Market supervision data exchange platform migration and security enhancement method and system based on credential environment

Abstract

The invention discloses a market supervision data exchange platform migration and security enhancement method and system based on a credential environment, and relates to the technical field of network security. In the data migration process, the consistency and privacy protection of the data are ensured through Hash check and a multi-party security computing technology; a domestic commercial cryptographic algorithm and a quantum resistance encryption technology are introduced, so that the protection capability of the platform to existing security threats is effectively improved, and the resistance to future quantum computing threats is enhanced; an intrusion detection system and a virtual honeypot node are integrated, and a defense strategy is monitored and updated in real time in combination with a deep learning model, so that novel attack behaviors can be detected and coped with in real time; according to an intrusion detection alarm and a block chain audit result, a security policy and resource allocation are dynamically adjusted in combination with a machine learning technology, it is ensured that high-risk nodes are monitored in time, resource allocation is automatically optimized, and the resource utilization efficiency and security of the system are improved.

Description

Technical Field

[0001] The present invention relates to the field of network security technology, and particularly to a method and system for migrating and enhancing the security of a market supervision data exchange platform based on the Xinchuang environment. Background Art

[0002] With the rapid development of information technology, data exchange is increasingly widely used in the field of market supervision. However, when traditional market supervision data exchange platforms are migrated to a new computing environment, they face a series of technical challenges.

[0003] Firstly, when existing platforms are migrated to domestic hardware, the compatibility issues of operating systems, databases, and middleware are relatively prominent, and there is a lack of effective automated tools for compatibility testing and performance optimization. Secondly, during the data migration process, how to ensure the consistency and privacy protection of large-scale data is a difficult task, and existing verification methods are difficult to achieve efficient and accurate data verification. Thirdly, traditional encryption and decryption algorithms are difficult to cope with the potential risks brought by quantum computing. Especially during the platform migration process, it is difficult to balance security and business performance when replacing the password system. In addition, existing intrusion detection systems cannot effectively deal with complex network attacks, and the deployment efficiency of honeypot systems is low, and attack features cannot be updated in real time. Although the introduction of blockchain technology can improve the data transparency and security of the platform, existing technologies are mostly single applications, lacking the implementation of functions such as multi-party authentication and reputation evaluation.

[0004] Therefore, developing a method for migrating and enhancing the security of a market supervision data exchange platform based on the Xinchuang environment that can effectively solve these problems is of great significance for improving the security, stability, and flexibility of the platform. Summary of the Invention

[0005] Aiming at the compatibility, data consistency, security, resource optimization and other problems faced by traditional platforms during the migration process, the present invention proposes a method and system for migrating and enhancing the security of a market supervision data exchange platform based on the Xinchuang environment, which can realize the smooth migration of the market supervision data exchange platform to the domestic software and hardware environment, and significantly improve the data security and operation reliability of the platform during and after the migration.

[0006] The present invention realizes the above object through the following technical solutions: A method for migrating and enhancing the security of a market supervision data exchange platform based on the Xinchuang environment, the method comprising: Deploying an adapted operating system, database, and middleware on the domestic hardware platform to complete the compatibility test of the operating environment; Modularly disassembling the system architecture of the existing platform, analyzing the call relationship between components through a dependency matrix, assigning migration priorities to each module in combination with business criticality, and formulating a migration plan; Import business data into the database of the domestic information technology platform in batches. After each batch migration, ensure data consistency through hash verification, and enhance data privacy protection through multi-party secure computing; Start business modules on the new platform according to the migration priority. During the parallel operation of the old and new platforms, conduct output comparison, and switch business traffic after verification; Replace the encryption and decryption algorithms with domestic commercial cryptographic algorithms, deploy a cryptographic service adaptation layer to connect to cryptographic hardware devices, and strengthen encryption protection by combining quantum-resistant encryption technology; Integrate an intrusion detection system and virtual honeypot nodes, monitor data exchange traffic in real time and trap attack behaviors, update attack features to the defense rule library, and update defense strategies in real time by combining deep learning models; Build a consortium blockchain network to record data exchange logs and security events, implement node identity authentication and dynamic reputation assessment based on smart contracts, and enhance the flexibility of identity authentication by combining centralized identity management; According to intrusion detection alerts and blockchain audit results, dynamically adjust security policies and resource allocation parameters by combining machine learning technologies to achieve adaptive defense.

[0007] Preferably, the deployment of adapted operating systems, databases, and middleware on the domestic hardware platform is achieved through automated adaptation tools and intelligent optimization algorithms, where: The automated adaptation tool evaluates the compatibility of the operating system, database, and middleware through a combination of static code analysis and runtime performance analysis. By analyzing the code and execution logs of the existing platform, a compatibility report is generated, and an adaptation configuration plan is automatically generated; The intelligent optimization algorithm specifically uses a machine learning model to predict the compatibility of domestic operating systems, databases, and middleware, and dynamically adjusts the operating system kernel parameters and database cache policies based on hardware resource requirements to minimize latency and maximize throughput. The machine learning model continuously optimizes the configuration according to historical data and real-time monitoring data; During the deployment process, combined with hardware acceleration technology, use GPU or FPGA to dynamically accelerate specified computing tasks. According to the task type and hardware resource availability, automatically select the hardware acceleration method, and automatically enable hardware acceleration resources when the system load is higher than the set threshold; Modularize the operating system, database, and middleware through containerization technology, support dynamic resource allocation, automatically monitor resource consumption, and adjust the computing and storage resources of the container.

[0008] Preferably, the method for formulating the migration plan specifically includes: Obtain the internal dependencies of the platform by combining static code analysis and runtime log analysis, and evaluate the resource consumption and performance bottlenecks of each module in combination with dynamic performance analysis; Based on the above dependencies and performance evaluation, construct a dependency matrix of functional modules and data units, and predict potential dependencies between modules in combination with a machine learning model; Use a multi-objective optimization algorithm to generate a migration sequence, optimize the migration order of key modules, reduce the impact on system services during the migration process, and simultaneously meet multiple objectives such as the shortest downtime and maximized resource utilization; Based on the above dependency matrix and multi-objective optimization algorithm, formulate a migration plan including the migration order, expected downtime, and resource utilization, and perform simulation model prediction before migration.

