A circuit breaker body fault diagnosis method, device and equipment of an intelligent high-voltage switch and a storage medium

Abstract

The application discloses a kind of intelligent high-voltage switch's circuit breaker body fault diagnosis method, device, equipment and storage medium, method includes: obtaining the historical fault data of circuit breaker in high-voltage switch, according to historical fault data determine suspected fault component set;Based on suspected fault component set, historical alarm information and fault reason are classified, determine the alarm information and fault reason corresponding to each fault component;Alarm timing information in historical fault data is checked with timing information constraint, and removes error fault information;The mapping relationship between the fault reason of each fault component and alarm information is analyzed using fault tree module;Based on the mapping relationship, the alarm information of target fault event is obtained by matrix form iteration operation by fuzzy inference mechanism, and the fault probability representation of high-voltage switch circuit breaker is obtained, and the fault diagnosis result is determined according to the fault probability representation of high-voltage switch circuit breaker.Effectively improve fault diagnosis efficiency.

Description

Technical Field

[0001] This invention relates to a method, apparatus, equipment, and storage medium for diagnosing faults in the circuit breaker body of an intelligent high-voltage switch, belonging to the field of intelligent high-voltage switch fault diagnosis technology. Background Technology

[0002] In intelligent high-voltage switchgear, fiber optic connections have replaced the traditional hard-wired cable connections in secondary circuits, with signal transmission primarily achieved through virtual loops. Information transmission is networked, meaning that a single line does not have only one receiving end, making fault diagnosis impossible based solely on line topology. Simultaneously, the substation is equipped with a highly automated integrated information analysis system, achieving a high degree of information sharing. When a system fault occurs, monitoring information tables reflecting abnormal conditions of various equipment within the substation can be obtained. This large amount of shared data is crucial for developing fault diagnosis methods for high-voltage switchgear circuit breakers. Therefore, for faults in certain critical functional structures within the substation, there is an urgent need to propose more intelligent and adaptable fault diagnosis methods based on the new characteristics of intelligent high-voltage switchgear.

[0003] The important equipment and functional structures within intelligent high-voltage switches mainly include high-voltage circuit breakers, protection devices, and communication networks. Currently, experts and scholars have conducted extensive research on the reliability of relay protection and communication networks in intelligent high-voltage switches, but systematic research on fault diagnosis of high-voltage circuit breakers in intelligent substations is relatively limited. Summary of the Invention

[0004] In intelligent high-voltage switches, the main functions of the circuit breaker are load control and fault protection. Incorrect operation of the circuit breaker will affect the stable operation of the system. Timely and accurate analysis of the cause of high-voltage circuit breaker failure is fundamental to ensuring the safe operation of the power grid and realizing the self-healing function of the smart grid. Intelligent high-voltage switches are equipped with various information acquisition modules, which can acquire sufficient data and monitor the operating status of various functional modules within the station in real time. When equipment fails, a large number of abnormal signals will appear within the station. These abnormal signals are the external manifestations of the fault. By analyzing their correlations, the actual cause of the fault can be quickly and effectively identified, thereby significantly improving fault diagnosis efficiency.

[0005] Objective: To overcome the shortcomings of existing technologies, this invention provides a method, device, equipment, and storage medium for diagnosing faults in the circuit breaker body of an intelligent high-voltage switch. It uses a weighted fuzzy Petri net model to represent the internal physical connections and fault propagation process of the high-voltage circuit breaker, resulting in low computational complexity and high accuracy.

