Method for false entry alarm of power transmission line area

Abstract

The application relates to the field of power transmission line safety protection, in particular to a method for false entry alarm of a power transmission line area. The method comprises the following steps: acquiring point cloud data and configuration data of the power transmission line area; wherein the configuration data comprises voltage grades of each tower section; constructing a space model according to the point cloud data and the configuration data; wherein the space model comprises protection area data and space index data; in real time, in response to positioning data sent by a preset end of a work equipment, determining space relationship information according to the positioning data and the space model; wherein the space relationship information represents the nearest neighbor distance and the azimuth state between the current position of the work equipment and a target tower section; if it is determined that the space relationship information meets preset alarm conditions, executing a false entry alarm strategy. The method is used to improve the identification ability and early warning timeliness of the risk of the work equipment approaching a conductor under the complex terrain and large range line scene.

Description

Technical Field

[0001] This application relates to the field of power transmission line safety protection, and in particular to a method for alarming unauthorized entry into a power transmission line area. Background Technology

[0002] In the construction and protection of transmission line protection zones, methods such as electric field induction, height limiting devices, satellite positioning or video recognition are commonly used to monitor the safety of operating equipment (such as cranes, excavators, etc.).

[0003] However, existing solutions generally struggle to meet the needs of spatial relationship judgment and real-time early warning in complex terrain. In particular, traditional positioning and judgment methods often focus on planar distance or are affected by the environment and line status, resulting in inaccurate identification of the actual three-dimensional proximity between machinery and conductors. At the same time, when facing large-scale line scenarios, the processing burden of related monitoring data is heavy, making it difficult to balance response speed and operational stability.

[0004] Therefore, how to improve the accuracy and real-time performance of judging the spatial relationship between construction machinery and conductors in power transmission line construction scenarios, while taking into account both adaptability to complex terrain and data processing efficiency, has become a technical problem that needs to be solved. Summary of the Invention

[0005] The method for alarming accidental entry into transmission line areas provided in this application can improve the ability to identify and provide timely warnings of the risk of working equipment approaching conductors in complex terrain and large-scale line scenarios.

[0006] In a first aspect, embodiments of this application provide a method for alarming unauthorized entry into a transmission line area, including:

[0007] Acquire point cloud data and configuration data for the transmission line area; the point cloud data represents the set of three-dimensional coordinate points of each conductor in each tower section, and the configuration data includes the voltage level of each tower section;

[0008] Based on point cloud data and configuration data, a spatial model is constructed. The spatial model includes protection area data and spatial index data. The protection area data represents the spatial constraint boundary corresponding to each tower segment, and the spatial index data represents the spatial proximity relationship corresponding to each tower segment. The spatial proximity relationship represents the index structure that supports spatial proximity queries.

[0009] The system responds in real time to the positioning data sent by the preset terminal of the working equipment, and determines the spatial relationship information based on the positioning data and spatial model. The spatial relationship information represents the nearest neighbor distance and orientation status between the current position of the working equipment and the target tower section.

[0010] If the spatial relationship information is determined to meet the preset alarm conditions, then the false entry alarm policy is executed.

[0011] Optionally, as described above, a spatial model is constructed based on point cloud data and configuration data, including:

[0012] For each tower section, determine the set of three-dimensional coordinate points of each conductor within the tower section, which is the point cloud sub-data;

[0013] Based on the voltage levels in the point cloud sub-data and configuration data, determine the spatial constraint boundaries corresponding to the tower sections;

[0014] Based on point cloud sub-data, determine the spatial proximity relationships corresponding to tower segments; where spatial constraint boundaries and spatial proximity relationships correspond to the same tower segment.

[0015] A spatial model is constructed based on the spatial constraint boundaries and spatial proximity relationships corresponding to each tower section.

[0016] Optionally, as described above, the spatial constraint boundary corresponding to the tower section is determined based on the voltage level in the point cloud sub-data and configuration data, including:

[0017] Clustering and principal component analysis are performed on the point cloud sub-data to determine the endpoint dataset; the endpoint dataset includes the planar coordinates of each conductor endpoint.

[0018] Based on the endpoint dataset, determine the convex hull region; the convex hull region represents the smallest convex polygon that contains all conductor endpoints within the tower section;

[0019] Based on the convex hull region and the corresponding voltage level in the configuration data, determine the spatial constraint boundary corresponding to the tower section.

[0020] Optionally, as described above, the spatial constraint boundary corresponding to the tower section is determined based on the convex hull region and the corresponding voltage level in the configuration data, including:

[0021] Based on a preset length correlation, the corresponding extension length for the voltage level is determined; wherein, the preset length correlation characterizes the correlation between the voltage level and the extension length;

[0022] Based on the outward expansion length, the convex hull region is expanded to obtain the spatial constraint boundary corresponding to the tower section.

[0023] Optionally, as described above, the spatial proximity relationships of tower sections are determined based on point cloud sub-data, including:

[0024] Based on a preset segmentation dimension, the point cloud sub-data is segmented to obtain the median node; where the preset segmentation dimension represents one dimension in three-dimensional space, and the median node represents the three-dimensional coordinate point at the middle position on the segmentation dimension.

[0025] The point cloud sub-data is divided by the median node to obtain a set of coordinate points; the set of coordinate points includes two coordinate point sets, and each coordinate point set includes at least one three-dimensional coordinate point.

[0026] The first median node is determined as the root node. The set of coordinate points is recursively processed until the preset convergence condition is met, resulting in multiple internal nodes and multiple leaf nodes. The internal nodes include the median nodes generated during the recursive processing.

[0027] Using the root node as the top level, the internal nodes and leaf nodes are hierarchically associated to obtain the spatial proximity relationships of the tower sections.

[0028] Optionally, as described above, spatial relationship information is determined based on positioning data and a spatial model, including:

[0029] The positioning data is transformed to obtain transformed coordinates; these transformed coordinates are consistent with the coordinate system of the spatial model.

[0030] The target tower section is determined based on the transformed coordinates and configuration data;

[0031] The spatial constraint boundary corresponding to the target tower segment is determined from the protection area data, which is the target spatial constraint boundary; and the spatial proximity relationship corresponding to the target tower segment is determined from the spatial model, which is the target spatial proximity relationship.

[0032] Based on the transformed coordinates and the target space constraint boundary, the first relationship information is determined; wherein, the first relationship information represents the positional relationship between the transformed coordinates and the target space constraint boundary;

[0033] Based on the transformed coordinates and the target spatial proximity relationship, the second relationship information is determined; wherein, the second relationship information represents the nearest neighbor distance from the transformed coordinates to the conductor within the target tower section;

[0034] Determine the first and second relationship information, which constitute spatial relationship information.

[0035] Optionally, as described above, the target spatial proximity relationship includes a root node and multiple leaf nodes; based on the transformed coordinates and the target spatial proximity relationship, the second relationship information is determined, including:

[0036] Using the transformed coordinates as the query point, starting from the root node, the target subtree is determined according to the splitting dimension corresponding to the root node; where the target subtree represents the set of nodes on the side where the query point is located;

[0037] Take the root node of the target subtree as the new root node, and determine the new target subtree based on the splitting dimension corresponding to the new root node;

[0038] The target subtree is determined recursively until the current node is a leaf node, and the initial candidate distance is determined; where the initial candidate distance represents the minimum spatial straight-line distance from the query point to each three-dimensional coordinate point in the leaf node;

[0039] The pruning decision is backtracked until the root node is reached to determine the pruning result; the pruning result represents the possibility that there is a closer point in the other subtree.

[0040] Based on the pruning results, the initial candidate distance is updated to obtain the target candidate distance, which is the second relation information.

[0041] Optionally, as described above, the leaf node includes at least one three-dimensional coordinate point; the determination of the target subtree is performed recursively until the current node is a leaf node, and the initial candidate distance is determined, including:

[0042] For each leaf node, determine the spatial straight-line distance from each 3D coordinate point in the leaf node to the query point;

[0043] The minimum value among the straight-line distances in each space is determined as the initial candidate distance.

[0044] Optionally, as described above, if the spatial relationship information is determined to meet preset alarm conditions, then a false entry alarm strategy is executed, including:

[0045] If the transformation coordinates of the first relation information are located inside the spatial constraint boundary, then output the regional alarm information;

[0046] If the second relationship information is less than the preset minimum safe distance threshold, then output distance alarm information;

[0047] Based on regional alarm information and / or distance alarm information, execute the false entry alarm policy.

[0048] Optionally, as described above, if the spatial relationship information is determined to meet preset alarm conditions, the method further includes the following steps before executing the false entry alarm strategy:

[0049] Obtain the quality information of the positioning data; whereby the quality information represents the positioning accuracy level of the positioning data;

[0050] If the quality information does not meet the preset accuracy conditions, the false entry alarm strategy will be terminated; whereby the accuracy conditions represent that the positioning accuracy level has reached the preset effective positioning state.

[0051] Secondly, embodiments of this application provide an alarm device for unauthorized entry into a transmission line area, comprising:

[0052] The data acquisition module is used to acquire point cloud data and configuration data of the transmission line area; the point cloud data represents the set of three-dimensional coordinate points of each conductor in each tower section, and the configuration data includes the voltage level of each tower section.

[0053] The model building module is used to build a spatial model based on point cloud data and configuration data. The spatial model includes protection area data and spatial index data. The protection area data represents the spatial constraint boundary corresponding to each tower segment, and the spatial index data represents the spatial proximity relationship corresponding to each tower segment. The spatial proximity relationship represents the index structure that supports spatial proximity queries.

