Complex terrain multi-machine lifting power line repair safety path optimization method and system

Abstract

The invention provides a method and a system for optimizing a safety path of multi-machine lifting power repair in a complex terrain, and relates to the technical field of power emergency repair, the method comprises the following steps: step 1, obtaining static three-dimensional geographic information of a repair operation area and dynamic obstacle monitoring data updated in real time, and performing fusion processing to obtain comprehensive environment data; 2, through the comprehensive environment data, independent three-dimensional path searching is carried out on each hoisting task, and multiple initial feasible paths are obtained; according to the invention, continuous, safe and efficient development of electric power emergency repair hoisting operation under complex terrains is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of power emergency repair technology, and in particular to a method and system for optimizing the safe route of multi-machine hoisting power repair in complex terrain. Background Technology

[0002] In complex terrain areas such as mountains and canyons, power facilities are susceptible to failure due to natural disasters. Multi-machine hoisting has become a key means of emergency power repair in such scenarios due to its flexible and efficient material transportation advantages. However, the scenarios are characterized by drastic terrain undulations, dense surface obstacles, and frequent dynamic hazards, which pose challenges to path planning and multi-machine collaborative operations.

[0003] After a power transmission line in a complex terrain area was damaged by a disaster, the repair team used multiple aircraft to carry out the hoisting task. However, the existing path planning method failed to fully integrate static terrain features and real-time dynamic hazard factors, and the multi-aircraft coordination lacked a unified time and air scheduling mechanism. This led to situations where the path conflicted with sudden dangerous areas and there were safety hazards in multi-aircraft flight. This exposed the deficiencies of traditional technologies in terms of the comprehensiveness of environmental perception, the accuracy of multi-aircraft coordination, and dynamic adaptability, making it difficult to ensure the continuous safety and efficiency of multi-aircraft hoisting and repair operations in complex terrain. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for optimizing the safe route of multi-machine hoisting for power emergency repair in complex terrain, so as to ensure the continuous safety and efficiency of power emergency repair hoisting operations in complex terrain.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: Firstly, a method for optimizing the safe route for multi-machine hoisting power emergency repair in complex terrain, the method comprising: Step 1: Obtain static three-dimensional geographic information and real-time updated dynamic obstacle monitoring data of the emergency repair operation area, and perform fusion processing to obtain comprehensive environmental data; Step 2: Based on comprehensive environmental data, perform independent 3D path search for each hoisting task to obtain multiple initial feasible paths; Step 3: On the critical flight sections of multiple initial feasible trajectories, a collaborative monitoring framework is constructed with high-voltage transmission towers, power distribution substations and power communication base station towers as reference nodes. The collaborative monitoring framework is adaptively divided into structures. By performing topological analysis on the spatial attributes and distribution characteristics of the divided structures, the trajectory safety correction coefficient reflecting the geometric characteristics of the space safety channel is obtained. Step 4: Enhance the safety margin of the initial feasible trajectory based on the trajectory safety correction coefficient to obtain an optimized single-aircraft flight trajectory with a high safety level; Step 5: Based on the optimized single-aircraft flight trajectory with a high safety level, joint scheduling and conflict resolution are carried out through spatiotemporal safety rules to obtain a global collaborative flight plan in which all helicopters are free from conflicts in space and time. Step 6: When performing hoisting operations according to the global collaborative flight plan, continuously monitor changes in the flight environment through real-time sensing data, and trigger local adjustments to the affected flight path in an instant to ensure the continuous and safe execution of hoisting operations.

[0006] Furthermore, static three-dimensional geographic information of the repair operation area and real-time updated dynamic obstacle monitoring data are acquired and fused to obtain comprehensive environmental data, including: Acquire static 3D geographic information data and dynamic obstacle monitoring data stream of the emergency repair operation area; Data cleaning, coordinate system standardization, and resolution standardization are performed on static 3D geographic information data to obtain high-precision static digital elevation data and surface feature data. By analyzing and aligning the dynamic obstacle monitoring data stream with spatiotemporal stamps, dynamic hazard elements are identified and classified. These dynamic hazard elements include moving weather clusters and temporary air traffic control areas, resulting in dynamic hazard element data with spatiotemporal attributes. Based on high-precision static digital elevation data, surface feature data, and dynamic hazard element data with spatiotemporal attributes, these data are fused under the same spatiotemporal reference. Using a rasterization method, comprehensive environmental data is constructed that integrates static terrain elevation, surface obstacle attributes, the spatiotemporal range of dynamic hazard areas, and corresponding hazard level indicators.

[0007] Furthermore, by integrating environmental data, an independent 3D path search is performed for each hoisting task, resulting in multiple initial feasible paths, including: Based on comprehensive environmental data, environmental constraint parameters such as terrain elevation constraints, static obstacle distribution, and dynamic hazardous area spatiotemporal range are extracted to obtain the constraint condition set for path search. Obtain task information for all current hoisting tasks, including the coordinates of the starting work point, the coordinates of the target hoisting point, the load weight, and volume parameters for each task, and obtain the task queue to be planned. For each hoisting task in the queue of tasks to be planned, multiple candidate paths from the starting point to the target hoisting point are independently calculated using a path search algorithm, based on the set of constraints. Multiple candidate paths are verified for safety feasibility, and initial feasible paths that meet the constraints of terrain obstacle avoidance, dynamic hazard avoidance and aircraft performance are selected. A corresponding set of initial feasible paths is generated for each hoisting task.

[0008] Furthermore, on the critical flight sections of multiple initially feasible trajectories, a collaborative monitoring framework is constructed using high-voltage transmission towers, power distribution substations, and power communication base station towers as reference nodes. The collaborative monitoring framework undergoes adaptive structural partitioning. Through topological analysis of the spatial attributes and distribution characteristics of the partitioned structure, trajectory safety correction coefficients reflecting the geometric characteristics of the spatial safety channel are obtained, including: Based on the initial feasible path set, high-risk flight sections that are parallel to or intersect with high-voltage transmission lines in each path are identified and marked as critical flight sections. The precise spatial coordinates of high-voltage transmission towers, distribution substations and power communication base station towers in the critical flight sections are extracted to obtain a set of reference nodes. A three-dimensional spatial collaborative monitoring framework is constructed within the key flight zone by referencing a set of nodes. An adaptive structural partitioning of the collaborative monitoring framework was performed. Based on the terrain undulation, obstacle density, and power facility distribution characteristics, the monitoring framework was divided into multiple sub-structural units with different spatial scales. By processing each sub-structural unit after division, spatial skeleton features are extracted, and the connection relationship, branch density and spatial extensibility topological properties between skeleton nodes are analyzed to obtain the topological property analysis results. Based on the topology analysis results, the width, height, and connectivity index of the spatial safety passage for each substructure unit are calculated to obtain the trajectory safety correction coefficient that reflects the geometric characteristics of the spatial safety passage.

[0009] Furthermore, the initial feasible trajectory is enhanced with a safety margin based on the trajectory safety correction coefficient, resulting in an optimized single-aircraft flight trajectory with a high safety level, including: Based on the trajectory safety correction coefficient and combined with the spatial geometric features of the corresponding initial feasible trajectory, the safe offset distance and offset direction of each trajectory segment are calculated to obtain the trajectory safety enhancement parameter set; Based on the trajectory safety enhancement parameter set, three-dimensional spatial offset processing is performed on each waypoint of the initial feasible trajectory. While maintaining the overall continuity of the trajectory, the spatial distance from dangerous obstacles is increased to obtain the offset trajectory. The trajectory after offset processing is smoothed and optimized to eliminate abrupt trajectory changes caused by offset, ensuring trajectory curvature continuity and aircraft dynamics feasibility, and obtaining a preliminary enhanced trajectory. Based on the preliminary enhanced trajectory and combined with comprehensive environmental data, it is verified whether the safe distance between the trajectory and static terrain obstacles and dynamic hazards meets the preset safety threshold. Trajectory segments that do not meet the requirements are iteratively corrected to obtain an optimized single-aircraft flight trajectory with a higher safety level.

