A security management system compatible adaptation method supporting a domestic middleware environment

Abstract

The application discloses a kind of support domestic middleware environment's security management system compatible adaptation method, it is related to information technology and network security technical field, including constructing middleware compatibility adaptation layer real-time monitoring the running state of multiple middleware platforms, according to the version change of middleware automatically adjusts adaptation strategy;Based on big data analysis and AI algorithm, the running data of multiple middleware platforms are analyzed in real time, potential failure is predicted, and security warning is generated;Introduce distributed disaster recovery backup mechanism, and automatically switch failure according to the failure state of middleware;Based on automatic recovery strategy, the failure data after introducing distributed disaster recovery backup mechanism is restored to the state before failure, uses rollback mechanism module, and stabilizes data state;The method described in the application realizes multiple middleware platforms automatic adaptation, intelligent fault prediction, fast failure switching, constructs closed-loop safety management system, and realizes high intelligence and automation.

Description

Technical Field

[0001] This invention relates to the fields of information technology and network security technology, specifically to a method for compatibility and adaptation of a security management system that supports domestic middleware environments. Background Technology

[0002] With the rapid development of information technology, middleware, as a key software platform supporting large-scale application systems, plays a vital role in data exchange, service integration, and application deployment. Especially in the field of domestic middleware, as the country's demand for independent control of information technology gradually increases, the research and application of domestic middleware have achieved significant development in recent years. After years of evolution, middleware technology has developed from a single-function support platform into a multi-functional solution integrating services, transaction management, and message passing. In this process, domestic middleware, such as Eastcom and DM, is gradually developing towards higher stability, security, and scalability, striving to gain a foothold in high-tech fields such as big data, cloud computing, and the Internet of Things.

[0003] However, most existing middleware management systems lack unified compatibility and adaptation for multiple middleware environments, especially when it comes to efficient compatibility and adaptation between different versions of middleware and heterogeneous systems, which presents significant technical challenges. Traditional middleware compatibility adaptation methods typically rely on manual configuration and intervention, failing to achieve automatic identification and adaptation of middleware versions, nor providing intelligent adaptation strategies for different middleware platforms. This not only increases system operation and maintenance costs but also affects the system's stability and scalability in different environments. Furthermore, in terms of data recovery, many existing solutions still rely on traditional full backup or single-point backup strategies, failing to provide real-time incremental backups and automatic recovery, resulting in excessively long system recovery times and failing to meet the low failure recovery time requirements of high-availability systems. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is that existing methods for supporting compatibility and adaptation, fault prediction and early warning, disaster recovery backup and data recovery in multi-middleware environments suffer from insufficient middleware compatibility and adaptation, low fault early warning and prediction capabilities, and low efficiency in disaster recovery backup and data recovery. The invention also addresses the issues of how to automate multi-middleware compatibility and adaptation, achieve intelligent fault prediction and response, quickly recover faulty data, and reduce response time.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a security management system compatibility and adaptation method supporting domestic middleware environments, comprising: constructing a middleware compatibility adaptation layer to monitor the operational status of multiple middleware platforms in real time and automatically adjusting the adaptation strategy according to middleware version changes; performing real-time analysis of the operational data of multiple middleware platforms based on big data analysis and AI algorithms to predict potential faults and generate security warnings; introducing a distributed disaster recovery backup mechanism to automatically switch faults according to the middleware fault status; and based on an automatic recovery strategy, restoring the fault data after the introduction of the distributed disaster recovery backup mechanism to the state before the fault, and using a rollback mechanism module to stabilize the data state.

[0007] As a preferred embodiment of the security management system compatibility and adaptation method supporting domestic middleware environment described in this invention, the method of constructing a middleware compatibility adaptation layer to monitor the running status of multiple middleware platforms in real time includes automatically identifying the current version of the middleware based on the middleware platform's version number and configuration file, and dynamically loading an adaptation layer compatible with the current version of the middleware.

[0008] As a preferred embodiment of the security management system compatibility and adaptation method supporting domestic middleware environments described in this invention, the real-time analysis of the operating data of multiple middleware platforms based on big data analysis and AI algorithms includes: using machine learning algorithms to train on historical fault data, analyzing the resource consumption, log data and operating status of middleware in real time, identifying abnormal behavior, and generating fault warning signals through set thresholds.

