Power transmission line safety early warning system based on artificial intelligence and Beidou positioning

Abstract

The invention relates to the technical field of safety monitoring, in particular to a power transmission line safety early warning system based on artificial intelligence and Beidou positioning. The system comprises a power transmission line data acquisition module, a power transmission line damage evaluation module, a tower safety fault identification module and a safety fault positioning and early warning module, and an image sensor, a current sensor and a voltage sensor are arranged on a power transmission line to acquire multi-source line data and transmit the data to a data processing center in real time; obtaining the distance of the corresponding trees and the number of built bird nests, and carrying out safety damage evaluation calculation and safety fault recognition analysis on the power transmission line to obtain a safety fault recognition result of the power transmission line tower; a Beidou positioning module is integrated and fault positioning early warning is carried out on a corresponding power transmission line tower with a safety fault, so that a fault early warning position of the power transmission line tower is obtained, and corresponding power transmission line fault early warning repair work is executed. Safe and stable operation of the power transmission line can be guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of safety monitoring, and in particular to a power transmission line safety warning system based on artificial intelligence and Beidou positioning. Background Art

[0002] As a key infrastructure for power transmission, the safe operation of power transmission lines is crucial for the stability of the power system. Power transmission lines are usually distributed in complex geographical environments, and corresponding poles and towers are also arranged around them, facing various natural and human threats. For example, adverse weather conditions (such as lightning strikes, strong winds, heavy rains, icing, etc.) can cause line failures; natural activities such as tree growth and bird nesting can also damage power transmission lines. However, existing monitoring systems based on artificial intelligence and positioning technology have many deficiencies, such as limited image and data processing capabilities in complex environments, and the positioning accuracy cannot meet the requirements of precise positioning of power transmission line failures, resulting in a reduction in the accuracy of safety warnings. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a power transmission line safety warning system based on artificial intelligence and Beidou positioning to solve at least one of the above technical problems.

[0004] To achieve the above object, a power transmission line safety warning system based on artificial intelligence and Beidou positioning includes the following modules: A power transmission line data acquisition module, which is used to deploy image sensors, current sensors, and voltage sensors on the power transmission line, and use the image sensors, current sensors, and voltage sensors to collect multi-source line data for the corresponding poles and towers around the power transmission line to obtain the environmental image of the power transmission line poles and towers, the current component of the power transmission line poles and towers, and the voltage component of the power transmission line poles and towers; and transmit the environmental image of the power transmission line poles and towers, the current component of the power transmission line poles and towers, and the voltage component of the power transmission line poles and towers to the data processing center in real time; A power transmission line damage assessment module, which is used to obtain the distance of the corresponding trees and the number of bird nests built through the environmental image of the power transmission line poles and towers, and perform a safety damage assessment calculation on the power transmission line corresponding to the poles and towers based on the distance of the trees and the number of bird nests built to obtain the safety damage rate of the power transmission line; A pole safety fault identification module, which is used to perform safety fault identification and analysis on the current component of the power transmission line poles and towers and the voltage component of the power transmission line poles and towers based on the safety damage rate of the power transmission line and in combination with a convolutional neural network to obtain the safety fault identification result of the power transmission line poles and towers; The safety fault location and warning module is used to integrate the Beidou positioning module on the corresponding poles and towers around the transmission line, and use the Beidou positioning module to perform fault location and warning on the transmission line poles and towers with safety faults corresponding to the safety fault identification results of the transmission line poles and towers, so as to obtain the fault warning positions of the transmission line poles and towers, and execute the corresponding transmission line fault warning and repair work.

[0005] Furthermore, the transmission line data acquisition module includes the following functions: By deploying image sensors, current sensors, and voltage sensors on the transmission line; Using the image sensor to collect the surrounding environment images of the corresponding poles and towers around the transmission line to obtain the environmental images of the transmission line poles and towers, including the distance of the surrounding trees and the condition of bird's nest building around the transmission line poles and towers; Using the current sensor to collect the real-time operating current of the corresponding poles and towers around the transmission line to obtain the current components of the transmission line poles and towers; Using the voltage sensor to collect the real-time operating voltage of the corresponding poles and towers around the transmission line to obtain the voltage components of the transmission line poles and towers; By building a data transmission network, the environmental images of the transmission line poles and towers, the current components of the transmission line poles and towers, and the voltage components of the transmission line poles and towers are transmitted to the data processing center in real time by using fiber optic transmission technology in nearby areas or areas with strong signals and establishing relay stations in remote areas or areas with weak signals.

[0006] Furthermore, the transmission line damage assessment module includes the following functions: Obtaining the corresponding distance between the transmission line and the surrounding trees from the environmental images of the transmission line poles and towers; Obtaining the corresponding number of bird's nest buildings from the bird's nest building conditions in the environmental images of the transmission line poles and towers; Obtaining the corresponding environmental humidity and environmental precipitation in the periodic area through the corresponding poles and towers around the transmission line, and calculating the leakage probability of the transmission line corresponding to the poles based on the environmental humidity and environmental precipitation to obtain the leakage probability of the transmission line poles and towers; Based on the distance between the trees, the number of bird's nest buildings, and the leakage probability of the transmission line poles and towers, perform a safety damage assessment calculation on the transmission line corresponding to the poles to obtain the safety damage rate of the transmission line.

[0007] Furthermore, the obtaining the corresponding environmental humidity and environmental precipitation in the periodic area through the corresponding poles and towers around the transmission line, and calculating the leakage probability of the transmission line corresponding to the poles based on the environmental humidity and environmental precipitation includes: Obtaining the corresponding environmental humidity and environmental precipitation in the surrounding area through the corresponding poles and towers around the transmission line and combining with the meteorological monitoring station; Perform arithmetic mean calculations on the ambient humidity and ambient precipitation corresponding to the surrounding area at fixed time intervals of 30 minutes to obtain the average ambient humidity and the average ambient precipitation; Use an inclination monitor to collect the inclination of the poles and towers corresponding to the transmission line to obtain the inclination angle of the transmission line poles and towers; Based on the inclination angle of the transmission line poles and towers, the average ambient humidity, and the average ambient precipitation, use the leakage probability calculation formula for pole lines to calculate the leakage probability of the transmission line corresponding to the poles to obtain the leakage probability of the transmission line poles and towers.

[0008] Further, the leakage probability calculation formula for pole lines is specifically: ; In the formula, is the leakage probability of the transmission line poles and towers, is the maximum inclination angle of the poles, is the minimum ambient humidity, is the maximum ambient humidity, is the minimum precipitation, is the maximum precipitation, is the pole inclination angle parameter, is the pole inclination contribution coefficient, is the ambient humidity parameter, is the average ambient humidity, is the standard deviation of the ambient humidity, is the ambient precipitation parameter, is the average ambient precipitation, is the standard deviation of the ambient precipitation, is the ambient parameter contribution coefficient, is the correction coefficient of the leakage probability of the transmission line poles and towers.

[0009] Further, the safety damage assessment calculation of the transmission line corresponding to the poles based on the distance of the trees, the number of bird nests built, and the leakage probability of the transmission line poles and towers includes: Obtain the growth trend of the tree branches corresponding to the distance of the trees, and based on the growth trend of the tree branches, conduct a mechanical damage assessment on the transmission line corresponding to the poles to obtain the mechanical damage coefficient of the transmission line; Measure the insulation resistance of the transmission line corresponding to the poles to obtain the insulation resistance of the transmission line; Based on the number of bird nests built, conduct an insulation impact assessment and analysis on the insulation resistance of the transmission line to obtain the insulation impact efficiency of the transmission line; Based on the mechanical damage coefficient of the transmission line, the insulation influence efficiency of the transmission line, and the leakage probability of the transmission line tower, the safety breakage rate calculation formula of the line is used to conduct a safety breakage assessment calculation on the transmission line corresponding to the tower, and the safety breakage rate of the transmission line is obtained.

[0010] Furthermore, the specific formula for the safety breakage rate of the line is: ; ; ; In the formula, is the safety breakage rate of the transmission line, is the time interval for safety breakage assessment, is the time variable parameter, is the mechanical damage coefficient of the transmission line at time ; is the distance between the tree and the transmission line at time ; is the distance mechanical damage weight, is the tree growth rate at time ; is the tree growth mechanical damage weight, is the tree growth height at time ; is the tree height mechanical damage weight, is the insulation influence efficiency of the transmission line at time ; is the number of bird nests built at time ; is the insulation resistance of the transmission line, is the insulation influence correction factor, is the leakage probability of the transmission line tower, is the leakage probability adjustment factor, is the correction coefficient of the safety breakage rate of the transmission line.

