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

Abstract

The embodiment of the application provides a data access method, a data access management system, a storage medium and an electronic device, the method comprises the following steps: obtaining access data of a target object and a data access mode corresponding to the access data monitored by a real-time monitoring module; obtaining a data access request output by a data analysis module based on the data access mode and the access data; sending the data access request to an adaptive cache strategy adjustment module, so that the adaptive cache strategy adjustment module determines an 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 to reduce the system energy consumption of the data access management system; determining data that is expected to be accessed in the cache data as priority access data through the data access module, and performing data access operation on the priority 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 field of computers, 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 user access behavior. With the rapid increase in data volume, cloud servers generally store a large amount of data. During peak service periods, data access delays and other situations will occur, affecting the user experience. 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 and lack flexibility in cache strategy adjustment. They are unable to dynamically optimize based on real-time data and user feedback, and can no longer meet user data access requests, seriously affecting data access efficiency. Therefore, in related technologies, data access efficiency for cloud servers that store large amounts of data is low.

[0003] Currently, no effective solution has been proposed to the problem of low data access efficiency for cloud servers storing large amounts of data in related technologies.

[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, comprising: applied to a data access management system, the data access management system is arranged in a cloud server, 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, comprising: obtaining the access data of the target object monitored by the real-time monitoring module and the data access mode corresponding to the access data; sending the data access mode and the access data to the data analysis module to obtain the data access request output by the data analysis module based on the data access mode and the access data, 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 strategy adjustment module, so that the adaptive cache strategy adjustment module determines the adjustment operation of the cache data in the data access management system based on the data access request; after the adjustment operation of the cache data in the data access management system, the energy consumption optimization module is used to perform energy consumption optimization operation on the data access management system to reduce the system energy consumption of the data access management system; through the data access module, the data that is expected to be accessed in the cache data is determined as priority access data, and in response to the current data access request sent by the target object, the priority access data is subjected to data access operation.

[0007] In one exemplary embodiment, the real-time monitoring module includes an access frequency analysis unit, a time period analysis unit and a user behavior analysis unit, the data access mode and the access data of the target object monitored by the real-time monitoring module are obtained, comprising: 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 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 corresponds to an access pattern, and selects target group objects corresponding to access patterns with periodic regularities from the multiple groups of objects; the behavior prediction unit predicts future behavior data of the target group objects based on historical access data of the target group objects, determines data expected to be accessed based on the future behavior data, and generates the data access request; wherein, the behavior prediction unit predicts future behavior data of the target group objects based on the historical access data of the target group objects includes: sorting the historical access data according to time series to obtain time series data, performing autocorrelation analysis on the time series data to obtain the autocorrelation function of the time series data, determining periodic access data in the historical access data based on the peak value of the autocorrelation function, and predicting 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 adjustment operations 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 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 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 establishment 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 establishment unit based on the historical energy consumption data of the data access management system and combined with the current system status 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 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 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; 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, comprising: the real-time monitoring module, configured to monitor access data of a target object and a data access pattern corresponding to the access data; send the data access pattern and the access data to the data analysis module; the data analysis module, configured to output a data access request based on the data access pattern and the access data, the data access request being used to request data that is expected to be accessed; send the data access request to the adaptive cache strategy adjustment module; the adaptive cache strategy adjustment module, configured to determine an adjustment operation on cache data in the data access management system based on the data access request; the energy consumption optimization module, configured to, after the adjustment operation on the cache data in the data access management system, perform an energy consumption optimization operation on the data access management system to reduce system energy consumption of the data access management system; and the data access module, configured to determine the data that is expected to be accessed as priority access data in the cache data pre-fetched by the data access request, and perform a data access operation on the priority access data in response to a current data access request sent by the target object.

[0014] According to still another embodiment of the present application, a computer readable storage medium is further provided, and the computer readable storage medium stores a computer program, wherein the computer program is configured to execute the steps in any of the method embodiments when running.

[0015] According to still another embodiment of the present application, an electronic device is further provided, comprising a memory and a processor, the memory stores a computer program, and the processor is configured to execute the computer program to perform the steps in any of the method embodiments.

