Data access method, data access management system, storage medium and electronic equipment

Abstract

The embodiment of the invention provides a data access method, a data access management system, a storage medium and electronic equipment. The method comprises the following steps: acquiring access data of a target object monitored by a real-time monitoring module and a data access mode corresponding to the access data; obtaining a data access request output by the data analysis module based on the data access mode and the access data; the data access request is sent to a self-adaptive cache strategy adjustment module, so that the self-adaptive cache strategy adjustment module determines to perform adjustment operation on cache data in the data access management system based on the data access request; performing energy consumption optimization operation on the data access management system through an energy consumption optimization module so as to reduce the system energy consumption of the data access management system; and determining prefetched data predicted to be accessed in the cache data as preferential access data through a data access module, and performing data access operation on the preferential access data in response to a current data access request sent by the target object.

Description

Technical Field

[0001] The embodiments of the present application relate to the computer field, and in particular, to a data access method, a data access management system, a storage medium, and an electronic device. Background Art

[0002] With the continuous development of cloud computing, cloud servers are increasingly dependent on data access. The amount of data access is related to the user's access behavior. With the sharp increase in the amount of data, cloud servers generally store a large amount of data. During peak service periods, data access delays and other situations that affect the user experience may occur. In order to solve the above problems, a variety of solutions have been proposed to process data, such as adding servers, data sharding, distributed storage, and increasing cache. However, traditional data access systems rely on fixed cache strategies, lack flexibility in adjusting cache strategies, and cannot be dynamically optimized based on real-time data and user feedback. They can no longer meet users' data access requests, which seriously affects data access efficiency. Therefore, in the related technology, the data access efficiency of cloud servers that store a large amount of data is low.

[0003] In view of the problem of low data access efficiency of cloud servers storing a large amount of data in related technologies, no effective solution has been proposed so far.

[0004] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology. Summary of the invention

[0005] The embodiments of the present application provide a data access method, a data access management system, a storage medium and an electronic device to at least solve the problem of low data access efficiency to a cloud server storing a large amount of data in the related art.

[0006] According to one embodiment of the present application, a data access method is provided, including: applied to a data access management system, the data access management system is arranged in a cloud server, the data access management system includes at least a real-time monitoring module, a data analysis module, an adaptive cache strategy adjustment module, an energy consumption optimization module and a data access module, including: obtaining access data of a target object monitored by the real-time monitoring module and a data access pattern corresponding to the access data; sending the data access pattern and the access data to the data analysis module, obtaining a data access request output by the data analysis module based on the data access pattern and the access data, the data access request being used to request an access request that is expected to be received the data access request; sending the data access request to the adaptive cache policy adjustment module, so that the adaptive cache policy adjustment module determines to adjust the cache data in the data access management system based on the data access request; after the cache data in the data access management system is adjusted, the energy consumption optimization operation is performed on the data access management system through the energy consumption optimization module to reduce the system energy consumption of the data access management system; the data access module determines the data that is expected to be accessed and pre-fetched in the cache data as priority access data, and performs data access operations on the priority access data in response to the current data access request sent by the target object.

[0007] In an exemplary embodiment, the real-time monitoring module includes an access frequency analysis unit, a time period analysis unit and a user behavior analysis unit, and obtains the data access pattern and access data of the target object monitored by the real-time monitoring module, including: obtaining the historical access frequency of different objects from the access frequency analysis unit, obtaining the access time period of the different objects from the time period analysis unit, and obtaining the behavior data of the different objects from the user behavior analysis unit; determining the access data corresponding to the historical access frequency, the access time period and the behavior data, and generating the access data based on the historical access frequency, the access time period and the behavior data.

[0008] In an exemplary embodiment, the data analysis module includes a pattern recognition unit and a behavior prediction unit. In the process of sending the data access pattern and the access data to the data analysis module and obtaining a data access request output by the data analysis module based on the data access pattern and the access data, the data access request is determined in the following manner: the pattern recognition unit uses a clustering algorithm to group the data objects corresponding to the access data to obtain multiple groups of objects, each group of objects corresponds to an access pattern, and selects a target group of objects corresponding to the access pattern with a periodic regularity from the multiple groups of objects; the behavior prediction unit predicts the future behavior data of the target group objects based on the historical access data of the target group objects, determines the data expected to be accessed based on the future behavior data, and generates the data access request; wherein, the behavior prediction unit predicts the future behavior data of the target group objects based on the historical access data of the target group objects, including: sorting the historical access data according to a time series to obtain time series data, performing autocorrelation analysis on the time series data to obtain an autocorrelation function of the time series data, determining the periodic access data in the historical access data based on the peak value of the autocorrelation function, and predicting the future behavior data of the target group objects based on the periodic access data.

[0009] In an exemplary embodiment, the adaptive cache policy adjustment module includes a machine learning unit, a prediction model update unit, a cache size adjustment unit and a content replacement policy unit, and performs an adjustment operation on the cache data in the data access management system in the following manner: sending the cache space difference output by the prediction model in the machine learning unit to the prediction model update unit, wherein the cache space difference represents the difference between a first cache space and a reserved cache space, the first cache space represents the cache space required for the data expected to be accessed, and the reserved cache space represents the additional cache space outside the second cache space of the cache data; comparing the cache space difference through the prediction model update unit, and when it is determined that the cache space difference is greater than a preset threshold, sending an adjustment instruction to the cache size adjustment unit, so that the cache size adjustment unit determines the adjustable space of the second cache space, and replaces the old data in the adjustable space with the data expected to be accessed through the content replacement policy unit.

[0010] In an exemplary embodiment, the energy consumption optimization module includes an energy consumption model building unit and a dynamic power consumption control unit, and performs energy consumption optimization operations on the data access management system in the following manner: obtaining the estimated energy consumption of the data access management system under different cache strategies predicted by the energy consumption model building unit based on the historical energy consumption data of the data access management system and combined with the current system state of the data access management system, determining an energy consumption optimization scheme corresponding to the estimated energy consumption, determining the current energy consumption optimization scheme of the data access management system under the current energy consumption from the energy consumption optimization scheme, and determining a first optimization instruction indicated by the energy consumption optimization scheme; obtaining a second optimization instruction generated by the dynamic power consumption control unit according to changes in network traffic; and determining the energy consumption optimization operation according to the optimization operation corresponding to the first optimization instruction and the optimization operation corresponding to the second optimization instruction.

