Fault detection method and apparatus for step-up station, electronic device and storage medium

Abstract

The present application relates to the technical field of data processing. Disclosed are a fault detection method and apparatus for a step-up station, an electronic device and a storage medium. A main technical solution comprises: acquiring electrical quantity data of an object under test; inputting the electrical quantity data into a preset fault detection model to obtain a fault determination result output by the preset fault detection model; and, when it is determined that fault information is present in the fault determination result, sending to a scheduling end the electrical quantity data and the fault information to be stored. The electrical quantity data of the object under test is acquired and inputted into the preset fault detection model, and detection is performed by means of the preset fault detection model to determine whether a fault occurs in said object. The present application improves the efficiency and accuracy of fault detection, and also ensures that a timely response can be made when a fault occurs. In addition, sending to the scheduling end the electrical quantity data containing the fault information to be stored helps subsequent fault analysis and processing.

Description

Fault detection methods and devices, electronic equipment and storage media for booster stations Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to a fault detection method and apparatus, electronic equipment and storage medium for a booster station. Background Technology

[0002] Vigorously developing low-carbon or zero-carbon energy systems such as renewable energy and building a new power system with new energy as the main body has become a strategic direction for technological transformation in the energy field. Among them, photovoltaic power generation is a recognized renewable, green and clean energy source with advantages such as huge energy source, environmental protection and no pollution, safety and sustainability.

[0003] As the power collection center of a photovoltaic power plant, the inspection and maintenance of the booster station is crucial for ensuring the safe operation and improving the efficiency of the power plant. Currently, fault detection at booster stations still relies on manual inspections. However, the inherent time lag in this method leads to untimely fault detection, which affects the power generation efficiency of the photovoltaic power plant. Summary of the Invention

[0004] This disclosure provides a method, apparatus, electronic device, and storage medium for fault detection in a booster station. Its main purpose is to achieve automatic fault detection in booster stations.

[0005] According to a first aspect of this disclosure, a fault detection method for a booster station is provided, comprising:

[0006] Acquire electrical quantity data of the target to be tested;

[0007] The electrical quantity data is input into a preset fault detection model to obtain the fault judgment result output by the preset fault detection model;

[0008] If the fault information is determined to exist in the fault judgment result, the electronic quantity data and the fault information are sent to the scheduling terminal for storage.

[0009] Optionally, after inputting the electrical quantity data into a preset fault detection model and obtaining the fault judgment result output by the preset fault detection model, the method further includes:

[0010] If no fault information is found in the fault judgment result, the target to be detected is re-detected based on the preset fault detection model and according to the preset time threshold.

[0011] Optionally, before inputting the electrical quantity data into a preset fault detection model and obtaining the fault judgment result output by the preset fault detection model, the method further includes:

[0012] Acquire training electrical quantity data for the target to be detected; wherein, the training electrical quantity data includes abnormal data and normal data;

[0013] The preset fault detection model is trained based on the electrical quantity data used for training, and training is stopped when the loss function of the preset fault detection model is less than or equal to a preset threshold, thus obtaining the trained preset fault detection model.

[0014] Optionally, the training electrical quantity data includes operational electrical quantity data and switching electrical quantity data.

[0015] Optionally, if the fault determination result indicates the presence of fault information, sending the electronic quantity data and the fault information to the scheduling terminal for storage further includes:

[0016] The fault information will be broadcast according to the preset alarm method.

[0017] According to a second aspect of this disclosure, a fault detection device for a booster substation is provided, comprising:

[0018] The first acquisition unit is used to acquire electrical quantity data of the target to be detected;

[0019] The judgment unit is used to input the electrical quantity data into a preset fault detection model and obtain the fault judgment result output by the preset fault detection model;

[0020] The sending unit is used to send the electronic quantity data and the fault information to the scheduling terminal for storage when the fault judgment result determines that fault information exists.

