A power transmission line external damage target internet of things information sensing detection method and system

Abstract

The application discloses a kind of power transmission line external damage target internet of things information sensing detection method and system, wherein, method includes steps: whether there is external damage target along power transmission line based on the detection data analysis monitor group;When there is no external damage target, the detection range of monitor group is optimized;When there is external damage target, external damage target is tracked and early warned;The method can accurately master weak link of monitoring coverage by actively acquiring environmental data and combining monitor parameter identification blind area, can be targeted to reduce or eliminate these monitoring blind area by dynamically adjusting the detection parameter of monitor adjacent to blind area according to blind area atlas, to improve the overall monitoring coverage effectiveness along power transmission line, significantly improve the sensing ability to external damage target, reduce the risk of missing report caused by the hidden activity of external damage target in blind area, enhance the adaptability and reliability of power transmission line safety monitoring system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of monitoring analysis, in particular to a power transmission line external damage target Internet of Things information sensing detection method and system. BACKGROUND

[0002] The power transmission line safety monitoring system, driven by the Internet of Things technology, through the deployment of optical cameras and millimeter wave radars and other sensors, combined with edge computing units, realizes efficient detection of cranes, excavators and other large construction machinery, effectively reducing the risk of accidents. The system performs well in flat and open areas, promoting the automation and intelligentization of power safety, and the trend is to integrate more environmental data sources to improve real-time response capabilities and promote the optimization and cost control of power transmission line operation and maintenance.

[0003] However, when the power transmission line passes through complex terrain areas such as deep valleys, steep ridges or winding river channels, fixed sensor deployment has inherent line-of-sight blockage, forming a continuous monitoring blind area. Seasonal changes cause dynamic changes in vegetation coverage, and the extensive tree canopy in summer expands the blind area, and the exposed mountain in autumn and winter still cannot completely eliminate the blockage; weather conditions such as heavy rain or snow reduce air visibility, weaken sensor detection capability, cause image blur and signal attenuation. External damage targets such as small excavators or illegal mining equipment use these blind areas for hidden operations, adopt intermittent movement strategies to evade monitoring, and the system cannot evaluate the effective coverage range in real time or dynamically adjust the monitoring strategy, resulting in unpredictable monitoring coverage gaps and increasing the risk of false negatives, threatening the safe operation of the power transmission line.

[0004] At present, there is no effective technical solution to the above problems. SUMMARY

[0005] The purpose of the present application is to provide a power transmission line external damage target Internet of Things information sensing detection method and system to solve the problem of monitoring blind area of power transmission line in complex terrain, vegetation and weather conditions.

[0006] In a first aspect, the present application provides a power transmission line external damage target Internet of Things information sensing detection method for detecting external damage targets along the power transmission line, the method comprising the following steps:

[0007] Analyzing whether there is an external damage target along the power transmission line based on the detection data of the monitor group;

[0008] When there is no external damage target, optimizing the detection range of the monitor group, the optimization process comprising:

[0009] A1, obtaining terrain data, vegetation data and weather data along the power transmission line;

[0010] A2, determining a monitoring blind area map of the power transmission line according to the terrain data, the vegetation data, the meteorological data, and the detection parameters of the monitor group, the monitoring blind area map comprising a plurality of monitoring blind areas;

[0011] A3, adjusting the detection parameters of the monitor adjacent to the monitoring blind area in the monitor group according to the monitoring blind area map, so as to reduce or eliminate the monitoring blind area;

[0012] When the external damage target exists, tracking and early warning are performed on the external damage target.

[0013] The method of the present application accurately identifies the monitoring blind area of the power transmission line by introducing the terrain data, the vegetation data, and the meteorological data, and actively adjusts the detection parameters of the monitor adjacent to the monitoring blind area according to the identified monitoring blind area map, effectively solving the problem of monitoring blind area of the power transmission line under complex terrain, vegetation, and meteorological conditions.

[0014] The power transmission line external damage target Internet of Things information sensing and detection method, wherein the monitoring blind area map further comprises an effective monitoring area and a blind area attribute corresponding to the monitoring blind area, and the tracking and early warning process comprises:

[0015] B1, recording trajectory information of the external damage target in the effective monitoring area;

[0016] B2, when the external damage target enters the monitoring blind area, predicting a behavior type of the external damage target in the monitoring blind area according to the trajectory information, the blind area attribute corresponding to the monitoring blind area, and the type of the external damage target;

[0017] B3, predicting an estimated position and an estimated time of the external damage target reappearing in the effective monitoring area according to the behavior type and the trajectory information;

[0018] B4, analyzing whether the external damage target has an abnormal behavior according to the appearance of the external damage target in the effective monitoring area, the estimated position, and the estimated time, and if so, triggering an alarm information.

[0019] In this example, the method of the present application solves the problem that when there is a monitoring blind area along the power transmission line, the external damage target enters the blind area, causing tracking interruption and difficulty in continuously grasping the target dynamics. By predicting the behavior and estimating the state when the target is invisible, the method of the present application effectively makes up for the information blind spot caused by the monitoring blind area, so that the method of the present application can maintain continuous attention to the external damage target, improve the monitoring continuity of the external damage target in complex environments, thereby more effectively predicting the target behavior and the position / time of reappearing, reducing the risk of false negatives, and enhancing the security capability of the safe operation of the power transmission line.

[0020] The power transmission line external damage target Internet of Things information perception detection method, wherein step B2 comprises:

[0021] B21, according to the trajectory information of the external damage target before entering the monitoring blind area, extracting the motion state parameters of the external damage target when entering the monitoring blind area;

[0022] B22, according to the blind area attribute of the monitoring blind area, inferring the topographic features of the monitoring blind area;

[0023] B23, according to the type of the external damage target, obtaining the corresponding typical behavior mode;

[0024] B24, according to the motion state parameters, the typical behavior mode and the topographic features, inferring the behavior type of the external damage target in the monitoring blind area.

[0025] The power transmission line external damage target Internet of Things information perception detection method, wherein the behavior type comprises continuous movement, stay operation or path change.

[0026] The power transmission line external damage target Internet of Things information perception detection method, wherein step B3 comprises:

[0027] B31, according to the geometric boundary of the monitoring blind area and the behavior type of the external damage target in the monitoring blind area, predicting several moving paths of the external damage target in the monitoring blind area;

[0028] B32, according to the motion state parameters obtained based on the trajectory information, the behavior type and the moving path, estimating the stay time of the external damage target in the monitoring blind area;

[0029] B33, according to the moving path and the stay time, inferring the estimated time and the estimated position of the external damage target appearing again in the effective monitoring area.

