Enterprise management system based on cloud computing

Abstract

An enterprise management system based on cloud computing relates to the technical field of cloud computing and comprises a cloud server which is in communication connection with a data acquisition module, a data analysis module, a day-ahead planning module, an intra-day scheduling module and an automatic expansion module. The data acquisition module is used for acquiring enterprise information; the data analysis module is used for acquiring a data packet flow prediction value of the enterprise communication terminal; the day-ahead planning module is used for carrying out cloud resource vertical extension planning or cloud resource horizontal extension planning on the enterprise communication terminals to obtain a pre-planned data packet flow processing upper limit of each enterprise communication terminal; the intra-day scheduling module performs difference significance analysis on the real-time data packet flow of each enterprise communication terminal and the corresponding data packet flow predicted value; and the automatic extension module is used for automatically performing cloud resource level extension on the enterprise communication terminals with significant difference in data packet traffic, so as to better deal with burst traffic and service growth.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud computing, and in particular to an enterprise management system based on cloud computing. Background Art

[0002] The prior art CN113516454A "An enterprise information management system and method based on cloud computing platform" includes enterprise terminals, management and control terminals and cloud; the enterprise terminal is used to upload enterprise information, the cloud is used to analyze and calculate enterprise information, the management and control terminal is used to organize the enterprise information uploaded by the enterprise terminal, and is also used to match and compare the calculation results on the cloud. An enterprise information update module is provided, so that it is possible to analyze whether the current enterprise information uploaded by the enterprise information upload module includes historical enterprise information, so that when updating enterprise information, different update methods can be selected, and there is no need to limit the way in which the enterprise terminal uploads enterprise information, which greatly reduces the difficulty of operating the enterprise terminal.

[0003] Prior art CN116502806A "A method and management system for enterprise information management based on cloud computing platform" The enterprise information management system includes a cloud computing management platform, and the cloud computing management platform includes an employee information management module, a customer information management module, a financial information management module, a registration and login management module, a permission management module, a distributed storage module and an information security supervision module; the present invention determines abnormal performance personnel and abnormal performance customers through attendance management analysis and cooperation management analysis, so that the corresponding management personnel can promptly strengthen the supervision of abnormal performance personnel and abnormal performance customers, improve the management effect of enterprise personnel and ensure the normal operation of the enterprise, and further ensure the storage security and access security of enterprise information by setting the corresponding enterprise's security network and security terminal and conducting risk analysis on the cloud computing management platform.

[0004] When an enterprise management system based on cloud computing faces the challenge of processing a large number of request tasks or complex computing tasks, the cloud server may not be able to meet the performance requirements under high load conditions, resulting in increased latency, longer response time and other issues. At the same time, the cloud server may face the risk of single point failure. Without sufficient redundancy and load balancing mechanisms, when a cloud server fails, it may cause service unavailability or increased downtime. How to easily increase or decrease cloud resources according to enterprise needs, automatically expand when demand increases, and automatically reduce when demand decreases, so that enterprises only need to pay for services actually used, thereby optimizing costs is a problem we continue to solve. Summary of the invention

[0005] In order to solve the above technical problems, the object of the present invention is to provide an enterprise management system based on cloud computing, including a cloud server, wherein the cloud server is communicatively connected with a data acquisition module, a data analysis module, a day-ahead planning module, an intraday scheduling module and an automation expansion module;

[0006] The data acquisition module and the cloud server are connected in a distributed manner through IoT nodes to obtain enterprise information uploaded by several enterprise communication terminals and transmit it to the data analysis module;

[0007] The data analysis module is used to package the received enterprise information, and build a traffic prediction model based on the data packets to obtain the data packet traffic prediction value of the enterprise communication terminal;

[0008] The day-ahead planning module performs cloud resource vertical expansion planning or cloud resource horizontal expansion planning for the enterprise communication terminal according to the predicted value of the data packet flow of each enterprise communication terminal, and obtains the data packet flow processing upper limit pre-planned by each enterprise communication terminal;

[0009] The intraday scheduling module performs a difference significance analysis between the real-time data packet flow of each enterprise communication terminal and the corresponding data packet flow prediction value, and performs smoothing processing on the enterprise communication terminals with significant differences in data packet flow according to the analysis results;

[0010] The automated expansion module is used to automatically expand cloud resources horizontally for enterprise communication terminals with significant differences in data packet traffic.