[0021] Optionally, the device further includes:

[0022] The detection unit is used to re-detect the target to be detected based on the preset fault detection model and according to a preset time threshold after the judgment unit inputs the electrical quantity data into the preset fault detection model and obtains the fault judgment result output by the preset fault detection model. If the fault judgment result determines that there is no fault information, the detection unit is used to re-detect the target to be detected based on the preset fault detection model and according to a preset time threshold.

[0023] Optionally, the device further includes:

[0024] The second acquisition unit is used to acquire training electrical quantity data of the target to be detected before the judgment unit inputs the electrical quantity data into the preset fault detection model and obtains the fault judgment result output by the preset fault detection model; wherein, the training electrical quantity data includes abnormal data and normal data;

[0025] The training unit is used to train the preset fault detection model based on the training electrical quantity data, and to stop training when the loss function of the preset fault detection model is less than or equal to a preset threshold, thereby obtaining the trained preset fault detection model.

[0026] Optionally, the training electrical quantity data includes operational electrical quantity data and switching electrical quantity data.

[0027] Optionally, the sending unit is further configured to:

[0028] The fault information will be broadcast according to the preset alarm method.

[0029] According to a third aspect of this disclosure, an electronic device is provided, comprising:

[0030] At least one processor; and

[0031] A memory communicatively connected to the at least one processor; wherein,

[0032] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect above.

[0033] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect above.

[0034] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0035] The fault detection method, apparatus, electronic equipment, and storage medium for substations disclosed in this disclosure mainly include: acquiring electrical quantity data of the target to be detected; inputting the electrical quantity data into a preset fault detection model to obtain a fault judgment result output by the preset fault detection model; and, if the fault judgment result determines the existence of fault information, sending the electrical quantity data and the fault information to the dispatching terminal for storage. Compared with related technologies, the embodiments of this application acquire electrical quantity data of the target to be detected and input it into a preset fault detection model for detection to determine whether the target to be detected has a fault. This improves the efficiency and accuracy of fault detection and ensures timely response when a fault occurs. Simultaneously, sending the electrical quantity data containing fault information to the dispatching terminal for storage facilitates subsequent fault analysis and processing.

[0036] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0037] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0038] Figure 1 is a flowchart illustrating a fault detection method for a booster station provided in an embodiment of this disclosure;

[0039] Figure 2 is a flowchart illustrating a fault detection method for a booster station provided in an embodiment of this disclosure;

[0040] Figure 3 is a structural schematic diagram of a fault detection device for a booster station provided in an embodiment of this disclosure;

[0041] Figure 4 is a structural schematic diagram of a fault detection device for a booster station provided in an embodiment of this disclosure;

[0042] Figure 5 is a schematic block diagram of an example electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0043] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0044] The following description, with reference to the accompanying drawings, outlines a method, apparatus, electronic device, and storage medium for detecting faults in a booster station according to embodiments of the present disclosure.

[0045] Figure 1 is a flowchart illustrating a fault detection method for a booster station provided in an embodiment of this disclosure.

[0046] As shown in Figure 1, the method includes the following steps:

[0047] Step 101: Obtain the electrical quantity data of the target to be tested.

[0048] In some embodiments, the electrical quantity data may include specific data items as needed, such as three-phase current, zero-sequence current, three-phase voltage, zero-sequence voltage, protection action time, power and frequency changes, etc. Specifically, the electrical quantity data to be collected may be determined according to the type of the target to be detected. In particular, the embodiments of this application do not limit the specific content and value of the electrical quantity data.

[0049] In some embodiments, data can be collected through methods such as sensor measurement, smart meter reading, or remote monitoring system collection. The specific method can be determined according to actual needs, and this application embodiment does not limit this.

[0050] In some embodiments, multiple targets may be set up, such as transformers, transmission lines, generators, etc. Specifically, the number and type of targets to be detected are not limited in the embodiments of this application.

