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

Abstract

The application discloses a market supervision data exchange platform migration and security enhancement method and system based on Xinyuan environment, and relates to the technical field of network security.In the data migration process, the application ensures the consistency and privacy protection of data through hash check and multi-party secure calculation technology; introduces domestic commercial cryptographic algorithms and quantum-resistant encryption technology, which not only effectively improves the protection ability of the platform against existing security threats, but also enhances the resistance to future quantum computing threats; integrates an intrusion detection system and a virtual honeypot node, combines a deep learning model to monitor and update defense strategies in real time, and can detect and respond to new attack behaviors in real time; according to the intrusion detection alarm and the blockchain audit result, combined with machine learning technology, the security strategy and resource allocation are dynamically adjusted to ensure that high-risk nodes are monitored in time, and the resource allocation is automatically optimized, improving the resource utilization efficiency and security of the system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of network security, and in particular to a market supervision data exchange platform migration and security enhancement method and system based on a Xinyuan environment. BACKGROUND

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

[0003] Firstly, the compatibility of the operating system, database and middleware is a prominent problem when the existing platform is migrated to a localized hardware platform, and there is a lack of effective automated tools for compatibility testing and performance optimization. Secondly, during the data migration process, ensuring the consistency and privacy protection of large-scale data is a difficult task, and the existing verification methods cannot achieve efficient and accurate data verification. Thirdly, traditional encryption and decryption algorithms cannot cope with the potential risks brought by quantum computing, especially during the platform migration process, the replacement of the password system cannot balance security and business performance. In addition, the existing intrusion detection system cannot effectively deal with complex network attacks, and the deployment efficiency of the honeypot system is low, and it cannot update attack features in real time. Although the introduction of blockchain technology can improve the data transparency and security of the platform, the existing technology is mostly single application, and lacks the implementation of multi-party authentication and reputation evaluation functions.

[0004] Therefore, it is of great significance to develop a market supervision data exchange platform migration and security enhancement method based on a Xinyuan environment that can effectively solve these problems, in order to improve the security, stability and flexibility of the platform. SUMMARY

[0005] In view of the compatibility, data consistency, security, resource optimization and other problems faced by the traditional platform during the migration process, the present application proposes a market supervision data exchange platform migration and security enhancement method and system based on a Xinyuan environment, which can realize the smooth migration of the market supervision data exchange platform to a 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 application achieves the above-mentioned purposes through the following technical solutions:

[0007] The market supervision data exchange platform migration and security enhancement method based on a Xinyuan environment comprises:

[0008] Deploying an adapted operating system, database and middleware on a localized hardware platform to complete the compatibility testing of the running environment;

[0009] The system architecture of the existing platform is modularly disassembled, the calling relationship between components is analyzed by relying on a matrix, each module is given a migration priority in combination with business criticality, and a migration plan is formulated;

[0010] The business data is imported into the Xingcheng platform database in batches, the data consistency is ensured after each batch migration through hash verification, and the data privacy protection is enhanced through multi-party secure calculation;

[0011] The business modules are started on the new platform according to the migration priority, and output comparison is performed during the parallel running of the new and old platforms, and the traffic is switched after verification;

[0012] The encryption and decryption algorithm is replaced by a domestic commercial cryptographic algorithm, a cryptographic service adaptation layer is deployed to interface with cryptographic hardware devices, and quantum-resistant encryption technology is used to strengthen encryption protection;

[0013] The intrusion detection system and virtual honeypot nodes are integrated to monitor data exchange traffic and lure attack behavior in real time, update attack features to the defense rule library, and update the defense strategy in real time combined with a deep learning model;

[0014] A consortium blockchain network is constructed to record data exchange logs and security events, node identity authentication and dynamic reputation evaluation are realized based on smart contracts, and identity authentication flexibility is improved combined with centralized identity management;

[0015] According to the intrusion detection alarm and the blockchain audit result, the security strategy and resource allocation parameters are dynamically adjusted combined with machine learning technology to realize adaptive defense.

[0016] Preferably, the deployment of the adapted operating system, database and middleware on the localized hardware platform is realized through an automatic adaptation tool and an intelligent optimization algorithm, wherein:

[0017] The automatic adaptation tool assesses the compatibility of the operating system, database and middleware by combining static code analysis and runtime performance analysis, generates a compatibility report by analyzing the code and execution logs of the existing platform, and automatically generates an adaptation configuration scheme;

[0018] The intelligent optimization algorithm specifically uses a machine learning model to predict the compatibility of the domestic operating system, database and middleware, dynamically adjusts the operating system kernel parameters and database cache strategy based on hardware resource requirements, and minimizes latency and maximizes throughput, and the machine learning model continuously optimizes the configuration according to historical data and real-time monitoring data;

[0019] During deployment, hardware acceleration technology is combined, GPU or FPGA is used to dynamically accelerate specified computing tasks, hardware acceleration mode is automatically selected according to task type and hardware resource availability, and hardware acceleration resources are automatically enabled when system load is higher than a set threshold.

[0020] Modularize operating system, database and middleware through containerization technology, support dynamic resource allocation, automatically monitor resource consumption and adjust computing and storage resources of containers.

[0021] Preferably, the method of formulating a migration plan specifically comprises:

[0022] The internal dependencies of the platform are obtained by combining static code analysis and running log analysis, and dynamic performance analysis is combined to evaluate the resource consumption and performance bottlenecks of each module.

[0023] Based on the dependency relationship and performance evaluation, a dependency matrix of functional modules and data units is constructed, and a machine learning model is used to predict potential dependencies between modules.

