A method and system for dispatching tree barrier processing tasks of a power transmission line based on spatial positions

Abstract

The application discloses a kind of transmission line tree barrier processing task dispatching method and system based on spatial position, mainly related to overhead transmission line operation and maintenance technical field.Its method includes: obtaining the laser radar point cloud data of tree along transmission line;Point cloud data is analyzed, and the spatial position coordinates of target tree with hidden danger of tree barrier, clearance distance and hidden danger grade are determined;According to spatial position coordinates, target tree is matched with pre-set responsible area, and the responsible operation terminal is determined;Tree barrier processing work order containing hidden danger information is generated and dispatched to the operation terminal;In response to the operation terminal receiving work order, navigation path from current position to target position is provided.The application realizes the automatic identification, accurate positioning and intelligent dispatch of hidden danger of tree barrier, reduces the manual communication link through spatial position matching and navigation guidance, significantly improves the efficiency and accuracy of tree barrier processing.

Description

Technical Field

[0001] This invention relates to the field of overhead transmission line operation and maintenance technology, specifically a method and system for dispatching tree obstruction handling tasks on transmission lines based on spatial location. Background Technology

[0002] Transmission lines are an important component of the power system, and their safe and stable operation is directly related to the reliability of power supply. In the operation and maintenance of overhead transmission lines, tree hazards (referred to as "tree obstacles") are one of the common threats. When trees in the transmission line corridor grow too fast or fall, resulting in insufficient clearance between them and live conductors, it is very easy to cause safety accidents such as line discharge tripping or forest fires. Therefore, timely and accurate detection and handling of tree obstacle hazards is a key task for line operation and maintenance departments.

[0003] Currently, the handling of tree obstruction hazards on power transmission lines usually relies on manual inspections or drone inspections. The traditional work process is as follows: line maintenance personnel discover tree hazards through visual inspections or use drones equipped with lidar to scan the line corridor, obtain point cloud data, and then generate a hazard report through internal analysis. Subsequently, the maintenance personnel need to inform the construction supervisor in charge of tree felling of the hazard point, such as the location of the tower and the terrain features, via telephone or instant messaging tools. The supervisor must first go to the site to conduct an on-site survey to confirm the specific location, number, and felling range of the trees before organizing personnel to carry out the work.

[0004] However, the aforementioned existing technologies have significant shortcomings: First, the entire process involves multiple stages such as inspection and discovery, internal analysis, task communication, on-site survey, and organization and implementation, which are cumbersome and involve long information transmission chains, resulting in low processing efficiency. Second, due to the lack of precise spatial location information transmission, construction supervisors often find it difficult to quickly and accurately locate target trees based on verbal descriptions or rough pole numbers, especially in mountainous areas with complex terrain, where wrong locations or trees are frequently found or cut down, resulting in waste of manpower and resources and rework. In addition, task assignment methods based on two-dimensional maps or text descriptions cannot intuitively provide operators with accurate navigation and cannot effectively support rapid response in on-site operations.

[0005] Therefore, how to simplify the task assignment process for tree obstacle handling and improve the accuracy of transmitting hazard location information and operational efficiency are technical problems that urgently need to be solved by those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for dispatching tree obstruction handling tasks for power transmission lines based on spatial location. It realizes automatic identification, precise positioning and intelligent dispatch of tree obstruction hazards. Through spatial location matching and navigation guidance, it reduces the manual communication links and significantly improves the efficiency and accuracy of tree obstruction handling.

[0007] To achieve the above objectives, the present invention employs the following technical solution: On one hand, the present invention provides a method for dispatching tree obstruction handling tasks on power transmission lines based on spatial location, comprising the following steps: Step S1: Obtain lidar point cloud data of trees along the power transmission line to be inspected; Step S2: Analyze and process the point cloud data to identify and determine the hazard information of the target trees with potential tree obstruction. The hazard information includes at least the spatial coordinates of the target trees, the clearance distance between them and the power transmission line, and the hazard level determined based on the clearance distance. Step S3: Match the target tree with the preset responsible area based on the spatial location coordinates to determine the operation terminal responsible for processing the target tree; Step S4: Generate a tree obstacle handling work order containing hazard information, and dispatch the tree obstacle handling work order to the matching operation terminal; Step S5: The work terminal receives the tree obstacle handling work order and provides a navigation path from the current location of the work terminal to the spatial coordinates to guide the workers to the site for handling.

[0008] Preferably, step S1 specifically involves: using a drone equipped with a lidar scanning device to scan the transmission line channel to be inspected along a preset flight path, and collecting original three-dimensional point cloud data including transmission line conductors, towers, trees, and ground objects.