[0006] Technical solution: To solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0007] In a first aspect, the present invention provides a method for diagnosing faults in the circuit breaker body of an intelligent high-voltage switch, comprising:

[0008] Step 1: Obtain historical fault data of the high-voltage switch circuit breaker, wherein the historical fault data includes historical fault events and corresponding alarm information and fault causes, and determine the set of suspected faulty components based on the historical fault data;

[0009] Step 2: Classify historical alarm information and fault causes based on the suspected faulty component set, and determine the alarm information and fault cause corresponding to each faulty component;

[0010] Step 3: Perform timing information constraint checks on the alarm timing information in the historical fault data and remove erroneous fault information;

[0011] Step 4: Based on the historical fault data after removing erroneous fault information, use the fault tree module to analyze the fault cause of each faulty component and the mapping relationship between the alarm information, that is, the types of alarm information corresponding to different fault causes;

[0012] Step 5: Based on the mapping relationship, the alarm information of the target fault event is used by the fuzzy reasoning mechanism through iterative calculation in matrix form to obtain the fault probability characterization of the high-voltage switch circuit breaker, and the fault diagnosis result is determined according to the fault probability characterization of the high-voltage switch circuit breaker.

[0013] In some embodiments, step 2 includes:

[0014] The FMEA model is used to conduct a preliminary analysis of historical alarm information and fault causes to determine the alarm information and fault causes corresponding to each faulty component.

[0015] In some embodiments, step 5 further includes: performing a timing information constraint check on the alarm information of the target fault event to remove erroneous fault information.

[0016] In some embodiments, the timing information constraint check includes:

[0017] Step 301: Identify the power outage area; all components within the power outage area are considered as potentially faulty components.

[0018] Step 302: For each suspected faulty component, classify the alarm information related to that component to form an alarm information set for each component;

[0019] Step 303: For each component's alarm information set, perform reverse timing reasoning using the actually obtained alarm timing information to obtain the univariate time point constraint of the fault occurrence;

[0020] Step 304: Merge all unary time point constraints of the fault occurrence to obtain the total unary time point constraint T of the fault occurrence;

[0021] Step 305: Perform forward timing reasoning on the total unary time point constraint T of the fault occurrence to obtain the unary time point constraint of each circuit breaker;

[0022] Step 306: Then, the operator compares the one-dimensional time point constraints of each circuit breaker with the actual obtained alarm timing information to identify erroneous alarm information that does not meet the timing information.

[0023] In some embodiments, step 5 includes:

[0024] The first step is to calculate the initial circuit breaker confidence level based on the preset initial matrix;

[0025] The second step is to calculate the confidence level of the equivalent fuzzy input to the fault event;

[0026] The third step is to compare the confidence level of the equivalent fuzzy input of the fault event with the preset threshold.

[0027] The fourth step is to remove fault events whose confidence level of the equivalent fuzzy input is less than a preset threshold, and to calculate the fault events whose confidence level of the equivalent fuzzy input is greater than a preset threshold.

[0028] Fifth step: Calculate the confidence level of all currently available circuit breakers;

[0029] The sixth step is to replace the original circuit breaker confidence level with the newly obtained confidence level, and repeat steps two through five until the calculation results meet the iteration termination condition, that is, the confidence levels of all circuit breakers no longer change, thus obtaining the fault probability characterization of the high-voltage switch circuit breaker.

[0030] Secondly, the present invention provides a fault diagnosis device for the circuit breaker body of an intelligent high-voltage switch, comprising:

[0031] The data acquisition module is configured to: acquire historical fault data of the high-voltage switch circuit breaker, wherein the historical fault data includes historical fault events and corresponding alarm information and fault causes, and determine a set of suspected faulty components based on the historical fault data;

[0032] The fault classification module is configured to classify historical alarm information and fault causes based on a set of suspected faulty components, and determine the alarm information and fault cause corresponding to each faulty component.

[0033] The timing constraint check module is configured to perform timing information constraint checks on alarm timing information in historical fault data and remove erroneous fault information.

[0034] The mapping relationship acquisition module is configured to: based on historical fault data after removing erroneous fault information, use the fault tree module to analyze the mapping relationship between the fault cause and alarm information of each faulty component, that is, the types of alarm information corresponding to different fault causes;

[0035] The fault result acquisition module is configured to: based on the mapping relationship, obtain the fault probability characterization of the high-voltage switch circuit breaker by iterative calculation in matrix form through fuzzy reasoning mechanism on the alarm information of the target fault event, and determine the fault diagnosis result based on the fault probability characterization of the high-voltage switch circuit breaker.

