Urban inspection data sharing method and device based on unmanned aerial vehicle

Abstract

The invention discloses a city inspection data sharing method and device based on an unmanned aerial vehicle, and the method comprises the steps: firstly determining an inspection time period when the inspection data needs to be shared, and collecting the position, speed, signal and meteorological data set of the unmanned aerial vehicle in the time period according to a preset sampling frequency; after all the data sets are aligned through timestamps, features are extracted respectively to form multi-dimensional feature vectors; converting the multi-dimensional feature vector into a one-dimensional array, and generating a hash feature value through a hash algorithm in combination with a timestamp and a random number; extracting a master key and an authentication key from the hash feature value, generating an initialization vector based on the master key, binding the master key, the authentication key and a timestamp, and then distributing the three to a receiver; inspection data is divided into blocks, a number and a timestamp are added to each block, encryption is performed by using a master key and an initialization vector, an authentication tag is generated in combination with an authentication key, and a data packet is packaged and then transmitted. According to the invention, the problem of low security during routing inspection data sharing of the unmanned aerial vehicle in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of drone inspection technology, and in particular to a drone-based urban inspection data sharing method and device. Background Art

[0002] At present, drone inspections have been widely used in urban security, power facility monitoring, traffic management and other fields due to their high efficiency and flexibility.

[0003] However, the data sharing process faces severe security threats. For example, the collected aerial images and sensor data may be tampered with, affecting the authenticity and reliability of the data. Therefore, the existing inspection data sharing has the problem of low security. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method and device for sharing urban inspection data based on drones, aiming to solve the problem of low security in the existing technology when sharing drone inspection data.

[0005] An object of the present invention is to provide a method for sharing urban inspection data based on drones, the method comprising: When it is detected that inspection data sharing is required, the inspection time period of the inspection data to be shared is obtained, and within the inspection time period, the position data group, speed data group, signal data group and meteorological data group collected by the UAV at a preset sampling frequency are obtained; Using timestamps to align the position data group, speed data group, signal data group, and meteorological data group, and extract features from the position data group, speed data group, signal data group, and meteorological data group to obtain a multidimensional feature vector containing position features, speed features, signal features, and meteorological features; Convert the multidimensional feature vector containing location features, speed features, signal features, and meteorological features into a one-dimensional array, and use the one-dimensional array combined with the timestamp and random number to obtain the hash feature value through a preset hash algorithm; Extract the master key and authentication key from the hash feature value, generate an initialization vector based on the master key, bind the generated master key, initialization vector, and authentication key to the corresponding timestamp, and distribute them to the receiver of the inspection data; The inspection data is divided into data blocks of fixed size, and a corresponding number and timestamp are added to each data block. Each data block is encrypted using a preset encryption algorithm combined with a master key and an initialization vector. An authentication tag is generated based on the authentication key and the content of each data block. Each encrypted data block is encapsulated into a data packet and transmitted to the receiver.

[0006] Furthermore, in the above-mentioned UAV-based urban inspection data sharing method, the step of extracting features from the position data group, the speed data group, the signal data group, and the meteorological data group to obtain a multidimensional feature vector containing position features, speed features, signal features, and meteorological features includes: Extract the maximum and minimum values ​​of the position coordinates in the position data group as position features respectively; Extract the mean and standard deviation of the relevant speed parameters in the speed data group as speed features; Extracting signal fluctuations and main frequency components from the signal data group as signal features; The temperature, humidity and wind speed in the meteorological data set are extracted as meteorological features.

[0007] Furthermore, in the above-mentioned method for sharing urban inspection data based on drones, after the step of extracting features from the position data group, the speed data group, the signal data group, and the meteorological data group to obtain a multidimensional feature vector including position features, speed features, signal features, and meteorological features, the method further includes: Add random noise to the extracted position features, speed features, signal features, and meteorological features, and perform nonlinear transformations; Among them, the expression for adding random noise is: ; The expression of nonlinear transformation is: ; in, They are the original features of position features, speed features, signal features and meteorological features, is the noise intensity coefficient, is a random number, 、 It is a trainable parameter and enhances the complexity of feature space.

[0008] Furthermore, in the above-mentioned UAV-based urban inspection data sharing method, the step of converting the multidimensional feature vector including position features, speed features, signal features, and meteorological features into a one-dimensional array, and using the one-dimensional array in combination with a timestamp and a random number to obtain a hash feature value through a preset hash algorithm includes: ； ; in, They are location characteristics, speed characteristics, signal characteristics and meteorological characteristics, is the timestamp, is a random number, It is an exclusive OR operation.

