Intelligent stereo distance measurement method for transmission lines based on multi-dimensional space topology analysis

Abstract

The present invention discloses an intelligent three-dimensional distance measurement method for transmission lines based on multi-dimensional space topological analysis, and relates to the technical field of transmission line distance detection. The method comprises: executing data collection of transmission lines and transmission environments, and establishing a multi-dimensional space data set; performing three-dimensional mapping fusion on the multi-dimensional space data set, and establishing a three-dimensional stereoscopic model of the transmission lines and the transmission environment; establishing a topological relationship model, and establishing key topological nodes of the topological relationship model; performing safety hazard analysis of the updated topological relationship model through a spatial distance analysis network; and updating the warning level in real time based on the safety hazard analysis results, and performing early warning. The method solves the technical problem that it is difficult to fully cover the complex and changeable transmission environment in the existing three-dimensional distance measurement of transmission lines, which leads to insufficient accuracy in distance measurement of transmission lines, and makes safety hazard identification and warning not timely and accurate enough, and achieves the technical effect of improving the accuracy of transmission line distance measurement, the accuracy and immediacy of safety hazard warning.

Description

Technical Field

[0001] The present application relates to the technical field related to transmission line distance detection, and in particular to an intelligent three-dimensional distance measurement method for transmission lines based on multi-dimensional space topology analysis. Background Art

[0002] With the continuous growth of global electricity demand, the safety and stability of transmission lines as the core infrastructure of power transmission have received increasing attention. Transmission lines usually span a wide geographical area, traverse different terrains and environments, and their surrounding environment is complex and changeable. Buildings, construction vehicles (including excavators, bulldozers, cement pump trucks, cranes, and crane booms, etc.), trees, terrain changes (mountains, lakes, etc.) and climatic conditions (such as strong winds, lightning, etc.) in the environment may pose a threat to the stability and safety of transmission lines, bringing unprecedented challenges to the safe operation and maintenance of transmission lines. In order to ensure the normal operation of transmission lines and avoid power outages and accidents, how to achieve efficient and accurate transmission line ranging has become an urgent problem to be solved. Although existing technical monitoring can identify and deal with safety hazards of transmission lines to a certain extent, it often has problems such as long response time, limited monitoring range, and poor real-time performance. It is difficult to fully cover and timely discover potential safety hazards, especially when facing complex terrain, bad weather or hidden obstacles. It is even more powerless, difficult to achieve transmission line ranging, and unable to timely and accurately detect potential risks of transmission lines.

[0003] At present, there is a technical problem in the related technologies of three-dimensional ranging of transmission lines, which is that it is difficult to fully cover the complex and changeable transmission environment, which leads to insufficient accuracy in transmission line distance measurement and makes the identification and warning of safety hazards not timely and accurate. Summary of the invention

[0004] This application provides an intelligent three-dimensional ranging method for transmission lines based on multi-dimensional space topology analysis, which solves the technical problem that the existing three-dimensional ranging of transmission lines is difficult to fully cover the complex and changeable transmission environment, which in turn leads to insufficient accuracy in transmission line distance measurement and makes the safety hazard identification and warning not timely and accurate. The application achieves the technical effect of improving the accuracy of transmission line ranging and the accuracy and timeliness of safety hazard warning.

[0005] The present application provides an intelligent three-dimensional ranging method for power transmission lines based on multi-dimensional space topological analysis, comprising: utilizing layout data to configure the collection focus of the target area, activating a laser radar and a panoramic scanning device based on the collection focus, executing data collection of the power transmission line and the power transmission environment, and establishing a multi-dimensional space data set; performing three-dimensional mapping fusion on the multi-dimensional space data set to establish a three-dimensional stereo model of the power transmission line and the power transmission environment; performing multi-modal data fusion analysis based on the three-dimensional stereo model to establish a topological relationship model, and establishing key topological nodes of the topological relationship model, wherein the key topological nodes have update frequency constraints; utilizing the laser radar and the panoramic scanning device to update the data of the key topological nodes based on the update frequency constraints, and executing a safety hazard analysis of the updated topological relationship model through a spatial distance analysis network; updating the warning level in real time based on the safety hazard analysis results, and executing a warning alarm.

[0006] In a possible implementation, the three-dimensional mapping fusion is performed on the multi-dimensional spatial data set to establish a three-dimensional stereo model of the transmission line and the transmission environment, and the following processing is also performed: based on the layout data, positioning key points are established, and the spatial coordinates of the positioning key points are generated, and the positioning key points include the transmission tower key points; using the spatial coordinates as coordinate matching points, performing feature matching of the positioning key points of the multi-dimensional spatial data set, generating feature matching results, and establishing corrected spatial coordinates based on the feature matching results; after correcting the layout data by the corrected spatial coordinates, using the feature matching results as the matching starting point, performing transmission line feature matching; and establishing a three-dimensional stereo model of the transmission line and the transmission environment according to the transmission line feature matching results.

