An unmanned aerial vehicle route planning method for low-altitude mapping

Abstract

The application discloses a kind of unmanned plane route planning method for low altitude mapping, and the present application relates to unmanned plane technical field.The method steps include: the mapping area is regularized and divided to obtain mapping subarea, and the topographic feature data and demand data of mapping subarea are collected, and the priority of mapping subarea is calculated;Collect unmanned plane performance data and mapping subarea performance demand data, and each characteristic matching value is obtained by single parameter performance matching, and the unmanned plane mapping subarea adaptation matrix is constructed in combination with the priority of mapping subarea;Based on unmanned plane mapping subarea adaptation matrix, the local initial flight path is generated by distributing unmanned plane through hungarian algorithm;The flight path conflict analysis model is constructed by terrain factor, the local initial flight path is input into the model, and the adjusted unmanned plane flight path is obtained;When unmanned plane executes task, real-time collection of operating state and progress data, risk value is calculated, threshold value is exceeded, and unmanned plane is exchanged, and safe and efficient route planning is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicles, in particular to a route planning method for low-altitude surveying and mapping unmanned aerial vehicles. BACKGROUND

[0002] Low-altitude surveying and mapping unmanned aerial vehicles have been widely used in urban planning, resource investigation, disaster monitoring, engineering construction and many other fields due to their flexibility, efficiency, low cost and high-resolution data acquisition advantages. In this field, efficient and safe route planning technology is the core key to ensuring the successful completion of surveying and mapping tasks and improving data acquisition efficiency and quality. Especially in the face of complex geographical environments (such as terrain undulations and building-intensive areas) and dynamic task requirements (such as emergency surveying and mapping response), higher intelligent requirements are put forward for unmanned aerial vehicle path planning.

[0003] However, the existing unmanned aerial vehicle path planning methods have obvious deficiencies in dealing with multi-vehicle path conflicts in low-altitude complex environments. The existing technology mainly uses fixed safety thresholds (such as horizontal distance and vertical height difference) for conflict detection, which lacks adaptability to dynamic topographic features (such as building density, terrain undulation, and geographical environment complexity) in the surveying and mapping area, resulting in high conflict risk of the planned route, which further affects the efficiency and data quality of low-altitude surveying and mapping. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a route planning method for low-altitude surveying and mapping unmanned aerial vehicles to solve the problems in the background art.

[0005] To achieve the above purpose, the present application is realized by the following technical scheme: a route planning method for low-altitude surveying and mapping unmanned aerial vehicles, comprising the following steps:

[0006] Step S1: Regularly partitioning the surveying and mapping area to obtain a plurality of surveying and mapping sub-areas; collecting the geographical features, terrain undulations and building density of the surveying and mapping sub-areas to obtain the topographic feature data of the surveying and mapping sub-areas;

[0007] Step S2: Collecting the task urgency and historical data missing situation of the surveying and mapping sub-areas to obtain the demand data of the surveying and mapping sub-areas; calculating the priority value of the surveying and mapping sub-areas according to the demand data and topographic feature data of the surveying and mapping sub-areas;

[0008] Step S3: Collecting data of endurance time, sensor resolution, and flight line density of the unmanned aerial vehicle to obtain unmanned aerial vehicle performance data; collecting data of performance requirements of the unmanned aerial vehicle in the surveying and mapping sub-area to obtain surveying and mapping sub-area performance requirement data; performing single-parameter performance matching on the unmanned aerial vehicle performance data and the surveying and mapping sub-area performance requirement data to obtain a performance matching value of each parameter in the unmanned aerial vehicle performance data; and constructing an unmanned aerial vehicle surveying and mapping sub-area adaptation matrix by combining the performance matching value and the surveying and mapping sub-area priority value.

[0009] Step S4: Based on the unmanned aerial vehicle surveying and mapping sub-area adaptation matrix, assigning the unmanned aerial vehicle to the corresponding surveying and mapping sub-area by using the Hungarian algorithm, and generating a local initial flight line for the surveying and mapping sub-area.

[0010] Step S5: Obtaining a terrain factor by calculating the terrain feature data of the surveying and mapping sub-area; constructing a flight path conflict analysis model based on a space-time grid based on the terrain factor; inputting the local initial flight line into the flight path conflict analysis model to obtain an adjusted unmanned aerial vehicle flight line, and realizing route planning for the unmanned aerial vehicle.

[0011] Preferably, the surveying and mapping sub-area priority value is calculated according to the surveying and mapping sub-area requirement data and the surveying and mapping sub-area terrain feature data, and includes the following specific steps:

[0012] The surveying and mapping sub-area priority value is calculated according to the surveying and mapping sub-area requirement data and the surveying and mapping sub-area terrain feature data.

[0013]

[0014] wherein Q is the surveying and mapping sub-area priority value, G is the geographical feature, DX is the terrain undulation, B is the building density, U is the task urgency, and LS is the historical data loss situation.

[0015] Preferably, the performance matching value of each parameter in the unmanned aerial vehicle performance data is obtained by performing single-parameter performance matching on the unmanned aerial vehicle performance data and the surveying and mapping sub-area performance requirement data, and includes the following steps:

[0016] The performance matching value of each parameter in the unmanned aerial vehicle performance data is obtained by performing single-parameter performance matching on the unmanned aerial vehicle performance data and the surveying and mapping sub-area performance requirement data.

[0017] The performance matching value of the endurance time is obtained by performing performance matching on the endurance time of the unmanned aerial vehicle and the required endurance time of the unmanned aerial vehicle.

[0018]

[0019] wherein, the performance matching value of the endurance time is the endurance time of the unmanned aerial vehicle, The endurance time required by the UAV, The penalty coefficient of the endurance time is 0.8 by default.

[0020] The UAV resolution and the UAV requirement resolution are matched in performance:

[0021]

[0022] Wherein, The performance matching value of the sensor resolution is The UAV requirement resolution is The UAV resolution is The penalty coefficient of the resolution is 0.7 by default.

[0023] The UAV route density and the UAV requirement route density are matched in performance:

[0024]

[0025] Wherein, The performance matching value of the route density is The UAV requirement route density is The UAV route density is The penalty coefficient of the route density is 0.5 by default.

[0026] Preferably, the UAV survey sub-area adaptation matrix is constructed by combining the performance matching value and the survey sub-area priority value, including the following steps:

[0027] The UAV survey sub-area adaptation matrix is constructed by combining the performance matching value and the survey sub-area priority value:

[0028]

[0029] Wherein, The UAV survey sub-area adaptation value of the kth UAV and the jth survey sub-area in the UAV survey sub-area adaptation matrix is represented by The weight of the ith feature of the UAV performance data is The performance matching value of the ith feature of the kth UAV is represented by The survey sub-area priority value of the jth survey sub-area is

[0030] Preferably, the UAV is assigned to the corresponding survey sub-area by the Hungarian algorithm based on the UAV survey sub-area adaptation matrix, including the following steps:

[0031] According to the number of UAVs and the number of survey sub-areas, the UAV survey sub-area adaptation matrix is adjusted to obtain an adjusted UAV survey sub-area adaptation matrix ;

[0032] Constructing a cost matrix C, wherein: =max( )- , is the value of the kth UAV and the jth mapping sub-area in the cost matrix, represents the value of the kth UAV and the jth mapping sub-area in the adjusted UAV mapping sub-area adaptation matrix, wherein max( ) represents the maximum value in the adjusted UAV mapping sub-area adaptation matrix;

[0033] Row reduction is performed on the cost matrix, and the value in each row of the cost matrix is reduced by the minimum value of the row. Then, column reduction is performed on the cost matrix, and the value in each column of the cost matrix is reduced by the minimum value of the column. Finally, the number of straight lines that can cover all zero elements in the cost matrix is obtained, which is the number of independent zero elements, and the independent zero elements are found.