[0009] As a preferred embodiment of the security management system compatibility and adaptation method supporting domestic middleware environment described in this invention, the step of using machine learning algorithms to train historical fault data includes using historical fault logs and performance data from multiple middleware platforms as training sets, using classification algorithms to identify normal and abnormal states, and gradually optimizing the early warning model.

[0010] As a preferred embodiment of the security management system compatibility and adaptation method supporting domestic middleware environment described in this invention, the automatic fault switching based on the middleware fault status includes setting up multiple backup nodes in a multi-middleware environment, and automatically switching to the backup node when the master node detects a fault or performance abnormality.

[0011] As a preferred embodiment of the security management system compatibility and adaptation method supporting domestic middleware environment described in this invention, the automatic switching to backup node includes dynamically selecting the most suitable backup node through a load balancing algorithm to minimize the delay during fault recovery and synchronize data status in real time.

[0012] As a preferred embodiment of the security management system compatibility and adaptation method supporting domestic middleware environment described in this invention, the step of dynamically selecting the most suitable backup node through load balancing algorithm includes intelligently scheduling traffic based on the health status, load, and geographical location of the backup node, and prioritizing the selection of nodes with sufficient resources and close to the user for switching.

[0013] As a preferred embodiment of the security management system compatibility and adaptation method supporting domestic middleware environment described in this invention, the step of restoring the fault data after introducing a distributed disaster recovery backup mechanism to the state before the fault includes, in the disaster recovery mechanism, real-time synchronization of data changes, ensuring data consistency between different nodes based on incremental backup and distributed consistency protocol, and synchronizing the latest operation data through incremental backup.

[0014] As a preferred embodiment of the security management system compatibility and adaptation method supporting domestic middleware environment described in this invention, the rollback mechanism module includes: after data recovery, performing consistency verification, verifying the correctness of data recovery through hash algorithm and checkpoint technology, detecting data inconsistency, automatically starting the rollback mechanism, and restoring the system to the stable state before the failure.

[0015] Another objective of this invention is to provide a security management system compatible with domestic middleware environments. This system can solve the problems of excessive manual intervention, low automation, and slow early warning response in current middleware compatibility adaptation methods by constructing a middleware compatibility adaptation layer and an intelligent fault early warning mechanism.

[0016] As a preferred embodiment of the security management system compatibility and adaptation system supporting domestic middleware environments described in this invention, the system includes: a middleware compatibility adaptation module, a fault early warning and intelligent analysis module, a disaster recovery backup and automatic recovery module, and a data consistency and rollback mechanism module. The middleware compatibility adaptation module is used to design a middleware compatibility adaptation layer to ensure seamless compatibility between different versions of domestic middleware, automatically identifying and deploying the most suitable adaptation layer. The fault early warning and intelligent analysis module is used to analyze the operating data of the middleware platform in real time, predict potential faults, and generate early warning signals. The disaster recovery backup and automatic recovery module is used to implement a distributed disaster recovery backup mechanism, automatically switching over in the event of a middleware failure. The data consistency and rollback mechanism module is used to ensure consistency during data recovery, providing a rollback mechanism that automatically rolls back to the previous stable state when inconsistent recovery data occurs.

[0017] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program as a step in implementing a security management system compatibility and adaptation method that supports a domestic middleware environment.

[0018] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of a security management system compatibility and adaptation method supporting a domestic middleware environment are disclosed.

[0019] The beneficial effects of this invention are as follows: The security management system compatibility and adaptation method for supporting domestic middleware environments provided by this invention reduces the workload of manual configuration and improves system stability and compatibility through real-time monitoring and automatic adaptation of the middleware compatibility and adaptation layer, effectively reducing operation and maintenance costs; the fault prediction and early warning mechanism based on machine learning algorithms identifies potential faults in advance, generates accurate early warning signals, and improves fault response speed and processing efficiency; through distributed disaster recovery backup and automatic fault switching mechanisms, it can quickly recover and ensure data consistency when a fault occurs, achieving efficient business continuity and rapid data recovery. This invention achieves better results in efficient compatibility and adaptation, security management and fault recovery in domestic middleware environments. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 The first embodiment of the present invention provides an overall flowchart of a security management system compatibility and adaptation method that supports domestic middleware environments.