[0007] In a possible implementation, the feature matching result is used as a matching starting point to perform transmission line feature matching, and the following processing is also performed: generating transmission line fitting coordinates based on the corrected layout data, and establishing transmission line matching features; using the feature matching result as a matching starting point, performing a concentrated position search of the multidimensional space data set at the transmission line fitting coordinates to generate a transmission line feature matching result, and the concentrated position search is a feature matching based on the transmission line matching features.

[0008] In a possible implementation, the key topological node for establishing the topological relationship model also performs the following processing: taking the transmission line as an evaluation point, calculating spatial distance data, the spatial distance data being the distance data from the evaluation point to the transmission environment, and generating a first key value based on the spatial distance data; performing meteorological monitoring on the target area, establishing a meteorological data set, and after dangerous expansion of the meteorological data set, using the expanded meteorological data to perform steady-state analysis of various locations of the transmission line to establish a second key value; using the expanded meteorological data to perform environmental steady-state analysis of the transmission environment to establish a third key value; and screening the transmission line topological nodes based on the first key value, the second key value, and the third key value to establish the key topological nodes of the topological relationship model.

[0009] In a possible implementation, the intelligent three-dimensional ranging method for transmission lines based on multi-dimensional space topology analysis also performs the following processing: performing an environmental change evaluation on the transmission environment, and establishing a growth trend prediction result of the transmission environment; establishing an update frequency constraint based on the growth trend prediction result and the comprehensive key value of the key topological nodes, and the comprehensive key value is calculated by the first key value, the second key value, and the third key value.

[0010] In a possible implementation, after the laser radar and panoramic scanning device are used to update the data of the key topological nodes based on the update frequency constraint, the following processing is also performed: establishing update constraints; performing adaptation analysis of the corresponding update frequency constraints based on the data of the updated key topological nodes, and establishing an adaptive update factor; optimizing the update frequency constraint after performing adaptive update factor compensation through the update constraint.

[0011] In a possible implementation, the warning level is updated in real time based on the safety hazard analysis results, and a warning alarm is issued. The following processing is also performed: a trigger mapping list of hazard-warning level is established; a list matching of the trigger mapping list is performed according to the safety hazard analysis results, and the warning level is updated in real time according to the list matching results, and a warning alarm is issued.

[0012] In a possible implementation, the intelligent three-dimensional ranging method for transmission lines based on multi-dimensional space topological analysis also performs the following processing: establishing a retrospective hidden danger analysis data set based on key topological nodes; performing hidden danger mutation evaluation based on the retrospective hidden danger analysis data set and the safety hidden danger analysis results to generate hidden danger mutation evaluation results; adjusting the warning level based on the hidden danger mutation evaluation results, and executing early warning alarm.

[0013] It is intended to use the intelligent three-dimensional distance measurement method for transmission lines based on multi-dimensional space topological analysis proposed in this application to perform data collection of transmission lines and transmission environment, establish a multi-dimensional space data set; perform three-dimensional mapping and fusion of the multi-dimensional space data set to establish a three-dimensional stereo model of the transmission line and transmission environment; establish a topological relationship model, and establish key topological nodes of the topological relationship model; perform safety hazard analysis of the updated topological relationship model through the spatial distance analysis network; update the warning level in real time based on the safety hazard analysis results, and perform early warning. It solves the technical problem that the existing three-dimensional distance measurement of transmission lines is difficult to fully cover the complex and changeable transmission environment, which leads to insufficient accuracy in transmission line distance measurement, making safety hazard identification and warning not timely and accurate, and achieves the technical effect of improving the accuracy of transmission line distance measurement, the accuracy and immediacy of safety hazard warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0015] Figure 1 A schematic diagram of a flow chart of a method for intelligent three-dimensional distance measurement of transmission lines based on multi-dimensional space topology analysis provided in an embodiment of the present application;

[0016] Figure 2 A schematic diagram of the process of establishing a three-dimensional model of a transmission line environment in the intelligent three-dimensional ranging method for transmission lines based on multi-dimensional space topology analysis provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0018] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0019] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0020] The present application embodiment provides a transmission line intelligent three-dimensional distance measurement method based on multi-dimensional space topology analysis, such as Figure 1 As shown, the method includes:

[0021] Step S100, using the layout data to configure the collection focus of the target area, activating the laser radar and panoramic scanning equipment based on the collection focus, performing data collection of the power transmission line and the power transmission environment, and establishing a multi-dimensional spatial data set.

