An adaptive real-time digital signal encryption processing system

Abstract

The application discloses a kind of self-adapting real-time digital signal encryption processing systems, including signal processing module, for collecting multi-source digital signal and time stamp mark and standard signal stream are generated with standardization;Risk assessment module is used to generate security level according to terminal security, firewall and historical attack feature evaluation risk;Parameter control module is used to construct control vector to determine elliptic curve encryption parameter, key length and update cycle;Correlation mapping module is used to generate digest and map control vector in sliding window hash;Encryption processing module is used to generate encrypted signal stream in elliptic curve segmented encryption;Block chain storage module is used to form verifiable storage according to time writing block chain;Security update module is used to output encrypted signal and synchronously update terminal security and firewall strategy.The application realizes digital signal self-adapting encryption, hash check and block chain storage, and improves security, integrity and traceability.

Description

Technical Field

[0001] This invention relates to the fields of information security and cryptography, and in particular to an adaptive real-time digital signal encryption processing system. Background Technology

[0002] In the existing fields of IoT communication and digital signal processing, real-time digital signals are vulnerable to data tampering, signal leakage, and malicious attacks during transmission and processing. Traditional encryption methods typically use fixed algorithms and static keys to encrypt digital signals. This method cannot dynamically adjust the encryption strategy according to the security status of different terminals, signal characteristics, and network environment, resulting in insufficient security in high-risk environments and wasting computing resources and transmission bandwidth in low-risk environments.

[0003] Furthermore, existing real-time digital signal encryption technologies often execute signal processing and encryption processing as independent sequential processes, lacking adaptive control mechanisms for the characteristics of time-series signals and dynamic network changes. This makes it difficult to ensure signal real-time performance and segmented processing efficiency while maintaining encryption strength. Especially in large-scale IoT deployments, the security status, firewall policies, and historical attack behaviors of different terminals vary significantly. Traditional static encryption cannot effectively cope with these dynamic changes, thus affecting the overall system security and reliability.

[0004] In terms of digital signal integrity verification, existing technologies typically rely on a single hash or message digest algorithm, but lack close correlation with encryption strategies, risk levels, and segmented time windows, making it impossible to accurately verify and trace real-time segmented signals. Furthermore, traditional methods for storing encrypted data are mostly limited to local or centralized storage, failing to achieve immutability, verifiability, and distributed management, thus making it difficult to meet the end-to-end data security and compliance requirements of the Internet of Things (IoT) environment.

[0005] Therefore, how to provide an adaptive real-time digital signal encryption processing system is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] One objective of this invention is to propose an adaptive real-time digital signal encryption processing system. This invention fully utilizes IoT acquisition technology, elliptic curve cryptography, sliding time window hashing, and blockchain distributed evidence storage technology. It details the specific implementation process of risk assessment of digital signal streams, parameter control vector construction, real-time segmented encryption, and verifiable evidence storage. It has the advantages of high security, strong real-time performance, good traceability, and distributed tamper-proof.

[0007] An adaptive real-time digital signal encryption processing system according to an embodiment of the present invention includes:

[0008] The signal processing module is used to acquire real-time digital signals from multiple signal sources, perform timestamp marking and signal format normalization processing on the real-time digital signals, and generate a standardized digital signal stream.

[0009] The risk assessment module is used to assess the risks of digital signal streams and generate corresponding security risk level labels based on terminal security status, firewall policy parameters and historical attack characteristic data.

[0010] The parameter control module is used to construct a parameter control vector based on the security risk level identifier. The parameter control vector is used to determine the curve parameters, key length and update period of the elliptic curve encryption algorithm.

[0011] The association mapping module is used to perform hash operations on the digital signal stream according to a sliding time window, generate a corresponding hash digest sequence, and associate the hash digest sequence with the parameter control vector.

[0012] The encryption processing module is used to perform real-time segmented encryption processing on the digital signal stream using the elliptic curve cryptography algorithm to generate an encrypted digital signal stream that is consistent with the hash digest sequence.

[0013] The blockchain evidence storage module is used to write the hash digest sequence corresponding to the encrypted digital signal stream and the security risk level identifier into the blockchain ledger in chronological order, forming a verifiable encrypted signal evidence storage record.

[0014] The security update module is used to output the encrypted digital signal stream that has been written to the blockchain to the transmission channel, and to perform synchronous updates on the terminal security status and firewall policy parameters based on the blockchain ledger records.

[0015] Optionally, modules can be integrated using the following methods:

[0016] S1. In an Internet of Things (IoT) environment, collect real-time digital signals from multiple signal sources, perform timestamp marking and signal format standardization processing on the real-time digital signals, and generate a standardized digital signal stream;

[0017] S2. Conduct risk assessment on digital signal streams and generate corresponding security risk level identifiers based on terminal security status, firewall policy parameters, and historical attack characteristic data.

[0018] S3. Construct a parameter control vector based on the security risk level identifier. The parameter control vector is used to determine the curve parameters, key length and update period of the elliptic curve encryption algorithm.

[0019] S4. Perform hash operation on the digital signal stream according to the sliding time window to generate a hash digest sequence that corresponds one-to-one with the sliding time window, and associate the hash digest sequence with the parameter control vector.

[0020] S5. Based on the parameter control vector, the elliptic curve cryptography algorithm is used to perform real-time segmented encryption processing on the digital signal stream to generate an encrypted digital signal stream that is consistent with the hash digest sequence.

[0021] S6. Write the hash digest sequence corresponding to the encrypted digital signal stream and the security risk level identifier into the blockchain ledger in chronological order to form a verifiable encrypted signal evidence record.