[0010] Furthermore, based on the optimized individual flight trajectories with a high safety level, joint scheduling and conflict resolution are performed through spatiotemporal safety rules to obtain a globally coordinated flight plan that ensures no conflicts between all helicopters in space and time, including: Based on the optimized single-aircraft flight trajectory, the spatiotemporal feature parameters of each trajectory are extracted, including flight altitude profile, velocity distribution curve, key time nodes and spatial coordinate sequence, to construct a multi-aircraft flight spatiotemporal feature database; Based on the multi-aircraft flight spatiotemporal feature database, multi-aircraft collaborative spatiotemporal safety rules are applied to detect spatiotemporal conflicts in all flight trajectories and identify flight trajectory pairs that have potential conflicts in terms of spatial distance and time window. For potential conflict trajectory pairs, a spatiotemporal priority scheduling strategy and trajectory fine-tuning algorithm are used to perform local time offset on the conflict trajectory to obtain the offset flight trajectory; The global consistency of the offset flight trajectory is verified, and it is checked whether the scheduled trajectory meets the aircraft performance constraints, mission timeliness requirements and safety interval standards. Trajectories that do not meet the requirements are iteratively optimized to obtain a global collaborative flight plan that includes the take-off time, flight path, operation sequence and landing arrangement of all helicopters.

[0011] Furthermore, when performing hoisting operations according to the global coordinated flight plan, changes in the flight environment are continuously monitored through real-time sensing data, and local adjustments to the affected flight path are triggered in real time to ensure the continuous and safe execution of hoisting operations, including: By receiving the global collaborative flight plan, it can acquire environmental perception data streams from airborne sensors and ground monitoring equipment in real time; Dynamically analyze the environmental perception data stream to identify hazardous factors that deviate from the preset environment of the global collaborative flight plan, and determine the affected helicopter flight trajectory and the affected temporal and spatial range. Based on the affected flight trajectories and their extent, and combined with the constructed comprehensive environmental data framework, the affected trajectory segments are locally replanned to obtain new trajectory segments that meet safety constraints. The new trajectory segment is subjected to safety verification, and the spatiotemporal compatibility of the new trajectory segment with the unaffected flight trajectory is checked, as well as the safe distance from static terrain obstacles and dynamic hazards, to obtain the adjusted trajectory. The adjusted trajectory is integrated with the original global collaborative flight plan to obtain an updated real-time collaborative flight plan, and flight commands are issued to relevant helicopters. At the same time, the adjustment effect is continuously monitored to form a closed-loop safety control mechanism.

[0012] Secondly, the multi-machine hoisting power emergency repair safety path optimization system for complex terrain includes: The acquisition module is used to acquire static three-dimensional geographic information of the emergency repair operation area and real-time updated dynamic obstacle monitoring data, and perform fusion processing to obtain comprehensive environmental data; The fusion module is used to perform independent 3D path search for each hoisting task by integrating environmental data, and obtain multiple initial feasible paths. The calculation module is used to construct a collaborative monitoring framework on the critical flight section of multiple initial feasible trajectories, with high-voltage transmission towers, power distribution substations and power communication base station towers as reference nodes. The collaborative monitoring framework is adaptively divided into structures, and the spatial attributes and distribution characteristics of the divided structure are analyzed by topology analysis to obtain the trajectory safety correction coefficient that reflects the geometric characteristics of the space safety channel. The correction module is used to enhance the safety margin of the initial feasible trajectory based on the trajectory safety correction coefficient, so as to obtain an optimized single-aircraft flight trajectory with a high safety level. The scheduling module is used to perform joint scheduling and conflict resolution based on optimized single-aircraft flight trajectories with high safety levels, through spatiotemporal safety rules, to obtain a global collaborative flight plan that is free from conflicts in both space and time for all helicopters; The processing module is used to continuously monitor changes in the flight environment through real-time sensing data when performing hoisting operations according to the global collaborative flight plan, and to trigger local adjustments to the affected flight path in an instant to ensure the continuous and safe execution of hoisting operations.

[0013] Thirdly, a computing device includes: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0014] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0015] The above-described solution of the present invention has at least the following beneficial effects: Because it employs a rasterized fusion technology of static 3D geographic information and dynamic obstacle monitoring data, a collaborative monitoring framework construction with power facilities as reference nodes and a topology analysis method for shape skeleton extraction, a conflict detection and trajectory fine-tuning algorithm under multi-aircraft collaborative spatiotemporal safety rules, and a dynamic adjustment mechanism of real-time perception, local replanning, and closed-loop verification, it overcomes the technical problems of incomplete environmental perception, insufficient safety margin in high-risk power areas, difficulty in resolving spatiotemporal conflicts among multiple aircraft, and poor adaptability to dynamic environments in traditional multi-aircraft hoisting power emergency repair path planning in complex terrain. As a result, it achieves the technical effects of providing a high-precision environmental base for all elements for path planning, improving the safety redundancy of single-aircraft trajectory, generating a global collaborative flight plan without spatiotemporal conflicts, and ensuring safe and efficient operation throughout the entire process. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the method for optimizing the safe path of multi-machine hoisting for power emergency repair in complex terrain, provided by an embodiment of the present invention. Figure 2 This is a schematic diagram of a multi-machine hoisting power emergency repair safety path optimization system for complex terrain provided by an embodiment of the present invention; Figure 3 This is a schematic diagram of a computing device. Detailed Implementation

[0017] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0018] like Figure 1 As shown, embodiments of the present invention propose a method for optimizing the safe route of multi-machine hoisting for emergency power repair in complex terrain. The method includes the following steps: Step 1: Obtain static three-dimensional geographic information and real-time updated dynamic obstacle monitoring data of the emergency repair operation area, and perform fusion processing to obtain comprehensive environmental data; Step 2: Based on comprehensive environmental data, perform independent 3D path search for each hoisting task to obtain multiple initial feasible paths; Step 3: On the critical flight sections of multiple initial feasible trajectories, a collaborative monitoring framework is constructed with high-voltage transmission towers, power distribution substations and power communication base station towers as reference nodes. The collaborative monitoring framework is adaptively divided into structures. By performing topological analysis on the spatial attributes and distribution characteristics of the divided structures, the trajectory safety correction coefficient reflecting the geometric characteristics of the space safety channel is obtained. Step 4: Enhance the safety margin of the initial feasible trajectory based on the trajectory safety correction coefficient to obtain an optimized single-aircraft flight trajectory with a high safety level; Step 5: Based on the optimized single-aircraft flight trajectory with a high safety level, joint scheduling and conflict resolution are carried out through spatiotemporal safety rules to obtain a global collaborative flight plan in which all helicopters are free from conflicts in space and time. Step 6: When performing hoisting operations according to the global collaborative flight plan, continuously monitor changes in the flight environment through real-time sensing data, and trigger local adjustments to the affected flight path in an instant to ensure the continuous and safe execution of hoisting operations.