[0009] Preferably, step S2 includes: Step S21: Preprocess the acquired raw point cloud data, including denoising, filtering and coordinate transformation, to obtain three-dimensional point cloud data that accurately represents the spatial location of ground features; Step S22: Use the progressive encryption triangular mesh filtering algorithm to extract ground points, and combine it with a machine learning classification algorithm based on random forest to classify the preprocessed point cloud data into tree point clouds, power line point clouds and other ground feature point clouds. Step S23: Based on tree point cloud data and transmission line point cloud data, calculate the minimum spatial distance between each tree and the nearest transmission line, as the clearance distance; when calculating the clearance distance, extract tree feature points from the tree point cloud, and the extraction rules for the feature points are as follows: For trees growing upright, the highest point of the canopy is extracted as the feature point; For trees growing at an angle, the point on the outer edge of the canopy closest to the power transmission line is extracted as the feature point; For trees with multiple trunks, the highest point of each trunk is extracted, and the point closest to the power transmission line is selected as the feature point. Step S24: Compare the clearance distance with the preset safe distance threshold. If the clearance distance is less than or equal to the safe distance threshold, the tree is determined to be a target tree with potential tree obstacle hazards. Step S25: Based on the voltage level of the transmission line to be inspected Query the preset voltage level-threshold mapping table to obtain the first threshold corresponding to the voltage level. Second threshold and the third threshold ,in The hazard levels are classified according to the following rules: ; in, Indicates the level of hazard. This indicates the clearance distance between the target tree and the power transmission line.

[0010] Preferably, step S23 specifically includes: For each tree, extract the coordinates of its highest point or the outer edge of its crown from the tree point cloud data; extract the conductor point cloud of the corresponding section from the transmission line point cloud data, and fit the spatial curve equation of the conductor; calculate the shortest Euclidean distance from the tree feature point to the conductor spatial curve, which is used as the clearance distance, expressed as: ; in, The three-dimensional coordinates of the tree's feature points. Represents the three-dimensional coordinates of any point on the space curve of the conductor.

[0011] Preferably, step S3 specifically includes: Each work terminal is pre-assigned a work area, which is defined by geographical coordinate range or line tower section; The spatial coordinates of the target tree are obtained by using the ray method or the grid index spatial query algorithm based on R-tree, and the work area in which the coordinate point falls is determined. The operation terminal corresponding to the operation area is identified as the operation terminal responsible for processing the target trees.

[0012] Preferably, step S4 specifically includes: The tree obstacle handling work order includes the task number, spatial coordinates of the target tree, clearance distance, hazard level, suggested handling time limit, and on-site environmental description information; The tree obstacle handling work order is pushed to the application installed on the work terminal via a wireless communication network. After receiving the work order, the application notifies the operator in the form of a pop-up window or message prompt.

[0013] Preferably, it also includes abnormal operating condition handling steps: When the work terminal is offline, the system temporarily stores the tree obstacle handling work order in the cloud and automatically pushes it after detecting that the terminal has returned to online; the terminal locally caches the received work orders and supports offline viewing of work order details and downloaded navigation maps; When the same target tree is covered by multiple work terminals, the system calculates a comprehensive priority based on the terminal's current workload, distance from the target, and terminal level to determine the responsible terminal, as shown below: ; in, This represents the number of tasks currently pending on the terminal. This represents the distance between the terminal's current location and the target. This is the terminal level coefficient. , , These are the weighting coefficients; When the terminal enters an area without mobile network and GPS signal, it switches to offline navigation mode, supporting guidance based on geodetic coordinates and azimuth. The azimuth calculation formula is as follows: ; in, The app displays a text prompt showing the azimuth and distance from the current location to the target point.

[0014] On the other hand, the present invention also provides a spatial location-based transmission line tree obstacle removal task dispatching system, for implementing the spatial location-based transmission line tree obstacle removal task dispatching method described above, including: The data acquisition module is used to acquire lidar point cloud data of trees along the transmission line to be inspected; The hazard analysis module is used to analyze and process the point cloud data, identify and determine the hazard information of target trees with potential tree obstruction hazards. The hazard information includes at least the spatial coordinates of the target tree, the clearance distance between it and the power transmission line, and the hazard level determined based on the clearance distance. The matching module is used to match the target tree with a preset responsible area based on the spatial location coordinates, and determine the operation terminal responsible for processing the target tree. The work order dispatch module is used to generate a tree obstacle handling work order containing the hazard information and dispatch the tree obstacle handling work order to the matching operation terminal. A navigation guidance module is used to provide a navigation path from the current location of the work terminal to the spatial location coordinates in response to the work terminal receiving the tree obstacle handling work order; The result feedback module is used to receive the on-site processing results uploaded by the operation terminal, including processing status, processing time, and before-and-after image data, and update the hazard processing status in the hazard information database according to the processing results, so as to realize closed-loop management of tasks.

