Digital twinning-oriented power grid dynamic ontology modeling method and system

Abstract

The invention discloses a digital twinborn-oriented power grid dynamic ontology modeling method and system, relates to the technical field of power grid dynamic ontology, and aims to solve the problem of inaccurate power grid dynamic modeling. According to the invention, through the power grid dynamic model after visualization processing, a worker can see the operation data of the power grid more visually, so that potential problems and risks can be found more easily. Therefore, the staff can make decisions in time and take corresponding measures to guarantee safe and stable operation of the power grid, after the interaction technology is added, the staff can perform interaction operation with the visual model through input equipment such as a mouse and a keyboard, the test result is analyzed by using a data analysis tool, and the test result is analyzed by using the data analysis tool. And error data in the model can be efficiently identified and diagnosed. According to the intelligent method, the accuracy and efficiency of error recognition are improved, and rapid positioning and problem solving are facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid dynamic ontology, and particularly to a power grid dynamic ontology modeling method and system for digital twin. Background Technique

[0002] Power grid dynamic ontology modeling refers to using the ontology method to construct a dynamic model of the power grid.

[0003] A Chinese patent with the publication number CN104217373B discloses a dynamic modeling and optimization control method for power energy statistics based on power grid topology. It mainly uses the power grid topology structure and data information, centered around the statistical entity, to find the information of n switch devices connected to the statistical entity and the gateway metering devices installed on each switch device, so as to realize the dynamic modeling of the power energy of the statistical entity; further improve the automation level of power energy statistics and greatly reduce the workload of power energy statistical modeling of provincial power grids or even larger-scale power grids; at the same time, obtain real-time measurement data at the gateway to realize the real-time calculation of electric energy for the statistical entity, providing a reliable method for the statistics of power purchase and sale business data indicators. Although the above patent solves the problem of power grid dynamic modeling, there are still the following problems in actual operation:

[0004] 1. No targeted multi-form model establishment is carried out according to the ontology situation of the power grid structure, resulting in an overly single ontology model.

[0005] 2. The established dynamic model is not effectively verified, and no targeted visualization processing and interaction technology are added to the established dynamic model, resulting in poor use effects of the generated power grid dynamic model.

[0006] 3. The power grid data is not effectively analyzed, and no abnormal degree judgment is made according to the analysis results, resulting in the inability to perform priority processing according to the abnormal degree. Summary of the Invention

[0007] The purpose of the present invention is to provide a power grid dynamic ontology modeling method and system for digital twin. Through the power grid dynamic model after visualization processing, staff can more intuitively see the operation data of the power grid, thus more easily discovering potential problems and risks. This helps staff make decisions in a timely manner and take corresponding measures to ensure the safe and stable operation of the power grid. An alarm is automatically triggered according to the data analysis results, realizing intelligent monitoring and management. The threshold setting can be adjusted according to different data attributes, making the alarm mechanism more flexible and adaptable, and can solve the problems in the prior art.

[0008] To achieve the above purpose, the present invention provides the following technical solutions:

[0009] A power grid dynamic ontology modeling method for digital twins, comprising:

[0010] Collect power grid operation data in the database, and after the data collection is completed, perform data preprocessing, and obtain target power grid data after the data preprocessing;

[0011] Use graph theory algorithms to construct a power grid structure model for the connection relationships of power grid components in the target power grid data, and obtain a power grid concept model after the power grid structure model is constructed;

[0012] Adopt ontology methods to define parameters for the attributes, states, and behaviors of power grid components in the power grid concept model, and establish a dynamic model according to the defined parameters. After the dynamic model is established, a power grid dynamic model is obtained;

[0013] Verify and correct the established power grid dynamic model. At the same time, update the pattern data of the corrected power grid dynamic model, and obtain a standard power grid dynamic model after the model data update is completed;

[0014] Visualize the standard power grid dynamic model using visualization processing technology, and provide an interaction function for the visualized standard power grid dynamic model to finally obtain real-time operation status data.

[0015] Preferably, collect power grid operation data in the database, and after the data collection is completed, perform data preprocessing, including:

[0016] Power grid operation data includes voltage values, current values, power data, frequency data, frequency status data, load data, environmental data, and fault event data;

[0017] Collect power grid operation data in real time from each monitoring terminal;

[0018] Store the real-time collected power grid operation data in the buffer area. At the same time, perform data preprocessing on the power grid operation data in the buffer area;

[0019] Data preprocessing includes data cleaning of the power grid operation data in the buffer area, and data standardization after data cleaning;

[0020] After data standardization, integrate the power grid operation data from different monitoring terminals, and align the integrated data in time series;

[0021] Perform data conversion on the power grid operation data with time series alignment, and complete the data preprocessing of the power grid operation data after data conversion;

[0022] Obtain target power grid data after data preprocessing.

[0023] Preferably, a graph theory algorithm is used to construct a power grid structure model for the connection relationships of power grid components in the target power grid data, including:

[0024] Identify the basic components in the target power grid data. The basic components include transformers, switches, lines, loads, and generators, and identify the connection relationships between each basic component;

[0025] Create nodes for each basic component and create edges for the connection relationships between each basic component;

[0026] Use the adjacency list method to create a graph model for the created nodes and edges. After creation, a power grid graph model is obtained;

[0027] Traverse the power grid graph model using a depth-first search or breadth-first search algorithm. After traversal, the nodes and loops of each basic component in the power grid graph model are obtained;

[0028] Use the Dijkstra algorithm or Bellman-Ford algorithm to calculate the paths between the nodes or loops of each basic component. After calculation, the shortest paths between the nodes or loops of each basic component are obtained;

[0029] Based on the shortest paths between the nodes or loops of each basic component, construct a model that reflects the connection relationships of all basic components in the power grid and the shortest paths in each connection relationship;

[0030] After the model construction is completed, a power grid conceptual model is obtained.

[0031] Preferably, an ontology method is used to define the attributes, states, and behaviors of power grid components in the power grid conceptual model and establish a dynamic model based on the defined parameters, including:

[0032] Define the ontology framework according to the power grid conceptual model. The ontology framework includes classes and attributes. Among them, the class is the category of basic components; the attribute is the attribute definition for each class, including characteristic attributes, state attributes, and behavior attributes;

[0033] Define the relationships between different classes, including inheritance relationships or association relationships. After definition, associate the attributes with the corresponding classes. After association, a specific attribute set for each class is obtained;

[0034] Define parameters for each basic component according to the specific attribute set, including static attribute parameterization, dynamic state parameterization, and behavior parameterization;

[0035] Static attributes are parameterized to define the data type and range of static attributes for each basic component; dynamic states are parameterized to design a real-time update mechanism for the states of each basic component; behaviors are parameterized to define the behavior models of basic components and establish the behavioral connections between basic components.

