Intelligent inspection device and method for power grid

Abstract

The invention discloses an intelligent inspection device and method for a power grid, and relates to the technical field of power grid inspection, and the device comprises an inspection vehicle, a control module located on the inspection vehicle, and an inspection unmanned aerial vehicle module carried on the inspection vehicle. When an operation instruction of an inspector is received, the control module controls the unmanned aerial vehicle to inspect the power grid; the inspection unmanned aerial vehicle module comprises an unmanned aerial vehicle and a data acquisition unit mounted on the unmanned aerial vehicle; a power grid is inspected through the unmanned aerial vehicle, and after comprehensive state data of the power grid are collected, the comprehensive state data are sent to the control module. According to the invention, automation and intelligentization of power grid inspection are realized, manual operation is reduced, inspection efficiency and accuracy are improved, and safe storage and effective utilization of data are ensured.

Description

Technical Field

[0001] The present application relates to the technical field of power grid inspection, and in particular to an intelligent inspection device and method for a power grid. Background Art

[0002] As a vital infrastructure in modern society, the power grid undertakes the critical task of transmitting and distributing electrical energy. Its safe and stable operation is directly related to the normal operation of social production and life. The power lines and power towers within the power grid are crucial components of the grid, and their operating status directly affects the reliability and stability of the entire grid. Therefore, regular inspection and maintenance of power lines and power towers to promptly detect and address potential faults is a crucial measure to ensure the safe operation of the power grid.

[0003] Currently, power grid inspections are primarily manual. Relevant personnel, carrying various inspection equipment, visit power grid sites at predetermined intervals to inspect power lines and power towers one by one, maintaining a paper-based inspection log. However, with the continuous expansion of power grids, the rapid increase in the number of devices, and the increasing level of intelligence, the drawbacks of manual inspections are becoming increasingly apparent. Firstly, manual inspections require inspectors to transport a large amount of inspection equipment to the site. This cumbersome process, involving numerous devices and requiring long operation times, is not only time-consuming and labor-intensive, but also prone to incomplete inspections due to negligence by inspectors, making it difficult to accurately and timely identify line faults and thus impacting the normal operation of the power grid. Secondly, inspection results are recorded on paper. As data continues to grow, effective data analysis and statistics become difficult, and data loss and incomplete records are prone to occur. Furthermore, the inspection process relies entirely on the discretion of inspectors, lacking effective oversight measures. This can lead to falsification of inspection data, compromising the quality and efficiency of inspections.

[0004] Therefore, there is an urgent need for a device and method for intelligent power grid inspection to solve the problems of traditional manual inspection, such as time-consuming and labor-intensive inspection, difficulty in preserving inspection data, and lack of supervision during the inspection process, so as to improve the efficiency and quality of power grid inspection work and ensure the safe and stable operation of the power grid. Summary of the Invention

[0005] The purpose of this application is to provide an intelligent inspection device and method for power grids, which can realize all-round and efficient intelligent inspection of power grid lines and power equipment, effectively solve the problems of traditional manual inspections such as time-consuming and labor-intensive, easy data loss and difficulty in analysis, and difficult supervision, and significantly improve the overall efficiency of power grid inspection work.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides an intelligent inspection device for a power grid, comprising:

[0008] An inspection vehicle, a control module located on the inspection vehicle, and an inspection drone module mounted on the inspection vehicle;

[0009] The inspection drone module includes a drone and a data acquisition unit installed on the drone; the drone is used to inspect the power grid; the data acquisition unit is used to collect comprehensive status data of the power grid and send the comprehensive status data to the control module;

[0010] The control module is used to control the UAV to inspect the power grid when receiving an operation instruction from an inspection personnel.

