Automatic early warning method, device, electronic equipment and medium for tree obstacle hazards of overhead power transmission lines

Abstract

The embodiment of the present application discloses an automatic early warning method, device, electronic device and storage medium for tree barrier hazards of overhead power transmission lines, including: acquiring image data of overhead power transmission lines through satellite remote sensing technology; automatically analyzing the image data using a deep learning model to identify trees within a preset range of the overhead power transmission line; determining whether there are tree barrier hazards within the preset range of the overhead power transmission line based on the distance between the overhead power transmission line and the trees; and providing early warning prompts for identified tree barrier hazards. The present application can effectively improve the accuracy of the prediction results of the occurrence time of tree barrier hazards of overhead power transmission lines, and thus can effectively improve the accuracy and reliability of tree barrier hazard early warnings for overhead power transmission lines.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an automatic early warning method, device, electronic equipment and storage medium for tree obstacle hazards of overhead power transmission lines. Background Art

[0002] Due to the large scale, wide coverage, and complex terrain environment of the transmission line corridors in some areas, the growth of trees near the transmission lines affects the safe and stable operation of the transmission lines. Therefore, the conflict between trees and lines has always been the focus of the power department's inspection. When the safety distance between trees and lines is insufficient, it is easy to cause accidents such as tripping and discharge. The defects of the existing tree barrier warning method are as follows: In order to accurately obtain tree species information, it is necessary to conduct a field survey of the transmission line, which is time-consuming and laborious. There are many types of trees along the power line channel, and the growth rates of different types of trees are different. The traditional method only judges the distance between the power lines and the tree tops along the way through field investigations by team members. The work intensity is high, the timeliness is poor, the accuracy is low, and the efficiency is low. In the case of lush trees, it is difficult to observe the relationship between the tree tops, tree crowns and power lines at the same time, resulting in misjudgment and missed judgment. If you want to accurately calculate the distance between the conductor sag and the trees to avoid indiscriminate felling and damage to vegetation, you need to carry professional height measuring tools, theodolites and other bulky instruments, so the line patrol personnel have a huge workload. Summary of the invention

[0003] The embodiments of the present application provide an automatic early warning system, method, electronic device and storage medium for tree barrier hazards of overhead transmission lines, which can effectively improve the accuracy of the prediction results of the occurrence time of tree barrier hazards of overhead transmission lines, and thus can effectively improve the accuracy and reliability of tree barrier hazard warnings for overhead transmission lines.

[0004] In a first aspect, an embodiment of the present application provides an automatic early warning method for tree obstacle hazards of overhead transmission lines, comprising:

[0005] Obtain image data of overhead power transmission lines through satellite remote sensing technology;

[0006] Automatically analyzing the image data using a deep learning model to identify trees within a preset range of the overhead transmission line;

[0007] Determining whether there is a tree obstacle hazard within a preset range of the overhead transmission line according to the distance between the overhead transmission line and the tree;

[0008] Provide early warning for identified tree obstacle hazards.

[0009] Optionally, in some embodiments of the present application, determining whether there is a tree obstacle hazard within a preset range of the overhead transmission line according to the distance between the overhead transmission line and the tree includes:

[0010] Obtain point cloud data within a preset range of overhead transmission lines;

[0011] Extracting the conductor point cloud corresponding to the overhead transmission line and the vegetation point cloud corresponding to the trees from the point cloud data;

[0012] Calculating the distance between the overhead transmission line and the tree based on the conductor point cloud and the vegetation point cloud;

[0013] Based on the preset safety distance and the distance between the overhead power transmission line and the tree, it is determined whether there is a tree obstacle hazard within the preset range of the overhead power transmission line.

[0014] Optionally, in some embodiments of the present application, calculating the distance between the overhead transmission line and the tree according to the conductor point cloud and the vegetation point cloud includes:

[0015] Determining a vegetation contour corresponding to the vegetation point cloud;

[0016] The distance between each guide line point in the guide line point cloud and the vegetation contour is calculated.