[0022] Figure 2 The third embodiment of the present invention provides an overall flowchart of a security management system compatible with and adapted to a domestic middleware environment. Detailed Implementation

[0023] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0024] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for compatibility and adaptation of a security management system supporting domestic middleware environments is provided, comprising:

[0025] S1: Build a middleware compatibility adaptation layer to monitor the running status of multiple middleware platforms in real time and automatically adjust the adaptation strategy according to changes in middleware versions.

[0026] Furthermore, the middleware compatibility adaptation layer is built to monitor the running status of multiple middleware platforms in real time. This includes automatically identifying the current version of the middleware based on the middleware platform's version number and configuration file, and dynamically loading an adaptation layer that is compatible with the current version of the middleware.

[0027] It should be noted that automatically identifying the current version of the middleware includes: automatically identifying the current version and configuration of the middleware by combining the version number and configuration file; periodically or on demand scanning the platform's version number and configuration file during middleware runtime to identify the currently running middleware version; calling the interface provided by the middleware platform to directly obtain the middleware version information; extracting the version number or related metadata from the middleware's configuration file; and after extracting the version number and configuration parameters from the middleware startup log file, accurately determining the adaptation layer that needs to be loaded, performing compatibility mapping, dynamically loading the adaptation layer, and generating an adaptation adjustment function, represented as follows:

[0028]

[0029] Where Φ(v) represents the compatibility and adaptability adjustment function generated after the middleware platform automatically identifies the version status, v represents the difference score between the current middleware version of the platform and the system compatibility mapping strategy, a represents the version adaptation square nonlinear response factor, and b represents the adaptation log weight response factor.

[0030] It should be noted that the interfaces provided by the middleware platform include JMX and REST API, but are not limited to JMX and REST API; the configuration files of the middleware include XML and JSON files, but are not limited to XML and JSON formats.

[0031] It should also be noted that the middleware compatibility adaptation layer can provide efficient version compatibility between multiple middleware platforms, and can also cope with differences and configuration changes between middleware versions. The system automatically loads and adjusts the adaptation strategy according to the version number and configuration file, realizing seamless cross-platform and cross-version integration, reducing the need for manual intervention, avoiding system failures and performance degradation caused by version incompatibility, greatly improving the system's compatibility, stability and scalability, and effectively reducing the complexity of middleware management and operation and maintenance costs.

[0032] S2: Based on big data analytics and AI algorithms, it performs real-time analysis of the operational data of multiple middleware platforms, predicts potential failures, and generates security alerts.

[0033] Furthermore, real-time analysis of the operational data of multiple middleware platforms based on big data analytics and AI algorithms includes using machine learning algorithms to train on historical fault data, analyzing the resource consumption, log data, and operational status of middleware in real time, identifying abnormal behaviors, and generating fault warning signals based on set thresholds.

[0034] Furthermore, training the historical fault data using machine learning algorithms involves using historical fault logs and performance data from multiple middleware platforms as the training set, and using classification algorithms to identify normal and abnormal states. The classification algorithm is represented as follows:

[0035]

[0036] in, The final classification result represents the middleware's running status; 1 indicates an abnormal state, and 0 indicates normal. "majority" represents the majority voting function. "x" represents the feature vector of the middleware platform's running status at a certain point in time. f1(x), f2(x), ..., f T (x) represents the independent decision trees of the random forest.

[0037] By combining the middleware platform's operational logs, resource consumption data, and error logs under normal and abnormal states, potential failure modes are identified and classified, and an accurate failure prediction model is trained, represented as follows:

[0038]

[0039] Among them, F p Φ(v) represents the final output score of the fault prediction model, Φ(v) represents the compatibility adjustment function generated by the automatic identification and loading mechanism of the middleware compatibility adaptation layer in stage S1, v represents the difference score between the current middleware version of the platform and the system compatibility mapping strategy, and θ represents the compatibility adjustment function. i Let ζ be the fault intensity factor generated after training with historical fault logs. i (τ) represents the degree of disturbance of the operating resources of the i-th middleware within the current time window τ, γ represents the upper limit of the anomalies that the disaster recovery backup mechanism can accept, δ represents the configuration state offset generated by the current platform after middleware switching or data recovery, and ω represents the standard deviation of the middleware operating state variation calculated based on the system's historical rollback experience.