[0036] Preferably, the visualized standard power grid dynamic model is provided with interactive functions, including:

[0037] Obtain the power grid management roles and construct user nodes according to the power grid management roles;

[0038] Distributively and bidirectionally connect each user node with the cloud processing center, and at the same time, bidirectionally connect the cloud processing center with the standard power grid dynamic model;

[0039] When there is an interactive request generated by a user node, transmit the interactive request of the current user node to the cloud processing center;

[0040] Based on the cloud processing center, read the interactive request, determine the request target and execution action of the interactive request, and at the same time, retrieve the model structure in the standard power grid dynamic model in the cloud processing center;

[0041] Perform a first match between the request target and the model structure in the standard power grid dynamic model to determine the adjustment area of the standard power grid dynamic model, and at the same time, generate an adjustment instruction according to the adjustment area and the execution action;

[0042] Transmit the adjustment instruction to the standard power grid dynamic model to perform the adjustment operation, and at the same time, read the response data in the power grid dynamic model;

[0043] Feed back the response data of the power grid dynamic model to the cloud processing center, and analyze the response data according to the cloud processing center to obtain the interactive result based on the current user node and the response results of the management scopes of the other user nodes for the response data;

[0044] Read the initial results of the management scopes of the other user nodes;

[0045] Perform a second match between the interactive result of the current user node and the target result of the interactive request, and at the same time, perform a third match between the response results of the management scopes of the user nodes and the corresponding initial results;

[0046] If the interactive result of the current user node is consistent with the target result of the interactive request, and the response results of the management scopes of the other user nodes are all consistent with the corresponding initial results, then the interactive operation is completed;

[0047] When the interaction result of the current user node is consistent with the target result of the interaction request, and there is a response result within the management scope of the target user node among the other user nodes that is inconsistent with the corresponding initial result, a first collaborative feedback report is generated and fed back to the corresponding target user management node;

[0048] When the interaction result of the current user node is inconsistent with the target result of the interaction request, and the response results within the management scope of the other user nodes are all consistent with the corresponding initial results, a second collaborative feedback report is generated and fed back to the current user management node;

[0049] Otherwise, the first collaborative feedback report is fed back to the corresponding target user management node, and the second collaborative feedback report is fed back to the current user management node.

[0050] Preferably, the ontology method is used to define the attributes, states, and behaviors of power grid components in the power grid concept model, and a dynamic model is established according to the defined parameters, further including:

[0051] A dynamic model is established according to the defined parameters. Before establishing the dynamic model, a modeling framework for the dynamic model is established first;

[0052] The modeling framework includes a state machine, an event-driven model, and a rule-based model; the state machine uses a state transition mechanism to define the states of basic components and their transition rules, and is used to manage the state changes of components; the event-driven model triggers corresponding behaviors by setting events and is used for complex power grid interaction simulation. The events include faults and load changes; the rule-based model uses a rule engine to process the behaviors of basic components according to the defined rules and is used for complex power grids with a large degree of freedom;

[0053] After the modeling framework of the dynamic model is established, the state machine is used for modeling. The state machine modeling defines the acceptable states for each basic component, lists the state transition conditions, creates a state transition diagram using a UML state diagram or other graphical tools, and at the same time, shows the transition relationships between states;

[0054] Modeling the event-driven model. The event-driven model modeling determines the key events in the defined parameters. The key events include load changes, equipment failures, and environmental changes, then confirms the trigger rules for the key events, and defines the trigger reactions for each key event;

[0055] Modeling the model of behavior parameters. The modeling of the behavior parameter model defines the operation logic of basic components according to the behavior attributes of basic components, and uses class or object programming to implement the behavior logic;

[0056] The modeled state machine, event-driven model, and behavior parameter model are combined, and after the combination is completed, a power grid dynamic model is obtained.

[0057] Preferably, verify and correct the established power grid dynamic model. At the same time, update the mode data of the corrected power grid dynamic model, including:

[0058] Collect the historical operation data in the database, including voltage, current, load changes, and fault events. The historical operation data is used to compare and verify the baseline of the model;

[0059] After the collection of historical operation data is completed, conduct qualitative verification and quantitative verification on the power grid dynamic model;

[0060] Qualitative verification is for the review staff to conduct a detailed review of the structure, logical relationship, state transition, and event response of the power grid dynamic model. At the same time, check the relevant descriptions in the documents and ontology;

[0061] Quantitative verification is to run the power grid dynamic model in the simulation environment, quantitatively compare the simulation results with the historical operation data, calculate the error between the simulation results and the historical operation data, and conduct statistical analysis;

[0062] After the qualitative verification and quantitative verification of the power grid dynamic model are completed, use the model checking tool to verify the logic of the model. At the same time, conduct unit testing, integration testing, and system testing on the model in sequence;

[0063] Use the data analysis tool to analyze the test results, and identify and diagnose the error data of the power grid dynamic model according to the analysis results;

[0064] Correct the model according to the identified and diagnosed error data. Model correction includes parameter adjustment, logic correction, and model structure update;

[0065] Simulate the power grid dynamic model after model correction again, and verify it using historical operation data and real-time operation data;

[0066] At the same time, during the verification and correction process of the power grid dynamic model, construct a cyclic feedback mechanism, continuously correct and iterate according to the results of each round of verification until the power grid dynamic model meets the predetermined accuracy and reliability standards;

[0067] Finally, obtain the standard power grid dynamic model.

[0068] Preferably, visually process the standard power grid dynamic model using visualization processing technology, and provide an interactive function for the visually processed standard power grid dynamic model, including:

[0069] Confirm the display indicators in the standard power grid dynamic model. The display indicators include voltage, load, frequency, and fault status;

[0070] After the display index is confirmed to be completed, a visual model of the standard power grid dynamic model is constructed;

[0071] The visual model is constructed by using a graphical tool to draw a power grid architecture diagram, showing basic components and their connection relationships, and expressed in a graphical way using nodes and edges;

[0072] Design dynamic indicators for each basic component to display the operating status of each basic component. Among them, the operating status includes normal, faulty, and maintenance, and different colored labels are used to represent different statuses;

[0073] Use line charts, bar charts, and pie charts to display index data;

[0074] Add interaction technology to the completed standard power grid dynamic model of the visual model;

[0075] After the addition of the interaction technology, real-time operating status data is obtained.

[0076] Preferably, when providing an interaction function for the visually processed standard power grid dynamic model, it includes:

[0077] Obtain the number of interactions with the standard power grid dynamic model and the total task volume of each interaction;

[0078] Calculate the interaction accuracy rate according to the total number of interactions with the standard power grid dynamic model and the total task volume of each interaction;

[0079] Calculate the target interaction efficiency for interacting with the standard power grid dynamic model according to the interaction accuracy rate;

[0080] Obtain a preset interaction efficiency threshold, and compare the target interaction efficiency with the preset interaction efficiency threshold to determine whether an alarm operation needs to be performed;

[0081] When the target interaction efficiency is equal to or greater than the preset interaction efficiency threshold, it is determined that no alarm operation needs to be performed;

[0082] Otherwise, it is determined that an alarm operation needs to be performed.

