Data storage management method and system of cloud computer and medium

Abstract

The invention relates to the technical field of data storage, in particular to a data storage management method and system for a cloud computer and a medium, and the method comprises the steps: obtaining access data of different terminal devices, carrying out the integration based on teaching data logic, obtaining the access data of each logic unit and the path distance between every two logic units, and storing the access data. The access habit of a single user is analyzed, and the access popularity index of each user to each logic unit is determined; for every two users with the same identity tags and interaction behaviors, according to the interaction times and the access popularity indexes of every two users and the access habits of edge storage node analysis groups of the two users, determining the storage priority index of each logic unit at the current moment; and adjusting a storage scheme according to the storage priority index and the access heat index of each logic unit at the current moment. According to the method, hierarchical storage is performed on group high-heat data and individual high-heat data, so that the data storage efficiency in a high-concurrent access scene is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data storage, in particular to a data storage management method and system of a cloud computer and a medium. BACKGROUND

[0002] At present, the data required to be stored by a teaching platform includes user learning data and resource cache. For the learning data, such as word query history, oral reading recording, self-defined word library, AI learning plan, etc. For the resource cache, such as offline dictionary package, electronic version of teaching material, etc. When the existing cloud computer stores and manages the above data of the teaching platform, due to the limited storage space of the terminal device, the hot data of the user is usually stored in the terminal device, the cold data is transmitted to the cloud storage, and the update is performed regularly. Among them, the hot data refers to the data frequently accessed in the recent period, and the cold data refers to the data not accessed in the recent period, such as knowledge points at the beginning of the current semester.

[0003] However, the existing data management method lacks a dynamic adjustment mechanism in the application of the actual teaching scene, which leads to a reduction in data storage access efficiency. For example, the same user may log in on multiple devices. The existing method only adjusts the cache data according to the local access information of a single device, which is not conducive to analyzing the personal access habits based on multiple devices to adjust the cache data. In addition, at the end of the term, a large number of users may access the cold data stored in the cloud at the same time, which greatly reduces the access efficiency. SUMMARY

[0004] In order to solve the technical problem of low access efficiency of the above-mentioned data management method applied to the teaching scene, the purpose of the present application is to provide a data storage management method, system and medium of a cloud computer, and the technical solution adopted is as follows: An embodiment of the present application provides a data storage management method of a cloud computer, which comprises the following steps: Obtain the access data of a plurality of users on different terminal devices in a plurality of consecutive monitoring periods; Integrate based on the teaching data logic to obtain the access data of the user in each monitoring period in each logical unit and the path distance between each two logical units; According to the access data of each user in each logical unit and the path distance between each two logical units, analyze the access habits of a single user, and determine the access heat index of each user for each logical unit; For each two users with the same identity tag and interactive behavior, analyze the access habits of the group according to the interaction times of each two users, the access heat index, and the edge storage node of the two users, and obtain each shared area; determining a storage priority index of each logical unit at the current time according to the access frequency of each teacher interacting with the shared area to the shared area in each monitoring period and the access heat index of each user to each logical unit; adjusting the storage scheme by the storage priority index of each logical unit at the current time and the access heat index.

[0005] Further, the access habit of each user is analyzed according to the access data of each user to each logical unit and the path distance between each two logical units, and the access heat index of each user to each logical unit is determined, comprising: For each user, the access frequency of the user to each logical unit in each monitoring period is obtained based on the access data, and the frequently accessed logical units are screened out by the access frequency of each logical unit, which are recorded as logical units to be analyzed; According to the path distance between each two logical units to be analyzed and the corresponding access frequency of each logical unit to be analyzed, the correlation degree of the high-frequency access data of the user on different terminal devices is determined; the correlation degree is inversely proportional to the path distance and proportional to the access frequency; The access heat index of the user to each logical unit is determined based on the access frequency and the access duration of the user to each logical unit in the current monitoring period, and combined with the correlation degree.

[0006] Further, the access heat index of the user to each logical unit is determined based on the access frequency and the access duration of the user to each logical unit in the current monitoring period, and combined with the correlation degree, comprising: The average value of the access duration of the user to all logical units in the current monitoring period is calculated, which is recorded as the access duration average value; The access heat index of the user to each logical unit is determined according to the first ratio of the access duration of the user to each logical unit in the current monitoring period to the access duration average value, the access frequency and the correlation degree; The first ratio, the access frequency and the correlation degree are all proportional to the access heat index.

[0007] Further, the access habit of the group is analyzed according to the interaction times of each two users, the access heat index and the edge storage node of the two users, and each shared area is obtained, comprising: According to the interaction times of each two users, the corresponding same high heat logical unit number, the average value of the access heat index corresponding to the same high heat logical unit and the total number of logical units, the evaluation index of each two users belonging to the same shared area is determined; the high heat logical unit is the logical unit with the access heat index greater than the set access heat threshold. An evaluation threshold is set, two users with the evaluation index greater than the evaluation threshold and having the same edge storage node are merged into a shared area, and each shared area is obtained.

[0008] Further, the evaluation index of each two users belonging to the same shared area is determined according to the interaction times of each two users, the corresponding same high-heat logical unit number, the mean value of the access heat index corresponding to the same high-heat logical unit, and the total number of logical units, and the evaluation index of each two users belonging to the same shared area is determined. The second ratio of the same high-heat logical unit number corresponding to each two users to the total number of logical units is determined. The evaluation index of each two users belonging to the same shared area is determined according to the interaction times of each two users, the second ratio, and the mean value of the access heat index. The interaction times, the second ratio, and the mean value of the access heat index are all proportional to the evaluation index.

