Land space planning surveying and mapping system based on aerial survey of unmanned aerial vehicle

Abstract

The invention relates to the technical field of surveying and mapping, in particular to a land space planning surveying and mapping system based on unmanned aerial vehicle aerial survey, which comprises an unmanned aerial vehicle module for acquiring surveying and mapping data, a central processing module for processing the surveying and mapping data, and a GPS positioning module for acquiring the area A of a surveying and mapping region and the real-time position coordinates of the unmanned aerial vehicle module. According to the invention, the new coordinates of the unmanned aerial vehicle module moving in the optimal aerial survey path are calculated through the unmanned aerial vehicle path updating model, so that when each unmanned aerial vehicle module moves in the corresponding optimal aerial survey path, the collection area of the unmanned aerial vehicle module can effectively cover the related sub-area; and the overlapping area between the acquisition areas of each unmanned aerial vehicle module is ensured to be minimum, and the overlapping part of the surveying and mapping data is reduced, so that the integrity and quality of the surveying and mapping data are improved.

Description

Technical Field

[0001] The present invention relates to the field of surveying and mapping technology, and in particular to a national land space planning surveying and mapping system based on unmanned aerial vehicle (UAV) aerial surveying. Background Art

[0002] The rapid development of drone aerial survey technology has provided strong technical support for national land space planning and surveying. Traditional surveying and mapping methods often rely on the consumption of manpower, time and resources, and cannot meet the needs of quickly and accurately obtaining national land space information. Drone aerial survey has the advantages of high efficiency, accuracy, flexibility and low cost, which can meet the needs of modern national land space planning.

[0003] After searching, Chinese patent No. CN202011412775.1 discloses a drone surveying and mapping method, device and system. The above scheme sets up multiple drones, and each drone corresponds to a set of first aerial photography tracks; after the drone takes real-time images according to the first aerial photography track corresponding to itself, multiple real-time images are obtained respectively, and the construction buildings are described by combining the multiple real-time images, and according to the multiple real-time images, it is judged whether multiple drones have repeated shooting; if so, the multiple first aerial photography tracks are adjusted to generate multiple second aerial photography tracks, and the multiple second aerial photography tracks are sent to multiple drones respectively, so that the multiple drones can shoot the construction site area according to the second aerial photography tracks corresponding to themselves, so as to ensure that the multiple drones can conduct comprehensive aerial photography of the construction buildings during the aerial photography process, and at the same time, the speed of drone aerial photography can be improved, thereby improving efficiency.

[0004] In the process of UAV mapping, whether multiple UAVs work together to shoot mapping data of multiple different aerial survey paths, or a single UAV repeatedly shoots mapping data of different aerial survey paths, overlapping areas will be generated in the mapping data. When stitching multiple mapping data later, the overlapping areas provide necessary reference points to ensure better matching of adjacent images during the stitching process, reduce the possibility of gaps and distortion, and make subsequent processing smoother and more accurate.

[0005] Therefore, in the process of UAV surveying and mapping, it is necessary to collect enough surveying and mapping data with overlapping areas to avoid insufficient overlapping areas between adjacent routes, which will result in the surveying and mapping data being unable to form a continuous picture at the connection point, causing damage to data integrity and reduced data accuracy, which will have an adverse impact on subsequent national land space planning decisions. However, when repeatedly collecting surveying and mapping data for multiple aerial photography routes, the higher the overlap rate of the aerial photography routes, the more surveying and mapping images are generated, and the amount of data increases accordingly, which in turn leads to a significant increase in the workload of post-processing and analysis. Therefore, a national land space planning surveying and mapping system based on UAV aerial survey is proposed to solve the above-mentioned problems. Summary of the invention

[0006] Technical issues solved

[0007] In view of the above-mentioned shortcomings of the prior art, the present invention provides a national land space planning surveying and mapping system based on UAV aerial survey, which can effectively solve the problem of low efficiency in processing and analyzing surveying and mapping data generated by a large number of repeated aerial photography routes in the prior art.

[0008] Technical Solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0010] The present invention provides a national land space planning surveying and mapping system based on UAV aerial survey, including a UAV module for collecting surveying and mapping data and a central processing module for processing the surveying and mapping data, as well as a GPS positioning module for obtaining the area A of the surveying and mapping area and the real-time position coordinates of the UAV module. k (X k ,Y k ,Z k ), and also includes:

[0011] Aerial survey path planning module, based on area A and real-time position coordinates UAV k (X k ,Y k ,Z k ) divides the survey area into several sub-areas V, and constructs an aerial survey path planning model to obtain the relevant sub-areas V (UAV k1 ) in the aerial survey path, where:

[0012] The aerial survey path is optimized through the path optimization model to obtain the optimal aerial survey path. The UAV path update model is constructed based on the optimal aerial survey path to obtain the new coordinates of the UAV module moving in the optimal aerial survey path. k ′(X k ′,Y k ′,Z k ′)=(X k +TOD·OR,Y k ,Z k );

[0013] Where TOD is the trajectory offset distance, and OR is the surveying and mapping overlap ratio.

[0014] Furthermore, the method of obtaining the area of ​​the surveying and mapping region is:

[0015] Where (X i ,Y i ) represents each vertex V in the surveying area i The coordinates of the vertex V i The number of

[0016] Furthermore, the method of dividing the surveying area into a plurality of sub-areas V includes: using the real-time position coordinates of the UAV k Construct the location set P of the drone module = {UAV1, UAV2, UAV3, ...., UAV K}, the position set P is used as the input feature and input into the UAV module distance calculation model to obtain the distance between the UAV modules In the formula, k1 and k2 are the corresponding UAV numbers;

[0017] Define the sub-area V, and get the initial position coordinates as UAV k1 The relevant sub-areas mapped by the drone module Where Q∈R 3 Represented in three-dimensional space R 3 Any point Q in is the surveying point; Indicates that the coordinates of any point Q to the initial position are UAV k1 The distance of the UAV module is less than any point Q to all initial position coordinates that are not UAV k1 The distance of the drone module.

