An industrial database management system

Abstract

The present application relates to the field of data management technology, and specifically discloses an industrial database management system. The present application mainly includes: a data acquisition module, a behavior acquisition module, a predicted access module and a data cache module. The data acquisition module is used to collect data generated during the production process and store the data in a hard disk drive. The behavior acquisition module is used to obtain historical access behavior records of different accounts to the database. The predicted access module is used to input the access behavior records into a trained prediction model to obtain predicted access data; the data cache module is used to cache the predicted access data into a preset pre-storage space in the memory. Due to the use of a data cache module, the present application caches the predicted access data into a preset pre-storage space in the memory according to different accounts, and can maximize the use of memory space to enable the system to have the best response speed to ensure the stability and good performance of the system, which is conducive to improving production efficiency and quality.

Description

Technical Field

[0001] The present application relates to the technical field of data management, and in particular to an industrial database management system. Background Art

[0002] An industrial database is a database specifically used for industrial automation and control systems. It is usually used to store and manage data and information generated in industrial processes. An industrial database can collect, store and process data and information generated in industrial processes in real time so as to control and adjust them in a timely manner, thereby optimizing the process and improving production efficiency and quality.

[0003] Industrial databases can analyze and mine data and information in industrial processes to discover potential problems and optimization space, thereby further improving production efficiency and quality. Users can access industrial databases to obtain various data and information in the production process for data analysis and optimization, improve production efficiency and quality, and reduce production costs and risks. Industrial databases cache commonly used data in memory space, which can greatly reduce the number of disk accesses, thereby improving database access speed and performance.

[0004] However, in the process of implementing relevant technical solutions, it was found that there are at least the following technical problems: the data and information generated in the industrial process are usually very large, involving multiple parameters and variables, and have high complexity. If different users access different contents of the industrial database, and the memory space is not large enough to store the content frequently accessed by all users, then when reading data, it is easy to read the data from the disk first, and then transfer the data to the memory, and finally process it by the CPU. This process requires a lot of time and resources, resulting in slow response speed of the system, low efficiency of obtaining user-read data, reducing user experience and not conducive to improving production efficiency and quality. Summary of the invention

[0005] The present application provides an industrial database management system to solve the problem in the prior art that the system memory is not large enough to store all the content frequently accessed by users, resulting in slow system data reading. It can speed up the system response speed, improve the user experience, effectively avoid the waste of system resources, reduce system crashes or slow operation to improve the stability of the system, and also reduce the workload of administrators in manually managing the cache to improve the management efficiency of the system.

[0006] In a first aspect, the present application provides an industrial database management system, which includes:

[0007] A data acquisition module, which is used to collect data generated during the production process and store the data in a hard disk drive;

[0008] A behavior acquisition module is used to obtain the historical access behavior records of different accounts to the database, where the access behavior records include access content; different access contents are respectively used as different data blocks, and the access behavior records also include the access time of the data block and the memory size occupied by the data block;

[0009] The access prediction module is used to input the historical access behavior records of the account into the trained prediction model when the account logs in, so as to obtain the predicted access data of the account. The predicted access data prediction results include the predicted value of the access probability of the data block, the predicted value of the memory occupied by the data block, and the number a of all the data blocks that will be accessed;

[0010] The data cache module is used to cache the data blocks located in the hard disk drive in sequence from high to low according to the predicted values ​​of the data block access probability to the preset pre-storage space in the memory, which can improve the access efficiency of data, reduce the number of disk accesses, improve the query efficiency and response speed, and improve the system performance.

[0011] By adopting a data cache module and caching the predicted access data into preset pre-stored space in the memory according to different accounts, the system response speed can be accelerated, the user experience can be improved, the waste of system resources can be effectively avoided, and system crashes or slow operations can be reduced to improve system stability. At the same time, it can also reduce the workload of administrators to manually manage the cache to improve system management efficiency.

[0012] Furthermore, the data in the pre-stored space is deleted when the account is logged out.

[0013] Furthermore, the prediction model is a support vector machine model, and the prediction model is a support vector machine model. The formula of the support vector machine model is:

[0014]

[0015] In the formula, represents the prediction result, which includes the predicted value of data block access probability, the predicted value of data block memory usage, and the number of all data blocks that will be accessed. x represents the feature vector of the data block. The feature vector representing the training data, represents the kernel function, Represents the bias term.

