Database comprehensive treatment system integrated with artificial intelligence technology and method thereof

Abstract

The invention discloses a database comprehensive treatment system and method integrated with an artificial intelligence technology, and relates to the field of artificial intelligence, and the method comprises the steps: collecting database operation parameters in real time through a multi-source intelligent probe; feature extraction and fusion are carried out to generate a treatment feature matrix of unified space-time association; inputting the matrix into an integrated AI decision engine cluster, and managing database data through each engine in the cluster; based on the decision instruction of each AI engine, conflict resolution and instruction fusion are carried out, and a final executable comprehensive treatment action sequence is generated; and issuing the sequence to a database, and re-inputting execution result data to an engine cluster by constructing a governance feedback loop to drive the next decision cycle. The method has the advantages that efficient and intelligent management and continuous optimization of the database are realized through AI engine analysis and decision fusion, and continuous improvement of system performance, safety and data quality is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of artificial intelligence, in particular to a database comprehensive management system integrating artificial intelligence technology and a method thereof. BACKGROUND

[0002] The database comprehensive management system integrating artificial intelligence technology and the method thereof aim to meet the increasingly complex data management and processing needs in today's big data era. The introduction of artificial intelligence technology brings new solutions to database management. Through AI technologies such as machine learning, natural language processing and data mining, processes such as data classification, data cleaning and data auditing can be automated, thereby improving the efficiency and accuracy of data management.

[0003] Most of the current database management methods on the market usually rely on a single monitoring or optimization method, lacking multi-dimensional intelligent collaboration. Traditional methods often treat performance optimization, security protection and data quality management as independent modules for processing, making it difficult to achieve comprehensive management across domains. For example, some methods only focus on database performance monitoring and index optimization, ignoring security and data quality issues, which makes it difficult for the database to respond comprehensively when facing complex business needs. At the same time, many existing methods do not fully utilize AI technology, relying on manual rules and experience-based judgments, resulting in decision-making lag and inaccuracy. Furthermore, the methods on the market often lack effective feedback mechanisms after execution, making it impossible to achieve continuous optimization. These shortcomings make traditional methods unable to provide timely and comprehensive management solutions when faced with complex and dynamic database environments. SUMMARY

[0004] To improve existing methods and systems, the database comprehensive management system integrating artificial intelligence technology and the method thereof are provided. The method realizes efficient and intelligent management and continuous optimization of the database through real-time data collection, multi-dimensional AI engine analysis and decision fusion, ensuring continuous improvement of system performance, security and data quality.

[0005] To achieve the above objectives, the technical solution adopted by the present application is as follows: The database comprehensive management method integrating artificial intelligence technology comprises: Real-time collection of database running parameters through multi-source intelligent probes deployed on database servers, network nodes and business application layers; Feature extraction and fusion based on the obtained database running parameters to generate a unified spatio-temporal correlation management feature matrix; The governance feature matrix is ​​input into the integrated AI decision engine cluster, and the database data is managed by each engine in the cluster. The integrated AI decision engine cluster includes a performance optimization AI engine, an active security protection AI engine, a data quality assessment AI engine, and an architecture evolution prediction AI engine. Based on the decision-making instructions of each AI engine, conflicts are resolved and instructions are integrated according to the priority of governance objectives and risk assessment, generating a final executable comprehensive governance action sequence; The comprehensive governance action sequence is distributed to the database, and the execution status and results are monitored in real time. By constructing a governance feedback loop, the execution result data is re-inputted into the integrated AI decision engine cluster to drive the next round of decision-making.

[0006] Preferably, the real-time collection of database operating parameters through multi-source intelligent probes deployed on the database server, network nodes, and business application layer specifically includes: The database operating parameters include operating performance indicators, access logs, SQL statements, network traffic metadata, and business load feature vectors. Capture database performance metrics, SQL execution, and access logs using probes deployed on the database server; Collect network traffic metadata, including IP traffic and transmission latency, using network node probes; Collect load characteristics and request / response information of the business layer through application layer probes; The collected data is cleaned and preprocessed.