[0011] In a second aspect, the present application provides a method for intelligent inspection of a power grid, comprising:

[0012] Collect the facial information of the inspection personnel, and compare and verify the facial information with the preset legal inspection personnel face database to obtain verification result data; the verification result data includes whether the verification is passed, the verification time and the inspection personnel's identity information;

[0013] If the facial information verification fails, the inspection task will be rejected and an alarm will be issued;

[0014] If the face information verification is successful, the inspection task will be started and the inspection will be carried out through the following steps:

[0015] Get the location information of the drone;

[0016] Based on the location information of the drone, the drone is controlled to perform inspections along a preset inspection route and obtain comprehensive status data of the power grid along the preset inspection route; the comprehensive status data includes power grid temperature distribution data, power grid corona discharge characteristic data, and power grid image data;

[0017] The operation status of the power grid is judged based on the comprehensive status data to obtain the operation status inspection information of the power grid; the operation status inspection information includes deformation status information, corrosion status information, peeling status information, foreign matter hanging status information and insulation status information;

[0018] An inspection record log is generated based on the operation status inspection information and verification result data; the inspection record log includes inspection time, inspection route, operation status inspection information and inspection personnel identity information.

[0019] According to the specific embodiments provided in this application, this application has the following technical effects:

[0020] The present application provides an intelligent inspection device and method for power grids. By providing an inspection vehicle as a mobile carrying platform, the problems of inconvenient mobility and limited coverage of traditional power grid inspection devices during complex terrain and long-distance inspections are solved, and efficient mobile inspections of large-area, multi-terrain power grid areas are realized. By equipping the inspection vehicle with an inspection drone module, which includes a drone and a data acquisition unit installed thereon, the problems of manual inspections being difficult to reach high altitudes and dangerous areas and low inspection efficiency are solved, and a comprehensive and meticulous inspection of the power grid with no blind spots is realized, especially the ability to conduct in-depth inspections of key locations such as the tops of towers and line crossings that are difficult for humans to reach. Through the control module located on the inspection vehicle, upon receiving the operating instructions of the inspection personnel, the drone can be accurately controlled to inspect the power grid, and at the same time, the comprehensive power grid status data sent by the data acquisition unit can be received, which solves the problems of separation of data acquisition and processing and untimely information transmission in traditional inspection methods, and realizes real-time monitoring, rapid feedback and accurate analysis of the power grid status, providing a strong guarantee for the stable operation and timely maintenance of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 A schematic diagram of the functional modules of an intelligent inspection device for a power grid provided in one embodiment of the present application. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0024] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0025] In an exemplary embodiment, Figure 1 As shown, a smart inspection device for a power grid is provided, comprising:

[0026] An inspection vehicle, a control module located on the inspection vehicle, and an inspection drone module mounted on the inspection vehicle.

[0027] The inspection drone module includes a drone and a data acquisition unit installed on the drone; the drone is used to inspect the power grid; the data acquisition unit is used to collect comprehensive status data of the power grid and send the comprehensive status data to the control module.

[0028] The control module is used to control the UAV to inspect the power grid when receiving an operation instruction from an inspection personnel.

[0029] As an optional implementation, the data acquisition unit includes an infrared imager, an ultraviolet imager and a camera unit.

[0030] The comprehensive status data includes temperature distribution data of the power grid, corona discharge characteristic data and appearance image data.

[0031] The infrared imager is used to collect power grid temperature distribution data; the ultraviolet imager is used to collect power grid corona discharge characteristic data; and the camera unit is used to collect power grid image data.

[0032] As an optional implementation manner, the intelligent inspection device for the power grid further includes: a communication module.

[0033] After the inspection task is completed, the control module uploads the comprehensive status data collected by the inspection drone module to the background server module through the communication module.

[0034] The background server module is used to judge the operation status of the power grid according to the comprehensive status data and obtain the operation status inspection information of the power grid.

[0035] As an optional implementation, the inspection drone module further includes: a first positioning module.

[0036] The first positioning module is located on the UAV and is used to obtain the UAV's location information in real time and send the UAV's location information to the control module.

[0037] The control module is also used to dynamically adjust the flight parameters of the drone according to the location information of the drone, so that the drone can perform inspections according to a preset inspection route.

[0038] As an optional implementation, the intelligent inspection device for the power grid further includes a second positioning module.

[0039] The second positioning module is a Beidou positioning module or a GPS positioning module, which is located on the inspection vehicle and is used to send the location information of the inspection vehicle to the control module.

[0040] The control module is further configured to obtain the location information of the inspection vehicle at preset time intervals, and send the location information of the inspection vehicle to the background server module via the communication module.

