A method and system for identifying and comprehensively evaluating repeated regions of a flight trajectory of a UAV

Abstract

The application discloses a method and system for identifying and comprehensively evaluating a repeated region of a flight track of an unmanned aerial vehicle, and belongs to the technical field of intelligent control of unmanned aerial vehicles. In order to solve the problem of accurately finding the repeated flight region of the unmanned aerial vehicle, the application converts all flight track segments of the unmanned aerial vehicle into track segment structure vectors; a multi-scale similarity evaluation method is constructed; the similarity of the track segment structure vectors is scored and evaluated from two aspects of structural similarity and point matching similarity; the repetition frequency of the track segments is calculated; based on a set threshold, the total number of similar segments between each track segment and all other track segments is counted, and a repeated track segment set is extracted; a kernel function is introduced to construct a track heat function, then the information entropy of a track region is calculated based on the heat function, and a heat map of the track region is established; an integrated scoring model of the repeated region of the flight track of the unmanned aerial vehicle is constructed by fusing the information entropy, the direction consistency and the historical track density, and multi-dimensional quantitative evaluation of the repeated region of the flight track of the unmanned aerial vehicle is realized.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) intelligent control technology, specifically relating to a method and system for identifying and comprehensively evaluating repetitive areas of UAV flight trajectories. Background Technology

[0002] With the widespread deployment of drones in urban patrols, security monitoring, logistics delivery, and emergency response, the flight trajectory data generated by drones performing missions in the air is becoming increasingly abundant. Analyzing this trajectory data and identifying frequently recurring areas has significant application value. On the one hand, recurring areas often reflect key target areas, high-value patrol routes, or important air corridors, which can be used to optimize route planning and resource deployment. On the other hand, recurring trajectories may also expose issues such as path redundancy and airspace congestion, providing data support for flight scheduling and airspace management. Therefore, automatically identifying recurring areas in drone flight trajectories is a crucial step in achieving efficient airspace utilization and intelligent trajectory management.

[0003] Existing methods for identifying recurring regions in flight trajectories largely rely on similarity comparisons between entire trajectories or on statistical analysis of trajectory point density based on grid distribution. These methods often struggle to accurately identify recurring flight regions when dealing with trajectories exhibiting diverse deformations, local overlaps, or inconsistent orientations. This is especially true in complex urban environments where drone trajectories are characterized by diverse shapes, frequent orientation changes, and uneven flight density distributions, making it difficult for traditional methods to capture local repetitions and structural features between trajectories. Furthermore, existing methods lack comprehensive consideration of factors such as directional consistency and trajectory density, easily leading to misjudgments of coincidentally close paths, resulting in insufficient accuracy and stability of the identification results.

[0004] After identifying areas with repeated trajectories, a comprehensive evaluation of these areas is crucial for further improving the intelligence of decision-making. This comprehensive evaluation not only determines the importance and representativeness of an area but can also be applied to various scenarios such as route optimization, airspace capacity assessment, and no-fly zone designation. By introducing multi-dimensional indicators such as heat distribution, directional consistency, and information entropy for quantitative analysis, a more comprehensive assessment of the area's flight characteristics can be achieved, providing a scientific basis for urban airspace management and drone scheduling strategies. Especially in multi-drone collaborative scenarios, comprehensive evaluation can effectively identify "high-value routes" and "air bottlenecks," improving overall operational efficiency and safety.

[0005] Existing trajectory region evaluation methods often focus on single attributes, such as trajectory density or repetition frequency, lacking comprehensive modeling of dynamic features such as directionality and heat concentration. Furthermore, existing models lack interpretability and adjustability, making it difficult to flexibly adjust evaluation dimensions and weights according to different application scenarios, thus limiting their practicality and generalizability. Summary of the Invention

[0006] The problem this invention aims to solve is to accurately identify areas where UAVs repeatedly fly, and proposes a method and system for identifying and comprehensively evaluating areas where UAV flight trajectories repeat.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for identifying and comprehensively evaluating overlapping regions in UAV flight trajectories includes the following steps:

[0009] S1. Collect UAV flight trajectory data, resample each collected UAV flight trajectory data, and then use a sliding window to divide it into UAV flight trajectory segments to form a set of UAV flight trajectory segments;

[0010] S2. Convert all the UAV flight trajectory segments obtained in step S1 into trajectory segment structure vectors;

[0011] S3. Construct a multi-scale similarity evaluation method to score and evaluate the similarity of trajectory segment structure vectors from two aspects: structural similarity and point matching similarity, and obtain the fusion similarity score of trajectory segment structure vectors;

[0012] S4. Calculate the trajectory segment repetition frequency using the fusion similarity score of the trajectory segment structure vector obtained in step S3, and count the total number of similar segments between each trajectory segment and all other trajectory segments based on a set threshold, and extract the set of repetitive trajectory segments.