[0017] Optionally, in some embodiments of the present application, determining whether there is a tree obstacle hazard within a preset range of the overhead transmission line based on a preset safety distance and a distance between the overhead transmission line and the tree includes:

[0018] Determining vegetation points within a preset range of the overhead power line based on the distance between the overhead power line and the trees;

[0019] Based on a preset safety distance, it is determined whether a vegetation point within a preset range of the overhead transmission line is a tree barrier hazard location, and based on the tree barrier hazard location, it is determined whether there is a tree barrier hazard within the preset range of the overhead transmission line.

[0020] Optionally, in some embodiments of the present application, determining whether a vegetation point within a preset range of the overhead transmission line is a tree barrier potential hazard location based on a preset safety distance includes:

[0021] When the vegetation point within the preset range of the overhead transmission line is less than or equal to the preset safety distance, the vegetation point is determined to be a tree obstacle potential location;

[0022] When the vegetation point within the preset range of the overhead power transmission line is greater than the preset safety distance, it is determined that the vegetation point is not a tree obstacle hazard site.

[0023] Optionally, in some embodiments of the present application, the using of a deep learning model to automatically analyze the image data to identify trees within a preset range of the overhead transmission line includes:

[0024] Preprocessing the image data to obtain a canopy height model and a normalized point cloud;

[0025] The deep learning model, the canopy height model and the normalized point cloud are used to identify trees within a preset range of the overhead transmission line.

[0026] Optionally, in some embodiments of the present application, the step of providing an early warning for the identified tree obstacle hazard includes:

[0027] Generate early warning information based on safety distance information at different voltage levels and identified tree obstacle hazards;

[0028] Based on the information type of the warning information, a warning prompt is given for the identified tree obstacle hidden danger.

[0029] In a second aspect, an embodiment of the present application provides an automatic early warning device for tree obstacle hazards of overhead power transmission lines, comprising:

[0030] An acquisition module, used to acquire image data of overhead power transmission lines through satellite remote sensing technology;

[0031] an identification module, configured to automatically analyze the image data using a deep learning model to identify trees within a preset range of the overhead transmission line;

[0032] A determination module, used to determine whether there is a tree obstacle hazard within a preset range of the overhead transmission line according to the distance between the overhead transmission line and the tree;

[0033] The prompt module is used to provide early warning for identified tree obstacle hazards.

[0034] Correspondingly, the present application also provides an electronic device, comprising a memory, a processor, and a processing program stored in the memory and executable on the processor, wherein the processor executes the program according to any of the steps of the method described above.

[0035] The present application also provides a storage medium, wherein the storage medium stores a processor program, and the processor program implements any of the above methods when the processor program is executed by a processor.

[0036] The present application provides an automatic early warning method, device, electronic device and storage medium for tree barrier hazards of overhead power transmission lines, which acquires image data of overhead power transmission lines through satellite remote sensing technology, and automatically analyzes the image data using a deep learning model to identify trees within a preset range of the overhead power transmission line: determines whether there are tree barrier hazards within the preset range of the overhead power transmission line based on the distance between the overhead power transmission line and the trees, and issues an early warning for the identified tree barrier hazards. In the automatic early warning scheme for tree barrier hazards of overhead power transmission lines provided in the present application, determines whether there are tree barrier hazards within the preset range of the overhead power transmission line based on the distance between the overhead power transmission line and the trees, which can effectively improve the accuracy of the prediction results of the occurrence time of tree barrier hazards of overhead power transmission lines, and further effectively improve the accuracy and reliability of early warnings for tree barrier hazards of overhead power transmission lines. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0038] Figure 1 It is a flow chart of an automatic early warning method for tree obstacle hazards of overhead power transmission lines provided in an embodiment of the present application;

[0039] Figure 2 It is a structural schematic diagram of an automatic early warning device for tree obstacle hazards of overhead power transmission lines provided in an embodiment of the present application;

[0040] Figure 3 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are merely examples of systems and methods consistent with the examples detailed in the attached claims, or with some aspects of the present application.

[0042] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover descriptions such as non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0043] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0044] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0045] Embodiments of the present application provide an automatic early warning method, device, electronic device, and storage medium for tree obstacle hazards on overhead power transmission lines.