[0009] Furthermore, in the above-mentioned UAV-based urban inspection data sharing method, the steps of extracting the master key and the authentication key from the hash feature value and generating the initialization vector based on the master key include: Extracting elements of preset bytes from the hash characteristic value as the master key, and using the remaining elements in the hash characteristic value as the authentication key; Use AES-CTR mode, combined with a random number and a counter, to generate an initialization vector based on the master key; The expression for generating the initialization vector is: ; in, is the master key, is a random number, For the counter.

[0010] Furthermore, the above-mentioned method for sharing urban inspection data based on drones further includes: When the receiver receives the data packet, it uses the corresponding master key and initialization vector to decrypt each encrypted data block to obtain the decrypted data block; The authentication tag is recalculated based on the decrypted data block using the authentication key and compared with the original authentication tag to ensure the integrity of the data block.

[0011] Furthermore, in the above-mentioned UAV-based urban inspection data sharing method, the step of converting the multidimensional feature vector containing position features, speed features, signal features, and meteorological features into a one-dimensional array includes: Obtain the number of feature categories among position features, speed features, signal features, and meteorological features respectively, and establish a grid matrix of preset specifications according to the number of feature categories; Fill different position features, speed features, signal features and meteorological features into the grids of the grid matrix in sequence; A preset number of characteristic elements in the grid matrix are selected, and then the grid matrix is ​​rotated to select characteristic elements again. After the characteristic elements are selected, the characteristic elements are connected in series in the order of selection to obtain a one-dimensional array.

[0012] Another object of the present invention is to provide a readable storage medium having a computer program stored thereon, wherein the program implements the steps of the above method when executed by a processor.

[0013] Another object of the present invention is to provide an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the steps of the above method are implemented when the processor executes the program.

[0014] The present invention obtains multi-dimensional data sets, such as a drone's position, speed, signal, and weather data, during an inspection period, performs feature extraction, converts these data into multi-dimensional feature vectors, and then generates a one-dimensional array. These multi-dimensional feature vectors encompass the drone's dynamic operating state and environmental information during a specific inspection period. This ensures that the generated hash value, as well as the subsequent master key and authentication key, are closely tied to the drone's current actual state. Different inspection periods, flight states, and environmental conditions produce different feature vectors, which in turn generate different keys. This significantly increases the uniqueness and unpredictability of the key, making it difficult for attackers to infer the current key from single-dimensional data or historical keys, effectively preventing key cracking. The inspection data is segmented into fixed-size data blocks, each of which is numbered and timestamped. The data blocks are encrypted using the master key and initialization vector, and an authentication tag is generated based on the authentication key. Block encryption reduces the complexity of encryption operations and improves encryption efficiency, making it suitable for processing large-scale inspection data. The use of authentication tags ensures that data has not been tampered with during transmission. The recipient can verify the data integrity by verifying the authentication tag. If the data is modified by an attacker, the authentication tag will fail verification. The recipient can promptly detect data anomalies and avoid using incorrect or tampered data, ensuring the accuracy and reliability of data sharing. This solves the low security problem of existing technologies when sharing drone inspection data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Flowchart of the method for sharing urban inspection data based on drones provided in the first embodiment of the present invention; Figure 2 This is a structural block diagram of a UAV-based urban inspection data sharing device in the third embodiment of the present invention.

[0016] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0017] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The drawings illustrate several embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0018] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be an intermediate element. When an element is referred to as being "connected to" another element, it may be directly connected to the other element or there may be an intermediate element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0020] Example 1 See also Figure 1 , which shows a method for sharing urban inspection data based on drones in a first embodiment of the present invention, and the method includes steps S10 to S14.

[0021] Step S10, when it is detected that inspection data sharing is required, the inspection time period of the inspection data to be shared is obtained, and within the inspection time period, the position data group, speed data group, signal data group and meteorological data group collected by the drone at a preset sampling frequency are obtained.