[0019] In this embodiment of the invention, by employing the fusion processing technology of static three-dimensional geographic information and real-time dynamic obstacle monitoring data, the construction of a collaborative monitoring framework with power facilities as reference nodes and topology analysis method, the joint scheduling and conflict resolution strategy under the multi-aircraft collaborative spatiotemporal safety rules, and the real-time perception-driven path dynamic adjustment mechanism, the technical problems of fragmented environmental information, insufficient safety margin in critical flight sections, frequent spatiotemporal conflicts in multi-aircraft operations, and weak adaptability to dynamic environments in traditional multi-aircraft hoisting power emergency repair path planning in complex terrain are overcome. Thus, the technical effects of constructing a comprehensive environmental base with all elements, improving the safety level of single-aircraft trajectory, generating conflict-free global collaborative flight plans, and ensuring the safe and efficient conduct of hoisting operations throughout the entire process are achieved.

[0020] In a preferred embodiment of the present invention, step 1 above may include: Step 1.1: Obtain static three-dimensional geographic information data and dynamic obstacle monitoring data stream of the emergency repair operation area. Specifically, this includes: obtaining digital elevation data, surface cover distribution data, and spatial location data of power facilities in the emergency repair operation area through satellite remote sensing mapping technology to form static three-dimensional geographic information data; and collecting wind speed, wind direction, and cloud intensity data related to moving weather clusters, control time periods and range data of temporary air traffic control zones, and movement trajectory data of low-altitude floating objects through airborne weather radar, ground automatic weather stations, air traffic control agencies, and low-altitude UAV monitoring networks to form dynamic obstacle monitoring data stream.

[0021] Step 1.2 involves data cleaning, coordinate system standardization, and resolution standardization of the static 3D geographic information data to obtain high-precision static digital elevation data and surface feature data. Specifically, this includes: removing noise data, abnormal elevation values, and duplicated surface obstacle records from the static 3D geographic information data using statistical filtering; converting static data from different sources to the WGS84 geodetic coordinate system using a coordinate transformation algorithm; adjusting the resolution of all static data to a 10m × 10m raster resolution according to the accuracy requirements of complex terrain path planning; and finally generating high-precision static digital elevation data containing information such as terrain undulation, slope, and aspect, as well as surface feature data with precise coordinates of surface cover types such as dense forests, bare rocks, buildings, high-voltage transmission towers, substations, and power and communication base station towers.

[0022] Step 1.3 involves parsing and aligning the dynamic obstacle monitoring data stream with its spatiotemporal stamps to identify and classify dynamic hazard elements. These dynamic hazard elements include moving weather clusters and temporary air control zones, resulting in dynamic hazard element data with spatiotemporal attributes. Specifically, this includes parsing the formats of various data in the dynamic obstacle monitoring data stream, extracting key parameters such as the moving speed, impact range, and hazard level of moving weather clusters, the boundary coordinates, start and end times of temporary air control zones, and the size and direction of movement of low-altitude floating objects. A time synchronization algorithm is used to uniformly calibrate the timestamps of all dynamic data to Coordinated Universal Time (UTC). Hazard elements are classified according to their nature and impact range, and attribute labels such as spatial location coordinates and effective time intervals are added to each type of dynamic hazard element, forming dynamic hazard element data with complete spatiotemporal attributes.

[0023] Step 1.4: Based on high-precision static digital elevation data, surface feature data, and dynamic hazard element data with spatiotemporal attributes, these are fused under the same spatiotemporal reference. A rasterization method is used to construct comprehensive environmental data that integrates static terrain elevation, surface obstacle attributes, the spatiotemporal range of dynamic hazard areas, and corresponding hazard level identifiers. Specifically, this includes: using the WGS84 coordinate system and UTC time reference as a unified spatiotemporal reference; employing a data association algorithm to correlate and match high-precision static digital elevation data, surface feature data, and dynamic hazard element data; using a rasterization modeling method to divide the entire emergency repair operation area into uniform three-dimensional grids; each grid cell storing the corresponding static terrain elevation value, surface obstacle type and height parameters, and the shortest distance to surrounding power facilities; and simultaneously associating the type identifier, spatiotemporal coverage, and corresponding hazard level quantification value of dynamic hazard elements. Finally, comprehensive environmental data is constructed that fully reflects the static terrain conditions, surface obstacle distribution, and dynamic hazard element changes in the operation area.

[0024] In this embodiment of the invention, because preprocessing techniques such as cleaning, coordinate unification, and resolution standardization of static three-dimensional geographic information data are adopted, and processing methods such as parsing, spatiotemporal stamp alignment, and hazard element classification of dynamic obstacle monitoring data streams are used, and high-precision static data and dynamic hazard element data with spatiotemporal attributes are integrated through rasterization technology under the same spatiotemporal reference, the technical problems of incomplete environmental cognition caused by the separation of static and dynamic environmental information, insufficient data accuracy, and lack of clear spatiotemporal identification of hazard elements in traditional path planning are overcome, thereby achieving the construction of comprehensive environmental data containing all elements, including static terrain elevation, surface obstacle attributes, spatiotemporal range of dynamic hazard areas, and corresponding hazard level identification.

[0025] In a preferred embodiment of the present invention, step 2 above may include: Step 2.1: Based on comprehensive environmental data, extract environmental constraint parameters such as terrain elevation constraints, static obstacle distribution, and the spatiotemporal range of dynamic hazard areas to obtain a set of constraints for path search. Specifically, this includes: extracting the highest and lowest limits of terrain elevation from the comprehensive environmental data to clarify the elevation boundary of the aircraft flight; extracting the spatial distribution boundary, height dimensions, and type attributes of static obstacles; determining the fixed hazard areas that need to be avoided; extracting the start time, end time, and spatial coverage of dynamic hazard areas and associating them with the corresponding hazard levels; clarifying the dynamic hazard range that needs to be avoided at different times; and integrating additional constraints such as the safety protection distance requirements for power facilities and the minimum turning radius limit of the aircraft. Finally, a set of path search constraints is formed, including elevation constraints, static obstacle avoidance constraints, dynamic obstacle avoidance constraints, and performance constraints.

[0026] Step 2.2: Obtain task information for all current hoisting tasks, including the coordinates of the starting work point, the coordinates of the target hoisting point, and the load weight and volume parameters for each task, to obtain a queue of tasks to be planned. Specifically, this includes: receiving dispatch instructions for all hoisting tasks through the power emergency repair command channel; collecting the specific coordinates of the starting work point for each hoisting task (usually a temporary take-off and landing field or fixed parking apron near the repair area); collecting the specific coordinates of the target hoisting point (the location where materials and equipment needed for power fault repair are deployed); collecting the load weight parameters for each task to determine the total mass of materials the aircraft needs to carry; collecting the load volume parameters, including length, width, and height data, to determine the space occupied by the load; and sorting all task information according to task priority to form a queue of tasks to be planned that includes task identifiers, coordinate parameters, and load parameters.

[0027] Step 2.3: For each hoisting task in the task queue to be planned, based on the constraint set, multiple candidate paths from the starting work point to the target hoisting point are independently calculated using a path search algorithm. Specifically, this includes: allocating independent computing resources to each hoisting task in the task queue to avoid mutual interference during the planning process; using the coordinates of the task's starting work point as the path start point and the coordinates of the target hoisting point as the path end point; selecting the fast exploration random tree algorithm as the core path search algorithm; incorporating the elevation restrictions and obstacle avoidance requirements parameters in the path search constraint set into the algorithm's cost considerations; setting the algorithm's search step size and iteration count to ensure that the search range covers the potential feasible area from the starting point to the target point; and generating 5 to 8 candidate paths with different directions and flight durations through algorithm iteration. Each candidate path contains a complete three-dimensional coordinate sequence and corresponding flight time information.

