Data encryption transmission method based on intelligent data acquisition instrument

Abstract

The invention discloses a data encryption transmission method based on an intelligent data acquisition instrument, and the method comprises the steps: firstly judging a data type, directly encrypting structured data, extracting the physical and operation characteristics of the acquisition instrument to generate a hash value for unstructured data, generating a tensor seed in combination with a random number, segmenting the data into blocks, and carrying out the convolution encryption through employing a dynamic confusion tensor; disrupting encryption blocks and generating position codes on the basis of equipment feature design rules, and constructing safe channel transmission for preventing side channel attack; the receiving end recovers the data according to the position code; according to the method, a multi-level protection system covering collection, encryption and transmission is formed through a dynamic key, equipment feature adaptive encryption, physical layer defense and a position code mechanism, and the confidentiality, integrity and anti-attack ability of data in a complex environment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data security transmission, and in particular to a data encryption transmission method based on an intelligent data collector. Background Art

[0002] Currently, intelligent data collectors are widely used in fields such as industrial monitoring, environmental sensing, and intelligent transportation. They collect diverse data types, including both structured and unstructured data. With increasing demands for data security, encrypted transmission has become a critical issue. Existing methods mostly design standard encryption algorithms for structured data, but their handling of unstructured data (such as images and audio) remains inadequate. Due to the complex semantics and dispersed features of unstructured data, traditional encryption can easily compromise data integrity or lead to low encryption efficiency. Furthermore, the physical characteristics (such as hardware identification) and operational characteristics (such as real-time status) of data collectors are not fully explored, making dynamic security protection difficult. An encrypted transmission solution that accounts for both data type differences and device characteristics is urgently needed. In practical applications, intelligent data collectors often face challenges in dynamic environments, such as device movement leading to changes in spatiotemporal characteristics and the complexities of data correlation caused by collaborative tasks involving multiple data collectors. Existing technologies either fail to adapt to dynamic characteristics due to static encryption strategies or compromise security by ignoring device physical and operational characteristics. Summary of the Invention

[0003] This application provides a data encryption and transmission method based on an intelligent data collector. By processing data by type, it extracts collector features for unstructured data to generate a dynamic obfuscation tensor, combines random rules with position codes to achieve data block obfuscation and secure transmission, and designs a side-channel attack prevention strategy to build a secure channel. It effectively solves the problems of adaptability, security, and integrity of data encryption in dynamic environments and meets the needs of high-reliability scenarios.

[0004] This application provides a data encryption transmission method based on an intelligent data acquisition instrument, comprising: S1, extract data from the data acquisition instrument and determine its data type. If the data is structured data, it will be directly encrypted; if the data is unstructured data, it will be executed in S2; S2, extracts the physical characteristics and operating 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 random tensor seed; S3 uses image processing algorithms to segment unstructured data into data blocks, and performs tensor convolution encryption operations on each data block using dynamic obfuscation tensors to generate encrypted data blocks; S4, designing a random rule based on the characteristics of the collector to scramble the encrypted data blocks and generate a position code for each data block; S5, design a strategy to prevent side-channel attacks and build a secure channel transmission channel; S6, transmits the scrambled encrypted data blocks and the corresponding position codes to the receiving end through a secure channel. The receiving end sorts and splices the encrypted data blocks according to the position codes and association rules to restore the original encrypted data block order and original data.

[0005] Preferably, the step S2, performing hash processing to generate a fixed-length characteristic hash value, specifically includes: The physical characteristics and operating characteristics are read from the hardware storage of the data acquisition instrument, the collected characteristic data are checked, and the characteristic data in different ranges are converted into a unified range to achieve data standardization; the standardized physical characteristics and operating characteristics are spliced ​​into a string; the spliced ​​string is hashed using the same hash algorithm as the fixed characteristic hash value to generate a dynamic characteristic hash value of a fixed length; the fixed characteristic hash value and the dynamic characteristic hash value are combined; a random number generator CSPRNG is used to generate a random number sequence, and the combined characteristic hash value is XORed with the random number sequence to generate a seed for the random tensor.