[0009] Further, the storage priority index of each logical unit at the current time is determined according to the access frequency of each teacher having an interaction behavior with the shared area for the shared area in each monitoring period and the access heat index of each user for each logical unit. For each shared area, the third ratio between the access frequency of each teacher having an interaction behavior with the shared area for the shared area in the current monitoring period and the mean value of the access frequency for the shared area in all monitoring periods is determined. The pre-examination behavior characteristic index of each teacher having an interaction behavior with the shared area is determined according to the third ratio between the access frequency and the mean value of the access frequency, and the mean value of the path distance between each two high-heat logical units accessed by the teacher in the current monitoring period; the third ratio and the mean value of the path distance are both proportional to the pre-examination behavior characteristic index. The number of students in the shared area marking the target logical unit as a high-heat logical unit at the current time and the total number of students in the shared area are obtained; the target logical unit is any logical unit. The storage priority index of the target logical unit at the current time is determined according to the fourth ratio of the number of students to the total number of students, the mean value of the access heat index of each teacher having an interaction behavior with the shared area for the target logical unit in the current monitoring period, and the mean value of the pre-examination behavior characteristic index.

[0010] Further, the fourth ratio of the number of students to the total number of students, the average of the access heat index of each teacher interacting with the shared area to the target logical unit in the current monitoring period, and the average of the pre-examination behavior characteristic index are used to determine the storage priority index of the target logical unit at the current time, comprising: obtaining a time characteristic value of the shared area at the current time, wherein the time characteristic value is determined by the time length from the current time to the next examination time; determining the storage priority index of the target logical unit at the current time according to the time characteristic value, the fourth ratio, the average of the access heat index, and the average of the pre-examination behavior characteristic index; The time characteristic value, the fourth ratio, the average of the access heat index, and the average of the pre-examination behavior characteristic index are all proportional to the storage priority index.

[0011] Further, the storage scheme is adjusted by the storage priority index and the access heat index of each logical unit at the current time, comprising: For any terminal device of any user, the first target cache data is screened out based on the access heat index, the first target cache data is moved from the device cache to the cloud storage, and the current remaining capacity of the terminal device is obtained; the first target cache data refers to the related data of the logical unit which is not marked as a high-heat logical unit from the last monitoring period of the current monitoring period to the current time, and the related data is all types of teaching data corresponding to the construction of the logical unit; According to the order of the access heat index of each logical unit from large to small, the related data of the logical unit meeting the storage data type of the terminal device is stored in the terminal device until the storage amount reaches a preset percentage of the current remaining capacity, and the remaining data is stored in the corresponding edge storage node of the user; For a single shared area, the second target cache data is screened out based on the storage priority index, the second target cache data is moved from the edge storage node to the cloud storage, and the third target cache data is stored in the corresponding edge storage node based on differential storage; The second target cache data refers to the related data of the logical unit whose storage priority index is less than a preset priority threshold from the last monitoring period of the current monitoring period to the current time, and the third target cache data refers to the related data of the logical unit whose storage priority index is not less than the preset priority threshold.

[0012] Another embodiment of the application provides a data storage management system of a cloud computer, comprising: The data acquisition module is used for acquiring access data of a plurality of users at different terminal devices in a plurality of monitoring periods in succession, and integrating the access data based on teaching data logic to obtain access data of the users at each logical unit and path distance between each two logical units; The data processing module is used for analyzing access habits of a single user according to the access data of each user at each logical unit and the path distance between each two logical units, determining an access heat index of each user for each logical unit, analyzing access habits of a group according to the number of interactions, the access heat index and edge storage nodes of each two users for each two users with the same identity tag and interactive behaviors, obtaining each shared area, and determining a storage priority index of each logical unit at a current time according to an access frequency of each teacher in each monitoring period to the shared area and the access heat index of each user to each logical unit for each user with interactive behaviors with the shared area. The data storage module is used for adjusting a storage scheme according to the storage priority index and the access heat index of each logical unit at the current time.

[0013] A data storage medium of a cloud computer is provided in still another embodiment of the application, which comprises a processor and a memory, and the processor is used for processing instructions stored in the memory to implement a data storage management method of a cloud computer.

[0014] The application has the following beneficial effects: The application provides a data storage management method, system and medium of a cloud computer, which determines an access heat index and a storage priority index of each logical unit, and stores group hot data and individual hot data in layers to reduce the situation that cache updating of cold data is not timely in a high concurrency scenario, and improve the storage efficiency of data. In order to adapt to the complex characteristics of access data in a teaching scenario, the cache is analyzed in units of logical units, which saves the efficiency of data storage analysis to a certain extent; the access heat index of each user to each logical unit is determined in combination with access data of a plurality of devices, which can fully analyze the access habits of individual users to reflect the real and comprehensive access habits of the users, and avoid wasting storage resources; users with similar access characteristics are divided into a shared area, the access frequency of each teacher in each monitoring period to the shared area and the access heat index of each user to each logical unit for each user with interactive behaviors with the shared area are analyzed, and the storage priority index of each logical unit at a current time is determined, which can avoid the situation that a large number of users send access requests to data of the same logical unit in a similar time period, and make a large number of users have the possibility of cache repeated data. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0016] Figure 1 An implementation flowchart of a data storage management method of a cloud computer provided for an embodiment of the present application is shown in the figure. Figure 2 A tree diagram of a logical unit in an embodiment of the present application is shown in the figure. Figure 3 An implementation flowchart of step S3 in an embodiment of the present application is shown in the figure. Figure 4 An implementation flowchart of step S4 in an embodiment of the present application is shown in the figure. Figure 5 An implementation flowchart of step S5 in an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0017] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the specific implementation, structure, features and effects of the technical solutions proposed according to the present application are described in detail below in combination with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0019] The application scenarios of the present application can be: The existing data management method applied to the actual teaching scene cannot adjust the cache data according to the actual situation, thereby reducing the efficiency of related data storage access. The present application analyzes the access habits of users and group access habits based on the access data of multiple terminal devices, so as to store individual hot data and group hot data in layers, thereby effectively improving the data storage efficiency in a high-concurrency access scene.

