Methods, devices, and computer equipment for constructing knowledge graphs for power grid equipment

Abstract

The application relates to a power grid equipment knowledge graph construction method and device and computer equipment. The method comprises the following steps: obtaining equipment physical characteristic data and equipment operation characteristic data corresponding to power grid equipment; constructing a power grid topology model corresponding to the power grid equipment according to equipment connection relationship information in the equipment physical characteristic data; extracting operation action condition data of the equipment physical characteristic data and the equipment operation characteristic data to obtain a relay protection rule model; inputting the equipment physical characteristic data and the equipment operation characteristic data into the power grid topology model and the relay protection rule model respectively, and mutually optimizing output results of the power grid topology model and the relay protection rule model to obtain an equipment knowledge graph corresponding to the power grid equipment. The method can improve the efficiency of constructing the power grid equipment knowledge graph, thereby improving the reliability, safety and efficiency of the power grid.

Description

Technical Field

[0001] This application relates to the field of smart grid technology, and in particular to a method, apparatus, computer equipment, storage medium and computer program product for constructing a knowledge graph of power grid equipment. Background Technology

[0002] With the development of computer technology, intelligent graph technology for power grid equipment has emerged. This technology is a structured information network used to describe and organize knowledge, data, and relationships related to power grid equipment. It consists of a series of entities (such as equipment, components, and events) and relationships (such as connections, dependencies, and effects), presented in graphical form, enabling users to intuitively understand and query power grid equipment and related information.

[0003] In traditional technologies, the construction of knowledge graphs for power grid equipment often faces challenges in terms of data consistency, completeness, and accuracy due to the diverse data sources and varying data quality. Data gaps, duplication, inconsistencies, or errors may exist, such as data drift or anomalies caused by sensor malfunctions, or errors in manual recording. These issues affect the modeling and analysis of knowledge graphs, leading to inefficiencies in constructing knowledge graphs for power grid equipment. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for constructing a power grid equipment knowledge graph that can improve the efficiency of constructing such a graph, in order to address the aforementioned technical problems.

[0005] Firstly, this application provides a method for constructing a knowledge graph of power grid equipment, including:

[0006] Acquire the physical characteristic data and operating characteristic data of the power grid equipment;

[0007] Based on the device connection relationship information in the device physical characteristic data, construct the power grid topology model corresponding to the power grid device;

[0008] Extract the physical characteristic data and the operational action condition data of the equipment's operating characteristic data to obtain a relay protection rule model;

[0009] The physical characteristic data and operating characteristic data of the equipment are respectively input into the power grid topology model and the relay protection rule model. The output results of the power grid topology model and the relay protection rule model are mutually optimized to obtain the equipment knowledge graph corresponding to the power grid equipment.

[0010] Secondly, this application also provides a power grid equipment knowledge graph construction apparatus, comprising:

[0011] The characteristic data acquisition module is used to acquire the physical characteristic data and operating characteristic data of the power grid equipment.

[0012] The topology model construction module is used to construct the power grid topology model corresponding to the power grid equipment based on the equipment connection relationship information in the equipment physical characteristic data;

[0013] The rule model acquisition module is used to extract the operation action condition data of the physical characteristic data and the operating characteristic data of the equipment to obtain the relay protection rule model;

[0014] The knowledge graph acquisition module is used to input the physical characteristic data and the operating characteristic data of the equipment into the power grid topology model and the relay protection rule model, respectively. By optimizing the output results of the power grid topology model and the relay protection rule model, the equipment knowledge graph corresponding to the power grid equipment is obtained.

[0015] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0016] Acquire the physical characteristic data and operating characteristic data of the power grid equipment;

[0017] Based on the device connection relationship information in the device physical characteristic data, construct the power grid topology model corresponding to the power grid device;

[0018] Extract the physical characteristic data and the operational action condition data of the equipment's operating characteristic data to obtain a relay protection rule model;

[0019] The physical characteristic data and operating characteristic data of the equipment are respectively input into the power grid topology model and the relay protection rule model. The output results of the power grid topology model and the relay protection rule model are mutually optimized to obtain the equipment knowledge graph corresponding to the power grid equipment.

[0020] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0021] Acquire the physical characteristic data and operating characteristic data of the power grid equipment;

[0022] Based on the device connection relationship information in the device physical characteristic data, construct the power grid topology model corresponding to the power grid device;

[0023] Extract the physical characteristic data and the operational action condition data of the equipment's operating characteristic data to obtain a relay protection rule model;

[0024] The physical characteristic data and operating characteristic data of the equipment are respectively input into the power grid topology model and the relay protection rule model. The output results of the power grid topology model and the relay protection rule model are mutually optimized to obtain the equipment knowledge graph corresponding to the power grid equipment.

[0025] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0026] Acquire the physical characteristic data and operating characteristic data of the power grid equipment;

[0027] Based on the device connection relationship information in the device physical characteristic data, construct the power grid topology model corresponding to the power grid device;

[0028] Extract the physical characteristic data and the operational action condition data of the equipment's operating characteristic data to obtain a relay protection rule model;

[0029] The physical characteristic data and operating characteristic data of the equipment are respectively input into the power grid topology model and the relay protection rule model. The output results of the power grid topology model and the relay protection rule model are mutually optimized to obtain the equipment knowledge graph corresponding to the power grid equipment.

