An industrial process control data access method supporting a multi-user data exchange protocol
By analyzing underlying data and cache performance information, and setting cache space and data flow priorities, the problems of inflexible and scalable data access in industrial process control systems are solved, improving data access speed and system performance, ensuring priority processing of important data flows, and preventing resource waste and network congestion.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHANGHAI MAIJIE TECH CO LTD BEIJING BRANCH
- Filing Date
- 2023-08-22
- Publication Date
- 2026-06-09
AI Technical Summary
In existing technologies, data access methods in industrial process control systems are not flexible and scalable enough, leading to increased access latency, resource waste, data consistency issues, and scalability limitations. They also fail to accurately assess data importance, resulting in unreasonable resource allocation and impacting system performance and efficiency.
By acquiring data information and access frequency from the underlying data, analyzing data evaluation coefficients, setting cache space, obtaining cache performance information, determining whether expansion is needed, and setting priorities based on the basic parameters of the data flow to optimize data access methods.
It improves data access speed and system performance, reduces resource waste, ensures priority processing of important data streams, prevents network congestion, and enhances system stability and scalability.
Smart Images
Figure CN116915859B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data access technology, and more specifically to an industrial process control data access method that supports a multi-user data exchange protocol. Background Technology
[0002] In industrial process control systems, multiple users typically access and control data from industrial equipment, sensors, and actuators. These users may include operators, engineers, and managers who need to acquire and process industrial process data in real time for monitoring, optimization, and decision support. To enable data exchange among multiple users, an industrial process control data access method that supports multi-user data exchange protocols is required.
[0003] Current industrial process control systems may involve a large number of users and devices. Current data access methods may not be flexible or scalable enough to meet the demands of large-scale data exchange. Clearly, this detection method has at least the following problems:
[0004] 1. Existing technologies cannot transfer the most frequently accessed data from the underlying data to the cache space, which will lead to problems such as increased access latency, decreased system performance, waste of resources, data consistency issues, and limitations on scalability. At the same time, if it is not possible to accurately determine whether the cache space needs to be expanded, it may lead to unnecessary waste of resources. If the cache space is already sufficient to store data, but expansion is carried out due to the inability to accurately assess the situation, it will waste additional storage resources. If the cache space is over-expanded, it may lead to increased cache latency, thereby affecting the data access speed and response time, thus reducing the overall system performance and occupying valuable storage resources, resulting in waste of resources.
[0005] 2. Current technology may not be able to accurately assess the importance of basic parameters for each user data stream. Inaccurate importance assessment coefficients for basic parameters and unreasonable priority settings may lead to delayed processing of some important data streams or over-processing of some unimportant data streams. If the priority settings are unreasonable, resources may be incorrectly allocated to unimportant data streams, thus wasting limited processing resources. This may result in degraded system performance, low processing efficiency, or latency issues, leading to inaccurate, delayed, or wasteful data stream processing. Summary of the Invention
[0006] To address the aforementioned technical shortcomings, the purpose of this invention is to provide an industrial process control data access method that supports a multi-user data exchange protocol.
[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides an industrial process control data access method supporting multi-user data exchange protocols, comprising:
[0008] Step 1: Data Acquisition: Acquire the data information corresponding to each data in the underlying data, including data size and access frequency;
[0009] Step 2: Data Information Analysis: Set up a cache space, analyze the data information corresponding to each data in the underlying data, obtain the data evaluation coefficients corresponding to each data in the underlying data, and determine whether each data in the underlying data is suitable for storage in the cache space.
[0010] Step 3: Performance Information Acquisition: Set several collection time points in the cache space to collect the corresponding performance information of the cache space at each collection time point. The performance information includes cache utilization, cache eviction rate and cache latency.
[0011] Step 4: Performance Information Analysis: Based on the performance information of the cache space at each collection time point, analyze the performance information of the cache space at each collection time point, obtain the performance evaluation coefficient corresponding to the cache space performance information at each collection time point, and determine whether the cache space at each collection time point needs to be expanded.
[0012] Step 5: Analysis of expansion value: When the cache space needs to be expanded at a certain data collection time point, the corresponding expansion value of the cache space is then analyzed.