[0054] The data response module is used to respond in real time to the positioning data sent by the preset terminal of the working equipment, and to determine the spatial relationship information based on the positioning data and the spatial model; wherein, the spatial relationship information represents the nearest neighbor distance and orientation status between the current position of the working equipment and the target tower section;

[0055] The false entry alarm module is used to execute the false entry alarm strategy if the spatial relationship information is determined to meet the preset alarm conditions.

[0056] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0057] The memory stores instructions that the computer executes;

[0058] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0059] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0060] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0061] The method for detecting unauthorized entry into transmission line areas provided in this application acquires a set of three-dimensional coordinate points of each conductor within each tower section as point cloud data. Combined with configuration data including the voltage levels of each tower section, a spatial model is constructed, incorporating protection area data and spatial index data. This allows the protection area data to characterize the spatial constraint boundaries corresponding to each tower section, and the spatial index data to characterize an index structure supporting spatial proximity queries. Thus, the method utilizes a real set of three-dimensional coordinate points to construct spatial constraint boundaries and spatial proximity relationships, replacing the traditional method that focuses on planar distance. This improves the accuracy of identifying the actual three-dimensional proximity between working equipment and conductors in complex terrain. The spatial model is characterized by the spatial index data... The proximity relationship method, when responding to the positioning data of the working equipment in real time, can quickly determine the nearest neighbor distance and orientation between the current position of the working equipment and the target tower section based on this index structure, replacing the point-by-point traversal calculation method, reducing the data processing burden in large-scale line scenarios, and balancing response speed and operational stability. By responding to the positioning data sent by the working equipment in real time, and determining the spatial relationship information based on the positioning data and spatial model, a false entry alarm strategy is executed when the spatial relationship information meets the preset alarm conditions. This makes the alarm triggering based on a comprehensive judgment of real-time positioning and three-dimensional spatial relationship, reducing dependence on the environment or line operating status, and improving the timeliness and reliability of the warning. The method of this application improves the ability to identify the risk of working equipment approaching the conductor and the timeliness of the warning in complex terrain and large-scale line scenarios. Attached Figure Description

[0062] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0063] Figure 1 A flowchart illustrating the method for detecting unauthorized entry into transmission line areas provided in this application. Figure 1 ;

[0064] Figure 2 A flowchart illustrating the method for detecting unauthorized entry into transmission line areas provided in this application. Figure 2 ;

[0065] Figure 3 A flowchart illustrating the method for detecting unauthorized entry into transmission line areas provided in this application. Figure 3 ;

[0066] Figure 4 A schematic diagram of the scene between the boom end and the ground end provided in this application;

[0067] Figure 5 A schematic diagram of the working equipment provided in this application;

[0068] Figure 6A schematic diagram of the structure of the intrusion alarm device for the transmission line area provided in this application;

[0069] Figure 7 A schematic diagram of the structure of the electronic device provided in this application.

[0070] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0071] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0072] In the construction and protection of transmission line protection zones, methods such as electric field induction, height limiting devices, satellite positioning or video recognition are commonly used to monitor the safety of operating equipment (such as cranes, excavators, etc.).

[0073] However, existing solutions generally struggle to meet the needs of spatial relationship judgment and real-time early warning in complex terrain. In particular, traditional positioning and judgment methods often focus on planar distance or are affected by the environment and line status, resulting in inaccurate identification of the actual three-dimensional proximity between machinery and conductors. At the same time, when facing large-scale line scenarios, the processing burden of related monitoring data is heavy, making it difficult to balance response speed and operational stability.

[0074] Therefore, how to improve the accuracy and real-time performance of judging the spatial relationship between construction machinery and conductors in power transmission line construction scenarios, while taking into account both adaptability to complex terrain and data processing efficiency, has become a technical problem that needs to be solved.

[0075] The method for detecting unauthorized entry into transmission line areas provided in this application acquires a set of three-dimensional coordinate points of each conductor within each tower section as point cloud data. Combined with configuration data including the voltage levels of each tower section, a spatial model is constructed, incorporating protection area data and spatial index data. This allows the protection area data to characterize the spatial constraint boundaries corresponding to each tower section, and the spatial index data to characterize an index structure supporting spatial proximity queries. Thus, the method utilizes a real set of three-dimensional coordinate points to construct spatial constraint boundaries and spatial proximity relationships, replacing the traditional method that focuses on planar distance. This improves the accuracy of identifying the actual three-dimensional proximity between working equipment and conductors in complex terrain. The spatial model is characterized by the spatial index data... The proximity relationship method, when responding to the positioning data of the working equipment in real time, can quickly determine the nearest neighbor distance and orientation between the current position of the working equipment and the target tower section based on this index structure, replacing the point-by-point traversal calculation method, reducing the data processing burden in large-scale line scenarios, and balancing response speed and operational stability. By responding to the positioning data sent by the working equipment in real time, and determining the spatial relationship information based on the positioning data and spatial model, a false entry alarm strategy is executed when the spatial relationship information meets the preset alarm conditions. This makes the alarm triggering based on a comprehensive judgment of real-time positioning and three-dimensional spatial relationship, reducing dependence on the environment or line operating status, and improving the timeliness and reliability of the warning. The method of this application improves the ability to identify the risk of working equipment approaching the conductor and the timeliness of the warning in complex terrain and large-scale line scenarios.

[0076] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0077] Figure 1 A flowchart illustrating the method for detecting unauthorized entry into transmission line areas provided in this application. Figure 1 The execution entity of this method can be an embedded main control device, an edge computing node, or a server. In this application, the execution entity corresponds to the ground end, responding to the positioning data sent in real time by the preset end of the operating equipment to monitor and alarm the risk of accidental entry into the power transmission line area. Figure 1 As shown, the method includes:

[0078] S101. Obtain point cloud data and configuration data for the transmission line area; wherein, the point cloud data represents the set of three-dimensional coordinate points of each conductor in each tower section, and the configuration data includes the voltage level of each tower section;

[0079] S102. Construct a spatial model based on point cloud data and configuration data; wherein, the spatial model includes protection area data and spatial index data, the protection area data represents the spatial constraint boundary corresponding to each tower segment, the spatial index data represents the spatial proximity relationship corresponding to each tower segment, and the spatial proximity relationship represents the index structure that supports spatial proximity query.

[0080] S103. Real-time response to positioning data sent by the preset terminal of the working equipment, and determination of spatial relationship information based on the positioning data and spatial model; wherein, the spatial relationship information represents the nearest neighbor distance and orientation status between the current position of the working equipment and the target tower section;

[0081] S104. If the spatial relationship information is determined to meet the preset alarm conditions, then the false entry alarm policy is executed.

[0082] In step S101, the transmission line area can refer to the overhead transmission line corridor requiring construction protection and the surrounding space within a certain range. For example, the transmission line area is divided into tower sections, where each tower section refers to the line segment between two adjacent towers.

[0083] Point cloud data refers to the set of three-dimensional coordinate points representing each conductor within a tower section, acquired through lidar scanning. Specifically, transmission line conductors can be scanned using UAV-borne or ground-based fixed lidar to obtain data files containing discrete three-dimensional coordinate points (X, Y, Z) on the conductor surface, saved in Lidar Aerial Survey (LAS) format or a similar format. Each LAS file corresponds to a tower section; for example, "110kV line 12-13.las" represents the point cloud data of the conductor between tower 12 and tower 13, containing thousands to tens of thousands of discrete three-dimensional coordinate points, which can realistically reflect the curve shape of the conductor in three-dimensional space.

[0084] Configuration data can refer to structured data that characterizes the attribute parameters of each tower section. For example, configuration data may include, but is not limited to: tower section number, voltage level (e.g., 110 kV, 220 kV, 500 kV), line name, tower latitude and longitude coordinates, etc.

[0085] Configuration data can be stored in tabular form (such as spreadsheet files, comma-separated values ​​(CSV) files, or database tables) for easy querying and updating.

[0086] In step S102, the spatial model can refer to a comprehensive data structure constructed based on point cloud data and configuration data, used to characterize the three-dimensional spatial constraints and proximity query capabilities of the transmission line area. Specifically, the spatial model organizes the discrete conductor point cloud data into a form that facilitates rapid querying, and combines it with the attribute information in the configuration data to form the spatial protection specifications corresponding to each tower section.

[0087] Protected area data can refer to data characterizing the spatial constraint boundaries corresponding to each tower section. Spatial constraint boundaries can refer to the three-dimensional spatial range generated based on conductor point cloud data, used to define the safe distance that needs to be maintained between the working equipment and the conductor.

[0088] In one possible implementation, determining the spatial constraint boundary can be achieved by using the projection distribution characteristics of the traverse point cloud data on a horizontal plane, and a pre-defined boundary extraction algorithm, such as the convex hull algorithm, to generate a planar polygon enclosing the projection set of the traverse endpoints. This planar polygon is then expanded outwards by a certain safety distance to obtain a protected area polygon representing the scope of the planar constraint. This protected area polygon defines the two-dimensional area that the working equipment should not enter on the horizontal plane. When the horizontal projection of the working equipment falls inside this polygon, it is determined that it has entered the spatial constraint boundary.

[0089] Spatial index data can refer to data that represents the spatial proximity relationships of each tower segment, and this spatial proximity relationship represents the index structure that supports spatial proximity queries.

