Cloud information management method for online education platform

Abstract

The invention relates to the technical field of cloud information management, in particular to a cloud information management method of an online education platform. The method comprises the following steps: acquiring frequency and load data of a refreshing request, a CPU utilization rate and a bandwidth utilization rate; determining a high-frequency refreshing node according to the fluctuation of the CPU utilization rate and the bandwidth utilization rate; according to the frequency distribution change and the load data change of the refresh request under the high-frequency refresh node, screening to obtain a high-frequency refresh moment; further determining a refreshing segment with densely distributed refreshing, and determining an unconscious refreshing performance evaluation value of the high-frequency refreshing node according to the load change of all high-frequency refreshing moments in the refreshing segment; and determining an unconscious influence node according to the unconscious refreshing performance evaluation value of each server node, and limiting the maximum refreshing frequency of the unconscious influence node. According to the method, unconscious refreshing can be accurately identified, cache resources of the part are liberated, the system burden is reduced, and the overall resource utilization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud information management, and in particular to a cloud information management method for an online education platform. Background Art

[0002] With the rapid development of online education, cloud information management has become a core component of the education platform infrastructure. Online education platforms need to process massive amounts of user data, course content, learning records, test scores, and other information, while also ensuring the platform's high availability, scalability, and data security. Cloud technology provides flexible computing resources and storage space, enabling the platform to dynamically adjust processing capabilities to meet different traffic demands, especially during critical moments such as exam peaks and live courses.

[0003] In related technologies, online education platforms may encounter performance bottlenecks during peak periods such as live classes, such as video freezes, slow page loading, request timeouts, and other problems. People will repeatedly refresh and click on the page, and the request volume of this node will be too large. The system will distribute the requests to other nodes through load balancing; however, sometimes there will be no performance bottlenecks and people's unconscious frequent refreshes will consume additional bandwidth and computing resources, thereby occupying server resources, reducing the response speed of other user requests, increasing the system burden, and affecting the overall resource utilization efficiency. Summary of the invention

[0004] In order to solve the technical problem in the related art that frequent refreshes by personnel unconsciously without encountering performance bottlenecks will consume additional bandwidth and computing resources, thereby occupying server resources, reducing the response speed of other user requests, increasing the system burden, and affecting the overall resource utilization efficiency, the present invention provides a cloud information management method for an online education platform, and the technical solution adopted is as follows: The present invention proposes a cloud information management method for an online education platform, the method comprising: Obtain the frequency and load data of user refresh requests at each collection time of each server node of the online education platform, as well as multi-dimensional real-time data collected at all collection times of all server nodes, wherein the real-time data includes: CPU utilization and bandwidth utilization; Determine the CPU peak fluctuation characteristic index and the bandwidth peak fluctuation characteristic index of each server node according to the fluctuation discreteness and range of the CPU utilization rate and the bandwidth utilization rate; and select high-frequency refresh nodes from the server nodes by combining the CPU peak fluctuation characteristic index and the bandwidth peak fluctuation characteristic index; According to the frequency distribution changes and load data changes of user refresh requests at each sampling moment of the high-frequency refresh node, the high-frequency refresh moment is screened from the sampling moment of each high-frequency refresh node; according to the distribution of the high-frequency refresh moment in the time series, the refresh segment with dense refresh distribution is determined, and according to the load changes of all high-frequency refresh moments in the refresh segment, the unconscious refresh performance evaluation value of the high-frequency refresh node is determined; The unconsciously affected nodes are determined according to the unconscious refresh performance evaluation value of each server node, and the maximum refresh frequency of the unconsciously affected nodes is limited.

[0005] Furthermore, the CPU peak fluctuation characteristic index and the bandwidth peak fluctuation characteristic index of each server node are determined according to the fluctuation discreteness and range of the CPU utilization and the bandwidth utilization, including: Calculate the product of the variance and range of the CPU utilization of each server node at all sampling times, and normalize them as the CPU peak fluctuation characteristic indicator; The product of the variance and range of the bandwidth utilization of each server node at all sampling times is calculated and normalized as the bandwidth peak fluctuation characteristic indicator.

[0006] Further, the combining the CPU peak fluctuation characteristic index and the bandwidth peak fluctuation characteristic index to select high-frequency refresh nodes from server nodes includes: Calculate the product value of the CPU peak fluctuation characteristic index and the bandwidth peak fluctuation characteristic index, and normalize them as the high-frequency refresh characteristic value; The server node whose high-frequency refresh characteristic value is greater than a preset refresh characteristic threshold is regarded as a high-frequency refresh node.

