Power grid intelligent anti-error method and system based on knowledge graph

Abstract

The invention discloses a power grid intelligent anti-error method and system based on a knowledge graph, and the method comprises the steps: obtaining multi-source heterogeneous data, and carrying out the preprocessing of the multi-source heterogeneous data, and obtaining a first data set; constructing a knowledge graph model based on the topological structure of the power system, wherein the knowledge graph model dynamically adjusts a knowledge graph according to the first data set; and carrying out risk assessment based on the map relationship in combination with the real-time data, and generating a corresponding anti-error strategy according to a risk assessment result. Through dynamic updating of the knowledge graph and real-time evaluation of the intelligent anti-misoperation model, misoperation early warning in dispatching operation is realized, and the safety of power grid dispatching is improved; in combination with a graph convolutional neural network technology, intelligent generation of an anti-error strategy is realized, a scheduling operation process is optimized, and potential risks caused by misoperation are avoided; by dynamically adjusting the mapping knowledge domain and the model parameters, the method can flexibly adapt to the change of the operation state of the power grid, and has relatively high environment adaptability and robustness.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching and error prevention management, and in particular to a knowledge graph-based power grid intelligent error prevention method and system. Background Art

[0002] As the scale and complexity of power systems increase, the risk of misoperation in power grid dispatching also increases. Traditional error prevention technologies mainly rely on static rules and manual inspections, which are difficult to adapt to the complex and changeable power grid operating environment, leading to problems such as slow response and insufficient accuracy. Existing error prevention systems have obvious deficiencies in integrating multi-source heterogeneous data, dynamically adjusting rules, and timely preventing misoperation, making it difficult to meet the actual needs of power grid dispatching. Among the existing technologies, although there are error prevention methods based on big data and intelligent algorithms, most of them rely on preset rules and static data, and are difficult to adapt to changes in the operating status of the power grid in real time. In addition, the existing methods lack systematicity and comprehensiveness in the construction of knowledge graphs and the design of intelligent error prevention models, and fail to effectively integrate multi-source heterogeneous data, thereby limiting the intelligence and coverage of the error prevention system.

[0003] Although the existing technology has made certain improvements in preventing misoperation of power grids, it still has problems such as insufficient intelligence, single data processing, limited flexibility and scalability, especially when dealing with complex and changeable power grid environments. In comparison, the present invention, by introducing knowledge graphs and intelligent misoperation prevention models, achieves dynamic adaptation to the real-time operating status of the power grid and comprehensive processing of multi-source data, significantly improving the intelligence level of misoperation prevention. In addition, the present invention is more in-depth in system integration and practical application, and can provide more efficient and intelligent misoperation prevention support for modern power grid dispatching, fully meeting the dispatching needs in complex power grid environments. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a knowledge graph-based intelligent power grid error prevention method and system to solve the problems mentioned in the background technology.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a knowledge graph-based intelligent power grid error prevention method, comprising: acquiring multi-source heterogeneous data, and preprocessing the multi-source heterogeneous data to obtain a first data set;

[0009] Building a knowledge graph model based on the topological structure of the power system, wherein the knowledge graph model dynamically adjusts the knowledge graph according to the first data set;

[0010] Risk assessment is performed based on graph relationships combined with real-time data, and corresponding error prevention strategies are generated according to the risk assessment results.

[0011] As a preferred solution of the knowledge graph-based intelligent power grid error prevention method described in the present invention, the acquisition of multi-source heterogeneous data includes at least: equipment status data, historical operation data, environmental data and sensor data.

[0012] As a preferred solution of the knowledge graph-based intelligent power grid error prevention method of the present invention, the preprocessing of the multi-source heterogeneous data to obtain the first data set includes:

[0013] The multi-source heterogeneous data are normalized, and features are extracted from the normalized multi-source heterogeneous data to construct feature vectors. A first data set is obtained based on the feature vectors, and the first data set is real-time updated data.

[0014] As a preferred solution of the power grid intelligent error prevention method based on knowledge graph described in the present invention, wherein: constructing a knowledge graph model based on the topological structure of the power system includes: defining nodes and connection relationships;

[0015] The nodes include equipment, operating status and risk events. The relationship between the equipment and the operating status is defined as an edge, the relationship between the operating status and the risk event is defined as an edge, and the relationship between the risk event and the equipment is defined as another edge. A knowledge graph model is constructed based on the defined nodes and edges. The knowledge graph model is used to represent an adjacency matrix A:

[0016]

[0017] Among them, A ij Represents node v i and node v j The strength of the relationship between them, E represents the edge set.