[0022] In practice, when the system detects the need to share inspection data, it first determines the specific time range corresponding to the inspection data to be shared, namely the inspection time period. This time period clearly defines the time boundary of the data to be shared. During this established inspection time period, according to the pre-set sampling frequency, the key data sets collected by various sensors on the drone are continuously and stably acquired; Specifically, these include: a location data set for accurately locating the drone's spatial position and trajectory, which covers the drone's three-dimensional coordinates (longitude, latitude, and altitude) in geographic space and information on its position changes over time; a velocity data set reflecting the drone's motion state, which includes parameters such as the drone's linear velocity, acceleration, and angular velocity, and can be used to analyze the drone's motion trends and dynamic characteristics; a signal data set reflecting the drone's communication status with the outside world, which primarily records indicators such as signal strength (RSSI) and signal-to-noise ratio (SNR), providing a direct reflection of the quality and stability of the communication link; and a meteorological data set describing the drone's environmental conditions, including meteorological parameters such as temperature, humidity, and wind speed. These data are crucial for assessing the impact of environmental factors on drone flight and data collection. Comprehensive and systematic collection of core data related to the drone's operating status and environment during a specific inspection period lays the foundation for subsequent data processing and shared security assurance.

[0023] Step S11, using timestamps to align the position data group, speed data group, signal data group and meteorological data group, and extract features from the position data group, speed data group, signal data group and meteorological data group respectively to obtain a multidimensional feature vector containing position features, speed features, signal features and meteorological features.

[0024] After acquiring the position, velocity, signal, and meteorological data sets, the sampling times of each sensor may vary slightly. To ensure data consistency and relevance, the timestamp information included in the data is used to precisely align the different data sets in chronological order. As an identifier of data collection time, the timestamp accurately records the moment each data point was generated. By comparing and matching timestamps, the position, velocity, signal, and meteorological data at the same moment can be aligned one-to-one, eliminating data confusion caused by asynchronous sampling.

[0025] After completing the data alignment, perform in-depth feature extraction operations on these four data groups. From the position data group, extract the maximum and minimum values ​​of the position coordinates (including longitude, latitude, and altitude) during the entire inspection period as position features. These two values ​​can intuitively reflect the boundaries of the drone's spatial activity range during this period. For example, the difference between the maximum and minimum values ​​of longitude can reflect the flight span in the east-west direction, and the maximum value of altitude can indicate the highest flight position reached by the drone. For the speed data group, calculate the mean and standard deviation of the speed parameters (such as linear velocity, acceleration, angular velocity, etc.) during this period. The mean value can represent the average motion state of the drone during this period, and the standard deviation is used to measure the degree of fluctuation of the speed parameters. , reflecting the stability of the drone's movement. For example, when the acceleration mean is high and the standard deviation is large, it may indicate that the drone has experienced complex movements of multiple accelerations or decelerations during this period. From the signal data set, the signal fluctuation characteristics (such as the amplitude of the change in signal strength, the frequency of fluctuations in the signal-to-noise ratio, etc.) and the main frequency component obtained by Fourier transform are extracted. The signal fluctuation characteristics can reflect the quality stability of the communication link, and the main frequency component can reveal the frequency components where energy is concentrated in the signal, helping to analyze the type of interference or multipath effects encountered by the signal. Finally, from the meteorological data set, the original data such as temperature, humidity, and wind speed during this period are directly extracted as meteorological features. In this way, representative key features are extracted from each data set, and a feature set containing multi-dimensional information is constructed, providing concise and effective data input for subsequent feature vector conversion and key generation.

[0026] Step S12: convert the multidimensional feature vector including the position feature, speed feature, signal feature and meteorological feature into a one-dimensional array, and use the one-dimensional array in combination with the timestamp and random number to obtain a hash feature value through a preset hash algorithm.

[0027] After extracting position, velocity, signal, and meteorological features and constructing a multidimensional feature vector, it is necessary to convert this multidimensional feature vector into a one-dimensional array for easier processing. Specifically, by concatenating the multidimensional feature vectors into a continuous one-dimensional sequence, the feature information, which originally had spatial or dimensional hierarchies, is converted into a linear structure, facilitating subsequent unified calculation and processing.