[0018] Furthermore, the method of constructing the aerial survey path planning model includes:

[0019] Define the cost function of the aerial survey path planning model as the path length Where PL represents the total length of the aerial survey path of the UAV module; d(Q i ,Q i+1 ) indicates that in the relevant sub-area V(UAV k1 ) any point Q i To another arbitrary point Q i+1 The distance; n is any point Q i The number of

[0020] Furthermore, the path optimization model optimizes the aerial survey path in the following ways:

[0021] Generate several groups of aerial survey paths as individuals, that is, path sequence I = [UAV k ,Q1,Q2,Q3,...,Q i ], where i represents the number of survey points Q;

[0022] Using several groups of path sequences I to construct a path collection CP = {I1, I2, I3, ..., I m}, i.e., the initial population, where m represents the number of path sequences I;

[0023] Calculate the fitness of each path sequence I Among them IPL represents the path length of the corresponding path sequence I;

[0024] Select the path sequence I with the largest fitness F value from the path collection CP, which is the optimal aerial survey path.

[0025] Furthermore, the method of selecting the path sequence I with a large fitness F value includes:

[0026] Randomly select j path sequences I from the path collection CP, where j is an integer less than m;

[0027] Compare the fitness F of j path sequences I, and select the path sequence I with the highest fitness F, which is the target of this selection; repeat the above steps until the required number of path sequences I are generated and retained as the next generation population;

[0028] Select two path sequences I from the next generation population as parent individuals, and obtain new child individuals through crossover operation; then perform mutation operation on the new child individuals to obtain mutated child individuals; combine the mutated child individuals with individuals in the initial race to obtain a new generation population; repeat the above steps until the preset number of iterations.

[0029] Furthermore, the method of constructing the drone path update model includes:

[0030] Through the UAV mapping range model, the field of view width FOV and field of view height H of the UAV module are calculated;

[0031] Based on the field of view width FOV and field of view height H, a surveying and mapping overlap area model is constructed to obtain the surveying and mapping overlap area OA of the UAV module;

[0032] The total surveying area TA = FOV*PL is calculated based on the field of view width FOV of the drone module and the path length PL in the optimal aerial survey path;

[0033] The surveying overlap rate is calculated based on the surveying overlap area OA and the surveying total area TA

[0034] Define the interval distance of the drone module SD = μ·FOV; where μ is a constant and μ≥1; define the trajectory offset distance of the drone module In the formula, γ is a constant,

[0035] The new coordinates of the mobile UAV′ k (X′ k ,Y′ k ,Z′ k )Add constraints:

[0036] |UAV′k+1 (X′ k+1 ,Y′ k+1 ,Z′ k+1 )-UAV′ k (X′ k ,Y′ k ,Z′ k )≥SD.

[0037] Furthermore, the calculation formula of the field of view width is:

[0038] The calculation formula of the visual field height is: H = H f sin(β);

[0039] In the formula, H f is the flight altitude of the UAV module, θ is the mapping field of view of the UAV module, and β is the mapping tilt angle of the UAV module.

[0040] Furthermore, the method of constructing the surveying and mapping overlapping area model includes:

[0041] Define the four corner points of the mapping boundary MB of the drone module, namely the left front point DOT LF , right front point DOT RF , left rear point DOT LB , right rear point DOT RB ;

[0042] Based on real-time position coordinates of UAV k (X k ,Y k ,Z k ), field of view width FOV and field of view height H, calculate the coordinates of the four corner points, respectively;

[0043] Left front point right front point Left rear point Right rear point

[0044] Then define the mapping boundaries:

[0045]

[0046] Using adjacent mapping boundaries MB k1 and mapping boundaries MB k2 Calculate the surveying and mapping overlap area OA, the calculation formula is:

[0047] OA=max[0,min(X k1 +FOV k1 ,X k2 +FOV k2 )-max(Xk1 ,X k2 )]*max[0,min(Y k1 +H k1 ,Y k2 +H k2 )-max(Y k1 ,Y k2 )];

[0048] Where: (X k1 ,Y k1 ) is the surveying boundary MB k1 The left rear point coordinates, FOV k1 MB for mapping boundaries k1 Width, H k1 MB for mapping boundaries k1 Height; (X k2 ,Y k2 ) is the surveying boundary MB k2 The left rear point coordinates, FOV k2 MB for mapping boundaries k2 Width, H k2 MB for mapping boundaries k2 Height;

[0049] max[0,min(X k1 +FOV k1 ,X k2 +FOV k2 )-max(X k1 ,X k2 )] represents the calculation of the width of the surveying and mapping overlapping area OA;

[0050] max[0,min(Y k1 +H k1 ,Y k2 +H k2 )-max(Y k1 ,Y k2 )] represents the calculation of the height of the surveying and mapping overlapping area OA.