[0016] Furthermore, when the predicted access data of the account is obtained, several data blocks with the highest predicted access probability values ​​are selected to construct a set of predicted data blocks. , ,in, Represents the bth prediction data block; the prediction data blocks cached to the pre-stored space are the set The prediction data block in .

[0017] Further, the collection The number b of predicted data blocks is twice the number a of all data blocks that will be visited.

[0018] Furthermore, the data in the hard drive is cached in the pre-stored space until the collection The prediction data block in the pre-stored space is completely accommodated in the pre-stored space or the pre-stored space cannot accommodate a complete prediction data block.

[0019] Furthermore, the preset pre-storage space is the optimal pre-storage space C. ; Where H represents the cache hit rate, B represents the cache block size, M represents the cache miss rate, and T represents the disk access time.

[0020] Furthermore, if the collection The predicted data blocks in the pre-stored space are not completely cached. Every time a user reads a data block in the pre-stored space, the data block is removed from the pre-stored space and the set Delete it and set the The remaining prediction data blocks that are not cached in the pre-stored space are cached in the pre-stored space preset in the memory in order of access probability from high to low until the set The data block in the pre-storage space is completely accommodated in the pre-storage space or the pre-storage space cannot accommodate a complete data block.

[0021] Furthermore, if the account has no historical access behavior record to the database, a specified number of data blocks with the highest access frequency in the database within a specified period will be cached in the pre-storage space in descending order of access probability until the specified number of data blocks with the highest access frequency are completely accommodated in the pre-storage space or the pre-storage space cannot accommodate a complete data block.

[0022] Furthermore, if the number of data blocks in the historical access behavior record of the account to the database is less than a preset specified number, and the data blocks in the historical access behavior record can be completely cached in the pre-storage space, the data blocks in the historical access behavior record of the account are first completely cached in the pre-storage space, and then the specified number of data blocks with the highest access frequency in the database within the specified period are cached in the pre-storage space in descending order of access probability, until the specified number of data blocks with the highest access frequency are completely accommodated in the pre-storage space or the pre-storage space cannot accommodate a complete data block.

[0023] In a second aspect, the present application provides an industrial database management method, which is applied to the industrial database management system of the first aspect, and comprises the following steps:

[0024] Collect data generated during the production process and store the data on a hard drive;

[0025] Obtain the historical access behavior records of different accounts to the database, where the access behavior records include the access content; different access contents are respectively regarded as different data blocks, and the access behavior records also include the access time of the data block and the memory size occupied by the data block;

[0026] When an account logs in, the historical access behavior records of the account are input into the trained prediction model to obtain the predicted access data of the account. The predicted access data prediction results include the predicted value of the data block access probability, the predicted value of the data block memory usage, and the number a of all data blocks that will be accessed;

[0027] The data blocks located in the hard disk drive are cached in sequence from high to low according to the predicted access probability of the data blocks to the preset pre-storage space in the memory.

[0028] In a third aspect, the present application provides an industrial database management device, which includes a memory and a processor.

[0029] The memory is used to store computer programs;

[0030] The processor is used to implement the steps of the industrial database management method of the second aspect when executing the computer program.

[0031] The technical solution provided by this application has at least the following technical effects or advantages:

[0032] 1. Due to the use of the data cache module, the predicted access data is cached to the preset pre-stored space in the memory according to different accounts, which effectively solves the problem of slow system data reading caused by insufficient memory space to store all frequently accessed content by all users, speeds up the system's response speed, improves the user experience, effectively avoids the waste of system resources, reduces system crashes or slow operation to improve system stability, and can also reduce the workload of administrators to manually manage the cache, thereby improving system management efficiency and helping to improve production efficiency and quality.

[0033] 2. Since the corresponding cache strategy can be determined according to different accounts, except for accounts with sufficient historical access behavior records, for newly registered accounts or recently registered accounts with no or few historical access behavior records, it can still improve the response speed while realizing the effective use of pre-stored space, reducing the waste of system resources and helping to improve the stability of the system.

