Relay protection device defect intelligent positioning method based on knowledge graph

Abstract

The invention discloses a relay protection device defect intelligent positioning method based on a knowledge graph, and relates to the technical field of intelligent power equipment fault diagnosis. The method comprises the following steps: data collection: collecting various data related to a relay protection device; a knowledge graph is constructed, a dynamic knowledge graph is constructed on the basis of collected data, the graph represents equipment information, defect types, maintenance records and the like in a node and edge mode, and when new data enter, the knowledge graph can be automatically adjusted and updated; and constructing an intelligent reasoning mechanism, automatically identifying possible defect reasons by analyzing information in the knowledge graph, and generating a defect positioning suggestion. By constructing the dynamic knowledge graph, the real-time monitoring data, the historical maintenance record and the environmental information of the sensor can be effectively integrated. The fusion of the multi-source data enables the system to comprehensively understand the operation state and potential problems of the equipment, thereby achieving higher-precision fault recognition.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent power equipment fault diagnosis, and in particular to a method for intelligently locating defects of a relay protection device based on a knowledge graph. Background Art

[0002] In recent years, in modern power systems, relay protection devices are key components to ensure the safe and stable operation of power equipment. These devices prevent equipment damage and power accidents by real-time monitoring and responding to abnormal conditions in the power system.

[0003] However, as the complexity of equipment increases, traditional fault diagnosis methods often face problems such as insufficient data processing capabilities, inaccurate fault location, and low maintenance efficiency. Existing technologies mostly rely on experience and manual judgment, lacking systematic analysis and intelligent decision support.

[0004] To this end, we proposed a knowledge graph-based intelligent defect location method for relay protection devices to solve the above problems. Summary of the invention

[0005] 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.

[0006] In view of the above problems existing in the prior art, the present invention is proposed.

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

[0008] A method for intelligently locating defects in a relay protection device based on a knowledge graph, the method comprising the following steps:

[0009] Step 1: Data collection: collect various data related to relay protection devices, including real-time monitoring data of sensors, historical maintenance records, and equipment use environment information, and integrate them together to form a basic database;

[0010] Step 2: Knowledge graph construction: Based on the collected data, a dynamic knowledge graph is constructed, and the graph represents equipment information, defect types, maintenance records, etc. in the form of nodes and edges. Whenever new data enters, the knowledge graph will be automatically adjusted and updated;

[0011] Step 3: Build an intelligent reasoning mechanism to automatically identify possible causes of defects by analyzing the information in the knowledge graph. Finally, generate suggestions for defect location based on the results obtained by the intelligent reasoning mechanism.

[0012] As a preferred solution of the method for intelligently locating defects of a relay protection device based on a knowledge graph described in the present invention, the method for constructing the knowledge graph is as follows:

[0013] S201: Define nodes and edges. Nodes represent equipment information, defect types, and maintenance records. Edges represent the relationship between nodes. Use edge weights A ij To describe, set the total number of nodes in the knowledge graph to G;

[0014] S202: Design a composite function f(D t , M, E);

[0015] S203: When new real-time monitoring data D t Enter, use f(D t , M, E) Calculate the edge weight A between nodes ij , that is, recalculate the edge weights between all nodes and adjust the knowledge graph structure.

[0016] As a preferred solution of the method for intelligently locating defects in a relay protection device based on a knowledge graph described in the present invention, wherein: the composite function f(D t , M, E) is:

[0017]

[0018] Among them, D t represents the real-time monitoring data of the sensor at time t; M represents the collection of historical maintenance records, including various types of maintenance information; E represents the equipment usage environment information, such as temperature and humidity; u M represents the mean of maintenance records; σ M Represents the standard deviation of the maintenance records.

[0019] As a preferred solution of the method for intelligently locating defects in a relay protection device based on a knowledge graph described in the present invention, in which: in the process of S203, the edge weight A ij The calculation formula is:

[0020]

[0021] Among them, R represents the normalization factor, g(N) represents the influence of the defect type, and Φ(k) represents the reliability score related to node k.

[0022] As a preferred solution of the method for intelligently locating defects of a relay protection device based on a knowledge graph described in the present invention, the method for constructing the intelligent reasoning mechanism is as follows:

[0023] S301: Set all edge weights A ij The sum of is taken as the knowledge graph dynamic value K(t);

[0024] S302: Define the impact of defect factors and calculate the impact of defect factors I c (t);

[0025]

[0026] S303: Based on the knowledge graph dynamic value K(t) and the influence degree of the defect factor I c (t) Generate defect location proposal P(t).