[0051] Beneficial Effects

[0052] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0053] This scheme divides the surveying area into several relevant sub-areas V (UAV k1 ), any point Q in each relevant sub-region V(UAVk1) is initially located at the coordinates of UAV k1The UAV modules are used for mapping, ensuring that the sub-areas V that each UAV module is responsible for do not overlap with each other and can completely cover the entire mapping area. Then, the UAV modules in their respective corresponding sub-areas V (UAV k1 ) to improve the efficiency of surveying and mapping;

[0054] This solution also calculates the new coordinates of the UAV module moving in the optimal aerial survey path through the UAV path update model k ′(X k ,Y k ,Z k ), ensuring that when each UAV module moves in its corresponding optimal aerial survey path, the collection area of ​​the UAV module can effectively cover the relevant sub-area V(UAV k1 ), and ensure that the overlapping area between the collection areas of each drone module is minimized, reducing the overlapping part of the surveying and mapping data, thereby improving the integrity and quality of the surveying and mapping data. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0056] Figure 1 Schematic diagram of the structural composition of the planning and mapping system in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] The present invention will be further described below in conjunction with the embodiments.

[0059] Example:

[0060] See attached Figure 1 This case proposes a national land space planning surveying and mapping system based on UAV aerial survey, which includes multiple UAV modules, GPS positioning module, aerial survey path planning module and central processing module.

[0061] in:

[0062] The drone module includes drones used for aerial surveys and related surveying and mapping equipment; the drone is used to fly within the surveying and mapping area and carry the surveying and mapping equipment, and the surveying and mapping equipment is used to collect data information within the surveying and mapping area, namely, surveying and mapping data. Surveying and mapping equipment includes cameras, laser scanners, radars, etc.; surveying and mapping data includes surface visual image data (such as land cover type, vegetation status, building distribution, etc.), lidar data (used to generate accurate topographic maps and three-dimensional models), microwave remote sensing data (used to obtain information such as surface humidity and soil type), and thermal infrared images (used to detect surface temperature distribution, assess vegetation health, urban heat island effect, etc.). After the surveying and mapping data is collected by the drone module, the drone module transmits the collected surveying and mapping data to the central processing module via wireless signals, and the central processing module analyzes the data, thereby providing rich and accurate spatial information support for national land space planning and decision-making.

[0063] The GPS positioning module is used to obtain the real-time position coordinates of the surveying area A and the drone module, thereby facilitating the subsequent effective and accurate planning of the aerial survey path of the drone module.

[0064] The aerial survey path planning module is used to coordinate the planning of the aerial survey paths of multiple drone modules, so that when multiple drone modules are operating in their respective aerial survey paths, each drone module can collect the least amount of surveying and mapping data while ensuring the complete collection of the surveying and mapping area, and ensuring that the overlapping areas between the collection areas of each drone module are minimized. Through the setting of the aerial survey path planning module, multiple drone modules can efficiently collaborate to complete aerial photography tasks while ensuring the complete collection of surveying and mapping data in the surveying and mapping area, and directly reduce the amount of data that needs to be processed when splicing surveying and mapping data, thereby optimizing the system's processing and analysis efficiency of surveying and mapping data.

[0065] The central processing module is used to receive the surveying and mapping data collected by each drone module and to splice multiple surveying and mapping data, so that the system can obtain complete, consistent and accurate surveying and mapping data, thereby ensuring the quality and accuracy of national land space planning.

[0066] Specifically, the GPS positioning module obtains the area A of the surveying area by: obtaining the coordinates V of each vertex position of the surveying area through a GPS device or map software i (X i ,Y i ), and then calculate the area of ​​the survey area based on the Shoelace formula Where n represents the number of vertex positions; (X i ,Y i ) represents the coordinates of the i-th vertex position in the survey area, that is, Vi (X i ,Y i ). By converting the calculation results into absolute values, the final area of ​​the surveyed area is standardized to a positive value.

[0067] For example, assume that the survey area is a pentagon, and the coordinates of its five vertices are: V1(X1,Y1), V2(X2,Y2), V3(X3,Y3), V4(X4,Y4), V5(X5,Y5). Then calculate using the shoe lace formula,

[0068] When multiple drone modules are working synchronously, they will be placed in different initial positions, so that each drone module can start from a different initial position, more effectively cover the surveying area, and ensure that the surveying area can be fully photographed. The real-time position coordinates of multiple drone modules are obtained through the GPS positioning module UAV k , namely UAV k (X k ,Y k ,Z k ), (X k ,Y k ,Z k ) represents the three-dimensional coordinates of the UAV module, and k is the corresponding UAV number.

[0069] Furthermore, the GPS positioning module obtains the survey area A and the real-time position coordinates of the UAV module UAV k After that, the coordinate data is transmitted to the aerial survey path planning module via wireless signals. The aerial survey path planning module is based on the survey area A and the real-time position coordinates of the UAV module. k , the surveying area is divided into several sub-areas V. Each sub-area V corresponds to a generation point, which is the initial position coordinate of the UAV module, that is, the real-time position coordinate of the UAV module at the initial position. k . This ensures that each sub-area V is surveyed and mapped by the nearest drone module.

[0070] The methods of dividing the surveying area include: using the real-time position coordinates of the UAV module k Construct the location set P of the drone module = {UAV1, UAV2, UAV3, ...., UAV K Then the position set P of the drone module is used as the input feature and input into the drone module distance calculation model to obtain the distance between the drone modules. Where k1 and k2 are the corresponding drone numbers; (X k1 -X k2 )2 Indicates that the initial position coordinates are UAV k1 and UAVs k2 The square of the coordinate difference of the two drone modules on the X axis; (Y k1 -Y k2 ) 2 Indicates that the initial position is UAV k1 and UAVs k2 The square of the coordinate difference of the two drone modules on the Y axis; (Z k1 -Z k2 ) 2 Indicates that the initial position is UAV k1 and UAVs k2 The square of the coordinate difference of the two UAV modules on the Z axis; the square of the coordinate difference can ensure that the calculation result is always non-negative, because the distance cannot be negative. Then, through the above formula, we can get the initial position coordinates of UAV k1 and UAVs k2 The Euclidean distance between the two UAV modules is d, that is, the distance between the two UAV modules.