[0028] Step 2.4 involves verifying the safety feasibility of multiple candidate paths, selecting initial feasible paths that meet the constraints of terrain obstacle avoidance, dynamic hazard avoidance, and aircraft performance, and generating a corresponding initial feasible path set for each lifting task. Specifically, this includes: verifying terrain obstacle avoidance for each candidate path by calculating the shortest spatial distance between all waypoints on the path and surrounding static terrain and surface obstacles, and determining whether the preset safety distance threshold is met; verifying dynamic hazard avoidance by checking whether the flight time of the path overlaps with the spatiotemporal range of the dynamic hazard area to ensure that the path does not enter a high-risk dynamic area; and verifying aircraft performance by combining the load weight and volume parameters to determine whether the climb gradient and flight distance of the path are within the allowable range of the aircraft's load endurance and maneuverability. All candidate paths that do not meet any verification requirements are eliminated, and the paths that pass all verifications are retained as initial feasible paths. An initial feasible path set containing 2 to 3 valid paths is then compiled for each lifting task.

[0029] In this embodiment of the invention, by employing multi-dimensional environmental constraint parameters such as terrain elevation, obstacle distribution, and dynamic hazard areas extracted from comprehensive environmental data, and combining task information such as the starting point, target point, and load characteristics of the hoisting task to construct a queue to be planned, and by using a path search algorithm to independently generate multiple candidate paths for each task, and then filtering them through safety feasibility verification based on terrain obstacle avoidance, dynamic hazard avoidance, and aircraft performance constraints, the technical problems of incomplete constraint consideration, lack of specificity of candidate paths, and low feasibility of initial paths due to insufficient integration of task characteristics and environmental limitations in traditional path search are overcome. Thus, the invention achieves the goal of generating an initial feasible path set for each hoisting task that meets actual operational requirements and satisfies multiple safety constraints.

[0030] In a preferred embodiment of the present invention, step 3 above may include: Step 3.1: Based on the initial feasible path set, identify high-risk flight sections that are parallel to or intersect with high-voltage transmission lines in each path and mark them as critical flight sections. Extract the precise spatial coordinates of high-voltage transmission towers, distribution substations, and power communication base station towers within the critical flight sections to obtain a reference node set. Specifically, this includes: comparing the three-dimensional coordinate sequence of each path in the initial feasible path set with the spatial orientation data of the high-voltage transmission lines, setting a distance threshold of 500 meters, identifying and marking segments in the path that are less than the threshold in distance from the high-voltage transmission lines and exhibit parallel extension or crossing states as high-risk flight sections, retrieving the spatial coordinate information of high-voltage transmission towers, distribution substations, and power communication base station towers within the critical flight sections from the surface feature data, cross-checking them with the on-site survey records of power facilities, eliminating data with excessive coordinate errors, and finally compiling a reference node set containing the precise three-dimensional coordinates of various power facilities.

[0031] Step 3.2 involves constructing a three-dimensional spatial collaborative monitoring framework within the critical flight zone using a set of reference nodes. This includes: determining the coverage area of ​​the three-dimensional spatial collaborative monitoring framework based on the precise coordinates of each node in the set of reference nodes and the spatial boundaries of the critical flight zone. The framework should cover a 500-meter area on each side of the critical flight zone laterally and a 200-meter area above and below the flight path altitude longitudinally. Using spatial point cloud modeling, the reference nodes are used as the core control points of the framework. Interpolation algorithms are used to complete the monitoring points between the control points, thus constructing a three-dimensional grid-like collaborative monitoring framework that comprehensively covers the critical flight zone and accurately correlates the location of power facilities.

[0032] Step 3.3 involves adaptively dividing the collaborative monitoring framework into sub-structural units with different spatial scales based on terrain undulation, obstacle density, and power facility distribution characteristics. Specifically, this includes: extracting terrain undulation data within the coverage area of ​​the collaborative monitoring framework; classifying the terrain into three levels—gentle, moderate, and severe undulation—based on the maximum elevation difference per kilometer; statistically analyzing the number and distribution density of static obstacles in each area within the framework, classifying them into three levels: low density, medium density, and high density; analyzing the spacing and arrangement patterns of power facilities such as high-voltage transmission towers; and using an adaptive grid partitioning algorithm based on the combination of terrain undulation level, obstacle density level, and power facility distribution characteristics to divide the collaborative monitoring framework into sub-structural units with different spatial scales. The unit scale for areas with gentle terrain and low obstacle density is set to 50m × 50m × 20m, while the unit scale for areas with severe terrain undulation or high obstacle density is adjusted to 20m × 20m × 10m.

[0033] Step 3.4 involves processing each substructure unit after division, extracting spatial skeleton features, analyzing the connection relationships, branch density, and spatial extensibility topological properties between skeleton nodes, and obtaining topological property analysis results. Specifically, this includes: extracting the spatial contour and skeletonizing each substructure unit, retaining the core lines reflecting the spatial form within the unit as spatial skeleton features, traversing the nodes on the skeleton lines, counting the number of connections between each node and other nodes and the length of the connection path, calculating the number of skeleton branches within a unit space to obtain the branch density, analyzing the extension direction, extension length, and whether there are interruptions in the skeleton lines in three-dimensional space, clarifying the spatial extensibility characteristics, and comprehensively organizing the skeleton node connection relationships, branch density, and spatial extensibility information to form the topological property analysis results for each substructure unit.

[0034] Step 3.5: Based on the topology attribute analysis results, calculate the width, height, and connectivity index of the spatial safety passage for each substructure unit to obtain the trajectory safety correction coefficient reflecting the geometric characteristics of the spatial safety passage. Specifically, this includes: measuring the maximum lateral distance that allows safe passage on both sides of the spatial skeleton within each substructure unit to obtain the width of the spatial safety passage; measuring the maximum longitudinal distance without obstructions above and below the skeleton to obtain the height of the spatial safety passage; calculating the connectivity index by statistically analyzing the proportion of continuous lengths of the safety passage without breaks within the unit; setting the weighting coefficients for the width, height, and connectivity index of the safety passage to 0.4, 0.3, and 0.3, respectively; calculating the comprehensive evaluation value of each substructure unit using a weighted summation method; mapping this evaluation value to a value between 0.8 and 2.0; and finally obtaining the trajectory safety correction coefficient reflecting the geometric characteristics of the spatial safety passage in each unit.

[0035] In this embodiment of the invention, by first identifying the critical flight sections related to high-voltage transmission lines in the initial path and extracting the precise coordinates of power facilities as reference nodes, then constructing a three-dimensional spatial collaborative monitoring framework and adaptively dividing sub-structural units according to the distribution characteristics of terrain, obstacles, and power facilities, and finally analyzing the topological attributes of the sub-structural units and calculating the width, height, and connectivity indicators of the spatial safety channel, the technical problem of insufficient analysis of the spatial safety characteristics of high-risk sections near power facilities and difficulty in accurately quantifying the geometric parameters of the safety channel in traditional path planning is overcome, which leads to a lack of targeted basis for subsequent trajectory safety optimization. Thus, the trajectory safety correction coefficient that accurately reflects the geometric characteristics of the spatial safety channel in the critical flight section is obtained.

[0036] In a preferred embodiment of the present invention, step 4 above may include: Step 4.1: Based on the trajectory safety correction coefficient and the spatial geometric features of the corresponding initial feasible trajectory, calculate the safe offset distance and offset direction for each trajectory segment to obtain the trajectory safety enhancement parameter set. Specifically, this includes: dividing the initial feasible trajectory into critical and non-critical flight sections, ensuring each trajectory segment corresponds one-to-one with the obtained sub-structural units; extracting the spatial geometric features of each trajectory segment, including trajectory direction, curvature changes, and relative orientation and distance relationships with dangerous obstacles such as high-voltage transmission towers and dense forests; combining the trajectory safety correction coefficient of the corresponding sub-structural unit (a larger correction coefficient value sets a larger basic safety offset value); determining the offset direction by analyzing the spatial positional relationship between the trajectory segment and dangerous obstacles, always pointing away from static obstacles and dynamic dangerous areas of high-voltage transmission lines; calculating the specific safe offset distance and precise offset direction for each trajectory segment; and organizing the offset parameters of all trajectory segments according to the trajectory order to form a trajectory safety enhancement parameter set containing trajectory segment identifiers, offset distances, and offset directions.