[0036] Thirdly, the present invention provides an apparatus comprising,

[0037] Memory;

[0038] processor;

[0039] as well as

[0040] Computer programs;

[0041] The computer program is stored in the memory and configured to be executed by the processor to implement the method described in the first aspect above.

[0042] Fourthly, the present invention provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.

[0043] Beneficial effects: The intelligent high-voltage switch circuit breaker body fault diagnosis method, device, equipment and storage medium provided by the present invention have the following advantages: By using historical fault data to perform correlation analysis between alarm information and fault causes, an intelligent high-voltage switch circuit breaker body fault diagnosis model with low computational complexity and high accuracy is established. Using this model, the actual fault cause can be found quickly and effectively, thereby effectively improving the fault diagnosis efficiency. Attached Figure Description

[0044] Figure 1 This is a schematic flowchart of a fault diagnosis method for an intelligent high-voltage switch provided in an embodiment of this application.

[0045] Figure 2 This is a schematic diagram of the timing information constraint check steps for fault diagnosis of a circuit breaker body of an intelligent high-voltage switch provided in an embodiment of this application.

[0046] Figure 3 This is a schematic diagram of the matrix-form iterative process in the fault identification of a fuzzy Petri net for circuit breaker fault diagnosis of an intelligent high-voltage switch provided in an embodiment of this application. Detailed Implementation

[0047] The present invention will be further described below with reference to the accompanying drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention, and should not be used to limit the scope of protection of the present invention.

[0048] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0049] In the description of this invention, the terms "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0050] Example 1

[0051] Firstly, such as Figure 1 As shown, this embodiment provides a method for diagnosing faults in the circuit breaker body of an intelligent high-voltage switch, including:

[0052] Step 1: Obtain historical fault data of the high-voltage switch circuit breaker, wherein the historical fault data includes historical fault events and corresponding alarm information and fault causes, and determine the set of suspected faulty components based on the historical fault data;

[0053] Step 2: Classify historical alarm information and fault causes based on the suspected faulty component set, and determine the alarm information and fault cause corresponding to each faulty component;

[0054] Step 3: Perform timing information constraint checks on the alarm timing information in the historical fault data and remove erroneous fault information;

[0055] Step 4: Based on the historical fault data after removing erroneous fault information, use the fault tree module to analyze the fault cause of each faulty component and the mapping relationship between the alarm information, that is, the types of alarm information corresponding to different fault causes;

[0056] Step 5: Based on the mapping relationship, the alarm information of the target fault event is used by the fuzzy reasoning mechanism through iterative calculation in matrix form to obtain the fault probability characterization of the high-voltage switch circuit breaker, and the fault diagnosis result is determined according to the fault probability characterization of the high-voltage switch circuit breaker.

[0057] In some embodiments, step 2 includes:

[0058] The FMEA model is used to conduct a preliminary analysis of historical alarm information and fault causes to determine the alarm information and fault causes corresponding to each faulty component.

[0059] In some embodiments, step 5 further includes: performing a timing information constraint check on the alarm information of the target fault event to remove erroneous fault information.

[0060] In some embodiments, the timing information constraint check includes:

[0061] Step 301: Identify the power outage area; all components within the power outage area are considered as potentially faulty components.

[0062] Step 302: For each suspected faulty component, classify the alarm information related to that component to form an alarm information set for each component;

[0063] Step 303: For each component's alarm information set, perform reverse timing reasoning using the actually obtained alarm timing information to obtain the univariate time point constraint of the fault occurrence;

[0064] Step 304: Merge all unary time point constraints of the fault occurrence to obtain the total unary time point constraint T of the fault occurrence;

[0065] Step 305: Perform forward timing reasoning on the total unary time point constraint T of the fault occurrence to obtain the unary time point constraint of each circuit breaker;

[0066] Step 306: Then, the operator compares the one-dimensional time point constraints of each circuit breaker with the actual obtained alarm timing information to identify erroneous alarm information that does not meet the timing information.

