Low-altitude intelligent aircraft data security protection method, system, medium and server

Abstract

The present application provides a method, system, medium and server for data security protection of low-altitude intelligent aircraft, which relates to the technical field of protecting data security. The method includes: analyzing internal data to determine whether the internal data contains image data. If it contains image data, the image data is stored; otherwise, the internal data is stored. Then, when the user calls the data, the user identity is verified. If the verification is passed, the relevant data is fed back; otherwise, an alarm is executed. The present application stores the image data in a stack manner, and only when the user identity verification is passed, the pixel values corresponding to the stack are fed back, improving the security of the image data.

Description

Technical Field

[0001] This application relates to the technical field of protecting data security, and particularly to a method, system, medium, and server for protecting the data security of low-altitude intelligent aircraft. Background Art

[0002] With the rapid development of low-altitude intelligent aircraft technology, such aircraft have shown unprecedented wide application potential in multiple fields such as aerial photography, agricultural monitoring, emergency rescue, cargo transportation, and even recreational entertainment, greatly improving work efficiency and quality of life. However, while fully enjoying the convenience and benefits brought by these high-tech products, an issue that cannot be ignored - data security - is gradually emerging and becoming one of the key factors restricting the further development of low-altitude intelligent aircraft.

[0003] Since low-altitude intelligent aircraft often carry advanced devices such as wide-angle high-definition cameras and sensors when performing tasks, they can capture and transmit a large amount of ground images, videos, and other sensitive information in real time. If this information is misused or leaked, it may pose a serious threat to individuals, enterprises, and even social stability. Especially during some special periods or in sensitive areas, no-fly zones are set up to maintain public safety, protect important facilities, or respond to specific events.

[0004] Although low-altitude intelligent aircraft will comply with the regulations and no longer enter these clearly demarcated no-fly zones after receiving a no-fly instruction, even if the low-altitude intelligent aircraft itself does not cross the boundary, when approaching the no-fly boundary, the low-altitude intelligent aircraft may still capture images of the edge area or even the content prohibited from being photographed within the no-fly zone, enabling the low-altitude intelligent aircraft to capture clear images when far away from the target area. Once these pictures containing the no-fly zone are illegally obtained, spread, or maliciously tampered with, the consequences will be unthinkable. Summary of the Invention

[0005] To improve the security of the internal data of low-altitude intelligent aircraft, this application provides a method, system, medium, and server for protecting the data security of low-altitude intelligent aircraft.

[0006] In a first aspect, this application provides a method for protecting the data security of low-altitude intelligent aircraft, adopting the following technical solution:

[0007] A method for protecting the data security of low-altitude intelligent aircraft includes the following steps:

[0008] Data acquisition: Obtain the internal data stored in the low-altitude intelligent aircraft;

[0009] First judgment: Judge whether the internal data contains image data. If so, execute the step of establishing a coordinate system; if not, execute the step of secondary storage;

[0010] Coordinate system establishment: Establish a coordinate system with any pixel point in the image data as the coordinate origin;

[0011] First acquisition: Acquire the coordinate values and pixel values of each pixel point in the image data;

[0012] Stack establishment: Establish stacks with the same number as the number of categories of pixel values;

[0013] First storage: Push the coordinate values of pixel points with the same pixel value into the stack, and record the pixel value corresponding to each stack;

[0014] Second storage: Store the internal data in a preset database;

[0015] User verification: Verify whether the user identity is legal. If so, execute the feedback step; if not, execute the warning step;

[0016] Feedback: Feedback the pixel value corresponding to each stack and / or the internal data to the user;

[0017] Warning: Send out a warning signal.

[0018] This application first acquires the internal data stored in the low-altitude intelligent aircraft, and then determines whether there is image data in the internal data. If there is, a coordinate system is established to obtain the coordinate values and pixel values of each pixel point. Then, stacks with the same number as the number of categories of pixel values are established, which facilitates the storage and management of the coordinate values of pixel points with the same pixel value, helps to quickly access and process these pixel points, and the use of stacks also improves the flexibility and efficiency of image data processing. Subsequently, this application pushes the coordinate values with the same pixel value into the stack in a preset order and records the pixel value corresponding to each stack. Otherwise, the internal data is stored. After the user identity verification is passed, the correct data will be fed back to the user. By adopting the above technical solution, this application realizes the classified storage and management of pixel points, and thus realizes the hiding of image data, improving the security of the internal image data of the low-altitude intelligent aircraft.

[0019] Optionally, after performing the data acquisition step and before performing the first judgment step, it further includes:

[0020] Clustering: Use the K-Means clustering algorithm to cluster the internal data to obtain the class labels of each internal data;

[0021] First calculation: Perform mean removal processing on the internal data under the same class label to obtain the internally processed data after the first processing; perform variance normalization processing on the internally processed data after the first processing to obtain the internally processed data after the second processing, denoted as the first data;

[0022] Construct a matrix: Construct a covariance matrix based on the first data, where the element in the p-th row and q-th column of the covariance matrix has the following calculation formula:

[0023] ;

[0024] where is the value of the p-th first data under the k1-th class label; is the mean value of all first data under the k1-th class label; is the value of the q-th first data under the k2-th class label; is the mean value of all first data under the k2-th class label; n is the number of class labels;

[0025] Second calculation: Perform eigenvalue decomposition on the covariance matrix to obtain the largest eigenvalue and the corresponding eigenvector;

[0026] Third calculation: Perform a multiplication operation on the first data and the eigenvector to obtain an operation result, and update the operation result as the internal data.