[0007] Preferably, the step of extracting and fusing features based on the acquired database operating parameters to generate a unified spatiotemporal correlation governance feature matrix specifically includes: Based on the obtained database operating parameters, database performance characteristics, SQL query characteristics, network traffic characteristics, and business load characteristics are extracted respectively. The time dimension is unified by aligning each feature data according to the timestamp, and the correlation between different feature data is integrated by calculating the correlation matrix between different features. Based on the above steps, a governance feature matrix with unified spatiotemporal correlation is generated.

[0008] Preferably, the step of inputting the governance feature matrix into the integrated AI decision engine cluster and managing the database data through each engine in the cluster, wherein the integrated AI decision engine cluster includes a performance optimization AI engine, a proactive security protection AI engine, a data quality assessment AI engine, and an architecture evolution prediction AI engine, specifically including: The acquired governance feature matrix is ​​input into the integrated AI decision engine cluster, and each AI engine in the cluster extracts and analyzes relevant data in the feature matrix according to different tasks. The performance optimization AI engine is based on reinforcement learning and time series prediction model, which analyzes historical performance data and real-time data to identify and correct performance bottlenecks, and generates optimal index strategy, resource dynamic allocation scheme and hot and cold data layer migration instructions; The active security protection AI engine is based on graph neural network to build an anomaly detection model, obtain a database access entity relationship graph, map the relationship between users, SQL, and access request entities, calculate the anomaly score of each node through graph attention network, obtain the diffusion path of anomalies, generate real-time blocking instructions, and intercept illegal database access requests; The data quality evaluation AI engine is based on a multi-task deep learning model, which correlates business logs and database operation features to build a cross-modal alignment model based on Transformer, aligns records representing the same business entity in different data sources, identifies abnormal data, and generates data cleaning and data anomaly tracing instructions; The architecture evolution prediction AI engine is based on graph representation learning and scenario simulation model, which trains the model through historical database architecture evolution data to predict future architecture bottlenecks and generates distributed architecture sharding and read-write separation instructions; Based on the obtained governance instructions of each AI engine, the database comprehensive governance instruction set is generated.

[0009] Preferably, the decision instructions based on each AI engine are conflict resolved and instruction fused according to the governance target priority and risk assessment to generate the final executable comprehensive governance action sequence, which specifically includes: Based on the governance instructions of each AI engine in the comprehensive governance instruction set, the output of each AI engine is converted into a unified decision format; The governance target priority is divided based on the governance target of the governance instruction, and the priority of the instruction is weighted processed according to the risk assessment result of each governance instruction to adjust the priority; The instruction fusion is performed based on the conflict of the governance instruction, and the fused instruction is sorted according to the priority to form the final executable governance action sequence.

[0010] Preferably, the comprehensive governance action sequence is issued to the database, and the execution state and result are monitored in real time, the execution result data is input to the integrated AI decision engine cluster again through the construction of the governance feedback loop to drive the next round of decision cycle, which specifically includes: Based on the obtained comprehensive governance action sequence, it is issued to the target system through an automatic process; By monitoring the execution state and result of each governance action, it is determined whether the instruction is executed as expected; Collect feedback data including performance indicators, security events, data quality repair, and re-input to the AI decision engine cluster for real-time analysis and optimization by each engine; Based on the feedback data and new analysis results, the AI engine will generate new decision instructions to form the next round of governance action sequences.

[0011] Further, a database comprehensive governance system integrating artificial intelligence technology is proposed, including: Intelligent probe module: the intelligent probe module is responsible for real-time collection of database running parameters, including performance indicators, access logs, SQL statements, network traffic and business load characteristics and other data; Feature extraction and fusion module: the feature extraction and fusion module extracts and fuses features based on the collected database running parameters to generate a unified spatiotemporal correlation governance feature matrix; Integrated AI decision engine cluster module: the integrated AI decision engine cluster module inputs the governance feature matrix into the integrated AI decision engine cluster, which includes performance optimization, active security protection, data quality evaluation and architecture evolution prediction, etc. multiple AI engines for intelligent management of databases; Conflict resolution and instruction fusion module: the conflict resolution and instruction fusion module resolves conflicts between the outputs of different AI engines according to the governance target priority and risk assessment, and generates a final executable governance action sequence; Instruction execution module: the instruction execution module issues the comprehensive governance action sequence to the database system, monitors the execution status and results in real time, and feeds back data according to the execution; Feedback loop module: the feedback loop module is used to collect execution feedback data and re-input the feedback data to the AI engine to drive the next round of decision cycle; Processor: the processor is used to process the calculation process of each formula and the construction calculation process of each model.

