Data security transmission method and system for unmanned aerial vehicle surveying and mapping, and storage medium

Abstract

The present application relates to the technical field of data security transmission, and particularly relates to a data security transmission method and system for unmanned aerial vehicle surveying and mapping and a storage medium. The method comprises the following steps: acquiring surveying and mapping images of continuous frames photographed by an unmanned aerial vehicle in an aerial photography process; determining transmission complexity according to gradient distribution, determining interference degree according to the proportion of different hierarchical regions contained in each frame of overlapping regions and hierarchical gradient sensitivity; determining flight instability of each frame of surveying and mapping images according to direction changes in actual flight under different frames, determining transmission importance of each frame of surveying and mapping images in combination with transmission complexity of each frame of surveying and mapping images, interference degree of overlapping regions and flight instability, and adopting an encryption algorithm with different encryption precision according to transmission importance. The present application can take into account the efficiency and precision of data transmission, and improve the security and flexibility of data transmission.

Description

Technical Field

[0001] This invention relates to the field of data security transmission technology, specifically to a data security transmission method, system, and storage medium for UAV mapping. Background Technology

[0002] With the rise of the low-altitude economy, drones, due to their low cost and high resolution, have become a key supplementary element in land spatial planning, construction, and other surveying and mapping work, playing an important role in urban modeling, engineering monitoring, and other fields. Surveying drones need to transmit large amounts of data, including imagery, attitude, and location information, back to ground stations or cloud platforms in real time to support rapid subsequent processing and decision-making. The importance of drone-based real-scene surveying data transmission—transmitting the complex and cumbersome surveying data collected by drones to the cloud for real-time stitching and 3D reconstruction—is self-evident.

[0003] Existing UAV mapping systems deploy edge nodes point-by-point along the flight path under an edge computing architecture. Each edge node employs a unified encryption transmission strategy to improve data transmission real-time performance while protecting the privacy of the node's original data. During UAV flight, images captured at adjacent points often overlap to some extent. Current edge encryption transmission mechanisms typically perform encryption independently at each node. Using a unified encryption method, however, leads to inconsistent texture representations across different image frames. If high precision is prioritized, the encryption process becomes redundant, increasing transmission complexity. Conversely, prioritizing low precision results in interference from encryption and transmission processes on sensitive texture information, affecting subsequent model building. In short, it's impossible to balance data transmission efficiency and accuracy, resulting in insufficient data transmission security and flexibility. Summary of the Invention

[0004] To address the technical problems of insufficient data transmission security and flexibility in related technologies, which cannot simultaneously achieve both efficiency and accuracy, this invention provides a data security transmission method, system, and storage medium for UAV mapping. The specific technical solution adopted is as follows:

[0005] This invention proposes a secure data transmission method for UAV mapping, the method comprising:

[0006] Acquire continuous frame survey images captured by the UAV during aerial photography; divide the pre-annotated areas contained in a single frame survey image into different levels, and determine the different level areas in each frame survey image;

[0007] Based on the gradient distribution of different hierarchical regions in each frame of the image, the hierarchical gradient sensitivity of each hierarchical region is determined, and the transmission complexity is determined based on the hierarchical gradient sensitivity of all hierarchical regions.

[0008] Feature point matching is performed on each frame of the mapped image to determine the overlapping area of ​​adjacent frames; based on the proportion of different hierarchical regions contained in the overlapping area of ​​each frame and the hierarchical gradient sensitivity, the degree of interference of the overlapping area on important information in the image is determined.

[0009] Based on the predetermined flight trajectory, the flight phase with the same flight direction is determined. Within any flight phase, the flight instability of each frame of the measured image is determined based on the actual flight direction changes in different frames.

[0010] The transmission importance of each frame of survey image is determined by combining the transmission complexity, interference level of overlapping areas, and flight instability. Based on the transmission importance, encryption algorithms with different encryption precision are adopted, wherein the transmission importance is proportional to the encryption precision.

[0011] Further, the surveyed image is an aerial photograph of urban planning, and the hierarchy includes at least one of facilities, buildings, ground texture, and traffic. Determining the hierarchy gradient sensitivity of each hierarchy region based on the gradient distribution of different hierarchy regions in each frame of the surveyed image includes:

[0012] The gradient of each pixel in each layer region is determined based on the gradient operator, and the gradient is processed based on the L1 norm to obtain the pixel gradient index.