[0027] In this application, first, the internal data under the same class label is de-meaned to eliminate the mean shift in the internal data under the same class label, making the internal data more concentrated and easier to analyze. The variance normalization process further eliminates the dimensional difference of the internal data in different dimensions, making the internal data comparable in different dimensions, thereby improving the accuracy and stability of subsequent data analysis. Subsequently, this application constructs a covariance matrix based on the internally processed data after the second processing to quantify the correlation and dependence between different internally processed data after the second processing. Then, eigenvalue decomposition is performed on the covariance matrix to obtain eigenvalues and corresponding eigenvectors. Then, a multiplication operation is performed on the internally processed data after the second processing and the eigenvector, so as to map the internally processed data after the second processing into the space represented by the eigenvector. This application can hide the original internal data while retaining the main information in the internal data. Even if an illegal user obtains the first data after dimensionality reduction, they cannot know the original dimension of the first data, thereby further improving the security of the internal data.

[0028] Optionally, after performing the step of the first acquisition and before performing the step of building a stack, it further includes:

[0029] Fourth calculation: Calculate the variance of all pixel values in the image data;

[0030] Second judgment: Judge whether the variance is equal to zero. If so, perform the step of building a stack; if not, perform the step of image segmentation;

[0031] Image segmentation: Use the region growing algorithm to segment the image data and obtain multiple segmented blocks;

[0032] Third judgment: Based on the eight-neighborhood pixel values, judge whether there are noise points in the segmented block. If so, modify the pixel value at the noise point to the average value of the eight-neighborhood pixel values of the noise point; if not, perform the image update step;

[0033] Image update: Update each segmented block into the image data respectively and perform the fourth calculation step.

[0034] This application first calculates the variance of all pixel values in the image data. A variance of zero indicates that the pixel values in the image data are the same or vary uniformly, that is, the image is a monochromatic image or a uniform image. Subsequently, this application determines whether the variance is equal to zero to identify whether the current image is a monochromatic image or a uniform image. When the variance is not zero, it indicates that the current image is a non-uniform image, that is, there may be noise points, and further image segmentation is required to determine whether there are indeed noise points. Subsequently, this application uses the region growing algorithm to segment the image data to obtain multiple segmented blocks with similar pixel values, and then separately processes and analyzes different regions in the image. Then, based on the eight-neighborhood pixel values, it is judged whether there are noise points in the current segmented block. If so, the pixel value at the noise point is modified to the average value of the pixel values of its eight-neighborhood pixel points, thereby smoothing the image. Through image segmentation and noise processing, this application can reduce the noise and interference in the image and improve the quality and usability of the image.

[0035] Optionally, after performing the image segmentation step and before performing the third judgment step, it further includes:

[0036] Fourth judgment: Judge whether the number of pixel points in the segmented block is one. If so, perform the third judgment step; if not, perform the image update step.

[0037] This application judges whether the number of pixel points in the segmented block is one. If so, it means that the segmented block is a monochromatic block, and directly performs the third judgment step to reduce the number of executions of the image segmentation step, thereby improving the overall processing efficiency. By first screening out the segmented blocks that only contain one pixel point, this application can optimize the allocation of computing resources, so that more resources can be concentrated on processing larger segmented blocks that contain more useful information, thereby improving the processing efficiency.

[0038] Optionally, after performing the first judgment step and before performing the second storage step, it further includes:

[0039] Second acquisition: Obtain the clustering result of the internal data and obtain n clustering centers based on the clustering result;

[0040] Database building: Construct n data sub - databases, and denote the i - th data sub - database as the i - th data sub - database;

[0041] Fifth calculation: Calculate the Euclidean distances between the j - th cluster center and the remaining cluster centers in sequence to obtain n - 1 Euclidean distances; Calculate the sum of the n - 1 Euclidean distances, and denote it as the second data;

[0042] First sorting: Arrange the n second data in a preset order, and denote the arranged result as the second data sequence;

[0043] Association: Associate the second data in the second data sequence with the data sub - database having the same serial number to obtain an association result;

[0044] Third storage: Store the internal data into the corresponding data sub - database according to the association result.

[0045] This application first obtains the clustering result of the internal data and obtains n cluster centers based on the clustering result. Subsequently, this application constructs n data sub - databases to facilitate the classified storage of the internal data of the low - altitude intelligent aircraft. Subsequently, this application calculates the Euclidean distances between the cluster centers, and respectively calculates the sum of the Euclidean distances from each cluster center to the remaining cluster centers, denoted as the second data, and arranges the n second data in a preset order. Subsequently, this application associates the second data in the second data sequence with the data sub - database having the same serial number, realizing the correspondence between the internal data and the data sub - database, which is convenient for subsequent internal data storage and access. Subsequently, this application stores the internal data into the corresponding data sub - database according to the association result, realizing the classified storage of the data, which helps to speed up the data retrieval speed and improve the data processing efficiency. This application enhances the data security through a dynamic fine - grained data storage method.