[0011] Furthermore, the data analysis module packages the received enterprise information and constructs a traffic prediction model according to the data packets. The process of obtaining the data packet traffic prediction value of the enterprise communication terminal includes:

[0012] Packing the enterprise information uploaded by the same enterprise communication terminal into a data packet, and obtaining the upload record of the corresponding enterprise communication terminal, wherein the upload record includes the upload time, the monitoring period, and the data packet flow;

[0013] Count the data packet traffic at different times of several monitoring cycles of the corresponding enterprise communication terminal, build a traffic prediction model based on deep learning, and use the data packet traffic as a training set and a test set; input the training set into the traffic prediction model for training until the loss function training is stable, save the model parameters, test the traffic prediction model with the test set until it meets the preset requirements, and output the traffic prediction model;

[0014] The traffic prediction module obtains the predicted value of the data packet traffic at each moment in the current monitoring period of the enterprise communication terminal, and constructs a reference curve chart showing the change of the data packet traffic of each enterprise communication terminal over time based on the predicted value of the data packet traffic at each moment in the current monitoring period of each enterprise communication terminal.

[0015] Furthermore, the process of the day-ahead planning module performing cloud resource vertical expansion planning for the enterprise communication terminal according to the data packet flow prediction value of each enterprise communication terminal includes:

[0016] Obtain all specification types of single virtual servers that can be loaded in the cloud server and the upper limits of data packet processing traffic corresponding to single virtual servers of each specification type, set a specification threshold interval, and divide specification sub-intervals of different specification levels into specification sub-intervals within the specification threshold interval using the upper limits of data packet processing traffic corresponding to single virtual servers of different specification types as threshold points;

[0017] Compare the reference curve graph of each enterprise communication terminal with the specification threshold interval, obtain several curve segments in the reference curve graph that are within the specification threshold interval and the specification sub-interval corresponding to each curve segment, obtain the time periods corresponding to the several curve segments contained in each specification sub-interval and the specification type of the single virtual server corresponding to each specification sub-interval, and according to the time periods corresponding to the several curve segments and the specification type of the single virtual server corresponding to each specification sub-interval, obtain the specification type corresponding to the single virtual server that needs to be pre-configured in several time periods of each enterprise communication terminal within the current monitoring period.

[0018] Furthermore, the process of the day-ahead planning module performing cloud resource horizontal expansion planning for the enterprise communication terminal according to the data packet flow prediction value of each enterprise communication terminal includes:

[0019] The reference curve graph of each enterprise communication terminal is compared with the specification threshold interval. If there is a curve segment in the reference curve graph that is not within the specification threshold interval, the time period corresponding to the curve segment and the packet flow prediction value corresponding to the curve segment are obtained, the specification type of the virtual server corresponding to the maximum packet processing flow upper limit is obtained, and it is marked as the highest specification virtual server, and the number of highest specification virtual servers corresponding to the curve segment is obtained according to the packet flow prediction value corresponding to the curve segment and the maximum packet processing flow upper limit, and the number of highest specification virtual servers that need to be pre-configured for each enterprise communication terminal in several time periods within the current monitoring cycle is obtained according to the time period corresponding to the curve segment and the number of highest specification virtual servers corresponding to the curve segment.

[0020] Furthermore, the process of the day-ahead planning module obtaining the upper limit of the data packet flow processing pre-planned by each enterprise communication terminal includes:

[0021] Obtain the cloud resource expansion type pre-planned for each enterprise communication terminal. If the enterprise communication terminal is planned for vertical expansion of cloud resources, obtain the data packet processing flow upper limit of a single virtual server according to the specification type of a single virtual server that needs to be pre-configured in several time periods of the current monitoring cycle of the enterprise communication terminal, and use the data packet processing flow upper limit as the data packet flow processing upper limit pre-planned for the enterprise communication terminal;

[0022] If the enterprise communication terminal is planned for vertical expansion of cloud resources, the pre-planned data packet flow processing upper limit of the enterprise communication terminal is obtained based on the maximum number of virtual servers that need to be pre-configured in the enterprise communication terminal in several time periods within the current monitoring cycle.

