Big data security transmission method based on encryption algorithm

Abstract

This invention relates to the field of encryption algorithm optimization, specifically to a secure transmission method for big data customs and trade platforms based on encryption algorithms. The invention achieves technological breakthroughs through improved algorithm modules: first, an improved segmented chaotic dynamic key encryption module, which constructs a chaotic key iteration formula adapted to the hierarchical characteristics of customs and trade data, enabling differentiated high-speed encryption of customs and trade data at different security levels; second, an adaptive weighted hash chain verification and error correction module, which optimizes the traditional single hash verification mechanism by introducing transmission link state weight parameters to achieve accurate location and dynamic error correction of data tampering during cross-border transmission. This invention addresses the core pain points of existing technologies through these two modules, balancing the security, real-time performance, and traceability of big data transmission in customs and trade, and adapting to the secure transmission needs of big data in various scenarios such as customs declaration, cross-border logistics, and tariff verification.

Description

Technical Field

[0001] This invention relates to the field of encryption algorithm optimization, specifically to a secure transmission method for big data customs and trade platforms based on encryption algorithms. Background Technology

[0002] With the digital upgrade of cross-border trade, the big data customs and trade platform has realized the information transmission of data throughout the entire process, including customs declaration, commodity quarantine, tariff calculation, and cross-border logistics traceability. It processes massive amounts of heterogeneous customs and trade big data daily, covering various levels of confidentiality, including general public logistics information, classified customs and tariff information, and confidential quarantine information for high-risk commodities. Customs and trade data has industry-specific characteristics such as massive data volume, large differences in data confidentiality levels, unstable cross-border transmission links, and high risk of data tampering, which places stringent requirements on the security, real-time performance, and traceability of data transmission.

[0003] Existing big data customs and trade platforms generally employ common AES and RSA encryption algorithms and a single SHA-256 hash verification mechanism for secure transmission, which have industry-wide technical shortcomings in practical applications: 1. Poor encryption adaptability and redundant high-concurrency transmission latency: Traditional general-purpose encryption algorithms use a fixed-key iterative mode and are not designed to differentiate themselves for the multi-level encryption characteristics of customs and trade data. Using high-complexity encryption for ordinary logistics data leads to wasted computing power and increased transmission latency, while using low-complexity encryption for top-secret quarantine data presents security vulnerabilities. This makes it unsuitable for the high-concurrency transmission scenarios of massive heterogeneous data on customs and trade platforms. During peak periods, the data transmission latency exceeds the standard by more than 35%, seriously affecting customs declaration and verification efficiency.

[0004] 2. Low accuracy in tamper tracing and lack of proactive error correction: Traditional single hash verification mechanisms can only verify the overall data integrity and cannot accurately locate the location of local data tampering, packet loss, or distortion caused by fluctuations in cross-border links. During cross-border transmission, the incidence of local data anomalies is high due to interference from cross-border network routing and fluctuations in link latency. Traditional verification methods can only determine data anomalies and cannot achieve accurate tracing and dynamic error correction, requiring data to be retransmitted, which greatly wastes cross-border transmission bandwidth resources.

[0005] 3. Weak link adaptability: The existing encryption and verification mechanism is designed with fixed parameters and cannot dynamically adjust the verification strategy according to the real-time latency, packet loss and interference status of the cross-border transmission link. When the link is unstable, the success rate of secure data transmission drops significantly.

[0006] Currently, there is no customized encrypted transmission solution specifically designed for the customs and trade industry's multi-level encryption, high concurrency, and large link fluctuation scenarios. The fixed design of general algorithms is severely out of touch with the actual transmission needs of the customs and trade industry. Therefore, there is an urgent need to design a secure transmission method that adapts to the unique characteristics of customs and trade big data, possesses differentiated encryption, and has precise traceability and error correction capabilities to solve the aforementioned technical pain points in the industry. Summary of the Invention

[0007] The purpose of this invention is to provide a secure transmission method for big data customs and trade platforms based on encryption algorithms, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a secure transmission method for a big data customs and trade platform based on encryption algorithms, comprising the following steps: S1. Customs and Trade Big Data Classification and Preprocessing: Collect big data to be transmitted from the customs and trade platform, including customs declaration data, commodity quarantine data, tariff calculation data, and cross-border logistics trajectory data. Classify the data into three categories according to the confidentiality level: ordinary level, confidential level, and top secret level. Complete the data format normalization and segmentation preprocessing. S2. Through the improved segmented chaotic dynamic key encryption module, dynamic encryption keys are generated for customs and trade segmented data of different security levels to complete differentiated encryption processing; the improved segmented chaotic dynamic key encryption module is an original improved algorithm module used to solve the problems of poor adaptability and high latency of traditional fixed key encryption. S3. Encapsulate the encrypted customs and trade data into an encrypted transmission message and send it to the receiving end through a dedicated cross-border transmission link; S4. The adaptive weighted hash chain verification and error correction module performs integrity verification, tamper location and dynamic error correction on the received message; the adaptive weighted hash chain verification and error correction module is an improved algorithm module used to solve the problems of low accuracy of traditional hash verification and inability to adapt to cross-border link fluctuations. S5. After successful verification, the data is decrypted in layers to restore the complete customs and trade big data and complete secure transmission.

[0009] Preferably, the specific implementation steps of S2 are as follows: S21. Parameter Initialization and Chaotic Model Construction: Based on the data security standards of the customs and trade industry, preset the data security level coefficient, segment length correction coefficient, and chaotic iteration control parameters; initialize the initial value of the chaotic key. Fixed balance correction factor , To ensure the stability and orderliness of key iteration; a dedicated chaotic iteration formula is constructed: ;in, The values ​​are for the chaotic iteration control parameters. ; The confidentiality coefficient for customs and trade data corresponds to the three core data levels of customs and trade: 1 for general logistics public data, 2 for routine customs and tax data, and 3 for confidential commodity quarantine data. This is the data segment length coefficient; S22. Tiered Key Iterative Generation: Based on the security level of the preprocessed data, a differentiated key iteration strategy is executed: Ordinary level data has low security requirements, so a single chaotic iteration is used to generate the key; confidential level data uses a double superposition iteration; top secret level quarantine and core tariff data uses a triple nested iteration. S23. Segmented Parallel Differentiated Encryption: The pre-processed variable-length data is segmented and mapped one-to-one with the corresponding dynamic chaotic key of the security level. Parallel group encryption is used to encrypt each segment. At the same time, a boundary redundancy shielding mechanism is added to automatically remove invalid redundant data generated by segmented encryption.

