Beidou positioning and AI fused intelligent line dynamic monitoring system

Abstract

The invention provides an intelligent line dynamic monitoring system integrating Beidou positioning and AI. The system comprises inspection equipment, a sensor network and central control equipment. The inspection equipment is used for receiving the inspection task sent by the central control equipment, planning an inspection path and executing the inspection task according to the inspection task and the position information obtained through the Beidou receiver, and obtaining an environment image through the visual monitoring equipment in the process of executing the inspection task and sending the environment image to the central control equipment. The sensor network is used for collecting line data of the power transmission line and environment data of the surrounding environment of the power transmission line and sending the data to the central control device. The central control device is used for generating an inspection report according to the environment image, the line data and the environment data, evaluating the health state of the power transmission line based on a line state analysis and prediction model, and generating an early warning notification and an inspection task. Omnibearing and multi-level monitoring of the power transmission line and the surrounding environment thereof is realized, and the inspection efficiency and safety are remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and specifically to a smart line dynamic monitoring system integrating Beidou positioning and AI. Background Art

[0002] With the rapid development of power systems, the safety and reliability of transmission lines have become particularly important. Traditional transmission line inspections mainly rely on manual inspections and regular maintenance, which is not only inefficient but also difficult to detect potential risks in a timely manner. In recent years, with the development of sensor technology, communication technology, and data analysis technology, smart line dynamic monitoring has gradually become an effective means to improve the efficiency and safety of transmission line operation and maintenance.

[0003] The intelligent line dynamic monitoring system refers to a system that uses advanced sensing technology, communication technology and data analysis technology to monitor and manage power transmission lines in real time. It aims to improve the safety, reliability and operation and maintenance efficiency of transmission lines and ensure the stable operation of the power grid.

[0004] However, in the existing intelligent line dynamic monitoring solutions, the positioning accuracy is limited in complex terrain or electromagnetic interference environments, and it is difficult to meet the needs of high-precision inspections. Summary of the invention

[0005] In view of this, the present invention is committed to providing an intelligent line dynamic monitoring system that integrates Beidou positioning and AI, which can combine the high-precision positioning capability of the Beidou satellite navigation system and the powerful data processing capability of the AI ​​algorithm to achieve all-round and multi-level monitoring of the transmission lines and their surrounding environment, and significantly improve the inspection efficiency and safety.

[0006] According to a first aspect of the present application, a smart line dynamic monitoring system integrating Beidou positioning and AI is provided, the system comprising: inspection equipment, a sensor network and a central control device;

[0007] The inspection device is provided with a Beidou receiver and a visual monitoring device; the inspection device is used to receive the inspection task sent by the central control device, plan the inspection path according to the inspection task and the location information obtained by the Beidou receiver, execute the inspection task according to the inspection path, and obtain the environment image through the visual monitoring device during the execution of the inspection task, and send the environment image data to the central control device;

[0008] The sensor network is used to collect line data of the transmission line and environmental data of the surrounding environment of the transmission line, and send the line data and the environmental data to the central control device;

[0009] The central control device is used to generate an inspection report based on the environmental image, the line data and the environmental data, evaluate the health status of the transmission line based on the line status analysis and prediction model, and generate early warning notifications and inspection tasks.

[0010] In a possible design of the first aspect, the sensor network includes: a temperature sensor, a humidity sensor, an anemometer, an inclination sensor, a vibration sensor, and a current and voltage sensor;

[0011] Wherein, the temperature sensor is arranged on the conductor and is used to collect temperature data of the conductor;

[0012] The humidity sensor is arranged on the top of the transmission tower and is used to collect humidity data in the air;

[0013] The anemometer is arranged on the top of the transmission tower to collect wind speed and direction data;

[0014] The tilt sensor is arranged at the bottom of the transmission tower and is used to collect the tilt angle of the transmission tower;

[0015] The vibration sensor is arranged on the conductor, the insulator string or the transmission tower, and is used to collect the vibration frequency and amplitude of the measurement object; the measurement object is any one or more of the conductor, the insulator string and the transmission tower;

[0016] The current and voltage sensors are arranged on conductors at the outlet lines of the substation or at important nodes, and are used to collect the current and voltage in the lines.

[0017] In a possible design of the first aspect, the visual monitoring device includes a high-definition camera, an infrared camera, and a thermal imager;

[0018] The high-definition camera is used to obtain video image data on the inspection path; the infrared camera is used to obtain infrared radiation images, and the thermal imager is used to obtain thermal distribution images.

[0019] In a possible design of the first aspect, the process of the inspection device planning an inspection path according to the inspection task and the location information obtained by the Beidou receiver includes:

[0020] The location information of each transmission tower is obtained through the Beidou receiver, and the inspection area is obtained according to the inspection task, and the obstacle distribution information in the inspection area is obtained through a pre-constructed map;

[0021] The inspection path is planned based on a heuristic search path planning algorithm, the location information, the inspection area, and the obstacle distribution information.

[0022] In a possible design of the first aspect, the inspection device is also used to: dynamically adjust the inspection path according to real-time perception data during the execution of the inspection task.

[0023] In a possible design of the first aspect, the inspection device is further used for:

[0024] Acquire edge information from the environment image using an edge detection algorithm to obtain the outline of the target object to locate the target object;

[0025] Acquire texture features of the target object to determine whether there is an abnormality on the surface of the target object according to the texture features;

[0026] When it is determined that an abnormality exists on the surface of the target object, an alarm message is sent to the central control device.

[0027] In a possible design of the first aspect, the process in which the central control device evaluates the health status of the transmission line based on the line status analysis and prediction model and generates an early warning notification and an inspection task includes:

[0028] Performing temporal and spatial alignment and matching processing on the line data and the environmental data respectively to obtain a plurality of long time series data;

[0029] Inputting the plurality of long time series data into a data fusion model to obtain fused data;

[0030] The fused data is input into a line status analysis and prediction model to obtain the health status of the transmission line, and to generate the early warning notification and the inspection task.

[0031] In a possible design of the first aspect, the line state analysis and prediction model includes: an input layer, a long short-term memory network LSTM layer, a fully connected layer, and an output layer connected in sequence;

[0032] The input layer is used to receive the fused data and pass the fused data to the LSTM layer;

[0033] The LSTM layer is used to extract multidimensional features in the time series from the fused data, and input the multidimensional features into the fully connected layer;

[0034] The fully connected layer is used to calculate the prediction result according to the multi-dimensional features and the activation function, and transmit the prediction result to the output layer;

[0035] The output layer is used to output the prediction result; the prediction result is the health status of the transmission line.