[0013] The standard deviation of the gradient indices of all pixels is normalized and used as the first sensitive indicator; the information entropy of the gradient indices of all pixels is normalized and used as the second sensitive indicator.

[0014] Calculate the product of the first and second sensitivity indices, and normalize it to obtain the hierarchical gradient sensitivity.

[0015] Furthermore, determining the transmission complexity based on the hierarchical gradient sensitivity of all hierarchical regions includes:

[0016] The mean of the hierarchical gradient sensitivity of all hierarchical regions is used as the transmission complexity.

[0017] Furthermore, the step of performing feature point matching on each frame of the mapped image to determine the overlapping region of adjacent frames includes:

[0018] The SIFT corner matching algorithm is used to identify feature points in the surveyed image and perform corner matching to determine the overlapping area between the surveyed image and the surveyed image in adjacent frames.

[0019] Furthermore, determining the degree of interference of the overlapping region on important information in the image based on the proportion of different hierarchical regions contained within the overlapping region of each frame and the hierarchical gradient sensitivity includes:

[0020] Calculate the product of the area proportion of each level region within the overlapping area and the level gradient sensitivity of the corresponding level region, and use it as the weighted interference term for the level region;

[0021] The sum of the weighted interference terms for all hierarchical regions is normalized to represent the degree of interference.

[0022] Furthermore, determining the flight instability of each frame's mapped image based on the actual flight direction changes in different frames includes:

[0023] Determine the angle between the actual flight direction and the predetermined flight trajectory in different frames;

[0024] The standard deviation of all included angles was normalized and used as the first contribution coefficient.

[0025] Determine the frame angle of the actual flight direction in any two frames, and normalize the maximum value of the frame angle as the second contribution coefficient.

[0026] The average of the first contribution coefficient and the second contribution coefficient is calculated as the flight instability of the mapped image.

[0027] Furthermore, determining the transmission importance of each frame of surveying imagery by combining the transmission complexity of each frame, the interference level of overlapping areas, and the flight instability includes:

[0028] The mean values ​​of transmission complexity, interference level in overlapping areas, and flight instability of each frame of survey image are calculated and normalized to represent the transmission importance of each frame of survey image.

[0029] Furthermore, the encryption algorithm employing different encryption precisions based on the importance of the transmission includes:

[0030] When the transmission importance is less than a preset importance threshold, the data is encrypted using the AES-128 method; when the transmission importance is greater than or equal to the preset importance threshold, the data is encrypted using the AES-256 method.

[0031] On the other hand, a data security transmission system for UAV mapping is also provided, the system comprising:

[0032] The acquisition module is used to acquire continuous frame survey images captured by the UAV during aerial photography; it divides the pre-annotated areas contained in a single frame survey image into different levels and determines the different level areas in each frame survey image;

[0033] The complexity analysis module is used to determine the gradient sensitivity of each layer region based on the gradient distribution of different layer regions in each frame of the mapped image, and to determine the transmission complexity based on the gradient sensitivity of all layer regions.

[0034] The interference analysis module is used to perform feature point matching on each frame of the mapped image to determine the overlapping area of ​​adjacent frames; based on the proportion of different level regions contained in the overlapping area of ​​each frame and the level gradient sensitivity, the degree of interference of the overlapping area on important information in the image is determined.

[0035] The contribution analysis module is used to determine the flight phase with the same flight direction based on the predetermined flight trajectory. Within any flight phase, the flight instability of each frame of the mapped image is determined based on the actual flight direction changes in different frames.

[0036] The encryption module is used to determine the transmission importance of each frame of survey image by combining the transmission complexity of each frame, the interference level of the overlapping area, and the flight instability. Based on the transmission importance, different encryption algorithms with different encryption precision are adopted, wherein the transmission importance is proportional to the encryption precision.

[0037] On the other hand, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps of a data secure transmission method for UAV mapping as described in any of the foregoing.