[0013] Step 6: Obtaining basic parameters of data streams: Obtain the basic parameters corresponding to each user's data stream, including data size, data frequency, and data timeliness;
[0014] Step 7: Analysis of basic parameters: Based on the basic parameters corresponding to each user data stream, the basic parameters corresponding to each user data stream are analyzed to obtain the importance evaluation coefficients corresponding to the basic parameters of each user data stream.
[0015] Step 8: Priority Setting: Based on the importance evaluation coefficients corresponding to the basic parameters of each user data stream, the priority of each user data stream is analyzed, and then the priority of each user data stream is set.
[0016] Preferably, the analysis of the data information corresponding to each data in the underlying data is carried out in the following specific process:
[0017] Let Q be the data size and access frequency of each data point in the underlying data. i and W i Where i represents the number corresponding to each data point, i = 1, 2, ..., u, and is substituted into the calculation formula. In this process, the data evaluation coefficient α corresponding to the data information of each data point in the underlying data is obtained.i Where Q′ and W′ represent the standard data size and standard access frequency corresponding to the set data, respectively, and ε1 and ε2 represent the weight factors corresponding to the data size and access frequency in the set data, respectively.
[0018] Preferably, the specific process for determining whether each piece of data in the underlying data is suitable for storage in the cache space is as follows:
[0019] The evaluation coefficients of each data point in the underlying data are compared with the evaluation coefficients of the data points in the set standard data. If the evaluation coefficient of a certain data point in the underlying data is less than the evaluation coefficient of the data points in the set standard data, then the data is determined to be unsuitable for storage in the cache space. If the evaluation coefficient of a certain data point in the underlying data is greater than or equal to the evaluation coefficient of the data points in the set standard data, then the data is determined to be suitable for storage in the cache space. This method is used to determine whether each data point in the underlying data is suitable for storage in the cache space.
[0020] Preferably, the analysis of the performance information corresponding to the cache space at each collection time point is carried out in the following specific analysis process:
[0021] Let e be the cache utilization rate, cache eviction rate, and cache latency corresponding to the cache space at each data collection time point. g r g and t g Where g represents the number corresponding to each collection time point, g = 1, 2, ..., n, and is substituted into the calculation formula. In this process, the performance evaluation coefficient β corresponding to the cache space performance information at each collection time point is obtained. g Where e′, r′, and t′ represent the standard cache utilization rate, standard cache eviction rate, and standard cache latency corresponding to the set cache space, respectively, and ω1, ω2, and ω3 represent the weight factors corresponding to the set cache space cache utilization rate, cache eviction rate, and cache latency, respectively.
[0022] Preferably, the specific determination process for whether the cache space at each collection time point needs to be expanded is as follows:
[0023] The performance evaluation coefficients corresponding to the cache space performance information at each collection time point are compared with the performance evaluation coefficients corresponding to the set standard cache space performance information. If the performance evaluation coefficient of the cache space performance information at a certain collection time point is less than the performance evaluation coefficient of the set standard cache space performance information, it is determined that the cache space at that collection time point does not need to be expanded. If the performance evaluation coefficient of the cache space performance information at a certain collection time point is greater than or equal to the performance evaluation coefficient of the set standard cache space performance information, it is determined that the cache space at that collection time point needs to be expanded. This method is used to determine whether the cache space at each collection time point needs to be expanded.
[0024] Preferably, the expansion value corresponding to the analysis cache space is analyzed in the following specific process:
[0025] When the cache space needs to be expanded at a certain collection time point, the performance evaluation coefficient corresponding to the performance information of the cache space at that collection time point is compared with the performance evaluation coefficients in the database to obtain the expansion value of the cache space corresponding to that collection time point.
[0026] Preferably, the analysis of the basic parameters corresponding to each user data stream is carried out as follows:
[0027] Let y denote the data size, data frequency, and data timeliness corresponding to each user data stream. x p x and h x Where x represents the number corresponding to each user data stream, x = 1, 2, ..., m, and is substituted into the calculation formula. In this process, the importance evaluation coefficient δ corresponding to the basic parameters of each user data stream is obtained. x , where y′, p′, and h′ represent the standard data size, standard data frequency, and standard data timeliness corresponding to the set user data stream, respectively, and σ1, σ2, and σ3 represent the weight factors corresponding to the data size, data frequency, and data timeliness in the set user data stream, respectively.
