Power distribution network anti-external damage fault early warning method based on cloud edge cooperation

Abstract

The invention discloses a power distribution network external damage prevention fault early warning method based on cloud edge cooperation, and the method comprises the steps: S1, obtaining the environment information of distribution network equipment through an edge calculation terminal, and carrying out the preprocessing of the environment information, and obtaining the dangerous condition feature information representing the external damage condition of a distribution network; s2, acquiring state information and danger feature information acquired by the edge computing terminal corresponding to each monitoring point based on the unmanned aerial vehicle inspection path; s3, the cloud server stores the state information and the dangerous condition feature information in combination with the inspection path of the unmanned aerial vehicle to obtain a data scheduling sequence; s4, the monitoring platform analyzes and judges corresponding sequence bit data in the data scheduling sequence in combination with the equipment standard state database, and determines a fault type and a fault point of the corresponding distribution network equipment; the problem that the power distribution network fault supervision efficiency is low due to limitation of a traditional power distribution network fault monitoring method is solved, the fault type and the fault point are judged and positioned quickly and accurately, the power distribution network fault monitoring efficiency is improved, and the safety and the stability of a power system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network security, and specifically to a method for preventing external damage and fault warning of a distribution network based on cloud-edge collaboration. Background Art

[0002] With the continuous development of the power system, in order to ensure the stability and reliability of power supply, it is necessary to detect and warn of distribution network faults in a timely manner. The scale and complexity of the distribution network are increasing continuously, so the demand for remote monitoring and equipment protection is also getting higher and higher. Solving distribution network faults has become the key to ensuring the stability of power supply; however, the traditional distribution network fault monitoring system faces multiple restrictions such as geography, power supply, and network, and cannot meet the real-time monitoring requirements for distribution network operation and maintenance equipment in remote, complex terrains or remote areas, and cannot timely judge the fault types and the location of fault points caused by the external environment.

[0003] Chinese Patent, Publication No.: CN112634590B, Publication Date: October 31, 2023, discloses a method and device for detecting faults in a substation area based on vision recognition technology, including: obtaining image information of a target area; detecting the obtained image information according to a preset learning model to obtain the position information and status information of a target device; judging the position information and the status information to determine whether the position information and the status information meet the preset warning conditions to obtain an alarm message; in response to the obtained alarm message, sending the alarm message to a terminal; this invention can detect the status of a target device in real time based on deep learning, and actively send an alarm message to a terminal after a fault is found, achieving the intelligent detection ability for faults in a substation area and improving the efficiency of detecting the status of distribution network equipment and the accuracy of identifying the fault location; however, this invention mainly proposes a method for judging the fault situation in a substation area by obtaining image information of a target area and performing a series of decoding and analysis processes on the image information, which has requirements for the network environment and cannot be applied to the monitoring requirements in complex terrains or remote areas. Summary of the Invention

[0004] The object of the present invention is to address the problem that the traditional distribution network fault monitoring method has limitations, resulting in low efficiency of distribution network fault supervision. A distribution network external damage prevention and fault warning method based on cloud-edge collaboration is designed. The edge computing terminal collects various environmental monitoring information of distribution network equipment, preprocesses the environmental information to obtain danger characteristic information representing the external damage situation of the distribution network, and collects the status information of distribution network equipment through the cruise path set by the unmanned aerial vehicle. At the same time, the danger characteristic information is sent to the cloud server for storage and processing to obtain a data scheduling sequence. The monitoring platform analyzes and judges the data corresponding to the sequence bits in the data scheduling sequence in combination with the equipment standard status database to determine the fault type and fault point of the corresponding distribution network equipment. It overcomes the problem that the traditional distribution network fault monitoring method has limitations, resulting in low efficiency of distribution network fault supervision, quickly and accurately judges and locates the fault type and fault point, monitors the environmental status of distribution network equipment in real time, can give early warnings in time when the distribution network equipment is damaged by the outside world, gives the response time for the staff to formulate corresponding fault solution strategies, and improves the equipment safety.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is: a distribution network external damage prevention and fault warning method based on cloud-edge collaboration, including the following steps: S1. The edge computing terminal obtains the environmental information of the distribution network equipment, and preprocesses the environmental information to obtain danger characteristic information representing the external damage situation of the distribution network; S2. Based on the unmanned aerial vehicle inspection path, obtain the status information and danger characteristic information obtained by the corresponding edge computing terminal at each monitoring point; S3. The cloud server stores the obtained status information and danger characteristic information in combination with the inspection path of the unmanned aerial vehicle to obtain a data scheduling sequence; S4. The monitoring platform analyzes and judges the data corresponding to the sequence bits in the data scheduling sequence in combination with the equipment standard status database to determine the fault type and fault point of the corresponding distribution network equipment.