[0016] According to still another embodiment of the present application, a computer program product is further provided, comprising a computer program, and the computer program is executed by a processor to implement the steps in any of the method embodiments.

[0017] Through the present application, a data access management system arranged in a cloud server and a data access method applied to the data access management system are provided. The data access management system arranged in the cloud server comprises 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. The access data of a target object monitored by the real-time monitoring module and the data access mode corresponding to the access data are obtained. The data access mode 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 mode and the access data. The data access request is used to request data that is expected to be accessed. The data access request is sent to the adaptive cache strategy adjustment module to enable the adaptive cache strategy adjustment module to determine an adjustment operation on the cache data in the data access management system based on the data access request. After the adjustment operation on the cache data in the data access management system, the energy consumption optimization module is used to perform an energy consumption optimization operation on the data access management system to reduce the system energy consumption of the data access management system. The data access module is used to determine the data that is expected to be accessed as priority access data in the cache data pre-fetched by the data access module. In response to the current data access request sent by the target object, the data access operation is performed on the priority access data. The data access management system is used to intelligently predict the access request of the target object, and the data that is expected to be accessed is loaded into the cache in advance. The data access efficiency of the cloud server storing a large amount of data is improved, the data access efficiency and response speed are significantly improved, the running cost of the system is effectively reduced through the adaptive cache strategy adjustment and energy consumption optimization, and the user experience is improved. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 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 flowchart of a data access method according to an embodiment of the present application;

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

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

[0022] Figure 5 is a structure diagram 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 conjunction with embodiments.

[0024] It should be noted that the terms "first", "second" and the like in the description and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily indicate 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 similar computing device. Taking the case of running on a server device, Figure 1 is a hardware structure block diagram of a server device of a data access method according to an embodiment of the present application. As shown in Figure 1 , the server device can include one or more (only one is shown in Figure 1 ) processors 102 (the processor 102 can 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 above-mentioned server device can further include a transmission device 106 for communication function and an input and output device 108. Those skilled in the art can understand that Figure 1 The structure shown is only schematic, which does not limit the structure of the above-mentioned server device. For example, the server device can further include more or less components than those shown in Figure 1 , or have a different configuration from that shown in Figure 1 .

[0026] The memory 104 can be used to store computer programs, for example, software programs of application software and modules, such as the computer program corresponding to the data access method in the embodiments 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, implements the above-mentioned method. The memory 104 can include a high-speed random access memory, and can also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 104 can further include a memory remotely arranged with respect to the processor 102, which can be connected to the server device through 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 a combination thereof.

[0027] The transmission device 106 is used to receive or send data via a network. A specific example of the aforementioned network may include a wireless network provided by a communication provider of the server device. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device 106 may 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: Acquire access data of the target object monitored by the real-time monitoring module and a data access pattern corresponding to the access data;

[0030] Step S204: 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 the data 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] In step S210 , the data access module determines the data that is pre-fetched from the cache data and is expected to be accessed as priority access data, and performs a data access operation on the priority access data in response to a current data access request sent by the target object.

[0034] By the above steps, by acquiring the access data of the target object monitored by the real-time monitoring module and the data access mode corresponding to the access data; sending the data access mode and the access data to the data analysis module to obtain the data access request output by the data analysis module based on the data access mode and the access data, the data access request is used to request the data expected to be accessed; sending the data access request to the adaptive cache strategy adjustment module, so that the adaptive cache strategy adjustment module determines the adjustment operation on the cache data in the data access management system based on the data access request; after the adjustment operation of the cache data in the data access management system, the energy consumption optimization module is used to perform energy consumption optimization operation on the data access management system to reduce the system energy consumption of the data access management system; the data access module is used to determine the data expected to be accessed in the cache data as the priority access data in response to the current data access request sent by the target object, and the priority access data is subjected to data access operation. The application uses the data access management system to intelligently predict the access request of the target object, and loads the data expected to be accessed into the cache in advance, which not only solves the problem of low data access efficiency of the cloud server storing a large amount of data, but also significantly improves the data access efficiency and response speed, reduces data delay, and at the same time, through adaptive cache strategy adjustment and energy consumption optimization, effectively reduces the running cost of the system, and improves the user experience.