[0036] In a possible design of the first aspect, the fully connected layer calculates the prediction result through the formula y=f(Wx+b), wherein x represents the multidimensional feature; y represents the prediction result, f represents the activation function, W represents the weight matrix, and b represents the bias vector.

[0037] In a possible design of the first aspect, the process in which the central control device evaluates the health status of the transmission line based on the line status analysis and prediction model and generates an early warning notification and an inspection task includes:

[0038] Performing temporal and spatial alignment and matching processing on the line data and the environmental data respectively to obtain a plurality of long time series data;

[0039] Inputting the plurality of long time series data into a data fusion model to obtain fused data;

[0040] Calculating the change rate between the long time series data according to the fused data;

[0041] The potential risk of the transmission line is predicted according to the change rate, the health status of the transmission line is obtained, and an early warning notification and inspection task corresponding to the potential risk are generated.

[0042] According to the technical solution of the present application, the inspection task sent by the central control device is received by the inspection device, the inspection path is planned and the inspection task is executed according to the inspection task and the location information obtained by the Beidou receiver, and the environmental image is acquired by the visual monitoring device during the execution of the inspection task and sent to the central control device. The line data of the transmission line and the environmental data of the surrounding environment of the transmission line are collected through the sensor network and sent to the central control device. The central control device generates an inspection report based on the environmental image, line data and environmental data, and evaluates the health status of the transmission line based on the line status analysis and prediction model, and generates early warning notifications and inspection tasks. Thus, it is possible to combine the high-precision positioning capability of the Beidou satellite navigation system and the powerful data processing capability of the AI ​​algorithm to achieve all-round and multi-level monitoring of the transmission line and its surrounding environment, significantly improving the inspection efficiency and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0044] Figure 1A schematic diagram of the structure of a smart line dynamic monitoring system integrating Beidou positioning and AI provided in an embodiment of the present application;

[0045] Figure 2 A schematic diagram of a sensor network in a smart line dynamic monitoring system integrating Beidou positioning and AI provided in an embodiment of the present application;

[0046] Figure 3 A schematic diagram of the structure of a line state analysis and prediction model in a smart line dynamic monitoring system integrating Beidou positioning and AI provided in an embodiment of the present application;

[0047] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0049] The technical terms involved in the embodiments of the present application are explained below:

[0050] Beidou Positioning System: It can provide centimeter-level or even higher positioning accuracy, which is crucial for understanding the specific location of transmission lines and their ancillary facilities, such as transmission towers, insulators, etc. Especially in complex geographical environments, such as mountainous areas, forests or near waters, traditional GPS may be affected by obstruction, while the multi-frequency design of the Beidou Positioning System can effectively reduce such problems and provide more stable positioning services.

[0051] Smart line dynamic monitoring: It is a new management mode that integrates modern information technology, sensor technology and network technology. It realizes real-time mastery of line operation status through comprehensive, continuous and intelligent monitoring of transmission lines, timely discovers and solves potential problems, and ensures safe and stable operation of the power grid. Its core lies in building a monitoring network covering the entire line, integrating various monitoring data, and using advanced analysis tools to assist decision-making.

[0052] With the rapid development of power systems, the safety and reliability of transmission lines have become particularly critical. Modern society is increasingly dependent on electricity, and any power outage may have a significant impact on the economy, society and daily life. Therefore, ensuring the stable operation of transmission lines is not only to ensure the continuity of power supply, but also to prevent potential safety risks and avoid accidents.

[0053] Traditional transmission line inspection mainly relies on manual inspection and regular maintenance. Although this method can detect some obvious faults to a certain extent, it has many limitations. For example, manual inspection requires a lot of manpower and time, especially on long-distance and complex terrain transmission lines. The work intensity of inspectors is high and the inspection cycle is long, making it difficult to achieve high-frequency comprehensive inspections. Manual inspections can usually only be carried out at a predetermined time point and cannot monitor the status of the line in real time. Many potential risks, such as overheating of wires, tilting of transmission towers, and dirty insulators, may gradually accumulate between two inspections, eventually leading to faults. In severe weather or complex terrain conditions, manual inspections have certain safety risks, and inspectors may face dangerous environments such as high-altitude operations, strong electromagnetic fields, and extreme climates. At the same time, manual inspections require a lot of resources, including manpower, equipment, and transportation costs. In the long run, the operation and maintenance costs are high.

[0054] In recent years, with the rapid development of sensor technology, communication technology and data analysis technology, intelligent line dynamic monitoring has gradually become an effective means to improve the efficiency and safety of power transmission line operation and maintenance. The intelligent line dynamic monitoring system refers to a system that uses advanced sensor technology, communication technology and data analysis technology to monitor and manage power transmission lines in real time. It aims to improve the safety, reliability and operation and maintenance efficiency of power transmission lines in an intelligent way and ensure the stable operation of the power grid.

[0055] Although the intelligent line dynamic monitoring system has achieved remarkable results in improving the safety and reliability of transmission lines, it still faces some challenges in practical applications, especially in complex terrain or electromagnetic interference environments. The existing positioning accuracy is limited and it is difficult to meet the needs of high-precision inspections.

[0056] Specifically, in complex terrains such as mountains, forests, and rivers, satellite signals (such as GPS / Beidou) may be blocked or reflected, resulting in reduced positioning accuracy. This makes it difficult for drones or inspection robots to navigate accurately when performing tasks, and they are prone to deviating from the intended path or even getting lost. In addition, the intelligent line dynamic monitoring system involves multiple types of sensor data. How to effectively integrate and analyze data from these different sources to generate valuable decision support information is the key to improving the intelligence level of the system.

[0057] Based on this, this application proposes a smart line dynamic monitoring system based on Beidou positioning and AI fusion, which achieves high-precision positioning and navigation by adopting multi-source fusion positioning technology, combining satellite positioning, inertial navigation system (INS), laser radar (LiDAR), visual simultaneous positioning and mapping (SLAM) and other sensor data. In addition, it can also help patrol equipment autonomously avoid obstacles and optimize paths in complex environments through pre-built three-dimensional maps and path planning algorithms.

[0058] In addition, in this application, multimodal data fusion technology is also introduced to build a unified data model by combining multiple sensor data such as images, videos, temperature, vibration, etc. Through deep learning and reinforcement learning algorithms, the system can automatically learn the correlation between different data and generate more accurate predictions and decision-making recommendations.