[0071] Then the concept of sub-area is further defined to obtain all relevant sub-areas belonging to the mapping of the specified drone module.

[0072] Where, V(UAV k1 ) indicates that the initial position coordinates are UAV k1 The relevant sub-area mapped by the drone module; Q∈R 3 Represented in three-dimensional space R 3 Any point Q in is the surveying point;

[0073] d(Q,UAV k1 ) indicates that the coordinates of any point Q to the initial position are UAV k1 The distance of the UAV module; d(Q,UAV k2 ) indicates that the coordinates of any point Q to the initial position are UAV k2 The distance of the drone module; Indicates that the coordinates of any point Q to the initial position are UAV k1 The distance of the UAV module is less than any point Q to all initial position coordinates that are not UAV k1 distance; Indicates that for each initial position coordinate is UAV k1 The UAV modules are all elements in the position set P of the UAV modules, and do not contain the UAV with the initial position coordinates k1 The drone module itself.

[0074] Then, the relevant sub-area V(UAV k1 ) is the point Q with the initial position coordinates of UAV k1 The drone module that is closest to the nearest drone module is used for mapping, which ensures that the sub-areas V that each drone module is responsible for do not overlap with each other and can completely cover the entire mapping area. Furthermore, by dividing the mapping area into several sub-areas V, the drone modules that perform mapping work can be effectively managed and scheduled.

[0075] Furthermore, in this solution, after the aerial survey path planning module divides the survey area into sub-areas V, the real-time position coordinates of the initial position of each UAV module are used. k , build an aerial survey path planning model, calculate the UAV module in each corresponding sub-area V (UAV k1 ) is the optimal aerial survey path.

[0076] Specifically, the cost function is constructed based on the traveling salesman problem (TSP) algorithm. First, the cost function of the aerial survey path planning model is defined as the path length In the formula, PL represents the total length of the aerial survey path of the UAV module, which reflects the cost from one point to another in the TSP algorithm. Generally, the lower the cost, the better the path; d(Q i ,Q i+1 ) indicates that in the relevant sub-area V(UAV k1 ) any point Q i To another arbitrary point Q i+1 The distance between the UAV module and the relevant sub-area V (UAV k1 ), so as to evaluate the advantages and disadvantages of different aerial survey paths when optimizing the aerial survey path of the subsequent UAV module and select the aerial survey path with the shortest path length.

[0077] For example, in the relevant sub-area V(UAV k1 ) contains three surveying points, namely, three arbitrary points Q1, Q2 and Q3; suppose the coordinates of the three surveying points are Q1(X Q1 ,Y Q1 ,Z Q1 )、Q2(X Q2 ,Y Q2 ,Z Q2 ) and Q3(X Q3 ,Y Q3 ,Z Q3 ), the real-time position coordinates of the initial position of the UAV module are UAV k1 (Xk1 ,Y k1 ,Z k1 ); Starting from the initial position, the single distance of the drone module to the three surveying points in turn is calculated as:

[0078] From UAV k1 (X k1 ,Y k1 ,Z k1 ) to Q1(X Q1 ,Y Q1 ,Z Q1 ):

[0079]

[0080] From Q1(X Q1 ,Y Q1 ,Z Q1 ) to Q2(X Q2 ,Y Q2 ,Z Q2 ):

[0081]

[0082] From Q3(X Q3 ,Y Q3 ,Z Q3 ) to Q2(X Q2 ,Y Q2 ,Z Q2 ):

[0083]

[0084] Finally, the path length of the UAV module PL = d(U1,Q1)+d(Q1,Q2)+d(Q2,Q3).

[0085] More specifically, in this case, the aerial survey path of the drone module is optimized through a path optimization model (i.e., a genetic algorithm model), and the optimization methods include:

[0086] First, several groups of aerial survey paths are generated, namely, individuals of the genetic algorithm, each of which represents the UAV module in the relevant sub-area V (UAV k1 ) is a path sequence I that starts from the initial position and reaches each mapping point in sequence; the path sequence I is usually represented by a list, which contains the order of all mapping points visited by the UAV module, such as path sequence I = [UAV k ,Q1,Q2,Q3,...,Q i ], where UAV k1 is the starting position of the drone module, that is, the real-time position coordinates of the drone module at the initial position, Q1, Q2, Q3, ..., Q iIt is the subsequent surveying point of the UAV module, and i represents the number of surveying points.

[0087] And through several groups of path sequences I, a path collection CP is constructed, which is the initial population of the genetic algorithm, representing the combination of all path sequences I in the aerial survey path, expressed as I∈CP={I1,I2,I3,...,I m}; where I1, I2, I3, ..., I m Represents different path sequences, and m is the number of path sequences.

[0088] Then, the fitness of each path sequence I is calculated I PL The path length of the corresponding path sequence is inversely proportional to the path length. The lower the path length, the higher the fitness. The fitness F is the standard for evaluating the quality of individuals. The path sequence I with high fitness indicates that the total distance or time required by the UAV module to survey each mapping point is the least, indicating that the path sequence is the best for the UAV module in the relevant sub-area V (UAV k1 ). Therefore, individuals with high fitness F are more likely to be retained in subsequent selections, i.e., excellent genes in the genetic algorithm, which have an impact on subsequent selection, crossover, and mutation operations.

[0089] Then, individuals with high fitness are selected from the initial population, that is, path sequences I with large fitness F values ​​are selected from the path collection CP. The selection method based on tournament selection includes: randomly selecting j path sequences I from the path collection CP, where j is an integer less than the total number of individuals m; then, the fitness F of the j path sequences I are compared, and the path sequence I with the highest fitness F is found as the winner of this tournament selection, that is, the target of this selection, and will be used as an excellent gene in the next generation population; finally, the above steps are repeated until the required number of individuals are generated. For example, if five individuals need to be selected, five rounds of tournament selection are required, that is, the next generation population contains five path sequences I that won in the tournament selection process.