[0022] S7. Output the encrypted digital signal stream that has completed the blockchain writing to the transmission channel, and perform synchronous updates on the terminal security status and firewall policy parameters based on the blockchain ledger records.

[0023] Optionally, the real-time digital signals are accessed in parallel through a multi-channel acquisition interface deployed on the IoT terminal side. During the acquisition process, a unified timestamp generated by a high-precision clock source is added to each real-time digital signal, and the sampling rate, quantization bit width, and data frame length are subjected to consistency correction processing according to the preset signal type identifier. Subsequently, the real-time digital signals that have been timestamped are subjected to byte order rearrangement, data field alignment, and redundant control field stripping operations, so as to reassemble real-time digital signals from different sources and in different formats into a time-continuous and structurally unified standardized data frame sequence, and finally form a standardized digital signal stream that can be processed sequentially according to the timestamp order.

[0024] Optionally, S2 specifically includes:

[0025] S21. Perform terminal binding parsing on the digital signal stream, read the terminal identification information corresponding to the digital signal stream, and retrieve the terminal security status data associated with the terminal identification information. The terminal security status data includes terminal firmware integrity flag, operating environment trust flag and access behavior statistical features.

[0026] S22. Match the transmission path information of the digital signal stream with the current firewall policy parameters, extract firewall rule hit records, port access sequences, and protocol behavior features to form a firewall behavior feature vector corresponding to the digital signal stream, specifically including:

[0027] Extract the transmission path information of the digital signal stream, which includes source IP, destination IP, source port, destination port, and protocol type;

[0028] The transmission path information is compared one by one with the access control rules recorded in the firewall policy table. The firewall policy table stores network access control rules, including source IP, destination IP, source port, destination port, protocol type, and rule priority information, specifically including:

[0029] Filter hit rules based on strategy priority;

[0030] The hit rules are validated, including port range matching, protocol consistency checks and time window limit determination. At the same time, the hit count and hit sequence of each rule are counted.

[0031] The hit results are converted into numerical feature vectors, including rule hit flags, access frequency, port anomaly index, and protocol anomaly index;

[0032] The feature vectors are arranged in the time sequence of the digital signal stream to form a complete sequence of firewall behavior feature vectors, providing input for the generation of security risk levels;

[0033] S23. Based on the temporal features, frequency domain features, and data packet structure features of the digital signal stream, perform feature alignment processing from the historical attack feature dataset to generate an attack similarity feature set that matches the current digital signal stream;

[0034] S24. Perform joint evaluation processing on terminal security status data, firewall behavior feature vector and attack similarity feature set, generate a unique security risk level identifier according to preset risk classification rules, and establish a one-to-one correspondence between the security risk level identifier and the digital signal stream.

[0035] Optionally, S3 specifically includes:

[0036] S31. Receive the security risk level identifier corresponding to the digital signal stream, and map the security risk level identifier to a preset parameter mapping table. The parameter mapping table records the elliptic curve type, curve parameter range, key length range and key update cycle range corresponding to different risk levels.

[0037] S32. Based on the curve parameter range corresponding to the risk level in the parameter mapping table, calculate and select the curve parameter values ​​according to the digital signal flow characteristic strength, encryption performance requirements, and real-time processing delay limitations. The curve parameter values ​​are determined by a dynamic weighting algorithm using the base point coordinates, curve coefficients, and finite field primes. The weights are jointly determined by the risk level and signal fluctuation characteristics, specifically including:

[0038] Extract the base point coordinates, curve coefficients, and finite field primes from the predefined parameter set of the elliptic curve type;

[0039] Dynamic weighting coefficients are generated based on the amplitude change rate, temporal fluctuation amplitude, and risk level weight of the digital signal stream. After standardizing each weight, the final value of the curve coefficient is calculated by linear combination.

[0040] Modular operations are performed on the base point coordinates based on the prime numbers of the finite field to adjust the curve to meet the requirements of non-singularity and encryption strength, and output curve parameter values ​​that match the characteristics of digital signal flow.

[0041] S33. Combining the security risk level identifier and the data packet length and transmission rate of the digital signal stream, calculate the key length of the elliptic curve encryption algorithm. The key length is generated within the selected key length range by a proportional scaling mapping method and adjusted to ensure that the encryption strength matches the real-time performance.

[0042] S34. Determine the update cycle of the elliptic curve key based on the security risk level identifier and the update frequency of the digital signal stream. The update cycle is adjusted by a risk weighting factor so that a shorter key update cycle corresponds to a higher risk level and a longer update cycle corresponds to a lower risk level.

[0043] S35. Integrate the curve parameters, key length, and key update period into a parameter control vector, specifically including:

[0044] By allocating contiguous storage units in memory, the base point coordinates, curve coefficients, and finite field primes of the elliptic curve are written into the starting storage area in a fixed byte order.

[0045] The calculated key length is appended to the curve parameters in binary form.

[0046] The key update cycle is appended to the key length in milliseconds, and the corresponding security risk level identifier and digital signal stream time window identifier are stored in the control vector header.

[0047] Perform a checksum generation operation, perform XOR and summation operations on the data blocks of curve parameters, key length and key update period to generate a checksum value, and append the checksum value to the end of the vector;

[0048] Ultimately, a parameter control vector is formed, enabling unified management and serializable transmission.

[0049] Optionally, S4 specifically includes:

[0050] S41. Divide the digital signal stream into segments according to a preset sliding time window. Each time window contains a fixed number of continuous data frames, and generate a unique identifier for each time window to index the corresponding data segment.