[0041] As an optional implementation, the inspection drone module includes multiple drones, and each drone performs inspections according to its own preset inspection route.

[0042] As an optional implementation, the intelligent inspection device for the power grid further includes a human-computer interaction display module.

[0043] The human-computer interaction display module is located on the inspection vehicle and is connected to the control module. It is used to display the inspection task page, display comprehensive status data, receive operation instructions from inspection personnel and send them to the control module.

[0044] The human-computer interaction display module is also used to display operation teaching videos to inspection personnel; the operation teaching videos include user interface operation guidance videos, safety operation guidance videos, comprehensive status data processing guidance videos and common problem solving guidance videos.

[0045] As an optional implementation manner, the control module is further configured to send an inspection task completion notification signal to the background server module when the inspection task is completed.

[0046] The background server module is also used to generate an inspection record log of the inspection personnel when receiving the inspection task completion notification signal.

[0047] As an optional embodiment, the inspection vehicle further includes a power module. The power module is used to provide power to the drone in the inspection drone module. The power module includes a battery or a solar panel.

[0048] The following describes the application using a specific power grid intelligent inspection process as an example.

[0049] The intelligent power grid inspection device of this embodiment includes an inspection vehicle and inspection equipment mounted on the vehicle. The inspection vehicle transports the inspection equipment to a designated location for inspection. In practice, inspectors can move the inspection vehicle to a designated inspection location, which can be set based on actual needs, such as the area near a power tower. Transporting inspection equipment via the inspection vehicle significantly saves manpower and avoids the tedious process of manually transporting the inspection equipment to the site.

[0050] As an optional implementation, the inspection equipment includes components such as a human-machine interactive display, an inspection drone, a facial recognition camera, a controller, a communication module, and memory. The interactive display facilitates interaction between the controller and the inspector. The controller is equipped with a system that displays relevant pages on the interactive display, providing an operational interface and information display platform for inspectors. The memory is used to temporarily store acquired inspection data. While the drone is performing an inspection, data captured by the controller is first stored in the memory until the inspection is complete.

[0051] After the inspection is complete, the controller uploads the drone's monitoring data to the backend server via the communication module. The backend server then uses this data to identify any power grid anomalies. The facial recognition camera is used to identify the inspector's face and monitor their progress. After the inspector moves the vehicle to the designated inspection location, they first log in to the system through the interactive display, which then displays the inspection page. At the start of the inspection, the inspector is logged into the system through facial recognition, verifying their identity. This process not only ensures the security of system data but also ensures the oversight of the inspector.

[0052] After successfully logging in, the human-computer interaction screen displays the inspection task page. The inspector clicks the Start Inspection button, and the controller automatically activates the inspection drone. The drone then patrols the power grid along the designated route, transmitting inspection data back to the controller. The controller also displays this data in real time on the human-computer interaction screen for the inspector to review.

[0053] In addition to the drone itself, the inspection drone is equipped with an infrared imager, a UV imager, a camera module, and a primary positioning module. The infrared imager senses temperature changes to identify heating fault points; the UV imager uses the generation and enhancement of corona and surface partial discharge to assess the insulation condition of operating equipment, thereby promptly detecting any insulation anomalies. The camera module captures images of power grid cables and towers as the drone passes through. The primary positioning module acquires the drone's location in real time and transmits it to the controller, assisting the drone in flying along its designated route.

[0054] The infrared imager, first positioning module, ultraviolet imager, and camera module are all electrically connected to the inspection drone's control module. This connection ensures that each module accurately transmits collected data or information to the inspection drone's control module. The inspection drone transmits data captured by the infrared imager, ultraviolet imager, and camera module to the controller and stores it in memory. The controller then uploads this data to a backend server, which performs appropriate data analysis to determine whether there are any anomalies in the transmission line cables and power towers, thereby enabling remote monitoring of power grid anomalies. For example, the image data captured by the camera can be used to determine whether there are any anomalies in the cables and power towers, enabling automated inspection of the power grid and timely monitoring of cable and power tower anomalies, such as cable deformation, corrosion, peeling, the presence of foreign objects on the towers, and the health of the cables and insulators on the towers. In addition, the infrared thermal imager and ultraviolet imager can assist in determining whether there are any anomalies in the cables and power towers.