[0015] Preferably, the hazard analysis module includes: The data preprocessing unit is used to perform noise reduction, filtering, and coordinate transformation on the acquired raw point cloud data; The point cloud classification unit is used to classify the preprocessed point cloud data and extract tree point cloud data and power line point cloud data. The clearance distance calculation unit is used to calculate the minimum spatial distance between each tree and the nearest power line based on the tree point cloud data and the power line point cloud data, and use it as the clearance distance; The hazard determination unit is used to compare the clearance distance with a preset safety distance threshold. If the clearance distance is less than or equal to the safety distance threshold, the tree is determined to be a target tree with a potential tree obstacle hazard. The hazard classification unit is used to determine the hazard level of the target tree based on the numerical value of the clearance distance, according to a preset hazard level classification rule. Preferably, the result feedback module is also used to generate processing result reports, statistically analyze the task completion rate, processing timeliness rate and average processing time of each work terminal, and synchronize the completed task data to the production management system for archiving.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention automatically identifies potentially hazardous trees through lidar point cloud data analysis and automatically matches the work terminals in the responsible area based on spatial location coordinates, generating work orders for direct dispatch without the need for manual communication and coordination, simplifying the task flow process and significantly improving management efficiency and response speed. 2. This invention can accurately obtain the three-dimensional spatial coordinates and clearance distance of each potentially hazardous tree, avoiding problems such as mislocating the wrong location or cutting down the wrong tree due to verbal description or rough positioning in traditional methods. This ensures that operators can accurately find the target, reduce rework, and save manpower and material costs. 3. After receiving a work order, the operation terminal of this invention can directly obtain the navigation path from the current location to the potentially hazardous trees, which solves the pain point of difficult location in complex terrain areas, helps operators to quickly reach the site, and improves the convenience and efficiency of on-site operations. 4. Through in-depth analysis of point cloud data, this invention can scientifically classify the level of potential hazards, providing data support for formulating differentiated handling strategies and prioritization, which helps to optimize resource allocation and improve the scientific and standardized nature of operation and maintenance work. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a flowchart of the point cloud data analysis and processing process of the present invention; Figure 3 This is the system structure of the present invention. Detailed Implementation

[0018] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined in this application.

[0019] In this invention, terms such as "upper," "lower," "left," "right," "front," "back," "vertical," "horizontal," "side," and "bottom" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used only to facilitate the description of the structural relationships of the various components or elements of this invention and do not specifically refer to any component or element in this invention. They should not be construed as limiting the invention.

[0020] Example: like Figure 1 As shown, this embodiment provides a method for dispatching tree obstacle handling tasks on power transmission lines based on spatial location, including the following steps: Step S1: Obtain lidar point cloud data of trees along the power transmission line to be inspected; Step S2: Analyze and process the point cloud data to identify and determine the hazard information of target trees with potential tree obstruction. The hazard information includes: the spatial coordinates of the target tree, the clearance distance between it and the power transmission line, and the hazard level determined based on the clearance distance. Step S3: Match the target tree with the preset responsible area based on the spatial location coordinates to determine the operation terminal responsible for processing the target tree; Step S4: Generate a tree obstacle handling work order containing hazard information, and dispatch the tree obstacle handling work order to the matching operation terminal; Step S5: The work terminal receives the tree obstacle handling work order and provides a navigation path from the current location of the work terminal to the spatial coordinates to guide the workers to the site for handling.

[0021] This embodiment uses the 110kV high-voltage transmission line B, maintained by Power Supply Company A, as an example to illustrate the method in detail: Line B is 25 kilometers long and passes through mountainous areas and woodlands. There are a large number of fast-growing poplar trees in the line corridor. Every spring and summer, the trees grow rapidly, which can easily cause insufficient distance between the conductor and the ground, creating tree obstacles. Traditional inspection methods require manual foot patrols or drone photography followed by manual analysis, and then telephone notification to the tree-cutting team. Often, inaccurate positioning leads to missed or incorrect felling, and the communication costs are high and the efficiency is low.

[0022] 1. Data Acquisition Phase: In this embodiment, a DJI Matrice M300 RTK drone equipped with a Zenmuse L1 LiDAR module is used to autonomously scan the entire "Weian Line" according to a pre-planned route. When planning the route, the flight altitude is set to 80 meters, the route overlap is 30%, and the scanning bandwidth is 100 meters to ensure that the ground features within 50 meters on both sides of the route are covered. Before the drone takes off, centimeter-level real-time differential positioning is obtained through the RTK base station to ensure that the collected point cloud data has high-precision geographic coordinates. During the flight of the UAV along the line, the lidar emits laser pulses and receives echoes, while the inertial navigation system records the UAV's attitude information. Finally, it generates original three-dimensional point cloud data including power line conductors, towers, trees, ground, buildings, etc. The original data is stored in the UAV's onboard computer in LAS format and exported to the ground workstation after the flight. In this embodiment, a total of about 250 million point cloud data were collected, covering the entire passage area of ​​towers 1 to 120 of line B.