[0009] Preferably, after each batch of migration, ensure data consistency through hash verification. The method includes: After each batch of migration, verify data consistency by comparing the record counts and hash values of the same-named tables in the old and new databases; When inconsistency is found, adopt an incremental synchronization strategy, only re-transmit the changed records, and simultaneously combine a distributed hash table and asynchronous multi-threaded verification technology to accelerate the data verification process; If the data is still inconsistent, compare the different records one by one and re-transmit the missing or inconsistent records until the corresponding data sets on the old and new platforms are exactly the same.

[0010] Preferably, perform output comparison during the parallel operation of the old and new platforms, specifically including: Implement intelligent test request scheduling for selected test requests, and dynamically select the test request type and priority according to the historical operation data and load conditions of the platform; Introduce a real-time performance monitoring system, continuously track the response time, load conditions, and error rate of the old and new platforms, and trigger an automatic rollback mechanism when an anomaly occurs to switch the service traffic back to the old platform to ensure the stable operation of the new platform; Adopt an incremental traffic switching strategy, gradually increase the traffic load of the new platform, and finally complete the full-volume traffic switch to ensure that the new platform can carry the service traffic under high load; Combine an intelligent anomaly detection algorithm to analyze potential anomalies in the comparison results in real time to ensure the high accuracy and timely feedback of the test comparison process.

[0011] Preferably, the domestic commercial cryptographic algorithms include SM2 / SM9 public key algorithms, SM4 symmetric encryption algorithms, and SM3 digest algorithms, and a post-quantum encryption algorithm is introduced to enhance the platform's resistance to future quantum computing threats; The password service adaptation layer encapsulates the call interfaces of the domestic commercial cryptographic algorithms, enabling the platform application layer to call the password service in the same way as the original algorithm, and adopting a multi-level encryption method to use different encryption algorithms for protection during data transmission and storage respectively.

[0012] Preferably, the honeypot node adopts a dynamic deployment mechanism. Based on the feedback of the real-time double-layer intrusion detection system, it automatically selects different virtual or physical nodes to trap attackers, and the honeypot node dynamically identifies and analyzes the behavior characteristics of attackers by using rule feature matching and machine learning techniques; The first-layer intrusion detection system quickly identifies known attack patterns through rule feature matching. The second-layer intrusion detection system detects unknown threat patterns based on the eigenvectors of the data exchange traffic analyzed by machine learning. The honeypot node records and stores the interaction data and behavior characteristics of the attackers and feeds them back to the first-layer intrusion detection system to optimize the attack feature library; The honeypot node updates the attack features in real time through an adaptive learning algorithm combined with deep learning and actively adjusts the honeypot strategy.

[0013] Preferably, the consortium blockchain network is deployed in a permissioned chain manner. The participating nodes include the main server nodes of the data exchange platform and the regulatory superior nodes. The smart contract is used to record information such as the participants, data digest, and timestamp of each data exchange, realize the tamper-proof storage of key operations, and calculate the node trust score according to the timeliness, accuracy of the data submitted by each node, and the occurrence of security events. The trust score is calculated based on the timeliness of data submission, the integrity of each data exchange, the security event response of each node, and the security of historical data exchange behaviors.

[0014] Preferably, based on the intrusion detection alarm data, use deep learning models or reinforcement learning algorithms to analyze and predict potential attack behaviors, and automatically adjust the firewall rules, response strategies of the intrusion prevention system, and traffic filtering strategies; According to the security events and node behaviors recorded in the blockchain audit results, analyze the security status and behavior performance of each node through clustering algorithms or anomaly detection algorithms, evaluate the risk level of the nodes, and then adjust the resource allocation parameters, including computing resources, bandwidth resources, and storage resources, to ensure that high-risk nodes obtain more monitoring resources, while low-risk nodes obtain optimized resource allocation; Automatically optimize the data encryption strategy and identity authentication strategy based on the analysis results, and dynamically adjust the encryption intensity or strengthen the identity authentication mechanism for high-risk nodes or traffic.

[0015] A migration and security enhancement system for the market supervision data exchange platform based on the domestic information technology innovation environment, which is used to execute the method for migrating and enhancing the security of the market supervision data exchange platform based on the domestic information technology innovation environment as described above. The system includes: An adaptation module, deployed on the domestic hardware platform, for supporting the compatibility testing of the operating system, database, and middleware; A migration planning module, for modularly disassembling the system architecture of the existing platform, generating a dependency matrix, and formulating a migration plan; A data migration module, for performing batch-by-batch migration of data, and using hash verification and multi-party secure computing to ensure data consistency and privacy protection; A service migration module, for starting service modules according to the migration priority, and performing output comparison during parallel operation to ensure smooth traffic switching; A cryptographic module, for replacing the encryption and decryption algorithms with domestic commercial cryptographic algorithms, and introducing quantum-resistant encryption technology to enhance data protection; A defense module, for integrating an intrusion detection system and honeypot nodes, monitoring traffic in real time, and automatically updating defense policies; A blockchain module, for constructing a consortium blockchain to record data exchange logs, and using smart contracts to implement identity authentication and reputation evaluation; A policy optimization module, for dynamically adjusting security policies and resource allocation according to intrusion detection alerts and blockchain audit results, combined with machine learning.

[0016] It can efficiently evaluate and ensure the compatibility of the platform in the domestic information technology innovation environment, automatically optimize the operating system kernel parameters and database cache policies, effectively improve the operating performance of the platform, maximize the throughput and minimize the latency; perform verification after each batch of data migration, adopt incremental synchronization and distributed hash tables to accelerate data verification, significantly improve the accuracy and security of data migration, and effectively prevent data loss and tampering; introduce domestic commercial cryptographic algorithms and quantum-resistant encryption technology, which not only effectively enhance the platform's protection ability against existing security threats, but also enhance its resistance to future quantum computing threats; the dynamic deployment mechanism of honeypot nodes and attack feature learning optimize the defense rule base, improve the defense intelligence level of the platform, and enhance the ability to respond to complex attack patterns; according to intrusion detection alerts and blockchain audit results, combined with machine learning technology, dynamically adjust security policies and resource allocation, ensure that high-risk nodes are monitored in a timely manner, and automatically optimize resource allocation, improving the resource utilization efficiency and security of the system. Description of the Drawings

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings. Among them: Figure 1 It is the flowchart of the method in the embodiments of the present invention; Figure 2 It is the system structure block diagram in the embodiments of the present invention. Specific implementation manners

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions of the embodiments of the present invention in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the described embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present invention.