[0037] Step 4.2: Based on the trajectory safety enhancement parameter set, perform three-dimensional spatial offset processing on each waypoint of the initial feasible trajectory. While maintaining the overall continuity of the trajectory, increase the spatial distance from dangerous obstacles to obtain the offset-processed trajectory. Specifically, according to the requirements of the trajectory safety enhancement parameter set, adjust the three-dimensional spatial coordinates of each waypoint of the initial feasible trajectory one by one. For each waypoint, according to the offset parameters of its trajectory segment, offset the corresponding distance in the X-axis, Y-axis, and Z-axis directions. During the offset process, refer to the offset of adjacent waypoints and adopt a gradual adjustment method to avoid abrupt and large offsets of individual waypoints, ensuring the overall continuity and consistency of the trajectory after offset. At the same time, focus on increasing the spatial distance between waypoints and power facilities such as high-voltage transmission towers, power distribution substations, power communication base station towers, and surface obstacles such as dense forests and bare rocks, so that the trajectory is far away from the potential influence range of dynamic hazard elements, and finally obtain the complete offset-processed trajectory.

[0038] Step 4.3 involves smoothing and optimizing the offset trajectory to eliminate abrupt changes caused by the offset, ensuring the continuity of trajectory curvature and the feasibility of aircraft dynamics, and obtaining a preliminary enhanced trajectory. Specifically, this includes: traversing all waypoints of the offset trajectory, calculating the change in angle and spatial distance between adjacent waypoints to identify abrupt changes caused by local offsets, and smoothing the trajectory segments containing these abrupt changes using continuous curve fitting. By optimizing the three-dimensional coordinates of the abrupt changes and surrounding waypoints, the curvature of the trajectory gradually transitions, eliminating abrupt angle changes. Simultaneously, the feasibility of the smoothed trajectory segments is verified by combining the aircraft's dynamic performance parameters, including minimum turning radius, maximum climb rate, and maximum descent rate, to ensure that the curvature continuity of the trajectory meets the aircraft's maneuverability requirements and that there are no trajectory segments exceeding the aircraft's operational limits, ultimately forming a preliminary enhanced trajectory.

[0039] Step 4.4: Based on the preliminary enhanced trajectory and combined with comprehensive environmental data, verify whether the safe distance between the trajectory and static terrain obstacles and dynamic hazard elements meets the preset safety threshold. Iteratively correct trajectory segments that do not meet the requirements to obtain an optimized single-aircraft flight trajectory with a higher safety level. Specifically, this includes: combining static terrain elevation data, surface obstacle attribute data, and spatiotemporal range data of dynamic hazard elements from the comprehensive environmental data, calculating the shortest spatial distance between each waypoint on the preliminary enhanced trajectory and surrounding static obstacles, and verifying whether the flight time period of each trajectory segment overlaps with the spatiotemporal coverage of the dynamic hazard area. The preset static safety distance threshold is not less than 30 meters, and the dynamic safety requirement is that the trajectory flight time period does not overlap with the spatiotemporal range of the dynamic hazard area. For trajectory segments that do not meet the safety requirements, readjust the safety offset distance and offset direction of the trajectory segment, perform smoothing optimization again, and then re-verify the safety distance. Repeat this iterative correction until all waypoints on the trajectory meet the static and dynamic safety threshold requirements, and finally obtain an optimized single-aircraft flight trajectory with a higher safety level.

[0040] In this embodiment of the invention, by employing technical means such as calculating safety offset parameters based on trajectory safety correction coefficients and initial trajectory spatial geometric features, performing three-dimensional spatial offset on waypoints, smoothing and optimizing the offset trajectory to eliminate abrupt changes, and combining comprehensive environmental data to conduct safety distance verification and iteratively correcting substandard trajectory segments, the invention overcomes the technical problems of traditional single-aircraft trajectory optimization, such as lack of precise basis for safety margin improvement, easy occurrence of dynamic infeasibility problems after trajectory offset, and insufficient safety level due to lack of closed-loop verification of safety threshold. Thus, it achieves the goal of increasing the safety distance between the trajectory and dangerous obstacles while ensuring trajectory continuity and aircraft dynamic feasibility, thereby generating an optimized single-aircraft flight trajectory with a higher safety level.

[0041] In a preferred embodiment of the present invention, step 5 above may include: Step 5.1: Based on the optimized single-aircraft flight trajectory, extract the spatiotemporal characteristic parameters of each trajectory, including flight altitude profile, velocity distribution curve, key time nodes, and spatial coordinate sequence, and construct a multi-aircraft flight spatiotemporal characteristic database. Specifically, this includes: for each optimized single-aircraft flight trajectory, extracting flight altitude data for different time periods according to the flight time sequence to form a complete flight altitude profile including takeoff and climb, level flight, and descent and landing phases; determining the velocity range for each flight phase based on the payload weight, terrain undulation, and aircraft performance parameters corresponding to the trajectory; generating a velocity distribution curve reflecting the velocity change over time; identifying and recording key time nodes such as takeoff time, entry time into the critical flight zone, exit time from the critical flight zone, and arrival time at the target hoisting point; extracting the three-dimensional coordinates of all waypoints on the trajectory and arranging them according to the flight sequence to form a spatial coordinate sequence; and associating and storing the flight altitude profile, velocity distribution curve, key time nodes, and spatial coordinate sequence of each trajectory with the corresponding mission identifier and aircraft model parameters to construct a unified and complete multi-aircraft flight spatiotemporal characteristic database.

[0042] Step 5.2: Based on the multi-aircraft flight spatiotemporal feature database, apply multi-aircraft collaborative spatiotemporal safety rules to perform spatiotemporal conflict detection on all flight trajectories, identifying flight trajectory pairs with potential conflicts in spatial distance and time window. Specifically, this includes: setting multi-aircraft collaborative spatiotemporal safety rules, where the spatial safety rule is that the straight-line spatial distance between any two aircraft at the same time point is not less than 500 meters, and the time safety rule is that the time interval between two aircraft passing through the same spatial point is not less than 30 seconds. Based on the multi-aircraft flight spatiotemporal feature database, iterate through all pairs of flight trajectories to form trajectory pairs, compare the spatial coordinate sequences of each trajectory pair, calculate the spatial distance between the two aircraft within the same time window, and simultaneously check the time difference between the trajectory pairs passing through the same or adjacent spatial points. If a trajectory pair has a spatial distance of less than 500 meters and overlapping time windows, or a time interval between passing through adjacent points of less than 30 seconds, it is marked as a trajectory pair with potential conflicts. Finally, potential conflict trajectory pairs are compiled.

[0043] Step 5.3: For potential conflict trajectory pairs, a spatiotemporal priority scheduling strategy and trajectory fine-tuning algorithm are used to locally time-shift the conflict trajectories to obtain the shifted flight trajectories. Specifically, this includes: formulating a spatiotemporal priority scheduling strategy, with priority determination based on factors such as the urgency of the hoisting task, the weight of the payload, and the importance level of the fault location. Trajectories with high task urgency, large payload, and critical fault locations receive higher priority. High-priority trajectories retain their original flight plans. For low-priority conflict trajectories, the trajectory fine-tuning algorithm is used to locally adjust the flight plans corresponding to the conflict period. Time shifting is achieved by appropriately extending or shortening the flight time of the level flight segment and fine-tuning the speed during the climb or descent phases. The adjustment range is strictly controlled during the shift process to ensure that the main spatial path of the trajectory remains unchanged, only changing the values ​​of key time nodes to avoid new spatial conflicts caused by time shifting. Finally, the low-priority flight trajectory after time shifting is obtained.