[0012] Compared with the prior art, the advantages of the present application are: By deploying multi-source intelligent probes to collect various parameters of database operation in real time, the comprehensiveness and accuracy of data are ensured. Then, through feature extraction and fusion, a unified spatiotemporal correlation governance feature matrix is formed, providing accurate data basis for subsequent decision-making. The integrated AI decision engine cluster combines performance optimization, active security protection, data quality evaluation, and architecture evolution prediction, etc. functions, and can intelligently analyze the running state of the database from different dimensions, timely identify and solve performance bottlenecks, data anomalies and security threats. In addition, through the conflict resolution and instruction fusion mechanism of governance target priority and risk assessment, the execution of governance instructions is ensured to be efficient and accurate. Most importantly, the comprehensive governance actions are continuously optimized through a real-time feedback mechanism, forming a closed loop, which can continuously improve the governance effect during the operation of the database, ensuring the long-term stability and security of the system. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 Method flowchart of the present application; Figure 2 Database operation parameter collection flowchart of the present application; Figure 3 Governance feature matrix generation flowchart of the present application; Figure 4 Integrated AI decision engine cluster flowchart of the present application; Figure 5 Comprehensive governance action sequence generation flowchart of the present application; Figure 6 Governance feedback loop flowchart of the present application. DETAILED DESCRIPTION

[0014] The following description is used to disclose the present application so that those skilled in the art can implement the present application. The preferred embodiments in the following description are only as examples, and other obvious modifications can be thought of by those skilled in the art.

[0015] The database comprehensive governance system integrating artificial intelligence technology comprises: Intelligent probe module: the intelligent probe module is responsible for real-time collection of database operation parameters, including performance indicators, access logs, SQL statements, network traffic and business load characteristics, etc. Feature extraction and fusion module: the feature extraction and fusion module extracts and fuses features based on the collected database operation parameters, and generates a unified spatiotemporal correlation governance feature matrix; Integrated AI decision engine cluster module: the integrated AI decision engine cluster module inputs the governance feature matrix into the integrated AI decision engine cluster, which contains multiple AI engines such as performance optimization, active security protection, data quality evaluation and architecture evolution prediction, etc. for intelligent management of the database; Conflict resolution and instruction fusion module: the conflict resolution and instruction fusion module resolves conflicts between different AI engine output decisions according to governance target priority and risk assessment, and generates a final executable governance action sequence; Instruction execution module: the instruction execution module issues the comprehensive governance action sequence to the database system, monitors the execution state and result in real time, and feeds back data according to the execution; Feedback loop module: the feedback loop module is used to collect execution feedback data and input the feedback data into the AI engine again to drive the next round of decision cycle; Processor: the processor is used to process the calculation process of each formula and the construction calculation process of each model.

[0016] Referring to Figure 1 The database comprehensive management method integrating artificial intelligence technology includes: Step one: real-time collection of database running parameters through multi-source intelligent probes deployed on database servers, network nodes and business application layers; Step two: feature extraction and fusion based on the obtained database running parameters to generate a unified spatiotemporal correlation governance feature matrix; Step three: input the governance feature matrix into the integrated AI decision engine cluster, and manage the database data through each engine in the cluster, the integrated AI decision engine cluster including performance optimization AI engine, active security protection AI engine, data quality evaluation AI engine and architecture evolution prediction AI engine; Step four: conflict resolution and instruction fusion based on the decision instructions of each AI engine according to governance target priority and risk assessment to generate a final executable comprehensive governance action sequence; Step five: issuing the comprehensive governance action sequence to the database and monitoring the execution state and result in real time, and inputting the execution result data into the integrated AI decision engine cluster again through the construction of the governance feedback loop to drive the next round of decision cycle.

