An intelligent information management method and system based on cloud computing

Abstract

The present invention relates to the field of intelligent information management technology, and in particular to an intelligent information management method and system based on cloud computing. The method comprises the following steps: performing load time series skewness analysis on enterprise database log data to obtain load fluctuation time series skewness data; performing table structure analysis on the enterprise database based on enterprise database management authority, and performing table structure performance bottleneck analysis to obtain table structure performance bottleneck data; performing asynchronous read-write cache processing on the database table structure according to the table structure performance bottleneck data and load fluctuation time series skewness data to obtain an asynchronous read-write cache database; performing compatibility matching on the asynchronous read-write cache database to obtain an asynchronous read-write compatibility cache database; and connecting the asynchronous read-write compatibility cache database to the enterprise database to perform intelligent information management. The present invention makes the intelligent information management technology more perfect by optimizing the intelligent information management technology.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent information management, and in particular to an intelligent information management method and system based on cloud computing. Background Art

[0002] As an emerging computing model, cloud computing has become one of the core technologies of modern information management with its powerful computing power, massive data storage and elastic scalability. In enterprise data management systems, databases often carry a large number of business operations, such as order processing, customer management, and financial settlement. With the increase in data volume and concurrent requests, database load fluctuations and performance bottlenecks have become key issues affecting enterprise operational efficiency. Intelligent information management methods rely on cloud computing infrastructure and can provide efficient, reliable and flexible solutions, especially suitable for processing complex data streams, massive data analysis and diversified business needs. In the face of data diversity, real-time and dynamic changes, previous information management methods often have problems such as low processing efficiency, limited storage capacity and poor scalability. The intelligent information management method based on cloud computing can dynamically allocate and adjust computing resources by making full use of the resource scheduling capabilities and virtualization technology of the cloud platform, thereby realizing on-demand resource allocation, reducing management costs, and improving the response speed and flexibility of the system. However, a traditional intelligent information management method based on cloud computing has the problem of inaccurate performance bottleneck analysis of database table structure and inability to cope with high-concurrency massive data. Summary of the invention

[0003] Based on this, it is necessary to provide an intelligent information management method and system based on cloud computing to solve at least one of the above technical problems.

[0004] To achieve the above object, a cloud computing-based intelligent information management method is provided, the method comprising the following steps:

[0005] Step S1: obtaining enterprise database log data and enterprise database management authority; performing load time series skewness analysis on the enterprise database log data to obtain load fluctuation time series skewness data;

[0006] Step S2: parsing the table structure of the enterprise database based on the enterprise database management authority to obtain the database table structure; performing table structure performance bottleneck analysis on the database table structure to obtain table structure performance bottleneck data; performing asynchronous read-write cache processing on the database table structure according to the table structure performance bottleneck data and load fluctuation time series skew data to obtain an asynchronous read-write cache database;

[0007] Step S3: performing compatibility matching on the asynchronous read-write cache database based on the containerization technology in cloud computing to obtain an asynchronous read-write compatibility cache database;

[0008] Step S4: Connect the asynchronous read-write compatibility cache database to the enterprise database to perform intelligent information management.

[0009] The present invention first obtains the log data of the enterprise database and its management authority, which is crucial. This step not only ensures the comprehensiveness of the analysis process, but also lays the foundation for subsequent performance optimization. By performing load time series skew analysis on the log data, the load changes of the database in different time periods can be identified, including peak load and valley load. This analysis can help enterprises understand the usage patterns of the system in different time periods, and provide important basis for capacity planning, resource allocation and fault prevention. In addition, the load fluctuation time series skew data provides real-time monitoring capabilities for the database managers of the enterprise, and can timely identify potential performance bottlenecks or abnormal situations, and then take corresponding measures for optimization. Finally, this step effectively improves the stability and response speed of the database, ensuring that the enterprise can still maintain good service quality under high load conditions. Based on the enterprise database management authority, it is very necessary to parse the database table structure, which enables managers to fully understand the design and architecture of the database. Through the table structure performance bottleneck analysis, it can be determined which tables or fields perform poorly under high load conditions, resulting in delays in query and write operations. This in-depth analysis provides specific directions for optimizing the database, such as by redesigning the table structure, adding indexes or archiving data. Next, based on the analysis results of the table structure performance bottleneck data and the load fluctuation time series skew data, it is very effective to implement asynchronous read and write cache processing. Asynchronous cache can significantly improve the read and write performance of the database, reduce response time, and improve the overall throughput of the system. This process not only improves the access speed of the database, but also reduces the pressure on the backend database, which can effectively improve the user's operating experience and provide strong support for the sustainable development of the enterprise's business. This step ultimately helps enterprises achieve more efficient data management and processing capabilities under high load. Using containerization technology in cloud computing to match the compatibility of asynchronous read and write cache databases is a key link. Containerization technology allows applications and all their dependencies to be packaged in a lightweight and portable environment, thereby ensuring consistent operating effects in different computing environments. In this process, by matching the compatibility of asynchronous read and write cache databases, potential compatibility issues between different system components can be identified and resolved, ensuring that data can flow seamlessly between different containers. This matching process can improve the flexibility of the system, allowing enterprises to quickly deploy and expand database services according to needs, while reducing maintenance complexity. Finally, the obtained asynchronous read and write compatibility cache database can run efficiently in multiple cloud environments, significantly improving data access speed and system reliability. This not only optimizes the enterprise's IT architecture, but also lays a solid foundation for subsequent intelligent information management. Connecting the asynchronous read-write compatibility cache database with the enterprise database is an important step in realizing intelligent information management. This connection process can achieve real-time data update and access, allowing enterprises to maintain high efficiency when processing massive data.By integrating the compatible cache database into the enterprise database, enterprises can conduct data analysis and decision-making faster, improving the intelligence level of business operations. The implementation of intelligent information management can not only improve the efficiency of data processing, but also use machine learning and data analysis technology to provide enterprises with in-depth insights to help decision makers identify trends, optimize business processes, and reduce costs. In addition, real-time data flow can also help enhance the agility of enterprises, enabling them to quickly respond to market changes and customer needs, thereby enhancing their competitiveness. This series of optimizations will ultimately help enterprises better utilize data resources in digital transformation and promote sustainable business development.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Obtaining enterprise database log data and enterprise database management authority;

[0012] Step S12: Fill missing values ​​in the enterprise database log data to obtain database log filling data;

[0013] Step S13: performing load fluctuation analysis on the database log filling data to obtain database load fluctuation data;

[0014] Step S14: performing load time series skewness analysis on the load fluctuation data in the database to obtain load fluctuation time series skewness data.

[0015] The present invention first obtains the log data and management authority of the enterprise database, which is the basis for analyzing and optimizing the performance of the database. This process ensures that the analyst can fully access the operation records of the database, so as to accurately understand the usage and performance bottlenecks of the system. At the same time, obtaining management authority also provides necessary authority guarantee for subsequent data processing and optimization. The log data of the enterprise database contains rich information, such as query frequency, execution time and error information, which provide valuable basis for analyzing and diagnosing problems. By fully obtaining these data, the enterprise can better monitor the operation status of the database and identify potential risk points, thereby laying a solid foundation for optimizing the database architecture and improving performance. The completion of this step creates a good data foundation for subsequent analysis. Filling missing values ​​in the database log data is a key link to improve data quality and analysis accuracy. There are missing values ​​in the database log data, which will cause deviations in subsequent analysis and affect the scientific nature of decision-making. By adopting a suitable filling strategy (such as mean filling, median filling or prediction filling based on a machine learning model), the missing parts in the data set can be effectively reduced, thereby generating complete database log filling data. This step not only improves the integrity of the data, but also can more comprehensively reflect the actual operation status of the database. In addition, the filled data will play a greater role in the subsequent load fluctuation analysis and performance evaluation, ensuring the reliability and effectiveness of the analysis results, thereby providing a more accurate basis for database optimization. Load fluctuation analysis of database log filling data can effectively reveal the usage and load characteristics of the database in different time periods. This analysis usually involves statistical calculations, trend analysis, and periodic inspections of the data to identify peak loads, valley loads, and their changing patterns. By obtaining database load fluctuation data, enterprises can gain an in-depth understanding of the system's performance in different time periods, including response time, number of concurrent connections, and query efficiency. This information can help the company's IT team identify potential bottlenecks in the system, optimize resource allocation, and thus improve system stability and availability. In addition, understanding the load fluctuation characteristics can also help enterprises make more reasonable capacity planning, ensuring that the system can still maintain good service quality during peak periods and improve user experience. Load time series skew analysis of database load fluctuation data is to deeply explore the database load characteristics and their changing patterns over time. Load time series skew analysis can help identify the distribution characteristics of data and detect potential abnormal fluctuations and extreme values. This process can not only reveal the skewed distribution of load data, but also evaluate the performance of the database under different load conditions. By obtaining load fluctuation time series skew data, enterprises can clearly understand key indicators such as system response time, processing capacity, and resource utilization under high load conditions. This information is crucial for formulating optimization strategies and helps enterprises consider load characteristics when designing and implementing database architectures, thereby more effectively performing performance tuning and risk management.Ultimately, this analysis provides important decision support for subsequent database optimization, ensuring that the enterprise can continue to operate efficiently in an ever-changing business environment.

[0016] Preferably, step S2 comprises the following steps:

[0017] Step S21: parsing the table structure of the enterprise database based on the enterprise database management authority to obtain the database table structure;

[0018] Step S22: performing a table structure performance bottleneck analysis on the database table structure according to the load fluctuation time series skew data to obtain table structure performance bottleneck data;

[0019] Step S23: performing high-load read / write delay response simulation according to the table structure performance bottleneck data and the load fluctuation timing skew data to obtain high-load read / write delay response data;

[0020] Step S24: performing read-write separation adaptation on the database table structure based on the high-load read-write delay response data to obtain database read-write separation adaptation data;

[0021] Step S25: performing asynchronous read-write cache processing on the database table structure according to the database read-write separation adaptation data to obtain an asynchronous read-write cache database.