[0034] The cost matrix after row reduction and column reduction is C', and independent zero elements are found in C'. A UAV allocation matrix FP is constructed:

[0035]

[0036] wherein, is the value of the kth UAV and the jth mapping sub-area in the UAV allocation matrix. If the value is 1, it indicates that the kth UAV is allocated to the jth mapping sub-area. If the value is 0, it indicates that the kth UAV is not allocated to the jth mapping sub-area.

[0037] Preferably, the method further comprises the following steps of generating a local initial flight path for each mapping sub-area:

[0038] The UAVs are allocated in each mapping sub-area through the UAV allocation matrix. For a regular-shaped mapping sub-area, a "Z" shaped scanning flight path is adopted. For a complex polygonal mapping sub-area, a Delaunay triangulation is performed, and then a boundary cruising is adopted. The local initial flight path of the kth UAV is ,

[0039] ={( , , , ),( , , , ),...,( , , , ),...,( , , , )},in, This represents the local initial flight path of the k-th drone. , , , This represents the coordinates of the m-th path of the local initial path of the k-th UAV. This represents the time of the k-th UAV at the m-th flight path coordinate, where M represents the total number of flight path coordinates.

[0040] Preferably, the step of calculating the terrain factor by analyzing the terrain feature data of the surveyed sub-region includes the following specific steps:

[0041] Using the terrain feature data of the sub-region of the conflict area mapped by the UAV, the terrain factor of the conflict area is calculated:

[0042]

[0043] Where TCI is the topographic factor, G is the geographical feature, DX is the topographic relief, and B is the building density.

[0044] Preferably, the step of constructing a spatiotemporal grid-based trajectory conflict analysis model based on the terrain factors includes the following specific steps:

[0045] The grid resolution of the conflict area is corrected using the aforementioned terrain factors: = *(1- ), = *(1- ), = *(1- ),in, The grid size is in the horizontal X direction. The grid size is in the horizontal Y direction. z is the grid size in the vertical Z direction. This is the attenuation coefficient, used to control the degree to which the grid size decreases with terrain factors. , , This is the basic grid size. = = Vertical resolution = , This represents the minimum safety threshold in the horizontal direction. The minimum safety threshold in the vertical direction, with a time step of [value missing]. t, mapping the local initial flight path into a discrete grid; then the spatio-temporal grid cell is represented as: [ , , , ], wherein, represents the spatio-temporal grid cell of the kth UAV, , , is the x, y, z coordinate of the mth flight path coordinate of the local initial flight path of the kth UAV mapped in the spatio-temporal grid cell, is the time of the mth flight path coordinate of the kth UAV mapped in the spatio-temporal grid cell, , , , ;

[0046] Conflict detection module:

[0047]

[0048] wherein F is a conflict detection function, , , is the coordinate of the mth flight path coordinate of the local initial flight path of the k1th UAV mapped in the spatio-temporal grid cell, , , is the x, y, z coordinate of the mth flight path coordinate of the local initial flight path of the k2th UAV mapped in the spatio-temporal grid cell, is the time of the mth flight path coordinate of the k1th UAV mapped in the spatio-temporal grid cell, is the time of the mth flight path coordinate of the k2th UAV mapped in the spatio-temporal grid cell, t represents the time step, is the time tolerance coefficient, when is 1, it means that the allowed time difference is less than when is 2, it means that the allowed time difference is less than 2 ;

[0049] Flight path adjustment module: the flight path adjustment module includes time adjustment, height adjustment, horizontal adjustment of the local initial flight path of the UAV;

[0050] Time adjustment:

[0051]

[0052] wherein, is the time of the adjusted k2th UAV in the mth route coordinate mapped in the time of the space-time grid unit, is the time step, py is the offset step number, and the time sequence rationality needs to be satisfied;

[0053] Height adjustment:

[0054]

[0055] wherein, is the z coordinate of the mth route coordinate of the adjusted k2th UAV mapped in the space-time grid unit, is the z coordinate of the mth route coordinate of the local initial route of the k2th UAV mapped in the space-time grid unit, py is the offset step number, and the collision with the terrain is avoided, is the grid size in the vertical Z direction;

[0056] Horizontal adjustment:

[0057]

[0058] wherein, is the x coordinate and y coordinate of the mth route coordinate of the adjusted k2th UAV mapped in the space-time grid unit, is the x coordinate and y coordinate of the mth route coordinate of the k2th UAV mapped in the space-time grid unit, is the offset angle, is the horizontal distance between the k1th UAV and the k2th UAV.

[0059] Preferably, the local initial route of the UAV is input into the route adjustment module to obtain the adjusted UAV route, so as to realize the route planning of the UAV, which comprises the following specific steps:

[0060] The local initial route of the UAV is input into the route conflict analysis model to obtain the adjusted UAV route , =[ , , , ].

[0061] Preferably, it further comprises step S6, which is specifically:

[0062] Step S6: When the UAV performs low-altitude mapping through the adjusted route, the running state data and the mapping progress data of the UAV are collected in real time, and the UAV running risk value is calculated by combining the running state data and the mapping progress data of the UAV. If the UAV running risk value is greater than the preset threshold, a warning is given and other UAVs are replaced to continue to perform the mapping task.

[0063] By combining the operation state data of the unmanned aerial vehicle and the unmanned aerial vehicle surveying progress data, the unmanned aerial vehicle operation risk value is calculated:

[0064]

[0065] Wherein, FX is the unmanned aerial vehicle operation risk value, DL is the remaining power, ZT is the attitude stability, XT is the flight control response delay, and QU is the area of the completed surveying sub-area.

[0066] If the unmanned aerial vehicle operation risk value is greater than a preset threshold value, a warning is given and other unmanned aerial vehicles are exchanged to continue to perform the surveying task.

[0067] Beneficial effects:

[0068] The application provides a kind of unmanned aerial vehicle route planning method for low altitude surveying, relate to image sensor technology, it has the following beneficial effects:

[0069] (1), the performance matching degree of unmanned aerial vehicle is combined with the priority weight of surveying sub-area to construct adaptive matrix, break through the limitation of traditional static task allocation. It is no longer a single consideration distance or simple priority, but realizes the dynamic quantitative fusion of two key dimensions: the satisfaction degree of the actual demand of sub-area by the ability of unmanned aerial vehicle (endurance, resolution, flight path density); The priority of sub-area itself due to task urgency and importance, the combination of the two can realize more accurate task matching, ensure that the unmanned aerial vehicle with high performance serves the complex key sub-area with high demand and high priority first, avoid resource mismatch (such as high-performance unmanned aerial vehicle for simple area, or unmanned aerial vehicle with insufficient capacity cannot meet the high requirement area); Significantly improve the utilization efficiency of the whole fleet and the completion effect of surveying task.