[0023] 2. Point cloud data analysis and processing stage: like Figure 2 As shown, the specific process for analyzing and processing point cloud data includes the following sub-steps: Step S21, Data Preprocessing: Import the raw point cloud data into professional point cloud processing software (such as LiDAR360 or TerraSolid). First, perform noise reduction to remove obvious isolated noise points (such as abnormal points caused by birds or electromagnetic interference). Then, perform filtering to distinguish between ground points and non-ground points. Finally, perform coordinate transformation to convert the WGS84 latitude and longitude coordinates into the plane coordinates required by the project (such as Gauss-Kruger projection coordinates) to facilitate subsequent distance calculations. In this embodiment, the high-precision point cloud data obtained after preprocessing has a plane coordinate accuracy better than 5 cm and an elevation accuracy better than 10 cm. Step S22, Point Cloud Classification: Ground points are extracted using a progressively encrypted triangular mesh filtering algorithm and combined with a machine learning classification algorithm based on random forest to classify the point cloud data into tree point clouds, power line point clouds and other ground feature point clouds. In this embodiment, the progressively encrypted triangular mesh filtering algorithm for extracting ground points includes the following steps: (1) Initial seed point selection: Divide the entire survey area into a 50m×50m grid and select the lowest point in each grid as the initial ground seed point; (2) Initial TIN construction: Construct an initial irregular triangular network based on seed points; (3) Iterative encryption: Traverse all unclassified points and calculate the distance from each point to the nearest triangle. The angle between the triangle face and the vertex ,like and (In this embodiment) rice, If the point is not found in the ground point set, then add the point to the ground point set and reconstruct the TIN. (4) Termination condition: Termination occurs when no new points are added in three consecutive iterations; The core parameters of the random forest classifier in this embodiment are as follows: Number of decision trees: ; Maximum depth: ; Minimum number of sample splits: ; Number of features: randomly selected for each tree Features ( (total characteristic number); Extracted features include 12-dimensional feature vectors such as elevation, echo intensity, local normal vector, flatness, verticality, point cloud density, and elevation variation coefficient. Using the above algorithm, the point cloud data is accurately classified into ground points, vegetation points (trees), power line points, pole points, building points, etc. In this embodiment, the classification accuracy rate reaches more than 95%, and a total of about 80 million tree point clouds and about 5 million power line point clouds are extracted. Step S23, Clearance Calculation: For each tree, calculate the minimum spatial distance between it and the nearest transmission line. When calculating the clearance distance, extract tree feature points from the tree point cloud. The extraction rules for these feature points are as follows: For trees growing upright, the highest point of the canopy is extracted as the feature point; For trees growing at an angle, the point on the outer edge of the canopy closest to the power transmission line is extracted as the feature point; For trees with multiple trunks, the highest point of each trunk is extracted, and the point closest to the power transmission line is selected as the feature point. In this embodiment, a differentiated feature point extraction strategy is adopted for different types of trees to ensure the accuracy of the clearance distance calculation: (1) Trees that grow upright: For trees with vertical trunks and symmetrical crowns, the region growing method is used to cluster the point clouds of individual trees, and the points with the largest Z-values ​​in the clustered point clouds are extracted as feature points. ; (2) Trees growing at an angle: For trees that grow at an angle due to factors such as sunlight and terrain, their highest point may not be the closest to the guide line. This embodiment uses the following steps to extract feature points: Determine the spatial plane where the conductor is located based on the route; Calculate the distance from each point in the tree point cloud to the traverse plane. ; Project the tree point cloud onto the traverse plane and calculate the distance from the projected points to the traverse curve. ; The point with the minimum comprehensive distance is selected as the feature point, and the comprehensive distance weighting coefficient is... ; (3) Trees with multiple trunks: For multi-trunk trees such as banyan and poplar, connected component analysis is used to separate the point clouds of each trunk. The highest point of each trunk is extracted, and the distance from each highest point to the traverse line is calculated. ,Pick The corresponding points are used as the final feature points; (4) Verification mechanism: After feature point extraction, the system automatically generates a feature point annotation map for manual verification. In this embodiment, 30 trees were randomly selected for inspection, and the error between the manual measurement value and the algorithm-extracted value was less than 0.2 meters, with an accuracy rate of over 98%. From the power line point cloud, conductor point clouds are extracted segment by segment based on tower locations. For each conductor span, a parabolic or catenary model is used to fit the conductor's spatial curve equation. Then, the shortest Euclidean distance from tree feature points to the conductor's spatial curve is calculated as the clearance distance D. The specific calculation formula is as follows: ; In the formula, The three-dimensional coordinates of the tree's feature points. This represents the three-dimensional coordinates of any point on the space curve of the traverse. In this embodiment, the iterative nearest point algorithm is used to search for the point on the traverse that is closest to the tree and to calculate the precise distance. Step S24, Hazard Assessment: The calculated clearance distance D is compared with the preset safety distance threshold. According to the "Operating Procedures for Overhead Transmission Lines" (DL / T 741-2019), for 110kV lines, the minimum vertical and horizontal distances between conductors and trees have different requirements under different conditions. To simplify the process, this embodiment sets a uniform safety distance threshold. Rice, if If the tree is identified as a potential tree obstacle, then it is determined to be a target tree with a potential tree obstacle. In this embodiment, a total of 157 potential trees were identified. Step S25, Hazard Level Classification: Based on the voltage level of the transmission line to be inspected. Query the preset voltage level-threshold mapping table to obtain the first threshold corresponding to the voltage level. Second threshold and the third threshold ,in The hazard levels are classified according to the following rules: ; in, Indicates the level of hazard. This indicates the clearance distance between the target tree and the power transmission line; In this embodiment, the classification of hidden danger levels fully considers the safety distance requirements of transmission lines at different voltage levels, and establishes a voltage level-threshold mapping table as shown in Table 1. Table 1 Voltage Level-Threshold Mapping Table

