Power transmission line channel tree barrier growth safety early warning method and device based on laser point cloud

Abstract

The invention discloses a laser point cloud-based power transmission line channel tree barrier growth safety early warning method. The method comprises the steps of route planning: designing a flight route of an unmanned aerial vehicle by an upper computer; field testing: the unmanned aerial vehicle carries a laser radar to acquire point cloud data and forward shooting images; tree obstacle hidden danger processing: analyzing the point cloud data to obtain the vertical distance and the horizontal distance between the tree obstacle and the line, testing the sag of the line on the same day, testing the height of the tree obstacle on the same day, and analyzing the tree species of the tree obstacle, the local soil competition condition and other information according to the positive image obtained by the unmanned aerial vehicle; predicting the growth amount: substituting the growth amount into a formula to calculate the daily growth amount of the tree and the height variation in the future i days; and performing safe distance early warning: predicting the safe distance in the ith day in the future and judging the risk level by combining the influence of the temperature on the line sag. Based on the influence of the laser point cloud data, the tree growth and the temperature on the conductor sag, the safety early warning of the tree barrier growth in the transmission line channel is realized, and the reliability is high.

Description

Technical Field

[0001] This invention relates to the field of safety early warning for tree growth in power transmission line corridors, specifically a method and device for safety early warning of tree growth in power transmission line corridors based on laser point clouds. Background Technology

[0002] With the widespread application of drone technology in the power transmission sector, the traditional power transmission operation and maintenance model is rapidly transforming towards digitalization and intelligence. While this transformation has significantly improved the accuracy of identifying tree-related safety hazards, it also places higher demands on the timely handling of hazards and the analysis of trend predictions.

[0003] Traditional tree obstacle hazard investigation mainly relies on manual line inspection, visual observation, or rangefinder measurement, which suffers from drawbacks such as low efficiency and large errors in judgment results. While the emerging drone-based lidar identification mode can achieve efficient and accurate tree obstacle detection, it still faces several challenges in practical application: First, the interval between two tree obstacle scans is relatively long, during which trees continue to grow, easily creating new safety hazards; second, the felling and clearing of some high-risk tree obstacles is delayed; more importantly, the sag of transmission lines changes significantly with temperature, and the combination of these factors can easily lead to line tripping accidents during the hazard mitigation period. Summary of the Invention

[0004] The purpose of this invention is to provide a method and device for early warning of tree growth safety in power transmission line corridors based on laser point clouds, which takes into account environmental factors and tree growth patterns to achieve early warning of tree growth safety in power transmission line corridors.

[0005] The technical solution to achieve the purpose of this invention is as follows: A method for early warning of tree growth in power transmission line corridors based on laser point clouds, characterized by comprising the following steps:

[0006] Step 1: Route planning. The flight route is designed for the measured route section using route planning software on the host computer to obtain the route package for the UAV's autonomous flight.

[0007] Step 2: On-site testing. The drone equipped with lidar is transported to the tower of the line section to be tested. The drone is started to receive the route package issued by the host computer. The drone flies along the predetermined route according to the route package. The lidar simultaneously collects on-site point cloud data and orthographic images.

[0008] Step 3: Analysis and processing of tree obstruction hazards. The collected point cloud data is transmitted to the host computer for processing through the point cloud storage module to obtain the vertical distance h0 and horizontal distance d0 between the tree obstruction and the line on the test day, the line sag f0 on the test day, and the tree obstruction height Y0 on the test day; the tree species and local soil competition information of the tree obstruction are identified based on the orthophoto images obtained by the UAV.