[0027] As a preferred solution of the method for intelligently locating defects of a relay protection device based on a knowledge graph described in the present invention, the expression of the dynamic value K(t) of the knowledge graph is:

[0028]

[0029] Where N and M represent the number and type of nodes respectively.

[0030] As a preferred solution of the method for intelligently locating defects of a relay protection device based on a knowledge graph described in the present invention, the expression of the defect location suggestion P(t) is:

[0031]

[0032] Among them, H represents the normalization factor that controls the output range, Nc represents the number of defect types, and C represents the set of defect factors.

[0033] A positioning system for a relay protection device defect intelligent positioning method based on a knowledge graph, wherein the system also includes the following functional modules:

[0034] Data acquisition module: collects sensor data, historical maintenance records and environmental information in real time to form a basic data set; Knowledge graph construction module: builds a dynamic knowledge graph based on the processed data, including equipment information, defect types, maintenance records, and is represented in the form of nodes and edges; Knowledge graph update module: monitors data changes in real time and automatically updates the knowledge graph to maintain the timeliness and accuracy of information;

[0035] And the reasoning engine module: use the intelligent reasoning mechanism to analyze the information in the knowledge graph, identify the possible causes of defects, and generate corresponding defect location suggestions; the visualization display module: display the knowledge graph and reasoning results in an intuitive graphical way to help users better understand and analyze fault information; the report generation module: automatically generate fault diagnosis and maintenance suggestion reports to provide decision support for maintenance personnel.

[0036] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned method for intelligently locating defects of a relay protection device based on a knowledge graph are implemented.

[0037] A computer-readable storage medium having a computer program stored thereon, characterized in that: when the computer program is executed by a processor, the steps of the above-mentioned method for intelligently locating defects in a relay protection device based on a knowledge graph are implemented.

[0038] Beneficial effects of the present invention:

[0039] 1. The present invention can effectively integrate real-time sensor monitoring data, historical maintenance records and environmental information by constructing a dynamic knowledge graph. This fusion of multi-source data enables the system to fully understand the operating status and potential problems of the equipment, thereby achieving more accurate fault identification; and by using the reasoning mechanism, based on the correlation analysis in the knowledge graph, it can identify the potential patterns and relationships of the fault causes, reduce the false alarm rate, and improve the reliability of fault location;

[0040] 2. As the operating environment and status of the equipment change, traditional methods often cannot adapt in time. The present invention ensures that the system can automatically adjust and optimize the inference rules when facing new situations by updating the knowledge graph in real time, thereby improving the adaptability of the overall system;

[0041] 3. The present invention uses an intelligent reasoning mechanism that is not limited to fault detection, but can also provide a basis for subsequent decision-making. For example, based on the operation history of the equipment, the system can predict potential fault risks and make preventive maintenance recommendations in advance, thereby reducing the probability of accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] 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 work. Among them:

[0043] Figure 1 This is a schematic diagram of the process structure of a method for intelligently locating defects in a relay protection device based on a knowledge graph proposed in the present invention. DETAILED DESCRIPTION

[0044] In order to make the above-mentioned objects, 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 accompanying drawings.

[0045] 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.

[0046] 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.

[0047] Reference Figure 1 , as an embodiment of the present invention, provides a method for intelligently locating defects of a relay protection device based on a knowledge graph, the method comprising the following steps:

[0048] Step 1: Data collection: collect various data related to relay protection devices, including real-time monitoring data of sensors, historical maintenance records, and equipment use environment information, and integrate them together to form a basic database;

[0049] Step 2: Knowledge graph construction: Based on the collected data, a dynamic knowledge graph is constructed, and the graph represents equipment information, defect types, maintenance records, etc. in the form of nodes and edges. Whenever new data enters, the knowledge graph will be automatically adjusted and updated;

[0050] Specifically, the knowledge graph construction method is:

[0051] S201: Define nodes and edges. Nodes represent equipment information, defect types, and maintenance records. Edges represent the relationship between nodes. Use edge weights A ij To describe, set the total number of nodes in the knowledge graph to G;

[0052] S202: Design a composite function f(D t , M, E);

[0053] S203: When new real-time monitoring data D t Enter, use f(D t , M, E) Calculate the edge weight A between nodes ij , that is, recalculate the edge weights between all nodes and adjust the knowledge graph structure.

[0054] Composite function f(D t , M, E) is:

[0055]

[0056] Among them, D t represents the real-time monitoring data of the sensor at time t; M represents the collection of historical maintenance records, including various types of maintenance information; E represents the equipment usage environment information, such as temperature and humidity; u M represents the mean of maintenance records; σ M Represents the standard deviation of the maintenance records.