[0040] It should be noted that when the final output score of the fault prediction model is greater than 0 and less than or equal to 1, it indicates that the system is in good condition; when the final output score of the fault prediction model is greater than 1 and less than or equal to 3, it indicates that some middleware in the system is abnormal or has a fault trend; when the final output score of the fault prediction model is greater than 3, it indicates that the system compatibility adaptation has failed, the middleware has failed, and an immediate switchback and rollback should be initiated.

[0041] It should be noted that training historical fault data using machine learning algorithms involves using historical fault logs and performance data from multiple middleware platforms as the training set, using classification algorithms to identify normal and abnormal states, and gradually optimizing the early warning model.

[0042] It should also be noted that using classification algorithms to identify normal and abnormal states includes using classification algorithms to identify abnormal states that occur under specific configurations and versions of middleware, adapting to specific failure modes of different middleware platforms, and improving the model's adaptability in dynamic and complex environments.

[0043] S3: Introduces a distributed disaster recovery and backup mechanism to automatically switch over based on the middleware failure status.

[0044] Furthermore, automatic failover based on middleware failure status includes setting up multiple backup nodes in a multi-middleware environment, automatically switching to the backup node when the primary node detects a failure or performance anomaly.

[0045] It should be noted that automatic switching to backup nodes includes dynamically selecting the most suitable backup node through a load balancing algorithm to minimize latency during fault recovery and synchronize data status in real time.

[0046] It should also be noted that dynamically selecting the most suitable backup node through a load balancing algorithm includes intelligently scheduling traffic based on the backup node's health status, load, and geographical location, prioritizing nodes with sufficient resources and proximity to users for switching. The load balancing algorithm is expressed as follows:

[0047]

[0048] Where R represents the resource consumption of the current backup node, r j Indicates the current load rate of the CPU or memory, l j a represents the normalized value of network latency. j d represents the reachability of node services. j The normalized value of the primary / backup synchronization delay is represented by w1, w2, w3, and w4, which represent weighting coefficients, satisfying w1+w2+w3+w4=1. D represents the distance between the node and the user's geographical location.

[0049] It should also be noted that the system dynamically selects backup nodes based on multiple factors to optimize the fault recovery process. When the middleware fails or its performance degrades, the most suitable backup node is dynamically selected through a load balancing algorithm. The backup node is selected based on the node's health status, taking into account the node's load and geographical location. This ensures that traffic can select the optimal backup node as much as possible, thereby reducing recovery latency and ensuring that the system can achieve seamless migration of data and traffic between different regions and different nodes. This improves the system's recovery efficiency in complex and dynamic environments and effectively reduces recovery bottlenecks caused by node overload or excessive distance.

[0050] S4: Based on the automatic recovery strategy, the faulty data after the introduction of the distributed disaster recovery backup mechanism will be restored to the state before the fault. The rollback mechanism module is used to stabilize the data state.

[0051] Furthermore, restoring faulty data to its pre-fault state after introducing a distributed disaster recovery backup mechanism includes real-time synchronization of data changes within the disaster recovery mechanism, ensuring data consistency across different nodes based on incremental backup and distributed consistency protocols, and synchronizing the latest operational data through incremental backup.

[0052] Furthermore, based on incremental backup and distributed consistency protocols, incremental backup only records data changes since the last backup. During the recovery process, each node updates its local data according to the changes recorded in the incremental backup, avoiding the storage and recovery time required for full backup. The distributed consistency protocol ensures data consistency among multiple nodes, guarantees data synchronization and consistency between different backup nodes, and prevents data conflicts and loss.

[0053] It should be noted that the rollback mechanism module includes performing consistency verification after data recovery, verifying the correctness of data recovery through hash algorithms and checkpoint technology, detecting data inconsistencies, automatically initiating the rollback mechanism, and restoring the system to its stable state before the failure.