[0022] The present invention analyzes the table structure of the database through the enterprise database management authority, and can fully understand the design and architecture of the database. This process involves analyzing various tables, fields and their data types, constraints and relationship structures in the database, so as to form a clear understanding of the database table structure. Through table structure analysis, enterprises can identify the logical layout of data storage, providing basic data support for subsequent performance optimization and troubleshooting. In addition, a deep understanding of the table structure helps to discover potential design defects, such as redundant data or unreasonable relationships, which will affect the performance and scalability of the database. Ultimately, this step not only improves the maintainability and flexibility of the database, but also provides favorable conditions for the future expansion and adjustment of the enterprise, thereby supporting its business development strategy. The performance bottleneck analysis of the database table structure based on the load fluctuation time series skew data is to identify the key factors affecting the performance of the database. By comparing the load fluctuation situation with the table structure, it can be found which tables or fields perform poorly under high load conditions, such as long response time, low query efficiency or poor concurrent processing capability. This analysis can not only help enterprises identify performance bottlenecks, but also gain a deep understanding of the performance of the database under different loads, thereby providing data support for the formulation of optimization plans. In addition, the results of performance bottleneck analysis can help enterprises make targeted decisions in adjusting database design, optimizing queries, adding indexes, etc., and ultimately significantly improve the overall performance and responsiveness of the database, ensuring that the enterprise can still operate stably under high load conditions. The purpose of high-load read and write delay response simulation based on table structure performance bottleneck data and load fluctuation time series skew data is to evaluate the performance of the database under extreme load. By simulating high-load scenarios, enterprises can obtain high-load read and write delay response data, thereby deeply understanding the behavior of the database under stress conditions. This simulation not only reveals the response time and processing capacity of the database under high concurrency, but also identifies specific performance bottlenecks and potential failure points. With the delay response data obtained, enterprises can take corresponding optimization measures, such as adjusting query strategies, improving index design, or upgrading hardware facilities, to ensure that they can better cope with the challenges brought by high load in actual operation. This process helps enterprises foresee problems before actual business operation, thereby improving the stability and reliability of the system and ensuring the efficiency of data processing. Read-write separation adaptation of the database table structure based on high-load read and write delay response data is an important means to improve database performance. Read-write separation technology can effectively reduce the pressure on the main database and improve data processing efficiency by allocating read requests and write requests to different database instances. By analyzing the read and write latency response data under high load conditions, enterprises can identify which queries can be optimized as read operations, thereby implementing read-write separation. This process not only improves the system's concurrent processing capabilities, but also reduces the impact of data updates on read operations and improves the response speed of user access.In addition, read-write separation can help enterprises achieve better load balancing, enhance system scalability, and ensure that a good user experience can still be maintained in high-load scenarios. This adaptation process will lay a solid foundation for subsequent asynchronous read-write cache processing. Asynchronous read-write cache processing of database table structures based on database read-write separation adaptation data is to further improve database performance and user experience. The introduction of asynchronous read-write cache can enable read operations and write operations to be processed in parallel, thereby greatly reducing response time and improving the overall throughput of the system. This processing method not only reduces the pressure on the database under high load conditions, but also improves the data reading efficiency by reducing the frequency of direct writes to the database. Asynchronous cache allows data to be processed in the background without affecting the operations of front-end users, ensuring that users can get faster feedback when accessing. In addition, through asynchronous read-write cache processing, enterprises can respond to high-concurrency requests more flexibly and enhance the availability and stability of the system. This optimization will provide strong support for enterprise data management and ensure that efficient and stable database operations can be maintained in a rapidly developing business environment.

[0023] Preferably, step S22 includes the following steps:

[0024] Step S221: Perform field constraint structure analysis on the database table structure to obtain field constraint structure data;

[0025] Step S222: Calculate the constraint nesting level of the database table structure according to the field constraint structure data to obtain the field constraint nesting level data;

[0026] Step S223: perform trigger-related logic retrieval on the database table structure to obtain trigger-related logic data;

[0027] Step S224: evaluating the event trigger logic complexity of the database table structure according to the trigger associated logic data to obtain event trigger logic complexity data;

[0028] Step S225: performing execution non-linear time complexity calculation based on the event triggering logic complexity data and the field constraint nesting level data to obtain execution non-linear time complexity data;

[0029] Step S226: mapping the execution complexity load fluctuation to the execution nonlinear time complexity data according to the load fluctuation time series skewness data, to obtain the execution complexity load fluctuation data;

[0030] Step S227: performing table structure performance bottleneck analysis on the database table structure according to the execution complexity load fluctuation data and the execution nonlinear time complexity data to obtain table structure performance bottleneck data.

[0031] The present invention performs field constraint structure analysis on the database table structure by identifying and extracting the constraint conditions (such as primary key, foreign key, unique constraint, non-empty constraint, etc.) of each field. The purpose of this analysis is to fully understand the relationship and constraints between fields, so as to facilitate the subsequent nested hierarchical calculation. By obtaining the field constraint structure data, the database designer can effectively identify data integrity problems and ensure the consistency and accuracy of data during storage. An effective constraint structure can not only prevent the insertion of invalid data, but also improve query efficiency, because the existence of constraints can allow the database optimizer to better select execution plans. In addition, a clear field constraint structure lays a solid foundation for subsequent complexity evaluation and performance analysis, making the performance of the database in complex transaction processing more stable and reliable. Constraint nesting level calculation is performed on the field constraint structure data. This process involves an in-depth analysis of the hierarchical relationship between each field constraint to form a hierarchical constraint structure map. By calculating the nested level of constraints, it is possible to identify which field constraints are interdependent, and then analyze the most complex constraint combination. This is crucial for the design and maintenance of databases, because too many or too complex nested constraints can lead to performance problems, especially when processing large amounts of data. At the same time, the results of this step can also be used to optimize the design of the database and simplify the constraint structure, thereby reducing redundancy and improving the efficiency of data operations. With a clear hierarchical structure, database administrators can better understand and manage the constraint characteristics of data, thereby improving the maintainability of the system. The trigger-related logic retrieval of the database table structure is executed to obtain the relationship data between the trigger and the table structure. Triggers are a mechanism that is automatically executed in the database. They can execute specific logic when data is modified, such as verifying data integrity, maintaining audit logs, etc. Therefore, accurately identifying the associated logic of triggers is crucial to understanding the behavior and performance of the database. This step can help developers and database administrators understand the execution conditions of triggers and their interaction with the fields in the table structure. This analysis not only helps to discover potential performance bottlenecks, but also evaluates the impact on trigger execution when modifying the table structure, ensuring the integrity and consistency of data operations. By combing the trigger logic, the predictability and reliability of the database can be improved, thereby reducing data problems caused by logical errors. The complexity of the event trigger logic is evaluated for the trigger-related logic data. The purpose of this step is to systematically analyze the logic executed by the trigger and evaluate its complexity under various event triggering situations. Complex trigger logic can cause performance degradation under high load, so evaluating its complexity is crucial. Through this process, we can identify the execution efficiency of triggers under different conditions, analyze their performance under high concurrent access, and find optimization directions. The evaluation results of the event trigger logic complexity provide data support for subsequent performance optimization and database adjustments, ensuring that the system can cope with the challenges of different loads in actual operation.Comprehensive consideration of logic complexity can effectively avoid performance bottlenecks caused by overly complex logic, thereby improving the response speed and processing capacity of the entire database system. Combined analysis of event trigger logic complexity data and field constraint nesting level data is performed to calculate the execution nonlinear time complexity. This calculation can reveal how the time complexity of database operations changes with the amount of input data under specific event trigger conditions. Through this analysis, it is possible to identify which operations will cause exponential growth in execution time under specific conditions, thereby affecting the overall performance of the database. Especially in a big data environment, the identification of nonlinear time complexity helps database administrators make more informed design decisions and optimize database structure and query logic to improve system processing efficiency and response speed. The results of this step provide an important mathematical basis for database optimization, allowing developers to consider performance factors in the design phase. The execution complexity load fluctuation mapping for load fluctuation time series skewed data aims to reveal the changing rules of execution complexity under different load conditions. This process helps database administrators understand how the system's performance changes under high and low load conditions by establishing a relationship model between load and execution complexity. This mapping analysis can not only provide the performance data of the database under different workloads, but also provide a reference for the capacity planning and performance tuning of the system. By analyzing the impact of load fluctuations on execution complexity, database administrators can effectively predict the performance bottlenecks that occur during peak hours, so as to take optimization measures in advance to ensure that the system can run stably under various load conditions. The results of this analysis help to achieve more efficient resource allocation and optimize the overall performance of the database system. Perform table structure performance bottleneck analysis based on execution complexity load fluctuation data and execution nonlinear time complexity data. By combining the data obtained in the previous steps, identify the performance bottlenecks of the database table structure when processing high loads and complex events. This analysis can reveal which specific field constraints, trigger logic or execution paths cause performance degradation. By identifying these bottlenecks, database administrators can make more targeted optimizations, such as redesigning table structures, optimizing indexes or adjusting trigger logic to improve overall performance. The ultimate goal of this process is to ensure that the database system can achieve optimal performance under various operating conditions and avoid performance problems caused by design defects. This in-depth bottleneck analysis provides an important basis for continuous optimization of the database, enabling the system to adapt to changing business needs.

[0032] Preferably, performing nonlinear time complexity calculation based on event triggering logic complexity data and field constraint nesting level data includes the following steps:

[0033] Nonlinear execution efficiency simulation is performed on the event trigger logic complexity data and the field constraint nesting level data respectively, and the event trigger nonlinear execution efficiency data and the nonlinear field constraint execution efficiency data are obtained respectively;

[0034] Performing hierarchical execution complexity error correction calculation on event-triggered nonlinear execution efficiency data to obtain trigger execution complexity error correction data;

[0035] The efficiency convergence mean difference is calculated for the nonlinear field constraint execution efficiency data to obtain the constraint efficiency convergence mean difference data;

[0036] The execution nonlinear time complexity is calculated based on the trigger execution complexity error correction data and the constraint efficiency convergence mean difference data to obtain the execution nonlinear time complexity data.

[0037] The present invention first needs to simulate the nonlinear execution efficiency of the event trigger logic complexity and the field constraint nested hierarchical data. This process includes modeling the trigger logic of different types of events and analyzing its complexity under multi-level and multi-dimensional conditions. For example, a mathematical model or simulation tool can be used to simulate the response time and resource consumption under different event trigger conditions. At the same time, the nested hierarchical data of field constraints also need to be similarly simulated to study the execution efficiency under different constraint levels. The key to this step is to obtain the execution efficiency of the two types of data through a detailed analysis of various variables and conditions, which provides basic data support for subsequent complexity analysis and optimization. After obtaining the event trigger nonlinear execution efficiency data, it is necessary to perform error correction calculations on the hierarchical execution complexity. Since there are certain errors in the simulation process, these errors will affect the final execution complexity analysis. In order to solve this problem, statistical methods can be used to evaluate the errors by comparing the event trigger efficiency and simulation results during actual operation. Common correction methods include regression analysis and least squares method, etc., which can effectively adjust the simulation data to make it closer to the actual situation. This process ensures that the subsequent execution complexity calculations are based on corrected and more accurate data, thereby improving the credibility and effectiveness of the analysis results. In the process of processing nonlinear field constraint execution efficiency data, convergence mean difference calculation is a key step. The goal of this step is to analyze the stability and consistency of execution efficiency under different constraints. By collecting execution efficiency data under different levels of constraints and calculating their mean and variance, the impact of each constraint on the overall execution efficiency can be evaluated. Specifically, the difference between the sample mean and each sample value in the data set can be used for analysis to obtain the convergence mean difference. This data not only helps to identify which field constraints cause performance bottlenecks, but also provides a basis for system optimization to ensure that the system can still maintain efficient execution performance under complex constraints. After obtaining the trigger execution complexity error correction data and constraint efficiency convergence mean difference data, it is necessary to calculate the execution nonlinear time complexity based on these data. This calculation usually involves the integration and analysis of multidimensional data, and the use of corresponding algorithm models (such as nonlinear regression or other optimization algorithms) to determine the final execution time complexity. At this stage, it is necessary to ensure the sufficiency and accuracy of the data, and use appropriate mathematical tools for complexity analysis and modeling. The final results will provide strong support for the system's performance evaluation, resource allocation and optimization, and lay the foundation for future improvements and development. The successful completion of this process will not only help to accurately grasp the current system's performance status, but also provide guidance for subsequent optimization plans to ensure the efficient operation of the system in a complex application environment.

