Rural power grid fault analysis method and device based on information credibility and electronic equipment

Abstract

The invention discloses a rural power grid fault analysis method and device based on information credibility and electronic equipment. The method comprises the following steps: acquiring power grid operation data of a first transformer area, and generating first information; determining a fault occurrence condition in the first transformer area by using the first information and a first model to obtain at least one piece of first fault information; based on the second information of the first transformer area, determining the first credibility of each piece of first fault information in the at least one piece of first fault information; the second information at least comprises environment information of the first transformer area and equipment information of the first transformer area; and based on the first credibility, determining credible first fault information from the at least one type of first fault information to obtain second fault information. According to the technical scheme provided by the invention, the accuracy and reliability of the rural power distribution network fault analysis result can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of power distribution networks, and in particular to a rural power grid fault analysis method, device and electronic equipment based on information credibility. Background Art

[0002] Distribution network fault detection is one of the important means to ensure the normal operation of the distribution network. In related technologies, by obtaining the grid operation data and analyzing the operation data to determine whether the distribution network equipment has failed, the faulty equipment can be maintained in time to ensure the safe operation of the grid. However, for the fault location of rural distribution networks, rural power grids have the characteristics of aging equipment and narrow line corridors, which easily lead to errors in the collected grid operation data, thereby affecting the accuracy of the fault location results.

[0003] Therefore, how to accurately and reliably analyze rural distribution network faults is an urgent problem to be solved. Summary of the invention

[0004] To solve the related technical problems, the embodiments of the present application provide a rural power grid fault analysis method, device and electronic equipment based on information credibility.

[0005] The technical solution of the embodiment of the present application is implemented as follows: The embodiment of the present application provides a rural power grid fault analysis method based on information credibility, the method comprising: Acquire power grid operation data of the first substation to generate first information; Using the first information and the first model, determine the occurrence of a fault in the first substation to obtain at least one first fault information; the first fault information at least includes a fault type, and different first fault information has different fault types; Determining a first credibility of each of the at least one first fault information based on second information of the first station area; the second information at least includes environmental information of the first station area and equipment information of the first station area; Based on the first credibility, reliable first fault information is determined from the at least one type of first fault information to obtain second fault information.

[0006] In the above solution, the determining the first credibility of each first fault information in the at least one first fault information based on the second information of the first station area includes: Based on the environmental information in the second information, determining at least one first environmental factor existing in the first station area; For each piece of first fault information, weighting the correlation between each first environmental factor and the first fault information to obtain a second credibility of the first fault information; Based on the second credibility, a first credibility of the first fault information is obtained.

[0007] In the above scheme, the method further comprises: Based on the probability and impact of each preset fault under different environmental factors, establish the correlation between the fault and the environmental factors; Based on the association relationship, a degree of association between each first environmental factor and the first fault information is determined.

[0008] In the above solution, obtaining the first credibility of the first fault information based on the second credibility includes: Based on the device information in the second information, using a second model to predict the life cycle of each device; the life cycle includes at least one fault node; For each first fault information and the life cycle of the corresponding device, determine a fault node matching the first fault information to obtain a first node; Based on the life cycle of the corresponding device and the current running time of the device, predict the position of the usage status of the device in the life cycle to obtain a second node; Obtaining a third credibility of the first fault information based on a position difference between the first node and the second node; Based on the second credibility and the third credibility, a first credibility of the first fault information is determined.

[0009] In the above solution, obtaining the third credibility of the first fault information based on the position difference between the first node and the second node includes: Determine whether the distance between the first node and the second node is less than a preset threshold; When the distance is less than the preset threshold, the first preset value is used as the third credibility; or, When the distance is greater than or equal to the preset threshold, the second preset value is used as the third credibility; wherein, The first preset value is greater than the second preset value.

[0010] In the above scheme, the method further comprises: Based on the second fault information, the first model is updated.

[0011] The embodiment of the present application also provides a rural power grid fault analysis device based on information credibility, comprising: A communication unit, used to obtain power grid operation data of the first substation and generate first information; A prediction unit, configured to determine the occurrence of a fault in the first substation by using the first information and the first model, and obtain at least one first fault information; the first fault information at least includes a fault type, and different first fault information has different fault types; A determination unit is used to determine the first credibility of each first fault information in the at least one first fault information based on the second information of the first substation; the second information at least includes the environmental information of the first substation and the equipment information of the first substation; and, based on the first credibility, determine the credible first fault information from the at least one type of first fault information to obtain the second fault information.