[0019] As Figure 1 shown, it is an embodiment of the present invention. This embodiment provides a method for migrating and enhancing the security of a market supervision data exchange platform based on an information and communication technology innovation (ICT innovation) environment, including the following steps: S1: Construction and adaptation of the ICT innovation environment Deploy the adapted operating system, database, and middleware on the domesticated hardware platform, and complete the compatibility test of the operating environment.

[0020] In one of the embodiments, deploying the adapted operating system, database, and middleware on the domesticated hardware platform realizes the efficient migration and adaptation of the platform through a combination of automated adaptation tools, intelligent optimization algorithms, hardware acceleration technologies, and containerized deployment.

[0021] In this embodiment, first use the automated adaptation tool to perform an adaptation analysis on the existing platform. The working process of this tool is divided into two stages: Static code analysis: The automated adaptation tool first performs static analysis on the code of the existing platform. The tool scans the source code of the operating system, database, and middleware, and generates a compatibility report, which includes hardware resource requirements, possible compatibility conflicts, and parts of the code that may affect migration. Static code analysis can discover potential code-level problems and provide suggestions for subsequent environment configuration and hardware selection.

[0022] Runtime Performance Analysis: After static analysis, the automated adaptation tool evaluates the performance bottlenecks of the system by real-time monitoring of the running logs. The goal of this stage is to identify potential performance bottlenecks or adaptation issues in the system by analyzing key performance indicators such as the running load, response time, memory, and CPU usage of the existing system. The results of this analysis will guide subsequent hardware configuration and operating system adjustments.

[0023] Through the above analysis, the adaptation tool can generate a compatibility report that details the configuration items that need to be optimized, possible hardware compatibility issues, and automatically generates an optimal configuration plan. This can reduce manual intervention and improve the efficiency and accuracy of the adaptation process.

[0024] According to the compatibility report, intelligent optimization algorithms are applied to optimize the configurations of the operating system, database, and middleware. The specific steps are as follows: Performance Prediction Model: Use machine learning models to predict the system's resource requirements (such as CPU, memory, storage, and I / O). This model evaluates the system's performance under different hardware resource conditions based on historical data and real-time monitoring data.

[0025] Configuration Dynamic Adjustment: Based on the prediction results of the machine learning model, the system automatically optimizes key parameters such as the kernel parameters of the operating system, the cache strategy of the database, and the connection pool configuration of the middleware. This optimization algorithm monitors the system load in real-time and dynamically adjusts the operating system kernel parameters and database cache according to changes in hardware resources (such as CPU, memory, storage) to ensure that the system has the best performance in the Xinchuang environment.

[0026] In terms of hardware acceleration, this implementation method introduces the hardware acceleration technologies of GPU and FPGA to handle compute-intensive tasks such as big data processing and encryption and decryption. The specific implementation methods are as follows: Hardware Resource Monitoring: The system monitors the usage of hardware resources in real-time and automatically selects the appropriate hardware acceleration method according to the type of current computing task and the system load.

[0027] Task Type Recognition: For example, for large-scale data processing tasks, the system preferentially selects the GPU to accelerate the calculation; while for tasks that require efficient execution of encryption and decryption operations, the system preferentially uses the FPGA to accelerate the processing. The system improves the overall computing efficiency by automatically identifying the task type and scheduling the most suitable hardware resources.

[0028] Hardware Resource Allocation: When the computing load is high, the system automatically enables the hardware acceleration resources. By using the GPU and FPGA, the system's processing ability in compute-intensive tasks can be greatly improved, reducing the computing bottleneck and enhancing the data processing speed.

[0029] To improve the flexibility of deployment and the scalability of the platform, this embodiment deploys the system through containerization technologies (such as Docker and Kubernetes). The specific implementation is as follows: Modular deployment: The system modularizes the operating system, database, and middleware, containerizes them respectively, and deploys them. Through Docker technology, each functional module runs in an independent container, which can greatly improve the flexibility of the system and reduce the dependence on hardware and the operating system.

[0030] Automatic resource scheduling: Kubernetes, as a container orchestration tool, is responsible for automatically managing the deployment, scheduling, and scaling of containers. Kubernetes will automatically allocate computing resources according to the system's load conditions and performance requirements to ensure that each container can obtain sufficient resource support and guarantee the high availability and high performance of the system.

[0031] Adaptive adjustment: The containerization solution supports dynamic resource allocation and can automatically adjust the computing and storage resources of containers according to the resource consumption situation during runtime. For example, the system can automatically increase the CPU and memory resources of the database container according to the change in database load, or automatically adjust the resource allocation of the middleware container according to the high concurrency requirements of the application service.

[0032] Cross-platform compatibility: Due to the adoption of containerization technology, the platform can be seamlessly migrated across different operating systems and hardware environments. In the Xinchuang environment, containerization enables the platform to better adapt to different hardware configurations and operating system versions, reducing hardware and software compatibility issues.

[0033] In this embodiment, the automated adaptation tool combines static code analysis and runtime performance analysis to help the system quickly evaluate compatibility issues and generate an optimal configuration plan. The intelligent optimization algorithm uses machine learning models to optimize the system in real time to maximize performance. Through dynamic hardware acceleration selection and containerized deployment, the present invention improves the adaptability and computing efficiency of the system in the Xinchuang environment, ensures seamless transition during the migration process of the platform, and enables stable operation under various load conditions. These methods not only optimize the adaptation process of the Xinchuang environment but also improve the performance, scalability, and flexibility of the platform, providing strong technical support for the migration and security enhancement of the market supervision data exchange platform.

