A Monitoring and Early Warning Method for Preventing External Damage to Transmission Lines Based on Laser Point Cloud Technology

Abstract

The present invention relates to the technical field of laser point monitoring, and in particular to a monitoring and early warning method for preventing external damage to transmission lines based on laser point cloud technology, including: monitoring the real-time height of trees and the real-time distance between the trees and the line, and using a number of wind speed sensors at a number of detection points deployed within the scope of the transmission line to monitor the real-time wind speed; determining the standard risk index range; determining the type of abnormality; and issuing corresponding prompts. By using laser point cloud technology to carefully monitor key areas, the present invention can capture key indicators such as the height of trees, the distance from the transmission line, and the wind speed in real time, evaluate and quantify the risks faced by the transmission line, determine a reasonable risk index range based on historical data, and when the risk index monitored in real time exceeds this range, analyze the specific reasons and take corresponding measures; in addition, adjust the monitoring duration according to the tree growth cycle and seasonal factors to ensure the accuracy and timeliness of risk assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of laser point monitoring, and particularly to a monitoring and early warning method for preventing external damage to transmission lines based on laser point cloud technology. Background Art

[0002] In the context of the era of globalization and informatization, with the acceleration of urbanization and the continuous improvement of industrialization level, as the core framework of the power system, transmission lines carry an increasing demand for energy transmission. At the same time, they also face security threats brought by natural environmental changes, frequent extreme climate events, and human factors such as surrounding construction activities. In this environment, society has higher and higher requirements for the stability and reliability of power supply. Traditional monitoring means for transmission lines are difficult to meet the current security needs. The risk of external force damage faced in the growing urbanization process and natural environmental changes, especially the real-time monitoring requirements for key parameters such as tree height, distance from the transmission line, and wind speed in key areas. With the increase in urban expansion and ecological protection requirements, transmission lines often pass through complex and changeable natural environments, such as urban green belts, forests, mountains, etc. The growth of trees and extreme climate events in these areas may pose threats to the safety of transmission lines.

[0003] Existing technologies usually cannot provide real-time and high-precision monitoring of the environmental changes around transmission lines, especially for key parameters such as tree height, distance from the line, and wind speed. The monitoring means of these technologies often rely on regular manual inspections, which are not only inefficient but also may miss key risk factors. In addition, existing technologies may lack a dynamic model that comprehensively considers multiple factors in risk assessment, resulting in the inability to accurately predict and evaluate potential external force damage risks. The deficiencies of the early warning system are also reflected in the slow and inaccurate response to abnormal situations, as well as the overly general risk management strategies, lacking pertinence and adaptability. Summary of the Invention

[0004] Therefore, the present invention provides a monitoring and early warning method for preventing external damage to transmission lines based on laser point cloud technology to overcome the problems of delay and inaccuracy in risk assessment and early warning caused by the insufficient real-time monitoring ability due to the low frequency and discontinuity of manual inspections in the prior art.

[0005] To achieve the above object, the present invention provides a monitoring and early warning method for preventing external damage to transmission lines based on laser point cloud technology, including:

[0006] Using a number of laser point sensors at a number of detection points deployed within the scope of the transmission line to monitor the real-time height of trees and the real-time distance between the trees and the line, and using a number of wind speed sensors at a number of detection points deployed within the scope of the transmission line to monitor the real-time wind speed;

[0007] Calculate the risk index based on the tree height, the distance between the tree and the line, and the wind speed at each event time point for each event location within the historical risk duration, and determine the standard risk index range according to the calculation results;

[0008] Based on the real-time risk index calculated from the real-time tree height, real-time distance, and real-time wind speed and the standard risk index range, determine the abnormal type according to the change speed of the tree height, the distance between the tree and the line, and the wind speed within the past detection duration. According to the abnormal type, select to adjust the preset height weight corresponding to the real-time tree height, or adjust the preset detection duration, or adjust the historical risk duration, and issue a prompt corresponding to the abnormal type;

[0009] The abnormal types include height abnormality, distance abnormality, wind speed abnormality, and historical risk duration abnormality.

[0010] Further, the calculating the risk index based on the tree height, the distance between the tree and the line, and the wind speed at each event time point for each event location within the historical risk duration includes:

[0011] Sum up the product of the standardized tree height and the height weight, the product of the standardized distance between the tree and the line and the preset distance weight, and the product of the standardized wind speed and the wind speed weight to obtain the risk index.