[0055] In this implementation, after receiving data from infrared imagers, ultraviolet imagers, and camera modules, the backend server performs data preprocessing. For temperature data collected by the infrared imager, the server applies specific filtering algorithms, such as median filtering, to remove abnormally high or low temperatures, taking into account the potential noise introduced by environmental factors and equipment errors, ensuring data accuracy and stability. Discharge data from the ultraviolet imager undergoes data smoothing to eliminate short-term random fluctuations and highlight true discharge trends. Image data from the camera module undergoes denoising and enhancement to improve image quality and facilitate more accurate feature extraction.

[0056] After the preprocessing is completed, the feature extraction stage begins. From the infrared data, the server extracts key features such as the maximum temperature, temperature standard deviation, and uniformity of temperature distribution. These features can intuitively reflect the thermal status of cables and power towers; for example, ① temperature features: extract the temperature values ​​of various parts of cables and power towers, including the maximum temperature, minimum temperature, average temperature, etc. Calculate the temperature standard deviation to reflect the degree of discreteness of the temperature distribution. A large temperature standard deviation may mean that there is local overheating or uneven temperature. ② Temperature gradient features: Analyze the rate of change of temperature with spatial position, that is, the temperature gradient. When cables and power towers are operating normally, the temperature gradient is usually within a certain range. If abnormal temperature gradient changes occur, it may indicate a fault.

[0057] For ultraviolet data, we focus on extracting the discharge intensity, discharge frequency, and spatial distribution characteristics of the discharge to determine whether there are abnormal conditions such as corona discharge or partial discharge. For example, ① discharge intensity characteristics: Count the number of photons or energy of the discharge in the ultraviolet imaging to measure the intensity of the discharge. Excessive discharge intensity may indicate that the equipment has insulation defects such as corona discharge or partial discharge. ② discharge frequency characteristics: Calculate the number of discharges per unit time. An abnormal increase in discharge frequency may be a signal of decreased insulation performance of the equipment. ③ discharge spatial distribution characteristics: Analyze the spatial distribution of discharges on cables and power towers, such as the shape, size, and position of the discharge area. Different discharge spatial distributions may correspond to different types of faults.

[0058] The camera module extracts appearance features from its image data. For example, ① Appearance shape features: For cables, this involves detecting deformations such as bends and twists. For power towers, this involves checking their structural integrity, tilt, and deformation. Image edge detection algorithms, such as the Canny algorithm, can be used to extract the edge contours of the object and then compare them with the normal shape. ② Surface texture features: Analyze the surface texture of cables and power towers to determine if corrosion, peeling, or other phenomena are present. Texture analysis methods, such as the gray-level co-occurrence matrix method, extract texture feature parameters such as contrast, correlation, and entropy. These parameters are then compared with normal texture features to identify anomalies. ③ Foreign object features: Detect the presence of foreign objects on the tower, such as bird nests and plastic bags. Object detection algorithms, such as the deep learning-based YOLO algorithm or the Faster R-CNN algorithm, are used to identify and locate foreign objects in the image. ④ Component condition features: Check the condition of individual cables and insulators on the tower, such as whether cables are broken or loose, or whether insulators are damaged or contaminated. The individual components are separated from the image through image segmentation algorithms, and then their status is evaluated.

[0059] After feature extraction is complete, the backend server uses multi-source data fusion analysis to make a comprehensive judgment. First, a weighted fusion strategy is used to assign weights to different features based on their importance in fault diagnosis. For example, temperature and discharge anomalies are crucial for cable fault diagnosis and are given higher weights, while appearance defects are relatively less important. The device status is then assessed by calculating a comprehensive score. Second, multi-source data correlation analysis is performed to delve into the inherent connections between data from different modules. For example, if infrared data indicates an elevated temperature in a certain area, while ultraviolet data indicates increased discharge intensity in that area, and camera images reveal appearance defects, the server will highly assess the potential for a fault in that area.

