Concentrator fault analysis method based on artificial intelligence and related equipment

Abstract

The embodiment of the invention provides a concentrator fault analysis method based on artificial intelligence and related equipment, and relates to the technical field of concentrator fault intelligent detection, and the method comprises the steps: obtaining signal lamp image data of concentrator equipment; performing data preprocessing on the signal lamp image data to obtain a training data set and a verification data set; extracting fault features from the training data set by adopting a target detection algorithm; performing model training based on the fault features, and performing model verification based on the verification data set; and inputting image data acquired in real time into the verified model for detection, and performing fault judgment and detection in combination with a fault preset rule. According to the technical scheme, the accuracy and efficiency of fault detection are improved, manual intervention is reduced, the operation cost is reduced, and predictive maintenance is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent detection of concentrator faults, and in particular to an artificial intelligence-based concentrator fault analysis method and related equipment. Background Art

[0002] Concentrators are core equipment of key power infrastructure such as power systems and communication base stations. Their normal operation is crucial to the stability of power system data collection. Concentrators are usually equipped with signal lights to indicate their working status, such as power, communication, fault, etc. By monitoring the status of these signal lights, operating problems of concentrators can be quickly identified and located.

[0003] Traditional fault detection methods mainly rely on manual inspections or automated identification systems with certain analytical capabilities. These methods have problems such as consuming manpower, low efficiency, high misjudgment rate, long response time, and inability to achieve predictive maintenance. Summary of the invention

[0004] The embodiments of the present application provide a concentrator fault analysis method and related equipment based on artificial intelligence to solve the technical problems existing in the prior art.

[0005] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0006] According to a first aspect of an embodiment of the present application, a concentrator fault analysis method based on artificial intelligence is provided, comprising:

[0007] Acquire the signal light image data of the concentrator device;

[0008] Performing data preprocessing on the signal light image data to obtain a training data set and a verification data set;

[0009] Use target detection algorithm to extract fault features from the training data set;

[0010] Performing model training based on the fault characteristics and the training data set, and performing model verification based on the verification data set;

[0011] The image data collected in real time is input into the verified model for detection, and fault judgment and detection are performed in combination with preset fault rules.

[0012] In some embodiments of the present application, based on the above solution, the data preprocessing of the signal light image data includes:

[0013] The signal light image data is subjected to data cleaning, missing value filling, noise filtering and data standardization processing.

[0014] In some embodiments of the present application, based on the above solution, the method of extracting fault features from a training data set using a target detection algorithm includes:

[0015] The target detection algorithm is used to extract the state features of the traffic lights from the training data set;

[0016] The classifier is trained to identify the state features of the traffic lights and extract the fault features related to the fault.

[0017] In some embodiments of the present application, based on the aforementioned solution, in the process of performing model training based on the fault features, the following is further included:

[0018] Cross-validation and data augmentation techniques are used to process the training data set.

[0019] In some embodiments of the present application, based on the aforementioned solution, it also includes: generating specific troubleshooting suggestions and handling measures based on the results of fault judgment and detection in combination with a fault handling knowledge base.

[0020] In some embodiments of the present application, based on the aforementioned solution, it also includes: making predictions based on historical fault analysis result data.

[0021] According to a second aspect of an embodiment of the present application, there is provided a concentrator fault analysis device based on artificial intelligence, comprising:

[0022] An acquisition unit, used for acquiring signal light image data of a concentrator device;

[0023] A data preprocessing unit, used to perform data preprocessing on the signal light image data to obtain a training data set and a verification data set;

[0024] An extraction unit, used for extracting fault features from a training data set using a target detection algorithm;

[0025] A training unit, configured to perform model training based on the fault characteristics and the training data set;

[0026] A verification unit, used for performing model verification based on the verification data set;

[0027] The fault judgment and detection unit is used to input the image data collected in real time into the verified model for detection, and perform fault judgment and detection in combination with the preset fault rules.

[0028] According to a third aspect of an embodiment of the present application, there is provided an electronic device, including: a memory and a processor;

[0029] The memory is used to store computer instructions;

[0030] The processor is used to call the computer instructions stored in the memory so that the electronic device executes the method as described in the first aspect.

[0031] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, wherein the storage medium stores computer instructions, and when the computer instructions are executed on a computer, the computer executes the method according to the first aspect.

[0032] The technical solution of the present application utilizes image recognition technology to monitor the signal lights on the concentrator equipment in real time, and uses deep learning and machine learning algorithms to perform fault analysis and prediction based on analysis results and historical data, thereby improving the accuracy and efficiency of fault detection, reducing manual intervention, reducing operating costs, and realizing predictive maintenance.