[0028] Preferably, the priority analysis of each user data stream is performed as follows:
[0029] The importance evaluation coefficients corresponding to the basic parameters of each user data stream are arranged in ascending order, and the user data stream with the largest importance evaluation coefficient is marked as the highest priority. Then, the priority of each user data stream is set according to the order of the importance evaluation coefficients corresponding to the basic parameters of each user data stream.
[0030] The beneficial effects of this invention are as follows:
[0031] 1. This invention sets up a cache space and analyzes the data information corresponding to each data in the underlying data to obtain a data evaluation coefficient and determine whether the data is suitable for storage in the cache space. Storing frequently accessed data in the cache space can reduce the number of accesses to the underlying data, thereby improving the data access speed. The cache space has a high read and write speed, which can respond to user requests faster. It can also reduce the load on the underlying data storage system and improve the performance and stability of the entire system.
[0032] 2. Simultaneously, by analyzing the cache space performance information at each collection time point, performance evaluation coefficients corresponding to the cache space performance information at each collection time point are obtained. These evaluation coefficients can be used to measure the performance of the cache space and determine whether it is necessary to expand its capacity. Increasing the capacity of the cache space allows it to store more data, which can reduce the occurrence of low cache hit rates, improve data access speed and response time. Expansion can effectively improve the performance of the cache space, avoid data loss or inability to retrieve data due to insufficient cache space, and improve the availability and stability of the cache. Expanding the cache space can improve the scalability and extensibility of the system. When the system load increases, the cache space can be expanded to meet higher demands, ensuring the performance and availability of the system.
[0033] 3. At the same time, by setting the priority of each user data stream, limited resources can be allocated more effectively to important data streams. This ensures that important data streams receive higher bandwidth, processing power, and other resources, thereby improving their transmission rate and response time. It also ensures that important data streams receive better service quality, allows for network traffic control and management, prevents network congestion, and prioritizes the transmission of important data streams to avoid data loss or transmission delays caused by network congestion. Attached Figure Description
[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0035] Figure 1 This is a flowchart illustrating the implementation steps of the method of the present invention. Detailed Implementation
[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0037] Examples of embodiments of the present invention Figure 1 As shown, an industrial process control data access method supporting a multi-user data exchange protocol includes:
[0038] Step 1: Data Acquisition: Acquire the data information corresponding to each data in the underlying data, including data size and access frequency.
[0039] It should be noted that the data sampler can record the data access situation during program execution and generate corresponding data access reports. Through the data access reports, the access frequency and data size can be obtained.
[0040] It should also be noted that some commonly used data samplers include IntelVTune, Perf, etc.
[0041] It should also be noted that underlying data refers to the raw data processed and stored in a computer system, including various input data, intermediate calculation results, and output data. Underlying data usually exists at the hardware level and the underlying software level of a computer system.
[0042] Step 2: Data Information Analysis: Set up a cache space, analyze the data information corresponding to each data in the underlying data, obtain the data evaluation coefficients corresponding to each data in the underlying data, and determine whether each data in the underlying data is suitable for storage in the cache space.
[0043] In a specific embodiment, the analysis of the data information corresponding to each data in the underlying data is carried out as follows:
[0044] Let Q be the data size and access frequency of each data point in the underlying data. i and W i Where i represents the number corresponding to each data point, i = 1, 2, ..., u, and is substituted into the calculation formula. In this process, the data evaluation coefficient α corresponding to the data information of each data point in the underlying data is obtained. i Where Q′ and W′ represent the standard data size and standard access frequency corresponding to the set data, respectively, and ε1 and ε2 represent the weight factors corresponding to the data size and access frequency in the set data, respectively.