[0060] To ensure accurate and reliable judgments, the backend server can employ machine learning-based anomaly detection and fault classification algorithms. For example, the isolation forest algorithm constructs an isolation tree model to effectively distinguish normal from abnormal data. This algorithm can quickly identify anomalies in all types of collected data. Alternatively, the algorithm can assess extracted features and comprehensive analysis results based on pre-set thresholds and judgment rules. If a feature parameter exceeds the normal range, or the comprehensive score exceeds a threshold, an abnormality is identified in that area.

[0061] Faults are classified based on their characteristics and manifestations. For fault classification, a decision tree algorithm is used to construct a decision tree model based on the extracted features. The tree's branching structure allows for precise fault classification. Furthermore, these algorithms are given scientifically sound parameters and optimized through methods such as cross-validation to improve their performance and generalization capabilities. For example, faults can be classified into categories such as overheating, discharge, mechanical damage, and insulation failure.

[0062] Reasonable threshold ranges are set for each characteristic parameter, based on extensive historical data statistics, industry standards, and expert experience. For example, a cable's normal operating temperature range is set; any temperature outside this range is considered abnormal. Discharge intensity also has corresponding thresholds to define normality. Furthermore, rules for comprehensive judgment based on multiple characteristics have been developed, clearly defining the fault type and severity corresponding to different feature combinations, as well as the priority and logical relationships of the judgment rules, to ensure accurate fault diagnosis even in complex situations.

[0063] To verify the effectiveness and accuracy of the entire data analysis and judgment process, extensive experimental validation was conducted. The experimental data was derived from actual transmission line monitoring data and simulated experimental data. A simulated transmission line environment was constructed in the laboratory, and real monitoring equipment was used to collect data under various fault conditions. Through detailed analysis of the experimental results, indicators such as fault detection accuracy, false alarm rate, and missed alarm rate were calculated. Based on these results, the data analysis methods and judgment rules were continuously optimized and improved to ensure that the backend server could accurately and reliably determine whether there were any anomalies in the transmission line cables and power towers.

[0064] The control module of the inspection drone and the controller of the inspection equipment can be implemented through LoRa, Bluetooth Low Energy, or Wi-Fi. For example, a network hotspot can be set up through the inspection equipment controller, and multiple inspection drones can communicate with the controller through the network hotspot. In actual use, the communication method between the inspection drone and the controller can be configured according to actual needs.

[0065] This embodiment uses an inspection drone for automatic inspection. Therefore, the inspection personnel do not need to get close to the cables or power poles, and the safety of the inspection personnel can be guaranteed by avoiding contact with the cables or power poles.

[0066] In addition, after the inspector logs in to the system through the human-computer interaction display, the human-computer interaction display will display the inspection task page to the inspector, and the inspector will inspect each inspection task in turn. Specifically, the inspector clicks the inspection button for each inspection task, which will activate the corresponding inspection drone to inspect along the corresponding inspection route. During the inspection process, the inspector does not need to perform any operation on the inspection drone, as the inspection drone will automatically complete the inspection operation for each inspection task.

[0067] After the inspection is completed, the inspection drone will send its own inspection data to the controller of the inspection equipment.

[0068] After all inspection tasks are completed, click the Inspection Complete button on the human-computer interaction display screen, and the controller will upload all the stored inspection data to the backend server. At the same time, each inspection task corresponds to an inspection data.

[0069] In this implementation, the controller not only stores this data but also displays it in real time on a human-machine interactive display screen for inspection personnel to review. During the inspection process, if an inspector discovers a location that may be abnormal, they can click on that location to initiate a focused inspection. This means that after the drone completes its inspection along the designated route, it will focus on the clicked location, for example, circling and approaching the location, and feeding detailed inspection data back to the controller.

[0070] Inspection personnel will conduct inspections according to each inspection task listed on the inspection task page. Specifically, the inspection personnel click the inspection button for each inspection task to launch the corresponding inspection drone to inspect the corresponding inspection route. During the entire inspection process, the inspection personnel do not need to perform any other operations on the inspection drone; the inspection drone will automatically complete the inspection operations for each inspection task.