[0022] Preferably, the deployment data refers to the basic information already available on the transmission line and the surrounding environment, which may include the specific location, height, suspension point, tower location of the transmission line, terrain features, buildings, trees, roads around the transmission line, and the collected historical transmission line status, environmental change records, etc.; using the deployment data to configure the collection focus of the target area refers to determining the areas and specific locations that need to be monitored and collected data based on the deployment data. Specifically, based on the deployment data, the key locations of the transmission line and its surrounding environment are identified. These locations may include the area around the transmission tower, the area where the transmission line is close to buildings or trees, the area with complex terrain or historically risky areas, etc. In these key areas, collection focus points are set as the target of subsequent data collection. These focus points may be a specific spatial location or a specific environmental condition (such as a windy area), and then the lidar and panoramic scanning equipment are activated to monitor the transmission line. The laser radar is used to accurately measure the position, shape and distance of objects in three-dimensional space. The laser radar can generate point cloud data to describe the detailed three-dimensional information of the transmission line and its surrounding environment. The panoramic scanning equipment is used to capture 360-degree environmental images. By performing a panoramic scan of the entire target area, two-dimensional image data is obtained, and combined with the laser radar data, more comprehensive environmental information is provided. The collected data includes the position and shape data of the transmission line (the three-dimensional point cloud generated by the laser radar accurately describes the spatial distribution and hanging shape of the transmission line), the spatial data of the surrounding environment (such as buildings, trees, terrain and other environmental information related to the transmission line, and their relative positions with the transmission line, etc.), and the collected data are integrated to form a multi-dimensional spatial data set, including three-dimensional point cloud data (spatial geometric information of the transmission line and its surrounding environment) and two-dimensional image data (images of the surrounding environment of the transmission line captured by the panoramic scanning equipment).

[0023] Step S200, performing three-dimensional mapping fusion on the multi-dimensional spatial data set to establish a three-dimensional stereo model of the power transmission line and the power transmission environment.

[0024] Preferably, the multidimensional spatial data set is processed and fused with three-dimensional mapping to generate an accurate three-dimensional stereo model to fully reflect the spatial structure of the transmission line and its surrounding environment. Specifically, before the three-dimensional mapping fusion is performed, the collected data are preprocessed, including denoising, data alignment, resolution adjustment, etc., to ensure the consistency and accuracy between different data sources. Then, coordinate transformation and registration are used to spatially align the data from different data sources, that is, map these data into the same coordinate system, such as using an ICP (iterative closest point) algorithm or a SIFT (scale-invariant feature transform) algorithm to align point cloud data, image data and other spatial data; key features (such as the sag of the transmission line, the top of the tower, the outline of the building, etc.) are extracted from the multidimensional spatial data set, and feature matching is performed to fuse the same or similar features in different data sources, and finally a three-dimensional stereo model of the transmission line and the transmission environment is generated, which can fully and accurately reflect the three-dimensional structure of the transmission line and its surrounding environment, and is used to evaluate the potential risks between the transmission line and the surrounding environment, for example, evaluating the distance between the transmission line and buildings and trees, and judging whether there is a collision or other safety hazard.

[0025] In one possible implementation, Figure 2 As shown, step S200 further includes step S210, establishing positioning key points based on the layout data and generating spatial coordinates of the positioning key points, wherein the positioning key points include transmission tower key points; step S220, using the spatial coordinates as coordinate matching points, performing feature matching of the positioning key points of the multidimensional spatial data set, generating feature matching results, and establishing corrected spatial coordinates based on the feature matching results; step S230, after correcting the layout data by the corrected spatial coordinates, using the feature matching results as the matching starting point, performing transmission line feature matching; step S240, establishing a three-dimensional stereo model of the transmission line and the transmission environment according to the transmission line feature matching results.

[0026] Preferably, on the basis of the layout data, some important key points for positioning are determined, which usually include the top of the transmission tower, the suspension point, the connection point of the cross arm, etc. These key points are the core support points of the overall structure of the transmission line. For each key point for positioning, accurate spatial coordinates are generated through the layout data. These coordinates are points in three-dimensional space and are used to describe the position of the key point. The generated spatial coordinates are used as matching points to find the corresponding positions in the multidimensional space data set. By comparing the data collected by the laser radar and the panoramic scanning equipment with these coordinate matching points, feature matching is performed. In the data collected in the field, feature points corresponding to the key points for positioning in the layout data are found, such as the top position of the transmission tower or the suspension point of the transmission line. The matching process generates a set of feature matching results, i.e., the correspondence between the actual positions in the field collected data and the key points for positioning in the layout data, including the matched point pairs and their The relative position in space is corrected according to the feature matching results. The spatial coordinates in the layout data are adjusted through the matching results so that the corrected coordinates reflect the actual situation more accurately and the corrected spatial coordinates are established. The corrected spatial coordinates are used to correct the entire layout data to ensure that all relevant data (such as the starting point, end point, sag point, etc. of the transmission line) are based on the corrected precise coordinates. The corrected coordinates and feature matching results are used as the starting point for a new round of feature matching to perform feature matching of the transmission line, including identifying the curvature, sag, connection points and other features of the transmission line, and ensuring that these features match the data collected on the spot. Finally, a three-dimensional model of the transmission line and the transmission environment is established based on the transmission line feature matching results, which not only reflects the actual position and shape of the transmission line, but also includes the spatial relationship between the transmission line and the environment (such as the distance from buildings and trees, the hanging height of the transmission line, etc.).