[0045] In another specific embodiment, the process of determining whether each piece of data in the underlying data is suitable for storage in the cache space is as follows:
[0046] The evaluation coefficients of each data point in the underlying data are compared with the evaluation coefficients of the data points in the set standard data. If the evaluation coefficient of a certain data point in the underlying data is less than the evaluation coefficient of the data points in the set standard data, then the data is determined to be unsuitable for storage in the cache space. If the evaluation coefficient of a certain data point in the underlying data is greater than or equal to the evaluation coefficient of the data points in the set standard data, then the data is determined to be suitable for storage in the cache space. This method is used to determine whether each data point in the underlying data is suitable for storage in the cache space.
[0047] This invention sets up a cache space and analyzes the data information corresponding to each data in the underlying data to obtain a data evaluation coefficient and determine whether the data is suitable for storage in the cache space. Storing frequently accessed data in the cache space can reduce the number of accesses to the underlying data, thereby improving the data access speed. The cache space has a high read and write speed, which can respond to user requests faster. It can also reduce the load on the underlying data storage system and improve the performance and stability of the entire system.
[0048] Step 3: Performance Information Acquisition: Set several collection time points in the cache space to collect the corresponding performance information of the cache space at each collection time point. The performance information includes cache utilization rate, cache eviction rate and cache latency.
[0049] It should be noted that a cache analyzer is a tool specifically designed for analyzing caches. It can help obtain cache usage and performance metrics. Through a cache analyzer, you can obtain information such as cache usage rate, eviction rate, and latency.
[0050] It should also be noted that some commonly used cache analyzers include CacheGrind and Cachetop.
[0051] Step 4: Performance Information Analysis: Based on the performance information of the cache space at each collection time point, analyze the performance information of the cache space at each collection time point, obtain the performance evaluation coefficient corresponding to the cache space performance information at each collection time point, and determine whether the cache space at each collection time point needs to be expanded.
[0052] In a specific embodiment, the analysis of the performance information corresponding to the cache space at each collection time point is carried out as follows:
[0053] Let e be the cache utilization rate, cache eviction rate, and cache latency corresponding to the cache space at each data collection time point. g r g and t g Where g represents the number corresponding to each collection time point, g = 1, 2, ..., n, and is substituted into the calculation formula. In this process, the performance evaluation coefficient β corresponding to the cache space performance information at each collection time point is obtained. g Where e′, r′, and t′ represent the standard cache utilization rate, standard cache eviction rate, and standard cache latency corresponding to the set cache space, respectively, and ω1, ω2, and ω3 represent the weight factors corresponding to the set cache space cache utilization rate, cache eviction rate, and cache latency, respectively.
[0054] In another specific embodiment, the process for determining whether the cache space at each acquisition time point needs to be expanded is as follows:
[0055] The performance evaluation coefficients corresponding to the cache space performance information at each collection time point are compared with the performance evaluation coefficients corresponding to the set standard cache space performance information. If the performance evaluation coefficient of the cache space performance information at a certain collection time point is less than the performance evaluation coefficient of the set standard cache space performance information, it is determined that the cache space at that collection time point does not need to be expanded. If the performance evaluation coefficient of the cache space performance information at a certain collection time point is greater than or equal to the performance evaluation coefficient of the set standard cache space performance information, it is determined that the cache space at that collection time point needs to be expanded. This method is used to determine whether the cache space at each collection time point needs to be expanded.
[0056] Meanwhile, by analyzing the cache space performance information at each collection time point, performance evaluation coefficients corresponding to the cache space performance information at each collection time point are obtained. These evaluation coefficients can be used to measure the performance of the cache space and determine whether it is necessary to expand the capacity. Increasing the capacity of the cache space allows the cache to store more data, which can reduce the situation of low cache hit rate, improve data access speed and response time. Expansion can effectively improve the performance of the cache space, avoid data loss or inability to retrieve due to insufficient cache space, improve the availability and stability of the cache, and improve the scalability and extensibility of the system. When the system load increases, the cache space can be expanded to meet higher demands and ensure the performance and availability of the system.
[0057] Step 5: Analysis of expansion value: When the cache space needs to be expanded at a certain collection time point, the corresponding expansion value of the cache space is then analyzed.