[0011] Furthermore, the tower safety fault identification module includes the following functions: By fitting the time series change curve of the safety breakage rate of the transmission line within a certain time range, a change trend curve of the breakage rate of the transmission line is generated; Determine the growth or decline rate of the breakage rate corresponding to the area where the transmission line is located through the change trend curve of the breakage rate of the transmission line; Based on the growth or decline rate of the damage rate corresponding to the area where the transmission line is located, conduct a damage fault analysis on the corresponding poles and towers around the transmission line. If the growth or decline rate of the damage rate is greater than or equal to the preset threshold, it is determined that there is a damage fault in the corresponding transmission line pole and tower; otherwise, it is determined that there is no damage fault in the corresponding transmission line pole and tower, so as to obtain the external cause damage fault result of the transmission line pole and tower; Use a convolutional neural network to conduct internal cause safety fault identification and analysis on the current component and voltage component of the transmission line pole and tower, so as to obtain the internal cause electrical parameter fault result of the transmission line pole and tower; Merge the external cause damage fault result of the transmission line pole and tower and the internal cause electrical parameter fault result of the transmission line pole and tower to obtain the safety fault identification result of the transmission line pole and tower.

[0012] Further, the use of a convolutional neural network to conduct internal cause safety fault identification and analysis on the current component and voltage component of the transmission line pole and tower includes: Conduct time series synchronization processing on the current component and voltage component of the transmission line pole and tower to obtain the corresponding pole current component sequence and pole voltage component sequence within the same time series range; Conduct frequency domain statistical analysis of the pole electrical parameters on the corresponding pole current component sequence and pole voltage component sequence within the same time series range to obtain the frequency domain statistical characteristics corresponding to the transmission line pole electrical parameters, including the frequency components, harmonic contents, and transient change characteristics corresponding to the current and voltage; Based on the frequency domain statistical characteristics corresponding to the transmission line pole electrical parameters, conduct instantaneous mutation analysis on the corresponding pole current component sequence and pole voltage component sequence within the same time series range to obtain the instantaneous mutation distribution characteristics corresponding to the transmission line pole electrical parameters; Use a convolutional neural network to conduct internal cause safety fault identification and analysis on the instantaneous mutation distribution characteristics corresponding to the transmission line pole electrical parameters, so as to input the instantaneous mutation distribution characteristics corresponding to the transmission line pole electrical parameters into the convolutional neural network to construct a pole electrical parameter line fault identification model, and output the overload and short circuit faults corresponding to the transmission line pole and tower, so as to obtain the internal cause electrical parameter fault result of the transmission line pole and tower.

[0013] Further, the safety fault location and warning module includes the following functions: Integrate and deploy a Beidou positioning module on the corresponding poles and towers around the transmission line; Use the Beidou positioning module to conduct fault location and warning on the transmission line poles and towers corresponding to the safety fault identification result of the transmission line pole and tower with safety faults to obtain the fault warning location of the transmission line pole and tower; Obtain the known positions of rescue teams, and perform repair positioning calculations on the known positions of rescue teams based on the fault warning positions of transmission line towers, so as to obtain the distance and azimuth between the transmission line fault point and the known positions, and notify the implementation of the corresponding transmission line fault warning repair work.

[0014] Advantages of the present invention: The power transmission line safety warning system based on artificial intelligence and Beidou positioning proposed by the present invention is generally composed of a power transmission line data acquisition module, a power transmission line damage assessment module, a tower safety fault identification module, and a safety fault positioning and warning module. Compared with the prior art, the beneficial effects of this application are as follows: By deploying image sensors, current sensors, and voltage sensors on the power transmission line, multi-source data acquisition of the power transmission line towers is realized. The image sensor can capture image information of the power transmission line and the surrounding environment of the tower, including influencing factors such as trees, bird nests, and weather changes, which helps to comprehensively understand the status of the surrounding environment. The current sensor and voltage sensor respectively record the current and voltage components of the power transmission line. Through all-round and multi-dimensional data acquisition, the environment and operating status of the power transmission line towers are comprehensively monitored, effectively improving the perception ability of equipment and environment. The cooperation of multiple sensors not only makes data collection more accurate but also can timely capture any abnormal situations that cause failures or dangers. For example, by identifying trees and bird nests in the environment through the image sensor, problems such as too-close trees and bird nest construction can be timely warned, effectively reducing the risk of line failures caused by natural factors. The real-time monitoring of current and voltage data can quickly locate problems when abnormalities occur, ensuring the efficient operation of the power transmission line. Secondly, by obtaining the distance between trees and bird nests based on the images of the power transmission line tower environment and combining these data to conduct a safety damage assessment of the line. The distance between trees and power facilities, the number and location of bird nest construction directly affect the operation safety of the power transmission line. If the trees are too close to the power transmission line, it may cause branches to contact the line, resulting in short circuits or fires; the construction of bird nests hinders the normal operation of the power transmission line and may even cause electrical failures, thus avoiding accidents and failures caused by environmental problems. Further, this assessment mechanism can be widely applied in large-scale power transmission line networks, thereby enhancing the image and data processing capabilities in complex environments. Then, by obtaining the environmental external cause faults corresponding to the power transmission line through the safety damage rate of the power transmission line, it helps to identify environmental external cause electrical faults caused by environmental problems (such as tree proximity, bird nest influence, etc.), and combines a convolutional neural network to conduct safety fault identification on current and voltage data, which can deeply analyze the operating status of the power transmission line. As a deep learning method, the convolutional neural network (CNN) can extract internal potential patterns and features from a large amount of current and voltage data, identify the differences between normal and abnormal states. Compared with traditional manual monitoring, the artificial intelligence analysis of the convolutional neural network greatly improves the accuracy and efficiency of fault identification, can quickly locate possible fault points, and predict future faults through algorithms. Through this intelligent safety analysis, the failure rate and accident probability of the power transmission line are greatly reduced, effectively improving the stability and safety of the power transmission line.Finally, by integrating the Beidou positioning module, the positioning and early warning of safety faults of transmission line towers are realized, providing accurate geographical information for fault repair. The Beidou positioning module can obtain the position of transmission line towers in real time and combine it with the fault identification result to quickly determine the specific tower position where the problem occurs, thus meeting the demand for precise positioning of transmission line faults. This process can accurately locate the fault position information to specific coordinates, providing intuitive guidance for on-site repair personnel, saving a lot of time. In this way, the integration of the Beidou positioning module makes the fault positioning more accurate, enabling the repair personnel to be accurately guided to the fault occurrence point, avoiding wasting time due to errors or unfamiliar terrain, and thus greatly improving the accuracy of safety early warning and the emergency response ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments read in conjunction with the accompanying drawings: Figure 1 FIG. is a schematic diagram of the modules of the transmission line safety early warning system based on artificial intelligence and Beidou positioning of the present invention; Figure 2 is Figure 1 a schematic diagram of the functional flow of the transmission line data acquisition module in; Figure 3 is Figure 1 a schematic diagram of the functional flow of the transmission line damage assessment module in. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The technical system of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0017] In addition, the drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor systems and / or microcontroller systems.

[0018] It should be understood that although terms such as "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly, the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0019] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides a transmission line safety warning system based on artificial intelligence and Beidou positioning. The system includes the following modules: A transmission line data acquisition module, configured to deploy image sensors, current sensors, and voltage sensors on the transmission line, and use the image sensors, current sensors, and voltage sensors to perform multi-source line data acquisition on the corresponding poles and towers around the transmission line, so as to obtain the transmission line pole and tower environment image, the transmission line pole and tower current component, and the transmission line pole and tower voltage component; and transmit the transmission line pole and tower environment image, the transmission line pole and tower current component, and the transmission line pole and tower voltage component to the data processing center in real time; A transmission line damage assessment module, configured to obtain the corresponding distance of the trees and the number of bird nests built through the transmission line pole and tower environment image, and perform a safety damage assessment calculation on the transmission line corresponding to the pole and tower based on the distance of the trees and the number of bird nests built, so as to obtain the transmission line safety damage rate; A pole and tower safety fault identification module, configured to perform safety fault identification and analysis on the transmission line pole and tower current component and the transmission line pole and tower voltage component based on the transmission line safety damage rate and in combination with a convolutional neural network, so as to obtain the transmission line pole and tower safety fault identification result; A safety fault location and warning module, configured to integrate a Beidou positioning module on the corresponding poles and towers around the transmission line, and use the Beidou positioning module to perform fault location and warning on the transmission line poles and towers corresponding to the transmission line pole and tower safety fault identification result where there are safety faults, so as to obtain the transmission line pole and tower fault warning position, and perform the corresponding transmission line fault warning and repair work.