[0012] The embodiment of the present application also provides an electronic device, including: a communication interface and a processor, wherein: The communication interface is used to obtain the power grid operation data of the first substation and generate the first information; The processor is used to use the first information and the first model to determine the occurrence of a fault in the first substation and obtain at least one first fault information; the first fault information at least includes the fault type, and different first fault information has different fault types; based on the second information of the first substation, the first credibility of each first fault information in the at least one first fault information is determined; the second information at least includes the environmental information of the first substation and the equipment information of the first substation; based on the first credibility, the credible first fault information is determined from the at least one type of first fault information to obtain the second fault information.

[0013] The present application also provides an electronic device, including: a processor and a memory for storing a computer program that can be run on the processor. Wherein, when the processor is used to run the computer program, it executes the steps of any one of the above methods.

[0014] An embodiment of the present application further provides a storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of any of the above-mentioned rural power grid fault analysis methods based on information credibility are implemented.

[0015] The rural power grid fault analysis method, device, electronic device and storage medium based on information credibility provided in the embodiment of the present application obtain the power grid operation data of the first substation to generate the first information; using the first information and the first model, determine the fault occurrence in the first substation to obtain at least one first fault information; the first fault information at least includes the fault type, and the fault types of different first fault information are different; based on the second information of the first substation, determine the first credibility of each first fault information in the at least one first fault information; the second information at least includes the environmental information of the first substation and the equipment information of the first substation; based on the first credibility, determine the credible first fault information from the at least one type of first fault information to obtain the second fault information. The scheme provided in the embodiment of the present application uses the distribution network operation data to perform fault analysis, and uses the environmental information of the equipment and the hardware information of the equipment to predict the credibility of the fault analysis results, so as to screen out the fault analysis results with higher credibility, thereby improving the accuracy and reliability of the fault analysis results of the rural distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a flow chart of a rural power grid fault analysis method based on information credibility according to an embodiment of the present application; Figure 2 This is a flow chart of step 103 in the rural power grid fault analysis method based on information credibility in an embodiment of the present application; Figure 3 This is a flow chart of step 203 in the rural power grid fault analysis method based on information credibility in an embodiment of the present application; Figure 4 This is a schematic diagram of the structure of a rural power grid fault analysis device based on information credibility according to an embodiment of the present application; Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Implementation

[0017] The present application is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0018] The present application embodiment provides a rural power grid fault analysis method based on information credibility, which is applied to electronic devices, and can be specifically applied to smart terminal devices such as personal computers (PCs), mobile phones, and tablet computers. Figure 1 As shown, the method includes: Step 101: Obtain power grid operation data of a first substation and generate first information.

[0019] In actual application, the power grid operation data can be dynamic data of the distribution network operation process, or it can be configuration data in the distribution network. For example, the power grid operation data can include line data (such as the number of lines, total length, transmission capacity, transmission distance, insulation status, number of switches and conductor conditions, etc.), capacity-load ratio, single-line single-transformer data, main transformer N-1, line N-1, load rate, etc.; among them, the capacity-load ratio is an important parameter for measuring the operating status of the distribution network, which represents the ratio of the capacitive reactance to the inductive reactance of the power system. The single-line single-transformer data represents the configuration of a single line and a single transformer in the distribution network. The main transformer N-1 represents the N-1 configuration of the main transformer in the distribution network, that is, when the main transformer fails, whether the standby transformer can meet the power demand. The line N-1 represents the N-1 configuration of the line in the distribution network, that is, when a line fails, whether the standby line can meet the power demand. The load rate represents the load condition of the distribution network, that is, the ratio of the load of the power system to the rated load.

[0020] In actual application, the first information may include the operating data of the first substation within the first time period; the operating data of the power distribution of the first substation can be acquired and stored in real time, and then the operating data in the previous time period of the current moment can be used as the power grid operating data to generate the first information.

[0021] Step 102: using the first information and the first model, determine the fault occurrence in the first substation to obtain at least one first fault information; the first fault information at least includes a fault type, and different first fault information has different fault types.

[0022] In actual application, historical operation data can be used to establish a sample set, and the established sample set can be used to train the first model; the first model can be a deep learning model.