[0027] In a possible implementation, step S230 further includes step S231, generating transmission line fitting coordinates based on the corrected layout data and establishing transmission line matching features; step S232, using the feature matching results as the matching starting point, performing a concentrated position search of the multidimensional space data set in the transmission line fitting coordinates, and generating a transmission line feature matching result, wherein the concentrated position search is a feature matching based on the transmission line matching features.

[0028] Preferably, more accurate layout data is obtained by correcting the initial layout data (for example, using laser radar and panoramic scanning data for accuracy adjustment). The corrected layout data reflects the position and state of the transmission line in the actual environment. The corrected layout data is used to generate the fitting coordinates of the transmission line through a mathematical fitting algorithm (such as polynomial fitting, spline curve fitting, etc.), that is, curve fitting is performed on the position data of the transmission line in three-dimensional space to obtain a smooth curve, which can approximately represent the ideal path or shape of the transmission line in three-dimensional space. These fitting coordinates are generated based on known discrete data points (such as the actual position of the transmission line at each hanging point or measuring point). Based on the fitting coordinates, key features of the transmission line are extracted, which may include sag and curvature information of the transmission line, connection points between the transmission line and the tower or other supporting structures, etc.; then, the feature matching result is used as the matching starting point, based on the previously generated transmission line Line fitting coordinates, perform position concentrated search, the search range is mainly concentrated in the area near the fitting coordinates to improve the search efficiency, find the actual position point that best matches the transmission line fitting coordinates in the multidimensional space data set, and find the closest matching point by comparing the spatial position and morphological characteristics of the actual data and the fitting coordinates, and generate the transmission line feature matching result, that is, the precise position and morphology of the actual transmission line in the multidimensional space data set. Among them, the position concentrated search is the feature matching based on the matching features of the transmission line. Feature matching is the core of the search process, which specifically refers to comparing the features of the fitting coordinates and the actual data within the search range. For example, using an iterative nearest point algorithm or an algorithm based on shape matching, by comparing the sag, curvature, suspension point position, etc. of the transmission line, the difference between the fitting coordinates and the actual data points is calculated, and the point with the smallest difference is selected as the final matching result, that is, determining which actual data points best meet the features of the fitting coordinates.

[0029] Step S300, performing multimodal data fusion analysis based on the three-dimensional model, establishing a topological relationship model, and establishing key topological nodes of the topological relationship model, wherein the key topological nodes have update frequency constraints.

[0030] Preferably, a multimodal data fusion analysis is performed based on the generated three-dimensional digital model of the transmission line and the surrounding environment. Specifically, a comprehensive analysis is performed by integrating data of different modes (such as lidar point cloud data, panoramic image data, meteorological data and historical monitoring data). For example, the three-dimensional model of the transmission line is combined with meteorological data to analyze the impact of wind on the transmission line; combined with historical monitoring data, the safety and stability of the transmission line under different conditions can be evaluated; key spatial points (such as the location of the transmission tower, the edge of the building, the top of the tree) are used as nodes, and the spatial relationship between the nodes (such as distance, angle, relative position, etc.) is used as the connecting edge to establish a topological relationship model to describe the spatial relationship between the transmission line and the surrounding environment elements (such as buildings, trees, terrain, etc.). The topological relationship is not only concerned with the position of each element, but also with the connection relationship and relative position between them, such as distance, adjacency, inclusion, intersection, etc. Then, based on the three-dimensional model, the specific relationship between these nodes and edges is analyzed and defined. For example, a transmission line node and a building node The distance relationship between them, or the relative height relationship between a tower node and a terrain node; then determine the key nodes in the topological relationship model, such as the closest point, sag point, suspension point between the transmission line and potential obstacles (such as buildings, trees, etc.), or the point where the transmission line is easily affected in complex terrain or environment. Among them, the key topological nodes have update frequency constraints. Because environmental conditions (such as wind speed, temperature, humidity) and transmission line status (such as cable tension, sag changes) are constantly changing, key topological nodes need to update their data regularly to ensure the accuracy and practicality of the model. Specifically, if a key node is in a high-risk environment (such as close to buildings or trees, or in an area frequently affected by strong winds), the update frequency of the node should be higher. For those nodes in a stable environment, a lower update frequency can be set to reduce unnecessary calculations and resource consumption. The update frequency can be dynamically adjusted according to real-time monitoring data or environmental changes. For example, when strong winds are predicted, the update frequency of related nodes is temporarily increased to ensure that the latest safety status is obtained in time.