[0010] Preferably, the specific implementation steps for the hierarchical key iteration generation in S22 are as follows: S22.1, Confidentiality Level Parameter Matching and Iteration Count Initialization: Based on the confidentiality level classification results of the preprocessed customs and trade data, bind the corresponding confidentiality level coefficients and iteration level thresholds: for ordinary-level cross-border logistics public data, match the confidentiality level coefficients. A single iteration mechanism is set; confidential-level routine customs and fee data are matched with the confidentiality level coefficient. A secondary overlay iteration mechanism is established; top-secret commodity quarantine data and core tariff confidential data are matched with a security classification coefficient. A three-nested iteration mechanism is set up; at the same time, the model's fixed parameters are retained: chaotic iteration control parameters. Balance correction coefficient , Substitute the length coefficient corresponding to each data segment. Complete the initialization of iterative operation parameters; S22.2, Iterative computation of hierarchical chaotic keys: (1) Single chaotic iteration of ordinary level data: with the initial chaotic key value Using the radix as the iteration base, substitute it into the iteration formula to complete a single operation and obtain the final encryption key. With a small amount of computation and low computing power consumption per iteration, it can minimize encryption latency for publicly available logistics data without confidentiality risks, adapt to high-concurrency transmission scenarios of massive amounts of ordinary data on the platform, and meet basic encryption protection requirements. (2) Confidential data double-overlay iteration: with initial value The intermediate key value is obtained after the first iteration. Then As a new iteration base, it is substituted into the chaotic iteration formula again for a second superposition operation, and the final encryption key is output. The superposition of two iterations can significantly improve the disorder and complexity of the key sequence, avoid the regularity defects of a single iteration key, adapt to the secure transmission requirements of routine classified data such as customs duties, and balance security and transmission efficiency; (3) Top Secret Data Three-Way Nested Iteration: The nested iterative operation logic is adopted, first through the initial value Iteratively obtained ,pass Iteratively obtain the second-level key Ultimately Complete the third nested iteration for the radix, and output a high-complexity encryption key. Three nested iterations can eliminate the periodicity of the key sequence, maximize the chaotic disorder of the key, effectively resist the risk of key cracking and data theft in cross-border open links, and adapt to the encryption needs of high-confidential customs and trade data such as commodity quarantine.

[0011] S22.3 Key Output and Computing Power Balance Verification: After completing the hierarchical iterative computation, the final key corresponding to different security levels of data is output. , , Validity checks are performed to remove invalid keys that exhibit iteration anomalies or out-of-bounds values. This step achieves dynamic adaptation through a differentiated iteration strategy that links security levels: low-security data reduces iteration computational power consumption and lowers high-concurrency transmission latency, while high-security data increases iteration levels and strengthens encryption security. This addresses the computational waste or security shortcomings caused by the "one-size-fits-all" approach of traditional fixed-iteration encryption modes, achieving a dynamic balance between security and real-time performance in customs and trade data transmission.

[0012] Preferably, the specific implementation steps of the segmented parallel differential encryption in S23 are as follows: S23.1 Based on the preprocessing results of step S1, the customs and trade data follows a negative correlation rule that the higher the security level, the shorter the segment length, and completes standardized segmentation: ordinary level logistics data. Segment length 1024 bytes, confidential level customs and fee data. Segment length 512B, Top Secret Quarantine Core Data The segment length is 256 bytes; each independent data segment corresponds to a unique segment length coefficient. Furthermore, it matches each of the unique chaotic keys generated in the previous step of hierarchical iteration to construct a precise mapping relationship. The mapping matching formula is as follows: ;in, For the first The encryption key corresponding to each data segment; , , These are the chaotic keys generated by a single iteration, a double superimposed iteration, and a triple nested iteration, respectively. S23.2 Multi-threaded parallel block differential encryption operation: A multi-threaded parallel encryption architecture is adopted, and encryption thread resources are dynamically allocated according to the number of data security level segments. Block encryption operation is performed synchronously on all data segments that have completed key mapping. Differentiated encryption strength matching is implemented for different security levels: ordinary data segments use low-computing-power single-key encryption to prioritize encryption speed; confidential and top-secret segments use high-complexity multi-layer iterative key encryption to prioritize transmission security; parallel computing mode processes massive heterogeneous customs and trade data segments simultaneously, solving the latency accumulation problem of traditional serial encryption in high-concurrency scenarios, and adapting to the business needs of the customs and trade platform for massive daily data transmission. S23.3 Intelligent Masking and Cleaning of Redundant Data at Segment Boundaries: Identifying invalid redundant data using a redundancy discrimination formula. ;in, This is a set of redundant data at the segment boundaries; , The data consists of redundant bits at the beginning and end of segments; the proportion of redundant data in a single segment is limited to no more than 8 bits to avoid accidental deletion of valid data.

[0013] Preferably, the specific implementation steps of S4 are as follows: S41. Real-time weight calculation of cross-border link status: Establish a cross-border transmission link status monitoring unit to collect three types of parameters in real time: latency fluctuation, packet loss rate, and signal interference intensity. After normalizing the parameters, calculate the comprehensive stability weight value of the link using a weighting formula. ;in This is the normalized value for link latency fluctuation, and its value is [value to be filled in]. The greater the fluctuation, the higher the value; This is the normalized value of the link packet loss rate; This is the normalized value of the link interference strength; , , This is a priority weighting coefficient, configured based on the industry characteristics that are most sensitive to latency in cross-border trade and customs transmission, prioritizing the verification efficiency of scenarios with stable latency. The larger the value, the stronger the link fluctuation and the worse the transmission stability; S42. Adaptive hash segmentation granularity adjustment: based on the link comprehensive weight value. Dynamically adjust hash check segment length: when The link is identified as experiencing high fluctuations. A 128B short segment check is used to improve the accuracy of local anomaly location. The condition is determined to be a normal fluctuation state of the link, and a 256B medium segmentation verification is used to balance accuracy and efficiency; when The link is determined to be in a stable state, and a 512B long segment verification is adopted to reduce the computational power consumption of verification. Based on the adjusted segment granularity, a continuous chained hash verification sequence is generated to replace the traditional overall hash verification mode. S43. Precise Tampering Location and Dynamic Error Correction: After receiving the encrypted message, the receiving end synchronously generates a local hash verification sequence and compares it segment by segment with the sender's baseline chain sequence. If the hash value of a certain segment does not match, the segment is accurately located as abnormally tampered / lost data, without the need for overall retransmission. Based on the adjacent normal segments of the abnormal segment, the data is dynamically corrected through an interpolation repair algorithm to restore the original information of the abnormal data, achieving "local anomaly, local repair", and completely solving the problem that traditional verification can only make overall judgments and cannot accurately repair.

[0014] Preferably, the specific implementation steps of the precise tamper location and dynamic error correction in S43 are as follows: S43.1, During the encrypted message encapsulation stage, the sending end determines the weight based on the real-time link weight. At the corresponding segmentation granularity, a baseline hash value is generated for each data segment, and these segments are concatenated in the data transmission order to form a baseline chained hash sequence at the sending end, which is synchronously transmitted to the receiving end along with the encrypted message. After receiving the encrypted message, the receiving end completely replicates the segmentation granularity rules of the sending end, performs hash operations on each segment of the received encrypted data, and generates a local chained hash sequence at the receiving end. Then, a segment-by-segment mapping precise comparison mechanism is adopted, using the adaptively split data segments as the smallest verification unit, and matching the hash feature values ​​of both the sending and receiving ends segment by segment. The comparison granularity and the adaptive segmentation rules of the link weight are unified: for high-fluctuation links... Using 128B as the smallest comparison unit, conventional fluctuating links Using 256 bytes as the smallest comparison unit, a stable link Using 512B as the smallest comparison unit, this solves the technical defect of traditional overall verification that cannot distinguish local anomalies; S43.2, Precise location and judgment of segmented abnormal data: During the segment comparison process, if the local hash value of a certain segment is completely consistent with the base hash value, the segment data is determined to be complete and untampered; if the hash feature value of a single segment does not match, the independent data segment is immediately and accurately locked as an abnormal object, and is determined to be a local data tampering, bit loss or transmission distortion caused by cross-border link interference. At the same time, the location number, segment granularity and corresponding data security level parameters of the abnormal segment are recorded. S43.3, Adaptive interpolation dynamic error correction based on neighborhood normal segmentation: For precisely located abnormal data segments, this invention adapts a neighborhood-weighted interpolation repair algorithm for customs and trade structured data. By using adjacent, valid segments that pass hash verification before and after the abnormal segment, it achieves lossless restoration and repair of the abnormal data. The error correction formula is as follows: ;in, The feature values ​​of the restored data for the abnormal segments to be repaired; The effective feature value for segmenting adjacent normal data before an abnormal segment; Effective feature values ​​for segmenting adjacent normal data after an abnormal segmentation; , The preset neighboring weights satisfy the constraints. ; S43.4 Secondary Verification Confirmation after Error Correction: After completing the repair of abnormal segmented data, the repaired complete data segments are re-hashed and compared with the baseline hash value of the sending end for the second time until the segment verification matches, confirming that the data repair is effective, without distortion or deviation, and finally outputting a complete, continuous, and secure encrypted customs and trade data message.