[0070] (2), based on terrain complexity factor (calculated by comprehensive geographical features, terrain undulation, building density), construct space-time grid track conflict analysis model, realize the self-adaptation and refinement of conflict detection and solution, can dynamically perceive the actual complexity of environment. The terrain complexity factor quantifies the challenge of the region (such as high-rise dense area or steep mountain). The model automatically adjusts the fineness of the space-time grid used for conflict detection according to this, uses finer grid in complex areas to improve detection sensitivity, and uses looser grid in open flat areas to avoid unnecessary conservative avoidance. This greatly improves the safety of multi-machine cooperative flight in low altitude, especially in complex environment.

[0071] (3), the initial local route generated after distribution is input into the conflict analysis model, and the final executable optimized route is output after detection and adjustment, which guarantees the safe and reliable and efficient execution of multi-machine cooperative operation. Through the model, the potential conflict points (such as meeting at the same position or being too close) of different unmanned aerial vehicle routes in time and space are accurately identified, and strategies such as time adjustment (arriving at different times), height adjustment (changing the flight height layer) or horizontal avoidance (lateral deviation of the route) are used to completely eliminate the collision risk. This is very important for safe flight in low-altitude complex environment (such as urban building group). BRIEF DESCRIPTION OF DRAWINGS

[0072] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0073] Fig. 1 A flow chart of a route planning method for a low-altitude surveying and mapping unmanned aerial vehicle is provided for the present application.

[0074] Fig. 2 A hierarchical diagram of a route planning method for a low-altitude surveying and mapping unmanned aerial vehicle is provided for the present application. DETAILED DESCRIPTION

[0075] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0076] Please refer to Figs. 1-2 The present application provides a technical solution: a route planning method for a low-altitude surveying and mapping unmanned aerial vehicle.

[0077] Step S1: obtain a plurality of surveying and mapping sub-areas by regularizing the surveying and mapping area; collect the geographical features, terrain undulations and building density of the surveying and mapping sub-area to obtain the terrain feature data of the surveying and mapping sub-area.

[0078] The surveying area is regularly partitioned to obtain a plurality of surveying sub-areas, and the partitioning rules are determined according to the surveying target and the area characteristics, for example, the natural geographical boundary (river, ridge line, etc.), artificial geographical boundary (road, street red line) or task priority (core work area, peripheral auxiliary area) is selected as the partitioning basis. If the geographical boundary is taken as an example, the survey area remote sensing image and vector map can be imported by means of the GIS platform, the boundary elements such as rivers and trunk roads are extracted, and the survey area is divided into a plurality of polygon sub-areas along the boundary line by using the spatial analysis tool, so as to ensure that the boundary of each sub-area is consistent with the actual geographical characteristics, for example, the city survey area is divided into independent street sub-areas according to the trunk road and street boundary, or the survey area is divided into left bank and right bank sub-areas along the river direction. After the partitioning is completed, the topographic feature data collection work is carried out for each surveying sub-area, the high-precision DEM data is obtained by using the unmanned aerial vehicle carrying the laser radar to calculate the terrain fluctuation parameters (such as slope and elevation difference), the building area and distribution density in the sub-area are counted by using the remote sensing image interpretation technology, the building height, type and other information are recorded by combining the field investigation, and other geographical characteristics such as vegetation coverage and water distribution are collected, so as to finally form the surveying sub-area topographic feature data set containing the terrain fluctuation index, building density data and other related attributes, which provides accurate basic data support for the subsequent regional priority weight evaluation and unmanned aerial vehicle task allocation.

[0079] Step S2: collecting the task urgency and the historical data missing situation of the surveying sub-area to obtain surveying sub-area demand data; calculating the surveying sub-area priority value according to the surveying sub-area demand data and the surveying sub-area topographic feature data.

[0080] The task urgency and the historical data missing situation of the surveying sub-area are collected to obtain the surveying sub-area demand data. The surveying sub-area topographic feature data and the surveying sub-area demand data are standardized.

[0081] It should be noted that the mapping sub-area terrain feature data includes: geographical features (G), terrain undulation (DX), and building density (B). Geographical features (G) are quantified as 0-1 according to landform types (mountainous, plain, water area, etc.) (such as mountainous = 1, water area = 0.6, plain = 0.3). The complex terrain of mountainous areas makes it difficult to plan the route, has a high risk of signal obstruction (requires frequent height adjustment), and requires high mapping accuracy (large terrain changes), so it needs to be mapped first and given the highest priority. When the landform type is mountainous, the value of the landform type is 1. The water surface of the water area interferes with the sensor (laser radar / camera), which requires a special flight height (to prevent splashing), and there is no terrain obstruction but signal interference. When the landform type is water area, the value of the landform type can be 0.6. Although the equipment loss of water area operation is higher (high humidity environment makes annual maintenance cost up to 8 thousand dollars, which is 8 times that of plain), but its key role in flood control decision and channel management is irreplaceable, so the priority is still significantly higher than that of plain. When the landform type is plain, the value of the landform type can be 0.3. Terrain undulation (DX) is determined by the value of the relative relief of the mapping sub-area, , , The relative relief reveals the inherent correlation between the horizontal extension and the vertical relief of the terrain. When the altitude variation is the same, the smaller the area, the more dramatic the terrain change, the stronger the challenge to the UAV route planning, and the greater the relative relief. The value of DX is also greater. The value of DX is limited to 0-1, and the maximum value of DX is 1. Building density (B) represents the proportion of building land area to the area of the mapping sub-area. The higher the building density, the greater the degree of obstruction of buildings in the mapping sub-area to the UAV mapping. The building density is standardized to 0-1 (for example, if the building density is 100%, it is standardized to 1, and if the building density is 70%, it is standardized to 0.7).

[0082] It should be noted that the mapping sub-area demand data includes: task urgency (U) and historical data loss situation (LS). Task urgency (U) is set to a level according to the type of operation (such as setting the disaster rescue area to level 10, and the value of the task urgency is 10, and setting the regular mapping area to level 5, and the value of the task urgency is 5); historical data loss situation (LS) is evaluated by the time span and resolution of the existing data in the sub-area (the value of the historical data loss situation is 0-10, such as data over 5 years or resolution below 1m is considered high loss, and the value of the historical data loss situation is 10, and data over 4 years or resolution below 2m is considered high loss, and the value of the historical data loss situation is 8).

[0083] According to the survey sub-area demand data and the survey sub-area terrain feature data, a survey sub-area priority value Q is calculated:

[0084]

[0085] wherein Q is the survey sub-area priority value, G is the geographic feature, DX is the terrain undulation, B is the building density, U is the task urgency, and LS is the historical data loss situation.