[0009] Step 4: Daily growth prediction. Obtain daily temperature T from weather data. Based on the tree barrier height Y0, tree species, local soil competition, and daily temperature T on the test day, predict the daily growth Y of the tree barrier on day i. i Based on the predicted daily growth Y of the tree barrier on day i i Calculate the predicted cumulative change in tree barrier height over day i, ΔY. i ;

[0010] Step 5: Safety Distance Warning. Based on the obtained temperature T0 and line sag f0 on the test day, calculate the equivalent line sag f for the i-th day. i Based on the cumulative change in tree barrier height over i days, the predicted value △Y i The equivalent sag of the line temperature on day i. i 1. Measure the vertical distance h0 and horizontal distance d0 between the tree obstruction and the railway line on the test day, and calculate the predicted safe distance H between the tree obstruction and the railway line on day i. i Based on the predicted safe distance H between the tree obstacle and the line on day i. i Determine the level of safety risk.

[0011] The flight path package includes the drone's flight trajectory, flight altitude, flight speed, and gimbal orientation parameters.

[0012] Based on the tree barrier height Y0, tree species, local soil competition, and daily temperature T on the test day, predict the predicted daily growth Y of the tree barrier on day i. i Specifically:

[0013] (1);

[0014] In the formula, Y i Let Y0 be the predicted growth of the tree barrier on day i, K1 be the tree species coefficient, K2 be the local soil competition coefficient, and K3 be the seasonal coefficient. K1, K2, and K3 are obtained by fitting historical inspection observation data using a multiple linear regression iterative algorithm. Y0 is the tree barrier height on the test day, and T is the tree height on day i. i Let N be the temperature of day i, and N be a positive integer.

[0015] Based on the predicted growth Y of the tree barrier on day i. i Calculate the predicted cumulative change in tree barrier height over day i, ΔY. i Specifically:

[0016] (2);

[0017] In the formula, △Y i Let i be the cumulative change in tree barrier height predicted over i days.

[0018] Based on the obtained temperature T0 and line sag f0 on the test day, calculate the equivalent line sag f on the i-th day. i Specifically:

[0019] (3);

[0020] In the formula, f i Let f0 be the line sag equivalent to the line temperature on day i, f0 be the line sag on the test day, and T0 be the temperature on the test day. imax The temperature is the highest temperature on day i.

[0021] Based on the predicted cumulative change in tree barrier height over i days, ΔY i The equivalent sag of the line temperature on day i. i 1. Measure the vertical distance h0 and horizontal distance d0 between the tree obstruction and the railway line on the test day, and calculate the predicted safe distance H between the tree obstruction and the railway line on day i. i Specifically:

[0022] (4);

[0023] In the formula, H i Let d0 be the predicted safe distance between the tree barrier and the line on day i, d0 be the horizontal distance between the tree barrier and the line on the test day, and h0 be the vertical distance between the tree barrier and the line on the test day.

[0024] A safety early warning device for tree obstruction growth along power transmission line corridors based on laser point clouds, comprising:

[0025] The route planning module is used to design flight routes for the measured route segments through route planning software on the host computer, and obtain the route package for the autonomous flight of the UAV.

[0026] The on-site testing module is used to transport a drone equipped with a lidar to the tower of the line section to be tested, start the drone to receive the flight path package issued by the host computer, and fly along the predetermined flight path according to the flight path package. The lidar simultaneously collects on-site point cloud data and orthographic images.

[0027] The tree obstacle hazard analysis module is used to transmit the collected point cloud data to the host computer for processing through the point cloud storage module, and obtain the vertical distance h0 and horizontal distance d0 between the tree obstacle and the line on the test day, the line sag f0 on the test day, and the tree obstacle height Y0 on the test day; and analyze the tree species and local soil competition information of the tree obstacle based on the orthophoto images obtained by the UAV.

[0028] The growth and height prediction module is used to predict the daily growth Y of the tree barrier on day i, based on the tree barrier height Y0, tree species, local soil competition, and daily temperature T on the test day.i The cumulative change in tree barrier height over i days is predicted to be ΔY. i ;

[0029] The safety distance early warning module is used to calculate the equivalent sag f of the line temperature on the i-th day based on the acquired temperature T0 and the line sag f0 on the test day. i Based on the cumulative change in tree barrier height over i days, the predicted value △Y i The equivalent sag of the line temperature on day i. i 1. Measure the vertical distance h0 and horizontal distance d0 between the tree obstruction and the railway line on the test day, and calculate the predicted safe distance H between the tree obstruction and the railway line on day i. i Based on the predicted safe distance H between the tree obstacle and the line on day i. i Determine the level of safety risk.