[0046] Optionally, after performing the step of the third storage, the method further includes:

[0047] First encryption: Use a symmetric encryption algorithm to encrypt the internal data stored in the i - th data sub - database to obtain the i - th encrypted data, and use the i - th encrypted data to update the internal data stored in the i - th data sub - database;

[0048] Second encryption: Use an asymmetric encryption algorithm to encrypt the i - th encrypted data and the internal data stored in the (i + 1) - th data sub - database to obtain the (i + 1) - th encrypted data, and use the (i + 1) - th encrypted data to update the internal data stored in the (i + 1) - th data sub - database;

[0049] Fifth judgment: Judge whether i + 1 is equal to n. If not, execute the iterative step;

[0050] Iteration: Use the (i + 1)-th data sub-library as the new i-th data sub-library, and the (i + 1)-th encrypted data as the new i-th encrypted data, and perform the steps of the second encryption until a preset stop condition is met.

[0051] This application uses a symmetric encryption algorithm to encrypt the internal data stored in the i-th data sub-library, which can improve the security of the internal data stored in the i-th data sub-library, making it difficult to decrypt and tamper with the encrypted internal data even if it is illegally obtained, thus protecting the privacy and integrity of the internal data. Subsequently, this application uses an asymmetric encryption algorithm to encrypt the i-th encrypted data and the internal data stored in the (i + 1)-th data sub-library, further enhancing the data security. Using different encryption algorithms can increase the difficulty of data being cracked and improve the hierarchical nature of data protection. Subsequently, by performing the steps of the fifth judgment, this application can check whether all internal data has completed the encryption process. If there is still unencrypted internal data, the encryption process is continued through the iteration steps until all internal data is encrypted, thereby reducing the risk of data leakage. By adopting the above solution, this application further enhances the security of the internal data of the low-altitude intelligent aircraft. The multi-level and multi-algorithm encryption method adopted by this application improves the intensity of data protection, making it difficult to decrypt and tamper with the internal data even when it is illegally obtained.

[0052] Optionally, after performing the associated steps and before performing the first storage step, it further includes:

[0053] Data screening: Identify sensitive data and non-sensitive data in the internal data;

[0054] Second sorting: Sort the sensitive data within each cluster in chronological order of collection time to obtain n sensitive data sequences;

[0055] Calculate distance: Calculate the distance matrix between the a-th sensitive data sequence and the remaining sensitive data sequences respectively, and calculate the cumulative distance matrix based on the distance matrix;

[0056] Determine sequence: Based on the cumulative distance matrix, use the dynamic path planning algorithm to obtain the sensitive data sequence with the highest similarity to the a-th sensitive data sequence, denoted as the similar data sequence;

[0057] Combination: Combine the sensitive data of the a-th sensitive data sequence with the non-sensitive data corresponding to the similar data sequence to obtain the combined data, and update the combined data as the internal data.

[0058] This application first identifies sensitive data and non-sensitive data in the internal data. Subsequently, the sensitive data within each cluster is sorted in chronological order to reveal the temporal characteristics and change trends of the data, facilitating subsequent data analysis and mining. Subsequently, this application calculates the distance matrix between the a-th sensitive data sequence and the remaining sensitive data sequences, and calculates the cumulative distance matrix based on the distance matrix, thereby quantifying the similarity between the a-th sensitive data sequence and the remaining sensitive data sequences. Subsequently, this application obtains the sensitive data sequence with the highest similarity to the a-th sensitive data sequence (i.e., the similar data sequence) according to the cumulative distance matrix. Then, the sensitive data of the a-th sensitive data sequence is combined with the non-sensitive data corresponding to the similar data sequence and updated as the internal data, which helps to better utilize the non-sensitive data of other data sequences to hide the sensitive data of the a-th data sequence while protecting the sensitive data, further improving data security.

[0059] In a second aspect, this application provides a data security protection system for low-altitude intelligent aircraft, adopting the following technical solutions:

[0060] The data security protection system for low-altitude intelligent aircraft includes:

[0061] A data acquisition module, used to obtain the internal data stored in the low-altitude intelligent aircraft;

[0062] A first judgment module, used to judge whether the internal data contains image data. If it contains image data, the coordinate system establishment module is triggered; otherwise, the second storage module is triggered;

[0063] A coordinate system establishment module, used to establish a coordinate system with any pixel point in the image data as the coordinate origin;

[0064] A first acquisition module, used to acquire the coordinate values and pixel values of each pixel point in the image data;

[0065] A stack establishment module, used to establish stacks with the same number as the number of pixel value categories;

[0066] A first storage module, used to push the coordinate values of pixel points with the same pixel value into the stack and record the pixel value corresponding to each stack;

[0067] A second storage module, used to store the internal data in a preset database;

[0068] A user verification module, used to verify whether the user identity is legal. If it is legal, the feedback module is triggered; otherwise, the alarm module is triggered;

[0069] A feedback module, used to feedback the pixel value corresponding to each stack and / or the internal data to the user;

[0070] An alarm module, used to emit an alarm signal.

[0071] Through a series of processing of image data, this application realizes the classified storage and processing of the internal data of the aircraft, improving the security of the internal image data of the aircraft.

[0072] In a third aspect, this application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is processed by a processor, it is used to implement the method described above.

[0073] In a fourth aspect, this application provides a server, on which the system described above is loaded. The server includes: a processor, and a memory communicatively connected to the processor;

[0074] The computer-readable storage medium described above is provided in the memory, and a computer program is stored on the computer-readable storage medium;

[0075] When the processor processes the computer program stored on the computer-readable storage medium, the method described above is implemented.