[0024] A multi-objective optimization algorithm is used to generate a migration sequence, optimize the migration order of key modules, reduce the impact on system business during migration, and meet multiple objectives of minimizing downtime and maximizing resource utilization.

[0025] Based on the dependency matrix and multi-objective optimization algorithm, a migration plan is formulated, including migration order, expected downtime and resource utilization, and a simulation model is used to predict the migration plan before migration.

[0026] Preferably, the data consistency is ensured by hash check after each batch migration, and the method comprises:

[0027] After each batch migration, the data consistency is verified by comparing the record quantity and hash value of the same table in the new and old databases.

[0028] When inconsistencies are found, an incremental synchronization strategy is used to retransmit only the changed records, and distributed hash tables and asynchronous multi-threaded verification techniques are combined to speed up the data verification process.

[0029] If the data is still inconsistent, the difference records are compared one by one and the missing or inconsistent records are retransmitted until the corresponding data sets of the new and old platforms are completely identical.

[0030] Preferably, the output comparison is performed during the parallel operation of the new and old platforms, and specifically comprises:

[0031] Intelligent test request scheduling is implemented for selected test requests, and test request types and priorities are dynamically selected based on historical running data and load conditions of the platform.

[0032] A real-time performance monitoring system is introduced to continuously track the response time, load condition and error rate of the new and old platforms, and an automatic rollback mechanism is triggered when an exception occurs to switch the business traffic back to the old platform, ensuring the stable operation of the new platform.

[0033] An incremental switching traffic strategy is adopted to gradually increase the traffic load of the new platform, and finally complete the switching of the full traffic, ensuring that the new platform can bear the service traffic under high load conditions;

[0034] Combined with intelligent anomaly detection algorithm, real-time analysis of potential anomalies in comparison results is performed to ensure high accuracy and timely feedback of the test comparison process.

[0035] Preferably, the domestic commercial cryptographic algorithm includes SM2 / SM9 public key algorithm, SM4 symmetric encryption algorithm and SM3 digest algorithm, and a post-quantum encryption algorithm is introduced to enhance the resistance of the platform to future quantum computing threats;

[0036] 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 uses multiple encryption layers to protect data transmission and storage using different encryption algorithms.

[0037] Preferably, the honeypot node adopts a dynamic deployment mechanism, automatically selects different virtual or physical nodes for attacker trapping based on the feedback of the real-time double-layer intrusion detection system, and dynamically identifies and analyzes the behavior characteristics of the attacker using rule feature matching and machine learning technology;

[0038] The first layer intrusion detection system quickly identifies known attack patterns through rule feature matching, and the second layer intrusion detection system analyzes the feature vectors of data exchange traffic based on machine learning to detect unknown threat patterns; the honeypot node records and stores the interaction data and behavior characteristics of the attacker, and feeds back to the first layer intrusion detection system to optimize the attack feature library;

[0039] 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.

[0040] Preferably, the alliance 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 supervisory superior nodes; the smart contract is used to record the participants, data digest and timestamp of each data exchange, realize the non-tamperable evidence of key operations, and calculate the node trust score according to the timeliness, accuracy and safety event occurrence of the submitted data; the trust score is calculated according to the timeliness of data submission, the integrity of each data exchange, the safety event response of each node, and the security of historical data exchange behavior.

[0041] Preferably, based on the intrusion detection alarm data, a deep learning model or reinforcement learning algorithm is used to analyze and predict potential attack behavior, and automatically adjust the response strategy of the firewall rules, intrusion prevention system, and traffic filtering strategy;

[0042] According to the security events and node behaviors recorded in the blockchain audit results, the security state and behavior performance of each node are analyzed through a clustering algorithm or an anomaly detection algorithm, the risk level of the node is evaluated, and then the resource allocation parameters, including computing resources, bandwidth resources and storage resources, are adjusted to ensure that high-risk nodes obtain more monitoring resources, and low-risk nodes obtain optimized resource allocation;

[0043] Based on the analysis results, the data encryption strategy and identity authentication strategy are automatically optimized, and the encryption strength or identity verification mechanism is dynamically adjusted for nodes or traffic with high risk levels.

[0044] The market supervision data exchange platform migration and security enhancement system based on the Xingcheng environment is used to perform the market supervision data exchange platform migration and security enhancement method based on the Xingcheng environment as described above, and the system comprises:

[0045] The adaptation module is deployed on the localized hardware platform and is used to support compatibility testing of the operating system, database and middleware;

[0046] The migration planning module is used to modularly disassemble the system architecture of the existing platform, generate a dependency matrix and develop a migration plan;

[0047] The data migration module is used to perform batch data migration, and uses hash checksum and multi-party secure calculation to ensure data consistency and privacy protection;

[0048] The business migration module is used to start the business module according to the migration priority, and perform output comparison during parallel running to ensure smooth traffic switching;

[0049] The password module is used to replace the encryption and decryption algorithm with a domestic commercial cryptographic algorithm, and introduce quantum-resistant encryption technology to enhance data protection;

[0050] The defense module is used to integrate an intrusion detection system and a honeypot node, monitor traffic in real time and automatically update defense strategies;

[0051] The blockchain module is used to build a consortium blockchain to record data exchange logs, and uses a smart contract to realize identity authentication and reputation evaluation;

[0052] The strategy optimization module is used to dynamically adjust security strategies and resource allocation according to intrusion detection alarms and blockchain audit results, combined with machine learning.