[0034] 3. Since the pre-stored space is set as the best cache space that is adjusted and changed according to the data access mode, system load conditions, etc., it can maximize the use of memory space to effectively reduce memory waste, so that the system has the best response speed to ensure system stability and good performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is the overall structural module diagram of the industrial database management system of this application;

[0036] Figure 2 This is a flowchart of the data displayed by the system when accessing database data for this application account. DETAILED DESCRIPTION

[0037] The present application proposes a database system that can pre-store and cache data according to different accounts. It can speed up the system response speed under limited memory space, improve the user experience, effectively avoid the waste of system resources, and improve the system stability and system management efficiency.

[0038] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0039] Embodiment 1

[0040] like Figure 1-Figure 2 As shown, this embodiment discloses an industrial database management system, which includes: a data collection module, a behavior acquisition module, a prediction access module and a data cache module.

[0041] 1. Data collection

[0042] The data acquisition module is used to collect data generated during the production process and store the data in the hard disk drive.

[0043] In the industrial production process, the data acquisition module can be used to collect the operating status, temperature, humidity, voltage and other parameters of the machine equipment, and store the data in the hard disk drive, which can ensure the integrity and accuracy of the data and provide a data basis for data analysis and processing.

[0044] In the process of collecting data, select the data source that needs to be collected, such as sensors and controllers on machinery and equipment. According to actual needs, determine the frequency and time of data collection, the format and storage method of collected data, and the method of data transmission and processing. For example, collect data once every second and store the data on the hard drive every hour. You can store data in text format and store the data on the local hard drive. You can use the network to transmit data and use local programs to process and analyze the data.

[0045] The database can be accessed by logging in with an account to verify the user's identity. Only verified users can access the database, which improves data security and enables user authority management. For example, different user authorities can be set to limit their access to and operations on the database, thereby better protecting the security and integrity of the database. At the same time, the user's login information can be recorded, such as login time, IP address, login method, etc., to facilitate subsequent auditing and monitoring, and it has traceability, and can record the user's operation history, modification records, etc., to facilitate tracing the source and change process of the data.

[0046] Since the database stores a large amount of production-related data, including production line status, equipment operating parameters, product quality data, etc., employees, engineers and managers in the factory may need different data to carry out production planning, equipment maintenance, quality control and other tasks. These personnel can set up their own database login accounts.

[0047] 2. Behavior Acquisition

[0048] The behavior acquisition module is used to obtain the historical access behavior records of different accounts to the database. The access behavior records include access content; different access contents are respectively used as different data blocks. The access behavior records also include the access time of the data block and the memory size occupied by the data block.

[0049] The data automatically cached in the memory is usually frequently accessed data. Therefore, since the content accessed by different accounts is generally different, the data accessed by the accounts will mostly not be cached in the memory, especially for industrial databases with very large amounts of information. It is even rarer that the accessed data happens to be stored in the memory. By obtaining the historical access behavior records of different accounts to the database, it is convenient to predict the access content of different accounts in the future.

[0050] In this embodiment, a manager of a production department may need to access the real-time status data and historical production data of the production line in order to adjust the production plan and make decisions in time. These production data can be regarded as data blocks in the database, including information such as production quantity, quality indicators, equipment operation status, etc. in each production link. When the manager logs in to the database and requests these data, the system will record his access behavior, including the access time and the memory size of the requested data block. In addition, the person in charge of the finance department may need to access data on production costs, resource utilization, etc. for cost control and financial analysis. These data are also data blocks in the database, including information such as raw material consumption, labor costs, equipment maintenance costs, etc. When the person in charge of finance logs in to the system and requests these data, his access behavior will also be recorded.

[0051] This module can help system administrators or other relevant personnel better understand the use of data blocks by different users, so as to better manage and optimize system resources. By analyzing the access behavior of managers to data blocks, factory managers can understand the needs and work priorities of various departments, thereby optimizing data storage and management strategies to ensure that key data can be accessed and used in a timely manner. At the same time, by monitoring the access to data blocks, possible security risks and abnormal behaviors can be discovered and resolved in a timely manner to ensure the security and integrity of production data.

[0052] 3. Predicted visits

[0053] The predicted access module is used to input the historical access behavior records of the account into the trained prediction model when the account logs in, so as to obtain the predicted access data of the account. The predicted access data prediction results include the predicted value of the data block access probability, the predicted value of the data block memory occupied, and the number a of all data blocks that will be accessed.