[0076] In summary, this application includes at least one of the following beneficial technical effects:

[0077] 1. This application first obtains the internal data stored in the low-altitude intelligent aircraft, and then determines whether there is image data in the internal data. If there is, a coordinate system is established to obtain the coordinate values and pixel values of each pixel point. Then, stacks with the same number as the number of categories of pixel values are established, which facilitates the storage and management of the coordinate values of pixel points with the same pixel value, helps to quickly access and process these pixel points, and the use of stacks also improves the flexibility and efficiency of image data processing.

[0078] 2. This application pushes the coordinate values with the same pixel value into the stack in a preset order and records the pixel value corresponding to each stack. Otherwise, the internal data is stored. Only after the user authentication is passed will the correct data be fed back to the user. By adopting the above technical solutions, this application realizes the classified storage and management of pixel points, and thus realizes the hiding of image data, improving the security of the internal image data of the low-altitude intelligent aircraft. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 is the flowchart of Embodiment 1 of this application;

[0080] Figure 2 is the flowchart of Embodiment 2 of this application;

[0081] Figure 3 is the flowchart of Embodiment 3 of this application;

[0082] Figure 4It is the flowchart of Embodiment 4 of the present application;

[0083] Figure 5 It is the flowchart of Embodiment 5 of the present application. Detailed implementation manners

[0084] The following is a further detailed description of the present application in conjunction with Figures 1 to 5 to further illustrate the present application in detail.

[0085] Embodiment 1: This embodiment discloses a method for protecting the data security of a low-altitude intelligent aircraft. Referring to Figure 1 , the method includes: S1 data acquisition, S2 first judgment, S3 image storage, S4 second storage, and S5 legal verification. First, internal data is acquired, and then it is judged whether the internal data contains image data. If it contains image data, any pixel point in the image is selected as the origin to establish a coordinate system, the coordinates and pixel values of each pixel are obtained, and a stack equal to the number of pixel value categories is established to store the coordinate information of the same pixel value; if it does not contain image data, the internal data is directly stored in a preset database. In addition, this embodiment also includes a user identity verification link. A legal user can obtain data feedback, while an illegal user triggers an alarm signal. This embodiment includes the following steps:

[0086] S1 data acquisition, obtaining the stored internal data from the aircraft, where the internal data includes flight parameters, sensor readings, device status information, and acquired image data, etc.

[0087] S2 first judgment, judging whether the internal data contains image data. If so, execute S3 image storage; otherwise, execute S4 second storage.

[0088] S3 image storage, including S31 coordinate system establishment, S32 first acquisition, S33 stack establishment, and S34 first storage.

[0089] S31 coordinate system establishment, selecting an arbitrary pixel point in the image data as the coordinate origin. This coordinate origin can be any pixel point at the upper left corner, center, or other positions of the image.

[0090] Determine the directions of the coordinate axes. In this embodiment, the horizontal direction is selected as the horizontal axis and the vertical direction is selected as the vertical axis to form a rectangular coordinate system.

[0091] In the rectangular coordinate system, each pixel point has a unique coordinate value to identify its position in the image data.

[0092] In other embodiments, other directions can also be selected as the horizontal axis and the vertical axis, as long as it can be determined that each pixel point in the image data has a unique coordinate value.

[0093] S32 First acquisition: Traverse each pixel point in the image data to obtain the coordinate value and pixel value of each pixel point.

[0094] S33 Stack building: Count the number of types of different pixel values in the image data. For each different pixel value, create a corresponding stack, which is used to store the coordinate values of the pixel points with the same pixel value. The elements stored in each stack represent the positions of the pixel points with this pixel value in the image.

[0095] S34 First storage: For each pixel point in the image data, find the corresponding stack according to its pixel value, push the coordinate values of the pixel points with the same pixel value into the same stack, and record the pixel value corresponding to each stack.

[0096] S4 Second storage: Store the internal data in a preset database.

[0097] S5 Legality verification includes S51 user verification, S52 feedback, and S53 alarm.

[0098] S51 User verification: Verify whether the user identity is legal. If so, execute S52 feedback; if not, execute S53 alarm.

[0099] The verification of the legality of the user identity can be achieved by inputting a username and password, using biometric technology, or other identity verification methods. The standard for the legal user identity is that the user successfully passes the verification.

[0100] S52 Feedback: Feed back the pixel value corresponding to each stack and / or the internal data to the user.

[0101] S53 Alarm: Send an alarm signal.

[0102] This embodiment realizes the function of securely storing the internal data stored in a low-altitude intelligent aircraft (hereinafter referred to as "aircraft" for short), strengthens the management and protection of the internal data of the aircraft, and improves the security of the internal data.

[0103] Embodiment 2: Refer to Figure 2 , the difference between this embodiment and Embodiment 1 is that after executing S1 data acquisition and before executing S2 first judgment, it further includes:

[0104] S61 Clustering: After obtaining the internal data, use the K-Means clustering algorithm to process these internal data. The K-Means clustering algorithm is an unsupervised learning algorithm used to divide data points into n clusters (the center of each cluster becomes a clustering center). After clustering, the class label of each internal data can be obtained.

[0105] S62 First calculation: perform mean removal processing on the internal data under the same category label to obtain the internal data after the first processing; perform variance normalization processing on the internal data after the first processing to obtain the internal data after the second processing, denoted as the first data.

[0106] Mean removal processing refers to subtracting the mean of all internal data under this category from each internal data to eliminate the offset of the internal data. Variance normalization refers to dividing the internal data after mean removal processing by the standard deviation of this internal data to scale the internal data on a unified scale.