[0030] The power transmission line external damage target Internet of Things information perception detection method, wherein step B4 comprises:

[0031] B41, according to the estimated time, generating the expected time range of the external damage target appearing again in the effective monitoring area, and according to the estimated position, generating the expected position range of the external damage target appearing again in the effective monitoring area;

[0032] B42, if the external damage target still does not appear again in the effective monitoring area within the expected time range, determining that the external damage target has abnormal behavior;

[0033] B43, if the external damage target appears again in the effective monitoring area within the expected time range, obtaining the actual appearing position of the external damage target, and determining whether the actual appearing position is within the expected position range, if the actual appearing position is not within the expected position range, determining that the external damage target has abnormal behavior;

[0034] B44, when determining that the external damage target has abnormal behavior, triggering an alarm information.

[0035] The power transmission line external damage target Internet of Things information sensing detection method, wherein step B41 comprises:

[0036] B411, according to the range of the monitoring blind area and the blind area attribute, determining the uncertainty influence parameter of the monitoring blind area on the behavior of the external damage target;

[0037] B412, according to the behavior type, determining a behavior influence factor;

[0038] B413, according to the uncertainty influence parameter, the behavior influence factor and the estimated time, calculating the expected time range;

[0039] B414, according to the expected time range and the estimated position, generating the expected position range.

[0040] The power transmission line external damage target Internet of Things information sensing detection method, wherein step A2 comprises:

[0041] A21, based on the deployment position and detection parameter of each monitor in the monitor group, combining the elevation information and slope information in the terrain data, calculating the initial effective monitoring area of each monitor in the monitor group;

[0042] A22, according to the vegetation height information and vegetation density information in the vegetation data, calculating the shielding range of the vegetation to the line of sight of the monitor group;

[0043] A23, determining the attenuation degree of the detection ability of different types of monitors based on the meteorological data;

[0044] A24, adjusting the initial effective monitoring area based on the shielding range and the attenuation degree to obtain the effective detection area of the monitor group, and identifying the monitoring blind area along the power transmission line based on the effective detection area;

[0045] A25, according to the dominant influencing factor of the monitoring blind area, configuring the blind area attribute;

[0046] A26, outputting the monitoring blind area map according to the effective monitoring area, the monitoring blind area and the blind area attribute.

[0047] The power transmission line external damage target Internet of Things information sensing detection method, wherein step A3 comprises:

[0048] A31, according to the monitoring blind area map, the position and range of each monitoring blind area are extracted;

[0049] A32, according to the position and range of the monitoring blind area, the monitor adjacent to each monitoring blind area in the monitor group is identified;

[0050] A33, according to the monitoring blind area type and the adjustable detection parameter and the position and range of the corresponding monitoring blind area, a detection parameter adjustment scheme is generated;

[0051] A34, the detection parameter adjustment scheme is sent to the corresponding monitor, so that the monitor adjusts its detection parameter according to the adjustment scheme, so as to reduce or eliminate the monitoring blind area.

[0052] In a second aspect, the application further provides a power transmission line external damage target Internet of Things information sensing detection system for detecting external damage targets of power transmission lines, the system comprising:

[0053] A monitoring module for analyzing whether there is an external damage target along the power transmission line based on the detection data of the monitor group;

[0054] An adjustment module for optimizing the detection range of the monitor group when there is no external damage target, and the optimization process comprises:

[0055] A1, acquiring topographic data, vegetation data and meteorological data along the power transmission line;

[0056] A2, according to the topographic data, the vegetation data, the meteorological data and the detection parameter of the monitor group, the monitoring blind area map of the power transmission line is determined, and the monitoring blind area map comprises a plurality of monitoring blind areas;

[0057] A3, according to the monitoring blind area map, the detection parameter of the monitor adjacent to the monitoring blind area in the monitor group is adjusted to reduce or eliminate the monitoring blind area;

[0058] A warning module for tracking and warning the external damage target when there is an external damage target.

[0059] The system of the application accurately identifies the monitoring blind area along the power transmission line by introducing topographic data, vegetation data and meteorological data, and actively adjusts the detection parameter of the monitor adjacent to the monitoring blind area according to the identified monitoring blind area map, effectively solving the problem of monitoring blind area of power transmission line under complex terrain, vegetation and meteorological conditions.

[0060] From the above, the application provides a transmission line external damage target Internet of Things information sensing detection method and system, wherein the method of the application can accurately master the weak links of monitoring coverage by actively acquiring environmental data and combining with the parameter identification of the monitor blind area, can reduce or eliminate these monitoring blind areas in a targeted manner by dynamically adjusting the detection parameters of the monitor adjacent to the blind area according to the blind area map, thereby improving the overall monitoring coverage effectiveness of the transmission line along the line, significantly improving the sensing ability of the external damage target, reducing the risk of missing report caused by the hidden activities of the external damage target in the blind area, and enhancing the adaptability and reliability of the transmission line safety monitoring system. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 The flowchart of the transmission line external damage target Internet of Things information sensing detection method provided by the embodiment of the application.

[0062] Figure 2 The flowchart for optimizing the detection range of the monitor group.

[0063] Figure 3 The flowchart for tracking and early warning.

[0064] Figure 4 The structural schematic diagram of the transmission line external damage target Internet of Things information sensing detection system provided by the embodiment of the application.

[0065] The figure mark: 201, monitoring module; 202, adjustment module; 203, early warning module. DETAILED DESCRIPTION

[0066] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. The components of the embodiments of the application described and shown in the drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the application provided in the drawings is not intended to limit the scope of the claimed application, but only represents selected embodiments of the application. Based on the embodiments of the application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the application.

[0067] It should be noted that: similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the application, the terms "first", "second" and the like are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0068] In the first aspect, please refer to Figures 1-3Some embodiments of the present application provide a power transmission line external damage target Internet of Things information sensing detection method for detecting external damage targets along the power transmission line. The method comprises the following steps:

[0069] Based on the detection data of the monitor group, analyze whether there is an external damage target along the power transmission line;

[0070] When there is no external damage target, optimize the detection range of the monitor group. The optimization process comprises:

[0071] A1, obtaining topographic data, vegetation data and meteorological data along the power transmission line;

[0072] A2, determining a monitoring blind area map along the power transmission line according to the topographic data, vegetation data, meteorological data and detection parameters of the monitor group, the monitoring blind area map comprising a plurality of monitoring blind areas;

[0073] A3, adjusting the detection parameters of the monitor adjacent to the monitoring blind area in the monitor group according to the monitoring blind area map to reduce or eliminate the monitoring blind area;

[0074] When there is an external damage target, track and warn the external damage target.