[0090] For example, suppose there are ten individuals in the initial population, that is, m = 10, then the path set CP = {I1, I2, I3, I4, I5, I6, I7, I8, I9, I 10}, three individuals are randomly selected, which may be I1, I7 and I9. Assuming that the fitness of I1, I7 and I9 are 0.8, 0.6 and 0.9 respectively, then I9 will be the winner of this round of selection and will be selected into the next generation population.

[0091] Next, two individuals are selected from the next generation population as parent individuals, and new child individuals are generated through crossover operation. During the crossover operation, the characteristics of the parent individuals (including path sequence I, path length I of the path sequence) are PL ) will be retained or combined. The crossover operation includes: selecting two individuals from the next generation population, and randomly selecting a cutting point between the two individuals. The cutting point can be any position in the path sequence I, and then exchanging the part after the cutting point to generate a new child individual.

[0092] For example, the two parents selected from the next generation population are parent 1 and parent 2. Assume that the path sequence of parent 1 is I = [UAV k ,Q1,Q2,Q3], the path sequence of parent 2 is I = [UAV k ,Q6,Q5,Q4], the randomly selected cutting point is position 2, then the path sequence of the new child individual I′=[UAV k ,Q1,Q5,Q4], indicating that the path sequence of the new child individual inherits the first two parts of the path sequence of parent 1 and the last two parts of the path sequence of parent 2.

[0093] The crossover operation combines the characteristics of the parent individuals so that the generated new child individuals may have different characteristics. For example, in this case, by combining the different path sequences I of the parent individuals, the new child individuals can generate a path sequence I' that is balanced in terms of cost, time, etc., that is, a new aerial survey path.

[0094] Next, mutation operations are performed on the new sub-individuals, including small-scale changes to the path sequence I' of the new sub-individuals, such as randomly exchanging the positions of two arbitrary points Q in the path sequence I', to increase the diversity of path combinations and prevent the genetic algorithm from staying at the local optimal solution. For example, if in a certain iteration, the genetic algorithm finds a relatively high-quality aerial survey path, but the aerial survey path may not be the best, if there is no mutation operation, the aerial survey path may be copied too much, resulting in the genetic algorithm being unable to explore new paths.

[0095] For example, suppose the path sequence of the new child individual I′=[UAV k ,Q1,Q5,Q4,Q9], randomly select the indexes g and h of two points, for example, g = 2 and h = 5, that is, Q1 and Q9 in the path sequence I', and by exchanging these two points, the mutated path sequence I' = [UAV k ,Q9,Q5,Q4,Q1]. The mutation operation ensures that the new child individuals do not only depend on the characteristics of the parent individuals, but also increases the differences between different new child individuals, making the solution set more diverse.

[0096] Secondly, the mutated sub-individuals are replaced, and the mutated sub-individuals are combined with individuals in the initial population to form a new generation of population. Usually, the mutated sub-individuals with higher fitness are selected and retained in the new generation of population. The replacement operation can ensure that the new generation of population contains excellent individuals, which promotes further optimization.

[0097] For example, assuming that there are five individuals in the initial population, that is, m=5, then the path set CP={I1,I2,I3,I4,I5}, and the fitness of the five individuals are 0.8, 0.6, 0.75, 0.9, and 0.65 respectively; assuming that there are two sub-individuals after mutation, that is, m=2, then the path set CP′={I1′,I2′} after mutation, and the fitness of the two sub-individuals after mutation are 0.85 and 0.7 respectively; then the sub-individuals after mutation are combined with the initial population, and in the process of aggregation, individuals with high fitness or sub-individuals after mutation are selected to be retained, for example, individual I4 and sub-individual I1′ after mutation are combined to form a new population, that is, the new path set CP″={I1′,I4}. This example uses partial replacement, that is, part of the initial population is replaced by the mutated sub-individuals. For example, the top M individuals in fitness are selected as the retained individuals, and the remaining positions are filled by the mutated sub-individuals. You can also choose full coverage replacement, that is, replace all individuals in the initial population with mutated sub-individuals; or elite retention, that is, ensure that the individuals with the highest fitness (i.e., elites) remain unchanged during the replacement process, while the remaining positions are updated by mutated individuals. By retaining individuals with high fitness, the overall fitness of the next generation can be effectively improved, and the convergence speed of the algorithm and the quality of the solution can be promoted. The replacement operation not only ensures the inheritance of high-quality genes, but also promotes the increase of diversity and adaptability, which is an important link in achieving efficient optimization.

[0098] Finally, the above fitness evaluation, selection operation, crossover operation, mutation operation and replacement operation are repeated. Through repeated iterations, the genetic algorithm gradually improves the path and gradually approaches the optimal solution until the preset number of iterations is reached or a satisfactory aerial survey path is found.

[0099] It is worth noting that the aerial survey path planning model obtains the UAV modules in their respective corresponding sub-areas V (UAV k1 ), in order to ensure that the shooting images of the drone module in each aerial survey path are different and to maintain the minimum overlap between the shooting areas, the drone mapping range model is constructed to obtain the field of view width FOV and field of view height H of the drone module. Then, based on the field of view width FOV and field of view height H, the mapping overlap area model is constructed to obtain the mapping overlap area OA of the drone module. Then, based on the field of view width FOV of the drone module and the drone module in the relevant sub-area V (UAV k1) is calculated by the path length PL of the optimal aerial survey path to obtain the total survey area TA = FOV * PL. Then, the survey overlap rate is calculated based on the survey overlap area OA of the drone module and the total survey area TA of the drone module.