[0058] In a specific embodiment, the analysis process for the expansion value corresponding to the analysis cache space is as follows:
[0059] When the cache space needs to be expanded at a certain collection time point, the performance evaluation coefficient corresponding to the performance information of the cache space at that collection time point is compared with the performance evaluation coefficients in the database to obtain the expansion value of the cache space corresponding to that collection time point.
[0060] Step 6: Obtaining basic parameters of data streams: Obtain the basic parameters corresponding to each user's data stream, including data size, data frequency, and data timeliness.
[0061] It should be noted that by querying information such as the amount of data, the data insertion time, and the data update time in the database, the data size, data frequency, and data timeliness of the data stream can be obtained.
[0062] Step 7: Analysis of basic parameters: Based on the basic parameters corresponding to each user data stream, analyze the basic parameters corresponding to each user data stream to obtain the importance evaluation coefficient corresponding to the basic parameters of each user data stream.
[0063] In a specific embodiment, the analysis of the basic parameters corresponding to each user data stream is carried out as follows:
[0064] Let y denote the data size, data frequency, and data timeliness corresponding to each user data stream. x p x and h x Where x represents the number corresponding to each user data stream, x = 1, 2, ..., m, and is substituted into the calculation formula. In this process, the importance evaluation coefficient δ corresponding to the basic parameters of each user data stream is obtained. x , where y′, p′, and h′ represent the standard data size, standard data frequency, and standard data timeliness corresponding to the set user data stream, respectively, and σ1, σ2, and σ3 represent the weight factors corresponding to the data size, data frequency, and data timeliness in the set user data stream, respectively.
[0065] Step 8: Priority Setting: Based on the importance evaluation coefficients corresponding to the basic parameters of each user data stream, the priority of each user data stream is analyzed, and then the priority of each user data stream is set.
[0066] In a specific embodiment, the priority analysis of each user data stream is performed as follows:
[0067] The importance evaluation coefficients corresponding to the basic parameters of each user data stream are arranged in ascending order, and the user data stream with the largest importance evaluation coefficient is marked as the highest priority. Then, the priority of each user data stream is set according to the order of the importance evaluation coefficients corresponding to the basic parameters of each user data stream.
[0068] At the same time, by setting the priority of each user's data stream, limited resources can be allocated more effectively to important data streams. This ensures that important data streams receive higher bandwidth, processing power, and other resources, thereby improving their transmission rate and response time. It also ensures that important data streams receive better service quality, allows for control and management of network traffic, prevents network congestion, and prioritizes the transmission of important data streams to avoid data loss or transmission delays caused by network congestion.
[0069] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in the claims, they should all fall within the protection scope of the present invention.
Claims
1. A method for accessing industrial process control data that supports a multi-user data exchange protocol, characterized in that, include: Step 1: Data Acquisition: Acquire the data information corresponding to each data in the underlying data, including data size and access frequency; Step 2: Data Information Analysis: Set up a cache space, analyze the data information corresponding to each data in the underlying data, obtain the data evaluation coefficients corresponding to each data in the underlying data, and determine whether each data in the underlying data is suitable for storage in the cache space. Step 3: Performance Information Acquisition: Set several collection time points in the cache space to collect the corresponding performance information of the cache space at each collection time point. The performance information includes cache utilization, cache eviction rate and cache latency. Step 4: Performance Information Analysis: Based on the performance information of the cache space at each collection time point, analyze the performance information of the cache space at each collection time point, obtain the performance evaluation coefficient corresponding to the cache space performance information at each collection time point, and determine whether the cache space at each collection time point needs to be expanded. Step 5: Analysis of expansion value: When the cache space needs to be expanded at a certain data collection time point, the corresponding expansion value of the cache space is then analyzed. Step 6: Obtaining basic parameters of data streams: Obtain the basic parameters corresponding to each user's data stream, including data size, data frequency, and data timeliness; Step 7: Analysis of basic parameters: Based on the basic parameters corresponding to each user data stream, the basic parameters corresponding to each user data stream are analyzed to obtain the importance evaluation coefficients corresponding to the basic parameters of each user data stream. The analysis of the basic parameters corresponding to each user data stream is as follows: The data size, data frequency, and data timeliness corresponding to each user data stream are respectively denoted as: , and ,in, This indicates the number corresponding to each user's data stream. Substitute into the calculation formula In this process, the importance evaluation coefficients corresponding to the basic parameters of each user data stream are obtained. ,in, , , These represent the standard data size, standard data frequency, and standard data timeliness corresponding to the set user data stream, respectively. , , These represent the weighting factors corresponding to the data size, data frequency, and data timeliness in the user data stream, respectively. Step 8: Priority setting: Based on the importance evaluation coefficients corresponding to the basic parameters of each user data stream, the priority of each user data stream is analyzed, and then the priority of each user data stream is set. The priority analysis of each user data stream is performed as follows: The importance evaluation coefficients corresponding to the basic parameters of each user data stream are arranged in ascending order, and the user data stream with the largest importance evaluation coefficient is marked as the highest priority. Then, the priority of each user data stream is set according to the order of the importance evaluation coefficients corresponding to the basic parameters of each user data stream.