[0053] The platform compatibility in the Xinchuang environment can be efficiently evaluated and ensured, the operating system kernel parameters and database cache strategies are automatically optimized, the running performance of the platform is effectively improved, the throughput is maximized and the delay is minimized; after each batch of data migration, verification is performed, incremental synchronization and distributed hash table are used to accelerate data verification, the accuracy and security of data migration are significantly improved, and data loss and tampering are effectively prevented; domestic commercial cryptographic algorithms and quantum-resistant encryption technologies are introduced, which not only effectively improve the protection capability of the platform against existing security threats, but also enhance the resistance to future quantum computing threats; the dynamic deployment mechanism of the honeypot node and the attack feature learning optimize the defense rule base, improve the intelligent level of the platform defense, and enhance the ability to cope with complex attack modes; according to the intrusion detection alarm and the blockchain audit result, combined with machine learning technology, the security policy and resource allocation are dynamically adjusted, the high-risk nodes are monitored in time, and the resource allocation is automatically optimized, thereby improving the resource utilization efficiency and security of the system. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Among them:

[0055] Figure 1 The method flowchart in the embodiment of the present application;

[0056] Figure 2 The system structure block diagram in the embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical scheme and advantages of the embodiments of the present application more clear, the technical scheme of the embodiments of the present application will be described clearly and completely in the following with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the described embodiments of the present application, all other embodiments obtained by those skilled in the art belong to the scope of protection of the present application.

[0058] As Figure 1 shown, one embodiment of the present application provides a market supervision data exchange platform migration and security enhancement method based on Xinchuang environment, including the following steps:

[0059] S1: Xinchuang environment construction and adaptation

[0060] Deploy the adapted operating system, database and middleware on the domestic hardware platform, and complete the running environment compatibility test.

[0061] In one embodiment, the deployment of adapted operating systems, databases, and middleware on a localized hardware platform is achieved through the combination of automated adaptation tools, intelligent optimization algorithms, hardware acceleration techniques, and containerized deployment, resulting in efficient migration and adaptation of the platform.

[0062] In this implementation, the existing platform is first analyzed for adaptation using an automated adaptation tool. The workflow of this tool is divided into two stages:

[0063] Static code analysis: The automated adaptation tool first performs a static analysis of the existing platform's code. The tool scans the source code of the operating system, database, and middleware and generates a compatibility report that includes hardware resource requirements, potential compatibility conflicts, and parts of the code that may affect migration. Static code analysis can identify potential code-level issues and provide recommendations for subsequent environment configuration and hardware selection.

[0064] Runtime performance analysis: After static analysis, the automated adaptation tool monitors the system's performance bottlenecks in real-time by analyzing the running logs. The goal of this stage is to find potential performance bottlenecks or adaptation issues in the system by analyzing key performance indicators such as system load, response time, memory and CPU usage, etc. This analysis result will guide the subsequent hardware configuration and operating system adjustment.

[0065] Through the above analysis, the adaptation tool can generate a compatibility report that lists the configuration items that need to be optimized, possible hardware compatibility issues, and automatically generates the optimal configuration scheme. This can reduce manual intervention and improve the efficiency and accuracy of the adaptation process.

[0066] Based on the compatibility report, intelligent optimization algorithms are applied to optimize the configuration of the operating system, database, and middleware. The specific steps are as follows:

[0067] Performance prediction model: A machine learning model is used to predict system resource requirements such as CPU, memory, storage, and I / O. The model evaluates the system's performance under different hardware resource conditions based on historical data and real-time monitoring data.

[0068] Dynamic configuration adjustment: Based on the prediction results of the machine learning model, the system automatically optimizes key parameters such as operating system kernel parameters, database cache strategies, and middleware connection pool configurations. The optimization algorithm monitors system load in real-time and dynamically adjusts operating system kernel parameters and database cache based on changes in hardware resources such as CPU, memory, and storage, ensuring that the system has optimal performance in the Xiongxin environment.

[0069] In terms of hardware acceleration, this implementation introduces hardware acceleration technology of GPU and FPGA to cope with computationally intensive tasks such as big data processing and encryption and decryption. The specific implementation is as follows:

[0070] Hardware resource monitoring: The system monitors the use of hardware resources in real time, and automatically selects the appropriate hardware acceleration method according to the type of current computing task and system load.

[0071] Task type identification: For example, for large-scale data processing tasks, the system preferentially selects the use of GPU to accelerate computation; while for tasks that require efficient execution of encryption and decryption operations, the system preferentially uses FPGA to accelerate processing. The system identifies the type of task automatically and schedules the most appropriate hardware resources to improve overall computing efficiency.

[0072] Hardware resource allocation: When the computing load is high, the system automatically enables hardware acceleration resources. By using GPU and FPGA, the processing capacity of the system in computationally intensive tasks can be greatly improved, reducing the computing bottleneck and improving the data processing speed.

[0073] In order to improve the flexibility of deployment and the scalability of the platform, this implementation deploys the system through containerization technology (such as Docker and Kubernetes). The specific implementation is as follows:

[0074] Modular deployment: The system modularizes the operating system, database and middleware, and deploys them separately. 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 operating system.

[0075] Automatic resource scheduling: Kubernetes, as a container orchestration tool, is responsible for automatically managing the deployment, scheduling and expansion of containers. Kubernetes will automatically allocate computing resources according to the load and performance requirements of the system, ensuring that each container can get enough resource support, guaranteeing the high availability and high performance of the system.

[0076] Adaptive adjustment: The containerization solution supports dynamic resource allocation, which can automatically adjust the computing and storage resources of containers according to the resource consumption during runtime. For example, the system can automatically increase the CPU and memory resources of the database container according to the changes in database load, or automatically adjust the resource allocation of the middleware container according to the high concurrency requirements of the application service.

