Wind power plant topographic surveying and mapping method and system based on unmanned aerial vehicle

Abstract

The invention provides a wind power plant topographic surveying and mapping method and system based on an unmanned aerial vehicle, and relates to the technical field of topographic surveying and mapping. Comprising the following steps: according to topographic characteristics of a wind power plant, adopting a multi-objective optimization model to respectively generate preliminary surveying and mapping routes for a plurality of unmanned aerial vehicles; the multi-objective optimization model takes coverage rate maximization as an optimization objective, and takes cross minimization and path length optimal solution among a plurality of unmanned aerial vehicle routes as constraint conditions; according to the real-time meteorological data, performing dynamic optimization on the preliminary surveying and mapping routes, and generating dynamically optimized surveying and mapping routes for the multiple unmanned aerial vehicles; the multiple unmanned aerial vehicles fly along the dynamically optimized surveying and mapping route, and surveying and mapping data are acquired through the data acquisition equipment. According to the wind power plant terrain complexity and the real-time meteorological data, the surveying and mapping route with the maximum coverage rate is generated for each unmanned aerial vehicle, and blind areas are eliminated through real-time supplementary measurement, so that the completeness of terrain data in the surveying and mapping area is ensured, and the missing of key terrain information is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of terrain surveying and mapping, and in particular to a method and system for terrain surveying and mapping a wind farm based on an unmanned aerial vehicle. Background Art

[0002] Topographic surveying of wind farms is a fundamental part of wind farm site selection and planning. The integrity and accuracy of the surveying data directly affect wind turbine layout, construction design and operating efficiency.

[0003] Among the related technologies, when drones are actually applied to wind farm terrain mapping, there are limitations in complex terrain conditions, including: First, the conventional surveying and mapping methods in related technologies are limited by the complexity of the terrain. In steep hills and areas with dense obstacles, blind spots are prone to appear, resulting in the omission of key terrain information. The problem of incomplete data coverage will affect the accuracy of three-dimensional terrain data mapping of wind farms, increasing the complexity of subsequent design and construction. Secondly, in the further application of drone technology, multiple drones will be used to work together to survey the terrain of wind farms. However, when multiple drones fly at the same time, their route planning and real-time adjustments often affect the surveying efficiency due to path conflicts and route intersections, and may even lead to duplicate or missed data collection.

[0004] Therefore, there is an urgent need to propose a wind farm terrain mapping method and system based on UAV. Summary of the invention

[0005] The present application provides a wind farm terrain mapping method and system based on drones, aiming to solve the technical problem that drone mapping in related technologies is limited by the complexity of the terrain, and further solve the path interference problem existing in collaborative mapping of multiple drones.

[0006] In order to achieve the above-mentioned purpose, the present application provides a wind farm terrain mapping method based on a drone, comprising the following steps: According to the terrain characteristics of the wind farm, a multi-objective optimization model is used to generate preliminary mapping routes for multiple UAVs. The multi-objective optimization model takes maximizing coverage as the optimization goal, and minimizing the intersection between multiple UAV routes and optimizing the path length as constraints.

[0007] The preliminary surveying and mapping routes are dynamically optimized according to the real-time meteorological data to generate dynamically optimized surveying and mapping routes for multiple UAVs.

[0008] Multiple UAVs fly along the dynamically optimized surveying and mapping routes and obtain surveying and mapping data through data acquisition equipment.

[0009] The surveying and mapping data obtained by multiple UAVs are processed separately, and a preliminary three-dimensional terrain model of the wind farm is generated using a multi-source data fusion algorithm based on the processed surveying and mapping data.

[0010] Monitor obstacles encountered by multiple drones during flight in real time, make judgments based on the size of the obstacles, the speed of the drones, and the flight environment conditions, and adjust the routes of each drone based on the judgment results to ensure that there are no path conflicts between drones and complete the mapping mission.

[0011] Multiple UAVs fly along their respective adjusted routes to conduct supplementary surveys on the uncovered areas in the preliminary three-dimensional terrain model of the wind farm, and improve the preliminary three-dimensional terrain model of the wind farm based on the surveying and mapping data obtained from the supplementary surveys.

[0012] The consistency correction and spatial weighted average method are used to integrate and optimize the surveying and mapping data collected by multiple drones. Based on the integration and optimization, duplicate regional data are eliminated and the surveying and mapping accuracy is improved to generate a complete three-dimensional terrain model of the wind farm.

[0013] As a preferred solution of the present invention, a multi-objective optimization model is used to generate preliminary mapping routes for multiple drones according to the terrain characteristics of the wind farm; the multi-objective optimization model takes maximizing coverage as the optimization goal, and takes minimizing the intersection between multiple drone routes and the optimal solution of path length as constraints, specifically including: Conduct data analysis on the terrain characteristics of the wind farm, and determine the distribution range of the survey area and the terrain complexity of each area based on the data analysis.

[0014] According to the distribution range and terrain complexity of the surveying area, the surveying task is decomposed into multiple sub-areas, and a corresponding drone is assigned to each sub-area.

[0015] In the multi-objective optimization model, the objective function is used to optimize the coverage rate of the sub-areas, the length of the drone routes, and the intersection of the routes. Specifically:

[0016] In the formula, represents the maximum optimization target value, represents the optimization target value, Indicates The coverage of the sub-areas, Indicates The length of the drone’s flight path, Indicates There are cross-route conflicts. The weight coefficient representing the coverage rate is determined by the complexity of the terrain and the importance of the survey area; The weight coefficients representing the route length and intersection conflict are determined according to the time requirement of the surveying and mapping task, resource consumption, and the impact of route conflicts between multiple UAVs.

[0017] During the optimization process, the following constraints are met: The first constraint is that only one drone is assigned to each sub-area, specifically:

[0018] In the formula, Represents the allocation matrix, used to represent sub-regions Whether it is by drone implement; The second constraint is that the total length of the route is less than the preset upper limit, specifically:

[0019] In the formula, is the allocation matrix used to represent the sub-region Whether it is by drone implement; Represents the upper limit of the route length; the optimization objective function is centered on maximizing coverage, combining the two secondary goals of optimizing route length and minimizing route intersections, and realizing task allocation and path planning through constraints.

[0020] Based on the optimization results, a preliminary mapping route is generated for each UAV to ensure that the route meets coverage requirements while reducing route conflicts between multiple UAVs, providing a basis for subsequent dynamic optimization.