[0013] S5. For the repeated trajectory segments obtained in step S4, introduce a kernel function to construct a trajectory heat function, and then calculate the information entropy of the trajectory region based on the heat function to establish a heat map of the trajectory region;

[0014] S6. Based on the heat map of the trajectory region obtained in step S5, integrate information entropy, directional consistency and historical trajectory density to construct a comprehensive scoring model for the repeated areas of UAV flight trajectories, so as to realize the multi-dimensional quantitative evaluation of the repeated areas of UAV flight trajectories.

[0015] Furthermore, the specific implementation method of step S1 includes the following steps:

[0016] S1.1. Use GPS equipment to record the latitude and longitude data of the UAV flight trajectory, and convert the latitude and longitude data into coordinates in a plane coordinate system to obtain the collected UAV flight trajectory data;

[0017] S1.2. The UAV flight trajectory data obtained in step S1.1 is spatially uniformly resampled using linear interpolation to obtain the resampled UAV flight trajectory data;

[0018] S1.3. The resampled UAV flight trajectory data obtained in step S1.2 is divided into several small segments using a sliding window method. Each UAV flight trajectory segment includes a fixed number of segments. From a series of points, we obtain:

[0019]

[0020] in, Let j be the trajectory segment starting from the j-th trajectory point. for The number of points in the trajectory, p j Let j be the coordinates of the j-th trajectory point. This represents the number of trajectory points in the resampled trajectory.

[0021] All trajectory segments constitute the set of drone flight trajectory segments. .

[0022] Furthermore, in step S2, each UAV flight trajectory segment is transformed into a structure vector, including the initial direction angle, average curvature, total length, and the difference vector between all adjacent points, resulting in:

[0023]

[0024] in, For trajectory segment structure vectors, This is the starting direction angle; For the mean curvature, This represents the total length of the trajectory segment; Let x be the difference vector between the l-th trajectory point and the (l-1)-th trajectory point. It is the difference vector of the y-axis between the l-th trajectory point and the (l-1)-th trajectory point.

[0025] Furthermore, the specific implementation method of step S3 includes the following steps:

[0026] S3.1. Structural similarity, from a holistic perspective, measures the similarity in the morphological structure of trajectory segments through their direction, curvature, and length. The formula for calculating the structural similarity score is as follows:

[0027]

[0028] in, For the a-th trajectory segment The b-th trajectory segment The structural similarity score of the structural vectors; , These are the a-th trajectory segments. The b-th trajectory segment The structure vector; , They are respectively , The initial direction angle; , They are respectively , The average curvature; , They are respectively , The total length of the trajectory segment; , , , These are the initial direction angle, average curvature, total length of the trajectory segment, and weight coefficients of the structure vector, respectively.

[0029] S3.2. Point matching similarity is calculated using a focused micro-alignment method;

[0030] S3.2.1. For trajectory segments , Collection trajectory segment and On Key points, including the start point, end point, midpoint, and points with equal length differences, are used to construct a set of point pairs. ,in, Where is the number of key points, and ji is the key point number. Representing trajectory segments and trajectory segment The first key point;

[0031] S3.2.2. Calculate the spatial difference between each pair of matched keypoints. The expression is:

[0032]

[0033] Then calculate the average difference between each pair of matched keypoints. ;

[0034] S3.2.3. Introduce the length of the trajectory segment as a normalization reference to calculate the point matching similarity score. The expression is:

[0035]

[0036] in, The similarity score is used to measure the location matching.

[0037] S3.3. Calculate the fusion similarity score of the trajectory segment structure vectors, expressed as:

[0038]

[0039] in, To incorporate similarity weighting coefficients; The fusion similarity score is used to evaluate the structural vectors of the trajectory segments.

[0040] Furthermore, the specific implementation method of step S5 includes the following steps:

[0041] S5.1. Consider forming a spatial heat map for each trajectory segment determined to be repeated in step S4. Use a two-dimensional Gaussian kernel function to map the trajectory segment to a set of spatial heat functions. By superimposing the trajectory segment kernel functions of all repeated segments, construct the heat map of the entire region.