[0006] Preferably, in S3, performing tensor convolution encryption operation on each data block using a dynamic obfuscation tensor includes: If the unstructured data itself is not in image format, it is first converted into a visual image representation; the image is divided into rectangular blocks of equal size; an algorithm is used to dynamically divide irregular areas based on the image content, and then mapped into regular blocks, with overlapping areas set between the blocks; when decrypting, the overlapping pattern must be recorded to correctly reassemble and obtain a set of data blocks , each block is a two-dimensional matrix or a three-dimensional tensor; use the seed generated in step S2 to initialize the cryptographically secure pseudo-random number generator; define the dimension of the four-dimensional tensor structure dynamic confusion tensor T as ,in, is the number of channels of the input data block, is the convolution kernel size (3×3, 5×5), 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 by CSPRNG; for each shape The input data block is encrypted and the encrypted data block is calculated 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, H and W are the height and width of the data block respectively.

[0007] Preferably, the design of a side channel attack prevention strategy specifically includes: A noise waveform is 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 pattern of the clock offset are dynamically adjusted, and a random mechanism is introduced to ensure that the clock offset pattern of each encryption operation is different. The electromagnetic radiation intensity is monitored in real time and analyzed in combination with the unique characteristic 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 characteristic vector and real-time monitoring data.

[0008] Preferably, the step S3, using an image processing algorithm to segment the unstructured data into data blocks, specifically includes: Extract edge strength and texture density from unstructured data to identify high-frequency change areas, identify key semantic areas, and dynamically adjust block sizes; calculate curvature changes in image blocks or time series mutation points of audio data to form point clusters; record the starting position, size, and association relationship of data blocks based on point clusters to generate an adaptive block mapping matrix; identify associated blocks based on the adaptive block mapping matrix, and generate a coupling key based on the unique characteristics of the data acquisition instrument and the characteristics of the associated blocks; use the coupling key to perform cross-block joint encryption operations on associated blocks to generate encrypted data blocks.

[0009] Preferably, the dynamically adjusting the block size includes: Initialize the basic block size, perform region growing algorithm clustering on the semantic mask matrix M(x,y), and calculate the area of ​​each semantic region; the calculation formula for dynamically adjusting the block size is: , where the basic block size is initialized, the semantic mask matrix is ​​clustered using the region growing algorithm, and the area of ​​each semantic region is calculated; the calculation formula for dynamically adjusting the block size is: ,in, is the calculated dynamically adjusted block size, is the initialization size of the basic block, is the area of ​​each semantic region, Average semantic region area, is the maximum block size.

[0010] Preferably, the generating of the coupling key includes: Collect the unique hardware identifier and real-time environment characteristics of the data collector, and generate the device feature fingerprint through SHA-256 hash ; According to the block mapping matrix S, calculate the semantic similarity between blocks, build the association graph G = (V, E), establish a one-to-one mapping relationship between vertex V and blocks, each vertex V represents a block, and use the semantic similarity between blocks as the weight value of the edge; for each block , select its adjacent block set ; Generate coupling key.

[0011] Preferably, the generating of the encrypted data block is specifically: Receive unstructured data from multiple collectors, extract the spatiotemporal and physical characteristics of each collector, and generate encrypted data blocks; divide multiple data collectors into several groups and assign unique group identifiers, and record the information of the collectors in the group; each collector in the group shares spatiotemporal and physical characteristics, the grouping center calculates the global spatiotemporal characteristics and global physical characteristic identifiers, analyzes the data correlation within the group to form a data correlation map; the grouping center generates a collaborative noise waveform and superimposes it on the power consumption waveform of each collector, generates a group shared key and distributes it to each collector, and each collector performs adaptive block identification of associated blocks and performs joint encryption; analyzes the spatiotemporal-physical characteristic correlation between groups to form an association map, and allocates collaborative transmission channels to different groups; monitors the group status in real time, and the receiving end jointly decrypts the encrypted data blocks based on the group identifier and shared key.

[0012] Preferably, the analyzing the data association within the group to form a data association graph includes: Each collector in the group shares its own spatiotemporal characteristics, and the group center calculates the global spatiotemporal characteristics of the group; each collector in the group shares its own physical characteristics, and the group center fuses and encodes the physical characteristics to generate a global physical characteristic identification of the group; the group center analyzes the correlation between the data collected by each collector in the group to form a data correlation map within the group.

[0013] Preferably, performing joint encryption specifically includes: Based on the global spatiotemporal characteristics and global physical feature identification of the group, the grouping center dynamically generates a collaborative noise waveform, which is superimposed on the encryption chip power consumption waveform of each collector in the group to form a collaborative confusion effect within the group; the grouping center generates a grouping shared key based on the physical characteristics and data correlation of each collector in the group and distributes it to each collector in the group through a secure channel; each collector in the group blocks the collected data according to the adaptive blocking strategy and identifies the associated blocks; the grouping shared key is used to perform cross-block joint encryption operations on the associated blocks to generate encrypted data blocks.

