A data encryption transmission method based on an intelligent data acquisition instrument

Abstract

This application discloses a data encryption transmission method based on an intelligent data acquisition instrument: First, the data type is determined; structured data is directly encrypted, while unstructured data is encrypted by extracting the physical and operational characteristics of the acquisition instrument to generate a hash value, which is then combined with a random number to generate a tensor seed. After the data is divided into blocks, it is encrypted using dynamic obfuscated tensor convolution. Next, based on the device characteristics, rules are designed to scramble the encrypted blocks and generate location codes, thus constructing a secure channel transmission that prevents side-channel attacks. The receiving end recovers the data based on the location codes. This method forms a multi-layered protection system covering acquisition, encryption, and transmission through dynamic keys, adaptive encryption based on device characteristics, physical layer defense, and location code mechanisms, thereby improving the confidentiality, integrity, and anti-attack capabilities of data in complex environments.

Description

Technical Field

[0001] This invention relates to the field of data security transmission technology, and in particular to a data encryption transmission method based on an intelligent data acquisition instrument. Background Technology

[0002] Currently, intelligent data acquisition instruments are widely used in industrial monitoring, environmental sensing, intelligent transportation, and other fields. They collect diverse data types, including both structured and unstructured data. With increasing demands for data security, encrypted transmission has become a critical element. Existing methods primarily design standard encryption algorithms for structured data, but their handling of unstructured data (such as images and audio) remains inadequate. Due to the semantic complexity and dispersed features of unstructured data, traditional encryption methods easily compromise data integrity or suffer from low encryption efficiency. Simultaneously, the physical characteristics (such as hardware identifiers) and operational characteristics (such as real-time status) of the acquisition instrument are not fully utilized, making dynamic security protection difficult. Therefore, an encrypted transmission scheme that balances data type differences and device characteristics is urgently needed. In practical applications, intelligent data acquisition instruments often face dynamic environmental challenges, such as changes in spatiotemporal characteristics caused by device movement and the increased complexity of data correlations resulting from multi-acquisition instrument collaborative tasks. Existing technologies either fail to adapt to dynamic characteristics due to static encryption strategies or reduce security by ignoring the physical and operational characteristics of the device. Summary of the Invention

[0003] This application provides a data encryption transmission method based on an intelligent data acquisition instrument. By processing data by type, extracting acquisition instrument features for unstructured data to generate a dynamic obfuscation tensor, and combining random rules and position codes to achieve data block obfuscation and secure transmission, a side-channel attack prevention strategy is designed to build a secure channel. This effectively solves the problems of adaptability, security and integrity of data encryption in dynamic environments and meets the requirements of high reliability scenarios.

[0004] This application provides a data encryption transmission method based on an intelligent data acquisition instrument, including:

[0005] S1: Extract data from the data acquisition device and determine its data type. If the data is structured, encrypt it directly; if the data is unstructured, proceed to S2.

[0006] S2, extract the physical and operational characteristics of the data acquisition instrument, perform hash processing to generate a fixed-length feature hash value, and combine it with a random number generator to generate a seed for a random tensor;

[0007] S3 uses image processing algorithms to segment unstructured data into data blocks, and performs tensor convolution encryption operation on each data block using dynamic obfuscated tensors to generate encrypted data blocks;

[0008] S4, design a random rule based on the characteristics of the data acquisition instrument to shuffle the encrypted data blocks, and generate a location code for each data block;

[0009] S5, design a strategy to prevent side-channel attacks and build a secure channel transmission channel;

[0010] S6. The scrambled encrypted data blocks and their corresponding position codes are transmitted to the receiving end through a secure channel. The receiving end sorts and reassembles the encrypted data blocks according to the position codes and association rules to restore the original encrypted data block order and the original data.

[0011] Preferably, step S2, which involves hashing to generate a fixed-length feature hash value, specifically includes:

[0012] The physical and operational characteristics are read from the hardware storage of the data acquisition instrument. The collected feature data is checked, and feature data from different ranges are converted to a unified range to achieve data standardization. The standardized physical and operational characteristics are concatenated into a string. The concatenated string is hashed using the same hash algorithm as the fixed feature hash value to generate a fixed-length dynamic feature hash value. The fixed feature hash value and the dynamic feature hash value are combined. A random number sequence is generated using the random number generator CSPRNG. The combined feature hash value is XORed with the random number sequence to generate the seed of the random tensor.

[0013] Preferably, in step S3, tensor convolution encryption is performed on each data block using a dynamically obfuscated tensor, including:

[0014] If the unstructured data is not in image format, it is first converted into a visual image representation; the image is then divided into rectangular blocks of the same size; irregular regions are dynamically divided based on the image content using an algorithm, and then mapped to regular blocks, with overlapping areas set between the blocks; during decryption, the overlapping pattern needs to be recorded for correct reconstruction, resulting in a set of data blocks. Each block is a two-dimensional matrix or a three-dimensional tensor; a cryptographically secure pseudo-random number generator is initialized using the seed generated in step S2; the dimension of the four-dimensional tensor structure dynamic confusion tensor T is defined as follows: ,in, This is the number of channels in the input data block. It is the kernel size (3×3, 5×5). This is the number of output channels; for each position (i,j,k,l) ​​in T, a random floating-point number T[i,j,k,l]∈[0,1] is generated using CSPRNG; for each shape... The input data block is encrypted, and the encrypted data block is computed through convolution operation; where i is the index of the first dimension, j is the index of the second dimension, k is the index of the third dimension, l is the index of the fourth dimension, and H and W are the height and width of the data block, respectively.