[0018] As a preferred solution of the power grid intelligent error prevention method based on knowledge graph described in the present invention, wherein: the knowledge graph model dynamically adjusts the knowledge graph according to the first data set, including:

[0019] If new equipment is added or replaced in the power grid, the knowledge graph needs to add or delete the corresponding equipment nodes accordingly;

[0020] If a device in the power grid fails, the fault node is updated with the fault type, impact range, and fault handling status. The new fault event triggers the update of related nodes in the knowledge graph.

[0021] When a power grid dispatcher performs an operation, the operation record also needs to be updated to the knowledge graph in a timely manner to mark the operation and the information associated with the equipment or status.

[0022] As a preferred solution of the power grid intelligent error prevention method based on knowledge graph described in the present invention, wherein: risk assessment is performed based on graph relationship combined with real-time data, and corresponding error prevention strategies are generated according to the risk assessment results, including: taking the first data set as the input of the graph convolutional neural network, taking the risk value as the output of the graph convolutional neural network, calculating the risk value of each node according to the graph convolutional neural network, and predicting risk faults;

[0023] If the risk value of any device exceeds the preset threshold, a fault warning will be issued for the device, and corresponding suggestions for preventing misoperation will be provided;

[0024] If the load in any area of ​​the power grid approaches the maximum carrying capacity, the system will issue an overload warning to prompt the dispatcher to take measures;

[0025] When the power grid dispatcher inputs an operation instruction, the system evaluates the risk of the operation in real time based on the graph embedding vector and node status updated by the graph convolutional neural network. If the operation causes an erroneous operation of the power grid, an anti-error warning will be issued.

[0026] As a preferred solution of the knowledge graph-based intelligent power grid error prevention method described in the present invention, it also includes: when any power grid area is assessed as high risk or potential failure, the system triggers early warning responses of other related equipment through the linkage relationship of the knowledge graph.

[0027] In a second aspect, the present invention provides a power grid intelligent error prevention system based on knowledge graph, comprising:

[0028] A data preprocessing module, used for acquiring multi-source heterogeneous data and preprocessing the multi-source heterogeneous data to obtain a first data set;

[0029] A knowledge graph construction module, used to construct a knowledge graph model based on the topological structure of the power system, wherein the knowledge graph model dynamically adjusts the knowledge graph according to the first data set;

[0030] The intelligent error prevention module is used to conduct risk assessment based on graph relationships combined with real-time data, and generate corresponding error prevention strategies according to the risk assessment results.

[0031] In a third aspect, the present invention provides an electronic device, comprising:

[0032] Memory and processor;

[0033] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the knowledge graph-based power grid intelligent error prevention method are implemented.

[0034] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the knowledge graph-based intelligent power grid error prevention method.

[0035] Compared with the prior art, the present invention has the following beneficial effects: the present invention realizes early warning of misoperation in dispatching operations through dynamic updating of knowledge graphs and real-time evaluation of intelligent error prevention models, thereby improving the safety of power grid dispatching; combined with graph convolutional neural network technology, it realizes intelligent generation of error prevention strategies, optimizes dispatching operation processes, and avoids potential risks caused by misoperation; by dynamically adjusting knowledge graphs and model parameters, it can flexibly adapt to changes in the operating status of the power grid, and has strong environmental adaptability and robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0037] Figure 1 A method flow chart of a knowledge graph-based intelligent power grid error prevention method and system according to an embodiment of the present invention;

[0038] Figure 2 A schematic diagram of the design of an intelligent error prevention model for an intelligent power grid error prevention method and system based on a knowledge graph according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0040] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0041] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0042] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0043] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0044] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0045] Example 1

[0046] Reference Figures 1-2 , is an embodiment of the present invention, which provides a power grid intelligent error prevention method based on knowledge graph, including:

[0047] S100: Acquire multi-source heterogeneous data, and pre-process the multi-source heterogeneous data to obtain a first data set;

[0048] In an embodiment of the present application, obtaining multi-source heterogeneous data includes at least: device status data, historical operation data, environmental data, and sensor data.

[0049] In an optional embodiment, the multi-source heterogeneous data collected from the power grid dispatching system is also stored in a data set D: D = {D1, D2, ..., D n};

[0050] Each dataset D i Contains m data points: D i ={d i1 ,d i2 ,…,d im}.

[0051] In an embodiment of the present application, preprocessing multi-source heterogeneous data to obtain a first data set includes:

[0052] Normalization is performed on the multi-source heterogeneous data, and features are extracted from the normalized multi-source heterogeneous data to construct feature vectors. A first data set is obtained based on the feature vectors, and the first data set is real-time updated data.