[0038] Preferably, performing hierarchical execution complexity error correction calculation on event-triggered nonlinear execution efficiency data includes the following steps:

[0039] Perform event-triggered execution level retrieval on event-triggered nonlinear execution efficiency data to obtain event-triggered execution level data;

[0040] According to the event-triggered execution level data, the execution efficiency variance between nested levels of the event-triggered nonlinear execution efficiency data is calculated to obtain the nested level execution efficiency variance data;

[0041] Based on the nested hierarchical execution efficiency variance data, the event-triggered nonlinear execution efficiency data is subjected to nested overlapping redundant execution efficiency identification to obtain nested overlapping redundant execution efficiency data;

[0042] Perform overlapping error correction processing on event-triggered nonlinear execution efficiency data according to the nested overlapping redundant execution efficiency data to obtain triggered nonlinear execution correction efficiency data;

[0043] Based on the divide-and-conquer method, the hierarchical execution complexity error correction calculation is performed on the triggered nonlinear execution correction efficiency data to obtain the triggered execution complexity error correction data.

[0044] The present invention retrieves the event-triggered execution level of event-triggered nonlinear execution efficiency data, aiming to clarify the execution level relationship between different events. Through this process, complex nonlinear execution efficiency data can be sorted and classified according to the hierarchical structure for subsequent analysis. Specifically, event-triggered execution level retrieval involves identifying and marking each event in the data set, and forming a hierarchical tree based on the dependency relationship and triggering order between events. This structure can help identify the hierarchical position of each event, thereby laying the foundation for the calculation and analysis of execution efficiency. The result of this step will provide the necessary framework for subsequent variance calculation and error correction to ensure the accuracy and reliability of the data. Based on the event-triggered execution level data, the variance of execution efficiency between nested levels of event-triggered nonlinear execution efficiency data is calculated. The purpose of this calculation is to identify the fluctuation of execution efficiency between different levels and reveal potential performance bottlenecks. Specifically, statistical methods will be used to calculate the variance between each level, and the differences will be found by comparing the execution efficiency of the same level with that of different levels. This variance analysis can help understand at which levels the execution efficiency is low and the impact of these inefficient levels. Finally, this step will generate nested hierarchical execution efficiency variance data, which will serve as the basis for subsequent redundancy identification and error correction, and provide guidance for optimizing execution efficiency. After identifying the nested hierarchical execution efficiency variance, the nested overlapping redundant execution efficiency identification stage is entered. The core of this process is to analyze whether there are repeated or overlapping parts of execution efficiency at different levels, and then identify the redundant execution efficiency. Redundancy not only leads to resource waste, but also reduces the execution efficiency of the overall system to a certain extent. Through in-depth analysis of the nested hierarchical execution efficiency variance data, it is possible to identify repeated work and unnecessary steps in the execution process. After identifying redundancy, it is possible to clearly understand which links in the event execution chain can be optimized and which resources can be reasonably configured. The results of this step provide a solid foundation for the subsequent error correction and optimization plan formulation. After identifying the nested overlapping redundant execution efficiency, the next step is to perform overlapping error correction processing on the event-triggered nonlinear execution efficiency data. The goal of this stage is to improve the accuracy and reliability of the overall execution efficiency by eliminating the redundant parts. Specifically, the redundant execution efficiencies identified in the previous step will be quantitatively analyzed to evaluate their impact on the overall execution efficiency data, and targeted corrections will be made accordingly. This involves optimizing certain steps, adjusting the execution order, or allocating resources reasonably to reduce duplication of work. This correction can not only improve the efficiency of event execution, but also lay a good foundation for subsequent complexity error correction. The final result of this step is the triggering of nonlinear execution correction efficiency data, which will be more accurate and convenient for subsequent analysis and decision-making. The hierarchical execution complexity error correction calculation is performed on the triggering of nonlinear execution correction efficiency data based on the divide-and-conquer method.The divide-and-conquer method is an efficient computing strategy that breaks down complex problems into multiple smaller problems and solves them one by one, and finally summarizes the results to obtain a global solution. (The traditional divide-and-conquer method will lead to repeated calculations if there are overlaps or dependencies between sub-problems. The above method removes the repeated parts and uses the divide-and-conquer method for calculation, which can greatly improve the accuracy of the data calculation results.) In this step, the event-triggered nonlinear execution correction efficiency data is first divided into levels, and complexity analysis and error correction are performed independently for each level. Through this method, the source of complexity in the execution of each level can be efficiently identified, and the corresponding error correction can be implemented. This hierarchical processing method can not only improve the efficiency of calculation, but also ensure that the mutual influence between different levels is taken into account during the correction process. The final result is the trigger execution complexity error correction data, which will greatly enhance the execution efficiency and reliability of the system and provide an important basis for future optimization.

[0045] Preferably, step S25 comprises the following steps:

[0046] Step S251: performing concurrent read-write frequency simulation on the database table structure according to the database read-write separation adaptation data to obtain concurrent read-write frequency simulation data;

[0047] Step S252: Designing a read-write cache structure for the database table structure based on the concurrent read-write frequency simulation data, and obtaining read cache structure data and write cache structure data respectively;

[0048] Step S253: performing asynchronous thread matching on the read cache structure data and the write cache structure data to obtain read cache asynchronous thread structure data and write cache asynchronous thread structure data respectively;

[0049] Step S254: Design conflict and concurrency control logic for the read cache asynchronous thread structure data and the write cache asynchronous thread structure data to obtain read-write conflict logic control data;

[0050] Step S255: performing asynchronous read-write cache processing on the database table structure according to the read cache asynchronous thread structure data, the write cache asynchronous thread structure data and the read-write conflict logic control data to obtain an asynchronous read-write cache database.

[0051] According to the principle of database read-write separation, the present invention simulates the concurrent read-write frequency of the database table structure. This simulation process can provide actual data support for subsequent database design and ensure that the system can adapt to high concurrent access requirements. By analyzing the read-write behavior of the database table in a high concurrent environment, potential bottlenecks and optimization points can be identified. In addition, this step lays the foundation for designing a reasonable cache mechanism by collecting and analyzing simulation data, so that the database can allocate resources more efficiently, reduce response time, and improve user experience when processing a large number of concurrent requests. According to the concurrent read-write frequency simulation data obtained in the previous step, a suitable read-write cache structure is designed. By deeply analyzing the concurrent data, the system can identify the characteristics and requirements of the read operation and the write operation, thereby respectively constructing an optimized read cache and write cache structure. This structural design can effectively reduce the frequency of direct access to the database, relieve the pressure on the database, and improve the overall data processing efficiency. The reasonable configuration of the read-write cache can not only improve the reading speed of the data, but also ensure the consistency and integrity of the data, and provide efficient support for subsequent read-write operations. Asynchronous thread matching is performed on the read cache structure data and the write cache structure data. This process optimizes concurrent operations by analyzing the characteristics of read and write operations and assigning them to corresponding asynchronous threads. This asynchronous processing mechanism allows multiple read and write requests to be performed simultaneously, making full use of system resources and reducing waiting time. Through effective thread matching, the system can not only improve the throughput of data processing, but also enhance the scalability of the system, adapt to larger-scale concurrent requests, and improve the overall performance of the system. By analyzing the read cache asynchronous thread structure data and the write cache asynchronous thread structure data, the system can identify potential read and write conflicts and establish corresponding control logic. This logic ensures that read and write operations can be performed safely and orderly in high concurrency situations to avoid data inconsistency and errors. This conflict control mechanism is crucial to maintaining the data integrity of the system, and can effectively coordinate operations in a multi-user environment to improve the reliability and stability of the system. Combined with the read cache asynchronous thread structure data, write cache asynchronous thread structure data and read-write conflict logic control data obtained in the previous steps, asynchronous read and write cache processing is performed on the database table structure. Through this processing, the system can achieve a more flexible and efficient database access mode and improve the overall response speed and processing capability. The introduction of asynchronous read-write cache means that the system can process more requests simultaneously, reduce latency, and improve user experience. At the same time, the optimized cache strategy also enables the database to continue to run under high load conditions, ensuring the stability and availability of the system.

[0052] Preferably, the present invention further provides an intelligent information management system based on cloud computing, which is used to execute the intelligent information management method based on cloud computing as described above, and the intelligent information management system based on cloud computing includes:

[0053] The load skewness analysis module is used to obtain the enterprise database log data and enterprise database management authority; perform load time series skewness analysis on the enterprise database log data to obtain load fluctuation time series skewness data;

[0054] The asynchronous read-write cache processing module is used to parse the table structure of the enterprise database based on the enterprise database management authority to obtain the database table structure; analyze the table structure performance bottleneck of the database table structure to obtain the table structure performance bottleneck data; perform asynchronous read-write cache processing on the database table structure according to the table structure performance bottleneck data and load fluctuation time series skew data to obtain an asynchronous read-write cache database;

[0055] A compatibility matching module is used to perform compatibility matching on the asynchronous read-write cache database based on the containerization technology in cloud computing to obtain an asynchronous read-write compatibility cache database;

[0056] The execution docking module is used to dock the asynchronous read-write compatibility cache database to the enterprise database to perform intelligent information management.

[0057] The beneficial effects of the present invention are that the present invention optimizes a traditional intelligent information management method based on cloud computing, solves the problems of inaccurate performance bottleneck analysis of database table structure and inability to cope with high-concurrency massive data in the traditional intelligent information management method based on cloud computing, improves the accuracy of performance bottleneck analysis of database table structure and enhances the ability to cope with high-concurrency massive data. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic diagram of the steps of an intelligent information management method based on cloud computing;

[0059] Figure 2 for Figure 1 The detailed implementation steps of step S2 are shown in FIG.