[0083] A power grid dynamic ontology modeling system for digital twins includes:

[0084] An early warning trigger and processing unit for:

[0085] Utilize a data flow and data processing framework to real-time process the collected real-time operating status data, and perform data analysis on the real-time operating status data through a real-time data processing engine;

[0086] Set the alarm mechanism according to the data analysis results. The alarm mechanism is set to set thresholds for different data attributes. When the data analysis results are not within the set threshold range, the alarm mechanism is automatically triggered;

[0087] At the same time, judge the degree of abnormality of the data that automatically triggers the alarm. The degree of abnormality is judged according to the threshold abnormality size of the data analysis results to confirm the degree of abnormality of the data analysis results;

[0088] The degree of abnormality is divided into mild abnormality, moderate abnormality and severe abnormality;

[0089] Perform processing with different priorities according to different degrees of abnormality.

[0090] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0091] 1. A method and system for power grid dynamic ontology modeling for digital twins provided by the present invention realizes efficient management of power grid component attributes through static attribute parameterization, dynamic state parameterization and behavior parameterization. This helps to reduce data redundancy and errors, improve the accuracy and reliability of the model, and the dynamic state parameterization design provides a real-time update mechanism for the state of each basic component. This helps to ensure that the model can accurately reflect the real-time state of the power grid system and provides strong support for decision-making and control.

[0092] 2. A method and system for power grid dynamic ontology modeling for digital twins provided by the present invention enables staff to more intuitively view the operation data of the power grid through the visualized power grid dynamic model, thus making it easier to discover potential problems and risks. This helps staff to make decisions in a timely manner and take corresponding measures to ensure the safe and stable operation of the power grid. After adding interactive technology, staff can interact with the visualized model through input devices such as mice and keyboards, and use data analysis tools to analyze the test results, and can efficiently identify and diagnose incorrect data in the model. This intelligent method improves the accuracy and efficiency of error identification and helps to quickly locate and solve problems.

[0093] 3. A method and system for power grid dynamic ontology modeling for digital twins provided by the present invention automatically triggers an alarm according to the data analysis results, realizing intelligent monitoring and management. The threshold setting can be adjusted according to different data attributes, making the alarm mechanism more flexible and adaptable. The solution supports processing with different priorities for different degrees of abnormality, is easy to expand and adapt to more complex scenarios, and through the degree of abnormality judgment, the degree of abnormality of the data analysis results can be accurately divided, which helps managers quickly locate problems and take targeted measures.

[0094] 4. By obtaining grid management roles and constructing user nodes corresponding to different grid management roles, and at the same time, connecting the constructed user nodes to the cloud processing center, it is convenient to receive and analyze the interaction requests sent by the user nodes. Secondly, the cloud processing center parses the received interaction requests, and realizes corresponding interaction control of the standard grid dynamic model according to the parsing results, ensuring the synchronous adjustment effect of the standard grid dynamic model. Finally, the corresponding data of the standard grid dynamic model is fed back to the cloud processing center for analysis, and the response results of the management scopes of different user nodes under the interaction requests are determined through the analysis results, and then the response results are discussed in different cases, realizing the generation and feedback of the collaborative feedback reports between different user nodes, improving the interaction effect and interaction reliability between the standard grid dynamic model and the user nodes, and at the same time, enhancing the practicability of the grid dynamic ontology modeling.

[0095] 5. By obtaining the number of interactions with the standard grid dynamic model and the total task volume of each interaction, the interaction accuracy rate is effectively calculated, and then the interaction efficiency is accurately calculated through the accuracy rate, so as to effectively measure the quality of the interaction with the standard grid dynamic model. When the preset interaction efficiency threshold is not reached, an alarm operation is performed, so as to effectively realize the timely grasp of the interaction dynamics and ensure the effectiveness of the interaction of the standard grid dynamic model. Brief Description of the Drawings

[0096] Figure 1 It is a schematic diagram of the steps of the grid dynamic ontology modeling of the present invention;

[0097] Figure 2 It is a schematic diagram of the grid dynamic ontology modeling process of the present invention. Detailed Embodiment

[0098] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0099] In order to solve the problem in the prior art that there is no targeted multi-form model establishment according to the ontology situation of the power grid structure, resulting in the overly single ontology model established, please refer to Figure 1 and Figure 2 , the following technical solutions are provided in this embodiment:

[0100] A grid dynamic ontology modeling method for digital twin, including:

[0101] Collect the power grid operation data in the database. After the data collection is completed, perform data preprocessing. After the data preprocessing, obtain the target power grid data;

[0102] Among them, the target power grid data after preprocessing has characteristics such as high quality, consistency, and comparability, which is convenient for subsequent advanced analysis tasks such as data mining, machine learning algorithm application, and decision support;

[0103] Use graph theory algorithms to construct a power grid structure model for the connection relationships of power grid components in the target power grid data. After the power grid structure model is constructed, obtain the power grid conceptual model;

[0104] Among them, by adopting path calculation algorithms such as the Dijkstra algorithm or the Bellman-Ford algorithm, the shortest paths between each basic component node or loop in the power grid can be calculated quickly and accurately;

[0105] Adopt the ontology method to define the attributes, states, and behaviors of power grid components in the power grid conceptual model, and establish a dynamic model according to the defined parameters. After the dynamic model is established, obtain the power grid dynamic model;

[0106] Among them, the ontology method provides an easily extensible mechanism, which can conveniently add new classes and attributes, as well as define the relationships between them;

[0107] Verify and correct the established power grid dynamic model. At the same time, update the model data of the corrected power grid dynamic model. After the model data update is completed, obtain the standard power grid dynamic model;

[0108] Among them, through unit testing, integration testing, and system testing, gradually verify the various parts and overall performance of the model, which helps to discover potential problems and repair them;

[0109] Visualize the standard power grid dynamic model using visualization processing technology, and provide an interactive function for the visualized standard power grid dynamic model to finally obtain the real-time operation status data;

[0110] Among them, through the visualized power grid dynamic model, staff can more intuitively see the operation data of the power grid, thus making it easier to discover potential problems and risks.

[0111] Collect the power grid operation data in the database. After the data collection is completed, perform data preprocessing, including:

[0112] The power grid operation data includes voltage values, current values, power data, frequency data, frequency status data, load data, environmental data, and fault event data;

[0113] Grid operation data is collected in real time from each monitoring terminal;

[0114] The grid operation data collected in real time is stored in the buffer area. At the same time, the grid operation data in the buffer area is preprocessed;

[0115] Data preprocessing includes data cleaning of the grid operation data in the buffer area, and data standardization is performed after data cleaning;

[0116] After data standardization, the grid operation data from different monitoring terminals is integrated, and the integrated data is aligned in time series;

[0117] The grid operation data with time series alignment is subjected to data conversion, and after data conversion, the data preprocessing of the grid operation data is completed;

[0118] Target grid data is obtained after data preprocessing.