[0053] In an optional embodiment, data preprocessing includes cleaning, normalizing and feature extraction of the collected multi-source heterogeneous data, and further includes noise removal, missing value filling, standardization and feature selection.

[0054] Specifically, for each data point d ij The normalized expression is:

[0055]

[0056] Among them, d ij ′ represents the normalized data value, max(D i ) and min(D i ) represent the data set D i The maximum and minimum values ​​of .

[0057] Specifically, feature extraction includes extracting operating time, number of faults, voltage mean and standard deviation, current mean and standard deviation, temperature mean and standard deviation, and fault type statistics (number of short circuits, number of overloads) from the equipment status data, constructing a feature vector, and merging the feature vector extracted from the equipment status data with the feature extracted data according to the timestamp to obtain the first data set, which is used to construct a knowledge graph and subsequent model training.

[0058] It should be noted that the feature vectors obtained after feature extraction: statistics of data such as load, voltage, current, etc. will change over time; when the feature vectors change, the new feature values ​​need to be reintegrated and updated into the knowledge graph.

[0059] Exemplarily, the integrated first data set is converted into a multidimensional matrix X, where each row of X represents the state of the power grid at a time point, and each column represents a feature obtained from different feature extraction steps, so that the data can be input into the model for training and analysis; assuming that there are T time points and the length of the data vector at each time point is F, then the latest data set X can be expressed as:

[0060]

[0061] Among them, x ij Indicates that at time point t i The value of the j-th feature on .

[0062] S200: constructing a knowledge graph model based on the topological structure of the power system, and the knowledge graph model dynamically adjusts the knowledge graph according to the first data set;

[0063] In an embodiment of the present application, constructing a knowledge graph model based on the topological structure of the power system includes: defining nodes and connection relationships;

[0064] Nodes include equipment, operating status, and risk events. The relationship between equipment and operating status is defined as an edge, the relationship between operating status and risk events is defined as an edge, and the relationship between risk events and equipment is defined as another edge. A knowledge graph model is constructed based on the defined nodes and edges. The knowledge graph model is used to represent an adjacency matrix A:

[0065]

[0066] Among them, A ij Represents node v i and node v j The strength of the relationship between them, E represents the edge set.

[0067] Specifically, in the embodiment of the present application, the node set is defined as V, where each node v i ∈V represents a specific device or event; extract entities from the integrated data, extract all devices from the device information table, create nodes; extract operation events from the operation record table, create nodes; extract load points from the load data table, create nodes; extract fault types from the fault record table, create nodes; extract environmental factors from the environmental data table, create nodes.

[0068] Specifically, the knowledge graph G = (V, E) forms a complete structural diagram of the power grid by constructing nodes and edges. In the embodiment of the present application, the relationship between entities is extracted from the data. Device connection: Create a "connection" edge based on the position and connection relationship in the device information table. Operation impact: Create an "execution" edge based on the operation type and the affected device ID in the operation record table. Fault impact: Create an "affected" edge based on the fault type and the affected device ID in the fault record table. Environmental dependency: Create a "dependency" edge based on the relationship between environmental factors and device status in the environmental data table.

[0069] It should be noted that the knowledge graph constructed can perform real-time data updates, including real-time updates of equipment status, load data, fault records, and environmental data; dynamically update nodes and edges in the knowledge graph based on real-time data; and update the knowledge graph based on real-time data to ensure that the error prevention model can dynamically reflect the actual operation of the power grid. Risk identification can also be performed using the relationships in the graph, such as the impact of a fault on a device, linkage analysis, and analysis of the linkage effects between devices, such as a fault that may affect multiple devices. The knowledge graph finally constructed provides a panoramic view of the power grid dispatching operation, supporting dynamic risk analysis and error prevention operations.

[0070] In an embodiment of the present application, the knowledge graph model dynamically adjusts the knowledge graph according to the first data set, including:

[0071] If new equipment is added or replaced in the power grid, the knowledge graph needs to add or delete the corresponding equipment nodes accordingly;

[0072] If a device in the power grid fails, the fault node is updated with the fault type, impact range, and fault handling status. The new fault event triggers the update of related nodes in the knowledge graph.

[0073] When a power grid dispatcher performs an operation, the operation record also needs to be updated to the knowledge graph in a timely manner to mark the operation and the information associated with the equipment or status.

