Intelligent station building inspection system and inspection method based on distribution automation cloud master station

Abstract

The invention belongs to the technical field of intelligent station building inspection, and relates to an intelligent station building inspection system and method based on a distribution automation cloud master station. The system comprises a cloud platform, an edge computing node and terminal equipment, the terminal device collects video data and sensor data and then transmits the data to the cloud platform through the edge computing node; wherein the cloud platform comprises a unified video platform, a production management and control platform, a power distribution cloud master station, a service supply platform and a relaxation electric assistant; the unified video platform and the power distribution cloud master station respectively receive video data and sensor data acquired by the edge computing nodes; the unified video platform and the power distribution cloud master station are connected with the production management and control platform, the unified video platform and the power distribution cloud master station are connected through an internal network, the power distribution cloud master station is connected to the service supply platform, and the service supply platform is connected with the relaxation electric assistant. According to the invention, a three-level architecture of the cloud platform, the edge computing node and the terminal equipment is adopted, comprehensive intelligent inspection monitoring of the operation state of the station building equipment is realized, and safe and stable operation of a power distribution system is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent inspection of station buildings, and particularly relates to an intelligent inspection system and inspection method for station buildings based on a distribution automation cloud master station. Background Art

[0002] The intelligent auxiliary monitoring system for distribution substation buildings is a comprehensive monitoring solution integrating a variety of advanced technologies, aiming to improve the operation safety and management efficiency of distribution substation buildings. However, the existing intelligent auxiliary monitoring system for distribution substation buildings still has deficiencies: (1) The construction of system functions lacks unified technical standards. The intelligent auxiliary monitoring system for distribution substation buildings lacks unified function requirement specifications, data model standards, equipment model standards, etc., and has not achieved unified design and construction. There are significant differences between systems, and it is difficult to integrate and interoperate function modules.

[0003] (2) The construction of the system framework lacks a unified access mode. Due to differences in local construction conditions for the construction of the framework of the intelligent auxiliary monitoring system for distribution substation buildings, most station building auxiliary monitoring systems operate independently at a single station, and the processing and analysis of data are completed on local terminals. The distribution substation building system is in a distributed operation state; in some areas, taking advantage of the characteristics of the Internet, a main station system for regional station building monitoring systems is built on the external network, and a cloud platform for the intelligent auxiliary monitoring system of regional station buildings is realized through the public network.

[0004] (3) There are no means to ensure data security and network security. Most intelligent auxiliary monitoring systems for distribution substation buildings are deployed on the information external network, and monitoring data is uploaded to the monitoring main station system deployed on the information external network through the public network. The data transmission is not encrypted or authenticated, posing significant information security risks.

[0005] Therefore, the technical problem that the present invention urgently needs to solve is whether it is possible to provide an intelligent inspection system and inspection method that can improve the edge computing ability of the fusion terminal and realize real-time monitoring and management of the station building environment. Summary of the Invention

[0006] In view of this, the present invention provides an intelligent inspection system and inspection method for station buildings based on a distribution automation cloud master station.

[0007] To solve the above technical problems, the technical solution adopted by the present invention is: An intelligent inspection system for station buildings based on a distribution automation cloud master station includes a cloud platform, an edge computing node, and a terminal device connected in sequence; after the terminal device collects video data and sensor data, it is transmitted to the cloud platform through the edge computing node; Among them, the cloud platform includes a unified video platform, a production control platform, a power distribution cloud master station, a power supply service platform, and Yudian Assistant; the unified video platform and the power distribution cloud master station respectively receive the video data and sensor data collected by the edge computing nodes through a 4G / 5G private network; the unified video platform and the power distribution cloud master station are respectively connected to the production control platform through the intranet, and the unified video platform and the power distribution cloud master station are also connected through the intranet. The power distribution cloud master station is connected to the power supply service platform through the intranet, and the power supply service platform is connected to Yudian Assistant.