[0119] Specifically, the grid operation data is collected in real time from each monitoring terminal, ensuring the timeliness and freshness of the data, which helps to timely reflect the actual operation status of the grid. Through a series of preprocessing steps such as data cleaning, standardization, integration, time series alignment and data conversion, the usability and accuracy of the data are effectively improved, providing an efficient way to obtain the target grid data. The preprocessed target grid data has characteristics such as high quality, consistency and comparability, which is convenient for subsequent advanced analysis tasks such as data mining, machine learning algorithm application and decision support. By collecting grid operation data in real time and performing a series of preprocessing steps, the data quality and analysis efficiency are effectively improved, providing a strong guarantee for the stable operation and optimized management of the smart grid. At the same time, this solution also enhances the stability and reliability of the system, and improves the scientificity and accuracy of decision-making.

[0120] Using graph theory algorithms to construct a power grid structure model for the connection relationships of power grid components in the target grid data, including:

[0121] Identify the basic components in the target grid data. The basic components include transformers, switches, lines, loads and generators, and identify the connection relationships between each basic component;

[0122] Create nodes for each basic component and create edges for the connection relationships between each basic component;

[0123] Create a graph model using the adjacency list method for the created nodes and edges. After creation, a power grid graph model is obtained;

[0124] Traverse the power grid graph model using depth-first search or breadth-first search algorithms. After traversal, the nodes and loops of each basic component in the power grid graph model are obtained;

[0125] Use the Dijkstra algorithm or the Bellman-Ford algorithm to calculate the paths between the nodes or loops of each basic component, and obtain the shortest paths between the nodes or loops of each basic component after the calculation;

[0126] Based on the shortest paths between the nodes or loops of each basic component, construct a model that reflects the connection relationships of all basic components in the power grid and the shortest paths in each connection relationship;

[0127] After the model is constructed, a power grid concept model is obtained.

[0128] Specifically, graph computing technology uses "nodes" to represent entities and "edges" to represent the association relationships between entities. This computing mode based on graph theory can flexibly record the complex connection relationships between basic components (such as transformers, switches, lines, loads, and generators) in the power grid. Compared with traditional relational databases, graph computing technology eliminates the computing performance overhead required to process "table associations", thereby improving the efficiency of data processing. Based on the graph model, multi-source and multi-type power grid data can be fused to support the cross-business data fusion management of the power grid. This data fusion ability helps to realize the analysis of the global characteristics of the power grid and the research of control strategies. At the same time, the graph model can also intuitively display the topological structure of the power grid, facilitating engineers to understand it visually and intuitively. By using path calculation algorithms such as the Dijkstra algorithm or the Bellman-Ford algorithm, the shortest paths between the nodes or loops of each basic component in the power grid can be calculated quickly and accurately. This is of great significance for optimizing the allocation of power resources, reducing power losses, and improving the operation efficiency of the power grid. After constructing the power grid concept model, the connection relationships of all basic components in the power grid and the shortest paths in each connection relationship can be clearly displayed. This helps engineers better understand the structure and characteristics of the power grid, so as to formulate more reasonable power grid planning and operation strategies, enhance the stability and reliability of the power grid. Using graph theory algorithms to construct a power grid structure model has the advantages of efficiently representing complex association relationships, enhancing data fusion and analysis capabilities, optimizing path calculation and resource allocation, enhancing the stability and reliability of the power grid, and supporting real-time analysis and calculation of large-scale power grids. These advantages make this solution have broad application prospects in aspects such as power grid planning, operation, and control.

[0129] Adopt the ontology method to define the attributes, states, and behaviors of power grid components in the power grid concept model, and establish a dynamic model according to the defined parameters, including:

[0130] Define the ontology framework according to the power grid concept model. The ontology framework includes classes and attributes. Among them, the class is the category of basic components; the attribute is the attribute definition for each class, including characteristic attributes, state attributes, and behavior attributes;

[0131] Define the relationships between different classes, including inheritance relationships or association relationships. After the definition is completed, associate the attributes with the corresponding classes. After the association, obtain the specific attribute set for each class;

[0132] Define the parameters for each basic component according to the specific attribute set, including static attribute parameterization, dynamic state parameterization, and behavior parameterization;

[0133] Static attribute parameterization defines the data type and range for the static attributes of each basic component; dynamic state parameterization designs a real-time update mechanism for the state of each basic component; behavior parameterization defines the behavior model of the basic components and establishes the behavior connections between the basic components.

[0134] Establish a dynamic model according to the defined parameters. Among them, before establishing the dynamic model, first establish the modeling framework of the dynamic model;

[0135] The modeling framework includes a state machine, an event-driven model, and a rule-based model; the state machine uses a state transition mechanism to define the states of the basic components and their transition rules, which is used to manage the state changes of the components; the event-driven model triggers corresponding behaviors by setting events, which is used for complex power grid interaction simulation. The events include faults and load changes; the rule-based model uses a rule engine to process the behaviors of the basic components according to the defined rules, which is used for complex power grids with a large degree of freedom;

[0136] After the modeling framework of the dynamic model is established, use the state machine for modeling. The state machine modeling defines the acceptable states for each basic component and lists the state transition conditions. Use the UML state diagram or other graphical tools to create a state transition diagram, and at the same time, show the transition relationships between the states;

[0137] Model the event-driven model. The event-driven model modeling determines the key events in the defined parameters. The key events include load changes, equipment failures, and environmental changes. Then confirm the trigger rules of the key events and define the trigger reactions for each key event;

[0138] Model the model of the behavior parameters. The modeling of the behavior parameter model defines the operation logic of the basic components according to the behavior attributes of the basic components and uses class or object programming to implement the behavior logic;

[0139] Combine the modeled state machine, event-driven model, and behavior parameter model. After the combination is completed, obtain the power grid dynamic model.

[0140] Specifically, the ontology method can clearly represent the attributes, states, and behaviors of power grid components in the power grid conceptual model, as well as the relationships between them. This helps to establish a unified understanding of the power grid system, reduce ambiguity and misunderstanding. For the unified concepts in different fields, the ontology method provides a standardized form of interpretation, achieving full sharing between different fields. In power grid modeling, this helps to ensure the generality and interoperability of the model, providing a variety of modeling frameworks including state machine, event-driven model, and rule-based model. These frameworks can be flexibly selected and combined according to the complexity and requirements of the power grid system to meet the modeling needs in different scenarios. As the power grid system continues to develop and change, new components and attributes may need to be incorporated into the model. The ontology method provides an easily extensible mechanism, which can conveniently add new classes and attributes, as well as define the relationships between them. Through static attribute parameterization, dynamic state parameterization, and behavior parameterization, this solution realizes the efficient management of the attributes of power grid components. This helps to reduce data redundancy and errors, improve the accuracy and reliability of the model. The dynamic state parameterization design provides a real-time update mechanism for the state of each basic component. This helps to ensure that the model can accurately reflect the real-time state of the power grid system and provide strong support for decision-making and control. The ontology method uses clear terms and concepts to describe all aspects of the power grid system, with strong semantic expression ability. This helps to establish a more accurate and detailed model, improve the description ability and interpretability of the model. Using the ontology method to dynamically model the power grid conceptual model has significant advantages such as clarity and standardization, flexibility and extensibility, efficiency and accuracy, as well as strong semantic expression ability and reasoning ability. These advantages help to establish a more accurate, reliable, and efficient power grid model, providing strong support for the planning, operation, and control of the power grid system.