[0044] Step 5.4 involves performing a global consistency verification on the offset flight trajectories. This check whether the scheduled trajectories meet aircraft performance constraints, mission timeliness requirements, and safety interval standards. Trajectories that do not meet the requirements are iteratively optimized to obtain a global coordinated flight plan that includes all helicopter takeoff times, flight paths, operation sequences, and landing arrangements. Specifically, this includes: conducting a global consistency verification on all offset flight trajectories; checking whether the climb angle, turning radius, and flight speed of each trajectory are within the allowable range of the corresponding aircraft's performance parameters; verifying whether the total flight time of each trajectory meets the timeliness requirements of the repair mission; and ensuring that all missions can be completed within the preset timeframe. Completed within the emergency repair window, the spatial distance and time interval between any two aircraft are re-verified to ensure they comply with the preset spatiotemporal safety rules. If a trajectory does not meet the aircraft performance constraints, the speed distribution curve of that trajectory is readjusted. If a trajectory exceeds the mission timeliness requirements, the flight speed of critical sections is optimized or the priority order is adjusted. If new spatiotemporal conflicts exist, the relevant trajectories are subjected to a second local time offset. This iterative optimization is repeated until all trajectories meet the performance constraints, timeliness requirements, and safety interval standards, ultimately forming a global collaborative flight plan that includes the takeoff time, complete flight path, operation execution sequence, and landing arrangements of all helicopters.

[0045] In this embodiment of the invention, by employing techniques such as extracting and optimizing the spatiotemporal feature parameters of a single-aircraft flight trajectory to construct a multi-aircraft flight spatiotemporal feature database, applying multi-aircraft collaborative spatiotemporal safety rules to conduct trajectory spatiotemporal conflict detection, implementing spatiotemporal priority scheduling and trajectory fine-tuning for potential conflict trajectories, and performing global consistency verification and iterative optimization on the scheduled trajectories, the invention overcomes the technical problems of disordered trajectory spatiotemporal feature management, inaccurate conflict detection, poor compatibility of conflict resolution schemes, and lack of global constraint verification in traditional multi-aircraft hoisting scheduling. These problems lead to spatiotemporal conflicts in multi-aircraft operations and scheduling plans that cannot balance aircraft performance and mission timeliness. As a result, the invention achieves the technical effect of accurately identifying and efficiently resolving potential spatiotemporal conflicts in multi-aircraft flight trajectories, generating a conflict-free global collaborative flight plan that includes the entire process operation sequence, and ensuring the orderly and efficient conduct of multi-aircraft hoisting operations.

[0046] In a preferred embodiment of the present invention, step 6 above may include: Step 6.1 involves receiving the global collaborative flight plan and acquiring environmental perception data streams from airborne sensors and ground monitoring equipment in real time. Specifically, this includes receiving the generated global collaborative flight plan and parsing the flight mission details of each helicopter within it; acquiring data such as wind speed and direction, real-time distance to surrounding obstacles, and current three-dimensional coordinates transmitted by the meteorological sensors, laser obstacle avoidance sensors, and GPS positioning equipment on each helicopter in real time via the airborne communication link; collecting monitoring data such as dynamic weather changes, temporary air traffic control adjustments, and secondary changes in terrain and landforms fed back by ground meteorological stations, power facility monitoring terminals, and low-altitude UAV patrol equipment; and organizing all multi-source environmental perception data from airborne and ground sources into a unified format to form a continuously updated environmental perception data stream.

[0047] Step 6.2 involves dynamically analyzing the environmental perception data stream to identify hazardous elements that deviate from the preset environment of the global collaborative flight plan, and determining the affected helicopter flight trajectories and the affected temporal and spatial ranges. Specifically, this includes: performing real-time dynamic analysis of the environmental perception data stream, comparing the real-time monitored dynamic hazardous element parameters with the preset environmental data of the global collaborative flight plan, identifying the types of hazardous elements with deviations, including moving meteorological masses exceeding the preset speed range, newly appearing low-altitude floating objects not included in the original plan, adjustments to the temporary air traffic control area or time period, and terrain changes caused by sudden small-scale landslides, etc. By overlaying and analyzing the spatiotemporal coverage of the deviated hazardous elements with the spatiotemporal coordinate sequence of each helicopter flight trajectory, determining the helicopter flight trajectories that will overlap with these hazardous elements in time and space, and clarifying the specific trajectory segments affected and their corresponding time intervals and spatial ranges.

[0048] Step 6.3: Based on the affected flight trajectory and range, and in conjunction with the constructed comprehensive environmental data framework, the affected trajectory segment is locally replanned to obtain a new trajectory segment that meets safety constraints. Specifically, this includes: retrieving the constructed comprehensive environmental data framework, incorporating the latest monitored deviation hazard data to update environmental constraints, using the start and end points of the affected trajectory segment as fixed nodes to maintain continuity with the unaffected trajectory segments before and after, using the previous path search algorithm and combining it with the aircraft performance parameters to recalculate the feasible path of the affected trajectory segment, focusing on avoiding newly added dynamic hazard areas and changed static terrain obstacles during the planning process, reasonably adjusting the three-dimensional coordinates of waypoints, and ensuring that the new trajectory segment meets the preset safety distance requirements and aircraft maneuverability limitations, ultimately obtaining a new trajectory segment that avoids the current hazard elements and has a smooth connection.

[0049] Step 6.4: Conduct safety verification of the new trajectory segment, checking its spatiotemporal compatibility with unaffected flight trajectories, as well as its safe distances from static terrain obstacles and dynamic hazards, to obtain the adjusted trajectory. Specifically, this includes: conducting multi-dimensional safety verification of the generated new trajectory segment. First, compare the spatiotemporal characteristic parameters of the new trajectory segment with those of other unaffected helicopter flight trajectories to check for potential conflicts arising from new spatial distances less than 500 meters or time intervals less than 30 seconds, verifying spatiotemporal compatibility. Then, calculate the shortest spatial distances between all waypoints on the new trajectory segment and surrounding static terrain, power facilities, and other obstacles. Verify whether the flight time period of the new trajectory segment overlaps with the spatiotemporal range of dynamic hazards, ensuring that the static safe distance is not less than 30 meters and there is no dynamic overlap. For compatibility issues or safety distance non-compliance discovered during the verification process, make minor adjustments to the new trajectory segment until all verification items meet the requirements, obtaining the adjusted complete flight trajectory.

[0050] Step 6.5 involves merging the adjusted trajectory with the original global collaborative flight plan to obtain an updated real-time collaborative flight plan. Flight instructions are then issued to the relevant helicopters, while the adjustment effect is continuously monitored to form a closed-loop safety control mechanism. Specifically, this includes: merging the adjusted complete flight trajectory with the original global collaborative flight plan, replacing the affected trajectory segments in the original plan, updating the helicopter takeoff time, flight path coordinate sequence, operation execution sequence, and landing arrangements, etc., to form an updated real-time collaborative flight plan; issuing the adjusted flight instructions to the relevant helicopter crews through an encrypted communication link and confirming receipt feedback; continuously receiving real-time data from airborne sensors and ground monitoring equipment; tracking the helicopter's execution of the new instructions; monitoring whether there are new changes in the work area environment; and immediately restarting the dynamic analysis, local replanning, and verification process if new hazards or trajectory execution deviations are found, thus forming a cyclical closed-loop safety control mechanism.