[0038] The present invention has the following beneficial effects:

[0039] This invention addresses the problem that using a single encryption method in encrypted transmission leads to a rigid encryption process, low flexibility, and compromised data transmission efficiency and encryption accuracy. By performing gradient analysis on the hierarchical regions contained in each frame to determine transmission complexity and characterize the sensitivity of the overall texture, and considering the susceptibility of overlapping regions to interference, the invention further analyzes the degree of interference of overlapping regions on important information within the image by considering the proportion of different hierarchical regions and their gradient sensitivity within each overlapping region. Then, based on changes in flight angle during actual flight, the invention determines flight instability, representing the instability of the flight path. By integrating data from transmission complexity, the degree of interference in overlapping regions, and flight instability, the invention performs adaptive texture analysis on each frame to determine the transmission importance of each mapped image. Based on this importance, different encryption algorithms with varying encryption precision are adaptively employed to ensure flexible encryption. For image frames with complex textures that are easily affected, high-precision encryption is used to ensure data transmission stability; for image frames with simple textures, low-precision encryption is used to provide faster encryption and decryption speeds and avoid unnecessary resource consumption caused by over-encryption. In summary, by determining an adaptive encryption transmission method for different image frames, this invention can combine image features for efficient and high-precision transmission, effectively improving the security and flexibility of data transmission. Attached Figure Description

[0040] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a flowchart of a data security transmission method for UAV mapping provided in one embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of a pre-annotated area of ​​a surveying image provided in one embodiment of the present invention;

[0043] Figure 3 This is a schematic diagram of a drone flight path provided for one embodiment of the present invention. Detailed Implementation

[0044] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a data security transmission method, system, and storage medium for UAV mapping proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0046] The following description, in conjunction with the accompanying drawings, details a specific scheme for a data security transmission method for UAV mapping provided by the present invention.

[0047] Please see Figure 1 The diagram illustrates a flowchart of a data security transmission method for UAV mapping according to an embodiment of the present invention, the method comprising:

[0048] S101: Acquire continuous frame survey images taken by the UAV during aerial photography; divide the pre-annotated areas contained in a single frame survey image into different levels, and determine the different level areas in each frame survey image.

[0049] With the rise of the low-altitude economy, drones, due to their advantages of low cost and high resolution, have become a key supplementary element in surveying and mapping work such as land spatial planning and construction, playing an important role in fields such as urban modeling and engineering monitoring. Surveying drones need to transmit large amounts of data, including imagery, attitude, and location information, back to ground stations or cloud platforms in real time to support subsequent rapid processing and decision-making.

[0050] The significance of using drones to map real-world data and transmitting the complex and cumbersome mapping data collected by drones to the cloud for real-time stitching and 3D reconstruction is self-evident.

[0051] Existing UAV mapping systems deploy edge nodes point-by-point along the flight path under an edge computing architecture. Each edge node employs a unified encryption transmission strategy to improve data transmission real-time performance while protecting the privacy of the node's original data. During UAV flight, images captured at adjacent points often overlap to some extent. Current edge encryption transmission mechanisms typically perform encryption independently at each node. Using a unified encryption method, however, leads to inconsistent texture representations across different image frames. If high precision is prioritized, the encryption process becomes redundant, increasing transmission complexity. Conversely, prioritizing low precision results in interference from encryption and transmission processes on sensitive texture information, affecting subsequent model building. In short, it's impossible to balance data transmission efficiency and accuracy, resulting in insufficient data transmission security and flexibility.

[0052] In this embodiment of the invention, a drone equipped with a professional aerial camera is used, employing oblique photography and an "S"-shaped flight path to ensure complete coverage of the sides of each area. This allows for the collection of multi-dimensional urban information, ensuring the identification of minute ground features at a resolution appropriate to the ground surface. Images are then sorted and distributed along the drone's flight path to obtain a series of consecutive survey images arranged in sequence.

[0053] Among them, the surveying images are aerial images of urban planning, and the layers include at least one of facilities, buildings, ground textures, and transportation. In the process of urban construction planning, the different characteristics of the images to be transmitted and stored in real time make the importance of image information in different areas different. The security requirements in the image transmission process also differ. For example, images containing critical infrastructure and special terrain features need to be pre-annotated and identified, and specifically classified according to type.

[0054] S102: Determine the gradient sensitivity of each layer region based on the gradient distribution of different layer regions in each frame of the measured image, and determine the transmission complexity based on the gradient sensitivity of all layer regions.