[0020] An embodiment of the present application provides a data storage management method of a cloud computer, as shown in the figure, comprising the following steps: Figure 1 S1, obtaining access data of a plurality of users on different terminal devices in a plurality of monitoring periods. S2, analyzing the access data to obtain individual high-heat data and group high-heat data.

[0021] Here, the monitoring period includes a current monitoring period and a historical monitoring period, the current monitoring period is a monitoring period containing a current time, and the monitoring period can be set to 24 hours; the terminal device can be a teaching device, and one user corresponds to one account in different teaching devices.

[0022] In this embodiment, in order to facilitate subsequent analysis of individual high-temperature data and group high-temperature data, the access data of each user on the corresponding teaching device in each monitoring period is obtained, such as the timestamp and access duration of chapter clicks, audio playback, and data download.

[0023] The number of monitoring periods can be set by the implementer according to the actual situation, and the experience value can be 7, which is not limited here. The teaching devices accessed by the user and the data accessed in the teaching device can be different in different monitoring periods; the types and numbers of teaching devices used by different users can be different, so only the access data of the teaching devices logged into the user account is obtained.

[0024] It should be noted that the weak network device such as the word machine transmits the access record of the user to the cloud storage through the differential synchronization protocol at a fixed time every day, and the rest of the devices are updated in real time.

[0025] At this point, the access data of each user on different terminal devices in each monitoring period is obtained.

[0026] S2, based on the teaching data logic, the access data of each user in each logical unit and the path distance between each two logical units in each monitoring period are obtained.

[0027] The data stored by the teaching platform is relatively disordered, which includes heterogeneous data such as text, pictures, and exercises. Different types of heterogeneous data are usually stored in different locations of the database, and different teaching devices can store different types of data. Therefore, in order to facilitate subsequent cache analysis, the access data of each user on different teaching devices in each monitoring period is integrated into the access data of the logical unit according to the teaching data logic.

[0028] As an exemplary embodiment, the access data of each logical unit includes: In order to realize the storage of chapter data, the knowledge points, pictures, exercises, etc. of a certain unit can be aggregated into a logical unit. Each user has multiple teaching devices, and each teaching device has its corresponding access data. The access data is divided into different logical units to obtain the access data of each teaching device in part of the logical unit, and then the access data of each user in each logical unit in each monitoring period is obtained.

[0029] It should be noted that the access data is only a part of all data corresponding to the logical unit, and subsequent cache allocation is adjusted in units of logical units. The specific logical unit storage granularity can be adjusted according to the specific teaching scene requirements.

[0030] In order to facilitate subsequent analysis of the correlation between the two logical units, the path distance between each two logical units needs to be determined. As an exemplary embodiment, the step of obtaining the path distance between each two logical units includes: Specifically, a tree diagram of the logical unit is drawn according to the logical relationship of the teaching data, and the path distance in the tree diagram is assigned through the logical relationship, to obtain the path distance between each two logical units.

[0031] Among them, the adjacent nodes between the same level are connected by a dashed line, and the path distance is assigned as 1. The path distance between adjacent levels is assigned as follows: the path distance from the last layer node to its parent node is 2, the path distance from the parent node to the grandparent node is 3, and so on. The tree diagram of the logical unit is shown in Figure 2 .

[0032] It should be noted that the path distance between two logical units can be the minimum value of the sum of the lengths of all line segments between the two logical units. The smaller the path distance, the stronger the correlation between the corresponding two logical units.

[0033] At this point, the access data of each user in each logical unit and the path distance between each two logical units in each monitoring period are obtained.

[0034] S3, according to the access data of each user in each logical unit and the path distance between each two logical units, analyze the access habit of a single user, and determine the access heat index of each user for each logical unit.

[0035] Here, the access heat index is an index determined by analyzing the access data characteristics of a single user, which can be used to represent the user's access to learning materials in each logical unit in different monitoring periods. The larger the access heat index, the more important the learning materials in the logical unit to the user, and the more it needs to be cached in the terminal device to facilitate user access.

[0036] It is common for the same user to log in to the same account on multiple teaching devices. Current data management methods mostly update the cached data of corresponding devices based on the user's data access patterns on their local devices. This results in the access data of a single teaching device failing to reflect the user's true and comprehensive access habits, and also easily leads to duplicate caching of learning materials across different teaching devices, causing a waste of storage resources. Therefore, it is necessary to analyze user access habits by combining access data from different teaching devices, that is, to determine the access frequency index of each user for each logical unit.

[0037] In this embodiment, taking a single user as an example, there may be situations where a user's access habits on different teaching devices are quite fragmented, such as only looking up English words on a single machine or only watching teaching videos on a computer. Therefore, based on the access data of each monitoring period, the correlation between the user's access habits on multiple teaching devices is analyzed, and the correlation between teaching devices is used as a weight to analyze the access popularity index of a single user for a certain logical unit.

[0038] As an exemplary implementation, step S3 described above can be achieved through... Figure 3 Steps S301 to S303 shown are implemented as follows: S301: Based on access data, obtain the user's access frequency for each logical unit in each monitoring period, and filter out the frequently accessed logical units by access frequency of each logical unit, and record them as the logical units to be analyzed.