[0015] Preferably, the design of the anti-side-channel attack strategy specifically includes:

[0016] The system generates noise waveforms by combining the unique characteristics of the data acquisition instrument and superimposes them onto the power consumption waveform of the encryption chip. It dynamically adjusts the amplitude, frequency, and offset mode of the clock offset, introducing a random mechanism so that the clock offset mode is not exactly the same for each encryption operation. It monitors the electromagnetic radiation intensity in real time and analyzes it in conjunction with the unique feature vector of the data acquisition instrument. When abnormal electromagnetic radiation is detected, it adaptively adjusts the channel switching strategy, random matrix reset method, and noise waveform generation parameters based on the unique feature vector and real-time monitoring data.

[0017] Preferably, step S3, which involves segmenting unstructured data into data blocks using an image processing algorithm, specifically includes:

[0018] The system extracts edge strength and texture density from unstructured data to identify high-frequency changing regions and key semantic regions, and dynamically adjusts the block size. It calculates the curvature changes of image blocks or the time-series abrupt change points of audio data to form point clusters. Based on the point clusters, it records the starting position, size, and correlation of data blocks to generate an adaptive block mapping matrix. It identifies associated blocks according to the adaptive block mapping matrix, and generates a coupling key by combining the unique features of the data acquisition instrument and the characteristics of associated blocks. It uses the coupling key to perform cross-block joint encryption operations on associated blocks to generate encrypted data blocks.

[0019] Preferably, the dynamic adjustment of the block size includes:

[0020] Initialize the basic block size, perform region growing clustering on the semantic mask matrix M(x,y), and calculate the area of ​​each semantic region; the formula for dynamically adjusting the block size is as follows: The process involves initializing the basic block size, performing region growing clustering on the semantic mask matrix, and calculating the area of ​​each semantic region. The formula for dynamically adjusting the block size is as follows: ,in, It is the dynamically adjusted block size obtained through calculation. This is the initial size of the basic block. It is the area of ​​each semantic region. Average semantic region area This is the maximum block size.

[0021] Preferably, the generation of the coupling key includes:

[0022] The hardware unique identifier and real-time environmental characteristics of the data acquisition instrument are collected, and a device feature fingerprint is generated using SHA-256 hashing. Based on the block mapping matrix S, calculate the semantic similarity between blocks, construct the association graph G=(V,E), establish a one-to-one mapping relationship between vertices V and blocks, where each vertex V represents a block, and use the semantic similarity between blocks as the weight value of the edges; for each block Select its adjacent block set Generate a coupling key.

[0023] Preferably, generating the encrypted data block specifically involves:

[0024] The system receives unstructured data from multiple data acquisition devices, extracts the spatiotemporal and physical characteristics of each device, and generates encrypted data blocks. It divides the data acquisition devices into several groups and assigns unique group identifiers, recording device information within each group. Each device within a group shares spatiotemporal and physical characteristics. The group center calculates global spatiotemporal and physical characteristic identifiers and analyzes data correlation within the group to form a data correlation graph. The group center generates a cooperative noise waveform, superimposed on the power consumption waveforms of each data acquisition device, and generates a shared group key, which is distributed to each device. Each device adaptively segments and identifies associated blocks, performing joint encryption. The system analyzes the spatiotemporal-physical characteristic correlations between groups to form a correlation graph and allocates cooperative transmission channels to different groups. The system monitors the group status in real time, and the receiving end performs joint decryption of the encrypted data blocks based on the group identifier and shared key.

[0025] Preferably, the data correlation graph formed by analyzing the data correlation within the group includes:

[0026] Each data acquisition instrument within the group shares its own spatiotemporal characteristics, and the group center calculates the global spatiotemporal characteristics of the group; each data acquisition instrument within the group shares its own physical characteristics, and the group center performs fusion encoding on the physical characteristics to generate a global physical characteristic identifier for the group; the group center analyzes the correlation between the data collected by each data acquisition instrument within the group to form a data correlation map within the group.

[0027] Preferably, the execution of joint encryption specifically includes:

[0028] Based on the global spatiotemporal and physical characteristics of the group, the grouping center dynamically generates a cooperative noise waveform. This cooperative noise waveform is superimposed on the power consumption waveforms of the encryption chips of each data acquisition device within the group, creating a cooperative obfuscation effect within the group. The grouping center generates a group-shared key based on the physical characteristics and data correlation of each data acquisition device within the group, and distributes it to each device through a secure channel. Each data acquisition device within the group divides the acquired data into blocks according to an adaptive block partitioning strategy and identifies associated blocks. The group-shared key is used to perform cross-block joint encryption operations on the associated blocks to generate encrypted data blocks.