[0034] S2: Dependency analysis and migration planning Modularly disassemble the system architecture of the existing platform, analyze the call relationships between components through a dependency matrix, assign migration priorities to the modules in combination with business criticality, and generate a migration plan.

[0035] In one of the embodiments, through technical means such as static code analysis, running log analysis, dynamic performance analysis, machine learning model prediction, multi-objective optimization algorithm, and simulation model prediction, the migration process of the market supervision data exchange platform is optimized to ensure that the platform achieves optimal performance and the shortest downtime during migration. The steps are as follows: S21: Static code analysis: Use static code analysis tools such as SonarQube to analyze the source code of the existing platform and identify the dependencies between various modules and functions. By analyzing program calls, data flows, and relationships between functions, obtain the internal call structure between modules. Combining with the architecture design of the platform, identify the potential performance bottlenecks and modules with high resource consumption in the system.

[0036] S22: Running log analysis: Collect running logs during the operation of the platform and analyze the performance data (such as CPU, memory, and IO usage) and module call information in the logs through log analysis tools (such as ELK Stack) to further refine the dependencies between modules. According to business criticality, identify the modules crucial to the system functions, and confirm the business continuity requirements and downtime tolerance.

[0037] S23: Dynamic performance analysis: In the Xinchuang environment, conduct dynamic performance analysis and use system monitoring tools (such as Prometheus, Grafana) to monitor the resource consumption and response time of each module of the platform in real time. Dynamically analyze the behavior of modules under high load, determine which modules are the priority modules during migration, and optimize their resource requirements during migration.

[0038] S24: Dependency matrix construction: According to the results of static code analysis and running log analysis, combined with the output of dynamic performance analysis, construct a dependency matrix that describes the call relationships, data exchange flows, and performance dependencies between functional modules. Assign a migration priority to each functional module, prioritize the migration of modules that affect critical businesses, and optimize the migration order to reduce the downtime of critical modules.

[0039] S25: Machine learning model prediction: Use machine learning models (such as graph neural networks) to analyze the potential dependencies between modules and further optimize the prediction of the dependency matrix. By learning historical migration data, the model can automatically adjust the migration order, predict potential bottleneck problems and resource conflicts during migration, and provide intelligent migration path suggestions.

[0040] S26: Multi-objective Optimization Algorithm: On the basis of considering the shortest downtime, a multi-objective optimization algorithm (such as genetic algorithm, particle swarm optimization) is adopted to optimize resource utilization and migration cost during the migration process simultaneously. This algorithm calculates the optimal migration order according to the resource occupancy of each module and the requirements during migration, maximizing the usage efficiency of system resources and minimizing the downtime and cost of the migration process.

[0041] S27: Simulation Model Prediction: Before formulating the migration plan, a simulation model (such as a migration prediction model based on the simulated annealing algorithm) is used to simulate different migration scenarios, predicting possible performance issues and system bottlenecks during the migration process. The feasibility and efficiency of the migration order are verified through the simulation results to ensure that the business functions of the system will not be affected during the actual migration process.

[0042] S28: Migration Plan Generation: Based on the dependency matrix, optimization algorithm, machine learning prediction, and dynamic performance analysis results, a migration plan including the migration order, expected downtime, and resource allocation is generated. This plan ensures that critical modules are migrated first during the migration process, the downtime is the shortest, and the system resources are optimally allocated.

[0043] S3: Data Segmented Migration and Integrity Verification The business data is imported into the Xinchuang platform database in batches. After each batch of migration, hash verification is used to ensure data consistency, and multi-party secure computing is used to enhance data privacy protection.

[0044] In one of the embodiments, step S3 specifically includes: S31: Before the migration starts, the system generates a hash value (such as using the SHA-256 or MD5 algorithm) for each database table and records this hash value; after each migration, the system recalculates the hash value of the corresponding table in the new platform database and compares it with the hash value of the old platform data. If the two hash values are the same, it is considered that the data migration of this batch is correct; if the hash values are different, continue to the next step for verification.

[0045] S32: When the hash values are different, the system will adopt an incremental synchronization strategy, that is, only the changed parts of the data are retransmitted instead of transmitting all records in full. This process identifies the changed records by comparing the records in the old and new platform databases: the system uses the primary key or timestamp field of the database to determine whether the data has changed, and for the changed records, they are updated through the incremental transmission mechanism, reducing unnecessary data transmission and improving the transmission efficiency.

[0046] S33: To accelerate the data verification process, a distributed hash table (DHT) and asynchronous multi-threaded verification technology are introduced during the incremental synchronization and data comparison process; Distributed Hash Table (DHT): The DHT technology is used to distribute and store the hash values of data on different nodes, and parallel verification is performed among multiple nodes, improving the efficiency of the verification process. Each node is responsible for calculating the hash values of a part of the data. When the data volume is large, the system can automatically adjust the node tasks to ensure load balancing during the verification process.

[0047] Asynchronous Multithreaded Verification: During the data consistency verification process, the system parallelizes the verification tasks through asynchronous multithreaded technology to ensure that different tables and data blocks can perform hash value comparison and data comparison simultaneously. This parallel processing method significantly improves the processing speed of the system. Especially when facing large-scale data, it can effectively reduce the time consumption of data comparison.

[0048] S34: If the data is still inconsistent after incremental synchronization, the system will enter the step-by-step comparison stage, where it compares the different data item by item and retransmits the missing or inconsistent records: The system will first locate the different records based on the primary key or unique identifier of the data. By comparing the data with the same primary key in the old and new platforms, the system can accurately find the different records; for each different record, the system will automatically initiate a retransmission and synchronize the missing or inconsistent data items to the new platform until the corresponding data sets in the old and new platforms are completely consistent.

[0049] S35: After all batches of data migration are completed, the system will perform a final consistency check on the full amount of data. At this time, the system will perform hash value comparison again and ensure that all data items have been successfully migrated and are consistent by record counting. If the hash values are consistent and the record numbers match, the system confirms that the data migration is completed, and the entire data migration process ends.