[0017] Referring to Figure 2 The real-time collection of database running parameters through multi-source intelligent probes deployed on database servers, network nodes and business application layers includes: The database running parameters include running performance indicators, access logs, SQL statements, network traffic metadata and business load feature vectors; The probes deployed on the database server capture database running indicators, SQL execution and access logs; The network node probes collect metadata of network traffic, including IP traffic and transmission delay; The application layer probe collects the load characteristics and request response information of the service layer by applying a layer probe; The collected data is cleaned and preprocessed.

[0018] Specifically, the real-time running indicators of the database are obtained through the probe, usually including CPU usage, memory usage, disk I / O, database cache hit rate, etc. By monitoring the performance of the database, the load of the database is evaluated. The network node probe captures the basic metadata of network traffic, such as IP traffic, transmission delay, network bandwidth, and packet loss rate. The application layer probe obtains the load characteristics of the application layer through the probe, such as response time of request, type of processed business, and request frequency. The data from different probes is integrated to build a comprehensive database. To avoid data loss or mismatch, timestamps and events can be used to synchronize data, providing support for subsequent data analysis and monitoring.

[0019] Referring to Figure 3 As shown, based on the obtained database running parameters, feature extraction and fusion are performed to generate a unified spatiotemporal correlation governance feature matrix, which specifically includes: Based on the obtained database running parameters, database performance features, SQL query features, network traffic features, and business load features are extracted. Aligning the feature data by timestamp to unify the time dimension, and fusing the correlation between different feature data by calculating the correlation matrix between different features. Based on the above steps, a unified spatiotemporal correlation governance feature matrix is generated.

[0020] Specifically, in order to better understand and analyze the comprehensive influence of the database, network and business layer, the data from different sources is fused to generate a unified feature matrix. Feature fusion usually relies on spatiotemporal correlation technology to ensure that feature matrices from different sources can be compared and analyzed in the same time and space dimensions. The correlation between different features is calculated, usually using a correlation coefficient matrix to measure the linear relationship strength between different features. The correlation coefficient formula is:

[0021] wherein, is the correlation coefficient, is the covariance of X and Y, is the standard deviation of X and Y, X and Y are feature data. The correlation between different feature data is fused by weighted average, and the features with strong correlation are combined into higher-level feature representation. The feature data after unified processing and fusion is used to generate a governance feature matrix according to the time and space correlation.

[0022] Referring to Figure 4 The governance feature matrix is input into the integrated AI decision engine cluster, and the database data is managed by each engine in the cluster, and the integrated AI decision engine cluster includes a performance optimization AI engine, an active security protection AI engine, a data quality evaluation AI engine, and an architecture evolution prediction AI engine, which specifically includes: Based on the obtained governance feature matrix, each AI engine in the cluster extracts and analyzes the relevant data in the feature matrix according to different tasks; The performance optimization AI engine is based on a reinforcement learning and time series prediction model, which analyzes historical performance data and real-time data to identify and correct performance bottlenecks, and generates optimal index strategies, resource dynamic allocation schemes, and hot and cold data layering migration instructions; The active security protection AI engine constructs an anomaly detection model based on a graph neural network, obtains a database access entity relationship graph, maps the relationships between users, SQLs, and access request entities, calculates the anomaly scores of each node through a graph attention network, obtains the diffusion path of the anomaly, generates real-time blocking instructions, and intercepts illegal database access requests; The data quality evaluation AI engine is based on a multi-task deep learning model, which associates business logs and database operation features to construct a cross-modal alignment model based on Transformer, aligns records representing the same business entity in different data sources, identifies abnormal data, and generates data cleaning and data anomaly tracing instructions; The architecture evolution prediction AI engine is based on a graph representation learning and scenario simulation model, which trains the model through historical database architecture evolution data to predict future architecture bottlenecks and generate distributed architecture sharding and read-write separation instructions; Based on the obtained governance instructions of each AI engine, a set of comprehensive database governance instructions is generated.