[0015] This step, through an integrated mechanism of chain-by-segment comparison, precise segment positioning, and neighborhood interpolation error correction, perfectly adapts to the aforementioned link weight adaptive adjustment logic. It solves the industry pain points of low accuracy in traditional single hash verification for tracing, inability to dynamically correct errors, and waste of overall retransmission resources, and significantly improves the transmission integrity and fault tolerance of customs and trade big data under unstable cross-border links.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention utilizes an improved segmented chaotic dynamic key encryption module to construct a chaotic iterative model that integrates data security level and segment length. This enables differentiated high-speed encryption of customs and trade data with different security levels, addressing the problems of wasted computing power and insufficient security adaptability in traditional encryption methods. An adaptive weighted hash chain verification and error correction module introduces cross-border link state weight parameters to dynamically adjust the granularity of hash verification segments, achieving precise segment-level location and dynamic error correction of data tampering. This solves the problems of vague traceability and high retransmission rates in traditional verification methods. While ensuring the security of customs and trade big data transmission, this invention significantly improves transmission efficiency and link adaptability, making it suitable for various customs big data cross-border security transmission scenarios, demonstrating good practicality and inventiveness. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall method flow of the present invention; Figure 2 This is a schematic diagram of the workflow of step S2 of the present invention; Figure 3 This is a schematic diagram of the workflow of step S4 of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1-3 This invention provides a technical solution: a secure transmission method for a big data customs and trade platform based on encryption algorithms, comprising the following steps: S1. Customs and Trade Big Data Tiered Preprocessing: Collect big data to be transmitted from the customs and trade platform, including customs declaration data, commodity quarantine data, tariff calculation data, and cross-border logistics trajectory data. Classify the data into three categories based on their confidentiality level: ordinary, confidential, and top secret. Complete data format normalization and segmentation preprocessing. Specific implementation steps are as follows: S11. Comprehensive Customs and Trade Data Collection and Precise Classification of Security Levels: This involves batch collection of comprehensive business data from the big data customs and trade platform awaiting cross-border transmission, specifically covering four major categories: cross-border logistics tracking data, routine customs declaration data, tariff calculation details, and import / export commodity quarantine filing data. Based on customs' classified data grading standards, the security level coefficient of subsequent encryption modules is matched. The parameter system enables precise three-level security classification and establishes a unique binding relationship between data types and security level parameters. Ordinary level data ( Publicly available cross-border logistics trajectory data and port clearance data that do not involve classified information; Confidential data ( ): Data for routine customs declaration forms and detailed data for general commodity tariff calculation; Top Secret Data ( ): Quarantine and inspection data for high-risk import and export commodities, and confidential data on core tariff verification.

[0020] This hierarchical rule is fully aligned with the subsequent chaos iteration formula. The density coefficient in This provides a preliminary quantitative basis for differentiated key iteration and hierarchical encryption.

[0021] S12. Cross-border Transmission Noise Data Cleaning: For noise data generated during cross-border network transmission, such as garbled characters, null values, duplicate redundancy, and incomplete messages, an adaptive window filtering algorithm is used to clean and purify the data, removing invalid and abnormal data. An adaptive filtering window threshold is set to filter out abnormal values ​​that deviate from the normal threshold range for customs and trade business data, retaining structured and valid business data. This avoids noise data interfering with subsequent encryption operations and hash verification accuracy, ensuring the purity and validity of the data source.

[0022] S13. Unified processing of global data format normalization: The cleaned heterogeneous customs and trade data is standardized and adapted, and the encoding format of all types of data is unified to UTF-8. The data field length, data storage format, and time sequence arrangement rules are synchronized and standardized to eliminate the problems of encryption segment misalignment and hash comparison failure caused by the differences in data formats of different business modules. This achieves the uniformity of the format of all data to be transmitted and adapts to the batch operation requirements of subsequent parallel encryption and chain verification.

[0023] S14, Confidentiality-Adaptive Variable-Length Adaptive Segmentation: Based on the core scenario characteristics of this invention, a negative correlation segmentation rule is established where higher data security level corresponds to shorter segment length. The matching relationship between segment length and security level coefficient is quantified, and the segment length formula is as follows: ;in, The final data segment length is determined by bytes. The maximum reference segment length; This is the adjustment coefficient for the security level segmentation; This is the data security level coefficient. Three types of standardized segment lengths are accurately generated based on the formula: Ordinary level data ( ): Large segment lengths are suitable for high-speed transmission of massive amounts of publicly available data; Confidential data ( ): The medium-segmentation design balances safety and efficiency. Top Secret Data ( ): Fine-grained segmentation improves the encryption accuracy and anomaly location accuracy of highly classified data.

[0024] Simultaneously, a segment length coefficient is generated based on the segment length. The longer the segment length, The larger the value, the more directly it is substituted into the aforementioned chaotic iterative formula to participate in key operations, thereby achieving deep binding between segmented features and key parameters.

[0025] S15. Preprocessed data archiving and solidification: Standardized data that has undergone grading, cleaning, normalization, and segmentation is archived uniformly, and each segment of data is labeled with a security classification tag. Segment length parameters This provides complete and unified pre-processing data support for key mapping, parallel encryption, and adaptive hash segmentation granularity matching in subsequent improved segmented chaotic dynamic key encryption modules.