[0031] In a possible implementation, step S300 further includes step S310, taking the transmission line as an evaluation point, calculating spatial distance data, the spatial distance data being the distance data from the evaluation point to the transmission environment, and generating a first key value based on the spatial distance data; step S320, performing meteorological monitoring on the target area, establishing a meteorological data set, and after dangerous expansion of the meteorological data set, using the expanded meteorological data to perform steady-state analysis of each location of the transmission line, and establishing a second key value; step S330, using the expanded meteorological data to perform environmental steady-state analysis of the transmission environment, and establishing a third key value; step S340, screening the transmission line topology nodes based on the first key value, the second key value, and the third key value, and establishing the key topology nodes of the topological relationship model.

[0032] Preferably, the transmission line is used as an evaluation point to calculate the spatial distance data. Specifically, the evaluation point refers to a specific position or node on the transmission line, such as a suspension point, a sag point, a connection point, etc. The distance data from these evaluation points to other objects in the surrounding environment of the transmission line (such as buildings, trees, and the ground) are calculated as spatial distance data. A first key value is generated based on the spatial distance data, such as the minimum distance from a certain evaluation point on the transmission line to the nearest obstacle. That is, by accurately measuring these distances, the spatial relationship between the transmission line and its surrounding environment can be evaluated, as well as whether there are potential safety hazards. In the target area where the transmission line and its environment are located, continuous meteorological monitoring is carried out, including collecting data such as wind speed, wind direction, temperature, humidity, and rainfall. These meteorological data are combined into a meteorological data set, and the meteorological data is dangerously expanded. For example, the possibility of extreme weather (such as typhoons and rainstorms) is predicted based on historical data or meteorological models, and these extreme conditions are included in the data set to more comprehensively evaluate the steady state of the transmission line under extreme meteorological conditions, and then the expanded meteorological data is used. A steady-state analysis is performed on each location of the transmission line to evaluate whether the transmission line can remain stable under specific meteorological conditions and whether there are displacements, sways or sag changes that exceed the safety threshold. A second key value is generated based on the evaluation results, which may indicate the maximum possible displacement of the transmission line under extreme meteorological conditions or the maximum change in the sag of the transmission line when the wind speed increases. The extended meteorological data is used to perform an environmental steady-state analysis of the transmission environment. For example, strong winds may cause trees to fall or buildings to be damaged. These environmental changes will affect the safety of the transmission line. Based on the results of the environmental steady-state analysis, a third key value is generated, which may include the possible displacement of trees or buildings under extreme weather or the risk of collapse of certain obstacles in the environment under strong winds or geological activities. The first key value, the second key value, and the third key value select the most critical transmission line topological nodes. For example, if the minimum spatial distance of an evaluation point is less than a threshold, the point is marked as a critical node. If the steady-state analysis results of an evaluation point under extreme meteorological conditions show obvious safety hazards, the point is also marked as a critical node.

[0033] In a possible implementation, step S300 further includes step S350, performing an environmental change evaluation on the power transmission environment and establishing a growth trend prediction result of the power transmission environment; step S360, establishing an update frequency constraint based on the growth trend prediction result and the comprehensive key value of the key topological node, and the comprehensive key value is calculated by the first key value, the second key value, and the third key value.