[0059] The following is a detailed description of the intelligent line dynamic monitoring system integrating Beidou positioning and AI provided in the embodiment of the present application in conjunction with the accompanying drawings.

[0060] Figure 1 This is a schematic diagram of the structure of the intelligent line dynamic monitoring system that integrates Beidou positioning and AI provided in the embodiment of this application. Figure 1 As shown, in an exemplary embodiment, the system 100 may include: a patrol device 110 , a sensor network 120 , and a central control device 130 .

[0061] Among them, the inspection device 110 is provided with a Beidou receiver and a visual monitoring device; the inspection device 110 is used to receive the inspection task sent by the central control device 130, plan the inspection path according to the inspection task and the location information obtained through the Beidou receiver, execute the inspection task according to the inspection path, and obtain the environmental image through the visual monitoring device during the execution of the inspection task, and send the environmental image data to the central control device 130.

[0062] The sensor network 120 is used to collect line data of the power transmission line and environmental data of the surrounding environment of the power transmission line, and send the line data and the environmental data to the central control device 130 .

[0063] The central control device 130 is used to generate an inspection report based on the environmental image, the line data and the environmental data, evaluate the health status of the transmission line based on the line status analysis and prediction model, and generate early warning notifications and inspection tasks.

[0064] In the intelligent line dynamic monitoring system 100 of this embodiment, the visual monitoring device set on the inspection device 110 may include a high-definition camera, an infrared camera and a thermal imager. The high-definition camera is used to obtain video image data on the inspection path; the infrared camera is used to obtain infrared radiation images, and the thermal imager is used to obtain thermal distribution images.

[0065] It should be noted that high-definition cameras, infrared cameras and thermal imagers are three important visual monitoring devices, each of which obtains different types of data and is used in different application scenarios.

[0066] Specifically, the data collected by high-definition cameras are visible light images or visible light videos. For example, high-definition cameras can capture clear visible light images of power transmission lines and their surroundings, that is, they can obtain high-resolution images, whose resolution can usually reach millions of pixels or even higher. These high-resolution images can provide rich details and help identify specific objects and structures. Real-time video streaming.

[0067] In addition to static high-resolution images, HD cameras can also record continuous video streams, recording dynamic changes during the inspection process by acquiring video image data (video stream) along the inspection path. Video streams can help analyze the movement trajectory of objects, such as the swaying of hanging foreign objects, bird activities, etc.

[0068] In this embodiment, the high-definition camera can shoot key components such as transmission towers, insulators, and hardware at close range to check whether there are problems such as looseness, corrosion, and cracks. In addition, by shooting from multiple angles, coverage without blind spots can be ensured. The images or video image data captured by the high-definition camera can also be used to identify foreign objects hanging on the wires, such as plastic bags, kite lines, bird nests, etc., so as to promptly discover hidden dangers that may affect the safety of the line. The images or video image data captured by the high-definition camera can also be used to monitor the growth of vegetation around the line, determine whether pruning is needed, and prevent trees from falling or branches from touching the wires. In addition, as a conventional inspection tool, the high-definition camera can regularly capture the status of the transmission line, accumulate historical data, and help analyze the long-term change trend of the line.

[0069] The data collected by the infrared camera is an infrared radiation image, which captures the infrared radiation emitted by the object instead of visible light. Infrared images can show the temperature distribution of the object. Areas with different temperatures appear in different colors or grayscales in the infrared radiation image. Through the infrared radiation image, you can intuitively see the temperature difference on the surface of the object.

[0070] Infrared cameras can not only generate infrared radiation images, but also accurately measure the temperature value of each pixel, which makes it possible to quantitatively analyze the temperature changes of objects and help identify overheated areas. For example, in the scenario of health assessment, infrared cameras can detect temperature changes in key parts such as wires, insulators, and joints to assess the health status of the equipment. For example, overheating of wires may be caused by excessive current or poor contact; abnormal surface temperature of insulators may be caused by partial discharge due to accumulation of dirt. For another example, in the scenario of early warning of faults, infrared cameras can continuously monitor the temperature changes of equipment to detect potential overheating problems before the fault occurs, and give early warning to avoid the expansion of accidents. For another example, in the scenario of night inspections, infrared cameras are not affected by light conditions and can work normally even at night or in low light environments, making them suitable for all-weather inspections. Hidden fault detection: For some hidden faults that are difficult to detect with visible light, such as internal short circuits and local overheating, infrared cameras can identify them through temperature anomalies, providing additional safety protection.

[0071] The data collected by the thermal imager is a heat map, which generates a complete heat map showing the temperature distribution of the entire scene. Similar to infrared cameras, thermal imagers can also capture infrared radiation emitted by objects, but their focus is on showing the temperature field of a large area rather than the temperature details of a single object. Specifically, thermal imagers can highlight areas with large temperature gradients and help identify places where the temperature changes dramatically. This is very useful for finding hot spots, cold spots, and areas with uneven temperatures.

[0072] In the application scenario of large-area temperature monitoring, thermal imagers are suitable for monitoring the temperature distribution of large areas, especially in complex environments such as long-distance transmission lines or substations. It can quickly scan the entire area and find equipment or areas with abnormal temperatures. In the application scenario of heat analysis of power equipment, thermal imagers can be used to analyze the heat of power equipment and help identify overheated transformers, switchgear, cable joints, etc. By analyzing the temperature field, it can be determined whether the working status of the equipment is normal and whether there is a potential risk of failure. In the application scenario of fire warning, thermal imagers can be used to monitor fire sources near transmission lines, especially in areas with dry vegetation or fire hazards. By detecting abnormal temperature increases, fire warnings can be issued in time to prevent the spread of fire. In the application scenario of building heat loss assessment, although mainly used in power systems, thermal imagers can also be used to assess the heat loss of buildings, help optimize energy utilization, and reduce unnecessary energy waste.

[0073] In summary, high-definition cameras are mainly used to capture visible light images and videos, and are suitable for scenarios such as structural integrity inspection, foreign object detection, vegetation management, and daily inspections. Infrared cameras focus on infrared radiation images and temperature data, and are suitable for scenarios such as equipment health assessment, early warning of faults, night inspections, and hidden fault detection. Thermal imagers generate thermal distribution maps, which are suitable for scenarios such as large-area temperature monitoring, power equipment heating analysis, fire warning, and building heat loss assessment.