[0090] In one possible implementation, the conductor point cloud data of each tower segment can be constructed as a three-dimensional spatial index of a tree data structure (KDTree, K-Dimensional Tree) as spatial index data, so that when a three-dimensional query point is given, the spatial straight-line distance from that point to the nearest point in the conductor point cloud and the index of the nearest point can be quickly returned.

[0091] In practice, the spatial model can be constructed during the offline phase of the system (i.e., the embedded master control device on the ground), pre-generated and stored in the ground storage medium; alternatively, it can be dynamically constructed during system startup or when loading data for a certain section for the first time. After construction, the spatial model is indexed and stored according to the tower section number, which facilitates quick location and loading of the spatial model data for the corresponding section based on the location of the operating equipment.

[0092] It should be understood that the protected area data and spatial index data in the spatial model can be used independently or in combination. For example, the protected area data can be used for coarse screening (to determine whether the operating equipment has entered the planar protected area), and then the spatial index data can be used for fine calculation (to calculate the three-dimensional nearest neighbor distance) to improve the overall query efficiency. In addition, the spatial index structure is not limited to KDTree, but can also be other index structures that support spatial proximity queries, such as R-Tree, Octree, etc.

[0093] In step S103, the working equipment can refer to large mechanical equipment that performs construction work within the protection zone of the transmission line and requires monitoring of its spatial relationship with the conductor. For example, the working equipment may include, but is not limited to, cranes, excavators, and pile drivers. Specifically, the working equipment may be equipped with a positioning module (e.g., a Real-time Kinematic (RTK) positioning module) capable of outputting high-precision positioning data in real time.

[0094] The preset end of the working equipment can refer to the boom end of the working equipment, that is, the part of the working equipment closest to the power line conductor and with the highest risk. Specifically, for cranes, the preset end can be the top of the boom or near the hook; for excavators, the preset end can be the end of the stick or near the bucket. Installing the RTK positioning module at the preset end can most directly monitor the spatial relationship between the most dangerous part of the working equipment and the conductor, rather than monitoring the overall position of the equipment, thus more accurately reflecting the actual risk situation.

[0095] Location data can refer to data that represents the current spatial location of the work equipment, transmitted in real time from a preset terminal. For example, the location data is collected and transmitted by an RTK positioning module installed on a preset terminal (e.g., the boom end) of the work equipment, and its content may include latitude, longitude, altitude, etc.

[0096] Spatial relationship information refers to data characterizing the spatial positional relationship between the current location of the working equipment and the target tower section, including nearest neighbor distance and orientation status. The nearest neighbor distance can refer to the minimum three-dimensional distance from the current location of the working equipment to the point cloud of the target tower section's conductor. For example, the nearest neighbor distance can be obtained by performing a nearest neighbor query by inputting the three-dimensional coordinates of the working equipment into the spatial index data of the section. Orientation status refers to the spatial positional description of the working equipment's current location relative to the target tower section. For example, it can include whether it is located inside the protected area polygon (planar orientation status), which can be determined by determining the inclusion relationship between the horizontal projected coordinates of the working equipment and the protected area polygon corresponding to the target tower section.

[0097] In step S104, the preset alarm conditions may refer to the judgment rules for determining whether an alarm needs to be triggered based on spatial relationship information. In one possible implementation, the preset alarm conditions may include, but are not limited to, at least one of the following: (1) the current position of the working equipment is located inside the protection polygon of the target tower section; (2) the nearest neighbor distance is less than or equal to the safe distance threshold; (3) the rate of descent of the nearest neighbor distance exceeds the preset rate threshold (e.g., descent of more than 1 meter per second), indicating that the working equipment is rapidly approaching the conductor. The above conditions may be used individually or in combination.

[0098] A false alarm strategy can refer to a series of warning operations executed when spatial relationship information meets preset alarm conditions. For example, an alarm strategy may include, but is not limited to: audible and visual alarms (e.g., driving a distance alarm indicator to remain constantly lit, a voice alarm indicator to flash, or playing a voice warning audio); display prompts (e.g., displaying the current distance and alarm status on a screen); data logging (e.g., recording the alarm time, location, and distance value); remote notification (e.g., sending alarm information to a monitoring center via a wireless network); and equipment control (e.g., sending deceleration or stop commands to the equipment).

[0099] It should be understood that the threshold parameters of alarm conditions can be configured and adjusted according to the actual construction environment, equipment type, and safety management requirements. In order to avoid false alarms caused by instantaneous jitter of positioning data or communication delay, a filtering mechanism (such as moving average or Kalman filtering) can be introduced to smooth the positioning data, or an alarm delay confirmation mechanism can be set (such as triggering an alarm only if the conditions are met for three consecutive queries).

[0100] This embodiment provides a method for alarming unauthorized entry into a transmission line area. It acquires a set of three-dimensional coordinate points of each conductor within each tower section as point cloud data. Combined with configuration data including the voltage levels of each tower section, a spatial model is constructed, incorporating protection area data and spatial index data. This allows the protection area data to characterize the spatial constraint boundaries corresponding to each tower section, and the spatial index data to characterize an index structure supporting spatial proximity queries. Thus, it utilizes the actual three-dimensional coordinate point set to construct spatial constraint boundaries and spatial proximity relationships, replacing the traditional method that focuses on planar distance. This improves the accuracy of identifying the actual three-dimensional proximity between working equipment and conductors in complex terrain. The spatial index data characterizes... The spatial proximity relationship, when responding to the positioning data of the working equipment in real time, can quickly determine the nearest neighbor distance and orientation between the current position of the working equipment and the target tower section based on the index structure, replacing the point-by-point traversal calculation method, reducing the data processing burden in large-scale line scenarios, and balancing response speed and operational stability. By responding to the positioning data sent by the working equipment in real time, and determining the spatial relationship information based on the positioning data and spatial model, a false entry alarm strategy is executed when the spatial relationship information meets the preset alarm conditions. This makes the alarm triggering based on a comprehensive judgment of real-time positioning and three-dimensional spatial relationship, reducing dependence on the environment or line operating status, and improving the timeliness and reliability of the warning. The method of this application improves the ability to identify the risk of working equipment approaching the conductor and the timeliness of the warning in complex terrain and large-scale line scenarios.

[0101] Figure 2 A flowchart illustrating the method for detecting unauthorized entry into transmission line areas provided in this application. Figure 2 , Figure 2 Implementation examples in Figure 1 Based on the embodiments, the method for alarming false entry into transmission line areas is described in detail, such as... Figure 2 As shown, the method includes:

[0102] S201. Obtain point cloud data and configuration data for the transmission line area; wherein, the point cloud data represents the set of three-dimensional coordinate points of each conductor in each tower section, and the configuration data includes the voltage level of each tower section.

[0103] S202. For each tower section, determine the set of three-dimensional coordinate points of each conductor within the tower section, which is the point cloud sub-data.

[0104] Specifically, point cloud sub-data is acquired by using lidar scanning on a specific tower section of a transmission line (e.g., the section between two adjacent towers), and saved as independent point cloud files (e.g., LAS format). Each file contains only the set of three-dimensional coordinate points of each conductor within that tower section, rather than the data for the entire line. By dividing the point cloud data of the entire line into multiple point cloud sub-data, it is easier to dynamically load the data as needed, reducing the amount of data processed per operation.

[0105] S203. Determine the spatial constraint boundary corresponding to the tower section based on the voltage level in the point cloud sub-data and configuration data.

[0106] Specifically, the spatial constraint boundary represents the space within the tower section where working equipment should not enter. Its generation needs to combine the actual spatial shape of the conductor and the safety distance requirements corresponding to the voltage level.

[0107] In an optional implementation, step S203 may include:

[0108] S2031. Perform clustering and principal component analysis on the point cloud sub-data to determine the endpoint dataset; the endpoint dataset includes the planar coordinates of each conductor endpoint.

[0109] Specifically, clustering can employ a density-based clustering algorithm (DBSCAN) to separate points belonging to different wire bundles within the point cloud subdata, resulting in multiple point cloud clusters. Each point cloud cluster corresponds to a wire bundle and contains the three-dimensional coordinates of all scanned points on that wire bundle. The DBSCAN algorithm can effectively separate density-connected points into the same cluster by setting a neighborhood radius (e.g., 0.5 meters) and a minimum sample size (e.g., 10), while simultaneously removing isolated noise points, thus achieving effective separation between different wire bundles.

[0110] Furthermore, principal component analysis (PCA) is performed on each cloud cluster. The first principal component is taken as the direction of the conductor. The average coordinates of a predetermined number (e.g., 100) points at both ends are extracted along this direction to serve as the two endpoints of the conductor bundle. The plane coordinates (X, Y) of the endpoints of all conductor bundles are summarized to form an endpoint dataset. It should be understood that the number of coordinate points in the endpoint dataset is twice the number of conductor bundles. For example, if a tower section contains three conductor bundles (phase A, phase B, and phase C), then the endpoint dataset contains the plane coordinates of six endpoints.

[0111] S2032. Based on the endpoint dataset, determine the convex hull region; the convex hull region represents the smallest convex polygon that contains all conductor endpoints within the tower section.

[0112] Specifically, based on all planar coordinates in the endpoint dataset, the convex hull algorithm can be used to calculate the smallest convex polygon covering all endpoints. This convex hull region contains the projection range of all conductors in the tower section onto the horizontal plane.

[0113] S2033. Determine the spatial constraint boundary corresponding to the tower section based on the convex hull region and the voltage level corresponding to the configuration data.