[0046] In order to achieve the purpose of efficient automatic warning of tree barrier hazards, the inventor of this application considers that a new data acquisition method and a new automatic calculation method must be adopted. In order to improve the accuracy of warning from the acquisition of data basis, the inventor of this application considers that a drone equipped with a three-dimensional laser scanner can be used to obtain point cloud data, and on the basis of point cloud data, through the innovative application of the crown height model, and the proposal of a new and highly reliable tree growth calculation model, it can effectively improve the accuracy of the prediction results of the occurrence time of tree barrier hazards of overhead transmission lines, and then effectively improve the accuracy and reliability of tree barrier hazard warnings for overhead transmission lines, which is conducive to technicians to eliminate the timeliness and accuracy of tree barrier hazard warnings, and can also effectively reduce the time and money costs of manual line patrols while avoiding vegetation damage caused by indiscriminate felling of trees in the overhead transmission line area.

[0047] Among them, the use of laser radars carried by drones for power line inspection has the advantages of not being restricted by terrain and environment, high efficiency, wide operating range and high degree of automation, making it a new means of data acquisition. The point cloud obtained by the laser radar can automatically extract and classify treetops, tree crowns, and towers, and then predict the position of treetops through vegetation growth models, and finally output information such as tree obstacle reports. This method can greatly reduce the burden on line inspectors, reduce labor costs, and improve line inspection efficiency and automation.

[0048] It should be noted that the order of description of the following embodiments is not intended to limit the priority order of the embodiments.

[0049] The present application provides an automatic early warning method for tree obstacle hazards of overhead transmission lines, comprising: acquiring image data of overhead transmission lines through satellite remote sensing technology; automatically analyzing the image data using a deep learning model to identify trees within a preset range of the overhead transmission lines; determining whether there are tree obstacle hazards within the preset range of the overhead transmission lines based on the distance between the overhead transmission lines and the trees; and providing early warning prompts for the identified tree obstacle hazards.

[0050] See also Figure 1 , Figure 1 : is a flow chart of an automatic early warning method for tree obstacle hazards of overhead transmission lines provided by the present application. The automatic early warning method for tree obstacle hazards of overhead transmission lines specifically includes the following process:

[0051] 101. Obtain image data of overhead transmission lines through satellite remote sensing technology.

[0052] For example, according to the needs of transmission line monitoring, appropriate remote sensing satellites and image types are selected. For example, high-resolution commercial satellite images such as Gaofen-2 and SuperView-1 (SV1) can be selected. These satellites can provide high-resolution image data, which are suitable for detailed monitoring of transmission lines. Image data of overhead transmission lines are obtained through satellite remote sensing technology. These data can include multispectral, hyperspectral or infrared images. The most appropriate data type is selected according to the needs. The acquired image data is preprocessed, including radiation correction, atmospheric correction and geometric correction, to improve the quality of image data. Remote sensing images are used to extract features of transmission line corridors, such as power lines, poles and towers. Information on transmission lines and towers can be extracted through image edge detection and line detection methods. Low-resolution multispectral images are fused with high-resolution panchromatic images to obtain clearer images of transmission lines and surrounding environment.

[0053] 102. Use deep learning models to automatically analyze image data and identify trees within the preset range of overhead transmission lines.

[0054] For example, the trained deep learning model is applied to the image data to automatically analyze and identify trees within the preset range of the overhead transmission line. The output of the deep learning model is analyzed to determine the location and type of trees, providing reliable data for in-depth investigation of trees in the transmission line corridor and accurate monitoring of hidden tree hazards.

[0055] Optionally, in some embodiments of the present application, the step of “using a deep learning model to automatically analyze the image data to identify trees within a preset range of the overhead transmission line” may specifically include:

[0056] Preprocess the image data to obtain the canopy height model and normalized point cloud;

[0057] Using deep learning models, canopy height models, and normalized point clouds, we identify trees within a preset range of overhead transmission lines.

[0058] For example, specifically, airborne LiDAR data is used in combination with SRTMGL1 terrain data to generate a canopy height model. This can be done by filtering the point cloud through an irregular triangulation algorithm, extracting ground points, and normalizing the point cloud to eliminate terrain effects to reflect the actual canopy height. The point cloud is normalized to eliminate terrain effects to more accurately reflect the actual height and distribution of trees. The image data is automatically analyzed using a trained deep learning model to identify trees within a preset range of overhead transmission lines, and the detection results are plotted on the image for visual inspection.