[0042] The kernel function K is calculated as follows:

[0043]

[0044] in, For point With trajectory segment The minimum Euclidean distance; To control the nuclear bandwidth of the heat diffusion range;

[0045] S5.2. Spatially superimpose the kernel functions of all repeating trajectory segments to form a heat function. This is used to represent the density of a location repeatedly traversed by a trajectory segment, and its expression is:

[0046]

[0047] in, A set of repeating trajectory segments;

[0048] S5.3. Divide the candidate region AR into multiple sub-regions c, calculate the heat distribution probability, and then calculate the information entropy of the candidate region;

[0049]

[0050]

[0051] in, Let c be the heat function of the c-th sub-region; This is the sum of the heat functions of all sub-regions; Let be the heat distribution probability of the c-th sub-region; Information entropy is used to measure the degree of dispersion in heat distribution.

[0052] Furthermore, the specific implementation method of step S6 includes the following steps:

[0053] S6.1. Extract the region where the heat value reaches the set threshold according to the heat function, and regard the region boundary as the critical position where the heat value changes significantly, representing the potential structural region traversed by the trajectory set;

[0054] The location of the regional boundary is denoted as The definition is as follows:

[0055]

[0056] in, This is the heat threshold; The heat value is equal to The boundary;

[0057] S6.2. Analyze whether the trajectory directions within the region boundary obtained in step S6.1 are consistent. Calculate the gradient direction of the heatmap at each trajectory point and statistically analyze the consistency of the directions on the region boundary.

[0058] First, using the spatial gradient of the heatmap, the gradient direction angle of each trajectory point is calculated, expressed as:

[0059]

[0060] in, The gradient direction angle; This is a two-dimensional arctangent function that returns the angle of the vector direction. These are the partial derivatives of the heat function in the x and y directions, respectively;

[0061] Then, the directional consistency score is defined as the calculation region boundary. The average angle between the orientation of all trajectory points and the gradient direction of the region. The consistency is obtained by taking the average of the cosine similarities:

[0062]

[0063] in, Scoring is given based on regional directional consistency.

[0064] S6.3. By weighted combination of information entropy, directional consistency, and historical trajectory density, a comprehensive scoring model for the overlapping areas of UAV flight trajectories is constructed, yielding:

[0065]

[0066] in, The final score for the candidate region. The maximum heat value within the candidate region; for Regional directional consistency score; , , These are the weighting coefficients for maximum popularity value, information entropy, and regional directional consistency score, respectively.

[0067] A system for identifying and comprehensively evaluating repetitive areas of UAV flight trajectories includes a processor, a memory, and a computer program stored in the memory and run on the processor. When the computer program runs, it implements the steps of the method for identifying and comprehensively evaluating repetitive areas of UAV flight trajectories.

[0068] The beneficial effects of this invention are:

[0069] This invention discloses a method for identifying and comprehensively evaluating repetitive regions in UAV flight trajectories. This method accurately identifies regions that have been repeatedly flown from a large number of UAV flight trajectories and provides reasonable evaluations of these regions. By resampling and sliding window segmentation, local trajectory segments are extracted. Combined with structural vectors and point pairing, multi-scale similarity assessment is achieved, effectively capturing microscopic repetitive patterns in the trajectories. A kernel function is introduced to model trajectory heat, and a comprehensive scoring model is constructed by integrating information entropy, directional consistency, and historical trajectory density, enabling multi-dimensional quantitative analysis of repetitive regions. This method is adaptable to the structural differences in different types of UAV trajectories, possesses good scalability and interpretability, and is suitable for UAV behavior mining and flight path optimization applications in complex airspace environments.

[0070] The method for identifying and comprehensively evaluating repetitive areas of UAV flight trajectories described in this invention has advantages such as detailed structural modeling, high identification accuracy, and reasonable comprehensive evaluation. Attached Figure Description

[0071] Figure 1 This is a flowchart of a method for identifying and comprehensively evaluating repetitive regions of UAV flight trajectories according to the present invention;

[0072] Figure 2 This is a comparison diagram of trajectories A and B corresponding to the present invention;

[0073] Figure 3 This is a heatmap of trajectories A and B of the present invention. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described specific embodiments are merely a part of the embodiments of the invention, and not all of them. The components of the specific embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations, and the invention may also have other embodiments.