[0077] Cross-platform compatibility: Due to the use of containerization technology, the platform can be seamlessly migrated in different operating systems and hardware environments. In the signal creation environment, containerization enables the platform to better adapt to different hardware configurations and operating system versions, reducing the compatibility problems of hardware and software.

[0078] In this embodiment, the automated adaptation tool helps the system quickly assess compatibility issues and generates the optimal configuration scheme by combining static code analysis and runtime performance analysis. 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 invention improves the adaptability and computing efficiency of the system in the ChinaSoft environment, ensuring seamless transition during migration and stable operation under various load conditions. These methods not only optimize the adaptation process in the ChinaSoft 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.

[0079] S2: Dependency analysis and migration planning

[0080] The system architecture of the existing platform is modularized and disassembled, and the calling relationship between components is analyzed through dependency matrix analysis. Combined with business criticality, the migration priority of the module is assigned, and the migration plan is generated.

[0081] In one embodiment, the migration process of the market supervision data exchange platform is optimized through static code analysis, runtime log analysis, dynamic performance analysis, machine learning model prediction, multi-objective optimization algorithm, and simulation model prediction. The platform achieves optimal performance and shortest downtime during migration. The steps are as follows:

[0082] 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 flow, and relationships between functions, the internal call structure between modules is obtained. Combined with the architecture design of the platform, identify the performance bottlenecks and resource-consuming modules that may exist in the system.

[0083] S22: Run log analysis: Collect running logs during platform operation and analyze performance data (such as CPU, memory, IO usage) and module call information in the logs through log analysis tools (such as ELKStack) to further refine the dependencies of each module. According to the business criticality, identify the modules that are crucial to the system function, and confirm the business continuity requirements and downtime tolerance.

[0084] S23: Dynamic performance analysis: In the ChinaSoft environment, implement dynamic performance analysis and monitor the resource consumption and response time of each module of the platform in real time through system monitoring tools (such as Prometheus, Grafana). Dynamic analysis module under high load to determine which modules are priority modules during migration and optimize their resource requirements during migration.

[0085] S24: Dependency Matrix Construction: Based on the results of static code analysis and runtime log analysis, combined with the output of dynamic performance analysis, a dependency matrix is constructed, which describes the calling relationship between functional modules, data exchange flow, and performance dependency. Each functional module is assigned a migration priority, and the modules that affect critical business are migrated first, and the migration order is optimized to reduce the downtime of critical modules.

[0086] S25: Machine Learning Model Prediction: Use machine learning models (such as graph neural networks) to analyze potential dependencies between modules and further optimize the dependency matrix. By learning from 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 recommendations.

[0087] 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 used to optimize resource utilization and migration cost during migration. The algorithm will calculate the best migration order according to the resource occupation of each module and the demand during migration, maximize the use efficiency of system resources, and minimize the downtime and cost of the migration process.

[0088] S27: Simulation Model Prediction: Before formulating the migration plan, use simulation models (such as migration prediction models based on simulated annealing algorithm) to simulate different migration schemes and predict potential performance problems and system bottlenecks during migration. Through simulation results, verify the feasibility and efficiency of the migration order to ensure that the actual migration process does not affect the business functions of the system.

[0089] S28: Migration Plan Generation: Based on the dependency matrix, optimization algorithm, machine learning prediction, and dynamic performance analysis results, generate a migration plan that includes migration order, expected downtime, and resource allocation. The plan ensures that critical modules are migrated first, downtime is minimized, and system resources are optimally allocated.

[0090] S3: Data Segmentation Migration and Integrity Verification

[0091] The business data is imported into the ChinaSoft platform database in batches, and after each batch migration, the data consistency is verified through hash verification, and the data privacy protection is enhanced through multi-party secure calculation.

[0092] In one embodiment, step S3 specifically includes:

[0093] S31: Before migration begins, the system generates a hash value (e.g., using SHA-256 or MD5 algorithm) for each database table and records the 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 hash values are consistent, it is considered that the batch of data migration is correct; if the hash values are inconsistent, the next step of verification is continued.

[0094] S32: When the hash values are inconsistent, the system will adopt an incremental synchronization strategy, i.e., only the changed part of the data is retransmitted, instead of transmitting all records. This process identifies the changed records by comparing the records in the new and old platform databases: the system uses the primary key or timestamp field of the database to determine whether the data has changed, and updates the changed records through the incremental transmission mechanism, reducing unnecessary data transmission and improving transmission efficiency.

[0095] S33: To speed up the data verification process, distributed hash table (DHT) and asynchronous multi-threaded verification technology are introduced in the incremental synchronization and data comparison process;

[0096] Distributed Hash Table (DHT): DHT technology is used to distribute data hash values in different nodes, and parallel verification is performed between multiple nodes to improve the efficiency of the verification process. Each node is responsible for calculating the hash value of a part of the data, and when the data volume is large, the system can automatically adjust the node tasks to ensure load balancing of the verification process.

[0097] Asynchronous multi-threaded verification: In the data consistency verification process, the system uses asynchronous multi-threaded technology to parallelize the verification tasks, ensuring that different tables and data blocks can simultaneously compare hash values and compare data. This parallel processing method significantly improves the processing speed of the system, especially when dealing with large-scale data, which can effectively reduce the time consumption of data comparison.

[0098] S34: If the data is still inconsistent after incremental synchronization, the system will enter the stage of comparing each record and retransmitting the missing or inconsistent records:

[0099] The system first locates the difference records according to the primary key or unique identifier of the data. By comparing the data of the same primary key in the new and old platforms, the system can accurately find the difference records; for each difference record, the system will automatically initiate retransmission and synchronize the missing or inconsistent data items to the new platform until the corresponding data sets in the new and old platforms are completely consistent.