[0075] Specifically, the monitor group refers to a set of sensors deployed along the power transmission line for sensing the external environment and targets, which can be implemented by combining different types of sensors, such as optical cameras, millimeter wave radars, and laser radars. Its main purpose is to realize the detection of the area along the power transmission line.

[0076] More specifically, the external damage target refers to an external object or activity subject that may pose a threat to the safety of the power transmission line, such as construction machinery, illegal operators or equipment, etc. Its type can be distinguished according to its physical characteristics or image recognition, such as vehicles, personnel or specific types of machinery.

[0077] More specifically, the topographic data refers to data describing the features of the ground along the power transmission line, such as undulations, slopes, and elevations. It can be obtained by using digital elevation models, contour maps or three-dimensional point cloud data. Its main purpose is to analyze the impact of ground shielding on monitoring line of sight.

[0078] More specifically, the vegetation data refers to data describing the characteristics of plants along the power transmission line, such as type, height, density, distribution, etc. It can be obtained by using remote sensing image analysis, ground investigation or vegetation model. Its main purpose is to analyze the shielding effect of vegetation on monitoring line of sight.

[0079] More specifically, the weather data refers to data describing weather conditions along the power transmission line, which can be obtained by weather station data, satellite remote sensing data or weather model prediction data, such as rainfall, snow depth, visibility, wind speed, mainly for analyzing the attenuation effect of weather conditions on the detection capability of the monitor.

[0080] More specifically, the monitoring blind area map refers to the distribution of areas that cannot be effectively covered by the monitor group along the power transmission line, which can be represented by two-dimensional map superposition, three-dimensional model rendering or area list, mainly for visualizing and locating the monitoring blind area.

[0081] More specifically, the adjustment process of the monitor can be achieved by adjusting the detection field of view, changing the detection direction or changing the detection distance, mainly for narrowing or eliminating the monitoring blind area.

[0082] Specifically, the method first uses the detection data collected by the monitor group to analyze whether there is an external damage target along the power transmission line. The analysis result determines the operation mode of the subsequent system. When the analysis result shows that there is no external damage target, the system enters the continuous or periodic detection range optimization process. In this process, the system first obtains the terrain data, vegetation data and weather data along the power transmission line. These environmental data are combined with the detection parameters of the monitor group to calculate and generate the monitoring blind area map of the power transmission line. The monitoring blind area map clearly identifies the effective monitoring area and monitoring blind area of the monitor group. Subsequently, the system identifies the monitors adjacent to the monitoring blind area in position according to the generated monitoring blind area map. For these adjacent monitors, the system generates a corresponding detection parameter adjustment scheme according to the position and range of the monitoring blind area. The scheme indicates how the adjacent monitor adjusts its detection parameters, such as changing the detection field of view, detection direction or detection distance. The adjustment scheme is sent to the corresponding monitor, and the monitor adjusts its parameters according to the scheme, so that the detection range is expanded to the original monitoring blind area, achieving the purpose of narrowing or eliminating the monitoring blind area. When the analysis result shows that there is an external damage target, the system enters the tracking and warning process to continuously track and warn the external damage target. By actively optimizing the monitoring coverage when there is no threat and responding in time when there is a threat, the method improves the overall perception ability of the system in complex environments.

[0083] The method of the present application accurately identifies the monitoring blind area along the power transmission line by introducing topographic data, vegetation data and meteorological data, and actively adjusts the detection parameters of the monitor adjacent to the monitoring blind area according to the identified monitoring blind area map, effectively solving the problem of monitoring blind area of the power transmission line under complex terrain, vegetation and weather conditions; The method can accurately identify the weak link of the monitoring coverage by actively obtaining environmental data and combining the monitor parameters, and can dynamically adjust the detection parameters of the monitor adjacent to the blind area according to the blind area map, so as to reduce or eliminate these monitoring blind areas, thereby improving the overall monitoring coverage effectiveness of the power transmission line, significantly improving the perception ability of external damage targets, reducing the risk of missing reports caused by the hidden activities of external damage targets in the blind area, and enhancing the adaptability and reliability of the power transmission line safety monitoring system.

[0084] In some preferred embodiments, the monitoring blind area map further comprises an effective monitoring area and a blind area attribute corresponding to the monitoring blind area, and the process of tracking and early warning comprises:

[0085] B1, record the trajectory information of the external damage target in the effective monitoring area;

[0086] B2, when the external damage target enters the monitoring blind area, according to the trajectory information, the blind area attribute corresponding to the monitoring blind area and the type of the external damage target, predict the behavior type of the external damage target in the monitoring blind area;

[0087] B3, according to the behavior type and the trajectory information, predict the estimated position and estimated time of the external damage target appearing in the effective monitoring area again;

[0088] B4, according to the appearance of the external damage target in the effective monitoring area, the estimated position and the estimated time, analyze whether the external damage target has abnormal behavior, and if so, trigger the alarm information.

[0089] Specifically, the effective monitoring area refers to the geographical range where the monitor group can effectively detect external targets, which can be calculated based on the deployment position of the monitor, the detection parameters (such as field of view angle, maximum detection distance) and the comprehensive influence of environmental factors (such as terrain, vegetation, weather) on detection ability. The blind area attribute refers to the information describing the characteristics of the monitoring blind area.

[0090] More specifically, the trajectory information refers to the position sequence data of the external damage target in the effective monitoring area within a period of time, which can be continuously obtained by the monitor in the effective monitoring area. The behavior type refers to the action or state that the external damage target can take in the monitoring blind area, such as continuing to move, stopping work, or changing the original path. The estimated position and the estimated time refer to the geographical coordinates and the corresponding time at which the system predicts that the external damage target can reappear in the effective monitoring area. The abnormal behavior refers to the case that the actual behavior of the external damage target deviates significantly from the system prediction, such as not appearing for a long time or appearing at an unexpected location.

[0091] Specifically, when the method of the present application detects the presence of an external damage target, it starts the tracking and warning process. First, the system continuously records the movement trajectory of the external damage target in the effective monitoring area, obtaining its motion state information before entering the blind area. Once the target enters the monitoring blind area, the direct monitoring is interrupted, and the system switches to the prediction mode. At this time, step B2 comprehensively utilizes the motion trend of the target before entering the blind area, the environmental characteristics of the monitoring blind area (reflected by the blind area attribute), and the type characteristics of the external damage target (different types of targets have different behavior tendencies), to infer the most likely behavior mode of the target in the blind area. Based on the predicted behavior type and the last known state of the target before entering the blind area, the system further estimates the time required for the target to pass through the blind area or the time spent in the blind area, and combines the geometry of the blind area to predict the position from which the target can re-enter the effective monitoring area and the time point at which it can re-enter. Finally, the system continuously focuses on the predicted appearance position and time window. If the target fails to appear as predicted or appears outside the predicted range, the system determines that the target behavior is abnormal and triggers an alarm to remind the operation and maintenance personnel. This scheme, combined with the overall external damage target detection method for the power transmission line, utilizes the monitoring blind area map and its attribute information generated in the method, so that the blind area problem can be addressed specifically when there is an external damage target, improving the robustness and effectiveness of the entire monitoring and warning system.