[0054] The predicted access data prediction results include the predicted value of data block access probability, the predicted value of data block memory occupation, and the number of all data blocks to be accessed. a represents the predicted probability of different data blocks that the account will access during this login, the memory occupied by all data blocks, and the number of all data blocks; the prediction model can be a support vector model, and the formula of the support vector machine model is:

[0055]

[0056] In the formula, represents the prediction result, which includes the predicted value of data block access probability, the predicted value of data block memory usage, and the number of all data blocks that will be accessed. x represents the feature vector of the data block. The feature vector representing the training data, represents the kernel function, Represents the bias term.

[0057] In the process of training the support vector machine model, the support vector machine model will find a hyperplane that maximizes the distance from all training samples to the hyperplane and satisfies the condition of minimizing the classification error. The specific formula is as follows:

[0058]

[0059]

[0060]

[0061] In the formula, represents the label of sample i, and the label represents the category or target value to which the sample belongs, n represents the number of samples, and C represents the regularization parameter. represents the slack variable, which is used to deal with inseparable samples. represents the bias term, is a regularization term used to control the complexity of the model and avoid overfitting. Represents the loss function term, which is used to penalize classification errors and the use of slack variables. It is also the coefficient of the regularization term. The training process of the model is to find the optimal hyperplane by optimizing this objective function to achieve sample classification.

[0062] Each sample corresponds to the historical access information of a data block, which may include the number of occurrences of the data block, access time interval, average access time, most recent access time, memory size, and other features. Since the support vector machine model is used to predict the access probability of a data block, the label of the sample may be a target value related to the access behavior of the data block, and the label may be a binary classification label. If the question is to predict whether the data block will be accessed, the label may be binary, such as "0" for no access and "1" for access. During the prediction process, the support vector machine model calculates the prediction result f(x) based on the feature vector of the input data and converts it into a category label.

[0063] Before using the support vector machine model for prediction, feature engineering is required to convert historical access records into feature vectors. When predicting and obtaining the predicted value of data block access probability, the following features can be used: the number of occurrences of data blocks, the access time interval of data blocks, the average access time of data blocks, the last access time of data blocks, or the size of memory occupied by data blocks. The number of occurrences, access time interval, and average access time of data blocks can reflect the access frequency and regularity of data blocks, the last access time can reflect the popularity of data blocks, and the size of memory occupied can reflect the size of data blocks. Combining these features into a feature vector x can be expressed as:

[0064]

[0065] In the formula, Represents the number of occurrences of a data block, Represents the access time interval of the data block. represents the average access time of all data blocks, Indicates the time of the most recent visit.

[0066] After the model training is completed, the feature vector x is substituted into the formula of the support vector machine model to obtain the prediction result f(x), thereby obtaining the predicted probability of different data blocks that the account will access during this login, the memory occupied by all the data blocks to be accessed, and the number a of all the data blocks to be accessed.

[0067] 4. Data caching

[0068] The data cache module is used to cache the data blocks located in the hard disk drive in order from high to low according to the predicted access probability of the data blocks to the preset pre-storage space in the memory.

[0069] Since access data is predicted by accounts, different accounts may access different data blocks. By determining corresponding caching strategies based on different accounts, frequently accessed data blocks can be cached in memory, thereby speeding up the system's response speed and improving the user experience. If all data blocks are cached in memory, a large amount of system resources will be occupied, resulting in a waste of resources. By determining corresponding caching strategies based on different accounts, only frequently accessed data blocks can be cached in memory, effectively avoiding a waste of system resources, reducing system crashes or slow operations, and improving system stability. In addition, it can also reduce the workload of administrators in manually managing caches and improve system management efficiency.

[0070] After obtaining the predicted access data of the account at this login, take several data blocks with the highest predicted access probability values ​​to build a set of predicted data blocks. , , where b represents the number of data blocks, Represents the bth prediction data block; the prediction data blocks cached in the pre-stored space are all sets The prediction data block in The number b of predicted data blocks is twice the number a of all data blocks that will be accessed, thereby ensuring that the data blocks that the account will access during this login can appear in the pre-stored space as much as possible.