[0029] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0030] By enhancing the security of structured data through dynamic key management and distributed storage, and achieving adaptive encryption of unstructured data by utilizing device feature hashing and random tensor generation, the system effectively defends against side-channel attacks by combining physical layer noise interference and clock skew technology. At the same time, the system adopts a feature fusion location code mechanism to ensure the integrity and traceability of data transmission. Ultimately, a multi-layered security protection system covering the entire process of data acquisition, encryption, and transmission is formed, which significantly improves the confidentiality, integrity, and anti-attack capabilities of data in complex environments.

[0031] By employing dynamic adaptive block segmentation and cross-block joint encryption techniques, this solution effectively addresses the problem of fixed-size block segmentation destroying the natural structural characteristics of unstructured data, thereby improving encryption effectiveness and data availability. Simultaneously, by identifying key semantic regions and generating coupled keys, it enhances the protection of critical data. Furthermore, the integration of a hardware-channel collaborative obfuscation system further improves data transmission security. This unique and innovative technical solution is particularly suitable for data acquisition instrument applications with extremely high data security requirements.

[0032] The dynamic grouping and collaborative encryption mechanism divides data into groups based on the spatiotemporal and physical characteristics of the data acquisition instrument and generates global identifiers. It utilizes collaborative noise waveforms to achieve power obfuscation and combines a shared group key with an adaptive block partitioning strategy to complete cross-block joint encryption. This scheme effectively improves the data transmission's resistance to attacks, optimizes channel allocation through inter-group correlation graphs to enhance system fault tolerance, and supports real-time monitoring and dynamic adjustment of group status to ensure continuous encryption effectiveness. Ultimately, it achieves secure and efficient collaborative transmission of unstructured data in complex environments. Attached Figure Description

[0033] Figure 1 This is a flowchart illustrating a data encryption transmission method based on an intelligent data acquisition instrument according to an embodiment of the present invention. Detailed Implementation

[0034] To facilitate understanding of the present invention, a more complete description of this application will be given below with reference to the accompanying drawings, which illustrate preferred embodiments of the invention. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to enable a more thorough and complete understanding of the disclosure of the present invention.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention; the term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0036] Example 1: Figure 1 This is a flowchart illustrating a data encryption transmission method based on an intelligent data acquisition instrument according to an embodiment of the present invention.

[0037] like Figure 1 As shown, a data encryption transmission method based on an intelligent data acquisition device includes the following steps:

[0038] S1: Extract data from the data acquisition instrument and determine its data type. If the data is structured, encrypt it directly; if the data is unstructured, proceed to S2.

[0039] If the data is structured, it will be directly encrypted, including:

[0040] 1A. Split the original structured data into multiple fragments. 1B. Generate a key (AES-256 key) for encryption of each data fragment. 1C. Distribute key processing using the Shamir secret sharing algorithm: divide each key into multiple fragments and distribute them across different storage nodes. The selection of storage nodes is based on network topology and data access requirements. 1D. Encrypt each data fragment using the generated key to obtain encrypted data fragments. 1E. Distribute the encrypted data fragments across different servers and nodes. 1F. Perform multi-level authentication (digital certificate verification, device fingerprint verification, behavioral analysis) on the requesting device and user. 1G. Dynamically allocate channels based on authentication results: dynamically allocate appropriate channels and permissions to devices based on factors such as device security level, data sensitivity, and network load. 1H. Establish a channel encryption mechanism to encrypt and protect transmitted data. 1I. Monitor the network and channel status (load, latency, security parameters) in real time and dynamically adjust channel allocation according to actual conditions. 1J. For received encrypted data: recover the encryption key for each data fragment using the secret sharing algorithm. The recovered key is used to decrypt the data fragments, yielding the original data fragments. After all data fragments have been decrypted, they are reassembled into the complete original data.

[0041] S2 extracts the physical and operational characteristics of the data acquisition instrument, performs hash processing to generate a fixed-length feature hash value, and combines it with a random number generator to generate a seed for a random tensor.

[0042] The physical characteristics include: hardware serial number, MAC address, device fingerprint, etc.; the operational characteristics include: device startup time, runtime, temperature, etc.; the feature hash values ​​include: fixed feature hash values ​​and dynamic feature hash values, with the total length remaining the same.

[0043] Specifically, the system reads the device's unique hardware sequence, MAC address, device fingerprint, etc. from the hardware storage of the data acquisition instrument, records the system time when the device starts up, continuously records the difference between the current time and the startup time during device operation to obtain the device's runtime, and reads the temperature value through the device's built-in temperature sensor, etc.

[0044] The collected feature data is inspected, and invalid data is removed. For example, if the temperature value read by the temperature sensor exceeds the normal range (e.g., -50℃ to 150℃), the data is considered invalid and needs to be corrected or discarded. Feature data from different ranges is converted to a unified range to achieve data standardization. For example, runtime (which may be in seconds, minutes, etc.) can be converted into a decimal form in hours; temperature values ​​can be normalized and mapped to the [0,1] interval.