[0033] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0035] Figure 1 A schematic diagram of a flow chart of a concentrator fault analysis method based on artificial intelligence according to an embodiment of the present application is shown;

[0036] Figure 2 A schematic diagram of a model training and verification process flow according to an embodiment of the present application is shown;

[0037] Figure 3 A block diagram of a concentrator fault analysis device based on artificial intelligence according to an embodiment of the present application is shown;

[0038] Figure 4 A block diagram of an electronic device according to an embodiment of the present application is shown;

[0039] Figure 5 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0040] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0041] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0042] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0043] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0044] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] Some embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0046] See also Figure 1 , shows a flow chart of a concentrator fault analysis method based on artificial intelligence according to an embodiment of the present application.

[0047] like Figure 1As shown, a concentrator fault analysis method based on artificial intelligence is demonstrated, which specifically includes steps S100 to S500.

[0048] refer to Figure 1 , step S100, obtaining the signal light image data of the concentrator device.

[0049] For example, a high-definition camera can be installed at a key position of the concentrator device to ensure that the status of the traffic light can be clearly captured; then the traffic light image data is captured in real time by the high-definition camera, and the real-time captured traffic light image data is transmitted to the back-end server for temporary storage for use in subsequent steps.

[0050] Continue to refer Figure 1 , step S200, performing data preprocessing on the traffic light image data to obtain a training data set and a verification data set.

[0051] In some feasible embodiments, based on the above solution, the data preprocessing of the signal light image data includes:

[0052] The signal light image data is subjected to data cleaning, missing value filling, noise filtering and data standardization processing.

[0053] It can be understood that preprocessing the image data can remove random noise in the image and improve the image quality. It can also enhance the image contrast and ensure that the color and status of the traffic light are clearly visible for subsequent identification. In addition, the image size is standardized through data normalization, and the image is adjusted to the input size of the YOLOv5 model (640x640 pixels), making the image processing process more efficient and unified.

[0054] It should be noted that after preprocessing, the preprocessed traffic light image data is divided into a training data set and a verification data set according to a ratio, and the division ratio is set according to actual needs and is not particularly limited.

[0055] Continue to refer Figure 1 , step S300, extracting fault features from the training data set using a target detection algorithm.

[0056] In some feasible embodiments, based on the above solution, the method of extracting fault features from a training data set using a target detection algorithm includes:

[0057] The target detection algorithm is used to extract the state features of the traffic lights from the training data set;

[0058] The classifier is trained to identify the state features of the traffic lights and extract the fault features related to the fault.

[0059] It should be noted that, in this embodiment, the YOLOv5 target detection algorithm is used for feature extraction.

[0060] It should be noted that the state characteristics of the signal light are mainly color (red, green), brightness and position, etc. By distinguishing the color, brightness and position of the signal light, it is possible to analyze whether there is a fault at present.

[0061] It should be noted that the extracted fault features are applied to the data set to extract useful and key information from the original data and associate it with its target object. These features determine how the model understands the data and ultimately predicts the target.

[0062] Fault features are used in training data sets to optimize model parameters so that the model can learn the key features of the target; they are used in validation data sets to evaluate the detection accuracy and generalization ability of the model.

[0063] By extracting fault features from the data set, the model can better understand the characteristics of targets of different categories; fault features can remove background information irrelevant to the target, focus on the target-related parts, and improve detection efficiency.

[0064] Continue to refer Figure 1 , step S400, performing model training based on the fault characteristics and the training data set, and performing model verification based on the verification data set.

[0065] In some feasible embodiments, based on the above solution, in the process of performing model training based on the fault features, the following is further included:

[0066] Cross-validation and data augmentation techniques are used to process the training data set.

[0067] Exemplarily, the model training and verification process is as follows:

[0068] Step S401: data collection, collecting the color status of the traffic light;

[0069] Step S402: annotate the collected image data and generate a label file;

[0070] Step S403, preprocessing the image data;

[0071] Step S404, splitting the preprocessed data set into K parts;

[0072] Step S405, using K-Fold Cross-Validation, and repeating it K times;

[0073] Step S406: Select and obtain a training set and a validation set;

[0074] Step S407, performing image data enhancement processing on the training set and the validation set;

[0075] Step S408, select the YOLOv5 model version and pre-train the weights;

[0076] Step S409: define training categories and configure training parameters;

[0077] Step S410, training starts;

[0078] Step S411, monitoring loss function;

[0079] Step S412, training is completed (all K iterations are completed), and the model network and weights are saved;

[0080] Step S413: Use the validation set to evaluate the satisfaction index. If it is not satisfied, adjust the model parameters and K value, and return to step S406;

[0081] Step S414: If satisfied, determine whether the performance evaluation satisfaction index is met. If so, derive the model; if not, adjust the model, specifically including hyperparameter adjustment and network structure adjustment, and return to step S413 for data preprocessing, and return to step S414 to re-divide the K part.