[0114] Specifically, based on the voltage level of the section in the configuration data, the corresponding safety distance threshold can be queried, the convex hull region can be expanded outward, and a protected area polygon can be generated as the spatial constraint boundary.

[0115] In an optional implementation, step S2033 may include:

[0116] Based on the preset length correlation, the expansion length corresponding to the voltage level is determined; wherein, the preset length correlation represents the correlation between the voltage level and the expansion length; according to the expansion length, the convex hull region is expanded to obtain the spatial constraint boundary corresponding to the tower section.

[0117] The preset length relationships can be stored in the configuration data or preset according to relevant power safety regulations. For example, the expansion length corresponding to a voltage level of 110 kV is 5 meters, the expansion length corresponding to a voltage level of 220 kV is 6 meters, and the expansion length corresponding to a voltage level of 500 kV is 8.5 meters. The expansion process can adopt an equidistant expansion method, that is, each edge of the convex hull region is translated outward along the normal direction by the expansion length, or each vertex of the convex hull region is moved outward along the angle bisector direction and then reconnected to form an expansion polygon.

[0118] It is understandable that by determining the corresponding extension length based on the voltage level, and applying differentiated safety distance constraints to tower sections of different voltage levels, the generation of spatial constraint boundaries can be matched with actual power safety regulations, thereby ensuring that the protection range under each voltage level meets safety requirements and achieving refined spatial protection by voltage level.

[0119] It is understandable that by performing cluster analysis, principal component analysis to extract endpoints, and calculating and expanding the convex hull of point cloud sub-data, spatial constraint boundaries can be determined. This allows spatial constraint boundaries to be generated based on the actual spatial distribution of the conductor, thereby replacing the traditional fixed electronic fence or manual boundary delineation method and achieving dynamic spatial constraints that are compatible with the actual direction of the conductor.

[0120] S204. Based on the point cloud sub-data, determine the spatial proximity relationship corresponding to the tower segment; where the spatial constraint boundary and the spatial proximity relationship correspond to the same tower segment.

[0121] Specifically, the spatial proximity relationship characterizes the spatial index structure of the conductor point cloud data within the tower section, supports fast nearest neighbor queries for any three-dimensional query point, and together with the spatial constraint boundary, constitutes the complete spatial model of the tower section.

[0122] In one alternative implementation, step S204 may include:

[0123] S2041. Based on the preset segmentation dimension, the point cloud sub-data is segmented to obtain the median node; wherein, the preset segmentation dimension represents one dimension in the three-dimensional space dimension, and the median node represents the three-dimensional coordinate point at the middle position on the segmentation dimension.

[0124] The preset segmentation dimension can be one of the X-axis, Y-axis, or Z-axis. In one possible implementation, the dimension with the most dispersed distribution of points in the current point set (i.e., the dimension with the largest variance) can be selected as the segmentation dimension. The median node is the point whose coordinate value is in the middle position on this segmentation dimension, that is, the point located in the middle position after sorting by the coordinate values ​​of this dimension.

[0125] S2042. Divide the point cloud sub-data into a set of coordinate points, using the median node as the boundary. The set of coordinate points includes two sets of coordinate points, and each set of coordinate points includes at least one three-dimensional coordinate point.

[0126] Specifically, the three-dimensional coordinate points whose coordinate values ​​are less than the coordinate values ​​corresponding to the median node constitute the left coordinate point set, and the three-dimensional coordinate points whose coordinate values ​​are greater than the coordinate values ​​corresponding to the median node constitute the right coordinate point set. The two coordinate point sets together form the coordinate point set.

[0127] S2043. Determine the first median node as the root node, and recursively process the set of coordinate points until the preset convergence condition is met, resulting in multiple internal nodes and multiple leaf nodes; among them, the internal nodes include each median node generated during the recursive processing.

[0128] Specifically, the recursive processing can be performed by repeatedly executing steps S2041 to S2042 for the left coordinate point set and the right coordinate point set, respectively, but the splitting dimension can be changed (for example, the first splitting dimension is the X-axis, the second splitting dimension is the Y-axis, and the third splitting dimension is the Z-axis, and the splitting dimension can be the dimension with the most dispersed distribution in the current point set (i.e., the dimension with the largest variance)); each recursion generates a new median node as an internal node, and the point set is further divided into two subsets; the division stops when the number of points in each subset is less than the preset leaf node size (e.g., 10 points), that is, the convergence condition is reached, and the remaining points are stored in the leaf nodes.

[0129] S2044. Using the root node as the top level, hierarchically associate each internal node and each leaf node to obtain the spatial proximity relationship corresponding to the tower section.

[0130] In one possible implementation, hierarchical relationships can be achieved using pointers or indexes. Specifically, each internal node records its split dimension, split value, left child pointer, and right child pointer; leaf nodes record a list of indices of their contained points in the origin cloud subdata. This forms a binary tree structure with the root node as the top level.

[0131] The relationship between the median node, root node, internal nodes, and leaf nodes should be understood as follows: The root node is the first determined median node, located at the top level of the tree structure; internal nodes are the collective term for all median nodes generated during the recursion, including the root node and all nodes below it and above the leaf nodes; the leaf node is the recursion termination node, storing an index list of the original node and no longer containing median nodes. In simpler terms, the root node is a special type of internal node, internal nodes are recursively generated median nodes, and leaf nodes are the termination nodes storing the original data. Together, these three constitute a hierarchical structure from the top to the bottom.

[0132] It is understandable that by recursively segmenting point cloud subdata, selecting median nodes, and hierarchically associating them, spatial proximity relationships can be determined. This allows unordered discrete point cloud data to be organized into a tree-like index structure with spatial partitioning capabilities. Consequently, nearest neighbor queries can quickly locate the search path based on spatial location, significantly reducing the time complexity of nearest neighbor queries for massive point cloud data and meeting the millisecond-level real-time response requirements of embedded platforms.

[0133] S205. Construct a spatial model based on the spatial constraint boundaries and spatial proximity relationships corresponding to each tower section.

[0134] Specifically, the spatial constraint boundaries and spatial proximity relationships of each tower section can be associated and stored according to the section number to form the spatial model of that section; further, the spatial models of all tower sections along the entire line can be summarized to form a complete spatial model.

[0135] It is understandable that by constructing and storing spatial constraint boundaries and spatial proximity relationships for each tower segment, a spatial model can be built. This allows the spatial model to be organized and managed in segments, thereby supporting the dynamic loading of spatial model data for the corresponding segment based on the real-time location of the operating equipment. This enables refined spatial protection of segments in large-scale line scenarios, while reducing the amount of data and memory usage per load.

[0136] S206. Real-time response to positioning data sent by the preset terminal of the working equipment, perform transformation processing on the positioning data to obtain transformed coordinates; wherein, the transformed coordinates are consistent with the coordinate system of the spatial model.

[0137] Specifically, the positioning data is collected and transmitted by the RTK positioning module on the operating equipment, and its content may include latitude, longitude, and altitude. The RTK positioning module is deployed in a pre-set terminal.

[0138] In one possible implementation, the location data is transformed to obtain transformed coordinates. This may include: converting the latitude and longitude in the location data from the geographic coordinate system to planar projected coordinates consistent with the spatial model through projection, and using the altitude as the Z-axis coordinate to obtain three-dimensional transformed coordinates.

[0139] S207. Determine the target tower section based on the transformed coordinates and configuration data.

[0140] Specifically, the distance between the plane coordinates of the transformed coordinates and the tower coordinates of each tower segment in the configuration data is calculated, and the tower segment with the closest distance is determined as the target tower segment.

[0141] S208. Determine the spatial constraint boundary corresponding to the target tower section from the protection area data, which is the target spatial constraint boundary; and determine the spatial proximity relationship corresponding to the target tower section from the spatial model, which is the target spatial proximity relationship.

[0142] Specifically, based on the determined target tower segment number, the spatial constraint boundary of the protection area data corresponding to the segment can be retrieved and extracted from the spatial model as the target spatial constraint boundary. At the same time, the spatial proximity relationship corresponding to the segment can be extracted as the target spatial proximity relationship.

[0143] In one possible implementation, if the spatial model data of the target tower segment has not yet been loaded into memory, the spatial model data of that segment and its adjacent segments are dynamically loaded from the ground-based storage medium before extraction. After extraction, the target spatial constraint boundary and the target spatial proximity relationship together constitute the complete spatial dataset currently used for spatial relationship determination.

[0144] It should be understood that dynamically loading spatial model data of adjacent segments refers to simultaneously loading the spatial model data of at least one spatially adjacent tower segment while loading the spatial model data of the target tower segment. For example, if the target tower segment is segment N (i.e., the segment between tower N-1 and tower N), then the spatial model data of its adjacent segments N-1 (tower N-2 to tower N-1) and N+1 (tower N to tower N+1) are loaded simultaneously, forming continuous protection data covering segments N-1, N, and N+1. This ensures that when the working equipment moves near the segment boundary, no protection blind spots will occur due to untimely data switching; after loading is completed, the spatial model data of the old segment can be released or cached to control the memory usage of the embedded platform.

[0145] S209. Determine the first relationship information based on the transformed coordinates and the target space constraint boundary; wherein, the first relationship information represents the positional relationship between the transformed coordinates and the target space constraint boundary.

