Business data testing method and system and storage medium

Abstract

The invention relates to the technical field of data transfer and verification, in particular to a business data testing method and system and a storage medium. The method comprises the following steps: acquiring an original service data stream; deploying a supervision submission shadow processing sandbox based on the original business data flow to obtain a data isolation verification environment; configuring an alliance block chain server based on the original service data flow, and constructing a distributed data processing traceability platform; based on the distributed data processing traceability platform, performing data processing proof generation by using the original service data stream to obtain a data processing hash chain; configuring differential privacy protection processor parameters based on the data isolation verification environment to obtain a privacy protection test data generator; a privacy protection test data generator is trained to generate a synthetic test data set. By combining the alliance block chain and the differential privacy technology, a high-credibility, automatic and traceable data testing system is constructed, and the data security, the verification efficiency and the system consistency are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data transfer and verification, and in particular to a business data testing method, system and storage medium. Background Art

[0002] Traditional business data testing methods mostly rely on centralized database systems for data verification and tracking, which is difficult to meet the needs of multi-source heterogeneous and dynamically changing data processing scenarios. In recent years, the rise of alliance blockchain technology has provided new solutions for secure data processing and trusted verification. With the help of blockchain's immutability, timestamp mechanism and consensus protocol, the verifiability and traceability of the entire business data processing process can be achieved. However, the existing testing methods still have the following shortcomings: most solutions lack effective support for encrypted data sharding and hash chain consistency verification, resulting in coarse granularity of data integrity verification; the existing consensus mechanism has high latency and poor real-time performance, and is not suitable for high-frequency business scenarios; the lack of compact proof structure design causes redundancy in index and backup data, increasing storage overhead. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a business data testing method, system and storage medium to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a business data testing method includes the following steps: Step S1: Obtain the original business data flow; deploy the regulatory reporting shadow processing sandbox based on the original business data flow to obtain a data isolation verification environment; Step S2: Configure the alliance blockchain server based on the original business data flow and build a distributed data processing traceability platform; Step S3: Generate data processing proof using the original business data stream based on the distributed data processing traceability platform to obtain a data processing hash chain; Step S4: configuring the differential privacy protection processor parameters based on the data isolation verification environment to obtain a privacy protection test data generator; training the privacy protection test data generator to generate a synthetic test data set; Step S5: Deploy smart contracts on the distributed data processing traceability platform based on the synthetic test data set to obtain an automated verification engine; perform data comparison and analysis between the source system and the target system based on the data processing hash chain and the automated verification engine to generate a risk calculation difference report; configure a risk calculation graph visualization server based on the synthetic test data set and the data isolation verification environment, and generate a risk calculation directed acyclic graph; Step S6: preset a difference positioning processor based on the risk calculation difference report and the risk calculation directed acyclic graph operation, configure an incremental compensation processing device, generate an automated correction workflow; execute the automated correction workflow, and generate a system consistency verification result.

[0005] The present invention constructs a highly reliable, automated, and traceable data testing system by introducing alliance blockchain and differential privacy technology, which is significantly better than the traditional data verification method that relies on centralized databases. First, the isolation of business data and test environment is realized to ensure that the real business system is not affected during the test process, and the test security is improved; secondly, with the help of the blockchain's immutability and timestamp mechanism, the authenticity and verifiability of the entire data processing process are ensured, and the shortcomings of the existing methods in data sharding encryption and hash chain consistency verification are solved; thirdly, the differential privacy protection mechanism is introduced to effectively shield sensitive information when generating test data, and improve test compliance and data security; in addition, automated verification and difference analysis are realized by combining synthetic test data with smart contracts, reducing manual intervention and improving test efficiency and accuracy; the risk calculation directed acyclic graph constructed provides a clear visualization of risk causal paths, which helps to accurately locate the source of differences; finally, through the automated correction workflow linked by difference positioning and incremental compensation mechanism, an integrated closed-loop processing from difference discovery to system repair is realized, ensuring that the system finally meets the consistency verification requirements, and significantly improving the intelligence and automation level of data quality management.

[0006] Preferably, the present invention further provides a business data testing system for executing the above-mentioned business data testing method, the business data testing system comprising: The data isolation sandbox deployment module is used to obtain the original business data flow; deploy the regulatory reporting shadow processing sandbox based on the original business data flow to obtain the data isolation verification environment; The blockchain traceability platform construction module is used to configure the alliance blockchain server based on the original business data flow and build a distributed data processing traceability platform; The data processing proof generation module is used to generate data processing proof based on the distributed data processing traceability platform using the original business data stream to obtain the data processing hash chain; A privacy synthetic test data generation module is used to configure the differential privacy protection processor parameters based on the data isolation verification environment to obtain a privacy protection test data generator; train the privacy protection test data generator to generate a synthetic test data set; The automated difference analysis and risk graph construction module is used to deploy smart contracts on the distributed data processing traceability platform based on the synthetic test data set to obtain the automated verification engine; perform data comparison and analysis between the source system and the target system based on the data processing hash chain and the automated verification engine to generate a risk calculation difference report; configure the risk calculation graph visualization server based on the synthetic test data set and the data isolation verification environment, and generate a risk calculation directed acyclic graph; The difference repair and consistency verification module is used to preset the difference positioning processor based on the risk calculation difference report and the risk calculation directed acyclic graph operation, and configure the incremental compensation processing equipment to generate an automated correction workflow; execute the automated correction workflow to generate a system consistency verification result.

[0007] The present invention forms an efficient, safe and automated data verification and repair system by organically integrating data isolation sandbox, blockchain traceability platform, privacy synthetic test data generation, automated difference analysis and risk map construction, and difference repair and consistency verification module. First, the data isolation sandbox deployment module provides isolation and security protection for the original business data flow, ensuring privacy and security during data processing; the blockchain traceability platform construction module provides traceability and non-tamperability for data processing through blockchain technology, ensuring transparency and credibility of the entire data processing process; the privacy synthetic test data generation module generates synthetic test data sets while ensuring privacy protection, providing compliance data that does not leak sensitive information for subsequent tests; the automated difference analysis and risk map construction module accurately identifies the differences between systems through the automated deployment of smart contracts and data comparison analysis, and uses visualization technology to help decision makers quickly grasp the risk sources and impacts; finally, the difference repair and consistency verification module ensures the automation, precision and efficiency of the system repair process through the application of incremental compensation and automated correction workflows, significantly improving the speed and quality of system consistency verification. Overall, this method greatly improves the accuracy, operability and security of data processing verification, and is suitable for cross-system and cross-platform data consistency verification and risk management.

[0008] Preferably, the present invention further provides a computer-readable storage medium on which a computer program is stored, and the computer program implements the business data testing method when executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings: Figure 1 A schematic diagram of the steps of the business data testing method of the present invention; Figure 2 for Figure 1Detailed step flow chart of step S1 in FIG. DETAILED DESCRIPTION

[0010] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0011] To achieve this, please refer to Figure 1 to Figure 2 The present invention provides a business data testing method, the method comprising the following steps: Step S1: Obtain the original business data flow; deploy the regulatory reporting shadow processing sandbox based on the original business data flow to obtain a data isolation verification environment; Step S2: Configure the alliance blockchain server based on the original business data flow and build a distributed data processing traceability platform; Step S3: Generate data processing proof using the original business data stream based on the distributed data processing traceability platform to obtain a data processing hash chain; Step S4: configuring the differential privacy protection processor parameters based on the data isolation verification environment to obtain a privacy protection test data generator; training the privacy protection test data generator to generate a synthetic test data set; Step S5: Deploy smart contracts on the distributed data processing traceability platform based on the synthetic test data set to obtain an automated verification engine; perform data comparison and analysis between the source system and the target system based on the data processing hash chain and the automated verification engine to generate a risk calculation difference report; configure a risk calculation graph visualization server based on the synthetic test data set and the data isolation verification environment, and generate a risk calculation directed acyclic graph; Step S6: preset a difference positioning processor based on the risk calculation difference report and the risk calculation directed acyclic graph operation, configure an incremental compensation processing device, generate an automated correction workflow; execute the automated correction workflow, and generate a system consistency verification result.

[0012] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a flow chart of steps of a business data testing method of the present invention. In this example, the business data testing method includes the following steps: Step S1: Obtain the original business data flow; deploy the regulatory reporting shadow processing sandbox based on the original business data flow to obtain a data isolation verification environment; In the embodiment of the present invention, the original business data stream is first obtained through the centralized management system of the transaction database. The data stream contains four fields: transaction date, transaction amount, transaction account, and transaction type. The data volume is 5 million structured records per day. Then, a server rack array is deployed based on the original business data stream. The specific configuration is 10 HP ProLiant DL380 servers, each equipped with an Intel Xeon E5-2699 v4 processor, 256GB ECC memory, and 10TB RAID. 10 storage arrays, build a shadow processing sandbox physical infrastructure; then, based on the shadow processing sandbox physical infrastructure, perform core component service orchestration, use Docker container technology to deploy 50 business microservice nodes, configure 4GB memory limit and 2-core CPU resource limit for each node, and achieve automatic expansion and contraction through Kubernetes cluster management system version 1.22.5 to obtain a microservice cluster; then, perform data normalization engine parameter tuning based on the original business data stream, including setting the field mapping matrix, configuring the data type conversion rule set (unifying all date formats to ISO-8601 standard, and unifying the amount to two decimal places), performing UTF-8 encoding standardization, and setting the data cleaning threshold to 99.95% accuracy, and finally obtain a standardized data conversion module; then, deploy a security isolation mechanism based on the standardized data conversion module , realize three-layer network partitioning (DMZ zone, security zone, core zone), configure deep packet inspection firewall with a blocking rate of 99.99%, implement strict access control list (ACL), limit the access range of IP address segment to 192.168.1.0 / 24, establish data transmission encryption channel using AES-256-GCM algorithm, and obtain data isolation control unit; finally, based on the standardized data conversion module and data isolation control unit, integrate and deploy the regulatory reporting shadow processing sandbox, configure real-time data synchronization mechanism (synchronization delay does not exceed 5 seconds), implement database table space isolation (each regulatory reporting module has an independent table space and does not interfere with each other), deploy memory isolation protection mechanism (address space layout randomization ASLR technology), implement mirror data backup strategy (incremental backup interval is 15 minutes, full backup once every 24 hours), and finally obtain data isolation verification environment.