[0074] In a feasible implementation, the inspection device 110 may be a drone and / or an inspection robot, depending on the usage scenario. When the inspection device 110 is a drone, the visual monitoring device is usually installed at the bottom or side of the drone to ensure that images and video data of the line and its surrounding environment can be captured in all directions. The drone can carry multiple cameras of different types to meet different monitoring needs. The Beidou receiver is installed inside or outside the drone, and it is necessary to ensure that its antenna can clearly receive satellite signals.

[0075] When the inspection device 110 is an inspection robot, the visual monitoring device is usually installed at the front or top of the inspection robot to ensure that key parts such as transmission towers and wires can be clearly photographed. The inspection robot can also be equipped with a mechanical arm for close-range inspection or obstacle removal. The Beidou receiver is installed inside or outside the body of the inspection robot. Similarly, it is necessary to ensure that its antenna can clearly receive satellite signals.

[0076] The Beidou receiver can obtain the current location information, such as latitude, longitude and altitude, in real time. And send the current location information to the flight controller or the main control unit of the mobile platform through a dedicated communication interface such as a serial interface and a CAN bus as a basis for automatic navigation. For example, when the inspection device 110 is a drone, the flight controller will adjust the speed of the propeller according to the difference between the current Beidou coordinate information and the target coordinates to achieve precise hovering or flight along a predetermined trajectory.

[0077] Similarly, the inspection device 110 can also send location information together with other sensor information, such as video image data obtained by a high-definition camera, to the central control device 130 through a wireless communication module, such as 4G / 5G, Wi-Fi, LoRa, NB-IoT, etc.

[0078] Through the real-time images and videos transmitted by drones or inspection robots, operation and maintenance personnel can remotely view the status of the transmission lines on the central control device 130, and promptly detect abnormal conditions such as hanging foreign objects, vegetation intrusion, insulator damage, etc., for remote monitoring and management.

[0079] In this embodiment, by combining the use of high-definition cameras, infrared cameras and thermal imagers, the intelligent line dynamic monitoring system 100 can comprehensively and multi-levelly monitor the transmission lines and their surrounding environment, promptly detect potential risks, and ensure the safe and stable operation of the power grid.

[0080] In the process of planning the inspection path according to the inspection task and the location information obtained by the Beidou receiver, the inspection device 110 first obtains the location information of each transmission tower through the Beidou receiver, obtains the inspection area according to the inspection task, and obtains the obstacle distribution information in the inspection area through the pre-built map. Then, the inspection path is planned based on the heuristic search path planning algorithm, the location information, the inspection area and the obstacle distribution information.

[0081] Specifically, the location information of each transmission tower includes longitude, latitude and altitude. The inspection device 110 also needs to define the starting point, end point and inspection area of ​​the inspection, obtain terrain information (such as mountains, rivers, forests, etc.) of the inspection area through a geographic information system (GIS) to evaluate the passability of the inspection device 110 in the inspection area, and obtain obstacle distribution information in the inspection area through sensors such as laser radar, ultrasonic sensors or pre-built maps.

[0082] Afterwards, a path planning algorithm based on heuristic search can be used for the acquired data to find the optimal path in a complex environment. In some other implementations, for indoor or underground environments without GPS signals, a feasible path from the starting point to the end point can be gradually constructed by random sampling and expanding the tree structure, and the feasible path is used as the inspection path.

[0083] Furthermore, in the absence of a pre-built map, that is, when the inspection device 110 is about to enter an unknown environment, it is possible to try to build a map and plan an inspection route through data from sensors such as lidar and cameras to ensure that the inspection device 110 can navigate autonomously even without a GPS signal.

[0084] In actual use, it is necessary to set a reasonable travel speed according to the performance and inspection requirements of the inspection device 110 to ensure the safety and efficiency of the inspection device 110 during the inspection process. At the same time, when planning the inspection route, it is also necessary to consider the battery life of the inspection device 110 to ensure that the inspection device 110 can successfully complete the inspection task and return to the base station for charging. It can be understood that during the inspection process, the inspection device 110 will use sensing devices such as laser radar and ultrasonic sensors to detect the surrounding environment in real time and avoid obstacles, thereby ensuring the safety of the inspection process.

[0085] In some feasible implementations, the inspection device 110 can also dynamically adjust the inspection path according to the real-time sensing data during the execution of the inspection task. Specifically, if the inspection device 110 encounters an emergency during the inspection process, such as rain, increased wind speed, obstacles, etc., the inspection device 110 can dynamically adjust the inspection path according to the real-time sensing data to ensure the smooth progress of the inspection task. When there are high-priority faults or abnormal conditions in certain inspection areas, the inspection device 110 can temporarily adjust the inspection path and give priority to these areas for detailed inspections.

[0086] Furthermore, after the inspection is completed, the inspection device 110 will upload the collected data to the central control device 130, so that the central control device 130 generates an inspection report based on the data, so as to optimize the planning of future inspection paths according to the inspection report.

[0087] For example, if the inspection device 110 is a drone, and it is necessary to plan the inspection tasks of the transmission lines for the drone in sequence, it is necessary to first obtain the longitude, latitude and altitude of all the transmission towers along the line through the Beidou system. Then, according to the requirements of the inspection task, define the starting point, end point and range of transmission towers to be covered by the drone, load the terrain data of the inspection area from the geographic information system (GIS), mark complex terrains such as mountains, rivers, and forests, and evaluate the passability of the drone.

[0088] Since transmission lines are usually located in open areas and the locations of transmission towers are known, algorithm A can be selected for path planning. Algorithm A can combine terrain data and the locations of transmission towers to find the optimal inspection path from the starting point to the end point.

[0089] In this example, the drone's flight altitude is set to 30 meters to ensure that it does not interfere with power transmission; the maximum flight speed is set to 10 meters per second to ensure the safety of the inspection process; considering that the drone's flight time is 30 minutes, the inspection route is reasonably planned to ensure that the drone can successfully complete the task and return to the base station for charging.

[0090] During the inspection, the drone uses a dynamic programming algorithm to optimize the path, minimize the energy consumption of the drone, and cover all transmission towers and key parts that need to be inspected. At the same time, during the inspection, the drone uses lidar and cameras to perceive the surrounding environment in real time and avoid obstacles. If an emergency occurs (such as rain or increased wind speed), the drone can dynamically adjust the path based on real-time data to ensure the smooth completion of the task.

[0091] After completing the inspection task, the drone uploads the collected images, videos and sensor data to the central control device 130 to generate a detailed inspection report. The central control device 130 can optimize the future inspection path based on the inspection report to further improve the inspection efficiency.