[0141] In order to solve the problem in the existing technology that the established dynamic model is not effectively verified, and there is no targeted visualization processing and interaction technology added to the established dynamic model, resulting in poor use effect of the generated power grid dynamic model, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0142] Verify and correct the established power grid dynamic model. At the same time, update the pattern data of the corrected power grid dynamic model, including:

[0143] Collect the historical operation data in the database, including voltage, current, load change, and fault events. The historical operation data is used to compare and verify the baseline of the model;

[0144] After the collection of historical operation data is completed, conduct qualitative verification and quantitative verification on the power grid dynamic model;

[0145] Qualitative verification is for the auditor to conduct a detailed review of the structure, logical relationship, state transition and event response of the power grid dynamic model, and at the same time, check the relevant descriptions of the documents and ontology;

[0146] Quantitative verification is to use the simulation environment to run the power grid dynamic model, and quantitatively compare the simulation results with the historical operation data, and calculate the error between the simulation results and the historical operation data, and perform statistical analysis;

[0147] After the qualitative and quantitative verification of the power grid dynamic model is completed, the model logic is verified using the model verification tool. At the same time, the model is sequentially tested for unit testing, integration testing, and system testing.

[0148] Analyze the test results using data analysis tools, and identify and diagnose erroneous data in the power grid dynamic model based on the analysis results;

[0149] Correct the model based on the identified and diagnosed error data, including parameter adjustment, logic correction and model structure update;

[0150] The modified power grid dynamic model is simulated again and verified using historical operation data and real-time operation data;

[0151] At the same time, during the verification and correction process of the power grid dynamic model, a loop feedback mechanism is built to continuously correct and iterate according to the results of each round of verification until the power grid dynamic model reaches the predetermined accuracy and reliability standards;

[0152] Finally, the standard power grid dynamic model is obtained.

[0153] Specifically, by collecting historical operation data (such as voltage, current, load changes, and fault events) as the verification baseline, the objectivity and accuracy of the verification process are ensured. Using historical data for quantitative verification, through the comparison of simulation results and actual data, the performance of the model can be intuitively reflected, including two dimensions of qualitative verification and quantitative verification, ensuring the comprehensiveness of the verification. Qualitative verification is carried out by manually reviewing the structure, logical relationships, etc. of the model, which helps to discover potential problems in the model design. Quantitative verification further verifies the accuracy and reliability of the model through simulation runs and error statistical analysis. After the verification is completed, a model checking tool is used to verify the logic of the model, ensuring the correctness of the model logic. Through unit testing, integration testing, and system testing, the various parts and overall performance of the model are gradually verified, which helps to discover and fix potential problems. Using data analysis tools to analyze the test results can efficiently identify and diagnose incorrect data in the model. This intelligent method improves the accuracy and efficiency of error identification, helps to quickly locate and solve problems, constructs a cyclic feedback mechanism, and continuously corrects and iterates the model according to the results of each round of verification. This iterative correction method helps to gradually optimize the model and make it gradually meet the predetermined accuracy and reliability standards. Not only historical operation data is used for verification, but also real-time operation data is introduced for verification. This enables the model to better adapt to the dynamic changes of the power grid, improving the practicality and reliability of the model. Through the advantages of data-driven, comprehensive verification, strict testing, intelligent error identification, cyclic feedback iteration, and real-time and dynamic nature, etc., the accuracy and reliability of the power grid dynamic model are ensured, providing a strong guarantee for the safe and stable operation of the power grid.

[0154] Visualize the standard power grid dynamic model using visualization processing technology, and provide interactive functions for the visually processed standard power grid dynamic model, including:

[0155] Confirm the display indicators in the standard power grid dynamic model, and the display indicators include voltage, load, frequency, and fault status;

[0156] After the display indicator confirmation is completed, construct a visualization model for the standard power grid dynamic model;

[0157] The visualization model construction is to draw a power grid architecture diagram through a graphical tool, display basic components and their connection relationships, and express them in a graphical way using nodes and edges;

[0158] Design dynamic indicators for each basic component to display the operating status of each basic component, where the operating status includes normal, faulty, and maintenance, and different color tags are used to represent different statuses;

[0159] Use line charts, bar charts, and pie charts to display indicator data;

[0160] Add interaction technology to the standard power grid dynamic model with the visualization model constructed.

[0161] After the interaction technology is added, real-time operation status data is obtained.

[0162] Specifically, the power grid architecture diagram drawn by the graphical tool can intuitively display the basic components of the power grid and their connection relationships. This graphical expression makes the power grid structure clear and easy for staff to quickly understand the overall layout and component distribution of the power grid. The dynamic indicators designed for each basic component can display the operation status of the component in real time. The tags of different colors intuitively reflect whether the component is in normal, faulty or maintenance status, enabling staff to quickly identify abnormal situations in the power grid. Diversified chart forms such as line charts, bar charts and pie charts are used to display key index data such as voltage, load, frequency and fault status. These charts can clearly present the trends and distributions of the data, helping staff better grasp the operation status of the power grid. Through the visualized power grid dynamic model, staff can more intuitively see the operation data of the power grid, thus making it easier to discover potential problems and risks. This helps staff make decisions in a timely manner and take corresponding measures to ensure the safe and stable operation of the power grid. After adding the interaction technology, staff can interact with the visualization model through input devices such as mice and keyboards. For example, they can zoom in or out of the view, rotate the model, select specific components for viewing, etc. This interactivity enables staff to understand the operation status of the power grid more deeply and improve work efficiency. The visualization model can receive and update the operation data of the power grid in real time. This means that staff can view the latest power grid status at any time without waiting for data updates or manually refreshing the view. This real-time nature helps staff respond to abnormal situations in the power grid in a timely manner and reduce the fault handling time. Visualizing the standard power grid dynamic model and adding interactive functions have many advantages, including intuitiveness and easy understandability, data visualization and decision support, interactivity and real-time nature, as well as flexibility and scalability. These advantages help improve the safety and stability of power grid operation and enhance the work efficiency and quality of staff.

[0163] To solve the problems in the prior art that there is no effective analysis of power grid data and no judgment of the degree of abnormality based on the analysis results, resulting in the inability to perform priority processing according to the degree of abnormality, please refer to Figure 1 and Figure 2 This embodiment provides the following technical solutions:

[0164] A power grid dynamic ontology modeling system for digital twin, comprising:

[0165] An early warning trigger and processing unit, for:

[0166] Utilize the data stream and data processing framework to process the collected real-time operation status data in real time, and perform data analysis on the real-time operation status data through the real-time data processing engine;

[0167] Set the alarm mechanism according to the data analysis results. The alarm mechanism is set to set thresholds for different data attributes. When the data analysis results are not within the set threshold range, the alarm mechanism will be automatically triggered;

[0168] Meanwhile, judge the degree of abnormality of the data for which the alarm is automatically triggered. The degree of abnormality is judged according to the threshold abnormality size of the data analysis results to confirm the degree of abnormality of the data analysis results;

[0169] The degree of abnormality is divided into minor abnormality, moderate abnormality, and severe abnormality;

[0170] Perform processing with different priorities according to different degrees of abnormality.