[0074] S300: Perform risk assessment based on the graph relationship combined with real-time data, and generate corresponding error prevention strategies according to the risk assessment results;

[0075] In an embodiment of the present application, risk assessment is performed based on the graph relationship combined with real-time data, and a corresponding error prevention strategy is generated according to the risk assessment result, including: using the first data set as a graph convolutional neural network input, the risk value as a graph convolutional neural network output, calculating the risk value of each node according to the graph convolutional neural network, and predicting risk failures;

[0076] If the risk value of any device exceeds the preset threshold, a fault warning will be issued for the device, and corresponding suggestions for preventing misoperation will be provided;

[0077] If the load in any area of ​​the power grid approaches the maximum carrying capacity, the system will issue an overload warning to prompt the dispatcher to take measures;

[0078] When the power grid dispatcher inputs an operation instruction, the system evaluates the risk of the operation in real time based on the graph embedding vector and node status updated by the graph convolutional neural network. If the operation causes an erroneous operation of the power grid, an anti-error warning will be issued.

[0079] In an embodiment of the present application, it also includes: when any power grid area is assessed as high risk or potential failure, the system triggers an early warning response of other related equipment through the linkage relationship of the knowledge graph.

[0080] In an optional embodiment, risk assessment based on graph relationships combined with real-time data can be used as an intelligent error prevention mechanism, which can prevent risks through an intelligent error prevention model. The intelligent error prevention model is a risk assessment and management system that comprehensively considers changes in the operating status of the power grid. The core of the model is to dynamically adjust the nodes and edges in the knowledge graph to achieve real-time identification, assessment and linkage response of risks, perform error prevention judgment, and avoid incorrect operations in the power grid. The model mainly includes a data input module, a knowledge graph construction module, a risk assessment module, and a linkage response module.

[0081] The data input module is responsible for receiving real-time data input from the power grid, including equipment status, operating parameters, environmental data, and historical operation records; data input is input through an API interface or data stream to ensure real-time and accuracy.

[0082] The knowledge graph construction module is used to update the knowledge graph regularly or on demand to reflect changes in the power grid operation status and prevent misoperation of the power grid in real time. The update cycle can be divided into real-time update: such as key data such as faults and loads; scheduled update: such as updating load data and environmental data once an hour; on-demand update: such as dynamic update after power grid operation or risk identification.

[0083] The risk assessment module is used to embed nodes and edges based on the knowledge graph using graph convolutional neural networks to obtain high-dimensional representations. Combined with real-time grid input data, the risk assessment of the current grid status is performed, including fault prediction, load overload risk assessment, etc. The relationship in the knowledge graph is dynamically adjusted, the weights of nodes and edges are updated in real time according to new data, and grid operations are judged in real time to prevent grid misoperation.

[0084] The linkage response module is used to generate linkage response strategies based on risk assessment results, including operation error prevention prompts, equipment warnings, operation suggestions, and user notifications. Through the linkage relationship in the knowledge graph, the penetrating dissemination of risk information is realized, the source of the fault and the scope of impact are quickly located, and real-time error prevention reminders are provided to improve the efficiency of scheduling operations and avoid scheduling operation risks.

[0085] Through the intelligent error prevention model, according to the risk assessment results, a linkage response strategy is generated, including power grid error prevention prompts, equipment warnings, operation suggestions and user notifications. Through the linkage relationship in the knowledge graph, intelligent error prevention of power grid operations is realized, which significantly improves the safety and stability of the power grid.

[0086] Example 2

[0087] The above embodiment is a schematic scheme of a knowledge graph-based intelligent power grid error prevention method. It should be noted that the technical solution of the knowledge graph-based intelligent power grid error prevention system and the above-mentioned knowledge graph-based intelligent power grid error prevention method belong to the same concept. The details of the technical solution of the knowledge graph-based intelligent power grid error prevention system that are not described in detail in this embodiment can be found in the description of the technical solution of the knowledge graph-based intelligent power grid error prevention method.

[0088] In this embodiment, a knowledge graph-based intelligent power grid error prevention system includes:

[0089] A data preprocessing module, used for acquiring multi-source heterogeneous data and preprocessing the multi-source heterogeneous data to obtain a first data set;

[0090] A knowledge graph construction module, used to construct a knowledge graph model based on the topological structure of the power system, and the knowledge graph model dynamically adjusts the knowledge graph according to the first data set;

[0091] The intelligent error prevention module is used to conduct risk assessment based on graph relationships combined with real-time data, and generate corresponding error prevention strategies according to the risk assessment results.