[0055] In the process of urban construction planning, the different characteristics of the images to be transmitted and stored in real time result in different levels of importance of image information in different areas, and the security requirements during image transmission also vary. For example, images containing critical infrastructure and special terrain features carry highly sensitive information, and the security risks caused by information leakage are higher, so stronger encryption measures are required. On the other hand, open ground or textured areas with high repetition frequency have relatively stable information, so relatively weaker encryption measures can be used to reduce the cost of data encryption processing. Therefore, this step determines the transmission complexity by quantifying and scoring the sensitivity of scene information in each frame of aerial image.

[0056] See Figure 2 , Figure 2 This is a schematic diagram of a pre-annotated area for a surveying image provided in one embodiment of the present invention. Further, in some embodiments of the present invention, the layer gradient sensitivity of each layer region is determined based on the gradient distribution of different layer regions in each frame of the surveying image, including: determining the gradient of each pixel in each layer region based on the gradient operator, and processing the gradient based on the L1 norm to obtain the pixel's point gradient index; normalizing the standard deviation of the point gradient indices of all pixels as a first sensitivity index; normalizing the information entropy of the point gradient indices of all pixels as a second sensitivity index; and calculating the product of the first sensitivity index and the second sensitivity index, and normalizing it as the layer gradient sensitivity.

[0057] Gradient calculation and L1 norm are calculation methods well known to those skilled in the art. The gradient operator in this embodiment of the invention can be, for example, the Sobel operator, which can achieve overall analysis of all pixels by determining the point gradient index of each pixel.

[0058] In areas with complex information (such as urban blocks), the gradient distribution of pixels is irregular and can vary greatly. In contrast, in areas with simple information (such as large areas of bare land), the distribution is more consistent and the variation is smaller. The importance of areas with complex information is significantly greater than that of areas with simple information, and the impact (sensitivity) of areas with complex information to transmission anomalies is also greater than that of areas with simple information. Based on this, a hierarchical gradient sensitivity analysis is conducted for different levels.

[0059] In this embodiment of the invention, the standard deviation of the point gradient index, after normalization, serves as the first sensitivity index, and the information entropy of the point gradient index, after normalization, serves as the second sensitivity index. The standard deviation represents the discreteness of the information distribution, while the information entropy represents the complexity of the information distribution. That is, the larger the values ​​of the first and second sensitivity indices, the more complex and discrete the texture gradient distribution within the corresponding layer region. Therefore, it contains more and more detailed texture information, making it more susceptible to damage during transmission, and thus its importance is higher. In this embodiment of the invention, the product of the first and second sensitivity indices is calculated and normalized to serve as the layer gradient sensitivity.

[0060] In one embodiment of the present invention, the normalization process can be specifically, for example, maximum and minimum value normalization. Furthermore, the normalization in subsequent steps can all adopt maximum and minimum value normalization. In other embodiments of the present invention, other normalization methods can be selected according to the specific range of the numerical values, which will not be elaborated further.

[0061] The higher the value of the hierarchical gradient sensitivity, the more chaotic the corresponding hierarchical changes, the more complex the texture distribution, the more resources it consumes during transmission, and the stronger its sensitivity.

[0062] The transmission complexity is determined based on the hierarchical gradient sensitivity of all hierarchical regions, including using the average hierarchical gradient sensitivity of all hierarchical regions as the transmission complexity.

[0063] Since the same image contains different layer regions, in this embodiment of the invention, the average value of the layer gradient sensitivity of all layer regions is used as the transmission complexity. That is, the transmission complexity is analyzed only based on the complex information of the texture itself. The larger the value of the transmission complexity, the more complex the texture information of the mapped image itself is, and the transmission process will be affected by the texture information.

[0064] S103: Perform feature point matching on each frame of the mapped image to determine the overlapping area of ​​adjacent frames; determine the degree of interference of the overlapping area on important information in the image based on the proportion of different level regions contained in the overlapping area of ​​each frame and the level gradient sensitivity.

[0065] When a drone is conducting surveying and mapping work along a predetermined route, in order to ensure the integrity of the information collected, there will be some spatial overlap between images collected from adjacent points. In order to ensure the high security of sensitive data transmission, it is necessary to analyze the overlap of sensitive information changes in multiple image frames along the flight path.