[0013] Step S2: Configure the alliance blockchain server based on the original business data flow and build a distributed data processing traceability platform; In the embodiment of the present invention, firstly, the hardware resources of the high-performance computing server cluster are allocated based on the original business data flow, and 7 Dell PowerEdge R740 servers are configured, each of which is equipped with a dual-channel Intel Xeon Gold 6258R processor (28 cores and 56 threads), 768GB DDR4-3200MHz ECC memory, 8TB NVMe SSD storage array, and 100Gbps network interface card to form a consortium blockchain physical node array; then, the consensus mechanism engine parameters are configured based on the consortium blockchain physical node array, and the practical Byzantine fault tolerance (PBFT) consensus algorithm is adopted. The number of verification nodes is configured to be 4, the number of fault-tolerant nodes is f=1 (that is, (3f+1)=4 verification nodes), the block generation cycle is set to 2 seconds, the upper limit of the number of transactions in a single block is 10,000, and the transaction verification timeout is set to 500 milliseconds to obtain a high-throughput consensus network; then, a blockchain server cluster is deployed based on the high-throughput consensus network, and Hyperledger Fabric is installed. v2.4 version core components, configure 4 organization nodes, each organization contains 2 peer nodes and 1 ordering node, set the number of channels to 2, the smart contract (Chaincode) execution environment to Docker container, the maximum number of parallel transactions to 5000 per second, and obtain the core service of the alliance blockchain; then install the hardware accelerator of the cryptographic security component, deploy the Intel QuickAssist Technology acceleration card QAT8970-AEPW, configure the RSA-4096 signature verification throughput to 20,000 times per second, the elliptic curve digital signature algorithm (ECDSA) P-256 signature verification throughput to 40,000 times per second, and the SHA-256 hash calculation throughput to 40Gbps, with a dedicated hardware random number generator (TRNG), and obtain a high-performance signature verification unit; then build a secure channel for the inter-node communication protocol based on the high-performance signature verification unit, and implement TLS1.3 encryption protocol, configure the parameters of the elliptic curve key exchange algorithm (ECDHE), use TLS_AES_256_GCM_SHA384 as the cipher suite, set the certificate rotation cycle to 7 days, update the communication key once an hour, set the data transmission encryption strength to 256 bits, establish a two-way authentication mechanism between nodes, and limit the maximum bandwidth of the transport layer to 10Gbps, and obtain an encrypted data transmission network; finally, deploy a distributed data processing traceability platform based on the alliance blockchain core service and the encrypted data transmission network, realize the blockchain storage of transaction data (transaction hash index links the original transaction), configure the data processing event recording mechanism (processing time is accurate to milliseconds), implement the traceability query interface (support multi-dimensional combined query, query response time does not exceed 200 milliseconds), deploy a real-time monitoring system (system status refresh frequency is 1 second), set up a full-link tracking log (log storage period is 180 days), and complete the construction and configuration of the distributed data processing traceability platform. .

[0014] Step S3: Generate data processing proof using the original business data stream based on the distributed data processing traceability platform to obtain a data processing hash chain; In the embodiment of the present invention, the original business data stream is preprocessed by format conversion, and the XML to JSON conversion algorithm is applied to convert the unstructured XML data (multi-layer nesting depth does not exceed 5 layers) into a standardized JSON format. The data cleaning process includes null value filling (using -1 to replace numeric null values, and using "N / A" to replace character null values), abnormal value processing (removing values ​​exceeding 3 standard deviations), and field standardization (unifying the date format to the ISO-8601 standard, and the amount precision to 2 decimal places), to obtain a standardized data packet; then the encryption component is loaded into the standardized data packet, and the AES-256-GCM encryption algorithm is applied to encrypt the key fields (encryption key length 256 bits, initialization vector IV length 12 bytes, authentication tag length 16 bytes), and the overall data packet is calculated. HMAC-SHA512 message authentication code (key length 512 bits) is used to obtain an encrypted data set. Then, the encrypted data set is sharded based on the alliance blockchain server (single-shard size 1MB, overlap rate 5%), and the hash processor parameters are configured based on the sharding results. The hash algorithm combination uses SHA3-512 and Blake2b for double hashing, the hash calculation thread pool size is set to 128 threads, the hash buffer size is set to 256MB, and the batch processing threshold is set to 10,000 records to obtain the hash generation engine. Then, the standardized data packet is timestamped and synchronized with the atomic clock using the Network Time Protocol (NTP) server (synchronization accuracy is in microseconds), and multiple hash calculations are performed on the timestamp synchronization results based on the hash generation engine. The calculation formula is HashFinal = SHA3-512(Blake2b(data) + timestamp + random entropy value), the random entropy value is taken from the 32-byte random number output by the hardware random number generator, and the original proof of data processing is obtained; then the original proof of data processing is processed by the proof aggregation processor, using the Merkle Tree structure, the tree height is limited to 16 layers, the leaf node hash value uses the original proof, the parent node hash value calculation formula is H(left child node hash + right child node hash), the root node calculation uses the weighted hash algorithm with the weight coefficient being the inverse of the child node depth, and the proof aggregation tree is obtained; then the proof aggregation tree is verified by consensus based on the distributed data processing traceability platform, using multiple rounds of PBFT consensus process (at least 3f+1 nodes reach a consensus, where f is the number of fault-tolerant nodes), the consensus threshold is set to 75% node recognition, the consensus timeout is set to 5 seconds, and the consensus verification result is written to the high-speed chain storage (the writing speed is not less than 200000 IOPS), and obtain persistent proof data; then operate the proof compression processor based on the persistent proof data, using the zlib compression algorithm (compression level is set to 9), with a compression ratio of not less than 60%, and a compressed block size of 64KB, to obtain a compact proof structure;The compact proof structure is monitored by redundant verification equipment, and Reed-Solomon forward error correction code (RS(10,4), i.e. 10 data blocks, 4 check blocks) is used. The error correction capability is that any loss of 4 blocks of data can still be restored, and the reliability enhanced proof is obtained; based on the reliability enhanced proof, the proof index generator is called, and the B+ tree index structure (leaf node capacity is 128 records, and the internal node branching factor is 64) is used. The index key is a combination of timestamp + transaction hash, and the total index entry points do not exceed 16, and the proof index table is obtained; then the proof index table is chained and encapsulated, and a chain hash pointer structure is used (each block contains the hash value of the previous block), the block size is fixed at 4MB, and the block header contains the current block hash value, the previous block hash value, the Merkle root, the timestamp and the random number, and is written to the hash chain backup device (using RAID 10 storage arrays, with a total capacity of 50TB), perform scheduled snapshot backup (once per hour) to obtain a distributed backup hash chain; finally, perform hash chain consistency verification on the distributed backup hash chain, using a chain backtracking verification algorithm (backtracking from the latest block to the genesis block), with a verification frequency of once every 10 minutes, and a consistency judgment standard of 100% matching of all node hash values ​​to obtain the final data processing hash chain. ;

[0015] Step S4: configuring the differential privacy protection processor parameters based on the data isolation verification environment to obtain a privacy protection test data generator; training the privacy protection test data generator to generate a synthetic test data set; In the embodiment of the present invention, firstly, a data distribution feature set is extracted based on a data isolation verification environment, and the statistical features of each field in the original business data stream, including mean, variance, median, quartile, kurtosis, skewness, correlation coefficient between fields, integrity features (missing rate does not exceed 0.1%), dimensionality features (high-dimensional sparsity does not exceed 20%), time series features (autocorrelation coefficient calculation window is set to 30 days), and field importance features (calculated by information gain ratio, with the threshold set to 0.05); then, a differential privacy sensitivity analyzer is calibrated based on the data distribution feature set, and the field sensitivity is hierarchically classified (high Sensitivity: ID number, account number, mobile phone number; medium sensitivity: name, address, amount; low sensitivity: transaction type, transaction time), calculate the global sensitivity δ value as the maximum change of the L1 norm of each field, set it to 10.5, generate the sensitivity threshold matrix (high sensitivity threshold 0.2, medium sensitivity threshold 0.5, low sensitivity threshold 1.0); then calculate the noise injection ratio of the sensitivity threshold matrix, using the Laplace noise mechanism, the noise ratio formula is b=Δf / ε, where Δf is the sensitivity, ε is the privacy budget, set the overall privacy budget ε=3.0, and allocate it according to field sensitivity (high sensitivity The privacy budget configuration table is obtained by setting the sensitivity of the query to ε1=0.5, the medium sensitivity to ε2=1.0, and the low sensitivity to ε3=1.5. Then, the differential privacy protection processor parameters are loaded based on the privacy budget configuration table, the seed value of the noise generator is set to a 128-bit random number generated by a hardware random number generator, the noise distribution parameter b=Δf / ε is configured (different for each field), the query sensitivity threshold is set (the impact of a single query result does not exceed 0.1%), and the data access frequency limit is set (the number of accesses to the same data set per hour does not exceed 10 times), and the initialized processor instance is obtained. Then, the initialized processor instance is privacy-protected. Utility balance test, calculate data availability index (statistical significance retention rate is not less than 95%), analyze privacy leakage risk index (differential privacy attack success rate is not more than 0.1%), calculate utility privacy balance coefficient α=0.7 (weight biased towards data availability), adjust privacy budget allocation by dichotomy, set the number of iterations to 50 times, and set the convergence threshold to 0.001 to obtain the optimized post-processor configuration; then assemble the data generation engine based on the optimized post-processor configuration, and build the generator architecture, including data distribution modeling module (each field distribution is modeled separately), correlation maintenance module (based on Gaussian Copula method, keep the correlation error between fields not more than 5%), privacy budget allocation module (dynamically adjust the privacy budget of each field, and keep the total budget at 3.0), and obtain the prototype of privacy protection test data generator; then conduct privacy protection strength verification equipment test on the privacy protection test data generator prototype, and perform member inference attack test (attack success rate must be less than 0.5%), attribute inference attack test (the accuracy of sensitive attribute inference needs to be close to the random guessing level of 50%), differential attack test (the probability of detecting differences in the outputs of adjacent data sets does not exceed e^ε=20.1%), and obtain the generator compliance report; finally, based on the generator compliance report, the parameters of the privacy protection test data generator prototype are fine-tuned, the privacy budget allocation ratio is adjusted (high sensitivity ε1=0.4, medium sensitivity ε2=1.1, low sensitivity ε3=1.5), the noise distribution parameters are optimized (the noise intensity is increased by 10% for highly sensitive fields), the data generation batch size is adjusted to 10,000 / batch, and the generation timeout limit is set The time per batch is 30 seconds, and the privacy protection test data generator is obtained. Then, based on the data isolation verification environment, training samples are extracted from the original business data stream, and a stratified sampling strategy is adopted (stratified by business type, and the sample size of each layer is consistent with the original ratio). The sampling ratio is 20% of the total data volume, and the minimum number of samples is limited to 50,000. The sample representativeness verification requires that the KS test p value is greater than 0.05 to obtain the training data set. Then, the training data set is input into the privacy protection test data generator, and the parameter learning process is executed. The number of iterations is set to 1000 times, the learning rate is set to 0.001, the batch size is set to 128 records, and the convergence condition is The loss function changes less than 0.0001 for 5 consecutive iterations, and the generator model in training is obtained; then, the data distribution consistency test is performed based on the generator model in training, the JS divergence between the original distribution and the generated distribution is calculated (must be less than 0.05), the two-sample KS test is performed (the p value must be greater than 0.05), the correlation retention between fields is calculated (the difference in Pearson correlation coefficient does not exceed 0.1), the feature retention rate is evaluated (the retention rate of key business features is not less than 95%), and the model performance evaluation report is obtained; then the parameter tuning device analysis of the model performance evaluation report is performed to identify the feature dimensions with poor performance (words with JS divergence greater than 0.05). segment), adjust the generation parameters of the corresponding dimensions in a targeted manner (increase the number of training rounds by 50%, improve the sampling accuracy by 30%), retrain the problem dimension 5 times, verify the adjustment effect, and obtain the optimized generator model; finally, call the high-performance data synthesis engine based on the optimized generator model, set the generated data volume to 10 times the original data volume, the generation speed is required to be no less than 10,000 pieces / second, the number of parallel generation threads is set to 16, and real-time quality monitoring is performed during the generation process (distribution consistency is checked every 100,000 pieces of data generated), perform batch-level data merging (the memory buffer size is set to 1GB), and obtain the final synthetic test data set. .