[0023] In actual application, in the process of establishing the sample set, the historical operation data of the first substation can be used for construction, and the historical power grid operation data of substations with similar climate to the first substation can be collected for construction, that is, the sample set can be constructed using the historical power grid data of the first substation and similar substations.

[0024] In actual application, multiple first models can be established according to the climate change situation in the first substation; specifically, each year can be divided into N time periods according to climate change, and the climate environment of each time period is similar, N is an integer greater than 2, and then a first model is established for the historical operating data of each time period; during the application of the first model, the first model of the corresponding time period can be obtained and the corresponding first model can be used to review the interlaced first fault information.

[0025] In actual application, the first fault information may also include relevant information of the fault location and the faulty device.

[0026] In actual application, during the process of training the first model, different fault levels can be configured for each fault type according to changes in the operating data; during the application process, the generated first fault information can include the fault level of the corresponding fault type; among them, for the output results with higher fault levels, manual re-inspection can be directly determined.

[0027] Step 103: Determine the first credibility of each first fault information in the at least one first fault information based on the second information of the first station area; the second information at least includes the environment information of the first station area and the equipment information of the first station area.

[0028] In practical applications, considering that different faults are closely related to the environment in which they are located, the correlation between environmental information and faults can intuitively reflect the probability of the corresponding faults, thereby judging the credibility of the output information of the first model.

[0029] Based on this, in one embodiment, if Figure 2 As shown, step 103 may specifically include: Step 201: Determine at least one first environmental factor existing in the first station area based on the environmental information in the second information; Step 202: for each piece of first fault information, weight the correlation between each first environmental factor and the first fault information to obtain a second credibility of the first fault information; Step 203: Based on the second credibility, obtain the first credibility of the first fault information.

[0030] In actual application, the association relationship between environmental factors and fault information can be established in advance based on historical operation data, so as to determine the correlation degree between environmental factors and fault types based on the association relationship.

[0031] Based on this, in one embodiment, the method further includes: Based on the probability and impact of each preset fault under different environmental factors, establish the correlation between the fault and the environmental factors; Based on the association relationship, a degree of association between each first environmental factor and the first fault information is determined.

[0032] In actual application, the probability of occurrence and impact of each fault under different environmental factors can be determined based on historical operating data and historical fault detection results; among them, for each environmental factor, the impact degree can be configured into different levels, for example, level one, level two, and level three, and a corresponding numerical value is configured for each level as the impact value. The larger the impact value, the greater the corresponding impact degree. Then, the impact value is used as a weight to weight the occurrence probability to obtain the corresponding correlation degree.

[0033] In actual applications, as the life cycle of the equipment develops, different faults may occur in the equipment at different operating periods. Therefore, by introducing the correlation between the operating cycle and the fault, the credibility of the fault analysis results can be more accurately reflected.

[0034] Based on this, Figure 3 As shown, in one embodiment, step 203 may specifically include: Step 301: Based on the device information in the second information, use a second model to predict the life cycle of each device; the life cycle includes at least one fault node; Step 302: for each first fault information and the life cycle of the corresponding device, determine a fault node matching the first fault information to obtain a first node; Step 303: Based on the life cycle of the corresponding device and the current running time of the device, predict the position of the use status of the device in the life cycle to obtain a second node; Step 304: obtaining a third credibility of the first fault information based on a position difference between the first node and the second node; Step 305: Determine a first credibility of the first fault information based on the second credibility and the third credibility.

[0035] In actual application, based on the device information in the second information, the second model is used to predict the life cycle of each device. Based on the device information, the possible failure of the device and the time node of the failure can be predicted, and the time node of the failure can be used as the failure node of the corresponding failure.

[0036] In actual application, the life cycle of the corresponding equipment can also be predicted based on the installation environment of each equipment. For example, in a humid environment, the equipment may be more prone to corrosion and oxidation, which may cause failures. This allows more accurate prediction of each failure node.

[0037] In actual application, the fault node may be the time point when the fault occurs, the fault types of different fault nodes may be different, and the impact degrees of different fault nodes may also be different.

[0038] In actual application, the first node can represent the node where the fault should occur, and the second node can represent the node where the fault actually occurs. The difference between the first node and the second node can show the deviation between the fault analysis result and the actual situation, thereby determining the credibility of the fault analysis result.