[0051] S42. For each time window data segment, read the byte stream sequentially from the memory buffer, and perform byte-by-byte accumulation and non-linear permutation operations according to the preset hash algorithm. Perform XOR and shift operations on each byte with the hash state value of the previous byte, and iteratively calculate to generate a preliminary hash value.

[0052] S43. Mix and map the initial hash value with the unique identifier of the current time window and the security risk level and curve parameter index in the parameter control vector, perform multiple rounds of nonlinear hashing operations, generate the final hash digest, and store the final hash digest in a time sequence array to form a hash digest sequence that corresponds one-to-one with each sliding time window.

[0053] S44. In the hash digest sequence, a timestamp and a sliding window index are added to each hash digest to achieve a precise correspondence with the original digital signal stream, parameter control vector, and security risk level, providing an input mapping basis for real-time segmented encryption processing.

[0054] Optionally, S5 specifically includes:

[0055] S51. Divide the digital signal stream into multiple segmented data units according to the sliding time window, and allocate a continuous storage buffer in memory for each segmented data unit to temporarily store the original data frame before encryption.

[0056] S52. Read the coordinates of the elliptic curve base point, curve coefficients, finite field prime number, key length and key update period of the corresponding time window from the parameter control vector. Apply the key length to generate the elliptic curve public and private key pair for this time window. During the key generation process, the initial key seed is perturbed by a weighted random number according to the risk level to ensure the uniqueness and security of the key.

[0057] S53. For each segmented data unit, perform byte-by-byte encryption operation according to the elliptic curve encryption algorithm, map the data value of each byte to the coordinates of a point on the elliptic curve, generate ciphertext coordinates through public key operation, and then store the ciphertext coordinate sequence in the encryption buffer according to the original data frame order to form an encrypted digital signal unit corresponding to the time window.

[0058] S54. After encryption is completed, each encrypted digital signal unit is verified and mapped to the corresponding hash digest sequence to ensure that the encrypted data corresponds exactly to the hash digest sequence. Then, each encrypted digital signal unit is combined in the order of the time window to output an encrypted digital signal stream that is consistent with the hash digest sequence, thereby realizing real-time segmented encryption processing.

[0059] Optionally, S6 specifically includes:

[0060] S61. The digital signal stream that has completed real-time segmented encryption is combined with the corresponding hash digest sequence and security risk level identifier in the order of time window to form encrypted data blocks. A timestamp, sliding window index and parameter control vector identifier are added to the header of each encrypted data block to uniquely identify the data block and its corresponding security attributes.

[0061] S62. For encrypted data blocks, the timestamp, hash digest sequence, security risk level identifier and the block hash value of the previous block are sequentially input into the hash operation to generate a block-level hash value, and the block-level hash value is appended to the end of the data block to achieve a chain-like integrity connection.

[0062] S63. Verify the generated blocks according to the blockchain consensus protocol, and perform distributed synchronization between nodes, including verifying the time order, hash consistency and risk level identifier mapping relationship. After consensus confirmation, write the blocks into the blockchain ledger and update the block index table to realize the verifiable storage record of each encrypted digital signal, its hash digest and security risk level.

[0063] Optionally, the process of generating the block-level hash value specifically includes:

[0064] The header timestamp, sliding window index, hash digest sequence, security risk level identifier, and block hash value of the previous block of each encrypted data block are loaded into the memory buffer in sequence.

[0065] The bytes are concatenated into a continuous byte stream in a fixed byte order, and the encrypted hash algorithm is iteratively operated on each segment of the byte stream. The current hash state value of each segment is updated through XOR, shift, non-linear permutation and accumulation operations.

[0066] The final iteration generates a unique block-level hash value, which is then appended to the end of the data block. At the same time, a chain of reference pointers pointing to the hash value of the previous block is established in memory. This realizes the integrity verification of the blockchain data block and the logical association between the previous and subsequent blocks, providing basic data for distributed verification and ledger writing.

[0067] The beneficial effects of this invention are:

[0068] First, this invention achieves dynamic security level judgment of digital signal streams by timestamping and standardizing multi-source real-time digital signals in the Internet of Things environment, and by combining terminal security status, firewall policy parameters and historical attack characteristic data for risk assessment. This enables encryption strategies to adaptively adjust to different signal characteristics and security risks, thereby significantly improving the overall security of digital signal transmission and processing.

[0069] Secondly, this invention constructs a parameter control vector to guide the selection of curve parameters, determination of key length, and control of key update cycle in elliptic curve encryption algorithms. At the same time, it performs hash operations on the digital signal stream according to a sliding time window and associates and maps it with the parameter control vector to achieve real-time segmented encryption and precise integrity verification. This ensures that the digital signal has both high-strength encryption protection and real-time performance and processing efficiency during the encryption process.

[0070] Finally, this invention writes the encrypted digital signal stream, hash digest sequence, and security risk level identifier into the blockchain ledger in chronological order to form a verifiable distributed evidence record. This ensures that the encrypted signal is tamper-proof, traceable, and can be updated synchronously with the terminal security status and firewall policies, thereby achieving end-to-end security assurance, evidence management, and real-time monitoring of digital signals, meeting the data security needs of large-scale IoT deployments and dynamic network environments. Attached Figure Description

[0071] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0072] Figure 1 This is a block diagram of an adaptive real-time digital signal encryption processing system proposed in this invention.

[0073] Figure 2 This is a flowchart of an adaptive real-time digital signal encryption processing system proposed in this invention.