[0014] One or more technical solutions provided in this application have at least the following technical effects or advantages: The security of structured data is enhanced through dynamic key management and distributed storage, and adaptive encryption of unstructured data is achieved by using device feature hashing and random tensor generation. Physical layer noise interference and clock offset technology are combined to effectively defend against side channel attacks. At the same time, a feature-fused position code mechanism is used to ensure the integrity and traceability of data transmission. Ultimately, a multi-level security protection system covering the entire process of data collection, encryption, and transmission is formed, significantly improving the confidentiality, integrity, and anti-attack capabilities of data in complex environments.

[0015] Dynamic adaptive block segmentation and cross-block joint encryption effectively address the problem of fixed-size block segmentation destroying the natural structural characteristics of unstructured data, improving encryption effectiveness and data availability. Furthermore, by identifying key semantic regions and generating coupling keys, the protection of critical data is enhanced. Combined with a hardware-channel collaborative obfuscation system, data transmission security is further improved. This unique and innovative technical solution is particularly suitable for data acquisition applications with extremely high data security requirements.

[0016] The dynamic grouping and collaborative encryption mechanism divides groups based on the temporal, spatial, and physical characteristics of the data collectors and generates global identifiers. It utilizes collaborative noise waveforms to achieve power consumption obfuscation and combines group shared keys with an adaptive block strategy to achieve cross-block joint encryption. This solution effectively improves data transmission resistance to attacks, optimizes channel allocation through inter-group association maps, enhances system fault tolerance, and supports real-time monitoring and dynamic adjustment of group status to ensure the continued effectiveness of encryption, ultimately achieving secure and efficient collaborative transmission of unstructured data in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The figure is a flow chart of a data encryption transmission method based on an intelligent data collector according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] To facilitate understanding of the present invention, the present application will be described more comprehensively below with reference to the relevant drawings; the drawings show preferred embodiments of the present invention, but the present invention can be implemented in many different forms and is not limited to the embodiments described herein; on the contrary, the purpose of providing these embodiments is to enable a more thorough and comprehensive understanding of the disclosed content of the present invention.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains; the terms used herein in the 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" used herein includes any and all combinations of one or more of the associated listed items.

[0020] Example 1: Figure 1 The present invention is a flowchart of a data encryption transmission method based on an intelligent data collector according to an embodiment of the present invention.

[0021] like Figure 1 As shown, a data encryption transmission method based on an intelligent data collector includes the following steps: 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, execute S2.

[0022] If the data is structured data, encryption is performed directly, including: 1A. Split the original structured data into multiple segments. 1B. Generate an encryption key (AES-256 key) for each data segment. 1C. Distribute the key using the Shamir secret sharing algorithm: Split each key into multiple segments and distribute them to different storage nodes. Storage nodes are selected based on network topology and data access requirements. 1D. Use the generated key to encrypt each data segment, generating the encrypted data segment. 1E. Distribute the encrypted data segments across different servers and nodes. 1F. Perform multi-level authentication (digital certificate verification, device fingerprint verification, and behavioral analysis) on the device and user requesting transmission. 1G. Dynamically allocate channels based on authentication results: Dynamically allocate channels and permissions to devices based on factors such as the device's security level, data sensitivity, and network load. 1H. Establish a channel encryption mechanism to encrypt and protect transmitted data. 1I. Monitor network and channel status (load, latency, and security parameters) in real time and dynamically adjust channel allocation based on actual conditions. 1J. For received encrypted data, use the secret sharing algorithm to recover the encryption key for each data segment. The recovered key is used to decrypt the data fragments to obtain the original data fragments. After all the data fragments are decrypted, they are combined into the complete original data.

[0023] 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 random tensor seed.

[0024] Among them, physical features include: hardware serial number, MAC address, device fingerprint, etc.; operating features include: device startup time, operating time, temperature, etc.; feature hash values ​​include: fixed feature hash values ​​and dynamic feature hash values, and the total length remains consistent.

[0025] Specifically, the device's unique hardware serial, MAC address, device fingerprint, etc. are read from the data acquisition instrument's hardware storage, and the system time when the device is started is recorded. During the operation of the device, the difference between the current time and the startup time is continuously recorded to obtain the device's operating time; the temperature value is read through the device's built-in temperature sensor, etc.