[0107] S63 Construct a matrix: construct a covariance matrix based on the first data, and the element in the p-th row and q-th column of the covariance matrix has the following calculation formula:

[0108] ;

[0109] where is the value of the p-th first data under the k1-th category label; is the mean of all first data under the k1-th category label; is the value of the q-th first data under the k2-th category label; is the mean of all first data under the k2-th category label; n is the number of category labels.

[0110] S64 Second calculation: perform eigenvalue decomposition on the covariance matrix to obtain the largest eigenvalue and the corresponding eigenvector.

[0111] S65 Third calculation: perform a multiplication operation on the first data and the eigenvector to obtain the operation result, and update the operation result as the internal data.

[0112] In this embodiment, the label of each internal data is obtained, and the data under different category labels is dimensionally reduced. While reducing the processing quantity, each internal data is camouflaged, improving the security of the internal data.

[0113] Embodiment 3: Referring to Figure 3 , the difference between this embodiment and Embodiment 1 is that after performing S32 First acquisition and before performing S33 Stack building, it further includes:

[0114] S71 Fourth calculation: calculate the variance of all pixel values in the image data.

[0115] S72 Second judgment: judge whether the variance is equal to zero. If so, it indicates that the current image is a monochromatic image or a uniform image, and then perform S33 Stack building; if not, it indicates that the current image is a non-uniform image, and then perform S73 Image segmentation.

[0116] S73 Image segmentation, using the region growing algorithm to segment the image data and obtain multiple segmented blocks.

[0117] S74 Fourth judgment, judge whether the number of pixel points in the segmented block is one. If so, it indicates that the current segmented block has been segmented into the smallest segmented block and cannot be segmented further. Then execute S75 Third judgment to judge whether there are noise points in the current segmented block; if not, it indicates that the current segmented block can be segmented further, and then execute S76 Image update.

[0118] S75 Third judgment, based on the eight-neighborhood pixel values of the current segmented block, judge whether there are noise points in the segmented block. If so, modify the pixel value at the noise point to the average value of the eight-neighborhood pixel values; if not, execute S76 Image update.

[0119] S76 Image update, update each segmented block into the image data respectively, and execute S71 Fourth calculation until all segmented blocks are updated, and then execute S33 Stack building.

[0120] In this embodiment, first, the variance of all pixel values in the image data is calculated, and it is judged whether the variance is zero to determine whether the image is a monochromatic image or a uniform image. If so, the stack building operation is executed; otherwise, image segmentation is performed. The image segmentation uses the region growing algorithm to obtain multiple segmented blocks. Then, the number of pixel points in the segmented block is judged. If it is one, it indicates that the smallest block has been segmented and it is judged whether there are noise points. If there are, the pixel value of the noise point is modified to the average value of the eight-neighborhood; otherwise, the image data is updated and steps such as calculating the variance are repeated until all segmented blocks are updated and stack building is executed. Through image segmentation and noise processing, this embodiment can reduce the noise and interference in the image and improve the quality and usability of the image.

[0121] Example 4: Refer to Figure 4 , the difference between this embodiment and Embodiment 2 is that after executing S2 First judgment and before executing S4 Second storage, it further includes:

[0122] S81 Second acquisition, obtain the clustering result of the internal data in S61 Clustering, and obtain n clustering centers based on the clustering result.

[0123] S82 Database building, according to the n clustering centers obtained by K-Means clustering, construct n data sub-libraries. Each data sub-library is used to store the internal data associated with the corresponding clustering center, and the i-th data sub-library is denoted as the i-th data sub-library (i = 1, 2,..., n).

[0124] S83 Fifth calculation, for each clustering center, calculate the Euclidean distance between it and the remaining clustering centers, and obtain n - 1 Euclidean distances (because each clustering center has a distance from the other n - 1 distance centers).

[0125] Add up the values of these Euclidean distances to obtain a sum, and denote the sum as the second data. This process will be repeated for each cluster center, and finally n second data will be obtained.

[0126] S84 First sorting: Arrange the obtained n second data in a preset order. The preset order is set based on actual needs, such as sorting from small to large or from large to small. The sorted result is called the second data sequence.

[0127] S85 Association: Associate each second data in the second data sequence with the data sub-library having the same serial number to obtain an association result. For example, associate the second data with the serial number one in the second data sequence with the first data sub-library, and the second data with the serial number two with the second data sub-library, and so on, until each data sub-library is associated with a second data.

[0128] S86 Third storage: Since each data sub-library is associated with a specified second data, this second data corresponds to a specified cluster center, and this cluster center corresponds to a specified internal data. Therefore, the internal data can be stored in the corresponding data sub-library through the association result.

[0129] The following elaborates on this embodiment with a specific case.

[0130] Suppose a low-altitude intelligent aircraft (hereinafter referred to as the aircraft) records a large amount of flight data, including parameters such as flight altitude, speed, temperature, pressure, etc., as well as readings of each sensor and device status information.

[0131] S1 Data acquisition: 1000 internal data records are collected from the aircraft, and each record contains flight parameters (such as altitude, speed), sensor readings (such as temperature, pressure), and device status information (such as engine status).

[0132] After S61 clustering, the K-Means clustering algorithm is used to process these internal data (set n = 5, that is, divide the internal data into 5 clusters), and 5 cluster centers are obtained. Each cluster center represents the average value of a group of internal data with similar characteristics.

[0133] S81 Second acquisition: Obtain the clustering result of the internal data and obtain 5 cluster centers.