[0026] S2. Using an improved segmented chaotic dynamic key encryption module, dynamic encryption keys are generated for customs and trade segmented data of different security levels to complete differentiated encryption processing. The improved segmented chaotic dynamic key encryption module is an original improved algorithm module used to solve the problems of poor adaptability and high latency of traditional fixed key encryption. The specific implementation steps are as follows: S21. Parameter Initialization and Chaotic Model Construction: Based on the data security standards of the customs and trade industry, preset the data security level coefficient, segment length correction coefficient, and chaotic iteration control parameters; initialize the initial value of the chaotic key. Fixed balance correction factor , To ensure the stability and orderliness of key iteration; a dedicated chaotic iteration formula is constructed: ;in, The value range is 3.95 to 4.0, which is the control parameter for chaotic iteration. This range ensures that the key sequence is in a completely chaotic state with no periodic repetition, thus eliminating the risk of the key being cracked. The confidentiality coefficient for customs and trade data corresponds to the three core data levels of customs and trade: 1 for general logistics public data, 2 for routine customs and tax data, and 3 for confidential commodity quarantine data. This is a coefficient for the length of data segments. The longer the segment data is in bytes, the larger the coefficient value becomes. It is used to balance the key complexity of encrypting long segment data. S22. Tiered Key Iterative Generation: Based on the security level of the preprocessed data, a differentiated key iteration strategy is executed: For ordinary-level data with low security requirements, a single chaotic iteration is used to generate the key, reducing computational power consumption and transmission latency; for confidential-level data, a two-stage superimposed iteration is used to increase key complexity and meet the security requirements of routine classified data; for top-secret quarantine and core customs data, a three-stage nested iteration is used to maximize key disorder and eliminate the risk of key cracking in cross-border links. This iterative mode achieves a dynamic balance between security level and computational power consumption; the specific implementation steps are as follows: S22.1, Confidentiality Level Parameter Matching and Iteration Count Initialization: Based on the confidentiality level classification results of the preprocessed customs and trade data, bind the corresponding confidentiality level coefficients and iteration level thresholds: for ordinary-level cross-border logistics public data, match the confidentiality level coefficients. A single iteration mechanism is set; confidential-level routine customs and fee data are matched with the confidentiality level coefficient. A secondary overlay iteration mechanism is established; top-secret commodity quarantine data and core tariff confidential data are matched with a security classification coefficient. A three-nested iteration mechanism is set up; at the same time, the model's fixed parameters are retained: chaotic iteration control parameters. Balance correction coefficient , Substitute the length coefficient corresponding to each data segment. Complete the initialization of iterative operation parameters; S22.2, Iterative computation of hierarchical chaotic keys: (1) Single chaotic iteration of ordinary level data: with the initial chaotic key value Using the radix as the iteration base, substitute it into the iteration formula to complete a single operation and obtain the final encryption key. With a small amount of computation and low computing power consumption per iteration, it can minimize encryption latency for publicly available logistics data without confidentiality risks, adapt to high-concurrency transmission scenarios of massive amounts of ordinary data on the platform, and meet basic encryption protection requirements. (2) Confidential data double-overlay iteration: with initial value The intermediate key value is obtained after the first iteration. Then As a new iteration base, it is substituted into the chaotic iteration formula again for a second superposition operation, and the final encryption key is output. The superposition of two iterations can significantly improve the disorder and complexity of the key sequence, avoid the regularity defects of a single iteration key, adapt to the secure transmission requirements of routine classified data such as customs duties, and balance security and transmission efficiency; (3) Top Secret Data Three-Way Nested Iteration: The nested iterative operation logic is adopted, first through the initial value Iteratively obtained ,pass Iteratively obtain the second-level key Ultimately Complete the third nested iteration for the radix, and output a high-complexity encryption key. Three nested iterations can eliminate the periodicity of the key sequence, maximize the chaotic disorder of the key, effectively resist the risk of key cracking and data theft in cross-border open links, and adapt to the encryption needs of high-confidential customs and trade data such as commodity quarantine.

[0027] S22.3 Key Output and Computing Power Balance Verification: After completing the hierarchical iterative computation, the final key corresponding to different security levels of data is output. , , Validity checks are performed to remove invalid keys that exhibit iteration anomalies or exceed numerical limits. This step achieves dynamic adaptation through a differentiated iteration strategy that links security levels: low-security data reduces iteration computational power consumption and lowers high-concurrency transmission latency, while high-security data increases iteration levels and strengthens encryption security. This addresses the computational waste or security shortcomings caused by the "one-size-fits-all" approach of traditional fixed-iteration encryption modes, achieving a dynamic balance between security and real-time performance in customs and trade data transmission. S23. Segmented Parallel Differentiated Encryption: The pre-processed variable-length data is segmented and mapped one-to-one with the corresponding dynamic chaotic key of the security level. Parallel block encryption is used to encrypt each segment sequentially. Simultaneously, a boundary redundancy shielding mechanism is added to automatically remove invalid redundant data generated during segmented encryption, avoiding bandwidth waste caused by redundant data and further improving encryption transmission efficiency in high-concurrency scenarios. The specific implementation steps are as follows: S23.1 Based on the preprocessing results of step S1, the customs and trade data follows a negative correlation rule that the higher the security level, the shorter the segment length, and completes standardized segmentation: ordinary level logistics data. Segment length 1024 bytes, confidential level customs and fee data. Segment length 512B, Top Secret Quarantine Core Data The segment length is 256 bytes; each independent data segment corresponds to a unique segment length coefficient. Furthermore, it matches each of the unique chaotic keys generated in the previous step of hierarchical iteration to construct a precise mapping relationship. The mapping matching formula is as follows: ;in, For the first The encryption key corresponding to each data segment; , , The chaotic keys are generated by single iteration, double superposition iteration, and triple nested iteration, respectively; the specific implementation details are as follows: S23.1.1 Binding of encryption strength level parameters: Combined with data density coefficient Segment length coefficient With hierarchical chaotic key , , A matching mechanism for encryption strength levels is established, where encryption strength is positively correlated with key iteration levels and security level coefficients, and fixed parameters of the model are used uniformly. , , The specific strength binding rules are as follows: ;in, This is the segmented encryption strength coefficient; the larger the value, the higher the encryption security level and the higher the computing power consumption. The balance coefficient is fixed and is set to 0.25. This is the data security level coefficient; This represents the number of iterations for the chaotic key. This formula quantifies and defines the encryption strength of different segments, achieving a precise match between encryption strength and data confidentiality level.

[0028] For general logistics data Low-complexity keys generated in a single iteration Encryption, encryption strength coefficient It minimizes computational overhead and, while meeting the basic security requirements of preventing tampering and data scraping of publicly available data, maximizes the compression of encryption computation time to ensure the rapid transmission of massive amounts of low-secret data.

[0029] For confidential reporting of customs and fees data Using a double-overlay iterative key Encryption, medium encryption strength Appropriately increase the disorder of keys and encryption complexity to resist the risks of routine data sniffing and tampering in cross-border links, while taking into account both the security of classified data and the efficiency of batch transmission.

[0030] For top-secret commodity quarantine and core tariff data Employing a three-nested iterative high-chaos key Encryption, encryption strength coefficient By maximizing the use of the non-periodic key characteristics of multi-layered nested iteration, the risks of key cracking, data theft, and precise tampering in cross-border public links are avoided, ensuring the absolute transmission security of highly confidential customs and trade data.

[0031] S23.1.2 Multi-threaded Dynamic Computing Power Scheduling Parallel Encryption: This invention employs a dynamic thread pool scheduling mechanism, adaptively allocating parallel computing thread resources based on the total amount of data segments to be encrypted in real time and the proportion of segments with different security levels. All 1024B ordinary level, 512B confidential level, and 256B top-secret level data segments do not need to be processed in sequence; they can be encrypted synchronously based on independent threads. During the operation, each segment independently calls its corresponding exclusive key and encryption strength parameters. Data between threads does not interfere with each other, and parameters do not conflict with each other, completely eliminating the latency defects of "single-segment blocking and full-data accumulation" in the serial encryption mode.

[0032] S23.1.3, Latency Optimization and Adaptation for High-Concurrency Scenarios: The total encryption delay of traditional serial encryption satisfies the summation formula. , For single-segment encryption latency, the amount of data segments... The larger the value, the linearly increasing the total latency becomes, making it completely unsuitable for the massive data transmission demands of the customs and trade platform during peak hours. The parallel encryption mode of this invention has a total latency that depends only on the maximum segment encryption time per batch, satisfying... This significantly reduces encryption latency buildup in high-concurrency scenarios.

[0033] By combining differentiated encryption strength matching with multi-threaded parallel computing, the problem of "redundant computing power for low-level encryption and lack of security for high-level encryption" in traditional general encryption is avoided, and the industry pain point of excessive latency in high-concurrency serial encryption is solved. It perfectly meets the high-frequency, high-speed, and secure transmission business needs of tens of thousands of heterogeneous data per day on the customs and trade platform.