[0007] Further, the high-frequency refresh time is obtained by screening the sampling time of each high-frequency refresh node according to the frequency distribution change of the user refresh request and the load data change at each sampling time of the high-frequency refresh node, including: Determine the demand factor of the refresh request at each sampling moment according to the frequency distribution and discrete characteristics of the user refresh request at all sampling moments of the same high-frequency refresh node; The demand factor of the load data at each sampling moment of the same high-frequency refresh node is calculated by analogy with the demand factor of the refresh request; Determine a refresh request quantity-load dependency index of the refresh request and the load at each sampling moment according to the correlation and value difference between the demand factor of the refresh request and the demand factor of the load data at each sampling moment; The sampling moment when the refresh request quantity-load dependency index is greater than a preset dependency threshold is used as the high-frequency refresh moment.

[0008] Furthermore, the step of determining the demand factor of the refresh request at each sampling moment according to the frequency distribution and discrete characteristics of the user refresh requests at all sampling moments of the same high-frequency refresh node includes: Taking any sampling time as the target time, calculating the average frequency of user refresh requests at all sampling times of the same high-frequency refresh node, and normalizing the difference between the frequency of user refresh requests at the target time and the average value as the refresh frequency difference; Calculate the inverse of the variance of the frequency of user refresh requests at all sampling moments of the same high-frequency refresh node, and perform maximum and minimum value normalization to obtain the refresh frequency stability index; The ratio of the refresh frequency difference to the refresh frequency stability index is used as a demand factor for a refresh request at a target time.

[0009] Further, the refresh request quantity-load dependency index of the refresh request and the load at each sampling moment is determined according to the correlation and numerical difference between the demand factor of the refresh request and the demand factor of the load data at each sampling moment, including: Calculate the correlation index between the demand factor of the refresh request and the demand factor of the load data at each moment based on the Pearson correlation coefficient; Calculate the absolute value of the difference between the demand factor of the refresh request and the demand factor of the load data at any sampling time, and normalize the inverse of the absolute value of the difference as the demand similarity index at the corresponding sampling time; The product of the demand similarity index and the correlation index is calculated and normalized to obtain the refresh request quantity-load dependency index at the corresponding sampling time.

[0010] Furthermore, the step of determining the refresh segments with dense refresh distribution according to the distribution of high-frequency refresh moments in the time sequence includes: The time interval between any high-frequency refresh moment and the previous high-frequency refresh moment is used as the high-frequency interval of the corresponding high-frequency refresh moment; The other high-frequency intervals on both sides of any high-frequency interval in the time series are regarded as adjacent intervals, and the average duration of the adjacent intervals is calculated as the impact interval value of the middle high-frequency interval; the ratio of the duration of the middle high-frequency interval to the impact interval value is calculated as the special ratio; When the special ratio is greater than a preset ratio, the middle high-frequency interval is used as a special interval; and the time interval between two special intervals closest to each other is used as a refresh segment with dense refresh distribution.

[0011] Further, the step of determining the unconscious refresh performance evaluation value of the high-frequency refresh node according to the load changes at all high-frequency refresh moments in the refresh segment includes: Calculate the mean of the load data at all high-frequency refresh moments in the refresh segment, and perform normalization processing to obtain the load coefficient of the refresh segment; The refresh segment whose load factor is less than a preset load threshold is regarded as an unconscious segment; An unconscious refresh performance evaluation value is determined according to the number of high-frequency refresh moments corresponding to the unconscious segments of the high-frequency refresh nodes.

[0012] Further, the step of determining the unconscious refresh performance evaluation value according to the number of high-frequency refresh moments corresponding to the unconscious segments of the high-frequency refresh nodes includes: The ratio of the number of high-frequency refresh moments corresponding to the unconscious segments of the high-frequency refresh nodes to the number of all high-frequency refresh moments is used as the unconscious refresh performance evaluation value.

[0013] Further, the step of determining the unconscious impact node according to the unconscious refresh performance evaluation value of each server node includes: The server node whose unconscious refresh performance evaluation value is greater than a preset performance threshold is regarded as an unconscious influencing node.