[0020] In the embodiment of the present invention, please refer to Figure 1 shown, which is a schematic diagram of the modules of the transmission line safety warning system based on artificial intelligence and Beidou positioning of the present invention. In this example, the transmission line safety warning system based on artificial intelligence and Beidou positioning includes the following modules: S1: Transmission line data acquisition module, which is used to deploy image sensors, current sensors, and voltage sensors on the transmission line, and use the image sensors, current sensors, and voltage sensors to collect multi-source line data for the corresponding transmission line towers around the transmission line, so as to obtain the transmission line tower environment image, the transmission line tower current component, and the transmission line tower voltage component; and transmit the transmission line tower environment image, the transmission line tower current component, and the transmission line tower voltage component to the data processing center in real time; In the embodiment of the present invention, by deploying image sensors, current sensors, and voltage sensors on each tower of the transmission line, the image sensor is used to capture the environment image around the tower, including information such as vegetation and bird nests. The current sensor is used to collect the current component of the transmission line on the tower, and the voltage sensor is used to collect the voltage component of the transmission line on the tower. Each sensor is equipped with high-precision measurement and acquisition equipment to ensure the accuracy of the data. The image sensor captures images in real time through a high-definition camera, and the current and voltage sensors convert the signals into digital data through a data acquisition card. The data collected by each sensor is transmitted to the data processing center in real time by wired or wireless means. During this process, the communication module ensures that the data will not be lost during transmission and maintains the real-time nature and integrity of the data. The data processing center receives and stores all the uploaded data for further analysis and processing.

[0021] S2: Transmission line damage assessment module, which is used to obtain the relative distance of the corresponding trees and the number of bird nests built through the transmission line tower environment image, and perform a safety damage assessment calculation on the transmission line corresponding to the tower based on the relative distance of the trees and the number of bird nests built, so as to obtain the transmission line safety damage rate; In the embodiment of the present invention, the environment image of the transmission line tower is processed by image recognition technology. Using image processing algorithms, the trees in the image and their relative positions to the tower are identified, and the relative distance of the trees is calculated. At the same time, deep learning algorithms are used to identify the number of bird nests built in the image, especially the number of bird nests near the structural part of the tower. Through the analysis of the relative distance of the trees and the number of bird nests built, combined with the previously known safety damage model, the safety damage rate of the transmission line tower is calculated. This model evaluates the possible damage risk based on the tree proximity, the number of bird nests, and other environmental factors (such as the probability of leakage). This damage rate represents the probability of damage to the transmission line caused by external environmental influences, and finally the transmission line safety damage rate is obtained.

[0022] S3: Tower safety fault identification module, which is used to perform safety fault identification and analysis on the transmission line tower current component and the transmission line tower voltage component based on the transmission line safety damage rate and combined with a convolutional neural network, so as to obtain the transmission line tower safety fault identification result; In an embodiment of the present invention, after obtaining the safety breakage rate of a transmission line tower, the safety breakage rate of the transmission line is used to obtain the environmental external factor faults corresponding to the transmission line, so as to help identify environmental external factor electrical faults caused by environmental problems (such as tree proximity, bird's nest influence, etc.), and combined with a convolutional neural network to perform safety fault identification on current and voltage data. First, using the trained convolutional neural network model, the current component and voltage component data of the transmission line tower are input for real-time analysis. Through the neural network's feature extraction of the current and voltage signals, the identified electrical fault modes, such as overload, short circuit, etc., the convolutional neural network can accurately identify potential faults in the power system through training of historical data, and by combining with the safety breakage rate, judge the severity and location of the faults, and finally obtain the safety fault identification result of the transmission line tower, including environmental external factor faults and line internal factor faults.

[0023] S4: A safety fault location and warning module, which is used to integrate a Beidou positioning module on the corresponding tower around the transmission line, and use the Beidou positioning module to perform fault location and warning on the transmission line tower with a safety fault corresponding to the safety fault identification result of the transmission line tower, so as to obtain the fault warning location of the transmission line tower and execute the corresponding transmission line fault warning and repair work.

[0024] In an embodiment of the present invention, by integrating a Beidou positioning module on each tower, this positioning module can obtain the position of the tower in real time and associate it with the fault identification result. When a safety fault occurs in a certain transmission line tower, the Beidou positioning system will accurately mark the geographical location of the tower. This position data and the fault diagnosis result are jointly transmitted to the data processing center, and real-time fault warning is carried out through the background system. The data processing center analyzes the location, type and severity of the fault according to the warning information, and promptly sends the fault warning information to the relevant maintenance department or repair team. After receiving the warning, the repair team quickly finds the fault location through the positioning information, starts the repair procedure, ensures timely handling of the fault problem of the transmission line, reduces the outage time, and guarantees the stability of power supply. Finally, the fault warning location of the transmission line tower is located and the corresponding transmission line fault warning and repair work is executed.

[0025] Furthermore, the transmission line data acquisition module includes the following functions: By deploying image sensors, current sensors and voltage sensors on the transmission line; Using the image sensor to collect the surrounding environment images of the corresponding towers around the transmission line to obtain the transmission line tower environment images, including the distance of the corresponding trees around the transmission line tower and the condition of bird's nest construction; Use a current sensor to collect the operating current of the corresponding poles and towers around the transmission line in real time to obtain the current components of the transmission line poles and towers; Use a voltage sensor to collect the operating voltage of the corresponding poles and towers around the transmission line in real time to obtain the voltage components of the transmission line poles and towers; By building a data transmission network, in the nearby area or the area with strong signal, use optical fiber transmission technology and establish relay stations in remote areas or areas with weak signal to transmit the environmental images of the transmission line poles and towers, the current components of the transmission line poles and towers, and the voltage components of the transmission line poles and towers to the data processing center in real time.

[0026] As an embodiment of the present invention, refer to Figure 2 shown in Figure 1 the functional flow diagram of the transmission line data acquisition module in S11: Deploy image sensors, current sensors, and voltage sensors on the transmission line; In the embodiment of the present invention, by deploying a variety of sensors on each pole and tower of the transmission line, in order to comprehensively collect information such as the environment and operating status. The image sensor is installed on the top of the pole or other open positions to ensure that the environment around the pole can be monitored, including potential influencing factors such as surrounding vegetation and bird nests. The current sensor and voltage sensor are respectively arranged at the connection between the power conductor of the pole and the ground to collect the current and voltage data of the transmission line in real time. The position of each sensor should be accurately arranged according to the terrain and operating needs of the transmission line to ensure the extensiveness and accuracy of data collection. In addition, the installation of the sensor needs to have protective measures to prevent bad weather or external interference from affecting the normal operation of the sensor.

[0027] S12: Use the image sensor to collect the surrounding environment images of the corresponding poles and towers around the transmission line to obtain the environmental images of the transmission line poles and towers, including the distance status of the surrounding trees and the nest building status of the bird nests corresponding to the transmission line poles and towers; In an embodiment of the present invention, environmental images around the pole tower are collected by an image sensor to identify factors affecting the safety of the transmission line, such as the growth condition of trees, the construction of bird nests, and other potential obstacles. The image sensor should have high resolution and the ability to provide clear imaging in low-light environments, and be able to provide stable image data under different weather conditions. The collected images are processed by image recognition technology to automatically identify the distance between the trees and the pole tower, as well as the presence and location of the bird nests. Using artificial intelligence technologies, such as convolutional neural networks (CNNs), the images are deeply analyzed to evaluate whether the trees pose a threat to the line and whether the construction of the bird nests causes short circuits or other safety problems. An environmental image will be generated and digitally fed back to the data center for subsequent processing, and finally, an environmental image of the transmission line pole tower is obtained.

[0028] S13: Use a current sensor to collect the operating current of the corresponding pole tower around the transmission line in real time to obtain the current component of the transmission line pole tower. In an embodiment of the present invention, the current component of each pole tower in the transmission line is monitored in real time by a current sensor. Through a high-precision current sensor, the magnitude and fluctuation of the current of each pole tower can be collected. These current sensors should be installed at the connection between the power conductor and the pole tower or in direct contact with electrical equipment to ensure the accuracy of the acquired current data. The current signal will be transmitted to the data acquisition module, and finally, the current component of the transmission line pole tower is obtained.

[0029] S14: Use a voltage sensor to collect the operating voltage of the corresponding pole tower around the transmission line in real time to obtain the voltage component of the transmission line pole tower. In an embodiment of the present invention, a voltage sensor is used in this step to monitor the real-time voltage situation of each pole tower. The voltage sensor is installed at each key position of the transmission line, such as the connection point between the power conductor and the pole tower. The voltage sensor can accurately measure the voltage fluctuation, and the data will be transmitted to the data acquisition unit in real time. By collecting the voltage component, it can be determined whether the voltage is within the normal range, and the identified voltage abnormalities or unstable factors can be used to monitor the change trend of the voltage in real time. Finally, the voltage component of the transmission line pole tower is obtained.

[0030] S15: By building a data transmission network, the environmental image of the transmission line pole tower, the current component of the transmission line pole tower, and the voltage component of the transmission line pole tower are transmitted to the data processing center in real time using fiber optic transmission technology in nearby areas or areas with strong signals and establishing relay stations in remote areas or areas with weak signals.