[0026] Check the collected feature data and remove invalid data. For example, if the temperature value read by a temperature sensor is outside the normal range (e.g., -50°C to 150°C), the data is considered invalid and needs to be corrected or discarded. Convert feature data with different ranges to a unified range to achieve data standardization. For example, the runtime (which may be in seconds, minutes, etc.) can be converted to a decimal in hours; the temperature value can be normalized and mapped to the interval [0, 1].

[0027] The cleaned and standardized physical characteristics are concatenated into a single string. For example, if 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 is SN12345600:11:22:33:44:55FP789012. A hash algorithm is used to hash the concatenated string to generate a fixed-length, fixed-characteristic hash value. The SHA-256 algorithm generates a 256-bit (32-byte) hash value.

[0028] Based on the device's operating status, regularly update operating characteristics (device startup time, operating hours, and temperature). For example, update the operating hours and temperature values ​​every minute. Concatenate the updated operating characteristics with some physical characteristics to form a new string. For example, if the updated operating hours are 2.5 hours, the temperature is 35°C, and the hardware serial number is SN123456, the concatenated string would be SN1234562.535. Hash the concatenated string using the same hash algorithm as the fixed characteristic hash value to generate a fixed-length dynamic characteristic hash value.

[0029] Combine the fixed feature hash value and the dynamic feature hash value to ensure that the total length is consistent. If the fixed feature hash value and the dynamic feature hash value have different lengths, you can adjust them by padding or truncating them. For example, if the fixed feature hash value is 32 bytes and the dynamic feature hash value is 28 bytes, you can pad the dynamic feature hash value with 4 bytes of zeros to make it the same length as the fixed feature hash value.

[0030] Generate a random number sequence using a random number generator (a cryptographically secure pseudorandom number generator, CSPRNG). For example, you can use the secrets module in Python. XOR the combined feature hash value with the random number sequence to generate the seed for the random tensor. For example, assuming the combined feature hash value is hash_value and the random number sequence is random_numbers, the seed for the random tensor can be calculated as follows: seed[i] = hash_value[i] ^ random_numbers[i], where i represents a byte index.

[0031] S3 uses image processing algorithms to segment unstructured data into data blocks, and performs tensor convolution encryption operations on each data block using dynamic obfuscation tensors to generate encrypted data blocks.

[0032] The dynamic confusion tensor is generated by using the generated seed to initialize the pseudo-random number generator to generate a four-dimensional dynamic confusion tensor.

[0033] Specifically, if the unstructured data is not in image format, first convert it into a visual image representation. Divide the image into rectangular blocks of equal 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.

[0034] Based on the image content, the algorithm (SLIC superpixel) is used to dynamically divide irregular areas and then map them into regular blocks. The overlapping areas (overlap 10%) are set between the blocks to avoid the loss of boundary information. When decrypting, the overlapping pattern needs to be recorded to correctly reassemble and obtain a set of data blocks. Each block is a two-dimensional matrix (grayscale image) or a three-dimensional tensor (RGB image). Use the seed (32-byte hash value) generated in step S2 to initialize a cryptographically secure pseudo-random number generator (CSPRNG) to ensure that the generated random numbers are unpredictable.

[0035] Define the dimension of the four-dimensional tensor structure dynamic confusion tensor T as ,in, is the number of channels of the input data block (1 for grayscale images and 3 for RGB images); is the convolution kernel size (3×3, 5×5); is the number of output channels (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] by 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 calculated through convolution operation; where H and W are the height and width of the data block respectively.

[0036] It should be noted that the calculation process of the convolution operation can be referred to the prior art, and this application will not go into details.

[0037] The encrypted data block Splice them in the original order to restore the encrypted complete image, save the blocking strategy (block size, overlapping area), confusion tensor seed and convolution parameters (step size, padding) for reconstruction during decryption.

[0038] S4, design a random rule based on the characteristics of the collector to scramble the encrypted data blocks and generate a position code for each data block.

[0039] The location code not only contains the data block's relative position within the original data but also incorporates the unique characteristics of the data collector (such as a number converted from a partial hash of the hardware serial number). For example, the fixed and dynamic features of the feature hash value can be converted into numbers (using three digits to represent a fixed feature and two digits to represent a dynamic feature). These numbers are then combined with the original location index to form a location code. The scrambled encrypted data block is then stored or transmitted along with the corresponding location code.

[0040] S5, design a strategy to prevent side-channel attacks and build a secure channel transmission channel.

[0041] Specifically, a noise waveform is 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 pattern of the clock offset are dynamically adjusted, and a randomness mechanism is introduced so that the clock offset pattern 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 characteristic 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 characteristic vector and real-time monitoring data.