[0023] Furthermore, the process of performing a difference significance analysis between the real-time data packet flow of each enterprise communication terminal and the corresponding data packet flow prediction value by the intraday scheduling module includes:

[0024] Acquire the real-time data packet traffic uploaded by each enterprise communication terminal during the current monitoring period, set a time window, segment the real-time data packet traffic and data packet traffic prediction value of each enterprise communication terminal during the current monitoring period according to the time window, divide the real-time data packet traffic and data packet traffic prediction value into several time windows, obtain the standard deviation of the real-time data packet traffic and the standard deviation of the data packet traffic prediction value in each time window, obtain the enterprise communication terminals with significant differences in data packet traffic according to the standard deviation, mark the enterprise communication terminals as key enterprise communication terminals, and mark the enterprise communication terminals without significant differences as normal enterprise communication terminals.

[0025] Furthermore, the process of the intraday scheduling module smoothing the enterprise communication terminals with significant differences in data packet flow includes:

[0026] Set the number of blocks n, divide the real-time data packets uploaded by the key enterprise communication terminal into n data blocks, each data block includes corresponding packet header information and payload, obtain the flow difference between the real-time data packet flow processed by each virtual server in the cloud server and the corresponding data packet flow processing upper limit, obtain the first n virtual servers with the largest flow difference, and obtain the size of the real-time data packet uploaded by the key enterprise communication terminal, and determine whether the sum of the flow differences of the first n virtual servers is greater than the size of the real-time data packet;

[0027] If it is greater, n data blocks are allocated to n virtual servers; if it is less, the automatic expansion mechanism is started.

[0028] Furthermore, the process in which the automated expansion module automatically expands the cloud resources horizontally for the enterprise communication terminals with significant differences in data packet traffic includes:

[0029] Obtain the maximum value of the historical data packet flow of the key enterprise communication terminal, compare the maximum value of the historical data packet flow of the key enterprise communication terminal with the upper limit of the data packet flow processing pre-planned by the key enterprise communication terminal, and obtain the flow difference between the maximum value of the historical data packet flow of the key enterprise communication terminal and the corresponding upper limit of the data packet flow processing;

[0030] Compare the flow difference with the specification threshold interval. If the flow difference is within the specification threshold interval, obtain the specification level corresponding to the specification sub-interval where the flow difference is located, and allocate virtual servers of corresponding specification types to key enterprise communication terminals according to the specification level;

[0031] If the traffic difference is not within the specification threshold range, the highest specification virtual server is first allocated to the key enterprise communication terminal, and the traffic difference between the real-time data packet traffic of the key enterprise communication terminal and the data packet traffic processing upper limit pre-planned for the key enterprise communication terminal is obtained. When the real-time data packet traffic is greater than the data packet traffic processing upper limit pre-planned for the key enterprise communication terminal, and the traffic difference is greater than or equal to the data packet traffic processing upper limit corresponding to the highest specification virtual server, the highest specification virtual server is again allocated to the key enterprise communication terminal, and so on, until the key enterprise communication terminal is marked as a normal enterprise communication terminal.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows: according to the predicted value of the data packet flow of each enterprise communication terminal, cloud resource vertical expansion planning or cloud resource horizontal expansion planning is performed for the enterprise communication terminal, and the upper limit of the data packet flow processing pre-planned by each enterprise communication terminal is obtained. For the case where the load fluctuation is small during the prediction monitoring period, such as during non-peak hours or for a relatively small number of users, a method of rapid expansion and performance improvement is provided through vertical expansion, and the real-time data packet flow of each enterprise communication terminal and the corresponding data packet flow prediction value are analyzed for difference significance, and according to the analysis results, the enterprise communication terminal with significant difference in data packet flow is smoothed, and the significant difference in data packet flow is apportioned through data block;

[0033] Automatically scale cloud resources horizontally for enterprise communication terminals with significant differences in packet traffic. For situations with large load fluctuations during the predicted monitoring period, such as sudden traffic peaks, horizontal scaling can be used to add multiple servers to handle the load and improve system availability. If one server fails or requires maintenance, other servers can continue to provide services, thereby avoiding the risk of single point failure in the system. Performance can be maintained when the load increases dramatically, and when the load decreases, resource usage can be quickly reduced to adapt to changing load demands, thereby improving resource utilization and better coping with sudden traffic and business growth. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A schematic diagram of a cloud computing-based enterprise management system according to an embodiment of the present application. DETAILED DESCRIPTION