[0171] Specifically, through the data stream and data processing framework, the operation status data can be collected and processed in real time, ensuring the timeliness and accuracy of the data. The real-time data processing engine can quickly analyze the data, discover problems in a timely manner, improve the response speed of the system. The alarm mechanism setting realizes automation, reduces manual intervention, and improves work efficiency. Automatically triggering the alarm according to the data analysis results realizes intelligent monitoring and management. The threshold setting can be adjusted according to different data attributes, making the alarm mechanism more flexible and adaptable. The solution supports processing with different priorities for different degrees of abnormality, is easy to expand and adapt to more complex scenarios. Through the judgment of the degree of abnormality, the degree of abnormality of the data analysis results can be accurately divided, which helps managers quickly locate problems and take targeted measures. Different degrees of abnormality correspond to different processing priorities, enabling resources to be more effectively allocated to the problems that require the most attention and processing. Real-time data processing and the alarm mechanism help to intervene before or at the initial stage of the problem, reducing potential risks. Through continuous data analysis and monitoring, potential problems can be discovered in advance, providing a basis for system optimization and improvement.

[0172] In one embodiment, provide an interactive function for the visualized standard power grid dynamic model, including:

[0173] Obtain the power grid management role and construct user nodes according to the power grid management role;

[0174] Distributively and bidirectionally connect each user node with the cloud processing center. Meanwhile, bidirectionally connect the cloud processing center with the standard power grid dynamic model;

[0175] When there is an interactive request generated by a user node, transmit the interactive request of the current user node to the cloud processing center;

[0176] Read the interaction request based on the cloud processing center, determine the request target and execution action of the interaction request, and at the same time, retrieve the model structure in the standard power grid dynamic model in the cloud processing center;

[0177] Perform a first match between the request target and the model structure in the standard power grid dynamic model to determine the adjustment area of the standard power grid dynamic model, and at the same time, generate an adjustment instruction according to the adjustment area and the execution action;

[0178] Transmit the adjustment instruction to the standard power grid dynamic model to perform the adjustment operation, and at the same time, read the response data in the power grid dynamic model;

[0179] Feed back the response data of the power grid dynamic model to the cloud processing center, and analyze the response data according to the cloud processing center to obtain the interaction result based on the current user node and the response results of the management scopes of the other user nodes for the response data;

[0180] Read the initial results of the management scopes of the other user nodes;

[0181] Perform a second match between the interaction result of the current user node and the target result of the interaction request, and at the same time, perform a third match between the response results of the management scopes of the user nodes and the corresponding initial results;

[0182] If the interaction result of the current user node is consistent with the target result of the interaction request, and the response results of the management scopes of the other user nodes are all consistent with the corresponding initial results, then the interaction operation is completed;

[0183] If the interaction result of the current user node is consistent with the target result of the interaction request, and there is a target user node whose response result of the management scope is inconsistent with the corresponding initial result among the other user nodes, then generate a first collaborative feedback report and feedback it to the corresponding target user management node;

[0184] If the interaction result of the current user node is inconsistent with the target result of the interaction request, and the response results of the management scopes of the other user nodes are all consistent with the corresponding initial results, then generate a second collaborative feedback report and feedback it to the current user management node;

[0185] Otherwise, feedback the first collaborative feedback report to the corresponding target user management node, and feedback the second collaborative feedback report to the current user management node.

[0186] In this embodiment, the power grid management role refers to the type of personnel who can manage the power grid, such as power grid engineers and power grid dispatchers, etc.

[0187] In this embodiment, the user node refers to a center constructed according to the power grid management role and used to receive and send data during interaction.

[0188] In this embodiment, the cloud processing center is a pre-set server used to connect the user node and the standard power grid dynamic model and, at the same time, process and analyze the data exchanged between the two.

[0189] In this embodiment, the request target refers to the specific object or block for which the current interaction request needs to be synchronously responded in the standard power grid dynamic model.

[0190] In this embodiment, the execution action refers to the actual management behavior of the power grid made by the user node represented by the current interaction request. For example, it can be the amount and direction of power grid scheduling.

[0191] In this embodiment, the first matching refers to matching the request target with the model structure of the standard power grid dynamic model, aiming to lock out the corresponding model area in the standard power grid dynamic model through the request target, that is, the obtained adjustment area.

[0192] In this embodiment, the response data refers to the operation data after the standard power grid dynamic model responds to the interaction request.

[0193] In this embodiment, the management scope of each user node refers to the management items or management services corresponding to different user nodes in the power grid.

[0194] In this embodiment, the initial result refers to the result corresponding to the management scope of the remaining user nodes when not affected by other user nodes.

[0195] In this embodiment, the target result of the interaction request refers to the final result after the current user node interacts with the standard power grid dynamic model and responds to the interaction request of the current user node.

[0196] In this embodiment, the first collaborative feedback report refers to a feedback report generated when the interaction result of the current user node is consistent with the target result of the interaction request and there is a response result of the target user node management scope in the remaining user nodes that is inconsistent with the corresponding initial result, and is used to characterize the influence on each user node.

[0197] In this embodiment, the target user management node refers to the user node in the remaining user nodes where the response result of the target user node management scope is inconsistent with the corresponding initial result.

[0198] In this embodiment, the second collaborative feedback report refers to a feedback report generated when the interaction result of the current user node is inconsistent with the target result of the interaction request, and the response results within the management scope of each other user node are all consistent with the corresponding initial results, and is used to characterize the influence on each user node.

[0199] The working principle and beneficial effects of the above technical solution are as follows: By obtaining the power grid management roles and constructing user nodes corresponding to different power grid management roles, and at the same time, connecting the constructed user nodes to the cloud processing center, it is convenient to receive and analyze the interaction requests sent by the user nodes. Secondly, the cloud processing center parses the received interaction requests to implement corresponding interaction control on the standard power grid dynamic model according to the parsing results, ensuring the synchronous adjustment effect of the standard power grid dynamic model. Finally, the corresponding data of the standard power grid dynamic model is fed back to the cloud processing center for analysis, and the response results within the management scope of different user nodes under the interaction request are determined through the analysis results, and then the response results are discussed in different cases to generate and feedback the collaborative feedback report between different user nodes, improving the interaction effect and interaction reliability between the standard power grid dynamic model and the user nodes, and at the same time, enhancing the practicality of the power grid dynamic ontology modeling.