[0045] The cleaned and standardized physical characteristics are concatenated into a string. For example, assuming the hardware serial number is SN123456, the MAC address is 00:11:22:33:44:55, and the device fingerprint is FP789012, the concatenated string would be SN12345600:11:22:33:44:55FP789012. A hash algorithm is then used to perform a hash calculation on the concatenated string, generating a fixed-length hash value for the fixed characteristics. The SHA-256 algorithm generates a 256-bit (32-byte) hash value.

[0046] Based on the device's operating status, periodically update its operating characteristics (device startup time, runtime, temperature). For example, update the runtime and temperature values ​​every minute. Concatenate the updated operating characteristics with some physical characteristics to form a new string. For instance, assuming the updated runtime is 2.5 hours, the temperature is 35℃, and the hardware serial number is SN123456, the concatenated string would be SN1234562.535. Use the same hash algorithm as the fixed characteristic hash value to perform a hash calculation on the concatenated string, generating a fixed-length dynamic characteristic hash value.

[0047] The fixed and dynamic feature hash values ​​are combined to ensure a consistent total length. If the fixed and dynamic feature hash values ​​differ in length, they can be adjusted by padding or truncation. For example, if the fixed feature hash value is 32 bytes and the dynamic feature hash value is 28 bytes, four bytes of zeros can be added to the end of the dynamic feature hash value to make its length the same as the fixed feature hash value.

[0048] Generate a random number sequence using a random number generator (CSPRNG, a cryptographically secure pseudo-random number generator). For example, the `secrets` module in Python can be used to generate random numbers. XOR the combined feature hash value with the random number sequence to generate the seed of the random tensor. For example, assuming the combined feature hash value is `hash_value` and the random number sequence is `random_numbers`, the seed of the random tensor can be calculated as follows: `seed[i] = hash_value[i]^random_numbers[i]`, where `i` represents the byte index.

[0049] S3 uses image processing algorithms to segment unstructured data into data blocks, and performs tensor convolution encryption operations on each data block using dynamic obfuscated tensors to generate encrypted data blocks.

[0050] Among them, the dynamic confusion tensor is generated by initializing the pseudo-random number generator with the generated seed to generate a four-dimensional dynamic confusion tensor.

[0051] Specifically, if the unstructured data is not in an image format, it should first be converted into a visual image representation. The image should be divided into rectangular blocks of the same size (e.g., 32×32 pixels). If the image size is not an integer multiple of the block size, the edges can be cropped or padded with zeros.

[0052] Irregular regions are dynamically divided based on image content using an algorithm (SLIC superpixels), and then mapped into regular blocks. A 10% overlap is set between blocks to avoid loss of boundary information. During decryption, the overlap pattern needs to be recorded for correct reconstruction, resulting in a set of data blocks. Each block is either a two-dimensional matrix (grayscale image) or a three-dimensional tensor (RGB image). A cryptographically secure pseudo-random number generator (CSPRNG) is initialized using the seed (32-byte hash value) generated in step S2 to ensure that the generated random numbers are unpredictable.

[0053] Define the dimension of the four-dimensional tensor structure dynamic confusion tensor T as: ,in, This is the number of channels in the input data block (1 for grayscale images and 3 for RGB images). It is the kernel size (3×3, 5×5); It is the number of output channels (which can be compared with...) (Same or larger). For each position (i,j,k,l) ​​in T, generate a random floating-point number T[i,j,k,l]∈[0,1] using CSPRNG, where i is the index of the first dimension, j is the index of the second dimension, k is the index of the third dimension, and l is the index of the fourth dimension. For each shape... The input data block is encrypted, and the encrypted data block is then computed using a convolution operation; where H and W are the height and width of the data block, respectively.

[0054] It should be noted that the calculation process of convolution operation is described in the prior art and will not be elaborated upon in this application.

[0055] The encrypted data block The images are stitched together in their original order to restore the encrypted complete image. The block segmentation strategy (block size, overlapping areas), obfuscation tensor seed, and convolution parameters (stride, padding) are saved for reconstruction during decryption.

[0056] S4. Design a random rule based on the characteristics of the data acquisition instrument to shuffle the encrypted data blocks and generate a location code for each data block.

[0057] The location code not only contains the relative position information of the data block within the original data, but also incorporates unique characteristics of the acquisition device (such as a partial hash value of the hardware serial number converted into a number). For example, the fixed and dynamic characteristics of the feature hash value can be converted into numbers (using 3 digits to represent a fixed characteristic and 2 digits to represent a dynamic characteristic), and these numbers are combined with the original location index to form the location code. The scrambled encrypted data block is then stored or transmitted together with the corresponding location code.

[0058] S5 designs a strategy to prevent side-channel attacks and constructs a secure channel transmission channel.

[0059] Specifically, noise waveforms are generated by combining the unique characteristics of the data acquisition instrument and superimposed on the power consumption waveform of the encryption chip; the amplitude, frequency, and offset mode of the clock offset are dynamically adjusted, and a random mechanism is introduced so that the clock offset mode of each encryption operation is not exactly the same; the electromagnetic radiation intensity is monitored in real time and analyzed in combination with the unique feature vector of the data acquisition instrument; when abnormal electromagnetic radiation is detected, the channel switching strategy, random matrix reset method, and noise waveform generation parameters are adaptively adjusted according to the unique feature vector and real-time monitoring data.