[0146] Specifically, the planar projection coordinates of the transformed coordinates can be converted into geometric point objects. A point-within-a-polygon determination algorithm (such as the ray method or the number of turns method) is used to determine whether the geometric point is located inside the target spatial constraint boundary. If yes, the first relationship information can be expressed, for example, as "located inside the spatial constraint boundary of the target tower section (N section)"; if no, the first relationship information can be expressed, for example, as "located outside the spatial constraint boundary of the target tower section (N section)".

[0147] For example, taking the ray method as an example of the polygon interior determination algorithm, a ray is drawn from the geometric point in any direction (e.g., the X-axis direction), and the number of intersection points between the ray and each side of the target space constraint boundary is calculated. If the number of intersection points is odd, the geometric point is determined to be inside the polygon; if the number of intersection points is even (including zero), the geometric point is determined to be outside the polygon.

[0148] For example, taking the polygon interior determination algorithm as an example of the rotation number method, a local coordinate system is established with the geometric point as the origin. Each directed edge of the target space constraint boundary is traversed, and the rotation angle of each edge relative to the geometric point is calculated and accumulated to obtain the total rotation number. If the total rotation number is not zero, the geometric point is determined to be inside the polygon; if the total rotation number is zero, the geometric point is determined to be outside the polygon.

[0149] S210. Determine the second relationship information based on the transformed coordinates and the target spatial proximity relationship; wherein, the second relationship information represents the nearest neighbor distance from the transformed coordinates to the conductor within the target tower section.

[0150] Specifically, the transformed coordinates can be used as the query point and input into the target space proximity relationship. A nearest neighbor query is then performed to obtain the minimum three-dimensional Euclidean distance from the query point to the traverse point cloud, which is used as the second relation information.

[0151] In an optional implementation, the target spatial proximity relationship includes a root node and multiple leaf nodes, and step S210 may include:

[0152] S2101. Using the transformed coordinates as the query point, starting from the root node, determine the target subtree according to the splitting dimension corresponding to the root node; where the target subtree represents the set of nodes on the side where the query point is located.

[0153] Specifically, the coordinates of the query point on the split dimension of the root node are compared with the coordinates of the root node on the same dimension: if the coordinates of the query point are less than or equal to the coordinates of the root node, the target subtree is the left subtree (the branch where the coordinates of the query point are less than the coordinates of the middle node); if the coordinates of the query point are greater than the coordinates of the root node, the target subtree is the right subtree (the branch where the coordinates of the query point are greater than the coordinates of the middle node).

[0154] It should be understood that this comparison operation essentially locates the query point to the same partitioned region as when the target space proximity relationship was created, ensuring that the subspace most likely to contain the nearest neighbor is searched first.

[0155] S2102. Take the root node of the target subtree as the new root node, and determine the new target subtree according to the splitting dimension corresponding to the new root node.

[0156] Specifically, the root node of the target subtree determined in step S2101 (i.e., the left or right child node of the original root node) is taken as the current root node. The comparison process of step S2101 is repeated according to the segmentation dimension corresponding to this node (which is consistent with the segmentation dimension used to determine the spatial proximity relationship mentioned above) to determine the target subtree of the next level. This step realizes a recursive descent from the top level to the bottom level, gradually narrowing the search range.

[0157] S2103. Recursively execute the determination of the target subtree until the current node is a leaf node, and determine the initial candidate distance; where the initial candidate distance represents the minimum spatial straight-line distance from the query point to each three-dimensional coordinate point in the leaf node.

[0158] Specifically, the recursive process from steps S2101 to S2102 is repeated, searching downwards from the root node layer by layer along the hierarchical structure of the target spatial proximity relationship. Each time, the target subtree is selected based on the splitting dimension of the current node, until a leaf node that no longer contains child nodes is reached. At this point, the spatial straight-line distance (specifically, three-dimensional Euclidean distance) from the query point to all three-dimensional coordinate points stored in the leaf node is calculated, and the minimum value is taken as the initial candidate distance. At the same time, the point that obtains the minimum value is recorded as the current nearest neighbor.

[0159] In an optional implementation, the leaf node includes at least one three-dimensional coordinate point; step S2103 may include:

[0160] For each leaf node, determine the spatial straight-line distance from each 3D coordinate point in the leaf node to the query point; determine the minimum value among the spatial straight-line distances, which is the initial candidate distance.

[0161] In one possible implementation, the spatial straight-line distance can be calculated using the three-dimensional Euclidean distance formula. Specifically, all three-dimensional coordinate points in the leaf nodes are traversed, and the spatial straight-line distance from each three-dimensional coordinate point to the query point is calculated one by one. The minimum value is selected as the initial candidate distance, which is an estimate of the distance from the query point to the nearest point in the currently searched area.

[0162] It is understandable that by calculating the distance to each 3D coordinate point in the leaf node, the accurate local nearest neighbor distance can be obtained within the smallest search unit. This can provide a reliable initial benchmark value for subsequent backtracking pruning, thereby avoiding the incorrect exclusion of potential nearest points due to inaccurate benchmark values ​​during the pruning process and ensuring the accuracy of nearest neighbor query.

[0163] S2104. Backtrack to perform pruning judgment until returning to the root node and determine the pruning result; where the pruning result represents the possibility that there is a closer point in the other subtree.

[0164] The pruning result can refer to the determination of whether the other subtree of the parent node needs to be searched, based on the comparison between the distance from the query point to the current parent node's splitting hyperplane (i.e., the plane that is perpendicular to the current splitting dimension and passes through the coordinate value of the parent node, which divides the space into two half-spaces, corresponding to the left and right subtrees respectively) and the current initial candidate distance. The pruning result can be represented by two states, such as "needs to be searched" (indicating that there may be a closer point in the other subtree) and "can be pruned" (indicating that there is no closer point in the other subtree).

[0165] In one possible implementation, the processing logic of step S2104 can be as follows: starting from the leaf node reached in step S2103, backtrack upwards along the search path to the parent node. Specifically, calculate the hyperplane distance from the query point to the current parent node, which is the absolute value of the difference between the coordinates of the query point and the coordinates of the parent node in the split dimension. If the hyperplane distance is less than the current initial candidate distance, it means that there may be a point closer to the current nearest neighbor in the spatial region represented by the other subtree of the parent node. The pruning result indicates that a search is needed, and the search should continue in the other subtree. If the hyperplane distance is greater than or equal to the current initial candidate distance, it means that the distance from all points in the other subtree to the query point is not less than the hyperplane distance. Since the hyperplane distance is not less than the current initial candidate distance, the other subtree cannot contain a closer point. The pruning result indicates that pruning can be performed, and the other subtree can be skipped. Repeat this backtracking judgment process until the root node is returned and the other subtree of the root node has also been checked.

[0166] S2105. Based on the pruning results, update the initial candidate distance to obtain the target candidate distance, which is the second relation information.

[0167] In one possible implementation, during the backtracking process, for each parent node: if the pruned result representation needs to be searched, then the other subtree of that parent node is entered. Following the recursive search logic of steps S2101 to S2103, the nearest point is found in that subtree, and its distance to the query point is calculated. This distance is compared with the current initial candidate distance, and the smaller of the two is taken as the new initial candidate distance. At the same time, the corresponding nearest neighbor index is updated. If the pruned result representation can be pruned, then the current initial candidate distance remains unchanged, and backtracking continues upward. When the backtracking is completed and returns to the root node, and the other subtree of the root node has also been processed, the final initial candidate distance is the target candidate distance, which serves as the second relation information.

[0168] It is understandable that by dynamically updating the initial candidate distance based on the pruning results, the global nearest neighbor can be gradually approached during the search process, thereby ensuring that the final output target candidate distance is the true minimum three-dimensional Euclidean distance from the query point to the conductor point cloud within the target tower section, thus ensuring the accuracy of the nearest neighbor query results.

[0169] S211. Determine the first relation information and the second relation information, which are spatial relation information.

[0170] It is understandable that by combining planar orientation status judgment and three-dimensional nearest neighbor distance calculation, spatial relationship information can be obtained. This allows for a comprehensive characterization of the spatial relationship between the working equipment and the guide wire from both two-dimensional plane and three-dimensional space dimensions. This avoids misjudgment or omission caused by single-dimensional judgment and achieves a multi-dimensional and three-dimensional accurate description of spatial relationships.

[0171] S212. If the spatial relationship information is determined to meet the preset alarm conditions, then the false entry alarm policy is executed.

[0172] In an optional implementation, step S212 may include:

[0173] S2121. If the transformation coordinates of the first relation information are located inside the spatial constraint boundary, then output the regional alarm information.

[0174] Specifically, the first relational information indicates that the transformation coordinates are located inside the spatial constraint boundary, which means that the working equipment has broken through the planar protection boundary determined based on the actual spatial distribution of the conductor and the corresponding extension length of the voltage level, and entered the clearly prohibited two-dimensional constraint area, posing a risk of directly touching the conductor.

[0175] The area alarm information represents the alarm identifier data indicating that the working equipment has entered the spatial constraint boundary. For example, it may include alarm type identifier, target tower section number, line name, voltage level, current timestamp, current location coordinates of the working equipment, etc., which is used to trigger the execution of the corresponding false entry alarm strategy.

[0176] S2122. If the second relationship information is less than the preset minimum safe distance threshold, then output distance alarm information.

[0177] Specifically, if the second relationship information is less than the preset minimum safe distance threshold, it means that the actual three-dimensional spatial distance between the working equipment and the conductor is less than the corresponding minimum safe distance threshold preset based on safety regulations. Even if the working equipment has not entered the polygon of the planar protection zone, there is a risk of discharge or mechanical collision, and an immediate warning is required.