[0067] In some embodiments, step 5 includes:

[0068] The first step is to calculate the initial circuit breaker confidence level based on the preset initial matrix;

[0069] The second step is to calculate the confidence level of the equivalent fuzzy input to the fault event;

[0070] The third step is to compare the confidence level of the equivalent fuzzy input of the fault event with the preset threshold.

[0071] The fourth step is to remove fault events whose confidence level of the equivalent fuzzy input is less than a preset threshold, and to calculate the fault events whose confidence level of the equivalent fuzzy input is greater than a preset threshold.

[0072] Fifth step: Calculate the confidence level of all currently available circuit breakers;

[0073] The sixth step is to replace the original circuit breaker confidence level with the newly obtained confidence level, and repeat steps two through five until the calculation results meet the iteration termination condition, that is, the confidence levels of all circuit breakers no longer change, thus obtaining the fault probability characterization of the high-voltage switch circuit breaker.

[0074] In some embodiments, a method for diagnosing faults in the circuit breaker body of an intelligent high-voltage switch includes the following steps:

[0075] Step 1: Store the historical fault events of the high-voltage switch circuit breaker and their corresponding alarm information into the database to identify the set of suspected faulty components.

[0076] Step 2: Classify the data after the fault occurs, and determine the alarm information and fault cause corresponding to each functional structure.

[0077] The FMEA model is used to conduct a preliminary analysis of historical alarm information and fault causes to determine the main correlations.

[0078] FMEA (Factors-Driven Analysis) can be used to evaluate potential product failures and the relationships between their influencing factors. By studying the product's internal structure and manufacturing process, it identifies all possible failure scenarios, analyzes the relationships between various factors and failure events that cause different system failures, and thus determines the possible combinations of influencing factors when a potential failure occurs. This method is beneficial for further improving product performance and reliability and reducing failure risk during product design, development, and subsequent maintenance. This method uses graphical representations of the influence relationships between events and can be applied to the failure analysis of complex, multi-factor, multi-objective systems.

[0079] Step 3: The weighted fuzzy Petri net model effectively utilizes the correlation and redundancy of alarm timing information, and uses timing constraints to filter and fully mine fault timing information. It can automatically identify erroneous fault information and reassign the initial circuit breaker confidence level.

[0080] In the power grid fault diagnosis method based on weighted fuzzy Petri nets, the storage unit represents the circuit breaker, the token represents the state of the storage unit, and the transition represents the event. According to the relay protection principle, when a high-voltage switch fails, the electrical quantity changes accordingly. The protection device performs setting calculations on the detected electrical quantity. If the operating conditions are met, an operating command is issued. Upon receiving the operating command, the corresponding high-voltage circuit breaker trips, disconnecting the faulty component. The occurrence of these events is distributed within a certain time range and is mutually coordinated and constrained in time, exhibiting a certain temporal constraint relationship. Therefore, the rational utilization of fault alarm timing information can effectively improve fault diagnosis performance.

[0081] By using time-series reasoning analysis to determine whether the timing information of fault alarms meets the timing constraints, erroneous alarm information can be identified, which can effectively improve the fault tolerance of fault diagnosis.

[0082] The timing information constraint check steps are as follows: Figure 2 As shown, it includes:

[0083] Step 1: Identify the power outage area; all components within the power outage area are considered potentially faulty.

[0084] Step 2: For each suspected faulty component, classify the alarm information related to that component to form a set of different components;

[0085] Step 3: For each set, perform reverse time-series reasoning using the actual alarm timing information to obtain the unary time point constraint of the fault occurrence.