[0006] Preferably, the environmental information includes danger correlation information of the environment where the distribution network equipment is located and video information of the position space of the distribution network equipment. Among them, the danger correlation information includes temperature information, humidity information, wind force information, sound information, smoke information, and fire information.

[0007] Preferably, the unmanned aerial vehicle inspection path is set according to the real-time positioning information and inspection frequency of the monitoring points, and is updated and corrected in real time according to the status information of the distribution network equipment.

[0008] In this solution, the edge computing terminal collects and uploads the environmental information of all devices within the management domain. The video information can intuitively display the operating status and fault conditions of the devices. The various risk-related information and image information are analyzed and matched to obtain risk characteristic information indicating the situation of external damage to the distribution network. Among them, the risk characteristic information includes risk-related information and the corresponding image information, so as to facilitate the monitoring platform to intuitively and clearly judge the current environmental situation of the devices, and then judge the type of device faults. The cruising path set by the drone can quickly and accurately locate the faulty devices, which helps the staff to make timely response and handling; uploading the collected environmental information to the cloud server for storage can reduce the load pressure of the monitoring platform on data storage. The monitoring platform can directly call the data from the cloud server. Here, a wireless transmission method is used, which can ensure that the real-time collected information is cloud-synchronized to the cloud server, avoiding data loss due to network delays and other reasons, and ensuring the timeliness, stability and reliability of the data; when detecting device faults, with the assistance of image information, the overall environmental status of the management domain area can be seen more comprehensively, which is beneficial to predicting and monitoring in advance the situation of devices being damaged by the outside world within a certain range. In this way, countermeasures can be taken in advance to resist and block the environmental damage factors, effectively reducing or directly avoiding device failures, improving the power supply reliability and security, and improving the efficiency of distribution network fault monitoring.

[0009] Preferably, the S1 includes the following sub-steps: S11. The risk-related information and video information of the distribution network devices collected by the environmental monitor are sent to the edge computing terminal. The edge computing terminal cleans and sorts out the risk-related information to obtain the first risk identification information; S12. Based on the first risk identification information, the video information is processed to obtain the second risk identification information, and the second risk identification information includes multiple picture frame information; S13. Based on the first risk identification information and the corresponding second risk identification information, risk characteristic information indicating the situation of external damage to the distribution network is constructed.

[0010] Preferably, the S12 includes the following sub-steps: S121. The video information is processed based on image processing technology to obtain multiple picture frame information; S122. An image data table for storing and managing the picture frame information and the corresponding time information is established. Based on the time data of the first risk identification information, the image data table is queried and matched to obtain the image information corresponding to the first risk identification information, and the image information is used as the second risk identification information.

[0011] In this solution, by processing the collected video information through image processing technology, a lot of frame image information can be obtained. Each frame of image information corresponds to a moment. By matching the time information of the first danger identification information with the moment information of the image frame, the environmental conditions of the space position where the network configuration device is located can be clearly observed through the image frame information, the first danger identification information can be verified, and it can also comprehensively and accurately assist the staff in judging the device failure state and failure type, improving the monitoring efficiency of the distribution network and enhancing the safety prevention mechanism of the distribution network.