[0050] S36: To ensure the fault tolerance during the migration process, the system adopts an automatic recovery mechanism. When data inconsistency or migration interruption is detected, it will automatically restart the data transmission and record the reason for failure. In this way, errors or exceptions during the migration process will not cause data loss, and the system can recover in time and continue the migration.

[0051] S4: Dual-Platform Parallel Switching Start the business modules on the new platform according to the migration priority, perform output comparison during the parallel operation of the old and new platforms, and switch the business traffic after verification.

[0052] In one of the embodiments, based on innovative technologies such as intelligent test request scheduling, real-time performance monitoring and automatic rollback mechanism, incremental traffic switching, and intelligent anomaly detection, the steps of dual-platform parallel switching are optimized, thereby improving the stability and reliability during the migration process of the new platform. The steps are as follows: S41: During the parallel operation of the old and new platforms, to ensure the accuracy and efficiency of the migration and switch, first deploy an intelligent test request scheduling system on the new platform. This system dynamically selects and schedules test requests based on the platform's historical operation data, current load conditions, and business traffic, so that they cover different business scenarios.

[0053] Data collection: The system collects the real-time load conditions and response times of the new and old platforms, and uses this data to evaluate the platform's workload.

[0054] Request scheduling: According to the load conditions, the system intelligently selects and arranges the types and priorities of test requests. For example, when the load on the new platform is light, select complex requests with higher priorities for testing; when the load is high, give priority to testing basic traffic requests.

[0055] Business scenario simulation: The system simulates actual business requests through intelligent algorithms, tests different request processing scenarios on the new platform, ensures a wide test coverage, and fully verifies the stability and reliability of the new platform.

[0056] S42: To ensure the stable operation of the new platform and its rapid recovery in case of anomalies, deploy a real-time performance monitoring system. This system continuously monitors the operating states of the old and new platforms and conducts real-time evaluations based on preset thresholds and rules.

[0057] Performance monitoring: The system collects and monitors in real time metrics such as the response times, error rates, and resource consumption of the old and new platforms. When it detects that the response time of the new platform is too long or the error rate increases, the system triggers an alarm and records the relevant data.

[0058] Automatic rollback mechanism: If the new platform fails to achieve the expected stability during the parallel operation, the system automatically switches all business traffic back to the old platform to ensure business continuity and stability. The execution of the rollback mechanism depends on predefined rollback thresholds. For example, when the error rate of the new platform exceeds the set value, the rollback operation will be automatically initiated.

[0059] Traffic switching process: Once the rollback is triggered, the system immediately stops sending traffic to the new platform and redirects all traffic back to the old platform until the problems on the new platform are resolved. This process is transparent to users and requires no manual intervention to ensure uninterrupted business.

[0060] S43: To further improve the stability of the new platform and the flexibility of system switching, adopt an incremental traffic switching strategy. The specific steps of incremental switching are as follows: Initial small-scale traffic switching: In the initial stage, the system guides a small amount of business traffic (such as specific test requests or low-load services) to the new platform for processing and monitors the response performance of the new platform.

[0061] Gradually increase traffic: As the traffic handling capacity of the new platform is gradually verified, the system will gradually increase the traffic load under normal performance conditions. Each time the traffic is increased, the system will re-evaluate the load and performance of the new platform to ensure that it can carry the gradually increasing traffic.

[0062] Full traffic switch: When the stability and performance of the new platform under high load are verified, the system will complete the full traffic switch, and all services will be fully transferred to the new platform.

[0063] S44: To more precisely monitor the stability of the new platform and identify potential problems, an intelligent anomaly detection system is introduced. This system is based on machine learning algorithms and deep learning models to automatically detect abnormal patterns during the operation of the platform.

[0064] Abnormal pattern recognition: The system analyzes the business requests and response results of the new and old platforms through deep learning models to identify possible abnormal behaviors when the new platform processes requests. For example, the system will train a model to identify problems such as excessive response time or high error rate.

[0065] Intelligent comparison and feedback: When the system compares the output results of the new and old platforms, it automatically discovers potential anomalies (such as data inconsistency or performance fluctuations). By comparing the processing results of the new platform with the standard output results of the old platform, the system can identify behaviors that do not meet expectations in real time and feedback them to the anomaly detection system to further optimize the model.

[0066] Automatic correction and reporting: When an anomaly is detected, the system will automatically record relevant data and trigger an automatic correction mechanism, such as rescheduling requests or reducing the request load. In addition, the system will generate an anomaly report for operation and maintenance personnel to analyze and solve problems.

[0067] S5: Reconstruct the password system Replace the encryption and decryption algorithms with domestic commercial password algorithms, deploy a password service adaptation layer to connect to password hardware devices, and strengthen encryption protection by combining quantum-resistant encryption technology.

[0068] In one of the embodiments, the domestic commercial password algorithms include: SM2 / SM9 public key algorithms: Used for data encryption and digital signatures. As a national commercial password public key algorithm, SM2 is mainly applied to public key encryption and digital signature scenarios. And as a public key algorithm supporting identity authentication, SM9 can perform more secure identity verification on the platform.

[0069] SM4 symmetric encryption algorithm: Used for symmetric encryption of data, especially suitable for encryption requirements in data storage and fast transmission. SM4 is a block encryption algorithm that supports a block length of 128 bits and can effectively handle large-scale data encryption tasks.

[0070] SM3 Digest Algorithm: Used for data integrity verification, the SM3 algorithm is one of the national cryptographic standards and is widely used in digital signatures and data verification to ensure the integrity and accuracy of data during transmission.

[0071] To address the potential cracking threats posed by quantum computing, the system introduces post-quantum encryption algorithms such as lattice-based encryption algorithms (e.g., NTRU and Lizard), which can maintain their encryption security in a quantum computing environment. These algorithms are integrated into the cryptographic service adaptation layer and provide quantum-resistant encryption protection for particularly sensitive data in the system.

[0072] To enhance data protection, a strategy of multiple encryption levels is adopted, and different encryption algorithms are used to protect data during different data transmission and storage processes: Data Transmission: During data transmission, the SM4 symmetric encryption algorithm is used to encrypt the data content. The SM4 algorithm is a symmetric encryption algorithm suitable for high-speed transmission and can ensure the confidentiality of data during transmission.