[0028] After the conversion is complete, the one-dimensional array is combined with a timestamp (the time of data collection) and a random number (generated from a secure random source such as a quantum random number generator to enhance the unpredictability of key generation) as input to a pre-defined hash algorithm (such as SHA-3, SM3, or other highly collision-resistant cryptographic hash functions). The hash algorithm compresses and maps the input data, generating a fixed-length hash value through complex nonlinear transformations and iterative calculations. This hash value not only incorporates the drone's operating status and environmental information carried by the multidimensional feature vector, but also incorporates the dynamic nature of time and the uncertainty of random numbers. This ensures that each generated hash value uniquely corresponds to the drone's status within a specific inspection period, making it difficult to infer the original feature data or generate the same value repeatedly through reverse engineering or statistical analysis. This provides a high-entropy, highly secure data source for subsequent key extraction.

[0029] Exemplarily, the step of converting a multidimensional feature vector including position features, speed features, signal features, and meteorological features into a one-dimensional array, and obtaining a hash feature value by using the one-dimensional array in combination with a timestamp and a random number through a preset hash algorithm includes: ； ; in, They are location characteristics, speed characteristics, signal characteristics and meteorological characteristics, is the timestamp, is a random number, It is an exclusive OR operation.

[0030] Step S13, extracting the master key and authentication key from the hash feature value respectively, generating an initialization vector based on the master key, binding the generated master key, initialization vector, authentication key with the corresponding timestamp and distributing them to the receiver of the inspection data.

[0031] After obtaining a hash characteristic value through a preset hash algorithm, key components for different purposes need to be separated from this fixed-length hash characteristic value. First, the master key (the core key used for data encryption, such as the 256-bit key required for AES-256) and the authentication key (the key used for data integrity verification, such as the key required for HMAC-SHA256) are extracted from the hash characteristic value according to established rules (such as division into specific byte intervals such as the first 128 bits and 256 bits). This hash-value-based key separation method ensures the consistency and security of the two at the data source. Next, an initialization vector (IV) is generated based on the master key. This VIV is typically generated using a secure random number generator (SSL). It is used in conjunction with the master key to ensure that the same plaintext generates different ciphertexts in different encryption cycles, thus avoiding security risks caused by repeated encryption. Exemplarily, the AES-CTR mode is used, combined with a random number and a counter, to generate an initialization vector based on the master key; The expression for generating the initialization vector is: ; in, is the master key, is a random number, For the counter; Subsequently, the generated master key, initialization vector, and authentication key are bound to the corresponding timestamp (indicating the specific time when the key is generated and takes effect) to form a unified structure containing key information and time attributes. This binding relationship makes each key valid only within the range of its corresponding timestamp, strengthening the time dimension constraint of the key; finally, the bound key structure is distributed to the receiver of the inspection data through a secure communication channel (such as an encrypted transmission protocol). After receiving the key, the receiver can verify the validity of the key through the timestamp, decrypt the encrypted data using the master key and initialization vector, and verify the data integrity through the authentication key to ensure the correct distribution, use and management of keys during data sharing, and prevent data security risks caused by key leakage or misuse.

[0032] Step S14: Divide the inspection data into data blocks of fixed size, add corresponding numbers and timestamps to each data block, use a preset encryption algorithm, combine the master key and initialization vector to encrypt each data block, generate an authentication tag based on the authentication key and the content of each data block, encapsulate each encrypted data block into a data packet and transmit it to the recipient.

[0033] After key distribution is completed, to ensure the security and integrity of inspection data during shared transmission, the original inspection data needs to be systematically processed: First, the complete inspection data (such as video, image, sensor monitoring data, etc.) is divided into multiple data blocks according to a pre-set fixed size. This data block operation not only facilitates the efficient processing of large amounts of data, but also reduces the computational complexity of the encryption process; Next, each data block is assigned a unique number and a corresponding timestamp. The number is used to identify the order of the data blocks in the original data, ensuring that the receiver can reassemble the data in the correct order. The timestamp records the time when the data block was generated or collected, facilitating the receiver to perform time consistency verification. Subsequently, each data block is encrypted using a preset encryption algorithm (such as AES-256-GCM, SM4, and other secure and reliable symmetric encryption algorithms), combined with the previously generated and distributed master key and initialization vector (IV). The master key determines the core transformation rules of the encryption, and the initialization vector ensures that the same plaintext generates different ciphertexts in different encryption cycles, preventing the encryption mode from being cracked. At the same time, based on the authentication key and the content of each data block, an authentication tag is generated using algorithms such as HMAC (Hash Message Authentication Code). The authentication tag contains hash information of the data block content and can be used to verify whether the data has been tampered with during transmission. Finally, the encrypted data block, the corresponding initialization vector, the authentication tag, and the necessary metadata (such as version number, key ID, etc.) are encapsulated to form a complete data packet. The various parts of the data packet work together to ensure that the receiver can correctly decrypt the data and verify its integrity. After encapsulation, the data packet is transmitted to the receiver through a secure communication link (such as the TLS protocol). After receiving the data packet, the receiver can use the distributed key and protocol rules to decrypt, reassemble, and verify the data to achieve secure and reliable data sharing.