[0092] Through the above processing, the method of the present application solves the problem that when there is a monitoring blind area along the power transmission line, the external damage target entering the blind area causes tracking interruption and makes it difficult to continuously monitor the target. By predicting the behavior and estimating the state when the target is not visible, the method effectively compensates for the information blind spot caused by the monitoring blind area, so that the method can maintain continuous attention to the external damage target, improve the monitoring continuity of the external damage target in complex environments, thereby more effectively predicting the target behavior and the position / time of reappearing, reducing the risk of false negatives, and enhancing the security capability of the safe operation of the power transmission line.

[0093] In some preferred embodiments, step B2 comprises:

[0094] B21. According to the trajectory information before the external damage target enters the monitoring blind area, the motion state parameters of the external damage target when entering the monitoring blind area are extracted;

[0095] B22. According to the blind area attribute of the monitoring blind area, the terrain feature of the monitoring blind area is inferred;

[0096] B23. According to the type of the external damage target, the corresponding typical behavior mode is obtained;

[0097] B24. According to the motion state parameters, the typical behavior mode and the terrain feature, the behavior type of the external damage target in the monitoring blind area is inferred.

[0098] Specifically, the blind area attribute can be represented by a preset classification label (for example, terrain blind area, vegetation blind area, meteorological blind area), which is usually determined according to the dominant influencing factors when the monitoring blind area map is generated. The typical behavior mode refers to the activity rule and ability range of a specific type of external damage target under normal or common conditions, which can be stored and obtained by a pre-established database or knowledge base, which contains information such as average or typical moving speed, working mode and working efficiency of different types of targets (such as excavators, personnel).

[0099] Specifically, the method of the present application comprehensively judges the activity state of the target in the blind area by analyzing the target's dynamics before entering the blind area, the characteristics of the blind area environment, and the inherent properties of the target itself. First, by processing the trajectory data of the target in the visible area, the motion state parameters including speed, direction, and last position, etc. at the moment when the target enters the boundary of the blind area can be accurately obtained. These motion state parameters directly reflect the inertia and initial intention of the target when it enters the unknown area. Then, by using the blind area attributes recorded in the blind area atlas, the environmental factors such as terrain, vegetation, or weather that may exist in the area can be inferred. For example, a terrain blind area may mean that there are steep slopes, gullies, or large obstacles, and these environmental characteristics will limit or affect the movement and operation ability of the target. At the same time, according to the known type of the target, the moving speed range and possible operation mode that the target usually has can be consulted, which provides prior knowledge and reasonable range for predicting the target's behavior. Finally, the motion state of the target when it enters the blind area, the behavior mode of the target of this type, and the environmental characteristics of the blind area are analyzed and integrated. For example, if a target identified as a excavator enters a terrain blind area speculated to be a steep slope at a low speed, combined with the usual operation mode of the excavator (which requires a relatively flat ground), it can be inferred that it may not operate in the blind area, but try to move slowly through or change the path to find an easier area to pass through. Through the comprehensive judgment of multi-dimensional information, it can more accurately predict whether the target will continue to move in the original direction, stop for some operation, or change its moving path due to environmental constraints. This prediction process is based on a comprehensive consideration of the target's own characteristics, entry state, and environmental constraints, and provides key inputs for subsequent estimation of the target's position and state during the period when the target is invisible, thereby supporting more reliable tracking and abnormal behavior judgment.

[0100] The above processing, combined with the overall process of target tracking in the effective monitoring area and subsequent prediction and abnormal judgment after the target enters the blind area, enables the method of the present application to maintain the grasp of the target's state based on prediction information when the target temporarily departs from the monitoring line of sight, significantly improving the continuous monitoring ability of the target in complex environments.

[0101] In some preferred embodiments, the behavior type includes continuous movement, stay operation, or path change.

[0102] Specifically, the scheme explicitly defines that the behavior type can be specifically three cases of continuous movement, stay operation or path change. Such limitation provides specific and operable basis for subsequent prediction of the estimated position and estimated time of the target reappearing in the effective monitoring area according to the behavior type and trajectory information, and analysis of whether the target has abnormal behavior according to the actual appearance of the target, the estimated position and the estimated time. By summarizing the complex behavior of the target in the monitoring blind area into these three discrete types, the system can adopt specific prediction models and abnormal judgment logic for each type. For example, for the target predicted as "continuous movement", its approximate traversal path and leaving time can be predicted based on its speed and direction before entering the blind area, combined with the blind area terrain; for the target predicted as "stay operation", its possible stay duration in the blind area can be considered, and its possible re-appearance near the blind area entrance or in a specific operation area can be predicted; for the target predicted as "path change", the system may need to consider the geometry and terrain of the blind area, and predict the alternative path it may choose and the corresponding leaving position and time. Such classification, prediction and analysis method based on explicit behavior type makes it possible to track and warn the external breaking target in the information-limited monitoring blind area, and improves the accuracy and effectiveness of the warning. By combining with the step of inferring the behavior type by using the trajectory information, the blind area attributes and the target type, the specific behavior type definition provided by the scheme enables the smooth execution of the entire tracking and warning process, and solves the problem that the prediction and analysis are difficult to implement due to the unclear behavior type.

[0103] In some preferred embodiments, step B3 comprises:

[0104] B31, predicting a plurality of movement paths of the external breaking target in the monitoring blind area according to the geometric boundary of the monitoring blind area and the behavior type of the external breaking target in the monitoring blind area;

[0105] B32, estimating the stay time of the external breaking target in the monitoring blind area according to the motion state parameters obtained by extracting based on the trajectory information, the behavior type and the movement path when entering the monitoring blind area;

[0106] B33, inferring the estimated time and the estimated position of the external breaking target reappearing in the effective monitoring area according to the movement path and the stay time.

[0107] Specifically, the geometric boundary of the monitoring blind area refers to the actual range and shape of the monitoring blind area in space; predicting a plurality of movement paths of the external breaking target in the monitoring blind area refers to simulating or calculating a plurality of movement routes that the target may follow in the blind area based on the spatial limitations of the blind area and the behavior mode of the target.

[0108] More specifically, the motion state parameters obtained by extracting based on the trajectory information when entering the monitoring blind area can directly use the motion state parameters extracted in the aforementioned step B21.