[0021] As a preferred solution of the present invention, data analysis is performed on the terrain characteristics of the wind farm, and the distribution range of the surveying area and the terrain complexity of each area are determined based on the data analysis, specifically including: Terrain complexity is used to quantify the complexity of the survey area and affects the path priority in route planning. The calculation formula for terrain complexity is:

[0022] In the formula, Represents the terrain complexity index; Indicates the area of ​​a region; Indicates the height of adjacent sampling points; Indicates the horizontal distance between adjacent sampling points; by calculating the cumulative change in elevation and horizontal distance within the survey area, the terrain undulation and obstacle distribution are quantified. The larger the value, the more complex the terrain.

[0023] As a preferred solution of the present invention, the preliminary surveying and mapping routes are dynamically optimized according to the real-time meteorological data to generate dynamically optimized surveying and mapping routes for multiple drones, specifically including: Get real-time weather data, including real-time wind speed, wind direction, temperature and air pressure.

[0024] The real-time meteorological data is associated with the preliminary mapped routes, and the adaptability score of each flight segment is calculated using the meteorological impact weight.

[0025] The preliminary mapping route is adjusted based on the adaptability score to generate a dynamically optimized mapping route.

[0026] The route optimization objectives of dynamic optimization are path stability and coverage, and the optimization objectives are described by the following formula:

[0027] In the formula, represents the maximum dynamic optimization target value, represents the dynamic optimization target value, Indicates The suitability score of the flight segments, For the Corrected deviation of a flight segment, used to indicate the impact of real-time weather data on the flight segment; The weight coefficient representing the meteorological impact is determined by analyzing the relationship between real-time meteorological data and flight segment stability using a statistical regression model. The numerical value of .

[0028] Adjust the flight paths of multiple drones based on dynamically optimized mapping routes.

[0029] As a preferred solution of the present invention, multiple drones fly along the dynamically optimized surveying and mapping routes, and obtain surveying and mapping data respectively through data acquisition equipment, specifically including: Data collection equipment includes lidar, optical cameras and infrared sensors, each used to obtain different types of surveying and mapping data.

[0030] LiDAR collects terrain point cloud data and records the three-dimensional coordinate information of the surveying area.

[0031] Optical cameras acquire surface texture data and capture detailed features of the surface.

[0032] Infrared sensors collect thermal signature data and analyze surface temperature distribution and coverage characteristics through thermal imaging.

[0033] The surveying and mapping data includes terrain point cloud data, surface texture data and thermal characteristic data. The surveying and mapping data is used as the data benchmark and provided to subsequent data processing and three-dimensional terrain model generation.

[0034] As a preferred solution of the present invention, the surveying and mapping data acquired by multiple drones are processed respectively, and a preliminary three-dimensional terrain model of the wind farm is generated by using a multi-source data fusion algorithm based on the surveying and mapping data after data processing, specifically including: Data processing includes denoising, image enhancement, feature extraction and temperature difference analysis.

[0035] The terrain point cloud data collected by the lidar is denoised to remove abnormal point clouds and improve spatial accuracy through consistency correction.

[0036] Perform image enhancement processing on the surface texture data collected by the optical camera, including brightness adjustment, edge sharpening and noise suppression.

[0037] Feature extraction and temperature difference analysis are performed on the thermal feature data collected by infrared sensors to identify the thermal distribution characteristics and coverage properties of the surface.

[0038] Combining the three-dimensional spatial information of point cloud data, the surface detail features of texture data and the coverage attributes of thermal feature data, the weighted fusion algorithm in the multi-source data fusion algorithm is used to generate a preliminary three-dimensional terrain model of the wind farm. The formula of the weighted fusion algorithm is:

[0039] In the formula, represents the eigenvalue of the fused terrain model, Represents the spatial information of point cloud data, Represents the detail features of texture data, represents the coverage attributes of thermal signature data, Represents the fusion weight coefficient, which is used to adjust the influence of different data types in the final model. Based on the quality of point cloud data, Determined by the clarity of texture data and image quality, Determined based on the importance of thermal feature data to the scene supplement.

[0040] As a preferred solution of the present invention, obstacles encountered by multiple drones during flight are monitored in real time, and judgments are made based on the size of the obstacles, the speed of the drones, and the flight environment conditions. The routes of each drone are adjusted according to the judgment results to ensure that there is no path conflict between the drones and the mapping task is completed. Specifically, it includes: Obstacles within the route range are monitored in real time through lidar and visual sensors, and the size, location and dynamic information of the obstacles are collected.

[0041] Based on the size of the obstacle and the flight speed of the drone, the minimum safe distance between the obstacle and the drone is calculated using the following formula:

[0042] In the formula, Indicates the minimum safe distance between the drone and obstacles. Indicates the flight speed of the drone. The reaction time of the drone is used to indicate the time required from detecting an obstacle to starting to adjust the route; Indicates the safe avoidance distance of an obstacle, and its value depends on the type and size of the obstacle.

[0043] When the actual distance between the obstacle and the drone is less than the minimum safety distance, the route adjustment is triggered.

[0044] According to the dynamic information of obstacles and the flight environment conditions of the drone, the adjusted route is calculated in combination with the dynamic obstacle avoidance algorithm. The objective function of the adjusted route is to optimize the path length and not affect the coverage rate. The adjusted path is based on the following constraints: The third constraint condition is that the angle between the flight direction of the drone and the obstacle avoidance direction is not less than the preset safety angle.

[0045] The fourth constraint is that the adjusted route does not cross or conflict with other UAV routes.

[0046] Output the adjusted route and update the flight path of the UAV in real time, so that the UAV can continue to perform the surveying and mapping task according to the adjusted path.

[0047] As a preferred solution of the present invention, multiple drones fly along their respective adjusted routes to conduct supplementary surveys on uncovered areas in the preliminary three-dimensional terrain model of the wind farm, and improve the preliminary three-dimensional terrain model of the wind farm based on the surveying and mapping data obtained from the supplementary survey, specifically including: By analyzing the preliminary three-dimensional terrain model of the wind farm, the distribution of uncovered areas and missing data points is identified.

[0048] Based on the location and scope of the uncovered area, a re-survey route is generated and the re-survey task is assigned to the corresponding drone.

[0049] Multiple drones flew along the supplementary survey route to collect terrain point cloud data, surface texture data and thermal feature data of the uncovered areas.