[0030] The aforementioned method, apparatus, computer equipment, storage medium, and computer program product for constructing a knowledge graph of power grid equipment involve: acquiring physical characteristic data and operational characteristic data of the corresponding power grid equipment; constructing a power grid topology model corresponding to the power grid equipment based on the equipment connection relationship information in the physical characteristic data; extracting operational action condition data from the physical characteristic data and operational characteristic data of the equipment to obtain a relay protection rule model; inputting the physical characteristic data and operational characteristic data of the equipment into the power grid topology model and the relay protection rule model respectively; and optimizing the output results of the power grid topology model and the relay protection rule model to obtain the equipment knowledge graph corresponding to the power grid equipment.

[0031] By collecting physical and operational characteristic data of power grid equipment, various parameters and status information about the equipment can be obtained. Combined with the connection relationships between equipment, a topology model of the power grid can be constructed. This model describes the connection methods and topological structure between various devices in the power grid, thus forming the overall framework of the power grid. This helps to understand the energy flow paths and potential risk points in the power grid. The operational conditions of the equipment's physical and operational characteristic data are extracted to generate a relay protection rule model. This model, based on the characteristics and operating conditions of the equipment, defines the conditions and triggering logic for various protection actions, ensuring that the power grid can react promptly in abnormal situations, protecting the safe and stable operation of equipment and the system. The equipment's physical and operational characteristic data are input into the power grid topology model and the relay protection rule model, respectively, and through mutual optimization, a knowledge graph of the power grid equipment is obtained. By integrating information such as the equipment's physical characteristics, operational characteristics, connection relationships, and protection rules, the comprehensive knowledge of power grid equipment can be abstracted and expressed, improving the efficiency of constructing the power grid equipment knowledge graph. Further utilizing the knowledge graph of power grid equipment as the foundation of the decision support system helps operation and maintenance personnel better understand the operating status of the power grid, predict potential problems, and take corresponding measures to optimize and improve it, thereby improving the reliability, security and efficiency of the power grid. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is an application environment diagram of a method for constructing a knowledge graph of power grid equipment in one embodiment.

[0034] Figure 2 This is a flowchart illustrating a method for constructing a knowledge graph for power grid equipment in one embodiment;

[0035] Figure 3 This is a flowchart illustrating a device knowledge graph construction method in one embodiment;

[0036] Figure 4 This is a flowchart illustrating a method for optimizing the power grid topology and obtaining optimized protection rule nodes in one embodiment.

[0037] Figure 5 This is a flowchart illustrating the method for optimizing the power grid topology and obtaining optimized protection rule nodes in another embodiment;

[0038] Figure 6 This is a flowchart illustrating the method for adjusting the topology in one embodiment;

[0039] Figure 7 This is a flowchart illustrating the method for adjusting protection rule nodes in one embodiment;

[0040] Figure 8 This is a structural block diagram of a knowledge graph construction device for power grid equipment in one embodiment;

[0041] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0043] This application provides a method for constructing a knowledge graph of power grid equipment, which can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed on a cloud or other network server. Server 104 can obtain physical characteristic data and operational characteristic data of the power grid equipment from terminal 102; based on the equipment connection relationship information in the physical characteristic data, it constructs a power grid topology model corresponding to the power grid equipment; it extracts the operational action condition data from the physical characteristic data and operational characteristic data to obtain a relay protection rule model; it inputs the physical characteristic data and operational characteristic data into the power grid topology model and the relay protection rule model respectively, and optimizes the output results of the power grid topology model and the relay protection rule model to obtain a knowledge graph of the power grid equipment. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can be smart speakers, smart TVs, smart air conditioners, smart vehicle devices, etc. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc. Server 104 can be implemented using a standalone server or a server cluster composed of multiple servers.

[0044] In one exemplary embodiment, such as Figure 2 As shown, a method for constructing a knowledge graph of power grid equipment is provided, which can be applied to... Figure 1Taking the server in the example, the explanation includes the following steps 202 to 208. Wherein:

[0045] Step 202: Obtain the physical characteristic data and operating characteristic data of the power grid equipment.

[0046] Among these, the physical characteristic data of the equipment can be data used to represent the inherent characteristics of the power grid equipment, such as basic equipment information: including equipment type (e.g., transformers, switches, relays, etc.), manufacturer, model, rated capacity, rated voltage, etc. Physical parameters of the power grid equipment: covering the physical characteristics of the equipment, such as resistance, reactance, capacitance, self-inductance, etc., these parameters are very important for the modeling and simulation of power grid equipment.

[0047] Among these, equipment operating characteristic data can be used to represent the characteristics of power grid equipment during operation, such as describing the performance characteristics of the equipment under different operating conditions, including operating temperature range, load capacity, and losses. Real-time or historical equipment operating status data includes parameters such as current, voltage, power, and temperature, as well as equipment fault and maintenance records. Parameter settings and operating conditions for the equipment protection system are defined, including overload protection, short-circuit protection, and grounding protection.

[0048] Specifically, before collecting data, a data acquisition system for power grid equipment needs to be established. This system can acquire real-time data from the equipment through sensors, monitoring devices, or remote interfaces. At the interface of the data acquisition system, this data is transmitted to a data processing platform. Data processing algorithms clean, analyze, and process the data to extract the equipment's physical and operational characteristics. Finally, the processed data is stored in a database, and a corresponding query interface is provided so that users can retrieve the required equipment characteristic data through query operations.

[0049] Step 204: Based on the equipment connection relationship information in the equipment physical characteristic data, construct the power grid topology model corresponding to the power grid equipment.