[0011] In an exemplary embodiment, the data access module includes a user feedback receiving module and a feedback processing module. Before determining the data that is expected to be accessed and pre-fetched from the cache data as priority access data and performing a data access operation on the priority access data in response to a current data access request sent by the target object, the method further includes: receiving the target object's satisfaction with the data access performance of the data access management system through the user feedback receiving module, and sending the satisfaction to the feedback processing module; using the feedback processing module to analyze the satisfaction in different evaluation dimensions to obtain analysis results, and generating an adjusted cache strategy based on the analysis results.

[0012] In an exemplary embodiment, the method further includes: deploying a multi-level cache architecture within the data access management system to support data migration between storage media of different performance levels through the cache areas of the multi-level cache architecture, wherein the cache areas of the multi-level cache architecture include at least one of the following: a high-speed cache area, a medium-speed cache area, and a low-speed storage area.

[0013] According to another embodiment of the present application, a data access management system is provided, including: the real-time monitoring module, which is used to monitor the access data of the target object and the data access pattern corresponding to the access data; sending the data access pattern and the access data to the data analysis module; the data analysis module, which is used to output a data access request based on the data access pattern and the access data, wherein the data access request is used to request data that is expected to be accessed; sending the data access request to the adaptive cache policy adjustment module; the adaptive cache policy adjustment module, which is used to determine to adjust the cache data in the data access management system based on the data access request; the energy consumption optimization module, which is used to reduce the system energy consumption of the data access management system by performing energy consumption optimization operations on the data access management system after the cache data in the data access management system is adjusted; the data access module, which is used to determine the data that is expected to be accessed pre-fetched in the cache data as priority access data, and perform data access operations on the priority access data in response to the current data access request sent by the target object.

[0014] According to another embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when run.

[0015] According to another embodiment of the present application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0016] According to another embodiment of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0017] Through the present application, a data access management system arranged on a cloud server and a data access method applied to the data access management system are proposed. The data access management system is arranged on a cloud server, and the data access management system at least includes a real-time monitoring module, a data analysis module, an adaptive cache strategy adjustment module, an energy consumption optimization module and a data access module. By acquiring the access data of the target object monitored by the real-time monitoring module and the data access pattern corresponding to the access data; sending the data access pattern and the access data to the data analysis module, obtaining the data access request output by the data analysis module based on the data access pattern and the access data, wherein the data access request is used to request the data that is expected to be accessed; sending the data access request to the adaptive cache policy adjustment module, so that the adaptive cache policy adjustment module determines to adjust the cache data in the data access management system based on the data access request; after the cache data in the data access management system is adjusted, the energy consumption optimization operation is performed on the data access management system through the energy consumption optimization module to reduce the system energy consumption of the data access management system; the data that is expected to be accessed pre-fetched in the cache data is determined as priority access data through the data access module, and in response to the current data access request sent by the target object, a data access operation is performed on the priority access data. This application uses a data access management system to intelligently predict access requests to target objects and loads the data that is expected to be accessed into the cache in advance. This not only solves the problem of low data access efficiency for cloud servers that store large amounts of data, but also significantly improves the efficiency and response speed of data access. At the same time, through adaptive cache strategy adjustment and energy consumption optimization, the system's operating costs are effectively reduced and the user experience is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a hardware structure block diagram of a server device of a data access method according to an embodiment of the present application;

[0019] Figure 2 is a flow chart of a data access method according to an embodiment of the present application;

[0020] Figure 3 is a schematic diagram of a process of data caching and pre-fetching according to an embodiment of the present application;

[0021] Figure 4 is a structural diagram of a data access management system according to an embodiment of the present application;

[0022] Figure 5 It is a schematic diagram of the structure of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] The embodiments of the present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0025] The method embodiments provided in the embodiments of the present application can be executed in a server device or a similar computing device. Taking running on a server device as an example, Figure 1 1 is a hardware structure block diagram of a server device of a data access method according to an embodiment of the present application. Figure 1 As shown, the server device may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the server device may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above server device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0026] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the data access method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the server device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0027] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the server device. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0028] In this embodiment, a data access method is provided. Figure 2 is a flow chart of a data access method according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps:

[0029] Step S202, obtaining access data of the target object monitored by the real-time monitoring module and a data access mode corresponding to the access data;

[0030] Step S204, sending the data access pattern and the access data to the data analysis module, obtaining a data access request output by the data analysis module based on the data access pattern and the access data, wherein the data access request is used to request the data that is expected to be accessed;

[0031] Step S206, sending the data access request to the adaptive cache policy adjustment module, so that the adaptive cache policy adjustment module determines to adjust the cache data in the data access management system based on the data access request;

[0032] Step S208, after the cache data in the data access management system is adjusted, the energy consumption optimization module is used to perform energy consumption optimization on the data access management system to reduce system energy consumption of the data access management system;

[0033] Step S210: determining the data that is expected to be accessed and pre-fetched from the cache data as priority access data through the data access module, and performing a data access operation on the priority access data in response to a current data access request sent by the target object.

[0034] Through the above steps, the access data of the target object monitored by the real-time monitoring module and the data access pattern corresponding to the access data are obtained; the data access pattern and the access data are sent to the data analysis module to obtain the data access request output by the data analysis module based on the data access pattern and the access data, and the data access request is used to request the data that is expected to be accessed; the data access request is sent to the adaptive cache policy adjustment module, so that the adaptive cache policy adjustment module determines to adjust the cache data in the data access management system based on the data access request; after the cache data in the data access management system is adjusted, the energy consumption optimization operation is performed on the data access management system through the energy consumption optimization module to reduce the system energy consumption of the data access management system; the data that is expected to be accessed pre-fetched in the cache data is determined as priority access data through the data access module, and in response to the current data access request sent by the target object, a data access operation is performed on the priority access data. This application uses a data access management system to intelligently predict access requests to target objects and loads the data that is expected to be accessed into the cache in advance. This not only solves the problem of low data access efficiency for cloud servers that store large amounts of data, but also significantly improves data access efficiency and response speed, reduces data latency, and at the same time, through adaptive cache strategy adjustment and energy consumption optimization, effectively reduces the system's operating costs and improves user experience.