[0134] S82 Database construction: According to the 5 cluster centers obtained by K-Means clustering, 5 corresponding data sub-libraries are constructed.

[0135] Each data sub - library will be used to store data points associated with the corresponding cluster center. For the convenience of management and reference, the i - th data sub - library is denoted as the i - th data sub - library (i = 1, 2, 3, 4, 5).

[0136] S83 First calculation: For each cluster center (e.g., the first cluster center), calculate the Euclidean distance between it and the remaining 4 cluster centers, obtaining 4 Euclidean distance values. Then sum up these 4 Euclidean distance values to get a total, and denote this total as the second data.

[0137] This step is performed for all 5 cluster centers, and finally 5 second data are obtained.

[0138] S84 First sorting: Sort the 5 obtained second data in ascending order to obtain an ordered data sequence, i.e., the second data sequence.

[0139] S85 Association: Associate each second data in the second data sequence with the data sub - library having the same serial number. For example, the second data sequence is: altitude data, speed data, temperature data, pressure data, engine status data; the second data corresponding to the altitude data is associated with the first data sub - library, the second data corresponding to the speed data is associated with the second data sub - library, the second data corresponding to the temperature data is associated with the third data sub - library, the second data corresponding to the pressure data is associated with the fourth data sub - library, and the second data corresponding to the engine status data is associated with the fifth data sub - library.

[0140] S86 Third storage: According to the association result, store the original internal data into the corresponding data sub - libraries. For example, altitude data is stored in the first data sub - library, speed data is stored in the second data sub - library, temperature data is stored in the third data sub - library, pressure data is stored in the fourth data sub - library, and engine status data is stored in the fifth data sub - library.

[0141] This embodiment involves obtaining internal data (such as flight parameters, sensor readings, equipment status information, etc.) from an aircraft, processing these internal data using the K - Means clustering algorithm to obtain n cluster centers, constructing n data sub - libraries, and each data sub - library is associated with the corresponding cluster center. Then, calculate the Euclidean distance between each cluster center and other cluster centers, and calculate the sum of these Euclidean distances to obtain n second data. After that, sort the second data to form a second data sequence. Associate the position of the second data in the second data sequence with the serial number of the data sub - library. Finally, store the internal data into the corresponding data sub - libraries according to the association result, realizing the dynamic storage and efficient management of data, and further improving the security of the internal data of the low - altitude intelligent aircraft.

[0142] Example 5: Refer to Figure 5 , the difference between this example and Example 4 is that after performing the steps associated with S85 and before performing the third storage of S86, the method further includes:

[0143] S91 Data screening, deeply analyze the internal data of the aircraft to identify sensitive data and non-sensitive data therein.

[0144] The method for identifying sensitive data can be an identification method based on data characteristics and uses, an identification method based on preset rules, or an identification method based on a machine learning model.

[0145] For example, the identification process based on data characteristics and uses is as follows: First, classify the internal data of the aircraft in detail, such as flight parameters, personnel information, system status data, etc. Then, based on the characteristics and uses of the data, preliminarily judge which data may belong to sensitive data and which belong to non-sensitive data. Then analyze the specific content of the data, such as: whether it contains personal privacy information (such as name, ID number, contact information, etc.), aircraft operation data (such as flight plan, etc.) or aircraft safety data (such as fault records, maintenance records, etc.). Finally, divide it into sensitive data and non-sensitive data according to the sensitivity of the data.

[0146] For example, the identification process based on a machine learning algorithm is: Use existing sensitive data and non-sensitive data samples to train a supervised learning model. Use the trained supervised learning model to identify the internal data and judge whether it contains sensitive data.

[0147] For example, the identification process based on rules is:

[0148] First, formulate sensitive data identification rules: According to the characteristics and uses of the internal data of the aircraft, formulate a set of sensitive data identification rules, and the identification rules can include requirements in aspects such as the content, format, and storage location of the data.

[0149] Then apply the identification rules: Apply the identification rules to the internal data of the aircraft, and identify sensitive data through matching and screening.

[0150] S92 Second sorting, within each cluster, sort the sensitive data in the order of collection time to obtain n sensitive data sequences, and each sensitive data sequence corresponds to a clustering center.

[0151] S93 Calculate distances. For the a-th sensitive data sequence, this step will calculate the distance matrix between it and the remaining sensitive data sequences. Each element in the distance matrix represents a certain distance metric (such as Euclidean distance, Manhattan distance, etc.) between the corresponding samples of two sensitive data sequences. The corresponding samples can be samples with the same serial number in each sensitive data sequence, or samples with the same acquisition time in each sensitive data sequence.

[0152] Based on a single distance matrix, this step will further calculate the cumulative distance matrix, which reflects the overall similarity or difference between sensitive data sequences.

[0153] S94 Determine the sequence. According to the cumulative distance matrix, this step can find the sensitive data sequence with the highest similarity to the a-th sensitive data sequence through the dynamic path planning algorithm, denoted as the similar data sequence.

[0154] S95 Combine. Combine the sensitive data of the a-th sensitive data sequence with the non-sensitive data corresponding to the similar data sequence for processing to obtain the disguised a-th data sequence. The disguised a-th sensitive data sequence will be updated as internal data, and then execute S86 Third Storage to store these internal data in the corresponding data sub-library.

[0155] S96 First encryption. Use the symmetric encryption algorithm to encrypt the internal data stored in the i-th data sub-library to obtain the i-th encrypted data and the key after i times of encryption. Update the internal data stored in the i-th data sub-library with the i-th encrypted data.