[0050] Among them, the equipment connection relationship information can be the connection relationship between various devices in the power grid.

[0051] In this context, a power grid topology model can be seen as a physical layout model that connects various devices in the power grid via transmission media. Similar to network topology models, the main structures of power grid topology models include bus topology, star topology, ring topology, tree topology, and mesh topology.

[0052] Specifically, based on the device connection relationship information in the analyzed physical characteristic data of the equipment, the topological relationships between various power grid devices are determined, such as cable connections and busbar connections. Further, a topological structure is established between power grid devices based on these topological relationships, including connection methods and transmission paths. Building upon this topological structure, a corresponding power grid topology model is constructed using the information from the topology. This model can be built using graph theory or network analysis methods to obtain a preliminary topology model. After verification and optimization, the final power grid topology model is obtained.

[0053] Step 206: Extract the operation action condition data of the equipment physical characteristic data and the equipment operation characteristic data to obtain the relay protection rule model.

[0054] Among them, the operation action condition data can be the data information of the conditions corresponding to the operation of the power grid equipment.

[0055] Among them, the relay protection rule model can be a model used to determine whether the actual operation data of the relay in the equipment physical characteristic data and equipment operation characteristic data meets the operation action condition data.

[0056] Specifically, the parameters and conditions in the physical and operational characteristic data of the equipment are analyzed to determine the operational action conditions required for the relay protection rules, including various threshold conditions such as current, voltage, and frequency. These operational action condition data are then organized and summarized, and combined with the physical model of the power grid equipment to construct an initial relay protection rule model. This initial relay protection rule model can be represented using logical expressions, decision trees, neural networks, etc. The final relay protection rule model is obtained by validating and optimizing the initial model.

[0057] Step 208: Input the equipment physical characteristic data and equipment operating characteristic data into the power grid topology model and the relay protection rule model respectively. Optimize the output results of the power grid topology model and the relay protection rule model to obtain the equipment knowledge graph corresponding to the power grid equipment.

[0058] Among them, the equipment knowledge graph can be a database used to guide how to deal with various situations that arise during the operation of various power grid equipment.

[0059] Specifically, physical characteristic data and operational characteristic data are input into the power grid topology model to determine the topology and connection relationships between various power grid devices. These topologies and connections are constructed based on the actual applications of the power grid devices and differ from the topology and connection relationships in the power grid topology model. The actual operation data of the relays from the physical and operational characteristic data are input into the relay protection rule model to evaluate the protection logic and operating conditions of the power grid devices in actual operation. The outputs of the power grid topology model and the relay protection rule model are mutually optimized; that is, the relay protection rule model is adjusted based on the output of the power grid topology model, and the power grid topology model is adjusted based on the output of the relay protection rule model. This eliminates logical contradictions or optimization space, resulting in a consistent and logically sound device knowledge graph.

[0060] In the aforementioned method for constructing a knowledge graph of power grid equipment, the following steps are taken: First, the physical characteristic data and operational characteristic data of the corresponding power grid equipment are obtained. Then, based on the equipment connection relationship information in the physical characteristic data, a power grid topology model corresponding to the power grid equipment is constructed. Next, the operational action condition data of the physical characteristic data and operational characteristic data are extracted to obtain a relay protection rule model. Finally, the physical characteristic data and operational characteristic data are input into the power grid topology model and the relay protection rule model, respectively. The output results of the power grid topology model and the relay protection rule model are mutually optimized to obtain the equipment knowledge graph corresponding to the power grid equipment.

[0061] By collecting physical and operational characteristic data of power grid equipment, various parameters and status information about the equipment can be obtained. Combined with the connection relationships between equipment, a topology model of the power grid can be constructed. This model describes the connection methods and topological structure between various devices in the power grid, thus forming the overall framework of the power grid. This helps to understand the energy flow paths and potential risk points in the power grid. The operational conditions of the equipment's physical and operational characteristic data are extracted to generate a relay protection rule model. This model, based on the characteristics and operating conditions of the equipment, defines the conditions and triggering logic for various protection actions, ensuring that the power grid can react promptly in abnormal situations, protecting the safe and stable operation of equipment and the system. The equipment's physical and operational characteristic data are input into the power grid topology model and the relay protection rule model, respectively, and through mutual optimization, a knowledge graph of the power grid equipment is obtained. By integrating information such as the equipment's physical characteristics, operational characteristics, connection relationships, and protection rules, the comprehensive knowledge of power grid equipment can be abstracted and expressed, improving the efficiency of constructing the power grid equipment knowledge graph. Further utilizing the knowledge graph of power grid equipment as the foundation of the decision support system helps operation and maintenance personnel better understand the operating status of the power grid, predict potential problems, and take corresponding measures to optimize and improve it, thereby improving the reliability, security and efficiency of the power grid.

[0062] In one exemplary embodiment, such as Figure 3 As shown, the physical characteristic data and operational characteristic data of the equipment are input into the power grid topology model and the relay protection rule model, respectively, to obtain the equipment knowledge graph corresponding to the power grid equipment, including steps 302 to 306. Wherein:

[0063] Step 302: Input the equipment physical characteristic data and equipment operating characteristic data into the power grid topology model and the relay protection rule model respectively to obtain the initial power grid topology and the initial protection rule nodes.

[0064] The initial power grid topology can be generated by various power grid devices, but it has not been optimized.

[0065] The initial protection rule nodes can be protection nodes generated by various power grid devices for relays, but they have not been optimized.