[0075] Therefore, the following detailed description of specific embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected specific embodiments of the invention. All other specific embodiments obtained by those skilled in the art based on these specific embodiments without inventive effort are within the scope of protection of this invention.

[0076] To further understand the invention's content, features, and effects, the following specific embodiments are provided, along with accompanying drawings. Figure 1 - Appendix Figure 3 Detailed explanation is as follows:

[0077] Example 1:

[0078] A method for identifying and comprehensively evaluating overlapping regions in UAV flight trajectories includes the following steps:

[0079] S1. Collect UAV flight trajectory data, resample each collected UAV flight trajectory data, and then use a sliding window to divide it into UAV flight trajectory segments to form a set of UAV flight trajectory segments;

[0080] Furthermore, the specific implementation method of step S1 includes the following steps:

[0081] S1.1. Use GPS equipment to record the latitude and longitude data of the UAV flight trajectory, and convert the latitude and longitude data into coordinates in a plane coordinate system to obtain the collected UAV flight trajectory data;

[0082] S1.2. The UAV flight trajectory data obtained in step S1.1 is spatially uniformly resampled using linear interpolation to obtain the resampled UAV flight trajectory data;

[0083] Furthermore, due to the different sampling frequencies of different devices, the spacing between trajectory points may be uneven, leading to significant structural differences between similar trajectories. Therefore, it is necessary to perform spatially uniform resampling on each trajectory to ensure that the distance between adjacent points is a fixed value. This improves the comparability between trajectory segments and avoids interference from trajectory point density in subsequent similarity calculations. Linear interpolation can be used for resampling.

[0084] ,

[0085] in, The trajectory after resampling; This represents the number of resampled trajectory points; Let be the coordinates of the i-th trajectory point. The planar coordinates of the i-th trajectory point are denoted as . ,in, Let x and y be the x and y coordinates of the i-th trajectory point, respectively. i = 1, 2, ..., n.

[0086] S1.3. The resampled UAV flight trajectory data obtained in step S1.2 is divided into several small segments using a sliding window method. Each UAV flight trajectory segment includes a fixed number of segments. From a series of points, we obtain:

[0087]

[0088] in, Let j be the trajectory segment starting from the j-th trajectory point. for The number of points in the trajectory, p j Let j be the coordinates of the j-th trajectory point. This represents the number of trajectory points in the resampled trajectory.

[0089] All trajectory segments constitute the set of drone flight trajectory segments. , .

[0090] Furthermore, to uncover recurring patterns in the trajectories, we divided each trajectory into several smaller segments using a sliding window approach. Each trajectory segment contains a fixed number of... Each continuous point represents a local shape of the trajectory. In this way, we can transform complex, long trajectories into multiple local segments, allowing similarity calculations to focus more on local structural features and better identify repeating paths. The sliding window typically uses a step size of 1 to ensure the complete coverage of the trajectory segments.

[0091] S2. Convert all the UAV flight trajectory segments obtained in step S1 into trajectory segment structure vectors;

[0092] Furthermore, in step S2, each UAV flight trajectory segment is transformed into a structure vector, including the initial direction angle, average curvature, total length, and the difference vector between all adjacent points, resulting in:

[0093]

[0094] in, For trajectory segment structure vectors, This is the starting direction angle; For the mean curvature, This represents the total length of the trajectory segment; Let x be the difference vector between the l-th trajectory point and the (l-1)-th trajectory point. It is the difference vector of the y-axis between the l-th trajectory point and the (l-1)-th trajectory point.

[0095] Furthermore, , refers to the difference vector Quantity; , refers to the difference vector Quantity.

[0096] in, , , These are the coordinates of the first two points of the trajectory segment; It is a two-parameter arctangent function. , It is the dot product of vectors; The number of trajectory points; Let be the coordinates of the j-th trajectory point. , It represents the magnitude of the vector.

[0097] Furthermore, this structured representation allows trajectory segments to be transformed into points in a high-dimensional space, enabling the use of vector distance to measure their similarity. This structural embedding method preserves both global and local features of the trajectory.

