models of energy systems in various regions are constructed, and different regional models are integrated to form a physical model framework for multi-regional energy interconnection; Data-driven model development: Use machine learning and deep learning technologies to develop prediction models and optimization models, and train models based on historical data and real-time data to improve prediction accuracy and scheduling efficiency; Model fusion and optimization: Integrate the physical model with the data-driven model to form a comprehensive model, and continuously optimize and adjust the model based on actual operation data feedback.

7. A data-model knowledge joint driving method for multi-regional energy interconnection according to claim 3, characterized in that Its knowledge base construction and reasoning: Knowledge base framework design: determine the structure and content scope of the knowledge base, including energy policies, technical standards, and system operation rules; Knowledge collection and organization: Collect relevant knowledge from authoritative institutions, professional websites, and industry report channels, organize, classify, and annotate the knowledge for subsequent query and use; Knowledge reasoning engine development: Based on rule reasoning and case reasoning methods, develop a knowledge reasoning engine, combine real-time data and model prediction results, and perform knowledge reasoning and decision support.

8. The data-model knowledge joint driving method for multi-regional energy interconnection according to claim 3 is characterized in that Its system integration pre-test: System integration design: Design the integration solution of multi-regional energy interconnection system, including data interface, model calling, and knowledge reasoning; System development and deployment: Develop a multi-regional energy interconnection system based on the design plan and deploy the system to each regional energy system to ensure system interconnection; System testing: Conduct unit testing, integration testing, and system testing to ensure the stability and reliability of the system, and make necessary adjustments and optimizations based on the test results.

9. A data-model knowledge joint driving method for multi-regional energy interconnection according to claim 3, characterized in that Its operation maintenance and iterative optimization: System operation monitoring: Real-time monitoring of the operating status of multi-regional energy interconnection systems, timely detection and handling of system anomalies and failures; Data analysis and evaluation: Conduct in-depth analysis of system operation data, evaluate system performance and optimization effects, and propose improvement suggestions and optimization plans based on the evaluation results; Iterative optimization: Based on the evaluation results and new changes in requirements, the system is iteratively optimized to continuously optimize data collection, model building, knowledge base and system integration.

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