[0071] After all inspection tasks are completed, the inspector clicks the Inspection Complete button on the interactive display. The controller then uploads all stored inspection data to the backend server via the communication module. The backend server determines the grid's operating status based on the received comprehensive status data (including grid temperature distribution data, corona discharge characteristics, and image data) and generates grid operating status inspection information.

[0072] In addition, the human-machine interactive display screen also displays instructional videos for inspectors. These videos include user interface operation guidance, safety operation guidance, comprehensive status data processing guidance, and common problem-solving guidance. Teaching inspectors through instructional videos lowers the barrier to entry for inspectors and improves inspection efficiency. Because this embodiment requires no human intervention for controlling the inspection equipment, inspectors can simply operate the system through the human-machine interactive display screen.

[0073] As an optional implementation, the inspection vehicle in this embodiment can be equipped with multiple inspection drones, each of which performs inspections along a predetermined inspection route independently and without interfering with each other. This allows a single inspector to automatically inspect multiple power towers, greatly improving inspection efficiency.

[0074] The equipment on the inspection drone in this embodiment is not limited to the modules listed in this embodiment, and it can integrate relevant modules according to actual needs.

[0075] As an optional implementation, the inspection vehicle is also equipped with a second positioning module, either a Beidou or GPS positioning module, which is electrically connected to the inspection equipment's controller. The controller obtains the inspection vehicle's location information at preset intervals and transmits this information to a backend server via a communication module. This allows the backend server to regularly monitor the inspector's location, enabling oversight and management of the inspector. For example, during working hours, it can determine whether the inspector is within the inspection area.

[0076] As an optional implementation, the inspection vehicle is also equipped with a power module to provide power to the inspection equipment. The power module can be implemented using batteries or solar panels, ensuring that the inspection equipment can function properly in various environments.

[0077] As an optional implementation, when the inspector logs out, the controller sends a patrol task completion notification signal to the backend server. Upon receiving this signal, the backend server generates a patrol record log for the inspector and archives the patrol record for subsequent inspection by the inspector.

[0078] The intelligent power grid inspection device of the present application is particularly suitable for the inspection of high-voltage cables and the like of transmission lines in the field. Through this device, it is possible to quickly collect image data of cables and power towers within a certain area, thereby realizing the inspection of the power grid, improving the inspection efficiency, lowering the inspection threshold, and ensuring the safe and stable operation of the power grid. The communication module can adopt Beidou satellite communication module, 5G communication module, etc. to ensure the stability and reliability of data transmission. At the same time, the equipment on the inspection drone can be integrated according to actual needs to meet different inspection requirements.

[0079] In terms of equipment transportation, the rational placement of various inspection equipment on inspection vehicles greatly facilitates the equipment transportation process and effectively saves labor costs. Inspection personnel only need to drive the inspection vehicle to easily transport the required equipment to the designated inspection location, avoiding the tedious and tiring manual handling of equipment.

[0080] Inspection drones play a key role in data collection and monitoring. The cameras onboard the drones accurately capture image data of cables and power towers, providing intuitive visual information for subsequent analysis. Furthermore, infrared and ultraviolet imagers, among other devices, leverage their unique capabilities to achieve comprehensive monitoring of cables and power towers. Infrared imagers can sense temperature fluctuations and promptly identify heating faults; ultraviolet imagers can assess equipment insulation conditions by detecting corona and surface partial discharge, identifying potential anomalies in advance.

[0081] Data recording and analysis are highly automated. The system automatically records and stores monitoring data, eliminating the need for inspectors to manually record it. The system then quickly uploads it to the backend server. The backend server automatically analyzes the data using advanced algorithms and models, quickly and accurately determining the operating status of power grid equipment, significantly improving data processing efficiency and accuracy.

[0082] When it comes to inspection drone control, the system automatically controls the drone's flight and inspection tasks, eliminating the need for manual operation by inspection personnel. This not only simplifies the operational process and makes inspections more convenient, but also ensures the standardization and consistency of inspections, reducing the impact of human factors on inspection results.

[0083] In terms of human-computer interaction, the system successfully achieves smooth interaction between the system and inspectors through the use of a human-computer interaction display. The system installed within the controller displays clear and intuitive pages on this display, allowing inspectors to easily access required information and perform corresponding operations. Furthermore, the inclusion of facial recognition cameras and GPS positioning modules further strengthens personnel supervision. The facial recognition cameras authenticate inspectors, ensuring system security and data confidentiality; the GPS positioning module tracks inspectors' locations in real time, enabling remote monitoring and ensuring on-time and high-quality inspections.