[0023] Specifically, the AI decision engine cluster is composed of the following four main engines: performance optimization AI engine, active security protection AI engine, data quality evaluation AI engine, and architecture evolution prediction AI engine; The performance optimization AI engine uses a reinforcement learning and time series prediction model to analyze database resource consumption, query execution time, index efficiency, and other indicators based on historical performance data and real-time performance data, identify potential performance bottlenecks, and optimize database resource scheduling strategies through continuous exploration and feedback. The specific model includes adjusting the database configuration through a reward function to achieve optimal performance, and the update formula in Q-learning is:

[0024] wherein, In the state Next action quality For learning rate, As a discount factor, For the current reward, The maximum expected reward for all possible actions in the next state; By using historical performance data and time series analysis methods such as the ARIMA model, future performance trends can be predicted, thereby adjusting the database's load balancing and resource allocation strategies. The optimal indexing strategy generates efficient indexes based on query frequency and execution plan; dynamic resource allocation adjusts the allocation of resources such as CPU, memory, and disk based on real-time data; and tiered migration of hot and cold data stores and manages frequently accessed and infrequently accessed data in layers, optimizing disk I / O and memory usage. The proactive security protection AI engine constructs a database access entity relationship graph through graph neural networks and calculates the anomaly score of each node. Through graph attention mechanism, the anomaly score of the node is calculated. The higher the weight of the node, the more likely it is to have abnormal behavior. Through these scores, potential attack paths or abnormal requests are discovered. Based on the anomaly detection results, real-time security protection instructions are generated to automatically block illegal requests and prevent data leakage or attacks. The data quality assessment AI engine learns through multi-tasks, allowing the model to share different features when handling different tasks, thus enhancing the model's generalization ability. It constructs a cross-modal alignment model based on Transformer, semantically aligning information from different data sources. By aligning records of the same business entity, it detects data anomalies, automatically generates data cleaning instructions based on data quality analysis, cleans up erroneous data, and traces anomalies. The architecture evolution prediction AI engine uses graph representation learning methods to construct a graph representation of the database architecture based on historical architecture evolution data. It then uses simulation technology to predict the future development and evolution trends of the database architecture, generating architecture optimization instructions including: distributed architecture sharding, which generates database sharding strategies based on prediction results to avoid single points of failure and overload; and read / write separation, which automatically identifies suitable scenarios for read / write separation through database load analysis and provides separation instructions. The governance commands generated by the above engines are integrated and summarized into a comprehensive database governance command set, covering optimization commands for performance optimization, security protection, data quality, and architecture evolution.

[0025] See Figure 5As shown, based on the decision instructions of each AI engine, conflict resolution and instruction fusion are performed according to the governance target priority and risk assessment to generate a final executable comprehensive governance action sequence, which specifically includes: Based on the governance instructions of each AI engine in the comprehensive governance instruction set, the output of each AI engine is converted into a unified decision format; Based on the governance target of the governance instruction, the target priority is divided, and the priority of the instruction is weighted according to the risk assessment result of each governance instruction to adjust the priority; Based on the instruction fusion under the condition of small conflict of governance instructions, the fused instructions are sorted according to the priority to form a final executable governance action sequence.

[0026] Specifically, in order to enable the governance instructions from different AI engines to be processed under the same framework, they need to be converted into a unified decision format. For each governance instruction, the priority is divided according to the importance of the governance target. High priority includes problems that have the greatest impact on system stability and user experience, such as database performance bottlenecks and serious security threats. Medium priority includes problems that have some impact on the system but do not affect normal operation, such as optimizing database query efficiency and data quality assessment. Low priority includes problems that can be delayed, such as predictive adjustment of architecture evolution. After the preliminary priority of the governance instruction is divided, the priority of each instruction needs to be weighted in combination with the risk assessment. Risk assessment usually involves the following dimensions: Potential impact: the potential impact of instruction execution failure or delay on the system; Feasibility: the implementability of the instruction, including the difficulty of technical implementation and resource requirements; Urgency: the execution urgency of the instruction, for example, security protection type instructions usually have high urgency; By calculating the weighted priority, tasks with high risk or high urgency are promoted in priority; By analyzing the target, parameters and execution object of the instruction, it is determined whether they will interfere with each other. If there is a conflict between instructions, adjustment or fusion may be needed. When there is a conflict between instructions, the conflict is solved through a fusion scheme, for example, if the targets of two instructions are similar, they can be combined into a comprehensive instruction, or they can be executed in stages. For cases where there is no obvious conflict between instructions, they can be directly fused; Based on the priority of each governance instruction, all instructions are sorted according to the priority to form a final executable governance action sequence.