[0156] S97 Second encryption. On the basis of S96 First encryption, use the asymmetric encryption algorithm to encrypt the i-th encrypted data and the internal data stored in the (i + 1)-th data sub-library to obtain the (i + 1)-th encrypted data and the key after (i + 1) times of encryption. Update the internal data stored in the (i + 1)-th data sub-library with the (i + 1)-th encrypted data.

[0157] S98 Fifth judgment. Judge whether i + 1 is equal to n. If so, no subsequent processing is performed; if not, it means there is still unencrypted internal data, and S99 iteration needs to be executed to continue encryption.

[0158] S99 Iteration. Take the (i + 1)-th data sub-library as the new i-th data sub-library and execute S96 First encryption. This process will continue until the preset stop condition is met (such as all data sub-libraries have been encrypted, the maximum number of iterations has been reached, etc.).

[0159] When the user calls relevant data, the user identity is first verified, and then starting from the last data sub-library, the corresponding decryption algorithm and key are used for decryption in reverse iteration until all data sub-libraries are decrypted. Subsequently, data recombination and verification are performed to identify and protect sensitive data.

[0160] In this embodiment, sensitive data in the internal data is first identified, and then the sensitive data in the current data sequence is combined with the non-sensitive data in the similar data sequence to obtain the disguised current data sequence. Then, the disguised current data sequence is updated to the data sub-library storing the current data sequence. Subsequently, an iterative encryption method is adopted to store all the disguised current data sequences to improve the security of the internal data.

[0161] Embodiment 6: This embodiment discloses a data security protection system for low-altitude intelligent aircraft, and the system includes:

[0162] A data acquisition module, which is responsible for collecting the internal data stored in the aircraft. These data include but are not limited to flight parameters (such as speed, altitude, heading, etc.), sensor data (such as temperature, pressure, humidity, etc.), system logs, fault diagnosis information, etc.

[0163] A first judgment module, which is used to judge whether the internal data contains image data according to a preset rule. If it contains image data, the coordinate system building module is triggered; otherwise, the second storage module is triggered.

[0164] A coordinate system building module, which is used to establish a coordinate system with any pixel point in the image data as the coordinate origin.

[0165] A first acquisition module, which is used to acquire the coordinate values and pixel values of each pixel point in the image data.

[0166] A stack building module, which is used to build stacks with the same number as the number of categories of pixel values, and each stack is responsible for storing one category of pixel values.

[0167] A first storage module, which is used to push the coordinate values of pixel points with the same pixel value into the stack and record the pixel value corresponding to each stack.

[0168] A second storage module, which is used to store the internal data in a preset database.

[0169] A user verification module, which is used to verify whether the user identity is legal. If it is legal, the feedback module is triggered; otherwise, the alarm module is triggered.

[0170] A feedback module, which is used to feedback the pixel value corresponding to each stack and / or the internal data to the user.

[0171] An alarm module, which is used to send an alarm signal.

[0172] In this embodiment, by analyzing the internal data, it is determined whether the internal data contains image data. If it contains image data, the image data is stored; otherwise, the internal data is stored. Then, when the user calls the data, the user identity is verified. If the verification passes, the relevant data is fed back; otherwise, an alarm is executed. This application stores the image data in a stack manner, and only when the user identity verification passes, the pixel values corresponding to the stack are fed back, improving the security of the image data.

[0173] Embodiment 7: This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the computer program is processed by a processor, it is used to implement the method described above.

[0174] Embodiment 8: This embodiment provides a server, on which the system described above is loaded. The server includes: a processor, and a memory communicatively connected to the processor;

[0175] The computer-readable storage medium described above is provided in the memory, and a computer program is stored on the computer-readable storage medium;

[0176] When the processor processes the computer program stored on the computer-readable storage medium, the method described above is implemented.

[0177] The above are all the preferred embodiments of this application. The protection scope of this application is not limited by this. Therefore, all equivalent changes made according to the structure, shape, and principle of this application should be covered within the protection scope of this application.

Claims

1. A method for protecting data security of low-altitude intelligent aircraft, characterized in that: include: Data collection: obtain internal data stored by low-altitude intelligent aircraft; First judgment: judging whether the internal data contains image data, if so, executing the step of establishing the system; if not, executing the second step of storing; Establishing a coordinate system: Establishing a coordinate system with any pixel point in the image data as the origin of the coordinate system; First acquisition: acquiring the coordinate value and pixel value of each pixel point in the image data; Stack building: Build stacks equal to the number of pixel value categories; First storage: push the coordinate values ​​of the pixel points with the same pixel value into the stack, and record the pixel value corresponding to each stack; Second storage: store internal data in a preset database; User verification: Verify whether the user's identity is legal. If yes, execute the feedback step; if not, execute the alarm step; Feedback: Feedback the pixel value and / or internal data corresponding to each stack to the user; Alarm: send out an alarm signal; After executing the first judging step and before executing the second storing step, the method further includes: Second acquisition: obtain the clustering results of internal data, and obtain n cluster centers based on the clustering results; Database construction: construct n data sub-databases, and record the i-th data sub-database as the i-th data sub-database; Fifth calculation: sequentially calculate the Euclidean distances between the j-th cluster center and the remaining cluster centers to obtain n-1 Euclidean distances; calculate the sum of the n-1 Euclidean distances and record it as the second data; First sorting: arranging the n second data in a preset order, and recording the result of the arrangement as a second data sequence; Association: Associating the second data in the second data sequence with the data sub-database having the same sequence number to obtain an association result; Third storage: store the internal data into the corresponding data sub-database according to the association results.