[0035] The above method of the present application is applicable to scenarios of large-scale data access and processing such as cloud computing, big data analysis, and online video streaming. It can significantly improve user experience and system performance, and provide efficient and energy-saving data access solutions for cloud computing, big data processing and other fields.

[0036] The execution subject of the above steps may be a server, but is not limited thereto.

[0037] Optionally, in one embodiment, the access module includes a user feedback receiving module and a feedback processing module, and can also implement the following process: collect information from the user feedback receiving module, which reflects the user's satisfaction with the data access performance (such as data caching and data prefetching) of the data access management system, and use the feedback processing module to optimize the caching strategy according to the analysis results of the satisfaction.

[0038] In an optional embodiment, a feedback loop mechanism may also be provided so that the data access management system can continuously optimize the cache and pre-fetch strategies according to new user behavior data and system performance indicators.

[0039] In an exemplary embodiment, the real-time monitoring module includes an access frequency analysis unit, a time period analysis unit, and a user behavior analysis unit, and obtains the data access pattern and access data of the target object monitored by the real-time monitoring module, including: obtaining the historical access frequency of different objects from the access frequency analysis unit, obtaining the access time period of the different objects from the time period analysis unit, and obtaining the behavior data of the different objects from the user behavior analysis unit; determining the access data corresponding to the historical access frequency, the access time period, and the behavior data, and generating the access data based on the historical access frequency, the access time period, and the behavior data. By deeply analyzing the historical access pattern, the user's future needs can be predicted more accurately, thereby loading data in advance, reducing access waiting time, and improving system response speed. This has a significant optimization effect on applications with high real-time requirements, such as online games, real-time data analysis, etc.

[0040] Furthermore, the above-mentioned real-time monitoring module can also realize the anomaly detection function of the access pattern within a specific time period. The anomaly detection function can be realized based on the anomaly detection model. Through the anomaly detection function, abnormal access behaviors that exceed the normal access pattern can be identified. For example, it is necessary to collect the user's access data first. The access data includes but is not limited to user ID, access timestamp, geographic location, device type, access frequency, and request type. Then the collected raw data is converted into a feature vector suitable for the machine learning model. Then use the deep learning model to capture the complex patterns in the time series data and perform anomaly detection in combination with contextual information.

[0041] The feature vectors are exemplified as follows:

[0042] Time feature: Extract the hour, day of the week, and month information from the timestamp. For example, use "hour_of_day" to represent the hour of the day; use "day_of_week" to represent the day of the week; and use "month" to represent the month of the year.

[0043] Geographic location features: Encode the geographic location as a numerical feature; for example, including: latitude, longitude.

[0044] Device type feature: Convert the device type into one-hot encoding. For example, use "is_mobile" to indicate whether it is a mobile device; use "is_tablet" to indicate whether it is a tablet device; use "is_pc" to indicate whether it is a PC.

[0045] Access frequency feature: Calculate the access frequency of each user in different time windows. For example, use "access_frequency_last_hour" to represent the number of visits in the past hour; use "access_frequency_last_day" to represent the number of visits in the past day; use "access_frequency_last_week" to represent the number of visits in the past week.

[0046] The request type feature indicates that the request type is converted into a one-hot encoding; for example, "is_get_request" is used to indicate whether it is a GET request; and "is_post_request" is used to indicate whether it is a POST request.

[0047] In an exemplary embodiment, the data analysis module includes a pattern recognition unit and a behavior prediction unit. In the process of sending the data access pattern and the access data to the data analysis module and obtaining a data access request output by the data analysis module based on the data access pattern and the access data, the data access request is determined in the following manner: the pattern recognition unit uses a clustering algorithm to group the data objects corresponding to the access data to obtain multiple groups of objects, each group of objects corresponds to an access pattern, and selects a target group of objects corresponding to the access pattern with a periodic regularity from the multiple groups of objects; the behavior prediction unit predicts the future behavior data of the target group objects based on the historical access data of the target group objects, determines the data expected to be accessed based on the future behavior data, and generates the data access request; wherein, the behavior prediction unit predicts the future behavior data of the target group objects based on the historical access data of the target group objects, including: sorting the historical access data according to a time series to obtain time series data, performing autocorrelation analysis on the time series data to obtain an autocorrelation function of the time series data, determining the periodic access data in the historical access data based on the peak value of the autocorrelation function, and predicting the future behavior data of the target group objects based on the periodic access data. This method can effectively identify and predict user access behavior. For applications with periodic access requests, such as financial statement analysis and meteorological data query, it can pre-fetch data in advance and significantly improve data access efficiency.

[0048] Optionally, in this embodiment, the principle of using the K-means algorithm in the pattern recognition unit to classify data objects with similar access patterns using a clustering algorithm is as follows:

[0049]

[0050] Among them, d(x i , c i) represents the Euclidean distance between the data point and the cluster center.

[0051] x i is a data point, c i is the cluster center, and n is the feature dimension.

[0052] The classification steps are:

[0053] 1. Assign the data point xi to the nearest cluster center c j , argmin j d(x i , c j ).

[0054] 2. Recalculate the center c of each cluster j .

[0055]

[0056] Among them, C j is the set of data points of the jth cluster, |C j | is the number of data points in this cluster.

[0057] The autocorrelation function is expressed as

[0058] Among them, x t is time series data, is the mean of the time series, N is the length of the time series, and k is the lag order. By analyzing the peak of the autocorrelation function R(k), the periodic components in the time series can be identified.