[0030] Furthermore, based on the tree barrier height Y0, tree species, local soil competition, and daily temperature T on the test day, the predicted daily growth Y of the tree barrier on day i is calculated. i Specifically:

[0031] (1);

[0032] In the formula, Y i Let Y0 be the predicted growth of the tree barrier on day i, K1 be the tree species coefficient, K2 be the local soil competition coefficient, K3 be the seasonal coefficient, Y0 be the tree barrier height on the test day, and T be the tree barrier height on the test day. i Let N be the temperature of day i, and N be a positive integer.

[0033] Based on the predicted growth Y of the tree barrier on day i. i Calculate the predicted cumulative change in tree barrier height over day i, ΔY. i Specifically:

[0034] (2);

[0035] In the formula, △Y i Let i be the cumulative change in tree barrier height predicted over i days.

[0036] Furthermore, based on the obtained temperature T0 and line sag f0 on the test day, the equivalent line sag f on the i-th day is calculated. i Specifically:

[0037] (3);

[0038] In the formula, f i Let f0 be the line sag equivalent to the line temperature on day i, f0 be the line sag on the test day, and T0 be the temperature on the test day. imax The temperature is the highest temperature on day i.

[0039] Furthermore, based on the cumulative change in tree barrier height predicted over i days, ΔY i The equivalent sag of the line temperature on day i. i 1. Measure the vertical distance h0 and horizontal distance d0 between the tree obstruction and the railway line on the test day, and calculate the predicted safe distance H between the tree obstruction and the railway line on day i. i Specifically:

[0040] (4);

[0041] In the formula, H i Let d0 be the predicted safe distance between the tree barrier and the line on day i, d0 be the horizontal distance between the tree barrier and the line on the test day, and h0 be the vertical distance between the tree barrier and the line on the test day.

[0042] This invention discloses a safety early warning method for tree growth obstruction in power transmission line corridors based on laser point clouds, which has the following advantages compared with the prior art:

[0043] With high measurement accuracy and strong early warning reliability, relying on the flight scanning of UAV lidar, it can accurately acquire the laser point cloud model data of tree obstacles in the power transmission line channel, and realize high-precision measurement of tree obstacle height and tree line spacing. At the same time, it innovatively introduces the influence of tree growth change trend and temperature on line sag, and constructs a dynamic safety distance calculation model, which breaks through the limitations of traditional static measurement, can predict tree obstacle safety hazards in advance, and greatly improve the reliability of early warning results.

[0044] The standardized operation process minimizes human interference. By pre-setting flight paths through flight path generation software, the quadcopter drone equipped with lidar flies according to the regulations, enabling precise control of key operational parameters such as flight path, flight altitude, and flight speed. This effectively avoids operational errors caused by manual line inspection or manual drone operation. At the same time, it simplifies the on-site operation process, reduces reliance on operator experience, and improves the standardization and consistency of tree obstacle data collection.

[0045] With strong practicality and adaptability to the actual needs of power transmission operation and maintenance, this method can effectively solve the tripping risk problems caused by long scanning intervals, delayed tree felling, and dynamic changes in line sag in the existing UAV lidar identification mode. It forms an integrated solution of "data acquisition - dynamic calculation - safety early warning", providing technical support for the routine management and forward-looking governance of tree obstacles in power transmission line corridors, and has extremely high practical application value. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the structure of the tree obstacle analysis device for power transmission line channels of the present invention;

[0047] Figure 2 This is a schematic diagram of the test circuit of the present invention;

[0048] Figure 3 This is a flowchart of a safety early warning method for tree growth in power transmission line corridors based on laser point clouds, according to the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] like Figure 1 , Figure 2 This invention provides a safety early warning platform for tree growth in power transmission line corridors using UAV point cloud modeling, comprising: a tree obstacle analysis device for power transmission line corridors based on laser point clouds and a 220 kV test line;

[0051] like Figure 1 The tree obstacle analysis device for power transmission line channels based on laser point clouds includes a host computer 03, flight path generation software 04, tree obstacle analysis software 05, flight path transmission module 06, point cloud storage module 07, lidar 08, and quadcopter UAV 10.