2. The low-altitude intelligent aircraft data security protection method according to claim 1 is characterized in that: After executing the step of data collection and before executing the step of first judgment, the method further includes: Clustering: clustering the internal data using the K-Means clustering algorithm to obtain a category label for each internal data; First calculation: perform mean removal processing on the internal data under the same category label to obtain the internal data after the first processing; perform variance normalization processing on the internal data after the first processing to obtain the internal data after the second processing, which is recorded as the first data; Construct a matrix: Construct a covariance matrix based on the first data, wherein the pth row and qth column element of the covariance matrix The calculation formula is as follows: ； in, is the value of the pth first data under the k1th category label; is the mean of all first data under the k1th category label; is the value of the qth first data under the k2th category label; is the mean of all first data under the k2th category label; n is the number of category labels; Second calculation: perform eigenvalue decomposition on the covariance matrix to obtain the largest eigenvalue and the corresponding eigenvector; The third calculation: performing a product operation on the first data and the characteristic vector to obtain an operation result, and updating the operation result as internal data.

3. The low-altitude intelligent aircraft data security protection method according to claim 2 is characterized in that: After executing the first acquisition step and before executing the stack building step, the method further includes: Fourth calculation: calculate the variance of all pixel values ​​in the image data; Second judgment: judging whether the variance is equal to zero, if so, executing the step of stacking; if not, executing the step of image segmentation; Image segmentation: Use the region growing algorithm to segment the image data and obtain multiple segments; The third judgment: judging whether there is a noise point in the segmented block based on the eight-neighborhood pixel values, if so, modifying the pixel value at the noise point to the average value of the eight-neighborhood pixel values ​​of the noise point; if not, executing the image update step; Image update: Update each segmented block to the image data respectively and perform the fourth calculation step.

4. The low-altitude intelligent aircraft data security protection method according to claim 3 is characterized in that: After performing the step of image segmentation and before performing the step of third determination, the method further includes: Fourth judgment: judge whether the number of pixel points in the segmented block is one, if so, execute the third judgment step; if not, execute the image update step.

5. The low-altitude intelligent aircraft data security protection method according to claim 1 is characterized in that: After executing the third storage step, the method further includes: First encryption: using a symmetric encryption algorithm to encrypt the internal data stored in the i-th data sub-database to obtain the i-th encrypted data, and using the i-th encrypted data to update the internal data stored in the i-th data sub-database; Second encryption: using an asymmetric encryption algorithm to encrypt the i-th encrypted data and the internal data stored in the i+1-th data sub-database to obtain the i+1-th encrypted data, and using the i+1-th encrypted data to update the internal data stored in the i+1-th data sub-database; Fifth judgment: judge whether i+1 is equal to n, if not, execute the iterative step; Iteration: Use the i+1th data sub-database as the new i-th data sub-database, use the i+1th encrypted data as the new i-th encrypted data, and perform the second encryption step until the preset stop condition is met.

6. The low-altitude intelligent aircraft data security protection method according to claim 5 is characterized in that: After executing the associating step and before executing the first storing step, the method further includes: Data screening: Identify sensitive and non-sensitive data in internal data; Second sorting: sort the sensitive data in each cluster according to the order of collection time to obtain n sensitive data sequences; Calculate the distance: Calculate the distance matrix between the ath sensitive data sequence and the remaining sensitive data sequences respectively, and calculate the cumulative distance matrix based on the distance matrix; Determine the sequence: Based on the cumulative distance matrix, use the dynamic path planning algorithm to obtain the sensitive data sequence with the greatest similarity to the a-th sensitive data sequence, which is recorded as the similar data sequence; Combining: combining the sensitive data of the a-th sensitive data sequence with the non-sensitive data corresponding to the similar data sequence to obtain combined data, and updating the combined data as internal data.

7. A low-altitude intelligent aircraft data security protection system, the system is used to implement the method according to any one of claims 1 to 6, characterized in that: include: Data acquisition module, used to obtain internal data stored in low-altitude intelligent aircraft; A first judgment module, used for judging whether the internal data contains image data, and triggering a system building module if the internal data contains image data; Otherwise, the second storage module is triggered; A system building module is used to build a coordinate system using any pixel point in the image data as the coordinate origin; A first acquisition module is used to acquire the coordinate value and pixel value of each pixel point in the image data; A stacking module, used to build stacks with the same number of categories as pixel values; A first storage module, used for pushing the coordinate values ​​of pixel points with the same pixel value into a stack, and recording the pixel value corresponding to each stack; A second storage module, used for storing internal data in a preset database; The user verification module is used to verify whether the user's identity is legal. If it is legal, the feedback module is triggered; Otherwise, the alarm module is triggered; A feedback module, used for feeding back the pixel value and / or internal data corresponding to each stack to the user; The alarm module is used to send out an alarm signal.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is processed by a processor, it is used to implement the method according to any one of claims 1 to 6.

9. A server, characterized in that: The server is loaded with the system according to claim 7, and the server comprises: a processor, and a memory in communication with the processor; The memory is provided with a computer-readable storage medium according to claim 8, and the computer-readable storage medium stores a computer program; When the processor processes the computer program stored on the computer-readable storage medium, the method according to any one of claims 1 to 6 is implemented.

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