[0008] Further, the unified video platform includes a video monitoring module, a video analysis module, and an alarm management module. The video monitoring module is used to centrally manage all video monitoring devices and provide real-time video streaming and historical video playback functions; the video analysis module uses artificial intelligence technology to analyze video data and identify abnormal behaviors and events, including illegal intrusion and equipment failures; the alarm management module is used to automatically trigger an alarm and notify relevant personnel when an abnormal situation is detected. The production control platform includes a production scheduling module, an equipment management module, and an operation management module; among them, the production scheduling module is used to optimize production plans and scheduling to ensure the efficient operation of power production; the equipment management module is used to monitor the equipment status, record the equipment maintenance and repair history, and provide equipment health management functions; the operation management module is used to manage daily operation tasks to ensure the standardization and standardization of the operation process. The power distribution cloud master station includes a data acquisition module, a data storage module, a data analysis module, and a remote monitoring module; the data acquisition module is used to collect real-time sensor data from each substation, including electrical parameters and environmental parameters; the data storage module is used to centrally store a large amount of data and provide efficient data query and retrieval functions; the data analysis module is used to deeply analyze the sensor data using big data analysis technology to generate reports and prediction models; the remote monitoring module is used to provide remote monitoring and management functions through the Web or mobile applications, enabling operation and maintenance personnel to access the system status anytime and anywhere. The power supply service platform includes a customer service module, a service scheduling module, and a fault handling module. The customer service module provides a customer service platform to handle customer inquiries, complaints, and repair requests; the service scheduling module is used to optimize the scheduling of service resources to improve the service response speed and service quality; the fault handling module is used to quickly locate the cause of the fault and coordinate resources for fault handling and power restoration. Yudian Assistant provides a mobile application, which facilitates operation and maintenance personnel to access the system status and data anytime and anywhere. At the same time, it supports the management and execution of on-site operation tasks and realizes real-time communication between operation and maintenance personnel.

[0009] Further, the terminal device includes a monitoring camera, an environmental monitoring sensor, a device control device, and a device status monitoring sensor. The monitoring camera is used to collect video data of the substation building; the environmental monitoring sensor, the device control device, and the device status monitoring sensor are used to collect sensor data of the substation building.

[0010] Further, the edge computing node includes a display screen, a hard disk recorder, and an intelligent fusion terminal connected to the switch. Among them, the monitoring camera transmits the collected video data to the switch through the hard disk recorder; the switch displays the video data on the display screen, and at the same time, the switch transmits the received video data to the unified video platform through the intelligent fusion terminal.

[0011] Further, the edge computing node further includes a wireless data aggregation unit, and the wireless data aggregation unit transmits the collected sensor data to the power distribution cloud master station through the intelligent fusion terminal.

[0012] A method for intelligent inspection of substation buildings based on a power distribution automation cloud master station includes the following steps: S1. Collect video data and sensor data in the substation building; S11. Through the monitoring camera installed in the substation building, capture the video images inside the substation building in real time; S12. Deploy various types of sensors inside the substation building, including temperature sensors, humidity sensors, smoke sensors, and current sensors, to monitor environmental parameters and equipment operation status in real time; S2. Transmit the collected video data and sensor data to the edge computing node, and the edge computing node preprocesses the data, including compression, filtering, and preliminary analysis, to reduce the amount of transmitted data and improve the processing speed. The preprocessed data is transmitted to the cloud platform through a 4G / 5G private network; S3. The unified video platform in the cloud platform performs in-depth analysis on the received video data, and uses image recognition and behavior analysis in artificial intelligence technology to identify abnormal behaviors and events, including equipment failures; S4. The power distribution cloud master station in the cloud platform processes and analyzes the received sensor data, uses big data analysis technology to deeply mine the sensor data, generates reports and prediction models, and helps operation and maintenance personnel understand the equipment operation status and predict potential failures; S5. When abnormal situations are found through video analysis or sensor data analysis, an alarm mechanism will be automatically triggered, and the alarm information will be transmitted to the production control platform through the internal network, and relevant personnel will be notified through the power supply service platform and Yudian Assistant. The notification methods include text messages, emails, and mobile application push notifications to ensure that relevant personnel can receive the alarm information in a timely manner and take corresponding measures.

[0013] The beneficial effects of the present invention are as follows: The present invention adopts a three - level architecture of cloud platform, edge computing nodes and terminal devices to achieve comprehensive and intelligent inspection and monitoring of the operation status of station house equipment, ensuring the safe and stable operation of the power distribution system.