[0012] Further, the determining the standard risk index range according to the calculation results includes:

[0013] Sort all the calculated risk indexes from small to large and calculate the median of all the risk indexes. Select the two risk indexes before and after the median as the minimum and maximum values of the standard risk index range.

[0014] Further, the determination process for the abnormal type being the height abnormality includes:

[0015] Calculate the real-time risk index based on the real-time tree height, real-time distance, and real-time wind speed;

[0016] Determine that the calculated real-time risk index is not within the standard risk index range;

[0017] Obtain the change speed of the tree height within the past preset detection duration;

[0018] Determine that the change speed is greater than the standard change speed.

[0019] Further, the adjusting the preset height weight corresponding to the real-time tree height according to the abnormal type includes:

[0020] Increase and adjust the height weight according to the change speed and the standard change speed when it is determined to be a height abnormality.

[0021] Further, the determination process for the abnormal type being the distance anomaly includes:

[0022] Calculate the real-time risk index based on the real-time height of the tree, the real-time distance, and the real-time wind speed;

[0023] Determine that the calculated real-time risk index is not within the standard risk index range;

[0024] Obtain the change speed of the tree height and the distance between the tree and the line within a preset detection duration in the past;

[0025] Determine that the change speed is less than or equal to the standard change speed, and the distance between the tree and the line is less than the standard distance.

[0026] Further, the determination process for the abnormal type being the wind speed anomaly includes:

[0027] Calculate the real-time risk index based on the real-time height of the tree, the real-time distance, and the real-time wind speed;

[0028] Determine that the calculated real-time risk index is not within the standard risk index range;

[0029] Obtain the distance between the tree and the line and the wind speed within a preset detection duration in the past;

[0030] Determine that the distance between the tree and the line is greater than or equal to the standard distance, and the wind speed is greater than the standard wind speed.

[0031] Further, the adjustment of the preset detection duration according to the abnormal type includes:

[0032] Reduce the preset detection duration for the abnormal type of wind speed anomaly according to the wind speed and the standard wind speed.

[0033] Further, the determination process for the abnormal type being the historical risk duration anomaly includes:

[0034] Calculate the real-time risk index based on the real-time height of the tree, the real-time distance, and the real-time wind speed;

[0035] Determine that the calculated real-time risk index is not within the standard risk index range;

[0036] Obtain the distance between the tree and the line and the wind speed within a preset detection duration in the past;

[0037] Determine that the distance between the tree and the line is greater than or equal to the standard distance, and the wind speed is less than or equal to the standard wind speed.

[0038] Further, the adjustment of the historical risk duration according to the abnormal type includes:

[0039] Adjust the historical risk duration when the abnormal type is the historical risk duration anomaly by increasing it according to the preset risk change period and duration adjustment factor.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows: By using laser point cloud technology to carefully monitor key areas, key indicators such as tree height, distance from the transmission line, and wind speed are captured in real time. Through this method, the technology can evaluate and quantify the risks faced by the transmission line, and determine a reasonable range of risk indices based on historical data. When the risk index monitored in real time exceeds this range, the system will analyze the specific reasons, such as abnormal tree growth rate or wind speed, and take corresponding measures, such as tree pruning or soil reinforcement. In addition, the system will also adjust the monitoring duration according to the tree growth cycle and seasonal factors to ensure the accuracy and timeliness of risk assessment. This comprehensive monitoring and early warning method not only improves the response speed to potential threats, but also enhances the overall safety of the transmission line, ensuring the continuity and reliability of power supply. Through continuous monitoring and timely maintenance, this technology significantly reduces accidents caused by external damage to the transmission line, providing strong support for the stable operation of the power system.

[0041] Furthermore, by precise sorting and median calculation, the standard range of the risk index is determined, making the risk assessment more standardized and systematic. It dynamically calculates the risk index using real-time data and compares it with the standard range to identify potential anomalies. When an abnormal tree growth rate is detected, the system will automatically adjust the weight and issue an early warning to promote timely tree management. This coherent process not only improves the accuracy and timeliness of risk response, but also significantly enhances the safety and stability of the transmission line through proactive management and maintenance, effectively preventing possible external damage and ensuring the continuity and reliability of power supply.