[0054] It should also be noted that verifying the correctness of data recovery through hash algorithms and checkpointing techniques includes using hash algorithms to calculate the hash value of data blocks. After data recovery, the hash value of the recovered data is recalculated and compared with the hash value before the failure. If the two are inconsistent, it indicates that the recovered data has changed or been lost, and the system will automatically initiate a rollback mechanism to recover the data. The hash algorithm is expressed as follows:

[0055] H(D) = Hash(D)

[0056] H(D′) = Hash(D) ' )

[0057] Here, H(D) represents the hash value of data D. After recovery, the system will calculate the hash value H(D′) of the recovered data D′ and determine data consistency by comparing H(D) and H(D′). If H(D) ≠ H(D′), the system will initiate a rollback mechanism. By periodically creating data checkpoints in the system, each checkpoint records the complete state of the system. During the recovery process, the system will roll back based on the most recent valid checkpoint, restoring the data state at that checkpoint. This avoids errors caused by data inconsistency during the recovery process and the manual intervention and potential data recovery problems in traditional methods, thereby improving the overall reliability and automation level of the system.

[0058] Example 2 is an embodiment of the present invention, which provides a method for compatibility and adaptation of a security management system that supports domestic middleware environments. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0059] To verify the adaptability, fault prediction, and recovery capabilities of this invention in a multi-middleware environment, the following experiment was designed. The experimental environment included multiple middleware platforms, including domestic middleware based on different versions (such as DM Database and Eastcom Middleware) and open-source middleware (such as Apache Kafka and Redis). The system was configured with one server as the master node and multiple backup nodes for disaster recovery backup and failover.

[0060] First, the middleware compatibility adaptation layer of this invention is deployed on all middleware platforms. The system automatically identifies the version of each middleware through its version number and configuration file, and dynamically loads the adaptation layer compatible with the current version based on preset compatibility rules. The system periodically checks the middleware version information and configuration through REST API and JMX interfaces to ensure the real-time update and correct loading of the adaptation layer.

[0061] Then, the middleware in the multi-platform environment is trained using historical fault logs and performance data; machine learning algorithms (using random forest and support vector machine algorithms) are used to analyze fault data and build fault prediction models; the system's resource consumption, log data and running status are analyzed in real time, and warning signals are generated using set thresholds; the system learns and updates the model regularly to ensure that the warning signals can accurately reflect the abnormal state of the current middleware platform.

[0062] Multiple backup nodes were configured, and the failure of the primary node was intentionally simulated in the experiment. A load balancing algorithm dynamically selects the most suitable backup node to minimize latency during fault recovery. The load balancing algorithm intelligently schedules backup nodes based on their health status, load, and geographical location to ensure optimal traffic switching. Incremental backup and a distributed consistency protocol synchronize data during fault recovery. A rollback mechanism module verifies the consistency of data recovery, using hash algorithms and checkpointing techniques to ensure the correctness of the recovered data. If data inconsistency occurs, the system automatically rolls back to its stable state before the fault.

[0063] Table 1 Data Comparison Table

[0064]

[0065] As can be seen from the data comparison table in Table 1, the DM database performs exceptionally well in disaster recovery backup, failover, recovery time, and data consistency, with a recovery time of 12 seconds and a data consistency verification pass rate of 100%. This performance is attributed to the dynamic adaptation layer and distributed disaster recovery backup mechanism introduced in this invention. In traditional technologies, especially in systems with relatively simple fault recovery mechanisms, recovery times are typically long, and data consistency is easily affected in multi-node environments. In contrast, this invention, through the combination of incremental backup and a distributed consistency protocol, maintains low latency and ensures data consistency during recovery in multi-platform environments.

[0066] In tests conducted on the Eastcom middleware and Apache Kafka platform, although there were slight differences in the number of backup nodes selected for load balancing and the success rate of fault recovery, the overall recovery time and data consistency verification pass rate were both quite satisfactory. In particular, the load balancing algorithm dynamically selects the most suitable backup node based on the node's health status, load, and geographical location, ensuring the system's efficient failover capability, thereby reducing recovery latency and improving system reliability.

[0067] Compared to traditional disaster recovery and restoration methods, this invention significantly reduces recovery time and improves the system's fault tolerance in complex multi-middleware environments by introducing intelligent fault prediction, disaster recovery switching, rollback mechanisms, and incremental backups. Furthermore, the rollback mechanism and consistency checks further enhance the accuracy of data recovery, preventing system errors and data loss caused by inconsistent recovered data—advantages that are often difficult to achieve in traditional technologies.