[0066] Specifically, physical and operational characteristic data are imported into the power grid topology model. The model analyzes the device connections and topology, determining the relationships and transmission paths between various devices in the power grid, thus forming the initial power grid topology. The physical and operational characteristic data are then input into the relay protection rule model. Based on the device characteristics and operating status, the model determines initial protection rule nodes for the relays. These initial protection rule nodes describe the protection actions and control strategies that the power grid devices need to take for the relays under different conditions.

[0067] Step 304: Optimize the dependency relationship between the initial power grid topology and the initial protection rule nodes to obtain the optimized power grid topology and optimized protection rule nodes.

[0068] Among them, optimizing the power grid topology and optimizing the protection rule nodes can be achieved through the optimized initial power grid topology and initial protection rule nodes.

[0069] Specifically, the relationships between the initial topology and each protection rule node are analyzed to determine their dependencies and influencing factors. These dependencies are evaluated and adjusted to meet optimization requirements, which can be achieved through simulation, optimization algorithms, and other methods. During this process, the interaction between the power grid topology and the protection rule nodes needs to be considered to ensure effective support for stable power grid operation during protection actions. Based on the optimized dependencies, the power grid topology and protection rule nodes are adjusted and updated to obtain the optimized power grid topology and optimized protection rule nodes.

[0070] Step 306: Construct a device knowledge graph based on the optimized power grid topology and optimized protection rule nodes.

[0071] Specifically, the process involves integrating and optimizing the data from the power grid topology and protection rule nodes. This includes integrating equipment connection information and transmission paths from the optimized power grid topology, as well as protection actions and conditions from the optimized protection rule nodes. The integrated data is then structured to establish relationships and logical connections between devices, forming an initial framework for a device knowledge graph. Based on actual needs and application scenarios, the device knowledge graph is further expanded and optimized. Detailed information such as device attributes, operating status, and fault modes can be added, along with functions for data integration with other systems, resulting in the final device knowledge graph. Finally, the initial device knowledge graph is validated and refined to obtain the final device knowledge graph.

[0072] In this embodiment, by inputting the physical and operational characteristic data of the equipment into the power grid topology model and the relay protection rule model, an initial power grid topology and protection rule nodes are obtained, thus establishing the basic model of the power grid. Subsequently, by optimizing the dependencies between the topology and protection rule nodes, a more efficient and accurate optimized power grid topology and protection rule nodes are obtained, improving the operational efficiency and security of the power grid. Finally, based on the optimized topology and protection rule nodes, a device knowledge graph is constructed, providing comprehensive information support for the management and operation and maintenance of power grid equipment, further enhancing the management level and operational efficiency of the power grid.

[0073] In one exemplary embodiment, such as Figure 4 As shown, the dependency relationship between the initial power grid topology and the initial protection rule nodes is optimized to obtain the optimized power grid topology and optimized protection rule nodes, including steps 402 to 408. Wherein:

[0074] Step 402: Analyze the initial dependencies between the initial power grid topology and the initial protection rule nodes to obtain dependency analysis data.

[0075] The initial dependency relationship can be the relationship between the initial power grid topology and each initial protection rule node.

[0076] Among them, dependency analysis data can be the analysis data obtained after analyzing the initial dependency relationships.

[0077] Specifically, the connection relationships and transmission paths of each device are extracted from the power grid topology, while the protection actions and conditions of each device are extracted from each protection rule node. The extracted data is integrated and matched to determine the initial protection dependencies between various power grid devices, i.e., the location and mutual influence of the power grid devices corresponding to the protection rule nodes within the power grid topology. Further analysis of these initial dependencies is conducted, including determining the degree of dependence between devices, the correlation between key devices and protection nodes, etc. Based on the analysis results, dependency analysis data is generated, including a list of key devices, the location of devices corresponding to protection rule nodes, and their impact range.

[0078] Step 404: Adjust the redundant connections and isolated nodes of the initial power grid topology based on the dependency analysis data to obtain the adjusted topology.

[0079] Specifically, dependency analysis data is used to determine which connections are redundant or unnecessary, and which nodes are isolated or unnecessary. Based on the analysis results, the initial power grid topology is adjusted by removing redundant connections and isolated nodes to optimize connectivity and efficiency. This may involve modifying connection methods, adjusting transmission paths, or redesigning node layouts, resulting in an unverified adjusted topology. This unverified adjusted topology is then further verified and optimized to ensure it meets the requirements of power grid operation and protection, while maintaining system stability and reliability. Once verification is successful, the adjusted topology is generated.

[0080] Step 406: Optimize and simplify the execution logic and execution order of the initial protection rule node based on the dependency analysis data to obtain the adjusted protection rule node.

[0081] Specifically, dependency analysis data is used to determine which rule nodes' execution logic can be simplified or optimized, and which rule nodes' execution order can be optimized. Based on the analysis results, the execution logic and order of each initial protection rule node are adjusted and optimized. This may involve redefining protection actions and conditions, adjusting protection strategies, or redesigning the logical flow of protection rules, resulting in unverified adjusted protection rule nodes. Finally, these unverified adjusted protection rule nodes are verified and optimized to ensure that they effectively support the protection needs of power grid equipment while improving system reliability and response speed. If the unverified adjusted protection rule nodes pass verification, the adjusted protection rule nodes are generated.

[0082] Step 408: Collaboratively process and adjust the topology and protection rule nodes to obtain the optimized power grid topology and optimized protection rule nodes.