[0074] Figure 3 This is a flowchart illustrating the segmented encryption and blockchain evidence storage process of an adaptive real-time digital signal encryption processing system proposed in this invention. Detailed Implementation

[0075] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0076] refer to Figure 1 An adaptive real-time digital signal encryption processing system, comprising:

[0077] The signal processing module is used to acquire real-time digital signals from multiple signal sources, perform timestamp marking and signal format normalization processing on the real-time digital signals, and generate a standardized digital signal stream.

[0078] The risk assessment module is used to assess the risks of digital signal streams and generate corresponding security risk level labels based on terminal security status, firewall policy parameters and historical attack characteristic data.

[0079] The parameter control module is used to construct a parameter control vector based on the security risk level identifier. The parameter control vector is used to determine the curve parameters, key length and update period of the elliptic curve encryption algorithm.

[0080] The association mapping module is used to perform hash operations on the digital signal stream according to a sliding time window, generate a corresponding hash digest sequence, and associate the hash digest sequence with the parameter control vector.

[0081] The encryption processing module is used to perform real-time segmented encryption processing on the digital signal stream using the elliptic curve cryptography algorithm to generate an encrypted digital signal stream that is consistent with the hash digest sequence.

[0082] The blockchain evidence storage module is used to write the hash digest sequence corresponding to the encrypted digital signal stream and the security risk level identifier into the blockchain ledger in chronological order, forming a verifiable encrypted signal evidence storage record.

[0083] The security update module is used to output the encrypted digital signal stream that has been written to the blockchain to the transmission channel, and to perform synchronous updates on the terminal security status and firewall policy parameters based on the blockchain ledger records.

[0084] refer to Figures 2-3 In this embodiment, the modules are interconnected using the following method:

[0085] S1. In an Internet of Things (IoT) environment, collect real-time digital signals from multiple signal sources, perform timestamp marking and signal format standardization processing on the real-time digital signals, and generate a standardized digital signal stream;

[0086] S2. Conduct risk assessment on digital signal streams and generate corresponding security risk level identifiers based on terminal security status, firewall policy parameters, and historical attack characteristic data.

[0087] S3. Construct a parameter control vector based on the security risk level identifier. The parameter control vector is used to determine the curve parameters, key length and update period of the elliptic curve encryption algorithm.

[0088] S4. Perform hash operation on the digital signal stream according to the sliding time window to generate a hash digest sequence that corresponds one-to-one with the sliding time window, and associate the hash digest sequence with the parameter control vector.

[0089] S5. Based on the parameter control vector, the elliptic curve cryptography algorithm is used to perform real-time segmented encryption processing on the digital signal stream to generate an encrypted digital signal stream that is consistent with the hash digest sequence.

[0090] S6. Write the hash digest sequence corresponding to the encrypted digital signal stream and the security risk level identifier into the blockchain ledger in chronological order to form a verifiable encrypted signal evidence record.

[0091] S7. Output the encrypted digital signal stream that has completed the blockchain writing to the transmission channel, and perform synchronous updates on the terminal security status and firewall policy parameters based on the blockchain ledger records.

[0092] In this embodiment, the real-time digital signals are accessed in parallel through a multi-channel acquisition interface deployed on the IoT terminal side. During the acquisition process, a unified timestamp generated by a high-precision clock source is added to each real-time digital signal, and the sampling rate, quantization bit width, and data frame length are corrected according to the preset signal type identifier. Then, the real-time digital signals with completed timestamps are subjected to byte order rearrangement, data field alignment, and redundant control field stripping operations. The real-time digital signals from different sources and in different formats are recombined into a time-continuous and structurally unified standardized data frame sequence, and finally, a standardized digital signal stream that can be processed sequentially is formed according to the timestamp order.

[0093] In this embodiment, S2 specifically includes:

[0094] S21. Perform terminal binding parsing on the digital signal stream, read the terminal identification information corresponding to the digital signal stream, and retrieve the terminal security status data associated with the terminal identification information. The terminal security status data includes terminal firmware integrity flags, operating environment trust flags, and access behavior statistical features.

[0095] S22. Match the transmission path information of the digital signal stream with the current firewall policy parameters, extract firewall rule hit records, port access sequences, and protocol behavior features to form a firewall behavior feature vector corresponding to the digital signal stream, specifically including:

[0096] Extract the transmission path information of the digital signal stream, which includes source IP, destination IP, source port, destination port, and protocol type;

[0097] The transmission path information is compared one by one with the access control rules recorded in the firewall policy table. The firewall policy table stores network access control rules, including source IP, destination IP, source port, destination port, protocol type, and rule priority information, specifically including:

[0098] Filter hit rules based on strategy priority;

[0099] The hit rules are validated, including port range matching, protocol consistency checks and time window limit determination. At the same time, the hit count and hit sequence of each rule are counted.

[0100] The hit results are converted into numerical feature vectors, including rule hit flags, access frequency, port anomaly index, and protocol anomaly index;

[0101] The feature vectors are arranged in the time sequence of the digital signal stream to form a complete sequence of firewall behavior feature vectors, providing input for the generation of security risk levels;

[0102] S23. Based on the temporal features, frequency domain features, and data packet structure features of the digital signal stream, perform feature alignment processing from the historical attack feature dataset to generate an attack similarity feature set that matches the current digital signal stream;

[0103] S24. Perform joint evaluation processing on terminal security status data, firewall behavior feature vector and attack similarity feature set, generate a unique security risk level identifier according to preset risk classification rules, and establish a one-to-one correspondence between the security risk level identifier and the digital signal stream.

[0104] In this embodiment, S3 specifically includes:

[0105] S31. Receive the security risk level identifier corresponding to the digital signal stream, and map the security risk level identifier to a preset parameter mapping table. The parameter mapping table records the elliptic curve type, curve parameter range, key length range and key update cycle range corresponding to different risk levels.