[0082] It should be noted that during the training process, the YOLOv5 algorithm was used for model training based on a large number of labeled traffic light status image datasets, and data enhancement techniques (random cropping, rotation, scaling, color jittering, etc.) were used to increase the diversity of training samples. The model learning rate, batch size, network structure and other parameters were optimized through cross-validation (the training set was divided into K subsets again, and K-fold cross-validation was performed. In each fold, a subset was selected as the validation set, and the remaining subsets were used as training sets. The model was trained and its performance on the validation set was evaluated. The cycle was repeated K times to ensure that each subset was evaluated once as a validation set.).

[0083] Continue to refer Figure 1 , step S500, input the real-time collected image data into the verified model for detection, and perform fault judgment and detection in combination with the preset fault rules.

[0084] For example, the real-time collected image data is input into the trained YOLOv5 model to detect the color status of the signal light. The model accurately identifies the color and brightness changes of the signal light, and combines the preset fault rules (for example, if the Alarm 1 light flashes red, it means the PLC module is faulty, flashing green means normal operation, or the red light lasts too long, etc.) to make fault judgment and prediction. The specific step logic is shown in the attached figure. Figure 2 -Fault analysis process shown.

[0085] Step S501: Real-time detection of the signal light to determine whether the signal light is in normal state. If so, continue monitoring; if not, perform fault detection;

[0086] Step S502: perform fault detection and determine whether the fault type can be identified. If so, perform fault classification. If not, further determine and process the problem, record and report it, formulate a fault handling strategy, feedback the operation and maintenance site processing, analyze the cause of the fault, output the solution, restore monitoring, and update the knowledge base system;

[0087] Step S503, fault classification, specifically divided into: power supply problem, check the power supply situation; phase problem, check the wiring situation; alarm, check the DCU status; network problem, check the network status; communication module problem, check the data transmission and reception situation; metering chip problem, check the active / reactive power;

[0088] Step S504: Generate specific troubleshooting suggestions and treatment measures through the knowledge base;

[0089] Step S505: Continue to detect the status of the signal light, and analyze and predict possible problems that may occur in the future based on the frequency of signal light failures.

[0090] In some feasible embodiments, based on the above solution, the method further includes:

[0091] Step S600, based on the results of fault judgment and detection, combined with the fault handling knowledge base, specific fault troubleshooting suggestions and handling measures are generated.

[0092] For example, if the duration of the red light of Alarm 1 is too long, it is recommended to check the PLC communication module or output the log to analyze the cause. If the duration of the red light of Phase L1 is long, it is necessary to check whether the power supply of the phase is normal.

[0093] In some feasible embodiments, based on the above solution, the method further includes:

[0094] Make predictions based on historical failure analysis results data.

[0095] It should be noted that predictions are made based on historical fault analysis results to analyze the most likely problems in the future and to carry out prevention and maintenance in advance. The signal light fault detection results and treatment effects are analyzed regularly, and the operating parameters and maintenance strategies of the concentrator are optimized based on the analysis results to improve the overall operating efficiency and reliability of the system.

[0096] In summary, this method has the following advantages:

[0097] 1. Through deep learning, image feature extraction and status recognition can more accurately identify the fault signal of the concentrator and reduce the misjudgment rate compared with traditional methods;

[0098] 2. The automated image acquisition and processing process significantly improves the speed of fault detection. Compared with manual inspection, it can significantly reduce inspection time and optimize cost-effectiveness;

[0099] 3. Through the fault prediction model, combined with historical fault data, the transition from passive maintenance to active prevention can be achieved, making the decision-making process more data-based;

[0100] 4. Better scalability and adaptability. This method can be adjusted and optimized according to concentrators of different types and sizes to adapt to more scenarios.

[0101] The following describes an embodiment of the device of the present application, which can be used to execute an artificial intelligence-based concentrator fault analysis method in the above embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the above method of the present application.