[0060] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0061] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0062] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0063] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0064] To achieve this, please refer to Figure 1 to Figure 2 , an intelligent information management method based on cloud computing, the method comprising the following steps:

[0065] Step S1: obtaining enterprise database log data and enterprise database management authority; performing load time series skewness analysis on the enterprise database log data to obtain load fluctuation time series skewness data;

[0066] Step S2: parsing the table structure of the enterprise database based on the enterprise database management authority to obtain the database table structure; performing table structure performance bottleneck analysis on the database table structure to obtain table structure performance bottleneck data; performing asynchronous read-write cache processing on the database table structure according to the table structure performance bottleneck data and load fluctuation time series skew data to obtain an asynchronous read-write cache database;

[0067] Step S3: performing compatibility matching on the asynchronous read-write cache database based on the containerization technology in cloud computing to obtain an asynchronous read-write compatibility cache database;

[0068] Step S4: Connect the asynchronous read-write compatibility cache database to the enterprise database to perform intelligent information management.

[0069] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of an intelligent information management method based on cloud computing of the present invention. In this example, the intelligent information management method based on cloud computing includes the following steps:

[0070] Step S1: obtaining enterprise database log data and enterprise database management authority; performing load time series skewness analysis on the enterprise database log data to obtain load fluctuation time series skewness data;

[0071] In the embodiment of the present invention, in the process of obtaining the enterprise database log data and the enterprise database management authority, firstly, a data connection is established with the database system of the enterprise, and the database log is read with the help of a database access tool (such as a database connection driver or a cloud platform database interface). The database log data generally includes database read and write operations, transaction execution status, error information, index usage, etc. These log data are subjected to time series load analysis through a dedicated time series data processing algorithm, wherein the load time series skew analysis technology used can adopt frequency domain and time domain hybrid analysis methods such as Fourier transform and Hilbert transform to identify the load fluctuation characteristics of the database in different time periods. The specific analysis process includes preprocessing the log data, removing irrelevant data points, segmenting the time series data through the sliding window technology, calculating the load peak, average value and skewness coefficient of each segment, and outputting the load fluctuation time series skewness data. This data provides an input basis for the subsequent optimization of the database table structure to reveal the database load during the system bottleneck period.

[0072] Step S2: parsing the table structure of the enterprise database based on the enterprise database management authority to obtain the database table structure; performing table structure performance bottleneck analysis on the database table structure to obtain table structure performance bottleneck data; performing asynchronous read-write cache processing on the database table structure according to the table structure performance bottleneck data and load fluctuation time series skew data to obtain an asynchronous read-write cache database;

[0073] In an embodiment of the present invention, after obtaining the enterprise database management authority, the table structure parsing step obtains the table structure information of the database by traversing the metadata table in the database (such as information_schema in MySQL or pg_catalog in PostgreSQL). The specific operation includes extracting the field name, data type, index configuration, constraint conditions, etc. of each table, and generating table structure mapping data based on this information. Subsequently, the table structure performance bottleneck analysis method is used, combined with the performance bottleneck information appearing in the database query log, and the database access cost model (such as the cost estimation model or QEP query plan evaluation technology) is used to conduct an in-depth analysis of the index, connection method and query mode of each table. This analysis method will focus on the bottlenecks of the database response time, index usage efficiency, locking mechanism, etc. under high load. By analyzing the association between log data and table structure information, the local bottleneck of database query performance is calculated, and table structure performance bottleneck data is generated. Based on the table structure performance bottleneck data and the load fluctuation time series skew data obtained in step S1, asynchronous read and write cache processing is performed by introducing an asynchronous data cache mechanism at the database layer. In specific operations, the hybrid cache strategy that combines the LRU cache replacement algorithm and the frequency and most recently used algorithm is combined to temporarily cache the data of high-frequency read and write operations in the memory. At the same time, asynchronous read and write processing of the database table is implemented through asynchronous I / O operations to reduce the load of the database directly writing to the disk. The implementation logic of this process is to obtain hot data directly from the cache when reading to reduce the peak load of the database; when writing, the log-ahead recording (WAL) technology is used to ensure data consistency and persistence, and avoid delays caused by frequent disk I / O operations to the system. The final output asynchronous read-write cache database has a significant improvement in response performance, ensuring the stable operation of the database under high load fluctuations.

[0074] Step S3: performing compatibility matching on the asynchronous read-write cache database based on the containerization technology in cloud computing to obtain an asynchronous read-write compatibility cache database;

[0075] In the embodiment of the present invention, in the process of matching the compatibility of the asynchronous read-write cache database based on the containerization technology in cloud computing, the introduction of containerization technology is intended to ensure that the asynchronous read-write cache database can maintain consistent performance and compatibility in different environments. The specific operation uses container orchestration technologies such as Docker or Kubernetes to encapsulate the asynchronous read-write cache database into a container to ensure that its operating environment (including operating system, dependency package, network configuration, etc.) is consistent with other services of the cloud computing platform. In the containerization encapsulation process, all dependent components and configuration files of the asynchronous read-write cache database are first extracted to create a unified container image. Subsequently, the containerization management tool performs dependency analysis and compatibility check on the image, focusing on analyzing the file system support of the database, the network I / O model, and the interactive behavior of the asynchronous cache strategy and the cloud platform container network. Through the construction of the container image and the standardized operation of the environment in the container, the database is ensured to be compatible with the storage service and network service in the cloud computing environment, and the stability of the asynchronous read-write mechanism in a high concurrency environment is verified through a series of load tests and stress tests. Finally, an asynchronous read-write compatibility cache database is generated, and it is ensured that it can run seamlessly in different cloud computing environments.

[0076] Step S4: Connect the asynchronous read-write compatibility cache database to the enterprise database to perform intelligent information management.

[0077] In the embodiment of the present invention, the asynchronous read-write compatibility cache database is connected to the enterprise database to perform intelligent information management. First, the asynchronous read-write compatibility cache database is connected to the core management module of the enterprise database through the database middleware or API interface. This process uses database gateway technology or database proxy technology based on connection pool to ensure efficient data transmission between the asynchronous read-write cache layer and the main storage layer of the enterprise database. First, define and configure the database connection pool parameters to ensure asynchronous and non-blocking transmission of data flows between the cache and the database. Secondly, with the help of load balancing algorithms (such as polling load balancing algorithms or consistent hashing algorithms), the query requests of the enterprise are allocated to the cache layer or the main storage layer, and high-frequency query operations are processed in the cache first to reduce the access pressure of the main storage layer. For write operations, asynchronous write log records are used to ensure data consistency and reliability. During the data flow process, the response time, concurrency and cache hit rate of database queries are monitored in real time to adjust the cache capacity and asynchronous read-write strategy. Finally, intelligent information management automatically optimizes and schedules caches according to business needs by monitoring the load of the enterprise database in real time, thereby realizing intelligent management of large-scale databases.

[0078] Preferably, step S1 comprises the following steps:

[0079] Step S11: Obtaining enterprise database log data and enterprise database management authority;

[0080] Step S12: Fill missing values ​​in the enterprise database log data to obtain database log filling data;

[0081] Step S13: performing load fluctuation analysis on the database log filling data to obtain database load fluctuation data;

[0082] Step S14: performing load time series skewness analysis on the load fluctuation data in the database to obtain load fluctuation time series skewness data.

[0083] In an embodiment of the present invention, in the process of obtaining enterprise database log data and enterprise database management rights, it is necessary to configure the rights management system through the database administrator (DBA) to obtain the permission set required for the operation. The specific operation includes configuring the access control policy in the database management system and obtaining the read permission for the log data through the role permission allocation method. The log data is usually stored in the system log table or independent log file inside the database. These logs record the historical information of the database operation, including query statements, transaction execution, resource usage, etc. A script or an automated scheduling tool (such as a Cron task) is used to regularly obtain the latest log data and store it in a designated analysis node. After the data collection is completed, the log data is transmitted to the subsequent data processing unit through a standardized transmission protocol (such as FTP, SFTP or enterprise intranet) to ensure the integrity and security of the data. For the missing value filling of the enterprise database log data, the log data is first cleaned by a data preprocessing technology. The cleaning step includes deleting duplicate log entries, eliminating irrelevant operation information, and marking abnormal format or incomplete logs. For the filling processing of missing values, an interpolation method based on time series or a historical trend fitting technology is used. Specifically, the missing time period data in the log is estimated and filled using the linear interpolation method or the Lagrange interpolation method. If the amount of missing data is large, the historical window regression prediction method is used to fill the missing points according to the load change trend of the previous and next time periods in combination with the periodicity and time series correlation of the log data, and generate complete database log filling data. The filling process ensures the integrity and consistency of the data and provides accurate data input for subsequent load analysis. When performing load fluctuation analysis on the database log filling data, the load calculation formula is used to analyze the parameters such as query frequency, transaction concurrency, CPU and I / O resource occupancy in the log to obtain the characteristic value of load fluctuation. This step uses the sliding window method to divide the log data into multiple time periods, and uses the calculation formula to calculate the average load, peak load and fluctuation amplitude of each time period. Specifically, the load fluctuation analysis includes the following calculations: setting a time window length, calculating the load change rate for each window, and evaluating the resource usage of the database in different time periods. By comparing the load peak and mean of each time window, the frequency and amplitude of load fluctuation are identified. The analysis process is performed using database-specific resource utilization assessment tools (such as DB Performance Analyzer) and outputs database load fluctuation data to provide accurate load characteristic information for subsequent steps. In the process of load time series skewness analysis of database load fluctuation data, the load data is further processed using time series analysis methods. The specific operations include converting database load fluctuation data into time series data and extracting the time series characteristics of load fluctuation using time domain and frequency domain analysis techniques.Time domain analysis identifies the skewed characteristics of load distribution by calculating the mean, standard deviation, skewness, kurtosis and other parameters of time series data; frequency domain analysis converts time series data into frequency components through discrete Fourier transform (DFT) or short-time Fourier transform (STFT) to analyze the frequency distribution of load fluctuations. The core of load time series skew analysis is to reveal the uneven distribution of database load in different time periods. The output load fluctuation time series skew data can reflect the concentration and abnormal fluctuation characteristics of load during peak hours, providing a scientific basis for database optimization.

[0084] Preferably, step S2 comprises the following steps:

[0085] Step S21: parsing the table structure of the enterprise database based on the enterprise database management authority to obtain the database table structure;

[0086] Step S22: performing a table structure performance bottleneck analysis on the database table structure according to the load fluctuation time series skew data to obtain table structure performance bottleneck data;

[0087] Step S23: performing high-load read / write delay response simulation according to the table structure performance bottleneck data and the load fluctuation timing skew data to obtain high-load read / write delay response data;

[0088] Step S24: performing read-write separation adaptation on the database table structure based on the high-load read-write delay response data to obtain database read-write separation adaptation data;

[0089] Step S25: performing asynchronous read-write cache processing on the database table structure according to the database read-write separation adaptation data to obtain an asynchronous read-write cache database.