[0098] S3. Construct a multi-scale similarity evaluation method to score and evaluate the similarity of trajectory segment structure vectors from two aspects: structural similarity and point matching similarity, and obtain the fusion similarity score of trajectory segment structure vectors;

[0099] Furthermore, the specific implementation method of step S3 includes the following steps:

[0100] S3.1. Structural similarity, from a holistic perspective, measures the similarity in the morphological structure of trajectory segments through their direction, curvature, and length. The formula for calculating the structural similarity score is as follows:

[0101]

[0102] in, For the a-th trajectory segment The b-th trajectory segment The structural similarity score of the structural vectors; , These are the a-th trajectory segments. The b-th trajectory segment The structure vector; , They are respectively , The initial direction angle; , They are respectively , The average curvature; , They are respectively , The total length of the trajectory segment; , , , These are the initial direction angle, average curvature, total length of the trajectory segment, and weight coefficients of the structure vector, respectively.

[0103] S3.2. Point matching similarity is calculated using a focused micro-alignment method;

[0104] set up For trajectory segment The number of trajectory points; For trajectory segment Number of trajectory points; trajectory segment The coordinates of the trajectory of any point on the trajectory are denoted as ; For trajectory segment The number of trajectory points; For trajectory segment Number of trajectory points; trajectory segment The coordinates of the trajectory of any point on the trajectory are denoted as ;

[0105] S3.2.1. For trajectory segments , Collection trajectory segment and On Key points, including the start point, end point, midpoint, and points with equal length differences, are used to construct a set of point pairs. ,in, Where is the number of key points, and ji is the key point number. Representing trajectory segments and trajectory segment The jith key point; each point represents the corresponding point of two trajectory segments at the same progress position.

[0106] S3.2.2. Calculate the spatial difference between each pair of matched keypoints. The expression is:

[0107]

[0108] Then calculate the average difference between each pair of matched keypoints. ;

[0109] S3.2.3. Introduce the length of the trajectory segment as a normalization reference to calculate the point matching similarity score. The expression is:

[0110]

[0111] in, The similarity score is used to measure the location matching.

[0112] S3.3. Calculate the fusion similarity score of the trajectory segment structure vectors, expressed as:

[0113]

[0114] in, To incorporate similarity weighting coefficients; The fusion similarity score is used to evaluate the structural vectors of the trajectory segments.

[0115] Furthermore, geometric features such as direction, curvature, and length of trajectory segments are used to measure morphological similarity. This method is computationally efficient and robust, suitable for quickly filtering trajectory segments with similar structural features. However, structural features alone often fail to capture the subtle differences in spatial location between trajectory points. Therefore, point-location matching similarity is introduced. By comparing sequences of trajectory points, it can more precisely reflect the local differences in the spatial distribution of trajectory segments. The two complement each other: structural similarity focuses on macroscopic morphology, while point-location matching similarity focuses on microscopic alignment. Their combined use can effectively improve the accuracy and stability of trajectory comparison.

[0116] S4. Calculate the trajectory segment repetition frequency using the fusion similarity score of the trajectory segment structure vector obtained in step S3, and count the total number of similar segments between each trajectory segment and all other trajectory segments based on a set threshold, and extract the set of repetitive trajectory segments.

[0117] Furthermore, the repetition of trajectory segments is a key indicator for determining whether they belong to a "frequently used path". This involves counting the total number of similar segments between each trajectory segment and all other trajectory segments (i.e., the number of segments with a similarity greater than a similarity threshold). (Number of repetitions). If the repetition frequency of a certain segment exceeds the set repetition frequency threshold. We consider these as "repeating segments". This method can effectively filter out randomly similar noise segments.

[0118] The repetition frequency is calculated as follows:

[0119] in, This represents the set consisting of the entire trajectory segment. In addition to the trajectory segment Other trajectory segments besides; The similarity threshold is determined by expert experience; The repetition frequency.

[0120] The above formula means that for a certain trajectory segment Find the set formed by the entire trajectory segment. Other trajectory segments Other trajectory segments whose similarity to each other is greater than a similarity threshold And count the number of such trajectory segments, i.e., the repetition frequency. .

[0121] The set of repeating segments is defined as follows: , A set of repeating trajectory segments; The repetition frequency threshold is determined by expert experience; the above formula means that all cases with a repetition frequency ≥ the threshold are included. Select the trajectory segments that are "" to form a set of repeated trajectory segments. .

