Intelligent sensing system and method for surface state of power grid equipment

Abstract

The invention discloses an intelligent sensing system and method for the surface state of power grid equipment, and belongs to the technical field of power grid equipment monitoring, and the system comprises a data collection module which is used for collecting the surface state data of the power grid equipment; the data processing module is responsible for processing and analyzing the data acquired by the data acquisition module; the intelligent diagnosis module is used for analyzing and comparing the processed data and carrying out fault prediction; the remote monitoring and early warning module is used for an operator to quickly check the state of the equipment and to give an alarm and notify a fault; by collecting equipment surface state data (such as temperature, humidity, stress, vibration and the like) and image data in real time, the system can comprehensively monitor the operation state of the power grid equipment, and the system can rapidly and accurately collect various parameters of the power grid equipment by utilizing the combination of the sensor and the image collection equipment, so that the system is convenient to use. And the data is transmitted to a data processing center in real time and passes through a high-precision sensor and a high-definition camera device.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid equipment monitoring, and in particular relates to an intelligent sensing system and method for the surface status of power grid equipment. Background Art

[0002] In modern power systems, the safe operation of power grid equipment is the basis for ensuring stable and reliable power supply. Power grid equipment includes substation equipment, power switches, power transformers, transmission lines and related auxiliary equipment, etc. These equipment need to operate for a long time and withstand various environmental conditions, such as high temperature, low temperature, humidity, corrosion, mechanical vibration, dust, etc. Therefore, the status monitoring and fault diagnosis of power grid equipment are particularly important, especially in the status monitoring of the equipment surface, which is directly related to the health of the equipment and the stability of the overall system.

[0003] 1. Common types of surface faults of power grid equipment

[0004] During operation, power grid equipment is often affected by external environment and internal factors, which may cause different types of faults or damage on the equipment surface. These common surface faults include but are not limited to:

[0005] Cracks: Due to material aging, mechanical stress, temperature changes, etc., cracks may appear on the surface of power grid equipment, especially metal or ceramic surfaces. Once cracks develop, they may cause equipment failure, and in severe cases, cause equipment failure.

[0006] Corrosion: When the surface of the equipment is exposed to corrosive substances such as air, moisture, and salt for a long time, it will cause corrosion of metal materials. Corrosion will not only reduce the mechanical strength of the equipment, but also affect its electrical performance.

[0007] Electrical aging and dirt accumulation: Long-term operation or the deposition of pollutants on the surface of the equipment may cause the electrical performance of the insulation material to deteriorate, or even cause short circuits, discharges and other problems. Dirt accumulation not only affects the performance of electrical equipment, but may also cause safety hazards such as overheating or fire.

[0008] Signs of overheating: When electrical equipment is in operation for a long time, it may cause local overheating due to excessive load or poor heat dissipation. Obvious signs of overheating, such as discoloration or burning, will appear on the surface of the electrical equipment.

[0009] 2. Existing Grid Equipment Surface Monitoring Technology

[0010] Currently, the surface monitoring of power grid equipment mainly relies on traditional manual inspections, regular inspections and some simple sensor monitoring. These methods have the following characteristics and limitations:

[0011] Manual inspection: Although manual inspection can intuitively find some obvious problems on the surface of equipment, such as cracks and corrosion, the periodicity and frequency of inspection are low, and there is a risk of human omission. Due to lack of experience or limited time and space conditions, the inspectors may not be able to fully and accurately check every corner, resulting in some problems not being discovered in time.

[0012] Regular inspection and maintenance: Grid equipment usually needs regular inspection and maintenance, but this method relies on a set maintenance cycle and cannot achieve real-time monitoring. When an abnormality occurs during equipment operation, regular inspections may not be able to detect the problem in time, delaying the repair and maintenance of the equipment.

[0013] Simple sensor monitoring: Some devices use temperature, humidity, pressure sensors, etc. for basic monitoring, but these sensors can only provide simple environmental data and cannot obtain specific damage or changes on the device surface in real time. Even if sensors are used to monitor environmental parameters, these data often cannot directly indicate whether there are specific problems such as cracks or corrosion on the device surface.