[0034] At the same time, when the receiver receives the data packet, it uses the corresponding master key and initialization vector to decrypt each encrypted data block to obtain the decrypted data block; The authentication tag is recalculated based on the decrypted data block using the authentication key and compared with the original authentication tag to ensure the integrity of the data block.

[0035] When the receiver receives the encapsulated data packet, it first extracts the corresponding initialization vector (IV) and the encrypted data block from the packet. Using the master key previously obtained through a secure channel and bound to the timestamp, combined with the extracted initialization vector, it uses the same pre-set decryption algorithm as the sender (such as AES-256-GCM decryption mode) to perform an inverse transformation on the encrypted data block, restoring the ciphertext to the original plaintext data block. During this process, the initialization vector ensures the parameter consistency of the decryption process and the encryption process, and the master key serves as a key parameter for decryption to control the correctness of the decryption transformation. Next, the receiver uses the same authentication tag generation algorithm (such as HMAC-SHA256) as the sender on the decrypted data block content based on the authentication key also obtained through the secure channel, and recalculates a new authentication tag. The newly generated authentication tag is then compared byte by byte with the original authentication tag carried in the data packet. If the two are completely consistent, it indicates that the data block has not been tampered with or damaged during transmission, and its integrity is guaranteed. If the comparison results are inconsistent, it means that the data block may have been tampered with by an attacker or an error occurred during transmission. The receiver will refuse to use the data block and trigger the corresponding security response mechanism (such as requesting retransmission, recording security events, etc.). Through this double verification mechanism (decryption verification and authentication tag verification), it ensures that the received inspection data is completely consistent with the data sent by the sender in content, effectively resisting security threats such as man-in-the-middle attacks and data tampering, and ensuring the reliability and security of the data sharing process.

[0036] In summary, the drone-based urban inspection data sharing method in the above-mentioned embodiments of the present invention obtains multi-dimensional data sets, such as the drone's location, speed, signal, and weather data, during an inspection period, performs feature extraction, converts these data sets into multi-dimensional feature vectors, and then generates a one-dimensional array. These multi-dimensional feature vectors contain the drone's dynamic operating state and environmental information during a specific inspection period. This ensures that the generated hash feature value, as well as the subsequent master key and authentication key, are closely tied to the drone's current actual state. Different inspection periods, flight states, and environmental conditions generate different feature vectors, which in turn generate different keys. This significantly increases the uniqueness and unpredictability of the key, making it difficult for attackers to infer the current key from single-dimensional data or historical keys, effectively preventing key cracking. The inspection data is segmented into fixed-size data blocks, each of which is numbered and timestamped. The data blocks are encrypted using the master key and initialization vector, and an authentication tag is generated based on the authentication key. Block encryption reduces the complexity of encryption operations and improves encryption efficiency, making it suitable for processing large-scale inspection data. The use of authentication tags ensures that data has not been tampered with during transmission. The recipient can verify the authentication tag to determine the data's integrity. If the data is modified by an attacker, the authentication tag will fail verification. The recipient can promptly detect data anomalies and avoid using incorrect or tampered data, ensuring the accuracy and reliability of data sharing. This solves the low security problem of existing technologies when sharing drone inspection data.

[0037] Example 2 This embodiment also proposes a method for sharing urban inspection data based on drones. The difference between the method for sharing urban inspection data based on drones in this embodiment and the method for sharing urban inspection data based on drones in the first embodiment is that: The step of converting the multidimensional feature vector including the position feature, the speed feature, the signal feature and the meteorological feature into a one-dimensional array comprises: Obtain the number of feature categories among position features, speed features, signal features, and meteorological features respectively, and establish a grid matrix of preset specifications according to the number of feature categories; Fill different position features, speed features, signal features and meteorological features into the grids of the grid matrix in sequence; A preset number of characteristic elements in the grid matrix are selected, and then the grid matrix is ​​rotated to select characteristic elements again. After the characteristic elements are selected, the characteristic elements are connected in series in the order of selection to obtain a one-dimensional array.