[0057] In addition, the edge weight A ij The calculation formula is:

[0058]

[0059] Among them, R represents the normalization factor, g(N) represents the influence of the defect type, avoiding simple linear weighting;

[0060] g(N)=log(N+1), where N is the number of defect types.

[0061] Φ(k) represents the reliability score associated with node k.

[0062] In summary, by defining the composite function f(D t , M, E) and g(N). The former quantifies the interactive impact of data by combining Gaussian distribution and Sigmoid function, while the latter reflects its importance by logarithmic transformation of defect types. These functions not only ij It provides a basis for calculation and ensures the dynamic update and accuracy of the knowledge graph.

[0063] Step 3: Build an intelligent reasoning mechanism to automatically identify possible causes of defects by analyzing the information in the knowledge graph. Finally, generate suggestions for defect location based on the results obtained by the intelligent reasoning mechanism.

[0064] Specifically, the construction method of the intelligent reasoning mechanism is:

[0065] S301: Set all edge weights to A ij The sum of is taken as the knowledge graph dynamic value K(t);

[0066] The expression of the knowledge graph dynamic value K(t) is:

[0067]

[0068] Where N and M represent the number and type of nodes respectively.

[0069] S302: Define the impact of defect factors and calculate the impact of defect factors I c (t);

[0070]

[0071] S303: Based on the knowledge graph dynamic value K(t) and the influence degree of the defect factor I c (t) Generate defect location proposal P(t).

[0072] The expression of defect location suggestion P(t) is:

[0073]

[0074] Among them, H represents the normalization factor that controls the output range, Nc represents the number of defect types, and C represents the set of defect factors.

[0075] The result of P(t) represents the defect location recommendation value inferred based on the dynamic information and influence of each defect factor in the knowledge graph at the current time t. This value is used to measure the comprehensive degree of equipment failure that may be caused by each defect factor, and to generate specific location recommendations:

[0076] The value of P(t) is generally within a certain range (for example, 0 to 1). The closer the value is to 1, the more influence the fault factor identified by the system has on the equipment failure, and the fault troubleshooting needs to be prioritized.

[0077] When P(t) is between 0.5 and 1, it indicates that the defect factors have a high degree of influence, and the system will locate these factors as the most likely causes of the failure.

[0078] When P(t) is lower than 0.5, it means that the equipment is currently in a relatively stable state, the possible defect factors have little impact, and no emergency intervention is required.

[0079] In summary, it can be seen that the core of building an intelligent reasoning mechanism lies in calculating the dynamic value K(t) of the knowledge graph, which is achieved by defining the edge weight A ij To reflect the relationship between nodes, and the sum is taken as K(t). Then, the Sigmoid function is used to calculate the influence of the defect factor. I c(t) to quantify the relative importance of each defect. Finally, by integrating this information, a defect location suggestion P(t) is generated, providing an accurate and reliable basis for fault diagnosis. This ensures a close connection and mutual support with the above steps.

[0080] A defect intelligent positioning system for a relay protection device defect intelligent positioning method based on a knowledge graph, the system includes the following modules:

[0081] Data acquisition module: collects sensor data, historical maintenance records and environmental information in real time to form a basic data set; Knowledge graph construction module: builds a dynamic knowledge graph based on the processed data, including equipment information, defect types, maintenance records, and is represented in the form of nodes and edges; Knowledge graph update module: monitors data changes in real time and automatically updates the knowledge graph to maintain the timeliness and accuracy of information;

[0082] And the reasoning engine module: use the intelligent reasoning mechanism to analyze the information in the knowledge graph, identify the possible causes of defects, and generate corresponding defect location suggestions; the visualization display module: display the knowledge graph and reasoning results in an intuitive graphical way to help users better understand and analyze fault information; the report generation module: automatically generate fault diagnosis and maintenance suggestion reports to provide decision support for maintenance personnel.

[0083] In summary, the present invention can effectively integrate real-time sensor monitoring data, historical maintenance records and environmental information by constructing a dynamic knowledge graph. This fusion of multi-source data enables the system to fully understand the operating status and potential problems of the equipment, thereby achieving more accurate fault identification; and by using the reasoning mechanism, according to the correlation analysis in the knowledge graph, it can identify the potential patterns and relationships of the cause of the fault, reduce the false alarm rate, and improve the reliability of fault location. As the operating environment and status of the equipment change, traditional methods are often unable to adapt in time. The present invention ensures that the system can automatically adjust and optimize the reasoning rules when facing new situations by updating the knowledge graph in real time, thereby improving the adaptability of the overall system. Through the intelligent reasoning mechanism, it is not limited to fault detection, but also provides a basis for subsequent decision-making. For example, based on the operating history of the equipment, the system can predict potential failure risks and make preventive maintenance suggestions in advance, thereby reducing the probability of accidents.