[0042] S6, transmits the scrambled encrypted data blocks and the corresponding position codes to the receiving end through a secure channel. The receiving end sorts and splices the encrypted data blocks according to the position codes and association rules to restore the original encrypted data block order and original data.

[0043] Among them, restoring the original data is to use the corresponding key vector and tensor convolution decryption algorithm to decrypt the data block and restore the original data.

[0044] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: The security of structured data is enhanced through dynamic key management and distributed storage, and adaptive encryption of unstructured data is achieved by using device feature hashing and random tensor generation. Physical layer noise interference and clock offset technology are combined to effectively defend against side channel attacks. At the same time, a feature-fused position code mechanism is used to ensure the integrity and traceability of data transmission. Ultimately, a multi-level security protection system covering the entire process of data collection, encryption, and transmission is formed, significantly improving the confidentiality, integrity, and anti-attack capabilities of data in complex environments.

[0045] 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 key region detection accuracy and limited encryption security. Because semantic regions in different data vary greatly in scale (such as text size and speech segment length) and there are spatial and temporal correlations between regions, static processing tends to overlook the distribution of semantic importance.

[0046] In some embodiments, the unstructured data is segmented into data blocks using an image processing algorithm, and step S3 further includes: S31, extracts edge strength and texture density from unstructured data to identify high-frequency change areas, identifies key semantic areas, and dynamically adjusts the block size.

[0047] 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 a dual threshold is used to detect connected edges. For audio data, a short-time Fourier transform (STFT) is used to extract spectral energy breakpoints and mark high-frequency components. The output is an edge strength matrix E(x,y), where (x,y) represents the pixel / sample coordinates.

[0048] For image data, we use the Gray Level Co-occurrence Matrix (GLCM) to calculate features such as contrast and entropy to quantify local texture complexity. For audio data, we use the difference values ​​of Mel-Frequency Cepstral Coefficients (MFCC) to measure spectral texture changes and output the texture density matrix T(x,y).

[0049] Define the semantic weight function: ,in, is the weight coefficient (adjusted according to the data type, such as image Binarize W(x,y) using Otsu threshold segmentation, marking high-weight areas (such as object outlines and text areas). Output the semantic mask matrix M(x,y), where high-weight pixels are 1 and the rest are 0.

[0050] Initialize the basic block size (32×32 pixels), perform region growing algorithm clustering on the semantic mask matrix M(x,y) and calculate the area of ​​each semantic region The block size is dynamically adjusted according to the actual area of ​​the semantic region, so that the block size can adapt to semantic regions of different sizes and be within a reasonable range, thereby calculating the dynamic adjustment of the block size.

[0051] The calculation formula for dynamically adjusting the block size is:

[0052] Average semantic region area, is the maximum block size (e.g. 128×128), and is mandatory for non-semantic areas (M(x,y)=0). size, avoid over-segmentation, is the initialization size of the basic block, is the calculated dynamically adjusted block size.

[0053] S32, calculating the curvature change of the image block or the time series mutation point of the audio data to form a point cluster.

[0054] Specifically, for the boundary pixels of the semantic region, the second-order difference (Laplacian operator) quantized curvature is calculated: , the absolute value of the curvature exceeds the threshold The point is the potential split point ( Take 1.5 times the mean edge strength). It is the second-order partial derivative of the energy function E, which is divided into: is the second-order partial derivative of E with respect to the variable x, which represents the curvature of E in the x direction; It is the second-order partial derivative of E with respect to the variable y, which represents the curvature of E in the y direction. For audio time series, the Teager Energy Operator (TEO) is used to calculate the energy mutation: ,mark Exceeding the threshold The sampling points of are potential split points. n is an integer index of discrete time or sequence, representing the current time point or sequence position. For example, x(n) is the signal value at the nth moment, x(n−1) is the value at the previous moment (the lag term), and x(n+1) is the value at the next moment (the lead term).

[0055] Perform DBSCAN clustering on potential split points (radius parameter ϵ, minimum number of points minPts), merge adjacent points to form split point clusters. Output split point cluster set , each cluster Contains a set of spatially / temporally adjacent split points.

[0056] It should be noted that is the Laplacian of E, which is used to describe the second-order spatial rate of change of the energy function.

[0057] S33, generating an adaptive block mapping matrix based on the starting position, size and association relationship of the point cluster record data block.