[0012] Preferably, the S13 includes the following sub-steps: S131. The monitoring platform sends a control instruction to the drone, and the drone arrives at the monitoring point to obtain real-time positioning information; S132. Search for and match the inspection path based on the real-time positioning information, and actively activate the communication interface of the edge computing terminal according to the search and match results to obtain a data channel for data interaction with it; S133. The edge computing terminal sends the danger feature information to the drone through the data channel.

[0013] In this solution, the drone uploads the danger feature information of the distribution network device analyzed and processed by the edge computing terminal to the cloud server for storage and management, which can improve the security and integrity of the monitoring data, and then improve the monitoring efficiency of the distribution network device failure; it avoids problems such as the collected monitoring data cannot be sent to the monitoring platform for analysis and processing in time due to poor network signals, resulting in the inability to discover equipment failure hidden dangers in time. The danger feature information can be stored in the cloud server for a long time, which is convenient for tracing and analyzing the historical operation and failure conditions of the equipment, and this helps to conduct long-term health management and performance optimization of the equipment.

[0014] Preferably, the S2 includes the following sub-steps: S21. Analyze the real-time state information of the distribution network device based on the regional distribution network topology, and obtain the danger feature data of all distribution network devices within the management domain by using the edge computing terminal as a node; S22. Send the state information and the danger feature information to the cloud server for storage and management through the wireless transmission channel of the drone.

[0015] In this solution, by analyzing the real-time state information of the distribution network device, the state of the device can be preliminarily evaluated, and the evaluation includes the load condition, operation efficiency, failure condition, etc. of the device, which is convenient for the monitoring platform to discover device anomalies in time according to the device state information.

[0016] Preferably, the S3 includes the following sub-steps: Divide the failure types according to the state information of the distribution network device; Generate a data scheduling sequence based on the correspondence between the critical feature information of the distribution network equipment and the fault type.

[0017] Preferably, the S4 includes the following sub-steps: S41. Obtain the data scheduling sequence in the cloud server and perform comparison and analysis based on the standard status database of the distribution network equipment in turn to judge the fault status of the distribution network equipment. If the fault status is abnormal, execute S42; if the fault status is normal, continuously monitor the operation status of the power distribution equipment. S42. Determine the management domain where the distribution network equipment is located according to the inspection path of the UAV, and determine the fault type and fault point of the distribution network equipment according to the critical feature information.

[0018] In this solution, by analyzing the real-time status information of the distribution network equipment, the status of the equipment can be preliminarily evaluated, including the load condition, operation efficiency, fault condition, etc. of the equipment, which is convenient for the monitoring platform to timely discover equipment anomalies according to the equipment status information; if the equipment status is abnormal, the monitoring platform can issue a warning to notify the operation and maintenance personnel to deal with it in time. At the same time, the system can also automatically handle some simple faults according to the preset rules to ensure the stable operation of the distribution network.

[0019] Preferably, determining the fault type and fault point of the distribution network equipment in S42 further includes the following sub-steps: Judge the fault type of the distribution network equipment according to the first critical situation identification information; Further verify the fault type based on the picture frame information in the second critical situation identification information, and locate the specific location of the distribution network equipment fault through the comparison and analysis of the time data of the picture frame information and the time data of the status information.

[0020] In this solution, by observing the picture frame information, the first critical situation identification information can be verified, the type and degree of equipment faults can be judged more accurately, and a more comprehensive basis for fault judgment can be provided. Potential faults can also be discovered and processed in time through the picture frame information to avoid equipment damage or accidents.

[0021] Preferably, the environmental monitor is powered by a photovoltaic power generation device.

[0022] In this solution, the photovoltaic power generation device provides power for the environmental monitor, so that the environmental monitor is no longer restricted by external power sources, realizing independent power supply. This not only enhances the applicability of the monitoring system in scenarios such as remote areas or lack of power and other infrastructure, but also reduces the operation and maintenance costs and improves the work efficiency of data collection.