[0178] Distance alarm information represents safety alarm data indicating insufficient three-dimensional spatial distance between the working equipment and the conductor. For example, it may include alarm type identifier, current nearest neighbor distance value, nearest neighbor coordinates, target tower section number, line name, voltage level, current timestamp, current position coordinates of the working equipment, preset minimum safe distance threshold, etc., which are used to trigger the execution of the corresponding false entry alarm strategy.

[0179] S2123. Execute the false entry alarm policy based on the area alarm information and / or distance alarm information.

[0180] In one possible implementation, the execution of the false alarm policy can be tiered based on the type of alarm information (including area alarm information and distance alarm information) or a combination thereof:

[0181] When only area alarm information is output, a level 1 alarm strategy is executed. For example, the level 1 alarm strategy includes driving the distance alarm indicator light to stay on, displaying "Entering the protected area" and the current section information on the screen, and playing a voice warning "Please evacuate immediately".

[0182] When only distance alarm information is output, a secondary alarm strategy is executed. For example, the secondary alarm strategy includes driving the distance alarm indicator to stay on, driving the voice alarm indicator to flash at a cycle of 0.25 seconds, displaying the current nearest neighbor distance value on the screen, and playing a voice warning of "Please keep your distance" with an interval of not less than 5 seconds between the two playbacks.

[0183] When both area alarm information and distance alarm information are output simultaneously, a three-level alarm strategy is executed. The three-level alarm strategy, based on the first-level and second-level alarm strategies, adds the sending of emergency alarm information to the monitoring center and the sending of deceleration or stop commands to the control system of the operating equipment.

[0184] When no alarm information is output, all alarm outputs are turned off, and the display shows a normal status.

[0185] It is understandable that by implementing differentiated false alarm strategies based on the hierarchical combination of regional alarm information and distance alarm information, the alarm level can be accurately matched with the risk level, avoiding insufficient or excessive alarms caused by a single alarm threshold, thereby improving the relevance and effectiveness of alarms.

[0186] In an optional implementation, prior to step S212, the following may also be included:

[0187] Acquire the quality information of the positioning data; where the quality information represents the positioning accuracy level of the positioning data; if the quality information does not meet the preset accuracy conditions, terminate the false entry alarm strategy; where the accuracy conditions represent that the positioning accuracy level has reached the preset effective positioning state.

[0188] Among them, quality information can be specified as a field or identifier in the bit data used to characterize the quality of the positioning solution.

[0189] In one possible implementation, the quality information is determined by the operating equipment based on the solution status of the satellite signals received by the RTK positioning module. Specifically, the RTK positioning module receives the carrier phase signal of the satellite system, performs real-time dynamic solution with the differential data from the reference station, and outputs the corresponding quality information code based on the degree of solution convergence and the fixed state.

[0190] For example, a quality information value of 0 indicates invalid positioning, meaning the working equipment did not receive a valid satellite signal or the calculation failed, and the location could not be determined; a quality information value of 1 indicates that the positioning data can only meet general navigation needs and cannot meet the requirements for refined judgment of the safety distance of transmission lines; a quality information value of 2 indicates that compared to a quality information value of 1, its accuracy has improved but still has not reached the centimeter level, and there is still a significant risk of misjudgment when used in transmission line scenarios; a quality information value of 3 indicates that the positioning accuracy is close but has not reached the convergence state of the fixed solution (the integer ambiguity of the carrier phase difference calculation is not yet completely fixed), and the stability is insufficient; a quality information value of 4 indicates that the carrier phase difference calculation is completely converged, the integer ambiguity is fixed, and the requirements for refined judgment of the safety distance of transmission lines at the centimeter level are met.

[0191] Specifically, if the quality information does not meet the preset accuracy conditions, the quality information value can be less than 4, indicating that the positioning accuracy has not reached the centimeter-level accuracy requirement of the RTK fixed solution.

[0192] Only when the quality information value is 4 is the relevant false entry alarm strategy judgment allowed; otherwise, the false entry alarm strategy is terminated, all alarm outputs are turned off to prevent false alarms due to insufficient positioning accuracy.

[0193] In one possible implementation, after responding to the positioning data sent by the preset terminal of the operating equipment, the quality information in the positioning data is verified. If the quality information does not meet the preset accuracy conditions, the false alarm strategy is terminated. This scheme can ensure that subsequent spatial relationship calculations and alarm judgments are always based on high-precision positioning data, avoiding false alarms or missed alarms caused by low-precision positioning.

[0194] It is understandable that by verifying the quality of positioning data before alarm judgment, the subsequent alarm process is only executed when the positioning quality meets the preset accuracy conditions. This can filter out the interference caused by low-precision positioning data, thereby fundamentally avoiding false alarms caused by positioning drift or signal instability, and ensuring the reliability of alarm triggering.

[0195] In this embodiment, based on the above embodiments, on the one hand, by dividing the point cloud sub-data according to tower sections and constructing spatial constraint boundaries and spatial proximity relationships respectively, the problem of excessive data volume and heavy processing burden on embedded platforms in large-scale line scenarios is solved; on the other hand, by combining positioning quality pre-verification, planar orientation judgment and three-dimensional nearest neighbor distance calculation, a multi-dimensional spatial relationship determination method is used to further improve the accuracy and reliability of judging the spatial relationship between operating equipment and conductors in complex terrain; thereby achieving the effect of dynamic adaptive section loading and millisecond-level real-time response, while taking into account both data processing efficiency and early warning accuracy.

[0196] Figure 3A flowchart illustrating the method for detecting unauthorized entry into transmission line areas provided in this application. Figure 3 , Figure 3 Based on the aforementioned embodiments, this embodiment provides another form of explanation for the false entry alarm method for transmission line areas, such as... Figure 3 As shown, the method may include:

[0197] S301. For each tower section in the transmission line area, obtain the LAS file for that tower section.

[0198] One LAS file represents the point cloud data of a tower section.

[0199] S302. Extract the three-dimensional coordinate points from the LAS file to obtain a set of three-dimensional coordinate points.

[0200] S303. Construct the KDTree three-dimensional spatial index based on the set of three-dimensional coordinate points.

[0201] Among them, the KDTree three-dimensional spatial index can be understood as the aforementioned spatial index data.

[0202] S304. Set the neighborhood radius of the DBSCAN clustering algorithm to 0.5 meters and the minimum number of samples to 10. Perform DBSCAN clustering on the set of three-dimensional coordinate points to obtain multiple point cloud clusters.

[0203] S305. Perform PCA processing on each cloud cluster, take the first principal component as the direction of the guide, extract the average coordinates of a preset number (100) of points at both ends along this direction, and use them as the two endpoints of the guide bundle; summarize the plane coordinates of the endpoints of all guide bundles to form the endpoint dataset.

[0204] S306. Determine the convex hull region based on the endpoint dataset.

[0205] S307. Determine the voltage level of the tower section.

[0206] S308. If the voltage level is 110 kV, then the external extension length is determined to be 5 meters.

[0207] S309. If the voltage level is 220 kV, then the external extension length is determined to be 6 meters.

[0208] S310. If the voltage level is 500 kV, then the external extension length is determined to be 8.5 meters.

[0209] S311. Based on the outward expansion length, the convex hull region is expanded to obtain the spatial constraint boundary corresponding to the tower section, which is the polygon of the protected area.

[0210] S312. Convert the latitude and longitude coordinates of the protected area polygon into planar coordinates consistent with the coordinate system of the KDTree three-dimensional spatial index to obtain the target protected area polygon.

[0211] S313, Store the target protected area polygon and KDTree three-dimensional spatial index to the ground storage unit.

[0212] S314, Start the system.

[0213] "Starting the system" means turning on the ground-based embedded main control device (such as a Raspberry Pi), loading the operating system and main program, and initializing the system operating environment.

[0214] S315. Initialize the interface of the ground terminal.

[0215] This interface can be a general purpose input / output (GPIO) interface, a serial port, an organic light-emitting diode (OLED) interface, etc.

[0216] By initializing the ground terminal interface, a communication channel can be established with peripherals such as RTK, indicator lights, OLED displays, and voice modules, providing hardware interface support for subsequent data reception, status display, and alarm output.

[0217] S316, Load configuration data, target protection zone polygon, and KDTree 3D spatial index.

[0218] S317, In response to the positioning data received from the boom end of the working equipment via the serial port.

[0219] Specifically, the format of the positioning data can be the standard positioning data (GGA, Global Positioning System Fix Data) format.

[0220] S318. Extract longitude, latitude, altitude, and mass information from positioning data.

[0221] S319. If the quality information value is not 4, then turn off the RTK indicator light, shut down all alarms, and wait for a new response to the positioning data received from the operating device via the serial port.

[0222] The RTK indicator light is used to visually display the current positioning quality status of the RTK positioning module.

[0223] S320. If the quality information value is 4, then the RTK indicator light will be lit based on the GPIO interface.

[0224] S321. Based on the longitude, latitude, and altitude in the positioning data, perform transformation processing to obtain transformed coordinates.

[0225] S322. Determine the target tower section corresponding to the transformed coordinates from the configuration data.

[0226] S323. Based on the target tower section number, determine whether the target protection zone polygon and KDTree 3D spatial index corresponding to the target tower section are currently loaded.

[0227] S324. If not currently loaded, load the target protection zone polygon and KDTree 3D spatial index corresponding to the target tower segment and the tower segments adjacent to the target tower segment, respectively.