[0100] The construction method of the UAV mapping range model includes: defining the flight altitude H of the UAV module f (in meters), the field of view angle θ of the surveying and mapping equipment (in degrees) and the tilt angle β of the surveying and mapping equipment (in degrees);

[0101] Field of view width In the formula, the field of view width FOV represents the width that the UAV module can cover during mapping, which is used to determine the mapping range of the UAV module and the overlap of the fields of view between adjacent UAV modules; the flight altitude H f Refers to the height of the drone module relative to the ground, the flight altitude H f It will affect the viewing angle θ of the surveying and mapping equipment and the size of the shooting area. The higher the flight altitude, the larger the field of view width FOV. The field of view angle θ represents the lateral range of the field of view of the surveying and mapping equipment, which determines the ground range that the drone module can capture at a specific altitude. The larger the angle, the wider the coverage area. Indicates that at a given flight altitude H f Under the 2·H, the top-view range of each side of the drone module is, because the field of view angle θ of the surveying and mapping equipment is divided from the center to the left and right, it is necessary to calculate half of the angle to calculate the range of each side of the surveying and mapping, so as to find the entire width; and then through 2·H f represents the projection of the drone's flight altitude on both sides, and It is used to calculate the field of view width FOV formed by the shooting field of view of the drone module at this height. The final field of view width FOV can help design a reasonable aerial survey path to ensure that the shooting areas between drone modules have minimal overlap when the drone modules complete the mapping of the entire survey area.

[0102] For example, assume that the flight altitude of the drone module is H f = 200, the field of view angle of the surveying and mapping equipment is θ = 60°, then the field of view width rice.

[0103] Field of view height H = H f sin(β); ​​where the tilt angle β determines the tilt angle of the surveying and mapping equipment when shooting downward or upward. For example, when the surveying and mapping equipment is shooting completely horizontally, that is, β = 0°, sin(0) = 0 indicates the ineffectiveness of the height when shooting without tilt, and thus H = H f ; When the surveying equipment is shooting vertically downward, that is, β = 90°, sin(90°) = 1, so H = Hf 1=H f , indicating that the surveying and mapping equipment completely covers the ground at this time; at any intermediate angle, the actual field of view height H will be the flight height H f A ratio of , affected by the tilt angle β, indicating the actual height of the surveying equipment projected on the ground.

[0104] The construction method of the surveying and mapping overlapping area model includes: firstly, defining the four corner points of the surveying and mapping boundary MB of the drone module, namely the left front point DOT LF , right front point DOT RF , left rear point DOT LB , right rear point DOT RB ;

[0105] Then based on the real-time position coordinates of the UAV module k (X k ,Y k ,Z k ), field of view width FOV and field of view height H, calculate the coordinates of the four corner points:

[0106] Left front point Indicates the upper left corner position of the mapping boundary MB of the drone module, which is moved to the left from the center of the drone module Then move up H f (indicates the flight height of the drone module) results, which can better reflect the drone module at the flight height H f The shooting area below;

[0107] right front point Indicates the upper right corner of the mapping boundary MB of the drone module, which moves to the right from the center of the drone module Then move up H f The result is that the UAV module is considered at the flight height H f The actual coverage area under

[0108] Left rear point Indicates the lower left corner of the mapping boundary MB of the drone module, which moves to the left from the center of the drone module The result is that Y k Remaining the same means that the point is at the same height;

[0109] Right rear point Indicates the lower right corner of the mapping boundary MB of the drone module, which moves to the right from the center of the drone module The result is the same as Y k Maintain base height.

[0110] Then define the mapping boundary of the drone module

[0111]

[0112] Mapping boundary MB between any two UAV modules k1 and mapping boundaries MB k2 Constructing a model of survey and mapping overlapping areas:

[0113] OA=max[0,min(X k1 +FOV k1 ,X k2 +FOV k2 )-max(X k1 ,X k2 )]*max[0,min(Y k1 +H k1 ,Y k2 +H k2 )-max(Y k1 ,Y k2 )];

[0114] Where OA is the surveying and mapping overlap area, i.e., the surveying and mapping boundary MB k1 and mapping boundaries MB k2 The overlapping part between (X k1 ,Y k1 ) is the surveying boundary MB k1 The left rear point coordinates, FOV k1 MB for mapping boundaries k1 Width (i.e., the field of view width FOV of the drone module), H k1 MB for mapping boundaries k1 height (i.e., the visual field height H of the drone module); similarly, (X k2 ,Y k2 ) is the surveying boundary MB k2 The left rear point coordinates, FOV k2 MB for mapping boundaries k2 Width, H k2 MB for mapping boundaries k2 Height;

[0115] max[0,min(X k1 +FOV k1 ,X k2 +FOV k2 )-max(X k1 ,X k2 )] indicates the calculation of the surveying boundary MB k1 and mapping boundaries MB k2 The width of the overlapping area;

[0116] min(Xk1 +FOV k1 ,X k2 +FOV k2 ) is used to calculate the surveying boundary MB k1 and mapping boundaries MB k2 The minimum value of the right boundary, that is, the smaller one of the right sides of the two mapping boundaries (that is, the maximum X-axis coordinate), indicates that the two mapping boundaries MB k The right edge of the overlap range in the horizontal direction;

[0117] max(X k1 ,X k2 ) is used to calculate the surveying boundary MB k1 and mapping boundaries MB k2 The maximum value of the left boundary, that is, the larger one of the left sides of the two mapping boundaries (that is, the minimum X-axis coordinate), indicates that the two mapping boundaries MB k The left edge of the overlap range in the horizontal direction;