[0014] Among them, the terminal devices are responsible for collecting various key data in the station house, including video data and sensor data. The video data is obtained through cameras and is used to monitor the operation status of the equipment in the station house in real - time and whether there are abnormal personnel breaking in, etc. The sensor data covers various types, such as temperature sensors monitoring the temperature of key parts of the equipment, humidity sensors detecting the humidity environment in the station house, and various electrical sensors for monitoring electrical parameters (such as current, voltage, power, etc.). The terminal devices are distributed at various key positions in the station house, with high reliability and stability, and can adapt to complex industrial environments to ensure the accuracy and timeliness of data collection.

[0015] The edge computing nodes receive the data collected by the terminal devices and perform preliminary processing and analysis. On the one hand, it reduces the computing pressure on the cloud platform, and on the other hand, it improves the real - time performance of data processing. For example, the edge computing nodes can perform real - time analysis on the video data to determine whether there are abnormal behaviors (such as equipment smoking, sparking, etc.), and only transmit valuable information (such as abnormal alarm information) to the cloud platform. At the same time, it also acts as a bridge for data transmission, accurately transmitting the processed data to the cloud platform according to certain protocols and formats. The edge computing nodes have strong local computing capabilities and data caching capabilities, and can temporarily store data in special situations such as network failures and upload it after the network is restored to ensure the integrity of the data.

[0016] The unified video platform in the cloud platform, as an important part of the cloud platform, is responsible for receiving, storing and managing the video data from the edge computing nodes. It can achieve real - time monitoring and display of multiple screens, facilitating the operation and maintenance personnel to view the video images of multiple station houses at the same time. Through intelligent video analysis algorithms, it deeply analyzes the images in the video, which can not only detect abnormal behaviors, but also identify the appearance status of the equipment, such as whether there are damages on the equipment surface and whether the labels are clear. In addition, this platform also supports the historical playback function of video data, which is convenient for tracing and analyzing specific events afterwards.

[0017] In addition to the unified video platform, the cloud platform also includes a data storage module, a data analysis module, an alarm management module, a user management module, etc. The data storage module is responsible for long-term storage of sensor data and processed video analysis data for subsequent query and statistical analysis; the data analysis module uses big data analysis technology and machine learning algorithms to mine a large amount of historical data and extract valuable information, such as predicting the fault trend of equipment and evaluating the health status of equipment; the alarm management module issues alarm information in a timely manner for the analyzed abnormal data according to preset thresholds and rules to notify relevant operation and maintenance personnel for processing; the user management module is responsible for permission management and authentication of users using the cloud platform to ensure the security of the system and the confidentiality of data. The cloud platform has powerful computing and storage capabilities, can process data of large-scale substations, and realizes centralized management and sharing of data. Through cloud computing technology, resources can be flexibly expanded according to actual needs to meet the growing data processing and analysis requirements of the system.

[0018] The intelligent substation inspection system with the architecture described in this application has advantages such as high efficiency, real-time performance, intelligence, and scalability. Through the collaborative work of terminal devices, edge computing nodes, and the cloud platform, it can achieve all-round and intelligent inspection of substation equipment, timely discover potential safety hazards, improve the operation and maintenance efficiency and reliability of the power distribution system, and reduce operation and maintenance costs. Brief Description of the Drawings

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 It is a schematic diagram of the overall framework of the present invention. Detailed Embodiments

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and in no way limits the present application and its application or use. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0022] It should be noted that the terms used herein are for the purpose of describing specific embodiments only and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they specify the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0023] Unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions, and values set forth in these embodiments do not limit the scope of the present application. At the same time, it should be understood that, for the sake of convenience of description, the dimensions of the various parts shown in the drawings are not drawn in actual proportional relationships. Technologies, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the said technologies, methods, and devices should be regarded as part of the authorized specification. In all the examples shown and discussed herein, any specific value should be construed as merely exemplary and not as a limitation.

[0024] Therefore, other examples of the exemplary embodiments may have different values. It should be noted that like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0025] Embodiment As Figure 1 shown, an embodiment of the present invention provides a substation intelligent inspection system and inspection method based on a distribution automation cloud master station.

[0026] The substation intelligent inspection system based on the distribution automation cloud master station includes a cloud platform, an edge computing node, and a terminal device connected in sequence; after the terminal device collects video data and sensor data, it is transmitted to the cloud platform through the edge computing node.