[0090] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0091] Step S21: parsing the table structure of the enterprise database based on the enterprise database management authority to obtain the database table structure;

[0092] In an embodiment of the present invention, when parsing the table structure of a database based on the enterprise database management authority, it is first necessary to extract the table structure metadata from the database. This process obtains the structural information of each table, including the column name, data type, index, foreign key constraint, and data distribution of the table, through the metadata management function of the database management system. The core of table structure parsing is to convert the physical storage form of the database into a logical structure representation. This process can be completed by parsing the "Data Dictionary" that comes with the database system. In order to accurately parse, SQL statements such as DESCRIBEtable_name or SHOW CREATE TABLE table_name are used to read the creation details of the table. The parsing process also needs to combine the access frequency in the load data to identify key tables and columns, lay the foundation for subsequent optimization steps, and finally obtain the database table structure.

[0093] Step S22: performing a table structure performance bottleneck analysis on the database table structure according to the load fluctuation time series skew data to obtain table structure performance bottleneck data;

[0094] In an embodiment of the present invention, when analyzing the performance bottleneck of a database table structure based on the load fluctuation time series skew data, the difference between the peak load and the off-peak load identified in the load data is used to evaluate the query frequency, data volume and index usage of the table structure. The performance bottleneck location is identified by performing a detailed analysis of the index, foreign key constraints, data distribution and the relationship between data tables of the table structure. During the analysis process, a specific performance bottleneck evaluation algorithm is used, such as an efficiency evaluation method based on a B-tree or hash index, to measure the index failure rate of the table and the frequency of full table scans, and to find the bottleneck in the query process in combination with the SQL execution plan. The results of the bottleneck analysis will reveal problems such as missing indexes, redundant data, foreign key locking, etc. in the table structure, and ultimately generate table structure performance bottleneck data.

[0095] Step S23: performing high-load read / write delay response simulation according to the table structure performance bottleneck data and the load fluctuation timing skew data to obtain high-load read / write delay response data;

[0096] In an embodiment of the present invention, when simulating the high-load read and write delay response based on the table structure performance bottleneck data and the load fluctuation time series skew data, a load generator tool (such as Apache JMeter) is used to simulate the actual operating environment of the database under high load, and multiple read and write request combinations are set to perform a large number of concurrent accesses to simulate the response of the database under different loads. The simulation process generates a delay curve by monitoring the I / O response time, CPU utilization, transaction processing delay and other performance indicators of the database in real time. In particular, for the performance bottleneck position of the table structure, the specific response time of the read and write operations is identified by analyzing the changes in data access delay under high load. The simulation is also dynamically adjusted in combination with the time series data to ensure that the simulation results can cover different load scenarios, and finally generate high-load read and write delay response data for subsequent optimization and adaptation processes.

[0097] Step S24: performing read-write separation adaptation on the database table structure based on the high-load read-write delay response data to obtain database read-write separation adaptation data;

[0098] In an embodiment of the present invention, when the database table structure is adapted for read-write separation based on high-load read-write delay response data, the delay bottlenecks of read and write operations are first identified based on the simulation data, especially those that affect the performance of the database under high load. Then, the read-write separation strategy is implemented to allocate read and write operations to different database instances or partitions respectively to reduce resource competition. The core of read-write separation adaptation is to replicate read operations in the database, synchronize data to multiple read-only instances, and concentrate write operations in the main database. To ensure data consistency, a real-time data synchronization mechanism based on transaction logs is adopted, such as the replication function in MySQL, to ensure that the synchronization delay between the main database and the read-only database is minimized. The entire adaptation process also needs to optimize the routing rules of read and write requests, and dynamically allocate read requests to different instances through a load balancer to finally obtain database read-write separation adaptation data.

[0099] Step S25: performing asynchronous read-write cache processing on the database table structure according to the database read-write separation adaptation data to obtain an asynchronous read-write cache database.

[0100] In an embodiment of the present invention, when asynchronous read-write cache processing is performed on the database table structure according to the database read-write separation adaptation data, cache technology is used to optimize the read-write efficiency of the database. First, a cache layer is introduced in the database, and a distributed cache system (such as Redis or Memcached) is used to store frequently accessed read operation data. The key to asynchronous read-write cache is to realize asynchronous processing of read and write requests. Read operations are obtained from the cache first, while write operations are submitted to the database asynchronously through the transaction queue. By introducing a multi-threaded queue manager, write operations are submitted with a delay while maintaining consistency requirements for database write operations. Asynchronous read-write cache processing also needs to consider cache invalidation strategies, and the LRU (Least Recently Used) algorithm is used to dynamically update cache data to ensure that the system maintains stable read and write performance under high load, and finally an asynchronous read-write cache database is generated to support the efficient operation of enterprise databases.

[0101] Preferably, step S22 includes the following steps:

[0102] Step S221: Perform field constraint structure analysis on the database table structure to obtain field constraint structure data;

[0103] Step S222: Calculate the constraint nesting level of the database table structure according to the field constraint structure data to obtain the field constraint nesting level data;

[0104] Step S223: perform trigger-related logic retrieval on the database table structure to obtain trigger-related logic data;

[0105] Step S224: evaluating the event trigger logic complexity of the database table structure according to the trigger associated logic data to obtain event trigger logic complexity data;

[0106] Step S225: performing execution non-linear time complexity calculation based on the event triggering logic complexity data and the field constraint nesting level data to obtain execution non-linear time complexity data;

[0107] Step S226: mapping the execution complexity load fluctuation to the execution nonlinear time complexity data according to the load fluctuation time series skewness data, to obtain the execution complexity load fluctuation data;

[0108] Step S227: performing table structure performance bottleneck analysis on the database table structure according to the execution complexity load fluctuation data and the execution nonlinear time complexity data to obtain table structure performance bottleneck data.

[0109] In the embodiment of the present invention, when the field constraint structure analysis is performed on the database table structure, it is first necessary to extract the definition information of the field from the table structure, including the type, length, default value, whether it is allowed to be empty, uniqueness constraint, foreign key constraint, etc. of the field. The definition of these field constraints directly affects the integrity and data consistency of the database table. By systematically analyzing the constraint conditions of each field, a field constraint structure model is constructed, and the constraint relationship between the fields, such as the dependency of the foreign key and the primary key, is identified in combination with the relevance of the fields in the table. In the specific analysis process, a constraint parsing algorithm (such as a recursive constraint tree analysis algorithm) is used to perform hierarchical decomposition, and the constraint relationship of the field is extracted step by step, and finally the field constraint structure data is generated, laying the foundation for the subsequent nested hierarchical analysis. When the constraint nesting level of the database table structure is calculated according to the field constraint structure data, it is first necessary to clarify the hierarchical relationship of the field constraint, including the nesting of the primary key constraint, foreign key constraint, uniqueness constraint, etc. By parsing the hierarchical dependency in the field constraint structure, a constraint nesting graph of each table is constructed using a tree structure. The graph evaluates the nesting level of the field by calculating the hierarchical depth, hierarchical nesting frequency and complexity of each constraint. In order to accurately calculate the nesting level, a graph traversal algorithm (such as depth-first search DFS) is applied to analyze the dependencies and constraint chains between fields layer by layer, and to identify bottlenecks and redundant constraints in the nested relationship. Finally, the field constraint nesting level data is generated, laying the foundation for the subsequent logical complexity evaluation. When performing trigger association logic retrieval on the database table structure, it is first necessary to traverse the trigger logic defined in the database, and retrieve the event trigger conditions, trigger timing (such as triggering when inserting, updating or deleting operations) and trigger execution logic related to the trigger on each table by querying the metadata table of the database. The execution order of each trigger and the conditional logic when triggering are obtained through SQL queries (such as SHOW TRIGGERS or accessing the trigger management table in the database). Then, the logic backtracking technology is applied to reversely analyze the triggers, parse the association relationship between triggers, and identify the dependency of mutual triggering. Finally, the association graph of the trigger is constructed through the recursive logic retrieval algorithm, and the trigger association logic data is generated to provide a basis for the subsequent complexity evaluation. When evaluating the logic complexity of event triggering of the database table structure based on the trigger-associated logic data, first traverse the dependency relationship in the trigger association graph to identify the length of the trigger chain, the number of concurrent triggers, and the execution order of the trigger events. During the complexity evaluation process, the combinatorial complexity analysis algorithm is applied to calculate the number of logical branches in the trigger chain and the number of events triggered by each branch. By analyzing the execution logic of each trigger (such as recursive calls, nested calls, etc.), the nesting depth and execution path complexity of the trigger are determined. Especially for those logics with multi-layer trigger dependencies, a multi-level trigger complexity model (such as a nested tree model) is used to quantify the execution complexity of the trigger.Finally, the event trigger logic complexity data is generated, indicating the key performance bottlenecks in the database trigger system. When calculating the execution nonlinear time complexity based on the event trigger logic complexity data and the field constraint nesting level data, it is necessary to first combine the execution logic complexity of the trigger and the nesting level of the field constraint, and comprehensively consider the read and write request response time of the database under high load. By analyzing the execution path of the trigger and the constraint relationship between the fields, a time complexity function is constructed. This function calculates the worst-case response time and the best-case response time of the trigger chain in a concurrent scenario based on the complexity analysis model, such as the big O symbol analysis method. At the same time, by recursively traversing the structure of the database table, the nonlinear impact of the constraint nesting depth on the performance is measured, and the execution nonlinear time complexity data is generated. This data reflects the response time behavior of the database under multi-level nesting and trigger concurrency. When mapping the execution complexity load fluctuation of the execution nonlinear time complexity data according to the load fluctuation time series skewness data, the time periods of peak and low load periods are first identified by analyzing the time series characteristics in the load fluctuation data. Then, the time complexity function in the execution nonlinear time complexity data is matched with the time series data in the load fluctuation data to construct the mapping relationship between time complexity and load fluctuation. The time series cross-mapping algorithm (such as Granger causality test) is used to evaluate the dynamic impact of load fluctuation on the database execution complexity and generate time complexity change curves under different load levels. Through this mapping analysis, the key period that affects the execution time complexity in the load fluctuation is identified, and finally the execution complexity load fluctuation data is generated to provide accurate load mapping information for performance bottleneck analysis. When the table structure performance bottleneck analysis of the database table structure is performed based on the execution complexity load fluctuation data and the execution nonlinear time complexity data, the table structure performance bottleneck under high load is identified by combining the relationship between execution time complexity and load fluctuation. In the analysis process, the performance degradation point of the database operation under high load is first determined based on the time series characteristics during load fluctuation. Then, the factors marked in the execution nonlinear time complexity data, such as trigger execution delay, constraint nesting bottleneck and index conflict, are taken as the key analysis objects. Using performance backtracking analysis technology, we can trace back the slow query, lock waiting and resource contention problems during the operation through the database execution plan (ExecutionPlan) to determine the location and cause of the performance bottleneck. Finally, we can generate table structure performance bottleneck data to optimize the database table structure design and trigger logic.