[0042] Furthermore, calculate the risk index through real-time data and compare it with the preset safety range to quickly identify any risk indicators that exceed the normal range. If an abnormal tree growth rate is detected, the weight of the tree height in the risk assessment is increased to ensure sufficient attention and timely management of fast-growing trees. At the same time, if the distance between the tree and the transmission line is less than the safety distance, even if the growth rate is normal, a warning for soil reinforcement will be issued to prevent potential threats to the line caused by possible collapse or root development. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic flowchart of the method for monitoring and early warning of preventing external damage to transmission lines based on laser point cloud technology in this embodiment;

[0044] Figure 2 It is a decision logic diagram for determining whether the risk index is abnormal in this embodiment;

[0045] Figure 3 This is the determination logic diagram for determining abnormal height in this embodiment;

[0046] Figure 4 This is the determination logic diagram for determining abnormal distance in this embodiment. Detailed implementation manners

[0047] In order to make the objectives and advantages of the present invention clearer, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0048] The preferred implementation manners of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these implementation manners are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0049] It should be noted that in the description of the present invention, the terms indicating directions or positional relationships such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings. This is only for convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0050] In addition, it should also be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0051] Please refer to Figure 1 As shown, it is a schematic flowchart of a method for monitoring and warning against external damage to a transmission line based on laser point cloud technology in this embodiment;

[0052] This embodiment provides a method for monitoring and warning against external damage to a transmission line based on laser point cloud technology, including:

[0053] Deploy laser point sensors in key areas of the transmission line to monitor the real-time height of trees and the real-time distance between the trees and the line, and use a number of wind speed sensors at a number of detection points deployed within the scope of the transmission line to monitor the real-time wind speed;

[0054] Collect all the transmission line risk event records related to trees within the preset historical risk duration. The transmission line risk event records include the event location, tree height, distance between the tree and the line, and wind speed.

[0055] Specifically, calculate the risk index according to the tree height, distance between the tree and the line, and wind speed at each event location. F = w1×(h - h min ) / (h max - h min ) + w2×(d - d min ) / (d max - d min ) + w3×(v - v min ) / (v max - v min ), where F is the risk index, h is the tree height, h min is the minimum tree height, h max is the maximum tree height, d min is the minimum distance, d max is the maximum distance, v min is the minimum wind speed, v max is the maximum wind speed, d is the distance between the tree and the line, v is the wind speed, w1 is the height weight, w2 is the distance weight, and w3 is the wind speed weight.

[0056] Specifically, sort all the risk indices from smallest to largest and calculate the median of all the risk indices. Select the two risk indices before and after the median as the minimum and maximum values of the standard risk index range.

[0057] Please continue to refer to Figure 2 shown, which is the decision logic diagram for determining whether the risk index is abnormal in this embodiment.

[0058] Specifically, calculate the real-time risk index according to the real-time tree height, real-time distance, and real-time wind speed, and compare the real-time risk index with the standard risk index range.

[0059] Specifically, if the real-time risk index is not within the standard risk index range, calculate the change speed of the tree height within the preset detection duration in the past, and compare the change speed with the standard change speed.

[0060] Please continue to refer to Figure 3 shown, which is the decision logic diagram for determining height abnormality in this embodiment.

[0061] Specifically, if the change speed is greater than the standard change speed, it is determined that the height is abnormal. Adjust the height weight according to the change speed and the standard change speed, and issue a prompt for tree management.

[0062] The standard range of the risk index is determined through precise sorting and median calculation, making the risk assessment more standardized and systematic. It dynamically calculates the risk index using real-time data and compares it with the standard range to identify potential anomalies. When an abnormal tree growth rate is detected, the system automatically adjusts the weights and issues a warning to facilitate timely tree management. This coherent process not only improves the accuracy and timeliness of risk response but also significantly enhances the safety and stability of the transmission line through proactive management and maintenance, effectively preventing possible external damage and ensuring the continuity and reliability of power supply.

[0063] Please continue to refer to Figure 4 as shown, which is the decision logic diagram for determining distance anomalies in this embodiment;

[0064] Specifically, if the change speed is less than or equal to the standard change speed, then compare the distance between the tree and the line with the preset standard distance. If the distance between the tree and the line within the preset detection duration in the past is less than the standard distance, it is determined that the distance is abnormal, and a prompt to reinforce the soil near the transmission line is issued;

[0065] First, calculate the current risk index through real-time monitoring data and compare it with the pre-determined standard risk index range.

[0066] If the real-time risk index is not within the standard range, this may indicate that there is some abnormal situation that requires further analysis.

[0067] Calculate the height change speed of the tree within the preset detection duration in the past, which is to evaluate whether the tree growth rate is abnormally fast, thus potentially approaching or contacting the transmission line faster.

[0068] Compare the calculated change speed with the preset standard change speed to determine whether the tree growth rate exceeds the normal range.