[0050] The data processing in the above steps is performed on the surveying and mapping data obtained from the supplementary survey, and the processed surveying and mapping data are fused with the preliminary three-dimensional terrain model of the wind farm. The spatial weighted average method is used to improve the data of the uncovered area. The fusion formula is:

[0051] In the formula, represents the fusion model feature value, represents the eigenvalues ​​of the corresponding points in the supplementary data and the preliminary model, is the weight coefficient, which is determined according to the confidence of the supplementary test data and the preliminary model data; Indicates the number of points involved in the supplementary test data and the preliminary model.

[0052] As a preferred solution of the present invention, the mapping data collected by multiple drones are integrated and optimized by using consistency correction and spatial weighted average method. Based on the integration and optimization, duplicate regional data are eliminated and the mapping accuracy is improved to generate a complete three-dimensional terrain model of the wind farm, which specifically includes: By analyzing mapping data collected by multiple drones, duplicate and missing areas in the data can be identified.

[0053] The data in the repeated area are corrected for consistency, and the spatial position of the overlapping points is adjusted by calculating the mean square error between adjacent data points.

[0054] The data in the missing areas are completed, and the data points from different sources are fused by the spatial weighted average method.

[0055] The optimized terrain data is checked for accuracy, and the spatial coordinate consistency and detail resolution of the data are analyzed; the accuracy of the three-dimensional terrain model of the wind farm is improved by adjusting the error distribution and correcting abnormal points.

[0056] Output the integrated and optimized three-dimensional terrain model of the wind farm, provide model data including complete spatial coordinates and detailed features, and provide data support for the subsequent design and planning of the wind farm.

[0057] The present application also provides a wind farm terrain mapping system based on a drone, comprising the following units: The route generation unit is used to generate preliminary mapping routes for multiple UAVs based on the terrain characteristics of the wind farm using a multi-objective optimization model; the multi-objective optimization model takes maximizing coverage as the optimization goal, and takes minimizing the intersection between multiple UAV routes and the optimal solution of path length as constraints.

[0058] The route optimization unit is used to dynamically optimize the preliminary mapping routes according to the real-time meteorological data and generate dynamically optimized mapping routes for multiple UAVs.

[0059] The data acquisition unit is used for multiple UAVs to fly along the dynamically optimized mapping routes and obtain the mapping data through data acquisition equipment.

[0060] The fusion modeling unit is used to process the surveying and mapping data obtained by multiple UAVs respectively, and generate a preliminary three-dimensional terrain model of the wind farm by adopting a multi-source data fusion algorithm based on the surveying and mapping data after data processing.

[0061] The dynamic avoidance unit is used to monitor in real time the obstacles encountered by multiple drones during flight, make judgments based on the size of the obstacles, the speed of the drones, and the flight environment conditions, and adjust the routes of each drone based on the judgment results to ensure that there is no path conflict between the drones and complete the surveying and mapping tasks.

[0062] The model re-survey unit is used for multiple UAVs to fly along their respective adjusted routes to re-survey the uncovered areas in the preliminary three-dimensional terrain model of the wind farm, and to improve the preliminary three-dimensional terrain model of the wind farm based on the surveying and mapping data obtained from the re-survey.

[0063] The integration and optimization unit is used to integrate and optimize the surveying and mapping data collected by multiple drones using consistency correction and spatial weighted average method. Based on the integration and optimization, duplicate regional data is eliminated and the surveying and mapping accuracy is improved to generate a complete three-dimensional terrain model of the wind farm.

[0064] The beneficial effects of this application are: 1. Through the multi-objective optimization model, according to the complexity of the wind farm terrain and real-time meteorological data, a mapping route with maximum coverage is generated for each drone, and blind spots are eliminated through real-time supplementary surveys to ensure the integrity of the terrain data in the mapping area and reduce the omission of key terrain information.

[0065] 2. Dynamic obstacle avoidance and real-time route adjustment technology are used to effectively avoid route crossing conflicts and path interference problems when UAVs work together, thereby improving the coordination of multi-UAV operations and surveying and mapping efficiency.

[0066] 3. Combine multi-source data fusion algorithm and consistency correction method to integrate and optimize surveying and mapping data to generate a three-dimensional terrain model of the wind farm with high spatial accuracy and detail integrity, providing reliable data support for subsequent wind farm planning and construction.

[0067] 4. By combining real-time meteorological data to adjust the surveying route and flight strategy, the present invention is suitable for complex terrain and changeable meteorological environment in wind farms, and enhances the adaptability and stability of terrain surveying.

[0068] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0070] Figure 1 A flow chart of a wind farm terrain mapping method based on a drone is provided in an embodiment of the present application.

[0071] Figure 2 An overall architecture diagram of a wind farm topographic mapping system based on a drone provided in an embodiment of the present application.

[0072] Attached figure numbers: 81, route generation unit; 82, route optimization unit; 83, data acquisition unit; 84, fusion modeling unit; 85, dynamic avoidance unit; 86, model supplementary measurement unit; 87, integrated optimization unit. DETAILED DESCRIPTION

[0073] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0074] See also Figure 1 , Figure 1 A flowchart of a wind farm terrain mapping method based on a drone is provided for an embodiment of the present application.

[0075] In this embodiment, a wind farm terrain mapping method based on a drone includes step S100, step S200, step S300, step S400, step S500, step S600 and step S700.

[0076] Step S100: According to the terrain characteristics of the wind farm, a multi-objective optimization model is used to generate preliminary mapping routes for multiple drones respectively; the multi-objective optimization model takes maximizing coverage as the optimization goal, and takes minimizing the intersection between multiple drone routes and the optimal solution of path length as constraints, specifically including: Conduct data analysis on the terrain characteristics of the wind farm, and determine the distribution range of the survey area and the terrain complexity of each area based on the data analysis.

[0077] It should be noted that data analysis of the terrain characteristics of the wind farm is achieved through the following steps: Remote sensing images, preliminary aerial data from drones and GIS data were used to obtain topographic data of the wind farm area, including elevation, slope, aspect and surface cover characteristics.

[0078] The digital elevation model is used to calculate the regional elevation distribution, and the terrain characteristics of each surveying and mapping unit are extracted by combining the slope and aspect information of the terrain.

[0079] The terrain complexity calculation formula is used to calculate, and the areas with low terrain complexity are marked as priority mapping areas, and the areas with high complexity are marked as key mapping areas. Combined with the planning and layout of the wind farm, the overall mapping scope is formed; in order to clarify the level of terrain complexity, a threshold is set Used to judge: local terrain complexity When , it is marked as a high complexity area; when When , it is marked as a low complexity area; the threshold The specific value of .