[0073] Data Storage: For data in storage, the SM3 digest algorithm is used to verify the integrity of the stored data, and the SM9 public key encryption algorithm is used to encrypt and protect key information. The identity authentication feature of the SM9 algorithm can effectively ensure the security of data storage.

[0074] Encryption Process Management: In the application of post-quantum encryption algorithms, the platform dynamically selects whether to enable the post-quantum encryption layer according to the sensitivity of different data, thus ensuring the security of data under the threat of quantum computing.

[0075] S6: Security Defense Deployment Integrate an intrusion detection system and virtual honeypot nodes to monitor data exchange traffic in real time, trap attack behaviors, update attack features to the defense rule library, and combine with a deep learning model to update defense strategies in real time.

[0076] A honeypot node refers to a virtual or physical defense node whose main purpose is to trap attackers and record attack behaviors. According to the real-time security situation of the platform, the honeypot node adopts a dynamic deployment mechanism and can automatically select different types of nodes (virtual or physical) to trap attackers. When the system detects potential security threats through a two-layer intrusion detection system (described below), the honeypot node will automatically switch the deployment strategy to best guide the attacker into the honeypot for interaction.

[0077] Based on the real-time feedback of the intrusion detection system, the honeypot node can intelligently select appropriate virtual or physical nodes for deployment and automatically adjust the deployment mode according to the attacker's behavior. The honeypot node can be deployed in different areas of the information technology application innovation environment to distract attackers and effectively enhance security protection.

[0078] The two-layer intrusion detection system of this embodiment includes two main parts: The first-layer intrusion detection system: This layer uses rule feature matching technology and is mainly responsible for quickly identifying known attack patterns. By matching the characteristics of attack behaviors with the rules in the pre-defined attack feature library, it can timely detect and block common known attacks. The first layer uses the attack features in the static rule library for quick matching and can real-time intercept known attack types such as DDoS attacks, SQL injections, cross-site scripting attacks (XSS), etc.

[0079] The second-layer intrusion detection system: This layer adopts machine learning methods and analyzes network traffic based on the feature vectors of data exchange traffic. Through in-depth analysis of data traffic, it identifies potential unknown threats and abnormal patterns. The machine learning model can provide stronger detection capabilities when facing new types of attacks, thus making up for the deficiencies of the rule matching system in detecting unknown threats. The second layer continuously analyzes a large amount of data traffic through feature learning and deep learning models, can discover new and unknown attack patterns, and timely identify potential attack behaviors.

[0080] The honeypot node can not only trap attackers and record their interaction behaviors, but also has efficient behavior analysis capabilities. Whenever an attacker interacts with the honeypot node, the honeypot node will record its behavior characteristics and attack methods, including but not limited to: detailed information such as the interfaces accessed by the attacker, the types of attack requests, input data, network traffic, etc.

[0081] Behavior recording and storage: The honeypot node stores the attacker's interaction data and behavior characteristics in the database and converts them into structured data for subsequent analysis.

[0082] Feature library update: The honeypot node feeds back the recorded attack behaviors to the first-layer intrusion detection system to update the attack feature library in real time. The updated feature library can help the first-layer intrusion detection system more accurately identify known attack behaviors.

[0083] The honeypot node can not only record the attacker's behavior data, but also can perform real-time optimization of attack features through adaptive learning algorithms and deep learning technologies. Through the analysis of attack behaviors, the honeypot node can automatically adapt to new attack patterns, adjust its trapping strategies, and improve its capture capabilities for complex attacks.

[0084] By combining deep learning and behavioral analysis, the honeypot nodes can not only passively entrap attackers, but also actively adjust defense strategies, identify different types of attacks, and adjust the deployment method of the honeypot strategy according to the complexity of the attacks. The flexibility of this strategy enables the system to respond to complex and covert attack patterns and reduce the risk of being detected by attackers.

[0085] S7: Blockchain Auditing and Trust Management Build a consortium blockchain network to record data exchange logs and security events, implement node identity authentication and dynamic reputation evaluation based on smart contracts, and improve the flexibility of identity authentication by combining decentralized identity management.

[0086] Specifically, the consortium blockchain network is deployed in a permissioned chain manner, and the participating nodes include the main server nodes of the data exchange platform and the regulatory superior nodes; the smart contract is used to record information such as the participants, data digest, and timestamp of each data exchange, implement non-tamperable evidence storage of key operations, and calculate the node trust score according to the timeliness, accuracy of the data submitted by each node, and the occurrence of security events; the trust score is calculated based on the timeliness of data submission, the integrity of each data exchange, the security event response of each node, and the security of historical data exchange behaviors.

[0087] S8: Operation Strategy Optimization According to intrusion detection alerts and blockchain audit results, dynamically adjust security policies and resource allocation parameters by combining machine learning techniques to achieve adaptive defense.

[0088] Furthermore, based on intrusion detection alert data, use deep learning models or reinforcement learning algorithms to analyze and predict potential attack behaviors, and automatically adjust firewall rules, response strategies of intrusion prevention systems, and traffic filtering strategies; According to the security events and node behaviors recorded in the blockchain audit results, analyze the security status and behavior performance of each node through clustering algorithms or anomaly detection algorithms, evaluate the risk level of the nodes, and then adjust resource allocation parameters, including computing resources, bandwidth resources, and storage resources, to ensure that high-risk nodes obtain more monitoring resources, while low-risk nodes obtain optimized resource allocation; Automatically optimize data encryption policies and identity authentication policies based on the analysis results, and dynamically adjust the encryption intensity or strengthen the identity verification mechanism for nodes or traffic with a high risk level.