[0060] S6. The scrambled encrypted data blocks and their corresponding position codes are transmitted to the receiving end through a secure channel. The receiving end sorts and reassembles the encrypted data blocks according to the position codes and association rules to restore the original encrypted data block order and the original data.

[0061] The process of restoring the original data involves using the corresponding key vector and tensor convolution decryption algorithm to decrypt the data block and recover the original data.

[0062] The technical solutions described in the embodiments of this application have at least the following technical effects or advantages:

[0063] By enhancing the security of structured data through dynamic key management and distributed storage, and achieving adaptive encryption of unstructured data by utilizing device feature hashing and random tensor generation, the system effectively defends against side-channel attacks by combining physical layer noise interference and clock skew technology. At the same time, the system adopts a feature fusion location code mechanism to ensure the integrity and traceability of data transmission. Ultimately, a multi-layered security protection system covering the entire process of data acquisition, encryption, and transmission is formed, which significantly improves the confidentiality, integrity, and anti-attack capabilities of data in complex environments.

[0064] Example 2: In Example 1, the fixed block size and local feature extraction method are difficult to adapt to the semantic heterogeneity of unstructured data (such as images and audio), resulting in insufficient detection accuracy of key regions and limited encryption security. Due to the large differences in semantic region scale in different data (such as text size and speech segment length), and the existence of spatial / temporal correlations between regions, static processing easily ignores the distribution of semantic importance.

[0065] In some embodiments, image processing algorithms are used to segment unstructured data into data blocks, and step S3 further includes:

[0066] S31 extracts edge strength and texture density from unstructured data to identify high-frequency changing regions, identifies key semantic regions, and dynamically adjusts block size.

[0067] Specifically, the Canny edge detection algorithm is used for image data. Gaussian filtering smooths noise, the Sobel operator calculates gradient magnitude and direction, non-maximum suppression refines edges, and dual thresholding detects connected edges. For audio data, short-time Fourier transform (STFT) extracts spectral energy abrupt changes to mark high-frequency component regions. The output edge intensity matrix E(x,y) is given, where (x,y) represents the pixel / sampling point coordinates.

[0068] For image data, features such as contrast and entropy are calculated using the gray-level co-occurrence matrix (GLCM) to quantify local texture complexity. For audio data, spectral texture variation is measured using the difference values ​​of Mel-frequency cepstral coefficients (MFCC), and the texture density matrix T(x,y) is output.

[0069] Define the semantic weight function: ,in, Weighting coefficients (adjusted according to data type, such as image data) The Otsu thresholding method binarizes W(x,y) and marks high-weight regions (such as object outlines and text regions). The output semantic mask matrix M(x,y) is then used, where high-weight pixels have a value of 1 and the rest are 0.

[0070] Initialize basic block size (32×32 pixels) Perform region growing clustering on the semantic mask matrix M(x,y) and calculate the area of ​​each semantic region. The block size is dynamically adjusted based on the actual area of ​​the semantic region, so that the block size can adapt to semantic regions of different sizes while remaining within a reasonable range. This is how the dynamic adjustment of the block size is calculated.

[0071] The formula for dynamically adjusting the block size is as follows:

[0072]

[0073] Average semantic region area For the maximum block size (e.g., 128×128), enforce the use of [a specific method / approach] for non-semantic regions (M(x,y)=0). Size, avoid excessive segmentation. This is the initial size of the basic block. It is the dynamically adjusted block size obtained through calculation.

[0074] S32 calculates the curvature changes of image patches or the time-series abrupt change points of audio data to form point clusters.

[0075] Specifically, for the boundary pixels of the semantic region, the second-order difference (Laplacian operator) quantization curvature is calculated: Mark the absolute value of curvature exceeding the threshold. The point is the potential split point ( (Take 1.5 times the average edge strength). It is the second-order partial derivative of the energy function E, specifically divided into: It is the second partial derivative of E with respect to the variable x, representing the curvature of E in the x direction; This is the second-order partial derivative of E with respect to the variable y, representing the curvature of E in the y-direction. For audio time series, the Teager energy operator (TEO) is used to calculate energy jumps: ,mark Exceeding the threshold The sampling points are potential segmentation points. Here, n is the index (integer) of the discrete time or sequence, representing the current time point or sequence position. For example: x(n) is the signal value at time n, x(n−1) is the value at the previous time (lag term), and x(n+1) is the value at the next time (lead term).

[0076] Potential split points are clustered using DBSCAN (radius parameter ϵ, minimum number of points minPts), and neighboring points are merged to form split point clusters. The output is a set of split point clusters. Each cluster It contains a set of spatially / temporally adjacent dividing points.

[0077] It should be noted that, It is the Laplacian operator of E, used to describe the second-order spatial rate of change of the energy function.

[0078] S33 generates an adaptive block mapping matrix based on the starting position, size, and association relationship of the data blocks recorded by the point cluster.