[0080] Among them, terrain complexity is used to quantify the complexity of the survey area and affects the path priority in route planning. The calculation formula of terrain complexity is:

[0081] In the formula, Represents the terrain complexity index; Indicates the area of ​​a region; Indicates the height of adjacent sampling points; Indicates the horizontal distance between adjacent sampling points; by calculating the cumulative change in elevation and horizontal distance within the survey area, the terrain undulation and obstacle distribution are quantified. The larger the value, the more complex the terrain.

[0082] According to the distribution range and terrain complexity of the surveying area, the surveying task is decomposed into multiple sub-areas, and a corresponding drone is assigned to each sub-area.

[0083] It should be noted that sub-area division is to divide the surveying and mapping area into multiple basic sub-areas according to a fixed grid size; each sub-area corresponds to a grid unit, and its center point is used as the surveying and mapping focus; grid division is based on changes in regional boundaries and terrain complexity, and the grid in complex areas can be appropriately reduced to enhance surveying and mapping accuracy.

[0084] It should be further explained that the task priority setting is based on the complexity of the terrain, and different priorities are assigned to sub-areas; areas with a complexity higher than 0.8 are set as priority mapping areas, and are preferentially assigned to drones with better performance to perform tasks.

[0085] It should be further explained that the UAV allocation is to generate a task allocation matrix based on the performance parameters of the UAV through a distributed task allocation algorithm, specifically:

[0086] In the formula, represents the allocation matrix, which is used to ensure that each sub-area is covered by a drone and the tasks are evenly distributed.

[0087] In the multi-objective optimization model, the objective function is used to optimize the coverage rate of the sub-areas, the length of the drone routes, and the intersection of the routes. Specifically:

[0088] In the formula, represents the maximum optimization target value, represents the optimization target value, Indicates The coverage rate of sub-areas, Indicates The length of the drone’s flight path, Indicates There are cross-route conflicts. The weight coefficient representing the coverage rate is determined by the complexity of the terrain and the importance of the survey area; The weight coefficients representing the route length and intersection conflict are determined according to the time requirement of the surveying and mapping task, resource consumption, and the impact of route conflicts between multiple UAVs.

[0089] It should be noted that .

[0090] During the optimization process, the following constraints are met: The first constraint is that only one drone is assigned to each sub-area, specifically:

[0091] In the formula, Represents the allocation matrix, used to represent sub-regions Whether it is by drone implement.

[0092] The second constraint is that the total length of the route is less than the preset upper limit, specifically:

[0093] In the formula, is the allocation matrix used to represent the sub-region Whether it is by drone implement; Represents the upper limit of the route length; the optimization objective function is centered on maximizing coverage, combining the two secondary goals of optimizing route length and minimizing route intersections, and realizing task allocation and path planning through constraints.

[0094] Based on the optimization results, a preliminary mapping route is generated for each UAV to ensure that the route meets coverage requirements while reducing route conflicts between multiple UAVs, providing a basis for subsequent dynamic optimization.

[0095] Step S200: dynamically optimizing the preliminary surveying and mapping routes according to the real-time meteorological data to generate dynamically optimized surveying and mapping routes for multiple drones, specifically including: Get real-time weather data, including real-time wind speed, wind direction, temperature and air pressure.

[0096] It should be noted that wind speed, wind direction, temperature and air pressure are obtained in real time through ground-based meteorological stations and online meteorological services, and the collected meteorological data are standardized, including the removal of outliers, time series interpolation and data area matching, to ensure the accuracy and consistency of meteorological data.

[0097] The real-time meteorological data is associated with the preliminary mapped routes, and the adaptability score of each flight segment is calculated using the meteorological impact weight.

[0098] It should be noted that by calculating the adaptability score of each flight segment, the impact of wind speed on the stability of the drone, the impact of wind direction on the flight path, and the impact of temperature and air pressure on the endurance of the drone are comprehensively considered; the higher the adaptability score, the more feasible the flight segment is under the current meteorological conditions.

[0099] The preliminary mapping route is adjusted based on the adaptability score to generate a dynamically optimized mapping route.

[0100] It should be noted that the calculation formula for adaptability score is:

[0101] In the formula, Indicates The suitability score of the flight segments, Indicates wind speed and wind direction The impact function on the flight segment, the score is reduced when the wind speed is large or perpendicular to the route direction; Indicates flight segment The regional coverage needs are met, with priority given to areas with lower coverage; Represents the weight coefficient used to balance the impact of wind speed, flight stability, and coverage requirements on the adaptability score.

[0102] It should be noted that .

[0103] The route optimization objectives of dynamic optimization are path stability and coverage, and the optimization objectives are described by the following formula:

[0104] In the formula, represents the maximum dynamic optimization target value, represents the dynamic optimization target value, Indicates The suitability score of the flight segments, For the Corrected deviation of a flight segment, used to indicate the impact of real-time weather data on the flight segment; The weight coefficient representing the meteorological impact is obtained by analyzing the relationship between real-time meteorological data and flight segment stability.

[0105] It should be noted that The specific value range is: .

[0106] Adjust the flight paths of multiple drones based on dynamically optimized mapping routes.

[0107] Step S300: multiple drones fly along the dynamically optimized surveying and mapping routes, and obtain surveying and mapping data through data acquisition equipment, specifically including: Data collection equipment includes lidar, optical cameras and infrared sensors, each used to obtain different types of surveying and mapping data.

[0108] LiDAR collects terrain point cloud data and records the three-dimensional coordinate information of the surveying area.

[0109] Optical cameras acquire surface texture data and capture detailed features of the surface.

[0110] Infrared sensors collect thermal signature data and analyze surface temperature distribution and coverage characteristics through thermal imaging.

[0111] The surveying and mapping data includes terrain point cloud data, surface texture data and thermal characteristic data. The surveying and mapping data is used as the data benchmark and provided to subsequent data processing and three-dimensional terrain model generation.

[0112] It should be noted that multiple drones fly along dynamically optimized routes, collecting terrain point cloud data through lidar, surface texture data through optical cameras, and thermal feature data through infrared sensors, providing basic data support for the subsequent generation of three-dimensional terrain models. Through multi-sensor collaborative collection, the diversity and accuracy of surveying and mapping data are improved, providing guarantees for comprehensive coverage of complex terrain areas and refinement of three-dimensional models.

[0113] Step S400: Process the surveying and mapping data acquired by the multiple drones respectively, and generate a preliminary three-dimensional terrain model of the wind farm using a multi-source data fusion algorithm based on the surveying and mapping data after the data processing, specifically including: Data processing includes denoising, image enhancement, feature extraction and temperature difference analysis.