2. The industrial process control data access method supporting multi-user data exchange protocol as described in claim 1, characterized in that, The analysis of the data information corresponding to each data in the underlying data is carried out in the following specific process: Let the data size and access frequency of each data in the underlying data be denoted as follows: and ,in, This indicates the number corresponding to each data point. Substitute into the calculation formula In this process, we obtain the data evaluation coefficients corresponding to the data information of each data point in the underlying data set. ,in, , These represent the standard data size and standard access frequency corresponding to the set data, respectively. , These represent the weighting factors corresponding to the data size and access frequency in the set data, respectively.
3. The industrial process control data access method supporting multi-user data exchange protocol as described in claim 2, characterized in that, The specific process for determining whether each piece of data in the underlying data is suitable for storage in the cache space is as follows: The evaluation coefficients of each data point in the underlying data are compared with the evaluation coefficients of the data points in the set standard data. If the evaluation coefficient of a certain data point in the underlying data is less than the evaluation coefficient of the data points in the set standard data, then the data is determined to be unsuitable for storage in the cache space. If the evaluation coefficient of a certain data point in the underlying data is greater than or equal to the evaluation coefficient of the data points in the set standard data, then the data is determined to be suitable for storage in the cache space. This method is used to determine whether each data point in the underlying data is suitable for storage in the cache space.
4. The industrial process control data access method supporting multi-user data exchange protocol as described in claim 1, characterized in that, The performance information corresponding to the cache space at each collection time point is analyzed, and the specific analysis process is as follows: The cache utilization rate, cache eviction rate, and cache latency corresponding to the data collection cache space at each data collection time point are respectively denoted as: , and ,in, This indicates the number corresponding to each data collection time point. Substitute into the calculation formula In this process, the performance evaluation coefficients corresponding to the cache space performance information at each collection time point are obtained. ,in, , , These represent the standard cache utilization rate, standard cache eviction rate, and standard cache latency corresponding to the set cache space, respectively. , , These represent the weighting factors corresponding to the set cache space utilization rate, cache eviction rate, and cache latency, respectively.
5. The industrial process control data access method supporting a multi-user data exchange protocol as described in claim 4, characterized in that, The specific determination process for whether the cache space at each data collection time point needs to be expanded is as follows: The performance evaluation coefficients corresponding to the cache space performance information at each collection time point are compared with the performance evaluation coefficients corresponding to the set standard cache space performance information. If the performance evaluation coefficient of the cache space performance information at a certain collection time point is less than the performance evaluation coefficient of the set standard cache space performance information, it is determined that the cache space at that collection time point does not need to be expanded. If the performance evaluation coefficient of the cache space performance information at a certain collection time point is greater than or equal to the performance evaluation coefficient of the set standard cache space performance information, it is determined that the cache space at that collection time point needs to be expanded. This method is used to determine whether the cache space at each collection time point needs to be expanded.
6. The industrial process control data access method supporting a multi-user data exchange protocol as described in claim 5, characterized in that, The analysis process for the expansion value corresponding to the analyzed cache space is as follows: When the cache space needs to be expanded at a certain collection time point, the performance evaluation coefficient corresponding to the performance information of the cache space at that collection time point is compared with the performance evaluation coefficients in the database to obtain the expansion value corresponding to the cache space at that collection time point.