[0068] In summary, this invention solves the problems of slow fault recovery and difficulty in ensuring data consistency in traditional technologies by using intelligent and automated middleware compatibility adaptation, fault prediction, distributed disaster recovery backup and rollback mechanisms, thereby improving the stability and high availability of the system. It is especially suitable for domestic middleware environments that require high reliability and business continuity.

[0069] Example 3, referring to Figure 2 As an embodiment of the present invention, a security management system compatible with domestic middleware environment is provided, including a middleware compatibility adaptation module 100, a fault early warning and intelligent analysis module 200, a disaster recovery backup and automatic recovery module 300, and a data consistency and rollback mechanism module 400.

[0070] X1: Middleware compatibility adaptation module 100 includes automatic identification submodule 101 and dynamic loading submodule 102.

[0071] It should be noted that the automatic identification submodule 101 is used to periodically or as needed identify the version information of the current middleware by calling the interface provided by the middleware platform, and extract the version number by analyzing the middleware configuration file or startup log; the dynamic loading submodule 102 is used to dynamically select and load an adaptation layer compatible with the current middleware version based on the middleware version information provided by the automatic identification submodule 101.

[0072] It should also be noted that the loading of the automatic identification submodule 101 adaptation layer is based on the version compatibility mapping table. After the dynamic loading submodule 102 receives the mapping information of the automatic identification submodule 101, the identification result is dynamically loaded with the compatibility adaptation logic. The middleware platform will expose the runtime data to the fault warning and intelligent analysis module 200.

[0073] X2: The fault early warning and intelligent analysis module 200 includes a historical data training submodule 201, a real-time data analysis submodule 202, and a fault early warning signal generation submodule 203.

[0074] It should be noted that the historical data training submodule 201 is used to train the system based on fault logs, system resource consumption, and error logs using machine learning algorithms; the real-time data analysis submodule 202 is used to analyze the middleware platform's resource consumption, log data, and operating status in real time to identify abnormal behavior; and the fault warning signal generation submodule 203 generates fault warning signals based on the abnormal behavior identified by the real-time data analysis submodule 202 and notifies the system administrator via email, SMS, or other means.

[0075] It should also be noted that the real-time data analysis 202 identifies and monitors the operating status in real time and triggers the fault warning signal generation submodule 203 to generate a warning signal. After the warning signal is transmitted to the disaster recovery backup and automatic recovery module 300, the distributed disaster recovery backup management submodule 301 and the automatic fault switching submodule 302 are activated.

[0076] X3: Disaster recovery backup and automatic recovery module 300 includes distributed disaster recovery backup management submodule 301, automatic failover submodule 302, and data synchronization and recovery submodule 303.

[0077] It should be noted that the distributed disaster recovery and backup management submodule 301 is used to manage multiple backup nodes, ensure real-time data backup in the system, and select a suitable node for fault recovery based on the health status of the backup node; the automatic failover submodule 302 is used to select the optimal backup node based on the current node's health status, load, and geographical location using a load balancing algorithm; and the data synchronization and recovery submodule 303 is used to start a data synchronization mechanism after the backup node is activated, ensuring data consistency between the backup node and the master node.

[0078] It should also be noted that after the disaster recovery backup and automatic recovery module 300 completes the activation of the backup node and data synchronization, it immediately hands over the restored data to the data consistency verification submodule 401 for consistency verification. If the consistency verification fails, the rollback mechanism submodule 402 will start the rollback mechanism to roll back the system to the nearest valid checkpoint. After the rollback is completed, the data consistency and rollback mechanism module 400 will feed back the result to the disaster recovery backup and automatic recovery module 300 to confirm that the system is in a stable state.

[0079] X4: The data consistency and rollback mechanism module 400 includes a data consistency verification submodule 401 and a rollback mechanism submodule 402.

[0080] It should be noted that the data consistency verification submodule 401 is used to perform data consistency verification using hash algorithms and checkpointing techniques; the rollback mechanism submodule 402 is used to initiate the rollback mechanism when data consistency verification fails, restoring the system to the last valid checkpoint.