[0014] 3. The need for intelligent monitoring technology

[0015] As the requirements for reliability and stability of power systems continue to increase, traditional monitoring methods can no longer meet the needs of modern power grid equipment management, and a technical means that can provide real-time, comprehensive and accurate monitoring is urgently needed. Traditional monitoring methods are often limited to one aspect and cannot fully obtain the status information of the equipment. In addition, fault diagnosis usually relies on manual experience and is prone to misdiagnosis or missed diagnosis. Therefore, how to improve the accuracy and efficiency of surface status monitoring of power grid equipment is a major challenge in the current maintenance and management of power grid equipment.

[0016] The specific needs are reflected in the following aspects:

[0017] Real-time: Changes in the surface state of equipment often accumulate gradually. If potential faults cannot be discovered and predicted in time, it may lead to unstable equipment operation and even cause large-scale power outages and other accidents. Therefore, the monitoring system of power grid equipment needs to be real-time and able to quickly feedback information on changes in the surface state of the equipment.

[0018] Intelligent diagnosis: There may be many types of faults on the surface of power grid equipment. The manifestations of these faults are complex and require comprehensive analysis based on multiple data (such as sensor data, image data, etc.). Existing manual inspections and monitoring methods based on a single sensor often cannot provide sufficiently comprehensive information and cannot detect equipment hidden dangers in a timely manner. Intelligent diagnosis based on artificial intelligence and deep learning technology can better achieve early identification and prediction of faults.

[0019] Multi-dimensional perception: The surface state of power grid equipment is affected by multiple factors, and a single sensor often cannot fully reflect the state of the equipment. In order to accurately monitor the surface state of the equipment, the system needs to be able to integrate multiple sensors (such as temperature and humidity sensors, stress sensors) and image acquisition devices, and accurately judge the health status of the equipment through comprehensive analysis of multi-dimensional data.

[0020] Remote monitoring and early warning: To prevent power grid equipment from shutting down due to failures, it is necessary to implement remote monitoring of the equipment. By transmitting the real-time collected data to the monitoring platform, operation and maintenance personnel can check the equipment status at any time, issue early warnings, and take corresponding maintenance measures.

[0021] 4. Challenges and bottlenecks of current technology

[0022] Although there are some monitoring methods based on sensors, image acquisition and other technologies, there are still some challenges and bottlenecks in practical applications:

[0023] Poor integration of sensors and image acquisition: Existing monitoring technologies focus on a certain type of data (such as sensor data or image data) and lack the ability to conduct comprehensive analysis. Sensors cannot directly identify changes in the surface morphology of equipment, and although image acquisition devices can provide high-resolution images, they are slow to respond to environmental factors and have limited detection accuracy for complex problems in images (such as fine cracks, hidden corrosion, etc.).

[0024] Insufficient data processing capabilities: Existing systems often rely on traditional processing methods and have limited processing capabilities for large amounts of monitoring data (especially image data). As the amount of data increases, how to efficiently process and analyze this data and extract effective information from it is an urgent problem to be solved.

[0025] Insufficient intelligent diagnosis: Although intelligent algorithms such as deep learning have achieved remarkable results in some fields, how to make accurate diagnosis based on multi-source data in the surface condition monitoring of power grid equipment is still a technical difficulty. Existing intelligent diagnosis technologies generally face problems such as insufficient data training sets and poor model adaptability.

[0026] Therefore, based on the shortcomings of the prior art, the present invention proposes a new intelligent perception system and method for the surface status of power grid equipment, which aims to achieve comprehensive and real-time monitoring of the surface status of power grid equipment by integrating multiple sensors and image acquisition technologies, combining deep learning with intelligent diagnosis algorithms, and providing early warning and prediction of potential faults. Summary of the invention

[0027] The purpose of the present invention is to provide an intelligent sensing system and method for the surface status of power grid equipment to solve the problems raised in the above background technology.

[0028] To achieve the above object, the present invention provides the following technical solution: an intelligent sensing system for the surface status of power grid equipment, comprising:

[0029] Data acquisition module, used to collect surface status data of power grid equipment;

[0030] The data processing module is responsible for processing and analyzing the data collected by the data acquisition module;

[0031] Intelligent diagnosis module, which analyzes and compares the processed data and predicts faults;

[0032] The remote monitoring and early warning module allows operators to quickly check the status of equipment and issue alarms and notifications for faults.

[0033] It should be noted that the data acquisition module includes:

[0034] Sensor array, including temperature sensor, humidity sensor, stress sensor, vibration sensor, corrosion sensor, used to obtain basic data on the surface status of power grid equipment in real time;

[0035] Image acquisition equipment, including high-definition cameras and infrared cameras, is used to obtain high-resolution image data on the surface of power grid equipment, especially for capturing small cracks, corrosion marks, and overheating marks on the equipment surface;

[0036] The data transmission module transmits the data and image data collected by the sensor to the central processing unit through a wireless or wired communication network.