[0092] This embodiment further provides an electronic device, which is applicable to the case of the intelligent error prevention method for a power grid based on a knowledge graph, including:

[0093] Memory and processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the intelligent power grid error prevention method based on knowledge graph as proposed in the above embodiment.

[0094] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for realizing intelligent error prevention of power grid based on knowledge graph as proposed in the above embodiment is implemented.

[0095] The storage medium proposed in this embodiment and the method for realizing the intelligent error prevention of power grid based on knowledge graph proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0096] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0097] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A knowledge graph-based intelligent power grid error prevention method, characterized in that: include: Acquire multi-source heterogeneous data, and preprocess the multi-source heterogeneous data to obtain a first data set; Building a knowledge graph model based on the topological structure of the power system, wherein the knowledge graph model dynamically adjusts the knowledge graph according to the first data set; Risk assessment is performed based on graph relationships combined with real-time data, and corresponding error prevention strategies are generated according to the risk assessment results.

2. The intelligent power grid error prevention method based on knowledge graph according to claim 1, characterized in that: Acquisition of multi-source heterogeneous data includes at least: equipment status data, historical operation data, environmental data and sensor data.

3. The intelligent power grid error prevention method based on knowledge graph according to claim 2 is characterized in that: Preprocessing the multi-source heterogeneous data to obtain a first data set includes: The multi-source heterogeneous data are normalized, and features are extracted from the normalized multi-source heterogeneous data to construct feature vectors. A first data set is obtained based on the feature vectors, and the first data set is real-time updated data.

4. The intelligent power grid error prevention method based on knowledge graph according to claim 3 is characterized in that: Building a knowledge graph model based on the topological structure of the power system includes: defining nodes and connection relationships; The nodes include equipment, operating status and risk events. The relationship between the equipment and the operating status is defined as an edge, the relationship between the operating status and the risk event is defined as an edge, and the relationship between the risk event and the equipment is defined as another edge. A knowledge graph model is constructed based on the defined nodes and edges. The knowledge graph model is used to represent an adjacency matrix A: Among them, A ij Represents node v i and node v j The strength of the relationship between them, E represents the edge set.

5. The intelligent power grid error prevention method based on knowledge graph according to claim 4 is characterized in that: The knowledge graph model dynamically adjusts the knowledge graph according to the first data set, including: If new equipment is added or replaced in the power grid, the knowledge graph needs to add or delete the corresponding equipment nodes accordingly; If a device in the power grid fails, the fault node is updated with the fault type, impact range, and fault handling status. The new fault event triggers the update of related nodes in the knowledge graph. When a power grid dispatcher performs an operation, the operation record also needs to be updated to the knowledge graph in a timely manner to mark the operation and the information associated with the equipment or status.

6. The intelligent power grid error prevention method based on knowledge graph according to claim 5, characterized in that: Performing risk assessment based on graph relationships combined with real-time data, and generating corresponding error prevention strategies according to the risk assessment results include: using the first data set as the input of the graph convolutional neural network, using the risk value as the output of the graph convolutional neural network, calculating the risk value of each node according to the graph convolutional neural network, and predicting risk failures; If the risk value of any device exceeds the preset threshold, a fault warning will be issued for the device, and corresponding suggestions for preventing misoperation will be provided; If the load in any area of ​​the power grid approaches the maximum carrying capacity, the system will issue an overload warning to prompt the dispatcher to take measures; When the power grid dispatcher inputs an operation instruction, the system evaluates the risk of the operation in real time based on the graph embedding vector and node status updated by the graph convolutional neural network. If the operation causes an erroneous operation of the power grid, an anti-error warning will be issued.

7. The method for preventing power grid errors based on knowledge graph according to claim 6, characterized in that: Also includes: When any power grid area is assessed as high risk or potential failure, the system triggers early warning responses from other related equipment through the linkage relationship of the knowledge graph.

8. A knowledge graph-based intelligent power grid error prevention system, characterized in that: include: A data preprocessing module, used for acquiring multi-source heterogeneous data and preprocessing the multi-source heterogeneous data to obtain a first data set; A knowledge graph construction module, used to construct a knowledge graph model based on the topological structure of the power system, wherein the knowledge graph model dynamically adjusts the knowledge graph according to the first data set; The intelligent error prevention module is used to conduct risk assessment based on graph relationships combined with real-time data, and generate corresponding error prevention strategies according to the risk assessment results.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the knowledge graph-based power grid intelligent error prevention method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the knowledge graph-based intelligent power grid error prevention method as described in any one of claims 1 to 7.

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