[0051] In this embodiment of the invention, by employing technical means such as real-time reception of environmental perception data streams from airborne and ground monitoring equipment, dynamic analysis of environmental deviations and determination of the affected trajectory range, local replanning of the affected trajectory segment, safety compatibility verification of the new trajectory segment, and fusion adjustment of the trajectory to form a closed-loop safety control mechanism, the invention overcomes the rigidity defects of traditional hoisting operation path planning, which involves one-time planning and unchanging throughout the entire process. This overcomes the technical problems of delayed response to sudden environmental changes during operations, lack of global compatibility verification for trajectory adjustments, and absence of closed-loop safety management. Consequently, the invention achieves real-time monitoring and rapid response to changes in the flight environment, ensuring that the adjusted trajectory has both safety constraint compliance and global spatiotemporal compatibility, forming a closed-loop safety control throughout the entire process, and guaranteeing the continuous and safe operation of hoisting operations in a dynamic environment.

[0052] like Figure 2 As shown, embodiments of the present invention also provide a multi-machine hoisting power emergency repair safety path optimization system for complex terrain, including: The acquisition module is used to acquire static three-dimensional geographic information of the emergency repair operation area and real-time updated dynamic obstacle monitoring data, and perform fusion processing to obtain comprehensive environmental data; The fusion module is used to perform independent 3D path search for each hoisting task by integrating environmental data, and obtain multiple initial feasible paths. The calculation module is used to construct a collaborative monitoring framework on the critical flight section of multiple initial feasible trajectories, with high-voltage transmission towers, power distribution substations and power communication base station towers as reference nodes. The collaborative monitoring framework is adaptively divided into structures, and the spatial attributes and distribution characteristics of the divided structure are analyzed by topology analysis to obtain the trajectory safety correction coefficient that reflects the geometric characteristics of the space safety channel. The correction module is used to enhance the safety margin of the initial feasible trajectory based on the trajectory safety correction coefficient, so as to obtain an optimized single-aircraft flight trajectory with a high safety level. The scheduling module is used to perform joint scheduling and conflict resolution based on optimized single-aircraft flight trajectories with high safety levels, through spatiotemporal safety rules, to obtain a global collaborative flight plan that is free from conflicts in both space and time for all helicopters; The processing module is used to continuously monitor changes in the flight environment through real-time sensing data when performing hoisting operations according to the global collaborative flight plan, and to trigger local adjustments to the affected flight path in an instant to ensure the continuous and safe execution of hoisting operations.

[0053] The optimization system according to embodiments of the present invention can correspond to performing the method described in the embodiments of the present invention, and the above and other operations and / or functions of each module of the optimization system are respectively for implementing Figure 1 The corresponding process of the method in the illustrated embodiment will not be described in detail here for the sake of brevity.

[0054] This application also provides a computing device. This computing device can utilize a server.

[0055] like Figure 3 As shown in the figure, this is a schematic diagram of a computing device provided in an embodiment of this application. The computing device 700 includes a bus 701, a processor 702, a communication interface 703, and a memory 704. The processor 702, the memory 704, and the communication interface 703 communicate with each other via the bus 701.

[0056] The 701 bus can be 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 representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0057] The processor 702 can be any one or more of the following processors: central processing unit (CPU), graphics processing unit (GPU), microprocessor (MP), or digital signal processor (DSP).

[0058] Communication interface 703 is used for external communication. Memory 704 may include volatile memory, such as random access memory (RAM). Memory 704 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid-state drive (SSD). Memory 704 stores executable code, which processor 702 executes to perform the aforementioned safe path optimization method for multi-machine hoisting power emergency repair in complex terrain.

[0059] Specifically, in implementing the embodiment of the multi-machine hoisting power emergency repair safety path optimization system for complex terrain described in the above embodiments, and where each module or unit of the multi-machine hoisting power emergency repair safety path optimization system for complex terrain described in the above embodiments is implemented by software, the software or program code required to execute the functions of each module / unit in the multi-machine hoisting power emergency repair safety path optimization system for complex terrain described in the above embodiments can be partially or entirely stored in the memory 704. The processor 702 executes the program code corresponding to each unit stored in the memory 704 to execute the aforementioned multi-machine hoisting power emergency repair safety path optimization method for complex terrain.

[0060] This application also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium that a computing device can store, or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive). The computer-readable storage medium includes instructions that instruct the computing device to execute the aforementioned method for optimizing the safe path for multi-machine hoisting power emergency repair in complex terrain.

[0061] This application also provides a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on a computing device, all or part of the processes or functions described in this application are generated.

[0062] The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, or data center to another website, computer, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0063] When the computer program product is executed by a computer, the computer executes any of the methods described in the aforementioned method for optimizing the safe path for multi-machine hoisting power repair in complex terrain. The computer program product can be a software installation package; when any of the methods described in the aforementioned method for optimizing the safe path for multi-machine hoisting power repair in complex terrain needs to be used, the computer program product can be downloaded and executed on the computer.

[0064] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing the safe route for multi-machine hoisting in complex terrain power emergency repairs, characterized in that: The method includes: Step 1: Obtain static three-dimensional geographic information and real-time updated dynamic obstacle monitoring data of the emergency repair operation area, and perform fusion processing to obtain comprehensive environmental data; Step 2: Based on comprehensive environmental data, perform independent 3D path search for each hoisting task to obtain multiple initial feasible paths; Step 3: On the critical flight sections of multiple initial feasible trajectories, a collaborative monitoring framework is constructed with high-voltage transmission towers, power distribution substations and power communication base station towers as reference nodes. The collaborative monitoring framework is adaptively divided into structures. By performing topological analysis on the spatial attributes and distribution characteristics of the divided structures, the trajectory safety correction coefficient reflecting the geometric characteristics of the space safety channel is obtained. Step 4: Enhance the safety margin of the initial feasible trajectory based on the trajectory safety correction coefficient to obtain an optimized single-aircraft flight trajectory with a high safety level; Step 5: Based on the optimized single-aircraft flight trajectory with a high safety level, joint scheduling and conflict resolution are carried out through spatiotemporal safety rules to obtain a global collaborative flight plan in which all helicopters are free from conflicts in space and time. Step 6: When performing hoisting operations according to the global collaborative flight plan, continuously monitor changes in the flight environment through real-time sensing data, and trigger local adjustments to the affected flight path in an instant to ensure the continuous and safe execution of hoisting operations.

2. The method for optimizing the safe route for multi-machine hoisting power emergency repair in complex terrain according to claim 1, characterized in that, The system acquires static 3D geographic information of the repair operation area and real-time updated dynamic obstacle monitoring data, and then fuses these data to obtain comprehensive environmental data, including: Acquire static 3D geographic information data and dynamic obstacle monitoring data stream of the emergency repair operation area; Data cleaning, coordinate system standardization, and resolution standardization are performed on static 3D geographic information data to obtain high-precision static digital elevation data and surface feature data. By analyzing and aligning the dynamic obstacle monitoring data stream with spatiotemporal stamps, dynamic hazard elements are identified and classified. These dynamic hazard elements include moving weather clusters and temporary air traffic control areas, resulting in dynamic hazard element data with spatiotemporal attributes. Based on high-precision static digital elevation data, surface feature data, and dynamic hazard element data with spatiotemporal attributes, these data are fused under the same spatiotemporal reference. Using a rasterization method, comprehensive environmental data is constructed that integrates static terrain elevation, surface obstacle attributes, the spatiotemporal range of dynamic hazard areas, and corresponding hazard level indicators.