[0016] Step S5: Deploy smart contracts on the distributed data processing traceability platform based on the synthetic test data set to obtain an automated verification engine; perform data comparison and analysis between the source system and the target system based on the data processing hash chain and the automated verification engine to generate a risk calculation difference report; configure a risk calculation graph visualization server based on the synthetic test data set and the data isolation verification environment, and generate a risk calculation directed acyclic graph; In the embodiment of the present invention, the identity authentication of the physical encryption module of the U-shield is first initialized, and the two-factor authentication process including PIN code verification (8-digit PIN code, the number of incorrect attempts is limited to 3 times) and fingerprint recognition (FAR error acceptance rate <0.0001%, FRR error rejection rate <1%) is performed through the Feitian Chengxin ePass3000 model U-shield connected to the PKCS#11 standard interface, the X.509 digital certificate stored in the U-shield is read (RSA-4096-bit key, the certificate is valid for 1 year), the certificate chain verification (root certificate, intermediate certificate, user certificate three-level verification chain) is performed, the certificate status is completed. Check the online (OCSP response timeout is set to 2 seconds), and obtain the hardware security authenticator; then the smart contract code is digitally signed based on the hardware security authenticator. First, the verification logic is encapsulated into a Solidity language smart contract (the number of code lines is controlled within 500 lines, and the number of functions does not exceed 20). The contract contains data hash verification functions, comparison Analyze the logic and difference report generation function, compile the smart contract to generate bytecode (the compilation optimization level is set to the highest), use the U shield to execute the ECDSA-secp256k1 signature algorithm (hash function is SHA3-256) to sign the bytecode, the signature data length is 72 bytes, verify the correctness of the signature, and obtain a secure and trusted deployment package; then deploy the smart contract on the distributed data processing traceability platform based on the synthetic test data set and the secure and trusted deployment package, execute the contract deployment transaction (the gas limit is set to 10,000,000 units, and the gas price is set to 20Gwei), wait for the transaction confirmation (the number of confirmations is set to 12 blocks), verify the validity of the deployed contract address, execute the contract initialization function (set the initial deployment parameters, such as the data source address, the target system address, and the permission control list), and obtain the automated verification engine; then load the verification instructions of the U shield security chip based on the data processing hash chain and the automated verification engine, and pass the U shield APDU instruction set (ISO / IEC 7816 standard) to write a verification command sequence, including SELECT (select application, AID length is 16 bytes), VERIFY (verify user, verification data length is 8 bytes), COMPUTE (perform hash calculation, support SHA-256 / 384 / 512 algorithm, data block size is 4KB), COMPARE (perform comparison, comparison speed is not less than 1GB / minute), U shield read and write speed is 48MB / second, parallel processing threads are limited to 4, and hardware accelerated verification unit is obtained;Then, based on the hardware acceleration verification unit, a data comparison and analysis between the source system and the target system is performed. The comparison and analysis adopts a three-stage process: first, a metadata structure comparison is performed (field type consistency comparison, field length comparison, field constraint comparison), then a data content hash comparison is performed (hash values ​​are generated in batches of 1,000 records, and hash value consistency is compared), and finally a statistical feature comparison is performed (the statistical distribution difference of each field is calculated, and the difference threshold is set to 1%), all differences are recorded (the difference record format is JSON, including difference fields, difference types, difference values, and location information), and a risk calculation difference report is generated; then, based on the synthetic test data set and the data isolation verification environment, a risk calculation graph visualization server is configured, and an NVIDIA Tesla T4 is deployed GPU accelerator card (16GB video memory, 2560 CUDA cores), configure graphics rendering parameters (resolution set to 4K, refresh rate 60Hz, color depth 32bit), deploy Neo4j graph database engine (version 4.4.0, index type is B-tree, cache size is set to 32GB), set the graph layout algorithm to force-directed layout (spring coefficient k=0.1, gravity coefficient g=0.05, iteration number 1000 times), configure the risk calculation graph visualization server; then input data to the risk calculation graph visualization server based on the synthetic test data set, control the data import rate to 10GB / hour, perform data normalization processing (attribute values ​​are normalized to the [0,1] interval), and build the graph data structure (section 2.3.1). Points are business entities, edges are business relationships, and attributes are business features), calculate node centrality indicators (degree centrality, betweenness centrality, and proximity centrality), execute the community discovery algorithm (Louvain algorithm, with the modularity threshold set to 0.6), and obtain the risk data preprocessing results; then, based on the risk data preprocessing results and the data isolation verification environment, U-shield command interaction is performed, and rendering instructions are transmitted through the U-shield security channel (using the SCP03 security channel protocol). The instructions include graphic layout update instructions (update frequency 1 time / second), node attribute calculation instructions (calculation time does not exceed 100 milliseconds / node), risk level marking instructions (risk levels are divided into 5 levels, marked with different colors), and GPU accelerated graphics rendering (using OpenGL 4.6API), get the hardware accelerated rendering unit; finally, build the risk data relationship topology based on the hardware accelerated rendering unit, control the number of nodes within 10,000, control the number of edges within 100,000, execute the force-oriented layout algorithm to layout the nodes (avoid the node overlap rate below 5%), calculate the critical path (using the Dijkstra algorithm, the edge weight is the risk value), mark the risk nodes (high-risk nodes are red, medium-risk nodes are yellow, and low-risk nodes are green), draw the risk propagation path (the arrow points to the direction of risk propagation), mark the risk value (accurate to two decimal places), and complete the generation and visualization of the risk calculation directed acyclic graph. ;

[0017] Step S6: preset a difference positioning processor based on the risk calculation difference report and the risk calculation directed acyclic graph operation, configure an incremental compensation processing device, generate an automated correction workflow; execute the automated correction workflow, and generate a system consistency verification result.

[0018] In the embodiment of the present invention, when the U-shield identity authentication is authorized based on the risk calculation difference report, the system starts the RSA-2048-bit asymmetric encryption algorithm to perform three-factor authentication. After the user inserts the U-shield physical device, enters the 8-digit PIN code and completes the fingerprint recognition. The system compares the recognition result with the template stored in the security area. When the comparison similarity reaches more than 98%, a 256-bit high-level management authority authentication token is generated. Then the system loads the node relationship matrix (dimension is n×n, n is the number of business data processing nodes) in the risk calculation directed acyclic graph to the preset difference positioning processor, which uses the graph theory depth-first search algorithm to traverse all node paths and calculate the weight value of each path (weight value calculation formula = ∑(r i ×d i ), r i is the risk value of node i, d i is the depth coefficient of node i), generating a 200×200 dimension difference correlation analysis matrix; then the built-in ARM security coprocessor of the USB shield executes the SHA-256 hash algorithm at a clock frequency of 1024MHz to perform root cause analysis on the difference correlation analysis matrix, and calculates the normalized centrality of each node (value range 0-1, calculation formula = k i / (n-1), k i=The difference root cause analysis report includes a difference severity score (1-10 points, 10 points is the most serious). The system configures the incremental compensation processing equipment according to the difference correlation analysis matrix, sets the data correction threshold to 0.05 (that is, when the data deviation exceeds 5%, the correction is triggered). The compensation strategy adopts a two-phase commit protocol to ensure transaction consistency, decomposes the correction operation into a preparation phase and a commit phase, and stores all correction rules as a compensation strategy configuration file in JSON format. Subsequently, the correction rules in the difference root cause analysis report are written into the EEPROM security memory built into the U shield (with a capacity of 256KB). The memory is encrypted with AES-256 bits and HMAC signature verification is set to form a tamper-proof correction rule base. The system uses a directed acyclic graph topological sorting algorithm (with a complexity of O(V+ E), V is the number of nodes, E is the number of edges) to arrange the execution order of the workflow to ensure that dependent tasks are executed first, set a 15-second timeout limit and a 3-time retry mechanism for each task node, and generate an automated correction workflow in the BPMN2.0 standard format; finally, the system calls the advanced management authority authentication token to execute the tasks in the automated correction workflow with 5 parallel threads, and takes a data snapshot before and after each correction operation (the snapshot interval is 200 milliseconds). The system consistency verification result is formed by calculating the difference rate of the data hash values ​​before and after the correction (the allowable error range is 0.001%) and recording the execution time (in milliseconds), resource consumption (CPU usage, memory usage), success rate (percentage) and other indicators of each operation. The result includes the complete execution log of all correction operations and the final data consistency measurement value (the value range is 0-100%, and it is required to reach 99.99% or more to be considered a successful verification).

[0019] The present invention constructs a highly reliable, automated, and traceable data testing system by introducing alliance blockchain and differential privacy technology, which is significantly better than the traditional data verification method that relies on centralized databases. First, the isolation of business data and test environment is realized to ensure that the real business system is not affected during the test process, and the test security is improved; secondly, with the help of the blockchain's immutability and timestamp mechanism, the authenticity and verifiability of the entire data processing process are ensured, and the shortcomings of the existing methods in data sharding encryption and hash chain consistency verification are solved; thirdly, the differential privacy protection mechanism is introduced to effectively shield sensitive information when generating test data, and improve test compliance and data security; in addition, automated verification and difference analysis are realized by combining synthetic test data with smart contracts, reducing manual intervention and improving test efficiency and accuracy; the risk calculation directed acyclic graph constructed provides a clear visualization of risk causal paths, which helps to accurately locate the source of differences; finally, through the automated correction workflow linked by difference positioning and incremental compensation mechanism, an integrated closed-loop processing from difference discovery to system repair is realized, ensuring that the system finally meets the consistency verification requirements, and significantly improving the intelligence and automation level of data quality management.

[0020] Preferably, step S1 comprises the following steps: Step S11: Obtaining the original service data stream; Step S12: deploying a server rack array based on the original business data flow to obtain a shadow processing sandbox physical infrastructure; Step S13: Perform core component service orchestration based on the shadow processing sandbox physical infrastructure to obtain a microservice cluster; Step S14: Optimize the parameters of the data normalization engine based on the original business data stream to obtain a standardized data conversion module; Step S15: deploying a security isolation mechanism based on the standardized data conversion module to obtain a data isolation control unit; Step S16: Based on the standardized data conversion module and the data isolation control unit, the regulatory reporting shadow processing sandbox is integrated and deployed to obtain a data isolation verification environment.