[0052] The measured 220 kV line section 02 includes flight path 09, tower 1 11, tower 2 12, tower 1 insulator string 13, tower 2 insulator string 14, conductor 15, ground wire 16, and tree obstruction in the passage 17.

[0053] After the host computer 03 completes the route planning through the route generation software 04, it transmits the generated route package to the quadcopter UAV 10 through the route transmission module 06 (this is an existing mature technology). The quadcopter UAV 10 executes the route package task and flies along the flight route 09.

[0054] During the flight mission of the quadcopter UAV 10, the lidar 08 works synchronously to measure the point cloud data within the measured section 02 of the route.

[0055] After completing the flight route mission, the quadcopter UAV 10 transmits the point cloud data to the host computer 03 through the point cloud storage module 07.

[0056] The route generation software 04 and tree obstacle analysis software 05 are built into the host computer 03.

[0057] Please see Figure 3This invention provides a method for early warning of tree growth safety in power transmission line corridors based on laser point clouds, comprising the following steps:

[0058] Step 1: Flight route planning. The flight route 09 of the measured route segment 02 is designed using the flight route generation software 04 on the host computer 03. Parameters such as the UAV flight trajectory, flight speed, and gimbal direction are set to determine the UAV flight route 09.

[0059] Step 2: On-site testing. The quadcopter drone 10, equipped with lidar 08, was transported to the 02 tower of the 220 kV line section to be tested. The pre-planned flight path was opened, and the drone was started to fly autonomously. LiDAR 08 collected point cloud data and orthographic images of the site.

[0060] Step 3: Tree obstacle hazard handling. The acquired point cloud data is transmitted to the host computer 03 through the point cloud storage module 07. The host computer 03 processes the point cloud data of the test route 02 through the tree obstacle analysis software 05 to obtain the vertical distance h0 and horizontal distance d0 between the tree obstacle and the route on the test day, the sag f0 of the route on the test day, and the height Y0 of the tree obstacle on the test day. Based on the orthophoto images acquired by the UAV, information such as the tree species and local soil competition is analyzed.

[0061] Step 4: Substitute the tree barrier height Y0, tree species, local soil competition, and daily temperature T obtained on the test day into formula (1) to obtain the predicted daily growth Y of the tree barrier on day i. i :

[0062] (1);

[0063] In the formula, Y i Let Y0 be the predicted daily growth of the tree barrier on day i, K1 be the tree species coefficient (range 0.0001–0.0015), K2 be the local soil competition coefficient (range 0.3–1.2), K3 be the seasonal coefficient (range 0–1.8), and Y0 be the tree barrier height on the test day. i Let N be the temperature of day i, and N be a positive integer.

[0064] The predicted growth amount of the tree barrier on day i, Y i Substituting into formula (2), we obtain the predicted cumulative change in tree barrier height over day i, ΔY. i :

[0065] (2);

[0066] In the formula, △Y i Let i be the cumulative change in tree barrier height predicted over i days.

[0067] Step 5: Safety Distance Warning. Based on the obtained temperature T0 and line sag f0 on the test day, calculate the equivalent line sag f for the i-th day. i ,

[0068] (3);

[0069] In the formula, f i Let f0 be the line sag equivalent to the line temperature on day i, f0 be the line sag on the test day, and T0 be the temperature on the test day. imax The highest temperature of day i.

[0070] Based on the predicted cumulative change in tree barrier height over i days, ΔY i The equivalent sag of the line temperature on day i. i 1. Measure the vertical distance h0 and horizontal distance d0 between the tree obstruction and the railway line on the test day, and calculate the predicted safe distance H between the tree obstruction and the railway line on day i. i ,

[0071] (4);

[0072] In the formula, H i Let d0 be the predicted safe distance between the tree barrier and the line on day i, d0 be the horizontal distance between the tree barrier and the line on the test day, and h0 be the vertical distance between the tree barrier and the line on the test day.