[0066] Furthermore, in some embodiments of the present invention, feature point matching is performed on each frame of the surveyed image to determine the overlapping area of ​​adjacent frame images, including: using the SIFT corner matching algorithm to identify feature points in the surveyed image and performing corner matching to determine the overlapping area matched between the surveyed image and the adjacent frame of the surveyed image.

[0067] Among them, adjacent frames are a preset number of frames that are closest in time to any frame of the surveyed image. In this embodiment of the invention, the nearest 4 frames can be set, specifically the 2 frames before and the 2 frames after any frame. Of course, it can also be determined as two adjacent frames or other numbers of frames according to the actual flight situation. There is no restriction on this. The part of the surveyed image that overlaps with the actual area captured by the adjacent frame image is the overlapping area.

[0068] In this embodiment of the invention, SIFT corner detection is used to identify feature points in the surveyed images. Feature point matching is performed between any frame of the surveyed image and the previous frame to identify overlapping image information, thus obtaining the overlapping region. The SIFT corner matching algorithm can effectively identify the same region in different images, maintaining good matching performance even under changes in scale, rotation, and illumination. It is an algorithm well-known to those skilled in the art, and therefore will not be further limited or described in detail.

[0069] These overlapping areas typically represent information about the same scene from different perspectives or scales along the flight path, ensuring more accurate data reconstruction after transmission. Understandably, overlapping areas are regions that represent repetition in two images. The higher the proportion of overlapping areas, the more easily the image is affected by the overlap of other images. This necessitates more precise matching of the overlapping parts during subsequent model matching and construction, requiring more accurate and secure transmission methods in actual transmission.

[0070] To ensure the accuracy of important building information or traffic information, when images have high overlap and continuity, a large proportion of overlapping parts in an image indicates a high proportion of non-independent parts. The greater the image interference caused by different overlapping parts, the more necessary it is to use a higher level of encryption for images with high information repetition and sensitivity to prevent data loss or transmission errors. For images with little change in information sensitivity, conventional encryption is sufficient to ensure the security of data transmission.

[0071] Furthermore, in some embodiments of the present invention, the degree of interference of the overlapping region on important information in the image is determined based on the proportion of different level regions contained in the overlapping region of each frame and the level gradient sensitivity. This includes: calculating the product of the area proportion of each level region in the overlapping region and the level gradient sensitivity of the corresponding level region as a weighted interference term of the level region; and normalizing the sum of the weighted interference terms of all level regions as the degree of interference.

[0072] To perform a more precise analysis of the overlapping region, it is necessary to analyze the area proportion of different levels of regions within the overlapping region. Then, the area proportion is used as a weight to weight the hierarchical gradient sensitivity of the hierarchical regions. In this embodiment of the invention, the weighting is achieved by calculating the product of the area proportion of each level of region within the overlapping region and the hierarchical gradient sensitivity of the corresponding level region, thereby obtaining the weighted interference term of the hierarchical region.

[0073] The overlapping region contains different hierarchical regions, and the proportion of different hierarchical regions also varies. Analyzing only the hierarchical gradient sensitivity of the overlapping region itself does not take into account the influence of different hierarchical regions on the entire frame of the image. Therefore, this embodiment of the invention uses the area of ​​the hierarchical regions as weights to analyze different hierarchical regions within the overlapping region. It integrates the sum of weighted interference terms of all hierarchical regions in the entire frame of the image to obtain the interference level. This interference level is an interference index obtained after accurate analysis of the overlapping region. The larger the value of the interference level, the more sensitive the texture of the overlapping region is in the image, meaning it contains more sensitive information and requires improved encryption security.

[0074] It is understandable that the regions in adjacent frames overlap with each other. When analyzing the interference level in adjacent frames, a large interference level value will also be obtained. As a result, all overlapping parts are processed more accurately, avoiding matching errors in subsequent modeling caused by inconsistencies in overlapping parts. The more consistent the degree of synchronous encryption of adjacent frame images, the better.

[0075] S104: Determine the flight phase with the same flight direction according to the predetermined flight trajectory. Within any flight phase, determine the flight instability of each frame of the measured image based on the actual flight direction changes in different frames.