[0031] In an embodiment of the present invention, a stable and efficient data transmission network is established to ensure that images, data, and sensor feedback can be transmitted to the data processing center in a timely manner. In areas with strong signals, fiber optic transmission technology is used to transmit a large amount of environmental images, current data, and voltage data with high bandwidth. In remote or weak-signal areas, relay stations are built or wireless communication technology is used to transmit the collected data to the main data processing center through the relay stations. These data will be transmitted to the data center through the network for further real-time processing and analysis. The data center uses artificial intelligence algorithms to perform risk assessment based on this real-time information and feedback warning information to the management system. The combination of fiber optic transmission and relay stations enables the system to operate stably and provide comprehensive monitoring and warning services in various complex geographical environments.

[0032] Further, the power transmission line damage assessment module includes the following functions: Obtain the corresponding distance between the trees and the power transmission line in the environmental image of the power transmission line tower; Obtain the corresponding number of bird nests built based on the bird nest building conditions in the environmental image of the power transmission line tower; Obtain the corresponding environmental humidity and environmental precipitation in the periodic area through the corresponding towers around the power transmission line, and calculate the leakage probability of the power transmission line corresponding to the tower based on the environmental humidity and environmental precipitation to obtain the leakage probability of the power transmission line tower; Perform a safety damage assessment calculation on the power transmission line corresponding to the tower based on the distance between the trees, the number of bird nests built, and the leakage probability of the power transmission line tower to obtain the safety damage rate of the power transmission line.

[0033] As an embodiment of the present invention, refer to Figure 3 shown, for Figure 1 the functional flowchart of the power transmission line damage assessment module in S21: Obtain the corresponding distance between the trees and the power transmission line in the environmental image of the power transmission line tower; In the embodiment of the present invention, by adopting the high-resolution environmental image acquisition technology, the image data of the transmission line tower and its surrounding environment is obtained. The images are collected by means of unmanned aerial vehicles, satellite remote sensing or ground shooting technology. When collecting images, it is necessary to ensure the clarity and integrity of the images to avoid data errors caused by unclear perspectives. After the image data is obtained, image processing algorithms, especially object detection methods based on deep learning, are used to segment and identify the trees and transmission lines in the images. The relative positions of the trees and transmission lines are extracted through image recognition technology, and the specific distances between the trees and transmission lines are calculated. Further, a machine learning model is used to analyze the size, type and growth form of the trees to judge the influence range of the trees, and the precise quantification of the tree distances is realized through a spatial coordinate positioning system, and finally the far and near distance relationships between each tree and the corresponding transmission line are obtained.

[0034] S22: Obtain the corresponding number of bird's nests built through the bird's nest building conditions corresponding in the environmental image of the transmission line tower. In the embodiment of the present invention, by obtaining the corresponding high-resolution images, image segmentation and object detection technologies are used to locate and identify the bird's nests in the images. First, the images are preprocessed by using image processing algorithms, including image denoising, contrast enhancement, etc., to improve the recognition accuracy of the bird's nest targets. Then, a convolutional neural network (CNN) model is applied, combined with a pre-trained bird's nest recognition data set, to accurately identify the positions of the bird's nests in the images. During the recognition process, special attention is paid to the spatial relationships between the bird's nests and the towers and transmission lines to ensure that the bird's nests built around the transmission lines and towers are accurately counted, the number of bird's nests around each tower in the image is counted, and detailed bird's nest position and quantity information is output, and finally the corresponding number of bird's nests built is obtained.

[0035] S23: Obtain the environmental humidity and environmental precipitation corresponding to the periodic area through the corresponding towers around the transmission line, and calculate the leakage probability of the transmission line corresponding to the tower based on the environmental humidity and environmental precipitation to obtain the leakage probability of the transmission line tower. In the embodiment of the present invention, by obtaining the meteorological data corresponding to the positions around each tower, the values of environmental humidity and precipitation can be obtained through the surrounding weather stations or through a mobile sensor network. These data can reflect the humidity and precipitation of the surrounding environment in real time. The obtained environmental humidity and precipitation are input into the leakage risk assessment model, and based on the existing empirical data and algorithms, the leakage probability of each tower area is calculated. The calculation of the leakage probability involves multiple parameters such as environmental factors and line load, and the insulation state of the line is evaluated by using these parameters, and finally the leakage probability of the transmission line tower is obtained.

[0036] S24: Based on the distance between trees, the number of bird nests built, and the leakage probability of transmission line towers, a safety damage assessment calculation is performed on the transmission line corresponding to the tower to obtain the safety damage rate of the transmission line.

[0037] In an embodiment of the present invention, a comprehensive safety assessment formula is constructed by combining previously acquired data on the distance between trees and the number of bird nests, and the obtained leakage probability. The formula comprehensively considers the possibility of physical contact between trees and transmission lines, the impact of bird nests on the insulation performance of lines, and the risk of leakage probability. If trees are close to transmission lines, especially under extreme weather conditions such as wind or precipitation, the risk of trees falling over increases, causing line damage; when there are a large number of bird nests, the activities of birds may affect the mechanical or electrical safety of the lines; and the increase in the probability of leakage, especially in conditions of high humidity and precipitation, increases the risk of power system failure. By incorporating these factors into a comprehensive weighted algorithm model, the safety damage rate of the line corresponding to each tower is calculated, and finally the safety damage rate of the transmission line is obtained.

[0038] Furthermore, the step of obtaining the ambient humidity and ambient precipitation corresponding to the periodic area through the corresponding towers around the transmission line, and calculating the leakage probability of the transmission line corresponding to the towers based on the ambient humidity and ambient precipitation includes: Obtain the corresponding ambient humidity and ambient precipitation in the surrounding area through the corresponding towers around the transmission lines and in combination with the meteorological monitoring station; In an embodiment of the present invention, the ambient humidity and ambient precipitation corresponding to the surrounding area are obtained through the corresponding pole towers around the transmission line in combination with the data from the meteorological monitoring station. First, meteorological monitoring equipment needs to be installed at various key locations around the transmission line. The equipment can collect ambient humidity and precipitation data in real time. The locations of these meteorological monitoring equipment should be reasonably arranged to cover all important areas along the transmission line to ensure that the collected environmental data are representative. By connecting to the data transmission system of the meteorological monitoring station, the monitored ambient humidity and precipitation data are transmitted to the central control system in real time. Through the data transmission network, the ambient humidity and precipitation data of each meteorological monitoring station within a specified time period can be obtained, and finally the ambient humidity and ambient precipitation corresponding to the surrounding area are obtained.

[0039] Preferably, the ambient humidity and ambient precipitation corresponding to the surrounding area are calculated by arithmetic average at fixed time intervals of 30 minutes to obtain the average ambient humidity and average ambient precipitation; In the embodiment of the present invention, by performing arithmetic mean calculation on the ambient humidity and ambient precipitation corresponding to the surrounding area at a fixed time interval of 30 minutes, after obtaining the humidity and precipitation data collected by the meteorological monitoring station, the data is grouped according to the time interval to ensure that each group of data represents the environmental state of 30 minutes. On this basis, the arithmetic mean method is used to calculate the average value of each group of data, and the average ambient humidity and average ambient precipitation during this period are obtained. Specifically, assuming that there are n humidity values and precipitation values in a certain time period, the average ambient humidity and average ambient precipitation can be respectively expressed as the sum of these n values divided by n, that is, the average values of humidity and precipitation in each 30-minute time period are calculated respectively, and finally the average ambient humidity and average ambient precipitation are obtained.

[0040] Preferably, a tilt monitor is used to collect the pole tilt of the poles corresponding to the transmission line to obtain the pole tilt angle of the transmission line tower. In the embodiment of the present invention, by using a tilt monitor to collect the tilt of the poles corresponding to the transmission line to obtain the pole tilt angle, at each key position of the transmission line, a fixed pole tilt monitor is set up, which can collect and record the tilt data of the pole in real time. The tilt monitor can use high-precision sensors, such as tilt sensors or acceleration sensors, to accurately measure the offset of the pole in the vertical direction. By connecting to the data acquisition system, the monitor can automatically record and upload data at regular intervals (such as every minute). At the system end, after receiving the tilt data of the pole, the data is analyzed through a preset algorithm to obtain the tilt angle of each pole at a specific moment, and finally the pole tilt angle of the transmission line tower is obtained.

[0041] Preferably, based on the pole tilt angle of the transmission line tower, the average ambient humidity, and the average ambient precipitation, the leakage probability calculation formula for the pole line is used to calculate the leakage probability of the transmission line corresponding to the pole to obtain the leakage probability of the transmission line tower.

[0042] In the embodiment of the present invention, by combining the maximum pole tilt angle, the minimum ambient humidity, the maximum ambient humidity, the minimum precipitation, the maximum precipitation, the pole tilt angle parameter, the pole tilt contribution coefficient, the ambient humidity parameter, the average ambient humidity, the ambient humidity standard deviation, the ambient precipitation parameter, the average ambient precipitation, the ambient precipitation standard deviation, the environmental parameter contribution coefficient, and related parameters, a suitable leakage probability calculation formula for the pole line is constructed for quantitative calculation of the probability to calculate the probability of the pole leakage under the given environmental conditions, and finally the leakage probability of the transmission line tower is obtained. In addition, this leakage probability calculation formula for the pole line can also use any leakage detection algorithm in the field to replace the process of leakage probability calculation, and is not limited to this leakage probability calculation formula for the pole line.