[0073] Based on the above steps, a tree growth safety early warning is issued when H... i When H ∈ (0, 2], it indicates that the safety of tree growth is at serious risk, and immediate action should be taken to prevent power outages; when H i When H ∈ (2, 4.5], it indicates that the tree barrier growth safety is at a moderate risk, and timely treatment can be arranged; when H i When H ∈ (4.5, 6], it indicates that the safety of tree growth is at a slight risk, and timely treatment can be arranged; when H i ∈(6,+ At this time, it indicates that there is no immediate risk to the safety of tree growth.

[0074] This invention also provides a corresponding early warning device for tree growth safety along power transmission line corridors based on laser point clouds, comprising:

[0075] The route planning module is used to design flight routes for the measured route segment through route planning software on the host computer, set the UAV flight trajectory, flight altitude, flight speed and gimbal direction parameters, and output route packages for autonomous flight of the UAV.

[0076] The on-site testing module is used to transport a drone equipped with a lidar to the tower of the line section to be tested, start the drone to receive the flight path package issued by the host computer, and fly along the predetermined flight path according to the flight path package. The lidar simultaneously collects point cloud data and orthographic images of the line section.

[0077] The tree obstacle analysis module is used to transmit the collected point cloud data to the host computer for processing through the point cloud storage module, and obtain the vertical distance h0 and horizontal distance d0 between the tree obstacle and the line on the test day, the line sag f0 on the test day, and the tree obstacle height Y0 on the test day; and analyze the tree species and local soil competition information of the tree obstacle based on the orthophoto images obtained by the UAV.

[0078] Growth and Height Prediction Module: The growth and height prediction module is used to calculate the predicted growth amount Y of the tree barrier on the i-th day based on the tree barrier height Y0, tree species, local soil competition, and daily temperature T on the test day, by substituting these into formula (1). i Then Y i Substituting into formula (2), we obtain the predicted cumulative change in tree barrier height over day i, ΔY. i ;

[0079] The safety distance early warning module is used to calculate the equivalent sag of the line temperature on the i-th day using formula (3) based on the acquired temperature T0 and the line sag f0 on the test day. i Based on the cumulative change in tree barrier height over i days, the predicted value △Y i The equivalent sag of the line temperature on day i. i On the test day, the vertical distance h0 and horizontal distance d0 between the tree obstacle and the line are measured, and the predicted safe distance H between the tree obstacle and the line on day i is calculated using formula (4). i According to the predicted safe distance H i Determine the level of safety risk.

[0080] This invention has the following features and effects:

[0081] 1. High-precision measurement enabled by laser point cloud technology: Point cloud data is collected by drones equipped with laser radar, and the host computer analyzes the data to obtain the high-precision vertical distance and coordinates between tree obstacles and guide lines, solving the pain points of low efficiency and poor accuracy of traditional manual line inspection, and significantly improving the accuracy of tree obstacle identification.

[0082] 2. Multi-parameter fusion to construct a dynamic prediction model: The daily growth of trees is calculated by integrating factors such as tree species, soil, season, tree age, and temperature. At the same time, a model of the influence of temperature on conductor sag is introduced to realize dynamic prediction of tree height and sag, covering multi-dimensional risk assessment of tree growth and conductor status.

[0083] 3. Early warning and avoidance of potential accidents: By predicting the safe distance between tree obstacles and power lines in the next i days and classifying the risk level (severe / moderate / mild / no risk), the problem of handling tripping accidents within the cycle in the traditional mode is effectively solved, providing a forward-looking basis for operation and maintenance decisions.

[0084] 4. Streamlined processes improve operational efficiency: Flight path planning enables autonomous flight of drones, reducing human intervention; point cloud analysis and parameter calculation are highly automated, helping power transmission operation and maintenance to transform towards digital intelligence, and significantly improving the efficiency and safety of tree obstacle management.