[0021] In the embodiment of the present invention, firstly, the core business system database (including Oracle 19c financial core business database, DB2 v11.5 customer information system and MySQL 8.0 transaction flow reservoir) is connected through the data acquisition gateway with a bandwidth of 10Gbps, and the ETL task is executed once every 15 minutes in an incremental extraction mode to obtain the original business data flow, and the extracted data includes structured data such as transaction flow table (number of fields 53), customer information table (number of fields 128), account information table (number of fields 76), etc., and the data volume is controlled to be no more than 500MB per batch; then, based on the obtained original business data flow, 4 Dell PowerEdge R940xa servers (each equipped with Intel Xeon Gold 6248R processor × 4, 768GB DDR4 memory, 30TB NVMe storage array) are deployed to form a server rack array, and the servers are interconnected through a 40Gbps InfiniBand network, and VMware ESXi is configured. The 7.0 virtualization platform implements resource isolation, allocates 32 virtual CPU cores and 256GB of memory for shadow processing sandbox operation, and sets up a physical network isolation area (VLAN ID is set to 4096) to build the physical infrastructure of the shadow processing sandbox. Then, based on the physical infrastructure of the shadow processing sandbox, a Kubernetes v1.24 cluster is deployed to orchestrate the core component services, 5 master nodes and 12 worker nodes are configured, Pod resource limits are set (CPU maximum utilization is 75%, memory limit is 192GB), 30 microservice containers are deployed (including data access service, transaction analysis service, rule verification service, report generation service, etc.), and 3 replica instances are configured for each service to ensure 99.999% high availability. The service grid architecture is used for communication between containers and the request timeout threshold is limited to 500 milliseconds to obtain a complete microservice cluster. Then, data normalization processing is performed based on the original business data flow, and Apache Spark is deployed 3.2 Data processing engine (the cluster is configured with 16 executors, and each executor is allocated 8GB of memory), write data cleaning rules (including field fill rate check, data type conversion, duplicate record removal, outlier detection, etc.), set field mapping conversion rules (the mapping table from source field name to target field name contains a total of 257 field mapping relationships), perform normalization conversion tasks (the conversion rate reaches 5000 records / second), and modify the data normalization engine parameters (including batch size set to 10,000 records, memory usage ratio set to 0.6. The garbage collection strategy is set to G1GC) to perform performance tuning and obtain a standardized data conversion module; a three-layer security isolation mechanism is implemented based on the standardized data conversion module. First, a physical isolation network gate device (one-way data flow control) is deployed to ensure one-way data flow, followed by configuring a firewall policy (limiting access to 34 necessary IP ports), and finally implementing data-level access control (using a role-based access control matrix to define 4 types of roles and 18 types of operation permissions), while implementing operation audit tracking (recording all data access operations, and the audit log retention period is 180 days), and obtaining a data isolation control unit with complete access control capabilities; finally, the standardized data conversion module is systematically integrated with the data isolation control unit, a dedicated processing environment for regulatory reporting is deployed, a data processing pipeline is implemented (a single pipeline throughput reaches 3,000 records / second), a real-time monitoring dashboard is configured (monitoring 15 key indicators, with a refresh frequency of 5 seconds / time), and abnormal alarm thresholds are set (alarms are triggered when the CPU usage exceeds 85%, the memory usage exceeds 80%, and the response time exceeds 2 seconds), and the integrated deployment of the regulatory reporting shadow processing sandbox is completed, and finally a complete, independent and fully functional data isolation verification environment is obtained. .

[0022] The present invention realizes a data isolation verification environment with high controllability, high security and high modularity by constructing a shadow processing sandbox and performing componentized service orchestration. First, through the deployment of physical infrastructure driven by original business data, the verification environment is physically isolated from the real business system to ensure business continuity and system stability during the test process; secondly, through the core component orchestration of the microservice cluster, a test service system with elastic scaling and rapid deployment capabilities is constructed, which improves the system response speed and resource utilization efficiency; thirdly, through the parameter tuning of the data normalization engine, the format unification and preprocessing standardization of multi-source heterogeneous data are realized, and the compatibility and adaptability of the test system to complex data streams are enhanced; in addition, combined with the deployed security isolation mechanism, the security protection of data access control and processing process is further strengthened to prevent sensitive data leakage or misuse; finally, the standardization module and the isolation control mechanism are integrated and deployed to build a complete data isolation verification environment, which provides a solid underlying support for subsequent synthetic data testing, risk assessment and consistency verification, and effectively improves the maintainability, security and scalability of the entire test platform.

[0023] Preferably, step S2 comprises the following steps: Step S21: Allocate hardware resources of a high-performance computing server cluster based on the original business data flow to obtain a physical node array of the alliance blockchain; Step S22: configuring consensus mechanism engine parameters based on the alliance blockchain physical node array to obtain a high-throughput consensus network; Step S23: Deploy a blockchain server cluster based on a high-throughput consensus network to obtain the alliance blockchain core service; Step S24: installing a hardware accelerator of a cryptographic security component to obtain a high-performance signature verification unit; Step S25: constructing an inter-node communication protocol secure channel based on a high-performance signature verification unit to obtain an encrypted data transmission network; Step S26: Deploy a distributed data processing traceability platform based on the alliance blockchain core service and encrypted data transmission network.

[0024] In the embodiment of the present invention, firstly, the hardware resources required for the calculation are quantified based on the processing requirements of the original business data flow, and 8 high-performance IBM Power System S924 servers (each configured with 12 cores of POWER9 processor, frequency of 3.8GHz, 512GB DDR4 memory, 12TB NVMe storage) are configured according to the peak processing volume of 7500 transaction records per second. A high-speed network interconnection architecture is constructed through 10 Cisco Nexus 9336C-FX2 switches (each configured with 36 100GbE ports), and the network delay is controlled within 10 microseconds. The RedHat Enterprise Linux 8.6 operating system is deployed and the real-time kernel patch (RT-PREEMPT) is enabled. Dedicated computing resources (10 CPU cores, 384GB memory capacity, 8TB storage space) are allocated to each node and NUMA affinity binding is set to form a physical node array of the alliance blockchain with physical isolation characteristics; then, a Practical Byzantine Fault is configured based on the physical node array of the alliance blockchain. Tolerance (PBFT) consensus mechanism engine, set consensus parameters including block generation time interval of 2 seconds, single block size upper limit of 16MB, maximum transaction processing volume of 3500 per second, configure the number of fault-tolerant nodes to be f=(n-1) / 3 (where n is the total number of nodes 8, so the number of fault-tolerant nodes is 2), deploy time synchronization service (using PTP protocol, time error is controlled within 50 nanoseconds), adjust the network communication buffer size to 64MB and enable zero copy technology, set the transaction verification thread pool size to the number of cores × 2, and finally achieve a high-throughput consensus network with TPS (transaction processing volume per second) of 3200. Then deploy Hyperledger Fabric v2 based on the high-throughput consensus network.4 blockchain server clusters, configured with 7 organizations (corresponding to different business departments), 28 endorsement nodes (4 for each organization) and 8 sorting nodes, set channel configuration parameters (including block cutting timeout of 200 milliseconds, maximum message count of 500, and preferred maximum byte count of 2MB), deploy chain code (smart contract) execution environment and set chain code execution timeout limit to 30 seconds, configure distributed ledger storage strategy (using CouchDB as the state database, setting the batch write size to 1000 records), implement event subscription mechanism (maximum event buffer size is 8192 events), and obtain the core service of alliance blockchain; then install a cryptographic accelerator based on dedicated hardware (Intel QuickAssist Technology acceleration card, each card provides 100Gbps throughput), integrate HSM (Hardware Security Module) hardware security module to protect private keys (FIPS 140-2 Level 4 certification), configure ECC (elliptic curve cryptography) parameters P-384 curve to implement digital signatures, deploy NIST-certified random number generator (DRBG SP 800-90A), integrated TEE (Trusted Execution Environment) to achieve secure computing isolation, and achieved signature verification performance of 8000 operations per second through 16 parallel processing units, and obtained a high-performance signature verification unit; based on the high-performance signature verification unit, a TLS 1.3 secure channel was built, the cipher suite was configured as TLS_AES_256_GCM_SHA384, a two-way authentication mechanism was implemented (based on X.509 v3 certificate, key length is 4096 bits), certificate transparency log monitoring service was deployed, the secure channel session timeout was set to 10 minutes, OCSP (Online Certificate Status Protocol) response binding was configured to ensure certificate validity check, link layer encryption was implemented (256-bit AES-GCM encryption algorithm, session keys were rotated every 24 hours), and a security audit log system that records all communication events was deployed to obtain an encrypted data transmission network with high throughput and low latency (average latency is less than 5 milliseconds); finally, a distributed data processing traceability platform was deployed based on the alliance blockchain core service and encrypted data transmission network, and a data ingestion module was integrated (processing rate is 5000 records / second), configure data processing pipeline (including data verification, business rule checking, data conversion, transaction packaging and other processing steps), implement distributed transaction management mechanism (using two-phase commit protocol, timeout set to 5 seconds), deploy distributed ledger query API (limit the size of a single query result set to 1000 records, query timeout to 3 seconds), configure traceability index service (based on B+ tree implementation, index node size is 4KB), set system monitoring alarm threshold (blockchain network delay exceeds 500 milliseconds, transaction rejection rate exceeds 0.1%, node CPU usage exceeds 80% when triggering alarm), complete the deployment of distributed data processing traceability platform. .

[0025] The present invention builds a distributed data processing platform with high throughput, strong security and traceability by constructing a high-performance alliance blockchain node architecture and secure communication mechanism. First, the resource scheduling of the physical nodes of the alliance blockchain is realized with the help of a high-performance computing server cluster, ensuring that the system has excellent computing power and concurrent processing capabilities when facing large-scale business data processing; secondly, through the configuration optimization of the high-throughput consensus mechanism, the efficiency of transaction confirmation on the chain is effectively improved, and the performance bottleneck of the traditional blockchain in high-frequency transactions or high-concurrency scenarios is solved; the deployed blockchain server cluster builds a stable and reliable core service system, enhancing the overall availability and scalability of the system; further, by integrating cryptographic hardware accelerators, the efficiency of signature verification is significantly improved, and the consensus verification delay is effectively reduced; the encrypted data transmission network builds a high-security transmission channel for inter-node communication to prevent data from being tampered with or stolen during transmission; finally, through the integrated deployment of the above hardware and protocol layers, the distributed data processing traceability platform built can realize the verifiable tracking of the entire process and all nodes of business data, and comprehensively improve the system's capabilities in data compliance processing, chain auditing and security supervision.

[0026] Preferably, step S3 comprises the following steps: Step S31: Perform format conversion preprocessing on the original business data stream to obtain a standardized data packet; load the encryption component on the standardized data packet to obtain an encrypted protection data set; Step S32: Slice the encrypted protection data set based on the alliance blockchain server, and configure the hash processor parameters based on the slicing processing results to obtain a hash generation engine; Step S33: synchronize the timestamp of the standardized data packet, and perform multiple hash calculations on the timestamp synchronization result based on the hash generation engine to obtain the original proof of data processing; Step S34: Aggregate, consensus verify and compress the original proof of data processing to generate a compact proof structure, build a tamper-proof storage link through chain encapsulation and distributed backup, and generate a data processing hash chain.