3. The method for optimizing the safe route for multi-machine hoisting power emergency repair in complex terrain according to claim 2, characterized in that, By integrating environmental data, an independent 3D path search was performed for each hoisting task, resulting in multiple initial feasible paths, including: Based on comprehensive environmental data, environmental constraint parameters such as terrain elevation constraints, static obstacle distribution, and dynamic hazardous area spatiotemporal range are extracted to obtain the constraint condition set for path search. Obtain task information for all current hoisting tasks, including the coordinates of the starting work point, the coordinates of the target hoisting point, the load weight, and volume parameters for each task, and obtain the task queue to be planned. For each hoisting task in the queue of tasks to be planned, multiple candidate paths from the starting point to the target hoisting point are independently calculated using a path search algorithm, based on the set of constraints. Multiple candidate paths are verified for safety feasibility, and initial feasible paths that meet the constraints of terrain obstacle avoidance, dynamic hazard avoidance and aircraft performance are selected. A corresponding set of initial feasible paths is generated for each hoisting task.

4. The method for optimizing the safe route for multi-machine hoisting power emergency repair in complex terrain according to claim 3, characterized in that, Step 3 includes: Based on the initial feasible path set, high-risk flight sections that are parallel to or intersect with high-voltage transmission lines in each path are identified and marked as critical flight sections. The precise spatial coordinates of high-voltage transmission towers, distribution substations and power communication base station towers in the critical flight sections are extracted to obtain a set of reference nodes. A three-dimensional spatial collaborative monitoring framework is constructed within the key flight zone by referencing a set of nodes. An adaptive structural partitioning of the collaborative monitoring framework was performed. Based on the terrain undulation, obstacle density, and power facility distribution characteristics, the monitoring framework was divided into multiple sub-structural units with different spatial scales. By processing each sub-structural unit after division, spatial skeleton features are extracted, and the connection relationship, branch density and spatial extensibility topological properties between skeleton nodes are analyzed to obtain the topological property analysis results. Based on the topology analysis results, the width, height and connectivity index of the spatial safety passage of each substructure unit are calculated to obtain the trajectory safety correction coefficient that reflects the geometric characteristics of the spatial safety passage.

5. The method for optimizing the safe route for multi-machine hoisting power emergency repair in complex terrain according to claim 4, characterized in that, The initial feasible trajectory is enhanced with a safety margin based on the trajectory safety correction coefficient to obtain an optimized single-aircraft flight trajectory with a high safety level, including: Based on the trajectory safety correction coefficient and combined with the spatial geometric features of the corresponding initial feasible trajectory, the safe offset distance and offset direction of each trajectory segment are calculated to obtain the trajectory safety enhancement parameter set; Based on the trajectory safety enhancement parameter set, three-dimensional spatial offset processing is performed on each waypoint of the initial feasible trajectory. While maintaining the overall continuity of the trajectory, the spatial distance from dangerous obstacles is increased to obtain the offset trajectory. The trajectory after offset processing is smoothed and optimized to eliminate abrupt trajectory changes caused by offset, ensuring trajectory curvature continuity and aircraft dynamics feasibility, and obtaining a preliminary enhanced trajectory. Based on the preliminary enhanced trajectory and combined with comprehensive environmental data, it is verified whether the safe distance between the trajectory and static terrain obstacles and dynamic hazards meets the preset safety threshold. Trajectory segments that do not meet the requirements are iteratively corrected to obtain an optimized single-aircraft flight trajectory with a higher safety level.

6. The method for optimizing the safe route for multi-machine hoisting power emergency repair in complex terrain according to claim 5, characterized in that, Based on optimized individual helicopter flight trajectories with high safety levels, joint scheduling and conflict resolution are performed through spatiotemporal safety rules to obtain a globally coordinated flight plan that ensures no conflicts between all helicopters in space and time, including: Based on the optimized single-aircraft flight trajectory, the spatiotemporal feature parameters of each trajectory are extracted, including flight altitude profile, velocity distribution curve, key time nodes and spatial coordinate sequence, to construct a multi-aircraft flight spatiotemporal feature database; Based on the multi-aircraft flight spatiotemporal feature database, multi-aircraft collaborative spatiotemporal safety rules are applied to detect spatiotemporal conflicts in all flight trajectories and identify flight trajectory pairs that have potential conflicts in terms of spatial distance and time window. For potential conflict trajectory pairs, a spatiotemporal priority scheduling strategy and trajectory fine-tuning algorithm are used to perform local time offset on the conflict trajectory to obtain the offset flight trajectory; The global consistency of the offset flight trajectory is verified, and it is checked whether the scheduled trajectory meets the aircraft performance constraints, mission timeliness requirements and safety interval standards. Trajectories that do not meet the requirements are iteratively optimized to obtain a global collaborative flight plan that includes the take-off time, flight path, operation sequence and landing arrangement of all helicopters.

7. The method for optimizing the safe route for multi-machine hoisting power emergency repair in complex terrain according to claim 6, characterized in that, When performing hoisting operations according to the global coordinated flight plan, changes in the flight environment are continuously monitored through real-time sensing data, and local adjustments to the affected flight path are triggered in real time to ensure the continuous and safe conduct of the hoisting operation, including: By receiving the global collaborative flight plan, it can acquire environmental perception data streams from airborne sensors and ground monitoring equipment in real time; Dynamically analyze the environmental perception data stream to identify hazardous factors that deviate from the preset environment of the global collaborative flight plan, and determine the affected helicopter flight trajectory and the affected temporal and spatial range. Based on the affected flight trajectories and their extent, and combined with the constructed comprehensive environmental data framework, the affected trajectory segments are locally replanned to obtain new trajectory segments that meet safety constraints. The new trajectory segment is subjected to safety verification, and the spatiotemporal compatibility of the new trajectory segment with the unaffected flight trajectory is checked, as well as the safe distance from static terrain obstacles and dynamic hazards, to obtain the adjusted trajectory. The adjusted trajectory is integrated with the original global collaborative flight plan to obtain an updated real-time collaborative flight plan, and flight commands are issued to relevant helicopters. At the same time, the adjustment effect is continuously monitored to form a closed-loop safety control mechanism.

8. A multi-machine hoisting safety path optimization system for emergency power repair in complex terrain, wherein the system implements the method as described in any one of claims 1 to 7, characterized in that, include: The acquisition module is used to acquire static three-dimensional geographic information of the emergency repair operation area and real-time updated dynamic obstacle monitoring data, and perform fusion processing to obtain comprehensive environmental data; The fusion module is used to perform independent 3D path search for each hoisting task by integrating environmental data, and obtain multiple initial feasible paths. The calculation module is used to construct a collaborative monitoring framework on the critical flight section of multiple initial feasible trajectories, with high-voltage transmission towers, power distribution substations and power communication base station towers as reference nodes. The collaborative monitoring framework is adaptively divided into structures, and the spatial attributes and distribution characteristics of the divided structure are analyzed by topology analysis to obtain the trajectory safety correction coefficient that reflects the geometric characteristics of the space safety channel. The correction module is used to enhance the safety margin of the initial feasible trajectory based on the trajectory safety correction coefficient, so as to obtain an optimized single-aircraft flight trajectory with a high safety level. The scheduling module is used to perform joint scheduling and conflict resolution based on optimized single-aircraft flight trajectories with high safety levels, through spatiotemporal safety rules, to obtain a global collaborative flight plan that is free from conflicts in both space and time for all helicopters; The processing module is used to continuously monitor changes in the flight environment through real-time sensing data when performing hoisting operations according to the global collaborative flight plan, and to trigger local adjustments to the affected flight path in an instant to ensure the continuous and safe execution of hoisting operations.

9. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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