[0059] Optionally, in some embodiments of the present application, a suitable deep learning model, such as VGG, ResNet, etc., can be pre-selected as a basic model, and the weight parameters of the pre-trained model can be initialized to the tree species recognition model through the transfer learning method, and fine-tuned for specific tasks. The training and test data sets are pre-processed, such as using the preprocess_input function to process the input data. The deep learning model is trained using the labeled tree data set to optimize the model structure and parameters.

[0060] 103. Based on the distance between the overhead transmission line and the trees, determine whether there is a hidden danger of tree obstacles within the preset range of the overhead transmission line.

[0061] For example, according to the "110kV ~ 750kV Overhead Transmission Line Construction and Acceptance Specification GB 50233-2014", the minimum vertical distance standards between transmission lines and trees of different voltage levels are obtained. Specifically, for the 110kV voltage level, the minimum vertical distance should not be less than 4.0 meters. Use laser radar (LiDAR) technology or other remote sensing technology to obtain the actual vertical distance between the transmission line and the tree. Compare the actual distance measured with the minimum vertical distance specified in GB 50233-2014. If the measured distance is less than the specified minimum vertical distance, it is considered that there is a tree barrier hazard. If the measured distance is greater than or equal to the specified minimum vertical distance, it is considered that there is no tree barrier hazard.

[0062] Optionally, in some embodiments of the present application, the step of “determining whether there is a tree obstacle hazard within a preset range of the overhead transmission line according to the distance between the overhead transmission line and the tree” may specifically include:

[0063] Obtain point cloud data within a preset range of overhead transmission lines;

[0064] Extracting the conductor point cloud corresponding to the overhead transmission line and the vegetation point cloud corresponding to the trees from the point cloud data;

[0065] Calculate the distance between overhead transmission lines and trees based on the conductor point cloud and vegetation point cloud;

[0066] Based on the preset safety distance and the distance between the overhead transmission line and the trees, determine whether there is a tree obstacle hazard within the preset range of the overhead transmission line.

[0067] For example, the conductor point cloud corresponding to the overhead transmission line and the vegetation point cloud corresponding to the tree are extracted from the point cloud data. The number and starting point of the power line are determined by using the k-means clustering analysis algorithm, the screening algorithm, and the point cloud fusion algorithm based on the normal constraint, and the lower conductor of the power line is determined based on the minimum height. At the same time, since the radar data has the characteristics of multiple echoes, there are generally no other obstructions above the power line, so the power line point cloud is mainly concentrated in the first echo of the radar. The point cloud distance calculation method is used to calculate the distance between the line and the ground / ground objects under the line corridor, and the voltage level is automatically compared with the standard safety distance, and a danger warning is issued for areas where the distance is less than the safety distance. According to the "GB50545-2010" 110kV~750kV overhead transmission line design specification, the minimum vertical distance standard between transmission lines and trees of different voltage levels is obtained. For example, for the 110kV voltage level, the minimum vertical distance should not be less than 4.0 meters. The actual distance measured is compared with the minimum vertical distance specified in GB 50233-2014. If the actual measured distance is less than the specified minimum vertical distance, it is considered that there is a tree obstacle hazard.

[0068] Optionally, in some embodiments of the present application, the step of “calculating the distance between the overhead transmission line and the tree according to the conductor point cloud and the vegetation point cloud” may specifically include:

[0069] Determine the vegetation contour corresponding to the vegetation point cloud;

[0070] Calculate the distance between each traverse point and the vegetation contour in the traverse point cloud.

[0071] For example, specifically, select a point from the vegetation point cloud, and then find some neighboring points around it. Determine the angular relationship between these neighboring points and the selected point, and find the points with the largest angles. If the largest of these angles exceeds a certain value (such as 90 degrees), then this point is considered to be a vegetation contour point. Perform the above check on each point in the vegetation point cloud to determine all contour points. For each point in the wire point cloud, calculate its distance to the vegetation contour point. This can be achieved by comparing the spatial position between each wire point and the vegetation contour point. Find the closest vegetation contour point, which can be considered as the shortest distance from the wire point to the vegetation contour.