[0059] In an exemplary embodiment, the adaptive cache policy adjustment module includes a machine learning unit, a prediction model update unit, a cache size adjustment unit, and a content replacement policy unit, and performs an adjustment operation on the cache data in the data access management system in the following manner: sending the cache space difference value output by the prediction model in the machine learning unit to the prediction model update unit, wherein the cache space difference value represents the difference between the first cache space and the reserved cache space, the first cache space represents the cache space required for the data expected to be accessed, and the reserved cache space represents the additional cache space outside the second cache space of the cache data; comparing the cache space difference value through the prediction model update unit, and sending an adjustment instruction to the cache size adjustment unit when it is determined that the cache space difference value is greater than a preset threshold, so that the cache size adjustment unit determines the adjustable space of the second cache space, and replaces the old data in the adjustable space with the data expected to be accessed through the content replacement policy unit. This adaptive cache policy adjustment can adjust the cache size and content according to the dynamic needs of data access, and can effectively improve the cache utilization and reduce data loading time for scenarios where data access patterns change frequently, such as social media, news websites, etc.

[0060] Optionally, in this embodiment, the machine learning unit improves the accuracy of data pre-fetching by training and updating the prediction model. The linear regression algorithm used in training the prediction model is as follows:

[0061] Assuming there are n samples and each sample has m features, the linear regression model is expressed as follows:

[0062] y i =β0+β 1Xi1 +β2x i2 +…+β m x im +∈ i .

[0063] Among them, y i is the target variable of the i-th sample, x ij is the jth feature of the i-th sample, β0 is the intercept term, β j is the coefficient of feature j, and ∈i is the error term.

[0064] The goal of linear regression is to minimize the mean squared error: in, is the predicted value of the model.

[0065] Prepare feature matrices, target vectors and other data through the following process: The feature data of all samples are organized into an n×m matrix X, where each row represents a sample and each column represents a feature; The target variables of all samples are organized into an n×1 vector y.

[0066] Among them, the model parameters of the linear regression model can be estimated by the least squares method. The specific steps are as follows:

[0067] 1. Add an intercept term: First, you need to add a column of intercept terms with all 1s in the feature matrix X to estimate the intercept term β0. Assuming there is a data set containing n observations, each observation has p features, then the feature matrix X is as follows:

[0068]

[0069] Each row represents an observation and the first column represents the intercept term.

[0070] 2. Estimate model parameters: The linear regression model is expressed as: y = Xβ+∈; where y is an n×1 vector containing the actual target variable with n observations, X is an n×(p+1) feature matrix, β is a (p+1)×1 parameter vector, and ∈ is an n×1 error vector.

[0071] The goal of the least squares method is to minimize the residual sum of squares:

[0072] Solving this optimization problem, we can obtain the closed-form solution of the model parameters:

[0073] The formula is explained as follows: X T represents the transposed matrix of X, X T X is a (p+1)×(p+1) matrix, called the pseudo-covariance matrix of the design matrix, (X T X) -1 Represents X T The inverse matrix of X, X T y is a (p+1)×1 vector representing the dot product of X and y.

[0074] In an exemplary embodiment, the energy consumption optimization module includes an energy consumption model building unit and a dynamic power consumption control unit, and performs energy consumption optimization operations on the data access management system in the following manner: obtaining the estimated energy consumption of the data access management system under different cache strategies predicted by the energy consumption model building unit based on the historical energy consumption data of the data access management system and the current system state of the data access management system, determining the energy consumption optimization scheme corresponding to the estimated energy consumption, determining the current energy consumption optimization scheme of the data access management system under the current energy consumption from the energy consumption optimization scheme, and determining the first optimization instruction indicated by the energy consumption optimization scheme; obtaining the second optimization instruction generated by the dynamic power consumption control unit according to the change of network traffic; determining the energy consumption optimization operation according to the optimization operation corresponding to the first optimization instruction and the optimization operation corresponding to the second optimization instruction. Through energy consumption optimization, not only can the operating cost of the system be reduced, but also the sustainability of the system can be improved. For applications that need to run for a long time and are sensitive to energy consumption, such as Internet of Things device management, remote education platform, etc., it has important economic and environmental significance.

[0075] Furthermore, the optimization operations corresponding to the first optimization instruction include but are not limited to: dynamically adjusting the cache update frequency to balance the relationship between energy consumption and cache freshness; reducing unnecessary prefetch operations during energy-sensitive periods to avoid unnecessary energy consumption increases. The optimization operations corresponding to the second optimization instruction include but are not limited to: adjusting the priority of cache operations according to changes in network traffic to balance performance and energy consumption; reducing data prefetch operations during network congestion periods to reduce network burden; prefetching data that is expected to be accessed in advance during network idle periods to increase access speed.

[0076] In an exemplary embodiment, the data access module includes a user feedback receiving module and a feedback processing module. Before determining the data that is expected to be accessed and pre-fetched from the cache data as priority access data and performing data access operations on the priority access data in response to the current data access request sent by the target object, the method further includes: receiving the target object's satisfaction with the data access performance of the data access management system through the user feedback receiving module, and sending the satisfaction to the feedback processing module; analyzing the satisfaction in different evaluation dimensions using the feedback processing module to obtain analysis results, and generating an adjustment cache strategy based on the analysis results. This method can dynamically adjust the cache strategy according to user feedback, ensure that the system performance always meets user needs, and provide more personalized and high-quality services for scenarios that are highly sensitive to user experience, such as online shopping, video conferencing, etc.

[0077] The user feedback receiving module includes a user satisfaction survey tool and a performance monitoring tool, and the feedback processing module includes a feedback analysis unit and a strategy optimization unit. The user satisfaction survey tool is used to support users to directly provide feedback on their satisfaction with cache and prefetch services. The performance monitoring tool is used to collect performance indicators such as cache hit rate and latency as indirect feedback information.