[0043] Furthermore, the specific formula for calculating the leakage probability of the pole and tower line is as follows: ; In the formula, is the leakage probability of the transmission line pole and tower, is the maximum inclination angle of the pole and tower, is the minimum value of the environmental humidity, is the maximum value of the environmental humidity, is the minimum value of the precipitation, is the maximum value of the precipitation, is the pole and tower inclination angle parameter, is the pole and tower inclination contribution coefficient, is the environmental humidity parameter, is the average environmental humidity, is the standard deviation of the environmental humidity, is the environmental precipitation parameter, is the average environmental precipitation, is the standard deviation of the environmental precipitation, is the environmental parameter contribution coefficient, is the correction coefficient of the leakage probability of the transmission line pole and tower.

[0044] The present invention has obtained a leakage probability calculation formula for tower lines through the use of a specific mathematical model and verification, which is used to calculate the leakage probability of the transmission line corresponding to the tower. This leakage probability calculation formula for tower lines comprehensively evaluates the leakage probability of the transmission line tower by combining multiple factors (such as tower inclination angle, environmental humidity, precipitation, etc.). Different environmental conditions (such as humidity, precipitation, tower inclination) will all affect the leakage risk. The formula can accurately take these factors into consideration, thereby providing an effective prediction for the safe operation of the transmission line. Tower inclination is an important factor affecting the safety of the transmission line. By adding a parameter of the inclination angle to the formula, the influence of the inclination on the leakage probability can be quantified. Especially in cases of adverse weather or geological activities (such as earthquakes, floods, etc.), the inclination angle of the tower may change, leading to an increase in the leakage risk. The introduction of this formula can predict in advance and take protective measures in these cases to avoid leakage problems caused by changes in the tower structure. By comprehensively analyzing the standard deviation and mean value of environmental humidity and precipitation, the formula can reflect the complex non-linear influence of humidity and precipitation on leakage. Excessively high humidity or precipitation will significantly increase the leakage risk, while lower humidity and precipitation will reduce this risk. By dynamically calculating the contribution of humidity and precipitation to leakage, it can provide a safety assessment for power companies based on real-time weather changes. In addition, the correction coefficient in the formula can be adjusted according to actual situations, so as to flexibly cope with the leakage risk under different environmental and operating conditions. This coefficient enables the formula to have the ability to adapt to different regions and different climate conditions, helping to more accurately evaluate the leakage probability and make targeted adjustments to measures. The and are respectively used to measure the influence degrees of tower inclination and environmental factors (humidity, precipitation). Through the adjustment of the weight coefficient, the roles of different factors in the leakage probability can be highlighted, thereby optimizing the maintenance and monitoring strategies of the transmission line in a targeted manner. To sum up, this formula fully considers the leakage probability of the transmission line tower , the maximum tower inclination angle , the minimum environmental humidity , the maximum environmental humidity , the minimum precipitation , the maximum precipitation , the tower inclination angle parameter , the tower inclination contribution coefficient , the environmental humidity parameter , the average environmental humidity , the environmental humidity standard deviation , the environmental precipitation parameter , the average environmental precipitation , the environmental precipitation standard deviation , the environmental parameter contribution coefficient , correction factor of the leakage probability of transmission line towers , according to the leakage probability of transmission line towers and the mutual correlation relationship with the above parameters constitutes a functional relationship , this formula can realize the calculation process of the leakage probability of the transmission line corresponding to the tower. At the same time, through the correction factor of the leakage probability of the transmission line tower The introduction of can be adjusted according to the error situation in the calculation process, thereby improving the accuracy and applicability of the leakage probability calculation formula of the tower line.

[0045] Further, the safety damage assessment calculation of the transmission line corresponding to the tower based on the distance of trees, the number of bird nests built, and the leakage probability of the transmission line tower includes: Obtain the growth trend of the tree branches corresponding to the distance of the trees, and conduct a mechanical damage assessment on the transmission line corresponding to the tower based on the growth trend of the tree branches to obtain the mechanical damage coefficient of the transmission line; In the embodiment of the present invention, by using high-precision lidar (LIDAR) technology or high-resolution satellite remote sensing images, the trees along the transmission line are measured to obtain the distance between the trees and the transmission line, the height of the trees, the distribution of branches and other relevant data. Then, apply artificial intelligence algorithms (such as convolutional neural networks in deep learning) to analyze the growth trend of the trees, predict the growth of the trees in a certain period of time in the future. The growth trend of the trees usually considers climatic conditions, tree species characteristics and the growth rate of the trees. By establishing a mathematical model and combining real-time data for simulation and calculation, the mechanical damage caused by the trees to the transmission line in a certain period of time in the future is obtained, and finally the mechanical damage coefficient of the transmission line is obtained.

[0046] Preferably, measure the insulation resistance of the transmission line corresponding to the tower to obtain the insulation resistance of the transmission line; In the embodiment of the present invention, it is measured by using a special insulation resistance test instrument. During the implementation process, by accurately determining the position of the transmission line tower, ensure that the measuring device can be correctly placed at a specific test point, connect the insulation resistance tester to the wiring terminal of the transmission line, and measure the insulation resistance value of each line. When measuring, it is necessary to ensure that the working voltage and current of the measuring instrument meet the working standards of the transmission line to avoid equipment damage or inaccurate measurement caused by excessive test current. This test is repeated at different time intervals and environmental conditions to obtain multiple readings to evaluate the insulation status of the transmission line, and finally the insulation resistance of the transmission line is obtained.

[0047] Preferably, based on the number of bird nests built, conduct an insulation impact assessment analysis on the insulation resistance of the transmission line to obtain the insulation impact efficiency of the transmission line; In the embodiments of the present invention, according to the environment where birds inhabit around the transmission line, the number and distribution of bird nests are obtained through high-resolution images or manual monitoring. The number of bird nests built is statistically analyzed using machine learning algorithms. Combining the bird inhabitation habits, the impact of the bird nests on the insulation resistance of the transmission line is evaluated. The activities of birds and the presence of bird nests cause the transmission line to be contaminated, thereby affecting its insulation performance. Based on the statistical data of the number and location of bird nests built and combined with the insulation resistance test results, a comprehensive impact assessment model is established to calculate the specific impact efficiency of bird inhabitation on the insulation performance of the transmission line. This efficiency reflects the negative impact degree of the bird nest building on the insulation resistance, and finally the insulation impact efficiency of the transmission line is obtained.

[0048] Preferably, based on the mechanical damage coefficient of the transmission line, the insulation impact efficiency of the transmission line, and the leakage probability of the transmission line tower, the safety breakage rate calculation formula of the line is used to perform a safety breakage assessment calculation on the transmission line corresponding to the tower, and the safety breakage rate of the transmission line is obtained.

[0049] In the embodiments of the present invention, by combining the safety breakage assessment time interval, time variable parameter, mechanical damage coefficient of the transmission line, distance between the tree and the transmission line, distance mechanical damage weight, tree growth speed, tree growth mechanical damage weight, tree growth height, tree height mechanical damage weight, insulation impact efficiency of the transmission line, number of bird nests built, insulation resistance of the transmission line, insulation impact correction factor, leakage probability of the transmission line tower, leakage probability adjustment factor, and related parameters, a suitable safety breakage rate calculation formula of the line is formed for quantitative calculation to quantitatively obtain the safety breakage rate of the transmission line. This breakage rate represents the probability of the line having a fault or damage within a certain period of time and is an important indicator for evaluating the safety status of the line. Finally, the safety breakage rate of the transmission line is obtained. In addition, this safety breakage rate calculation formula of the line can also use any line breakage monitoring technology in the field to replace the process of safety breakage assessment calculation, and is not limited to this safety breakage rate calculation formula of the line.