[0037] It is further worth noting that the data processing module includes:

[0038] The data fusion module is responsible for fusing the data obtained from the data acquisition module acquisition device;

[0039] Image processing module, which analyzes the acquired image data through image processing algorithms to identify whether there are cracks, corrosion, dirt or overheating problems on the surface of the equipment;

[0040] The feature extraction module extracts key features from the fused data, such as temperature change curve, stress distribution, size and shape of surface cracks, and corrosion area.

[0041] It should be further explained that the intelligent diagnosis module includes:

[0042] Deep learning model: Use a deep learning-based classification model to analyze the processed data and identify potential fault types. The deep learning model can learn the association between the surface state of the equipment and the fault through the training data set, and predict whether the equipment is in a normal state or at risk of failure based on the current data;

[0043] Anomaly detection and fault prediction, using anomaly detection algorithms, compare and analyze real-time data with historical data, identify whether there are abnormal fluctuations, and make fault predictions.

[0044] As a preferred implementation, the remote monitoring and early warning module includes:

[0045] The user interface provides a friendly monitoring interface for power operation and maintenance personnel, displaying real-time data, equipment health status, and historical data trend information, making it easy for operators to quickly check equipment status;

[0046] The alarm system will automatically trigger an alarm and notify relevant maintenance personnel when a failure risk or abnormal equipment status is detected on the equipment surface, providing information on the type, location, and severity of the failure to support rapid handling of the failure.

[0047] In addition, the present invention provides the following technical solution: an intelligent sensing method for the surface state of a power grid device, comprising the following steps:

[0048] S1. Collecting the surface status data of power grid equipment through the data acquisition module;

[0049] S2, transmitting the collected data to the central processing unit and preparing for further data processing through the data processing module;

[0050] S3. The data fusion module is used to fuse data from different sources to ensure data consistency and integrity. The image data is analyzed by the image processing module. The processed data is passed through the feature extraction module to extract key equipment surface status features.

[0051] S4. The results of data processing will be intelligently analyzed through the deep learning model to predict possible faults. The anomaly detection module will monitor data fluctuations in real time. When an abnormality is detected on the surface of the equipment, an alarm will be triggered and a detailed analysis will be conducted to provide a fault diagnosis report.

[0052] S5. Operation and maintenance personnel can monitor the equipment status in real time through the user interface. When the system detects a potential fault or abnormal state, it will send an early warning message to the operation and maintenance personnel and provide a detailed description, location and solution of the equipment fault to help the operation and maintenance personnel to handle it in a timely manner.

[0053] Compared with the prior art, the intelligent sensing system and method for the surface status of power grid equipment provided by the present invention have at least the following beneficial effects:

[0054] (1) By collecting equipment surface status data (such as temperature, humidity, stress, vibration, etc.) and image data in real time, the present invention can provide comprehensive monitoring of the operating status of power grid equipment. By using a combination of sensors and image acquisition equipment, the system can quickly and accurately collect various parameters of power grid equipment and transmit the data to the data processing center in real time. Through high-precision sensors and high-definition camera equipment, the system can accurately identify subtle changes on the surface of the equipment, such as cracks, corrosion, overheating, etc., thereby avoiding possible missed detection and false detection in traditional manual detection methods, and ensuring the accuracy of equipment status monitoring.

[0055] (2) The system can automatically analyze the collected sensor data and image data, and perform intelligent diagnosis of equipment status through deep learning models. When the equipment status is abnormal, the model will quickly identify and give a judgment on the fault type. Compared with the traditional method that relies on human experience and judgment, intelligent diagnosis is more accurate and efficient, and reduces the subjective bias of human judgment. In addition to fault diagnosis, the system can also predict possible equipment failures based on historical data and real-time data. This prediction can provide operation and maintenance personnel with the opportunity to intervene in advance, discover potential faults in advance, avoid sudden failures of power grid equipment during operation, and reduce downtime and maintenance costs.

[0056] (3) Through intelligent diagnosis and fault prediction, operation and maintenance personnel can perform preventive maintenance before equipment failure occurs, thereby avoiding large-scale equipment downtime, extending equipment service life, and reducing maintenance costs. By providing timely warnings and accurately diagnosing the type of fault, operation and maintenance personnel can accurately locate the problem and reduce unnecessary maintenance and component replacement work.