[0086] It should be noted that the survey sub-area priority value can be used to construct a survey sub-area priority level map for importance level division and spatial mapping. The survey sub-area priority value is divided into three levels, i.e., high priority (0.8-1), medium priority (0.5-0.79), and low priority (<0.5). With the aid of a GIS platform, the score of each sub-area is mapped to a color gradient (for example, red represents high priority, blue represents medium priority, and yellow represents low priority), and a survey sub-area priority level map is generated. A dynamic correction mechanism is established. If the environment changes (such as sudden strong wind) or the task is adjusted (such as the addition of an emergency demand) during the operation, the environmental complexity or task urgency indicators are updated in real time, the survey sub-area priority value is recalculated, and the survey sub-area priority level map is updated to ensure that the priority ranking is synchronized with the actual operation demand. For example, when a disaster occurs in a survey sub-area, the task urgency indicator is increased from level 5 to level 10, the survey sub-area priority value is correspondingly increased, the color in the survey sub-area priority level map is changed from yellow to red, and the survey sub-area is automatically adjusted to a high-priority operation area.

[0087] Step S3: Collecting data of the endurance time, sensor resolution, and flight line density of the unmanned aerial vehicle to obtain unmanned aerial vehicle performance data; collecting data of the performance demand of the unmanned aerial vehicle in the survey sub-area to obtain survey sub-area performance demand data; performing single-parameter performance matching on the unmanned aerial vehicle performance data and the survey sub-area performance demand data to obtain a performance matching value of each parameter in the unmanned aerial vehicle performance data; and constructing an unmanned aerial vehicle survey sub-area adaptation matrix by combining the performance matching value and the survey sub-area priority value.

[0088] Collecting data of the endurance time, sensor resolution, and flight line density of the unmanned aerial vehicle to obtain unmanned aerial vehicle performance data. Collecting data of the performance demand of the unmanned aerial vehicle in the survey sub-area to obtain survey sub-area performance demand data, wherein the survey sub-area performance demand data includes unmanned aerial vehicle required endurance time, unmanned aerial vehicle required resolution, and unmanned aerial vehicle required flight line density.

[0089] Performing single-parameter performance matching on the unmanned aerial vehicle performance data and the survey sub-area performance demand data to obtain a performance matching value of each parameter in the unmanned aerial vehicle performance data.

[0090] Performing performance matching on the unmanned aerial vehicle endurance time and the unmanned aerial vehicle required endurance time:

[0091]

[0092] wherein, is the performance matching value of the endurance time, is the endurance time of the UAV, is the required endurance time of the UAV, is the penalty coefficient of the endurance time, and the default value is 0.8.

[0093] It should be noted that when the endurance time of the UAV meets the required endurance time of the survey sub-area, the matching value is directly assigned as 1 point (completely adapted); if the endurance time of the UAV is insufficient, the matching value is calculated according to the ratio and multiplied by the penalty coefficient , which embodies the principle of "ability gap needs to be deducted". For example, the required endurance time of the survey sub-area is 100 minutes, and the endurance time of the UAV is 80 minutes, = 0.8, then the performance matching value of the endurance time is 0.64.

[0094] The performance of the UAV resolution and the UAV required resolution is matched by:

[0095]

[0096] wherein, is the performance matching value of the sensor resolution, is the UAV required resolution, is the UAV resolution, is the penalty coefficient of the resolution, and the default value is 0.7.

[0097] The smaller the sensor resolution value (the higher the accuracy), the stronger the ability, so when the resolution of the UAV ≤ the required resolution of the sub-area (such as 2 cm ≤ 5 cm), the matching value is 1 point (the ability is better than the requirement); if the resolution value is larger (the accuracy is insufficient, such as 6 cm > 5 cm), the matching value is calculated according to the ratio of the requirement and the ability and multiplied by the penalty coefficient of the resolution . For example, the UAV required resolution of the survey sub-area is ≤ 5 cm, and the UAV resolution is 6 cm, = 0.7, then the performance matching value of the sensor resolution is 0.58.

[0098] The performance of the UAV flight line density and the UAV required flight line density is matched by:

[0099]

[0100] wherein, is the performance matching value of the flight line density, The demand route density for the UAV, The route density for the UAV, The penalty coefficient for the route density, which is 0.5 by default.

[0101] It should be noted that the "the closer the route density is to the demand, the higher the matching value" characteristic is described by the exponential function. - |represents the difference between the route density of the UAV and the demand of the survey sub-area, multiplied by the sensitivity coefficient , through the form of an exponential, the smaller the difference, the closer the matching value is to 1. For example: the route density of the survey sub-area is 8 lines / km, the route density of the UAV is 10 lines / km, 0.5, then the performance matching value of the route density is 0.37, indicating that the matching value is low due to the large density difference (the route needs to be adjusted).

[0102] It should be noted that the endurance time (threshold type matching): endurance is the basic condition for the UAV to complete the task. If the ability does not meet the demand (such as a UAV with an endurance of 100 minutes performing a 120-minute task), the task will be interrupted halfway. Threshold type matching ensures the basic ability bottom line through the logic of "full score if meeting the standard, and proportional penalty if not meeting the standard", avoiding task failure due to insufficient endurance. Sensor resolution (reverse type matching): resolution value and accuracy are inversely proportional (such as 0.5cm resolution is better than 1cm), if the resolution value of the UAV is greater than the demand (such as 1cm ability, 0.5cm demand), it cannot meet the high-precision surveying and mapping requirement. Reverse matching ensures that the data acquisition accuracy meets the standard through "full score when the ability value ≤ demand, and penalty when it exceeds". Route density (progressive type matching): too high route density will lead to redundant flight (waste of endurance), and too low route density will miss surveying and mapping areas. Progressive type matching makes the matching value approach 1 as the gap narrows through the exponential function, allowing the density to fluctuate within a reasonable range, balancing task efficiency and coverage integrity.

[0103] By combining the performance matching value and the survey sub-area priority value, a UAV survey sub-area adaptation matrix is constructed:

[0104]

[0105] Among them, represents the UAV survey sub-area adaptation value of the kth UAV and the jth survey sub-area in the UAV survey sub-area adaptation matrix, is the weight of the ith feature of the UAV performance data, represents the performance matching value of the ith feature of the kth UAV, is the survey sub-area priority value of the jth survey sub-area.

[0106] Step S4: based on the UAV survey sub-area adaptation matrix, the UAVs are assigned to the corresponding survey sub-areas by the Hungarian algorithm, and a local initial flight path is generated for the survey sub-area.

[0107] Based on the UAV survey sub-area adaptation matrix, the UAVs are assigned to the corresponding survey sub-areas by the Hungarian algorithm, and a local initial flight path is generated for the survey sub-area.

[0108] According to the number of UAVs and the number of survey sub-areas, the UAV survey sub-area adaptation matrix is adjusted to obtain an adjusted UAV survey sub-area adaptation matrix .