[0035] The following is a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0036] like Figure 1 As shown, an enterprise management system based on cloud computing includes a cloud server, and the cloud server is communicatively connected with a data acquisition module, a data analysis module, a day-ahead planning module, a day-intraday scheduling module, and an automation expansion module;

[0037] The data acquisition module and the cloud server are connected in a distributed manner through IoT nodes to obtain enterprise information uploaded by several enterprise communication terminals and transmit it to the data analysis module;

[0038] The data analysis module is used to package the received enterprise information, and build a traffic prediction model based on the data packets to obtain the data packet traffic prediction value of the enterprise communication terminal;

[0039] The day-ahead planning module performs cloud resource vertical expansion planning or cloud resource horizontal expansion planning for the enterprise communication terminal according to the predicted value of the data packet flow of each enterprise communication terminal, and obtains the data packet flow processing upper limit pre-planned by each enterprise communication terminal;

[0040] The intraday scheduling module performs a difference significance analysis between the real-time data packet flow of each enterprise communication terminal and the corresponding data packet flow prediction value, and performs smoothing processing on the enterprise communication terminals with significant differences in data packet flow according to the analysis results;

[0041] The automated expansion module is used to automatically expand cloud resources horizontally for enterprise communication terminals with significant differences in data packet traffic.

[0042] It should be further explained that, in the specific implementation process, the data analysis module packages the received enterprise information, and builds a traffic prediction model according to the data packet, and the process of obtaining the data packet traffic prediction value of the enterprise communication terminal includes:

[0043] Packing the enterprise information uploaded by the same enterprise communication terminal into a data packet, and obtaining the upload record of the corresponding enterprise communication terminal, wherein the upload record includes the upload time, the monitoring period, and the data packet flow;

[0044] Count the data packet traffic at different times of several monitoring cycles of the corresponding enterprise communication terminal, build a traffic prediction model based on deep learning, and use the data packet traffic as a training set and a test set; input the training set into the traffic prediction model for training until the loss function training is stable, save the model parameters, test the traffic prediction model with the test set until it meets the preset requirements, and output the traffic prediction model;

[0045] The traffic prediction module obtains the predicted value of the data packet traffic at each moment in the current monitoring period of the enterprise communication terminal, and constructs a reference curve chart showing the change of the data packet traffic of each enterprise communication terminal over time based on the predicted value of the data packet traffic at each moment in the current monitoring period of each enterprise communication terminal.

[0046] It should be further explained that, in the specific implementation process, the process of the day-ahead planning module performing cloud resource vertical expansion planning for the enterprise communication terminal according to the data packet flow prediction value of each enterprise communication terminal includes:

[0047] Obtain all specification types of single virtual servers that can be loaded in the cloud server and the upper limits of data packet processing traffic corresponding to single virtual servers of each specification type, set a specification threshold interval, and divide specification sub-intervals of different specification levels into specification sub-intervals within the specification threshold interval using the upper limits of data packet processing traffic corresponding to single virtual servers of different specification types as threshold points;

[0048] Compare the reference curve graph of each enterprise communication terminal with the specification threshold interval, obtain several curve segments in the reference curve graph that are within the specification threshold interval and the specification sub-interval corresponding to each curve segment, obtain the time periods corresponding to the several curve segments contained in each specification sub-interval and the specification type of the single virtual server corresponding to each specification sub-interval, and according to the time periods corresponding to the several curve segments and the specification type of the single virtual server corresponding to each specification sub-interval, obtain the specification type corresponding to the single virtual server that needs to be pre-configured in several time periods of each enterprise communication terminal within the current monitoring period.