[0027] The cloud platform includes a unified video platform, a production management and control platform, a distribution cloud master station, a power supply service platform, and a Yudian Assistant; the unified video platform and the distribution cloud master station respectively receive the video data and sensor data collected by the edge computing node through a 4G / 5G private network; the unified video platform and the distribution cloud master station are respectively connected to the production management and control platform through an intranet, and the unified video platform and the distribution cloud master station are also connected through an intranet. The distribution cloud master station is connected to the power supply service platform through an intranet, and the power supply service platform is connected to the Yudian Assistant.

[0028] The unified video platform includes a video monitoring module, a video analysis module, and an alarm management module. The video monitoring module is used to centrally manage all video monitoring devices and provide real-time video streaming and historical video playback functions; the video analysis module uses artificial intelligence technology to analyze video data and identify abnormal behaviors and events, including illegal intrusion and equipment failures; the alarm management module is used to automatically trigger an alarm and notify relevant personnel when an abnormal situation is detected.

[0029] The production control platform includes a production scheduling module, an equipment management module, and an operation management module. Among them, the production scheduling module is used to optimize production plans and scheduling to ensure the efficient operation of power production; the equipment management module is used to monitor the equipment status, record the equipment maintenance and repair history, and provide equipment health management functions; the operation management module is used to manage daily operation tasks to ensure the standardization and standardization of the operation process.

[0030] The distribution cloud master station includes a data acquisition module, a data storage module, a data analysis module, and a remote monitoring module. The data acquisition module is used to collect real-time sensor data from each substation building, including electrical parameters and environmental parameters; the data storage module is used to centrally store a large amount of data and provide efficient data query and retrieval functions; the data analysis module is used to deeply analyze the sensor data using big data analysis technology to generate reports and prediction models; the remote monitoring module is used to provide remote monitoring and management functions through Web or mobile applications, enabling operation and maintenance personnel to access the system status anytime and anywhere.

[0031] The power supply service platform includes a customer service module, a service scheduling module, and a fault handling module. The customer service module provides a customer service platform to handle customer inquiries, complaints, and repair requests; the service scheduling module is used to optimize the scheduling of service resources to improve service response speed and service quality; the fault handling module is used to quickly locate the cause of the fault and coordinate resources for fault handling and power restoration.

[0032] The Yudian Assistant provides a mobile application, which facilitates operation and maintenance personnel to access the system status and data anytime and anywhere, and at the same time supports the management and execution of on-site operation tasks, realizing real-time communication among operation and maintenance personnel.

[0033] The terminal device includes a monitoring camera, an environmental monitoring sensor, a device control device, and a device status monitoring sensor. The monitoring camera is used to collect video data of the substation building; the environmental monitoring sensor, the device control device, and the device status monitoring sensor are used to collect sensor data of the substation building.

[0034] The edge computing node includes a display screen, a hard disk recorder, and an intelligent fusion terminal connected to a switch. Among them, the monitoring camera transmits the collected video data to the switch through the hard disk recorder, and the connection method is an RJ45 network interface; the switch displays the video data on the display screen, and at the same time, the switch transmits the received video data to the unified video platform through the intelligent fusion terminal.

[0035] The edge computing node further includes a wireless data aggregation unit, and the wireless data aggregation unit transmits the collected sensor data to the power distribution cloud master station through the intelligent fusion terminal.

[0036] The embodiment of the present invention also provides a method for intelligent inspection of a substation building based on a power distribution automation cloud master station, including the following steps: S1. Collect video data and sensor data in the substation building; S11. Through the monitoring cameras installed in the substation building, capture the video images inside the substation building in real time; S12. Deploy various types of sensors inside the substation building, including temperature sensors, humidity sensors, smoke sensors, and current sensors, to monitor the environmental parameters and the operating status of equipment in real time; S2. Transmit the collected video data and sensor data to the edge computing node, and the edge computing node preprocesses the data, including compression, filtering, and preliminary analysis, to reduce the amount of transmitted data and improve the processing speed. The preprocessed data is transmitted to the cloud platform through a 4G / 5G private network; S3. The unified video platform in the cloud platform performs in-depth analysis on the received video data, and uses image recognition and behavior analysis in artificial intelligence technology to identify abnormal behaviors and events, including equipment failures; S4. The power distribution cloud master station in the cloud platform processes and analyzes the received sensor data, uses big data analysis technology to deeply mine the sensor data, and generates reports and prediction models to help operation and maintenance personnel understand the operating status of equipment and predict potential failures; S5. When abnormal situations are found through video analysis or sensor data analysis, the alarm mechanism will be automatically triggered, and the alarm information will be transmitted to the production control platform through the intranet, and relevant personnel will be notified through the power supply service platform and Yudian Assistant. The notification methods include text messages, emails, and mobile application push notifications, ensuring that relevant personnel can receive the alarm information in a timely manner and take corresponding measures.