[0034] Preferably, environmental succession assessment refers to analyzing and evaluating the changing trends of various elements (such as trees, vegetation, buildings, etc.) in the environment around the transmission line, that is, analyzing and predicting the growth rate and direction of vegetation such as trees and shrubs, and evaluating whether they will touch or approach the transmission line in the future; analyzing and predicting possible changes in surrounding buildings (such as new construction, expansion, demolition, etc.), and evaluating the impact of these changes on the safety of the transmission line; based on the results of environmental succession assessment, using historical data and prediction models, predicting the growth trend prediction results of various elements in the transmission environment, including the growth height and expansion range of vegetation in the future, and possible changes in buildings, wherein the prediction model is constructed based on time series analysis or regression model, trained with historical data, and predicting the average annual growth rate of trees and the expansion trend of buildings; then based on the growth trend prediction results, combined with the comprehensive key values ​​of key topological nodes, establish update frequency constraints. Specifically, the comprehensive key value is A comprehensive index is calculated by integrating the previously calculated first key value (spatial distance data), second key value (steady-state analysis results under meteorological data), and third key value (steady-state analysis results of the environment), which reflects the risk level of each key topological node under various factors. The first, second, and third key values ​​are weighted and summed. Different weights reflect the importance of different factors. For example, meteorological factors may have a greater weight in the windy season, while environmental factors (such as vegetation growth) may have a greater weight in spring and summer. Then, the data update frequency of each key topological node is set. For those nodes with high comprehensive key values ​​and significant changes in environmental growth trend forecasts, the update frequency should be higher. For example, if it is predicted that some trees will grow rapidly and approach transmission lines in the next few months, these nodes should be set to a more frequent update cycle; for nodes with low comprehensive key values ​​and slow environmental changes, a lower update frequency can be set to reduce unnecessary resource consumption. By setting the update frequency reasonably, the key nodes of the transmission line can be monitored in real time, potential safety hazards can be discovered in time, and early warnings can be issued.

[0035] Step S400, using the laser radar and panoramic scanning equipment to update the data of key topological nodes based on the update frequency constraint, and performing safety hazard analysis of the updated topological relationship model through a spatial distance analysis network.

[0036] Preferably, according to the set update frequency constraint, the latest data of the transmission line and its surrounding environment are collected by using lidar equipment and panoramic scanning equipment, the newly collected data are compared and integrated with the existing key topological node data, and the spatial position, geometric shape and relative relationship of these nodes are updated to ensure that the key nodes in the model can accurately reflect the current actual environment; then, the safety hazard analysis of the updated topological relationship model is performed through the spatial distance analysis network. Specifically, the spatial distance analysis network is used to analyze and calculate the spatial relationship between the transmission line and the surrounding environment, that is, by analyzing the distance between the nodes and edges in the topological relationship model, potential safety hazards are identified, and obstacles between the transmission line and the surrounding environment are calculated. The system automatically identifies the potential safety hazards between the transmission line and the surrounding environment through the spatial distance analysis network. For example, if a section of the transmission line shows that the distance to a fast-growing tree has narrowed to a dangerous range in the most recent data update, this situation will be marked as a transmission line safety hazard. After the safety hazard is identified, an alarm will be issued according to the set early warning mechanism to notify maintenance personnel to take timely measures, such as pruning trees or reinforcing transmission lines.

[0037] In a possible implementation, step S400 further includes step S410, establishing an update constraint; step S420, performing adaptation analysis of the corresponding update frequency constraint based on the data of the updated key topological nodes to establish an adaptive update factor; step S430, optimizing the update frequency constraint after performing adaptive update factor compensation through the update constraint.

[0038] Preferably, the update constraints are initially set according to initial conditions (such as risk level, speed of environmental change, etc.). For example, for nodes in high-risk areas, the initial update constraints may be set to a higher update frequency, wherein the update constraint refers to a preliminary update frequency set for key topological nodes, which determines how often the data of these nodes are updated; as time goes by and environmental conditions change, the data of key topological nodes will be updated in real time, and the update frequency constraints are analyzed based on the updated key topological node data to determine whether they need to be adjusted. Specifically, if the new data indicates an increase or decrease in risk, the update frequency may need to be adjusted. For example, faster tree growth may mean that more frequent updates are needed. The speed of environmental changes around the node is analyzed to determine whether the current update frequency is timely enough, and then an adaptive update factor, i.e., the adaptive update factor, is generated based on the results of the adaptation analysis. The factor is a dynamically adjusted parameter used to reflect the degree of change in the environment or risk. For example, if the risk increases, the adaptive update factor may increase, prompting an increase in the update frequency. Finally, the adaptive update factor is applied to the initially set update constraint to perform adaptive update factor compensation to dynamically adjust the update frequency. Specifically, if the adaptive update factor shows that the risk increases or the environmental change accelerates, the compensation mechanism will increase the update frequency. If the factor shows that the risk decreases or the environmental change slows down, the compensation mechanism may reduce the update frequency. As new data is continuously input, the update frequency can be dynamically adjusted to perform real-time, adaptive update factor compensation, and ultimately optimize the update frequency constraint, which will more accurately reflect the current risk and environmental changes without being too frequent (wasting resources) or not frequent enough (ignoring potential risks), ensuring the efficiency and reliability of the transmission line monitoring system and being able to respond to changes in the environment and risks in a timely manner.