[0200] In one embodiment, when providing an interaction function for the visualized standard power grid dynamic model, it includes:

[0201] Obtain the number of interactions with the standard power grid dynamic model and the total task volume of each interaction;

[0202] Calculate the interaction accuracy rate according to the total number of interactions with the standard power grid dynamic model and the total task volume of each interaction;

[0203]

[0204] Among them, φ represents the interaction accuracy rate; n represents the total number of interactions with the standard power grid dynamic model; n1 represents the number of correct interactions in the total number of interactions with the standard power grid dynamic model; n0 represents the number of incorrect interactions in the total number of interactions with the standard power grid dynamic model; i represents the serial number value of the interaction times; M i represents the total task volume at the i-th interaction; M ir represents the task volume with correct interaction at the i-th interaction;

[0205] Calculate the target interaction efficiency for interacting with the standard power grid dynamic model according to the interaction accuracy rate;

[0206]

[0207] Among them, η represents the target interaction efficiency for interacting with the standard power grid dynamic model; t iIt represents the interaction transmission time at the i-th interaction; τ i It represents the interaction response time at the i-th interaction; T represents the unit time of 1 h; δ represents the number of updates of the standard power grid dynamic model;

[0208] Obtain a preset interaction efficiency threshold, compare the target interaction efficiency with the preset interaction efficiency threshold, and determine whether an alarm operation needs to be performed;

[0209] When the target interaction efficiency is equal to or greater than the preset interaction efficiency threshold, it is determined that no alarm operation needs to be performed;

[0210] Otherwise, it is determined that an alarm operation needs to be performed.

[0211] In this embodiment, the preset interaction efficiency threshold is set in advance and used as a measurement standard for determining whether an alarm operation needs to be performed.

[0212] In this embodiment, the alarm operation can be a sound alarm.

[0213] The working principle and beneficial effects of the above technical solution are as follows: By obtaining the number of interactions with the standard power grid dynamic model and the total task volume of each interaction, the interaction accuracy rate can be effectively calculated. Furthermore, the interaction efficiency can be accurately calculated through the accuracy rate, so as to effectively measure the quality of the interaction with the standard power grid dynamic model. When the preset interaction efficiency threshold is not reached, an alarm operation is performed, so as to effectively achieve the timely grasp of the interaction dynamics and ensure the effectiveness of the interaction with the standard power grid dynamic model.

[0214] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0215] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention.

Claims

1. A method for dynamic ontology modeling of power grids for digital twins, characterized in that, Including: Collect the power grid operation data in the database. After data collection, perform data preprocessing. After data preprocessing, obtain the target power grid data. Use graph theory algorithms to construct a power grid structure model for the connection relationships of power grid components in the target power grid data. After the power grid structure model is constructed, obtain the power grid concept model. Adopt the ontology method to define the attributes, states, and behaviors of power grid components in the power grid concept model, and establish a dynamic model according to the defined parameters. After the dynamic model is established, obtain the power grid dynamic model. Verify and correct the established power grid dynamic model. At the same time, update the pattern data of the corrected power grid dynamic model. After the model data update is completed, obtain the standard power grid dynamic model. Visualize the standard power grid dynamic model using visualization processing technology, and provide an interactive function for the visualized standard power grid dynamic model to finally obtain the real-time operation status data.

2. The method for dynamically modeling an ontology of a power grid for digital twin according to claim 1, wherein, Collect the power grid operation data in the database. After data collection, perform data preprocessing, including: The power grid operation data includes voltage values, current values, power data, frequency data, frequency status data, load data, environmental data, and fault event data. The power grid operation data is collected in real time from each monitoring terminal. Store the real-time collected power grid operation data in the buffer area. At the same time, perform data preprocessing on the power grid operation data in the buffer area. Data preprocessing includes data cleaning of the power grid operation data in the buffer area. After data cleaning, perform data standardization. After data standardization, integrate the power grid operation data from different monitoring terminals, and align the integrated data in time series. Perform data conversion on the power grid operation data with time series alignment. After data conversion, complete the data preprocessing of the power grid operation data. After data preprocessing, obtain the target power grid data.

3. A method for dynamic ontology modeling of power grid oriented to digital twin according to claim 2, characterized in that Use graph theory algorithms to construct a power grid structure model for the connection relationships of power grid components in the target power grid data, including: Identify the basic components in the target power grid data. The basic components include transformers, switches, lines, loads, and generators, and identify the connection relationships between each basic component. Create nodes for each basic component and create edges for the connection relationships between each basic component. Create a graph model for the created nodes and edges using the adjacency list method. After creation, obtain the power grid graph model. Traverse the power grid graph model using the depth-first search or breadth-first search algorithm. After traversal, obtain the nodes and loops of each basic component in the power grid graph model. Use the Dijkstra algorithm or Bellman-Ford algorithm to calculate the paths between the nodes or loops of each basic component. After calculation, obtain the shortest paths between the nodes or loops of each basic component. According to the shortest paths between the nodes or loops of each basic component, construct a model that reflects the connection relationships of all basic components in the power grid and the shortest paths in each connection relationship. After the model is constructed, obtain the power grid concept model.

4. A method for dynamically modeling an ontology of a power grid for digital twins according to claim 3, characterized in that Using the ontology method to define the parameters of the attributes, states, and behaviors of power grid components in the power grid conceptual model, and establishing a dynamic model based on the defined parameters, including: Defining the ontology framework according to the power grid conceptual model. The ontology framework includes classes and attributes. Among them, the classes are the categories of basic components; the attributes are the attribute definitions for each class, including characteristic attributes, state attributes, and behavior attributes; Defining the relationships between different classes, including inheritance relationships or association relationships. After the definition is completed, associate the attributes with the corresponding categories to obtain a specific attribute set for each class; Defining the parameters for each basic component according to the specific attribute set, including static attribute parameterization, dynamic state parameterization, and behavior parameterization; Static attribute parameterization is to define the data type and range for the static attributes of each basic component; dynamic state parameterization is to design a real-time update mechanism for the states of each basic component; behavior parameterization is to define the behavior model of basic components and establish the behavior connections between basic components.