[0058] S34, identifying the associated blocks according to the adaptive block mapping matrix, and generating a coupling key by combining the unique characteristics of the data acquisition instrument and the characteristics of the associated blocks.

[0059] Specifically, the hardware unique identifier and real-time environment characteristics of the data collector are collected, and the device feature fingerprint is generated through SHA-256 hashing. According to the block mapping matrix S, the semantic similarity between blocks is calculated (the color histogram intersection of image blocks and the MFCC distance of audio blocks).

[0060] For each block (V is the set of vertices in the association graph, each vertex represents a block), select its adjacent block set , in the incident graph G=(V,E), the vertex It is an abstract element used to represent the node of the block in the graph structure. It does not directly contain the data content of the block, but serves as an identifier. The edge E connects the vertices and reflects the relationship between the blocks. For example, if the vertex With vertex and vertices Connected by edges, then Generate coupling key ,in For block The semantic feature hash of . Among them, is the key or authentication code calculated for the i-th time; It is a message authentication code algorithm based on SHA-256 hash; is a set of adjacent blocks The hash value of It is a string concatenation operation. The specific process is to and After splicing, use the device key pass Generate Key .

[0061] It should be noted that the specific steps for generating the coupling key can be referred to in the prior art and will not be described in detail in this article.

[0062] S35, using the coupling key to perform a cross-block joint encryption operation on the associated blocks to generate an encrypted data block.

[0063] Specifically, for the associated block pairs (edges in the association graph, where yes Directly related blocks (adjacent blocks). Perform XOR coupling encryption: ,in yes The block data, yes The block data, is the shared key between blocks (by and (Coupling keys of adjacent blocks) XOR generation). The specific process is for each pair of associated blocks , using their chunked data and , and the shared key between them , generate encrypted block data through XOR operation Arrange the encrypted blocks in their original spatial order and insert the compressed version of the block mapping matrix S as the metadata header. Combined with the anti-side channel attack strategy in Example 1 (such as noise waveform superposition and dynamic clock offset), the encrypted data packet is transmitted.

[0064] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: Dynamic adaptive block segmentation and cross-block joint encryption effectively address the problem of fixed-size block segmentation destroying the natural structural characteristics of unstructured data, improving encryption effectiveness and data availability. Furthermore, by identifying key semantic regions and generating coupling keys, the protection of critical data is enhanced. Combined with a hardware-channel collaborative obfuscation system, data transmission security is further improved. This unique and innovative technical solution is particularly suitable for data acquisition applications with extremely high data security requirements.

[0065] Example 3: While Example 2 leverages semantic features to achieve dynamic block segmentation and coupled key encryption, when the spatiotemporal characteristics of multiple collectors change dynamically (e.g., when devices rapidly move and reorganize), the local segmentation strategy of a single collector can easily lead to misalignment of cross-device associated blocks. Furthermore, key generation fails to incorporate the group's global characteristics, reducing attack resistance during group state transitions. Furthermore, cross-block encryption relies solely on direct block associations and fails to utilize the group's global characteristics to optimize parameters, resulting in insufficient security in complex environments.

[0066] In some embodiments, in step S35, generating an encrypted data block further includes: S351, receiving unstructured data from multiple collectors, extracting the spatiotemporal characteristics and physical characteristics of each collector, and generating encrypted data blocks.

[0067] Among them, each collector records the timestamp and geographic location information of its own data collection, and generates a spatiotemporal feature vector, which includes the collection time, longitude and latitude coordinates, device number, etc.

[0068] S352, divide multiple data collectors into several groups and assign unique group identifiers, and record the information of the collectors in the group.

[0069] Among them, the division of groups can be based on factors such as the geographical location of the data collector, the type of collection task, and data relevance.

[0070] Specifically, multiple data collectors are divided into several groups based on factors such as their geographic location, collection task type, and data relevance. The collectors within each group have similar spatiotemporal characteristics or data relevance, facilitating collaborative obfuscation and encryption. Each group is assigned a unique group identifier, which records information such as the list of collectors within the group and the location of the group's center (e.g., geographic center or temporal center).

[0071] S353: Each collector in the group shares spatiotemporal characteristics and physical characteristics. The group center calculates the global spatiotemporal characteristics and global physical characteristic identifiers, analyzes the data correlation within the group and forms a data correlation map.