[0069] If the change speed is greater than the standard change speed, increase the weight w1 of the tree height. This is because a rapidly growing tree may approach the transmission line faster, increasing the risk of contact with it, so greater attention needs to be given in the risk assessment.

[0070] If the change speed is less than or equal to the standard change speed, it means that the tree growth rate is within the acceptable range. At this time, other risk factors need to be concerned, such as the distance between the tree and the line.

[0071] If the distance between the tree and the line is less than the preset standard distance, even if the tree growth rate is not abnormal, there is still a risk that the tree may collapse or the root development may pose a threat to the transmission line. Therefore, a prompt to reinforce the soil near the transmission line is issued to reduce this risk.

[0072] Calculate the risk index through real-time data and compare it with the preset safety range to quickly identify any risk indicators beyond the norm. If an abnormal tree growth rate is detected, increase the weight of tree height in the risk assessment to ensure sufficient attention and timely management of fast-growing trees. At the same time, if the distance between a tree and a transmission line is less than the safe distance, even if the growth rate is normal, a warning to reinforce the soil will be issued to prevent possible collapse or threats to the line caused by root development.

[0073] Through the above analysis, the source of the risk can be identified more precisely, and corresponding measures can be taken, such as adjusting the tree management strategy, strengthening the maintenance of the transmission line, or improving construction activities, to ensure the safety of the transmission line.

[0074] If the distance between a tree and a line within the preset detection duration in the past is greater than or equal to the standard distance, then compare the wind speed with the standard wind speed. If the wind speed is greater than the standard wind speed, it is determined that the wind speed is abnormal. Adjust the preset detection duration according to the wind speed and the standard wind speed, and issue a prompt to prune the branches at this location;

[0075] A wind speed greater than the standard wind speed indicates that the tree sways in strong winds and poses a threat to the transmission line. Tree pruning measures need to be taken, and the wind speed monitoring frequency at this location should be increased;

[0076] If the wind speed is less than or equal to the standard wind speed, it is determined that the historical risk duration is abnormal. Issue a prompt to adjust the historical risk duration, and increase the adjusted historical risk duration according to the preset risk change cycle and duration adjustment factor, where T’ = T + α×(Tz - T), T’ is the adjusted historical risk duration, T is the preset historical risk duration, α is the duration adjustment factor, and Tz is the preset risk change cycle;

[0077] The risk change cycle is the periodic duration of tree growth changes. For example, if tree growth is mainly affected by seasons.

[0078] α ranges from 0 to 1 and is used to adjust the historical risk duration according to the risk change cycle. When α = 0, the risk change cycle is not considered, and the historical risk duration is used; when α = 1, the historical risk duration is exactly equal to the risk change cycle.

[0079] For example, if the historical risk duration is set to 1 year, the risk change cycle is 2 years, and it is desired that the historical risk duration can cover at least one complete risk change cycle, then set α = 0.5. In this way, the historical risk duration will be:

[0080] T’ = 1 + 0.5×(2 - 1) = 1.5 years,

[0081] This duration provides a balance point, taking into account both the risk change cycle and retaining a certain historical risk duration to ensure the stability of the assessment.

[0082] By using laser point cloud technology to meticulously monitor key areas, key indicators such as tree height, distance from the transmission line, and wind speed are captured in real time. Through this method, the technology can evaluate and quantify the risks faced by the transmission line and determine a reasonable risk index range based on historical data. When the risk index monitored in real time exceeds this range, the system will analyze the specific reasons, such as abnormal tree growth rate or wind speed, and take corresponding measures, such as tree pruning or soil reinforcement. In addition, the system will also adjust the monitoring duration according to the tree growth cycle and seasonal factors to ensure the accuracy and timeliness of risk assessment. This comprehensive monitoring and early warning method not only improves the response speed to potential threats but also enhances the overall safety of the transmission line, ensuring the continuity and reliability of power supply. Through continuous monitoring and timely maintenance, this technology significantly reduces accidents caused by external damage to the transmission line, providing strong support for the stable operation of the power system.

[0083] Specifically, for a transmission line, multiple monitoring points are arranged along the line, and each monitoring point is equipped with a laser point sensor that can monitor tree height, distance between the tree and the line, and wind speed in real time.