[0109] It should be noted that according to the number of UAVs and the number of survey sub-areas, the UAV survey sub-area adaptation matrix is adjusted: the Hungarian algorithm requires the UAV survey sub-area adaptation matrix to be a square matrix, when the number of UAVs is greater than the number of survey sub-areas, the UAV survey sub-area adaptation matrix is expanded, the newly added K-j list in the expanded UAV survey sub-area adaptation matrix represents a virtual sub-area, and the UAV survey sub-area adaptation value of the virtual sub-area is 0, K represents the total number of UAVs, and j represents the total number of survey sub-areas, for example, the original UAV survey sub-area adaptation matrix is , and the adjusted UAV survey sub-area adaptation matrix is ; when the number of UAVs is less than the number of survey sub-areas, the UAV survey sub-area adaptation matrix is expanded, and the newly added j-K row in the expanded UAV survey sub-area adaptation matrix represents a virtual sub-area; when the number of UAVs is equal to the number of survey sub-areas, the UAV survey sub-area adaptation matrix remains unchanged.

[0110] The cost matrix C in the Hungarian algorithm is constructed, which is the core input of the Hungarian algorithm, quantifies the cost of each task assignment combination, and the row index represents the UAV and the column index represents the survey sub-area, wherein: =max( )- , is the value of the kth UAV and the jth survey sub-area in the cost matrix, represents the value of the kth UAV and the jth survey sub-area in the adjusted UAV survey sub-area adaptation matrix, wherein max( ) represents the maximum value in the adjusted UAV survey sub-area adaptation matrix.

[0111] The row of the cost matrix is reduced, the value in each row of the cost matrix is reduced by the minimum value of the row, and then the column of the cost matrix is reduced, the value in each column of the cost matrix is reduced by the minimum value of the column, finally the number of straight lines that can cover all zero elements in the cost matrix is obtained, that is, the number of independent zero elements, and the independent zero elements are found.

[0112] Note that independent zero elements: a set of zero elements located in different rows and different columns, that is, there are no two independent zero elements in the same row or column in the matrix Each independent zero element corresponds to an effective allocation pair: row index → UAV number, column index → mapping sub-area number

[0113] Note that find the minimum number of horizontal and vertical lines that can cover all zero elements in the cost matrix C. 1. Mark all rows that do not have independent zero elements allocated, 2. For each zero element in the marked row (whether it is an independent zero element or not), mark the column where it is located, 3. For each zero element in the marked column (whether it is an independent zero element or not), mark the row where it is located, repeat steps 2-3 until it is not possible to continue marking, cover all unmarked rows and marked columns with straight lines. If the number of covered straight lines is less than the matrix order, perform the following adjustment: find the minimum value among the elements not covered by any straight line , the uncovered element minus , the element covered by two straight lines plus , the element covered by only one straight line remains unchanged; if the number of covered straight lines is equal to the matrix order, the current allocation of independent zeros is the optimal solution.

[0114] Then the cost matrix after row reduction and column reduction is C', find the independent zero elements in C', and construct the UAV allocation matrix FP:

[0115]

[0116] Wherein, is the value of the kth UAV and the jth mapping sub-area in the UAV allocation matrix, a value of 1 indicates that the kth UAV is allocated to the jth mapping sub-area, and a value of 0 indicates that the kth UAV is not allocated to the jth mapping sub-area.

[0117] Through the UAV allocation matrix, the UAV is allocated to the mapping sub-area (when the number of UAVs is greater than the number of mapping sub-areas, then part of the UAVs will be left over; when the number of UAVs is less than the number of mapping sub-areas, then part of the mapping sub-areas will not be allocated to the UAV, then enter the waiting sequence, when all UAVs execute the task, recompute the UAV allocation matrix of all UAVs and mapping sub-areas in the waiting sequence, allocate the UAV to the UAV in the waiting sequence), for regular-shaped mapping sub-areas (such as rectangles), use "Z" zigzag scanning route; for complex polygonal mapping sub-areas, use Delaunay triangulation and then cruise along the boundary; then the local initial route of the kth UAV is , ={( , , , ),( , , , ),...,( , , , ),...,( , , , )},in, This represents the local initial flight path of the k-th drone. , , , This represents the coordinates of the m-th path of the local initial path of the k-th UAV. This represents the time of the k-th UAV at the m-th flight path coordinate, where M represents the total number of flight path coordinates.

[0118] Step S5: Calculate the terrain feature data of the surveyed sub-area to obtain the terrain factor; construct a trajectory conflict analysis model based on the terrain factor; input the local initial route into the trajectory conflict analysis model to obtain the adjusted UAV route, thereby realizing route planning for the UAV.

[0119] A spatiotemporal grid-based trajectory conflict analysis model is constructed to make local adjustments to trajectory segments with potential conflicts, thereby achieving route planning for decoupled flight.

[0120] The flight path conflict analysis model includes: a time grid partitioning module, a conflict detection module, and a flight path adjustment module.

[0121] Time grid partitioning module:

[0122] Using the terrain feature data of the sub-region of the conflict area mapped by the UAV, the terrain factor of the conflict area is calculated:

[0123]

[0124] Where TCI is the topographic factor, G is the geographical feature, DX is the topographic relief, and B is the building density.

[0125] It should be noted that the conflict area of ​​the UAV is the boundary zone of the surveying sub-region. The boundary zone is often a region of abrupt change in geographical features (such as the transition zone between plains and mountains, or the edge of a building complex), which leads to increased climbing or turning maneuvers of the UAV and reduced flight path stability.

[0126] Conflicts are addressed by gridding, and the grid resolution is corrected using terrain factors. In low-altitude mapping, the complexity of the terrain directly determines the effectiveness requirements of sensor accuracy. The terrain factor (TCI) quantifies the combined effects of geographical features, terrain undulation, and building density. When the TCI approaches 1 (e.g., in steep mountains or densely built-up urban areas), abrupt changes in surface geometry can cause high distortion rates in scan data at standard resolution. This distortion stems from multiple reflections in three-dimensional space: in densely built-up areas, lidar pulses create multiple echoes between adjacent buildings, resulting in ghost outlines in the point cloud data; in complex mountainous terrain, when the surface inclination exceeds 15°, fixed-resolution scanning will lose key terrain features due to projection distortion. By dynamically attenuating the resolution using terrain factors, an inversely proportional adaptation mechanism between resolution and terrain complexity is established. This avoids wasted runtime due to oversampling in plains areas while ensuring mapping quality in complex regions.

[0127] The grid resolution of the conflict area is corrected using the aforementioned terrain factors: = *(1- ), = *(1- ), = *(1- ),in, The grid size is in the horizontal X direction. The grid size is in the horizontal Y direction. z is the grid size in the vertical Z direction. This is the attenuation coefficient, used to control the degree to which the grid size decreases with terrain factors. , , This is the basic grid size. = = Vertical resolution = , This represents the minimum safety threshold in the horizontal direction. The minimum safety threshold in the vertical direction, with a time step of [value missing]. If the local initial flight path is mapped onto a discrete grid, then the spatiotemporal grid cell representation is as follows: =[ , , , ],in, This represents the spatiotemporal grid cell of the k-th UAV. , , x, y, z coordinates of the x, y, z coordinates of the mth track coordinate of the local initial track of the kth UAV mapped in the space-time grid cell, time of the time of the mth track coordinate of the kth UAV mapped in the space-time grid cell, , , , .