5. A method for dynamically modeling an ontology of a power grid for digital twins according to claim 1, characterized in that, Provide interactive functions for the standardized power grid dynamic model after visualization processing, including: Obtain the power grid management roles and construct user nodes according to the power grid management roles; Distributively and bidirectionally connect each user node to the cloud processing center. At the same time, bidirectionally connect the cloud processing center to the standardized power grid dynamic model; When there is an interactive request generated by a user node, transmit the interactive request of the current user node to the cloud processing center; Read the interactive request based on the cloud processing center to determine the request target and execution action of the interactive request. At the same time, retrieve the model structure in the standardized power grid dynamic model in the cloud processing center; Perform the first match between the request target and the model structure in the standardized power grid dynamic model to determine the adjustment area of the standardized power grid dynamic model. At the same time, generate an adjustment instruction according to the adjustment area and the execution action; Transmit the adjustment instruction to the standardized power grid dynamic model to perform the adjustment operation, and at the same time, read the response data in the power grid dynamic model; Feed back the response data of the power grid dynamic model to the cloud processing center, and analyze the response data according to the cloud processing center to obtain the interactive result based on the current user node and the response result of the management scope of the remaining user nodes to the response data; Read the initial results of the management scopes of the remaining user nodes; Perform the second match between the interactive result of the current user node and the target result of the interactive request. At the same time, perform the third match between the response results of the management scopes of each user node and the corresponding initial results; If the interactive result of the current user node is consistent with the target result of the interactive request, and the response results of the management scopes of the remaining user nodes are all consistent with the corresponding initial results, then the interactive operation is completed; If the interactive result of the current user node is consistent with the target result of the interactive request, and there is a response result of the management scope of the target user node among the remaining user nodes that is inconsistent with the corresponding initial result, then generate a first collaborative feedback report and feedback it to the corresponding target user management node; When the interaction result of the current user node is inconsistent with the target result of the interaction request, and the response results within the management scope of the remaining user nodes are all consistent with the corresponding initial results, a second collaborative feedback report is generated and fed back to the current user management node; Otherwise, the first collaborative feedback report is fed back to the corresponding target user management node, and the second collaborative feedback report is fed back to the current user management node.

6. A method for dynamically modeling an ontology of a power grid for digital twins according to claim 4, characterized in that, Using the ontology method to define the attributes, states and behaviors of power grid components in the power grid conceptual model, and establishing a dynamic model according to the defined parameters, further including: Establishing a dynamic model according to the defined parameters, where a modeling framework for the dynamic model is established before establishing the dynamic model; The modeling framework includes a state machine, an event-driven model and a rule-based model; the state machine uses a state transition mechanism to define the states of basic components and their transition rules, and is used to manage the state changes of components; the event-driven model triggers corresponding behaviors by setting events and is used for complex power grid interaction simulations, and the events include faults and load changes; the rule-based model uses a rule engine to process the behaviors of basic components according to the defined rules and is used for complex power grids with a large degree of freedom; After the modeling framework of the dynamic model is established, the state machine is used for modeling. The state machine modeling defines acceptable states for each basic component and lists the state transition conditions, and creates a state transition diagram using a UML state diagram or other graphical tools. At the same time, the transition relationships between states are shown; Modeling the event-driven model. The event-driven model modeling determines the key events in the defined parameters. The key events include load changes, equipment failures and environmental changes, then confirms the triggering rules of the key events, and defines the triggering reactions for each key event; Modeling the model of behavior parameters. The modeling of the behavior parameter model defines the operation logic of basic components according to the behavior attributes of basic components, and uses class or object programming to implement the behavior logic; Combining the modeled state machine, event-driven model and behavior parameter model, and obtaining a power grid dynamic model after the combination is completed.

7. A method for dynamically modeling an ontology of a power grid for digital twins according to claim 6, characterized in that, Verifying and correcting the established power grid dynamic model. At the same time, updating the pattern data of the corrected power grid dynamic model, including: Collecting the historical operation data in the database, including voltage, current, load changes and fault events. The historical operation data is used to compare and verify the baseline of the model; After the collection of historical operation data is completed, qualitative verification and quantitative verification are carried out on the power grid dynamic model; Qualitative verification is that the audit staff conducts a detailed audit of the structure, logical relationship, state transition and event reaction of the power grid dynamic model. At the same time, the relevant descriptions in the documents and ontology are checked; Quantitative verification is to run the power grid dynamic model in a simulation environment, quantitatively compare the simulation results with the historical operation data, calculate the error between the simulation results and the historical operation data, and conduct statistical analysis; After the power grid dynamic model completes qualitative verification and quantitative verification, use a model checking tool to verify the logic of the model. At the same time, unit testing, integration testing and system testing are carried out on the model in turn; Use data analysis tools to analyze the test results, and identify and diagnose the incorrect data in the power grid dynamic model according to the analysis results; Modify the model according to the identified and diagnosed incorrect data. The model modification includes parameter adjustment, logic correction, and model structure update; Simulate the power grid dynamic model again after model modification, and verify it using historical operation data and real-time operation data; At the same time, during the verification and modification process of the power grid dynamic model, construct a loop feedback mechanism, and continuously correct and iterate according to the results of each round of verification until the power grid dynamic model meets the predetermined accuracy and reliability standards; Finally, obtain the standard power grid dynamic model.

8. A method for dynamic ontology modeling of a power grid oriented to digital twins according to claim 7, characterized in that, Visualize the standard power grid dynamic model using visualization processing technology, and provide interactive functions for the visualized standard power grid dynamic model, including: Confirm the display indicators in the standard power grid dynamic model. The display indicators include voltage, load, frequency, and fault status; After the display indicator confirmation is completed, construct a visualization model for the standard power grid dynamic model; The visualization model construction is to draw a power grid architecture diagram through a graphical tool, display the basic components and their connection relationships, and express them in a graphical way using nodes and edges; Design dynamic indicators for each basic component to display the operating status of each basic component. Among them, the operating status includes normal, faulty, and maintenance, and different colored labels are used to represent different statuses; Use line charts, bar charts, and pie charts to display indicator data; Add interactive technology to the standard power grid dynamic model with the completed visualization model construction; After the interactive technology is added, obtain the real-time operation status data.

9. A method for dynamically modeling an ontology of a power grid for digital twins according to claim 1, characterized in that When providing interactive functions for the visualized standard power grid dynamic model, it includes: Obtain the number of interactions with the standard power grid dynamic model and the total task volume of each interaction; Calculate the interaction accuracy rate according to the total number of interactions with the standard power grid dynamic model and the total task volume of each interaction; Calculate the target interaction efficiency for interacting with the standard power grid dynamic model according to the interaction accuracy rate; Obtain the preset interaction efficiency threshold, and compare the target interaction efficiency with the preset interaction efficiency threshold to determine whether an alarm operation needs to be performed; When the target interaction efficiency is equal to or greater than the preset interaction efficiency threshold, it is determined that no alarm operation needs to be performed; Otherwise, it is determined that an alarm operation needs to be performed.

10. A power grid dynamic ontology modeling system for digital twins, which is used to implement the power grid dynamic ontology modeling method for digital twins described in claim 7, and is characterized in that, It includes: An early warning trigger and processing unit, which is used for: Use the data stream and data processing framework to real-time process the collected real-time operation status data, and perform data analysis on the real-time operation status data through the real-time data processing engine; Set the alarm mechanism according to the data analysis results. The alarm mechanism is set to set thresholds for different data attributes, and when the data analysis results are not within the set threshold range, the alarm mechanism will be automatically triggered; At the same time, judge the degree of abnormality of the automatically triggered alarm data. The degree of abnormality judgment is to confirm the degree of abnormality of the data analysis results according to the threshold abnormality size of the data analysis results; The degree of abnormality is divided into minor abnormality, moderate abnormality, and severe abnormality; Perform processing with different priorities according to different degrees of abnormality.

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