[0100] 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 again compare the hash values and ensure that all data items have been successfully migrated and remain consistent through record counts. If the hash values are consistent and the number of records matches, the system confirms that the data migration is complete, and the entire data migration process ends.

[0101] S36: To ensure fault tolerance during migration, the system uses an automatic recovery mechanism that automatically restarts data transmission and records the failure reason when data inconsistency or migration interruption is detected. In this way, errors or exceptions during the migration process will not result in data loss, and the system can recover in time and continue migration.

[0102] S4: Parallel switching of dual platforms

[0103] According to the migration priority, start the business module on the new platform, and perform output comparison during the parallel running of the new and old platforms. After verification, switch the business traffic.

[0104] In one embodiment, based on intelligent test request scheduling, real-time performance monitoring and automatic rollback mechanism, incremental switching traffic and intelligent anomaly detection, the dual-platform parallel switching steps are optimized, thereby improving the stability and reliability of the new platform migration process. The steps are as follows:

[0105] S41: During the parallel running of the new and old platforms, to ensure the accuracy and efficiency of migration switching, an intelligent test request scheduling system is first deployed on the new platform. This system will dynamically select and schedule test requests based on platform historical running data, current load conditions and business traffic, so as to cover different business scenarios.

[0106] Data collection: The system collects real-time load conditions and response times of the new and old platforms, and uses these data to evaluate the workload of the platforms.

[0107] Request scheduling: According to the load conditions, the system intelligently selects and arranges the types and priorities of test requests. For example, when the new platform load is light, complex requests with high priority are selected for testing; when the load is high, basic traffic requests are tested first.

[0108] Business scenario simulation: The system simulates actual business requests through intelligent algorithms to test different request processing scenarios on the new platform, ensuring that the test coverage is extensive and fully verifying the stability and reliability of the new platform.

[0109] S42: To ensure that the new platform can run stably and recover quickly when an exception occurs, a real-time performance monitoring system is deployed. This system will continuously monitor the running status of the new and old platforms and perform real-time evaluation through pre-set thresholds and rules.

[0110] Performance Monitoring: The system collects and monitors real-time response times, error rates, and resource consumption of both the new and old platforms. When the system detects that the response time of the new platform is too long or the error rate increases, it triggers an alarm and records relevant data.

[0111] Automatic Rollback Mechanism: If the new platform fails to achieve the expected stability during 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, such as when the error rate of the new platform exceeds a set value, the rollback operation is automatically initiated.

[0112] Traffic Switching Process: Once the rollback is triggered, the system immediately stops sending traffic to the new platform and redirects all traffic to the old platform until the new platform problem is resolved. This process is transparent to users and does not require manual intervention, ensuring uninterrupted business.

[0113] S43: To further improve the stability of the new platform and the flexibility of system switching, an incremental traffic switching strategy is adopted. The specific steps of incremental switching are as follows:

[0114] Initial small-scale traffic switching: In the initial stage, the system will direct a small amount of business traffic (such as specific test requests or low-load business) to the new platform for processing and monitor the response performance of the new platform.

[0115] Gradually increase traffic: As the processing capacity of the new platform is gradually verified, the system will gradually increase the traffic load under normal performance conditions. At each traffic increase, the system will re-evaluate the load and performance of the new platform to ensure that it can handle the gradually increasing traffic.

[0116] Full traffic switching: When the stability and performance of the new platform under high load are verified, the system will complete full traffic switching, and all business will be completely transferred to the new platform.

[0117] S44: To more accurately monitor the stability of the new platform and identify potential problems, an intelligent anomaly detection system is introduced. Based on machine learning algorithms and deep learning models, the system automatically detects abnormal patterns during platform operation.

[0118] Abnormal pattern recognition: The system analyzes the business requests and response results of the new and old platforms through deep learning models to identify abnormal behaviors that may occur when the new platform processes requests. For example, the system will train a model to identify whether there are problems such as excessively long response times, excessively high error rates, etc.

[0119] Intelligent comparison and feedback: The system automatically detects potential anomalies (e.g., data inconsistencies or performance fluctuations) when comparing the output results of the new and old platforms. 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 feed them back to the anomaly detection system for further optimization of the model.

[0120] Automatic correction and reporting: When anomalies are detected, the system automatically records relevant data and triggers automatic correction mechanisms, such as rescheduling requests or reducing request loads. In addition, the system generates an anomaly report for operations and maintenance personnel to analyze and solve problems.

[0121] S5: Password system reconstruction

[0122] Replace the encryption and decryption algorithms with domestic commercial encryption algorithms, deploy a password service adaptation layer to interface with password hardware devices, and strengthen encryption protection with quantum-resistant encryption technology.

[0123] In one embodiment, the domestic commercial encryption algorithm includes:

[0124] SM2 / SM9 public key algorithm: used for data encryption and digital signature. SM2 is a national commercial public key algorithm, mainly used in public key encryption and digital signature scenarios. While SM9 is a public key algorithm that supports identity authentication, allowing for more secure identity verification on the platform.

[0125] SM4 symmetric encryption algorithm: used for symmetric encryption of data, particularly suitable for encryption needs in data storage and fast transmission. SM4 is a block encryption algorithm that supports 128-bit block length, effectively handling large-scale data encryption tasks.

[0126] SM3 digest algorithm: used for data integrity verification, SM3 algorithm is one of the national cryptographic standards, widely used in digital signature and data verification, ensuring the integrity and accuracy of data transmission.

[0127] To counter the threat of 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 password service adaptation layer and provide quantum-resistant encryption protection for particularly sensitive data in the system.