[0057] (4) By real-time monitoring of the equipment surface status, equipment abnormalities can be discovered in time, thereby preventing power outages caused by equipment failures and ensuring the normal operation of the power grid system. Due to the timely discovery and handling of equipment failures, the failure rate of power grid equipment is reduced, thereby improving the overall stability and operational reliability of the power grid.

[0058] (5) Intelligent perception and fault diagnosis systems can greatly improve the work efficiency of operation and maintenance personnel. Through the data analysis and intelligent early warning provided by the system, operation and maintenance personnel can not only save a lot of time to deal with the repetitive labor brought by traditional manual inspections, but also focus on more complex maintenance tasks and improve work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a structural schematic diagram of an intelligent sensing system for the surface status of power grid equipment according to the present invention;

[0060] Figure 2 This is a structural block diagram of an intelligent sensing system for the surface status of power grid equipment according to the present invention;

[0061] Figure 3 The present invention is a flowchart of an intelligent sensing method for the surface status of power grid equipment. DETAILED DESCRIPTION

[0062] The present invention will be further described below in conjunction with the embodiments.

[0063] See also Figure 1-2 The present invention provides an intelligent sensing system for the surface state of power grid equipment, comprising:

[0064] Data acquisition module, used to collect surface status data of power grid equipment;

[0065] The data processing module is responsible for processing and analyzing the data collected by the data acquisition module;

[0066] Intelligent diagnosis module, which analyzes and compares the processed data and predicts faults;

[0067] The remote monitoring and early warning module allows operators to quickly check the status of equipment and issue alarms and notifications for faults.

[0068] Further as Figure 2 As shown, it is worth noting that the data acquisition module includes:

[0069] Sensor array, including temperature sensor, humidity sensor, stress sensor, vibration sensor, corrosion sensor, used to obtain basic data on the surface status of power grid equipment in real time;

[0070] Image acquisition equipment, including high-definition cameras and infrared cameras, is used to obtain high-resolution image data on the surface of power grid equipment, especially for capturing small cracks, corrosion marks, and overheating marks on the equipment surface;

[0071] The data transmission module transmits the data and image data collected by the sensor to the central processing unit through a wireless or wired communication network.

[0072] By collecting equipment surface status data (such as temperature, humidity, stress, vibration, etc.) and image data in real time, the present invention can provide comprehensive monitoring of the operating status of power grid equipment. By utilizing the combination of sensors and image acquisition equipment, the system can quickly and accurately collect various parameters of power grid equipment and transmit the data to the data processing center in real time. Through high-precision sensors and high-definition camera equipment, the system can accurately identify slight changes on the surface of the equipment, such as cracks, corrosion, overheating, etc., thereby avoiding possible missed detections and false detections in traditional manual detection methods, and ensuring the accuracy of equipment status monitoring.

[0073] Further as Figure 2 As shown, it is worth noting that the data processing module includes:

[0074] The data fusion module is responsible for fusing the data obtained from the data acquisition module acquisition device;

[0075] Image processing module, which analyzes the acquired image data through image processing algorithms to identify whether there are cracks, corrosion, dirt or overheating problems on the surface of the equipment;

[0076] The feature extraction module extracts key features from the fused data, such as temperature change curve, stress distribution, size and shape of surface cracks, and corrosion area.

[0077] Further as Figure 2 As shown, it is worth noting that the intelligent diagnosis module includes:

[0078] Deep learning model: Use a deep learning-based classification model to analyze the processed data and identify potential fault types. The deep learning model can learn the association between the surface state of the equipment and the fault through the training data set, and predict whether the equipment is in a normal state or at risk of failure based on the current data;

[0079] Anomaly detection and fault prediction, using anomaly detection algorithms, compare and analyze real-time data with historical data, identify whether there are abnormal fluctuations, and make fault predictions.

[0080] Through the deep learning model, intelligent diagnosis of equipment status is performed. When the equipment status is abnormal, the model will quickly identify and give a judgment on the fault type. Compared with the traditional method that relies on human experience and judgment, intelligent diagnosis is more accurate and efficient, and reduces the subjective bias of human judgment. In addition to fault diagnosis, the system can also predict possible equipment failures based on historical data and real-time data. This prediction can provide operation and maintenance personnel with the opportunity to intervene in advance, discover potential faults in advance, avoid sudden failures of power grid equipment during operation, and reduce downtime and maintenance costs.