[0109] More specifically, the estimated time and the estimated position of the re- appearance of the external damage target in the effective monitoring area refer to the time points and the corresponding spatial positions at which the target is likely to appear along the predicted moving paths and the estimated staying time.

[0110] Specifically, the prediction of the moving path combines the actual spatial limitations of the blind area and the previously speculated behavior type, so that the simulation of the moving trajectory of the target in the blind area is more close to the actual situation. Subsequently, the staying time of the external damage target in the blind area is estimated by using the motion state parameters before the external damage target enters the blind area, the speculated behavior type and the predicted moving path. This estimation method integrates the inertia of the target, the behavior intention and the time required through different paths, so that the prediction of the staying time is more reasonable. Finally, the estimated time and the estimated position of the re-appearance of the external damage target in the effective monitoring area are inferred by combining the predicted moving path and the estimated staying time. By considering multiple paths and corresponding staying times, a set of possible time points and positions of the re-appearance of the target can be obtained, which provides a more rich and accurate information basis for subsequent judgment of abnormal behavior of the target. The scheme effectively deals with the uncertainty caused by the blind area by refining the prediction process of the behavior of the target in the blind area, and improves the accuracy of tracking and early warning. The scheme, in combination with the method of predicting the behavior type of the target according to the trajectory information and the attributes of the blind area, forms a more complete chain of behavior prediction of the target in the blind area, and provides a more reliable input for subsequent abnormal behavior judgment.

[0111] Through the above scheme, the method of the present application improves the prediction accuracy of the time and position of the re-appearance of the external damage target after entering the monitoring blind area, reduces the risk of false negatives caused by inaccurate prediction, and enhances the reliability of tracking and early warning of the external damage target of the power transmission line.

[0112] In some preferred embodiments, step B4 comprises:

[0113] B41, generating an expected time range of the re-appearance of the external damage target in the effective monitoring area according to the estimated time, and generating an expected position range of the re-appearance of the external damage target in the effective monitoring area according to the estimated position;

[0114] B42, if the external damage target still does not re-appear in the effective monitoring area within the expected time range, determining that the external damage target has abnormal behavior;

[0115] B43, if the external damage target re-appears in the effective monitoring area within the expected time range, obtaining the actual appearance position of the external damage target, and determining whether the actual appearance position is within the expected position range, if the actual appearance position is not within the expected position range, determining that the external damage target has abnormal behavior;

[0116] B44, triggering an alarm information when it is determined that the external damage target has abnormal behavior.

[0117] Specifically, the expected time range refers to the time interval in which the external damage target is estimated to reappear in the effective monitoring area based on the prediction of the target's behavior in the monitoring blind area. It can be determined by adding a time margin calculated based on uncertainty factors to the estimated time. The expected position range refers to the spatial area in which the external damage target is estimated to reappear in the effective monitoring area based on the prediction of the target's behavior in the monitoring blind area. It can be determined by defining a spatial area based on the estimated position and calculated based on uncertainty factors.

[0118] Specifically, step B41 generates an expected time range based on the predicted estimated time, and an expected position range based on the predicted estimated position. These two ranges are based on the prediction of the target's behavior in the blind area, providing specific reference standards for subsequent determination of whether the external damage target appears as expected. Then, step B42 provides an abnormality determination rule: if the external damage target does not reappear in the effective monitoring area within the set expected time range, it is considered abnormal. This is because the target's behavior in the blind area and the time of its reappearance are predicted based on previous trajectory information, blind area attributes and target type. If it does not appear for a long time, it may mean that the target stays in the blind area for too long, changes its path or stops moving, which may be signs of destructive activities. Then, step B43 also provides another abnormality determination rule: if the external damage target reappears in the effective monitoring area within the expected time range, its actual appearance position is further obtained, and then the actual appearance position is compared with the generated expected position range. If the actual appearance position is not within the expected position range, it is also determined that the external damage target has abnormal behavior. This is because according to the predicted path and behavior pattern, the position of the target's reappearance in the effective area should be within the predicted range. If it deviates from the expected range, it indicates that the target may have taken an unexpected movement way or performed other abnormal activities in the blind area. Once it is determined that the external damage target has abnormal behavior according to any of the above rules, the system immediately triggers an alarm information. This ensures that potential threats can be identified in time to issue an early warning so that the operation and maintenance personnel can take further measures to improve the timeliness and accuracy of the warning.

[0119] Through the above processing, the method of the application provides a clear and quantifiable abnormal behavior determination standard and process for external damage targets, enabling the system to automatically identify abnormal situations according to whether the target reappears within the expected time and position range, improving the accuracy and efficiency of identifying potential external damage risks, reducing the possibility of false positives and false negatives, and thus enhancing the timeliness and reliability of external damage warning for power transmission lines.

[0120] In some preferred embodiments, step B41 comprises:

[0121] B411, according to the range of the monitoring blind area and the blind area attribute, determining the uncertainty influence parameter of the monitoring blind area on the behavior of the external target;

[0122] B412, according to the behavior type, determining the behavior influence factor;

[0123] B413, according to the uncertainty influence parameter, the behavior influence factor and the estimated time, calculating the expected time range;

[0124] B414, according to the expected time range and the estimated position, generating the expected position range.

[0125] Specifically, the uncertainty influence parameter refers to a numerical value quantifying the influence of the complexity of the monitoring blind area environment on the target behavior trajectory and time uncertainty; the behavior influence factor refers to a numerical value quantifying the influence of a specific behavior type on the target's stay or movement time in the blind area.

[0126] Specifically, step B411 determines the uncertainty influence parameter of the monitoring blind area on the behavior of the external breaking target according to the range and the blind area attribute of the monitoring blind area. Blind areas of different sizes and attributes have different levels of complexity in their internal environment, and have different uncertainty influences on the behavior trajectory and time of the external breaking target moving or staying in them. Step B411 can quantify this uncertainty according to a preset mapping relationship or database, providing a basis for subsequent calculation of the expected range. Next, step B412 determines the behavior influence factor according to the predicted behavior type. The behavior type of the external breaking target in the blind area itself has different time consumption and path change characteristics. For example, stay operations usually introduce more time uncertainty than continuous movement. Determining the behavior influence factor is to take into account the influence of the behavior pattern of the external breaking target itself on the time prediction. Similarly, step B412 can quantify the behavior influence factor according to a preset mapping relationship or database. Then, step B413 calculates the expected time range according to the determined uncertainty influence parameter, the determined behavior influence factor, and the predicted estimated time. This step no longer relies solely on a single estimated time, but rather takes into account the uncertainty brought about by the blind area environment and target behavior. By quantifying these influence factors into parameters and factors and combining them with the estimated time, an expected time window with a certain width is calculated. This window reflects the time range in which the target is most likely to appear again after considering various uncertainty factors. Finally, step B414 generates an expected location range according to the calculated expected time range and the predicted estimated location. After obtaining a more reliable expected time range, combined with the estimated location, the spatial area in which the target is likely to appear within the expected time range can be derived, thereby generating the corresponding expected location range. This location range matches the optimized time range, and together they provide a more accurate reference benchmark for subsequent abnormal behavior determination.