[0089] In the present invention, the construction and adaptation of the Xinchuang environment are the foundation, ensuring that the system can run smoothly in the Xinchuang environment and laying the foundation for subsequent migration, data processing, and security protection. After completing the environment construction, dependency analysis and migration planning are carried out to ensure that the migration process is carried out according to priorities, avoiding downtime or performance degradation of key business modules during the migration process. Data migration is a key part of platform migration. By migrating in batches and performing hash verification, it is ensured that data is not lost or tampered with during migration, while protecting privacy. This step follows the migration planning and actually executes the data migration operation. After the data migration is completed, it enters the parallel switching stage. The functions of the new platform are gradually enabled, and the old platform and the new platform work in parallel to ensure that the functions are consistent before the switch. During the platform migration and data switching process, security is of utmost importance. The reconstruction of the password system is an important step to enhance the platform security after the data migration is completed. This step ensures that encryption algorithms compliant with national security standards are adopted during the data exchange process and adds the protection of quantum-resistant encryption. The security defense is deployed after the data migration and platform switching to ensure that the new platform can prevent external attacks and abnormal behaviors during formal operation. Attack features are collected through honeypot nodes, and the defense rule base is optimized in combination with deep learning models. During the operation of the system, the blockchain audit system continuously records transaction data and security events to ensure the traceability and transparency of all operations within the platform. This step guarantees the trust management of the system and the integrity of subsequent audits. During the operation of the system, by continuously collecting intrusion detection and blockchain audit results, the security strategy is optimized to ensure that the platform can respond and adjust in real time when facing new threats.

[0090] As Figure 2 shown, this is another embodiment of the present invention. This embodiment provides a migration and security enhancement system for the market supervision data exchange platform based on the Xinchuang environment, which is used to execute the migration and security enhancement method for the market supervision data exchange platform based on the Xinchuang environment as described above, including: An adaptation module, deployed on a domestic hardware platform, is used to support the compatibility testing of operating systems, databases, and middleware; A migration planning module, which is used to modularly disassemble the system architecture of the existing platform, generate a dependency matrix, and formulate a migration plan; A data migration module, which is used to execute the batch migration of data, and uses hash verification and multi-party secure computing to ensure data consistency and privacy protection; A business migration module, which is used to start business modules according to the migration priority and perform output comparison during parallel operation to ensure smooth traffic switching; A password module, which is used to replace the encryption and decryption algorithms with domestic commercial password algorithms and introduce quantum-resistant encryption technology to enhance data protection; A defense module, which is used to integrate an intrusion detection system and honeypot nodes, monitor traffic in real time, and automatically update the defense strategy; A blockchain module for constructing a consortium blockchain to record data exchange logs and implementing identity authentication and reputation assessment using smart contracts; A policy optimization module for dynamically adjusting security policies and resource allocation according to intrusion detection alerts and blockchain audit results in combination with machine learning.

[0091] In summary, the present invention combines innovative technologies such as domestic hardware, encryption technology, intrusion prevention mechanisms, resource optimization, and adaptive defense, not only effectively solves various problems of the prior art in the migration and security protection of the market supervision data exchange platform, but also improves the compatibility, performance, security, and intelligence level of the platform, ensuring the efficient and stable operation of the platform in the Xinchuang environment.

[0092] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of various changes or substitutions, and these should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for migrating and enhancing security of a market supervision data exchange platform based on a trusted innovation environment, characterized in that: The method comprises: Deploy the appropriate operating system, database, and middleware on the domestic hardware platform and complete the compatibility test of the operating environment; Modularize the system architecture of the existing platform, analyze the calling relationships between components through the dependency matrix, assign migration priorities to each module based on business criticality, and formulate a migration plan; Import business data into the Xinchuang platform database in batches. After each batch is migrated, hash verification is performed to ensure data consistency, and multi-party secure computing is used to enhance data privacy protection. Start the business module on the new platform according to the migration priority, compare the outputs of the old and new platforms while they are running in parallel, and switch the business traffic after verification. Replace the encryption and decryption algorithms with domestic commercial encryption algorithms, deploy a cryptographic service adaptation layer to connect to cryptographic hardware devices, and combine quantum-resistant encryption technology to strengthen encryption protection; Integrate intrusion detection system and virtual honeypot nodes to monitor data exchange traffic in real time and trap attack behaviors, update attack features to the defense rule library, and update defense strategies in real time in combination with deep learning models; Build a consortium blockchain network to record data exchange logs and security events, implement node identity authentication and dynamic reputation evaluation based on smart contracts, and improve the flexibility of identity authentication in combination with centralized identity management; Based on intrusion detection alerts and blockchain audit results, security policies and resource allocation parameters are dynamically adjusted in combination with machine learning technology to achieve adaptive defense.

2. According to claim 1, the method for migrating and enhancing security of the market supervision data exchange platform based on the trust innovation environment is characterized in that: The deployment of the adapted operating system, database and middleware on the domestic hardware platform is achieved through automated adaptation tools and intelligent optimization algorithms, where: The automated adaptation tool evaluates the compatibility of the operating system, database, and middleware by combining static code analysis and runtime performance analysis. It generates a compatibility report by analyzing the code and execution logs of the existing platform and automatically generates an adaptation configuration plan. The intelligent optimization algorithm specifically uses a machine learning model to predict the compatibility of domestic operating systems, databases, and middleware, and dynamically adjusts operating system kernel parameters and database cache strategies based on hardware resource requirements to minimize latency and maximize throughput. The machine learning model continuously optimizes configuration based on historical data and real-time monitoring data; During the deployment process, hardware acceleration technology is combined to dynamically accelerate specified computing tasks using GPU or FPGA. The hardware acceleration method is automatically selected based on the task type and hardware resource availability, and hardware acceleration resources are automatically enabled when the system load exceeds the set threshold. Through containerization technology, the operating system, database and middleware are modularized to support dynamic resource allocation, automatically monitor resource consumption, and adjust the computing and storage resources of the container.

3. According to claim 1, the method for migrating and enhancing security of the market supervision data exchange platform based on the trust innovation environment is characterized in that: The method for formulating the migration plan specifically includes: Use static code analysis and run log analysis to obtain the internal dependencies of the platform, and combine dynamic performance analysis to evaluate the resource consumption and performance bottlenecks of each module; Based on the dependencies and performance evaluation, a dependency matrix of functional modules and data units is constructed, and potential dependencies between modules are predicted in combination with a machine learning model; A multi-objective optimization algorithm is used to generate a migration sequence, optimize the migration order of key modules, reduce the impact on system services during the migration process, and meet multiple goals of minimizing downtime and maximizing resource utilization; Based on the dependency matrix and the multi-objective optimization algorithm, a migration plan including migration sequence, expected downtime and resource utilization is formulated, and a simulation model prediction is performed before migration.