[0128] The conflict detection module:

[0129]

[0130] wherein F is a conflict detection function, , , x, y, z coordinates of the x, y, z coordinates of the mth track coordinate of the local initial track of the kth UAV mapped in the space-time grid cell, , , x, y, z coordinates of the x, y, z coordinates of the mth track coordinate of the local initial track of the kth UAV mapped in the space-time grid cell, time of the time of the mth track coordinate of the kth UAV mapped in the space-time grid cell, time of the time of the mth track coordinate of the kth UAV mapped in the space-time grid cell, t represents a time step, is a time tolerance coefficient, when is 1, it means that the allowed time difference is less than when is 2, it means that the allowed time difference is less than 2 .

[0131] It should be noted that the UAV conflict monitoring module, the conflict monitoring module is the core component of the track conflict analysis model, and the conflict is judged through double conditions: when two UAVs are in the same space grid coordinate (i.e. three-dimensional position overlap) and the time difference is less than the set tolerance threshold , it is judged that there is a collision risk. For physical collision avoidance, the spatial coordinates must be strictly consistent and the time difference tends to zero (η≈0.1); and for surveying and mapping task conflict (such as repeated scanning of the same area), the time condition is relaxed (η=1.5).

[0132] The track adjustment module: the track adjustment module includes time adjustment, height adjustment, and horizontal adjustment of the local initial track of the UAV.

[0133] When the conflict monitoring module identifies a route conflict, the mapping sub-area priority of the mapping sub-area corresponding to the two unmanned aerial vehicles is determined, for example: the k2th unmanned aerial vehicle is adjusted, and the mapping sub-area priority of the mapping sub-area corresponding to the k2th unmanned aerial vehicle should be less than the mapping sub-area priority of the mapping sub-area corresponding to the k1th unmanned aerial vehicle.

[0134] Time adjustment: ,

[0135] wherein, is the time of the adjusted k2th unmanned aerial vehicle at the mth route coordinate mapped in the time-space grid unit, is the time step, and py is the offset step number, which needs to meet the time sequence rationality.

[0136] Height adjustment:

[0137] wherein, is the z coordinate of the adjusted k2th unmanned aerial vehicle at the mth route coordinate mapped in the time-space grid unit, is the z coordinate of the mth route coordinate of the local initial route of the k2th unmanned aerial vehicle mapped in the time-space grid unit, and py is the offset step number, is the grid size in the vertical Z direction.

[0138] Horizontal adjustment:

[0139]

[0140] wherein, is the x coordinate and y coordinate of the adjusted k2th unmanned aerial vehicle at the mth route coordinate mapped in the time-space grid unit, is the x coordinate and y coordinate of the k2th unmanned aerial vehicle at the mth route coordinate mapped in the time-space grid unit, is the offset angle, is the horizontal distance between the k1th unmanned aerial vehicle and the k2th unmanned aerial vehicle.

[0141] It should be noted that when the two unmanned aerial vehicles are in the same space grid coordinate (i.e., the three-dimensional positions overlap) and the time difference is less than the set tolerance threshold , there is a risk of collision, and the mapping sub-area priority of the mapping sub-area corresponding to the two unmanned aerial vehicles is determined, for example: the k2th unmanned aerial vehicle is adjusted, and the mapping sub-area priority of the mapping sub-area corresponding to the k2th unmanned aerial vehicle should be less than the mapping sub-area priority of the mapping sub-area corresponding to the k1th unmanned aerial vehicle. Before the collision of the two unmanned aerial vehicles , the situation of the two unmanned aerial vehicles is determined by using the corresponding adjustment, and the horizontal adjustment is: before the collision of the two unmanned aerial vehicles When the actual horizontal distance between the k1th UAV and the k2th UAV is less than the safety threshold in the horizontal direction, the horizontal offset is calculated; for example, when the horizontal distance between the k1th UAV and the k2th UAV is 150 m and the minimum safety threshold in the horizontal direction is 200 m, the horizontal offset distance required to reach the safety threshold is 200-150=50 m, and the offset direction is determined by the angle The offset is decomposed into the x-axis and the y-axis by a trigonometric function, and the coordinates of the adjusted k2th UAV in the mth route mapped in the space-time grid unit at time t are: Height adjustment: before the two UAVs collide When the distance between the k1th UAV and the k2th UAV in the vertical direction is less than the safety threshold at time t, the height is adjusted; time adjustment: when the allowed time tolerance is too small, it may cause rear-end collision or there are obstacles when adjusting the horizontal offset distance, then the time offset is adjusted.

[0142] By inputting the local initial route of the UAV into the route adjustment module, the adjusted UAV route '', '=[ , , , ]。

[0143] Step S6: When the UAV performs low-altitude mapping through the adjusted route, the operation state data and the mapping progress data of the UAV are collected in real time, and the UAV operation risk value is calculated by combining the operation state data and the mapping progress data of the UAV. If the UAV operation risk value is greater than the preset threshold, a warning is given and other UAVs are exchanged to continue the mapping task.

[0144] When the UAV performs low-altitude mapping through the adjusted route, the operation state data and the mapping progress data of the UAV are collected in real time and normalized to [0, 1]. The operation state data of the UAV includes the remaining power, attitude stability, and flight control response delay of the UAV. The mapping progress data of the UAV includes the area ratio of the completed mapping sub-area.

[0145] By combining the operation state data and the mapping progress data of the UAV, the UAV operation risk value is calculated:

[0146]

[0147] wherein FX is the UAV operation risk value, DL is the remaining power, ZT is the attitude stability, XT is the flight control response delay, and QU is the area ratio of the completed mapping sub-area.

[0148] If the operation risk value of the UAV is greater than the preset threshold value, a warning is given and other UAVs are exchanged to continue the mapping task.

[0149] It should be noted that when the operation risk value of the UAV exceeds the preset threshold value (such as 0.7), a warning is started and the UAV exchange mechanism is triggered. Around the risk UAV, within a set radius (such as 1000 meters), search for nearby UAVs that meet the power requirement (remaining power is more than 10% above the minimum safety value) as candidates, and the mapping sub-area priority of the candidate UAV is less than the mapping sub-area corresponding to the UAV whose operation risk value exceeds the threshold value. Then, by the performance matching value of the candidate UAV to the mapping sub-area, the UAV with greater performance matching value to the mapping sub-area and more sufficient power is preferentially selected to reduce the task interruption time. After determining the optimal candidate UAV, the system sends the unfinished mapping task coordinates of the original UAV to the candidate machine, and re-plans the route for the candidate machine through the flight path conflict analysis model. The original risk UAV executes the return or standby instruction according to its own state, and the whole exchange process is completed through real-time data interaction, ensuring seamless connection of the mapping task and reducing the risk of task interruption caused by single machine failure.

[0150] An intelligent multi-UAV cooperative route planning method for low-altitude mapping scenarios is proposed in this paper. First, the mapping area is divided into regular sub-areas, and the topographic data of each sub-area, such as geographical features, terrain undulations, and building density, as well as demand data such as task urgency and historical data missing, are collected. Based on these data, the priority weight of each sub-area is calculated. At the same time, the performance parameters (such as endurance time, sensor resolution, and flight path density) of available UAVs are collected and evaluated. Then, the UAV performance is matched with the sub-area demand, and an adaptation matrix is constructed combining the sub-area priority, and an optimization algorithm (such as the Hungarian algorithm) is used to assign the optimal mapping sub-area to the UAV and generate the initial flight path. Subsequently, a flight path conflict analysis model is dynamically constructed considering the terrain complexity of the sub-area, and the initial flight path is adjusted for safety. Finally, the state and progress of the UAV during task execution are monitored in real time, the operation risk is evaluated, and the UAV is dynamically exchanged when the risk is too high to ensure that the mapping task is completed efficiently, safely, and reliably.