[0014] The present invention has the following beneficial effects: The present invention takes CPU utilization and bandwidth utilization as a group through the refresh request frequency, load data, CPU utilization and bandwidth utilization, and further screens out high-frequency refresh nodes from server nodes through specific analysis of CPU state changes and bandwidth state changes; then, for the high-frequency refresh nodes, the high-frequency refresh moment is determined through the frequency distribution changes of refresh requests and the load data changes, that is, the high-frequency refresh moments of the unconscious refresh and jammed refresh sets are preliminarily screened out, and then the unconscious refresh performance evaluation value of the high-frequency refresh node is determined according to the distribution and load changes in the timing of the high-frequency refresh moment. The unconscious refresh performance evaluation value can accurately characterize the unconscious impact nodes that generate unconscious refreshes more frequently, and further limit the maximum refresh frequency of the unconscious impact nodes, which can free up this part of cache resources, reduce the system burden, and improve the overall resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A flow chart of a cloud information management method for an online education platform provided by an embodiment of the present invention; Figure 2 A schematic diagram of a CPU utilization curve provided by an embodiment of the present invention; Figure 3 A schematic diagram of refresh segmentation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the cloud information management method of an online education platform proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

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

[0019] The following is a detailed description of a specific solution of a cloud information management method for an online education platform provided by the present invention in conjunction with the accompanying drawings.

[0020] See also Figure 1 , which shows a flow chart of a cloud information management method of an online education platform provided by an embodiment of the present invention, the method comprising: S101: Obtain the frequency and load data of user refresh requests at each collection moment of each server node of the online education platform, as well as multi-dimensional real-time data collected at all collection moments of all server nodes, wherein the real-time data includes: CPU utilization and bandwidth utilization.

[0021] With the rapid development of online education, cloud information management has become a core component of the education platform infrastructure. Online education platforms need to process massive amounts of user data, course content, learning records, test scores, and other information, while also ensuring the platform's high availability, scalability, and data security. Cloud technology provides flexible computing resources and storage space, enabling the platform to dynamically adjust processing capabilities to meet different traffic demands, especially during critical moments such as exam peaks and live courses.

[0022] Online education platforms may encounter performance bottlenecks during peak periods such as live classes, such as video freezes, slow page loading, request timeouts, etc., and people may repeatedly refresh and click on the page. The number of requests for this node is too large, and the system will distribute the requests to other nodes through load balancing; however, sometimes there are no performance bottlenecks and people unconsciously refresh frequently, which will consume additional bandwidth and computing resources, thereby occupying server resources, reducing the response speed of other user requests, increasing the system burden, and affecting the overall resource utilization efficiency. Therefore, the embodiments of the present invention are intended to identify unconscious frequent refreshes and restrict them.

[0023] The distributed cloud computing architecture disperses the various components of the education platform (such as user management, course management, live video, test assessment, learning analysis, etc.) on servers in multiple geographical locations, working together through network connections.

[0024] Therefore, in the embodiment of the present invention, the frequency and load data of user refresh requests at each collection moment of each server node of the online education platform can be obtained. Then, one second can be used as a collection moment, and correspondingly, multi-dimensional real-time data can be monitored once from each component of the online education platform every second as a collection moment, so as to obtain multi-dimensional real-time data collected at all times of all server nodes in a day. Real-time data includes: CPU utilization, bandwidth utilization. Every second as a collection moment, the number of user refresh requests and load conditions at each server node in a day at each moment can be obtained by logging on the server side.

[0025] S102: Determine the CPU peak fluctuation characteristic index and the bandwidth peak fluctuation characteristic index of each server node according to the fluctuation discreteness and range of the CPU utilization and the bandwidth utilization; and screen high-frequency refresh nodes from the server nodes by combining the CPU peak fluctuation characteristic index and the bandwidth peak fluctuation characteristic index.

[0026] Online education platforms may encounter performance bottlenecks during peak periods such as live classes, such as video freezes, slow page loading, and request timeouts. People will frequently refresh and click on the page, and frequent requests will cause the server to require more computing resources to process each request, especially when there are complex calculations or data processing (such as video decoding, user authentication, and data access). The CPU load will increase. If the refresh request involves a large amount of data processing (for example, each refresh requires reloading a complex page), the CPU utilization will increase significantly.

[0027] During the data monitoring process, the CPU utilization curve of each server node can be obtained with time as the horizontal axis and CPU utilization as the vertical axis. The CPU utilization curve fluctuates relatively smoothly during the non-peak period. When the online education platform encounters a peak period, the CPU utilization will increase instantly. Therefore, the CPU utilization curve of the online education platform will fluctuate greatly during peak periods such as live classes. Figure 2 As shown, Figure 2 A schematic diagram of a CPU utilization curve provided by an embodiment of the present invention.

[0028] At the same time, each page refresh consumes bandwidth, especially when the resources such as video streams and pictures are large. Frequent page refreshes will increase the use of network bandwidth and may cause bandwidth saturation, which in turn affects the experience of other users. Increased bandwidth utilization will also cause problems such as video stuttering and slow page loading.