[0083] Specifically, the topology and various protection rule nodes are matched to ensure that changes in the topology are covered by the protection rules, and changes in the protection rules are adapted to the topology adjustments. The matched data is then subjected to joint optimization processing. This process considers the mutual influence between the topology and the protection rules, as well as their combined impact on grid stability and security. Therefore, it may be necessary to adjust the triggering conditions and action response strategies of the protection rules to adapt to topology changes; simultaneously, it may also be necessary to adjust the topology layout and transmission paths to ensure the effective execution of the protection rules. The final output is the optimized grid topology and optimized protection rule nodes after joint optimization.

[0084] In this embodiment, dependency analysis data was obtained by analyzing the dependencies between the initial power grid topology and protection rule nodes, providing a foundation for subsequent optimization. Subsequently, based on this data, the initial power grid topology was adjusted, eliminating redundant connections and isolated nodes, thereby improving the power grid's transmission efficiency and stability. Simultaneously, the execution logic and order of the initial protection rule nodes were optimized and simplified, making the protection rules clearer and more efficient. Finally, through collaborative processing of the adjusted topology and protection rule nodes, an optimized power grid topology and protection rule nodes were obtained, improving the power grid's security and reliability, and providing better support and assurance for power grid operation and management.

[0085] In one exemplary embodiment, such as Figure 5 As shown, the collaborative processing adjusts the topology and protection rule nodes to obtain an optimized power grid topology and optimized protection rule nodes, including steps 502 to 506. Wherein:

[0086] Step 502: Dynamically adjust the dependency relationship between the protection rule node and the topology structure to determine the optimal dependency relationship.

[0087] Among them, optimizing dependencies can be a new dependency obtained by adjusting the original dependencies based on dynamic adjustments.

[0088] Specifically, the process involves analyzing the dependencies between the current topology adjustments and the protection rule nodes. This includes identifying which protection rule nodes depend on specific parts of the topology and which topology changes affect the execution logic of the protection rule nodes. Then, based on real-time monitoring of the power grid's status and operation, the optimized dependencies between the protection rule nodes and the topology are dynamically adjusted. This adjustment process may involve real-time updates to the triggering conditions and action response strategies of the protection rule nodes to adapt to topology changes; simultaneously, it may also require real-time adjustments to the topology layout and transmission paths to ensure the effective execution of the protection rules. Throughout this process, continuous monitoring and analysis of the power grid's operating status and topology changes are necessary, and timely adjustments and optimizations should be made based on this feedback to ensure that the protection rules can respond promptly and effectively to the power grid's operational needs.

[0089] Step 504: Based on the determined optimization dependencies, adjust the position of the optimization protection rule node in the adjusted topology to obtain the optimization rule node position.

[0090] Among them, the optimized rule node position can be the new position after adjusting the node position in the topology.

[0091] Specifically, based on the optimization results of dependencies, the analysis adjusts the protection rule nodes and the topology to determine which protection rule nodes need to be repositioned to adapt to changes and optimizations in the topology. Next, these protection rule nodes that need repositioning are relocated, considering their optimal positions in the new topology to ensure effective coverage of the required equipment and transmission paths, thus obtaining optimized rule node positions. The adjustment process may involve moving the protection rule nodes, redefining their triggering conditions and execution logic, while ensuring that the adjusted rule node positions meet the protection requirements of the power grid.

[0092] Step 506: Determine the optimized power grid topology and optimized protection rule nodes based on the optimization dependencies and the locations of the optimization rule nodes.

[0093] Specifically, the process involves integrating and matching optimized dependencies and optimized rule node positions. This includes determining which topology adjustments affect protection rule nodes based on the optimized dependencies, and determining the new positions of protection rule nodes within the topology based on the optimized rule node positions. Then, based on the integrated data, corresponding feedback adjustments and optimizations are performed on the topology adjustments and optimized rule node positions (i.e., the topology adjustments and optimized rule node positions adjust according to changes in each other). This may include adjusting the layout and connection methods of equipment in the topology, as well as adjusting the triggering conditions and action response strategies of protection rule nodes. During the optimization process, it is crucial to ensure that the optimized power grid topology and protection rule nodes can coordinate and support each other to improve the stability, reliability, and security of the power grid. Finally, the optimized power grid topology and optimized protection rule nodes are generated.

[0094] In this embodiment, by dynamically adjusting the dependency relationship between protection rule nodes and the topology, the system can adapt more promptly to changes in the power grid's operating status and the occurrence of emergencies. This enables the protection rules to respond more quickly to power grid faults and anomalies, improving the power grid's response speed and fault handling capabilities. Simultaneously, by determining the optimized power grid topology and protection rule nodes based on the optimized dependency relationship and rule node positions, the system can effectively reduce misoperations and false alarms, improving the stability and reliability of power grid operation and lowering power grid operation risks.

[0095] In one exemplary embodiment, such as Figure 6 As shown, the redundant connections and isolated nodes of the initial power grid topology are adjusted based on dependency analysis data to obtain the adjusted topology, including steps 602 to 608. Wherein:

[0096] Step 602: Based on dependency analysis data, identify redundant connections and isolated nodes from the initial power grid topology.

[0097] Specifically, dependency analysis data is used to determine which power grid devices have duplicate connections or redundant paths, and which nodes are isolated in the power grid, i.e., not connected to other devices. Further, based on the analysis results, the initial power grid topology is identified and marked, marking redundant connections and isolated nodes. This may involve finding duplicate connections, identifying unnecessary transmission paths, and identifying individual nodes not connected to other devices.