[0084] Furthermore, this inspection device boasts a high level of integration, integrating multiple advanced devices and boasting powerful functionality. It can simultaneously control multiple inspection drones to inspect cables and power towers, significantly improving inspection efficiency. Furthermore, the electronic nature of monitoring data eliminates the need for manual recording, preventing human error and ensuring data accuracy and integrity. The interactive display allows inspectors to operate the system more conveniently, lowering the barrier to entry and enabling more personnel to quickly master inspection skills.

[0085] In summary, this smart grid inspection solution performs well in equipment transportation, data collection and monitoring, data recording and analysis, drone control, human-computer interaction, and personnel supervision, providing a strong guarantee for the safe and stable operation of the power grid.

[0086] Based on the same inventive concept, the embodiments of the present application also provide a method for intelligent inspection of a power grid for implementing the aforementioned intelligent inspection device for the power grid. The solution provided by this method is similar to the solution described in the aforementioned device. Therefore, the specific limitations of one or more embodiments of the intelligent inspection method for a power grid provided below can be found in the above-mentioned limitations on the intelligent inspection device for the power grid, and will not be repeated here.

[0087] In an exemplary embodiment, a method for intelligent inspection of a power grid is provided, comprising:

[0088] The facial information of the inspection personnel is collected and compared with the preset legal inspection personnel face database to obtain verification result data; the verification result data includes whether the verification is passed, the verification time and the inspection personnel identity information.

[0089] If the facial information verification fails, the inspection task will be refused and an alarm will be issued.

[0090] If the facial information verification is successful, the inspection task will be started and the inspection will be carried out through the following steps.

[0091] Get the drone's location information.

[0092] Based on the location information of the drone, the drone is controlled to perform inspections along the preset inspection route, and the comprehensive status data of the power grid along the corresponding preset inspection route is obtained; the comprehensive status data includes the temperature distribution data of the power grid equipment, the corona discharge characteristic data of the power grid equipment, and the appearance image data of the power grid equipment.

[0093] The operation status of the power grid is judged based on the comprehensive status data to obtain the operation status inspection information of the power grid; the operation status inspection information includes deformation status information, corrosion status information, peeling status information, foreign matter hanging status information and insulation status information.

[0094] An inspection record log is generated based on the operation status inspection information and verification result data; the inspection record log includes inspection time, inspection route, operation status inspection information and inspection personnel identity information.

[0095] In summary, this application has the following beneficial effects:

[0096] 1. This application places the inspection equipment on the inspection vehicle, making it easier for inspection personnel to transport the inspection equipment and saving manpower.

[0097] 2. This application integrates a patrol drone into an inspection vehicle. By controlling the drone to inspect the power grid along a predetermined route, the inspection data is collected and uploaded to a backend server, eliminating the need for inspectors to manually record monitoring data, thus enabling remote inspections. Furthermore, the drone requires no manual operation; the system can be operated via a human-computer interactive display screen, making it easy to operate. Furthermore, the human-computer interactive display screen provides instructional support for inspectors, making it easy to get started and providing a low barrier to entry.

[0098] 3. By integrating facial recognition cameras into patrol vehicles, facial verification of patrol personnel can be achieved, the legitimacy of patrol personnel can be ensured, and remote supervision of patrol personnel can be achieved.

[0099] 4. By installing a GPS positioning module on the inspection vehicle, the backend server can obtain the location of the inspection vehicle at regular intervals, thereby enabling supervision of the inspection personnel.

[0100] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0101] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0102] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0103] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0104] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. An intelligent inspection device for a power grid, characterized in that: The intelligent inspection device of the power grid includes: An inspection vehicle, a control module located on the inspection vehicle, and an inspection drone module mounted on the inspection vehicle; The inspection drone module includes a drone and a data acquisition unit installed on the drone; the drone is used to inspect the power grid; the data acquisition unit is used to collect comprehensive status data of the power grid and send the comprehensive status data to the control module; The control module is used to control the UAV to inspect the power grid when receiving an operation instruction from an inspection personnel.