[0110] Preferably, performing nonlinear time complexity calculation based on event triggering logic complexity data and field constraint nesting level data includes the following steps:

[0111] Nonlinear execution efficiency simulation is performed on the event trigger logic complexity data and the field constraint nesting level data respectively, and the event trigger nonlinear execution efficiency data and the nonlinear field constraint execution efficiency data are obtained respectively;

[0112] Performing hierarchical execution complexity error correction calculation on event-triggered nonlinear execution efficiency data to obtain trigger execution complexity error correction data;

[0113] The efficiency convergence mean difference is calculated for the nonlinear field constraint execution efficiency data to obtain the constraint efficiency convergence mean difference data;

[0114] The execution nonlinear time complexity is calculated based on the trigger execution complexity error correction data and the constraint efficiency convergence mean difference data to obtain the execution nonlinear time complexity data.

[0115] In an embodiment of the present invention, nonlinear execution efficiency simulation is performed for event trigger logic complexity data. This step calculates the execution efficiency of event triggering by analyzing the execution path of the trigger in the database. During the execution efficiency simulation process, a trigger path tracing algorithm (such as a dependency analysis method based on a directed acyclic graph) is used to gradually analyze each step of the trigger chain and its dependencies. During the analysis process, the concurrency and nested behaviors between triggers are identified, especially the performance bottleneck of triggers under large-scale concurrent operations. In order to perform nonlinear simulation, the time-consuming operations in the trigger path are simulated using a polynomial approximation method, especially the impact of trigger nested logic and concurrent conflicts, the growth trend of nonlinear behavior is derived, and finally the event trigger nonlinear execution efficiency data is generated. When performing nonlinear execution efficiency simulation based on field constraint nested level data, the field constraints in the database are first analyzed in depth, especially the nested levels of foreign keys, primary keys and uniqueness constraints are simulated and analyzed. A recursive traversal algorithm (such as a tree-based hierarchical recursive algorithm) is used to hierarchically divide the nested levels and evaluate the execution cost of each level of constraints. During the simulation, the exponential growth model is used to simulate the constraint execution behavior in the nested hierarchy, and the downward trend of execution efficiency in the constraint verification process is derived when the constraint hierarchy increases. The nonlinear time complexity analysis method is used to evaluate the execution time and resource consumption of each level of constraints under large data processing, and finally the nonlinear field constraint execution efficiency data is generated. When correcting the hierarchical execution complexity error of the event-triggered nonlinear execution efficiency data, the impact of different levels of trigger logic on the execution efficiency must be considered first. The hierarchical error in the trigger execution path is analyzed and adjusted by the error correction algorithm (such as the Kalman filter algorithm). The complexity of the trigger execution path leads to a decrease in efficiency or an increase in execution error due to logical nesting. Therefore, in the error correction process, the bottleneck points and delay hotspots in the trigger chain are identified, and the execution time is adjusted according to these bottlenecks. Especially in high concurrency situations, the trigger execution efficiency is abnormally reduced due to waiting or lock contention. The error correction algorithm ensures the accuracy of the final output data by correcting these abnormal points. Finally, the trigger execution complexity error correction data is generated to reflect the execution efficiency fluctuation of the trigger system and the corrected execution complexity. When calculating the efficiency convergence mean difference based on the nonlinear field constraint execution efficiency data, firstly, statistically analyze the efficiency fluctuations generated during the field constraint execution process, and use the mean difference calculation method (such as the Bayesian mean method) to perform mean difference analysis on the execution efficiency of each layer of constraints. In the specific operation, the execution efficiency data of each layer of constraints are processed in layers, and the convergence speed and fluctuation range of each layer are calculated. Through the convergence mean difference calculation, the stability of the execution efficiency in the constraint nesting level is identified, and whether the execution efficiency of the field constraint can be stabilized within a certain range during long-term execution is evaluated.This step can not only identify the execution bottleneck of the constraint through the mean difference calculation, but also provide an efficiency benchmark for the subsequent complexity calculation. Finally, the constraint efficiency convergence mean difference data is generated, indicating the balance of efficiency in constraints at different levels. When performing nonlinear time complexity calculation based on the trigger execution complexity error correction data and the constraint efficiency convergence mean difference data, the impact of the trigger execution efficiency and the field constraint efficiency on the time complexity is first analyzed by combining the two types of data. In this process, the composite complexity analysis method is used to merge the complexity of the trigger execution and the complexity of the constraint execution to construct a comprehensive time complexity function. Through the time complexity theory (such as the logarithmic linear complexity model), the execution efficiency of the trigger and field constraints is dynamically weighted, and their execution time under different load conditions is calculated. In particular, for scenarios with uneven complexity, the nonlinear growth factor in the complexity calculation is adjusted to ensure that the time complexity model can accurately reflect the overall execution behavior of the system. Finally, the execution nonlinear time complexity data is generated, providing an important reference for database performance optimization and load prediction.

[0116] Preferably, performing hierarchical execution complexity error correction calculation on event-triggered nonlinear execution efficiency data includes the following steps:

[0117] Perform event-triggered execution level retrieval on event-triggered nonlinear execution efficiency data to obtain event-triggered execution level data;

[0118] According to the event-triggered execution level data, the execution efficiency variance between nested levels of the event-triggered nonlinear execution efficiency data is calculated to obtain the nested level execution efficiency variance data;

[0119] Based on the nested hierarchical execution efficiency variance data, the event-triggered nonlinear execution efficiency data is subjected to nested overlapping redundant execution efficiency identification to obtain nested overlapping redundant execution efficiency data;

[0120] Perform overlapping error correction processing on event-triggered nonlinear execution efficiency data according to the nested overlapping redundant execution efficiency data to obtain triggered nonlinear execution correction efficiency data;

[0121] Based on the divide-and-conquer method, the hierarchical execution complexity error correction calculation is performed on the triggered nonlinear execution correction efficiency data to obtain the triggered execution complexity error correction data.

[0122] In an embodiment of the present invention, when performing event-triggered execution level retrieval on event-triggered nonlinear execution efficiency data, firstly, the logical structure of the trigger in the database is analyzed to identify the execution level of each trigger in the event triggering process. This step uses a hierarchical traversal algorithm (such as a depth-first search algorithm) to parse the trigger chain layer by layer, and record the call relationship and nesting depth of each trigger. During the execution process, the direct and indirect dependencies between the triggers are classified, and the triggers of each level are organized into an execution level table through a tree structure to generate event-triggered execution level data. This data clarifies the execution order and nesting relationship of the triggers, laying the foundation for subsequent efficiency analysis. Based on the event-triggered execution level data, the variance of the execution efficiency between the nested levels of the event-triggered nonlinear execution efficiency data is calculated. In this step, the variance analysis method (such as a variance analysis model based on ANOVA) is used to compare the execution efficiency of the triggers at each level layer by layer. First, the execution time of each trigger level is counted to calculate the mean and variance of its execution efficiency; then, the efficiency fluctuations between different levels are compared to analyze the imbalance of execution efficiency in different levels of nesting. In particular, for the deeply nested levels, the efficiency is prone to decrease significantly. The variance calculation can accurately locate the level where this phenomenon occurs. Finally, the nested level execution efficiency variance data is generated to provide a basis for subsequent redundancy identification. According to the nested level execution efficiency variance data, the event-triggered nonlinear execution efficiency data is identified for nested overlapping redundant execution efficiency. This step uses an overlapping redundancy detection algorithm (such as a redundancy detection algorithm based on pattern recognition) to further analyze the redundant trigger execution paths for the levels with large variance. By performing pattern matching on the call chain of each trigger, redundant operations that are repeatedly executed at different levels are identified, especially the parts that are repeatedly triggered in multiple nestings. In the process of redundancy identification, the dependency between triggers, repeated logical conditions, and call conflicts of parallel triggers are focused on. Finally, the nested overlapping redundant execution efficiency data is generated, which can clearly show the redundant parts of the triggers and their impact on the overall efficiency. Based on the nested overlapping redundant execution efficiency data, the event-triggered nonlinear execution efficiency data is corrected for overlapping errors. This step corrects the detected redundant execution paths and uses error correction algorithms (such as Taylor series expansion) to optimize and adjust the execution time of redundant paths. In specific operations, redundant trigger chains are removed or simplified to reduce repeated calls and recalculate the corrected execution time and efficiency. Especially under high concurrency conditions, the redundant execution paths of triggers have a greater impact on the overall system load, and correcting redundant paths can significantly reduce the system's resource consumption. Finally, the trigger nonlinear execution correction efficiency data is generated, which can reflect the optimized trigger execution path and its time efficiency changes.Based on the divide-and-conquer method, the hierarchical execution complexity error correction calculation is performed on the trigger nonlinear execution correction efficiency data. The divide-and-conquer method decomposes the complex trigger execution path into multiple smaller sub-problems, solves each sub-problem independently, and then merges the results. During the correction process, the hierarchical execution path of the trigger is first segmented, and the execution complexity of each level is analyzed separately. The errors of each level are corrected layer by layer through the step-by-step calculation of the execution complexity. In each sub-problem, the dynamic programming method is used to optimize the execution time and complexity to ensure that the error continues to converge during the correction process. Finally, the correction results of all sub-problems are merged to obtain the global hierarchical execution complexity error correction data, which is used to measure the complexity of the overall execution of the trigger system and provide a basis for subsequent performance optimization.

[0123] Preferably, step S25 comprises the following steps:

[0124] Step S251: performing concurrent read-write frequency simulation on the database table structure according to the database read-write separation adaptation data to obtain concurrent read-write frequency simulation data;

[0125] Step S252: Designing a read-write cache structure for the database table structure based on the concurrent read-write frequency simulation data, and obtaining read cache structure data and write cache structure data respectively;

[0126] Step S253: performing asynchronous thread matching on the read cache structure data and the write cache structure data to obtain read cache asynchronous thread structure data and write cache asynchronous thread structure data respectively;

[0127] Step S254: Design conflict and concurrency control logic for the read cache asynchronous thread structure data and the write cache asynchronous thread structure data to obtain read-write conflict logic control data;

[0128] Step S255: performing asynchronous read-write cache processing on the database table structure according to the read cache asynchronous thread structure data, the write cache asynchronous thread structure data and the read-write conflict logic control data to obtain an asynchronous read-write cache database.