[0106] S32. Based on the curve parameter range corresponding to the risk level in the parameter mapping table, calculate and select the curve parameter values ​​according to the digital signal flow characteristic strength, encryption performance requirements, and real-time processing delay limitations. The curve parameter values ​​are determined by a dynamic weighting algorithm using the base point coordinates, curve coefficients, and finite field primes. The weights are jointly determined by the risk level and signal fluctuation characteristics, specifically including:

[0107] Extract the base point coordinates, curve coefficients, and finite field primes from the predefined parameter set of the elliptic curve type;

[0108] Dynamic weighting coefficients are generated based on the amplitude change rate, temporal fluctuation amplitude, and risk level weight of the digital signal stream. After standardizing each weight, the final value of the curve coefficient is calculated by linear combination.

[0109] Modular operations are performed on the base point coordinates based on the prime numbers of the finite field to adjust the curve to meet the requirements of non-singularity and encryption strength, and output curve parameter values ​​that match the characteristics of digital signal flow.

[0110] S33. Combining the security risk level identifier and the data packet length and transmission rate of the digital signal stream, calculate the key length of the elliptic curve encryption algorithm. The key length is generated within the selected key length range by a proportional scaling mapping method and adjusted to ensure that the encryption strength matches the real-time performance.

[0111] S34. Determine the update cycle of the elliptic curve key based on the security risk level identifier and the update frequency of the digital signal stream. The update cycle is adjusted by a risk weighting factor so that a shorter key update cycle corresponds to a higher risk level and a longer update cycle corresponds to a lower risk level.

[0112] S35. Integrate the curve parameters, key length, and key update period into a parameter control vector, specifically including:

[0113] By allocating contiguous storage units in memory, the base point coordinates, curve coefficients, and finite field primes of the elliptic curve are written into the starting storage area in a fixed byte order.

[0114] The calculated key length is appended to the curve parameters in binary form.

[0115] The key update cycle is appended to the key length in milliseconds, and the corresponding security risk level identifier and digital signal stream time window identifier are stored in the control vector header.

[0116] Perform a checksum generation operation, perform XOR and summation operations on the data blocks of curve parameters, key length and key update period to generate a checksum value, and append the checksum value to the end of the vector;

[0117] Ultimately, a parameter control vector is formed, enabling unified management and serializable transmission.

[0118] In this embodiment, S4 specifically includes:

[0119] S41. Divide the digital signal stream into segments according to a preset sliding time window. Each time window contains a fixed number of continuous data frames, and generate a unique identifier for each time window to index the corresponding data segment.

[0120] S42. For each time window data segment, read the byte stream sequentially from the memory buffer, and perform byte-by-byte accumulation and non-linear permutation operations according to the preset hash algorithm. Perform XOR and shift operations on each byte with the hash state value of the previous byte, and iteratively calculate to generate a preliminary hash value.

[0121] The data segments are loaded into the cache sequentially in byte order, and a fixed-length hash status register is initialized. Each byte is then XORed bit by bit with the current value in the hash status register.

[0122] Perform a left circular shift and a non-linear permutation table lookup, then mix the permutation table output with the XOR result bitwise again to complete one iteration and update the hash state;

[0123] This iterative operation is repeated for all bytes in the entire time window. After each round of byte loop, the hash state is added to the final hash value of the previous time window to achieve continuous linking and generate an initial hash value.

[0124] S43. Mix and map the initial hash value with the unique identifier of the current time window and the security risk level and curve parameter index in the parameter control vector, perform multiple rounds of nonlinear hashing operations, generate the final hash digest, and store the final hash digest in a time sequence array to form a hash digest sequence that corresponds one-to-one with each sliding time window.

[0125] S44. In the hash digest sequence, a timestamp and a sliding window index are added to each hash digest to achieve a precise correspondence with the original digital signal stream, parameter control vector, and security risk level, providing an input mapping basis for real-time segmented encryption processing.

[0126] In this embodiment, S5 specifically includes:

[0127] S51. Divide the digital signal stream into multiple segmented data units according to the sliding time window, and allocate a continuous storage buffer in memory for each segmented data unit to temporarily store the original data frame before encryption.

[0128] S52. Read the elliptic curve base point coordinates, curve coefficients, finite field prime number, key length, and key update period for the corresponding time window from the parameter control vector. Apply the key length to generate the elliptic curve public-private key pair for this time window. During key generation, the initial key seed is perturbed using risk-level weighted random numbers to ensure the uniqueness and security of the key. Specifically, this includes:

[0129] The key generation algorithm is initialized by reading the key length and curve parameters from the parameter control vector;

[0130] Input the risk level weighting factor and time window identifier into the pseudo-random number generator to generate a private key seed of a specified length;

[0131] The corresponding public key is generated using elliptic curve base points and finite field primes according to standard elliptic curve multiplication.

[0132] The private and public keys are stored in a contiguous memory buffer, and a key update counter and timestamp are recorded in memory to ensure that the public and private key pairs generated in each time window are unique and can be called in sequence.

[0133] S53. For each segmented data unit, perform byte-by-byte encryption operation according to the elliptic curve encryption algorithm, map the data value of each byte to the coordinates of a point on the elliptic curve, generate ciphertext coordinates through public key operation, and then store the ciphertext coordinate sequence in the encryption buffer according to the original data frame order to form an encrypted digital signal unit corresponding to the time window.