[0071] Data on the hard drive is cached in pre-stored space until it is collected The prediction data block in the pre-stored space is completely accommodated in the pre-stored space or the pre-stored space cannot accommodate a complete prediction data block.

[0072] The preset pre-storage space is the optimal pre-storage space C, where C is:

[0073]

[0074] In the formula, H represents the cache hit rate, B represents the cache block size, M represents the cache miss rate, and T represents the disk access time; the cache hit rate refers to the probability that a data block that already exists in the cache is accessed again when it is accessed, and the cache miss rate refers to the probability that a data block that does not exist in the cache needs to be read from the disk when it is accessed. The cache block size is the smallest unit used to cache data in the computer, usually a continuous space in the memory or a physical block on the disk. The disk access time refers to the average time required to read a data block from the disk. Usually, the cache hit rate and cache miss rate can be obtained through experiments or simulations. The cache block size can be determined based on the actual situation. It is generally recommended to select an appropriate size to improve cache efficiency. The disk access time can be determined through testing or reference to hardware specifications.

[0075] There are three situations in which data in the hard disk drive is cached to the pre-storage space:

[0076] 1. Collection The prediction data block in is not completely cached into the pre-stored space

[0077] If the account has sufficient historical access behavior records, the collection The prediction data blocks in are not completely cached in the pre-stored space, that is, the pre-stored space is not enough to accommodate the set All the predicted data blocks in the pre-stored space, then every time a user reads a data block in the pre-stored space, the data block is removed from the pre-stored space and the set Delete it and set the The remaining prediction data blocks that are not cached in the pre-stored space are cached in the pre-stored space preset in the memory in order of access probability from high to low until the set The data block in the storage is completely accommodated in the pre-stored space or the pre-stored space cannot accommodate a complete data block.

[0078] 2. The account has no historical access records to the database

[0079] If the account is a newly registered account and has no historical access behavior record to the database, a specified number of data blocks with the highest access frequency in the database within a specified period will be cached in the pre-storage space in descending order of access probability until the specified number of data blocks with the highest access frequency are completely accommodated in the pre-storage space or the pre-storage space cannot accommodate a complete data block; wherein, the specified number may be the number of data blocks with the highest access frequency that can be stored in the pre-storage space at the same time, and the specified period may be within one day, within one week, etc., and may be set according to actual conditions.

[0080] 3. The data blocks in the historical access behavior records of the account to the database are less than the preset specified number

[0081] The account is a recently registered account and has fewer historical access behavior records to the database. If the data blocks in the historical access behavior records to the database are less than the preset specified number, and the data blocks in the historical access behavior records can be completely cached in the pre-storage space, the data blocks in the historical access behavior records of the account will first be completely cached in the pre-storage space, and then the specified number of data blocks with the highest access frequency in the database within the specified period will be cached in the pre-storage space in descending order of access probability, until the specified number of data blocks with the highest access frequency are completely accommodated in the pre-storage space or the pre-storage space cannot accommodate a complete data block.

[0082] Because the corresponding caching strategy is determined according to different accounts, except for accounts with sufficient historical access behavior records, for newly registered accounts or recently registered accounts with no or few historical access behavior records, it can still improve the response speed while realizing effective utilization of pre-stored space, reducing the waste of system resources and helping to improve system stability.

[0083] Finally, the system can display data content according to the account logged in. The data cache module is also used to delete the data in the pre-stored space when the account logs out, so that the pre-stored space has enough space to cache the predicted access data of the next account, thereby maximizing the use of memory space to effectively reduce memory waste, so that the system has the best response speed to ensure system stability and good performance.

[0084] It should be noted that deleting data from the pre-stored space does not delete the data from the hard disk drive and will not cause damage to the data.

[0085] Embodiment 2

[0086] This embodiment discloses an industrial database management method, which is applied to the industrial database management system in the first embodiment, and includes the following steps:

[0087] Collect data generated during the production process and store the data on a hard drive;

[0088] Obtain the historical access behavior records of different accounts to the database, where the access behavior records include the access content; different access contents are respectively regarded as different data blocks, and the access behavior records also include the access time of the data block and the memory size occupied by the data block;

[0089] When an account logs in, the historical access behavior records of the account are input into the trained prediction model to obtain the predicted access data of the account. The predicted access data prediction results include the predicted value of the data block access probability, the predicted value of the data block memory usage, and the number a of all data blocks that will be accessed;

[0090] The data blocks located in the hard disk drive are cached in sequence from high to low according to the predicted access probability of the data blocks to the preset pre-storage space in the memory.