[0129] In an embodiment of the present invention, when simulating the concurrent read and write frequency of a database table structure, by analyzing the historical read and write operation logs of the database, concurrent transaction analysis technology (such as the timestamp sorting algorithm in the transaction concurrency model) is used to count the read and write requests of different tables to obtain the read and write request frequency per second. Next, according to the read and write separation adaptation data of the database, the read and write ratio calculation formula based on the mathematical model is applied to simulate the concurrent read and write operations in a multi-threaded environment, and the read and write frequency is gradually adjusted to ensure consistency with the actual operating environment. In the concurrent simulation process, focus on the read and write operation conflicts and bottleneck problems during peak hours, and finally generate concurrent read and write frequency simulation data, which can quantify the read and write pressure distribution of different table structures under concurrent conditions. Based on the concurrent read and write frequency simulation data, the read and write cache structure of the database table structure is designed. In this step, the hierarchical cache design theory is used to allocate the access data of the table into two parts, the read cache and the write cache, according to the frequency of read and write operations in the database. Specifically, for tables with frequent read operations, the read cache structure is designed first, and a cache replacement algorithm similar to LRU (least recently used) is applied to optimize the cache of frequently accessed data blocks in the table. For tables with frequent write operations, an asynchronous write cache mechanism is adopted to improve the write cache efficiency by merging write technology (such as batch write technology). Finally, read cache structure data and write cache structure data are generated respectively, which record the cache design details of each table in detail, including cache size and cache strategy. According to the designed read cache structure data and write cache structure data, the cache system is subjected to asynchronous thread matching operation. In this step, a thread pool optimization algorithm (such as a dynamic thread allocation algorithm) is used to allocate independent asynchronous threads to each read cache and write cache structure to realize asynchronous parallel processing. By analyzing the access frequency and operation complexity of each cache area, the number of threads in the thread pool is dynamically adjusted to ensure the maximization of the concurrent performance of read and write operations. In the matching process, high-frequency read and write operations are preferentially allocated to multi-thread processing to avoid thread blocking. Finally, read cache asynchronous thread structure data and write cache asynchronous thread structure data are generated, which respectively describe the matching relationship and allocation strategy between cache structure and asynchronous thread. After the read-write cache structure completes the asynchronous thread matching, the conflict concurrency control logic design is performed for the asynchronous read-write operations. This step applies a control strategy that combines a lock mechanism with a lock-free mechanism, using optimistic locking and a lock-free algorithm based on CAS (Compare-And-Swap) to ensure that read-write operations do not cause resource competition and data conflicts in a high-concurrency environment. In specific operations, the lock-free mechanism is used first for read operations to improve concurrent processing performance; optimistic locking is applied to write operations to avoid multi-threaded write conflicts. At the same time, transaction isolation technology (such as multi-version concurrency control, MVCC) is used to refine conflict control to ensure the correctness of read and write operations.Finally, the read-write conflict logic control data is generated, which clearly records the conflict resolution methods in various concurrent scenarios. According to the read cache asynchronous thread structure data, the write cache asynchronous thread structure data and the read-write conflict logic control data, the database table structure is asynchronously read and write cached. This step realizes the parallelization of read and write operations by running the read cache and write cache independently in asynchronous threads, thereby improving the overall processing capacity of the database. In specific operations, the aforementioned LRU strategy is used in the read cache processing process to asynchronously read commonly used data and quickly return query results; the write cache processing ensures the efficiency and consistency of write operations by combining batch writing with transaction logs. Combined with the conflict control logic, the concurrency problems between read and write operations are effectively avoided, ensuring that the system remains stable under high load. Finally, an asynchronous read-write cache database is generated, and its structure has been optimized, which can significantly improve the processing efficiency and stability of the database in large-scale concurrent scenarios.

[0130] Preferably, designing a read-write cache structure for a database table structure based on concurrent read-write frequency simulation data includes the following steps:

[0131] Based on the concurrent read and write frequency simulation data, the database table structure is sorted by the field read hotspot frequency in different time periods to obtain the field read hotspot frequency sorting data;

[0132] Assigning hotspot field priority values ​​to the field read hotspot frequency sorting data to obtain the hotspot field priority values;

[0133] According to the priority value of the hotspot field, the LRU priority startup strategy is formulated for the field reading hotspot frequency sorting data to obtain the read field priority startup strategy;

[0134] According to the read field priority start strategy, the read cache structure is designed for the field read hot spot frequency sorting data to obtain the read cache structure data;

[0135] Based on the concurrent read and write frequency simulation data, the field write request density of the database table structure is analyzed to obtain the field write request density data;

[0136] Use the cache database to perform cache sharding on the field write request density data to obtain the field write cache sharding data;

[0137] Formulate a batch write-back cache strategy for the field write cache shard data to obtain a batch write-back cache strategy;

[0138] The write cache structure is designed according to the field write cache shard data and the batch write back cache strategy to obtain the write cache structure data.

[0139] In the embodiment of the present invention, based on the concurrent read and write frequency simulation data, the reading frequency of each field in the database table structure in different time periods is statistically analyzed, and the number of reads in each time period is quantified by the time series analysis method. By sorting the read operations in different time periods, the read hotspot frequency of each field is obtained. In the specific operation, a discrete time model is used to divide a day into multiple time slices, and the number of reads of each field in each time slice is recorded. Finally, the field read hotspot frequency sorting data is obtained according to the read frequency from high to low. This data can clearly reflect the access hotspots of each field in different time periods. After obtaining the field read hotspot frequency sorting data, the priority value is assigned based on the read hotspot degree of the field. First, the fields are grouped according to the read frequency, and the hierarchical weight allocation method is used to assign different priorities to the fields of different groups. The weight value is assigned according to the hotspot degree of each field, and the field with a high read frequency will be assigned a higher priority. The priority setting is normalized to a specific interval by linear normalization to ensure that there are obvious differences between the field priority values. Finally, the hotspot field priority value is obtained, which will be used to guide the subsequent cache strategy formulation. According to the priority value of the hotspot field, the LRU (least recently used) preferred startup strategy is formulated for the field read hotspot frequency sorting data. In this step, the LRU cache replacement algorithm is used to give priority to hotspot fields during the field read cache startup process. Specifically, based on the priority value of the hotspot field, the high-priority field is placed at the front of the cache queue, and the low-priority field enters the back end. The LRU algorithm dynamically adjusts the data in the cache according to the frequency of use of the field. When the cache space is insufficient, the field that has not been accessed for the longest time will be evicted from the cache. Through the formulation of this strategy, it is ensured that the frequently accessed fields are given priority in the cache, and finally a read field priority startup strategy is generated. According to the read field priority startup strategy, the read cache structure is designed for the field read hotspot frequency sorting data. In this step, the hierarchical cache design method is used to partition the hotspot fields according to priority and set a multi-level cache mechanism. The first-level cache is used to store the most frequently accessed fields, and the second-level cache stores the secondary hotspot fields. Through the multi-level cache structure, the read efficiency of fields with different priorities is maximized. At the same time, for the cache allocation of high-priority fields, a dynamic adjustment algorithm can be used to automatically adjust the size of the cache space to cope with changes in data access. Finally, the read cache structure data is obtained, which describes the allocation and management strategy of the cache area in detail. Based on the concurrent read and write frequency simulation data, the field write request density in the database table structure is analyzed. By counting the number of writes and data block size of each field, the write request density of the field is calculated using the density estimation method.Density estimation is based on the write frequency and data volume of the field. Kernel Density Estimation technology is used to generate a write density distribution map for each field. This distribution map can intuitively display the write frequency and data pressure of each field, provide data support for the subsequent write cache design, and finally obtain the field write request density data. The cache database is used to perform cache sharding on the field write request density data. First, the write pressure of the field is sharded according to the write request density data, and the high-density fields are allocated to multiple cache shards using a partitioning strategy to balance the pressure of each cache area. The sharding process uses a hash sharding algorithm to map frequently written fields to different cache blocks by performing hash operations on the fields. This can effectively avoid write conflicts and data bottlenecks in the cache, ensuring stable performance in high-concurrency write scenarios. Finally, the field write cache sharding data is generated, which describes the cache allocation scheme for each field in detail. According to the field write cache sharding data, a batch write back cache strategy is formulated for the cache write back operation. This step uses batch write optimization technology to merge multiple small write requests into a large request to reduce the number of database write operations and improve write efficiency. In the specific operation, based on the delayed write (Write Behind) mechanism, the data in the cache is accumulated, and a write-back threshold is set. When the data volume reaches the threshold, the batch write-back operation is triggered. Combined with the write request density of the field, high-density fields are written back to the database first to ensure that the write efficiency of the system is maximized. Finally, the batch write-back cache strategy is obtained. According to the field write cache shard data and the batch write-back cache strategy, the write cache structure is designed. In this step, the asynchronous write cache mechanism is used to design a multi-level write cache architecture by combining partitioning and batch writing. First, an independent write cache area is set up for high-frequency write fields, and a dedicated write thread is assigned to it to ensure that the write operation is not affected by other fields. Secondly, the batch write-back strategy is used to write the data in the cache back to the database in batches to reduce the write pressure of the database. Finally, the asynchronous write mechanism is used to ensure that the write operation is performed in the background and does not affect the read and write performance of the foreground. Finally, the write cache structure data is generated.

[0140] Preferably, the conflict concurrency control logic design for the read cache asynchronous thread structure data and the write cache asynchronous thread structure data includes the following steps:

[0141] Perform conflict field simulation on the read cache asynchronous thread structure data and the write cache asynchronous thread structure data to obtain conflict field simulation data;

[0142] Perform cache field row-level lock granularity optimization on conflict field simulation data to obtain conflict field row-level lock granularity optimization data;

[0143] Perform optimistic lock concurrency control processing on the conflict field location data according to the conflict field row-level lock granularity optimization data to obtain the conflict field optimistic lock control data;

[0144] Based on the conflict field optimistic lock control data and the conflict field row-level lock granularity optimization data, the conflict concurrency control logic is designed to obtain the read-write conflict logic control data.

[0145] In the embodiment of the present invention, the conflict fields of the read cache asynchronous thread structure data and the write cache asynchronous thread structure data are simulated and analyzed, with the purpose of identifying the fields where concurrent conflicts occur during the read and write operations. This step adopts a conflict detection algorithm based on concurrent access, and by analyzing the fields accessed in the cache, those fields that are read or written by multiple threads at the same time period are identified. By simulating these conflict fields, detailed statistical data on the access frequency and conflict degree of the conflict fields can be generated. By using the time-space overlap analysis technology, the behaviors of different threads accessing the same field at different times are compared, and finally the conflict field simulation data is obtained. This data provides basic information for the subsequent optimization of the lock mechanism. After obtaining the conflict field simulation data, the row-level lock granularity of the cache field is optimized. Based on the simulation data, the dynamic lock granularity adjustment algorithm is used to set the row-level lock granularity for each conflict field. The algorithm flexibly adjusts the lock granularity according to the frequency and intensity of the field conflict, ensuring that the lock operation under high concurrency does not affect the overall performance of the system. By refining the row-level lock, the scope of the lock conflict can be reduced to avoid locking the entire table or large blocks of data. The optimized row-level lock granularity ensures that the granularity of the lock operation is small enough when the conflicting fields are accessed concurrently, so as to reduce lock competition and improve the concurrent processing capability of the system. Finally, the row-level lock granularity optimization data of the conflicting fields is obtained, which describes the locking strategy of each field. Based on the row-level lock granularity optimization data of the conflicting fields, the optimistic lock concurrency control processing is performed on the conflicting field positioning data. In this step, the optimistic lock mechanism is adopted to detect concurrency conflicts by recording the version number of each conflicting field. The specific operation includes setting a unique version number for each field, reading the version number first during read and write operations, and checking whether the version number has changed during write operations. If there is no change, the write operation is allowed to be submitted. The introduction of optimistic locks can reduce unnecessary lock operations and thus improve concurrency performance. Based on version control technology, this process synchronizes multi-threaded read and write operations to ensure that concurrency conflicts are effectively controlled. Finally, the optimistic lock control data of the conflicting fields is generated, and a specific concurrency control mechanism and field version number management method are provided. Combining the optimistic lock control data of the conflicting fields and the row-level lock granularity optimization data of the conflicting fields, the conflict concurrency control logic is designed. In this step, we use the conflict control algorithm to combine row-level locks with optimistic locks to design a set of concurrency control logic. This logic dynamically selects optimistic locks or row-level locks for control based on the access frequency and conflict intensity of the conflicting fields. In the case of less conflict, optimistic locks are used for concurrency control first, while in the case of more severe conflict, row-level locks are used to ensure data consistency. Through this hybrid control strategy, the optimal control of read-write concurrency can be achieved while ensuring system stability. Finally, the read-write conflict logic control data is obtained, and the specific execution rules and processes of the control logic are described in detail.