[0039] Specifically, based on all access data corresponding to the user, the access frequency of each logical unit on all teaching devices in all monitoring periods is obtained, and then the average access frequency of all logical units on all teaching devices is determined as the frequency threshold. Logical units with access frequencies greater than the frequency threshold are regarded as frequently accessed logical units and are recorded as logical units to be analyzed.

[0040] Here, access frequency refers to the number of times a user accesses learning materials belonging to the same logical unit multiple times through different terminal devices.

[0041] S302, based on the path distance between every two logical units to be analyzed and the access frequency corresponding to each logical unit to be analyzed, determine the degree of correlation of high-frequency access data of users on different terminal devices.

[0042] Here, the degree of correlation is inversely proportional to the path distance and directly proportional to the access frequency.

[0043] The shorter the path distance between two to-be-analyzed logical units in the embodiment, the smaller the length of the associated path between the frequently accessed logical units, the stronger the correlation of the data frequently accessed by the user on the multiple teaching devices, and the higher the correlation degree of the access habits of different terminal devices. The access features of the logical units frequently accessed by the user can be identified according to the access frequency corresponding to the to-be-analyzed logical units, and the access habits of the user can be further inferred, for example, the access frequency can represent the stability of the access within a period of time. If the access frequency of some logical units is maintained at a high level for a long time, it indicates that the user has a fixed access habit for these logical units, and therefore these frequently accessed logical units can reflect the regular needs or preferences of the user.

[0044] As an example, the calculation formula of the correlation degree of the data frequently accessed by the i th user on different terminal devices can be: In the formula, the correlation degree of the data frequently accessed by the i th user on different terminal devices is represented by D i, the normalization function is represented by N, the average value of the access frequency corresponding to all to-be-analyzed logical units of the i th user is represented by F i, and the average value of the path distance between every two to-be-analyzed logical units corresponding to the i th user is represented by D i.

[0045] In the calculation formula of the correlation degree, the smaller the average value of the path distance, the higher the correlation degree of the frequently accessed logical units, and the larger the average value of the access frequency, the more frequently the user accesses the to-be-analyzed logical units, which can reflect the access habits of the user, discover the potential rules of the user habits, highlight the data features of the average value of the path distance, and make the value of the correlation degree determined by D i more reliable.

[0046] In S303, the access frequency and the access duration of the user for each logical unit in the current monitoring period are obtained based on the access data, and the access heat index of the user for each logical unit is determined in combination with the correlation degree.

[0047] In the embodiment, in the current monitoring period containing the current time, the higher the access frequency of the user for a certain logical unit in all teaching devices up to the current time and the longer the access duration each time, and the larger the correlation degree of the access habits of the user on multiple teaching devices, the higher the access heat index of the user for the corresponding logical unit at the current time.

[0048] ​​​​​​​​​Specifically, an average value of the access duration of the user for all logical units in the current monitoring period is calculated, denoted as the access duration average value; according to a first ratio of the access duration of the user for each logical unit in the current monitoring period to the access duration average value, the access frequency and the association degree, the access heat index of the user for each logical unit is determined; wherein the first ratio, the access frequency and the association degree are all proportional to the access heat index.

[0049] As an example, the calculation formula of the access heat index of the i th user for the k th logical unit at the current time can be: ; in the formula, denotes the access heat index of the i th user for the k th logical unit at the current time, denotes the association degree of the i th user to frequently access data on different terminal devices, denotes the access frequency of the i th user for the k th logical unit in the current monitoring period, denotes the access duration of the i th user for the k th logical unit in the current monitoring period, denotes the access duration of the i th user for all logical units in the current monitoring period, that is, the access duration average value, denotes the first ratio of the access duration of the i th user for the k th logical unit to the access duration average value, and norm denotes a normalization function for limiting data between 0 and 1.

[0050] Referring to the access heat index of the i th user for the k th logical unit, the access heat index of each user for each logical unit can be obtained.

[0051] Thus, the access heat index of each user for each logical unit is obtained.

[0052] S4, for each two users with the same identity tag and interactive behavior, the access habits of the group are analyzed according to the interaction times, the access heat index of each two users and the edge storage node of the two users, and each sharing area is obtained.

[0053] Here, for the edge storage node of the user, the edge storage node corresponding to the user and the edge storage node with the largest remaining capacity and the nearest spatial distance at the current time is obtained, and the edge storage node of each user is obtained. The sharing area is to divide users with the same access characteristics into a group, and the sharing area is composed of multiple users, which can be used to analyze the group access characteristics.

[0054] Generally, there can be certain similarities in the access habits between users who have information sharing behaviors, such as sharing knowledge points among classmates or distributing exercises from a teacher to students, and if the cache is updated only based on the access habits of a single user, a large number of users can simultaneously send access requests for access data of the same logical unit in a similar time period, which can cause a large number of users to have the possibility of repeated data in the cache. Therefore, a shared area containing multiple users for analyzing group access characteristics needs to be obtained based on the access correlation between users.

[0055] In this embodiment, two users with the same identity tag and interactive behaviors are taken as an example. When the interaction frequency of the two users in all monitoring periods is high and the access to the logical units is similar, the possibility that the two users belong to the same shared area is greater. If the remaining users also meet the above requirements with any user in the shared area, the users meeting the requirements are added to the current shared area, and the above operation is repeated until there is no user meeting the requirements, so as to obtain each shared area.