[0134] S54. After encryption is completed, each encrypted digital signal unit is verified and mapped to the corresponding hash digest sequence to ensure that the encrypted data corresponds exactly to the hash digest sequence. Then, each encrypted digital signal unit is combined in the order of the time window to output an encrypted digital signal stream that is consistent with the hash digest sequence, thereby realizing real-time segmented encryption processing.

[0135] In this embodiment, S6 specifically includes:

[0136] S61. The digital signal stream that has completed real-time segmented encryption is combined with the corresponding hash digest sequence and security risk level identifier in the order of time window to form encrypted data blocks. A timestamp, sliding window index and parameter control vector identifier are added to the header of each encrypted data block to uniquely identify the data block and its corresponding security attributes.

[0137] S62. For encrypted data blocks, the timestamp, hash digest sequence, security risk level identifier and the block hash value of the previous block are sequentially input into the hash operation to generate a block-level hash value, and the block-level hash value is appended to the end of the data block to achieve a chain-like integrity connection.

[0138] S63. Verify the generated blocks according to the blockchain consensus protocol, and perform distributed synchronization between nodes, including verifying the time order, hash consistency and risk level identifier mapping relationship. After consensus confirmation, write the blocks into the blockchain ledger and update the block index table to realize the verifiable storage record of each encrypted digital signal, its hash digest and security risk level.

[0139] In this embodiment, the process of generating the block-level hash value specifically includes:

[0140] The header timestamp, sliding window index, hash digest sequence, security risk level identifier, and block hash value of the previous block of each encrypted data block are loaded into the memory buffer in sequence.

[0141] The bytes are concatenated into a continuous byte stream in a fixed byte order, and the encrypted hash algorithm is iteratively operated on each segment of the byte stream. The current hash state value of each segment is updated through XOR, shift, non-linear permutation and accumulation operations.

[0142] The final iteration generates a unique block-level hash value, which is then appended to the end of the data block. At the same time, a chain of reference pointers pointing to the hash value of the previous block is established in memory. This realizes the integrity verification of the blockchain data block and the logical association between the previous and subsequent blocks, providing basic data for distributed verification and ledger writing.

[0143] Example 1:

[0144] To verify the feasibility of this invention in practice, it was applied to the encryption processing of multi-source real-time digital signals in an IoT environment. In this scenario, the digital signals originate from a large number of terminal sensors and edge devices, and their characteristics include various types such as temperature, current, vibration, and pressure. Existing technologies, when processing these real-time signals, suffer from problems such as a single encryption strategy, inability to dynamically adapt to terminal security status and network risks, and difficulty in verifying encrypted data. This makes the signals vulnerable to tampering and information leakage during transmission.

[0145] When applying this invention, each digital signal stream is first timestamped and formatted, unifying signals from different sources and types into a continuous, standardized data frame sequence. Based on this, the system performs a risk assessment on the digital signal streams, combining terminal security status, firewall policy parameters, and historical attack characteristics to generate a security risk level identifier. For example, in an experiment, 10,000 continuous signal streams were assessed, with high-risk signals accounting for approximately 12%, medium-risk signals approximately 38%, and low-risk signals approximately 50%. The system can accurately identify high-risk signals, providing a basis for subsequent encryption strategies.

[0146] Subsequently, a parameter control vector is constructed based on the security risk level identifier to determine the curve parameters, key length, and key update period of the elliptic curve cryptography algorithm. Experiments in Table 1 verify that the key length for high-risk signals is 512 bits with an update period of 10 seconds; the key length for medium-risk signals is 384 bits with an update period of 30 seconds; and the key length for low-risk signals is 256 bits with an update period of 60 seconds. Within a sliding time window, the digital signal stream performs hash operations to generate a hash digest sequence corresponding to the window, and associates and maps this sequence with the parameter control vector to achieve integrity verification before encryption. In the experiments in Table 1, window hash calculations were performed on 1000 signals per second, with an average hash digest generation time of approximately 0.8 milliseconds per signal, ensuring high-throughput real-time processing capabilities.

[0147] Table 1 Comparison of Digital Signal Encryption Processing Effects

[0148] Signal Category Number of signals Security Risk Level Elliptic curve key length (bits) Key update cycle (seconds) Average hash generation time (ms) Average encryption time (ms) Encryption accuracy (%) Blockchain notarization success rate (%) Temperature signal 2500 high 512 10 0.8 1.2 99.98 100 Current signal 3000 middle 384 30 0.7 1.1 99.97 100 Vibration signal 2000 Low 256 60 0.8 1.2 99.99 100 pressure signal 2500 middle 384 30 0.7 1.1 99.98 100

[0149] In the real-time segmented encryption stage, the system uses an elliptic curve cryptography algorithm to perform segmented encryption processing on the digital signal stream, ensuring that the encrypted digital signal stream is consistent with the hash digest sequence. In the experiment shown in Table 1, 10,000 signals were encrypted, with an average encryption time of 1.2 milliseconds per signal and an encryption accuracy of 99.98%. Subsequently, the encrypted digital signal stream, hash digest sequence, and security risk level identifier were written into the blockchain ledger in chronological order to achieve verifiable distributed evidence storage. In the experimental simulation environment, the write operations of 1,000 blocks were statistically analyzed, achieving a 100% success rate, and the average blockchain verification time was 3.5 milliseconds per block. Through comparative experiments, the traditional encryption method without this invention, under the same conditions, had a high-risk signal recognition rate of only 65%, an average encryption processing delay of 3.5 milliseconds, and a blockchain evidence storage unverifiable or lost rate of approximately 7%, significantly lower than the implementation effect of this invention.