[0027] In the embodiment of the present invention, the format conversion processor is used to unify the data format of the original business data stream, and the data in different formats such as JSON, XML, CSV, etc. are converted into the standard JSON format encoded in UTF-8 to form a standardized data packet. Then the system calls the AES-256 encryption algorithm module to perform full-field encryption processing on the standardized data packet. Each field uses an independent encryption key, and the key length is fixed to 256 bits to generate an encrypted protection data set; the alliance blockchain server calls the data sharding processor to shard the encrypted protection data set according to the size of each 256KB, and each shard is attached with a 32-byte checksum, and then A hash processor of the SHA-256 hash algorithm is configured based on the sharding processing result. The processor's buffer size is set to 4MB, and the single processing capacity is 1 million hashes / second, forming a hash generation engine. A timestamp issued by the RFC3161 standard timestamp server is added to each data record in the standardized data packet, with an accuracy of milliseconds, and the timestamp synchronization result is input into the hash generation engine. The hash generation engine uses a dual SHA-256 algorithm and a SHA-3 algorithm in parallel to generate multiple hash values. The hash value length is 512 bits, which constitute the original proof of data processing. The original proof of data processing is processed by the proof aggregation processor according to the default The Kerr tree structure is aggregated, the tree depth is set to 20 layers, each node contains up to 64 child nodes, and a proof aggregation tree is generated; the distributed data processing traceability platform uses the PBFT consensus algorithm to verify the proof aggregation tree, requiring at least 75% of the nodes to reach an agreement, and the verified results are written to the SSD high-speed chain storage with a writing speed of no less than 3GB / s to obtain persistent proof data; the proof compression processor of the ZK-SNARK algorithm is operated with a compression ratio of 10:1, and the persistent proof data is processed to obtain a compact proof structure, which is then monitored in 4+2 verification mode through the Reed-Solomon redundant verification device, and the redundancy The ratio is 30%, and the reliability enhancement proof is obtained. Based on this, the proof index generator of the B+ tree structure is called, the index depth is 5 layers, and a proof index table is established; the proof index table is chain-encapsulated using the RSA-4096 algorithm, and the encapsulation block size is 1MB, and the encapsulation result is written to the distributed hash chain backup device. The backup device adopts a 5-node remote redundant architecture, and the write delay does not exceed 200 milliseconds to form a distributed backup hash chain; the distributed backup hash chain is verified by hash chain consistency through SHA-256 fingerprint comparison, the verification cycle is 5 minutes, and the consistency requirement is 100% matching, so as to obtain the final data processing hash chain.

[0028] The present invention constructs an efficient, secure and verifiable data processing proof system by introducing multi-layer processing mechanisms such as standardized preprocessing, encryption protection, hash calculation and chain encapsulation. First, by standardizing and encrypting the original business data flow, the unification of data structure and the security protection of content are realized, laying the foundation for subsequent processing; then, the encrypted data is sharded and the hash parameters are tuned in combination with the blockchain server, making the hash generation engine more targeted and high-performance, significantly improving the adaptability and hash calculation efficiency of multi-source data processing; the original proof of data processing generated based on timestamp synchronization and multiple hashing technology ensures the integrity and verifiability of the data processing process, and effectively prevents tampering and backtracking risks; further through aggregation, consensus verification and compression optimization, a large number of original proof structures are converted into compact data structures, greatly reducing the storage burden; finally, with the help of chain encapsulation and distributed backup mechanism, a tamper-proof and highly available data processing hash chain is constructed, which not only improves the data audit capability of the system, but also enhances the traceability and evidence validity in regulatory compliance scenarios.

[0029] It is particularly important that step S34 includes the following steps: Step S341: Process the original proof of data processing by the proof aggregation processor to obtain a proof aggregation tree; Step S342: Perform consensus verification on the proof aggregation tree based on the distributed data processing traceability platform, and write the consensus verification result into the high-speed chain storage to obtain persistent proof data; Step S343: operating the proof compression processor based on the persistent proof data to obtain a compact proof structure; performing redundant verification device monitoring on the compact proof structure to obtain a reliability-enhanced proof; calling the proof index generator based on the reliability-enhanced proof to obtain a proof index table; Step S344: chain encapsulation processing is performed on the proof index table, and the table is written into the hash chain backup device to obtain a distributed backup hash chain; Step S345: Perform hash chain consistency verification on the distributed backup hash chain to obtain the final data processing hash chain.

[0030] In the embodiment of the present invention, the original proof of data processing is organized according to the Merkle tree algorithm through the proof aggregation processor, the tree height is set to 20 layers, each parent node is associated with a maximum of 64 child nodes, and the proof aggregation processor uses GPU acceleration to build a proof aggregation tree at a speed of processing 1 million hash values ​​per second. Each node contains a 256-bit hash value and a 32-bit timestamp. The root node hash value of the proof aggregation tree is used as the unique identifier of the entire batch of data; the distributed data processing traceability platform calls the PBFT consensus algorithm to perform consensus verification on the proof aggregation tree, sets the consensus threshold to 75% of the total number of nodes, and the consensus timeout is 5 seconds. After successful verification, the consensus result is written to the high-speed chain storage, and the storage adopts NVMe SSD array configuration, write speed of 3GB / s, data redundancy of 2.0, to form persistent proof data; activate the proof compression processor of the ZK-SNARK algorithm, set the compression parameters so that the compression ratio reaches 10:1, and process the persistent proof data to obtain a compact proof structure. The size of the compact proof structure does not exceed 10% of the original data and retains 100% verification capability. Then, the 4+2 verification mode is monitored through the Reed-Solomon encoded redundant verification device, the verification block size is set to 4KB, the redundancy ratio is 30%, and the verification result reaches 99.9999% reliability requirements to obtain reliability enhanced proof. Then, based on the proof, the proof index generator using the B+ tree structure is called. The index tree depth is 5 layers, each node contains up to 256 index entries, and the index key length is 32 bytes to build a proof index table; the proof index table is verified by The RSA-4096 asymmetric encryption algorithm is used for chain encapsulation processing. The size of each encapsulation block is 1MB. The hash value of the previous block is used as the encryption seed of the next block to achieve chain association. The encapsulated data is written to a hash chain backup device with a 5-node remote deployment architecture. The geographical distance between nodes is not less than 300 kilometers, and the data synchronization delay is controlled within 200 milliseconds. All nodes adopt a full data replication strategy to form a distributed backup hash chain; the system calculates the data fingerprint of each node in the distributed backup hash chain through the SHA-256 hash algorithm. The fingerprint length is 256 bits. The data fingerprints of all nodes are compared within a fixed 5-minute verification cycle. The verification standard is set to 100% complete match. Any mismatch will trigger an automatic data repair mechanism. The repair uses a majority vote to determine the correct data version. The verified data constitutes the final data processing hash chain.

[0031] The present invention constructs an efficient, reliable and traceable data processing proof mechanism through multi-level proof structure optimization and chain encapsulation processing. First, by aggregating the original proof of data processing, a structured proof aggregation tree is formed, which realizes the efficient merging and logical organization of the original scattered proofs and improves the verification efficiency; the consensus verification completed in the distributed traceability platform ensures the global credibility of the aggregation results, and realizes persistence through high-speed chain storage, enhancing the system's data writing ability and anti-loss ability under high concurrency; then, through compression processing and redundant verification, a compact reliability proof with verification enhancement capability is constructed, which greatly reduces the storage and transmission overhead and improves the data integrity detection capability; the proof index table generated further facilitates subsequent efficient query and rapid positioning of related proof data, and supports large-scale concurrent retrieval requirements; finally, through chain encapsulation and writing hash chain backup devices, a tamper-proof distributed backup mechanism is realized, combined with hash chain consistency verification, to ensure that the final generated data processing hash chain meets high standards in terms of integrity, consistency and audit availability, providing solid technical support for business data testing and compliance auditing.

[0032] Preferably, configuring the differential privacy protection processor parameters based on the data isolation verification environment in step S4 includes: Extracting data distribution feature sets from the data isolation verification environment; Calibrate the differential privacy sensitivity analyzer based on the data distribution feature set and generate a sensitivity threshold matrix; Calculate the noise injection ratio of the sensitivity threshold matrix to obtain the privacy budget configuration table; Load the differential privacy protection processor parameters based on the privacy budget configuration table to obtain the initialized processor instance; Perform a privacy utility balance test on the initialized processor instance to obtain the optimized processor configuration; Based on the optimized post-processor configuration, the data generation engine is assembled to obtain the prototype of the privacy-preserving test data generator; Conduct privacy protection strength verification equipment test on the privacy protection test data generator prototype and obtain the generator compliance report; Based on the generator compliance report, the parameters of the privacy-preserving test data generator prototype are fine-tuned to obtain the privacy-preserving test data generator.

[0033] In the embodiment of the present invention, a feature extraction operation is first performed on the original business data in the data isolation verification environment, and a statistical analysis is performed on 257 business fields through a data analysis engine to calculate the numerical distribution (including minimum value, maximum value, mean value, median, standard deviation, quantile) of each field, frequency distribution (Top-K value occurrence frequency, K is set to 10) and correlation matrix (calculated using Pearson correlation coefficient, threshold set to ±0.7), and corresponding feature extraction algorithms are applied to different data types (Z-score standardization is used for numerical types, One-hot encoding is used for categorical types, and Fourier transform is used to extract frequency features for time series), and finally a data distribution feature set containing 1024 feature dimensions is formed; then, the differential privacy sensitivity analysis is calibrated based on the data distribution feature set. The instrument calculates the global sensitivity value △f for each feature dimension (defined as the maximum difference between the query function f of any two adjacent data sets D and D', that is, △f=max|f(D)-f(D')|). For continuous features, the interval clamping method is used to limit the sensitivity to the range of [μ-3σ,μ+3σ] (μ is the mean, σ is the standard deviation). For discrete features, the influence range of feature changes is calculated through frequency analysis, and the exponential decay function (attenuation coefficient α=0.85) is used to adjust the sensitivity as the influence range increases. The sensitivity values ​​of all features are organized into a 257×4 sensitivity threshold matrix (the four dimensions are maximum sensitivity, average sensitivity, weighted sensitivity, and combined sensitivity). Then, the noise injection ratio of the sensitivity threshold matrix is ​​calculated, and the Laplace mechanism is used. The noise scale b = △f / ε (where ε is the privacy budget parameter) is determined by the privacy mechanism, and differentiated privacy budgets are set for different business scenarios (ε = 0.1 for core transaction data, ε = 0.5 for customer information data, and ε = 1.0 for statistical summary data). The final noise injection ratio is calculated based on the business importance weight of each feature (determined by business analysis, ranging from 0 to 1), and a privacy budget configuration table containing the noise parameters required for each query operation is generated; based on the privacy budget configuration table, the differential privacy protection processor parameters are loaded, and the processor adopts a GPU acceleration architecture (4 NVIDIA A100 GPU, each with 40GB video memory), parallel processing capability of 10,000 queries / second, memory cache size of 128GB, privacy parameter ε setting range limited to [0.01, 2.0], noise generation using a high-precision random number generator (entropy source is a quantum random number generator, entropy value> 7.5bits / byte), for each type of query operation (including counting statistics, sum calculation, mean calculation, median calculation, quantile calculation) pre-compiled optimization processing function to obtain the initialized processor instance; then the initialized processor instance is subjected to the privacy utility balance test, the test set contains 1,000 typical query scenarios, and the test matrix is ​​designed to cover different privacy budgets ε (from 0.01 to 2.0, with a step size of 0.01) Calculate the utility loss rate (defined as |original result - privacy-processed result| / |original result|) and privacy protection strength (quantified using KL divergence) under different query complexities (from single-field query to 25-field joint query) under each configuration, optimize the parameter configuration through binary search algorithm (find the optimal parameter combination with utility loss rate <15% and meeting privacy requirements), apply the parameter adaptive adjustment mechanism (dynamically adjust the privacy budget allocation according to query frequency and data sensitivity), and obtain the optimized post-processor configuration; assemble the data generation engine based on the optimized post-processor configuration, the engine architecture includes data reading module (throughput 500MB / s), feature extraction module (supports 257 field types), privacy parameter control module (implements budget management and consumption records), noise generation module (supports Laplace distribution and Gaussian distribution) and data synthesis module (synthesis rate 1000 records / s), the system integrates HDFS storage backend (capacity set to 50TB) and Spark distributed computing framework (configured with 64 executors, each with 4 cores and 16GB memory), and obtain the privacy protection test data generator prototype; perform privacy protection on the privacy protection test data generator prototype The protection strength verification test includes differential attack test (insert / delete a single record and observe the output change), link attack test (try to associate synthetic data with external data sources), reconstruction attack test (try to reconstruct the original data from synthetic data). The test results require that the privacy leakage risk is less than 0.001 (that is, the attack success rate is <0.1%), the generator utilization rate (defined as the proportion of useful information of the original data retained by the synthetic data) is greater than 85%, and the comprehensive score (privacy × 0.6 + utilization rate × 0.4) is greater than 0.8. All test indicators are organized into a generator compliance report; finally, based on the generator The device compliance report fine-tuned the parameters of the privacy protection test data generator prototype, adjusted the noise distribution function parameters (the standard deviation was adjusted to 0.95 times the original value), updated the budget allocation strategy (increased the budget by 10% for high-frequency queries), optimized the cache management algorithm (LRU replacement strategy, the cache hit rate was increased to 92%), and adjusted the memory usage configuration (limited the working set size to 75% of the total memory). After fine-tuning, the system increased the data usage rate by 3.5 percentage points while maintaining the same privacy guarantee, and reduced the query response time by 25%, completing the final configuration of the privacy protection test data generator. .