[0029] Therefore, fluctuation characteristic analysis can be performed based on CPU utilization and bandwidth utilization. Furthermore, in some embodiments of the present invention, the CPU peak fluctuation characteristic index and the bandwidth peak fluctuation characteristic index of each server node are determined based on the fluctuation discreteness and range of CPU utilization and bandwidth utilization, including: calculating the product of the variance and range of the CPU utilization of each server node at all sampling moments, and normalizing them as the CPU peak fluctuation characteristic index; calculating the product of the variance and range of the bandwidth utilization of each server node at all sampling moments, and normalizing them as the bandwidth peak fluctuation characteristic index.

[0030] The variance characterizes the discreteness of fluctuations. The larger the variance value, the greater the fluctuation of the corresponding CPU utilization. The larger the range value, the larger the overall distribution of CPU utilization values. Therefore, the more likely it is to have a peak effect. In this embodiment of the present invention, the product of the variance and the range of CPU utilization is directly calculated and normalized as a CPU peak fluctuation characteristic indicator. Similarly, the product of the variance and the range of bandwidth utilization is calculated and normalized as a bandwidth peak fluctuation characteristic indicator.

[0031] When the peak fluctuation performance of the CPU utilization and the peak fluctuation performance of the bandwidth utilization of the server node are greater, it means that there is obvious consumption of the CPU utilization and bandwidth utilization of the node, and the node may have a refresh peak period.

[0032] Therefore, in an embodiment of the present invention, the CPU peak fluctuation characteristic index and the bandwidth peak fluctuation characteristic index are combined to screen high-frequency refresh nodes from server nodes, including: calculating the product value of the CPU peak fluctuation characteristic index and the bandwidth peak fluctuation characteristic index, and normalizing it as a high-frequency refresh characteristic value; and taking a server node whose high-frequency refresh characteristic value is greater than a preset refresh characteristic threshold as a high-frequency refresh node.

[0033] The preset refresh feature threshold is a threshold value of the high-frequency refresh feature value. The specific value of the preset refresh feature threshold may be, for example, 0.5, that is, the server node with a high-frequency refresh feature value greater than 0.5 is regarded as a high-frequency refresh node.

[0034] S103: According to the frequency distribution changes and load data changes of user refresh requests at each sampling moment of the high-frequency refresh node, the high-frequency refresh moment is filtered out from the sampling moment of each high-frequency refresh node; according to the distribution of the high-frequency refresh moment in time series, the refresh segment with dense refresh distribution is determined, and according to the load changes of all high-frequency refresh moments in the refresh segment, the unconscious refresh performance evaluation value of the high-frequency refresh node is determined.

[0035] During peak periods such as live classes, online education platforms may encounter performance bottlenecks due to a large number of users accessing the platform at the same time. For example, video streaming freezes, page loading is slow, and request timeouts frequently occur, seriously affecting the user experience. When users encounter problems such as page loading failures or video playback interruptions, they frequently refresh the page and try to reload the content, resulting in a sharp increase in node requests.

[0036] When the user's page does not have problems such as video streaming freezes, slow page loading, and request timeouts, the frequent and unconscious page refreshes by people will also cause a sharp increase in node requests, increasing the system load. If not distinguished, these unconscious refresh requests will be confused with normal user requests, causing the load balancing algorithm to allocate too many requests to certain nodes, thus creating a new performance bottleneck.

[0037] In addition, the resource consumption caused by these repeated requests may slow down the response speed of other normal requests, further affecting the overall performance of the platform. Therefore, it is necessary to distinguish between the moments of refresh jams and unconscious refreshes in high-frequency refresh nodes, that is, to conduct a specific analysis of unconscious refresh performance.

[0038] Furthermore, in some embodiments of the present invention, based on the frequency distribution changes and load data changes of user refresh requests at each sampling moment of the high-frequency refresh node, the high-frequency refresh moment is screened from the sampling moment of each high-frequency refresh node, including: determining the demand factor of the refresh request at each sampling moment based on the frequency distribution and discrete characteristics of the user refresh requests at all sampling moments of the same high-frequency refresh node; calculating the demand factor of the load data at each sampling moment of the same high-frequency refresh node by analogy with the demand factor of the refresh request; determining the refresh request amount-load dependency index of the refresh request and the load at each sampling moment based on the correlation and numerical difference between the demand factor of the refresh request and the demand factor of the load data at each sampling moment; and taking the sampling moment at which the refresh request amount-load dependency index is greater than a preset dependency threshold as the high-frequency refresh moment.

[0039] Among them, the demand factor of the refresh request indicates the degree to which a refresh request is needed at the corresponding sampling moment. That is, the larger the value of the demand factor of the refresh request, the more a refresh request is needed at the corresponding sampling moment. It is specifically manifested as: while the overall refresh request distribution is relatively stable, the amount of refresh requests is relatively large. Based on this, the demand factor of the refresh request is specifically calculated.