[0078] In an exemplary embodiment, the method further includes: deploying a multi-level cache architecture within the data access management system to support data migration between storage media of different performance levels through the cache area of ​​the multi-level cache architecture, wherein the cache area of ​​the multi-level cache architecture includes at least one of the following: a high-speed cache area, a medium-speed cache area, and a low-speed storage area. Through the multi-level cache architecture, the system can automatically adjust the data storage location according to the access frequency and importance of the data, realize fast access and efficient storage of the data, and can significantly improve the data processing efficiency and storage space utilization for scenarios that need to process a large amount of heterogeneous data, such as scientific research data management, enterprise-level databases, etc.

[0079] Among them, the high-speed cache area uses high-performance storage media such as SSD to store high-frequency access data; the medium-speed cache area uses medium-performance storage media such as SAS hard disk to store medium-frequency access data; the low-speed storage area uses low-cost storage media such as SATA hard disk to store low-frequency access data.

[0080] Obviously, the embodiments described above are only some embodiments of the present application, not all embodiments. In order to better understand the above method, the above process is described below in conjunction with the embodiments, but it is not intended to limit the technical solutions of the embodiments of the present application, specifically:

[0081] In one embodiment, by using a variety of technical means such as real-time monitoring, data analysis, adaptive cache strategy adjustment, energy consumption optimization, user feedback processing, and multi-level cache architecture, data caching and pre-fetching are realized in an intelligent manner, and efficient management and access of server-side data are achieved, access delays are reduced, energy consumption is reduced, and user experience is improved. Figure 3 As shown:

[0082] Step S1, continuously monitoring data access patterns and user behaviors through a real-time monitoring module;

[0083] Step S2, using a data analysis module to analyze access patterns and predict future data requests based on the data obtained in step S1;

[0084] Step S3, using an adaptive cache strategy adjustment module to dynamically adjust the data content in the cache according to the prediction result in step S2;

[0085] Step S4: Implement the energy consumption optimization module to minimize energy consumption while ensuring data access performance;

[0086] The upper data access module includes a user feedback receiving module and a feedback processing module.

[0087] Step S5: collecting information from the user feedback receiving module, where the information reflects the terminal user's satisfaction with the cache and prefetching effects;

[0088] Step S6: using the feedback processing module to process the information collected in step S5, and optimizing the cache strategy accordingly;

[0089] Step S7: deploy a multi-level cache architecture to support data migration between storage media of different performance levels to achieve the best performance and cost ratio;

[0090] In S1, the access frequency analysis unit counts the access frequency of each data object in the past period of time, such as the number of times it was accessed in the past hour, and records the access frequency of each data object;

[0091] The time period analysis unit identifies the time pattern of data access, including peak access periods and trough access periods. For example, it analyzes whether there is an access peak between 9 am and 5 pm every day.

[0092] The user behavior analysis unit analyzes user access behavior, including user access preferences, access time, and access frequency, such as identifying that a user often accesses a specific type of data between 8 p.m. and 10 p.m.;

[0093] In addition, the real-time monitoring module further includes an abnormal detection function for access patterns within a specific time period, through which abnormal access behaviors that exceed normal access patterns can be identified. Once abnormal access is detected, the emergency response process is immediately initiated to prevent service interruption or resource waste caused by abnormal access.

[0094] In S2, the pattern recognition unit uses the K-means clustering algorithm to classify data objects with similar access patterns and identify periodic access patterns.

[0095] The behavior prediction unit predicts future user behavior based on historical access data, and uses an autocorrelation analysis method to calculate the autocorrelation function of time series data and find the peak value in the autocorrelation function.

[0096] In S3, the machine learning unit uses a linear regression algorithm to train and update the prediction model to improve the accuracy of data prefetching. The prediction model update unit automatically triggers the model update process when the error of the prediction model exceeds a predetermined threshold. The cache size adjustment unit dynamically adjusts the cache size according to the current network conditions and computing resources. The content replacement strategy unit determines when to replace old data in the cache to make room for new data, and selects a replacement strategy based on the Least Recently Used (LRU) algorithm or the Least Frequently Used (LFU) algorithm;

[0097] In S4, the energy consumption model building unit predicts the energy consumption under different cache strategies based on historical energy consumption data and the current system status, and formulates energy consumption optimization plans accordingly. For example, the cache update frequency is dynamically adjusted to balance the relationship between energy consumption and cache freshness, or unnecessary prefetch operations are reduced during energy-sensitive periods to avoid unnecessary energy consumption increases.

[0098] The dynamic power control unit adjusts the priority of cache operations according to changes in network traffic to balance performance and energy consumption. For example, it reduces data prefetching operations during network congestion to reduce network burden, or prefetches data that is expected to be accessed during network idle periods to increase access speed.

[0099] In S5, the user satisfaction survey tool allows users to directly provide feedback on their satisfaction with cache and prefetch services. Performance monitoring tools: collect performance indicators such as cache hit rate and latency, and use them as indirect feedback information.

[0100] In S6, the feedback analysis unit analyzes the user feedback data to identify key factors affecting the user experience. The policy optimization unit adjusts the cache policy based on the feedback analysis result to improve the user experience.

[0101] In S7, the multi-level cache architecture specifically includes but is not limited to: a high-speed cache area, a medium-speed cache area, and a low-speed storage area.

[0102] The cache area uses high-performance storage media such as SSD to store high-frequency access data.

[0103] The medium-speed cache area uses medium-performance storage media such as SAS hard disks to store medium-frequency access data.

[0104] The low-speed storage area uses low-cost storage media such as SATA hard disks to store low-frequency access data.

[0105] The above-mentioned embodiment uses a real-time monitoring module and a data analysis module, and utilizes a clustering algorithm and an autocorrelation analysis method to more accurately identify access patterns and periodic access patterns, thereby improving the accuracy of predictions of future data requests. An adaptive cache strategy adjustment module is used to dynamically adjust cache content according to prediction results, so that the cache strategy is closer to actual needs and reduces the space occupied by invalid data. An energy consumption optimization module is implemented, and through an energy consumption model establishment unit and a dynamic power consumption control unit, energy consumption can be minimized while ensuring data access performance, which is in line with the trend of green computing. The method also includes a feedback loop mechanism, and the system can continuously optimize its cache and pre-fetch strategies based on new user behavior data and system performance indicators, thereby maintaining the dynamic adaptability of the system. By introducing a context-aware anomaly detection function, this solution can more accurately identify abnormal access behaviors and improve the security and stability of the system.