[0086] Step 4: Merge the unary time point constraints of the fault occurrence obtained from the reverse temporal reasoning of each set to obtain the total unary time point constraints of the fault occurrence;

[0087] Step 5: Perform forward temporal reasoning on T to obtain the unary time point constraints of each repository. Then, compare these constraints with the actual obtained temporal information using operators to identify erroneous alarm information that does not meet the temporal information requirements.

[0088] Step 4: Use the fault tree module to analyze the mapping relationship between the fault causes and alarm information of each part, that is, different fault causes correspond to all possible alarm information types, and after eliminating them according to the structure module, the main mapping relationship is obtained.

[0089] Step 5: The fault probability representation of the high-voltage switch circuit breaker is obtained through iterative calculation in matrix form using the fuzzy inference mechanism.

[0090] The matrix-form iterative reasoning process in fault identification using fuzzy Petri nets is as follows: Figure 3 As shown,

[0091] The first step is to write the initial matrix and calculate the initial storage state values.

[0092] The second step is to calculate the confidence level of the synthetic input of the transition, that is, to calculate the confidence level of the equivalent fuzzy input.

[0093] The third step is to compare the confidence level of the synthesized input with the magnitude of the transition threshold.

[0094] The fourth step is to remove equivalent fuzzy input terms with confidence levels lower than the transition threshold, i.e., to calculate the confidence level of the synthesized input terms with confidence levels greater than the transition threshold.

[0095] The fifth step is to calculate the confidence scores for all currently available libraries.

[0096] The sixth step is to replace the original place confidence with the newly obtained place confidence and repeat steps one through five until the calculation results meet the iteration termination condition, that is, the confidence of all places no longer changes, and the inference operation ends.

[0097] Example 2

[0098] Secondly, based on Embodiment 1, this embodiment provides a fault diagnosis device for the circuit breaker body of an intelligent high-voltage switch, comprising:

[0099] The data acquisition module is configured to: acquire historical fault data of the high-voltage switch circuit breaker, wherein the historical fault data includes historical fault events and corresponding alarm information and fault causes, and determine a set of suspected faulty components based on the historical fault data;

[0100] The fault classification module is configured to classify historical alarm information and fault causes based on a set of suspected faulty components, and determine the alarm information and fault cause corresponding to each faulty component.

[0101] The timing constraint check module is configured to perform timing information constraint checks on alarm timing information in historical fault data and remove erroneous fault information.

[0102] The mapping relationship acquisition module is configured to: based on historical fault data after removing erroneous fault information, use the fault tree module to analyze the mapping relationship between the fault cause and alarm information of each faulty component, that is, the types of alarm information corresponding to different fault causes;

[0103] The fault result acquisition module is configured to: based on the mapping relationship, obtain the fault probability characterization of the high-voltage switch circuit breaker by iterative calculation in matrix form through fuzzy reasoning mechanism on the alarm information of the target fault event, and determine the fault diagnosis result based on the fault probability characterization of the high-voltage switch circuit breaker.

[0104] In some embodiments, the timing constraint checking module is specifically used for:

[0105] Step 301: Identify the power outage area; all components within the power outage area are considered as potentially faulty components.

[0106] Step 302: For each suspected faulty component, classify the alarm information related to that component to form an alarm information set for each component;

[0107] Step 303: For each component's alarm information set, perform reverse timing reasoning using the actually obtained alarm timing information to obtain the univariate time point constraint of the fault occurrence;

[0108] Step 304: Merge all unary time point constraints of the fault occurrence to obtain the total unary time point constraint T of the fault occurrence;

[0109] Step 305: Perform forward timing reasoning on the total unary time point constraint T of the fault occurrence to obtain the unary time point constraint of each circuit breaker;

[0110] Step 306: Then, the operator compares the one-dimensional time point constraints of each circuit breaker with the actual obtained alarm timing information to identify erroneous alarm information that does not meet the timing information.

[0111] In some embodiments, the fault result acquisition module is specifically used for:

[0112] The first step is to calculate the initial circuit breaker confidence level based on the preset initial matrix;

[0113] The second step is to calculate the confidence level of the equivalent fuzzy input to the fault event;

[0114] The third step is to compare the confidence level of the equivalent fuzzy input of the fault event with the preset threshold.