[0118] [min(...)-max(...)] means that by calculating the difference between the above two operations, that is, the width of the mapping overlap area OA, if the mapping boundary MB k1 and mapping boundaries MB k2 If there is no overlap between them, this value may be negative, so the negative value is constrained to 0 by max[0,...];

[0119] max[0,min(Y k1 +H k1 ,Y k2 +H k2 )-max(Y k1 ,Y k2 )] indicates the calculation of the surveying boundary MB k1 and mapping boundaries MB k2 The height of the overlapping area;

[0120] min(Y k1 +H k1 ,Y k2 +H k2 ) is used to calculate the surveying boundary MB k1 and mapping boundaries MB k2 The minimum value of the upper boundary, that is, the smaller of the tops of the two mapping boundaries (that is, the maximum Y-axis coordinate), indicates that the two mapping boundaries MB k The top boundary of the overlap range in the vertical direction;

[0121] max(Y k1 ,Y k2 ) is used to calculate the surveying boundary MB k1 and measure the boundary MB k2The maximum value of the lower boundary, that is, the larger of the bottoms of the two mapping boundaries (that is, the minimum Y-axis coordinate), indicates that the two mapping boundaries MB k The lower boundary of the overlap range in the vertical direction;

[0122] Similarly, by calculating the difference between the above two operations, that is, the height of the mapping overlap area OA, if the mapping boundary MB k1 and mapping boundaries MB k2 There is no overlap between them, so the negative values ​​are constrained to 0 by max[0,...];

[0123] Then, through the above formula, the area of ​​the mapping overlap area OA is obtained, which can accurately evaluate the mapping overlap area between drone modules and provide a data basis for subsequent image processing and aerial photography task optimization. By ensuring the minimization of the mapping overlap area, the efficiency and data quality of drone module aerial photography can be effectively improved.

[0124] Finally, the real-time position coordinates of the UAV based on the UAV module k , field of view width FOV to build the UAV path update model and obtain the new coordinates of the UAV module moving in the optimal aerial survey path k ′(X k ,Y k ,Z k ), the construction of the path update model includes:

[0125] The interval distance SD of the drone module is defined, which represents the horizontal spacing of each movement of the drone module when it moves and maps in the aerial survey path, that is, the distance between the drone module from one mapping point to the next mapping point; interval distance SD = μ·FOV, μ is a constant and μ≥1, further ensuring that the interval distance SD≥FOV, ensuring that the mapping area captured each time during the flight of the drone module will not be missed, that is, ensuring that the area between two adjacent shots is fully covered, ensuring that the final mapping data is continuous and complete, and avoiding the loss of mapping data in the mapping area; at the same time, when the interval distance SD is greater than the field of view width FOV, the overlapping distribution between images can be reduced, thereby improving the efficiency of subsequent data processing.

[0126] Define the trajectory offset distance of the drone module

[0127] In the formula, γ is a constant, Used to control the interval between drone modules; by setting the track offset distance TOD, the paths of two adjacent drone modules can be kept at a certain horizontal or vertical distance, thus avoiding shooting the same area. Furthermore, by adjusting the γ value, the distance between drone modules can be adjusted. The relationship between the mapping boundary MB is to ensure that during the mapping process, the data collected by different drone modules will not overlap too much.

[0128] New coordinates of the move UAV′ k (X′ k ,Y′ k ,Z′ k )=(X k +TOD·RO,Y k ,Z k ), after obtaining the new coordinates UAV′ of each UAV module k (X′ k ,Y′ k ,Z′ k ), it is necessary to ensure that the minimum interval between the UAV modules is the interval distance SD, that is, each time the path is planned, ensure that the next starting point UAV′ of the UAV module k+1 The interval from the previous surveying point must meet the requirements:

[0129] |UAV′ k+1 (X′ k+1 ,Y′ k+1 ,Z′ k+1 )-UAV′ k (X′ k ,Y′ k ,Z′ k )|≥SD.

[0130] By reasonably calculating the spacing d between UAV modules and the mapping overlap rate OR of the mapping boundary MB, it can be ensured that each UAV module effectively covers the relevant sub-area V (UAV k1 ), reducing the overlap of surveying and mapping data, thereby improving the integrity and quality of surveying and mapping data. And updating the real-time position coordinates of multiple drone modules can reduce the errors caused by differences in individual drone modules, thereby ensuring the reliability of surveying and mapping data.

[0131] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of the present invention.

Claims

1. The national land space planning surveying and mapping system based on UAV aerial survey includes a UAV module for collecting surveying and mapping data, a central processing module for processing surveying and mapping data, and a GPS positioning module for obtaining the surveying and mapping area A and the real-time position coordinates of the UAV module. k (X k ,Y k ,Z k ), characterized in that Also includes: Aerial survey path planning module, based on area A and real-time position coordinates UAV k (X k ,Y k ,Z k ) divides the survey area into several sub-areas V, and constructs an aerial survey path planning model to obtain the relevant sub-areas V (UAV k1 ) in the aerial survey path, where: The aerial survey path is optimized through the path optimization model to obtain the optimal aerial survey path. The UAV path update model is constructed based on the optimal aerial survey path to obtain the new coordinates of the UAV module moving in the optimal aerial survey path. k ′(X k ′,Y k ′,Z k ′)=(X k +TOD·OR,Y k ,Z k ); Where TOD is the trajectory offset distance, and OR is the surveying and mapping overlap ratio.