[0098] Step 604: Simulate the functions of redundant connections and isolated nodes to obtain the first simulated function.

[0099] The simulation function refers to the simulation data obtained after simulating the simulated object.

[0100] Specifically, the functions and roles of redundant connections and isolated nodes are determined. This includes determining whether redundant connections affect the transmission efficiency and stability of the power grid, and whether isolated nodes cause isolation or communication failures in certain areas or equipment. Simulations are conducted based on the functions of redundant connections and isolated nodes, using simulation software or network simulation tools. During the simulation, scenarios involving disconnecting or deleting redundant connections can be simulated to observe changes in power grid transmission efficiency and stability; scenarios involving connecting isolated nodes or redesigning connection paths can also be simulated to observe improvements in power grid communication and operational status. The simulation results are analyzed to evaluate the impact of redundant connections and isolated nodes on the power grid, and the effectiveness of adjusting or optimizing them through simulation. Finally, based on the simulation results and analysis conclusions, the primary simulation function is determined.

[0101] Step 606: Simulate the initial power grid topology based on dependency analysis data to obtain the second simulation function.

[0102] Specifically, dependency analysis data is transformed into parameters and conditions required for simulation. This includes determining the dependencies between various devices, transmission paths, and triggering conditions for protection rules. Based on these parameters and conditions, the initial power grid topology is simulated using simulation software or network simulation tools. During the simulation, faults or changes in the operating status of different devices can be simulated to observe their impact on the overall power grid topology and protection rules. The simulation results are analyzed to evaluate the operating status and protection effectiveness of the power grid under different conditions, as well as the stability and reliability of the topology and protection rules. Finally, based on the simulation results and analysis conclusions, a second simulation function is determined.

[0103] Step 608: Based on the operation of the first simulation function in the second simulation function, adjust the redundant connections and isolated nodes of the initial power grid topology to obtain the adjusted topology.

[0104] Specifically, the impact of the operational results of the first and second simulation functions on the power grid is analyzed. This includes assessing the impact of redundant connections and isolated nodes on the power grid, as well as the improvement in power grid operation and protection effectiveness after adjustments. Based on the analysis results, corresponding adjustments are made to redundant connections and isolated nodes. For redundant connections, transmission path redundancy can be reduced by deleting or adjusting connection paths; for isolated nodes, redesigning or adding connections can be considered to reintegrate them into the power grid topology, thus obtaining an adjusted topology. However, further evaluation of the improvement effect of the adjusted topology on power grid operation and protection is needed. If the operational requirements of the power grid are not met, redundant connections and isolated nodes can be further optimized and adjusted until the adjusted topology reaches its optimal state.

[0105] In this embodiment, by analyzing dependency data, redundant connections and isolated nodes in the initial power grid topology are first identified, providing a foundation for power grid optimization. Subsequently, by simulating the functions of redundant connections and isolated nodes, a first simulation function is obtained, enabling a deeper understanding of the impact of these structures on power grid operation. Next, based on the dependency data, the initial topology is simulated again, resulting in a second simulation function, further exploring the impact of topology changes on power grid operation. Finally, based on the performance of the first simulation function in the second simulation function, the redundant connections and isolated nodes in the initial topology are adjusted, resulting in an adjusted topology. This improved power grid transmission efficiency and stability, reduced resource waste, and enhanced power grid responsiveness and reliability.

[0106] In one exemplary embodiment, such as Figure 7 As shown, the execution logic and execution order of the initial protection rule node are optimized and simplified based on dependency analysis data to obtain the adjusted protection rule node, including steps 702 to 706. Wherein:

[0107] Step 702: Based on the dependency analysis data, merge the identical nodes in the initial protection rule nodes to obtain simplified protection rule nodes.

[0108] The simplified protection rule node can be the result of merging nodes that have the same characteristics among the initial protection rule nodes.

[0109] Specifically, the dependencies and similarities between protection rule nodes are analyzed. This includes identifying which protection rule nodes share the same triggering conditions, action response strategies, or protected objects. Based on the analysis results, protection rule nodes with the same characteristics are merged into a unified node. This may involve adjusting the triggering conditions and action response strategies of the protection rule nodes to ensure that the merged node can effectively cover all relevant protection needs, resulting in a simplified protection rule node.

[0110] Step 704: Generate the protection rule logical structure based on the simplified protection rule nodes.

[0111] Among them, the protection rule logic structure can be the structure of the implementation logic of the protection rule.

[0112] Specifically, based on the simplification of protection rule nodes, the logical relationships, triggering conditions, and action response strategies between these nodes are determined. According to these logical relationships, the logical structure of the protection rules is established, which can be represented using logic diagrams, decision trees, state machines, etc. During the establishment of the logical structure, the priority relationships and triggering order between different protection rule nodes need to be considered to ensure the execution order and effectiveness of protection measures, thus generating the final logical structure of the protection rules.

[0113] Step 706: Set the execution order of protection rules according to the logical structure of the protection rules.

[0114] Specifically, based on the logical structure of the protection rules, the dependencies and logical flow between each protection rule are understood. This includes determining which protection rules need to be executed before others, and which rules have priority or sequential relationships. The execution order of the protection rules is then set based on the analysis results. This setting process includes determining the triggering conditions, action response strategies, and execution timing of the rules, as well as establishing the priority or sequential relationships between rules, ultimately generating the final execution order of the protection rules.