[0122] S5. For the repeated trajectory segments obtained in step S4, introduce a kernel function to construct a trajectory heat function, and then calculate the information entropy of the trajectory region based on the heat function to establish a heat map of the trajectory region;

[0123] Furthermore, each trajectory segment identified as repeating will spread its heat influence in the surrounding area, forming a spatial heat region. A two-dimensional Gaussian kernel function is used to map the trajectory segment to a set of spatial heat functions, representing its probability or influence of propagation in space. The bandwidth of the kernel function... This determines the spatial diffusion range. By superimposing the kernel functions of all repeating segments, a heatmap of the entire region can be constructed.

[0124] Furthermore, the specific implementation method of step S5 includes the following steps:

[0125] S5.1. Consider forming a spatial heat map for each trajectory segment determined to be repeated in step S4. Use a two-dimensional Gaussian kernel function to map the trajectory segment to a set of spatial heat functions. By superimposing the trajectory segment kernel functions of all repeated segments, construct the heat map of the entire region.

[0126] The kernel function K is calculated as follows:

[0127]

[0128] in, For point With trajectory segment The minimum Euclidean distance; To control the nuclear bandwidth of the heat diffusion range;

[0129] S5.2. Spatially superimpose the kernel functions of all repeating trajectory segments to form a heat function. This is used to represent the density of a location repeatedly traversed by a trajectory segment, and its expression is:

[0130]

[0131] in, A set of repeating trajectory segments;

[0132] S5.3. Divide the candidate region AR into multiple sub-regions c, calculate the heat distribution probability, and then calculate the information entropy of the candidate region;

[0133]

[0134]

[0135] in, Let c be the heat function of the c-th sub-region; This is the sum of the heat functions of all sub-regions; Let be the heat distribution probability of the c-th sub-region; Information entropy is used to measure the degree of dispersion in heat distribution.

[0136] S6. Based on the heat map of the trajectory region obtained in step S5, integrate information entropy, directional consistency and historical trajectory density to construct a comprehensive scoring model for the repeated areas of UAV flight trajectories, so as to realize the multi-dimensional quantitative evaluation of the repeated areas of UAV flight trajectories.

[0137] Furthermore, the specific implementation method of step S6 includes the following steps:

[0138] S6.1. Extract the region where the heat value reaches the set threshold according to the heat function, and regard the region boundary as the critical position where the heat value changes significantly, representing the potential structural region traversed by the trajectory set;

[0139] The location of the regional boundary is denoted as The definition is as follows:

[0140]

[0141] in, This is the heat threshold; The heat value is equal to The boundary;

[0142] S6.2. Analyze whether the trajectory directions within the region boundary obtained in step S6.1 are consistent. Calculate the gradient direction of the heatmap at each trajectory point and statistically analyze the consistency of the directions on the region boundary.

[0143] First, using the spatial gradient of the heatmap, the gradient direction angle of each trajectory point is calculated, expressed as:

[0144]

[0145] in, The gradient direction angle; This is a two-dimensional arctangent function that returns the angle of the vector direction. These are the partial derivatives of the heat function in the x and y directions, respectively;

[0146] Then, the directional consistency score is defined as the calculation region boundary. The average angle between the orientation of all trajectory points and the gradient direction of the region. The consistency is obtained by taking the average of the cosine similarities:

[0147]

[0148] in, Scoring is given based on regional directional consistency.

[0149] S6.3. By weighted combination of information entropy, directional consistency, and historical trajectory density, a comprehensive scoring model for the overlapping areas of UAV flight trajectories is constructed, yielding:

[0150]

[0151] in, The final score for the candidate region. The maximum heat value within the candidate region; for Regional directional consistency score; , , These are the weighting coefficients for maximum popularity value, information entropy, and regional directional consistency score, respectively.

[0152] The application scenario based on this embodiment is shown below:

[0153] Given two drone flight paths, A and B, the trajectory points corresponding to path A are as follows: , , , , , , , , , ,

[0154] The trajectory points corresponding to trajectory B are as follows: , , , , , , , , , Extract the initial 5 points from each of the two trajectories as a sliding window trajectory segment:

[0155] Trajectory A corresponds to: , , , , ,

[0156] Trajectory B corresponds to: , , , , .

[0157] The results of the structural similarity score, point matching similarity score, and fusion similarity score calculation are shown in Table 1.

[0158] Table 1

[0159]

[0160] The calculation results of the maximum heat value, information entropy, and final score of the repeated segment are shown in Table 2.