[0072] Specifically, each data collector within a train shares its own spatiotemporal characteristics (timestamp, geographic location). The train center calculates the train's global spatiotemporal characteristics, reflecting the overall spatiotemporal distribution of data collectors within the train. Each data collector within a train shares its own physical characteristics (hardware serial number, MAC address, etc.). The train center fuses and encodes these physical characteristics to generate a global physical characteristic identifier for the train. The train center analyzes the correlations (e.g., temporal, spatial, and content) between the data collected by each data collector within the train, forming a data correlation map within the train.

[0073] In step S354, the marshaling center generates a collaborative noise waveform and superimposes it on the power consumption waveform of each collector, generates a marshaling shared key and distributes it to each collector, and each collector performs adaptive block identification of associated blocks and executes joint encryption.

[0074] Specifically, based on the global spatiotemporal characteristics and global physical feature identifiers of the group, the grouping center dynamically generates a collaborative noise waveform, which is superimposed on the power consumption waveform of the encryption chip of each collector in the group, forming a collaborative obfuscation effect within the group. Based on the physical characteristics and data correlation of each collector in the group, the grouping center generates a group shared key and distributes it to each collector in the group through a secure channel for subsequent joint encryption. Each collector in the group divides the collected data into blocks according to the adaptive block strategy and identifies the 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 jointly decrypts the encrypted data blocks based on the group identifier and shared key to restore the original data.

[0075] S355: Analyze the correlation between the spatiotemporal and physical characteristics of the groups to form a correlation map, and allocate cooperative transmission channels to different groups.

[0076] Specifically, the temporal and spatial correlations and physical similarities between different marshaling groups are analyzed to form a correlation map between them. Based on this correlation map, cooperative transmission channels are allocated to different marshaling groups to prevent data transmission from interfering with each other in time and frequency between adjacent or related groups, thereby improving overall anti-attack capabilities. The status of data collectors within the marshaling group is monitored in real time (such as changes in position and data collection tasks), and the marshaling division and marshaling center position are dynamically adjusted. Based on the results of the marshaling adjustments, the marshaling information, cooperative noise waveform, and shared key are updated to ensure effective coordinated obfuscation and encryption within the marshaling group.

[0077] S356, real-time monitoring of the grouping status, and the receiving end jointly decrypting the encrypted data block according to the grouping identifier and the shared key.

[0078] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: The dynamic grouping and collaborative encryption mechanism divides groups based on the temporal, spatial, and physical characteristics of the data collectors and generates global identifiers. It utilizes collaborative noise waveforms to achieve power consumption obfuscation and combines group shared keys with an adaptive block strategy to achieve cross-block joint encryption. This solution effectively improves data transmission resistance to attacks, optimizes channel allocation through inter-group association maps, enhances system fault tolerance, and supports real-time monitoring and dynamic adjustment of group status to ensure the continued effectiveness of encryption, ultimately achieving secure and efficient collaborative transmission of unstructured data in complex environments.

[0079] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Various modifications and variations are readily apparent to those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall 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, extracting data from the data acquisition instrument and determining its data type; If the data is structured data, it is encrypted directly; if the data is unstructured data, S2 is executed; S2, extracts the physical characteristics and operating 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 random tensor seed; S3 uses image processing algorithms to segment unstructured data into data blocks, and performs tensor convolution encryption operations on each data block using dynamic obfuscation tensors to generate encrypted data blocks; S4, designing a random rule based on the characteristics of the collector to scramble the encrypted data blocks and generate a position code for each data block; S5, design a strategy to prevent side-channel attacks and build a secure channel transmission channel; S6, transmits the scrambled encrypted data blocks and the corresponding position codes to the receiving end through a secure channel. The receiving end sorts and splices the encrypted data blocks according to the position codes and association rules to restore the original encrypted data block order and original data.

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

3. The data encryption transmission method based on the intelligent data acquisition instrument according to claim 1, characterized in that: In S3, a tensor convolution encryption operation is performed on each data block using a dynamic obfuscation tensor, including: If the unstructured data itself is not in image format, it is first converted into a visual image representation; the image is divided into rectangular blocks of equal size; an algorithm is used to dynamically divide irregular areas based on the image content, and then mapped into regular blocks, with overlapping areas set between the blocks; when decrypting, the overlapping pattern must be recorded to correctly reassemble and obtain a set of data blocks , each block is a two-dimensional matrix; use the pseudo-random number generator of step S2; define the dimension of the four-dimensional tensor structure dynamic confusion tensor T as ,in, is the number of channels of the input data block, is the convolution kernel size, is the number of output channels; for each position (i, j, k, l) in T, a random floating point number is generated as T[i, j, k, l] ∈ [0, 1]; for each shape The input data block is encrypted and the encrypted data block is calculated 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, H and W are the height and width of the data block respectively.