[0023] The beneficial effects of the present invention: 1. By analyzing and matching various crisis-related information and video image information collected by the environmental monitor, and uploading the matched crisis-related information and image information as crisis feature information to the cloud server for storage, it is convenient for the monitoring platform to call and for the later maintenance and management of data, providing real-time, safe, reliable, and stable data support for the distribution network fault monitoring, helping to timely detect and locate faults, and improving the power supply reliability and safety. 2. The photovoltaic power generation device provides power for the environmental monitor, enabling the environmental monitor to be no longer restricted by external power sources, achieving independent power supply. This not only enhances the applicability of the monitoring system in scenarios such as remote areas or lacking power and other infrastructure, but also reduces the operation and maintenance costs and improves the work efficiency of data collection. 3. Through the video information of the management domain, based on image processing technology, the video information is cut and feature extracted. After obtaining the image information, the entire environmental status of the management domain area can be seen more comprehensively, which is conducive to predicting in advance and real-time monitoring the situation of equipment being damaged by the outside world within a certain range, conducive to making countermeasures in advance, resisting and blocking environmental damage factors, effectively reducing or directly avoiding equipment failures, improving the power supply reliability and safety, and improving the distribution network fault monitoring efficiency. Description of the Drawings

[0024] By reading the detailed description of the non-restrictive embodiments with reference to the following drawings, other features, purposes, and advantages of the present invention will become more obvious. The drawings are only for the purpose of showing the preferred embodiments and are not considered as limiting the present invention. Moreover, throughout the drawings, the same reference signs are used to represent the same components.

[0025] Figure 1 It is a method flow chart of a method for preventing external damage and fault warning of a distribution network based on cloud-edge collaboration according to an embodiment of the present invention. Detailed Embodiments

[0026] To make the purpose, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only the best embodiments of the present invention, only used to explain the present invention, and do not limit the protection scope of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0027] Embodiment 1: As Figure 1 shown, the method for preventing external damage and fault warning of a distribution network based on cloud-edge collaboration includes steps S1 - S4, where: S1. The edge computing terminal obtains the environmental information of the distribution network equipment, and preprocesses the environmental information to obtain crisis feature information representing the external damage situation of the distribution network.

[0028] Specifically, the S1 includes the following sub-steps: S11. Upload the crisis-related information and video information of the distribution network equipment collected by the environmental monitor to the edge computing terminal. The edge computing terminal preprocesses the crisis-related information, including data cleaning, sorting, and standardization, to ensure the accuracy and consistency of the data, and obtains the first crisis identification information. S12. Process the video information based on the first crisis identification information to obtain the second crisis identification information including multiple picture frame information. S13. Construct crisis feature information representing the external damage situation of the distribution network based on the first crisis identification information and the corresponding second crisis identification information.

[0029] Further, the S12 includes the following sub-steps: S121. Process the video information based on image processing technology to obtain multiple picture frame information. S122. Establish an image data table for storing and managing the picture frame information and the corresponding time information. Query and match the image data table based on the time data of the first crisis identification information to obtain the image information corresponding to the first crisis identification information, and use the image information as the second crisis identification information.

[0030] In this embodiment, by collecting the video information of the distribution network equipment and obtaining the picture frame information to judge the type of equipment failure, it has the advantages of intuitiveness, comprehensiveness, real-time, reliability, and traceability, which helps to improve the accuracy and efficiency of equipment failure judgment; the video information can intuitively display the operation status and failure conditions of the equipment. By observing the picture frame information in the video, the type and degree of equipment failure can be judged more accurately. Compared with single sensor data, the video information can more comprehensively reflect the operation conditions and failure characteristics of the equipment; by real-time monitoring the video information of the equipment, potential failures can be discovered and processed in time to avoid equipment damage or accidents; the degree of automation of video information collection and processing is relatively high, which can reduce the influence of human factors on failure judgment; at the same time, through computer vision and image processing technology, more accurate and reliable failure judgment can be made on the video information. The video information can also be stored for a long time, which is convenient for tracing and analyzing the historical operation conditions and failure conditions of the distribution network equipment, and this helps to conduct long-term health management and performance optimization of the equipment.