[0076] like Figure 3 As shown, Figure 3 This is a schematic diagram of a drone flight path provided in an embodiment of the present invention. The preset flight path (solid arrow part) reflects the geographical features and flight mission requirements of the target area during the drone's flight. However, due to the complex texture distribution or terrain environment of different areas, more detailed information needs to be collected in the current area. The real-time flight path of the drone (dashed line part) may deviate significantly from the preset flight path. Therefore, the higher the importance of the image information collected by the drone during the flight in the area, the higher the security requirement for the transmission of the regional image information captured by the drone in real time, in order to protect the complex regional texture information.

[0077] In this embodiment of the invention, the predetermined flight trajectory is determined as follows: Figure 3The "S"-shaped straight line shown represents a single-direction navigation trajectory phase as a flight phase, allowing for analysis of the navigation situation within each flight phase.

[0078] Furthermore, in some embodiments of the present invention, determining the flight instability of each frame of the mapped image based on the actual flight direction changes in different frames includes: determining the angle between the actual flight direction in different frames and the flight direction of the predetermined flight trajectory; normalizing the standard deviation of all angles as a first contribution coefficient; determining the frame angle of the actual flight direction in any two frames, normalizing the maximum value of the frame angle as a second contribution coefficient; and calculating the mean of the first contribution coefficient and the second contribution coefficient as the flight instability of the mapped image.

[0079] In this embodiment of the invention, the standard deviation of all included angles is normalized as the first contribution coefficient. The larger the value of the first contribution coefficient, the more discrete the included angles are, which means that the overall flight process is less stable. The larger the frame angle of the actual flight direction in any two frames, the larger the second contribution coefficient is, which also indicates that a large range of angle changes occurred during the flight phase. Therefore, the greater the deviation of the flight path, the more unstable the flight state of the UAV is. The average of the first contribution coefficient and the second contribution coefficient is calculated as the flight instability of the mapping image. The larger the value of the flight instability, the more unstable the flight path is. It is necessary to perform high-precision data security protection to avoid distortion caused by low data precision, which would affect the subsequent modeling due to the original instability.

[0080] S105: Combining the transmission complexity of each frame of survey image, the degree of interference in the overlapping area, and the flight instability, determine the transmission importance of each frame of survey image, and adopt encryption algorithms with different encryption precision according to the transmission importance, wherein the transmission importance is proportional to the encryption precision.

[0081] The feature data obtained in the above steps are integrated to analyze each frame of the mapped image.

[0082] Furthermore, in some embodiments of the present invention, the transmission importance of each frame of surveying image is determined by combining the transmission complexity of each frame of surveying image, the interference level of the overlapping area, and the flight instability, including: calculating the mean of the transmission complexity of each frame of surveying image, the interference level of the overlapping area, and the flight instability, and normalizing the mean as the transmission importance of each frame of surveying image.

[0083] The higher the transmission complexity value, the more complex the texture information of the mapped image itself, which will affect the transmission process. The higher the interference value, the more sensitive the texture representation in the overlapping area is in the image, meaning it contains more sensitive information and requires improved encryption security. The higher the flight instability value, the more unstable the flight path, requiring higher precision data security protection to avoid distortion caused by low data precision, which would affect subsequent modeling. Therefore, the transmission importance is obtained by fusing the information of the three dimensions by averaging. The higher the transmission importance, the more precise the texture details in the corresponding frame of the mapped image need to be transmitted, and the higher the required transmission resources and encryption requirements.

[0084] The encryption algorithm with different encryption precision is used according to the importance of transmission, including: when the importance of transmission is less than a preset importance threshold, the AES-128 method is used for data encryption; when the importance of transmission is greater than or equal to the preset importance threshold, the AES-256 method is used for data encryption.

[0085] The preset importance threshold is a threshold value for the importance of transmission. In this embodiment of the invention, the preset importance threshold can be, for example, 0.5. That is, for mapping images with a transmission importance greater than or equal to 0.5, a high-strength AES-256 method is selected for data encryption to ensure the security of data transmission. For mapping images with a transmission importance less than 0.5, a low-computational-consumption AES-128 method is selected for data encryption to provide faster encryption and decryption speeds and avoid unnecessary resource consumption caused by excessive encryption.

[0086] Of course, in other embodiments of the present invention, other encryption methods may be used to ensure flexible encryption requirements, and there are no limitations on this.

[0087] By employing layered adaptive encryption for transmission, the security, reliability, and real-time performance of data transmission in the cloud are ensured in complex smart city construction scenarios.