[0040] Furthermore, in some embodiments of the present invention, a demand factor for refresh requests at each sampling moment is determined based on the frequency distribution and discrete characteristics of user refresh requests at all sampling moments of the same high-frequency refresh node, including: taking any sampling moment as the target moment, calculating the mean frequency of user refresh requests at all sampling moments of the same high-frequency refresh node, normalizing the difference between the frequency of user refresh requests at the target moment and the mean as the refresh frequency difference; calculating the inverse of the variance of the frequency of user refresh requests at all sampling moments of the same high-frequency refresh node, and performing maximum and minimum value normalization to obtain a refresh frequency stability index; and taking the ratio of the refresh frequency difference to the refresh frequency stability index as the demand factor for refresh requests at the target moment.

[0041] That is, the refresh frequency difference is obtained through the difference between the frequency and the mean. The larger the value of the refresh frequency difference, the greater the difference between the frequency and the overall value. The higher the value, the more abnormal it is. The larger the demand factor of the refresh request. The larger the value of the variance, the more unstable it is. The reliability of the obtained demand factor value is lower. The calculated refresh frequency stability index shows the stability characteristics. Therefore, the ratio of the refresh frequency difference and the refresh frequency stability index is used as the demand factor of the refresh request at the target moment.

[0042] Among them, the calculation method of the demand factor of the refresh request is similar to that of the demand factor of the load data. The specific calculation method of the demand factor of the load data is to calculate the mean of the load data at all sampling times of the same high-frequency refresh node, and normalize the difference between the load data at the target time and the mean as the load data difference; calculate the inverse of the variance of the load data at all sampling times of the same high-frequency refresh node, and perform maximum and minimum value normalization to obtain the load stability index; and use the ratio of the load data difference to the load stability index as the demand factor of the load data at the target time.

[0043] In the embodiment of the present invention, the demand factor of the refresh request and the demand factor of the load data both represent the characteristics of the refresh request and the characteristics of the load data at the corresponding sampling time. Based on the correlation analysis, the correlation between the refresh request and the load change can be determined, thereby determining the dependence of the refresh request on the load change.

[0044] Furthermore, in some embodiments of the present invention, a refresh request quantity-load dependence index of the refresh request and the load at each sampling moment is determined based on the correlation and numerical difference between the demand factor of the refresh request and the demand factor of the load data at each sampling moment, including: calculating the correlation index of the demand factor of the refresh request and the demand factor of the load data at each moment based on the Pearson correlation coefficient; calculating the absolute value of the difference between the demand factor of the refresh request and the demand factor of the load data at any sampling moment, and normalizing the opposite of the absolute value of the difference as the demand similarity index at the corresponding sampling moment; calculating the product of the demand similarity index and the correlation index, and normalizing to obtain the refresh request quantity-load dependence index at the corresponding sampling moment.

[0045] Among them, the Pearson correlation coefficient is a correlation calculation method well known to those skilled in the art. The correlation index is calculated, and the larger the value is, the more positively correlated the fluctuations are.

[0046] Then, the larger the demand similarity index at the sampling moment, the closer the numerical value of the demand factor of the refresh request is to the demand factor of the load data, which in turn indicates the numerical similarity. Therefore, the product of the demand similarity index and the correlation index is calculated, and the refresh request quantity-load dependency index at the corresponding sampling moment is obtained by normalization. The larger the value of the refresh request quantity-load dependency index, the higher the similarity between the refresh request and the load data in both the numerical value and the fluctuation, that is, the greater the dependence of the refresh request on the load.

[0047] When each client refreshes a page, it sends a request to the server node to which it belongs, occupying certain system resources, such as CPU, memory, network bandwidth, etc. In the scenario of high-frequency refresh requests, the processing time of each request is relatively short, but frequent requests will cause the system to continue to occupy resources, resulting in increased load. For live broadcasts on online education platforms, if the frequency of refresh requests is too high, the server must continuously process these requests, which will correspondingly cause excessive load; when the number of refresh requests is small, that is, when the refresh is not high-frequency, the load will be relatively light.

[0048] In the embodiment of the present invention, the refresh request amount-load dependence index is positively correlated with the demand factor of the refresh request, and negatively correlated with the demand factor of the load data. That is, the larger the value of the refresh request amount-load dependence index is, the more refresh requests there are under low load conditions, which can be expressed as a high-frequency refresh situation. Therefore, high-frequency refresh timing analysis can be performed based on the refresh request amount-load dependence index.