[0034] The present invention constructs a privacy protection test data generation system with high adaptability, high security and strong compliance through dynamic configuration and iterative optimization of differential privacy parameters. First, by extracting the data distribution characteristics in the data isolation verification environment, the differential privacy parameter configuration is more in line with the characteristics of real business data, and the pertinence and effectiveness of the privacy protection strategy are improved; combined with sensitivity analysis and privacy budget calculation, the mathematical rationality of privacy protection and the controllability of protection are ensured; through the privacy utility balance test, the contradiction between data availability and privacy protection is effectively reconciled to ensure that the generated data is representative and has application value in the test; the constructed data generation engine has the characteristics of strong configurability and flexible response, which is easy to integrate into diversified test scenarios; in the generator compliance verification and parameter fine-tuning link, it effectively ensures that the generator output complies with relevant data protection laws and security specifications, and reduces the risk of data leakage caused by improper synthetic data; the final privacy protection test data generator can not only generate high-quality synthetic data with statistical authenticity, but also take into account regulatory compliance and system security, providing a robust and secure input basis for business data testing and algorithm verification.

[0035] It is particularly important that in step S4, training the privacy-preserving test data generator to generate a synthetic test data set includes: Extract training samples from the original business data stream based on the data isolation verification environment to obtain a training data set; Input the training data set into the privacy-preserving test data generator to obtain the generator model in training; Perform data distribution consistency detection based on the generator model in training and obtain a model performance evaluation report; Perform parameter tuning device analysis on the model performance evaluation report to obtain the optimized generator model; Based on the optimized generator model, a high-performance data synthesis engine is called to obtain a synthetic test dataset.

[0036] In the embodiment of the present invention, first, the system performs a training sample extraction operation on the original business data stream based on the data isolation verification environment, extracts 20% of the records from the original data through a random stratified sampling algorithm, and keeps the data distribution characteristics unchanged, thereby obtaining a training data set with a training data capacity of 500MB; then, the system inputs the training data set into a privacy-preserving test data generator through a TLS1.3 secure channel. The generator adopts a Gaussian distribution noise injection mechanism and sets the noise scale parameters ε=0.1, δ=10^-5, performs 10,000 iterations of training in a 512GB memory space, and obtains a generator model in training; then, the system uses the Kolmogorov-Smirnov The test method performs data distribution consistency detection on the generator model in training, sets the confidence level α=0.05, calculates the KS statistic of the numeric field and the chi-square value of the categorical field, and generates a model performance evaluation report containing 95 feature distribution comparison results; then, based on the 12 distribution deviation points identified in the model performance evaluation report, the system calls the parameter tuning device for analysis. The device uses the Bayesian optimization algorithm, sets the learning rate to 0.001, the batch size to 256, performs 50 rounds of exploration and search, adjusts the weight parameters for each deviation point, and obtains the optimized generator model with the deviation mean reduced to below 0.03; finally, based on the optimized generator model, the system calls the CPU with 32 cores and 128GB RAM. RAM's high-performance data synthesis engine sets the batch size to 1,000 records, performs 500 batch generation operations, and applies the forward propagation algorithm and probabilistic sampling method to generate a synthetic test data set containing 500,000 records, retaining the original data structure and a total size of 2.5GB within 30 minutes. At the same time, it ensures that the statistical characteristics of the generated data differ from the original data within 5% and does not contain any explicit identifiers that can be directly mapped to the original records.

[0037] The present invention realizes the generation of high-quality, privacy-protecting synthetic test data sets by constructing a closed-loop generator training and evaluation optimization process. First, by extracting training samples in a data isolation verification environment, it ensures that the training data is representative and isolated from the real production system, effectively preventing the leakage of original data; secondly, by training the privacy protection test data generator and combining the data distribution consistency detection method, the model is highly fitted with the original data in terms of data structure, statistical characteristics, etc., ensuring that the generated data has real business characteristics; the model performance evaluation and parameter tuning process improves the generalization ability and generation quality of the generator, so that the synthetic data maintains stability and availability in multi-scenario testing; the synthetic test data set finally generated neither leaks the original sensitive information nor retains the real business laws of the data, providing a high-quality, low-risk synthetic data foundation for subsequent testing, verification, risk analysis and other applications, significantly improving the security, effectiveness and compliance of data testing.

[0038] Preferably, step S5 comprises the following steps: Step S51: Initialize the identity authentication of the U-shield physical encryption module to obtain a hardware security authenticator; Step S52: Digitally sign the smart contract code based on the hardware security authenticator to obtain a secure and trusted deployment package; Step S53: deploying smart contracts on the distributed data processing traceability platform based on the synthetic test data set and the secure and trusted deployment package to obtain an automated verification engine; Step S54: Load the verification instruction of the U-Shield security chip based on the data processing hash chain and the automated verification engine to obtain a hardware accelerated verification unit; Step S55: performing data comparison and analysis between the source system and the target system based on the hardware acceleration verification unit, and generating a risk calculation difference report; Step S56: configuring a risk calculation graph visualization server based on the synthetic test data set and the data isolation verification environment; Step S57: inputting data into the risk calculation graph visualization server based on the synthetic test data set to obtain risk data preprocessing results; Step S58: Based on the risk data preprocessing results and the data isolation verification environment, U-shield command interaction is performed to obtain a hardware accelerated rendering unit; Step S59: constructing a risk data relationship topology based on the hardware accelerated rendering unit to obtain a risk calculation directed acyclic graph.

[0039] In the embodiment of the present invention, the system connects to the physical encryption module of the U shield and initializes the identity authentication process through the three-way handshake protocol. The authentication process first requires the input of an 8-digit PIN code. If the PIN code is entered incorrectly more than 3 times, the U shield will be locked. After the authentication is successful, the system calls the RSA-4096 key pair generator built into the U shield to create a temporary session key. The session key is valid for 30 minutes. The system uses the key to establish a 256-bit encrypted communication channel with the U shield. After the channel is established, the system obtains a hardware security authenticator, which includes a 512-bit digital certificate and a 128-bit session token; the smart contract code to be deployed is submitted to the hardware security authenticator. The contract code is written in Solidity language, and the code length is controlled within 10KB. The system calls the ECDSA-P256 algorithm built into the U shield through the hardware security authenticator to digitally sign the smart contract code. The signing process is completed in the physically isolated environment of the U shield. The signature result is 64 bytes long, and the system prints the signature result together with the smart contract code. The package is a secure and trusted deployment package, which is encapsulated in PKCS#7 format; the distributed data processing traceability platform is prepared for deployment based on a synthetic test data set, and the fuel limit for contract deployment is set to 5 million units. The secure and trusted deployment package is submitted to the contract deployment service of the distributed data processing traceability platform through the HTTPS protocol. After the service verifies the digital signature in the deployment package, it executes the bytecode compilation of the smart contract. The compiled code size does not exceed 15KB, and then the compiled bytecode is deployed to all nodes of the blockchain network. The deployment adopts a two-phase submission protocol to ensure consistency. After the deployment is completed, the system obtains an automated verification engine instance, which contains the contract address and ABI interface definition; the verification path and hash summary are extracted from the data processing hash chain. The path depth does not exceed 20 layers, and the summary length is fixed to 256 bits. This information is formatted into APDU instructions that comply with the U-Shield security chip interface specification. The instruction length is controlled within 256 bytes, and super-long instructions are transmitted in fragments through USB. The 3.0 interface loads the verification instruction to the U-Shield security chip at a rate of 480Mbps. After receiving the instruction, the chip starts the built-in SHA-256 hash verification circuit. The operating frequency of the circuit is 1.2GHz, and the verification throughput reaches 5 million hash operations per second. After the verification is completed, the system obtains a hardware acceleration verification unit; based on the hardware acceleration verification unit, the data comparison and analysis process between the source system and the target system is started, and the standardized business data records are extracted from the source system. The total number of records is controlled within 100,000 per batch. At the same time, the corresponding processing result data is extracted from the target system. The data on both sides use the same 256-bit SHA-3 algorithm to calculate the checksum. The system performs precise comparison of each field through the hardware acceleration verification unit. The comparison uses bit-by-bit XOR to calculate the difference. The comparison results of each field are summarized and counted. The difference exceeds the threshold of 0.01% of the fields are marked as risk points. The system sorts all risk points according to severity and generates a structured risk calculation difference report. The report includes the total difference, difference distribution and typical difference samples. Based on the synthetic test data set and data isolation verification environment, a risk calculation graph visualization server is configured. The server is configured with a 32-core CPU, 256GB memory and 4TB NVMe storage. A distributed graph database is deployed on the server. The database is configured as a 5-node cluster. Each node has a processing capacity of 6000TPS, and the overall cluster throughput reaches 30000TPS. The synthetic test data set is imported into the risk calculation graph visualization server. The import process adopts batch parallel loading. The batch size is set to 1000 records and the parallelism is set to 16. The imported data is cleaned and standardized. The processing includes type conversion, null value filling and outlier filtering. The processing parameters are set to uniformly use UTF-8 encoding, the date and time format is unified to ISO-8601 standard, and the numerical precision is unified to 4 decimal places. After the processing is completed, the system obtains the risk data preprocessing results. Based on the risk data preprocessing results and the data isolation verification environment, the U shield physical device is connected through an encrypted channel. The connection adopts TLS 1.3 The protocol ensures security. Graphics rendering acceleration instructions are sent to the U-Shield. The instructions include data structure description and rendering parameters. After receiving the instructions, the U-Shield activates the built-in graphics processing unit, which has 1024 CUDA cores, a processing frequency of 1.5GHz, and a memory bandwidth of 336GB / s. The system uses this hardware resource to accelerate graphics rendering calculations. The rendering uses a force-directed algorithm to optimize the graph layout. The number of iterations is set to 500 times, and the convergence threshold is set to 0.001. After the calculation is completed, the system obtains a hardware-accelerated rendering unit; based on the hardware-accelerated rendering unit, the relationship topology between risk data is constructed. The topology construction adopts a hierarchical structure, the maximum level depth is set to 5 layers, and the number of nodes in each layer is limited to 200. The connection weight between nodes is calculated based on data correlation. The correlation is calculated using the Pearson correlation coefficient method. The correlation coefficient threshold is set to 0.7. Node pairs exceeding the threshold are connected, and the constructed topology is organized into a directed acyclic graph to ensure that there is no circular dependency. The total number of nodes in the graph is controlled within 1000, and the number of edges is controlled within 5000. Finally, a directed acyclic graph for risk calculation is generated. .