[0072] Optionally, in some embodiments of the present application, the step of “determining whether there is a tree obstacle hazard within a preset range of the overhead transmission line based on a preset safety distance and a distance between the overhead transmission line and the tree” may specifically include:

[0073] determining vegetation points within a preset range of the overhead transmission line based on the distance between the overhead transmission line and the trees;

[0074] Based on the preset safety distance, determine whether the vegetation point within the preset range of the overhead transmission line is a tree barrier hazard location, and determine whether there is a tree barrier hazard within the preset range of the overhead transmission line based on the tree barrier hazard location.

[0075] For example, specifically, keep the conductor point cloud and the vegetation point cloud in the same coordinate system to facilitate accurate distance calculation. For each conductor point, calculate its straight-line distance to the nearest vegetation point. Determine the minimum safe distance that should be maintained between the transmission line and the trees according to the standards and specifications of the power industry. Compare the calculated distance from each conductor point to the vegetation point with the preset safety distance. If the distance from a conductor point to a vegetation point is less than the preset safety distance, mark the vegetation point as a tree barrier hazard site. Summarize all vegetation points marked as tree barrier hazard sites and analyze their distribution within the preset range of the transmission line. Based on the number, location and severity of the hazard sites, assess whether there are tree barrier hazards within the preset range of the entire transmission line.

[0076] Optionally, in some embodiments of the present application, the step of “determining whether a vegetation point within a preset range of the overhead transmission line is a tree barrier potential hazard location based on a preset safety distance” may specifically include:

[0077] When the vegetation point within the preset range of the overhead transmission line is less than or equal to the preset safety distance, the vegetation point is determined to be a tree obstacle potential location;

[0078] When the vegetation point within the preset range of the overhead transmission line is greater than the preset safety distance, it is determined that the vegetation point is not a tree obstacle hazard site.

[0079] 104. Provide early warning for identified tree obstacle hazards.

[0080] For example, based on the identified tree obstacle hazards, pruning warnings or pruning and felling warnings are issued to operation and maintenance personnel. The tree obstacle hazard quantitative analysis system based on three-dimensional temporal GIS and feature extraction, by constructing a three-dimensional temporal GIS model of the transmission corridor, solves the problem that the current manual analysis method of tree obstacle hazards is inefficient and easily affected by human subjective factors.

[0081] Optionally, in some embodiments of the present application, the step of “providing an early warning prompt for the identified tree obstacle hidden danger” may specifically include:

[0082] Generate early warning information based on safety distance information at different voltage levels and identified tree obstacle hazards;

[0083] Based on the information type of the warning information, early warning prompts are issued for the identified tree obstacle hazards.

[0084] Obtain safety distance information under different voltage levels. For example, for voltage levels of 1-10 kV, the safety distance should be 1.5 meters. Using satellite remote sensing images and photogrammetry principles, match spatial point cloud information from stereo image pairs and construct a three-dimensional stereo model. On this basis, combine crown recognition and calculation of transmission line spatial parameters to achieve line-tree distance measurement. Compare the measured line-tree distance with the safety distance standard to determine which vegetation points are less than or equal to the preset safety distance, thereby identifying tree barrier hidden danger sites. Generate early warning information based on the comparison results. If the distance of the vegetation point is less than or equal to the preset safety distance, it is determined to be a tree barrier hidden danger site, and record relevant information, including location, distance, voltage level, etc.

[0085] The warning information is classified according to its urgency and impact scope, for example, it can be divided into "urgent", "high risk", "medium risk" and "low risk".

[0086] Different early warning measures are taken according to the classification of early warning information. For "urgent" and "high-risk" tree barrier hazards, the operation and maintenance personnel are notified immediately to conduct on-site inspections and treatment. For "medium-risk" and "low-risk" tree barrier hazards, regular inspections and monitoring can be arranged, and relevant personnel can be notified to pay attention to the development of the hazard.

[0087] The above is the automatic early warning process for tree obstacle hazards on overhead transmission lines provided in the embodiment of the present application.