[0081] It should also be noted that after the data consistency and rollback mechanism module 400 completes the rollback, it will report the rollback result or consistency verification result. If a compatibility defect is found in a specific middleware adaptation layer during the rollback process, the information will be fed back to the middleware compatibility adaptation module 100. Based on the feedback, the middleware compatibility adaptation module 100 will update the adaptation layer rules or trigger the adaptation layer reload to optimize the next processing capability.

[0082] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0083] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0084] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0085] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for compatibility and adaptation of a security management system supporting domestic middleware environments, characterized in that, include: Build a middleware compatibility adaptation layer to monitor the running status of multiple middleware platforms in real time and automatically adjust the adaptation strategy according to middleware version changes; Real-time analysis of operational data from multiple middleware platforms is performed using big data analytics and AI algorithms to predict potential failures and generate security alerts. A distributed disaster recovery and backup mechanism is introduced to automatically switch over based on the middleware failure status; Based on the automatic recovery strategy, the faulty data after the introduction of the distributed disaster recovery backup mechanism will be restored to the state before the fault, and the rollback mechanism module will be used to stabilize the data state. The construction of the middleware compatibility adaptation layer for real-time monitoring of the operation status of multiple middleware platforms includes automatically identifying the current version of the middleware based on the middleware platform's version number and configuration file, and dynamically loading an adaptation layer that is compatible with the current version of the middleware. The automatic fault switching based on the middleware fault status includes setting up multiple backup nodes in a multi-middleware environment, and automatically switching to the backup node when the master node detects a fault or performance abnormality. The process of restoring fault data to its pre-fault state after the introduction of a distributed disaster recovery backup mechanism includes: in the disaster recovery mechanism, real-time synchronization of data changes, ensuring data consistency between different nodes based on incremental backup and distributed consistency protocol, and synchronizing the latest operation data through incremental backup. The rollback mechanism module includes performing consistency verification after data recovery, verifying the correctness of data recovery through hash algorithms and checkpoint technology, detecting data inconsistencies, automatically starting the rollback mechanism, and restoring the system to the stable state before the failure. The real-time analysis of the operational data of the multi-middleware platform based on big data analysis and AI algorithms includes using machine learning algorithms to train on historical fault data, analyzing the resource consumption, log data and operating status of the middleware in real time, identifying abnormal behavior, and generating fault warning signals through set thresholds.

2. The compatibility and adaptation method for a security management system supporting domestic middleware environments as described in claim 1, characterized in that: The process of training historical fault data using machine learning algorithms includes, Historical fault logs and performance data from multiple middleware platforms are used as training sets. Classification algorithms are used to identify normal and abnormal states, and the early warning model is gradually optimized.

3. The compatibility and adaptation method for a security management system supporting domestic middleware environments as described in claim 2, characterized in that: The automatic switch to the backup node includes, By dynamically selecting the most suitable backup node through a load balancing algorithm, the latency during fault recovery is minimized, and data status is synchronized in real time.

4. The compatibility and adaptation method for a security management system supporting domestic middleware environments as described in claim 3, characterized in that: The dynamic selection of the most suitable backup node through a load balancing algorithm includes... Based on the health status, load, and geographical location of backup nodes, traffic is intelligently scheduled, prioritizing nodes with sufficient resources and close to users for switching.

5. A security management system compatibility and adaptation system supporting domestic middleware environments, used to implement the security management system compatibility and adaptation method supporting domestic middleware environments as described in any one of claims 1 to 4, characterized in that: This includes a middleware compatibility adaptation module, a fault warning and intelligent analysis module, a disaster recovery backup and automatic recovery module, and a data consistency and rollback mechanism module; The middleware compatibility adaptation module is used to design a middleware compatibility adaptation layer to ensure seamless compatibility between different versions of domestic middleware, and to automatically identify and deploy the most suitable adaptation layer. The fault warning and intelligent analysis module is used to analyze the middleware platform's operating data in real time, predict potential faults, and generate warning signals. The disaster recovery backup and automatic recovery module is used to implement a distributed disaster recovery backup mechanism and automatically switch over when the middleware fails. The data consistency and rollback mechanism module is used to ensure consistency during data recovery and provides a rollback mechanism that automatically rolls back to the previous stable state when data inconsistency occurs.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the security management system compatibility and adaptation method supporting domestic middleware environment as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the security management system compatibility and adaptation method supporting domestic middleware environment as described in any one of claims 1 to 4.

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