[0079] S34. Identify associated blocks based on the adaptive block mapping matrix, and generate a coupling key by combining the unique features of the data acquisition instrument and the characteristics of the associated blocks.

[0080] Specifically, the unique hardware identifier of the data acquisition device and real-time environmental characteristics are used to generate a device feature fingerprint through SHA-256 hashing. Based on the block mapping matrix S, calculate the semantic similarity between blocks (intersection of color histograms of image blocks, MFCC distance of audio blocks).

[0081] For each block (V is the set of vertices in the association graph, where each vertex represents a block), select its adjacent block set. In the association graph G=(V,E), the vertices The `<node>` element is an abstract element used to represent a block within a graph structure. It does not directly contain the block's data content but serves as an identifier. Edges `E` connect vertices, reflecting the relationships between blocks. For example, if a vertex... With vertex and vertex Connected by edges, then Generate coupling key ,in For block The semantic feature hash. Among them, It is the key or authentication code obtained from the i-th calculation; It is a message authentication code algorithm based on SHA-256 hashing; It is an adjacency block set The hash value; This is a string concatenation operation. The specific process is to concatenate the strings... and After splicing, use the device key pass Generate key .

[0082] It should be noted that the specific steps for generating the coupling key can be found in existing technologies, and will not be elaborated upon in this article.

[0083] S35, using the coupling key to perform cross-block joint encryption operation on the associated blocks, generating encrypted data blocks.

[0084] Specifically, for associated block pairs (The edges in the associated graph, where) yes Directly related blocks (adjacent blocks)). Perform XOR coupling encryption: ,in yes Blocked data, yes Blocked data, For inter-block shared keys (by and (Generation of the coupling key between adjacent blocks using XOR). The specific process involves XORing each pair of associated blocks. Use their block data and and the shared keys between them. The encrypted data blocks are generated through XOR operations. The encrypted blocks are arranged in their original spatial order, and a compressed version of the block mapping matrix S is inserted as the metadata header. Encrypted data packets are transmitted using the side-channel attack prevention strategies described in Example 1 (such as noise waveform overlay and dynamic clock offset).

[0085] The technical solutions described in the embodiments of this application have at least the following technical effects or advantages:

[0086] By employing dynamic adaptive block segmentation and cross-block joint encryption techniques, this solution effectively addresses the problem of fixed-size block segmentation destroying the natural structural characteristics of unstructured data, thereby improving encryption effectiveness and data availability. Simultaneously, by identifying key semantic regions and generating coupled keys, it enhances the protection of critical data. Furthermore, the integration of a hardware-channel collaborative obfuscation system further improves data transmission security. This unique and innovative technical solution is particularly suitable for data acquisition instrument applications with extremely high data security requirements.

[0087] Example 3: In Example 2, although dynamic block partitioning and coupled key encryption were achieved using semantic features, when the spatiotemporal characteristics of multiple data acquisition devices change dynamically (such as rapid device relocation and reorganization), the local block partitioning strategy of a single data acquisition device is prone to misalignment of cross-device associated blocks. Furthermore, the key generation does not incorporate global grouping features, resulting in decreased resistance to attacks during grouping state switching. In addition, cross-block encryption relies solely on the direct correlation between blocks, without utilizing global grouping characteristics to optimize parameters, leading to insufficient security in complex environments.

[0088] In some embodiments, step S35, generating the encrypted data block, further includes:

[0089] S351 receives unstructured data from multiple data acquisition devices, extracts the spatiotemporal and physical characteristics of each data acquisition device, and generates encrypted data blocks.

[0090] Each data acquisition device records its own data acquisition timestamp and geographical location information, generating a spatiotemporal feature vector. The spatiotemporal feature vector includes acquisition time, latitude and longitude coordinates, device number, etc.

[0091] S352 divides multiple data acquisition devices into several groups and assigns a unique group identifier, and records the information of the acquisition devices within the group.

[0092] The grouping can be based on factors such as the geographical location of the data acquisition device, the type of acquisition task, and the correlation of data.

[0093] Specifically, based on factors such as the geographical location of the data acquisition devices, the type of acquisition task, and the correlation of data, multiple data acquisition devices are divided into several groups. The acquisition devices in each group have similar spatiotemporal characteristics or data correlations, which facilitates collaborative obfuscation and encryption. A unique group identifier is assigned to each group, and information such as the list of acquisition devices in the group and the group center location (such as the geographical center or time center) is recorded.

[0094] S353: Each data acquisition instrument in the group shares spatiotemporal and physical characteristics. The group center calculates global spatiotemporal and physical characteristic identifiers and analyzes the data correlation within the group to form a data correlation map.

[0095] Specifically, each data acquisition device within a group shares its own spatiotemporal characteristics (timestamp, geographical location). The grouping center calculates the group's global spatiotemporal characteristics, reflecting the overall spatiotemporal distribution of the data acquisition devices within the group. Each data acquisition device within a group shares its own physical characteristics (hardware serial number, MAC address, etc.). The grouping center performs fusion encoding on these physical characteristics to generate a global physical characteristic identifier for the group. The grouping center analyzes the correlations between the data acquired by each data acquisition device within the group (such as temporal correlation, spatial correlation, and content correlation), forming a data correlation map within the group.