[0084] Monitoring point data:

[0085] Data of monitoring point A:

[0086] Tree height hA = 15 meters;

[0087] Distance between the tree and the line dA = 2 meters;

[0088] Real-time wind speed vA = 20 m / s;

[0089] Data of monitoring point B:

[0090] Tree height hB = 10 meters;

[0091] Distance between the tree and the line dB = 4 meters;

[0092] Real-time wind speed vB = 12 m / s;

[0093] Tree height weight w1 = 0.5;

[0094] Distance between the tree and the line weight w2 = 0.3;

[0095] Wind speed weight w3 = 0.2;

[0096] Risk assessment formula: F = w1×(h - h min ) / (h max -hmin ) + w2×(d - d min ) / (d max - d min ) + w3×(v - v min ) / (v max - v min );

[0097] Risk index FA of monitoring point A: FA = 0.7;

[0098] Risk index FB of monitoring point B: FB = 0.3;

[0099] Collect the risk index data of all monitoring points in the past year to obtain a historical risk index list, for example: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0];

[0100] The past detection duration is set to one year. Collect the risk index data of all monitoring points within one year, and there is sufficient data to support the risk assessment.

[0101] Calculate the median. Assuming there are 14 risk indices in the historical data, the median is the average of the 7th and 8th values, that is, the median is 0.375.

[0102] Determine the standard risk index range, for example, the two values before and after the median, that is, 0.25 and 0.45.

[0103] The risk index FA = 0.7 of monitoring point A exceeds the standard range (0.25 - 0.45), and risk response measures need to be taken.

[0104] The risk index FB = 0.3 of monitoring point B is within the standard range, and no special measures are temporarily required.

[0105] Risk response for monitoring point A:

[0106] Analyze the reasons why the risk index exceeds the standard range and determine whether it is the tree height, distance, or wind speed that causes the increased risk.

[0107] Since hA and vA are the main risk factors, consider the following measures:

[0108] Tree management:

[0109] Evaluate the contribution of the tree height hA = 15 meters to the risk index and arrange a professional tree management team for on-site assessment.

[0110] If the tree growth rate is too fast, formulate a pruning plan to reduce the tree height to no more than the safe height (for example, 13 meters).

[0111] Enhanced wind speed monitoring:

[0112] Since the wind speed vA = 20 m / s is another major risk factor, increase the wind speed monitoring frequency at monitoring point A, especially when bad weather is forecasted.

[0113] When the wind speed is normal, collect the risk index data of monitoring point A in the past year and analyze the periodicity of risk changes, such as seasonal changes.

[0114] Suppose the analysis finds that the growth of trees is mainly affected by seasons, and determine a risk change cycle, for example, Tz = 2 years.

[0115] According to the risk change cycle, select a duration adjustment factor α. For example, if it is desired that the historical risk duration can cover at least one complete risk change cycle, α = 0.5 can be selected.

[0116] The currently preset historical risk duration T is 1 year, and T’ = 1 + 0.5×(2 - 1) = 1 + 0.5 = 1.5 years, indicating that the time window for historical risk assessment needs to be adjusted to 1.5 years to better reflect the risk changes.

[0117] Subsequent monitoring and evaluation:

[0118] After taking response measures, continue to monitor the risk index of monitoring point A and evaluate the effectiveness of the measures.

[0119] If the risk index remains high, consider further risk mitigation measures.

[0120] Risk response for monitoring point B:

[0121] Risk identification:

[0122] The risk index FB of monitoring point B is 0.3, within the standard risk index range.

[0123] Response measures:

[0124] Continuous monitoring:

[0125] Even if the risk index is within the acceptable range, regular monitoring of monitoring point B should still be continued.

[0126] Tree growth monitoring:

[0127] Regularly check the growth of the tree with hB = 10 m and predict the possible risks to the transmission line in the future.

[0128] Risk assessment update:

[0129] Regularly update the risk assessment of monitoring point B according to the new monitoring data and environmental changes.

[0130] Preventive measure planning:

[0131] Even if the current risk index is acceptable, preventive measures such as a regular tree pruning plan should be planned to prevent the future risk index from rising.