[0228] If the target protection zone polygon and KDTree 3D spatial index corresponding to the tower section do not exist, the LAS file corresponding to the tower section is obtained, and the target protection zone polygon and KDTree 3D spatial index corresponding to the tower section are determined based on the LAS file.

[0229] S325. If currently loaded, calculate the nearest neighbor distance from the transformed coordinates to the conductor within the target tower section, and determine whether the transformed coordinates are located inside the polygon of the target protection zone.

[0230] S326. Determine whether the nearest neighbor distance is less than the preset minimum safe distance threshold, or whether the coordinates are located inside the target protection zone polygon.

[0231] S327. If so, then illuminate the distance alarm indicator, activate the voice alarm indicator to flash and play the preset alarm audio, and update the OLED display to show the current distance value and alarm status.

[0232] The preset alarm audio can be, for example, a prompt tone saying "Please keep your distance," and the current distance value and alarm status can be, for example, "Distance: 3.2 meters, alarm!"

[0233] S328. If not, turn off the distance alarm indicator light and stop flashing, stop the voice playback, and update the OLED display to show the normal status.

[0234] This embodiment, taking a specific engineering implementation process as an example, details the complete processing chain from the original LAS point cloud file to the final alarm output, according to two stages: offline modeling (including steps S301-S313) and real-time operation (including steps S314-S328). By separating offline modeling from real-time operation, and pre-generating and storing spatial model data for each tower section, the embedded platform can avoid undertaking heavy point cloud processing and index building tasks during the real-time operation stage. This allows the real-time stage to perform only lightweight data loading and spatial query operations, achieving efficient and stable operation on resource-constrained embedded devices.

[0235] For example, based on the foregoing embodiments, in order to intuitively illustrate the overall deployment architecture of the transmission line intrusion alarm system and the data interaction relationships between its components, Figure 4 This is a schematic diagram of the scene between the boom end and the ground end provided in this application, such as... Figure 4 As shown, the system consists of two parts: the boom end and the ground end.

[0236] The boom end is installed on the working equipment (such as a crane boom) and integrates an RTK positioning module, a boom end microcontroller, and a wireless data transmission module (such as WiFi). The RTK positioning module receives BeiDou satellite signals in real time and sends the positioning data (which can be in standard positioning data (GNGGA, Global Navigation Satellite System Fix Data) format) to the boom end microcontroller via a serial port. The boom end microcontroller then sends the data to the ground end via the wireless data transmission module.

[0237] The ground terminal can be installed in the cab of the operating equipment or at a ground monitoring point, and includes an embedded main controller (such as a Raspberry Pi), a wireless data receiving module, a storage module (for storing relevant data), a serial communication module, a GPIO interface, an OLED display, an audible and visual alarm module (indicator lights and voice broadcast module), and a power supply module.

[0238] In practice, a wireless local area network communication channel is established between the boom end and the ground end via WiFi to ensure real-time and reliable transmission of positioning data. The ground end forwards the data to the embedded main controller via serial port. After parsing the positioning data, the main controller performs spatial relationship calculation and alarm judgment, and drives indicator lights (such as RTK indicator light, distance alarm light, and voice alarm light) through GPIO, calls and plays voice alarms, and drives the OLED display to display status information in real time.

[0239] based on Figure 4 In conjunction with the foregoing embodiments, to illustrate in detail the hardware connection relationships and signal flow between the ground-based embedded main control unit and each functional module, an example of the ground-based unit being deployed on the operating equipment is provided. Figure 5The structural diagram of the operating equipment provided in this application is as follows: Figure 5 As shown, the ground terminal is based on an embedded main controller (Raspberry Pi), and each functional module is connected to the main controller through different interfaces.

[0240] Specifically, the RTK positioning module transmits positioning data via WiFi at the boom end to the WiFi receiving module at the ground end, and then connects to the main control via a serial port. GPIO25, GPIO22, GPIO27, and GPIO5 are connected to LED indicators of corresponding colors. Specifically, GPIO25 connects to the RTK indicator, GPIO22 connects to the distance alarm indicator, GPIO27 connects to the voice alarm indicator, GPIO5 connects to the serial data connection indicator, and GPIO24 connects to the one-button power-off button. The OLED display is connected via an I2C bus, and the voice broadcast module is connected via an audio output interface. The main control runs the main program, coordinating the various modules to complete data reception, spatial query, alarm judgment, and output control.

[0241] It is understandable that by separating the positioning function at the boom end from the calculation and alarm function at the ground end, the boom end can focus on high-precision positioning data acquisition and wireless transmission, while the ground end can focus on complex spatial relationship calculations and alarm output. This reduces the power consumption and complexity of the boom end, while utilizing the relatively abundant computing resources and display conditions at the ground end to achieve refined alarm prompts, thereby optimizing the overall system functionality and improving engineering practicality.

[0242] Figure 6 This is a schematic diagram of the structure of the intrusion alarm device for the transmission line area provided in this application, as shown below. Figure 6 As shown, the intrusion alarm device 60 for the transmission line area includes: a data acquisition module 601, a model construction module 602, a data response module 603, and an intrusion alarm module 604.

[0243] The data acquisition module 601 is used to acquire point cloud data and configuration data of the transmission line area; wherein, the point cloud data represents the set of three-dimensional coordinate points of each conductor in each tower section, and the configuration data includes the voltage level of each tower section;

[0244] The model building module 602 is used to build a spatial model based on point cloud data and configuration data. The spatial model includes protection area data and spatial index data. The protection area data represents the spatial constraint boundary corresponding to each tower segment, and the spatial index data represents the spatial proximity relationship corresponding to each tower segment. The spatial proximity relationship represents the index structure that supports spatial proximity query.

[0245] The data response module 603 is used to respond in real time to the positioning data sent by the preset terminal of the working equipment, and to determine the spatial relationship information based on the positioning data and the spatial model; wherein, the spatial relationship information represents the nearest neighbor distance and orientation status between the current position of the working equipment and the target tower section;

[0246] The false entry alarm module 604 is used to execute the false entry alarm strategy if the spatial relationship information is determined to meet the preset alarm conditions.

[0247] In an optional example, model building module 602 is also used for:

[0248] For each tower section, determine the set of three-dimensional coordinate points of each conductor within the tower section, which is the point cloud sub-data;

[0249] Based on the voltage levels in the point cloud sub-data and configuration data, determine the spatial constraint boundaries corresponding to the tower sections;

[0250] Based on point cloud sub-data, determine the spatial proximity relationships corresponding to tower segments; where spatial constraint boundaries and spatial proximity relationships correspond to the same tower segment.

[0251] A spatial model is constructed based on the spatial constraint boundaries and spatial proximity relationships corresponding to each tower section.

[0252] In an optional example, model building module 602 is also used for:

[0253] Clustering and principal component analysis are performed on the point cloud sub-data to determine the endpoint dataset; the endpoint dataset includes the planar coordinates of each conductor endpoint.

[0254] Based on the endpoint dataset, determine the convex hull region; the convex hull region represents the smallest convex polygon that contains all conductor endpoints within the tower section;

[0255] Based on the convex hull region and the corresponding voltage level in the configuration data, determine the spatial constraint boundary corresponding to the tower section.

[0256] In an optional example, model building module 602 is also used for:

[0257] Based on a preset length correlation, the corresponding extension length for the voltage level is determined; wherein, the preset length correlation characterizes the correlation between the voltage level and the extension length;

[0258] Based on the outward expansion length, the convex hull region is expanded to obtain the spatial constraint boundary corresponding to the tower section.

[0259] In an optional example, model building module 602 is also used for:

[0260] Based on a preset segmentation dimension, the point cloud sub-data is segmented to obtain the median node; where the preset segmentation dimension represents one dimension in three-dimensional space, and the median node represents the three-dimensional coordinate point at the middle position on the segmentation dimension.

[0261] The point cloud sub-data is divided by the median node to obtain a set of coordinate points; the set of coordinate points includes two coordinate point sets, and each coordinate point set includes at least one three-dimensional coordinate point.

[0262] The first median node is determined as the root node. The set of coordinate points is recursively processed until the preset convergence condition is met, resulting in multiple internal nodes and multiple leaf nodes. The internal nodes include the median nodes generated during the recursive processing.

[0263] Using the root node as the top level, the internal nodes and leaf nodes are hierarchically associated to obtain the spatial proximity relationships of the tower sections.

[0264] In an optional example, the data response module 603 is also used for:

[0265] The positioning data is transformed to obtain transformed coordinates; these transformed coordinates are consistent with the coordinate system of the spatial model.

[0266] The target tower section is determined based on the transformed coordinates and configuration data;

[0267] The spatial constraint boundary corresponding to the target tower segment is determined from the protection area data, which is the target spatial constraint boundary; and the spatial proximity relationship corresponding to the target tower segment is determined from the spatial model, which is the target spatial proximity relationship.

[0268] Based on the transformed coordinates and the target space constraint boundary, the first relationship information is determined; wherein, the first relationship information represents the positional relationship between the transformed coordinates and the target space constraint boundary;

[0269] Based on the transformed coordinates and the target spatial proximity relationship, the second relationship information is determined; wherein, the second relationship information represents the nearest neighbor distance from the transformed coordinates to the conductor within the target tower section;

[0270] Determine the first and second relationship information, which constitute spatial relationship information.