[0024] In this embodiment, line A is a 110kV line, therefore it is taken as... rice, rice, According to calculations, among the 157 trees with potential hazards, 23 are critical hazards, 65 are serious hazards, and 69 are general hazards. Dynamic adjustment mechanism: The system supports managers to adjust the thresholds based on actual operating experience. For example, in areas with strong winds, heavy icing, or densely populated areas, the thresholds can be increased by 10% to 20%. When adjusting, simply modify the mapping table in the background, and the system will automatically recalculate the hazard level without modifying the code. Multi-voltage level mixed scenario: If the same inspection includes multiple lines of different voltage levels (such as 110kV and 220kV double circuits on the same tower), when calculating the clearance distance, the system calculates the distance between the tree and each circuit conductor separately, and takes the stricter level as the standard. For example, if a tree is 3.2 meters away from the 110kV conductor and 4.5 meters away from the 220kV conductor, then based on the "general" level of 110kV (below 3.5 meters) and the "safe" level of 220kV (above 5 meters), the stricter "general" level is finally taken. At this point, the hazard information for each potentially hazardous tree has been determined, including: a unique identifier ID and its spatial coordinates. Clearance distance Hidden danger level And the line section to which it belongs (obtained by matching coordinates with tower locations, for example, located between towers 45 and 46).

[0025] 3. Task matching and dispatch phase: In this embodiment, Power Supply Company A has three tree-cutting work teams, each responsible for different sections of Line B: Team C is responsible for towers 1 to 40, Team D is responsible for towers 41 to 80, and Team E is responsible for towers 81 to 120. Each team leader is equipped with a smartphone with a dedicated APP installed as the work terminal. The APP backend has pre-entered the boundary coordinate range of the area responsible for each team. The spatial coordinates of the 157 potentially hazardous trees were matched with the areas of responsibility of each work team. A ray casting method or a grid-indexed spatial query algorithm based on R-trees was used to quickly determine the work area to which each coordinate point belonged. In this embodiment, spatial matching is accelerated using an R-tree index. The specific steps are as follows: (1) Constructing an index: Use the smallest outer rectangle of the polygon of the area to which each work group is responsible as the leaf node of the R-tree to construct the R-tree spatial index; (2) Preliminary screening: For the coordinates of the target trees Quickly retrieve possible matches using R-trees Candidate sets of intersecting polygons; (3) Precise judgment: For each polygon in the candidate set, the ray method is used to determine the points. Is it inside the polygon? (4) Complexity: The time complexity of this algorithm is from Down to In this embodiment, matching 157 points takes only 0.3 seconds; For example, if a tree with coordinates (35467892.34, 4051234.56) is calculated to fall into the section between towers 41 and 80, it will be automatically assigned to team D. The matching result generates a task allocation list to ensure that each potential hazard point has a unique responsible team. The system automatically generates tree obstacle handling work orders. Each work order contains the following information: task number (e.g., WF-20231027-001), hazard tree ID, spatial location coordinates (accurate to centimeters), clearance distance, hazard level (critical / serious / general), suggested handling time limit (set according to the level: critical hazards within 24 hours, serious hazards within 7 days, and general hazards within 30 days), on-site environment description (e.g., "located approximately 50 meters to the side of the small side of tower No. 45, in the middle of the hillside, with no other obstacles around"), and tree photos (if visible light images are available). After the work order is generated, it is pushed to the corresponding work team's work terminal APP via 4G / 5G wireless communication network. The APP will issue an audio prompt upon receiving the work order and display the number of pending tasks on the main interface.