[0132] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0133] The above are only the preferred embodiments of the present invention and are not used to limit the present invention; for those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A monitoring and early warning method for preventing external damage to transmission lines based on laser point cloud technology, characterized in that, Including: Using a number of laser point sensors at several detection points deployed within the scope of the transmission line to monitor the real-time height of the trees and the real-time distance between the trees and the line, and using a number of wind speed sensors at several detection points deployed within the scope of the transmission line to monitor the real-time wind speed; Calculating a risk index based on the tree height, the distance between the tree and the line, and the wind speed at each event time point for each event location within the historical risk duration, and determining a standard risk index range based on the calculation results; Based on the real-time risk index calculated from the real-time height of the tree, the real-time distance, and the real-time wind speed and the standard risk index range, determining the abnormal type based on the change rate of the tree height, the distance between the tree and the line, and the wind speed within the past detection duration, and selecting to adjust the preset height weight corresponding to the real-time height of the tree, or adjusting the preset detection duration, or adjusting the historical risk duration according to the abnormal type, and sending a prompt corresponding to the abnormal type; The abnormal types include height abnormality, distance abnormality, wind speed abnormality, and historical risk duration abnormality; Among them, the determination process for the abnormal type of the height abnormality includes: Calculating a real-time risk index based on the real-time height of the tree, the real-time distance, and the real-time wind speed; Determining that the calculated real-time risk index is not within the standard risk index range; Obtaining the change rate of the tree height within the past preset detection duration; If the change rate is greater than the standard change rate, it is determined as a height abnormality, increasing and adjusting the height weight according to the change rate and the standard change rate, and sending a prompt for tree management; The determination process for the abnormal type of the distance abnormality includes: Calculating a real-time risk index based on the real-time height of the tree, the real-time distance, and the real-time wind speed; Determining that the calculated real-time risk index is not within the standard risk index range; Obtaining the change rate of the tree height and the distance between the tree and the line within the past preset detection duration; If the change rate is less than or equal to the standard change rate, then comparing the distance between the tree and the line with the preset standard distance. If the distance between the tree and the line within the past preset detection duration is less than the standard distance, it is determined as a distance abnormality, and a prompt for strengthening the soil near the transmission line is sent; The determination process for the abnormal type of the wind speed abnormality includes: Calculating a real-time risk index based on the real-time height of the tree, the real-time distance, and the real-time wind speed; Determining that the calculated real-time risk index is not within the standard risk index range; Obtaining the distance between the tree and the line and the wind speed within the past preset detection duration; If the distance between the tree and the line within the past preset detection duration is greater than or equal to the standard distance, then comparing the wind speed with the standard wind speed. If the wind speed is greater than the standard wind speed, it is determined as a wind speed abnormality, reducing and adjusting the preset detection duration according to the wind speed and the standard wind speed, and sending a prompt for pruning the branches at this location.

2. The monitoring and early warning method for preventing external damage to transmission lines based on laser point cloud technology according to claim 1, wherein The calculating the risk index based on the tree height, the distance between the tree and the line, and the wind speed at each event time point for each event location within the historical risk duration includes: Summing up the product of the standardized tree height and the height weight, the product of the standardized distance between the tree and the line and the preset distance weight, and the product of the standardized wind speed and the wind speed weight to obtain the risk index.

3. The method for monitoring and early warning of preventing external damage to transmission lines based on laser point cloud technology according to claim 2, wherein, The determination of the standard risk index range according to the calculation results includes: Sort all the calculated risk indices from smallest to largest and calculate the median of all the risk indices. Select the two risk indices before and after the median as the minimum and maximum values of the standard risk index range.

4. The monitoring and early warning method for preventing external damage to transmission lines based on laser point cloud technology according to claim 3, characterized in that The adjustment of the preset height weight corresponding to the real-time height of the tree according to the abnormal type includes: Increase and adjust the height weight according to the change speed and the standard change speed when it is determined that the height is abnormal.

5. The monitoring and early warning method for preventing external damage to transmission lines based on laser point cloud technology according to claim 4, characterized in that The adjustment of the preset detection duration according to the abnormal type includes: Reduce and adjust the preset detection duration when the abnormal type is wind speed abnormality according to the wind speed and the standard wind speed.

6. The method for monitoring and early warning of preventing external damage to transmission lines based on laser point cloud technology according to claim 5, characterized in that The determination process of the abnormal type being the abnormal historical risk duration includes: Calculate the real-time risk index according to the real-time height, real-time distance and real-time wind speed of the tree; Determine that the calculated real-time risk index is not within the standard risk index range; Obtain the distance and wind speed between the tree and the line within the preset detection duration in the past; Determine that the distance between the tree and the line is greater than or equal to the standard distance, and the wind speed is less than or equal to the standard wind speed.

7. The method for monitoring and early warning of preventing external damage to transmission lines based on laser point cloud technology according to claim 6, wherein The adjustment of the historical risk duration according to the abnormal type includes: Increase and adjust the historical risk duration when the abnormal type is the abnormal historical risk duration according to the preset risk change period and duration adjustment factor; The risk change period is the periodic duration of the growth change of the tree.

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