[0115] Step 708: Based on the simplified protection rule node and the execution order of the protection rules, the adjusted protection rule node is obtained.

[0116] Specifically, simplified protection rule nodes are executed according to the order in which protection rules are executed. The execution process maps protection rule nodes to their execution order, ensuring that each rule node is triggered in the correct sequence. If a rule node fails to trigger in the correct order, the simplified protection rule nodes are adjusted in reverse based on the data after partial execution. This may involve adjusting the triggering conditions, action response strategies, or execution timing of the rule nodes until they can still be executed correctly in the order after adjustment, generating adjusted protection rule nodes.

[0117] In this embodiment, simplified protection rule nodes are obtained by merging identical nodes in the initial protection rule nodes, reducing the complexity and redundancy of the rule nodes and improving the management efficiency of protection rules. Subsequently, a protection rule logical structure is generated based on the simplified protection rule nodes, making the logical relationships between protection rules clearer and more understandable. Next, the execution order of the protection rules is set according to the logical structure, enabling protection measures to be triggered in a reasonable order, improving the safety and reliability of the power grid. Finally, based on the simplified protection rule nodes and the execution order, adjusted protection rule nodes are obtained, further optimizing the execution logic and effects of the protection rules, providing better support and guidance for power grid protection control and decision-making. It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction for the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the steps or stages in other steps.

[0118] Based on the same inventive concept, this application also provides a power grid equipment knowledge graph construction device for implementing the aforementioned power grid equipment knowledge graph construction method. The solution provided by this device is similar to the implementation scheme described in the above method. Therefore, the specific limitations of one or more power grid equipment knowledge graph construction device embodiments provided below can be found in the limitations of the power grid equipment knowledge graph construction method described above, and will not be repeated here.

[0119] In one exemplary embodiment, such as Figure 8 As shown, a device for constructing a knowledge graph for power grid equipment is provided, comprising: a characteristic data acquisition module 802, a topology model construction module 804, a rule model acquisition module 806, and a knowledge graph acquisition module 808, wherein:

[0120] The characteristic data acquisition module 802 is used to acquire the physical characteristic data and operating characteristic data of the power grid equipment.

[0121] The topology model building module 804 is used to build a power grid topology model corresponding to the power grid equipment based on the equipment connection relationship information in the equipment physical characteristic data.

[0122] The rule model is obtained by module 806, which is used to extract the operation action condition data of the equipment physical characteristic data and the equipment operating characteristic data to obtain the relay protection rule model;

[0123] The knowledge graph acquisition module 808 is used to input the physical characteristic data and operating characteristic data of the equipment into the power grid topology model and the relay protection rule model, respectively. By optimizing the output results of the power grid topology model and the relay protection rule model, the equipment knowledge graph corresponding to the power grid equipment is obtained.

[0124] In one embodiment, the knowledge graph acquisition module 808 is further configured to input the equipment physical characteristic data and the equipment operating characteristic data into the power grid topology model and the relay protection rule model respectively to obtain the initial power grid topology and the initial protection rule node; optimize the dependency relationship between the initial power grid topology and the initial protection rule node to obtain the optimized power grid topology and the optimized protection rule node; and construct the equipment knowledge graph based on the optimized power grid topology and the optimized protection rule node.

[0125] In one embodiment, the knowledge graph acquisition module 808 is further used to analyze the initial dependencies between the initial power grid topology and the initial protection rule nodes to obtain dependency analysis data; adjust the redundant connections and isolated nodes of the initial power grid topology according to the dependency analysis data to obtain the adjusted topology; optimize and simplify the execution logic and execution order of the initial protection rule nodes according to the dependency analysis data to obtain the adjusted protection rule nodes; and collaboratively process the adjusted topology and the adjusted protection rule nodes to obtain the optimized power grid topology and the optimized protection rule nodes.

[0126] In one embodiment, the knowledge graph acquisition module 808 is further used to dynamically adjust the dependency relationship between the adjustment protection rule node and the adjustment topology to determine the optimization dependency relationship; based on the determined optimization dependency relationship, adjust the position of the adjustment protection rule node in the adjustment topology to obtain the position of the optimization rule node; and based on the optimization dependency relationship and the position of the optimization rule node, determine the optimized power grid topology and the optimized protection rule node.

[0127] In one embodiment, the knowledge graph acquisition module 808 is further configured to: identify redundant connections and isolated nodes from the initial power grid topology based on dependency analysis data; simulate the functions of redundant connections and isolated nodes to obtain a first simulation function; simulate the initial power grid topology based on dependency analysis data to obtain a second simulation function; and adjust the redundant connections and isolated nodes of the initial power grid topology based on the operation of the first simulation function in the second simulation function to obtain an adjusted topology.

[0128] In one embodiment, the knowledge graph obtaining module 808 is further configured to analyze data based on dependency relationships, merge identical nodes in the initial protection rule nodes to obtain simplified protection rule nodes; generate a protection rule logical structure based on the simplified protection rule nodes; set the protection rule execution order based on the protection rule logical structure; and obtain an adjusted protection rule node based on the simplified protection rule nodes and the protection rule execution order.

[0129] The modules in the aforementioned power grid equipment knowledge graph construction device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0130] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores server data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for constructing a knowledge graph for power grid equipment.

[0131] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0132] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0133] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0134] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the computer device to perform the steps in the above method embodiments.