[0026] 4. On-site operation and navigation phase: The team leader of Team D opened the mobile app and saw the newly assigned task of 73 trees with potential hazards, including 23 critical hazards. The team leader clicked on one of the critical hazard tasks, and the work order details page showed that the tree was located near Tower No. 45, with a clearance distance of only 1.8 meters, and the level was critical, requiring immediate handling. The app automatically called the built-in map engine of the phone (such as Gaode Map or Baidu Map) and planned the optimal driving and walking routes based on the team leader's current location (work area) and the coordinates of the target tree. Since the tree was located in the mountainous area, the map may not be accurate to the path, so the app also provided the geodetic coordinates and azimuth of the coordinate point. Lao Zhang can combine it with a handheld GPS or drone to find the point on site. The person in charge drove to the vicinity of Tower No. 45 according to the navigation, and then walked about 200 meters to find the target tree based on the electronic compass and distance indication on the APP. After confirming the distance between the tree and the overhead power line, the person in charge organized the personnel to start felling. During the felling process, photos and brief descriptions of the site were uploaded through the APP, and the task processing status was marked as "in progress". After the felling was completed, the clearance distance was measured again to confirm that it was greater than the safety threshold. The person in charge clicked "complete" in the APP and uploaded the processing results (including photos after felling, remaining distance, etc.). The system background automatically updated the processing status of the hazard, forming a closed-loop management. In case of special circumstances, such as the tree location not matching the description (which may be due to point cloud classification errors), the person in charge can report the anomaly through the APP, and the back-end management personnel will review and reissue or correct the data.

[0027] 5. Abnormal operating condition handling mechanism This embodiment fully considers various abnormal situations that may be encountered in field operations and designs a multi-layered fault tolerance mechanism: (1) Terminal offline processing mechanism: When Team C enters the mountainous area for work, the mobile phone goes offline due to a signal blind spot. After the system backend detects that the terminal's heartbeat packet has timed out, it automatically marks the work order as "pending push" and temporarily stores it in the message queue. At the same time, the terminal APP has automatically downloaded the task list and high-definition offline map (based on MBTiles format, covering the entire 20-kilometer range of Line A) before going offline. The person in charge can still view the work order details and coordinates, but cannot report the progress in real time. First, the local log (photos, text notes) is recorded in the APP. When returning to the area with signal, the APP automatically uploads the locally cached operation records to the server, and the system merges and updates the work order status. In this embodiment, the message queue is implemented using RabbitMQ, which supports disconnection reconnection and message confirmation mechanisms. (2) Priority allocation for multiple terminals: During a certain inspection, the terminals of Team C and Team D simultaneously entered the area near Tower 45. A critically endangered tree in this area had not yet been assigned a responsibilities assignment. The system detected that the tree was located in the overlapping boundary area between the two teams' responsibilities, triggering the priority assignment algorithm. There are currently 5 tasks pending in team C. Team D has 2 tasks pending processing. ); Team C is currently 1.2 kilometers away from the target. Team D is 0.8 kilometers away from the target. ); The team / group grade coefficient is 1.0. ); Take weight , , ;Calculation yields: ; ; Team D has higher priority, so the system will assign tasks to team D and push notifications via the APP to avoid duplicate work. (3) Navigation without map signal: The leader of team D entered the depths of the dense forest. His mobile phone lost GPS signal and mobile network coverage was completely gone. The app automatically detected the anomaly and switched to offline navigation mode. When the leader opened the task on the app, the interface displayed: The last known position of the current record: ; Target location: ; Based on the azimuth calculated from the last positioning. (Magnetic declination correction has been taken into account); Text prompt: "The target is located approximately 230 meters northeast of your last recorded location. Please proceed at a 45° angle." The person in charge, holding a compass, walked along the azimuth direction and roughly measured the distance with a tape measure, eventually successfully locating the target tree. This function is especially useful in areas with no signal, such as dense forests and deep valleys.