[0039] In actual application, when determining the first credibility of the first fault information based on the second credibility and the third credibility, the second credibility and the third credibility may be weighted to obtain the first credibility.

[0040] In one embodiment, obtaining the third credibility of the first fault information based on the position difference between the first node and the second node may include: Determine whether the distance between the first node and the second node is less than a preset threshold; When the distance is less than the preset threshold, the first preset value is used as the third credibility; or, When the distance is greater than or equal to the preset threshold, the second preset value is used as the third credibility; wherein, The first preset value is greater than the second preset value.

[0041] In practical applications, the distance between the first node and the second node may be the time difference between the first node and the second node.

[0042] Step 104: Based on the first credibility, determine credible first fault information from the at least one type of first fault information to obtain second fault information.

[0043] In actual application, the first credibility of each first fault information can be compared with a preset credibility threshold. When it is greater than or equal to the credibility threshold, the first fault information can be considered as credible information and the first fault information can be used as second fault information; when it is less than the credibility threshold, the first fault information can be considered as uncredible information.

[0044] In actual application, all first fault information may be sorted according to credibility, and M first fault information with the highest credibility are selected as credible second fault information; wherein M is an integer greater than or equal to 1.

[0045] In actual application, in order to improve the accuracy of the fault analysis result output by the first model, the finally generated second fault information may be fed back to the first model to update the first model.

[0046] Based on this, in one embodiment, the method may further include: Based on the second fault information, the first model is updated.

[0047] In actual application, during the process of updating the first model, the second fault information can be used as an ideal output result to adjust the model parameters of the first model.

[0048] In summary, the solution provided in the embodiment of the present application uses distribution network operation data to perform fault analysis, and uses the environmental information of the equipment and the hardware information of the equipment to predict the credibility of the fault analysis results, so as to screen out fault analysis results with higher credibility and improve the accuracy and reliability of the fault analysis results of the rural distribution network.

[0049] In order to implement the rural power grid fault analysis method based on information credibility of the present application, the embodiment of the present application also provides a rural power grid fault analysis device based on information credibility, which is arranged on an electronic device, such as Figure 4 As shown, the device comprises: The communication unit 401 is used to obtain the power grid operation data of the first substation and generate the first information; A prediction unit 402 is used to determine the occurrence of a fault in the first substation by using the first information and the first model, and obtain at least one first fault information; the first fault information at least includes a fault type, and different first fault information has different fault types; The determination unit 403 is used to determine the first credibility of each first fault information in the at least one first fault information based on the second information of the first substation; the second information at least includes the environmental information of the first substation and the equipment information of the first substation; and, based on the first credibility, determine the credible first fault information from the at least one type of first fault information to obtain the second fault information.

[0050] In one embodiment, the determining unit 403 may be specifically configured to: Based on the environmental information in the second information, determining at least one first environmental factor existing in the first station area; For each piece of first fault information, weighting the correlation between each first environmental factor and the first fault information to obtain a second credibility of the first fault information; Based on the second credibility, a first credibility of the first fault information is obtained.

[0051] In one embodiment, the determining unit 403 may also be used to: Based on the probability and impact of each preset fault under different environmental factors, establish the correlation between the fault and the environmental factors; Based on the association relationship, a degree of association between each first environmental factor and the first fault information is determined.

[0052] In one embodiment, the determining unit 403 may be specifically configured to: Based on the device information in the second information, using a second model to predict the life cycle of each device; the life cycle includes at least one fault node; For each first fault information and the life cycle of the corresponding device, determine a fault node matching the first fault information to obtain a first node; Based on the life cycle of the corresponding device and the current running time of the device, predict the position of the usage status of the device in the life cycle to obtain a second node; Obtaining a third credibility of the first fault information based on a position difference between the first node and the second node; Based on the second credibility and the third credibility, a first credibility of the first fault information is determined.

[0053] In one embodiment, the determining unit 403 may be specifically configured to: Determine whether the distance between the first node and the second node is less than a preset threshold; When the distance is less than the preset threshold, the first preset value is used as the third credibility; or, When the distance is greater than or equal to the preset threshold, the second preset value is used as the third credibility; wherein, The first preset value is greater than the second preset value.

[0054] In one embodiment, the prediction unit 402 may also be used to: Based on the second fault information, the first model is updated.