[0034] S23.2 Multi-threaded parallel block differential encryption operation: A multi-threaded parallel encryption architecture is adopted, and encryption thread resources are dynamically allocated according to the number of data security level segments. Block encryption operation is performed synchronously on all data segments that have completed key mapping. Differentiated encryption strength matching is implemented for different security levels: ordinary data segments use low-computing-power single-key encryption to prioritize encryption speed; confidential and top-secret segments use high-complexity multi-layer iterative key encryption to prioritize transmission security; parallel computing mode processes massive heterogeneous customs and trade data segments simultaneously, solving the latency accumulation problem of traditional serial encryption in high-concurrency scenarios, and adapting to the business needs of the customs and trade platform for massive daily data transmission. S23.3 Intelligent Shielding and Cleaning of Redundant Data at Segment Boundaries: Addressing the inherent defects of segmented encryption, this invention adds a unique boundary redundancy shielding mechanism. During the data segmentation and encryption splicing process, a small amount of invalid padding bits and iterative residual redundant data will be generated at the beginning and end of each segment. This data contains no valid information and will additionally occupy cross-border transmission bandwidth and increase transmission latency. This step identifies invalid redundant data using a redundancy discrimination formula: ;in, This is a set of redundant data at the segment boundaries; , The data consists of redundant bits at the beginning and end of segments; the proportion of redundant data in a single segment is limited to no more than 8 bits to avoid accidental deletion of valid data.

[0035] Before encryption is completed and message encapsulation is performed, the boundary areas of all encrypted segments are automatically traversed, all invalid and redundant data are removed, and only valid encrypted data segments are retained for subsequent message encapsulation and transmission. This eliminates the waste of bandwidth resources caused by segmented encryption, further optimizes the encryption transmission efficiency in high-concurrency scenarios, and realizes integrated encryption processing of "hierarchical encryption, parallel operation, and redundancy clearing".

[0036] S3. Encapsulate the encrypted customs and trade data into an encrypted transmission message and send it to the receiving end via a dedicated cross-border transmission link; the specific implementation steps are as follows: S31. Encrypted Valid Data Consolidation and Verification: After encryption and redundancy masking cleaning by the S2 module, the entire segmented encrypted data is consolidated and verified. Invalid bits remaining at boundaries are filtered out, retaining only valid encrypted data segments. The data is matched to the three standard segment specifications formed in the S1 preprocessing stage: 1024B for ordinary level data, 512B for confidential level data, and 256B for top-secret level data, ensuring that all data segments to be encapsulated have uniform specifications and are free from errors or offsets. Simultaneously, the encryption key level corresponding to each segment is verified, and the security level coefficient is matched. Confirm that ordinary, confidential, and top-secret data correspond to the single-iteration keys respectively. Second iteration key Three-stage iteration key Encryption is used to prevent mismatches between encryption levels and data security levels.

[0037] S32, Standardized Encapsulation of Customs and Trade Tiered Encrypted Messages: Given the confidential nature of customs and trade data, a layered, independent encapsulation mechanism combined with overall packaging is adopted, abandoning the traditional unified encapsulation model. Sub-messages are independently encapsulated for data segments of different security levels, and then integrated into a complete transmission message. The encapsulation process embeds end-to-end related parameters, providing a basis for adaptive verification and layered decryption at the receiving end. The encapsulation parameter binding formula is as follows: ;in, It is a single-segment encrypted sub-message; Segment the cleaned and effectively encrypted data; This is the data security level coefficient; Divide the data into segments of byte length; The real-time link comprehensive weight value is based on the S41 link status calculation result; The adaptive hash verification segmentation granularity for the current link is matched with the 128B, 256B, and 512B verification specifications corresponding to the $$W$$ threshold range.

[0038] All sub-messages carrying parameter tags are concatenated according to their original transmission sequence to generate a complete encrypted transmission message. The message is stored in partitions according to security level to achieve physical isolation of different classified data and avoid the security risks caused by the mixing of high and low security level data.

[0039] S33, Cross-border transmission dynamic priority scheduling: To address the business requirement of prioritizing the transmission of classified data on the customs and trade platform, a priority mechanism for classified data transmission is established, with the priority quantification formula as follows: ;in, This indicates transmission priority; higher values ​​indicate higher priority. Top Secret Data Highest priority, prioritizes cross-border link bandwidth; confidential data. Secondly; general-level logistics data Lowest priority. In scenarios with limited link bandwidth and high congestion, it automatically prioritizes the transmission of highly confidential customs and trade data to ensure the timeliness of transmission of high-value data such as quarantine and core tariffs, and adapts to the urgent transmission needs of core customs business.

[0040] S34. Adaptive Transmission via Dedicated Cross-Border Link: This invention employs a customs-dedicated, isolated cross-border transmission link, distinct from public network transmission, thus avoiding risks such as public network routing jumps, malicious sniffing, and link hijacking. This is combined with real-time link weight values. Adaptive adjustment of message transmission rate and packet interval: when When the link experiences high fluctuations, reduce the size of each packet and increase the packet interval to avoid packet loss and distortion. During normal fluctuations, a standard balanced packet sending rate is used; when When the link is stable, increase the number of concurrent packets to maximize transmission efficiency.

[0041] S35. Timing synchronization and base hash sequence are sent in conjunction: During message transmission, the baseline chained hash sequence of the sending end is synchronously bundled with the encrypted message for transmission, ensuring that the timing and segmentation granularity of the message data at the receiving end are completely aligned with the hash verification sequence. This completely avoids the verification failure problem caused by encapsulation and transmission misalignment, and provides complete matching baseline data support for the S4 module's segmented hash comparison, accurate tamper location and dynamic error correction, realizing a closed loop of parameter linkage throughout the encryption, transmission and verification process.

[0042] S4. The received message is subjected to integrity verification, tamper location, and dynamic error correction through an adaptive weighted hash chain verification and error correction module. The adaptive weighted hash chain verification and error correction module is used to solve the problems of low accuracy in traditional hash verification and inability to adapt to cross-border link fluctuations. The specific implementation steps are as follows: S41. Real-time weight calculation of cross-border link status: Establish a cross-border transmission link status monitoring unit to collect three types of parameters in real time: latency fluctuation, packet loss rate, and signal interference intensity. After normalizing the parameters, calculate the comprehensive stability weight value of the link using a weighting formula. ;in This is the normalized value for link latency fluctuation, and its value is [value to be filled in]. The greater the fluctuation, the higher the value; This is the normalized value of the link packet loss rate; This is the normalized value of the link interference strength; , , This is a priority weighting coefficient, configured based on the industry characteristics that are most sensitive to latency in cross-border trade and customs transmission, prioritizing the verification efficiency of scenarios with stable latency. The larger the value, the stronger the link fluctuation and the worse the transmission stability; S42. Adaptive hash segmentation granularity adjustment: based on the link comprehensive weight value. Dynamically adjust hash check segment length: when The link is identified as experiencing high fluctuations. A 128B short segment check is used to improve the accuracy of local anomaly location. The condition is determined to be a normal fluctuation state of the link, and a 256B medium segmentation verification is used to balance accuracy and efficiency; when The link is determined to be in a stable state, and a 512B long segment verification is adopted to reduce the computational cost of verification. Based on the adjusted segment granularity, a continuous chained hash verification sequence is generated to replace the traditional overall hash verification mode; the specific implementation logic is as follows: S42.1 Scenario-based calibration of link weight threshold: Based on massive amounts of measured data from customs cross-border transmission links, combined with the link weight parameter system of this invention ( , , Two core dividing thresholds of 0.3 and 0.6 are set to classify the link status into three levels: stable, normal fluctuation, and high fluctuation. The thresholds are fully adapted to the weight characteristics of the customs and trade link, which prioritizes latency, takes packet loss as a secondary factor, and compensates for interference. This enables accurate classification and determination of the link status and provides a quantitative basis for granular adaptive adjustment.