[0115] The fourth step is to remove fault events whose confidence level of the equivalent fuzzy input is less than a preset threshold, and to calculate the fault events whose confidence level of the equivalent fuzzy input is greater than a preset threshold.

[0116] Fifth step: Calculate the confidence level of all currently available circuit breakers;

[0117] The sixth step is to replace the original circuit breaker confidence level with the newly obtained confidence level, and repeat steps two through five until the calculation results meet the iteration termination condition, that is, the confidence levels of all circuit breakers no longer change, thus obtaining the fault probability characterization of the high-voltage switch circuit breaker.

[0118] Example 3

[0119] Thirdly, based on Embodiment 1, this embodiment provides a device, including,

[0120] Memory;

[0121] processor;

[0122] as well as

[0123] Computer programs;

[0124] The computer program is stored in the memory and configured to be executed by the processor to implement the method described in Embodiment 1.

[0125] Example 4

[0126] Fourthly, based on Embodiment 1, this embodiment provides a storage medium on which a computer program is stored, and when the computer program is executed by a processor, it implements the method described in Embodiment 1.

[0127] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0128] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0129] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0130] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0131] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for diagnosing faults in the circuit breaker body of an intelligent high-voltage switch, characterized in that, The method includes: Step 1: Obtain historical fault data of the high-voltage switch circuit breaker, wherein the historical fault data includes historical fault events and corresponding alarm information and fault causes, and determine the set of suspected faulty components based on the historical fault data; Step 2: Classify historical alarm information and fault causes based on the suspected faulty component set, and determine the alarm information and fault cause corresponding to each faulty component; Step 3: Perform time-series information constraint checks on alarm information in historical fault data and remove erroneous alarm information; Step 4: Based on the historical fault data after removing error alarm information, use the fault tree module to analyze the fault cause of each faulty component and the mapping relationship between alarm information; Step 5: Based on the mapping relationship, the alarm information of the target fault event is used by the fuzzy reasoning mechanism through iterative calculation in matrix form to obtain the fault probability characterization of the high-voltage switch circuit breaker, and the fault diagnosis result is determined according to the fault probability characterization of the high-voltage switch circuit breaker.

2. The method for fault diagnosis of the circuit breaker body of the intelligent high-voltage switch according to claim 1, characterized in that, Step 2 includes: The FMEA model is used to conduct a preliminary analysis of historical alarm information and fault causes to determine the alarm information and fault causes corresponding to each faulty component.

3. The method for fault diagnosis of the circuit breaker body of the intelligent high-voltage switch according to claim 1, characterized in that, Step 5 also includes: performing a timing information constraint check on the alarm information of the target fault event and removing erroneous alarm information.

4. The method for fault diagnosis of the circuit breaker body of the intelligent high-voltage switch according to claim 1 or 3, characterized in that, The timing information constraint check includes: Step 301: Identify the power outage area; all components within the power outage area are considered as potentially faulty components. Step 302: For each suspected faulty component, classify the alarm information related to that component to form an alarm information set for each component; Step 303: For each component's alarm information set, perform reverse timing reasoning using the actual obtained alarm information to obtain the univariate time point constraint of the fault occurrence. Step 304: Merge all unary time point constraints of the fault occurrence to obtain the total unary time point constraint T of the fault occurrence; Step 305: Perform forward timing reasoning on the total unary time point constraint T of the fault occurrence to obtain the unary time point constraint of each circuit breaker; Step 306: Then, the operator compares the one-dimensional time point constraints of each circuit breaker with the actual alarm information obtained to identify erroneous alarm information that does not meet the timing information.