[0127] As a preferred implementation, the scheme of the present application is implemented as follows: the range of the blind area can be represented by a set of geometric boundary coordinates, and the attribute of the blind area can be represented by an enumeration type, such as terrain, vegetation, or weather. The uncertainty influence parameter can be a numerical value, such as obtained by consulting a preset table that gives corresponding uncertainty numerical values according to the blind area range (such as the size of the area) and the blind area attribute (such as the complexity level of the terrain). The behavior influence factor can be a numerical value, such as obtained by consulting another preset table that gives corresponding influence factors according to the behavior type, such as the behavior influence factor of staying operation being higher than that of continuous movement. The expected time range can be calculated as the estimated time plus or minus an offset, which is calculated by the product or combination of the uncertainty influence parameter and the behavior influence factor. For example, the expected time range can be represented as a time interval [estimated time - offset, estimated time + offset]. The expected position range can be generated according to the expected time range and the estimated position, for example, assuming that the target starts from the estimated position at the maximum possible speed within the expected time range, moving in all directions to the maximum distance that can be reached, thereby generating a circular region with the estimated position as the center and the maximum possible moving distance as the radius or a polygonal region considering the terrain restrictions as the expected position range.

[0128] Through the above processing, when the re-appeared external damage target appears in the expected time range and the expected position range of the effective monitoring area generated by the present application, the characteristics of the monitoring blind area and the uncertainty introduced by the behavior type of the external damage target are fully considered, making the generated expected range more reasonable and accurate, thereby improving the reliability of the abnormal behavior judgment based on these ranges and reducing the risk of false positives and false negatives.

[0129] In some preferred embodiments, step A2 includes:

[0130] A21, based on the deployment position and detection parameter of each monitor in the monitor group, combining the elevation information and slope information in the terrain data, calculating the initial effective monitoring area of each monitor in the monitor group;

[0131] A22, according to the vegetation height information and vegetation density information in the vegetation data, calculating the shielding range of the monitor group line of sight by the vegetation;

[0132] A23, determining the attenuation degree of the detection capability of different types of monitors based on the weather data;

[0133] A24, adjusting the initial effective monitoring area based on the shielding range and the attenuation degree to obtain the effective detection area of the monitor group, and identifying the monitoring blind area along the power transmission line based on the effective detection area;

[0134] A25, configuring blind area attributes according to the dominant influencing factors of the monitoring blind area;

[0135] A26, outputting a monitoring blind area map according to the effective monitoring area, the monitoring blind area and the blind area attributes.

[0136] Specifically, the detection parameter refers to the technical performance index possessed by the monitor when performing target detection, which can be realized by using a detection field of view angle, a maximum detection distance, a detection frequency, a resolution or a sensitivity, etc.

[0137] More specifically, the attenuation degree of the detection capability refers to the influence degree of the meteorological condition on the effective detection distance, signal strength or image definition of the monitor, which can be quantified by using an attenuation coefficient, a signal-to-noise ratio reduction amount or a visibility reduction amount.

[0138] More specifically, the initial effective monitoring area refers to an area that can be effectively detected by the monitor based on its own parameters and terrain conditions under ideal conditions without considering the influence of vegetation and meteorology, which can be calculated by using a line-of-sight analysis or a ray tracing method.

[0139] More specifically, the dominant influencing factor refers to the most important environmental factor leading to the formation of the monitoring blind area, which can be classified by using terrain, vegetation or meteorology, etc.

[0140] Specifically, the above steps elaborate the specific implementation mode of the step of determining the monitoring blind area map: first, step A21 calculates the initial effective monitoring area under the condition of no vegetation and meteorological interference based on the deployment position of the monitor, the detection parameter and the elevation and slope information of the terrain, which lays a foundation for subsequent adjustment. Then, step A22 calculates the specific occlusion range of the monitor line of sight by using the height and density information of the vegetation. At the same time, step A23 evaluates the attenuation degree of the detection capability of different types of monitors according to the current meteorological data. Subsequently, step A24 combines the initial area calculated in step A21 with the occlusion range in step A22 and the attenuation degree in step A23 to correct the initial effective monitoring area, so as to obtain the effective detection area under the actual environment, and identify the uncovered monitoring blind area based thereon. Step A25 analyzes the main reason for the formation of each monitoring blind area and marks it as a terrain blind area, a vegetation blind area or a meteorological blind area, etc. Finally, step A26 integrates the calculated effective monitoring area, the identified monitoring blind area and its attributes into a monitoring blind area map and outputs it. The whole process forms a complete data processing chain, which starts from the basic environmental data and the monitor parameters, gradually refines the calculation and finally generates a blind area map reflecting the real coverage. In this way, the present scheme provides an accurate and reliable basis for subsequent adjustment of the monitor detection parameter according to the blind area map, so that the optimization process can be carried out in a targeted manner.

[0141] In some preferred embodiments, step A3 comprises:

[0142] A31, extracting the location and range of each monitoring blind zone according to the monitoring blind zone map;

[0143] A32, identifying the monitors in the monitor group that are adjacent to each monitoring blind zone according to the location and range of the monitoring blind zone;

[0144] A33, generating a detection parameter adjustment scheme according to the monitoring blind zone type and the adjustable detection parameter and the location and range of the corresponding monitoring blind zone;

[0145] A34, sending the detection parameter adjustment scheme to the corresponding monitor, so that the monitor adjusts its detection parameter according to the adjustment scheme to reduce or eliminate the monitoring blind zone.

[0146] Specifically, the adjacent monitor refers to the monitor that is close to the specific monitoring blind zone in geographical location and whose detection range is likely to cover or affect the blind zone after adjustment, and its identification can be determined based on the distance threshold, the overlap calculation of the potential coverage area or the network topology relationship.