[0271] In an optional example, the target spatial proximity relationship includes a root node and multiple leaf nodes; the data response module 603 is also specifically used for:

[0272] Using the transformed coordinates as the query point, starting from the root node, the target subtree is determined according to the splitting dimension corresponding to the root node; where the target subtree represents the set of nodes on the side where the query point is located;

[0273] Take the root node of the target subtree as the new root node, and determine the new target subtree based on the splitting dimension corresponding to the new root node;

[0274] The target subtree is determined recursively until the current node is a leaf node, and the initial candidate distance is determined; where the initial candidate distance represents the minimum spatial straight-line distance from the query point to each three-dimensional coordinate point in the leaf node;

[0275] The pruning decision is backtracked until the root node is reached to determine the pruning result; the pruning result represents the possibility that there is a closer point in the other subtree.

[0276] Based on the pruning results, the initial candidate distance is updated to obtain the target candidate distance, which is the second relation information.

[0277] In an optional example, the leaf node includes at least one three-dimensional coordinate point; the data response module 603 is also specifically used for:

[0278] For each leaf node, determine the spatial straight-line distance from each 3D coordinate point in the leaf node to the query point;

[0279] The minimum value among the straight-line distances in each space is determined as the initial candidate distance.

[0280] In an optional example, the false entry alarm module 604 is also used for:

[0281] If the transformation coordinates of the first relation information are located inside the spatial constraint boundary, then output the regional alarm information;

[0282] If the second relationship information is less than the preset minimum safe distance threshold, then output distance alarm information;

[0283] Based on regional alarm information and / or distance alarm information, execute the false entry alarm policy.

[0284] In an optional example, the false entry alarm module 604 is also used for:

[0285] Obtain the quality information of the positioning data; whereby the quality information represents the positioning accuracy level of the positioning data;

[0286] If the quality information does not meet the preset accuracy conditions, the false entry alarm strategy will be terminated; whereby the accuracy conditions represent that the positioning accuracy level has reached the preset effective positioning state.

[0287] The false entry alarm device for the transmission line area in this embodiment can execute the technical solution in the above method. Its specific implementation process and technical principle are the same, and will not be described again here.

[0288] Figure 7 A schematic diagram of the structure of the electronic device provided in this application, such as... Figure 7 As shown, the electronic device 70 provided in this embodiment includes at least one processor 701 and a memory 702. Optionally, the electronic device 70 further includes a communication component 703. The processor 701, memory 702, and communication component 703 are connected via a bus 704.

[0289] In a specific implementation, at least one processor 701 executes computer execution instructions stored in memory 702, causing at least one processor 701 to perform the above-described method.

[0290] The specific implementation process of processor 701 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0291] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0292] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0293] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0294] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0295] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0296] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as magnetic disks or optical disks.

[0297] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for alarming false incursions into a transmission line area, characterized in that, include: Acquire point cloud data and configuration data for the transmission line area; wherein, the point cloud data represents the set of three-dimensional coordinate points of each conductor within each tower section, and the configuration data includes the voltage level of each tower section; Based on the point cloud data and the configuration data, a spatial model is constructed; wherein, the spatial model includes protection area data and spatial index data, the protection area data represents the spatial constraint boundary corresponding to each tower segment, the spatial index data represents the spatial proximity relationship corresponding to each tower segment, and the spatial proximity relationship represents the index structure that supports spatial proximity query; The system responds in real time to positioning data sent from a preset terminal of the working equipment, and determines spatial relationship information based on the positioning data and the spatial model; wherein, the spatial relationship information represents the nearest neighbor distance and orientation status between the current position of the working equipment and the target tower section; If the spatial relationship information is determined to meet the preset alarm conditions, then the false entry alarm strategy is executed.

2. The method according to claim 1, characterized in that, The step of constructing a spatial model based on the point cloud data and the configuration data includes: For each tower section, the set of three-dimensional coordinate points of each conductor within the tower section is determined, which is the point cloud sub-data; Based on the voltage levels corresponding to the point cloud sub-data and the configuration data, the spatial constraint boundary corresponding to the tower section is determined; Based on the point cloud sub-data, the spatial proximity relationship corresponding to the tower segment is determined; wherein, the spatial constraint boundary and the spatial proximity relationship correspond to the same tower segment; The spatial model is constructed based on the spatial constraint boundaries and spatial proximity relationships corresponding to each tower segment.

3. The method according to claim 2, characterized in that, The step of determining the spatial constraint boundary corresponding to the tower section based on the voltage level corresponding to the point cloud sub-data and the configuration data includes: Clustering and principal component analysis are performed on the point cloud sub-data to determine the endpoint dataset; the endpoint dataset includes the planar coordinates of each conductor endpoint; Based on the endpoint dataset, the convex hull region is determined; the convex hull region represents the smallest convex polygon that contains all conductor endpoints within the tower section; Based on the convex hull region and the corresponding voltage level in the configuration data, the spatial constraint boundary corresponding to the tower section is determined.

4. The method according to claim 3, characterized in that, The step of determining the spatial constraint boundary corresponding to the tower section based on the convex hull region and the voltage level corresponding to the configuration data includes: Based on a preset length correlation, the expansion length corresponding to the voltage level is determined; wherein, the preset length correlation characterizes the correlation between the voltage level and the expansion length; Based on the expansion length, the convex hull region is expanded to obtain the spatial constraint boundary corresponding to the tower section.

5. The method according to claim 2, characterized in that, The step of determining the spatial proximity relationship corresponding to the tower segment based on the point cloud sub-data includes: Based on a preset segmentation dimension, the point cloud sub-data is segmented to obtain a median node; wherein, the preset segmentation dimension represents one dimension in three-dimensional space, and the median node represents the three-dimensional coordinate point at the middle position on the segmentation dimension. The point cloud sub-data is divided into two sets of coordinate points, with the median node as the boundary, to obtain a set of coordinate point sets. The set of coordinate point sets includes two coordinate point sets, and each coordinate point set includes at least one three-dimensional coordinate point. The first median node is determined as the root node, and the set of coordinate points is recursively processed until the preset convergence condition is met, resulting in multiple internal nodes and multiple leaf nodes; wherein, the internal nodes include each median node generated during the recursive processing. Using the root node as the top layer, the internal nodes and leaf nodes are hierarchically associated to obtain the spatial proximity relationship corresponding to the tower segment.

6. The method according to claim 1, characterized in that, The step of determining spatial relationship information based on the positioning data and the spatial model includes: The positioning data is transformed to obtain transformed coordinates; wherein the transformed coordinates are consistent with the coordinate system of the spatial model. The target tower section is determined based on the transformed coordinates and the configuration data; The spatial constraint boundary corresponding to the target tower segment is determined from the protected area data, which is the target spatial constraint boundary; and the spatial proximity relationship corresponding to the target tower segment is determined from the spatial model, which is the target spatial proximity relationship. Based on the transformed coordinates and the target space constraint boundary, first relationship information is determined; wherein, the first relationship information characterizes the positional relationship between the transformed coordinates and the target space constraint boundary; Based on the transformed coordinates and the target spatial proximity relationship, a second relationship information is determined; wherein, the second relationship information represents the nearest neighbor distance from the transformed coordinates to the conductor within the target tower section; The first relationship information and the second relationship information are determined to be the spatial relationship information.

7. The method according to claim 6, characterized in that, The target spatial proximity relationship includes a root node and multiple leaf nodes; the step of determining the second relationship information based on the transformed coordinates and the target spatial proximity relationship includes: Using the transformed coordinates as the query point, starting from the root node, the target subtree is determined according to the segmentation dimension corresponding to the root node; wherein, the target subtree represents the set of nodes on the side where the query point is located; The root node of the target subtree is taken as the new root node, and the new target subtree is determined according to the splitting dimension corresponding to the new root node. The determination of the target subtree is performed recursively until the current node is the leaf node, and the initial candidate distance is determined; wherein, the initial candidate distance represents the minimum spatial straight-line distance from the query point to each three-dimensional coordinate point in the leaf node; The pruning decision is backtracked until the root node is reached, and the pruning result is determined; wherein, the pruning result represents the possibility that there is a closer point in the other subtree; Based on the pruning results, the initial candidate distance is updated to obtain the target candidate distance, which is the second relationship information.

8. The method according to claim 7, characterized in that, The leaf node includes at least one three-dimensional coordinate point; The recursive execution of determining the target subtree continues until the current node becomes a leaf node, determining the initial candidate distance, including: For each leaf node, determine the spatial straight-line distance from each three-dimensional coordinate point in the leaf node to the query point; The minimum value among the aforementioned spatial straight-line distances is determined as the initial candidate distance.

9. The method according to claim 6, characterized in that, If the spatial relationship information is determined to meet the preset alarm conditions, then the false entry alarm strategy is executed, including: If the transformation coordinates represented by the first relation information are located inside the spatial constraint boundary, then output the regional alarm information; If the second relationship information is less than the preset minimum safe distance threshold, then a distance alarm message is output; The false entry alarm strategy is executed based on the area alarm information and / or the distance alarm information.

10. The method according to any one of claims 1-9, characterized in that, Before executing the false entry alarm strategy if the spatial relationship information is determined to meet the preset alarm conditions, the method further includes: Obtain the quality information of the positioning data; wherein, the quality information characterizes the positioning accuracy level of the positioning data; If the quality information does not meet the preset accuracy conditions, the false entry alarm strategy is terminated; wherein, the accuracy conditions represent that the positioning accuracy level has reached the preset effective positioning state.

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