[0040] The present invention integrates the U-shield physical encryption module, smart contract security deployment, high-performance verification and visual rendering mechanism to build a business data verification system that is safe, reliable, highly automated and clearly visually expressed. First, with the help of the identity authentication mechanism of the U-shield hardware, the identity credibility of the key links of the system and the physical security of instruction execution are ensured, effectively preventing illegal tampering and signature forgery; by digitally signing and trustworthy deployment of smart contracts, the source of the verification logic is traceable and the execution process is auditable; combined with the hardware acceleration verification unit, the processing speed and accuracy of data comparison and risk analysis are significantly improved, which is suitable for high-frequency and high-concurrency data verification scenarios; synthetic test data and isolation environments are introduced to achieve dual guarantees of security and effectiveness of business data testing; in terms of risk calculation, the graph visualization server and the U-shield instruction interaction mechanism are integrated, and a risk calculation directed acyclic graph with clear structure and rigorous logic is constructed through the hardware acceleration rendering unit, making the risk propagation path of complex data relationships more intuitive and visible; overall, it not only improves the automation and accuracy of data consistency verification and difference analysis, but also provides highly secure and fully controllable technical support for scenarios such as regulatory audits and compliance testing.

[0041] Preferably, step S54 includes the following steps: Step S541: extracting the key verification path and data summary index from the data processing hash chain to generate a verification task instruction set; Step S542: formatting the verification task instruction set into an instruction stream that complies with the U-Shield security chip interface protocol; Step S543: Start the USB-shield communication driver in the automated verification engine, and load the verification instruction stream to the USB-shield security chip through the USB / Type-C interface; Step S544: Start the hardware verification engine in the U-Shield security chip, complete key matching, digital signature verification and hash chain recalculation, and generate a verification result; Step S545: Compare the verification result with the data processing hash chain to generate a verification consistency flag; Step S546: Report the verification consistency flag to the automated verification engine, trigger the risk difference analysis / consistency confirmation process, and form a hardware accelerated verification unit.

[0042] In an embodiment of the present invention, a hash chain analyzer is called to extract a key verification path from a data processing hash chain. The analyzer uses a depth-first search algorithm to traverse the hash chain structure, identify the path nodes required for verification, and extract the data summary index associated with each path node. The index format is a 64-bit hexadecimal string. Each index corresponds to a specific business data processing operation. After the extraction is completed, the system organizes these paths and indexes into a structured XML format verification task instruction set. The instruction set contains the number of verification nodes, the verification path topology, and the summary index value of each node; the format conversion processor is started to convert the verification task instruction set into an APDU instruction stream that complies with the U-shield security chip interface protocol. The APDU instruction header length is fixed to 5 bytes, the maximum length of the instruction body is 255 bytes, and the fragment transmission technology is used for super-long instructions. The fragment size is 240 bytes, and each fragment is attached with 16 bytes of verification information; the U-shield communication driver in the automated verification engine is started, and the driver uses USB 3.1 or Type-C interface to establish a communication connection with the U-Shield security chip, the communication rate is set to 480Mbps, the transmission adopts asynchronous mode, and the timeout threshold is set to 2 seconds. After the connection is successfully established, the system will load the formatted verification instruction stream to the U-Shield security chip in sequence through the established communication channel. The loading process adopts a block transmission and confirmation mechanism to ensure that each instruction block is received correctly; after receiving the verification instruction stream, the U-Shield security chip activates the built-in hardware verification engine. The verification engine runs at a frequency of 1.2GHz. First, the RSA-2048 algorithm is executed for key matching operations to compare the key fingerprint in the instruction with the master key stored in the security chip. Then the verification engine uses the ECDSA-P256 algorithm to verify the validity of the digital signature. After the verification is passed, the engine calls the SHA-256 algorithm to recalculate the hash value of the key node in the hash chain. The hash calculation speed is 5 million hash operations per second. After the calculation is completed Generate a verification result data packet containing all recalculated hash values; compare the recalculated hash values ​​in the verification result data packet with the original hash values ​​stored in the data processing hash chain one by one. The comparison algorithm uses a bitwise XOR operation followed by summation. A comparison result of zero indicates a complete match, and any non-zero result indicates an inconsistency. After the comparison is completed, the system generates a verification consistency flag in binary format, 1 indicates complete consistency, and 0 indicates a difference. The verification consistency flag and detailed verification comparison data are reported to the automated verification engine through the U-Shield communication driver. The report adopts an encrypted channel and uses the AES-256-GCM algorithm to ensure data transmission security. When the received consistency flag value is 1, the automated verification engine triggers the consistency confirmation process and generates a confirmation report. When the consistency flag value is 0, the risk difference analysis process is triggered and a difference detail report is generated. The above verification results and processing logic together constitute a hardware accelerated verification unit.

[0043] The present invention realizes efficient, reliable and consistent verification of data processing hash chain by integrating the instruction interaction and hardware acceleration verification capabilities of U shield security chip. By extracting key verification paths and data summary indexes from hash chain, it can accurately focus on core verification content and improve the pertinence and execution efficiency of verification tasks; instruction set standardization ensures its compatibility with U shield hardware interface and enhances system versatility and stability; combined with U shield communication driver and interface loading mechanism, it realizes automation and high-speed injection of verification tasks, reduces manual participation cost and error risk; completes key verification, signature verification and hash recalculation in the hardware verification process, has extremely high computing performance and security level, and effectively prevents software-level attacks or tampering behaviors; through the automatic comparison mechanism of verification results and original hash chain, it can quickly generate consistency marks and realize result reporting, further drive difference analysis and consistency confirmation process, and build a complete hardware acceleration verification unit; the overall solution can significantly improve the automation, security and response speed of data processing verification process, and is particularly suitable for business scenarios with extremely high requirements for verification strength, real-time and non-repudiation.

[0044] Preferably, step S6 comprises the following steps: Step S61: Based on the risk calculation difference report authorization verification U shield identity authentication, obtain the senior management authority authentication token; Step S62: Loading the topological data of the preset difference positioning processor based on the risk calculation directed acyclic graph to obtain a difference correlation analysis matrix; Step S63: Perform U-shield security computing co-processing on the preset difference positioning processor to obtain a difference root cause analysis report; Step S64: configuring the incremental compensation processing device operating parameters based on the difference correlation analysis matrix to obtain a compensation strategy configuration file; Step S65: Write the correction rules into the built-in secure memory of the USB shield based on the difference root cause analysis report to obtain an anti-tampering correction rule library; Step S66: arranging a correction workflow engine strategy based on the compensation strategy configuration file and the anti-tampering correction rule library to obtain an automated correction workflow; Step S67: Based on the senior management authority authentication token, the automated correction workflow is scheduled for execution to obtain a system consistency verification result.

[0045] In the embodiment of the present invention, after receiving the risk calculation difference report, the U shield identity authentication process is triggered. The process adopts a multi-factor authentication mechanism, including physical contact verification, 8-digit PIN code verification and fingerprint biometric verification. After all three verifications are passed, the system calls the RSA-4096 key generator built into the U shield to create a temporary management key with a validity period of 3 hours. The key length is 4096 bits. The system uses the key to establish an AES-256-GCM encryption channel with the management backend. After the channel is successfully established, the system obtains a high-level management authority authentication token. The token contains a 512-bit digital signature and a 256-bit timestamp; the topology data is exported from the risk calculation directed acyclic graph. The topology data contains a structured description with a node number not exceeding 1000 and an edge number not exceeding 5000. The system loads the topology data into a preset difference positioning processor. The processor is configured with a 32-core CPU, 128GB of memory and 2TB of NVMe storage, the system performs sparse matrix conversion on the loaded data, the matrix dimension does not exceed 1000×1000, and the matrix elements use 32-bit floating point numbers to represent the difference weights. The system performs a singular value decomposition algorithm on the converted matrix, sets the singular value truncation threshold to 0.01, and obtains a compressed representation of the difference correlation analysis matrix; the difference correlation analysis matrix is ​​transmitted to the U-shield security computing co-processing module in a block-by-block manner, with each block size of 4KB and a transmission rate of 400Mbps. After receiving the data, the U-shield starts the built-in graph algorithm accelerator, which has 256 dedicated computing cores and a frequency of 1.8GH z, the system executes the PageRank algorithm and community detection algorithm in the U-Shield security environment, the number of algorithm iterations is set to 200 times, the convergence threshold is set to 0.0001, and the algorithm execution results are returned to the main system after being signed by the RSA-2048 algorithm. The system builds a difference root cause analysis report based on the returned results. The report contains up to 10 key root cause nodes and 50 secondary impact nodes; the incremental compensation processing device is configured based on the difference correlation analysis matrix. The device adopts an FPGA-based hardware acceleration architecture. The FPGA chip is the Xilinx Ultrascale+ series, and the number of logic units is 1.3M, the number of DSP units is 6840, the system compiles the difference data and compensation strategy into FPGA firmware, the firmware size does not exceed 20MB, the system loads the firmware into the FPGA chip and initializes the operating environment, the environment configuration includes 32-bit floating-point operation precision, 128KB cache size and 16-channel parallel processing capability, the system generates a compensation strategy configuration file, the file is in XML format, the size does not exceed 5MB, and contains a complete description of the compensation rules and execution order; the difference root cause analysis report is converted into a binary correction rule format, the rule set size is controlled within 2MB, each rule contains a 128-bit condition description and a 256-bit execution operation, the system writes the rule set into the built-in secure memory of the U shield through a secure channel, the memory adopts an anti-tampering design, and the writing speed is 50MB / s. After the writing is completed, the system performs SHA-256 hash verification to ensure data integrity. After the verification is passed, the system obtains the anti-tampering correction rule library; the workflow orchestration engine is called based on the compensation strategy configuration file and the anti-tampering correction rule library, and the engine adopts BPMN 2.0 standard defines workflow. The system first builds a task dependency graph in the form of DAG. The graph contains no more than 100 task nodes, and each node is associated with a specific correction operation. The system executes a topological sorting algorithm on the graph to determine the order of task execution. After sorting, the system allocates task execution resources, including the number of CPU cores, memory capacity, and storage bandwidth. The system generates an automated correction workflow containing a complete execution plan. The workflow file uses the JSON format and is no larger than 10MB in size. The automated correction workflow is scheduled for execution based on the advanced management authority authentication token. The system first verifies the digital signature and validity period of the token. After the verification is passed, the system starts the distributed task scheduler and schedules the task. The server adopts a master-slave architecture. The master node is responsible for global scheduling, and the slave node performs specific tasks. The communication between nodes uses a message queue based on ZeroMQ. The system decomposes the workflow into atomic tasks. The execution time of each atomic task does not exceed 5 minutes. The system uses a transaction processing mechanism to ensure the atomicity of task execution. Data verification is performed before and after each task is executed. The verification uses the CRC32 algorithm. If the verification value does not match, it will automatically roll back. After all tasks are executed, the system summarizes the execution results and generates a system consistency verification result containing consistency indicators and correction details. The results are presented in HTML format. The report size does not exceed 20MB, including two display forms: charts and data tables. .