[0056] Among them, the interactive behaviors are counted by background recording information, and the interactive behaviors can be resource sharing, joint editing, handwriting interaction, group learning, and mistake question help, and each occurrence of the interactive behaviors is counted as one interaction, such as sharing once being equal to one interaction.

[0057] As an exemplary embodiment, the above step S4 can be implemented by the steps S401 to S402 shown in the figure: Figure 4 S401, according to the interaction times of each two users, the number of corresponding same hot logical units, the mean value of the access heat index corresponding to the same hot logical units, and the total number of logical units, to determine the evaluation index of each two users belonging to the same shared area.

[0058] Here, the hot logical unit is a logical unit with an access heat index greater than a set access heat threshold, and the access heat threshold can be set to 0.5. The implementer can set it according to the specific actual situation, which is not limited here.

[0059] For each two users, the i-th user and the j-th user are taken as an example. Since there can be a case that the interaction is frequent but the distance is far or unstable, for the i-th user, the user with a similar access time and a high access interaction frequency is preferred in the traversal, and the user without interaction with the i-th user is not involved in the traversal. If the i-th user and the j-th user do not meet the above requirements, the i-th user is not involved in the traversal. ​​​If two users interact frequently and share many identical high-frequency logic units, then it is highly likely that the two users belong to the same shared group, meaning that the evaluation index of the two users belonging to the same shared area is relatively high.

[0060] Specifically, determine the i-th user and the... The second ratio of the number of identical high-heat logic units corresponding to each user to the total number of logic units; based on the i-th user and the... The average of the number of interactions of each user, the second ratio, and the access popularity index are used to determine the relationship between the i-th user and the i-th user. Evaluation metrics for users belonging to the same shared area; the average of interaction count, second ratio, and access popularity index are all directly proportional to the evaluation metrics.

[0061] As an example, the i-th user and the i-th The formula for calculating the evaluation index for users belonging to the same shared area can be: In the formula, Represents the i-th user and the i-th... The evaluation metric for users belonging to the same shared area, where t represents the current time. This indicates that, up to the current moment, the i-th user and the... Number of interactions per user Represents the i-th user and the i-th... The average access popularity index of each user for the same high-popularity logical unit. Represents the i-th user and the i-th... The number of identical high-heat logic units corresponding to each user, where n represents the total number of logic units, is used to analyze the proportion of identical high-heat logic units. Represents the i-th user and the i-th... The second ratio of the number of identical high-temperature logic units corresponding to each user to the total number of logic units, where norm represents the normalization function used to limit the data to between 0 and 1.

[0062] Referring to the i-th user and the i-th... The evaluation metric for each user belonging to the same shared area can be obtained by comparing the evaluation metrics for each pair of users belonging to the same shared area.

[0063] S402, set the evaluation threshold, merge two users with the same edge storage node whose evaluation index is greater than the evaluation threshold into a shared area, and obtain each shared area.

[0064] In this embodiment, the evaluation threshold is set to 0.5, and two users with the same edge storage node and whose evaluation indexes are greater than the evaluation threshold 0.5 are divided into the same shared area, thereby obtaining each shared area. The evaluation threshold can be set by the implementer according to specific actual conditions, which is not specifically limited here.

[0065] In a single shared area, the number of users of a certain logical unit that are marked as high-heat logical units in the current shared area is obtained. If the number of users marked as high-heat logical units is greater than half the number of all users in the current shared area, it is determined that the corresponding logical unit is a group high-heat logical unit, i.e., group high-heat access data.

[0066] It should be noted that, for the current user, in addition to the group high-heat logical unit, the remaining high-heat logical unit corresponding to the user itself is individual high-heat access data. For individual high-heat access data, such as a high-frequency error question set established by the user itself.

[0067] Up to now, the users with similar access characteristics are divided into the same shared area in this embodiment, and each shared area is obtained.

[0068] S5, according to the access frequency of each teacher who has interactive behavior with the shared area in each monitoring period for the shared area and the access heat index of each user for each logical unit, determining the storage priority index of each logical unit at the current time.

[0069] Here, the storage priority index represents the priority of the access data of different logical units when performing cache management.

[0070] In the teaching scenario, there are a large number of users simultaneously accessing a group of logical units with low heat, for example, before the final exam, a large number of students begin to review the knowledge points at the beginning of the semester, at this time, it is necessary to store the related data in the adjacent edge storage node in advance, otherwise a large number of concurrent access requests may cause a significant reduction in storage efficiency, but the existing data management method cannot realize the above operation, and depends on the test time input by the user.

[0071] In order to realize the above operation, the storage priority index of each logical unit is analyzed based on the timing characteristics and teacher behavior.

[0072] As an exemplary embodiment, the above step S5 can be implemented by the steps S501 to S504 shown in the following table: Figure 5 S501, for each shared area, determining the third ratio between the access frequency of each teacher who has interactive behavior with the shared area in the current monitoring period for the shared area and the average access frequency of the shared area in all monitoring periods. ​

[0073] In this embodiment, for each teacher, the access frequency of the shared area in the current monitoring period refers to the total frequency of access to all student users in the shared area, such as the cumulative value of the access frequency of the student mistake statistics page in the shared area, and the average access frequency refers to the average value of the access frequency of the shared area by the teacher with interactive behavior in all monitoring periods.

[0074] Among them, the third ratio can represent the proportion of the access frequency of the shared area in the current monitoring period to all monitoring periods, for example, the larger the third ratio, the greater the access frequency of the student mistake statistics page in the shared area by the teacher up to the current time in the current period compared to the historical data.