[0038] In the process of converting the multidimensional feature vector into a one-dimensional array, the number of feature categories contained in each of the position feature, speed feature, signal feature, and meteorological feature is first counted (for example, the position feature may contain three feature categories, such as the maximum longitude, the minimum longitude, and the maximum latitude; the speed feature contains two feature categories, such as the speed mean and the speed standard deviation). The preset specifications of the grid matrix (such as the number of rows × the number of columns, to ensure that the matrix can accommodate all feature categories) are determined based on the sum of these numbers. For example, if the total number of feature categories is 12, a 4×3 grid matrix can be constructed. Next, the values ​​corresponding to each feature category are filled into each grid of the grid matrix in a predetermined order (such as position features first, then speed features, then signal features, and finally meteorological features). Each grid stores a feature value, forming a structured two-dimensional feature representation. Subsequently, a specific feature element selection strategy is adopted to select a preset number of elements from the grid matrix (such as 4 elements each time). The selection method can be row-first, column-first, or diagonal order. After completing one selection, the grid matrix is ​​rotated clockwise or counterclockwise by a preset angle, such as 90 degrees, and feature elements are selected again according to the same or different row-first, column-first, or diagonal order rules. This process is repeated until all feature elements have been selected. Finally, the selected feature elements are concatenated in the order of selection to form a continuous one-dimensional array. This method of grid matrix rotation and multiple rounds of selection not only realizes the conversion of multidimensional features to one-dimensional structures, but also enhances the obfuscation and security of the features through a specific arrangement order, providing more complex and unpredictable input data for subsequent hash calculations.

[0039] Furthermore, after the step of extracting features from the position data group, the speed data group, the signal data group, and the meteorological data group to obtain a multidimensional feature vector including position features, speed features, signal features, and meteorological features, the following steps are further included: Add random noise to the extracted position features, speed features, signal features, and meteorological features, and perform nonlinear transformations; Among them, the expression for adding random noise is: ; The expression of nonlinear transformation is: ; in, They are the original features of position features, speed features, signal features and meteorological features, is the noise intensity coefficient, is a random number, 、 It is a trainable parameter and enhances the complexity of feature space.

[0040] After extracting features from the position, velocity, signal, and meteorological data groups, and obtaining multidimensional feature vectors containing position, velocity, signal, and meteorological features, these features are then subjected to dual processing to further enhance the security and complexity of the features. First, random noise is added to disrupt the original feature values. This involves multiplying the original features (i.e., the initial values ​​of the position, velocity, signal, and meteorological features) by a noise intensity coefficient and a random number. The noise intensity coefficient is used to control the degree of noise addition, and the random number is obtained from a secure random number generator. This incorporates unpredictable random interference into the original features, making the extracted features no longer completely identical to the original data, and increasing the difficulty for attackers to infer the original data through the features.

[0041] Next, the noise-added features are subjected to a nonlinear transformation, running them through a nonlinear function containing trainable parameters. These trainable parameters can be optimized and adjusted based on historical data or specific security requirements. Leveraging the complex mapping properties of nonlinear functions, the feature values ​​are mapped to a new spatial dimension, breaking the linear relationship between the original features, further obfuscating feature information, and significantly increasing the complexity of the feature space. In specific implementations, random noise and nonlinear transformation can be combined or applied separately, with random noise added to a subset of features and nonlinear transformation applied to others.

[0042] In summary, the drone-based urban inspection data sharing method in the above-mentioned embodiments of the present invention obtains multi-dimensional data sets, such as the drone's location, speed, signal, and weather data, during an inspection period, performs feature extraction, converts these data sets into multi-dimensional feature vectors, and then generates a one-dimensional array. These multi-dimensional feature vectors contain the drone's dynamic operating state and environmental information during a specific inspection period. This ensures that the generated hash feature value, as well as the subsequent master key and authentication key, are closely tied to the drone's current actual state. Different inspection periods, flight states, and environmental conditions generate different feature vectors, which in turn generate different keys. This significantly increases the uniqueness and unpredictability of the key, making it difficult for attackers to infer the current key from single-dimensional data or historical keys, effectively preventing key cracking. The inspection data is segmented into fixed-size data blocks, each of which is numbered and timestamped. The data blocks are encrypted using the master key and initialization vector, and an authentication tag is generated based on the authentication key. Block encryption reduces the complexity of encryption operations and improves encryption efficiency, making it suitable for processing large-scale inspection data. The use of authentication tags ensures that data has not been tampered with during transmission. The recipient can verify the authentication tag to determine the data's integrity. If the data is modified by an attacker, the authentication tag will fail verification. The recipient can promptly detect data anomalies and avoid using incorrect or tampered data, ensuring the accuracy and reliability of data sharing. This solves the low security problem of existing technologies when sharing drone inspection data.