[0046] The present invention effectively builds a set of high-security, high-precision, and fully automatic system consistency repair mechanism by integrating U-shield identity authentication, secure computing, difference root cause analysis, and automated correction process. The U-shield is used to realize the secure authorization of senior management rights, ensuring that the startup and scheduling of the correction process have strong identity protection to prevent unauthorized operations; the loading of difference positioning and correlation analysis matrix realizes the structured understanding of complex data dependencies and the accurate modeling of difference propagation paths, and improves the depth and breadth of problem identification; the root cause analysis is completed through the co-processing capability of the U-shield, and the highly reliable difference identification results are provided in combination with the hardware trust environment; the combination of compensation strategy configuration and anti-tampering correction rules not only realizes the generation of flexible and dynamic correction schemes, but also ensures the integrity and non-tamperability of the correction rules during the execution process; finally, the difference closed-loop correction and system consistency verification are completed through the automated workflow engine, effectively reducing manual intervention and improving response speed and execution accuracy; the mechanism is widely applicable to application scenarios such as business data synchronization, regulatory compliance auditing, and cross-system consistency verification, providing solid technical support for building a trusted business data processing and correction system.

[0047] Preferably, the present invention further provides a business data testing system for executing the above-mentioned business data testing method, the business data testing system comprising: The data isolation sandbox deployment module is used to obtain the original business data flow; deploy the regulatory reporting shadow processing sandbox based on the original business data flow to obtain the data isolation verification environment; The blockchain traceability platform construction module is used to configure the alliance blockchain server based on the original business data flow and build a distributed data processing traceability platform; The data processing proof generation module is used to generate data processing proof based on the distributed data processing traceability platform using the original business data stream to obtain the data processing hash chain; A privacy synthetic test data generation module is used to configure the differential privacy protection processor parameters based on the data isolation verification environment to obtain a privacy protection test data generator; train the privacy protection test data generator to generate a synthetic test data set; The automated difference analysis and risk graph construction module is used to deploy smart contracts on the distributed data processing traceability platform based on the synthetic test data set to obtain the automated verification engine; perform data comparison and analysis between the source system and the target system based on the data processing hash chain and the automated verification engine to generate a risk calculation difference report; configure the risk calculation graph visualization server based on the synthetic test data set and the data isolation verification environment, and generate a risk calculation directed acyclic graph; The difference repair and consistency verification module is used to preset the difference positioning processor based on the risk calculation difference report and the risk calculation directed acyclic graph operation, and configure the incremental compensation processing equipment to generate an automated correction workflow; execute the automated correction workflow to generate a system consistency verification result.

[0048] Preferably, the present invention further provides a computer-readable storage medium on which a computer program is stored, and the computer program implements the business data testing method when executed.

Claims

1. A business data testing method, characterized in that: The following steps are involved: Step S1: Obtain the original business data flow; deploy the regulatory reporting shadow processing sandbox based on the original business data flow to obtain a data isolation verification environment; Step S2: Configure the alliance blockchain server based on the original business data flow and build a distributed data processing traceability platform; Step S3: Generate data processing proof using the original business data stream based on the distributed data processing traceability platform to obtain a data processing hash chain; Step S4: configuring the differential privacy protection processor parameters based on the data isolation verification environment to obtain a privacy protection test data generator; Train the privacy-preserving test data generator to generate a synthetic test dataset; Step S5: Deploy smart contracts on the distributed data processing traceability platform based on the synthetic test data set to obtain an automated verification engine; Compare and analyze data between source and target systems based on data processing hash chains and automated verification engines, and generate risk calculation difference reports; Configure the risk calculation graph visualization server based on the synthetic test data set and data isolation verification environment, and generate a risk calculation directed acyclic graph; Step S6: presetting a difference positioning processor based on the risk calculation difference report and the risk calculation directed acyclic graph operation, configuring an incremental compensation processing device, and generating an automated correction workflow; Execute automated remediation workflows and generate system consistency verification results.

2. The service data testing method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Obtaining the original service data stream; Step S12: deploying a server rack array based on the original business data flow to obtain a shadow processing sandbox physical infrastructure; Step S13: Perform core component service orchestration based on the shadow processing sandbox physical infrastructure to obtain a microservice cluster; Step S14: Optimize the parameters of the data normalization engine based on the original business data stream to obtain a standardized data conversion module; Step S15: deploying a security isolation mechanism based on the standardized data conversion module to obtain a data isolation control unit; Step S16: Based on the standardized data conversion module and the data isolation control unit, the regulatory reporting shadow processing sandbox is integrated and deployed to obtain a data isolation verification environment.

3. The service data testing method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Allocate hardware resources of a high-performance computing server cluster based on the original business data flow to obtain a physical node array of the alliance blockchain; Step S22: configuring consensus mechanism engine parameters based on the alliance blockchain physical node array to obtain a high-throughput consensus network; Step S23: Deploy a blockchain server cluster based on a high-throughput consensus network to obtain the alliance blockchain core service; Step S24: installing a hardware accelerator of a cryptographic security component to obtain a high-performance signature verification unit; Step S25: constructing an inter-node communication protocol secure channel based on a high-performance signature verification unit to obtain an encrypted data transmission network; Step S26: Deploy a distributed data processing traceability platform based on the alliance blockchain core service and encrypted data transmission network.

4. The service data testing method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Perform format conversion preprocessing on the original business data stream to obtain a standardized data packet; load the encryption component on the standardized data packet to obtain an encrypted protection data set; Step S32: Slice the encrypted protection data set based on the alliance blockchain server, and configure the hash processor parameters based on the slicing processing results to obtain a hash generation engine; Step S33: synchronize the timestamp of the standardized data packet, and perform multiple hash calculations on the timestamp synchronization result based on the hash generation engine to obtain the original proof of data processing; Step S34: Aggregate, consensus verify and compress the original proof of data processing to generate a compact proof structure, build a tamper-proof storage link through chain encapsulation and distributed backup, and generate a data processing hash chain.

5. The service data testing method according to claim 1, characterized in that: In step S4, the differential privacy protection processor parameters are configured based on the data isolation verification environment, including: Extracting data distribution feature sets from the data isolation verification environment; Calibrate the differential privacy sensitivity analyzer based on the data distribution feature set and generate a sensitivity threshold matrix; Calculate the noise injection ratio of the sensitivity threshold matrix to obtain the privacy budget configuration table; Load the differential privacy protection processor parameters based on the privacy budget configuration table to obtain the initialized processor instance; Perform a privacy utility balance test on the initialized processor instance to obtain the optimized processor configuration; Based on the optimized post-processor configuration, the data generation engine is assembled to obtain the prototype of the privacy-preserving test data generator; Conduct privacy protection strength verification equipment test on the privacy protection test data generator prototype and obtain the generator compliance report; Based on the generator compliance report, the parameters of the privacy-preserving test data generator prototype are fine-tuned to obtain the privacy-preserving test data generator.

6. The service data testing method according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: Initialize the identity authentication of the U-shield physical encryption module to obtain a hardware security authenticator; Step S52: Digitally sign the smart contract code based on the hardware security authenticator to obtain a secure and trusted deployment package; Step S53: deploying smart contracts on the distributed data processing traceability platform based on the synthetic test data set and the secure and trusted deployment package to obtain an automated verification engine; Step S54: Load the verification instruction of the U-Shield security chip based on the data processing hash chain and the automated verification engine to obtain a hardware accelerated verification unit; Step S55: performing data comparison and analysis between the source system and the target system based on the hardware acceleration verification unit, and generating a risk calculation difference report; Step S56: configuring a risk calculation graph visualization server based on the synthetic test data set and the data isolation verification environment; Step S57: inputting data into the risk calculation graph visualization server based on the synthetic test data set to obtain risk data preprocessing results; Step S58: Based on the risk data preprocessing results and the data isolation verification environment, U-shield command interaction is performed to obtain a hardware accelerated rendering unit; Step S59: constructing a risk data relationship topology based on the hardware accelerated rendering unit to obtain a risk calculation directed acyclic graph.

7. The service data testing method according to claim 6, characterized in that: Step S54 includes the following steps: Step S541: extracting the key verification path and data summary index from the data processing hash chain to generate a verification task instruction set; Step S542: formatting the verification task instruction set into an instruction stream that complies with the U-Shield security chip interface protocol; Step S543: Start the USB-shield communication driver in the automated verification engine, and load the verification instruction stream to the USB-shield security chip through the USB / Type-C interface; Step S544: Start the hardware verification engine in the U-Shield security chip, complete key matching, digital signature verification and hash chain recalculation, and generate a verification result; Step S545: Compare the verification result with the data processing hash chain to generate a verification consistency flag; Step S546: Report the verification consistency flag to the automated verification engine, trigger the risk difference analysis / consistency confirmation process, and form a hardware accelerated verification unit.

8. The service data testing method according to claim 1, characterized in that: Step S6 includes the following steps: Step S61: Based on the risk calculation difference report authorization verification U shield identity authentication, obtain the senior management authority authentication token; Step S62: Loading the topological data of the preset difference positioning processor based on the risk calculation directed acyclic graph to obtain a difference correlation analysis matrix; Step S63: Perform U-shield security computing co-processing on the preset difference positioning processor to obtain a difference root cause analysis report; Step S64: configuring the incremental compensation processing device operating parameters based on the difference correlation analysis matrix to obtain a compensation strategy configuration file; Step S65: Write the correction rules into the built-in secure memory of the USB shield based on the difference root cause analysis report to obtain an anti-tampering correction rule library; Step S66: arranging a correction workflow engine strategy based on the compensation strategy configuration file and the anti-tampering correction rule library to obtain an automated correction workflow; Step S67: Based on the senior management authority authentication token, the automated correction workflow is scheduled for execution to obtain a system consistency verification result.

9. A business data testing system, characterized in that: Used to execute the service data testing method according to claim 1, the service data testing system comprises: The data isolation sandbox deployment module is used to obtain the original business data flow; deploy the regulatory reporting shadow processing sandbox based on the original business data flow to obtain the data isolation verification environment; The blockchain traceability platform construction module is used to configure the alliance blockchain server based on the original business data flow and build a distributed data processing traceability platform; The data processing proof generation module is used to generate data processing proof based on the distributed data processing traceability platform using the original business data stream to obtain the data processing hash chain; A privacy synthetic test data generation module is used to configure the differential privacy protection processor parameters based on the data isolation verification environment to obtain a privacy protection test data generator; train the privacy protection test data generator to generate a synthetic test data set; The automated difference analysis and risk graph construction module is used to deploy smart contracts on the distributed data processing traceability platform based on the synthetic test data set to obtain the automated verification engine; perform data comparison and analysis between the source system and the target system based on the data processing hash chain and the automated verification engine to generate a risk calculation difference report; configure the risk calculation graph visualization server based on the synthetic test data set and the data isolation verification environment, and generate a risk calculation directed acyclic graph; The difference repair and consistency verification module is used to preset the difference positioning processor based on the risk calculation difference report and the risk calculation directed acyclic graph operation, and configure the incremental compensation processing equipment to generate an automated correction workflow; execute the automated correction workflow to generate a system consistency verification result.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the business data testing method according to any one of claims 1 to 8 is implemented.

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