[0050] Furthermore, the safety breakage rate calculation formula of the line is specifically: ; ; ; In the formula, is the safety breakage rate of the transmission line, is the safety breakage assessment time interval, is the time variable parameter, is the mechanical damage coefficient of the transmission line at time , is at time The distance between the trees and the transmission line is the weight of mechanical damage at time is the tree growth rate is the weight of mechanical damage caused by tree growth at time is the tree growth height is the weight of mechanical damage caused by tree height at time is the insulation influence efficiency of the transmission line at time is the number of bird nests built is the insulation resistance of the transmission line is the insulation influence correction factor is the leakage probability of the transmission line tower is the leakage probability adjustment factor is the correction coefficient of the safety breakage rate of the transmission line

[0051] The present invention has obtained a calculation formula for the line safety breakage rate through the use of a specific mathematical model and verification, which is used to calculate the safety breakage assessment of the transmission line corresponding to the pole tower. This calculation formula for the line safety breakage rate comprehensively considers the impacts of multiple factors (such as mechanical damage, insulation influence, and leakage probability, etc.) on the safety of the transmission line, and can comprehensively evaluate the safety breakage rate of the line. By introducing different parameters and correction factors, it can effectively reflect the comprehensive effects of various external and internal factors on the line safety, avoiding the limitations of a single factor. Through parameters such as the distance between the tree and the transmission line, the tree growth rate, and the height, combined with the mechanical damage weight, the formula can dynamically evaluate the impact of the tree growth process on the mechanical damage of the transmission line. This consideration of dynamic changes helps to predict future safety risks and take measures in advance to reduce potential line failures. By evaluating the number of bird nests built and combining with the insulation resistance of the transmission line, the formula can estimate the downward trend of the insulation performance. The building of bird nests may affect the integrity of the insulation, leading to leakage or short - circuit risks. Through this evaluation, it can provide a scientific basis for the insulation maintenance of the line. By considering the leakage probability, the formula can accurately evaluate the electrical safety of the line under different conditions. By introducing a leakage probability adjustment factor and a safety breakage correction coefficient, the formula can be flexibly adjusted according to the actual situation, making the prediction of the breakage rate more accurate and reliable. Multiple correction coefficients and dynamic time variables (such as time, tree growth status, etc.) in the formula can be adjusted at any time according to environmental changes and actual conditions, making the evaluation results more precise. Different environmental and operating conditions (such as tree growth, bird nest building, etc.) can affect the calculation of the line safety breakage rate in real - time, providing flexibility and accuracy. Since the formula can consider multiple dynamic factors, it can help engineers and maintenance personnel predict potential line damage in advance and take preventive measures in a timely manner. This not only improves the safety of the transmission line but also reduces power outages caused by line breakage, ensuring the stable operation of the power system. In summary, this formula fully considers the line safety breakage rate , the safety breakage assessment time interval , the time - variable parameter , at time , the mechanical damage coefficient of the transmission line , at time , the distance between the tree and the transmission line , the distance mechanical damage weight , at time , the tree growth rate , the tree growth mechanical damage weight , at time , the tree growth height , the tree height mechanical damage weight , at time The insulation of the transmission line at [location] affects the efficiency At time The number of bird nests built at [location] The insulation resistance of the transmission line The insulation influence correction factor The leakage probability of the transmission line tower The leakage probability adjustment factor The correction coefficient of the safety breakage rate of the transmission line Among them, by combining the time variable parameter At time The distance between the tree and the transmission line at [location] The distance mechanical damage weight At time The tree growth rate at [location] The tree growth mechanical damage weight At time The tree growth height at [location] And the tree height mechanical damage weight Constitute a transmission line mechanical damage coefficient at time at [location] Functional relationship Also, by combining the time variable parameter And at time The number of bird nests built at [location] Constitute a transmission line insulation influence efficiency at time at [location] Functional relationship According to the safety breakage rate of the transmission line The mutual correlation relationship with the above parameters constitutes a functional relationship This formula can realize the safety breakage assessment calculation process of the transmission line corresponding to the tower. At the same time, through the introduction of the correction coefficient of the safety breakage rate of the transmission line, it can be adjusted according to the error situation in the calculation process, thereby improving the accuracy and applicability of the formula for calculating the safety breakage rate of the line.

[0052] Furthermore, the tower safety fault identification module includes the following functions: By fitting the time series change curve of the safety breakage rate of the transmission line within a certain time range to generate the change trend curve of the transmission line breakage rate; In an embodiment of the present invention, by obtaining historical breakage rate data of a transmission line within a certain time range, the breakage rate data includes the occurrence frequency or damage degree of breakage events of the transmission line at each time point. These historical data are cleaned and sorted by a data processing tool (such as the Pandas library in Python) to remove outliers and missing values to ensure the accuracy of the data. Then, a mathematical fitting method, such as the least squares method, is used to fit the time series change curve. This process can be completed using a curve fitting algorithm (such as curve_fit in Scipy). A suitable data fitting model (such as an exponential decay model or a linear regression model) is selected to fit the historical breakage rate data to obtain a trend curve of the breakage rate changing with time. This trend curve can reflect the change of the breakage rate of the transmission line within a certain time period, and finally generate a trend curve of the change of the breakage rate of the transmission line.

[0053] Preferably, the growth or decline rate of the breakage rate corresponding to the area where the transmission line is located is determined through the trend curve of the change of the breakage rate of the transmission line. In an embodiment of the present invention, by analyzing the derivative of the curve according to the previously obtained trend curve of the change of the breakage rate of the transmission line, the change rate of the breakage rate is determined. First, a numerical derivative is taken of the trend curve (for example, using the difference method or the derivative calculation formula) to obtain the change rate of the breakage rate at each time point. If the curve shows an upward trend, it means the breakage rate is increasing; if the curve shows a downward trend, it means the breakage rate is decreasing. The calculated rate value can be judged by a set threshold. If the rate is greater than the preset growth rate threshold, it is considered that the breakage rate is increasing rapidly; if the rate is less than the preset decline rate threshold, it is considered that the breakage rate is decreasing slowly. Through this analysis, the change trend of the breakage rate in the area where the transmission line is located can be clearly understood, and finally the growth or decline rate of the breakage rate corresponding to the area where the transmission line is located is obtained.

[0054] Preferably, a breakage fault analysis is performed on the corresponding poles and towers around the transmission line based on the growth or decline rate of the breakage rate corresponding to the area where the transmission line is located. If the growth or decline rate of the breakage rate is greater than or equal to the preset threshold, it is determined that there is a breakage fault in the corresponding transmission line pole and tower; otherwise, it is determined that there is no breakage fault in the corresponding transmission line pole and tower to obtain the result of the external breakage fault of the transmission line pole and tower. In an embodiment of the present invention, by combining geographical information and historical damage data of poles and towers around a transmission line according to the growth or decline rate of the previously obtained damage rate, a fault analysis of the poles and towers is carried out. First, according to the change rate of the damage rate in the area where the pole or tower is located, a certain algorithm (such as linear regression, support vector machine, etc.) is used to evaluate the risk of each pole and tower. If the growth or decline rate of the damage rate in this area exceeds a preset threshold (such as the growth rate exceeds 0.2% per month), it is determined that the poles and towers in this area have a relatively high risk of damage, and then it is determined that these poles and towers have external damage faults. According to the analysis results, fault identification will be carried out on the poles and towers around the transmission line, and the result of "there is a damage fault" or "there is no damage fault" will be output, and finally the result of external damage faults of the transmission line poles and towers is obtained.

[0055] Preferably, a convolutional neural network is used to identify and analyze the internal safety faults of the current components and voltage components of the transmission line poles and towers to obtain the results of internal electrical parameter faults of the transmission line poles and towers; In an embodiment of the present invention, the current and voltage data of the poles and towers are collected in real time through sensors installed on the transmission line poles and towers, and the current and voltage data will be transmitted to the data processing system through wireless communication technology (such as a solution combining the Beidou positioning system and the wireless sensor network). Subsequently, a convolutional neural network (CNN) is used to identify and analyze the current and voltage data. Specifically, a trained convolutional neural network model is used, which is specifically used to analyze the current and voltage data of electrical equipment and identify possible electrical faults, including whether there is overload (that is, the current gradually increases and the voltage gradually decreases), short circuit (that is, the current suddenly surges and the voltage suddenly drops) and other safety faults in the transmission line. The input data is processed by the convolutional layer and the pooling layer to extract the characteristics of the current and voltage signals, and then classified and output through the fully connected layer. If the current or voltage fluctuates abnormally, it is determined that the pole and tower has an internal electrical fault, and the corresponding fault type is output, and finally the result of internal electrical parameter faults of the transmission line poles and towers is obtained.

[0056] Preferably, the results of external damage faults of the transmission line poles and towers and the results of internal electrical parameter faults of the transmission line poles and towers are combined to obtain the results of safety fault identification of the transmission line poles and towers.

[0057] In an embodiment of the present invention, by combining the previously obtained results of external factor damage faults with the results of internal factor electrical parameter faults identified by a convolutional neural network, first, the results of the two are subjected to data fusion, and a decision tree algorithm, weighted average or other suitable fusion method is used to synthesize the external and internal factor fault information. If one or both results indicate that there is a fault in the pole tower, it is finally determined that the pole tower has a safety fault; if both the external factor fault result and the internal factor fault result indicate that the pole tower has no fault, it is determined that the pole tower is safe. This result can not only identify whether there is a fault in the pole tower, but also further distinguish whether the nature of the fault is caused by external environmental factors or electrical factors, and finally obtain the identification result of the safety fault of the transmission line pole tower.