[0043] S42.2, Precise matching of link status classification and verification granularity: Based on the real-time calculated comprehensive link weight value The range is determined by differential matching of the corresponding hash verification segment byte granularity, while also adapting to the 1024B, 512B, and 256B data segmentation characteristics of the encryption module mentioned above, ensuring a high degree of compatibility between the encryption segment and the verification segment. The specific matching rules are as follows: (1) High volatility link granularity matching: when the following conditions are met When significant latency fluctuations, packet loss, or signal interference are detected in cross-border links, the link transmission stability is extremely poor. In this case, a 128-bit ultra-short segmented verification granularity is adopted. By reducing the size of the verification unit, the accuracy of identifying local bit tampering and minor packet loss is significantly improved. This solves the problem of subtle data anomalies going undetected under unstable links, accurately meeting the stringent verification requirements of highly confidential data such as top-secret quarantine data and core tariff data.

[0044] (2) Regular fluctuation link granularity matching: when the following conditions are met At this point, the link is determined to have slight fluctuations but no serious transmission failure, which is the most common state for cross-border customs and trade data transmission. In this case, a medium segmented verification granularity of 256B is adopted, which balances verification accuracy and computing power consumption. It can accurately identify most local transmission anomalies while avoiding the computing power redundancy caused by short segmented verification, and is suitable for the routine verification scenarios of confidential customs and tariff data.

[0045] (3) Stable link granularity matching: when the following conditions are met At this point, the link latency, packet loss, and interference parameters are all within the normal range, indicating a stable and reliable transmission status. A 512B long segment verification granularity is then employed. By expanding the verification range of a single segment and reducing the overall number of verification segments, the number of hash operations and computational overhead are effectively reduced. This approach is suitable for high-efficiency batch verification scenarios involving massive amounts of ordinary cross-border logistics data, solving the resource waste problem of traditional fine-grained verification under stable link conditions.

[0046] S42.3, Segmented Granularity Normalization and Alignment Processing: After completing the granularity matching, all encrypted data to be verified is adaptively and uniformly segmented to ensure that the boundaries of the verification segments are aligned with the boundaries of the preceding encrypted segments, thus avoiding hash matching failures caused by segment misalignment. At the same time, the hash operation baseline parameters of each segment are unified to ensure that the hash feature values ​​of segments with different granularities have a unified comparison standard, eliminating verification deviations caused by granularity differences.

[0047] S42.4, Dynamic Chained Hash Verification Sequence Generation: Following the chronological order of cross-border data transmission, hash operations are performed on each segment of the adaptively split data. The hash feature values ​​of all segments are then concatenated to construct a segmented chained hash verification sequence. This sequence abandons the traditional single-value verification mode, using adaptive granularity as the smallest independent verification unit. Each segment hash value independently corresponds to a segment of encrypted customs and trade data, providing core data support for subsequent segment-level precise tampering location and local dynamic error correction. This mechanism addresses the technical deficiency of traditional overall hash verification, which can only make global judgments and cannot trace local sources.

[0048] S43. Precise Tamper Location and Dynamic Error Correction: After receiving an encrypted message, the receiving end synchronously generates a local hash verification sequence and compares it segment by segment with the sender's baseline chained sequence. If the hash value of a segment does not match, the segment is precisely located as abnormally tampered / lost data, without the need for overall retransmission. Based on the adjacent normal segments of the abnormal segment, an interpolation repair algorithm is used to dynamically correct the data, restoring the original information of the abnormal data and achieving "local anomaly, local repair," completely solving the problem that traditional verification can only make overall judgments and cannot accurately repair data. The specific implementation steps are as follows: S43.1, During the encrypted message encapsulation stage, the sending end determines the weight based on the real-time link weight. At the corresponding segmentation granularity, a baseline hash value is generated for each data segment, and these segments are concatenated in the data transmission order to form a baseline chained hash sequence at the sending end, which is synchronously transmitted to the receiving end along with the encrypted message. After receiving the encrypted message, the receiving end completely replicates the segmentation granularity rules of the sending end, performs hash operations on each segment of the received encrypted data, and generates a local chained hash sequence at the receiving end. Then, a segment-by-segment mapping precise comparison mechanism is adopted, using the adaptively split data segments as the smallest verification unit, and matching the hash feature values ​​of both the sending and receiving ends segment by segment. The comparison granularity and the adaptive segmentation rules of the link weight are unified: for high-fluctuation links... Using 128B as the smallest comparison unit, conventional fluctuating links Using 256 bytes as the smallest comparison unit, a stable link Using 512B as the smallest comparison unit, this solves the technical defect of traditional overall verification that cannot distinguish local anomalies; S43.2, Precise location and judgment of segmented abnormal data: During the segment comparison process, if the local hash value of a certain segment is completely consistent with the base hash value, the segment data is determined to be complete and untampered; if the hash feature value of a single segment does not match, the independent data segment is immediately and accurately locked as an abnormal object, and is determined to be a local data tampering, bit loss or transmission distortion caused by cross-border link interference. At the same time, the location number, segment granularity and corresponding data security level parameters of the abnormal segment are recorded. This positioning mechanism is based on adaptive segmentation. It only locks down one or more abnormal segments without determining the overall data failure. It avoids the drawback of the traditional verification mechanism of "single segment abnormality, overall invalidation" and does not require retransmission of the entire message. It reduces cross-border transmission bandwidth loss and retransmission latency from the source.

[0049] S43.3, Adaptive interpolation dynamic error correction based on neighborhood normal segmentation: For precisely located abnormal data segments, this invention adapts a neighborhood-weighted interpolation repair algorithm for customs and trade structured data. By using adjacent, valid segments that pass hash verification before and after the abnormal segment, it achieves lossless restoration and repair of the abnormal data. The error correction formula is as follows: ;in, The feature values ​​of the restored data for the abnormal segments to be repaired; The effective feature value for segmenting adjacent normal data before an abnormal segment; Effective feature values ​​for segmenting adjacent normal data after an abnormal segmentation; , The preset neighboring weights satisfy the constraints. ; The coefficient values ​​are adapted to the link weight scenario of this invention: link fluctuation weight. The larger the value, the stronger the correlation between the preceding and following links. In this embodiment, we take... Balanced weighted repair: When the link status is stable, the coefficients are fine-tuned according to the time sequence characteristics of the customs and trade data, giving priority to matching the characteristics of the previous stable transmission data, so as to ensure that the repaired data matches the original customs and trade data patterns.

[0050] Simultaneously, by leveraging the structured characteristics of customs and trade data (customs declaration parameters, quarantine values, and logistics trajectories are all continuous structured data), interpolation algorithms are used to fit the missing or tampered bit information of abnormal segments, accurately restoring the original data content of the segments and achieving dynamic lossless error correction of local abnormal data.

[0051] S43.4 Secondary Verification Confirmation after Error Correction: After completing the repair of abnormal segmented data, the repaired complete data segments are re-hashed and compared with the baseline hash value of the sending end for the second time until the segment verification matches, confirming that the data repair is effective, without distortion or deviation, and finally outputting a complete, continuous, and secure encrypted customs and trade data message.

[0052] This step, through an integrated mechanism of chain-by-segment comparison, precise segment positioning, and neighborhood interpolation error correction, perfectly adapts to the aforementioned link weight adaptive adjustment logic. It solves the industry pain points of low accuracy in traditional single hash verification for tracing, inability to dynamically correct errors, and waste of overall retransmission resources, and significantly improves the transmission integrity and fault tolerance of customs and trade big data under unstable cross-border links.

[0053] After receiving the encrypted message, the receiving end synchronously generates a local hash verification sequence and compares it segment by segment with the sender's baseline chained sequence. If the hash value of a segment does not match, the segment is accurately located as abnormally tampered / lost data, eliminating the need for overall retransmission. Based on the adjacent normal segments of the abnormal segment, an interpolation repair algorithm is used to dynamically correct the data and restore the original information of the abnormal data.