[0114] The terrain point cloud data collected by the lidar is denoised to remove abnormal point clouds and improve spatial accuracy through consistency correction.

[0115] It should be noted that the denoising of the terrain point cloud data collected by the lidar is achieved through an outlier removal method based on statistical filtering; this method calculates the neighborhood mean and standard deviation of each point in the point cloud data, removes outliers that deviate from the mean by more than a preset threshold, and thus improves the spatial accuracy of the point cloud data.

[0116] Perform image enhancement processing on the surface texture data collected by the optical camera, including brightness adjustment, edge sharpening and noise suppression.

[0117] It should be noted that the brightness adjustment, edge sharpening and noise suppression of the surface texture data collected by the optical camera are achieved by the following technical means: Brightness adjustment improves the overall brightness distribution of texture images and enhances dark details through histogram equalization.

[0118] Edge sharpening uses the Laplacian operator enhancement algorithm to highlight the edge features of surface texture.

[0119] Noise suppression uses Gaussian filtering to smooth random noise in the image while retaining texture detail information.

[0120] Feature extraction and temperature difference analysis are performed on the thermal feature data collected by infrared sensors to identify the thermal distribution characteristics and coverage properties of the surface.

[0121] It should be noted that the feature extraction and temperature difference analysis of the thermal feature data collected by the infrared sensor are achieved through the region growing algorithm and the temperature gradient calculation method: Feature extraction is based on the region growing algorithm, which extracts regions with the same temperature characteristics in the thermal feature data and marks the coverage type of the surface.

[0122] Temperature difference analysis calculates the temperature gradient of adjacent pixels in infrared images, identifies the changes in thermal distribution in different areas, and provides a basis for the classification of surface cover properties.

[0123] It should be further explained that the above-mentioned denoising, image enhancement, feature extraction and temperature difference analysis are all implemented by technical means known in the art, and their specific implementation methods and steps have been disclosed in relevant technical literature, so they will not be repeated here.

[0124] Combining the three-dimensional spatial information of point cloud data, the surface detail features of texture data and the coverage attributes of thermal feature data, the weighted fusion algorithm in the multi-source data fusion algorithm is used to generate a preliminary three-dimensional terrain model of the wind farm. The formula of the weighted fusion algorithm is:

[0125] In the formula, represents the eigenvalue of the fused terrain model, Represents the spatial information of point cloud data, Represents the detail features of texture data, represents the coverage attributes of thermal signature data, Represents the fusion weight coefficient, which is used to adjust the influence of different data types in the final model. Based on the quality of point cloud data, Determined by the clarity of texture data and image quality, Determined based on the importance of thermal feature data to the scene supplement.

[0126] It should be noted that The value range must satisfy .

[0127] Step S500: monitor obstacles encountered by multiple drones during flight in real time, make judgments based on obstacle size, drone speed, and flight environment conditions, and adjust the routes of each drone based on the judgment results to ensure that there is no path conflict between drones and complete the mapping task, specifically including: Obstacles within the route range are monitored in real time through lidar and visual sensors, and the size, location and dynamic information of the obstacles are collected.

[0128] Based on the size of the obstacle and the flight speed of the drone, the minimum safe distance between the obstacle and the drone is calculated using the following formula:

[0129] In the formula, Indicates the minimum safe distance between the drone and obstacles. Indicates the flight speed of the drone. The reaction time of the drone is used to indicate the time required from detecting an obstacle to starting to adjust the route; Indicates the safe avoidance distance of an obstacle, and its value depends on the type and size of the obstacle.

[0130] When the actual distance between the obstacle and the drone is less than the minimum safety distance, the route adjustment is triggered.

[0131] According to the dynamic information of obstacles and the flight environment conditions of the drone, the adjusted route is calculated in combination with the dynamic obstacle avoidance algorithm. The objective function of the adjusted route is to optimize the path length and not affect the coverage rate. The adjusted path is based on the following constraints: The third constraint condition is that the angle between the flight direction of the drone and the obstacle avoidance direction is not less than the preset safety angle.

[0132] It should be noted that the preset safety angle is calculated based on the relative position of the drone and the obstacle and the avoidance direction. The calculation formula is:

[0133] In the formula, Indicates the preset safety angle. Indicates the distance difference between the drone and the obstacle in the vertical and horizontal directions; For safety correction angle, the size and dynamic characteristics of the obstacle are preset, and the correction angle is increased for larger obstacles to ensure sufficient avoidance space.

[0134] Output the adjusted route and update the flight path of the UAV in real time, so that the UAV can continue to perform the surveying and mapping task according to the adjusted path.

[0135] The dynamic obstacle avoidance algorithm is implemented using the DWA dynamic window algorithm based on path planning. The DWA dynamic window algorithm generates a feasible obstacle avoidance path in real time by comprehensively considering the flight speed, flight direction and dynamic information of obstacles of the drone. The specific steps include: Under the current flight environment, establish the speed space and heading space of the UAV.

[0136] In the speed and direction space, the feasibility of different paths is dynamically evaluated based on the objective functions of optimal path length and maximum route coverage.

[0137] Combined with the dynamic information of obstacles, paths that intersect or conflict with obstacles are eliminated.

[0138] Finally, the route with the shortest path length and unaffected coverage is selected as the adjusted path.

[0139] Step S600: multiple drones fly along their adjusted routes to conduct supplementary surveys on uncovered areas in the preliminary three-dimensional terrain model of the wind farm, and improve the preliminary three-dimensional terrain model of the wind farm based on the surveying and mapping data obtained from the supplementary survey, specifically including: By analyzing the preliminary three-dimensional terrain model of the wind farm, the distribution of uncovered areas and missing data points is identified.

[0140] It should be noted that the distribution of uncovered areas and missing data points is achieved through a missing detection algorithm based on spatial difference analysis: Firstly, the preliminary three-dimensional terrain model of the wind farm is divided into a regular grid structure, and each grid unit corresponds to a spatial area. Then, the distribution of sampling points in the grid is analyzed, and the grids with point cloud density lower than the critical point cloud density are identified and marked as missing data areas. The critical point cloud density is not less than 10 sampling points per square meter. Furthermore, the coverage of surface texture data and thermal feature data is combined to mark the uncovered grid units, and finally a spatial distribution map of the uncovered area is formed.