[0055] It should be noted that: the rural power grid fault analysis device based on information credibility provided in the above embodiment only uses the division of the above program modules as an example when performing rural power grid fault analysis based on information credibility. In actual applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the device is divided into different program modules to complete all or part of the processing described above. In addition, the rural power grid fault analysis device based on information credibility provided in the above embodiment and the rural power grid fault analysis method embodiment based on information credibility belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0056] Based on the hardware implementation of the above program modules, and in order to implement the method of the embodiment of the present application, the embodiment of the present application also provides an electronic device, such as Figure 5 As shown, the electronic device 500 includes: Communication interface 501, capable of exchanging information with other devices; A processor 502, connected to the communication interface 501 to implement information interaction with other devices, and used when running a computer program; A memory 503 , on which the computer program is stored.

[0057] Specifically, the processor 502 is configured to: Using the communication interface 501 to obtain the power grid operation data of the first substation, and generate first information; Using the first information and the first model, determine the occurrence of a fault in the first substation to obtain at least one first fault information; the first fault information at least includes a fault type, and different first fault information has different fault types; Based on the second information of the first station area, determine the first credibility of each first fault information in the at least one first fault information; the second information at least includes the environmental information of the first station area and the equipment information of the first station area; and, based on the first credibility, determine the credible first fault information from the at least one type of first fault information to obtain the second fault information.

[0058] In one embodiment, the processor 502 may be specifically configured to: Based on the environmental information in the second information, determining at least one first environmental factor existing in the first station area; For each piece of first fault information, weighting the correlation between each first environmental factor and the first fault information to obtain a second credibility of the first fault information; Based on the second credibility, a first credibility of the first fault information is obtained.

[0059] In one embodiment, the processor 502 may also be configured to: Based on the probability and impact of each preset fault under different environmental factors, establish the correlation between the fault and the environmental factors; Based on the association relationship, a degree of association between each first environmental factor and the first fault information is determined.

[0060] In one embodiment, the processor 502 may be specifically configured to: Based on the device information in the second information, using a second model to predict the life cycle of each device; the life cycle includes at least one fault node; For each first fault information and the life cycle of the corresponding device, determine a fault node matching the first fault information to obtain a first node; Based on the life cycle of the corresponding device and the current running time of the device, predict the position of the usage status of the device in the life cycle to obtain a second node; Obtaining a third credibility of the first fault information based on a position difference between the first node and the second node; Based on the second credibility and the third credibility, a first credibility of the first fault information is determined.

[0061] In one embodiment, the processor 502 may be specifically configured to: Determine whether the distance between the first node and the second node is less than a preset threshold; When the distance is less than the preset threshold, the first preset value is used as the third credibility; or, When the distance is greater than or equal to the preset threshold, the second preset value is used as the third credibility; wherein, The first preset value is greater than the second preset value.

[0062] In one embodiment, the processor 502 may also be configured to: Based on the second fault information, the first model is updated.

[0063] It should be noted that the specific processing process of the processor 502 can be understood by referring to the above method.

[0064] Of course, in actual application, the various components in the electronic device 500 are coupled together through the bus system 504. It can be understood that the bus system 504 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 504 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Figure 5 Various buses are labeled as bus system 504 .

[0065] The memory 503 in the embodiment of the present application is used to store various types of data to support the operation of the electronic device 500. Examples of such data include: any computer program used to operate on the electronic device 500.

[0066] The method disclosed in the above embodiment of the present application can be applied to the processor 502, or implemented by the processor 502. The processor 502 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 502. The above-mentioned processor 502 may be a general-purpose processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 502 can implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor may be a microprocessor or any conventional processor, etc. In combination with the steps of the method disclosed in the embodiment of the present application, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in the memory 503, and the processor 502 reads the information in the memory 503 and completes the steps of the above method in combination with its hardware.

[0067] In an exemplary embodiment, the electronic device 500 may be implemented by one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to execute the aforementioned method.

[0068] It can be understood that the memory 503 of the embodiment of the present application can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory (FlashMemory), a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAMbus random access memory (DRRAM, Direct Rambus Random Access Memory).The memories described in the embodiments of the present application are intended to include, but are not limited to, these and any other suitable types of memories.