[0085] This invention combines technological innovation with practical operability, providing an efficient and reliable solution for the safety management of tree obstructions along power transmission line corridors.

[0086] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for safety early warning of tree growth obstruction in power transmission line corridors based on laser point clouds, characterized in that, Includes the following steps: Step 1: Route planning. The flight route is designed for the measured route section using route planning software on the host computer to obtain the route package for the UAV's autonomous flight. Step 2: On-site testing. The drone equipped with lidar is transported to the tower of the line section to be tested. The drone is started to receive the route package issued by the host computer. The drone flies along the predetermined route according to the route package. The lidar simultaneously collects on-site point cloud data and orthographic images. Step 3: Analysis and processing of tree obstruction hazards. The collected point cloud data is transmitted to the host computer for processing through the point cloud storage module to obtain the vertical distance h0 and horizontal distance d0 between the tree obstruction and the line on the test day, the line sag f0 on the test day, and the tree obstruction height Y0 on the test day; the tree species and local soil competition information of the tree obstruction are identified based on the orthophoto images obtained by the UAV. Step 4: Daily growth prediction. Obtain daily temperature T from weather data. Based on the tree barrier height Y0, tree species, local soil competition, and daily temperature T on the test day, predict the daily growth Y of the tree barrier on day i. i Based on the predicted daily growth Y of the tree barrier on day i i Calculate the predicted cumulative change in tree barrier height over day i, ΔY. i ; Step 5: Safety Distance Warning. Based on the obtained temperature T0 and line sag f0 on the test day, calculate the equivalent line sag f for the i-th day. i Based on the cumulative change in tree barrier height over i days, the predicted value △Y i The equivalent sag of the line temperature on day i. i 1. Measure the vertical distance h0 and horizontal distance d0 between the tree obstruction and the railway line on the test day, and calculate the predicted safe distance H between the tree obstruction and the railway line on day i. i Based on the predicted safe distance H between the tree obstacle and the line on day i. i Determine the level of safety risk.

2. The method for safety early warning of tree growth in power transmission line corridors based on laser point clouds as described in claim 1, characterized in that, The flight path package includes the drone's flight trajectory, flight altitude, flight speed, and gimbal orientation parameters.

3. The method for safety early warning of tree growth in power transmission line corridors based on laser point clouds as described in claim 1, characterized in that, Based on the tree barrier height Y0, tree species, local soil competition, and daily temperature T on the test day, predict the predicted daily growth Y of the tree barrier on day i. i Specifically: （1）； In the formula, Y i Let Y0 be the predicted growth of the tree barrier on day i, K1 be the tree species coefficient, K2 be the local soil competition coefficient, K3 be the seasonal coefficient, Y0 be the tree barrier height on the test day, and T be the tree barrier height on the test day. i Let N be the temperature of day i, and N be a positive integer.

4. The method for safety early warning of tree obstruction growth in power transmission line corridors based on laser point clouds as described in claim 1 or 3, characterized in that, Based on the predicted growth Y of the tree barrier on day i. i Calculate the predicted cumulative change in tree barrier height over day i, ΔY. i Specifically: （2）； In the formula, △Y i Let i be the cumulative change in tree barrier height predicted over i days.

5. The method for safety early warning of tree growth in power transmission line corridors based on laser point clouds as described in claim 4, characterized in that, Based on the obtained temperature T0 and line sag f0 on the test day, calculate the equivalent line sag f on the i-th day. i Specifically: （3）； In the formula, f i Let f0 be the line sag equivalent to the line temperature on day i, f0 be the line sag on the test day, and T0 be the temperature on the test day. imax The highest temperature on day i is [value].

6. The method for safety early warning of tree growth in transmission line corridors based on laser point clouds as described in claims 1-3 or 5, characterized in that, Based on the predicted cumulative change in tree barrier height over i days, ΔY i The equivalent sag of the line temperature on day i. i 1. Measure the vertical distance h0 and horizontal distance d0 between the tree obstruction and the railway line on the test day, and calculate the predicted safe distance H between the tree obstruction and the railway line on day i. i Specifically: （4）； In the formula, H i Let d0 be the predicted safe distance between the tree barrier and the line on day i, d0 be the horizontal distance between the tree barrier and the line on the test day, and h0 be the vertical distance between the tree barrier and the line on the test day.