[0039] Step S500, based on the safety hazard analysis results, the warning level is updated in real time, and a pre-alarm is issued. According to the analysis results, the current safety hazards are evaluated, and the warning level is adjusted according to the severity of these hazards. Then, an alarm or notification is issued according to the updated warning level to ensure that relevant personnel can take timely action. Specifically, the safety hazard analysis results include various factors that affect the safety of transmission lines, such as tree growth, building expansion, etc. that may approach or touch the transmission lines; natural factors such as strong winds, heavy rains, and snow accumulation may cause transmission line shaking, sag changes or other abnormalities; aging, corrosion or damage of the transmission lines or transmission towers themselves; warning levels are usually divided into different levels according to the severity of the hazards, such as low, medium, high and emergency levels. Each level corresponds to different risk levels and response measures. A low-level warning may only remind maintenance personnel to pay attention; an intermediate warning may require Arrange inspections; advanced warnings may require dispatching a maintenance team for on-site inspection and processing; emergency warnings require immediate emergency actions, such as cutting off power supply, evacuating personnel, and activating emergency plans; dynamically adjust the warning level based on the latest safety hazard analysis results. For example, if a sudden increase in wind speed is detected and the shaking amplitude of the transmission line exceeds the safety threshold, the warning level may be raised from low to high or emergency level; take corresponding actions based on the updated warning level and issue alarms to relevant personnel, such as triggering visual (such as red warning lights) or auditory (such as alarm sounds) alarms in the monitoring center or on-site equipment. Through timely early warning alerts, potential accidents can be effectively prevented, losses can be minimized, and more serious consequences can be avoided, such as large-scale power outages, equipment damage, or casualties.

[0040] In a possible implementation, step S500 further includes step S510, establishing a trigger mapping list of hidden dangers and warning levels; step S520, performing list matching of the trigger mapping list according to the safety hidden danger analysis results, updating the warning level in real time according to the list matching results, and executing a pre-alarm.

[0041] Preferably, a trigger mapping list of hidden dangers and warning levels is established, which lists possible safety hazards and their corresponding warning levels. Each type of hidden danger has its own specific threshold or condition. When these conditions are met, the corresponding warning level will be triggered. Specific conditions or thresholds are set for each type of hidden danger, which may be numerical values ​​(such as distance, wind speed) or states (such as equipment aging, environmental changes). For example, the distance between the transmission line and the tree is less than a threshold, the wind speed exceeds a certain threshold, the sag of the transmission line exceeds a safe range, etc. When the detected hidden danger parameters reach or exceed these conditions, the corresponding warning level is triggered; then the safety hazard analysis results are compared and matched with the entries in the trigger mapping list to determine which warning level should be triggered. For example, if it is detected that the distance between the transmission line and the tree is reduced to below a certain critical value, the warning level corresponding to the distance range is searched in the mapping list. After the list matching is completed, the current warning level is updated according to the matching results. If the new hidden danger condition is more serious than the previous one, the warning level will be upgraded; if the conditions are alleviated, the warning level may be reduced.

[0042] In a possible implementation, step S500 further includes step S530, establishing a retrospective hidden danger analysis data set based on key topological nodes; step S540, performing hidden danger mutation evaluation based on the retrospective hidden danger analysis data set and the safety hidden danger analysis results, and generating hidden danger mutation evaluation results; step S550, adjusting the warning level based on the hidden danger mutation evaluation results, and executing early warning output.

[0043] Preferably, historical data is collected based on key topological nodes to form a retrospective hidden danger analysis data set, including all monitoring data of these nodes in the past period of time, such as environmental changes, transmission line status, meteorological conditions, etc., for analyzing the historical evolution of hidden dangers in order to identify long-term trends or potential mutations, where mutation refers to a significant and sudden change in a hidden danger parameter, which may exceed the normal fluctuation range, usually meaning a sharp increase in potential risks, such as rapid growth of trees resulting in a sharp shortening of the distance to the transmission line, or a sudden increase in wind speed resulting in a significant increase in transmission line shaking; hidden danger mutation evaluation refers to evaluating whether the hidden danger parameter has mutated by comparing the current safety hidden danger analysis results with the retrospective hidden danger analysis data set. Specifically, if the retrospective data shows that the status of a node is relatively stable, but the recent monitoring data show that the node is in a state of If the situation suddenly deteriorates (such as the distance suddenly decreases), the situation may be marked as a mutation. The focus is on evaluating the magnitude and speed of the change, identifying those mutations that exceed the normal fluctuation range, and then generating hidden danger mutation evaluation results based on the mutation evaluation, including the severity of the mutation, potential impact range, specific location and cause of the occurrence, etc.; finally, the warning level is adjusted based on the hidden danger mutation evaluation results, that is, if the hidden danger mutation evaluation results show that a node or area has a significant mutation, the current warning level is adjusted according to the severity of the mutation. For example, if the mutation of a node is assessed as severe (such as a hidden danger deteriorates sharply in a short period of time), the warning level may be rapidly increased, or even directly enter the advanced or emergency warning state; if the mutation is relatively minor, only a small adjustment is made to the current warning level or it remains unchanged. Finally, according to the adjusted warning level, the early warning is automatically executed.