[0147] Specifically, step A31 obtains the specific location and range information of each monitoring blind area from the pre-determined monitoring blind area map. Then, step A32 identifies those monitoring devices in the monitoring device group that are geographically close to the blind areas and have the potential to cover the blind areas by adjusting their own parameters, and these identified monitoring devices are the objects of the parameter adjustment operation. Then, step A33, in combination with the specific type of the monitoring blind area (e.g. caused by terrain, vegetation or weather influence), considers the adjustable detection parameters and their adjustment ranges possessed by the adjacent monitoring devices, as well as the location and range of the blind area, and the system generates a detailed detection parameter adjustment scheme. This scheme specifies which parameters of which adjacent monitoring devices need to be adjusted (e.g. adjusting the detection field of view, changing the detection direction or increasing the detection distance), and how to adjust (e.g. specific angle value, focal length setting or power level). Finally, step A34 sends the generated detection parameter adjustment scheme to the corresponding adjacent monitoring devices through the communication network, and the monitoring devices automatically or remotely adjust their own detection parameters according to the requirements of the scheme after receiving the instructions. In this way, the detection range of the monitoring devices is changed, thereby specifically reducing or eliminating the original monitoring blind area and improving the overall monitoring coverage rate along the transmission line. The method of the present application makes the process of parameter adjustment based on the monitoring blind area map systematic and operable by providing a structured step sequence, solves the problem of how to effectively determine the parameter adjustment scheme, thereby enabling the optimization process to effectively reduce or eliminate the monitoring blind area, improves the efficiency and effectiveness of the entire optimization process, and enhances the coverage ability and safety of the transmission line monitoring system.

[0148] In some preferred embodiments, step A33 comprises:

[0149] A331, determining a candidate set of detection parameter adjustments according to the location and range of the monitoring blind area and the adjustable detection parameter range of the adjacent monitoring devices;

[0150] A332, for each parameter combination in the candidate set, calculating the coverage effect of all adjacent monitoring devices on the monitoring blind area after adjustment;

[0151] A333, selecting the parameter combination that makes the monitoring blind area coverage effect meet the pre-set requirements or maximizes the coverage effect from the candidate set as the detection parameter adjustment scheme.

[0152] Specifically, the candidate set of detection parameter adjustment refers to a set of all possible and feasible adjustment schemes formed by combinations of different parameters (such as detection field of view, detection direction, and detection distance) within the respective adjustable parameter ranges of the adjacent monitors. It can be implemented in various ways, such as traversing all discretized parameter value combinations, generating parameter combinations based on optimization algorithms, or filtering parameter combinations according to preset rules. The coverage effect refers to a quantitative indicator of the actual coverage degree of the monitoring blind area by the adjacent monitors under a specific parameter combination, which can be implemented in various ways, such as calculating the proportion of the covered blind area, calculating the number of key points in the covered blind area, or evaluating the detectable probability of targets in the blind area.

[0153] Specifically, the above steps provide a specific method for generating a detection parameter adjustment scheme, the process of which includes: first, determining a candidate set of detection parameter adjustment according to the location and range of the monitoring blind area, and the adjustable detection parameter range of the adjacent monitors. This step defines all possible and feasible parameter adjustment options, providing a basis and range for subsequent optimization and avoiding blind attempts. Next, for each parameter combination in the candidate set, calculate the coverage effect of the monitoring blind area by all adjacent monitors after adjustment. This step establishes an evaluation mechanism to quantify the actual coverage ability of the monitors in the blind area under different parameter combinations, providing an objective basis for comparison and selection. By calculating the coverage effect, the advantages and disadvantages of each adjustment scheme can be intuitively understood. Finally, select the parameter combination that maximizes the coverage effect or meets the preset requirements of the monitoring blind area from the candidate set as the detection parameter adjustment scheme. This step selects the optimal solution from the evaluated candidate schemes based on the set optimization target (meeting the preset requirements or achieving maximum coverage effect). The adjustment scheme thus determined can ensure that the capabilities of adjacent monitors are effectively utilized to reduce or eliminate the monitoring blind area, thereby improving the overall monitoring coverage. By determining the candidate set, evaluating the effect of each candidate scheme, and selecting the optimal one, this scheme specifically implements the adjustment scheme generation process, making it operable and optimized, and overcoming the shortcomings of merely proposing a generation scheme without specific selection criteria.

[0154] Through the above processing, the method of the present application can select the scheme with the optimal coverage effect or meeting the preset requirements for the monitoring blind area from a variety of possible parameter adjustment schemes, avoiding the uncertainty and inefficiency caused by random or empirical adjustment, improving the effectiveness and pertinence of monitoring blind area optimization, and thereby improving the overall monitoring coverage and safety of the power transmission line.

[0155] In a second aspect, referring to Figure 4 Some embodiments of the present application also provide a power transmission line external damage target Internet of Things information sensing and detection system for detecting external damage targets of a power transmission line, which comprises:

[0156] The monitoring module 201 is configured to analyze whether there is an external damage target along the power transmission line based on the detection data of the monitor group.

[0157] The adjusting module 202 is configured to optimize the detection range of the monitor group when there is no external damage target, and the optimization process comprises:

[0158] A1, acquiring topographic data, vegetation data and meteorological data along the power transmission line;

[0159] A2, determining a monitoring blind area map along the power transmission line according to the topographic data, the vegetation data, the meteorological data and the detection parameters of the monitor group, wherein the monitoring blind area map comprises a plurality of monitoring blind areas;

[0160] A3, adjusting the detection parameters of the monitor adjacent to the monitoring blind area in the monitor group according to the monitoring blind area map, so as to reduce or eliminate the monitoring blind area;

[0161] The early warning module 203 is configured to track and warn the external damage target when there is an external damage target.

[0162] The system of the present application can accurately identify the monitoring blind area along the power transmission line by introducing topographic data, vegetation data and meteorological data, and actively adjust the detection parameters of the monitor adjacent to the monitoring blind area according to the identified monitoring blind area map, effectively solving the problem of monitoring blind area of the power transmission line under complex terrain, vegetation and meteorological conditions; The system can accurately master the weak link of monitoring coverage by actively acquiring environmental data and identifying blind areas combined with monitor parameters, and can reduce or eliminate these monitoring blind areas by dynamically adjusting the detection parameters of the monitor adjacent to the blind area according to the blind area map, thereby improving the overall monitoring coverage effectiveness of the power transmission line, significantly improving the perception ability of the external damage target, reducing the risk of missing report caused by the hidden activity of the external damage target in the blind area, and enhancing the adaptability and reliability of the power transmission line safety monitoring system.

[0163] In addition, the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the present embodiment.

[0164] Furthermore, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0165] In this document, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.

[0166] The above description is merely illustrative of the application and not in limitation of the principles of the application. Numerous modifications and adaptations thereof will be readily apparent to those skilled in the art without departing from the spirit and scope of the application as defined in the following claims.