[0088] This invention addresses the problem that using a single encryption method in encrypted transmission leads to a rigid encryption process, low flexibility, and compromised data transmission efficiency and encryption accuracy. By performing gradient analysis on the hierarchical regions contained in each frame to determine transmission complexity and characterize the sensitivity of the overall texture, and considering the susceptibility of overlapping regions to interference, the invention further analyzes the degree of interference of overlapping regions on important information within the image by considering the proportion of different hierarchical regions and their gradient sensitivity within each overlapping region. Then, based on changes in flight angle during actual flight, the invention determines flight instability, representing the instability of the flight path. By integrating data from transmission complexity, the degree of interference in overlapping regions, and flight instability, the invention performs adaptive texture analysis on each frame to determine the transmission importance of each mapped image. Based on this importance, different encryption algorithms with varying encryption precision are adaptively employed to ensure flexible encryption. For image frames with complex textures that are easily affected, high-precision encryption is used to ensure data transmission stability; for image frames with simple textures, low-precision encryption is used to provide faster encryption and decryption speeds and avoid unnecessary resource consumption caused by over-encryption. In summary, by determining an adaptive encryption transmission method for different image frames, this invention can combine image features for efficient and high-precision transmission, effectively improving the security and flexibility of data transmission.

[0089] On the other hand, a data security transmission system for UAV mapping is also provided, the system including:

[0090] The acquisition module is used to acquire continuous frame survey images captured by the UAV during aerial photography; it divides the pre-annotated areas contained in a single frame survey image into different levels and determines the different level areas in each frame survey image;

[0091] The complexity analysis module is used to determine the gradient sensitivity of each layer region based on the gradient distribution of different layer regions in each frame of the mapped image, and to determine the transmission complexity based on the gradient sensitivity of all layer regions.

[0092] The interference analysis module is used to perform feature point matching on each frame of the mapped image to determine the overlapping area of ​​adjacent frames; based on the proportion of different level regions contained in the overlapping area of ​​each frame and the level gradient sensitivity, the degree of interference of the overlapping area on important information in the image is determined.

[0093] The contribution analysis module is used to determine the flight phase with the same flight direction based on the predetermined flight trajectory. Within any flight phase, the flight instability of each frame of the mapped image is determined based on the actual flight direction changes in different frames.

[0094] The encryption module is used to determine the transmission importance of each frame of survey image by combining the transmission complexity of each frame, the interference level of the overlapping area, and the flight instability. Based on the transmission importance, different encryption algorithms with different encryption precision are adopted, wherein the transmission importance is proportional to the encryption precision.

[0095] It should be noted that the specific content of a data security transmission system for UAV mapping refers to the relevant steps of a data security transmission method for UAV mapping in the above embodiments, and will not be further limited or elaborated.

[0096] On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a data security transmission method for UAV mapping as described in any of the foregoing.

[0097] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0098] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for secure data transmission in UAV mapping, characterized in that, The method includes: Acquire continuous frame survey images captured by the UAV during aerial photography; divide the pre-annotated areas contained in a single frame survey image into different levels, and determine the different level areas in each frame survey image; Based on the gradient distribution of different hierarchical regions in each frame of the image, the hierarchical gradient sensitivity of each hierarchical region is determined, and the transmission complexity is determined based on the hierarchical gradient sensitivity of all hierarchical regions. Feature point matching is performed on each frame of the mapped image to determine the overlapping area of ​​adjacent frames; based on the proportion of different hierarchical regions contained in the overlapping area of ​​each frame and the hierarchical gradient sensitivity, the degree of interference of the overlapping area on important information in the image is determined. Based on the predetermined flight trajectory, the flight phase with the same flight direction is determined. Within any flight phase, the flight instability of each frame of the measured image is determined based on the actual flight direction changes in different frames. The transmission importance of each frame of survey image is determined by combining the transmission complexity, interference level of overlapping areas, and flight instability. Based on the transmission importance, encryption algorithms with different encryption precision are adopted, wherein the transmission importance is proportional to the encryption precision.