[0150] In summary, this invention enables adaptive encryption processing of multi-source real-time digital signals in an IoT environment. It dynamically adjusts the encryption strategy through risk assessment, ensures integrity through hash operations, and distributes the encryption results to a blockchain, achieving technical effects such as signal immutability, traceability, high security, and strong real-time performance. Experimental data fully demonstrates the advantages of this invention in maintaining encryption processing efficiency and security even in high-throughput, high-risk environments.

[0151] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An adaptive real-time digital signal encryption processing system, characterized in that, include: Signal processing module, risk assessment module, parameter control module, correlation mapping module, encryption processing module, blockchain evidence storage module, security update module; The modules are connected in the following way: S1. Through the signal processing module, real-time digital signals from multiple signal sources are collected in the Internet of Things environment, and the real-time digital signals are timestamped and the signal format is standardized to generate a standardized digital signal stream. S2. Through the risk assessment module, the digital signal stream is assessed for risk, and a corresponding security risk level label is generated based on the terminal security status, firewall policy parameters and historical attack characteristic data. S2 specifically includes: S21. Perform terminal binding parsing on the digital signal stream, read the terminal identification information corresponding to the digital signal stream, and retrieve the terminal security status data associated with the terminal identification information. The terminal security status data includes terminal firmware integrity flags, operating environment trust flags, and access behavior statistical features. S22. Match the transmission path information of the digital signal stream with the current firewall policy parameters, extract firewall rule hit records, port access sequences, and protocol behavior features to form a firewall behavior feature vector corresponding to the digital signal stream, specifically including: Extract the transmission path information of the digital signal stream, which includes source IP, destination IP, source port, destination port, and protocol type; The transmission path information is compared one by one with the access control rules recorded in the firewall policy table. The firewall policy table stores network access control rules, including source IP, destination IP, source port, destination port, protocol type, and rule priority information, specifically including: Filter hit rules based on strategy priority; The hit rules are validated, including port range matching, protocol consistency checks and time window limit determination. At the same time, the hit count and hit sequence of each rule are counted. The hit results are converted into numerical feature vectors, including rule hit flags, access frequency, port anomaly index, and protocol anomaly index; The feature vectors are arranged in the time sequence of the digital signal stream to form a complete sequence of firewall behavior feature vectors, providing input for the generation of security risk levels; S23. Based on the temporal features, frequency domain features, and data packet structure features of the digital signal stream, perform feature alignment processing from the historical attack feature dataset to generate an attack similarity feature set that matches the current digital signal stream; S24. Jointly evaluate and process the terminal security status data, firewall behavior feature vector and attack similarity feature set, generate a unique security risk level identifier according to the preset risk classification rules, and establish a one-to-one correspondence between the security risk level identifier and the digital signal stream. S3. Through the parameter control module, a parameter control vector is constructed based on the security risk level identifier. The parameter control vector is used to determine the curve parameters, key length and update period of the elliptic curve encryption algorithm. S3 specifically includes: S31. Receive the security risk level identifier corresponding to the digital signal stream, and map the security risk level identifier to a preset parameter mapping table. The parameter mapping table records the elliptic curve type, curve parameter range, key length range and key update cycle range corresponding to different risk levels. S32. Based on the curve parameter range corresponding to the risk level in the parameter mapping table, calculate and select the curve parameter value according to the characteristic strength of digital signal flow, encryption performance requirements and real-time processing delay limits. The curve parameter value is determined by the base point coordinates, curve coefficients and finite field primes through a dynamic weighting algorithm. The weight is determined by the risk level and signal fluctuation characteristics. S33. Combining the security risk level identifier and the data packet length and transmission rate of the digital signal stream, calculate the key length of the elliptic curve encryption algorithm. The key length is generated within the selected key length range by a proportional scaling mapping method and adjusted to ensure that the encryption strength matches the real-time performance. S34. Determine the update cycle of the elliptic curve key based on the security risk level identifier and the update frequency of the digital signal stream. The update cycle is adjusted by a risk weighting factor so that a shorter key update cycle corresponds to a higher risk level and a longer update cycle corresponds to a lower risk level. S35. Integrate curve parameters, key length, and key update period into a parameter control vector; S4. Through the association mapping module, perform hash operation on the digital signal stream according to the sliding time window to generate a hash digest sequence that corresponds one-to-one with the sliding time window, and associate the hash digest sequence with the parameter control vector. S5. Through the encryption processing module, based on the parameter control vector, the elliptic curve cryptography algorithm is used to perform real-time segmented encryption processing on the digital signal stream, generating an encrypted digital signal stream that is consistent with the hash digest sequence. S6. Through the blockchain evidence storage module, the hash digest sequence corresponding to the encrypted digital signal stream and the security risk level identifier are written into the blockchain ledger in chronological order to form a verifiable encrypted signal evidence storage record. S7. Through the security update module, the encrypted digital signal stream that has completed the blockchain writing is output to the transmission channel, and the terminal security status and firewall policy parameters are synchronously updated based on the blockchain ledger records.

2. The adaptive real-time digital signal encryption processing system according to claim 1, characterized in that, The real-time digital signals are accessed in parallel through a multi-channel acquisition interface deployed on the IoT terminal side. During the acquisition process, a unified timestamp generated by a high-precision clock source is added to each real-time digital signal. The sampling rate, quantization bit width, and data frame length are corrected according to the preset signal type identifier. Then, the real-time digital signals with completed timestamps are reordered by bytes, aligned by data fields, and stripped of redundant control fields. The real-time digital signals from different sources and in different formats are recombined into a time-continuous and structurally unified standardized data frame sequence. Finally, a standardized digital signal stream that can be processed sequentially is formed according to the timestamp order.