[0096] S354, the grouping center generates a collaborative noise waveform that is superimposed on the power consumption waveform of each data acquisition unit, generates a grouping shared key and distributes it to each data acquisition unit, and each data acquisition unit performs adaptive block identification of associated blocks and performs joint encryption.

[0097] Specifically, based on the global spatiotemporal and physical characteristics of the group, the grouping center dynamically generates a cooperative noise waveform. This cooperative noise waveform is superimposed on the power consumption waveforms of the encryption chips of each data acquisition device within the group, creating a cooperative obfuscation effect within the group. The grouping center generates a group-shared key based on the physical characteristics and data correlation of each data acquisition device within the group, and distributes it to each device through a secure channel for subsequent joint encryption. Each data acquisition device within the group divides the acquired data into blocks according to an adaptive block partitioning strategy and identifies associated blocks. The group-shared key is used to perform cross-block joint encryption operations on the associated blocks to generate encrypted data blocks. The receiving end uses the group identifier and the shared key to jointly decrypt the encrypted data blocks and recover the original data.

[0098] S355 analyzes the spatiotemporal-physical characteristics of different groups to form a correlation map, and allocates cooperative transmission channels to different groups.

[0099] Specifically, the spatiotemporal correlations and physical similarities between different data acquisition groups are analyzed to form a correlation map between the groups. Based on the correlation map, cooperative transmission channels are allocated to different groups to ensure that data transmission between adjacent or related groups does not interfere with each other in time and frequency, thereby improving the overall anti-attack capability. The status of the acquisition devices within the group is monitored in real time (such as changes in position or data acquisition tasks), and the group division and group center position are dynamically adjusted. Based on the group adjustment results, group information, cooperative noise waveforms, shared keys, etc., are updated to ensure the cooperative obfuscation and encryption effects within the group.

[0100] S356 monitors the group status in real time, and the receiving end performs joint decryption of encrypted data blocks based on the group identifier and shared key.

[0101] The technical solutions described in the embodiments of this application have at least the following technical effects or advantages:

[0102] The dynamic grouping and collaborative encryption mechanism divides data into groups based on the spatiotemporal and physical characteristics of the data acquisition instrument and generates global identifiers. It utilizes collaborative noise waveforms to achieve power obfuscation and combines a shared group key with an adaptive block partitioning strategy to complete cross-block joint encryption. This scheme effectively improves the data transmission's resistance to attacks, optimizes channel allocation through inter-group correlation graphs to enhance system fault tolerance, and supports real-time monitoring and dynamic adjustment of group status to ensure continuous encryption effectiveness. Ultimately, it achieves secure and efficient collaborative transmission of unstructured data in complex environments.

[0103] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A data encryption transmission method based on an intelligent data acquisition instrument, characterized in that, include: S1, extract data from the data acquisition instrument and determine its data type; If the data is structured, it is directly encrypted; if the data is unstructured, S2 is executed. S2, extract the physical and operational characteristics of the data acquisition instrument, perform hash processing to generate a fixed-length feature hash value, and combine it with a random number generator to generate a seed for a random tensor; S3 uses image processing algorithms to segment unstructured data into data blocks, and performs tensor convolution encryption on each data block using a dynamically obfuscated tensor to generate encrypted data blocks. Specifically, the tensor convolution encryption operation on each data block using a dynamically obfuscated tensor involves: if the unstructured data is not in image format, it is first converted into a visual image representation; the image is divided into rectangular blocks of the same size; irregular regions are dynamically divided based on the image content using an algorithm, and then mapped to regular blocks, with overlapping areas set between blocks; during decryption, the overlapping pattern needs to be recorded for correct reconstruction, resulting in a set of data blocks. Each block is a two-dimensional matrix; the pseudo-random number generator from step S2 is used; the dimension of the four-dimensional tensor structure dynamic confusion tensor T is defined as follows: ,in, This is the number of channels in the input data block. It is the kernel size. This is the number of output channels; in T, a random floating-point number is generated for each position (i,j,k,l). For each shape The input data block is encrypted, and the encrypted data block is then computed through convolution. Here, i is the index of the first dimension, j is the index of the second dimension, k is the index of the third dimension, l is the index of the fourth dimension, and H and W are the height and width of the data block, respectively. Specifically, image processing algorithms are used to segment unstructured data into data blocks, including: extracting edge strength and texture density from the unstructured data to determine high-frequency change regions, identifying key semantic regions, and dynamically adjusting the block size; calculating the curvature changes of image blocks or the time-series abrupt change points of audio data to form point clusters; recording the starting position, size, and association relationships of data blocks based on the point clusters to generate an adaptive block mapping matrix; identifying associated blocks according to the adaptive block mapping matrix, and generating a coupling key by combining the unique features of the data acquisition instrument and the characteristics of the associated blocks; and using the coupling key to perform cross-block joint encryption operations on the associated blocks to generate encrypted data blocks. S4, design a random rule based on the characteristics of the data acquisition instrument to shuffle the encrypted data blocks, and generate a location code for each data block; S5, design a strategy to prevent side-channel attacks and build a secure channel transmission channel; S6. The scrambled encrypted data blocks and their corresponding position codes are transmitted to the receiving end through a secure channel. The receiving end sorts and reassembles the encrypted data blocks according to the position codes and association rules to restore the original encrypted data block order and the original data.