[0031] It can be understood that the video information collected by the machine is digitally processed through image processing technology to achieve various functions, such as enhancement, analysis, compression, and reconstruction, etc., to obtain image information. The image information includes several video frames (stored in the format of picture frames here), which is helpful for uploading to the cloud server for storage and invocation. Among them, digital image processing technology can be used to process the video, which is beneficial to monitor the power distribution equipment. Digital image processing technology is a method and technology that processes images such as denoising, enhancement, restoration, segmentation, and feature extraction through a computer. First of all, the basic principle of digital image processing is to convert the image into a digital signal, and then process and analyze these digital signals. This conversion process can be achieved through devices such as scanners and cameras, or through digital photography and other methods. Secondly, digital image processing technology utilizes the basic principles of digital signal processing, such as filtering and transformation, to process the image for denoising, enhancement, restoration, etc. Among them, filtering is a common digital signal processing technology that can be used to reduce noise and interference in the image. The transformation technology can convert the image from the spatial domain to the frequency domain, so as to better analyze and process the image. In addition, digital image processing technology also involves some special algorithms and technologies, such as image compression coding, pattern recognition, etc. These algorithms and technologies can further improve the effect and efficiency of image processing.

[0032] Specifically, the environmental information includes the danger-related information of the environment where the power distribution equipment is located and the video information of the position space of the power distribution equipment. Among them, the danger-related information includes temperature information, humidity information, wind force information, sound information, smoke information, and fire information.

[0033] Further, the S13 includes the following sub-steps: S131. The monitoring platform sends a control instruction to the drone, and the drone arrives at the monitoring point to obtain real-time positioning information. S132. Search for and match the inspection path based on the real-time positioning information, and actively activate the communication interface of the edge computing terminal according to the search and match results to obtain a data channel for data interaction between the drone and the edge computing terminal. S133. The edge computing terminal sends the danger feature information to the drone through the data channel.

[0034] In this embodiment, the UAV uploads the danger characteristic information of the distribution network equipment obtained by the edge computing terminal analysis and processing to the cloud server for storage and management, which can improve the security and integrity of the monitoring data, and then improve the fault monitoring efficiency of the distribution network equipment; it avoids problems such as the collected monitoring data not being able to be sent to the monitoring platform for analysis and processing in time due to poor network signals, resulting in the inability to detect equipment fault hidden dangers in time; the transmission of equipment environmental monitoring data by the UAV can also avoid personnel from performing high-altitude dangerous operations, and at the same time reduce the safety risks of personnel on site. By remotely monitoring and collecting equipment danger characteristic data, it avoids the problem of data loss caused by the environmental information monitoring device being damaged by the external environment. For example, the overheating point in the wire may cause a fire if not solved. Therefore, the UAV not only makes the operation of electric power workers safer, but also maintains the safety of the surrounding community.

[0035] Specifically, the UAV inspection path is set according to the real-time positioning information of the monitoring points and the inspection frequency, and is updated and corrected in real time according to the status information of the distribution network equipment.

[0036] In this embodiment, the environmental monitor includes a sound collector, a smoke collector, a fire detector, etc. to collect information on factors such as sound, temperature, and humidity in the environment where the power equipment is located. These devices can be used alone or in combination to provide more comprehensive and accurate environmental information. Combining the image information collected by the camera device to judge whether there is danger in the environment where the equipment is located and the type of fault that has occurred to the equipment; for example, when the image information determines that a foreign object has entered the danger warning area; further combine the sound information to judge whether the equipment is damaged, and combine the sound information to obtain the accompanying image information, and package the sound information and the accompanying image information as the danger characteristic information; further, when the smoke collector and the fire detector obtain the corresponding warning information, the sound information, the accompanying image information, and the warning information are used as the danger characteristic information. According to the danger characteristic information, accurately judge the equipment fault situation. The inspection path is set in the UAV. When the monitoring platform detects that a device has a fault, it can automatically locate the fault point quickly and accurately according to the inspection path.