[0106] It should be noted that the scope of protection of this patent is not limited to this. Any person familiar with the technical field can easily think of changes or substitutions within the technical scope involved in this patent, which should be covered within the scope of protection of this patent. Therefore, the scope of protection of this patent should be based on the scope of protection of the claims. After considering the specification and practicing the patent here, those skilled in the art will easily think of other implementation schemes of this application. This application is intended to cover any variations, uses or adaptive changes of this application, which follow the general principles of this application and include common knowledge or customary technical means in this technical field that are not disclosed in this application.

[0107] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0108] In this embodiment, a data access management system is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the system described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0109] Figure 4 is a structural block diagram of a data access management system according to an embodiment of the present application, such as Figure 4 As shown, the system includes:

[0110] The real-time monitoring module 402 is used to monitor the access data of the target object and the data access pattern corresponding to the access data; and send the data access pattern and the access data to the data analysis module;

[0111] The data analysis module 404 is connected to the real-time monitoring module and is used to output a data access request based on the data access mode and the access data, wherein the data access request is used to request data that is expected to be accessed; and send the data access request to the adaptive cache strategy adjustment module;

[0112] The adaptive cache strategy adjustment module 406 is connected to the data analysis module and is used to determine to adjust the cache data in the data access management system based on the data access request;

[0113] The energy consumption optimization module 408 is connected to the adaptive cache strategy adjustment module and is used to reduce the system energy consumption of the data access management system by performing energy consumption optimization operation on the data access management system after the cache data in the data access management system is adjusted;

[0114] The data access module 410 is connected to at least the adaptive cache strategy adjustment module, and is used to determine the pre-fetched data expected to be accessed in the cache data as priority access data, and perform data access operations on the priority access data in response to the current data access request sent by the target object.

[0115] Through the above system, the access data of the target object monitored by the real-time monitoring module and the data access pattern corresponding to the access data are obtained; the data access pattern and the access data are sent to the data analysis module to obtain the data access request output by the data analysis module based on the data access pattern and the access data, and the data access request is used to request the data that is expected to be accessed; the data access request is sent to the adaptive cache policy adjustment module, so that the adaptive cache policy adjustment module determines to adjust the cache data in the data access management system based on the data access request; after the cache data in the data access management system is adjusted, the energy consumption optimization operation is performed on the data access management system through the energy consumption optimization module to reduce the system energy consumption of the data access management system; the data that is expected to be accessed pre-fetched in the cache data is determined as priority access data through the data access module, and in response to the current data access request sent by the target object, a data access operation is performed on the priority access data. The data access management system of this application can realize intelligent data pre-fetching and cache management, solve the problem of low data access efficiency of cloud servers storing large amounts of data, effectively improve data access efficiency, reduce data access delays, and at the same time, reduce the operating costs of the system through energy consumption optimization, and improve the sustainability and environmental performance of the system. The solution of this application is particularly suitable for scenarios that need to process large-scale data and provide real-time services, such as cloud computing platforms, big data analysis systems, online video services, social media platforms, etc., which can significantly improve system performance, reduce operating costs, and provide users with a smoother, more efficient, and energy-saving service experience. In the future digital transformation and intelligent upgrades, this efficient and energy-saving data access management solution will become one of the key technologies to improve system performance, reduce energy consumption, and optimize user experience, and it has important strategic value and application prospects.

[0116] In an exemplary embodiment, the real-time monitoring module includes an access frequency analysis unit, a time period analysis unit and a user behavior analysis unit, and obtains the data access pattern and access data of the target object monitored by the real-time monitoring module, including: obtaining the historical access frequency of different objects from the access frequency analysis unit, obtaining the access time period of the different objects from the time period analysis unit, and obtaining the behavior data of the different objects from the user behavior analysis unit; determining the access data corresponding to the historical access frequency, the access time period and the behavior data, and generating the access data based on the historical access frequency, the access time period and the behavior data.

[0117] In an exemplary embodiment, the data analysis module includes a pattern recognition unit and a behavior prediction unit. In the process of sending the data access pattern and the access data to the data analysis module and obtaining a data access request output by the data analysis module based on the data access pattern and the access data, the data access request is determined in the following manner: the pattern recognition unit uses a clustering algorithm to group the data objects corresponding to the access data to obtain multiple groups of objects, each group of objects corresponds to an access pattern, and selects a target group of objects corresponding to the access pattern with a periodic regularity from the multiple groups of objects; the behavior prediction unit predicts the future behavior data of the target group objects based on the historical access data of the target group objects, determines the data expected to be accessed based on the future behavior data, and generates the data access request; wherein, the behavior prediction unit predicts the future behavior data of the target group objects based on the historical access data of the target group objects, including: sorting the historical access data according to a time series to obtain time series data, performing autocorrelation analysis on the time series data to obtain an autocorrelation function of the time series data, determining the periodic access data in the historical access data based on the peak value of the autocorrelation function, and predicting the future behavior data of the target group objects based on the periodic access data.

[0118] In an exemplary embodiment, the adaptive cache policy adjustment module includes a machine learning unit, a prediction model update unit, a cache size adjustment unit and a content replacement policy unit, and performs an adjustment operation on the cache data in the data access management system in the following manner: sending the cache space difference output by the prediction model in the machine learning unit to the prediction model update unit, wherein the cache space difference represents the difference between a first cache space and a reserved cache space, the first cache space represents the cache space required for the data expected to be accessed, and the reserved cache space represents the additional cache space outside the second cache space of the cache data; comparing the cache space difference through the prediction model update unit, and when it is determined that the cache space difference is greater than a preset threshold, sending an adjustment instruction to the cache size adjustment unit, so that the cache size adjustment unit determines the adjustable space of the second cache space, and replaces the old data in the adjustable space with the data expected to be accessed through the content replacement policy unit.