[0141] Based on the location and scope of the uncovered area, a re-survey route is generated and the re-survey task is assigned to the corresponding drone.

[0142] It should be noted that the generation of supplementary survey routes is achieved through regional priority division and path planning algorithm: The uncovered areas are prioritized according to their spatial distribution and size, and high-priority areas are allocated to drones with higher performance. In each priority area, a path planning algorithm is used to generate the optimal supplementary survey route based on the obstacle distribution and the initial position of the drone. The generated supplementary survey route must meet the constraints of maximizing path coverage and minimizing route intersections.

[0143] Multiple drones flew along the supplementary survey route to collect terrain point cloud data, surface texture data and thermal feature data of the uncovered areas.

[0144] The data processing in the above steps is performed on the surveying and mapping data obtained from the supplementary survey, and the processed surveying and mapping data are fused with the preliminary three-dimensional terrain model of the wind farm. The spatial weighted average method is used to improve the data of the uncovered area. The fusion formula is:

[0145] In the formula, represents the eigenvalue of the fused model, represents the eigenvalues ​​of the corresponding points in the supplementary data and the preliminary model, is the weight coefficient, which is determined according to the confidence of the supplementary test data and the preliminary model data; Indicates the number of points involved in the supplementary test data and the preliminary model.

[0146] It should be noted that the fusion weight coefficient of the supplementary test data and the preliminary model data is It is calculated based on the confidence level of the data, which is determined by the accuracy of the data acquisition equipment and the error correction in the data processing steps: For supplementary measurement data, the higher the accuracy of the acquisition equipment and the smaller the processing error, the greater the weight assigned.

[0147] For the preliminary model data, the weights are calculated by combining the spatial consistency of the point cloud data and the clarity of the texture data to ensure the accuracy of the fused data.

[0148] Step S700: Integrate and optimize the surveying and mapping data collected by multiple drones using consistency correction and spatial weighted average method, eliminate duplicate regional data and improve surveying and mapping accuracy based on integration and optimization, and generate a complete three-dimensional terrain model of the wind farm, specifically including: By analyzing mapping data collected by multiple drones, duplicate and missing areas in the data can be identified.

[0149] It should be noted that the identification of repeated areas is done by spatially comparing the data collected by multiple drones and identifying areas with spatial coordinate differences less than a threshold. The point cloud area is marked as a duplicate area.

[0150] The missing area is identified by mapping the survey data to a regular grid structure, analyzing the density of data points within the grid, and identifying points with a density below the critical density. The grid cells are marked as missing areas.

[0151] The data in the repeated area are corrected for consistency, and the spatial position of the overlapping points is adjusted by calculating the mean square error between adjacent data points.

[0152] It should be noted that in the repeated area, the mean square error of data points from different sources is calculated using the following formula:

[0153] In the formula, Indicates The spatial coordinates of the points, is the average coordinate value of the area, is the number of data points; adjust the spatial position of the repeated points so that their coordinate values ​​are close to the mean , reducing the error between points.

[0154] The data in the missing areas are completed, and the data points from different sources are fused by the spatial weighted average method.

[0155] It should be noted that the spatial weighted average method assigns different weights to the existing data points around the missing point, and the weight value is based on the distance The inverse function calculation of :

[0156] In the formula, For the The weight of the data point, The missing point and The distance between data points, is a smoothing factor to prevent the weight from being infinite when the distance is zero.

[0157] The coordinate values ​​of the fused and completed data points are:

[0158] In the formula, is the completion value of the missing point, is the characteristic value of the surrounding data points.

[0159] The optimized terrain data is checked for accuracy, and the spatial coordinate consistency and detail resolution of the data are analyzed; the accuracy of the three-dimensional terrain model of the wind farm is improved by adjusting the error distribution and correcting abnormal points.

[0160] It should be noted that the accuracy check of the optimized terrain data is achieved by adopting the inspection method based on distribution error analysis: Check the consistency of spatial coordinates and calculate the mean square error between the optimized model and the original collected data to ensure that the error is less than the preset threshold ; Check detail resolution, analyze the resolution of texture data and the continuity of thermal feature data, and ensure that the detail information of the optimized model meets the accuracy requirements of wind farm design.

[0161] Output the integrated and optimized three-dimensional terrain model of the wind farm, provide model data including complete spatial coordinates and detailed features, and provide data support for the subsequent design and planning of the wind farm.

[0162] At this point, a wind farm terrain mapping method based on drones has been completed.

[0163] See also Figure 2 , Figure 2 An overall architecture diagram of a wind farm terrain mapping system based on a drone is provided for an embodiment of the present application.

[0164] A wind farm terrain mapping system based on drones, including the following units: The route generation unit 81 is used to generate preliminary mapping routes for multiple drones based on the terrain characteristics of the wind farm using a multi-objective optimization model; the multi-objective optimization model takes maximizing coverage as the optimization goal, and takes minimizing the intersection between multiple drone routes and the optimal solution of path length as constraints.

[0165] The route optimization unit 82 is used to dynamically optimize the preliminary surveying and mapping routes according to the real-time meteorological data, and generate dynamically optimized surveying and mapping routes for multiple UAVs.

[0166] The data acquisition unit 83 is used for multiple UAVs to fly along the dynamically optimized surveying and mapping routes, and respectively acquire surveying and mapping data through data acquisition equipment.

[0167] The fusion modeling unit 84 is used to process the surveying and mapping data acquired by multiple UAVs respectively, and generate a preliminary three-dimensional terrain model of the wind farm by adopting a multi-source data fusion algorithm based on the surveying and mapping data after the data processing.

[0168] The dynamic avoidance unit 85 is used to monitor in real time the obstacles encountered by multiple drones during flight, make judgments based on the size of the obstacles, the speed of the drones and the flight environment conditions, and adjust the routes of each drone based on the judgment results to ensure that there is no path conflict between the drones and complete the mapping task.

[0169] The model re-surveying unit 86 is used for multiple UAVs to fly along their adjusted routes to re-survey uncovered areas in the preliminary three-dimensional terrain model of the wind farm, and to improve the preliminary three-dimensional terrain model of the wind farm based on the surveying and mapping data obtained from the re-surveying.

[0170] The integration and optimization unit 87 is used to integrate and optimize the surveying and mapping data collected by multiple drones by using consistency correction and spatial weighted average method, eliminate duplicate regional data based on integration and optimization and improve surveying and mapping accuracy, so as to generate a complete three-dimensional terrain model of the wind farm.