[0084] This embodiment also provides a computer device, which is suitable for a method for intelligently locating defects in a relay protection device based on a knowledge graph, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement a method for intelligently locating defects in a relay protection device based on a knowledge graph as proposed in the above embodiment.

[0085] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0086] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a method for intelligently locating defects in a relay protection device based on a knowledge graph as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a disk or an optical disk.

[0087] 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 method for intelligently locating defects in relay protection devices based on knowledge graph, characterized in that: The method comprises the following steps: Step 1: Data collection: collect various data related to relay protection devices, including real-time monitoring data of sensors, historical maintenance records, and equipment use environment information, and integrate them together to form a basic database; Step 2: Knowledge graph construction: Based on the collected data, a dynamic knowledge graph is constructed, and the graph represents equipment information, defect types, maintenance records, etc. in the form of nodes and edges. Whenever new data enters, the knowledge graph will be automatically adjusted and updated; Step 3: Build an intelligent reasoning mechanism to automatically identify possible causes of defects by analyzing the information in the knowledge graph. Finally, generate suggestions for defect location based on the results obtained by the intelligent reasoning mechanism.

2. According to claim 1, a method for intelligently locating defects in a relay protection device based on a knowledge graph is characterized in that: The knowledge graph construction method is: S201: Define nodes and edges. Nodes represent equipment information, defect types, and maintenance records. Edges represent the relationship between nodes. Use edge weights A ij To describe, set the total number of nodes in the knowledge graph to G; S202: Design a composite function f(D t ,M,E); S203: When new real-time monitoring data D t Enter, use f(D t ,M,E) Calculate the edge weight A between nodes ij , that is, recalculate the edge weights between all nodes and adjust the knowledge graph structure.

3. According to claim 2, a method for intelligently locating defects in a relay protection device based on a knowledge graph is characterized in that: The composite function f(D t ,M,E) is: Among them, D t represents the real-time monitoring data of the sensor at time t; M represents the collection of historical maintenance records, including various types of maintenance information; E represents the equipment usage environment information, such as temperature and humidity; u M represents the mean of maintenance records; σ M Represents the standard deviation of the maintenance records.

4. According to claim 3, a method for intelligently locating defects in a relay protection device based on a knowledge graph is characterized in that: In the process of S203, the edge weight A ij The calculation formula is: Among them, R represents the normalization factor, g(N) represents the influence of the defect type, and Φ(k) represents the reliability score related to node k.

5. According to claim 4, a method for intelligently locating defects in a relay protection device based on a knowledge graph is characterized in that: The construction method of the intelligent reasoning mechanism is: S301: Set all edge weights A ij The sum of is taken as the knowledge graph dynamic value K(t); S302: Define the impact of defect factors and calculate the impact of defect factors I c (t); S303: Based on the knowledge graph dynamic value K(t) and the influence degree of the defect factor I c (t) Generate defect location proposal P(t0.

6. According to claim 5, a method for intelligently locating defects in a relay protection device based on a knowledge graph is characterized in that: The expression of the knowledge graph dynamic value K(t0) is: Where N and M represent the number and type of nodes respectively.

7. According to claim 5, a method for intelligently locating defects in a relay protection device based on a knowledge graph is characterized in that: The expression of the defect location suggestion P(t) is: Among them, H represents the normalization factor that controls the output range, Nc represents the number of defect types, and C represents the set of defect factors.

8. According to claim 7, the defect intelligent positioning system of the relay protection device defect intelligent positioning method based on knowledge graph is characterized in that: The system includes the following modules: Data acquisition module: collects sensor data, historical maintenance records and environmental information in real time to form a basic data set; Knowledge graph construction module: builds a dynamic knowledge graph based on the processed data, including equipment information, defect types, maintenance records, and is represented in the form of nodes and edges; Knowledge graph update module: monitors data changes in real time and automatically updates the knowledge graph to maintain the timeliness and accuracy of information; And the reasoning engine module: Use intelligent reasoning mechanisms to analyze the information in the knowledge graph, identify possible causes of defects, and generate corresponding defect location suggestions; Visualization display module: displays the knowledge graph and reasoning results in an intuitive graphical way to help users better understand and analyze fault information; Report generation module: automatically generates fault diagnosis and maintenance recommendation reports to provide decision support for maintenance personnel.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a method for intelligently locating defects of a relay protection device based on a knowledge graph as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for intelligently locating defects of a relay protection device based on a knowledge graph as described in any one of claims 1 to 7 are implemented.

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