[0054] S5. After successful verification, the data is decrypted in layers to restore the complete customs and trade big data, completing secure transmission. The specific implementation steps are as follows: S51. Decryption Pre-parameter Parsing and Initialization: The receiving end reads the verified standardized encrypted message, parses the bound parameters built into the message, initializes the decryption parameters, and accurately matches all core parameters from the encryption phase to ensure complete alignment between the decryption and encryption logic. Key parsed parameters include: data security level coefficient. Data segment length Segment length coefficient Chaotic Iteration Control Parameters Balance correction coefficient , The encrypted segments are further classified based on parameter labels to distinguish between ordinary, confidential, and top-secret encrypted data segments, providing a quantitative basis for layered and differentiated decryption.

[0055] S52. Hierarchical Chaotic Key Reverse Restoration: The encryption process of this invention adopts a forward generation logic with higher security levels, more iteration levels, and higher key complexity. The decryption process strictly follows the principle of reverse iteration matching at the same level. For the exclusive encryption keys of different security levels, the original key parameters are reverse restored. The key matching rules are as follows: ;in A dedicated key is used for layered decryption; The single, double, and triple nested iterative chaotic keys generated by the S2 module are respectively. The decryption key and the encryption key are one-to-one and perfectly matched, eliminating decryption failure and data distortion problems caused by key mismatch.

[0056] S53, Differentiated Reverse Decryption Operation Based on Classification of Security Levels: Based on the encryption strength and iteration level of data with different security levels, a corresponding inverse decryption algorithm is used to complete the differentiated decryption operation segment by segment, fully adapting to the improved chaotic iterative model. The inverse operation formula is as follows: The parameters are defined exactly as in the encryption stage. The initial value of the original chaotic key is derived in reverse using a reverse formula to achieve accurate restoration of the encrypted data. The specific rules for layered decryption are as follows: (1) Layered decryption of ordinary-level data: targeting 1024B segments of publicly available logistics data, based on a single iteration key. It performs a single reverse decryption operation with low computational load and fast decryption speed, efficiently restoring public logistics original data and adapting to scenarios of rapid decryption of massive amounts of low-security data.

[0057] (2) Layered decryption of confidential data: targeting 512B segmented customs duty data, based on a double-overlay iterative key. Perform a two-layer reverse decryption operation to peel off the superimposed features of secondary encryption layer by layer, accurately restore classified customs declaration data, and ensure the integrity and accuracy of decryption of moderately classified data.

[0058] (3) Layered decryption of top-secret data: targeting 256B segment of quarantine and core customs data, classified based on three nested iteration keys. It performs a three-layer nested reverse decryption operation, disassembling the highly complex chaotic encryption features layer by layer, completely restoring the highly classified original data, eliminating the problems of decryption residue and information distortion of high-level classified data, and ensuring the secure restoration of core customs and trade data.

[0059] S54. Segmented Data Consolidation and Full-Domain Restoration: After completing the layered decryption of all segmented data, the original data sequence rules and field formats of the S1 preprocessing stage are replicated. Temporary redundant bits generated during the decryption process are removed. The decrypted segmented data of three different specifications (1024B, 512B, and 256B) are sequentially concatenated to eliminate segment boundary gaps and restore the complete customs and trade big data in UTF-8 standardized format. At the same time, the field integrity and temporal continuity of the concatenated data are verified to avoid data incompleteness caused by segment concatenation misalignment. S55. Final Verification and Closed-Loop Confirmation of Full-Domain Data: A final verification is performed on the complete original customs and trade data after splicing. The field parameters and numerical characteristics of the original collected data are compared to confirm that the ordinary, confidential, and top-secret data are all lossless, unaltered, and distortion-free. This completes the secure closed-loop transmission of customs and trade big data throughout the entire process, from preprocessing, differentiated encryption, adaptive transmission, chain-based error correction to layered decryption. This perfectly solves the technical shortcomings of traditional unified decryption modes, ensuring the security of high-secret data decryption while improving the efficiency of low-secret data decryption, adapting to the full-scenario data transmission needs of the customs and trade platform.

[0060] This invention discloses a secure transmission method for big data customs and trade platforms based on encryption algorithms. Addressing the specific industry-specific technical problems of existing general-purpose encryption algorithms in customs and trade big data transmission, such as poor adaptability to multiple data security levels, high latency in high-concurrency transmission, low accuracy in tracing data tampering in cross-border links, and lack of dynamic error correction capabilities, this invention utilizes an improved segmented chaotic dynamic key encryption module. This module constructs a chaotic iterative model that integrates data security level and segment length, achieving differentiated high-speed encryption of customs and trade data with different security levels. This solves the problems of wasted computing power and insufficient security adaptability in traditional encryption modes. An adaptive weighted hash chain verification and error correction module introduces cross-border link state weight parameters to dynamically adjust the hash verification segment granularity, achieving precise segment-level location and dynamic error correction of data tampering. This solves the problems of fuzzy verification and traceability and high retransmission rates in traditional methods. This invention significantly improves transmission efficiency and link adaptability while ensuring the security of customs and trade big data transmission, making it suitable for various customs big data cross-border secure transmission scenarios and demonstrating good practicality and inventiveness.

[0061] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A secure transmission method for a big data customs and trade platform based on encryption algorithms, characterized in that: Includes the following steps: S1. Customs and Trade Big Data Classification and Preprocessing: Collect big data to be transmitted from the customs and trade platform, including customs declaration data, commodity quarantine data, tariff calculation data, and cross-border logistics trajectory data. Classify the data into three categories according to the confidentiality level: ordinary level, confidential level, and top secret level. Complete the data format normalization and segmentation preprocessing. S2. Through the improved segmented chaotic dynamic key encryption module, dynamic encryption keys are generated for customs and trade segmented data of different security levels to complete differentiated encryption processing; S3. Encapsulate the encrypted customs and trade data into an encrypted transmission message and send it to the receiving end through a dedicated cross-border transmission link; S4. The adaptive weighted hash chain verification and error correction module performs integrity verification, tamper location and dynamic error correction on the received message. S5. After successful verification, the data is decrypted in layers to restore the complete customs and trade big data and complete secure transmission.

2. The secure transmission method for a big data customs and trade platform based on encryption algorithms according to claim 1, characterized in that: The specific implementation steps of S2 are as follows: S21. Parameter Initialization and Chaotic Model Construction: Based on the data security standards of the customs and trade industry, preset the data security level coefficient, segment length correction coefficient, and chaotic iteration control parameters; initialize the initial value of the chaotic key. Fixed balance correction factor , To ensure the stability and orderliness of key iteration; a dedicated chaotic iteration formula is constructed: ;in, The control parameter for chaotic iteration takes a value of 3.95 to 4.

0. The confidentiality coefficient for customs and trade data corresponds to the three core data levels of customs and trade: 1 for general logistics public data, 2 for routine customs and tax data, and 3 for confidential commodity quarantine data. This is the data segment length coefficient; S22. Tiered Key Iterative Generation: Based on the security level of the preprocessed data, a differentiated key iteration strategy is executed: Ordinary level data has low security requirements, so a single chaotic iteration is used to generate the key; confidential level data uses a double superposition iteration; top secret level quarantine and core tariff data uses a triple nested iteration. S23. Segmented Parallel Differentiated Encryption: The pre-processed variable-length data is segmented and mapped one-to-one with the corresponding dynamic chaotic key of the security level. Parallel group encryption is used to encrypt each segment. At the same time, a boundary redundancy shielding mechanism is added to automatically remove invalid redundant data generated by segmented encryption.