[0051] Step 102: Input the electrical quantity data into the preset fault detection model to obtain the fault judgment result output by the preset fault detection model.

[0052] In some embodiments, the preset fault detection model can be determined according to specific needs, such as a neural network model or a decision tree, or specific instruments can be used for detection, such as a microcomputer fault recorder. Specifically, the embodiments of this application do not limit this.

[0053] Step 103: If the fault information is determined to exist in the fault judgment result, the electronic quantity data and the fault information are sent to the scheduling terminal for storage.

[0054] In some embodiments, before sending the fault information to the dispatcher, the electronic quantity data containing the fault information and the fault information can be packaged and encrypted to ensure data security. After receiving the data, the dispatcher stores it for subsequent analysis and querying.

[0055] The fault detection method for substations disclosed in this paper mainly includes: acquiring electrical quantity data of the target to be detected; inputting the electrical quantity data into a preset fault detection model to obtain a fault judgment result output by the preset fault detection model; and, if the fault judgment result determines that fault information exists, sending the electrical quantity data and the fault information to the dispatching terminal for storage. Compared with related technologies, this application embodiment acquires the electrical quantity data of the target to be detected and inputs it into a preset fault detection model for detection, thereby determining whether the target to be detected has experienced a fault. This improves the efficiency and accuracy of fault detection and ensures timely response when a fault occurs. Furthermore, sending the electrical quantity data containing fault information to the dispatching terminal for storage facilitates subsequent fault analysis and processing.

[0056] In some embodiments, a protection and fault information remote transmission system substation can be configured at the booster station. This substation should employ an embedded real-time operating system to collect and organize various status information from all protection devices and waveform recorders, and transmit it to the dispatching terminal as needed. The protection and fault information remote transmission system substation communicates with each protection device within the station via a communication interface, using standard protocols, and connects to the power dispatching data network via an Ethernet interface to transmit information to the dispatching terminal.

[0057] In some embodiments, after the fault judgment result is output in step 102, two situations may occur; if no fault information is found, the following steps are also included:

[0058] If no fault information is found in the fault judgment result, the target to be detected is re-detected based on the preset fault detection model and according to the preset time threshold.

[0059] In some embodiments, the preset time threshold is an empirical value that can be determined based on historical data, system characteristics, etc. However, this application does not limit this specific time threshold.

[0060] In practical applications, the model needs to be updated and optimized in a timely manner based on updates to the equipment under test and the discovery of new fault modes or anomalies.

[0061] Optionally, before inputting the electrical quantity data into a preset fault detection model and obtaining the fault judgment result output by the preset fault detection model, the method further includes:

[0062] Step 201: Obtain training electrical quantity data for the target to be detected; wherein the training electrical quantity data includes abnormal data and normal data.

[0063] In some embodiments, waveforms of various analog and digital quantities can be recorded to train a preset fault detection model. Electrical quantity data under different operating conditions can be collected, covering changes in electrical quantities of the power system under different operating states, including key indicators such as current, voltage, and power factor. Simultaneously, we need to ensure that the data includes both electrical quantity data during normal operation and abnormal data during fault occurrences, so that the characteristics of normal and fault data can be learned during subsequent model training.

[0064] Before training, you can also set clear type labels for each electrical quantity data used in training, such as normal or abnormal, to facilitate subsequent training of the model.

[0065] It should be noted that the electrical quantity data used for training includes operational electrical quantity data and switching electrical quantity data.

[0066] Step 202: Train the preset fault detection model based on the electrical quantity data used for training, and stop training when the loss function of the preset fault detection model is less than or equal to a preset threshold, thereby obtaining the trained preset fault detection model.

[0067] In some embodiments, during each training iteration, the preset fault detection model calculates the prediction result based on the current parameters and input data, calculates the value of the loss function, and uses gradient descent or other optimization algorithms to update the parameters to reduce the value of the loss function. This process is repeated until a preset number of iterations is reached or the loss function value is less than a preset threshold. The preset threshold is an empirical value and can be set according to accuracy requirements or computer performance; this embodiment does not limit its setting.