[0049] The sampling moment when the refresh request amount-load dependency index is greater than the preset dependency threshold is used as the high-frequency refresh moment, wherein the preset dependency threshold is the threshold value of the refresh request amount-load dependency index. In an embodiment of the present invention, the preset dependency threshold can be set to 0.8, that is, the sampling moment when the refresh request amount-load dependency index is greater than 0.8 is used as the high-frequency refresh moment.

[0050] The high-frequency refresh moment indicates the moment when the refresh far exceeds the influence of the current load. Therefore, the high-frequency refresh moment can be specifically segmented to determine the refresh segment that is more affected by the high-frequency refresh.

[0051] Furthermore, in some embodiments of the present invention, based on the distribution of high-frequency refresh moments in timing, a refresh segment with a dense refresh distribution is determined, including: taking the time interval between any high-frequency refresh moment and the previous high-frequency refresh moment as the high-frequency interval of the corresponding high-frequency refresh moment; taking the other high-frequency intervals on both sides of any high-frequency interval in timing as adjacent intervals, and calculating the average duration of the adjacent intervals as the impact interval value of the middle high-frequency interval; calculating the ratio of the duration of the middle high-frequency interval to the impact interval value as a special ratio; when the special ratio is greater than the preset ratio, taking the middle high-frequency interval as a special interval; taking the time interval between the two closest special intervals as a refresh segment with a dense refresh distribution.

[0052] See also Figure 3 , Figure 3A refresh segmentation schematic diagram provided for an embodiment of the present invention; the time interval between two high-frequency refresh moments is a high-frequency interval, and when the high-frequency interval is larger (meaning it lasts longer than the left and right sides), it is treated as a special interval, and the time interval between the two special intervals is treated as a refresh segment.

[0053] When calculating the special interval, this scheme introduces a feature ratio to specifically target the feature of "large high-frequency intervals", and regards the high-frequency interval whose special ratio is greater than a preset ratio as a special interval. The preset ratio can be specifically 3, for example, that is, when the high-frequency interval is greater than 3 times the average of the high-frequency intervals adjacent to it, the corresponding high-frequency interval is recorded as a special interval.

[0054] It should be noted that in this solution, refresh is divided into two situations. One is the freeze effect caused by load changes, which leads to the need for refresh, and the other is the unconscious refresh caused by the user's personal operating habits.

[0055] Stuttering refreshes are usually accompanied by an increase in network or system load. The increase in refresh requests directly reflects the bottleneck problem of platform performance. Therefore, the relationship between the number of refresh requests and the load is closer, and the load gradually increases with the increase in the number of requests. The number of requests for unconscious page refreshes by users does not match the platform load, which means that requests may accumulate in a short period of time, but the system load does not show obvious abnormal changes. The corresponding refresh request-load dependency at high-frequency refresh moments is less than that of stuttering refreshes. However, refresh requests are more dependent on the load under stuttering refreshes, while refresh requests are less dependent on the load under unconscious refreshes.

[0056] Furthermore, in some embodiments of the present invention, an unconscious refresh performance evaluation value of a high-frequency refresh node is determined based on the load changes of all high-frequency refresh moments in a refresh segment, including: calculating the mean of the load data of all high-frequency refresh moments in the refresh segment, and performing normalization processing to obtain the load coefficient of the refresh segment; treating a refresh segment whose load coefficient is less than a preset load threshold as an unconscious segment; and determining an unconscious refresh performance evaluation value based on the number of high-frequency refresh moments corresponding to the unconscious segment of the high-frequency refresh node.

[0057] Since the value of the refresh request amount-load dependency index has filtered out some unconscious features, it is necessary to further analyze the filtered high-frequency refresh moments, wherein the load coefficient represents the load value characteristics. When the load is large, the refresh is more likely to be caused by jamming, and when the load is small, the refresh is more likely to be an unconscious performance. Therefore, the refresh segment with a load coefficient less than the preset load threshold is regarded as an unconscious segment, wherein the preset load threshold is the threshold value of the load coefficient. In the embodiment of the present invention, the preset load threshold is set to 0.5, that is, the refresh segment with a load coefficient less than 0.5 is regarded as an unconscious segment.

[0058] Then, the performance evaluation can be performed in combination with the number of high-frequency refresh moments in the unconscious segment. Further, in some embodiments of the present invention, the unconscious refresh performance evaluation value is determined according to the number of high-frequency refresh moments corresponding to the unconscious segment of the high-frequency refresh node, including: taking the ratio of the number of high-frequency refresh moments corresponding to the unconscious segment of the high-frequency refresh node to the number of all high-frequency refresh moments as the unconscious refresh performance evaluation value.

[0059] The larger the ratio of the number of high-frequency refresh moments corresponding to the unconscious segment to the number of all high-frequency refresh moments, that is, the larger the proportion of unconscious refreshes compared to stuttering refreshes, the larger the unconscious refresh performance evaluation value.