2. The data encryption transmission method based on an intelligent data acquisition instrument as described in claim 1, characterized in that, S2 involves performing hash processing to generate a fixed-length feature hash value, specifically including: The physical and operational characteristics are read from the hardware storage of the data acquisition instrument. The collected feature data is checked, and feature data from different ranges are converted to a unified range to achieve data standardization. The standardized physical and operational characteristics are concatenated into a string. The concatenated string is hashed using the same hash algorithm as the fixed feature hash value to generate a fixed-length dynamic feature hash value. The fixed feature hash value and the dynamic feature hash value are combined. A random number generator is used to generate a random number sequence. The combined feature hash value is XORed with the random number sequence to generate the seed of the random tensor.

3. The data encryption transmission method based on an intelligent data acquisition instrument as described in claim 1, characterized in that, The design of the side-channel attack prevention strategy specifically includes: The system generates noise waveforms by combining the unique characteristics of the data acquisition instrument and superimposes them onto the power consumption waveform of the encryption chip. It dynamically adjusts the amplitude, frequency, and offset mode of the clock offset, introducing a random mechanism so that the clock offset mode is not exactly the same for each encryption operation. It monitors the electromagnetic radiation intensity in real time and analyzes it in conjunction with the unique feature vector of the data acquisition instrument. When abnormal electromagnetic radiation is detected, it adaptively adjusts the channel switching strategy, random matrix reset method, and noise waveform generation parameters based on the unique feature vector and real-time monitoring data.

4. The data encryption transmission method based on an intelligent data acquisition instrument as described in claim 1, characterized in that, The dynamic adjustment of block size includes: Initialize the basic block size, perform region growing clustering on the semantic mask matrix, and calculate the area of ​​each semantic region; the formula for dynamically adjusting the block size is as follows: ,in, It is the dynamically adjusted block size obtained through calculation. This is the initial size of the basic block. It is the area of ​​each semantic region. Average semantic region area This is the maximum block size.

5. The data encryption transmission method based on an intelligent data acquisition instrument as described in claim 1, characterized in that, The generation of the coupling key includes: The hardware unique identifier and real-time environmental characteristics of the data acquisition instrument are collected, and a device feature fingerprint is generated using SHA-256 hashing; the semantic similarity between blocks is calculated based on the block mapping matrix; and an association graph is constructed. Establish a one-to-one mapping relationship between vertices V and blocks, where each vertex V represents a block, and use the semantic similarity between blocks as the weight of the edges; for each block Select its adjacent block set Generate a coupling key.

6. The data encryption transmission method based on an intelligent data acquisition instrument as described in claim 1, characterized in that, The generation of the encrypted data block specifically involves: The system receives unstructured data from multiple data acquisition devices, extracts the spatiotemporal and physical characteristics of each device, and generates encrypted data blocks. It divides the data acquisition devices into several groups and assigns unique group identifiers, recording device information within each group. Each device within a group shares spatiotemporal and physical characteristics. The group center calculates global spatiotemporal and physical characteristic identifiers and analyzes data correlation within the group to form a data correlation graph. The group center generates a cooperative noise waveform, superimposed on the power consumption waveforms of each data acquisition device, and generates a shared group key, which is distributed to each device. Each device adaptively segments and identifies associated blocks, performing joint encryption. The system analyzes the spatiotemporal-physical characteristic correlations between groups to form a correlation graph and allocates cooperative transmission channels to different groups. The system monitors the group status in real time, and the receiving end performs joint decryption of the encrypted data blocks based on the group identifier and shared key.

7. The data encryption transmission method based on an intelligent data acquisition instrument as described in claim 6, characterized in that, The analysis of data relationships within the group forms a data relationship graph, including: Each data acquisition instrument within the group shares its own spatiotemporal characteristics, and the group center calculates the global spatiotemporal characteristics of the group; each data acquisition instrument within the group shares its own physical characteristics, and the group center performs fusion encoding on the physical characteristics to generate a global physical characteristic identifier for the group; the group center analyzes the correlation between the data collected by each data acquisition instrument within the group to form a data correlation map within the group.

8. The data encryption transmission method based on an intelligent data acquisition instrument as described in claim 6, characterized in that, The execution of joint encryption specifically includes: Based on the global spatiotemporal and physical characteristics of the group, the grouping center dynamically generates a cooperative noise waveform. This cooperative noise waveform is superimposed on the power consumption waveforms of the encryption chips of each data acquisition device within the group, creating a cooperative obfuscation effect within the group. The grouping center generates a group-shared key based on the physical characteristics and data correlation of each data acquisition device within the group, and distributes it to each device through a secure channel. Each data acquisition device within the group divides the acquired data into blocks according to an adaptive block partitioning strategy and identifies associated blocks. The group-shared key is used to perform cross-block joint encryption operations on the associated blocks to generate encrypted data blocks.

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