[0035] The above method of the application is applicable to large-scale data access and processing scenes such as cloud computing, big data analysis, online video streaming media, etc., can significantly improve user experience and system performance, and provides an efficient and energy-saving data access solution for the field of cloud computing, big data processing, etc.

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

[0037] Optionally, in an embodiment, the access module includes a user feedback receiving module and a feedback processing module, and the following process can also be implemented: collecting information from the user feedback receiving module, which reflects the satisfaction of users with the data access performance (such as data caching and data prefetching) of the data access management system, and optimizing the cache strategy according to the analysis result of the satisfaction degree by using the feedback processing module.

[0038] In an optional embodiment, a feedback cycle mechanism can also be provided, so that the data access management system can continuously optimize the cache and prefetch strategy 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. The module obtains data access patterns and target object access data monitored by the real-time monitoring module, including: obtaining the historical access frequencies of different objects from the access frequency analysis unit, obtaining the access time periods of the different objects from the time period analysis unit, and obtaining the behavioral data of the different objects from the user behavior analysis unit; determining the access data corresponding to the historical access frequencies, access time periods, and behavioral data, and generating the access data based on the historical access frequencies, access time periods, and behavioral data. By deeply analyzing historical access patterns, users' future needs can be more accurately predicted, thereby preloading data, reducing access wait time, and improving system response speed. This has a significant optimization effect on applications with high real-time requirements, such as online games and real-time data analysis.

[0040] Furthermore, the real-time monitoring module can also implement anomaly detection for access patterns within a specific time period. This can be implemented based on an anomaly detection model. This anomaly detection function can identify unusual access behaviors that fall outside normal access patterns. For example, this requires first collecting user access data, including but not limited to user ID, access timestamp, geographic location, device type, access frequency, and request type. This collected raw data is then converted into feature vectors suitable for machine learning models. Deep learning models are then used to capture complex patterns in time series data and incorporate contextual information for anomaly detection.

[0041] The following are examples of eigenvectors:

[0042] Time features: 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; "day_of_week" to represent the day of the week; and "month" to represent the month of the year.

[0043] Geographic location features: Encode geographic location as numerical features; for example, including: latitude (latitude), longitude (longitude).

[0044] Device type feature: Convert the device type into a 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; and use "is_pc" to indicate whether it is a PC.

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

[0046] The request type feature converts the request type into a one-hot encoding. For example, "is_get_request" indicates whether it is a GET request; "is_post_request" indicates 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 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 corresponds to an access pattern, and selects target group objects corresponding to access patterns with periodic regularities from the multiple groups of objects; the behavior prediction unit predicts future behavior data of the target group objects based on historical access data of the target group objects, determines data expected to be accessed based on the future behavior data, and generates the data access request; wherein, the behavior prediction unit predicts future behavior data of the target group objects based on the historical access data of the target group objects includes: sorting the historical access data according to time series to obtain time series data, performing autocorrelation analysis on the time series data to obtain the autocorrelation function of the time series data, determining periodic access data in the historical access data based on the peak value of the autocorrelation function, and predicting 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, significantly improving 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 irepresents the Euclidean distance between a data point and a cluster center.

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

[0052] The classification step is:

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

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

[0055]

[0056] where C j is the set of data points in the jth cluster, and |C j | is the number of data points in the cluster.

[0057] where the autocorrelation function is denoted as

[0058] where x t is the 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 peaks of the autocorrelation function R(k), 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 adjusts the cache data in the data access management system in the following manner: sending a 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 a first cache space and a reserved cache space, the first cache space representing the cache space required for the data expected to be accessed, and the reserved cache space representing the additional cache space outside the second cache space of the cached data; the prediction model update unit compares the cache space difference value, and when it is determined that the cache space difference value is greater than a preset threshold, sends 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 by 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. For scenarios where data access patterns frequently change, such as social media and news websites, it can effectively improve cache utilization and reduce data loading time.