[0171] The above is only a specific implementation of the present invention, but the protection scope of the embodiments of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the embodiments of the present invention, which should be included in the protection scope of the embodiments of the present invention. Therefore, the protection scope of the embodiments of the present invention shall be based on the protection scope of the claims.

Claims

1. A wind farm terrain mapping method based on drone, characterized in that: The following steps are involved: According to the terrain characteristics of the wind farm, a multi-objective optimization model is used to generate preliminary mapping routes for multiple drones; Dynamically optimize the preliminary survey routes according to real-time meteorological data to generate dynamically optimized survey routes for multiple drones; Multiple drones fly along the dynamically optimized surveying and mapping routes and obtain surveying and mapping data through data acquisition equipment; The surveying and mapping data acquired by multiple drones are processed separately, and a preliminary three-dimensional terrain model of the wind farm is generated using a multi-source data fusion algorithm based on the processed surveying and mapping data; Monitor obstacles encountered by multiple drones in real time during flight, make judgments based on obstacle size, drone speed, and flight environment conditions, and adjust the routes of each drone based on the judgment results; Multiple drones fly along their adjusted routes to conduct supplementary surveys on uncovered areas in the preliminary wind farm 3D terrain model, and improve the preliminary wind farm 3D terrain model based on the surveying and mapping data obtained from the supplementary surveys; The consistency correction and spatial weighted average method are used to integrate and optimize the surveying and mapping data collected by multiple drones. Based on the integration and optimization, duplicate regional data are eliminated and the surveying and mapping accuracy is improved to generate a complete three-dimensional terrain model of the wind farm.

2. A wind farm terrain mapping method based on an unmanned aerial vehicle according to claim 1, characterized in that: According to the terrain characteristics of the wind farm, a multi-objective optimization model is used to generate preliminary mapping routes for multiple drones, including: Conduct data analysis on the terrain characteristics of the wind farm, and determine the distribution range of the survey area and the terrain complexity of each area based on the data analysis; According to the distribution range and terrain complexity of the survey area, the survey task is divided into multiple sub-areas, and a corresponding drone is assigned to each sub-area; In the multi-objective optimization model, the objective function is used to optimize the coverage rate of the sub-areas, the length of the drone routes, and the intersection of the routes. Specifically: In the formula, represents the maximum optimization target value, represents the optimization target value, Indicates The coverage rate of sub-areas, Indicates The length of the drone’s flight path, Indicates There are cross-route conflicts. The weight coefficient representing the coverage rate is determined by the complexity of the terrain and the importance of the survey area; The weight coefficients representing the route length and cross-conflict are determined according to the time requirement of the surveying and mapping task, resource consumption, and the impact of route conflicts between multiple UAVs; During the optimization process, the following constraints are met: The first constraint is that only one drone is assigned to each sub-area, specifically: In the formula, Represents the allocation matrix, used to represent sub-regions Whether it is by drone implement; The second constraint is that the total length of the route is less than the preset upper limit, specifically: In the formula, is the allocation matrix, used to represent the sub-region Whether it is by drone implement; Indicates the upper limit of the route length; the optimization objective function is centered on maximizing coverage, combining the two secondary goals of optimizing route length and minimizing route intersections, and realizing task allocation and path planning through constraints; Based on the optimization results, a preliminary mapping route is generated for each UAV to ensure that the route meets coverage requirements while reducing route conflicts between multiple UAVs, providing a basis for subsequent dynamic optimization.

3. A wind farm terrain mapping method based on an unmanned aerial vehicle as claimed in claim 2, characterized in that: Conduct data analysis on the terrain characteristics of the wind farm, and determine the distribution range of the survey area and the terrain complexity of each area based on the data analysis, including: Terrain complexity is used to quantify the complexity of the survey area and affects the path priority in route planning. The calculation formula for terrain complexity is: In the formula, represents the terrain complexity index; Indicates the area of ​​a region; Indicates the height of adjacent sampling points; Indicates the horizontal distance between adjacent sampling points; by calculating the cumulative change in elevation and horizontal distance within the survey area, the terrain undulation and obstacle distribution are quantified. The larger the value, the more complex the terrain.

4. A wind farm terrain mapping method based on an unmanned aerial vehicle according to claim 1, characterized in that: The preliminary surveying routes are dynamically optimized according to real-time meteorological data to generate dynamically optimized surveying routes for multiple drones, including: Get real-time weather data, including real-time wind speed, wind direction, temperature and air pressure; The real-time weather data is associated with the preliminary survey route, and the suitability score of each flight segment is calculated by the weather impact weight; Adjust the preliminary mapping route based on the adaptability score to generate a dynamically optimized mapping route; The route optimization objectives of dynamic optimization are path stability and coverage, and the optimization objectives are described by the following formula: In the formula, represents the maximum dynamic optimization target value, represents the dynamic optimization target value, Indicates The suitability score of the flight segments, For the Corrected deviation of a flight segment, used to indicate the impact of real-time weather data on the flight segment; The weight coefficient representing the meteorological impact is determined by analyzing the relationship between real-time meteorological data and flight segment stability using a statistical regression model. The value of Adjust the flight paths of multiple drones based on dynamically optimized mapping routes.

5. The method for wind farm terrain mapping based on drone according to claim 1, characterized in that: Multiple drones fly along the dynamically optimized surveying and mapping routes and obtain surveying and mapping data through data acquisition equipment, including: Data collection equipment includes lidar, optical cameras, and infrared sensors, each used to obtain different types of surveying and mapping data; LiDAR collects terrain point cloud data and records the three-dimensional coordinate information of the surveying area; The optical camera obtains surface texture data and captures the detailed features of the surface; Infrared sensors collect thermal signature data and analyze surface temperature distribution and coverage characteristics through thermal imaging; The surveying and mapping data includes terrain point cloud data, surface texture data and thermal characteristic data. The surveying and mapping data is used as the data benchmark and provided to subsequent data processing and three-dimensional terrain model generation.