2. The national land space planning surveying and mapping system based on drone aerial surveying according to claim 1 is characterized in that: The method for obtaining the area of ​​the surveying and mapping region is: Where (X i ,Y i ) represents each vertex V in the surveying area i The coordinates of the vertex V i The number of 3. The national land space planning surveying and mapping system based on drone aerial surveying according to claim 2 is characterized in that: The method of dividing the surveying area into a plurality of sub-areas V includes: using the real-time position coordinates UAV k Construct the location set P of the drone module = {UAV1, UAV2, UAV3, ...., UAV K }, the position set P is used as the input feature and input into the UAV module distance calculation model to obtain the distance between the UAV modules In the formula, k1 and k2 are the corresponding UAV numbers; Define the sub-area V, and get the initial position coordinates as UAV k1 The relevant sub-areas mapped by the drone module Where Q∈R 3 Represented in three-dimensional space R 3 Any point Q in is the surveying point; Indicates that the coordinates of any point Q to the initial position are UAV k1 The distance of the UAV module is less than any point Q to all initial position coordinates that are not UAV k1 The distance of the drone module.

4. The national land space planning surveying and mapping system based on drone aerial surveying according to claim 3 is characterized in that: The method of constructing the aerial survey path planning model includes: Define the cost function of the aerial survey path planning model as the path length Where PL represents the total length of the aerial survey path of the UAV module; d(Q i ,Q i+1 ) indicates that in the relevant sub-area V(UAV k1 ) any point Q i To another arbitrary point Q i+1 The distance; n is any point Q i The number of 5. The national land space planning surveying and mapping system based on drone aerial surveying according to claim 4 is characterized in that: The path optimization model optimizes the aerial survey path in the following ways: Generate several groups of aerial survey paths as individuals, that is, path sequence I = [UAV k ,Q1,Q2,Q3,...,Q i ], where i represents the number of survey points Q; Using several groups of path sequences I to construct a path collection CP = {I1, I2, I3, ..., I m }, i.e., the initial population, where m represents the number of path sequences I; Calculate the fitness of each path sequence I Among them I PL represents the path length of the corresponding path sequence I; Select the path sequence I with the largest fitness F value from the path collection CP, which is the optimal aerial survey path.

6. The national land space planning surveying and mapping system based on drone aerial surveying according to claim 5 is characterized in that: The method of selecting the path sequence I with a large fitness F value includes: Randomly select j path sequences I from the path collection CP, where j is an integer less than m; Compare the fitness F of j path sequences I, and select the path sequence I with the highest fitness F, which is the target of this selection; repeat the above steps until the required number of path sequences I are generated and retained as the next generation population; Select two path sequences I from the next generation population as parent individuals, and obtain new child individuals through crossover operation; then perform mutation operation on the new child individuals to obtain mutated child individuals; combine the mutated child individuals with individuals in the initial race to obtain a new generation population; repeat the above steps until the preset number of iterations.

7. The national land space planning surveying and mapping system based on drone aerial surveying according to claim 6 is characterized in that: The method of constructing the UAV path update model includes: Through the UAV mapping range model, the field of view width FOV and field of view height H of the UAV module are calculated; Based on the field of view width FOV and field of view height H, a surveying and mapping overlap area model is constructed to obtain the surveying and mapping overlap area OA of the UAV module; The total surveying area TA = FOV*PL is calculated based on the field of view width FOV of the drone module and the path length PL in the optimal aerial survey path; The surveying overlap rate is calculated based on the surveying overlap area OA and the surveying total area TA Define the interval distance of the drone module SD = μ·FOV; where μ is a constant and μ≥1; define the trajectory offset distance of the drone module In the formula, γ is a constant, New coordinates for moving UAV k ′(X′ k ,Y k ′,Z′ k )Add constraints: |UAV' k+1 (X′ k+1 ,Y′ k+1 ,Z′ k+1 )-UAV′ k (X′ k ,Y′ k ,Z′ k )|≥SD.

8. The national land space planning surveying and mapping system based on drone aerial surveying according to claim 7 is characterized in that: The calculation formula of the field of view width is: The calculation formula of the visual field height is: H = H f sin(β); In the formula, H f is the flight altitude of the UAV module, θ is the mapping field of view of the UAV module, and β is the mapping tilt angle of the UAV module.

9. The national land space planning surveying and mapping system based on drone aerial surveying according to claim 8 is characterized in that: The method of constructing the surveying and mapping overlapping area model includes: Define the four corner points of the mapping boundary MB of the drone module, namely the left front point DOT LF , right front point DOT RF , left rear point DOT LB , right rear point DOT RB ; Based on real-time position coordinates of UAV k (X k ,Y k ,Z k ), field of view width FOV and field of view height H, calculate the coordinates of the four corner points, respectively; Left front point right front point Left rear point Right rear point Then define the mapping boundaries: Using adjacent mapping boundaries MB k1 and mapping boundaries MB k2 Calculate the surveying and mapping overlap area OA, the calculation formula is: OA=max[0,min(X k1 +FOV k1 ,X k2 +FOV k2 )-max(X k1 ,X k2 )]*max[0,min(Y k1 +H k1 ,AND k2 +H k2 )-max(Y k1 ,AND k2 )]; Where: (X k1 ,Y k1 ) is the surveying boundary MB k1 The left rear point coordinates, FOV k1 MB for mapping boundaries k1 Width, H k1 MB for mapping boundaries k1 Height; (X k2 ,Y k2 ) is the surveying boundary MB k2 The left rear point coordinates, FOV k2 MB for mapping boundaries k2 Width, H k2 MB for mapping boundaries k2 Height; max[0,min(X k1 +FOV k1 ,X k2 +FOV k2 )-max(X k1 ,X k2 )] represents the calculation of the width of the surveying and mapping overlapping area OA; max[0,min(Y k1 +H k1 ,Y k2 +H k2 )-max(Y k1 ,Y k2 )] represents the calculation of the height of the surveying and mapping overlapping area OA.

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