[0081] Further as Figure 2 As shown, it is worth noting that the remote monitoring and early warning module includes:

[0082] The user interface provides a friendly monitoring interface for power operation and maintenance personnel, displaying real-time data, equipment health status, and historical data trend information, making it easy for operators to quickly check equipment status;

[0083] The alarm system will automatically trigger an alarm and notify relevant maintenance personnel when a failure risk or abnormal equipment status is detected on the equipment surface, providing information on the type, location, and severity of the failure to support rapid handling of the failure.

[0084] Through intelligent diagnosis and fault prediction, operation and maintenance personnel can perform preventive maintenance before equipment fails, thereby avoiding large-scale equipment downtime, extending equipment life, and reducing maintenance costs. By providing timely warnings and accurately diagnosing the type of fault, operation and maintenance personnel can accurately locate the problem and reduce unnecessary maintenance and component replacement work.

[0085] See also Figure 3 The present invention provides an intelligent sensing method for the surface state of power grid equipment, comprising the following steps:

[0086] S1. Collecting the surface status data of power grid equipment through the data acquisition module;

[0087] S2, transmitting the collected data to the central processing unit and preparing for further data processing through the data processing module;

[0088] S3. The data fusion module is used to fuse data from different sources to ensure data consistency and integrity. The image data is analyzed by the image processing module. The processed data is passed through the feature extraction module to extract key equipment surface status features.

[0089] S4. The results of data processing will be intelligently analyzed through the deep learning model to predict possible faults. The anomaly detection module will monitor data fluctuations in real time. When an abnormality is detected on the surface of the equipment, an alarm will be triggered and a detailed analysis will be conducted to provide a fault diagnosis report.

[0090] S5. Operation and maintenance personnel can monitor the equipment status in real time through the user interface. When the system detects a potential fault or abnormal state, it will send an early warning message to the operation and maintenance personnel and provide a detailed description, location and solution of the equipment fault to help the operation and maintenance personnel to handle it in a timely manner.

[0091] The data quality is ensured by denoising, standardizing and normalizing the raw data collected by the sensor, and feature information such as temperature change trend, stress distribution, crack size, corrosion area, etc. is extracted from the sensor data and image data. Historical data and failure cases are used to train the deep learning model, so that the model can learn the relationship between the surface state of the equipment and the failure, infer the data collected in real time, predict the health status of the equipment, and determine whether there is a potential failure risk. Based on the diagnosis results, the system automatically generates a fault report and performs fault prediction, predicts the time of occurrence of the failure and the possible consequences, and provides decision support for operation and maintenance personnel. When the system determines that the equipment status is abnormal or there is a risk of failure, it automatically triggers an alarm and transmits the information to the operation and maintenance personnel.

[0092] This solution has the following working process: by integrating multiple sensors and image acquisition devices, it monitors the operating status of power grid equipment in real time, collects parameters such as temperature, humidity, stress, vibration and equipment surface image data. The system uses deep learning and machine learning algorithms to perform intelligent analysis on the collected data to achieve fault diagnosis and prediction, timely detect equipment abnormalities and issue early warnings. By identifying potential faults in advance, operation and maintenance personnel can perform preventive maintenance, reduce equipment downtime and maintenance costs, and improve the safety, reliability and operation efficiency of the power grid. At the same time, this system supports remote monitoring and data visualization, which further improves the work efficiency of operation and maintenance personnel, and provides data-driven decision support for power grid managers, promoting the intelligent and refined management of the power industry.