[0075] S502, according to the third ratio between the access frequency and the average access frequency, and the average path distance between each two high-heat logical units accessed by the teacher in the current monitoring period, determine the pre-examination behavior characteristic index of each teacher with interactive behavior in the shared area.

[0076] Here, the third ratio and the average path distance are directly proportional to the pre-examination behavior characteristic index.

[0077] Since the teacher access behavior has a greater impact on the group access habits in the teaching scenario, the pre-examination behavior characteristic index is quantified based on the teacher access situation, so as to facilitate subsequent determination of the storage priority index of the logical unit at the current time.

[0078] In this embodiment, the teacher with interactive behavior in the shared area accesses the mistake statistics data of the student users in the corresponding shared area in a short period of time, and frequently accesses the access data of two logical units with far path distance, which indicates that the student users in the shared area may be close to the examination or the teaching focus of the next learning stage will change at the current time, that is, the possibility of a large number of student users in the shared area accessing related logical units in a short period of time is greater.

[0079] It should be noted that if the teacher accesses the student mistake page, the teacher can only obtain the statistical data of the student mistakes in the shared area, and cannot obtain the personal information of the student; the two logical units with far path distance may be knowledge points with low correlation.

[0080] Taking the zth teacher with interactive behavior in the jth shared area as an example, the access frequency of the zth teacher to the student mistake statistics page in the jth shared area up to the current time in the current monitoring period is higher than the historical data, and the path distance between the logical units accessed by the zth teacher is far from each other, the degree of the zth teacher behavior conforming to the pre-examination behavior characteristic is higher, that is, the pre-examination behavior characteristic index is larger.

[0081] As an example, the calculation formula of the pre-examination behavior characteristic index of the zth teacher who has interactive behavior with the jth shared area can be: ; wherein, represents the pre-examination behavior characteristic index of the zth teacher who has interactive behavior with the jth shared area, represents the access frequency of the zth teacher who has interactive behavior with the jth shared area to the shared area in the current monitoring period, represents the average access frequency of the zth teacher who has interactive behavior with the jth shared area to the shared area in all monitoring periods, represents the third ratio between the access frequency and the average access frequency, represents the average path distance between every two hot logic units accessed by the zth teacher who has interactive behavior with the jth shared area in the current monitoring period.

[0082] Referring to the pre-examination behavior characteristic index of the zth teacher who has interactive behavior with the jth shared area described above, the pre-examination behavior characteristic index of each teacher who has interactive behavior with the shared area can be obtained.

[0083] S503, obtaining the number of students who mark the target logic unit as a hot logic unit in the shared area at the current time, and the total number of students in the shared area.

[0084] Here, the target logic unit is any logic unit.

[0085] In this embodiment, for the kth logic unit, the number of students who are marked as hot logic units in the current monitoring period containing the current time and are located in the shared area.

[0086] S504, determining the storage priority index of the target logic unit at the current time according to the fourth ratio between the number of students and the total number of students, the average access heat index of each teacher who has interactive behavior with the shared area to the target logic unit in the current monitoring period, and the average pre-examination behavior characteristic index.

[0087] In this embodiment, the fourth ratio can represent the degree to which the access data of the target logical unit belongs to the group of high heat data, the greater the fourth ratio, the greater the proportion of the target logical unit being marked as a high heat logical unit in the shared area, the greater the possibility of the target logical unit being accessed simultaneously, and therefore the greater the storage priority index of the target logical unit at the current time; the access heat index mean can represent the access heat of the teacher to the target logical unit, the greater the access heat index mean, since the access of the teacher in the teaching system has a guiding effect on the students, the students will also access the target logical unit, and the storage level of the target logical unit should be higher; the pre-examination behavior characteristic index can represent the degree to which the teacher access behavior conforms to the pre-examination behavior characteristic, the greater the mean of the pre-examination behavior characteristic index, the greater the influence of the teacher access behavior on the group access habits of the student users in the shared area, and the greater the storage priority index of the target logical unit corresponding to the user in the shared area at the current time.

[0088] Specifically, a time characteristic value of the shared area at the current time is obtained, and the storage priority index of the target logical unit at the current time is determined according to the time characteristic value, the fourth ratio, the access heat index mean, and the mean of the pre-examination behavior characteristic index; wherein the time characteristic value is determined by the time length from the current time to the next examination time, and the time characteristic value, the fourth ratio, the access heat index mean, and the mean of the pre-examination behavior characteristic index are all directly proportional to the storage priority index.

[0089] As an exemplary embodiment, the calculation formula of the storage priority index of the kth logical unit at the current time can be: ; in the formula, represents the storage priority index of the kth logical unit at the current time, represents the time characteristic value at the current time, represents the number of students in the jth shared area who mark the kth logical unit as a high heat logical unit at the current time, represents the total number of students in the jth shared area, represents the access heat index mean of the kth logical unit by all teachers who have interactive behavior with the jth shared area at the current monitoring period, represents the mean of the pre-examination behavior characteristic index of all teachers who have interactive behavior with the jth shared area.

[0090] The time characteristic value is: represents the time length from the current time to the next examination time, represents a non-zero constant, which is used to avoid the case that the denominator of the fraction is zero; if there is no examination time input by the user, ​As to the time characteristic value, it is mainly used to ensure that the examination time is zero, and does not affect the subsequent storage priority analysis, that is, if there is no user to input the accurate examination time in the system, the priority can still be adjusted according to the subsequent calculation.

[0091] Referring to the determination process of the storage priority index of the kth logical unit at the current time, the storage priority index of each logical unit at the current time can be obtained.

[0092] Up to now, the storage priority index of each logical unit in each shared area at the current time is obtained in the embodiment.