5. The method for fault diagnosis of the circuit breaker body of the intelligent high-voltage switch according to claim 1, characterized in that, Step 5 includes: The first step is to calculate the initial circuit breaker confidence level based on the preset initial matrix; The second step is to calculate the confidence level of the equivalent fuzzy input to the fault event; The third step is to compare the confidence level of the equivalent fuzzy input of the fault event with the preset threshold. The fourth step is to remove fault events whose confidence level of the equivalent fuzzy input is less than a preset threshold, and to calculate fault events whose confidence level of the equivalent fuzzy input is greater than a preset threshold. Fifth step: Calculate the confidence level of all currently available circuit breakers; The sixth step is to replace the original circuit breaker confidence level with the newly obtained confidence level, and repeat steps two through five until the calculation results meet the iteration termination condition, that is, the confidence levels of all circuit breakers no longer change, thus obtaining the fault probability characterization of the high-voltage switch circuit breaker.

6. A fault diagnosis device for the circuit breaker body of an intelligent high-voltage switch, characterized in that, include: The data acquisition module is configured to: acquire historical fault data of the high-voltage switch circuit breaker, wherein the historical fault data includes historical fault events and corresponding alarm information and fault causes, and determine a set of suspected faulty components based on the historical fault data; The fault classification module is configured to classify historical alarm information and fault causes based on a set of suspected faulty components, and determine the alarm information and fault cause corresponding to each faulty component. The timing constraint check module is configured to perform timing information constraint checks on alarm information in historical fault data and remove erroneous alarm information. The mapping relationship acquisition module is configured to: analyze the fault cause of each faulty component and the mapping relationship between alarm information based on historical fault data after removing error alarm information using the fault tree module; The fault result acquisition module is configured to: based on the mapping relationship, obtain the fault probability characterization of the high-voltage switch circuit breaker by iterative calculation in matrix form through fuzzy reasoning mechanism on the alarm information of the target fault event, and determine the fault diagnosis result based on the fault probability characterization of the high-voltage switch circuit breaker.

7. The circuit breaker body fault diagnosis device for intelligent high-voltage switches according to claim 6, characterized in that, The timing constraint checking module is specifically used for: Step 301: Identify the power outage area; all components within the power outage area are considered as potentially faulty components. Step 302: For each suspected faulty component, classify the alarm information related to that component to form an alarm information set for each component; Step 303: For each component's alarm information set, perform reverse timing reasoning using the actual obtained alarm information to obtain the univariate time point constraint of the fault occurrence. Step 304: Merge all unary time point constraints of the fault occurrence to obtain the total unary time point constraint T of the fault occurrence; Step 305: Perform forward timing reasoning on the total unary time point constraint T of the fault occurrence to obtain the unary time point constraint of each circuit breaker; Step 306: Then, the operator compares the one-dimensional time point constraints of each circuit breaker with the actual alarm information obtained to identify erroneous alarm information that does not meet the timing information.

8. The circuit breaker body fault diagnosis device for intelligent high-voltage switches according to claim 6, characterized in that, The fault result acquisition module is specifically used for: The first step is to calculate the initial circuit breaker confidence level based on the preset initial matrix; The second step is to calculate the confidence level of the equivalent fuzzy input to the fault event; The third step is to compare the confidence level of the equivalent fuzzy input of the fault event with the preset threshold. The fourth step is to remove fault events whose confidence level of the equivalent fuzzy input is less than a preset threshold, and to calculate fault events whose confidence level of the equivalent fuzzy input is greater than a preset threshold. Fifth step: Calculate the confidence level of all currently available circuit breakers; The sixth step is to replace the original circuit breaker confidence level with the newly obtained confidence level, and repeat steps two through five until the calculation results meet the iteration termination condition, that is, the confidence levels of all circuit breakers no longer change, thus obtaining the fault probability characterization of the high-voltage switch circuit breaker.

9. A device, characterized in that, include: Memory; processor; as well as Computer programs; The computer program is stored in the memory and configured to be executed by the processor to implement the method as described in any one of claims 1 to 5.

10. A storage medium, characterized in that, It stores a computer program thereon, which, when executed by a processor, implements the method described in any one of claims 1 to 5.

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