[0091] The industrial database management method in this embodiment has all the advantages of the industrial database management system in the first embodiment, so it will not be described in detail.

[0092] Embodiment 3

[0093] This embodiment provides an industrial database management device, which includes a memory and a processor. The memory is used to store a computer program; the processor is used to implement the steps of an industrial database management method in the second embodiment when executing the computer program.

[0094] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps of the functions specified in a box or multiple boxes. Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the attached claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0096] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0097] The above are only preferred specific implementation methods of the embodiments of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can make equivalent substitutions or changes according to the technical solution and concept of the present application within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims

1. An industrial database management system, characterized in that: It includes: A data acquisition module, which is used to collect data generated during the production process and store the data in a hard disk drive; A behavior acquisition module is used to obtain the historical access behavior records of different accounts to the database, where the access behavior records include access content; different access contents are respectively used as different data blocks, and the access behavior records also include the access time of the data block and the memory size occupied by the data block; The access prediction module is used to input the historical access behavior records of the account into the trained prediction model when the account logs in, so as to obtain the predicted access data of the account. The predicted access data prediction results include the predicted value of the access probability of the data block, the predicted value of the memory occupied by the data block, and the number a of all the data blocks that will be accessed; A data cache module is used to cache data blocks located in the hard disk drive in order from high to low according to the predicted values ​​of access probability of the data blocks to a preset pre-storage space in the memory; When the predicted access data of the account is obtained, several data blocks with the highest predicted access probability values ​​are taken to construct a set of predicted data blocks. , , where Represents the bth prediction data block; the prediction data blocks cached to the pre-stored space are the set The prediction data block in ; gather The number b of predicted data blocks is twice the number a of all data blocks that will be visited.

2. The industrial database management system according to claim 1, characterized in that: The data in the pre-stored space will be deleted when the account is logged out.

3. The industrial database management system according to claim 1, characterized in that: The prediction model is the support vector model, and the formula of the support vector machine model is: In the formula, represents the prediction result, which includes the predicted value of data block access probability, the predicted value of data block memory usage, and the number of all data blocks that will be accessed. x represents the feature vector of the data block. The feature vector representing the training data, represents the kernel function, Represents the bias term.

4. The industrial database management system according to claim 1, characterized in that: Data on the hard drive is cached in pre-stored space until it is collected The prediction data block in the pre-stored space is completely accommodated in the pre-stored space or the pre-stored space cannot accommodate a complete prediction data block.

5. The industrial database management system according to claim 4, characterized in that: The preset storage space is the optimal storage space C. ; Where H represents the cache hit rate, B represents the cache block size, M represents the cache miss rate, and T represents the disk access time.

6. The industrial database management system according to claim 5, characterized in that: If the collection The predicted data blocks in the pre-stored space are not completely cached. Every time a user reads a data block in the pre-stored space, the data block is removed from the pre-stored space and the set Delete it and set the The remaining prediction data blocks that are not cached in the pre-stored space are cached in the pre-stored space preset in the memory in order of access probability from high to low until the set The data block in the pre-storage space is completely accommodated in the pre-storage space or the pre-storage space cannot accommodate a complete data block.

7. The industrial database management system according to claim 1, characterized in that: If the account has no historical access record to the database, a specified number of data blocks with the highest access frequency in the database within a specified period will be cached in the pre-storage space in descending order of access probability until the specified number of data blocks with the highest access frequency are completely stored in the pre-storage space or the pre-storage space cannot accommodate a complete data block.

8. The industrial database management system according to claim 1, characterized in that: If the number of data blocks in the historical access behavior record of the account to the database is less than the preset specified number, and the data blocks in the historical access behavior record can be completely cached in the pre-storage space, the data blocks in the historical access behavior record of the account will be completely cached in the pre-storage space first, and then the specified number of data blocks with the highest access frequency in the database within the specified period will be cached in the pre-storage space in descending order of access probability, until the specified number of data blocks with the highest access frequency are completely accommodated in the pre-storage space or the pre-storage space cannot accommodate a complete data block.

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