[0068] In some embodiments, when step 103 determines that fault information exists in the fault judgment result and sends the electronic quantity data and the fault information to the scheduling terminal for storage, the following steps are also included:

[0069] The fault information will be broadcast according to the preset alarm method.

[0070] In some embodiments, multiple alarm methods can be set to meet the alarm needs of different scenarios. For example, in the power system control room, sound and light alarms can be used to remind on-duty personnel; for remote monitoring centers, relevant personnel can be notified via SMS or email; and for important customers or equipment managers, real-time alarm information can be pushed via a mobile APP.

[0071] Specifically, the equipment can be bound to the maintenance personnel in advance. When a fault occurs, the maintenance personnel can be identified based on the faulty equipment, and a fault alarm can be sent to that maintenance personnel.

[0072] Corresponding to the aforementioned fault detection method for booster substations, this invention also proposes a fault detection device for booster substations. Since the device embodiments of this invention correspond to the aforementioned method embodiments, details not disclosed in the device embodiments can be referred to the aforementioned method embodiments, and will not be repeated here.

[0073] Figure 3 is a schematic diagram of a fault detection device for a booster station provided in an embodiment of this disclosure. As shown in Figure 3, it includes:

[0074] The first acquisition unit 31 is used to acquire electrical quantity data of the target to be detected;

[0075] The judgment unit 32 is used to input the electrical quantity data into a preset fault detection model and obtain the fault judgment result output by the preset fault detection model;

[0076] The sending unit 33 is used to send the electronic quantity data and the fault information to the scheduling terminal for storage when the fault judgment result determines that there is fault information.

[0077] The fault detection device for a booster station provided in this disclosure mainly includes: acquiring electrical quantity data of the target to be detected; inputting the electrical quantity data into a preset fault detection model to obtain a fault judgment result output by the preset fault detection model; and, if the fault judgment result determines that fault information exists, sending the electrical quantity data and the fault information to a dispatching terminal for storage. Compared with related technologies, this application embodiment acquires the electrical quantity data of the target to be detected and inputs it into a preset fault detection model for detection, thereby determining whether a fault has occurred in the target. This improves the efficiency and accuracy of fault detection and ensures timely response when a fault occurs. Simultaneously, sending the electrical quantity data containing fault information to the dispatching terminal for storage facilitates subsequent fault analysis and processing.

[0078] Furthermore, in one possible implementation of this disclosure embodiment, as shown in FIG4, the apparatus further includes:

[0079] The detection unit 34 is used to re-detect the target to be detected based on the preset fault detection model and according to a preset time threshold after the judgment unit 32 inputs the electrical quantity data into the preset fault detection model and obtains the fault judgment result output by the preset fault detection model. If the fault judgment result determines that there is no fault information, the detection unit 34 is used to re-detect the target to be detected based on the preset fault detection model and according to a preset time threshold.

[0080] Furthermore, in one possible implementation of this disclosure embodiment, as shown in FIG4, the apparatus further includes:

[0081] The second acquisition unit 35 is used to acquire training electrical quantity data of the target to be detected before the judgment unit 32 inputs the electrical quantity data into the preset fault detection model and obtains the fault judgment result output by the preset fault detection model; wherein, the training electrical quantity data includes abnormal data and normal data;

[0082] Training unit 36 ​​is used to train the preset fault detection model based on the training electrical quantity data, and to stop training when the loss function of the preset fault detection model is less than or equal to a preset threshold, so as to obtain the trained preset fault detection model.

[0083] Furthermore, in one possible implementation of this disclosure embodiment, as shown in FIG4, the training electrical quantity data includes operating electrical quantity data and switching electrical quantity data.