[0037] The implementation principle of step S3 includes steps S31 - S35.

[0038] S31. Image recognition, identifying specific objects and scenes from the video data; First, train a convolutional neural network using a deep learning framework to achieve object detection and classification; use an object detection algorithm to identify specific objects in the video; use a face recognition algorithm to identify faces in the video and perform identity verification.

[0039] S32. Device fault detection, identifying the fault status of devices in the video; Use an image segmentation algorithm to segment the device area in the video data; extract the features of the device from the video frames, including color, shape, and texture, for fault detection; use classification algorithms, including support vector machine and random forest classification algorithms, etc., to classify the device status and identify normal and fault statuses.

[0040] S33. Real-time video analysis and alarm, promptly notifying relevant personnel to handle abnormal situations; Use a real-time stream processing framework to achieve real-time video data processing and analysis; when detecting abnormal behaviors or device faults, push notifications to relevant personnel in a timely manner via text messages, emails, or mobile applications.

[0041] S34. Data storage and management; Use a distributed storage system to achieve the storage and management of large-scale video data; use a relational database or a NoSQL database to store and manage video metadata.

[0042] S35. Visualization and report generation, presenting the analysis results to users in the form of visualization and reports; Use a reporting tool to generate a detailed analysis report, including data statistics, trend analysis, prediction results, etc.

[0043] The specific steps of step S4 include steps S41 - S46.

[0044] S41. Data preprocessing, successively through data cleaning, data standardization, and feature extraction, cleaning and organizing the collected sensor data to make it suitable for subsequent analysis.

[0045] S42. Through data analysis and modeling, generate reports and prediction models; Use statistical methods to analyze the distribution and trend of data, use machine learning algorithms to establish prediction models, and use a deep learning framework to establish neural network models for more accurate prediction.

[0046] S43. Timely detect abnormal situations during device operation. First, set the sensor data threshold, and trigger an alarm when the sensor data exceeds the threshold; then use the statistical method of box plot to detect outliers; based on machine learning methods, use clustering algorithms to detect abnormal data.

[0047] S44. Predict the future operating status and potential faults of the device; First, perform time series analysis, use the LSTM time series model to predict the change trend of future data; Then, perform regression analysis, use the linear regression method to establish a relationship model between input variables and output variables; Next, use the logistic regression classification algorithm to predict the fault type of the device.

[0048] S45. Visualization and report generation, use reporting tools to generate detailed analysis reports, including data statistics, trend analysis, and prediction results.

[0049] S46. Real-time monitoring and alarming, timely notify the operation and maintenance personnel to handle abnormal situations.

[0050] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: They can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; As is obvious to those skilled in the art, combinations of multiple technical solutions of the present invention are possible. And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A station house intelligent inspection system based on distribution automation cloud master station, characterized in that: It includes a cloud platform, an edge computing node and a terminal device connected in sequence; after the terminal device collects video data and sensor data, it transmits the data to the cloud platform through the edge computing node; The cloud platform includes a unified video platform, a production control platform, a distribution cloud master station, a supply and service platform and Henan Electricity Assistant; the unified video platform and the distribution cloud master station respectively receive the video data and sensor data collected by the edge computing node through the 4G / 5G private network; the unified video platform and the distribution cloud master station are respectively connected to the production control platform through the intranet, and the unified video platform and the distribution cloud master station are also connected through the intranet. The distribution cloud master station is connected to the supply and service platform through the intranet, and the supply and service platform is connected to the Henan Electricity Assistant.