[0092] It can be seen that in this embodiment, the inspection path planning based on Beidou positioning obtains geographic coordinates, selects a suitable path planning algorithm, considers actual constraints, optimizes the path and makes real-time adjustments to ensure that the inspection equipment 110 can complete the inspection task efficiently and safely.

[0093] In some feasible implementations, during the inspection process, the inspection device 110 may also use an edge detection algorithm to obtain edge information from the environmental image to obtain the outline of the target object to locate the target object; obtain the texture features of the target object to determine whether there is any abnormality on the surface of the target object based on the texture features; and send an alarm message to the central control device 130 when it is determined that there is an abnormality on the surface of the target object.

[0094] Among them, edge detection can help outline the contours of objects, especially for distinguishing the boundaries of different objects. In power line inspections, edge detection can be used to identify the contours of structures such as wires and transmission towers to help accurately locate these objects.

[0095] In practical applications, the environmental image can also be divided into different color regions through color space conversion, such as RGB to HSV, Lab, etc., so as to distinguish different parts such as wires, transmission towers, and backgrounds. For example, wires are usually silver or gray, while the background may be green (vegetation) or blue (sky). These regions can be quickly separated through color segmentation. Among them, common color segmentation algorithms include K-means clustering and threshold segmentation.

[0096] Traditional target detection algorithms include sliding window method, Haar cascade classifier, DPM (Deformable Part Model), etc. These algorithms scan images step by step to find target objects through predefined features and classifiers. Although these methods perform well in simple scenarios, they have low accuracy and efficiency in complex transmission line environments and are gradually replaced by deep learning methods. Modern target detection tasks mainly rely on deep learning models such as Faster R-CNN, YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), etc. These models extract image features through convolutional neural networks (CNN) and directly generate bounding boxes and category labels of targets in combination with region proposal networks (RPN) or anchor mechanisms. In transmission line inspection, target detection based on deep learning can be used to identify foreign objects on wires, defects on transmission towers, the status of insulators, etc. For example, YOLOv5 can quickly detect and classify multiple targets in real-time video streams, which is suitable for dynamic inspection tasks of drones or inspection robots.

[0097] Similarly, by analyzing the texture features of the object surface, such as gray-level co-occurrence matrix, local binary pattern LBP, etc., the material type or surface condition can be identified. Texture analysis can help distinguish objects of different materials, such as metal surfaces and non-metal surfaces. Texture analysis can be used to detect whether there are abnormal conditions such as corrosion and dirt on the surface of the wire, or to identify the degree of rust on the surface of the transmission tower.

[0098] By setting reasonable thresholds or using machine learning algorithms (such as support vector machines (SVMs), random forests, etc.), abnormal situations that do not conform to normal patterns can be identified. Autoencoders and generative adversarial networks (GANs) in deep learning can also be used for anomaly detection. By learning the characteristics of normal samples, abnormal samples can be identified. For example, anomaly detection can be used to identify situations such as excessively high conductor temperatures, excessively large tilt angles of transmission towers, and abnormal conductor vibration frequencies, and timely alarms can be issued.

[0099] In the intelligent line dynamic monitoring system 100 provided in the embodiment of the present application, different types of sensors are strategically deployed in different parts of the transmission line and its surrounding environment to comprehensively monitor the health status and environmental impact of the line. Figure 2 A schematic diagram of the sensor network in the intelligent line dynamic monitoring system integrating Beidou positioning and AI provided in the embodiment of the present application. Figure 2 As shown, the sensor network 120 of this embodiment may include a temperature sensor 1201 , a humidity sensor 1202 , an anemometer 1203 , an inclination sensor 1204 , a vibration sensor 1205 , and a current and voltage sensor 1206 .

[0100] The temperature sensor 1201 is arranged on the conductor and is used to collect the temperature data of the conductor. The temperature data of the conductor collected by the temperature sensor 1201 can be used for overheat detection. High temperature may accelerate the aging of the conductor material and reduce the mechanical strength; it may also cause thermal expansion and increase sag, thereby affecting the safe distance of the line. The temperature data of the conductor collected by the temperature sensor 1201 can also be used for insulation performance detection. For example, an abnormal increase in the surface temperature of the insulator may be a sign of dirty discharge or local overheating, which needs to be handled in time.

[0101] The humidity sensor 1202 is arranged at the top of the transmission tower and is used to collect humidity data in the air. A high humidity environment may aggravate the corrosion of metal parts, especially at the joints; it may also affect the electrical performance of the insulators, resulting in an increased risk of flashover. Therefore, the humidity data collected by the humidity sensor 1202 can be used to prompt the adoption of moisture-proof measures when the humidity is high. In addition, the humidity data collected by the humidity sensor 1202 can be combined with other meteorological data, such as temperature and wind speed, to predict possible severe weather and provide meteorological warnings so as to make protective preparations in advance.

[0102] The anemometer 1203 is arranged at the top of the transmission tower to collect wind speed and direction data. Since strong winds may impose additional loads on the transmission tower and the conductor, especially on the windward side, and long-term uneven force may cause structural deformation or damage, the wind speed and direction data collected by the anemometer 1203 can be used for structural safety warning. At the same time, under a specific combination of wind direction and speed, the conductor may dance, threatening the stability of the line and the safety of surrounding facilities. Therefore, the wind speed and direction data collected by the anemometer 1203 can also be used for dancing prediction.

[0103] The tilt sensor 1204 is arranged at the bottom of the transmission tower and is used to collect the tilt angle of the transmission tower. The tilt of the transmission tower may be caused by foundation settlement or soil erosion, which will affect the stability of the entire structure. If an abnormal tilt is found, it may indicate that there is a structural problem with the transmission tower. By collecting the tilt angle of the transmission tower through the tilt sensor 1204, foundation settlement and structural completion problems can be discovered in time, and timely inspection and repair can be carried out.

[0104] The vibration sensor 1205 is arranged on the conductor, insulator string or transmission tower, and is used to collect the vibration frequency and amplitude of the measurement object; the measurement object is any one or more of the conductor, insulator string and transmission tower. Abnormal vibration may be caused by looseness, wear or breakage of mechanical parts, especially for suspended insulators and hardware. Abnormal vibration may also be caused by external factors (such as vehicle passing, construction activities), and long-term effects may also accumulate damage. In this embodiment, the vibration sensor 1205 collects the vibration frequency and amplitude of the measurement object, which can prompt mechanical failures and external interference factors in time, so that the operation and maintenance personnel can take corresponding measures in time.