[0058] Furthermore, the use of the convolutional neural network to perform internal factor safety fault identification and analysis on the current component of the transmission line pole tower and the voltage component of the transmission line pole tower includes: Performing time series synchronization processing on the current component of the transmission line pole tower and the voltage component of the transmission line pole tower to obtain the corresponding pole tower current component sequence and pole tower voltage component sequence within the same time series range; In an embodiment of the present invention, by performing time series synchronization processing on the current component of the transmission line pole tower and the voltage component of the transmission line pole tower, first, the current and voltage signal data of each transmission line pole tower are collected. During the collection process, high-precision current and voltage sensors are used to monitor the electrical parameters of each pole tower on the line in real time, and the collected current and voltage signals are time series aligned through a synchronous sampling device. The specific operation is to calibrate through the time stamp and the synchronous signal to ensure that the acquisition time points of all pole tower current and voltage signals are consistent within the same time series range, so that the current and voltage data are completely aligned in the time domain. After the time series synchronization processing is completed, the corresponding pole tower current component sequence and pole tower voltage component sequence within the same time series range can be obtained.

[0059] Preferably, frequency domain statistical analysis of the pole tower electrical parameters is performed on the corresponding pole tower current component sequence and pole tower voltage component sequence within the same time series range to obtain the frequency domain statistical characteristics corresponding to the transmission line pole tower electrical parameters, including the frequency components, harmonic contents, and transient change characteristics corresponding to the current and voltage; In an embodiment of the present invention, through frequency-domain statistical analysis of the corresponding tower current component sequence and tower voltage component sequence within the same time sequence range, first, frequency-domain conversion is performed on the synchronized current and voltage sequences through fast Fourier transform (FFT). The current and voltage signals are divided into different frequency components in the frequency domain, and the FFT algorithm can extract these frequency components. For the electrical parameters of each transmission line, by analyzing the amplitude spectrum in the frequency domain, the main frequency components and harmonic contents are extracted. Through frequency-domain analysis, the low-frequency components, intermediate-frequency components, and high-frequency components in the current and voltage signals are calculated, and the existing harmonic components are particularly identified. Further, statistical methods are used to evaluate the intensity, phase, and harmonic distortion of the components in each frequency band to obtain frequency-domain statistical features, and finally, the frequency-domain statistical features corresponding to the electrical parameters of the transmission line tower are obtained.

[0060] Preferably, based on the frequency-domain statistical features corresponding to the electrical parameters of the transmission line tower, instantaneous mutation analysis is performed on the corresponding tower current component sequence and tower voltage component sequence within the same time sequence range to obtain the instantaneous mutation distribution features corresponding to the electrical parameters of the transmission line tower; In an embodiment of the present invention, through instantaneous mutation analysis of the tower current component sequence and voltage component sequence based on the frequency-domain statistical features, first, the instantaneous change regions in the signals are extracted through the frequency-domain statistical features. The purpose of instantaneous mutation analysis is to identify sudden events in the current and voltage signals, such as sharp increases, decreases, or fluctuations in current or voltage, which are usually caused by line failures. Using wavelet transform or time-domain difference method, mutation detection is performed on the current and voltage signals. Specifically, the sliding window technique is adopted to gradually analyze the changes in current and voltage in each time period, calculate their change rates and change amplitudes in this time period, and identify whether the instantaneous change exceeds a predetermined threshold. By extracting the mutation intervals, the distribution features of the mutation phenomena in the transmission line can be accurately obtained, including the time, amplitude, and duration of the mutation, and finally, the instantaneous mutation distribution features corresponding to the electrical parameters of the transmission line tower are obtained.

[0061] Preferably, a convolutional neural network is used to perform internal cause safety fault identification analysis on the instantaneous mutation distribution features corresponding to the electrical parameters of the transmission line tower, so as to input the instantaneous mutation distribution features corresponding to the electrical parameters of the transmission line tower into the convolutional neural network to construct a tower electrical parameter line fault identification model, and output the overload and short-circuit faults corresponding to the transmission line tower to obtain the internal cause electrical parameter fault results of the transmission line tower.

[0062] In the embodiment of the present invention, a convolutional neural network (CNN) is used to perform fault identification and analysis on the instantaneous mutation distribution characteristics of electrical parameters. First, the mutation characteristics of current and voltage extracted from the instantaneous mutation analysis are input into the convolutional neural network model. This model first performs multi-layer convolutional operations on the input mutation characteristics to learn the spatial characteristics of current and voltage mutations. Then, the pooling layer is used to further reduce the data dimension and improve the efficiency of the model. Next, the fully connected layer is used to convert the feature map into the determination result of electrical parameter faults. At this time, the convolutional neural network performs in-depth learning on the mutation distribution characteristics of electrical parameters through the trained model, and automatically identifies whether there are safety faults such as overload (i.e., the current gradually increases and the voltage gradually decreases) and short circuit (i.e., the current suddenly surges and the voltage suddenly drops) in the transmission line. Through network training, the model can be continuously optimized based on historical data, so as to realize the prediction and alarm of future fault events. When the model outputs a signal of overload or short circuit fault, an alarm will be automatically generated to prompt the safety risk of the transmission line, and finally the internal electrical parameter fault result of the transmission line tower will be obtained.

[0063] Furthermore, the safety fault location and early warning module includes the following functions: Integrate and deploy the Beidou positioning module on the corresponding towers around the transmission line; In the embodiment of the present invention, by integrating and deploying the Beidou positioning module on each tower of the transmission line, to ensure the positioning accuracy and stability, each tower should be equipped with a set of integrated Beidou receiving modules, which have the ability to receive satellite signals in real time. This Beidou positioning module should be able to maintain high-precision positioning under strong electromagnetic interference, complex environments, etc. During the actual deployment process, first, according to the path of the transmission line and the specific location of the tower, select a suitable installation location and ensure that the module can maintain a continuous and stable satellite connection. The power supply of the module is powered by an external solar panel and a storage battery to ensure 24-hour all-weather operation. Each positioning module is connected to the central control platform through a wireless communication network (such as 5G, LoRa, etc.) to transmit real-time positioning data.

[0064] Preferably, the Beidou positioning module is used to perform fault location and early warning on the transmission line towers corresponding to the safety fault identification results of the transmission line towers with safety faults, so as to obtain the fault warning position of the transmission line towers; In an embodiment of the present invention, the deployed Beidou positioning module is used to monitor each transmission line tower in real time, identify safety faults, and determine whether there are transmission line towers with safety faults in the previously identified safety fault identification results of the transmission line towers. If so, the Beidou positioning module will be automatically activated for fault location. When a fault is identified, the Beidou positioning module will obtain the specific tower position where the fault occurs and transmit the coordinates and fault information of this fault position to the central monitoring system through a wireless network. At this time, the system processes and analyzes the data according to the input algorithm model, judges the severity of the fault, generates corresponding early warning information, marks the transmission line towers with safety faults, and issues an early warning to relevant operators, and finally locates the early warning position of the transmission line tower fault.

[0065] Preferably, obtain the known positions of rescue teams, and perform repair positioning calculations on the known positions of rescue teams based on the early warning position of the transmission line tower fault to obtain the distance and azimuth between the transmission line fault point and the known position points, and notify the execution of the corresponding transmission line fault early warning repair work.

[0066] In an embodiment of the present invention, after receiving the early warning of the transmission line tower fault, the position information of the known rescue teams is further obtained. The positions of these rescue teams can be tracked in real time through the Beidou positioning module or other GPS positioning technologies. According to the early warning position of the faulty tower, the distance and azimuth from the rescue team to the fault position will be calculated by triangulation using the positions of the known rescue teams and the coordinates of the faulty tower. This calculation takes into account terrain factors and transportation routes to ensure high accuracy of the calculated distance and azimuth. Based on the calculation results, the system sends repair instructions to each rescue team, guiding them the shortest path and real-time traffic information to ensure timely repair work. At the same time, the rescue team members can receive repair information in real time through mobile terminals and accurately reach the fault point for repair under the guidance of the Beidou positioning system. They can also adjust the early warning and repair scheduling plan according to the real-time feedback during the repair process to optimize the repair efficiency and fault recovery speed, and finally notify the execution of the corresponding transmission line fault early warning repair work.

[0067] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to encompass all changes falling within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0068] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A power transmission line safety early warning system based on artificial intelligence and Beidou positioning, characterized in that: Includes the following modules: The transmission line data acquisition module is used to deploy image sensors, current sensors and voltage sensors on the transmission line, and use the image sensors, current sensors and voltage sensors to perform multi-source line data acquisition on the corresponding towers around the transmission line, so as to obtain the transmission line tower environment image, the transmission line tower current component and the transmission line tower voltage component; Transmitting the environmental image of the transmission line tower, the current component of the transmission line tower, and the voltage component of the transmission line tower to the data processing center in real time; The power transmission line damage assessment module is used to obtain the corresponding tree distance and the number of bird nests through the transmission line tower environment image, and perform safety damage assessment calculation on the transmission line corresponding to the tower based on the tree distance and the number of bird nests to obtain the transmission line safety damage rate; The tower safety fault identification module is used to perform safety fault identification analysis on the transmission line tower current component and the transmission line tower voltage component based on the transmission line safety damage rate and in combination with the convolutional neural network to obtain the transmission line tower safety fault identification result; The safety fault location and warning module is used to integrate the Beidou positioning module on the corresponding towers around the transmission line, and use the Beidou positioning module to perform fault location and warning on the transmission line towers with safety faults corresponding to the safety fault identification results of the transmission line towers, so as to obtain the fault warning position of the transmission line towers and perform the corresponding transmission line fault warning emergency repair work.