[0043] Example 3 See also Figure 2 , shown is a UAV-based urban inspection data sharing device proposed in the third embodiment of the present invention, the device comprising: The acquisition module 100 is used to obtain the inspection time period of the inspection data to be shared when it is detected that inspection data sharing is required. During the inspection time period, the acquisition module 100 obtains the position data group, speed data group, signal data group, and meteorological data group collected by the UAV at a preset sampling frequency; An extraction module 200 is configured to align the position data set, the velocity data set, the signal data set, and the meteorological data set using timestamps, and to perform feature extraction on each of the position data set, the velocity data set, the signal data set, and the meteorological data set to obtain a multidimensional feature vector including position features, velocity features, signal features, and meteorological features; The series connection module 300 is used to convert the multidimensional feature vector containing the position feature, speed feature, signal feature and meteorological feature into a one-dimensional array, and use the one-dimensional array in combination with the timestamp and random number to obtain a hash feature value through a preset hash algorithm; The generation module 400 is used to extract the master key and the authentication key from the hash characteristic value, generate an initialization vector based on the master key, bind the generated master key, initialization vector, and authentication key with the corresponding timestamp, and distribute them to the receiver of the inspection data; The shared module 500 is used to divide the inspection data into data blocks of fixed size, add a corresponding number and timestamp to each data block, use a preset encryption algorithm, combine the master key and the initialization vector to encrypt each data block, generate an authentication tag based on the authentication key and the content of each data block, encapsulate each encrypted data block into a data packet and transmit it to the recipient.

[0044] The functions or operation steps implemented when the above modules are executed are substantially the same as those in the above method embodiments and will not be repeated here.

[0045] Example 4 Another aspect of the present invention further provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the above-mentioned embodiments 1 to 2.

[0046] Example 5 On the other hand, the present invention also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and runnable on the processor, and when the processor executes the program, the steps of the method described in any one of the above embodiments one to two are implemented.

[0047] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0048] Those skilled in the art will appreciate that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable storage medium for use by an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or for use in conjunction with such instruction execution system, apparatus, or device. For purposes of this specification, a "computer-readable storage medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device.

[0049] More specific examples (a non-exhaustive list) of computer-readable storage media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable storage medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0050] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following technologies known in the art may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0051] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0052] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for sharing urban inspection data based on drones, characterized in that: The method comprises: When it is detected that inspection data sharing is required, the inspection time period of the inspection data to be shared is obtained, and within the inspection time period, the position data group, speed data group, signal data group and meteorological data group collected by the UAV at a preset sampling frequency are obtained; Using timestamps to align the position data group, speed data group, signal data group, and meteorological data group, and extract features from the position data group, speed data group, signal data group, and meteorological data group to obtain a multidimensional feature vector containing position features, speed features, signal features, and meteorological features; Convert the multidimensional feature vector containing location features, speed features, signal features, and meteorological features into a one-dimensional array, and use the one-dimensional array combined with the timestamp and random number to obtain the hash feature value through a preset hash algorithm; Extract the master key and authentication key from the hash feature value, generate an initialization vector based on the master key, bind the generated master key, initialization vector, and authentication key to the corresponding timestamp, and distribute them to the receiver of the inspection data; The inspection data is divided into data blocks of fixed size, and a corresponding number and timestamp are added to each data block. Each data block is encrypted using a preset encryption algorithm combined with a master key and an initialization vector. An authentication tag is generated based on the authentication key and the content of each data block. Each encrypted data block is encapsulated into a data packet and transmitted to the receiver.