[0027] Referring to Figure 6As shown, the comprehensive management action sequence is issued to the database, and the execution state and result are monitored in real time. Through the construction of the management feedback loop, the execution result data is re-input to the integrated AI decision engine cluster to drive the next round of decision cycle. Specifically, the method comprises the following steps: Based on the obtained comprehensive management action sequence, the sequence is issued to the target system through an automatic process; By monitoring the execution state and result of each management action, it is determined whether the instruction is executed as expected; Collect feedback data including performance indicators, security events, and data quality fixes, and re-input them to the AI decision engine cluster for real-time analysis and optimization by each engine; Based on the feedback data and new analysis results, the AI engine will generate new decision instructions to form the next round of management action sequence.

[0028] Specifically, the management action is issued to the target system through an automatic process. Task scheduling system, workflow engine and other tools are used to issue and execute these management tasks. During the execution of the management action, the execution state and result of each action are monitored to verify whether the instruction is executed as expected. After the execution of the management action, feedback data including performance indicators, security events, and data quality fixes are collected, including performance indicators, security events, and data quality. The collected feedback data is re-input to the AI decision engine. The AI engine optimizes the current management strategy through real-time analysis. Based on the feedback data and new analysis results, the AI engine generates new management instructions. These instructions form the next round of management action sequence to continue optimizing the target system.

[0029] It should be noted that the above-mentioned embodiments of the present application are in the order of description only, and do not represent the advantages and disadvantages of the embodiments. Moreover, the above describes a specific embodiment of the present application. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or can be advantageous.