[0088] In summary, the embodiment of the present application obtains image data of overhead power transmission lines through satellite remote sensing technology, uses a deep learning model to automatically analyze the image data, and identifies trees within a preset range of the overhead power transmission line: determines whether there are tree barrier hazards within the preset range of the overhead power transmission line based on the distance between the overhead power transmission line and the trees, and issues an early warning for the identified tree barrier hazards. In the automatic early warning scheme for tree barrier hazards of overhead power transmission lines provided in the present application, determines whether there are tree barrier hazards within the preset range of the overhead power transmission line based on the distance between the overhead power transmission line and the trees, which can effectively improve the accuracy of the prediction results of the occurrence time of tree barrier hazards of overhead power transmission lines, and further effectively improve the accuracy and reliability of early warnings for tree barrier hazards of overhead power transmission lines.

[0089] In order to facilitate the implementation of the automatic early warning method for tree barrier hidden dangers of overhead power transmission lines in the embodiment of the present application, the embodiment of the present application also provides an automatic early warning device for tree barrier hidden dangers of overhead power transmission lines. The meanings of the terms are the same as those in the automatic early warning method for tree barrier hidden dangers of overhead power transmission lines mentioned above, and the specific implementation details can refer to the description in the system embodiment.

[0090] See also Figure 2 , Figure 2 A schematic diagram of the structure of an automatic early warning device for tree-obstruction hazards of overhead transmission lines provided in an embodiment of the present application, wherein the automatic early warning device for tree-obstruction hazards of overhead transmission lines may specifically include an acquisition module 201, an identification module 202, a determination module 203, and a prompt module 204, which may be specifically as follows:

[0091] The acquisition module 201 is used to acquire image data of overhead power transmission lines through satellite remote sensing technology.

[0092] The identification module 202 is used to automatically analyze the image data using a deep learning model to identify trees within a preset range of the overhead power line.

[0093] Optionally, in some embodiments of the present application, the recognition module 202 can be specifically used to: pre-process the image data to obtain a canopy height model and a normalized point cloud; and use a deep learning model, a canopy height model and a normalized point cloud to identify trees within a preset range of the overhead transmission line.

[0094] The determination module 203 is used to determine whether there is a tree obstacle hazard within a preset range of the overhead transmission line according to the distance between the overhead transmission line and the tree.

[0095] Optionally, in some embodiments of the present application, the determination module 203 can be specifically used to: obtain point cloud data within a preset range of the overhead transmission line; extract the conductor point cloud corresponding to the overhead transmission line and the vegetation point cloud corresponding to the tree from the point cloud data; calculate the distance between the overhead transmission line and the tree based on the conductor point cloud and the vegetation point cloud; based on the preset safety distance and the distance between the overhead transmission line and the tree, determine whether there is a tree obstacle hazard within the preset range of the overhead transmission line.

[0096] Optionally, in some embodiments of the present application, the determination module 203 may be specifically used to: determine a vegetation contour corresponding to the vegetation point cloud; and calculate a distance between each guide wire point in the guide wire point cloud and the vegetation contour.

[0097] Optionally, in some embodiments of the present application, the determination module 203 can be specifically used to: determine the vegetation points within a preset range of the overhead transmission line based on the distance between the overhead transmission line and the trees; determine whether the vegetation points within the preset range of the overhead transmission line are tree barrier hazard locations based on a preset safety distance, and determine whether there is a tree barrier hazard within the preset range of the overhead transmission line based on the tree barrier hazard locations.

[0098] Optionally, in some embodiments of the present application, the determination module 203 can be specifically used to: when the vegetation point within the preset range of the overhead transmission line is less than or equal to the preset safety distance, determine that the vegetation point is a tree barrier hazard site; when the vegetation point within the preset range of the overhead transmission line is greater than the preset safety distance, determine that the vegetation point is not a tree barrier hazard site.

[0099] Prompt module 204 is used to provide early warning for identified tree obstacle hazards.

[0100] Optionally, in some embodiments of the present application, the prompt module 204 can be specifically used to: generate warning information based on safety distance information under different voltage levels and identified tree obstacle hazards; and provide warning prompts for identified tree obstacle hazards based on the information type of the warning information.