[0135] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0136] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0137] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0138] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for constructing a power grid equipment knowledge graph, characterized in that, The method comprises: acquiring device physical characteristic data and device operation characteristic data corresponding to the power grid equipment; the device physical characteristic data comprises any one of device type, manufacturer, model, rated capacity, rated voltage, resistance, reactance, capacitance, and self-inductance; the device operation characteristic data comprises any one of working temperature range, load capacity, loss, device current, voltage, power, temperature, and fault and maintenance records of the device; constructing a power grid topology model corresponding to the power grid equipment according to device connection relationship information in the device physical characteristic data; the power grid topology model is a topology model obtained by modeling using graph theory; extracting operation action condition data of the device physical characteristic data and the device operation characteristic data to obtain a relay protection rule model; the operation action condition data is data information of conditions corresponding to actions of operating the power grid equipment; the relay protection rule model is a model for judging whether actual operation data of the device physical characteristic data and the device operation characteristic data on the relay conforms to the operation action condition data; inputting the device physical characteristic data and the device operation characteristic data into the power grid topology model and the relay protection rule model respectively, and mutually optimizing output results of the power grid topology model and the relay protection rule model to obtain a device knowledge graph corresponding to the power grid equipment; creating a database of a decision support system according to the device knowledge graph; the database is used to determine coping methods for various situations generated in the operation process of each power grid equipment.

2. The method of claim 1, wherein, The method comprises: inputting the device physical characteristic data and the device operation characteristic data into the power grid topology model and the relay protection rule model respectively to obtain an initial power grid topology structure and an initial protection rule node; optimizing a dependency relationship between the initial power grid topology structure and the initial protection rule node to obtain an optimized power grid topology structure and an optimized protection rule node; constructing the device knowledge graph according to the optimized power grid topology structure and the optimized protection rule node.

3. The method of claim 2, wherein, The method comprises: analyzing an initial dependency relationship between the initial power grid topology structure and the initial protection rule node to obtain dependency relationship analysis data; adjusting redundant connections and isolated nodes of the initial power grid topology structure according to the dependency relationship analysis data to obtain an adjusted topology structure; optimizing and simplifying execution logic and execution order of the initial protection rule node according to the dependency relationship analysis data to obtain an adjusted protection rule node; co-processing the adjusted topology structure and the adjusted protection rule node to obtain the optimized power grid topology structure and the optimized protection rule node.

4. The method of claim 3, wherein, The cooperative processing of the adjustment topology and the adjustment protection rule node obtains the optimized power grid topology and the optimized protection rule node, and comprises: dynamically adjusting the dependency relationship between the adjustment protection rule node and the adjustment topology to determine an optimized dependency relationship; adjusting the position of the adjustment protection rule node in the adjustment topology according to the determined optimized dependency relationship to obtain an optimized rule node position; determining the optimized power grid topology and the optimized protection rule node according to the optimized dependency relationship and the optimized rule node position.

5. The method of claim 3, wherein, The adjustment of the redundant connection and the isolated node of the initial power grid topology according to the dependency relationship analysis data to obtain an adjustment topology comprises: identifying the redundant connection and the isolated node from the initial power grid topology according to the dependency relationship analysis data; simulating the function of the redundant connection and the isolated node to obtain a first simulated function; simulating the initial power grid topology according to the dependency relationship analysis data to obtain a second simulated function; adjusting the redundant connection and the isolated node of the initial power grid topology according to the operation of the first simulated function in the second simulated function to obtain the adjustment topology.

6. The method of claim 3, wherein, The optimization and simplification of the execution logic and the execution sequence of the initial protection rule node according to the dependency relationship analysis data to obtain an adjustment protection rule node comprises: merging the same nodes in the initial protection rule node according to the dependency relationship analysis data to obtain a simplified protection rule node; generating a protection rule logic structure according to the simplified protection rule node; setting a protection rule execution sequence according to the protection rule logic structure; obtaining the adjustment protection rule node according to the simplified protection rule node and the protection rule execution sequence.

7. A power grid equipment knowledge graph construction apparatus, characterized in that, The device comprises: a characteristic data acquisition module configured to acquire device physical characteristic data and device operation characteristic data corresponding to power grid equipment; the device physical characteristic data comprises any one or more of device type, manufacturer, model, rated capacity, rated voltage, resistance, reactance, capacitance, and self-inductance; the device operation characteristic data comprises any one or more of working temperature range, load capacity, loss, device current, voltage, power, temperature, and fault and maintenance records of the device; a topology model construction module configured to construct a power grid topology model corresponding to the power grid equipment according to device connection relationship information in the device physical characteristic data; the power grid topology model is a topology model obtained by modeling using graph theory; a rule model obtaining module configured to extract operation action condition data of the device physical characteristic data and the device operation characteristic data to obtain a relay protection rule model; the operation action condition data is data information of a condition corresponding to an operation action on the power grid equipment; the relay protection rule model is a model for judging whether actual operation data of the device physical characteristic data and the device operation characteristic data on a relay conforms to the operation action condition data. The knowledge graph obtaining module is configured to input the device physical characteristic data and the device operation characteristic data into the power grid topology model and the relay protection rule model respectively, and obtain a device knowledge graph corresponding to the power grid device through mutual optimization of output results of the power grid topology model and the relay protection rule model. According to the device knowledge graph, a database of a decision support system is created; the database is used to determine coping methods for various situations generated in the operation process of each power grid device.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to implement the steps of the method in any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 6.

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