[0069] In an exemplary embodiment, the embodiment of the present application further provides a storage medium, namely a computer storage medium, specifically a computer-readable storage medium, for example, including a memory 503 storing a computer program, and the computer program can be executed by a processor 502 of an electronic device 500 to complete the steps of the rural power grid fault analysis method based on information credibility. For another example, including a memory 503 storing a computer program, the computer program can be executed by a processor 502 of an electronic device 500 to complete the steps of the poster image processing method. The computer-readable storage medium can be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface storage, optical disk, or CD-ROM.

[0070] It should be noted that: "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0071] In addition, the technical solutions described in the embodiments of the present application can be combined arbitrarily without conflict.

[0072] The above description is only a preferred embodiment of the present application and is not intended to limit the protection scope of the present application.

Claims

1. A rural power grid fault analysis method based on information credibility, characterized in that: include: Acquire power grid operation data of the first substation to generate first information; Determine the occurrence of a fault in the first substation by using the first information and the first model, and obtain at least one first fault information; The first fault information includes at least a fault type, and different first fault information has different fault types; Determining a first credibility of each first fault information in the at least one first fault information based on the second information of the first station area; The second information includes at least the environment information of the first station area and the equipment information of the first station area; Based on the first credibility, reliable first fault information is determined from the at least one type of first fault information to obtain second fault information.

2. The method according to claim 1, characterized in that The determining, based on the second information of the first station area, the first credibility of each first fault information in the at least one first fault information comprises: Based on the environmental information in the second information, determining at least one first environmental factor existing in the first station area; For each piece of first fault information, weighting the correlation between each first environmental factor and the first fault information to obtain a second credibility of the first fault information; Based on the second credibility, a first credibility of the first fault information is obtained.

3. The method according to claim 2, characterized in that The method further comprises: Based on the probability and impact of each preset fault under different environmental factors, establish the correlation between the fault and the environmental factors; Based on the association relationship, a degree of association between each first environmental factor and the first fault information is determined.

4. The method according to claim 2, characterized in that: The obtaining, based on the second credibility, a first credibility of the first fault information includes: Based on the device information in the second information, using a second model to predict the life cycle of each device; the life cycle includes at least one fault node; For each first fault information and the life cycle of the corresponding device, determine a fault node matching the first fault information to obtain a first node; Based on the life cycle of the corresponding device and the current running time of the device, predict the position of the usage status of the device in the life cycle to obtain a second node; Obtaining a third credibility of the first fault information based on a position difference between the first node and the second node; Based on the second credibility and the third credibility, a first credibility of the first fault information is determined.

5. The method according to claim 4, characterized in that The obtaining, based on the position difference between the first node and the second node, a third credibility of the first fault information includes: Determine whether the distance between the first node and the second node is less than a preset threshold; When the distance is less than the preset threshold, the first preset value is used as the third credibility; or, When the distance is greater than or equal to the preset threshold, the second preset value is used as the third credibility; wherein, The first preset value is greater than the second preset value.

6. The method according to any one of claims 1 to 5, characterized in that The method further comprises: Based on the second fault information, the first model is updated.

7. A rural power grid fault analysis device based on information credibility, characterized in that: include: A communication unit, used to obtain power grid operation data of the first substation and generate first information; A prediction unit, configured to determine the occurrence of a fault in the first substation by using the first information and the first model, and obtain at least one first fault information; The first fault information includes at least a fault type, and different first fault information has different fault types; A determination unit, configured to determine a first credibility of each first fault information in the at least one first fault information based on the second information of the first station area; The second information includes at least the environment information of the first station area and the equipment information of the first station area; And, based on the first credibility, reliable first fault information is determined from the at least one type of first fault information to obtain second fault information.

8. An electronic device, characterized in that: include: A communication interface and a processor, wherein The communication interface is used to obtain the power grid operation data of the first substation and generate the first information; The processor is used to use the first information and the first model to determine the occurrence of a fault in the first substation and obtain at least one first fault information; the first fault information at least includes the fault type, and different first fault information has different fault types; based on the second information of the first substation, the first credibility of each first fault information in the at least one first fault information is determined; the second information at least includes the environmental information of the first substation and the equipment information of the first substation; based on the first credibility, the credible first fault information is determined from the at least one type of first fault information to obtain the second fault information.

9. An electronic device, characterized in that: include: a processor and a memory for storing a computer program capable of being executed on the processor, Wherein, when the processor is used to run the computer program, it executes the steps of the method described in any one of claims 1 to 6.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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