7. A safety early warning device for tree growth obstruction in power transmission line corridors based on laser point clouds, characterized in that, include: The route planning module is used to design flight routes for the measured route segments through route planning software on the host computer, and obtain the route package for the autonomous flight of the UAV. The on-site testing module is used to transport a drone equipped with a lidar to the tower of the line section to be tested, start the drone to receive the flight path package issued by the host computer, and fly along the predetermined flight path according to the flight path package. The lidar simultaneously collects on-site point cloud data and orthographic images. The tree obstacle hazard analysis module is used to transmit the collected point cloud data to the host computer for processing through the point cloud storage module, and obtain the vertical distance h0 and horizontal distance d0 between the tree obstacle and the line on the test day, the line sag f0 on the test day, and the tree obstacle height Y0 on the test day; and analyze the tree species and local soil competition information of the tree obstacle based on the orthophoto images obtained by the UAV. The growth and height prediction module is used to predict the daily growth Y of the tree barrier on day i, based on the tree barrier height Y0, tree species, local soil competition, and daily temperature T on the test day. i The cumulative change in tree barrier height over i days is predicted to be ΔY. i ; The safety distance early warning module is used to calculate the equivalent sag f of the line temperature on the i-th day based on the acquired temperature T0 and the line sag f0 on the test day. i Based on the cumulative change in tree barrier height over i days, the predicted value △Y i The equivalent sag of the line temperature on day i. i 1. Measure the vertical distance h0 and horizontal distance d0 between the tree obstruction and the railway line on the test day, and calculate the predicted safe distance H between the tree obstruction and the railway line on day i. i Based on the predicted safe distance H between the tree obstacle and the line on day i. i Determine the level of safety risk.

8. The method for safety early warning of tree growth in power transmission line corridors based on laser point clouds as described in claim 7, characterized in that, Based on the tree barrier height Y0, tree species, local soil competition, and daily temperature T on the test day, predict the predicted daily growth Y of the tree barrier on day i. i Specifically: （1）； In the formula, Y i Let Y0 be the predicted growth of the tree barrier on day i, K1 be the tree species coefficient, K2 be the local soil competition coefficient, K3 be the seasonal coefficient, Y0 be the tree barrier height on the test day, and T be the tree barrier height on the test day. i Let N be the temperature of day i, and N be a positive integer. Based on the predicted growth Y of the tree barrier on day i. i Calculate the predicted cumulative change in tree barrier height over day i, ΔY. i Specifically: （2）； In the formula, △Y i Let i be the cumulative change in tree barrier height predicted over i days.

9. The method for safety early warning of tree obstruction growth in power transmission line corridors based on laser point clouds as described in claim 7, characterized in that, Based on the obtained temperature T0 and line sag f0 on the test day, calculate the equivalent line sag f on the i-th day. i Specifically: （3）； In the formula, f i Let f0 be the line sag equivalent to the line temperature on day i, f0 be the line sag on the test day, and T0 be the temperature on the test day. imax The highest temperature on day i is [value].

10. The method for safety early warning of tree obstruction growth in power transmission line corridors based on laser point clouds as described in claim 7, characterized in that, Based on the predicted cumulative change in tree barrier height over i days, ΔY i The equivalent sag of the line temperature on day i. i 1. Measure the vertical distance h0 and horizontal distance d0 between the tree obstruction and the railway line on the test day, and calculate the predicted safe distance H between the tree obstruction and the railway line on day i. i Specifically: （4）； In the formula, H i Let d0 be the predicted safe distance between the tree barrier and the line on day i, d0 be the horizontal distance between the tree barrier and the line on the test day, and h0 be the vertical distance between the tree barrier and the line on the test day.

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