[0161] Table 2

[0162]

[0163] The trajectory comparison chart and heatmap corresponding to trajectories A and B are obtained as follows: Figure 2 and Figure 3 As shown in the figure, the UAV flight trajectory repetition region identification and comprehensive evaluation method proposed in this embodiment has the advantages of fine structural modeling, high identification accuracy, and reasonable comprehensive evaluation. By resampling the trajectory and using sliding window segmentation, local trajectory segments are extracted. Multi-scale similarity evaluation is achieved by combining structural vectors and point pairing, effectively capturing the micro-repetition patterns of the trajectory. A kernel function is introduced to model trajectory heat, and a comprehensive scoring model is constructed by integrating information entropy, directional consistency, and historical trajectory density to achieve multi-dimensional quantitative analysis of repetitive regions. This method can adapt to the structural differences in trajectories of different types of UAVs, has good scalability and interpretability, and is suitable for UAV behavior mining and flight path optimization applications in complex airspace environments.

[0164] Example 2:

[0165] A system for identifying and comprehensively evaluating repetitive areas of UAV flight trajectories includes a processor, a memory, and a computer program stored in the memory and run on the processor. When the computer program is run, it implements the steps of the method for identifying and comprehensively evaluating repetitive areas of UAV flight trajectories as described in Example 1.

[0166] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0167] Although this application has been described above with reference to specific embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of this application. In particular, as long as there is no structural conflict, the features in the specific embodiments disclosed in this application can be combined with each other in any way. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, this application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for identifying and comprehensively evaluating repetitive regions of UAV flight trajectories, characterized in that, Includes the following steps: S1. Collect UAV flight trajectory data, resample each collected UAV flight trajectory data, and then use a sliding window to divide it into UAV flight trajectory segments to form a set of UAV flight trajectory segments; S2. Convert all the UAV flight trajectory segments obtained in step S1 into trajectory segment structure vectors; In step S2, each UAV flight trajectory segment is converted into a structure vector, including the initial direction angle, average curvature, total length, and the difference vector between all adjacent points, resulting in: ； in, For trajectory segment structure vectors, This is the starting direction angle; For the mean curvature, This represents the total length of the trajectory segment; Let x be the difference vector between the l-th trajectory point and the (l-1)-th trajectory point. Let y be the difference vector between the l-th trajectory point and the (l-1)-th trajectory point; S3. Construct a multi-scale similarity evaluation method to score and evaluate the similarity of trajectory segment structure vectors from two aspects: structural similarity and point matching similarity, and obtain the fusion similarity score of trajectory segment structure vectors; The specific implementation method of step S3 includes the following steps: S3.

1. Structural similarity, from a holistic perspective, measures the similarity in the morphological structure of trajectory segments through their direction, curvature, and length. The formula for calculating the structural similarity score is as follows: ； in, For the a-th trajectory segment The b-th trajectory segment The structural similarity score of the structural vectors; , These are the a-th trajectory segments. The b-th trajectory segment The structure vector; , They are respectively , The initial direction angle; , They are respectively , The average curvature; , They are respectively , The total length of the trajectory segment; , , , These are the initial direction angle, average curvature, total length of the trajectory segment, and weight coefficients of the structure vector, respectively. S3.

2. Point matching similarity is calculated using a focused micro-alignment method; S3.2.

1. For trajectory segments , Collection trajectory segment and On Key points, including the start point, end point, midpoint, and points with equal length differences, are used to construct a set of point pairs. ,in, Where is the number of key points, and ji is the key point number. Representing trajectory segments and trajectory segment The first key point; S3.2.

2. Calculate the spatial difference between each pair of matched keypoints. The expression is: ； Then calculate the average difference between each pair of matched keypoints. ; S3.2.

3. Introduce the length of the trajectory segment as a normalization reference to calculate the point matching similarity score. The expression is: ； in, The similarity score is used to measure the location matching. S3.

3. Calculate the fusion similarity score of the trajectory segment structure vectors, expressed as: ； in, To incorporate similarity weighting coefficients; The fusion similarity score is used to evaluate the structural vectors of the trajectory segments. S4. Calculate the trajectory segment repetition frequency using the fusion similarity score of the trajectory segment structure vector obtained in step S3, and count the total number of similar segments between each trajectory segment and all other trajectory segments based on a set threshold, and extract the set of repetitive trajectory segments. S5. For the repeated trajectory segments obtained in step S4, introduce a kernel function to construct a trajectory heat function, and then calculate the information entropy of the trajectory region based on the heat function to establish a heat map of the trajectory region; S6. Based on the heat map of the trajectory region obtained in step S5, integrate information entropy, directional consistency and historical trajectory density to construct a comprehensive scoring model for the repeated areas of UAV flight trajectories, so as to realize the multi-dimensional quantitative evaluation of the repeated areas of UAV flight trajectories.