[0060] Optionally, in this embodiment, the machine learning unit improves the accuracy of data prefetching 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 combined 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 combined 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 values ​​1 to 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. Estimation of 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 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. Through energy consumption optimization, not only can the operating cost of the system be reduced, but the sustainability of the system can also be improved. For applications that require long-term operation and are sensitive to energy consumption, such as Internet of Things device management, remote education platforms, 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 cache update frequency to balance energy consumption and cache freshness; reducing unnecessary prefetching 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 cache operation priorities based on changes in network traffic to balance performance and energy consumption; reducing data prefetching operations during periods of network congestion to reduce network burden; and prefetching data expected to be accessed during periods of network idleness 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 cached 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, via the user feedback receiving module, the target object's satisfaction with the data access performance of the data access management system 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 adjusted cache strategy based on the analysis results. This method can dynamically adjust the cache strategy based on user feedback, ensuring that system performance always meets user needs. For scenarios that are highly sensitive to user experience, such as online shopping and video conferencing, it can provide more personalized and high-quality services.

[0077] The user feedback receiving module includes a user satisfaction survey tool and a performance monitoring tool, while the feedback processing module includes a feedback analysis unit and a policy optimization unit. The user satisfaction survey tool allows users to directly provide feedback on their satisfaction with cache and prefetching services. The performance monitoring tool collects performance metrics 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 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. Through the multi-level cache architecture, the system can automatically adjust the data storage location based on the access frequency and importance of the data, achieving fast data access and efficient storage. For scenarios that require processing large amounts of heterogeneous data, such as scientific research data management and enterprise-level databases, it can significantly improve data processing efficiency and storage space utilization.

[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; and 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 part of the embodiments of the present application, rather than all the 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 achieved 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 monitor data access patterns and user behavior through a real-time monitoring module;

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

[0084] Step S3: Adopting 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 end 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] Among them, 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 temporal patterns of data access, including peak access periods and trough access periods. For example, it analyzes whether there is an access peak between 9 a.m. and 5 p.m. every day.

[0092] The user behavior analysis unit analyzes user access behavior, including user access preferences, access time, and access frequency. For example, it identifies that a user frequently accesses a specific type of data between 8 p.m. and 10 p.m.

[0093] The real-time monitoring module also includes an anomaly detection function for access patterns within a specific time period. This function can identify abnormal access behaviors that exceed normal access patterns. Once an abnormal access is detected, the emergency response process is immediately initiated to prevent service interruptions 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 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 based on 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 modeling unit uses historical energy consumption data and the current system state to predict energy consumption under different cache strategies and formulate energy optimization plans accordingly. For example, it dynamically adjusts the cache update frequency to balance energy consumption with cache freshness, or reduces unnecessary prefetching operations during energy-sensitive periods to avoid unnecessary energy consumption increases.

[0098] The dynamic power control unit adjusts cache operation priorities based on network traffic to balance performance and energy consumption. For example, it reduces data prefetching during periods of network congestion to reduce network burden, or prefetches data expected to be accessed during periods of network inactivity to improve access speed.

[0099] In S5, the user satisfaction survey tool allows users to directly provide feedback on their satisfaction with the cache and prefetching services. Performance monitoring tools collect performance metrics 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 results 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 frequently accessed 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 drives to store low-frequency access data.

[0105] The above embodiment can more accurately identify access patterns and periodic access patterns by using a real-time monitoring module and a data analysis module, a clustering algorithm and an autocorrelation analysis method, thereby improving the accuracy of future data request prediction, and using an adaptive cache strategy adjustment module, the cache content can be dynamically adjusted according to the prediction result, so that the cache strategy is closer to the actual demand, and the invalid data occupies the space is reduced, the energy consumption optimization module is implemented, through the energy consumption model establishment unit and the dynamic power consumption control unit, the data access performance can be guaranteed while the energy consumption is maximized, which meets the trend of green computing, and the method includes a feedback loop mechanism, the system can continuously optimize its cache and prefetch strategy according to new user behavior data and system performance indicators, and the dynamic adaptability of the system is maintained, and through the introduction of the context-aware anomaly detection function, the abnormal access behavior can be more accurately identified, and the security and stability of the system are improved.