[0084] Furthermore, in one possible implementation of this disclosure embodiment, as shown in FIG4, the sending unit 33 is further configured to:

[0085] The fault information will be broadcast according to the preset alarm method.

[0086] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of the embodiments of this disclosure, and the principle is the same. Therefore, the embodiments of this disclosure are not limited thereto.

[0087] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0088] Figure 5 illustrates a schematic block diagram of an example electronic device 400 that can be used to implement embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0089] As shown in Figure 5, device 400 includes a computing unit 401, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 402 or a computer program loaded from storage unit 408 into RAM (Random Access Memory) 403. RAM 403 can also store various programs and data required for the operation of device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via bus 404. I / O (Input / Output) interface 405 is also connected to bus 404.

[0090] Multiple components in device 400 are connected to I / O interface 405, including: input unit 406, such as keyboard, mouse, etc.; output unit 407, such as various types of monitors, speakers, etc.; storage unit 408, such as disk, optical disk, etc.; and communication unit 409, such as network card, modem, wireless transceiver, etc. Communication unit 409 allows device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0091] The computing unit 401 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as a fault detection method for a booster station. For example, in some embodiments, the fault detection method for a booster station may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of the methods described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the aforementioned fault detection method for the booster station by any other suitable means (e.g., by means of firmware).

[0092] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0093] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0094] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0095] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0096] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0097] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system that addresses the management difficulties and weak business scalability inherent in traditional physical hosts and VPS (Virtual Private Server) services. Servers can also be servers for distributed systems or servers integrated with blockchain technology.

[0098] It's important to note that artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0099] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0100] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method of fault detection for a step-up station, characterized in that, include: Acquire electrical quantity data of the target to be tested; The electrical quantity data is input into a preset fault detection model to obtain the fault judgment result output by the preset fault detection model; If the fault information is determined to exist in the fault judgment result, the electronic quantity data and the fault information are sent to the scheduling terminal for storage.

2. The method of claim 1, wherein, After inputting the electrical quantity data into a preset fault detection model and obtaining the fault judgment result output by the preset fault detection model, the method further includes: If no fault information is found in the fault judgment result, the target to be detected is re-detected based on the preset fault detection model and according to the preset time threshold.

3. The method of claim 1, wherein, Before inputting the electrical quantity data into a preset fault detection model and obtaining the fault judgment result output by the preset fault detection model, the method further includes: Acquire training electrical quantity data for the target to be detected; wherein, the training electrical quantity data includes abnormal data and normal data; The preset fault detection model is trained based on the electrical quantity data used for training, and training is stopped when the loss function of the preset fault detection model is less than or equal to a preset threshold, thus obtaining the trained preset fault detection model.

4. The method of claim 1, wherein, The electrical quantity data used for training includes operational electrical quantity data and switching electrical quantity data.

5. The method according to any one of claims 1-4, characterized in that, The step of sending the electronic quantity data and the fault information to the scheduling terminal for storage when the fault judgment result determines that fault information exists further includes: The fault information will be broadcast according to the preset alarm method.

6. A fault detection device for a step-up station, characterized in that include: The first acquisition unit is used to acquire electrical quantity data of the target to be detected; The judgment unit is used to input the electrical quantity data into a preset fault detection model and obtain the fault judgment result output by the preset fault detection model; The sending unit is used to send the electronic quantity data and the fault information to the scheduling terminal for storage when the fault judgment result determines that fault information exists.

7. The apparatus of claim 6, wherein, The device further includes: The detection unit is used to re-detect the target to be detected based on the preset fault detection model and according to a preset time threshold after the judgment unit inputs the electrical quantity data into the preset fault detection model and obtains the fault judgment result output by the preset fault detection model. If the fault judgment result determines that there is no fault information, the detection unit is used to re-detect the target to be detected based on the preset fault detection model and according to a preset time threshold.

8. An electronic device, comprising: include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

9. A non-transitory computer-readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-5.

10. A computer program product, characterised in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-5.

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