[0049] It should be further explained that, in the specific implementation process, the process of the day-ahead planning module performing cloud resource horizontal expansion planning for the enterprise communication terminal according to the data packet flow prediction value of each enterprise communication terminal includes:

[0050] The reference curve graph of each enterprise communication terminal is compared with the specification threshold interval. If there is a curve segment in the reference curve graph that is not within the specification threshold interval, the time period corresponding to the curve segment and the packet flow prediction value corresponding to the curve segment are obtained, the specification type of the virtual server corresponding to the maximum packet processing flow upper limit is obtained, and it is marked as the highest specification virtual server, and the number of highest specification virtual servers corresponding to the curve segment is obtained according to the packet flow prediction value corresponding to the curve segment and the maximum packet processing flow upper limit, and the number of highest specification virtual servers that need to be pre-configured for each enterprise communication terminal in several time periods within the current monitoring cycle is obtained according to the time period corresponding to the curve segment and the number of highest specification virtual servers corresponding to the curve segment.

[0051] It should be further explained that, in the specific implementation process, the process of the day-ahead planning module obtaining the upper limit of the data packet flow processing pre-planned by each enterprise communication terminal includes:

[0052] Obtain the cloud resource expansion type pre-planned for each enterprise communication terminal. If the enterprise communication terminal is planned for vertical expansion of cloud resources, obtain the data packet processing flow upper limit of a single virtual server according to the specification type of a single virtual server that needs to be pre-configured in several time periods of the current monitoring cycle of the enterprise communication terminal, and use the data packet processing flow upper limit as the data packet flow processing upper limit pre-planned for the enterprise communication terminal;

[0053] If the enterprise communication terminal is planned for vertical expansion of cloud resources, the pre-planned data packet flow processing upper limit of the enterprise communication terminal is obtained based on the maximum number of virtual servers that need to be pre-configured in the enterprise communication terminal in several time periods within the current monitoring cycle.

[0054] It should be further explained that, in the specific implementation process, the process of the intraday scheduling module performing a difference significance analysis between the real-time data packet flow of each enterprise communication terminal and the corresponding data packet flow prediction value includes:

[0055] Acquire the real-time data packet traffic uploaded by each enterprise communication terminal during the current monitoring period, set a time window, segment the real-time data packet traffic and data packet traffic prediction value of each enterprise communication terminal during the current monitoring period according to the time window, divide the real-time data packet traffic and data packet traffic prediction value into several time windows, obtain the standard deviation of the real-time data packet traffic and the standard deviation of the data packet traffic prediction value in each time window, obtain the enterprise communication terminals with significant differences in data packet traffic according to the standard deviation, mark the enterprise communication terminals as key enterprise communication terminals, and mark the enterprise communication terminals without significant differences as normal enterprise communication terminals.

[0056] It should be further explained that, in the specific implementation process, the calculation formula for obtaining the standard deviation of the real-time data packet flow in each time window and the standard deviation of the data packet flow prediction value is:

[0057]

[0058] Where S represents the standard deviation; u i represents the data packet flow at the i-th moment; represents the average value of the data packet flow in the time window; n represents the length of the time window; i represents the time mark in the time window;

[0059] It should be further explained that, in the specific implementation process, the specific process of obtaining the enterprise communication terminals with significant differences in data packet flow according to the standard deviation is:

[0060] A multiple change value T is obtained according to the standard deviation S(A) of the real-time data packet flow in each time window and the standard deviation S(B) of the data packet flow prediction value, a multiple change threshold TD is preset, and a time window whose multiple change value T is greater than the multiple change threshold TD is screened out, and a start timestamp of the time window is obtained, and the time windows are arranged in order from small to large according to the start timestamps, and a time window timing sequence is obtained, and a standard deviation S(Aj) of each time window in the time window timing sequence is obtained, where j represents the timing sequence number of each time window, and then the standard deviation S(Aj) of each time window in the time window timing sequence except the first time window is compared with the standard deviation S(A(j-1)) of the previous time window. If the standard deviation of each time window except the first time window satisfies S(Aj)>S(A(j-1)), the enterprise communication terminal is marked as a key enterprise communication terminal;

[0061] First, the time window whose multiple change value T(j) is greater than the multiple change threshold TD is selected. When the multiple change value T(j) is greater than the multiple change threshold TD, it is considered that the real-time data packet flow of the enterprise communication terminal deviates from the expected dynamic trajectory and has a significant difference from the expected value.