3. The secure transmission method for a big data customs and trade platform based on encryption algorithms according to claim 2, characterized in that: The specific implementation steps for the hierarchical key iterative generation in S22 are as follows: S22.1, Confidentiality Level Parameter Matching and Iteration Count Initialization: Based on the confidentiality level classification results of the preprocessed customs and trade data, bind the corresponding confidentiality level coefficients and iteration level thresholds: for ordinary-level cross-border logistics public data, match the confidentiality level coefficients. A single iteration mechanism is set; confidential-level routine customs and fee data are matched with the confidentiality level coefficient. A secondary overlay iteration mechanism is established; top-secret commodity quarantine data and core tariff confidential data are matched with a security classification coefficient. A three-nested iteration mechanism is set; chaotic iteration control parameters are used. Balance correction coefficient , Substitute the length coefficient corresponding to each data segment. Complete the initialization of iterative operation parameters; S22.2, Iterative computation of hierarchical chaotic keys: (1) Single chaotic iteration of ordinary level data: with the initial chaotic key value Using the radix as the iteration base, substitute it into the iteration formula to complete a single operation and obtain the final encryption key. ; (2) Confidential data double-overlay iteration: with initial value The intermediate key value is obtained after the first iteration. Then As a new iteration base, it is substituted again into the chaotic iteration formula for a second superposition operation, outputting the final encryption key. ; (3) Top Secret Data Three-Way Nested Iteration: The nested iterative operation logic is adopted, first through the initial value Iteratively obtained ,pass Iteratively obtain the second-level key Ultimately Complete the third nested iteration for the radix, and output a high-complexity encryption key. ; S22.3 Key Output and Computing Power Balance Verification: After completing the hierarchical iterative computation, the final key corresponding to different security levels of data is output. , , Perform validity checks and remove invalid keys that have iteration errors or out-of-bounds values.

4. The secure transmission method for a big data customs and trade platform based on encryption algorithms according to claim 2, characterized in that: The specific implementation steps of the segmented parallel differential encryption in S23 are as follows: S23.1 Based on the preprocessing results of step S1, the customs and trade data follows a negative correlation rule that the higher the security level, the shorter the segment length, and completes standardized segmentation: ordinary level logistics data. Segment length 1024 bytes, confidential level customs and fee data. Segment length 512B, Top Secret Quarantine Core Data The segment length is 256 bytes; each independent data segment corresponds to a unique segment length coefficient. Furthermore, it matches each of the unique chaotic keys generated in the previous step of hierarchical iteration to construct a precise mapping relationship. The mapping matching formula is as follows: ;in, For the first The encryption key corresponding to each data segment; , , These are the chaotic keys generated by a single iteration, a double superimposed iteration, and a triple nested iteration, respectively. S23.2 Multi-threaded parallel block differential encryption operation: A multi-threaded parallel encryption architecture is adopted, and encryption thread resources are dynamically allocated according to the number of data security level segments. Block encryption operation is performed synchronously on all data segments that have completed key mapping. Differentiated encryption strength matching is applied to different security levels of data segments: ordinary data segments use low-computing-power single-key encryption to prioritize encryption speed; confidential and top-secret data segments use high-complexity multi-layer iterative key encryption to prioritize transmission security; and parallel computing mode processes massive heterogeneous customs and trade data segments simultaneously. S23.3 Intelligent Masking and Cleaning of Redundant Data at Segment Boundaries: Identifying invalid redundant data using a redundancy discrimination formula. ;in, This is a set of redundant data at the segment boundaries; , The data consists of redundant bits at the beginning and end of segments; the proportion of redundant data in a single segment is limited to no more than 8 bits to avoid accidental deletion of valid data.

5. The secure transmission method for a big data customs and trade platform based on encryption algorithms according to claim 1, characterized in that: The specific implementation steps of S4 are as follows: S41. Real-time weight calculation of cross-border link status: Establish a cross-border transmission link status monitoring unit to collect three types of parameters in real time: latency fluctuation, packet loss rate, and signal interference intensity. After normalizing the parameters, calculate the comprehensive stability weight value of the link using a weighting formula. ;in This is the normalized value for link latency fluctuation, and its value is [value to be filled in]. The greater the fluctuation, the higher the value; This is the normalized value of the link packet loss rate. This is the normalized value of the link interference strength; , , The priority weighting coefficient is configured based on the industry characteristics that are most sensitive to the latency of cross-border trade and customs transmission. S42. Adaptive hash segmentation granularity adjustment: based on the link comprehensive weight value. Dynamically adjust hash check segment length: when The link is identified as experiencing high fluctuations. A 128B short segment check is used to improve the accuracy of local anomaly location. The condition is determined to be a normal fluctuation state of the link, and a 256B medium segmentation verification is used to balance accuracy and efficiency; when The link is determined to be in a stable state, and 512B long segment verification is adopted to reduce the verification computing power consumption. S43. Precise Tampering Location and Dynamic Error Correction: After receiving the encrypted message, the receiving end synchronously generates a local hash verification sequence and compares it segment by segment with the sender's baseline chain sequence. If the hash value of a certain segment does not match, the segment is accurately located as abnormally tampered / lost data. Based on the adjacent normal segments before and after the abnormal segment, the data is dynamically corrected through an interpolation repair algorithm to restore the original information of the abnormal data.

6. The secure transmission method for a big data customs and trade platform based on encryption algorithms according to claim 5, characterized in that: The specific implementation steps of S43's precise tamper location and dynamic error correction are as follows: S43.1, During the encrypted message encapsulation stage, the sending end determines the weight based on the real-time link weight. At the corresponding segmentation granularity, a baseline hash value is generated for each data segment, and these segments are concatenated in the data transmission order to form a baseline chained hash sequence for the sending end, which is synchronously transmitted to the receiving end along with the encrypted message. After receiving the encrypted message, the receiving end performs hash operations on each segment of the received encrypted data to generate a local chained hash sequence. Then, a segment-by-segment mapping precise comparison mechanism is adopted, using the adaptively split data segments as the smallest verification unit, and matching the hash feature values ​​of both the sending and receiving ends segment by segment. The comparison granularity and the adaptive segmentation rules of the link weight are consistent: for high-fluctuation links... Using 128B as the smallest comparison unit, conventional fluctuating links Using 256 bytes as the smallest comparison unit, a stable link Using 512B as the smallest comparison unit; S43.2, Precise location and judgment of segmented abnormal data: During the segment comparison process, if the local hash value of a certain segment is completely consistent with the base hash value, the segment data is determined to be complete and untampered; if the hash feature value of a single segment does not match, the independent data segment is immediately and accurately locked as an abnormal object, and is determined to be a local data tampering, bit loss or transmission distortion caused by cross-border link interference. At the same time, the location number, segment granularity and corresponding data security level parameters of the abnormal segment are recorded. S43.3, Adaptive interpolation dynamic error correction based on neighborhood normal segmentation: For accurately located abnormal data segments, lossless restoration and repair of the abnormal data is achieved by using adjacent, valid segments that pass hash verification. The error correction formula is as follows: ;in, The feature values ​​of the restored data for the abnormal segments to be repaired; The effective feature value for segmenting adjacent normal data before the abnormal segment; Effective feature values ​​for segmenting adjacent normal data after an abnormal segmentation; , The preset neighboring weights satisfy the constraints. ; S43.4 Secondary Verification Confirmation after Error Correction: After completing the repair of abnormal segmented data, the repaired complete data segments are re-hashed and compared with the baseline hash value of the sending end for the second time until the segment verification matches, confirming that the data repair is effective, without distortion or deviation, and finally outputting a complete, continuous, and secure encrypted customs and trade data message.

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