[0037] It can be understood that the smoke collector can use devices such as a smoke alarm or an air quality detector to collect smoke information in the environment. These devices can detect indicators such as the smoke concentration and particulate matter in the air to help the staff discover fire hazards in time.

[0038] S2. Obtain the status information and danger characteristic information obtained by the corresponding edge computing terminal for each monitoring point based on the UAV inspection path.

[0039] Specifically, the S2 includes the following sub-steps: S21. Analyze the real-time status information of distribution network equipment based on regional distribution network topology, and obtain the crisis characteristic data of all distribution network equipment within the management domain by using the edge computing terminal as a node; S22. The drone uploads the status information and the crisis characteristic information obtained from the edge computing terminal to the cloud server for storage and management through a wireless transmission channel.

[0040] In this embodiment, by analyzing the real-time status information of distribution network equipment, the status of the equipment can be preliminarily evaluated. The evaluation includes the load condition, operation efficiency, fault condition, etc. of the equipment, which is convenient for the monitoring platform to timely detect abnormal conditions of the equipment according to the equipment status information; if the equipment status is abnormal, the monitoring platform can issue a warning to notify the operation and maintenance personnel to handle it in time. At the same time, the system can also automatically handle some simple faults according to the preset rules to ensure the stable operation of the distribution network; among them, the status information of distribution network equipment obtained based on regional distribution network topology analysis can go through the following steps: (1) Collect the status data of equipment in the distribution network, which can be realized through various sensors and monitoring systems, including voltage, current, power factor, active power, reactive power, etc.; (2) The collected data needs to be preprocessed, including data cleaning, sorting, and standardization to ensure the accuracy and consistency of the data; (3) Conduct regional distribution network topology analysis based on the collected data, including analyzing the topological structure of equipment in the distribution network to understand the connection relationship and operation status of the equipment; (4) Evaluate the status of the equipment according to the results of regional distribution network topology analysis, including the load condition, operation efficiency, fault condition, etc. of the distribution network equipment; (5) According to the status of the distribution network equipment and the results of regional distribution network topology analysis, a reasonable data scheduling sequence can be generated, which can help the operation and maintenance personnel better arrange the operation and maintenance plan of the distribution network equipment; The analysis of the status of distribution network equipment can be combined with various methods, such as signal processing technology, pattern recognition, neural network, etc. to improve the accuracy and reliability of judgment. And when analyzing the status of distribution network equipment, the overall status of the equipment needs to be considered, including the operation history, maintenance records, fault history, etc. of the equipment to comprehensively judge the health status of the equipment.

[0041] S3. The cloud server combines the inspection path of the drone to store the obtained status information and crisis characteristic information to obtain a data scheduling sequence, where: Divide the fault types according to the status information of the distribution network equipment; Generate a data scheduling sequence based on the corresponding relationship between the crisis characteristic information of the distribution network equipment and the fault types.

[0042] In this embodiment, the characteristics of the fault are analyzed based on the collected device status information. For example, if there are abnormal voltage or current fluctuations in the device, it may indicate an electrical fault in the device; if there are abnormal situations such as too high or too low temperature in the device, it may indicate a mechanical fault in the device. If there is an electrical fault in the device, it may be a circuit breaker fault, a line fault, a transformer fault, etc.; if there is a mechanical fault in the device, it may be bearing wear, gear wear, lubrication system fault, etc. By discriminating through the critical situation characteristic information, the type of device fault can be further determined. For example, if a line fault occurs, the specific situation of the line fault can be verified through the image information in the critical situation characteristic information. In addition, the fault type is divided according to the status information of the distribution network equipment, and then corresponding maintenance plans and measures can be formulated in advance according to the fault type, which helps to provide countermeasures in time when an emergency occurs in the distribution network equipment, and further efficiently maintain the safety and stability of the distribution network.