[0151] The performance matching degree of the UAV is combined with the priority weight of the surveying and mapping sub-area to construct an adaptation matrix, which breaks through the limitations of traditional static task allocation. Instead of simply considering distance or priority, the dynamic quantitative fusion of two key dimensions is achieved: the degree to which the UAV's own capabilities (endurance, resolution, flight path density) meet the actual needs of the sub-area; and the priority of the sub-area itself, which is reflected by the urgency and importance of the task. By combining the two, more accurate task matching can be achieved, ensuring that high-performance UAVs are preferentially served in complex and critical sub-areas with high demand and high priority, and avoiding resource mismatching (such as using high-performance UAVs in simple areas or using UAVs with insufficient capabilities in high-demand areas); the overall fleet utilization efficiency and the completion effect of the surveying and mapping task are significantly improved.

[0152] Based on the terrain complexity factor (calculated by integrating geographical features, terrain undulation, and building density), a space-time grid conflict analysis model is constructed to achieve adaptive and refined conflict detection and resolution, and to dynamically perceive the actual complexity of the environment. The terrain complexity factor quantifies the challenging nature of the region (such as high-rise dense areas or steep mountains). The model automatically adjusts the fineness of the space-time grid used for conflict detection based on this, using finer grids in complex areas to improve detection sensitivity and using looser grids in open flat areas to avoid unnecessary conservative avoidance. This greatly improves the safety of multi-UAV cooperative flight in low-altitude, especially in complex environments.

[0153] The initial local flight path generated after allocation is input into the above conflict analysis model, and the final executable optimized flight path is output after detection and adjustment, ensuring the safety, reliability, and efficient execution of multi-UAV cooperative operation. The model accurately identifies potential conflict points (such as meeting at the same location or being too close) in time and space for different UAV flight paths, and uses time adjustment (offset arrival time), height adjustment (change flight altitude), or horizontal avoidance (lateral offset flight path) strategies to completely eliminate collision risks. This is crucial for safe flight in low-altitude complex environments (such as urban building groups).

[0154] It has to be noted that, in the present document, the terms "first", "second", etc. merely serve to identify different entities or actions and do not necessarily require or imply any actual relationship or order between these entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. In other words, without further restriction, reference to elements will not, without more limitations, exclude additional, unrecited elements of a process, method, article, or apparatus.

[0155] While embodiments of the application have been shown and described, it is to be understood that the application is not limited to the details of the embodiments described, since numerous modifications and changes can be made to the embodiments without departing from the spirit and scope of the application as defined by the appended claims and their equivalents.

Claims

1. A method for route planning of a UAV for low altitude mapping, characterized in that: The method comprises the following steps: Step S1: obtaining a plurality of surveying and mapping sub-areas by regularly partitioning a surveying and mapping area; collecting geographical features, terrain undulations, and building density of the surveying and mapping sub-areas to obtain terrain feature data of the surveying and mapping sub-areas; Step S2: collecting task urgency and historical data missing conditions of the surveying and mapping sub-areas to obtain demand data of the surveying and mapping sub-areas; and calculating a priority value of the surveying and mapping sub-areas according to the demand data and the terrain feature data of the surveying and mapping sub-areas; Step S3: collecting data of endurance time, sensor resolution, and flight line density of the unmanned aerial vehicle to obtain unmanned aerial vehicle performance data; collecting data of unmanned aerial vehicle performance requirements of the surveying and mapping sub-areas to obtain performance requirement data of the surveying and mapping sub-areas; performing single-parameter performance matching on the unmanned aerial vehicle performance data and the performance requirement data to obtain a performance matching value of each parameter in the unmanned aerial vehicle performance data; and constructing an unmanned aerial vehicle-surveying and mapping sub-area matching matrix by combining the performance matching value and the priority value of the surveying and mapping sub-areas; Step S4: distributing the unmanned aerial vehicle to the corresponding surveying and mapping sub-area by using the Hungarian algorithm based on the unmanned aerial vehicle-surveying and mapping sub-area matching matrix, and generating a local initial flight line for the surveying and mapping sub-area; Step S5: calculating a terrain factor by calculating the terrain feature data of the surveying and mapping sub-areas; constructing a flight path conflict analysis model based on a space-time grid based on the terrain factor; inputting the local initial flight line into the flight path conflict analysis model to obtain an adjusted unmanned aerial vehicle flight line, and realizing route planning for the unmanned aerial vehicle. 2.The unmanned aerial vehicle route planning method for low-altitude mapping of claim 1, wherein: The calculation of the priority value of the surveying and mapping sub-areas according to the demand data and the terrain feature data of the surveying and mapping sub-areas comprises the following specific steps: The calculation of the priority value of the surveying and mapping sub-areas according to the demand data and the terrain feature data of the surveying and mapping sub-areas comprises the following specific steps: ； Wherein, Q is the priority value of the surveying and mapping sub-area, G is the geographical feature, DX is the terrain undulation, B is the building density, U is the task urgency, and LS is the historical data missing condition. 3.The UAV route planning method for low-altitude mapping of claim 2, wherein: The single-parameter performance matching of the unmanned aerial vehicle performance data and the performance requirement data to obtain the performance matching value of each parameter in the unmanned aerial vehicle performance data comprises the following steps: The single-parameter performance matching of the unmanned aerial vehicle performance data and the performance requirement data to obtain the performance matching value of each parameter in the unmanned aerial vehicle performance data comprises the following steps: The performance matching of the endurance time of the unmanned aerial vehicle and the required endurance time of the unmanned aerial vehicle is performed as follows: ； wherein, is a performance matching value for the endurance time, is the endurance time of the UAV, is the required endurance time of the UAV, is a penalty coefficient for the endurance time, and is 0.8 by default. The performance matching of the resolution of the unmanned aerial vehicle and the required resolution of the unmanned aerial vehicle is performed as follows: ； wherein, a performance matching value for the sensor resolution, a drone demand resolution, a drone resolution, a penalty coefficient for the resolution, defaulting to 0.7; The performance matching of the flight line density of the unmanned aerial vehicle and the required flight line density of the unmanned aerial vehicle is performed as follows: ； wherein, is a performance matching value of the route density, is a UAV required route density, is a UAV route density, is a penalty coefficient of the route density, and the default value is 0.

5. 4.The UAV route planning method for low-altitude mapping according to claim 3, wherein: The performance matching of the flight line density of the unmanned aerial vehicle and the required flight line density of the unmanned aerial vehicle is performed as follows: The construction of the unmanned aerial vehicle-surveying and mapping sub-area matching matrix by combining the performance matching value and the priority value of the surveying and mapping sub-areas comprises the following steps: ； wherein, represents a UAV mapping sub-region adaptation value of the kth UAV and the jth mapping sub-region in the UAV mapping sub-region adaptation matrix, is a weight of the ith feature of the UAV performance data, represents a performance matching value of the ith feature of the kth UAV, is a mapping sub-region priority value of the jth mapping sub-region.