6. A wind farm terrain mapping method based on an unmanned aerial vehicle according to claim 1, characterized in that: The surveying and mapping data obtained by multiple drones are processed separately. Based on the processed surveying and mapping data, a multi-source data fusion algorithm is used to generate a preliminary three-dimensional terrain model of the wind farm, including: Data processing includes denoising, image enhancement, feature extraction and temperature difference analysis; De-noising the terrain point cloud data collected by the LiDAR, removing abnormal point clouds and improving spatial accuracy through consistency correction; Perform image enhancement processing on the surface texture data collected by the optical camera, including brightness adjustment, edge sharpening and noise suppression; Perform feature extraction and temperature difference analysis on the thermal feature data collected by infrared sensors to identify the thermal distribution characteristics and coverage properties of the surface; Combining the three-dimensional spatial information of point cloud data, the surface detail features of texture data and the coverage attributes of thermal feature data, the weighted fusion algorithm in the multi-source data fusion algorithm is used to generate a preliminary three-dimensional terrain model of the wind farm. The formula of the weighted fusion algorithm is: In the formula, represents the eigenvalue of the fused terrain model, Represents the spatial information of point cloud data, Represents the detail features of texture data, represents the coverage attributes of thermal signature data, Represents the fusion weight coefficient, which is used to adjust the influence of different data types in the final model. Based on the quality of point cloud data, Determined by the clarity and image quality of the texture data, Determined based on the importance of thermal feature data to the scene supplement.

7. A wind farm terrain mapping method based on an unmanned aerial vehicle according to claim 1, characterized in that: Monitor obstacles encountered by multiple drones in real time during flight, make judgments based on obstacle size, drone speed, and flight environment conditions, and adjust the routes of each drone based on the judgment results to ensure that there is no path conflict between drones and complete the mapping task. Specifically, it includes: Obstacles within the route are monitored in real time through LiDAR and visual sensors, and their size, location and dynamic information are collected; Based on the size of the obstacle and the flight speed of the drone, the minimum safe distance between the obstacle and the drone is calculated using the following formula: In the formula, Indicates the minimum safe distance between the drone and obstacles. Indicates the flight speed of the drone. The reaction time of the drone is used to indicate the time required from detecting an obstacle to starting to adjust the route; Indicates the safe avoidance distance of obstacles, and its value depends on the type and size of the obstacle; When the actual distance between the obstacle and the drone is less than the minimum safety distance, the route adjustment is triggered; According to the dynamic information of obstacles and the flight environment conditions of the drone, the adjusted route is calculated in combination with the dynamic obstacle avoidance algorithm. The objective function of the adjusted route is to optimize the path length and not affect the coverage rate. The adjusted path is based on the following constraints: The third constraint condition is that the angle between the flight direction of the drone and the obstacle avoidance direction is not less than the preset safety angle; The fourth constraint is that the adjusted route does not conflict with other UAV routes; Output the adjusted route and update the flight path of the UAV in real time, so that the UAV can continue to perform the surveying and mapping task according to the adjusted path.

8. The method for wind farm terrain mapping based on drone according to claim 1, characterized in that: Multiple drones fly along their adjusted routes to conduct supplementary surveys on uncovered areas in the preliminary wind farm 3D terrain model. The preliminary wind farm 3D terrain model is improved based on the surveying and mapping data obtained from the supplementary surveys, including: By analyzing the preliminary wind farm 3D terrain model, the distribution of uncovered areas and missing data points is identified; Generate re-survey routes based on the location and scope of uncovered areas, and assign re-survey tasks to corresponding drones; Multiple drones fly along the supplementary survey route to collect terrain point cloud data, surface texture data, and thermal feature data of uncovered areas; The data processing in the above steps is performed on the surveying and mapping data obtained from the supplementary survey, and the processed surveying and mapping data are fused with the preliminary three-dimensional terrain model of the wind farm. The spatial weighted average method is used to improve the data of the uncovered area. The fusion formula is: In the formula, represents the eigenvalue of the fused model, represents the eigenvalues ​​of the corresponding points in the supplementary data and the preliminary model, is the weight coefficient, which is determined according to the confidence of the supplementary test data and the preliminary model data; Indicates the number of points involved in the supplementary test data and the preliminary model.

9. The method for wind farm terrain mapping based on drone according to claim 1, characterized in that: The mapping data collected by multiple drones are integrated and optimized using consistency correction and spatial weighted average method. Based on the integration and optimization, duplicate regional data are eliminated and mapping accuracy is improved to generate a complete three-dimensional terrain model of the wind farm, including: By analyzing the mapping data collected by multiple drones, duplicate and missing areas in the data can be identified; The consistency correction is performed on the data of the repeated area, and the spatial position of the overlapping points is adjusted by calculating the mean square error between adjacent data points; The data of the missing areas are completed and the data points from different sources are fused by spatial weighted averaging method; Check the accuracy of the optimized terrain data, analyze the spatial coordinate consistency and detail resolution of the data; improve the accuracy of the wind farm 3D terrain model by adjusting the error distribution and correcting abnormal points; Output the integrated and optimized three-dimensional terrain model of the wind farm, provide model data including complete spatial coordinates and detailed features, and provide data support for the subsequent design and planning of the wind farm.

10. A wind farm terrain mapping system based on an unmanned aerial vehicle, the system being applied to a wind farm terrain mapping method based on an unmanned aerial vehicle as described in any one of claims 1 to 9, characterized in that: The following units are included: A route generation unit is used to generate preliminary mapping routes for multiple drones respectively using a multi-objective optimization model according to the terrain characteristics of the wind farm; the multi-objective optimization model takes maximizing coverage as an optimization goal, and takes minimizing intersections between multiple drone routes and optimizing path lengths as constraints; A route optimization unit is used to dynamically optimize the preliminary survey routes according to real-time meteorological data and generate dynamically optimized survey routes for multiple UAVs; A data acquisition unit is used for multiple UAVs to fly along the dynamically optimized surveying and mapping routes and obtain surveying and mapping data through data acquisition equipment; A fusion modeling unit is used to process the surveying and mapping data obtained by multiple UAVs respectively, and generate a preliminary three-dimensional terrain model of the wind farm using a multi-source data fusion algorithm based on the surveying and mapping data after data processing; The dynamic avoidance unit is used to monitor the obstacles encountered by multiple drones in real time during flight, make judgments based on the size of the obstacles, the speed of the drones, and the flight environment conditions, and adjust the routes of each drone based on the judgment results to ensure that there is no path conflict between drones and complete the mapping task; The model re-survey unit is used for multiple UAVs to fly along their adjusted routes to re-survey the uncovered areas in the preliminary wind farm 3D terrain model, and to improve the preliminary wind farm 3D terrain model based on the surveying and mapping data obtained from the re-survey; The integration and optimization unit is used to integrate and optimize the surveying and mapping data collected by multiple drones using consistency correction and spatial weighted average method. Based on the integration and optimization, duplicate regional data is eliminated and the surveying and mapping accuracy is improved to generate a complete three-dimensional terrain model of the wind farm.

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