[0105] The current and voltage sensor 1206 is arranged on the conductor at the outlet line of the substation or at an important node to collect the current and voltage in the line. Real-time monitoring of current can help optimize the operation of the power grid and avoid overload. At the same time, excessive voltage fluctuations may affect the quality of electricity and damage user equipment. When faults such as short circuit and grounding occur, the change pattern of current and voltage helps to quickly locate the fault point and shorten the repair time.

[0106] The central control device 130, as the control center of the entire intelligent line dynamic monitoring system 100, is responsible for receiving data from various monitoring points, and using AI algorithms for comprehensive analysis to generate inspection reports and early warning notifications. Among them, the monitoring point refers to the specific location where various sensors, visual monitoring equipment and other hardware facilities are installed in the transmission line and its surrounding environment. These monitoring points are responsible for collecting various types of data, such as temperature, humidity, wind speed, vibration, current and voltage, as well as image and video data.

[0107] In one implementation, after receiving the environmental image, the line data, and the environmental data, the central control device 130 performs temporal and spatial alignment and matching processing on the line data and the environmental data to obtain multiple long time series data; inputs the multiple long time series data into a data fusion model to obtain fused data; calculates the change rate of the long time series data based on the fused data; predicts the potential risk of the transmission line based on the change rate, obtains the health status of the transmission line, and generates early warning notifications and inspection tasks corresponding to the potential risks.

[0108] Among them, the change rate of long time series data can be calculated by the following formula:

[0109] ΔY t =αβ′Y t-1 +Γ1ΔY t-1 +…+Γ p-1 ΔY t-(p-1) +∈ t ;

[0110] Among them, ΔY t Yes t The first-order difference of represents the rate of change of long time series data, α and β are the coefficient matrices of error correction terms, β′Y t-1 represents the long-term equilibrium deviation, Γ i It is the coefficient matrix of short-term dynamic adjustment, which represents the dynamic changes in the short term.

[0111] Based on the change rate, future time series can be predicted. Specifically, based on the change rate, the temperature change of the wire in a certain period of time in the future can be predicted to help operation and maintenance personnel assess whether the wire will overheat. If the wire temperature is predicted to be too high, the load can be adjusted or cooled in time to prevent the wire from overheating and damage.

[0112] Based on this change rate, the change in the tilt angle of the transmission tower in a certain period of time in the future can be predicted to help evaluate the stability of the transmission tower. If it is predicted that the tilt angle of the transmission tower exceeds the safety threshold, maintenance or reinforcement measures can be arranged immediately to prevent the transmission tower from collapsing.

[0113] Based on this rate of change, the current and voltage changes on the transmission line can be predicted to help identify potential short circuits or ground faults. If abnormal fluctuations in current or voltage are predicted, possible short circuits or ground faults can be checked in advance to avoid the expansion of accidents.

[0114] Based on this rate of change, changes in environmental factors such as wind speed, humidity, and rainfall can be predicted, helping to assess the impact of external conditions on transmission lines.

[0115] Furthermore, in this embodiment, by analyzing the causal relationship between different sensor data, it is also possible to analyze which factors have a significant impact on the health of the transmission line. For example, by analyzing the causal relationship between the conductor temperature and the current, it is possible to determine whether current fluctuations will cause the conductor to overheat. By analyzing the causal relationship between wind speed and the inclination angle of the transmission tower, it is possible to determine whether wind speed will affect the stability of the transmission tower. By analyzing the causal relationship between humidity and surface contamination of the insulator, it is possible to determine whether humidity will affect the discharge performance of the insulator.

[0116] In another implementation, after receiving the environmental image, the line data and the environmental data, the central control device 130 can perform temporal and spatial alignment and matching processing on the line data and the environmental data respectively to obtain multiple long time series data; input the multiple long time series data into a data fusion model to obtain fused data; input the fused data into a line status analysis and prediction model to obtain the health status of the transmission line, and generate the early warning notification and the inspection task.

[0117] In an example, Figure 3 As shown, the line state analysis prediction model 300 may include: an input layer, a long short-term memory network (LSTM) layer, a fully connected layer, and an output layer connected in sequence. The input layer is used to receive the fused data and pass the fused data to the LSTM layer; the LSTM layer is used to extract multidimensional features in the time series from the fused data and input the multidimensional features to the fully connected layer; the fully connected layer is used to calculate the prediction result based on the multidimensional features and the activation function, and pass the prediction result to the output layer; the output layer is used to output the prediction result; the prediction result is the health status of the transmission line.

[0118] Specifically, the fully connected layer calculates the prediction result through the formula y=f(Wx+b), wherein x represents the multidimensional feature; y represents the prediction result, f represents the activation function, W represents the weight matrix, and b represents the bias vector.

[0119] When training the line state analysis prediction model, after predicting the sample data based on the initial prediction model to obtain the prediction result, the loss value between the prediction result and the true value of the sample is calculated based on the loss function. Then, based on the loss value and the optimization algorithm, the parameter value of the prediction model is adjusted to minimize the loss value.

[0120] Exemplarily, the inspection report generated by the central control device 130 based on the environmental image, line data, and environmental data may include health index, fault statistics, prediction results, maintenance suggestions, and performance evaluation.

[0121] Among them, the health index is used to reflect the overall health status of the transmission line. The report can show the changing trend of the health index by time period (such as daily, weekly, and monthly). The fault statistics can list the fault types, locations, times, and handling situations that occurred in the past period of time. The report can analyze the causes of the faults and help operation and maintenance personnel summarize their experience and improve maintenance strategies. The prediction results can show the possible faults or risks that may occur in the future, including the specific prediction time and the affected areas. The report can provide preventive maintenance suggestions to help operation and maintenance personnel prepare in advance. The maintenance suggestions can make specific maintenance suggestions based on the analysis results, such as pruning vegetation along the line, replacing aging insulators, and strengthening the foundation of transmission towers. The report can describe in detail the implementation steps and expected results of each suggestion. Performance evaluation evaluates the working efficiency and accuracy of the current inspection equipment 110 (such as drones and inspection robots) and puts forward improvement suggestions. The performance of different equipment can be compared in the inspection report to help select the optimal inspection plan.

[0122] The early warning notification sent by the central control device 130 is an instant alarm issued for abnormal conditions or potential risks detected in real time, and can usually include the alarm level, alarm problem description, abnormal location, recommended measures, and the time when the alarm problem occurred and related sensor data or image data.