[0028] like Figure 3 As shown, this embodiment also provides a power transmission line tree obstacle handling task dispatching system based on spatial location, including: The data acquisition module is used to acquire lidar point cloud data of trees along the power transmission line to be inspected. In specific implementation, this module may include hardware such as drones, lidar sensors, and storage devices, as well as a ground control station that communicates with the drones and is responsible for data download and preliminary processing. The hazard analysis module is used to analyze and process the point cloud data, identify and determine the hazard information of target trees with potential tree obstruction hazards. The hazard information includes at least the spatial coordinates of the target tree, the clearance distance between it and the power transmission line, and the hazard level determined based on the clearance distance. The hazard analysis module further includes: a data preprocessing unit for denoising, filtering, and coordinate transformation of the acquired raw point cloud data; a point cloud classification unit for classifying the preprocessed point cloud data and extracting tree point cloud data and transmission line point cloud data; a clearance distance calculation unit for calculating the minimum spatial distance between each tree and the nearest transmission line based on the tree point cloud data and transmission line point cloud data, as the clearance distance; a hazard determination unit for comparing the clearance distance with a preset safety distance threshold, and determining that the tree is a target tree with a potential tree obstacle hazard if the clearance distance is less than or equal to the safety distance threshold; and a hazard classification unit for determining the hazard level of the target tree based on the value of the clearance distance according to preset hazard level classification rules. These units can be deployed on a server or workstation and implemented through software programs. The matching module is used to match the target tree with a preset responsible area based on the spatial location coordinates, and determine the operation terminal responsible for processing the target tree. The matching module can be implemented based on a GIS spatial database, pre-store the geometric boundaries of each operation area, and quickly match them through spatial query algorithms (such as spatial indexing, point within polygon judgment). The work order dispatch module is used to generate tree obstacle handling work orders containing the hazard information and dispatch the tree obstacle handling work orders to the matching operation terminal. The work order dispatch module includes a work order generation unit and a communication interface. The work order generation unit assembles the hazard information into structured data (such as JSON or XML) according to the template. The communication interface interacts with the operation terminal APP through the mobile network and supports push notifications and message queues. The navigation guidance module is used to respond to the work terminal receiving the tree obstacle handling work order and provide a navigation path from the current location of the work terminal to the spatial coordinates. The navigation guidance module is usually integrated into the work terminal APP, uses the terminal device's GPS positioning module to obtain the current location, calls a third-party map SDK or built-in navigation algorithm to plan the path, and displays an electronic map, path, and direction indicators on the APP interface.

[0029] The data flow and control flow relationships between the above modules are as follows: The point cloud data collected by the data acquisition module is transmitted to the hazard analysis module for processing, and the generated hazard information table is stored in the database; the matching module periodically scans newly marked hazard points in the database, performs spatial matching, and writes the matching results into the task table; after the work order dispatch module detects a new task, it generates a work order and sends it to the corresponding terminal via message push; after the terminal receives the work order, the navigation guidance module provides navigation services; the terminal processing results are fed back to the system to update the task status.

[0030] The above is a detailed description of the preferred embodiments of the present invention, but the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A method for dispatching tree obstacle handling tasks on power transmission lines based on spatial location, characterized in that, Includes the following steps: Step S1: Obtain lidar point cloud data of trees along the power transmission line to be inspected; Step S2: Analyze and process the point cloud data to identify and determine the hazard information of the target trees with potential tree obstruction. The hazard information includes at least the spatial coordinates of the target trees, the clearance distance between them and the power transmission line, and the hazard level determined based on the clearance distance. Step S3: Match the target tree with the preset responsible area based on the spatial location coordinates to determine the operation terminal responsible for processing the target tree; Step S4: Generate a tree obstacle handling work order containing hazard information, and dispatch the tree obstacle handling work order to the matching operation terminal; Step S5: The work terminal receives the tree obstacle handling work order and provides a navigation path from the current location of the work terminal to the spatial coordinates to guide the workers to the site for handling.

2. The method for dispatching tree obstacle handling tasks on transmission lines based on spatial location according to claim 1, characterized in that, Step S1 specifically involves: using a drone equipped with a lidar scanning device to scan the transmission line channel to be inspected along a preset flight path, and collecting original three-dimensional point cloud data including transmission line conductors, towers, trees, and ground objects.

3. The method for dispatching tree obstacle handling tasks on transmission lines based on spatial location according to claim 1, characterized in that, Step S2 includes: Step S21: Preprocess the acquired raw point cloud data, including denoising, filtering and coordinate transformation, to obtain three-dimensional point cloud data that accurately represents the spatial location of ground features; Step S22: Use the progressive encryption triangular mesh filtering algorithm to extract ground points, and combine it with a machine learning classification algorithm based on random forest to classify the preprocessed point cloud data into tree point clouds, power line point clouds and other ground feature point clouds. Step S23: Based on tree point cloud data and transmission line point cloud data, calculate the minimum spatial distance between each tree and the nearest transmission line, as the clearance distance; when calculating the clearance distance, extract tree feature points from the tree point cloud, and the extraction rules for the feature points are as follows: For trees growing upright, the highest point of the canopy is extracted as the feature point; For trees growing at an angle, the point on the outer edge of the canopy closest to the power transmission line is extracted as the feature point; For trees with multiple trunks, the highest point of each trunk is extracted, and the point closest to the power transmission line is selected as the feature point. Step S24: Compare the clearance distance with the preset safe distance threshold. If the clearance distance is less than or equal to the safe distance threshold, the tree is determined to be a target tree with potential tree obstacle hazards. Step S25: Based on the voltage level of the transmission line to be inspected Query the preset voltage level-threshold mapping table to obtain the first threshold corresponding to the voltage level. Second threshold and the third threshold ,in The hazard levels are classified according to the following rules: ； in, Indicates the level of hazard. This indicates the clearance distance between the target tree and the power transmission line.