[0101] In summary, the acquisition module 201 of the embodiment of the present application acquires the image data of the overhead transmission line through satellite remote sensing technology, and the identification module 202 automatically analyzes the image data using a deep learning model to identify the trees within the preset range of the overhead transmission line: the determination module 203 determines whether there is a tree barrier hazard within the preset range of the overhead transmission line according to the distance between the overhead transmission line and the tree, and the prompt module 204 issues an early warning for the identified tree barrier hazard. In the automatic early warning scheme for tree barrier hazards of overhead transmission lines provided in the present application, it is determined whether there is a tree barrier hazard within the preset range of the overhead transmission line according to the distance between the overhead transmission line and the tree, which can effectively improve the accuracy of the prediction results of the occurrence time of tree barrier hazards of overhead transmission lines, and further effectively improve the accuracy and reliability of early warning of tree barrier hazards for overhead transmission lines.

[0102] In addition, the present application also provides an electronic device, such as Figure 3 As shown, it shows a schematic diagram of the structure of the electronic device involved in the embodiment of the present application, specifically:

[0103] The electronic device may include components such as a processor 301 with one or more processing cores, a memory 302 with one or more processor-readable storage media, a power supply 303, and an input unit 304. Those skilled in the art will appreciate that Figure 3 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0104] The processor 301 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. By running or executing software programs and / or modules stored in the memory 302, and calling data stored in the memory 302, it executes various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 301.

[0105] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and distributed power load control methods by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 302 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0106] The electronic device also includes a power supply 303 for supplying power to each component. Preferably, the power supply 303 can be logically connected to the processor 301 through a power management system, so that the power management system can manage charging, discharging, power consumption and other functions. The power supply 303 can also include one or more DC or AC power supplies, recharging systems, power anomaly detection circuits, power converters or inverters, power status indicators and other arbitrary components.

[0107] The electronic device may further include an input unit 304, which may be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0108] Although not shown, the electronic device may further include a display unit, etc., which will not be described in detail herein. Specifically in this embodiment, the processor 301 in the electronic device will load the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 will run the application programs stored in the memory 302, thereby realizing various functions, as follows:

[0109] Obtain image data of overhead transmission lines through satellite remote sensing technology; use deep learning models to automatically analyze image data and identify trees within the preset range of overhead transmission lines; determine whether there are tree barrier hazards within the preset range of overhead transmission lines based on the distance between overhead transmission lines and trees; and issue early warnings for identified tree barrier hazards.

[0110] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0111] The embodiment of the present application acquires image data of overhead power transmission lines through satellite remote sensing technology, uses a deep learning model to automatically analyze the image data, and identifies trees within a preset range of the overhead power transmission line: determines whether there are tree barrier hazards within the preset range of the overhead power transmission line based on the distance between the overhead power transmission line and the trees, and issues an early warning for the identified tree barrier hazards. In the automatic early warning scheme for tree barrier hazards of overhead power transmission lines provided in the present application, determines whether there are tree barrier hazards within the preset range of the overhead power transmission line based on the distance between the overhead power transmission line and the trees, which can effectively improve the accuracy of the prediction results of the occurrence time of tree barrier hazards of overhead power transmission lines, and further effectively improve the accuracy and reliability of early warnings for tree barrier hazards of overhead power transmission lines.

[0112] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a processor-readable storage medium and loaded and executed by a processor.

[0113] To this end, an embodiment of the present application provides a storage medium in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the steps in any of the distributed power load control methods provided in the embodiments of the present application. For example, the instructions can execute the following steps:

[0114] Obtain image data of overhead transmission lines through satellite remote sensing technology; use deep learning models to automatically analyze image data and identify trees within the preset range of overhead transmission lines; determine whether there are tree barrier hazards within the preset range of overhead transmission lines based on the distance between overhead transmission lines and trees; and issue early warnings for identified tree barrier hazards.

[0115] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0116] The storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0117] Since the instructions stored in the storage medium can execute the steps in any distributed power supply load control method provided in the embodiments of the present application, the beneficial effects that can be achieved by any distributed power supply load control method provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0118] The above is a detailed introduction to a distributed power supply load control method, device, electronic device and storage medium provided in the embodiments of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, according to the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. An automatic early warning method for tree obstacle hazards in overhead power transmission lines, characterized in that: include: Obtain image data of overhead power transmission lines through satellite remote sensing technology; Automatically analyzing the image data using a deep learning model to identify trees within a preset range of the overhead transmission line; Determining whether there is a tree obstacle hazard within a preset range of the overhead transmission line according to the distance between the overhead transmission line and the tree; Provide early warning for identified tree obstacle hazards.