[0119] In an exemplary embodiment, the energy consumption optimization module includes an energy consumption model building unit and a dynamic power consumption control unit, and performs energy consumption optimization operations on the data access management system in the following manner: obtaining the estimated energy consumption of the data access management system under different cache strategies predicted by the energy consumption model building unit based on the historical energy consumption data of the data access management system and combined with the current system state of the data access management system, determining an energy consumption optimization scheme corresponding to the estimated energy consumption, determining the current energy consumption optimization scheme of the data access management system under the current energy consumption from the energy consumption optimization scheme, and determining a first optimization instruction indicated by the energy consumption optimization scheme; obtaining a second optimization instruction generated by the dynamic power consumption control unit according to changes in network traffic; and determining the energy consumption optimization operation according to the optimization operation corresponding to the first optimization instruction and the optimization operation corresponding to the second optimization instruction.

[0120] In an exemplary embodiment, the data access module includes a user feedback receiving module and a feedback processing module. Before determining the data that is expected to be accessed and pre-fetched from the cache data as priority access data and performing a data access operation on the priority access data in response to a current data access request sent by the target object, the method further includes: receiving the target object's satisfaction with the data access performance of the data access management system through the user feedback receiving module, and sending the satisfaction to the feedback processing module; using the feedback processing module to analyze the satisfaction in different evaluation dimensions to obtain analysis results, and generating an adjusted cache strategy based on the analysis results.

[0121] In an exemplary embodiment, the method further includes: deploying a multi-level cache architecture within the data access management system to support data migration between storage media of different performance levels through the cache areas of the multi-level cache architecture, wherein the cache areas of the multi-level cache architecture include at least one of the following: a high-speed cache area, a medium-speed cache area, and a low-speed storage area.

[0122] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0123] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0124] Optionally, in this embodiment, the computer program may be configured to perform the following steps by means of the computer program:

[0125] S1, obtaining access data of the target object monitored by the real-time monitoring module and a data access mode corresponding to the access data;

[0126] S2, sending the data access pattern and the access data to the data analysis module, obtaining a data access request output by the data analysis module based on the data access pattern and the access data, wherein the data access request is used to request the data that is expected to be accessed;

[0127] S3, sending the data access request to the adaptive cache policy adjustment module, so that the adaptive cache policy adjustment module determines to adjust the cache data in the data access management system based on the data access request;

[0128] S4, after the cache data in the data access management system is adjusted, the energy consumption optimization module performs an energy consumption optimization operation on the data access management system to reduce the system energy consumption of the data access management system;

[0129] S5, determining the data that is expected to be accessed and pre-fetched from the cache data as priority access data through the data access module, and performing a data access operation on the priority access data in response to a current data access request sent by the target object.

[0130] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0131] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0132] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0133] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0134] The embodiment of the present application also provides an electronic device, such as Figure 5 As shown, the electronic device includes a memory 502 and a processor 504. The memory 502 stores a computer program, and the processor 504 is configured to execute the steps in any of the above method embodiments through the computer program.

[0135] Optionally, in this embodiment, the processor 504 may be configured to perform the following steps through a computer program:

[0136] S1, obtaining access data of the target object monitored by the real-time monitoring module and a data access mode corresponding to the access data;

[0137] S2, sending the data access pattern and the access data to the data analysis module, obtaining a data access request output by the data analysis module based on the data access pattern and the access data, wherein the data access request is used to request the data that is expected to be accessed;

[0138] S3, sending the data access request to the adaptive cache policy adjustment module, so that the adaptive cache policy adjustment module determines to adjust the cache data in the data access management system based on the data access request;

[0139] S4, after the cache data in the data access management system is adjusted, the energy consumption optimization module performs an energy consumption optimization operation on the data access management system to reduce the system energy consumption of the data access management system;

[0140] S5, determining the data that is expected to be accessed and pre-fetched from the cache data as priority access data through the data access module, and performing a data access operation on the priority access data in response to a current data access request sent by the target object.

[0141] Alternatively, a person skilled in the art may understand that: Figure 5 The structure shown is for illustration only. Figure 5 The structure of the electronic device is not limited. For example, the electronic device may also include Figure 5 More or fewer components (such as network interfaces, etc.) as shown in, or with Figure 5 Different configurations shown.

[0142] Among them, the memory 502 can be used to store software programs and modules, such as program instructions / modules corresponding to the data access method and device in the embodiments of the present application. The processor 504 executes various functional applications and data processing by running the software programs and modules stored in the memory 502, that is, realizing the above-mentioned data access method. The memory 502 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 502 may further include a memory remotely located relative to the processor 504, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. Among them, the memory 502 can be specifically, but not limited to, used to store information such as distributed storage system configuration files. As an example, such as Figure 5 As shown, the memory 502 may include but is not limited to all modules in the data access management system. In addition, it may also include but is not limited to other module units in the data access management system, which will not be described in detail in this example.

[0143] Optionally, the transmission device 506 is used to receive or send data via a network. Specific examples of the network may include wired networks and wireless networks. In one example, the transmission device 506 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices and routers via a network cable so as to communicate with the Internet or a local area network. In one example, the transmission device 506 is a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0144] In addition, the electronic device mentioned above further includes: a display 508; and a connection bus 510, which is used to connect various module components in the electronic device mentioned above.

[0145] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0146] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0147] An embodiment of the present application also provides a computer program, which includes computer instructions, which are stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in any one of the above method embodiments.