2. The method for identifying and comprehensively evaluating repetitive regions of UAV flight trajectories according to claim 1, characterized in that, The specific implementation method of step S1 includes the following steps: S1.

1. Use GPS equipment to record the latitude and longitude data of the UAV flight trajectory, and convert the latitude and longitude data into coordinates in a plane coordinate system to obtain the collected UAV flight trajectory data; S1.

2. The UAV flight trajectory data obtained in step S1.1 is spatially uniformly resampled using linear interpolation to obtain the resampled UAV flight trajectory data; S1.

3. The resampled UAV flight trajectory data obtained in step S1.2 is divided into several small segments using a sliding window method. Each UAV flight trajectory segment includes a fixed number of segments. From a series of points, we obtain: ； in, Let j be the trajectory segment starting from the j-th trajectory point. for The number of points in the trajectory, p j Let j be the coordinates of the j-th trajectory point. This represents the number of trajectory points in the resampled trajectory. All trajectory segments constitute the set of drone flight trajectory segments. .

3. The method for identifying and comprehensively evaluating repetitive regions of UAV flight trajectories according to claim 2, characterized in that, The specific implementation method of step S5 includes the following steps: S5.

1. Consider forming a spatial heat map for each trajectory segment determined to be repeated in step S4. Use a two-dimensional Gaussian kernel function to map the trajectory segment to a set of spatial heat functions. By superimposing the trajectory segment kernel functions of all repeated segments, construct the heat map of the entire region. The kernel function K is calculated as follows: ； in, For point With trajectory segment The minimum Euclidean distance; To control the nuclear bandwidth of the heat diffusion range; S5.

2. Spatially superimpose the kernel functions of all repeating trajectory segments to form a heat function. This is used to represent the density of a location repeatedly traversed by a trajectory segment, and its expression is: ； in, A set of repeating trajectory segments; S5.

3. Divide the candidate region AR into multiple sub-regions c, calculate the heat distribution probability, and then calculate the information entropy of the candidate region; ； ； in, Let c be the heat function of the c-th sub-region; This is the sum of the heat functions of all sub-regions; Let be the heat distribution probability of the c-th sub-region; Information entropy is used to measure the degree of dispersion in heat distribution.

4. The method for identifying and comprehensively evaluating repetitive regions of UAV flight trajectories according to claim 3, characterized in that, The specific implementation method of step S6 includes the following steps: S6.

1. Extract the region where the heat value reaches the set threshold according to the heat function, and regard the region boundary as the critical position where the heat value changes significantly, representing the potential structural region traversed by the trajectory set; The location of the regional boundary is denoted as The definition is as follows: ； in, This is the heat threshold; The heat value is equal to The boundary; S6.

2. Analyze whether the trajectory directions within the region boundary obtained in step S6.1 are consistent. Calculate the gradient direction of the heatmap at each trajectory point and statistically analyze the consistency of the directions on the region boundary. First, using the spatial gradient of the heatmap, the gradient direction angle of each trajectory point is calculated, expressed as: ； in, The gradient direction angle; This is a two-dimensional arctangent function that returns the angle of the vector direction. These are the partial derivatives of the heat function in the x and y directions, respectively; Then, the directional consistency score is defined as the calculation region boundary. The average angle between the orientation of all trajectory points and the gradient direction of the region. The consistency is obtained by taking the average of the cosine similarities: ； in, Scoring is given based on regional directional consistency. S6.

3. By weighted combination of information entropy, directional consistency, and historical trajectory density, a comprehensive scoring model for the overlapping areas of UAV flight trajectories is constructed, yielding: ； in, The final score for the candidate region. The maximum heat value within the candidate region; for Regional directional consistency score; , , These are the weighting coefficients for maximum popularity value, information entropy, and regional directional consistency score, respectively.

5. A system for identifying and comprehensively evaluating repetitive areas of UAV flight trajectories, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed, it implements the steps of the method for identifying and comprehensively evaluating the repetitive areas of a UAV flight trajectory as described in any one of claims 1-4.

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