[0093] In summary: by collecting equipment surface status data (such as temperature, humidity, stress, vibration, etc.) and image data in real time, the present invention can provide comprehensive monitoring of the operating status of power grid equipment. By using a combination of sensors and image acquisition equipment, the system can quickly and accurately collect various parameters of power grid equipment and transmit the data to the data processing center in real time. Through high-precision sensors and high-definition camera equipment, the system can accurately identify subtle changes on the surface of the equipment, such as cracks, corrosion, overheating, etc., thereby avoiding possible missed detections and false detections in traditional manual detection methods, and ensuring the accuracy of equipment status monitoring; the system can automatically analyze the collected sensor data and image data, and perform intelligent diagnosis of equipment status through deep learning models. When the equipment status is abnormal, the model will quickly identify and give an Compared with the traditional method that relies on human experience and judgment, intelligent diagnosis is more accurate and efficient in determining the type of fault, and reduces the subjective bias of human judgment. In addition to fault diagnosis, the system can also predict possible equipment failures based on historical data and real-time data. This prediction can provide operation and maintenance personnel with the opportunity to intervene in advance, discover potential faults in advance, avoid sudden failures of power grid equipment during operation, and reduce downtime and maintenance costs. Through intelligent diagnosis and fault prediction, operation and maintenance personnel can perform preventive maintenance before equipment fails, thereby avoiding large-scale equipment downtime, extending the service life of equipment, and reducing maintenance costs. Through timely warning and accurate diagnosis of the type of fault, operation and maintenance personnel can accurately locate the problem and reduce unnecessary maintenance and replacement of parts.

Claims

1. An intelligent sensing system for the surface status of power grid equipment, characterized in that: include: Data acquisition module, used to collect surface status data of power grid equipment; The data processing module is responsible for processing and analyzing the data collected by the data acquisition module; Intelligent diagnosis module, which analyzes and compares the processed data and predicts faults; The remote monitoring and early warning module allows operators to quickly check the status of equipment and issue alarms and notifications for faults.

2. The intelligent sensing system for the surface status of power grid equipment according to claim 1, characterized in that: The data acquisition module comprises: Sensor array, including temperature sensor, humidity sensor, stress sensor, vibration sensor, corrosion sensor, used to obtain basic data on the surface status of power grid equipment in real time; Image acquisition equipment, including high-definition cameras and infrared cameras, is used to obtain high-resolution image data on the surface of power grid equipment, especially for capturing small cracks, corrosion marks, and overheating marks on the surface of equipment; The data transmission module transmits the data and image data collected by the sensor to the central processing unit through a wireless or wired communication network.

3. The intelligent sensing system for the surface status of power grid equipment according to claim 2, characterized in that: The data processing module comprises: The data fusion module is responsible for fusing the data obtained from the data acquisition module acquisition device; Image processing module, which analyzes the acquired image data through image processing algorithms to identify whether there are cracks, corrosion, dirt or overheating problems on the surface of the equipment; The feature extraction module extracts key features from the fused data, such as temperature change curve, stress distribution, size and shape of surface cracks, and corrosion area.

4. The intelligent sensing system for the surface status of power grid equipment according to claim 3 is characterized by: The intelligent diagnosis module comprises: Deep learning model: Use a deep learning-based classification model to analyze the processed data and identify potential fault types. The deep learning model can learn the association between the surface state of the equipment and the fault through the training data set, and predict whether the equipment is in a normal state or at risk of failure based on the current data; Anomaly detection and fault prediction, using anomaly detection algorithms, compare and analyze real-time data with historical data, identify whether there are abnormal fluctuations, and make fault predictions.

5. The intelligent sensing system for the surface status of power grid equipment according to claim 4, characterized in that: The remote monitoring and early warning module includes: The user interface provides a friendly monitoring interface for power operation and maintenance personnel, displaying real-time data, equipment health status, and historical data trend information, making it easy for operators to quickly check equipment status; The alarm system will automatically trigger an alarm and notify relevant maintenance personnel when a failure risk or abnormal equipment status is detected on the equipment surface, providing information on the type, location, and severity of the failure to support rapid handling of the failure.

6. An intelligent sensing method for the surface status of power grid equipment, characterized in that: The following steps are included S1. Collecting the surface status data of power grid equipment through the data acquisition module; S2, transmitting the collected data to the central processing unit and preparing for further data processing through the data processing module; S3. Through the data fusion module, data from different sources are integrated to ensure data consistency and integrity. Image data is analyzed through the image processing module. The processed data will pass through the feature extraction module to extract key equipment surface state features. S4. The results of data processing will be intelligently analyzed through the deep learning model to predict possible faults. The anomaly detection module will monitor data fluctuations in real time. When an abnormality is detected on the surface of the equipment, an alarm will be triggered and a detailed analysis will be conducted to provide a fault diagnosis report. S5. Operation and maintenance personnel can monitor the equipment status in real time through the user interface. When the system detects a potential fault or abnormal state, it will send an early warning message to the operation and maintenance personnel and provide a detailed description, location and solution of the equipment fault to help the operation and maintenance personnel to handle it in a timely manner.

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