[0128] To enhance data protection, a multi-encryption layer strategy is adopted, using different encryption algorithms for protection during different data transmission and storage processes:

[0129] 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, which can ensure the confidentiality of data during transmission.

[0130] Data Storage: The data in storage uses the SM3 digest algorithm for integrity verification of the stored data, and the SM9 public key encryption algorithm for encryption protection of critical information. The identity authentication feature of the SM9 algorithm can effectively ensure the security of data storage.

[0131] Encryption Process Management: In the application of post-quantum encryption algorithms, the platform will dynamically select whether to enable the post-quantum encryption layer according to the sensitivity of different data, so as to ensure the security of data under the threat of quantum computing.

[0132] S6: Security Defense Deployment

[0133] Integrate intrusion detection systems and virtual honeypot nodes to monitor data exchange traffic in real time and lure attack behavior, update attack features to defense rule library, and combine deep learning models to update defense strategies in real time.

[0134] Honeypot nodes refer to virtual or physical defense nodes, whose main purpose is to lure attackers and record attack behavior. According to the real-time security situation of the platform, the honeypot node adopts a dynamic deployment mechanism, which can automatically select different types of nodes (virtual or physical) for the luring of attackers. When the system detects potential security threats through a double-layer intrusion detection system (see description below), the honeypot node will automatically switch deployment strategies to guide attackers to the honeypot for interaction in the best way.

[0135] According to the real-time feedback of the intrusion detection system, the honeypot node can intelligently select appropriate virtual nodes or physical nodes for deployment, and automatically adjust the deployment mode according to the behavior of the attacker. The honeypot node can be deployed in different areas of the China's information creation environment to distract the attention of the attacker and effectively enhance security protection.

[0136] The double-layer intrusion detection system of this embodiment includes two main parts:

[0137] First layer intrusion detection system: This layer uses rule feature matching technology, mainly responsible for quickly identifying known attack patterns. By matching the characteristics of attack behavior with the rules in the pre-defined attack feature library, it can timely discover and block common known attacks. The first layer uses attack features in the static rule library for fast matching, which can intercept known attack types such as DDoS attacks, SQL injection, cross-site scripting (XSS), etc. in real time.

[0138] Second-tier intrusion detection system: This layer uses machine learning methods to analyze network traffic based on feature vectors of data exchange traffic. Through deep analysis of data traffic, potential unknown threats and abnormal patterns are identified. Machine learning models can provide stronger detection capabilities when facing new attacks, making up for the shortcomings of rule matching systems in detecting unknown threats. The second layer continuously analyzes massive data traffic through feature learning and deep learning models, enabling the discovery of new and unknown attack patterns and the timely identification of potential attack behavior.

[0139] Honeypot nodes not only trap attackers and record their interaction behavior, but also have efficient behavior analysis capabilities. When an attacker interacts with a honeypot node, the honeypot node records the behavior characteristics and attack methods of the attacker, including but not limited to: the interfaces accessed by the attacker, the types of attack requests, input data, network traffic, and other detailed information.

[0140] Behavior recording and storage: Honeypot nodes store the interaction data and behavior characteristics of attackers in databases and convert them into structured data for subsequent analysis.

[0141] Feature library update: Honeypot nodes feed the attack behavior they record back to the first-tier intrusion detection system to update the attack feature library in real time. The updated feature library can help the first-tier intrusion detection system more accurately identify known attack behavior.

[0142] Honeypot nodes not only record the behavior data of attackers, but also optimize attack features in real time through adaptive learning algorithms and deep learning techniques. Through analysis of attack behavior, honeypot nodes can automatically adapt to new attack patterns and adjust their trapping strategies to improve their ability to capture complex attacks.

[0143] Through the combination of deep learning and behavior analysis, honeypot nodes can not only passively trap attackers, but also actively adjust defense strategies, identify different types of attacks, and adjust the deployment of honeypot strategies based on the complexity of attacks. The flexibility of this strategy enables the system to respond to complex and covert attack patterns and reduce the risk of being attacked.

[0144] S7: Blockchain audit and trust management

[0145] A consortium blockchain network is built 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.

[0146] Specifically, the alliance 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 supervisory superior nodes; the smart contract is used to record the information of the participants, data digest and timestamp of each data exchange, realize the tamper-proof evidence of key operations, and calculate the node trust score according to the timeliness, accuracy and security event occurrence of the submitted data of each node; the trust score is calculated according to 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 behavior.

[0147] S8: Run strategy optimization

[0148] According to the intrusion detection alarm and the blockchain audit result, the security policy and the resource allocation parameter are dynamically adjusted combined with the machine learning technology, and the adaptive defense is realized.

[0149] Further, based on the intrusion detection alarm data, the potential attack behavior is analyzed and predicted by using a deep learning model or a reinforcement learning algorithm, and the firewall rules, the response strategy of the intrusion prevention system and the traffic filtering strategy are automatically adjusted;

[0150] According to the security events and node behaviors recorded in the blockchain audit result, the security state and behavior performance of each node are analyzed by using a clustering algorithm or an anomaly detection algorithm, the risk level of the node is evaluated, and then the resource allocation parameter is adjusted, including the computing resource, the bandwidth resource and the storage resource, so as to ensure that the high-risk node obtains more monitoring resources, and the low-risk node obtains optimized resource allocation;

[0151] Based on the analysis result, the data encryption strategy and the identity authentication strategy are automatically optimized, and the encryption strength or the identity verification mechanism is dynamically adjusted for the node or the traffic with high risk level.