2. The automatic early warning method for tree obstacle hazards of overhead power transmission lines according to claim 1 is characterized in that: The determining, based on the distance between the overhead transmission line and the tree, whether there is a tree obstacle hazard within a preset range of the overhead transmission line includes: Obtain point cloud data within a preset range of overhead transmission lines; Extracting the conductor point cloud corresponding to the overhead transmission line and the vegetation point cloud corresponding to the trees from the point cloud data; Calculating the distance between the overhead transmission line and the tree based on the conductor point cloud and the vegetation point cloud; Based on the preset safety distance and the distance between the overhead power transmission line and the tree, it is determined whether there is a tree obstacle hazard within the preset range of the overhead power transmission line.

3. The automatic early warning method for tree obstacle hazards of overhead power transmission lines according to claim 2 is characterized in that: The step of calculating the distance between the overhead transmission line and the tree according to the conductor point cloud and the vegetation point cloud comprises: Determining a vegetation contour corresponding to the vegetation point cloud; The distance between each guide line point in the guide line point cloud and the vegetation contour is calculated.

4. The automatic early warning method for tree obstacle hazards of overhead power transmission lines according to claim 2 is characterized in that: The determining whether there is a tree obstacle hazard within a preset range of the overhead transmission line based on a preset safety distance and a distance between the overhead transmission line and the tree comprises: Determining vegetation points within a preset range of the overhead power line based on the distance between the overhead power line and the trees; Based on a preset safety distance, it is determined whether a vegetation point within a preset range of the overhead transmission line is a tree barrier hazard location, and based on the tree barrier hazard location, it is determined whether there is a tree barrier hazard within the preset range of the overhead transmission line.

5. The automatic early warning method for tree obstacle hidden dangers of overhead power transmission lines according to claim 4 is characterized in that: The determining, based on the preset safety distance, whether a vegetation point within a preset range of the overhead transmission line is a tree barrier potential hazard location includes: When the vegetation point within the preset range of the overhead transmission line is less than or equal to the preset safety distance, the vegetation point is determined to be a tree obstacle potential location; When the vegetation point within the preset range of the overhead power transmission line is greater than the preset safety distance, it is determined that the vegetation point is not a tree obstacle hazard site.

6. The automatic early warning method for tree obstacle hazards of overhead power transmission lines according to any one of claims 1 to 5, characterized in that: The method of automatically analyzing the image data using a deep learning model to identify trees within a preset range of the overhead transmission line includes: Preprocessing the image data to obtain a canopy height model and a normalized point cloud; Trees within a preset range of the overhead transmission line are identified using a deep learning model, a canopy height model, and a normalized point cloud.

7. The automatic early warning method for tree obstacle hazards of overhead power transmission lines according to any one of claims 1 to 5, characterized in that: The early warning of the identified tree obstacle hidden dangers includes: Generate early warning information based on safety distance information at different voltage levels and identified tree obstacle hazards; Based on the information type of the warning information, a warning prompt is given for the identified tree obstacle hidden danger.

8. An automatic early warning device for tree obstacle hazards in overhead power transmission lines, characterized in that: include: An acquisition module, used to acquire image data of overhead power transmission lines through satellite remote sensing technology; an identification module, configured to automatically analyze the image data using a deep learning model to identify trees within a preset range of the overhead transmission line; A determination module, used to determine whether there is a tree obstacle hazard within a preset range of the overhead transmission line according to the distance between the overhead transmission line and the tree; The prompt module is used to provide early warning for identified tree obstacle hazards.

9. An electronic device, characterized in that: include: A memory, a processor and a processing program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the automatic early warning method for tree obstacle hazards of overhead transmission lines as described in any one of claims 1 to 7 are performed.

10. A storage medium, characterized in that: A computer processing program is stored which can be loaded by a processor and execute the automatic early warning method for tree obstacle hazards of overhead power transmission lines as claimed in any one of claims 1 to 7.

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