[0060] S104: Determine the unconsciously affected nodes according to the unconscious refresh performance evaluation value of each server node, and limit the maximum refresh frequency of the unconsciously affected nodes.

[0061] In the embodiment of the present invention, the unconscious refresh performance evaluation value can effectively characterize the unconscious effect. Therefore, the unconsciously affected server nodes can be directly determined. The unconsciously affected nodes are determined according to the unconscious refresh performance evaluation value of each server node, including: the server nodes whose unconscious refresh performance evaluation values ​​are greater than the preset performance threshold are regarded as unconsciously affected nodes.

[0062] Among them, the preset performance threshold is the threshold value of the unconscious refresh performance evaluation value. The preset performance threshold in the embodiment of the present invention can be specifically 0.5, that is, the server node with the unconscious refresh performance evaluation value greater than 0.5 is regarded as an unconscious impact node. That is, when the number of unconscious refresh moments in each node is greater than half of the number of high-frequency refresh moments, the node is recorded as an unconscious impact node. The unconscious impact node indicates that the corresponding server node is prone to unconscious refresh impact, and its maximum refresh frequency can be limited, that is, its refresh frequency can be reduced, thereby freeing up this part of cache resources, reducing the system burden, improving the overall resource utilization efficiency, and completing cloud information management.

[0063] For example, in a normal server node, the number of refreshes is unlimited. However, when a node is identified as an unconsciously affected node, the refresh is limited to once every 3 seconds, or only 10 times within 1 minute, so as to limit the refresh of the unconsciously affected node and release cache resources.

[0064] The present invention takes CPU utilization and bandwidth utilization as a group through the refresh request frequency, load data, CPU utilization and bandwidth utilization, and further screens out high-frequency refresh nodes from server nodes through specific analysis of CPU state changes and bandwidth state changes; then, for the high-frequency refresh nodes, the high-frequency refresh moment is determined through the frequency distribution changes of refresh requests and the load data changes, that is, the high-frequency refresh moments of the unconscious refresh and jammed refresh sets are preliminarily screened out, and then the unconscious refresh performance evaluation value of the high-frequency refresh node is determined according to the distribution and load changes in the timing of the high-frequency refresh moment. The unconscious refresh performance evaluation value can accurately characterize the unconscious impact nodes that generate unconscious refreshes more frequently, and further limit the maximum refresh frequency of the unconscious impact nodes, which can free up this part of cache resources, reduce the system burden, and improve the overall resource utilization efficiency.

[0065] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0066] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A cloud information management method for an online education platform, characterized in that: The method comprises: Obtain the frequency and load data of user refresh requests at each collection time of each server node of the online education platform, as well as multi-dimensional real-time data collected at all collection times of all server nodes, wherein the real-time data includes: CPU utilization and bandwidth utilization; Determine the CPU peak fluctuation characteristic index and the bandwidth peak fluctuation characteristic index of each server node according to the fluctuation discreteness and range of the CPU utilization rate and the bandwidth utilization rate; and select high-frequency refresh nodes from the server nodes by combining the CPU peak fluctuation characteristic index and the bandwidth peak fluctuation characteristic index; According to the frequency distribution changes and load data changes of user refresh requests at each sampling moment of the high-frequency refresh node, the high-frequency refresh moment is screened from the sampling moment of each high-frequency refresh node; according to the distribution of the high-frequency refresh moment in the time series, the refresh segment with dense refresh distribution is determined, and according to the load changes of all high-frequency refresh moments in the refresh segment, the unconscious refresh performance evaluation value of the high-frequency refresh node is determined; The unconsciously affected nodes are determined according to the unconscious refresh performance evaluation value of each server node, and the maximum refresh frequency of the unconsciously affected nodes is limited.

2. A cloud information management method for an online education platform as claimed in claim 1, characterized in that: Determining the CPU peak fluctuation characteristic index and the bandwidth peak fluctuation characteristic index of each server node according to the fluctuation discreteness and range of the CPU utilization and the bandwidth utilization includes: Calculate the product of the variance and range of the CPU utilization of each server node at all sampling times, and normalize them as the CPU peak fluctuation characteristic indicator; The product of the variance and range of the bandwidth utilization of each server node at all sampling times is calculated and normalized as the bandwidth peak fluctuation characteristic indicator.

3. The cloud information management method of an online education platform according to claim 1, characterized in that: The step of combining the CPU peak fluctuation characteristic index and the bandwidth peak fluctuation characteristic index to select high-frequency refresh nodes from server nodes includes: Calculate the product value of the CPU peak fluctuation characteristic index and the bandwidth peak fluctuation characteristic index, and normalize them as the high-frequency refresh characteristic value; The server node whose high-frequency refresh characteristic value is greater than a preset refresh characteristic threshold is regarded as a high-frequency refresh node.