4. The method for dispatching tree obstacle handling tasks on transmission lines based on spatial location according to claim 3, characterized in that, Step S23 specifically involves: For each tree, extract the coordinates of its highest point or the outer edge of its crown from the tree point cloud data; extract the conductor point cloud of the corresponding section from the transmission line point cloud data, and fit the spatial curve equation of the conductor; calculate the shortest Euclidean distance from the tree feature point to the conductor spatial curve, which is used as the clearance distance, expressed as: ； in, The three-dimensional coordinates of the tree's feature points. Represents the three-dimensional coordinates of any point on the space curve of the conductor.

5. The method for dispatching tree obstacle handling tasks on transmission lines based on spatial location according to claim 1, characterized in that, Step S3 is as follows: Each work terminal is pre-assigned a work area, which is defined by geographical coordinate range or line tower section; The spatial coordinates of the target tree are obtained by using the ray method or the grid index spatial query algorithm based on R-tree, and the work area in which the coordinate point falls is determined. The operation terminal corresponding to the operation area is identified as the operation terminal responsible for processing the target trees.

6. The method for dispatching tree obstacle handling tasks on transmission lines based on spatial location according to claim 1, characterized in that, Step S4 is as follows: The tree obstacle handling work order includes the task number, spatial coordinates of the target tree, clearance distance, hazard level, suggested handling time limit, and on-site environmental description information; The tree obstacle handling work order is pushed to the application installed on the work terminal via a wireless communication network. After receiving the work order, the application notifies the operator in the form of a pop-up window or message prompt.

7. The method for dispatching tree obstacle handling tasks on transmission lines based on spatial location according to claim 1, characterized in that, It also includes abnormal operating condition handling procedures: When the work terminal is offline, the system temporarily stores the tree obstacle handling work order in the cloud and automatically pushes it after detecting that the terminal has returned to online; the terminal locally caches the received work orders and supports offline viewing of work order details and downloaded navigation maps; When the same target tree is covered by multiple work terminals, the system calculates a comprehensive priority based on the terminal's current workload, distance from the target, and terminal level to determine the responsible terminal, as shown below: ； in, This represents the number of tasks currently pending on the terminal. This represents the distance between the terminal's current location and the target. This is the terminal level coefficient. , , These are the weighting coefficients; When the terminal enters an area without mobile network and GPS signal, it switches to offline navigation mode, supporting guidance based on geodetic coordinates and azimuth. The azimuth calculation formula is as follows: ； in, The app displays a text prompt showing the azimuth and distance from the current location to the target point.

8. A spatial location-based transmission line tree obstruction task dispatching system, used to implement the spatial location-based transmission line tree obstruction task dispatching method as described in any one of claims 1-7, characterized in that, include: The data acquisition module is used to acquire lidar point cloud data of trees along the transmission line to be inspected; The hazard analysis module is used to analyze and process the point cloud data, identify and determine the hazard information of target trees with potential tree obstruction hazards. The hazard information includes at least the spatial coordinates of the target tree, the clearance distance between it and the power transmission line, and the hazard level determined based on the clearance distance. The matching module is used to match the target tree with a preset responsible area based on the spatial location coordinates, and determine the operation terminal responsible for processing the target tree. The work order dispatch module is used to generate a tree obstacle handling work order containing the hazard information and dispatch the tree obstacle handling work order to the matching operation terminal. A navigation guidance module is used to provide a navigation path from the current location of the work terminal to the spatial location coordinates in response to the work terminal receiving the tree obstacle handling work order; The result feedback module is used to receive the on-site processing results uploaded by the operation terminal, including processing status, processing time, and before-and-after image data, and update the hazard processing status in the hazard information database according to the processing results, so as to realize closed-loop management of tasks.

9. A power transmission line tree obstacle handling task dispatching system based on spatial location according to claim 8, characterized in that, The hazard analysis module includes: The data preprocessing unit is used to perform noise reduction, filtering, and coordinate transformation on the acquired raw point cloud data; The point cloud classification unit is used to classify the preprocessed point cloud data and extract tree point cloud data and power line point cloud data. The clearance distance calculation unit is used to calculate the minimum spatial distance between each tree and the nearest power line based on the tree point cloud data and the power line point cloud data, and use it as the clearance distance; The hazard determination unit is used to compare the clearance distance with a preset safety distance threshold. If the clearance distance is less than or equal to the safety distance threshold, the tree is determined to be a target tree with a potential tree obstacle hazard. The hazard classification unit is used to determine the hazard level of the target tree based on the numerical value of the clearance distance, according to the preset hazard level classification rules.

10. A power transmission line tree obstacle handling task dispatching system based on spatial location according to claim 8, characterized in that, The result feedback module is also used to generate processing result reports, statistically analyze the task completion rate, processing timeliness rate and average processing time of each work terminal, and synchronize the completed task data to the production management system for archiving.

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