[0106] It should be noted that the protection scope of the patent is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope of the patent, which should be covered within the protection scope of the patent. Therefore, the protection scope of the patent should be subject to the protection scope of the claims. Those skilled in the art can easily think of other embodiments of the application after considering the specification and practicing the patent. The present application is intended to cover any variations, uses or adaptations of the application that follow the general principles of the application and include common knowledge or conventional technical means in the art that are not disclosed in the application.

[0107] From the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be realized by means of software and necessary general hardware platform, of course, it can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application 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 plurality of instructions for making a terminal device (which can be a mobile phone, computer, server or network device) execute the methods described in the embodiments of the present application.

[0108] In the present embodiment, a data access management system is also provided, which is used to implement the above embodiments and preferred embodiments, which have been described and 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, hardware or a combination of software and hardware is also possible and contemplated.

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

[0110] The real-time monitoring module 402 is configured to monitor access data of a target object and a data access mode corresponding to the access data, and send the data access mode and the access data to the data analysis module.

[0111] The data analysis module 404 is connected with the real-time monitoring module and is configured to output a data access request based on the data access mode and the access data, the data access request being 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 with the data analysis module and is configured to determine an adjustment operation on cache data in the data access management system based on the data access request.

[0113] The energy consumption optimization module 408 is connected with the adaptive cache strategy adjustment module and is configured to, after the adjustment operation on the cache data in the data access management system, perform an energy consumption optimization operation on the data access management system to reduce system energy consumption of the data access management system.

[0114] The data access module 410 is connected at least with the adaptive cache strategy adjustment module and is configured to determine the data that is expected to be accessed in the cache data as priority access data, and perform a data access operation on the priority access data in response to a current data access request sent by the target object.

[0115] ​Through the above-mentioned 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 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 access module determines the data expected to be accessed pre-fetched in the cache data as priority access data, and performs data access operation on the priority access data in response to the current data access request sent by the target object. 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 delay, and at the same time, reduce the operating cost 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 upgrade, 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 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 corresponds to an access pattern, and selects target group objects corresponding to access patterns with periodic regularities from the multiple groups of objects; the behavior prediction unit predicts future behavior data of the target group objects based on historical access data of the target group objects, determines data expected to be accessed based on the future behavior data, and generates the data access request; wherein, the behavior prediction unit predicts future behavior data of the target group objects based on the historical access data of the target group objects includes: sorting the historical access data according to time series to obtain time series data, performing autocorrelation analysis on the time series data to obtain the autocorrelation function of the time series data, determining periodic access data in the historical access data based on the peak value of the autocorrelation function, and predicting 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 adjustment operations 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 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 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 example embodiment, the energy consumption optimization module comprises an energy consumption model establishing unit and a dynamic power consumption control unit, and the energy consumption optimization operation on the data access management system is performed by the following manner: obtaining the predicted energy consumption of the data access management system under different cache strategies based on the historical energy consumption data of the data access management system and the predicted energy consumption of the data access management system under different cache strategies predicted by the energy consumption model establishing unit in combination with the current system state of the data access management system, determining the energy consumption optimization scheme corresponding to the predicted 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; 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 example embodiment, the data access module comprises a user feedback receiving module and a feedback processing module, and before the data determined to be accessed in the pre-fetching of the cache data is determined as the priority access data, the method further comprises the following steps: receiving the satisfaction of the target object to the data access performance of the data access management system by the user feedback receiving module, and sending the satisfaction to the feedback processing module; analyzing the satisfaction in different evaluation dimensions by the feedback processing module to obtain an analysis result, and generating an adjusted cache strategy based on the analysis result.

[0121] In an example embodiment, the method further comprises the following steps: deploying a multi-level cache architecture in the data access management system to support data migration between storage media with different performance levels through the cache area of the multi-level cache architecture, wherein the cache area of the multi-level cache architecture at least comprises 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 each of the above modules can be implemented by software or hardware, and for the latter, the following implementation manners can be used, but are not limited thereto: all the above modules are located in the same processor; or the above modules are located in different processors in any combination.

[0123] Embodiments of the present application also provide a computer readable storage medium, which stores a computer program, wherein the computer program is configured to execute the steps in any of the above method embodiments when running.