4. The cloud information management method of an online education platform according to claim 1, characterized in that: The method of filtering out the high-frequency refresh time from the sampling time of each high-frequency refresh node according to the frequency distribution change of the user refresh request and the load data change at each sampling time of the high-frequency refresh node includes: Determine the demand factor of the refresh request at each sampling moment according to the frequency distribution and discrete characteristics of the user refresh request at all sampling moments of the same high-frequency refresh node; The demand factor of the load data at each sampling moment of the same high-frequency refresh node is calculated by analogy with the demand factor of the refresh request; Determine a refresh request quantity-load dependency index of the refresh request and the load at each sampling moment according to the correlation and value difference between the demand factor of the refresh request and the demand factor of the load data at each sampling moment; The sampling moment when the refresh request quantity-load dependency index is greater than a preset dependency threshold is used as the high-frequency refresh moment.

5. A cloud information management method for an online education platform as claimed in claim 4, characterized in that: The step of determining the demand factor of the refresh request at each sampling moment according to the frequency distribution and discrete characteristics of the user refresh requests at all sampling moments of the same high-frequency refresh node includes: Taking any sampling time as the target time, calculating the average frequency of user refresh requests at all sampling times of the same high-frequency refresh node, and normalizing the difference between the frequency of user refresh requests at the target time and the average value as the refresh frequency difference; Calculate the inverse of the variance of the frequency of user refresh requests at all sampling moments of the same high-frequency refresh node, and perform maximum and minimum value normalization to obtain the refresh frequency stability index; The ratio of the refresh frequency difference to the refresh frequency stability index is used as a demand factor for a refresh request at a target time.

6. A cloud information management method for an online education platform as claimed in claim 4, characterized in that: Determining the refresh request quantity-load dependency index of the refresh request and the load at each sampling moment according to the correlation and numerical difference between the demand factor of the refresh request and the demand factor of the load data at each sampling moment includes: Calculate the correlation index between the demand factor of the refresh request and the demand factor of the load data at each moment based on the Pearson correlation coefficient; Calculate the absolute value of the difference between the demand factor of the refresh request and the demand factor of the load data at any sampling time, and normalize the inverse of the absolute value of the difference as the demand similarity index at the corresponding sampling time; The product of the demand similarity index and the correlation index is calculated and normalized to obtain the refresh request quantity-load dependency index at the corresponding sampling time.

7. The cloud information management method of an online education platform according to claim 1, characterized in that: The step of determining the refresh segments with dense refresh distribution according to the distribution of high-frequency refresh moments in the time sequence includes: The time interval between any high-frequency refresh moment and the previous high-frequency refresh moment is used as the high-frequency interval of the corresponding high-frequency refresh moment; The other high-frequency intervals on both sides of any high-frequency interval in the time series are regarded as adjacent intervals, and the average duration of the adjacent intervals is calculated as the impact interval value of the middle high-frequency interval; the ratio of the duration of the middle high-frequency interval to the impact interval value is calculated as the special ratio; When the special ratio is greater than a preset ratio, the middle high-frequency interval is used as a special interval; and the time interval between two special intervals closest to each other is used as a refresh segment with dense refresh distribution.

8. The cloud information management method of an online education platform according to claim 1, characterized in that: The step of determining the unconscious refresh performance evaluation value of the high-frequency refresh node according to the load changes at all high-frequency refresh moments in the refresh segment includes: Calculate the mean of the load data at all high-frequency refresh moments in the refresh segment, and perform normalization processing to obtain the load coefficient of the refresh segment; The refresh segment whose load factor is less than a preset load threshold is regarded as an unconscious segment; An unconscious refresh performance evaluation value is determined according to the number of high-frequency refresh moments corresponding to the unconscious segments of the high-frequency refresh nodes.

9. A cloud information management method for an online education platform as claimed in claim 8, characterized in that: The step of determining the unconscious refresh performance evaluation value according to the number of high-frequency refresh moments corresponding to the unconscious segments of the high-frequency refresh nodes includes: The ratio of the number of high-frequency refresh moments corresponding to the unconscious segments of the high-frequency refresh nodes to the number of all high-frequency refresh moments is used as the unconscious refresh performance evaluation value.

10. The cloud information management method of an online education platform according to claim 1, characterized in that: The step of determining the unconscious impact node according to the unconscious refresh performance evaluation value of each server node includes: The server node whose unconscious refresh performance evaluation value is greater than a preset performance threshold is regarded as an unconscious influencing node.

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