[0152] In the present application, the construction and adaptation of the Xinyuan environment is the foundation, ensuring that the system can run smoothly in the Xinyuan environment, 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 the priority, avoiding downtime or performance degradation of key business modules during the migration process. Data migration is the key part of platform migration, through batch migration and hash verification, to ensure that data is not lost or tampered with during migration, while protecting privacy. This step follows the migration planning and actually performs the data migration operation. After data migration is completed, the parallel switching stage is entered. 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 switching. During platform migration and data switching, security is crucial, and password system reconstruction is an important step to enhance platform security after data migration. This step ensures that encryption algorithms that meet national security standards are used during data exchange, and quantum-resistant encryption protection is added. Security defense deployment is carried out after data migration and platform switching to ensure that the new platform can prevent external attacks and abnormal behavior when it is formally running. Attack features are collected through honeypot nodes, and deep learning models are used to optimize the defense rule library. During system operation, 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 ensures the integrity of the system's trust management and subsequent audit. During system operation, security policies are optimized by continuously collecting intrusion detection and blockchain audit results to ensure that the platform can respond and adjust in real time when facing new threats.

[0153] As Figure 2 shown, another embodiment of the present application provides a market supervision data exchange platform migration and security enhancement system based on a Xinyuan environment, for performing the market supervision data exchange platform migration and security enhancement method based on the Xinyuan environment as described above, comprising:

[0154] An adaptation module is deployed on a localized hardware platform to support compatibility testing of operating systems, databases and middleware;

[0155] A migration planning module is used to modularly disassemble the system architecture of the existing platform, generate a dependency matrix and develop a migration plan;

[0156] A data migration module is used to perform batch data migration, and hash verification and multi-party secure calculation are used to ensure data consistency and privacy protection;

[0157] A business migration module is used to start business modules according to migration priorities and perform output comparison during parallel operation to ensure smooth traffic switching;

[0158] A cryptographic module is used to replace encryption and decryption algorithms with domestic commercial cryptographic algorithms and introduce quantum-resistant encryption technology to enhance data protection.

[0159] A defense module is used to integrate an intrusion detection system and a honeypot node, monitor traffic in real time and automatically update defense strategies.

[0160] A blockchain module is used to build a consortium blockchain record data exchange log and use a smart contract to realize identity authentication and reputation evaluation.

[0161] A strategy optimization module is used to dynamically adjust security strategies and resource allocation according to intrusion detection alarms and blockchain audit results combined with machine learning.

[0162] In summary, the application combines innovative technologies such as localization hardware, encryption technology, intrusion prevention mechanism, resource optimization and adaptive defense, effectively solving various problems in the migration and security protection of the market supervision data exchange platform in the prior art, and improving the compatibility, performance, security and intelligent level of the platform, ensuring efficient and stable operation of the platform in the signal creation environment.

[0163] The above is only a specific embodiment of the application, but the protection scope of the application is not limited thereto, and any skilled person in the art can easily think of various changes or replacements within the technical scope disclosed in the application, which should be covered within the protection scope of the application. Therefore, the protection scope of the application should 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 Xinyuan environment, characterized in that, The method comprises: Deploying adapted operating systems, databases and middleware on a localized hardware platform, completing compatibility testing of the running environment; Modularly disassembling the system architecture of the existing platform, analyzing the calling relationship between components through a dependency matrix, assigning migration priorities to each module in combination with business criticality, and formulating a migration plan; Batching business data into the Xinda platform database, ensuring data consistency through hash verification after each batch migration, and enhancing data privacy protection through multi-party secure computation; Starting business modules on the new platform according to the migration priorities, comparing outputs during parallel operation of the new and old platforms, and switching business traffic after verification of consistency; Replacing encryption and decryption algorithms with domestic commercial cryptographic algorithms, deploying a cryptographic service adaptation layer to interface cryptographic hardware devices, and strengthening encryption protection in combination with quantum-resistant encryption technology; Integrating an intrusion detection system and virtual honeypot nodes to monitor data exchange traffic and lure attack behaviors in real time, updating attack features to a defense rule library, and updating defense strategies in real time in combination with a deep learning model; Building a consortium blockchain network to record data exchange logs and security events, implementing node identity authentication and dynamic reputation evaluation based on smart contracts, and improving identity authentication flexibility in combination with centralized identity management; According to the intrusion detection alarm and the blockchain audit result, dynamically adjusting the security strategy and resource allocation parameters in combination with machine learning technology to realize adaptive defense.

2. The method of claim 1, wherein the method further comprises: The deployment of adapted operating systems, databases and middleware on a localized hardware platform is achieved through automated adaptation tools and intelligent optimization algorithms, wherein: The automated adaptation tools assess the compatibility of operating systems, databases and middleware through a combination of static code analysis and runtime performance analysis, generate a compatibility report by analyzing the code and execution logs of the existing platform, and automatically generate an adaptation configuration scheme; The intelligent optimization algorithm specifically uses a machine learning model to predict the compatibility of domestic operating systems, databases and middleware, dynamically adjusts operating system kernel parameters and database cache strategies based on hardware resource requirements to minimize latency and maximize throughput, and the machine learning model continuously optimizes the configuration based on historical data and real-time monitoring data; During deployment, hardware acceleration technology is used to dynamically accelerate specified computing tasks using GPUs or FPGAs, automatically select hardware acceleration methods based on task type and hardware resource availability, and automatically enable hardware acceleration resources when system load exceeds a set threshold; Modularize operating systems, databases and middleware through containerization technology, support dynamic resource allocation, automatically monitor resource consumption, and adjust the computing and storage resources of containers.