[0093] S6, adjusting the storage scheme through the storage priority index of each logical unit at the current time and the access heat index.

[0094] After determining the storage priority index and the access heat index, the group high-heat access data and the personal high-heat access data are determined based on the two indexes. Since the storage capacity of different educational devices is limited, and the types of data that can be stored are different, the data that is personally high-heat access but has low group access heat is cached in the personal terminal device based on the storage capacity of the terminal device, the group high-heat access data is transmitted to the edge storage node, and the remaining access data is transmitted to the cloud.

[0095] As an exemplary embodiment, the above step S6 can be realized by the steps S601 to S603 shown (not shown in the figure): S601, for any terminal device of any user, the first target cache data is screened out based on the access heat index, the first target cache data is moved from the device cache to the cloud storage, and the current remaining capacity of the terminal device is obtained.

[0096] In the embodiment, the first target cache data refers to the related data of the logical unit that is not marked as high heat from the beginning of the last monitoring period of the current monitoring period to the current time. The related data is all types of teaching data corresponding to the construction of the logical unit, that is, the knowledge points, pictures, exercises, etc. of a unit are aggregated into a logical unit, which can also be called all types of learning materials in a logical unit.

[0097] S602, according to the order of the access heat index of each logical unit from large to small, the related data of the logical unit that meets the storage data type of the terminal device is stored in the terminal device until the storage amount reaches the preset percentage of the current remaining capacity, and the remaining data is stored in the edge storage node corresponding to the user.

[0098] In the embodiment, the preset percentage can be set to 80%, which can be set by the implementer according to the specific actual situation, which is not limited here.

[0099] S603, for a single shared area, screening out second target cache data based on the storage priority index, moving the second target cache data from the edge storage node to the cloud storage, and storing the third target cache data to the corresponding edge storage node based on the differential storage.

[0100] In this embodiment, the second target cache data refers to the relevant data of the logical unit whose storage priority index is less than the preset priority threshold from the beginning of the last monitoring period to the current time in the current monitoring period, and the third target cache data refers to the relevant data of the logical unit whose storage priority index is not less than the preset priority threshold.

[0101] The preset priority threshold can be set to 0.6, which can be set by the implementer according to the actual situation, and is not limited here.

[0102] At this point, after obtaining the storage scheme, data storage can be performed based on the storage scheme to improve storage efficiency.

[0103] Another embodiment of the application provides a data storage management system of a cloud computer, comprising: The data acquisition module is configured to acquire access data of a plurality of users on different terminal devices in a plurality of consecutive monitoring periods, and integrate the access data based on the teaching data logic to obtain access data of the users on each logical unit and path distance between each two logical units. The data processing module is configured to analyze the access habits of each user based on the access data of each user on each logical unit and the path distance between each two logical units, determine the access heat index of each user on each logical unit, analyze the access habits of a group of users based on the interaction times of each two users, the access heat index, and the edge storage nodes of the two users for each two users with the same identity tag and interaction behavior, and obtain each shared area, and determine the storage priority index of each logical unit at the current time based on the access frequency of each teacher on each shared area in each monitoring period and the access heat index of each user on each logical unit. The data storage module is configured to adjust the storage scheme based on the storage priority index and the access heat index of each logical unit at the current time.

[0104] Still another embodiment of the application provides a data storage medium of a cloud computer, comprising a processor and a memory, wherein the processor is configured to process instructions stored in the memory to implement a data storage management method of a cloud computer.

[0105] The above-described embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A data storage management method of a cloud computer, characterized by, The method comprises the following steps: obtaining access data of a plurality of users on different terminal devices in a plurality of monitoring periods in succession; integrating the access data based on teaching data logic to obtain access data of each user on each logical unit and path distance between each two logical units in each monitoring period; analyzing access habits of each user according to the access data of each user on each logical unit and the path distance between each two logical units to determine an access heat index of each user on each logical unit; for each two users with the same identity tag and interactive behavior, analyzing access habits of a group according to the number of interactions of each two users, the access heat index and edge storage nodes of the two users to obtain each shared area; determining a storage priority index of each logical unit at a current time according to the access frequency of each teacher on each shared area in each monitoring period and the access heat index of each user on each logical unit; adjusting a storage scheme according to the storage priority index and the access heat index of each logical unit at the current time.

2. The data storage management method of claim 1, wherein, The method of determining the access heat index of each user on each logical unit according to the access data of each user on each logical unit and the path distance between each two logical units comprises: for each user, obtaining the access frequency of the user on each logical unit in each monitoring period based on the access data, and screening out frequently accessed logical units as to-be-analyzed logical units according to the access frequency of each logical unit; determining the correlation degree of high-frequency access data of the user on different terminal devices according to the path distance between each two to-be-analyzed logical units of the user and the access frequency corresponding to each to-be-analyzed logical unit; the correlation degree is inversely proportional to the path distance and proportional to the access frequency; obtaining the access frequency and access duration of the user on each logical unit in the current monitoring period based on the access data, and determining the access heat index of the user on each logical unit in combination with the correlation degree.

3. The data storage management method of claim 2, wherein, The method of determining the access heat index of the user on each logical unit in combination with the correlation degree according to the access frequency and access duration of the user on each logical unit in the current monitoring period comprises: calculating the average value of the access duration of the user on all logical units in the current monitoring period, denoted as the access duration average value; determining the access heat index of the user on each logical unit according to the first ratio of the access duration of the user on each logical unit in the current monitoring period to the access duration average value, the access frequency and the correlation degree; the first ratio, the access frequency and the correlation degree are all proportional to the access heat index.