[0102] Reference Figure 3 As shown, according to an embodiment of the present application, a concentrator fault analysis device 300 based on artificial intelligence includes:

[0103] An acquisition unit 301 is used to acquire signal light image data of a concentrator device;

[0104] A data preprocessing unit 302, configured to perform data preprocessing on the signal light image data to obtain a training data set and a verification data set;

[0105] An extraction unit 303 is used to extract fault features from a training data set using a target detection algorithm;

[0106] A training unit 304, configured to perform model training based on the fault characteristics and the training data set;

[0107] A verification unit 305, configured to perform model verification based on the verification data set;

[0108] The fault judgment and detection unit 306 is used to input the image data collected in real time into the verified model for detection, and perform fault judgment and detection in combination with the preset fault rules.

[0109] like Figure 4As shown, an embodiment of the present application also provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor. When the processor 420 executes the computer program 411, the steps of the above-mentioned concentrator fault analysis method based on artificial intelligence are implemented.

[0110] Since the electronic device introduced in this embodiment is a device used to implement an artificial intelligence-based concentrator fault analysis device in the embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation mode of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.

[0111] During the specific implementation process, when the computer program 411 is executed by a processor, any implementation method in the embodiments corresponding to the first aspect can be implemented.

[0112] Figure 5 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.

[0113] It should be noted that Figure 5 The computer system 500 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0114] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 502 or the program loaded from the storage part 508 to the random access memory (RAM) 503, such as executing the method described in the above embodiment. In the RAM 503, various programs and data required for system operation are also stored. The CPU 501, the ROM 502 and the RAM 503 are connected to each other through the bus 504. The input / output (I / O) interface 505 is also connected to the bus 504.

[0115] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed so that a computer program read therefrom is installed into the storage section 508 as needed.

[0116] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 509, and / or installed from a removable medium 511. When the computer program is executed by a central processing unit (CPU) 501, various functions defined in the system of the present application are executed.

[0117] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by an instruction execution system, device or device or used in combination with it. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0118] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0119] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not constitute limitations on the units themselves in some cases.

[0120] As another aspect, the present application further provides a computer program product or a computer program, the computer program product or the computer program including computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes a concentrator fault analysis method based on artificial intelligence described in the above embodiment.

[0121] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the concentrator fault analysis method based on artificial intelligence described in the above embodiment.

[0122] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0123] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0124] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary techniques in the art that are not disclosed in the present application. It should be understood that the present application is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A concentrator fault analysis method based on artificial intelligence, characterized in that: include: Acquire the signal light image data of the concentrator device; Performing data preprocessing on the signal light image data to obtain a training data set and a verification data set; Use target detection algorithm to extract fault features from the training data set; Performing model training based on the fault characteristics and the training data set, and performing model verification based on the verification data set; The image data collected in real time is input into the verified model for detection, and fault judgment and detection are performed in combination with preset fault rules.

2. The method according to claim 1, characterized in that The data preprocessing of the signal light image data comprises: The signal light image data is subjected to data cleaning, missing value filling, noise filtering and data standardization processing.

3. The method according to claim 1, characterized in that The method of extracting fault features from a training data set using a target detection algorithm includes: The target detection algorithm is used to extract the state features of the traffic lights from the training data set; The classifier is trained to identify the state features of the traffic lights and extract the fault features related to the fault.

4. The method according to claim 1, characterized in that: The process of training the model based on the fault characteristics also includes: Cross-validation and data augmentation techniques are used to process the training data set.

5. The method according to claim 1, characterized in that Also includes: Based on the results of fault judgment and detection, combined with the fault handling knowledge base, specific fault troubleshooting suggestions and handling measures are generated.

6. The method according to claim 1, characterized in that Also includes: Make predictions based on historical failure analysis results data.

7. A concentrator fault analysis device based on artificial intelligence, characterized in that: include: An acquisition unit, used for acquiring signal light image data of a concentrator device; A data preprocessing unit, used to perform data preprocessing on the signal light image data to obtain a training data set and a verification data set; An extraction unit, used for extracting fault features from a training data set using a target detection algorithm; A training unit, used for performing model training based on the fault characteristics and the training data set; A verification unit, used for performing model verification based on the verification data set; The fault judgment and detection unit is used to input the image data collected in real time into the verified model for detection, and perform fault judgment and detection in combination with the preset fault rules.

8. An electronic device, characterized in that: include: Memory and processor; The memory is used to store computer instructions; The processor is used to call the computer instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: The storage medium stores computer instructions, and when the computer instructions are executed on a computer, the computer is enabled to execute the method according to any one of claims 1 to 6.

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