3. The method of claim 1, wherein the method further comprises: The method of formulating a migration plan specifically comprises: Obtain the internal dependencies of the platform using a combination of 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 dependency relationship and performance evaluation, construct a dependency matrix of functional modules and data units, and predict potential inter-module dependencies using 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 business during migration, and meet multiple objectives of minimizing downtime and maximizing resource utilization; Based on the dependency matrix and multi-objective optimization algorithm, a migration plan is developed that includes migration order, expected downtime, and resource utilization, and a simulation model is used to predict the results before migration.

4. The method of claim 1, wherein the method further comprises: After each batch migration, the data consistency is ensured through hash verification, and the method includes: After each batch migration, the data consistency is verified by comparing the record quantity and hash value of the same table in the new and old databases; When inconsistencies are found, an incremental synchronization strategy is used to retransmit only the changed records, and the distributed hash table and asynchronous multi-thread verification technology are combined to speed up the data verification process; If the data is still inconsistent, the difference records are compared one by one, and the missing or inconsistent records are retransmitted until the corresponding data sets on the new and old platforms are identical.

5. The method of claim 1, wherein the method further comprises: During the parallel operation of the new and old platforms, output comparison is performed, which includes: Intelligent test request scheduling is implemented for selected test requests, and test request types and priorities are dynamically selected based on historical operation data and load conditions of the platform; A real-time performance monitoring system is introduced to continuously track the response time, load condition, and error rate of the new and old platforms, and an automatic rollback mechanism is triggered when abnormalities occur to switch business traffic back to the old platform, ensuring stable operation of the new platform; An incremental switching traffic strategy is used to gradually increase the traffic load of the new platform, and finally complete the switching of full traffic, ensuring that the new platform can handle high-load business traffic; Intelligent anomaly detection algorithms are used to analyze potential anomalies in the comparison results in real time, ensuring high accuracy and timely feedback in the test comparison process.

6. The method of claim 1, wherein the method further comprises: The domestic commercial cryptographic algorithms include SM2 / SM9 public key algorithms, SM4 symmetric encryption algorithms, and SM3 digest algorithms, and post-quantum encryption algorithms are introduced to enhance the resistance of the platform to future quantum computing threats; The cryptographic service adaptation layer encapsulates the calling interfaces of the domestic commercial cryptographic algorithms, allowing the platform application layer to call the cryptographic service in the same way as the original algorithm, and using a multi-encryption layer approach, different encryption algorithms are used for data transmission and storage protection.

7. The method of claim 1, wherein the method further comprises: receiving a request for a data exchange from a client; and sending a response to the request to the client. The honeypot node uses a dynamic deployment mechanism to automatically select different virtual or physical nodes for attacker trapping based on feedback from a real-time two-layer intrusion detection system, and the honeypot node uses rule feature matching and machine learning techniques to dynamically identify and analyze the behavior characteristics of attackers; The first layer intrusion detection system quickly identifies known attack patterns through rule feature matching, and the second layer intrusion detection system analyzes the feature vectors of data exchange traffic based on machine learning to detect unknown threat patterns; the honeypot node records and stores the interaction data and behavior characteristics of attackers and feeds back to the first layer intrusion detection system to optimize the attack feature library; The honeypot node uses an adaptive learning algorithm combined with deep learning to update attack features in real time and actively adjust the honeypot strategy.

8. The method of claim 1, wherein the method further comprises: The alliance blockchain network is deployed in a permissioned chain manner, and the participating nodes include main server nodes of the data exchange platform and supervisory superior nodes; the smart contract is used to record the participants, data digest and timestamp information of each data exchange, realize tamper-proof evidence of key operations, and calculate node trust scores according to the timeliness, accuracy and safety event occurrence of the submitted data of each node; The trust score is calculated according to the timeliness of data submission, the integrity of each data exchange, the safety event response of each node, and the security of historical data exchange behavior.

9. The method of claim 1, wherein the method further comprises: Based on the intrusion detection alarm data, a deep learning model or reinforcement learning algorithm is used to analyze and predict potential attack behaviors, and automatically adjust the 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 safety state and behavior performance of each node are analyzed through clustering algorithm or anomaly detection algorithm, the risk level of the node is evaluated, and then the resource allocation parameters including computing resources, bandwidth resources and storage resources are adjusted to ensure that high-risk nodes obtain more monitoring resources, and low-risk nodes obtain optimized resource allocation; Based on the analysis results, the data encryption strategy and identity authentication strategy are automatically optimized, and the encryption strength or identity verification mechanism is dynamically adjusted for nodes or traffic with high risk level.

10. The system for migrating and enhancing security of the market supervision data exchange platform based on the Xinyuan environment, which is used for executing the method for migrating and enhancing security of the market supervision data exchange platform based on the Xinyuan environment as claimed in any one of claims 1 to 9, characterized in that, The system comprises: An adaptation module deployed on a localized hardware platform for supporting compatibility testing of operating systems, databases and middleware; A migration planning module for modularizing the system architecture of an existing platform, generating a dependency matrix and formulating a migration plan; A data migration module for performing batch data migration, using hash check and multi-party secure computation to ensure data consistency and privacy protection; A business migration module for starting business modules according to migration priorities, performing output comparison during parallel running, and ensuring smooth traffic switching; A cryptography module for replacing encryption and decryption algorithms with domestic commercial cryptography algorithms, and introducing quantum-resistant encryption technology to enhance data protection; A defense module for integrating an intrusion detection system and a honeypot node, monitoring traffic in real time and automatically updating defense strategies; A blockchain module for constructing an alliance blockchain to record data exchange logs, and using a smart contract to realize identity authentication and reputation evaluation; A strategy optimization module for dynamically adjusting security strategies and resource allocation according to intrusion detection alarms and blockchain audit results, combined with machine learning.

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