[0124] Optionally, in the present embodiment, the above computer program can be configured to execute the following steps by the computer program:

[0125] S1, obtaining access data of the target object monitored by the real-time monitoring module and a data access pattern 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 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, performing an energy consumption optimization operation on the data access management system through the energy consumption optimization module 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, comprising 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.

[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 execute 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 pattern 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 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, performing an energy consumption optimization operation on the data access management system through the energy consumption optimization module 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, those skilled in the art will appreciate 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 embodiment 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 used specifically, but not limited to, to store information such as distributed storage system configuration files. As an example, if Figure 5 As shown, the memory 502 may include, but is not limited to, all modules of 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 configured to receive or send data via a network. Specific examples of the network may include a wired network and a wireless network. In one embodiment, the transmission device 506 includes a network interface controller (NIC), which can be connected to other network devices and a router via a network cable to communicate with the Internet or a local area network. In one embodiment, the transmission device 506 is a radio frequency (RF) module, which is configured to communicate with the Internet wirelessly.

[0144] In addition, the electronic device further includes: a display 508; and a connection bus 510 for connecting various module components in the electronic device.

[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, comprising 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 of 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 of any 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 here.

[0149] Obviously, those skilled in the art should understand that the modules or steps of the present application described above can be implemented using a general-purpose computing device, they can be concentrated on a single computing device, or distributed across a network composed of multiple computing devices, they can be implemented using program code executable by the computing device, and thus, 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 performed in a different order than herein, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated 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 merely a preferred embodiment of the present application and is not intended to limit the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application shall be included within the scope of protection 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 up in a cloud server, and 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: Acquire access data of the 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 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 an energy consumption optimization operation on the data access management system to reduce the system energy consumption of the data access management system; Determining, by the data access module, the data that is pre-fetched from the cache data and is expected to be accessed 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 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 historical energy consumption data of the data access management system and in combination with the current system state of the data access management system, determining an energy consumption optimization scheme corresponding to the estimated energy consumption, determining a current energy consumption optimization scheme of the data access management system under the current energy consumption from the energy consumption optimization schemes, 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 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.

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 target object access data 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 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, by the behavior prediction unit, future behavior data of the target group objects based on historical access data of the target group objects, determining data expected to be accessed according to the future behavior data, and generating the data access request; The step of 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, the time series data is subjected to autocorrelation analysis to obtain the autocorrelation function of the time series data, the periodic access data in the historical access data is determined according to the peak value of the autocorrelation function, and the future behavior data of the target group objects is predicted according to the periodic access data.

4. The method according to claim 1, wherein The adaptive cache policy adjustment module includes a machine learning unit, a prediction model updating unit, a cache size adjustment unit, and a content replacement policy unit, and adjusts the cache data in the data access management system in the following manner: Sending a cache space difference value output by the prediction model in the machine learning unit to a prediction model updating unit, wherein the cache space difference value represents a difference between a first cache space and a reserved cache space, the first cache space represents a cache space required for the data expected to be accessed, and the reserved cache space represents an additional cache space outside the second cache space for the cached data; The cache space difference is compared by the prediction model update unit. 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 through the content replacement strategy unit.

5. The method according to claim 1, wherein The data access module includes a user feedback receiving module and a feedback processing module. Before determining the pre-fetched data expected to be accessed from the cached 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 target object's satisfaction with 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.

6. 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.

7. A data access management system, It is characterized by: Includes: 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 configured to monitor access data of a target object and a 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 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; and send the data access request to the adaptive cache policy adjustment module; The adaptive cache policy adjustment module is connected to the data analysis module and is used to determine an adjustment operation to be performed on the cached data in the data access management system based on the data access request; The energy consumption optimization module is connected to the adaptive cache policy adjustment module and is used to reduce the system energy consumption of the data access management system by performing an energy consumption optimization operation on the data access management system after the cache data in the data access management system is adjusted; The energy consumption optimization module includes an energy consumption model establishment unit and a dynamic power consumption control unit, which 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 establishment 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, 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; The data access module is connected to at least the adaptive cache policy adjustment module, and is used to determine the data that is pre-fetched from the cache data and is expected to be accessed 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.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 6 are implemented.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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