2. The method for sharing urban inspection data based on drones according to claim 1 is characterized in that: The step of extracting features from the position data group, the speed data group, the signal data group, and the meteorological data group to obtain a multidimensional feature vector including position features, speed features, signal features, and meteorological features comprises: Extract the maximum and minimum values ​​of the position coordinates in the position data group as position features respectively; Extract the mean and standard deviation of the relevant speed parameters in the speed data group as speed features; Extracting signal fluctuations and main frequency components from the signal data group as signal features; The temperature, humidity and wind speed in the meteorological data set are extracted as meteorological features.

3. The method for sharing urban inspection data based on drones according to claim 2 is characterized in that: After the step of extracting features from the position data group, the speed data group, the signal data group, and the meteorological data group to obtain a multidimensional feature vector including position features, speed features, signal features, and meteorological features, the following steps are further included: Add random noise to the extracted position features, speed features, signal features, and meteorological features, and perform nonlinear transformations; Among them, the expression for adding random noise is: ； The expression of nonlinear transformation is: ； in, They are the original features of position features, speed features, signal features and meteorological features, is the noise intensity coefficient, is a random number, 、 It is a trainable parameter and enhances the complexity of feature space.

4. The method for sharing urban inspection data based on drones according to claim 3 is characterized in that: The step of converting a multidimensional feature vector including position features, speed features, signal features, and meteorological features into a one-dimensional array, and obtaining a hash feature value by using the one-dimensional array in combination with a timestamp and a random number through a preset hash algorithm includes: ； ； in, They are location characteristics, speed characteristics, signal characteristics and meteorological characteristics, is the timestamp, is a random number, It is an exclusive OR operation.

5. The method for sharing urban inspection data based on drones according to claim 4 is characterized in that: The master key and authentication key are extracted from the hash feature value. The steps for generating the initialization vector based on the master key include: Extracting elements of preset bytes from the hash characteristic value as the master key, and using the remaining elements in the hash characteristic value as the authentication key; Use AES-CTR mode, combined with a random number and a counter, to generate an initialization vector based on the master key; The expression for generating the initialization vector is: ； in, is the master key, is a random number, For the counter.

6. The method for sharing urban inspection data based on drones according to claim 1 is characterized in that: The method further comprises: When the receiver receives the data packet, it uses the corresponding master key and initialization vector to decrypt each encrypted data block to obtain the decrypted data block; The authentication tag is recalculated based on the decrypted data block using the authentication key and compared with the original authentication tag to ensure the integrity of the data block.

7. The method for sharing urban inspection data based on drones according to claim 2 is characterized in that: The step of converting the multidimensional feature vector including the position feature, the speed feature, the signal feature and the meteorological feature into a one-dimensional array comprises: Obtain the number of feature categories among position features, speed features, signal features, and meteorological features respectively, and establish a grid matrix of preset specifications according to the number of feature categories; Fill different position features, speed features, signal features and meteorological features into the grids of the grid matrix in sequence; A preset number of characteristic elements in the grid matrix are selected, and then the grid matrix is ​​rotated to select characteristic elements again. After the characteristic elements are selected, the characteristic elements are connected in series in the order of selection to obtain a one-dimensional array.

8. A drone-based urban inspection data sharing device, characterized in that: The device comprises: The acquisition module is used to obtain the inspection time period of the inspection data to be shared when it is detected that inspection data sharing is required. During the inspection time period, the acquisition module obtains the position data group, speed data group, signal data group and meteorological data group collected by the UAV at a preset sampling frequency; an extraction module, configured to align the position data group, the speed data group, the signal data group, and the meteorological data group using timestamps, and to extract features from the position data group, the speed data group, the signal data group, and the meteorological data group, respectively, to obtain a multidimensional feature vector including position features, speed features, signal features, and meteorological features; A series connection module is used to convert a multidimensional feature vector containing position features, speed features, signal features, and meteorological features into a one-dimensional array, and use the one-dimensional array in combination with a timestamp and a random number to obtain a hash feature value through a preset hash algorithm; A generation module is used to extract the master key and authentication key from the hash feature value, generate an initialization vector based on the master key, bind the generated master key, initialization vector, and authentication key with the corresponding timestamp, and distribute them to the receiver of the inspection data; The shared module is used to divide the inspection data into data blocks of fixed size, add corresponding numbers and timestamps to each data block, use the preset encryption algorithm, combine the master key and initialization vector to encrypt each data block, generate an authentication tag based on the authentication key and the content of each data block, encapsulate each encrypted data block into a data packet and transmit it to the recipient.

9. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 7 when executing the program.

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