2. According to the intelligent inspection system for station buildings based on the distribution automation cloud master station of claim 1, it is characterized in that: The unified video platform includes a video monitoring module, a video analysis module and an alarm management module. The video monitoring module is used to centrally manage all video monitoring devices and provide real-time video streaming and historical video playback functions; the video analysis module uses artificial intelligence technology to analyze video data and identify abnormal behaviors and events, including illegal intrusions and equipment failures; the alarm management module is used to automatically trigger an alarm and notify relevant personnel when an abnormal situation is detected; The production control platform includes a production scheduling module, an equipment management module and an operation management module; The production scheduling module is used to optimize production planning and scheduling to ensure efficient operation of power production; the equipment management module is used to monitor equipment status, record equipment maintenance and repair history, and provide equipment health management functions; the operation management module is used to manage daily operation tasks to ensure the normalization and standardization of operation processes; The power distribution cloud master station includes a data acquisition module, a data storage module, a data analysis module and a remote monitoring module; the data acquisition module is used to collect real-time sensor data from each station room, including electrical parameters and environmental parameters; the data storage module is used to centrally store massive data and provide efficient data query and retrieval functions; the data analysis module is used to use big data analysis technology to conduct in-depth analysis of sensor data and generate reports and prediction models; the remote monitoring module is used to provide remote monitoring and management functions through the Web or mobile applications, so that operation and maintenance personnel can access the system status anytime and anywhere; The service supply platform includes a customer service module, a service scheduling module and a fault handling module; the customer service module provides a customer service platform to handle customer inquiries, complaints and repair requests; the service scheduling module is used to optimize the scheduling of service resources, improve service response speed and service quality; the fault handling module is used to quickly locate the cause of the fault, coordinate resources to handle the fault and restore power supply; The Yudian Assistant provides a mobile application that allows operation and maintenance personnel to access system status and data anytime and anywhere, while supporting the management and execution of on-site work tasks and realizing real-time communication between operation and maintenance personnel.

3. According to the intelligent inspection system for station buildings based on the distribution automation cloud master station of claim 2, it is characterized in that: The terminal equipment includes a monitoring camera, an environmental monitoring sensor, an equipment control device and an equipment status monitoring sensor. The monitoring camera is used to collect video data of the station room; The environmental monitoring sensors, equipment control devices and equipment status monitoring sensors are used to collect sensor data of the station building.

4. A station house intelligent inspection system based on a distribution automation cloud master station according to claim 3, characterized in that: The edge computing node includes a display screen, a hard disk burner and an intelligent fusion terminal connected to a switch, wherein the surveillance camera transmits the collected video data to the switch through the hard disk burner; the switch displays the video data on the display screen, and the switch simultaneously transmits the received video data to the unified video platform via the intelligent fusion terminal.

5. A station house intelligent inspection system based on a distribution automation cloud master station according to claim 4, characterized in that: The edge computing node also includes a wireless data aggregation unit, which transmits the collected sensor data to the power distribution cloud master station via the intelligent fusion terminal.

6. A station house intelligent inspection method based on distribution automation cloud master station, characterized in that: The following steps are involved: S1, collect video data and sensor data in the station room; S11. Capture video images inside the station building in real time through surveillance cameras installed in the station building; S12. Deploy various types of sensors in the station building, including temperature sensors, humidity sensors, smoke sensors, and current sensors, to monitor environmental parameters and equipment operating status in real time; S2. The collected video data and sensor data are transmitted to the edge computing node. The edge computing node pre-processes the data, including compression, filtering and preliminary analysis, to reduce the amount of transmitted data and increase the processing speed. The pre-processed data is transmitted to the cloud platform via the 4G / 5G private network; S3. The unified video platform in the cloud platform conducts in-depth analysis of the received video data, using image recognition and behavior analysis in artificial intelligence technology to identify abnormal behaviors and events, including equipment failures; S4. The power distribution cloud master station in the cloud platform processes and analyzes the received sensor data, uses big data analysis technology to conduct in-depth mining of sensor data, generates reports and prediction models, and helps operation and maintenance personnel understand the equipment operation status and predict potential failures; S5. When video analysis or sensor data analysis finds an abnormal situation, the alarm mechanism will be automatically triggered. The alarm information will be transmitted to the production control platform through the intranet, and the relevant personnel will be notified through the supply and service platform and Yudian Assistant. The notification methods include SMS, email and mobile application push to ensure that the relevant personnel can receive the alarm information in time and take corresponding measures.

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