2. The intelligent inspection device for power grid according to claim 1, characterized in that: The data acquisition unit includes an infrared imager, an ultraviolet imager and a camera unit; The comprehensive status data includes temperature distribution data of the power grid, corona discharge characteristic data and appearance image data; The infrared imager is used to collect power grid temperature distribution data; the ultraviolet imager is used to collect power grid corona discharge characteristic data; and the camera unit is used to collect power grid image data.

3. The intelligent inspection device for power grid according to claim 1, characterized in that: The intelligent inspection device for the power grid further includes: a communication module; After the inspection task is completed, the control module uploads the comprehensive status data collected by the inspection drone module to the background server module through the communication module; The background server module is used to judge the operation status of the power grid according to the comprehensive status data and obtain the operation status inspection information of the power grid.

4. The intelligent inspection device for power grid according to claim 1, characterized in that: The inspection drone module further includes: a first positioning module; The first positioning module is located on the UAV and is used to obtain the UAV's location information in real time and send the UAV's location information to the control module; The control module is also used to dynamically adjust the flight parameters of the drone according to the location information of the drone, so that the drone can perform inspections according to a preset inspection route.

5. The intelligent inspection device for power grid according to claim 1, characterized in that: The intelligent inspection device for the power grid further includes a second positioning module; The second positioning module is a Beidou positioning module or a GPS positioning module, which is located on the inspection vehicle and is used to send the location information of the inspection vehicle to the control module; The control module is further configured to obtain the location information of the inspection vehicle at preset time intervals, and send the location information of the inspection vehicle to the background server module via the communication module.

6. The intelligent inspection device for power grid according to claim 1, characterized in that: The inspection drone module includes multiple drones, each of which performs inspections according to its own preset inspection route.

7. The intelligent inspection device for power grid according to claim 1, characterized in that: The intelligent inspection device of the power grid also includes a human-computer interaction display module; The human-computer interaction display module is located on the inspection vehicle and is connected to the control module, and is used to display the inspection task page, display comprehensive status data, receive the inspection personnel's operation instructions and send them to the control module; The human-computer interaction display module is also used to display operation teaching videos to inspection personnel; the operation teaching videos include user interface operation guidance videos, safety operation guidance videos, comprehensive status data processing guidance videos and common problem solving guidance videos.

8. The intelligent inspection device for power grid according to claim 1, characterized in that: The control module is also used to send an inspection task completion notification signal to the background server module when the inspection task is completed; The background server module is also used to generate an inspection record log of the inspection personnel when receiving the inspection task completion notification signal.

9. The intelligent inspection device for power grid according to claim 1, characterized in that: The inspection vehicle also includes a power module; The power supply module is used to provide power supply for the drone in the inspection drone module; The power supply module includes a battery or a solar power generation panel.

10. An intelligent inspection method for a power grid, characterized in that: The intelligent inspection method for a power grid is applied to the intelligent inspection device for a power grid according to any one of claims 1 to 9, and the intelligent inspection method for a power grid includes: Collect the facial information of the inspection personnel, and compare and verify the facial information with the preset legal inspection personnel face database to obtain verification result data; the verification result data includes whether the verification is passed, the verification time and the inspection personnel's identity information; If the facial information verification fails, the inspection task will be rejected and an alarm will be issued; If the face information verification is successful, the inspection task will be started and the inspection will be carried out through the following steps: Get the location information of the drone; Based on the location information of the drone, the drone is controlled to perform inspections along a preset inspection route and obtain comprehensive status data of the power grid along the preset inspection route; the comprehensive status data includes power grid temperature distribution data, power grid corona discharge characteristic data, and power grid image data; The operation status of the power grid is judged based on the comprehensive status data to obtain the operation status inspection information of the power grid; the operation status inspection information includes deformation status information, corrosion status information, peeling status information, foreign matter hanging status information and insulation status information; An inspection record log is generated based on the operation status inspection information and verification result data; the inspection record log includes inspection time, inspection route, operation status inspection information and inspection personnel identity information.

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