[0148] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0149] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0150] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A data access method, characterized in that: Applied to a data access management system, the data access management system is set in a cloud server, and the data access management system at least includes a real-time monitoring module, a data analysis module, an adaptive cache strategy adjustment module, an energy consumption optimization module and a data access module, including: Acquire access data of the target object monitored by the real-time monitoring module and a data access pattern corresponding to the access data; send the data access pattern and the access data to the data analysis module to obtain a data access request output by the data analysis module based on the data access pattern and the access data, wherein the data access request is used to request data that is expected to be accessed; Sending the data access request to the adaptive cache policy adjustment module, so that the adaptive cache policy adjustment module determines to adjust the cache data in the data access management system based on the data access request; After the cache data in the data access management system is adjusted, the energy consumption optimization module is used to perform energy consumption optimization on the data access management system to reduce system energy consumption of the data access management system; The data that is expected to be accessed and pre-fetched from the cache data is determined as priority access data by the data access module, and a data access operation is performed on the priority access data in response to a current data access request sent by the target object.

2. The method according to claim 1, characterized in that: The real-time monitoring module includes an access frequency analysis unit, a time period analysis unit, and a user behavior analysis unit. Acquiring the data access pattern and access data of the target object monitored by the real-time monitoring module includes: Acquire historical access frequencies of different objects from the access frequency analysis unit, acquire access time periods of the different objects from the time period analysis unit, and acquire behavior data of the different objects from the user behavior analysis unit; Determine the access data corresponding to the historical access frequency, the access period and the behavior data, and generate the access data based on the historical access frequency, the access period and the behavior data.

3. The method according to claim 1, characterized in that The data analysis module includes a pattern recognition unit and a behavior prediction unit. In the process of sending the data access pattern and the access data to the data analysis module and obtaining the data access request output by the data analysis module based on the data access pattern and the access data, the data access request is determined in the following manner: The pattern recognition unit uses a clustering algorithm to group the data objects corresponding to the access data to obtain multiple groups of objects, each group of objects corresponding to an access pattern, and selects a target group of objects corresponding to the access pattern with a periodic regularity from the multiple groups of objects; Predicting future behavior data of the target group objects based on historical access data of the target group objects by the behavior prediction unit, determining data that is expected to be accessed according to the future behavior data, and generating the data access request; Wherein, predicting the future behavior data of the target group objects based on the historical access data of the target group objects by the behavior prediction unit includes: The historical access data is sorted according to time series to obtain time series data, autocorrelation analysis is performed on the time series data to obtain the autocorrelation function of the time series data, periodic access data in the historical access data is determined according to the peak value of the autocorrelation function, and future behavior data of the target group objects is predicted according to the periodic access data.

4. The method according to claim 1, characterized in that The adaptive cache strategy adjustment module includes a machine learning unit, a prediction model updating unit, a cache size adjustment unit, and a content replacement strategy unit, and performs an adjustment operation on the cache data in the data access management system in the following manner: Sending the cache space difference value output by the prediction model in the machine learning unit to the prediction model updating unit, wherein the cache space difference value represents the difference between the first cache space and the reserved cache space, the first cache space represents the cache space required for the data expected to be accessed, and the reserved cache space represents the additional cache space outside the second cache space of the cache data; The cache space difference is compared by the prediction model updating unit, and when it is determined that the cache space difference is greater than a preset threshold, an adjustment instruction is sent to the cache size adjustment unit, so that the cache size adjustment unit determines the adjustable space of the second cache space, and replaces the old data in the adjustable space with the data that is expected to be accessed by the content replacement strategy unit.

5. The method according to claim 1, characterized in that: The energy consumption optimization module includes an energy consumption model building unit and a dynamic power consumption control unit, and performs energy consumption optimization operations on the data access management system in the following manner: Obtain the estimated energy consumption of the data access management system under different cache strategies predicted by the energy consumption model building unit based on the historical energy consumption data of the data access management system and in combination with the current system state of the data access management system, determine the energy consumption optimization scheme corresponding to the estimated energy consumption, determine the current energy consumption optimization scheme of the data access management system under the current energy consumption from the energy consumption optimization scheme, and determine the first optimization instruction indicated by the energy consumption optimization scheme; Obtaining a second optimization instruction generated by the dynamic power consumption control unit according to a change in network traffic; The energy consumption optimization operation is determined according to the optimization operation corresponding to the first optimization instruction and the optimization operation corresponding to the second optimization instruction.

6. The method according to claim 1, characterized in that The data access module includes a user feedback receiving module and a feedback processing module. Before determining the data that is expected to be accessed and pre-fetched from the cache data as priority access data and performing a data access operation on the priority access data in response to a current data access request sent by the target object, the method further includes: receiving, through the user feedback receiving module, the satisfaction of the target object with respect to the data access performance of the data access management system, and sending the satisfaction to the feedback processing module; The feedback processing module is used to analyze the satisfaction in different evaluation dimensions to obtain analysis results, and an adjustment cache strategy is generated based on the analysis results.

7. The method according to claim 1, characterized in that The method further comprises: A multi-level cache architecture is deployed in the data access management system to support data migration between storage media of different performance levels through the cache areas of the multi-level cache architecture, wherein the cache areas of the multi-level cache architecture include at least one of the following: a high-speed cache area, a medium-speed cache area, and a low-speed storage area.

8. A data access management system, It is characterized in that Including: real-time monitoring module, data analysis module, adaptive cache strategy adjustment module, energy consumption optimization module and data access module; The real-time monitoring module is used to monitor the access data of the target object and the data access pattern corresponding to the access data; and send the data access pattern and the access data to the data analysis module; The data analysis module is connected to the real-time monitoring module and is used to request data access based on the data access mode and the access data output, wherein the data access request is used to request data that is expected to be accessed; and send the data access request to the adaptive cache strategy adjustment module; The adaptive cache strategy adjustment module is connected to the data analysis module and is used to determine an adjustment operation to be performed on the cache data in the data access management system based on the data access request; The energy consumption optimization module is connected to the adaptive cache strategy adjustment module and is used to reduce the system energy consumption of the data access management system by performing energy consumption optimization operations on the data access management system after the cache data in the data access management system is adjusted; The data access module is at least connected to the adaptive cache strategy adjustment module, and is used to determine the pre-fetched data expected to be accessed in the cache data as priority access data, and perform data access operations on the priority access data in response to the current data access request sent by the target object.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the method described in any one of claims 1 to 7 when executed by a processor.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method described in any one of claims 1 to 7 are implemented.

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