4. The data encryption transmission method based on the intelligent data acquisition instrument according to claim 1, characterized in that: The design of the anti-side channel attack strategy specifically includes: A noise waveform is 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 pattern of the clock offset are dynamically adjusted, and a random mechanism is introduced to ensure that the clock offset pattern of each encryption operation is different. The electromagnetic radiation intensity is monitored in real time and analyzed in combination with the unique characteristic 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 characteristic vector and real-time monitoring data.

5. The data encryption transmission method based on the intelligent data acquisition instrument according to claim 1, characterized in that: S3 uses an image processing algorithm to segment the unstructured data into data blocks, specifically including: Extract edge strength and texture density from unstructured data to identify high-frequency change areas, identify key semantic areas, and dynamically adjust block sizes; calculate curvature changes in image blocks or time series mutation points of audio data to form point clusters; record the starting position, size, and association relationship of data blocks based on point clusters to generate an adaptive block mapping matrix; identify associated blocks based on the adaptive block mapping matrix, and generate a coupling key based on the unique characteristics of the data acquisition instrument and the characteristics of the associated blocks; use the coupling key to perform cross-block joint encryption operations on associated blocks to generate encrypted data blocks.

6. The data encryption transmission method based on the intelligent data acquisition instrument according to claim 5, characterized in that: The dynamically adjusting the block size includes: Initialize the basic block size, perform region growing algorithm clustering on the semantic mask matrix, and calculate the area of ​​each semantic region; the calculation formula for dynamically adjusting the block size is: ,in, is the calculated dynamically adjusted block size, is the initialization size of the basic block, is the area of ​​each semantic region, Average semantic region area, is the maximum block size.

7. The data encryption transmission method based on the intelligent data acquisition instrument according to claim 5, characterized in that: The generating of the coupling key includes: Collect the hardware unique identifier and real-time environment characteristics of the data collector, and generate the device feature fingerprint through SHA-256 hash; calculate the semantic similarity between blocks according to the block mapping matrix; construct the association graph G=(V,E), establish a one-to-one mapping relationship between vertex V and blocks, each vertex V represents a block, and use the semantic similarity between blocks as the weight of the edge; for each block , select its adjacent block set ; Generate coupling key.

8. The data encryption transmission method based on the intelligent data acquisition instrument according to claim 5, characterized in that: The generated encrypted data block is specifically: Receive unstructured data from multiple collectors, extract the spatiotemporal and physical characteristics of each collector, and generate encrypted data blocks; divide multiple data collectors into several groups and assign unique group identifiers, and record the information of the collectors in the group; each collector in the group shares spatiotemporal and physical characteristics, the grouping center calculates the global spatiotemporal characteristics and global physical characteristic identifiers, analyzes the data correlation within the group to form a data correlation map; the grouping center generates a collaborative noise waveform and superimposes it on the power consumption waveform of each collector, generates a group shared key and distributes it to each collector, and each collector performs adaptive block identification of associated blocks and performs joint encryption; analyzes the spatiotemporal-physical characteristic correlation between groups to form an association map, and allocates collaborative transmission channels to different groups; monitors the group status in real time, and the receiving end jointly decrypts the encrypted data blocks based on the group identifier and shared key.

9. The data encryption transmission method based on the intelligent data acquisition instrument according to claim 8, characterized in that: The analysis of the data association within the group to form a data association graph includes: Each collector in the group shares its own spatiotemporal characteristics, and the group center calculates the global spatiotemporal characteristics of the group; each collector in the group shares its own physical characteristics, and the group center fuses and encodes the physical characteristics to generate a global physical characteristic identification of the group; the group center analyzes the correlation between the data collected by each collector in the group to form a data correlation map within the group.

10. The data encryption transmission method based on the intelligent data acquisition instrument according to claim 8, characterized in that: The performing of joint encryption specifically includes: Based on the global spatiotemporal characteristics and global physical feature identification of the group, the grouping center dynamically generates a collaborative noise waveform, which is superimposed on the encryption chip power consumption waveform of each collector in the group to form a collaborative confusion effect within the group; the grouping center generates a grouping shared key based on the physical characteristics and data correlation of each collector in the group and distributes it to each collector in the group through a secure channel; each collector in the group blocks the collected data according to the adaptive blocking strategy and identifies the associated blocks; the grouping shared key is used to perform cross-block joint encryption operations on the associated blocks to generate encrypted data blocks.

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