[0043] S4. The monitoring platform analyzes and judges the data corresponding to the sequence bits in the data scheduling sequence in combination with the device standard status database to determine the fault type and fault point of the corresponding distribution network equipment.

[0044] Specifically, the S4 includes the following sub-steps: S41. Obtain the data scheduling sequence in the cloud server and perform comparison and analysis based on the distribution network equipment standard status database in turn to judge the fault status of the distribution network equipment. If the fault status is abnormal, execute S42; if the fault status is normal, continuously monitor the operation status of the distribution equipment, where: According to the comparison and analysis result of the data scheduling sequence and the distribution network equipment standard status database, judge whether the electrical signal of the distribution network equipment is abnormal. For example, if the electrical signal value fluctuates or exceeds the normal value range, it is determined that the electrical signal is abnormal, and then the distribution equipment has a fault; S42. Determine the management domain where the distribution network equipment is located according to the inspection path of the unmanned aerial vehicle, and determine the fault type and fault point of the distribution network equipment according to the critical situation characteristic information.

[0045] Further, the sub-steps of determining the fault type and fault point of the distribution network equipment in S42 also include: initially judge the fault type of the distribution network equipment according to the first critical situation identification information. For example, if the temperature of the distribution network equipment is too high and smoke information is collected, it means that there may be a fire in the environment where the distribution network equipment is located; Based on the picture frame information in the second critical situation identification information, further verify the fault type. By using the characteristics of the picture frame information that can clearly observe the environmental conditions of the spatial position where the distribution network equipment is located, further judge the fault type that occurs in the distribution network equipment, and then compare and analyze the time data of the current picture frame information with the time data of the current status information of the distribution network equipment, and accurately locate the distribution network equipment in the management domain through the coincident time data.

[0046] In this embodiment, a lot of frame picture information can be obtained by processing video information. Each frame of picture information corresponds to a moment. By matching the time information of the first danger identification information with the moment information of the picture frame, the environmental conditions of the space where the network configuration device is located can be clearly observed through the picture frame information, and the first danger identification information can be verified. By observing the picture frame information, the type and degree of equipment failure can be judged more accurately, providing a more comprehensive basis for fault judgment. Potential faults can also be discovered and processed in time through the picture frame information, avoiding equipment damage or accidents, improving the monitoring efficiency of the distribution network, and enhancing the safety prevention mechanism of the distribution network.

[0047] Specifically, the environmental monitor is powered by a photovoltaic power generation device.

[0048] In this embodiment, the photovoltaic power generation device can use solar power supply technology. Solar energy is a renewable energy source that is inexhaustible for humans, with advantages such as sufficient cleanliness, absolute safety, relative universality, definite long life and maintenance-free, resource adequacy, and potential economy. Using solar power supply can make the environmental monitoring equipment no longer restricted by external power sources, especially suitable for remote areas or scenarios that require long-term operation.

[0049] The above specific implementation manners are the preferred implementation manners of the present invention, and do not limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation manner. Any equivalent changes made according to the shape, structure, and method of the present invention are within the protection scope of the present invention.

Claims

1. A method for preventing external damage and fault warning of a distribution network based on cloud-edge collaboration, characterized in that: It includes the following steps: S1. The edge computing terminal obtains the environmental information of the power distribution equipment, preprocesses the environmental information to obtain the danger characteristic information indicating the external damage situation of the power distribution network; S2. Based on the UAV inspection path, obtain the status information and danger characteristic information obtained by the corresponding edge computing terminal for each monitoring point; S3. The cloud server combines the UAV inspection path to store the obtained status information and danger characteristic information to obtain a data scheduling sequence; S4. The monitoring platform combines the equipment standard status database to analyze and judge the data corresponding to the sequence bits in the data scheduling sequence to determine the fault type and fault point of the corresponding power distribution equipment.