3. The adaptive real-time digital signal encryption processing system according to claim 1, characterized in that, Specifically, S32 includes: S321. Extract the base point coordinates, curve coefficients, and finite field primes from the predefined parameter set of the elliptic curve type. S322. Generate dynamic weighting coefficients based on the amplitude change rate, time-series fluctuation amplitude, and risk level weights of the digital signal stream. After standardizing each weight, perform a linear combination to calculate the final value of the curve coefficients. S323. Adjust the coordinates of the base point by modulo operation based on the prime number of the finite field, so that the curve meets the requirements of non-singularity and encryption strength, and output curve parameter values ​​that match the characteristics of digital signal flow.

4. The adaptive real-time digital signal encryption processing system according to claim 1, characterized in that, Specifically, S35 includes: S351. By allocating contiguous storage units in memory, the base point coordinates, curve coefficients, and finite field primes of the elliptic curve are written sequentially into the starting storage area in a fixed byte order. S352. Append the calculated key length in binary form to the curve parameters; S353. Append the key update cycle to the key length in milliseconds, and store the corresponding security risk level identifier and digital signal stream time window identifier in the control vector header. S354. Perform a checksum generation operation, perform XOR and accumulation operations on the data blocks of curve parameters, key length and key update period to generate a check value, and append the check value to the end of the vector. S355. Finally, a parameter control vector is formed, enabling unified management and serializable transmission.

5. The adaptive real-time digital signal encryption processing system according to claim 1, characterized in that, S4 specifically includes: S41. Divide the digital signal stream into segments according to a preset sliding time window. Each time window contains a fixed number of continuous data frames, and generate a unique identifier for each time window to index the corresponding data segment. S42. For each time window data segment, read the byte stream sequentially from the memory buffer, and perform byte-by-byte accumulation and non-linear permutation operations according to the preset hash algorithm. Perform XOR and shift operations on each byte with the hash state value of the previous byte, and iteratively calculate to generate a preliminary hash value. S43. Mix and map the initial hash value with the unique identifier of the current time window and the security risk level and curve parameter index in the parameter control vector, perform multiple rounds of nonlinear hashing operations, generate the final hash digest, and store the final hash digest in a time sequence array to form a hash digest sequence that corresponds one-to-one with each sliding time window. S44. In the hash digest sequence, a timestamp and a sliding window index are added to each hash digest to achieve a precise correspondence with the original digital signal stream, parameter control vector, and security risk level, providing an input mapping basis for real-time segmented encryption processing.

6. The adaptive real-time digital signal encryption processing system according to claim 1, characterized in that, S5 specifically includes: S51. Divide the digital signal stream into multiple segmented data units according to the sliding time window, and allocate a continuous storage buffer in memory for each segmented data unit to temporarily store the original data frame before encryption. S52. Read the coordinates of the elliptic curve base point, curve coefficients, finite field prime number, key length and key update period of the corresponding time window from the parameter control vector. Apply the key length to generate the elliptic curve public and private key pair for this time window. During the key generation process, the initial key seed is perturbed by a weighted random number according to the risk level to ensure the uniqueness and security of the key. S53. For each segmented data unit, perform byte-by-byte encryption operation according to the elliptic curve encryption algorithm, map the data value of each byte to the coordinates of a point on the elliptic curve, generate ciphertext coordinates through public key operation, and then store the ciphertext coordinate sequence in the encryption buffer according to the original data frame order to form an encrypted digital signal unit corresponding to the time window. S54. After encryption is completed, each encrypted digital signal unit is verified and mapped to the corresponding hash digest sequence to ensure that the encrypted data corresponds exactly to the hash digest sequence. Then, each encrypted digital signal unit is combined in the order of the time window to output an encrypted digital signal stream that is consistent with the hash digest sequence, thereby realizing real-time segmented encryption processing.

7. The adaptive real-time digital signal encryption processing system according to claim 1, characterized in that, S6 specifically includes: S61. The digital signal stream that has completed real-time segmented encryption is combined with the corresponding hash digest sequence and security risk level identifier in the order of time window to form encrypted data blocks. A timestamp, sliding window index and parameter control vector identifier are added to the header of each encrypted data block to uniquely identify the data block and its corresponding security attributes. S62. For encrypted data blocks, the timestamp, hash digest sequence, security risk level identifier and the block hash value of the previous block are sequentially input into the hash operation to generate a block-level hash value, and the block-level hash value is appended to the end of the data block to achieve a chain-like integrity connection. S63. Verify the generated blocks according to the blockchain consensus protocol, and perform distributed synchronization between nodes, including verifying the time order, hash consistency and risk level identifier mapping relationship. After consensus confirmation, write the blocks into the blockchain ledger and update the block index table to realize the verifiable storage record of each encrypted digital signal, its hash digest and security risk level.

8. The adaptive real-time digital signal encryption processing system according to claim 7, characterized in that, The process of generating the block-level hash value specifically includes: The header timestamp, sliding window index, hash digest sequence, security risk level identifier, and block hash value of the previous block of each encrypted data block are loaded into the memory buffer in sequence. The bytes are concatenated into a continuous byte stream in a fixed byte order, and the encrypted hash algorithm is iteratively operated on each segment of the byte stream. The current hash state value of each segment is updated through XOR, shift, non-linear permutation and accumulation operations. The final iteration generates a unique block-level hash value, which is then appended to the end of the data block. At the same time, a chain of reference pointers pointing to the hash value of the previous block is established in memory. This realizes the integrity verification of the blockchain data block and the logical association between the previous and subsequent blocks, providing basic data for distributed verification and ledger writing.

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