4. The data storage management method of claim 1, wherein, The method of obtaining each shared area by analyzing the access habits of the group according to the number of interactions of each two users, the access heat index and the edge storage nodes of the two users comprises: According to the interaction times of each two users, the corresponding same high-heat logical unit number, the mean of the access heat index corresponding to the same high-heat logical unit, and the total number of logical units, an evaluation index of each two users belonging to the same shared area is determined; the high-heat logical unit is a logical unit with an access heat index greater than a set access heat threshold value; An evaluation threshold value is set, and two users with the evaluation index greater than the evaluation threshold value and having the same edge storage node are merged into a shared area to obtain each shared area.

5. The data storage management method of claim 4, wherein, The evaluation index of each two users belonging to the same shared area is determined according to the interaction times of each two users, the corresponding same high-heat logical unit number, the mean of the access heat index corresponding to the same high-heat logical unit, and the total number of logical units, and the evaluation index is determined. A second ratio of the same high-heat logical unit number corresponding to each two users to the total number of logical units is determined. The evaluation index of each two users belonging to the same shared area is determined according to the interaction times of each two users, the second ratio, and the mean of the access heat index. The interaction times, the second ratio, and the mean of the access heat index are all directly proportional to the evaluation index.

6. The data storage management method of a cloud computer according to claim 4, wherein, The storage priority index of each logical unit at the current time is determined according to the access frequency of each teacher having an interactive behavior with the shared area for the shared area in each monitoring period and the access heat index of each user for each logical unit. For each shared area, a third ratio between the access frequency of each teacher having an interactive behavior with the shared area for the shared area in the current monitoring period and the mean of the access frequency for the shared area in all monitoring periods is determined. The pre-examination behavior characteristic index of each teacher having an interactive behavior with the shared area is determined according to the third ratio between the access frequency and the mean of the access frequency, and the mean of the path distance between each two high-heat logical units accessed by the teacher in the current monitoring period; the third ratio and the mean of the path distance are both directly proportional to the pre-examination behavior characteristic index. The number of students in the shared area who mark the target logical unit as a high-heat logical unit at the current time is obtained, and the total number of students in the shared area is obtained; the target logical unit is any logical unit. The storage priority index of the target logical unit at the current time is determined according to the fourth ratio of the number of students to the total number of students, the mean of the access heat index of each teacher having an interactive behavior with the shared area for the target logical unit in the current monitoring period, and the mean of the pre-examination behavior characteristic index.

7. The data storage management method of a cloud computer according to claim 6, wherein, The storage priority index of the target logical unit at the current time is determined according to the fourth ratio of the number of students to the total number of students, the mean of the access heat index of each teacher having an interactive behavior with the shared area for the target logical unit in the current monitoring period, and the mean of the pre-examination behavior characteristic index, which comprises: A time characteristic value of the shared area at the current time is obtained, and the time characteristic value is determined by the time length from the current time to the next examination time. Determine the storage priority index of the target logical unit at the current time according to the time characteristic value, the fourth ratio, the average of the access heat index, and the average of the pre-examination behavior characteristic index; The time characteristic value, the fourth ratio, the average of the access heat index, and the average of the pre-examination behavior characteristic index are all proportional to the storage priority index.

8. The data storage management method of a cloud computer according to claim 1, wherein, Adjust the storage scheme through the storage priority index and the access heat index of each logical unit at the current time, including: For any terminal device of any user, filter the first target cache data based on the access heat index, move the first target cache data from the device cache to the cloud storage, and obtain the current remaining capacity of the terminal device; The first target cache data refers to the relevant data of the logical unit that is not marked as a high-heat logical unit from the beginning of the last monitoring period of the current monitoring period to the current time, and the relevant data is all types of teaching data corresponding to the construction of the logical unit; According to the order of the access heat index of each logical unit from large to small, store the relevant data of the logical unit that meets the data type of the terminal device into the terminal device until the storage amount reaches a preset percentage of the current remaining capacity, and store the remaining data into the corresponding edge storage node of the user; For a single shared area, filter the second target cache data based on the storage priority index, move the second target cache data from the edge storage node to the cloud storage, and store the third target cache data into the corresponding edge storage node based on differential storage; The second target cache data refers to the relevant data of the logical unit whose storage priority index is less than a preset priority threshold from the beginning of the last monitoring period of the current monitoring period to the current time, and the third target cache data refers to the relevant data of the logical unit whose storage priority index is not less than the preset priority threshold.

9. A data storage management system of a cloud computer, characterized by Comprise: A data acquisition module for acquiring access data of a plurality of users on different terminal devices in a plurality of consecutive monitoring periods, and integrating based on teaching data logic to obtain access data of each user on each logical unit and path distance between each two logical units; A data processing module for analyzing the access habits of each user according to the access data of each user on each logical unit and the path distance between each two logical units, determining the access heat index of each user for each logical unit; for each two users with the same identity tag and interactive behavior, analyzing the access habits of the group according to the number of interactions, access heat index, and edge storage nodes of the two users, obtaining each shared area; determining the storage priority index of each logical unit at the current time according to the access frequency of each teacher to the shared area in each monitoring period and the access heat index of each user to each logical unit; A data storage module for adjusting the storage scheme through the storage priority index and the access heat index of each logical unit at the current time.

10. A data storage medium for a cloud computer, characterized by A cloud computer comprising a processor for processing instructions stored in a memory to implement a data storage management method as claimed in any one of claims 1-8. A cloud computer comprising a processor for processing instructions stored in a memory to implement a data storage management method as claimed in any one of claims 1-8.

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