2. The method for early warning of external damage prevention failure of a distribution network based on cloud-edge collaboration according to claim 1, wherein: The environmental information includes the danger-related information of the environment where the power distribution equipment is located and the video information of the location space of the power distribution equipment. Among them, the danger-related information includes temperature information, humidity information, wind force information, sound information, smoke information, and fire information.

3. The method for preventing external damage and fault warning of a distribution network based on cloud-edge collaboration according to claim 1, characterized in that: The S1 includes the following sub-steps: S11. The environmental monitor sends the collected danger-related information and video information of the power distribution equipment to the edge computing terminal. According to the edge computing terminal, the danger-related information is cleaned and sorted to obtain the first danger identification information; S12. Based on the first danger identification information, process the video information to obtain the second danger identification information, and the second danger identification information includes multiple picture frame information; S13. Based on the first danger identification information and the corresponding second danger identification information, construct the danger characteristic information indicating the external damage situation of the power distribution network.

4. The method for preventing external damage and fault warning of a distribution network based on cloud-edge collaboration according to claim 3, wherein: The S12 includes the following sub-steps: S121. Process the video information based on image processing technology to obtain multiple picture frame information; S122. Establish an image data table for storing and managing the picture frame information and the corresponding time information. Query and match the image data table based on the time data of the first danger identification information to obtain the image information corresponding to the first danger identification information, and use the image information as the second danger identification information.

5. The method for preventing external damage fault warning of a distribution network based on cloud-edge collaboration according to claim 3, wherein: The S13 includes the following sub-steps: S131. The monitoring platform sends a control command to the UAV, and the UAV arrives at the monitoring point to obtain real-time positioning information; S132. Search for and match the inspection path based on the real-time positioning information, and actively activate the communication interface of the edge computing terminal according to the search and match results to obtain a data channel for data interaction with it; S133. The edge computing terminal sends the danger characteristic information to the UAV through the data channel.

6. The method for early warning of external damage prevention faults in a distribution network based on cloud-edge collaboration according to claim 3, wherein: The S2 includes the following sub-steps: S21. Analyze the real-time status information of the power distribution equipment based on the regional power distribution topology, and obtain the danger characteristic data of all power distribution equipment in the management domain with the edge computing terminal as a node; S22. Send the status information and the danger characteristic information to the cloud server for storage and management through the wireless transmission channel of the UAV.

7. The method for preventing external damage fault warning of a distribution network based on cloud-edge collaboration according to claim 3, characterized in that: The S3 includes the following sub-steps: Divide the fault types according to the status information of the power distribution equipment; Generate a data scheduling sequence based on the corresponding relationship between the danger characteristic information of the power distribution equipment and the fault types.

8. The method for preventing external damage and fault warning of a distribution network based on cloud-edge collaboration according to claim 7, characterized in that: The S4 includes the following sub-steps: S41. Obtain the data scheduling sequence in the cloud server and perform comparison and analysis based on the standard status database of distribution network equipment in sequence to judge the fault status of the distribution network equipment. If the fault status is abnormal, execute S42; if the fault status is normal, continuously monitor the operation status of the distribution equipment. S42. Determine the management domain where the distribution network equipment is located according to the inspection path of the UAV, and determine the fault type and fault point of the distribution network equipment according to the danger feature information.

9. The method for preventing external damage and fault warning of a distribution network based on cloud-edge collaboration according to claim 7, characterized in that: In S42, determining the fault type and fault point of the distribution network equipment further includes the following sub-steps: Judge the fault type of the distribution network equipment according to the first danger identification information; Further verify the fault type based on the picture frame information in the second danger identification information. By comparing and analyzing the time data of the picture frame information with the time data of the status information, locate the specific position of the fault of the distribution network equipment.

10. The method for preventing external damage and fault warning of a distribution network based on cloud-edge collaboration according to claim 1, wherein: The inspection path of the UAV is set according to the real-time positioning information of the monitoring points and the inspection frequency, and is updated and corrected in real time according to the status information of the distribution network equipment.

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