Claims

1. A power transmission line external damage target object Internet of Things information sensing detection method for detecting external damage targets of a power transmission line, characterized by, The method comprises the following steps: Based on the monitoring group's detection data, analyze whether there is an external damage target along the power transmission line; When there is no external damage target, optimize the detection range of the monitoring group, and the optimization process comprises: A1, obtain the terrain data, vegetation data and weather data along the power transmission line; A2, according to the terrain data, vegetation data, weather data and monitoring group's detection parameters, determine the monitoring blind area map of the power transmission line, the monitoring blind area map comprises several monitoring blind areas; A3, according to the monitoring blind area map, adjust the detection parameters of the monitoring device adjacent to the monitoring blind area in the monitoring group, so as to reduce or eliminate the monitoring blind area; When there is an external damage target, track and warn the external damage target; Step A3 comprises: A31, according to the monitoring blind area map, extract the position and range of each monitoring blind area; A32, according to the position and range of the monitoring blind area, identify the monitoring device adjacent to each monitoring blind area in the monitoring group; A33, according to the monitoring blind area type and the adjustable detection parameters and the position and range of the corresponding monitoring blind area, generate a detection parameter adjustment scheme; A34, send the detection parameter adjustment scheme to the corresponding monitoring device, so that the monitoring device adjusts its detection parameters according to the adjustment scheme to reduce or eliminate the monitoring blind area.

2. The power transmission line external damage target IoT information sensing and detection method according to claim 1, characterized by, The monitoring blind area map also includes an effective monitoring area and a blind area attribute corresponding to the monitoring blind area, and the tracking and warning process comprises: B1, record the trajectory information of the external damage target in the effective monitoring area; B2, when the external damage target enters the monitoring blind area, according to the trajectory information, the blind area attribute corresponding to the monitoring blind area and the type of the external damage target, predict the behavior type of the external damage target in the monitoring blind area; B3, according to the behavior type and the trajectory information, predict the estimated position and estimated time of the external damage target appearing in the effective monitoring area again; B4, according to the appearance of the external damage target in the effective monitoring area, the estimated position and the estimated time, analyze whether the external damage target has abnormal behavior, if so, trigger the alarm information.

3. The power transmission line external damage target IoT information sensing and detection method according to claim 2, characterized by, Step B2 comprises: B21, according to the trajectory information of the external damage target before entering the monitoring blind area, extract the motion state parameters of the external damage target when entering the monitoring blind area; B22, according to the blind area attribute of the monitoring blind area, infer the topographic features of the monitoring blind area; B23, according to the type of the external damage target, obtain the corresponding typical behavior mode; B24, according to the motion state parameters, the typical behavior mode and the topographic features, infer the behavior type of the external damage target in the monitoring blind area.

4. The power transmission line external damage target IoT information sensing and detection method of claim 2, characterized by, The behavior type comprises continuous movement, stay operation or path change.

5. The power transmission line external damage target IoT information sensing and detection method of claim 2, characterized by, Step B3 comprises: B31, according to the geometric boundary of the monitoring blind area and the behavior type of the external damage target in the monitoring blind area, predict several moving paths of the external damage target in the monitoring blind area; B32, estimating the staying time of the external damage target in the monitoring blind area according to the motion state parameter, the behavior type and the moving path obtained based on the trajectory information; B33, inferring the estimated time and the estimated position of the external damage target reappearing in the effective monitoring area according to the moving path and the staying time.

6. The power transmission line external damage target IoT information sensing and detection method of claim 2, characterized by, Step B4 comprises: B41, generating the expected time range of the external damage target reappearing in the effective monitoring area according to the estimated time, and generating the expected position range of the external damage target reappearing in the effective monitoring area according to the estimated position; B42, if the external damage target still does not reappear in the effective monitoring area within the expected time range, determining that the external damage target has abnormal behavior; B43, if the external damage target reappears in the effective monitoring area within the expected time range, obtaining the actual appearing position of the external damage target, and determining whether the actual appearing position is within the expected position range, if the actual appearing position is not within the expected position range, determining that the external damage target has abnormal behavior; B44, when it is determined that the external damage target has abnormal behavior, triggering an alarm information.

7. The power transmission line external damage target IoT information sensing and detection method of claim 6, characterized by, Step B41 comprises: B411, determining the uncertainty influence parameter of the monitoring blind area on the behavior of the external damage target according to the range of the monitoring blind area and the blind area attribute; B412, determining the behavior influence factor according to the behavior type; B413, calculating the expected time range according to the uncertainty influence parameter, the behavior influence factor and the estimated time; B414, generating the expected position range according to the expected time range and the estimated position. 8.The power transmission line external damage target object IoT information sensing and detection method of claim 1, wherein, Step A2 comprises: A21, calculating the initial effective monitoring area of each monitor in the monitor group based on the deployment position and the detection parameter of each monitor in the monitor group, combining the elevation information and the slope information in the terrain data; A22, calculating the shielding range of the line of sight of the monitor group according to the vegetation height information and the vegetation density information in the vegetation data; A23, determining the attenuation degree of the detection capability of different types of monitors based on the meteorological data; A24, adjusting the initial effective monitoring area based on the shielding range and the attenuation degree to obtain the effective detection area of the monitor group, and identifying the monitoring blind area along the transmission line based on the effective detection area; A25, configuring the blind area attribute according to the dominant influence factor of the monitoring blind area; A26, outputting the monitoring blind area map according to the effective monitoring area, the monitoring blind area and the blind area attribute.

9. A power transmission line external damage target object Internet of Things information sensing and detection system for detecting external damage targets of a power transmission line, characterized by, The system comprises: a monitoring module for analyzing whether there is an external damage target along the transmission line based on the detection data of the monitor group; an adjusting module for optimizing the detection range of the monitor group when there is no external damage target, and the optimization process comprises: A1, obtaining the terrain data, the vegetation data and the meteorological data along the transmission line; A2, determining a monitoring blind area map along the power transmission line according to the terrain data, the vegetation data, the weather data, and the detection parameters of the monitor group, the monitoring blind area map including a plurality of monitoring blind areas; A3, adjusting the detection parameters of the monitors adjacent to the monitoring blind areas in the monitor group according to the monitoring blind area map, so as to reduce or eliminate the monitoring blind areas; an early warning module, configured to track and early warn the external breaking target when the external breaking target exists; Step A3 includes: A31, extracting the position and range of each monitoring blind area according to the monitoring blind area map; A32, identifying the monitors adjacent to each monitoring blind area in the monitor group according to the position and range of the monitoring blind area; A33, generating a detection parameter adjustment scheme according to the type of the monitoring blind area, the adjustable detection parameter, and the position and range of the corresponding monitoring blind area; A34, sending the detection parameter adjustment scheme to the corresponding monitor, so that the monitor adjusts its detection parameter according to the adjustment scheme, so as to reduce or eliminate the monitoring blind area.

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