5. The unmanned aerial vehicle route planning method for low-altitude mapping according to claim 4, characterized in that: The construction of the unmanned aerial vehicle-surveying and mapping sub-area matching matrix by combining the performance matching value and the priority value of the surveying and mapping sub-areas comprises the following steps: The distribution of the unmanned aerial vehicle to the corresponding surveying and mapping sub-area based on the unmanned aerial vehicle-surveying and mapping sub-area matching matrix by using the Hungarian algorithm comprises the following steps: According to the number of unmanned aerial vehicles and the number of surveying and mapping sub-areas, the unmanned aerial vehicle surveying and mapping sub-area adaptation matrix is adjusted to obtain an adjusted unmanned aerial vehicle surveying and mapping sub-area adaptation matrix ; A cost matrix C is constructed, where: =max( )- , is the value in the cost matrix for the kth drone and jth mapping sub-region, denotes the value in the adjusted drone mapping sub-region fit matrix for the kth drone and jth mapping sub-region, where max( ) denotes the maximum value in the adjusted drone mapping sub-region fit matrix; The cost matrix is reduced by rows, and the values in each row of the cost matrix are reduced by the minimum value of the row. Then the cost matrix is reduced by columns, and the values in each column of the cost matrix are reduced by the minimum value of the column. Finally, the number of straight lines that can cover all the zero elements in the cost matrix is obtained, that is, the number of independent zero elements, and the independent zero elements are found. The cost matrix after row reduction and column reduction is C', and the independent zero elements are found in C', and the UAV allocation matrix FP is constructed: ； wherein, is a value of the kth UAV and the jth mapping sub-area in the UAV allocation matrix, and the value of 1 indicates that the kth UAV is allocated to the jth mapping sub-area, and the value of 0 indicates that the kth UAV is not allocated to the jth mapping sub-area. 6.The UAV route planning method for low-altitude mapping of claim 5, wherein: The local initial flight path of the survey sub-area is generated, including the following specific steps: The unmanned aerial vehicles are distributed in each surveying and mapping sub-area through the unmanned aerial vehicle distribution matrix, a "Z" shaped scanning route is adopted for the regular shaped surveying and mapping sub-area, and a Delaunay triangulation is adopted for the complex polygonal surveying and mapping sub-area, and then the boundary is patrolled; , = {( , , , ),( , , , ),...,( , , , ),...,( , , , )}, wherein represents the local initial flight path of the kth UAV, , , , represents the mth flight path coordinate of the local initial flight path of the kth UAV, represents the time of the kth UAV at the mth flight path coordinate, and M represents the total number of flight path coordinates.

7. The unmanned aerial vehicle route planning method for low-altitude mapping of claim 6, wherein: The terrain factor is obtained by calculating the terrain feature data of the survey sub-area, including the following specific steps: The terrain factor of the conflict local area is calculated through the terrain feature data of the survey sub-area of the conflict area of the UAV: ； Wherein, TCI is the terrain factor, G is the geographical feature, DX is the terrain undulation, and B is the building density. 8.The UAV route planning method for low-altitude mapping of claim 7, wherein: Based on the terrain factor, a flight path conflict analysis model based on a space-time grid is constructed, including the following specific steps: The grid resolution of the conflict area is modified by the terrain factor: = *(1- ), = *(1- ), = *(1- ), wherein, is the grid size in the horizontal X direction, is the grid size in the horizontal Y direction, z is the grid size in the vertical Z direction, is the attenuation coefficient for controlling the degree of attenuation of the grid size with the terrain factor, , , is the basic grid size, = = , the vertical resolution = , is the minimum safety threshold in the horizontal direction, is the minimum safety threshold in the vertical direction, and the time step is t, the local initial flight path is mapped into a discrete grid; then the space-time grid cell represents: =[ , , , ], wherein, represents the space-time grid cell of the kth UAV, , , is the x, y, z coordinate of the mth flight path coordinate representing the local initial flight path of the kth UAV mapped in the space-time grid cell, represents the time of the mth flight path coordinate of the kth UAV mapped in the space-time grid cell, , , , ; The conflict detection module: ； wherein F is a collision detection function, , , is the x, y, z coordinate of the mth track coordinate of the local initial track of the k1th UAV mapped in the space-time grid cell, , , is the x, y, z coordinate of the mth track coordinate of the local initial track of the k2th UAV mapped in the space-time grid cell, is the time of the mth track coordinate of the k1th UAV mapped in the space-time grid cell, is the time of the mth track coordinate of the k2th UAV mapped in the space-time grid cell, t is the time step, is a time tolerance coefficient, when is 1, it means that the allowed time difference is less than when is 2, it means that the allowed time difference is less than 2 ; The flight path adjustment module includes time adjustment, height adjustment, and horizontal adjustment of the local initial flight path of the UAV. Time adjustment: ； wherein, is the time of the adjusted k2th UAV at the mth route coordinate mapped in the time of the space grid unit, is the time step, pyis the offset step number, and the time sequence rationality needs to be met; Height adjustment: ； wherein, is the z coordinate of the mth track coordinate of the adjusted local initial track of the k2th UAV mapped in the space-time grid cell, is the z coordinate of the mth track coordinate of the local initial track of the k2th UAV mapped in the space-time grid cell, and pyis the offset step number, is the grid size in the vertical Z direction; Horizontal adjustment: ； wherein, is the x coordinate and y coordinate of the adjusted k2th UAV in the mth flight line coordinate mapped in the space-time grid cell, is the x coordinate and y coordinate of the k2th UAV in the mth flight line coordinate mapped in the space-time grid cell, is the offset angle, is the horizontal distance between the k1th UAV and the k2th UAV. 9.The UAV route planning method for low-altitude mapping of claim 8, wherein: The local initial flight path is input into the flight path conflict analysis model to obtain the adjusted UAV flight path, realizing the route planning of the UAV, including the following specific steps: The local initial flight path of the unmanned aerial vehicle is input into the flight path adjustment module to obtain an adjusted flight path of the unmanned aerial vehicle , [ , , , ]. 10.The UAV route planning method for low-altitude mapping of claim 9, wherein: Further comprising step S6, specifically: Step S6: When the UAV performs low-altitude surveying through the adjusted flight path, real-time collection of the operation state data of the UAV and the surveying progress data of the UAV is performed, and the UAV operation risk value is calculated by combining the operation state data of the UAV and the surveying progress data of the UAV. If the UAV operation risk value is greater than a preset threshold, a warning is given and other UAVs are exchanged to continue to perform the surveying task. The UAV operation risk value is calculated by combining the operation state data of the UAV and the surveying progress data of the UAV: ； Wherein, FX is the UAV operation risk value, DL is the remaining power, ZT is the attitude stability, XT is the flight control response delay, and QU is the completed surveying sub-area; If the UAV operation risk value is greater than a preset threshold, a warning is given and other UAVs are exchanged to continue to perform the surveying task.

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