[0044] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. An intelligent three-dimensional distance measurement method for transmission lines based on multi-dimensional space topology analysis, characterized in that: The method comprises: Using the deployment data to configure the collection focus of the target area, activating the laser radar and the panoramic scanning equipment based on the collection focus, performing data collection of the power transmission line and the power transmission environment, and establishing a multi-dimensional spatial data set; Performing three-dimensional mapping fusion on the multi-dimensional spatial data set to establish a three-dimensional stereo model of the power transmission line and the power transmission environment; Perform multimodal data fusion analysis based on the three-dimensional model, establish a topological relationship model, and establish key topological nodes of the topological relationship model, wherein the key topological nodes have update frequency constraints; Using the laser radar and panoramic scanning equipment to update the data of key topological nodes based on the update frequency constraint, and performing safety hazard analysis of the updated topological relationship model through a spatial distance analysis network; Update the warning level in real time based on the safety hazard analysis results and issue early warning alerts; The three-dimensional mapping and fusion of the multi-dimensional space data set to establish a three-dimensional stereoscopic model of the power transmission line and the power transmission environment includes: Establishing key positioning points based on the layout data and generating spatial coordinates of the key positioning points, wherein the key positioning points include key points of a transmission tower; Using the spatial coordinates as coordinate matching points, performing feature matching of the positioning key points of the multidimensional spatial data set, generating feature matching results, and establishing correction spatial coordinates based on the feature matching results; After the layout data is corrected by the correction space coordinates, the feature matching of the transmission line is performed using the feature matching result as the matching starting point; A three-dimensional model of the transmission line and the transmission environment is established based on the transmission line feature matching results; The step of performing transmission line feature matching using the feature matching result as a matching starting point includes: Generating transmission line fitting coordinates according to the corrected layout data, and establishing transmission line matching features; Taking the feature matching result as a matching starting point, performing a position-concentrated search of the multidimensional space data set at the transmission line fitting coordinates to generate a transmission line feature matching result, wherein the position-concentrated search is a feature matching based on the transmission line matching feature; The key topological nodes for establishing the topological relationship model include: Taking the transmission line as an evaluation point, calculating spatial distance data, the spatial distance data being distance data from the evaluation point to the power transmission environment, and generating a first key value according to the spatial distance data; Conducting meteorological monitoring on the target area, establishing a meteorological data set, and after dangerously expanding the meteorological data set, using the expanded meteorological data to conduct steady-state analysis of each position of the transmission line, and establishing a second key value; Using the expanded meteorological data to perform an environmental steady-state analysis of the power transmission environment, and establish a third key value; Screening the transmission line topology nodes according to the first key value, the second key value, and the third key value to establish the key topology nodes of the topology relationship model; Conducting environmental change evaluation on the power transmission environment and establishing growth trend prediction results of the power transmission environment; Establishing an update frequency constraint according to the growth trend prediction result and the comprehensive key value of the key topological node, wherein the comprehensive key value is calculated by the first key value, the second key value, and the third key value; After the laser radar and the panoramic scanning device are used to update the data of the key topological nodes based on the update frequency constraint, the method further includes: Establish update constraints; According to the data of the updated key topological nodes, an adaptive analysis of the corresponding update frequency constraints is performed to establish an adaptive update factor; After adaptive update factor compensation is performed through the update constraint, the update frequency constraint is optimized.

2. The intelligent three-dimensional distance measurement method for power transmission lines based on multi-dimensional space topology analysis according to claim 1, characterized in that: The method of updating the warning level in real time based on the safety hazard analysis result and executing the early warning alarm also includes: Establish a trigger mapping list of hidden dangers and warning levels; According to the safety hazard analysis results, a list match of the trigger mapping list is performed, and the warning level is updated in real time according to the list matching results, and a pre-warning alarm is issued.

3. The intelligent three-dimensional distance measurement method for power transmission lines based on multi-dimensional space topology analysis according to claim 2, characterized in that: The method further comprises: Establish a retrospective hidden danger analysis data set based on key topological nodes; Performing a hidden danger mutation evaluation based on the retrospective hidden danger analysis data set and the safety hidden danger analysis result to generate a hidden danger mutation evaluation result; The warning level is adjusted based on the hidden danger mutation assessment result, and a pre-warning alarm is issued.

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