[0030] Each embodiment in the specification is described in a progressive manner, and the same or similar parts of each embodiment can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0031] The above is only the preferred embodiment of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A database integrated management method incorporating artificial intelligence technology, characterized by, The application relates to a database management method based on an integrated AI decision engine cluster. Real-time collection of database running parameters through multi-source intelligent probes deployed on database servers, network nodes and business application layers; Feature extraction and fusion based on the obtained database running parameters to generate a unified spatiotemporal correlation governance feature matrix; Input of the governance feature matrix into the integrated AI decision engine cluster, and management of database data through each engine in the cluster, wherein the integrated AI decision engine cluster comprises a performance optimization AI engine, an active security protection AI engine, a data quality evaluation AI engine and an architecture evolution prediction AI engine; Based on the decision instructions of each AI engine, conflict resolution and instruction fusion are performed according to the governance target priority and risk assessment to generate a final executable comprehensive governance action sequence; The comprehensive governance action sequence is issued to the database, and the execution state and result are monitored in real time, and the execution result data is re-input into the integrated AI decision engine cluster through the construction of a governance feedback loop to drive the next round of decision cycle. 2.The database comprehensive management method integrating artificial intelligence technologies according to claim 1, characterized in that, The real-time collection of database running parameters through multi-source intelligent probes deployed on database servers, network nodes and business application layers specifically comprises: The database running parameters include running performance indicators, access logs, SQL statements, network traffic metadata and business load feature vectors; The probes deployed on the database servers capture database running indicators, SQL execution and access logs; The network node probes collect metadata of network traffic, including IP traffic and transmission delay; The application layer probes collect load features and request response information of the business layer; The collected data is cleaned and preprocessed. 3.The database comprehensive management method integrating artificial intelligence technologies according to claim 1, characterized in that, The feature extraction and fusion based on the obtained database running parameters to generate a unified spatiotemporal correlation governance feature matrix specifically comprises: Database performance features, SQL query features, network traffic features and business load features are extracted based on the obtained database running parameters; The feature data is aligned according to timestamps to unify the time dimension, and the correlation between different feature data is fused by calculating the correlation matrix between different features; The unified spatiotemporal correlation governance feature matrix is generated based on the above steps. 4.The database comprehensive management method integrating artificial intelligence technologies according to claim 1, wherein, The input of the governance feature matrix into the integrated AI decision engine cluster, and the management of database data through each engine in the cluster, wherein the integrated AI decision engine cluster comprises a performance optimization AI engine, an active security protection AI engine, a data quality evaluation AI engine and an architecture evolution prediction AI engine specifically comprises: The obtained governance feature matrix is input into the integrated AI decision engine cluster, and each AI engine in the cluster extracts and analyzes relevant data in the feature matrix according to different tasks; The performance optimization AI engine identifies and corrects performance bottlenecks based on reinforcement learning and time series prediction models, generates optimal index strategies, resource dynamic allocation schemes and cold and hot data layer migration instructions by analyzing historical performance data and real-time data; The active security protection AI engine constructs an anomaly detection model based on a graph neural network, obtains a database access entity relationship graph, maps the relationship between users, SQLs, and access request entities, calculates the anomaly scores of each node through a graph attention network, obtains the diffusion path of the anomaly, generates real-time blocking instructions, and intercepts illegal database access requests; The data quality evaluation AI engine is based on a multi-task deep learning model, correlates business logs and database operation features, constructs a cross-modal alignment model based on a Transformer, aligns records representing the same business entity in different data sources, identifies abnormal data, and generates data cleaning and data anomaly tracing instructions; The architecture evolution prediction AI engine is based on graph representation learning and scenario simulation models, trains the model through historical database architecture evolution data, predicts future architecture bottlenecks, and generates distributed architecture sharding and read-write separation instructions; Based on the governance instructions of each AI engine, a comprehensive database governance instruction set is generated. 5.The database comprehensive management method integrating artificial intelligence technologies according to claim 1, characterized in that, The decision instructions of each AI engine are subjected to conflict resolution and instruction fusion according to the priority of the governance target and the risk assessment, and a final executable comprehensive governance action sequence is generated, which specifically includes: Based on the governance instructions of each AI engine in the comprehensive governance instruction set, the output of each AI engine is converted into a unified decision format; The governance target of the governance instruction is prioritized, and the priority of the instruction is weighted according to the risk assessment result of each governance instruction to adjust the priority; The instructions are fused based on the condition that the conflicts are small, and the fused instructions are sorted according to the priority to form the final executable governance action sequence. 6.The database comprehensive management method integrating artificial intelligence technologies according to claim 1, wherein, The comprehensive governance action sequence is issued to the database, and the execution status and results are monitored in real time. The execution result data is re-input into the integrated AI decision engine cluster through the construction of a governance feedback loop to drive the next round of decision cycle, which specifically includes: Based on the obtained comprehensive governance action sequence, it is issued to the target system through an automatic process; By monitoring the execution status and results of each governance action, it is determined whether the instruction is executed as expected; Feedback data including performance indicators, security events, and data quality repair are collected and re-input into the AI decision engine cluster for real-time analysis and optimization by each engine; Based on the feedback data and new analysis results, the AI engine will generate new decision instructions to form the next round of governance action sequence.

7. A database comprehensive management system integrating artificial intelligence technology, used to implement the database comprehensive management method integrating artificial intelligence technology according to any one of claims 1-6, characterized in that, It includes: Intelligent probe module: The intelligent probe module is responsible for real-time collection of database running parameters, including performance indicators, access logs, SQL statements, network traffic, and business load characteristics, etc. Feature extraction and fusion module: The feature extraction and fusion module extracts and fuses features based on the collected database running parameters to generate a unified spatiotemporal correlation governance feature matrix; Integrated AI decision engine cluster module: The integrated AI decision engine cluster module inputs the governance feature matrix into the integrated AI decision engine cluster, which includes performance optimization, active security protection, data quality evaluation, and architecture evolution prediction AI engines, for intelligent management of the database. The conflict resolution and instruction fusion module resolves conflicts between the decision outputs of different AI engines according to the governance target priority and risk assessment, and generates a final executable governance action sequence. The instruction execution module sends the comprehensive governance action sequence to the database system, monitors the execution state and result in real time, and feeds back data according to the execution condition. The feedback loop module is used to collect execution feedback data and re-input the feedback data to the AI engine to drive the next round of decision cycle. The processor is used to process the calculation process of each formula and the construction calculation process of each model.

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