4. According to claim 1, the method for migrating and enhancing security of the market supervision data exchange platform based on the trust innovation environment is characterized in that: After each batch of migration, the data consistency is ensured by hash verification, and the method includes: After each batch of migration, the data consistency is verified by comparing the number of records and hash values ​​of the tables with the same name in the new and old databases; When inconsistencies are found, an incremental synchronization strategy is adopted to retransmit only the changed records, while the distributed hash table and asynchronous multi-threaded verification technology are combined to speed up the data verification process; If the data is still inconsistent, compare the difference records one by one and retransmit the missing or inconsistent records until the corresponding data sets of the new and old platforms are exactly the same.

5. According to claim 1, the method for migrating and enhancing security of the market supervision data exchange platform based on the trust innovation environment is characterized in that: The output comparison is performed during the parallel operation of the new and old platforms, specifically including: Implement intelligent test request scheduling for selected test requests, and dynamically select test request type and priority based on the platform's historical operation data and load conditions; Introducing a real-time performance monitoring system to continuously track the response time, load, and error rate of the new and old platforms, and triggering an automatic rollback mechanism when an abnormality occurs to switch business traffic back to the old platform to ensure the stable operation of the new platform; Adopt an incremental traffic switching strategy to gradually increase the traffic load of the new platform, and finally complete the switching of the full traffic to ensure that the new platform can carry the business traffic under high load conditions; Combined with intelligent anomaly detection algorithms, potential anomalies in comparison results are analyzed in real time to ensure high accuracy and timely feedback during the test comparison process.

6. According to claim 1, the method for migrating and enhancing security of the market supervision data exchange platform based on the trust innovation environment is characterized in that: The domestically produced commercial cryptographic algorithms include SM2 / SM9 public key algorithms, SM4 symmetric encryption algorithms and SM3 digest algorithms, and the introduction of post-quantum encryption algorithms is used to enhance the platform's resistance to future quantum computing threats; The cryptographic service adaptation layer encapsulates the calling interface of the domestic commercial cryptographic algorithm, so that the platform application layer calls the cryptographic service in the same way as the original algorithm, and adopts a multiple encryption level approach, using different encryption algorithms for protection in data transmission and storage.

7. According to claim 1, the method for migrating and enhancing security of the market supervision data exchange platform based on the trust innovation environment is characterized in that: The honeypot node adopts a dynamic deployment mechanism, automatically selects different virtual or physical nodes to trap attackers based on the feedback of the real-time two-layer intrusion detection system, and the honeypot node dynamically identifies and analyzes the behavioral characteristics of attackers using rule feature matching and machine learning technology; The first-layer intrusion detection system quickly identifies known attack patterns through rule feature matching. The second-layer intrusion detection system analyzes the feature vectors of data exchange traffic based on machine learning to detect unknown threat patterns. Honeypot nodes record and store attackers’ interaction data and behavior characteristics, and feed them back to the first-layer intrusion detection system to optimize the attack feature library. Honeypot nodes use adaptive learning algorithms combined with deep learning to update attack features in real time and actively adjust honeypot strategies.

8. According to claim 1, the method for migrating and enhancing security of the market supervision data exchange platform based on the trust innovation environment is characterized in that: The alliance blockchain network is deployed in a permissioned chain manner, and the participating nodes include the main server nodes and supervisory superior nodes of the data exchange platform; the smart contract is used to record the participants, data summary, timestamp and other information of each data exchange, realize the tamper-proof evidence of key operations, and calculate the node trust score based on the timeliness and accuracy of the data submitted by each node and the occurrence of security incidents; The trust score is calculated based on the timeliness of data submission, the integrity of each data exchange, the security incident response of each node, and the security of historical data exchange behavior.

9. The method for migrating and enhancing security of a market supervision data exchange platform based on a trusted innovation environment according to claim 1 is characterized in that: Based on intrusion detection alarm data, use deep learning models or reinforcement learning algorithms to analyze and predict potential attack behaviors, and automatically adjust firewall rules, intrusion prevention system response strategies, and traffic filtering strategies; According to the security events and node behaviors recorded in the blockchain audit results, the clustering algorithm or anomaly detection algorithm is used to analyze the security status and behavior performance of each node, evaluate the risk level of the node, and then adjust the resource allocation parameters, including computing resources, bandwidth resources, and storage resources, to ensure that high-risk nodes obtain more monitoring resources, while low-risk nodes obtain optimized resource allocation; Automatically optimize data encryption and identity authentication strategies based on analysis results, dynamically adjust encryption strength or strengthen identity authentication mechanisms for nodes or traffic with high risk levels.

10. A market supervision data exchange platform migration and security enhancement system based on a trusted innovation environment, used to execute a market supervision data exchange platform migration and security enhancement method based on a trusted innovation environment as described in any one of claims 1 to 9, characterized in that: The system comprises: Adaptation modules are deployed on domestic hardware platforms to support compatibility testing of operating systems, databases, and middleware; Migration planning module, which is used to modularize the system architecture of the existing platform, generate a dependency matrix and formulate a migration plan; The data migration module is used to perform batch migration of data, using hash verification and multi-party secure computing to ensure data consistency and privacy protection; The service migration module is used to start the service module according to the migration priority and perform output comparison during parallel operation to ensure smooth traffic switching; Cryptographic module, used to replace encryption and decryption algorithms with domestic commercial cryptographic algorithms, and introduce quantum-resistant encryption technology to enhance data protection; Defense module, which is used to integrate intrusion detection systems and honeypot nodes, monitor traffic in real time and automatically update defense strategies; Blockchain module, used to build a consortium blockchain to record data exchange logs and use smart contracts to implement identity authentication and reputation assessment; The policy optimization module is used to dynamically adjust security policies and resource allocation based on intrusion detection alerts and blockchain audit results combined with machine learning.

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