[0146] Preferably, the present invention further provides an intelligent information management system based on cloud computing, which is used to execute the intelligent information management method based on cloud computing as described above, and the intelligent information management system based on cloud computing includes:

[0147] The load skewness analysis module is used to obtain the enterprise database log data and enterprise database management authority; perform load time series skewness analysis on the enterprise database log data to obtain load fluctuation time series skewness data;

[0148] The asynchronous read-write cache processing module is used to parse the table structure of the enterprise database based on the enterprise database management authority to obtain the database table structure; analyze the table structure performance bottleneck of the database table structure to obtain the table structure performance bottleneck data; perform asynchronous read-write cache processing on the database table structure according to the table structure performance bottleneck data and load fluctuation time series skew data to obtain an asynchronous read-write cache database;

[0149] A compatibility matching module is used to perform compatibility matching on the asynchronous read-write cache database based on the containerization technology in cloud computing to obtain an asynchronous read-write compatibility cache database;

[0150] The execution docking module is used to dock the asynchronous read-write compatibility cache database to the enterprise database to perform intelligent information management.

[0151] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0152] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An intelligent information management method based on cloud computing, characterized in that: The following steps are involved: Step S1: Obtain enterprise database log data and enterprise database management authority; Perform load time series skewness analysis on enterprise database log data to obtain load fluctuation time series skewness data; Step S2: parsing the table structure of the enterprise database based on the enterprise database management authority to obtain the database table structure; performing table structure performance bottleneck analysis on the database table structure to obtain table structure performance bottleneck data; Asynchronous read-write cache processing is performed on the database table structure according to the table structure performance bottleneck data and the load fluctuation time series skew data to obtain an asynchronous read-write cache database; wherein step S2 includes: Step S21: parsing the table structure of the enterprise database based on the enterprise database management authority to obtain the database table structure; Step S22: performing a table structure performance bottleneck analysis on the database table structure according to the load fluctuation time series skew data to obtain table structure performance bottleneck data; wherein step S22 includes: Step S221: Perform field constraint structure analysis on the database table structure to obtain field constraint structure data; Step S222: Calculate the constraint nesting level of the database table structure according to the field constraint structure data to obtain the field constraint nesting level data; Step S223: perform trigger-related logic retrieval on the database table structure to obtain trigger-related logic data; Step S224: evaluating the event trigger logic complexity of the database table structure according to the trigger associated logic data to obtain event trigger logic complexity data; Step S225: performing execution non-linear time complexity calculation based on the event triggering logic complexity data and the field constraint nesting level data to obtain execution non-linear time complexity data; Step S226: mapping the execution complexity load fluctuation to the execution nonlinear time complexity data according to the load fluctuation time series skewness data, to obtain the execution complexity load fluctuation data; Step S227: performing table structure performance bottleneck analysis on the database table structure according to the execution complexity load fluctuation data and the execution nonlinear time complexity data to obtain table structure performance bottleneck data; Step S23: performing high-load read / write delay response simulation according to the table structure performance bottleneck data and the load fluctuation timing skew data to obtain high-load read / write delay response data; Step S24: performing read-write separation adaptation on the database table structure based on the high-load read-write delay response data to obtain database read-write separation adaptation data; Step S25: performing asynchronous read-write cache processing on the database table structure according to the database read-write separation adaptation data to obtain an asynchronous read-write cache database; wherein step S25 includes: Step S251: performing concurrent read-write frequency simulation on the database table structure according to the database read-write separation adaptation data to obtain concurrent read-write frequency simulation data; Step S252: Designing a read-write cache structure for the database table structure based on the concurrent read-write frequency simulation data, and obtaining read cache structure data and write cache structure data respectively; Step S253: performing asynchronous thread matching on the read cache structure data and the write cache structure data to obtain read cache asynchronous thread structure data and write cache asynchronous thread structure data respectively; Step S254: Design conflict and concurrency control logic for the read cache asynchronous thread structure data and the write cache asynchronous thread structure data to obtain read-write conflict logic control data; Step S255: performing asynchronous read-write cache processing on the database table structure according to the read cache asynchronous thread structure data, the write cache asynchronous thread structure data and the read-write conflict logic control data to obtain an asynchronous read-write cache database; Step S3: performing compatibility matching on the asynchronous read-write cache database based on the containerization technology in cloud computing to obtain an asynchronous read-write compatibility cache database; Step S4: Connect the asynchronous read-write compatibility cache database to the enterprise database to perform intelligent information management.

2. The intelligent information management method based on cloud computing according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Obtaining enterprise database log data and enterprise database management authority; Step S12: Fill missing values ​​in the enterprise database log data to obtain database log filling data; Step S13: performing load fluctuation analysis on the database log filling data to obtain database load fluctuation data; Step S14: performing load time series skewness analysis on the load fluctuation data in the database to obtain load fluctuation time series skewness data.

3. The intelligent information management method based on cloud computing according to claim 1 is characterized in that: The nonlinear time complexity calculation based on the event trigger logic complexity data and field constraint nested level data includes the following steps: Nonlinear execution efficiency simulation is performed on the event trigger logic complexity data and the field constraint nesting level data respectively, and the event trigger nonlinear execution efficiency data and the nonlinear field constraint execution efficiency data are obtained respectively; Performing hierarchical execution complexity error correction calculation on event-triggered nonlinear execution efficiency data to obtain trigger execution complexity error correction data; The efficiency convergence mean difference is calculated for the nonlinear field constraint execution efficiency data to obtain the constraint efficiency convergence mean difference data; The execution nonlinear time complexity is calculated based on the trigger execution complexity error correction data and the constraint efficiency convergence mean difference data to obtain the execution nonlinear time complexity data.

4. The intelligent information management method based on cloud computing according to claim 3 is characterized in that: The hierarchical execution complexity error correction calculation for event-triggered nonlinear execution efficiency data includes the following steps: Perform event-triggered execution level retrieval on event-triggered nonlinear execution efficiency data to obtain event-triggered execution level data; According to the event-triggered execution level data, the execution efficiency variance between nested levels of the event-triggered nonlinear execution efficiency data is calculated to obtain the nested level execution efficiency variance data; Based on the nested hierarchical execution efficiency variance data, the event-triggered nonlinear execution efficiency data is subjected to nested overlapping redundant execution efficiency identification to obtain nested overlapping redundant execution efficiency data; Perform overlapping error correction processing on event-triggered nonlinear execution efficiency data according to the nested overlapping redundant execution efficiency data to obtain triggered nonlinear execution correction efficiency data; Based on the divide-and-conquer method, the hierarchical execution complexity error correction calculation is performed on the triggered nonlinear execution correction efficiency data to obtain the triggered execution complexity error correction data.

5. The intelligent information management method based on cloud computing according to claim 1 is characterized in that: The design of the read-write cache structure for the database table structure based on concurrent read-write frequency simulation data includes the following steps: Based on the concurrent read and write frequency simulation data, the database table structure is sorted by the field read hotspot frequency in different time periods to obtain the field read hotspot frequency sorting data; Assigning hotspot field priority values ​​to the field reading hotspot frequency sorting data to obtain the hotspot field priority values; According to the priority value of the hotspot field, the LRU priority startup strategy is formulated for the field reading hotspot frequency sorting data to obtain the read field priority startup strategy; According to the read field priority start strategy, the read cache structure is designed for the field read hot spot frequency sorting data to obtain the read cache structure data; Based on the concurrent read and write frequency simulation data, the field write request density of the database table structure is analyzed to obtain the field write request density data; Use the cache database to perform cache sharding on the field write request density data to obtain the field write cache sharding data; Formulate a batch write-back cache strategy for the field write cache shard data to obtain a batch write-back cache strategy; The write cache structure is designed according to the field write cache shard data and the batch write back cache strategy to obtain the write cache structure data.

6. The intelligent information management method based on cloud computing according to claim 1 is characterized in that: The conflict concurrency control logic design for read cache asynchronous thread structure data and write cache asynchronous thread structure data includes the following steps: Perform conflict field simulation on the read cache asynchronous thread structure data and the write cache asynchronous thread structure data to obtain conflict field simulation data; Perform cache field row-level lock granularity optimization on conflict field simulation data to obtain conflict field row-level lock granularity optimization data; Perform optimistic lock concurrency control processing on the conflict field location data according to the conflict field row-level lock granularity optimization data to obtain the conflict field optimistic lock control data; Based on the conflict field optimistic lock control data and the conflict field row-level lock granularity optimization data, the conflict concurrency control logic is designed to obtain the read-write conflict logic control data.

7. An intelligent information management system based on cloud computing, characterized in that: Used to execute the intelligent information management method based on cloud computing as claimed in claim 1, the intelligent information management system based on cloud computing comprises: The load skewness analysis module is used to obtain the enterprise database log data and enterprise database management authority; perform load time series skewness analysis on the enterprise database log data to obtain load fluctuation time series skewness data; The asynchronous read-write cache processing module is used to parse the table structure of the enterprise database based on the enterprise database management authority to obtain the database table structure; analyze the table structure performance bottleneck of the database table structure to obtain the table structure performance bottleneck data; perform asynchronous read-write cache processing on the database table structure according to the table structure performance bottleneck data and load fluctuation time series skew data to obtain an asynchronous read-write cache database; A compatibility matching module is used to perform compatibility matching on the asynchronous read-write cache database based on the containerization technology in cloud computing to obtain an asynchronous read-write compatibility cache database; The execution docking module is used to dock the asynchronous read-write compatibility cache database to the enterprise database to perform intelligent information management.

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