2. The power transmission line safety early warning system based on artificial intelligence and Beidou positioning according to claim 1 is characterized in that: The transmission line data acquisition module includes the following functions: By deploying image sensors, current sensors, and voltage sensors on transmission lines; The image sensor is used to collect the surrounding environment images of the towers corresponding to the transmission line, so as to obtain the environmental images of the transmission line towers, including the distance of the trees corresponding to the transmission line towers and the construction of bird nests; Use current sensors to collect real-time operating currents of the corresponding towers around the transmission lines to obtain the current components of the transmission line towers; Use voltage sensors to collect the operating voltage of the corresponding towers around the transmission line in real time to obtain the voltage components of the transmission line towers; By building a data transmission network, using optical fiber transmission technology in nearby areas or areas with strong signals and establishing relay stations in remote areas or areas with weak signals, the environmental images of transmission line towers, current components of transmission line towers, and voltage components of transmission line towers are transmitted to the data processing center in real time.

3. The power transmission line safety early warning system based on artificial intelligence and Beidou positioning according to claim 1 is characterized in that: The transmission line damage assessment module includes the following functions: Obtain the corresponding distance between trees through the transmission line and surrounding trees in the transmission line tower environment image; Obtain the corresponding number of bird nests built according to the corresponding bird nest building conditions in the transmission line tower environment image; The environmental humidity and environmental precipitation corresponding to the periodic area are obtained through the corresponding towers around the transmission line, and the leakage probability of the transmission line corresponding to the tower is calculated based on the environmental humidity and environmental precipitation to obtain the leakage probability of the transmission line tower; Based on the distance between trees, the number of bird nests built and the leakage probability of transmission line towers, a safety damage assessment calculation is performed on the transmission line corresponding to the tower to obtain the safety damage rate of the transmission line.

4. The power transmission line safety early warning system based on artificial intelligence and Beidou positioning according to claim 3 is characterized in that: The step of obtaining the ambient humidity and ambient precipitation corresponding to the periodic area through the corresponding pole towers around the transmission line, and calculating the leakage probability of the transmission line corresponding to the pole towers based on the ambient humidity and ambient precipitation includes: Obtain the corresponding ambient humidity and ambient precipitation in the surrounding area through the corresponding towers around the transmission lines and in combination with the meteorological monitoring station; The ambient humidity and ambient precipitation corresponding to the surrounding area are calculated by arithmetic average at fixed time intervals of 30 minutes to obtain the average ambient humidity and average ambient precipitation; Use the tilt monitor to collect the tilt of the corresponding towers around the transmission line to obtain the tilt angle of the transmission line tower; Based on the inclination angle of the transmission line tower, the average ambient humidity and the average ambient precipitation, the leakage probability of the transmission line corresponding to the tower is calculated using the tower line leakage probability calculation formula to obtain the transmission line tower leakage probability.

5. The power transmission line safety early warning system based on artificial intelligence and Beidou positioning according to claim 4 is characterized in that: The calculation formula for the tower line leakage probability is as follows: ； In the formula, is the leakage probability of the transmission line tower, is the maximum inclination angle of the tower, is the minimum value of ambient humidity, is the maximum ambient humidity, is the minimum precipitation value, is the maximum precipitation amount, is the tower inclination angle parameter, is the tower tilt contribution coefficient, is the ambient humidity parameter, is the average ambient humidity, is the standard deviation of ambient humidity, is the environmental precipitation parameter, is the average ambient precipitation, is the standard deviation of ambient precipitation, is the environmental parameter contribution coefficient, It is the correction factor of the leakage probability of transmission line tower.

6. The power transmission line safety early warning system based on artificial intelligence and Beidou positioning according to claim 3 is characterized in that: The safety damage assessment calculation of the transmission line corresponding to the tower based on the distance between the trees, the number of bird nests and the leakage probability of the transmission line tower includes: The corresponding tree trunk growth trend is obtained by the distance between trees, and the mechanical damage of the transmission line corresponding to the tower is evaluated based on the tree trunk growth trend to obtain the mechanical damage coefficient of the transmission line; Measuring the insulation resistance of the transmission line corresponding to the tower to obtain the insulation resistance of the transmission line; The insulation impact assessment and analysis of the insulation resistance of the transmission line was conducted based on the number of bird nests built, and the insulation impact efficiency of the transmission line was obtained; Based on the mechanical damage coefficient of the transmission line, the insulation influence efficiency of the transmission line and the leakage probability of the transmission line tower, the line safety damage rate calculation formula is used to perform a safety damage assessment calculation on the transmission line corresponding to the tower to obtain the transmission line safety damage rate.

7. The power transmission line safety early warning system based on artificial intelligence and Beidou positioning according to claim 6 is characterized in that: The line safety damage rate calculation formula is specifically: ； ； ； In the formula, is the safety damage rate of transmission lines, The safety damage assessment time interval is is the time variable parameter, For in time The mechanical damage coefficient of the transmission line at For in time The distance between trees and transmission lines, is the distance mechanical damage weight, For in time The growth rate of trees at is the mechanical damage weight of tree growth, For in time The height of trees growing at is the mechanical damage weight of tree height, For in time The insulation of the transmission line affects the efficiency. For in time The number of bird nests built at is the insulation resistance of the transmission line, is the insulation effect correction factor, is the leakage probability of the transmission line tower, is the leakage probability adjustment factor, It is the correction factor for the safety damage rate of the transmission line.

8. The power transmission line safety early warning system based on artificial intelligence and Beidou positioning according to claim 1 is characterized in that: The tower safety fault identification module includes the following functions: By fitting the time series change curve of the safety damage rate of the transmission line within a certain time range, a change trend curve of the damage rate of the transmission line is generated; Determine the growth or decline rate of the damage rate corresponding to the area where the transmission line is located through the transmission line damage rate change trend curve; Based on the damage rate growth or decline rate corresponding to the area where the transmission line is located, the damage fault analysis is performed on the corresponding towers around the transmission line. If the damage rate growth or decline rate is greater than or equal to a preset threshold, it is determined that the corresponding transmission line tower has a damage fault. Otherwise, it is determined that the corresponding transmission line tower does not have a damage fault, so as to obtain the external damage fault result of the transmission line tower; Convolutional neural network is used to identify and analyze the internal safety faults of transmission line tower current components and transmission line tower voltage components, so as to obtain the internal electrical parameter fault results of transmission line tower; The transmission line tower external damage failure results and the transmission line tower internal electrical parameter failure results are combined to obtain the transmission line tower safety fault identification results.

9. The power transmission line safety early warning system based on artificial intelligence and Beidou positioning according to claim 8 is characterized in that: The method of using a convolutional neural network to perform internal safety fault identification analysis on a transmission line tower current component and a transmission line tower voltage component includes: Performing time-series synchronization processing on the transmission line tower current component and the transmission line tower voltage component to obtain the tower current component sequence and the tower voltage component sequence corresponding to the same time-series range; Perform frequency domain statistical analysis on the tower electric parameters of the tower current component sequence and the tower voltage component sequence corresponding to the same time range to obtain the frequency domain statistical characteristics of the transmission line tower electric parameters, including the frequency components, harmonic content and transient change characteristics of the current and voltage. Based on the frequency domain statistical characteristics of the electric parameters of the transmission line towers, the tower current component sequence and the tower voltage component sequence corresponding to the same time range are analyzed for instantaneous mutations, and the instantaneous mutation distribution characteristics corresponding to the electric parameters of the transmission line towers are obtained; The convolutional neural network is used to perform intrinsic safety fault identification analysis on the instantaneous mutation distribution characteristics corresponding to the electrical parameters of the transmission line towers, so as to input the instantaneous mutation distribution characteristics corresponding to the electrical parameters of the transmission line towers into the convolutional neural network to construct a tower electrical parameter line fault identification model, and output the overload and short-circuit faults corresponding to the transmission line towers to obtain the intrinsic electrical parameter fault results of the transmission line towers.

10. The power transmission line safety early warning system based on artificial intelligence and Beidou positioning according to claim 1 is characterized in that: The safety fault location warning module includes the following functions: By integrating and deploying Beidou positioning modules on the corresponding towers around the transmission lines; The Beidou positioning module is used to carry out fault location warning for the transmission line tower with safety faults corresponding to the transmission line tower safety fault identification results, so as to obtain the transmission line tower fault warning position; Obtain the known rescue team location point, and perform emergency repair positioning calculation on the known rescue team location point based on the transmission line tower fault warning position, so as to obtain the distance and direction between the transmission line fault point and the known location point, and notify the execution of the corresponding transmission line fault warning emergency repair work.

Images