2. The data security transmission method for UAV mapping as described in claim 1, characterized in that, The surveyed image is an aerial photograph of the city plan. The hierarchy includes at least one of facilities, buildings, ground texture, and traffic. Determining the hierarchy gradient sensitivity of each hierarchy region based on the gradient distribution of different hierarchy regions in each frame of the surveyed image includes: The gradient of each pixel in each layer region is determined based on the gradient operator, and the gradient is processed based on the L1 norm to obtain the pixel gradient index. The standard deviation of the gradient indices of all pixels is normalized and used as the first sensitive indicator; the information entropy of the gradient indices of all pixels is normalized and used as the second sensitive indicator. Calculate the product of the first and second sensitivity indices, and normalize it to obtain the hierarchical gradient sensitivity.

3. The data security transmission method for UAV mapping as described in claim 1, characterized in that, The determination of transmission complexity based on the hierarchical gradient sensitivity of all hierarchical regions includes: The mean of the hierarchical gradient sensitivity of all hierarchical regions is used as the transmission complexity.

4. The data security transmission method for UAV mapping as described in claim 1, characterized in that, The step of performing feature point matching on each frame of the mapped image to determine the overlapping region of adjacent frames includes: The SIFT corner matching algorithm is used to identify feature points in the surveyed image and perform corner matching to determine the overlapping area between the surveyed image and the surveyed image in adjacent frames.

5. A data security transmission method for UAV mapping as described in claim 1, characterized in that, The determination of the degree of interference of the overlapping region on important information in the image based on the proportion of different hierarchical regions contained in the overlapping region of each frame and the hierarchical gradient sensitivity includes: Calculate the product of the area proportion of each level region within the overlapping area and the level gradient sensitivity of the corresponding level region, and use it as the weighted interference term for the level region; The sum of the weighted interference terms for all hierarchical regions is normalized to represent the degree of interference.

6. A data security transmission method for UAV mapping as described in claim 1, characterized in that, The determination of flight instability of each frame of the mapped image based on the actual flight direction changes in different frames includes: Determine the angle between the actual flight direction and the predetermined flight trajectory in different frames; The standard deviation of all included angles was normalized and used as the first contribution coefficient. Determine the frame angle of the actual flight direction in any two frames, and normalize the maximum value of the frame angle as the second contribution coefficient. The average of the first contribution coefficient and the second contribution coefficient is calculated as the flight instability of the mapped image.

7. A data security transmission method for UAV mapping as described in claim 1, characterized in that, The determination of the transmission importance of each frame of surveying imagery, by combining the transmission complexity of each frame, the interference level of overlapping areas, and the flight instability, includes: The mean values ​​of transmission complexity, interference level in overlapping areas, and flight instability of each frame of survey image are calculated and normalized to represent the transmission importance of each frame of survey image.

8. A data security transmission method for UAV mapping as described in claim 1, characterized in that, The encryption algorithm employing different encryption precisions based on the importance of the transmission includes: When the transmission importance is less than a preset importance threshold, the data is encrypted using the AES-128 method; when the transmission importance is greater than or equal to the preset importance threshold, the data is encrypted using the AES-256 method.

9. A data security transmission system for UAV mapping, characterized in that, The system includes: The acquisition module is used to acquire continuous frame survey images captured by the UAV during aerial photography; it divides the pre-annotated areas contained in a single frame survey image into different levels and determines the different level areas in each frame survey image; The complexity analysis module is used to determine the gradient sensitivity of each layer region based on the gradient distribution of different layer regions in each frame of the mapped image, and to determine the transmission complexity based on the gradient sensitivity of all layer regions. The interference analysis module is used to perform feature point matching on each frame of the mapped image to determine the overlapping area of ​​adjacent frames; based on the proportion of different level regions contained in the overlapping area of ​​each frame and the level gradient sensitivity, the degree of interference of the overlapping area on important information in the image is determined. The contribution analysis module is used to determine the flight phase with the same flight direction based on the predetermined flight trajectory. Within any flight phase, the flight instability of each frame of the mapped image is determined based on the actual flight direction changes in different frames. The encryption module is used to determine the transmission importance of each frame of survey image by combining the transmission complexity of each frame, the interference level of the overlapping area, and the flight instability. Based on the transmission importance, different encryption algorithms with different encryption precision are adopted, wherein the transmission importance is proportional to the encryption precision.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the steps of a data security transmission method for UAV mapping as described in any one of claims 1-8.

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