[0123] According to the Beidou positioning and AI fusion intelligent line dynamic monitoring system 100 of this embodiment, the inspection task sent by the central control device 130 is received through the inspection device 110, and the inspection path is planned and the inspection task is performed according to the inspection task and the location information obtained through the Beidou receiver. In the process of performing the inspection task, the environmental image is obtained by the visual monitoring device and sent to the central control device 130. The line data of the transmission line and the environmental data of the surrounding environment of the transmission line are collected through the sensor network 120 and sent to the central control device 130. The central control device 130 generates an inspection report based on the environmental image, line data and environmental data, and evaluates the health status of the transmission line based on the line status analysis and prediction model, and generates early warning notifications and inspection tasks. Thus, it is possible to combine the high-precision positioning capability of the Beidou satellite navigation system and the powerful data processing capability of the AI ​​algorithm to achieve all-round and multi-level monitoring of the transmission line and its surrounding environment, and significantly improve the inspection efficiency and safety.

[0124] It should be understood that the modules in the above devices can be implemented in the form of a processor calling software. For example, the device includes a processor, the processor is connected to a memory, instructions are stored in the memory, and the processor calls the instructions stored in the memory to realize the functions of each unit of the device, wherein the processor can be a general-purpose processor, such as a CPU or a microprocessor, etc., and the memory can be a memory in the device or a memory outside the device. Alternatively, the units in the device can be implemented in the form of hardware circuits, and the functions of some or all units can be realized by designing the hardware circuits. The hardware circuit can be understood as one or more processors; for example, in one implementation, the hardware circuit is an ASIC, and the functions of some or all of the above units are realized by designing the logical relationship of the components in the circuit; for another example, in another implementation, the hardware circuit can be implemented by PLD, taking FPGA as an example, which can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by the configuration file, so as to realize the functions of some or all of the above units. All units of the above devices can be implemented in the form of a processor calling software, or in the form of hardware circuits, or in part by a processor calling software, and the remaining part is implemented in the form of hardware circuits.

[0125] In an embodiment of the present application, a processor is a circuit with the ability to process signals. In one implementation, the processor may be a circuit with the ability to read and run instructions, such as a CPU, a microprocessor, a GPU, or a DSP; in another implementation, the processor may implement certain functions through the logical relationship of a hardware circuit, and the logical relationship of the hardware circuit is fixed or reconfigurable, such as a hardware circuit implemented by an ASIC or PLD, such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as an NPU, TPU, DPU, etc.

[0126] It can be seen that each unit in the above device can be one or more processors (or processing circuits) configured to implement the above method, such as: CPU, GPU, NPU, TPU, DPU, microprocessor, DSP, ASIC, FPGA, or a combination of at least two of these processor forms.

[0127] In addition, all or part of the units in the above devices can be integrated together, or can be implemented independently. In one implementation, these units are integrated together and implemented in the form of a SOC. The SOC may include at least one processor for implementing any of the above methods or implementing the functions of each unit of the device. The type of the at least one processor may be different, for example, including a CPU and an FPGA, a CPU and an artificial intelligence processor, a CPU and a GPU, etc.

[0128] The present application embodiment provides an electronic device, see Figure 4 As shown, the electronic device may be the central control device 130 in the intelligent line dynamic monitoring system 100 integrating Beidou positioning and AI, or the inspection device 110 in the intelligent line dynamic monitoring system 100 integrating Beidou positioning and AI. Specifically, the electronic device may include a memory 400 and a processor 410 connected to the memory 400.

[0129] The memory 400 is used to store programs.

[0130] The processor 410 is used to implement the functions of the central control device 130 in any of the above embodiments of the intelligent line dynamic monitoring system 100 integrating Beidou positioning and AI, or to implement the functions of the inspection device 110 in any of the above embodiments of the intelligent line dynamic monitoring system 100 integrating Beidou positioning and AI. By combining the high-precision positioning capability of the Beidou satellite navigation system and the powerful data processing capability of the AI ​​algorithm, all-round and multi-level monitoring of the transmission line and its surrounding environment can be achieved, significantly improving the inspection efficiency and safety.

[0131] The specific processing process of the processor 410 can refer to the introduction of the above embodiment, and the specific implementation method of the processor 410 can also refer to the introduction of the above embodiment.

[0132] Specifically, the electronic device may further include: a bus, a communication interface 420 , an input device 430 and an output device 440 .

[0133] The processor 410, the memory 400, the communication interface 420, the input device 430 and the output device 440 are connected to each other via a bus.

[0134] A bus may include a pathway that transfers information between components of a computer system.

[0135] Processor 410 may be a general purpose processor, such as a general purpose central processing unit (CPU), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the scheme of the present invention. It may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0136] The processor 410 may include a main processor, and may also include a baseband chip, a modem, and the like.

[0137] The memory 400 stores a program for executing the technical solution of the present invention, and may also store an operating system and other key services. Specifically, the program may include a program code, and the program code includes a computer operation instruction. More specifically, the memory 400 may include a read-only memory (ROM), other types of static storage devices that can store static information and instructions, a random access memory (RAM), other types of dynamic storage devices that can store information and instructions, a disk storage, a flash, and the like.

[0138] The input device 430 may include a device for receiving data and information input by a user, such as an error microphone, a keyboard, a mouse, a camera, a scanner, a light pen, a voice input device, a touch screen, a pedometer, or a gravity sensor.

[0139] Output device 440 may include a device that allows information to be output to a user, such as a speaker, a display screen, a printer, a speaker, etc.

[0140] The communication interface 420 may include any transceiver or the like to communicate with other devices or communication networks, such as Ethernet, a radio access network (RAN), a wireless local area network (WLAN), etc.

[0141] The processor 410 executes the program stored in the memory 400 and calls other devices, which can be used to implement the functions of the central control device 130 or the inspection device 110 in any one of the Beidou positioning and AI-integrated smart line dynamic monitoring systems 100 provided in the above-mentioned embodiments of the present application.

[0142] The embodiment of the present application also proposes a chip, which includes a processor and a data interface. The processor reads and runs the program stored in the memory through the data interface to realize the functions of the central control device 130 or the inspection device 110 in the Beidou positioning and AI integrated smart line dynamic monitoring system 100 introduced in any of the above embodiments. The specific processing process and its beneficial effects can be found in the above-mentioned Beidou positioning and AI integrated smart line dynamic monitoring system 100 embodiment introduction.

[0143] In addition to the above-mentioned systems and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to perform the functions of the central control device 130 or the inspection device 110 in the smart line dynamic monitoring system 100 integrating Beidou positioning and AI according to various embodiments of the present application as described in any of the above embodiments of this specification.

[0144] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0145] In addition, the embodiment of the present application may also be a storage medium on which a computer program is stored, and the computer program is executed by a processor in any of the above embodiments of this specification according to the Beidou positioning and AI fusion intelligent line dynamic monitoring system 100 of various embodiments of the present application. Specifically, the following steps can be implemented:

[0146] For the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0147] It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0148] The steps in the methods of each embodiment of the present application can be adjusted in order, combined and deleted according to actual needs, and the technical features recorded in each embodiment can be replaced or combined.

[0149] The modules and sub-modules in the devices and terminals of the various embodiments of the present application can be combined, divided and deleted according to actual needs.

[0150] In the several embodiments provided in the present application, it should be understood that the disclosed terminals, devices and methods can be implemented in other ways. For example, the terminal embodiments described above are only schematic, for example, the division of modules or submodules is only a logical function division, and there may be other division methods in actual implementation, for example, multiple submodules or modules can be combined or integrated into another module, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0151] The modules or submodules described as separate components may or may not be physically separated, and the components of the modules or submodules may or may not be physical modules or submodules, that is, they may be located in one place, or they may be distributed on multiple network modules or submodules. Some or all of the modules or submodules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0152] In addition, each functional module or submodule in each embodiment of the present application may be integrated into one processing module, or each module or submodule may exist physically separately, or two or more modules or submodules may be integrated into one module. The above-mentioned integrated modules or submodules may be implemented in the form of hardware or in the form of software functional modules or submodules.

[0153] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0154] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly by hardware, software units executed by a processor, or a combination of the two. The software units may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0155] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0156] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A smart line dynamic monitoring system integrating Beidou positioning and AI, characterized in that: The system includes: inspection equipment, sensor network and central control equipment; The inspection device is provided with a Beidou receiver and a visual monitoring device; the inspection device is used to receive the inspection task sent by the central control device, plan the inspection path according to the inspection task and the location information obtained by the Beidou receiver, execute the inspection task according to the inspection path, and obtain the environment image through the visual monitoring device during the execution of the inspection task, and send the environment image data to the central control device; The sensor network is used to collect line data of the transmission line and environmental data of the surrounding environment of the transmission line, and send the line data and the environmental data to the central control device; The central control device is used to generate an inspection report based on the environmental image, the line data and the environmental data, evaluate the health status of the transmission line based on the line status analysis and prediction model, and generate early warning notifications and inspection tasks.

2. The system according to claim 1, characterized in that The sensor network includes: a temperature sensor, a humidity sensor, an anemometer, an inclination sensor, a vibration sensor, and a current and voltage sensor; Wherein, the temperature sensor is arranged on the conductor and is used to collect temperature data of the conductor; The humidity sensor is arranged on the top of the transmission tower and is used to collect humidity data in the air; The anemometer is arranged on the top of the transmission tower to collect wind speed and direction data; The tilt sensor is arranged at the bottom of the transmission tower and is used to collect the tilt angle of the transmission tower; The vibration sensor is arranged on the conductor, the insulator string or the transmission tower, and is used to collect the vibration frequency and amplitude of the measurement object; the measurement object is any one or more of the conductor, the insulator string and the transmission tower; The current and voltage sensors are arranged on conductors at the outlet lines of the substation or at important nodes, and are used to collect the current and voltage in the lines.

3. The system according to claim 1, characterized in that The visual monitoring equipment includes a high-definition camera, an infrared camera, and a thermal imager; The high-definition camera is used to obtain video image data on the inspection path; the infrared camera is used to obtain infrared radiation images, and the thermal imager is used to obtain thermal distribution images.

4. The system according to claim 1, characterized in that The process of planning the inspection route by the inspection device according to the inspection task and the location information obtained by the Beidou receiver includes: The location information of each transmission tower is obtained through the Beidou receiver, and the inspection area is obtained according to the inspection task, and the obstacle distribution information in the inspection area is obtained through a pre-constructed map; The inspection path is planned based on a heuristic search path planning algorithm, the location information, the inspection area, and the obstacle distribution information.

5. The system according to claim 4, characterized in that The inspection equipment is also used for: In the process of executing the inspection task, the inspection path is dynamically adjusted according to the real-time perception data.

6. The system according to claim 1, characterized in that The inspection equipment is also used for: Acquire edge information from the environment image using an edge detection algorithm to obtain the outline of the target object to locate the target object; Acquire texture features of the target object to determine whether there is an abnormality on the surface of the target object according to the texture features; When it is determined that an abnormality exists on the surface of the target object, an alarm message is sent to the central control device.

7. The system according to claim 2, characterized in that The process in which the central control device evaluates the health status of the transmission line based on the line status analysis and prediction model and generates early warning notifications and inspection tasks includes: Performing temporal and spatial alignment and matching processing on the line data and the environmental data respectively to obtain a plurality of long time series data; Inputting the plurality of long time series data into a data fusion model to obtain fused data; The fused data is input into a line status analysis and prediction model to obtain the health status of the transmission line, and to generate the early warning notification and the inspection task.

8. The system according to claim 7, characterized in that The line state analysis and prediction model comprises: an input layer, a long short-term memory network LSTM layer, a fully connected layer and an output layer connected in sequence; The input layer is used to receive the fused data and pass the fused data to the LSTM layer; The LSTM layer is used to extract multidimensional features in the time series from the fused data, and input the multidimensional features into the fully connected layer; The fully connected layer is used to calculate the prediction result according to the multi-dimensional features and the activation function, and transmit the prediction result to the output layer; The output layer is used to output the prediction result; the prediction result is the health status of the transmission line.

9. The system according to claim 8, characterized in that The fully connected layer calculates the prediction result through the formula y=f(Wx+b), where x represents the multidimensional feature; y represents the prediction result, f represents the activation function, W represents the weight matrix, and b represents the bias vector.

10. The system according to claim 2, characterized in that The process in which the central control device evaluates the health status of the transmission line based on the line status analysis and prediction model and generates early warning notifications and inspection tasks includes: Performing temporal and spatial alignment and matching processing on the line data and the environmental data respectively to obtain a plurality of long time series data; Inputting the plurality of long time series data into a data fusion model to obtain fused data; Calculating the change rate between the long time series data according to the fused data; The potential risk of the transmission line is predicted according to the change rate, the health status of the transmission line is obtained, and an early warning notification and inspection task corresponding to the potential risk are generated.

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