[0062] Then, determine whether the standard deviation of each time window in the time window timing sequence except the first time window satisfies S(Aj)>S(A(j-1)). If S(Aj)>S(A(j-1)) is satisfied, it proves that the real-time data packet flow of the enterprise communication terminal is significantly different from the expected data packet flow, and the data packet flow shows an abnormal and stable expansion trend. There is a possibility that the real-time data packet flow is greater than the data packet flow processing upper limit of the enterprise communication terminal. In this case, smoothing processing or automatic expansion operation is performed.

[0063] It should be further explained that, in the specific implementation process, the calculation formula for obtaining the multiple change value T according to the standard deviation S(A) of the real-time data packet flow in each time window and the standard deviation S(B) of the predicted value of the data packet flow is:

[0064]

[0065] It should be further explained that, in the specific implementation process, the process of the intraday scheduling module smoothing the enterprise communication terminals with significant differences in data packet traffic includes:

[0066] Set the number of blocks n, divide the real-time data packets uploaded by the key enterprise communication terminal into n data blocks, each data block includes corresponding packet header information and payload, obtain the flow difference between the real-time data packet flow processed by each virtual server in the cloud server and the corresponding data packet flow processing upper limit, obtain the first n virtual servers with the largest flow difference, and obtain the size of the real-time data packet uploaded by the key enterprise communication terminal, and determine whether the sum of the flow differences of the first n virtual servers is greater than the size of the real-time data packet;

[0067] If it is greater, n data blocks are allocated to n virtual servers; if it is less, the automatic expansion mechanism is started.

[0068] It should be further explained that, in the specific implementation process, the process in which the automatic expansion module automatically expands the cloud resources horizontally for the enterprise communication terminals with significant differences in data packet traffic includes:

[0069] Obtain the maximum value of the historical data packet flow of the key enterprise communication terminal, compare the maximum value of the historical data packet flow of the key enterprise communication terminal with the upper limit of the data packet flow processing pre-planned by the key enterprise communication terminal, and obtain the flow difference between the maximum value of the historical data packet flow of the key enterprise communication terminal and the corresponding upper limit of the data packet flow processing;

[0070] Compare the flow difference with the specification threshold interval. If the flow difference is within the specification threshold interval, obtain the specification level corresponding to the specification sub-interval where the flow difference is located, and allocate virtual servers of corresponding specification types to key enterprise communication terminals according to the specification level;

[0071] If the traffic difference is not within the specification threshold range, the highest specification virtual server is first allocated to the key enterprise communication terminal, and the traffic difference between the real-time data packet traffic of the key enterprise communication terminal and the data packet traffic processing upper limit pre-planned for the key enterprise communication terminal is obtained. When the real-time data packet traffic is greater than the data packet traffic processing upper limit pre-planned for the key enterprise communication terminal, and the traffic difference is greater than or equal to the data packet traffic processing upper limit corresponding to the highest specification virtual server, the highest specification virtual server is again allocated to the key enterprise communication terminal, and so on, until the key enterprise communication terminal is marked as a normal enterprise communication terminal.

[0072] The data collection module and the cloud server are connected in a distributed manner through IoT nodes to obtain enterprise information collected by several enterprise communication terminals;

[0073] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An enterprise management system based on cloud computing, comprising a cloud server, characterized in that: The cloud server is communicatively connected to a data acquisition module, a data analysis module, a day-ahead planning module, an intraday scheduling module, and an automation expansion module; The data acquisition module and the cloud server are connected in a distributed manner through IoT nodes to obtain enterprise information uploaded by several enterprise communication terminals and transmit it to the data analysis module; The data analysis module is used to package the received enterprise information, and build a traffic prediction model based on the data packets to obtain the data packet traffic prediction value of the enterprise communication terminal; The day-ahead planning module performs cloud resource vertical expansion planning or cloud resource horizontal expansion planning for the enterprise communication terminal according to the predicted value of the data packet flow of each enterprise communication terminal, and obtains the data packet flow processing upper limit pre-planned by each enterprise communication terminal; The intraday scheduling module performs a difference significance analysis between the real-time data packet flow of each enterprise communication terminal and the corresponding data packet flow prediction value, and performs smoothing processing on the enterprise communication terminals with significant differences in data packet flow according to the analysis results; The automated expansion module is used to automatically expand cloud resources horizontally for enterprise communication terminals with significant differences in data packet traffic.

2. The cloud computing-based enterprise management system according to claim 1, characterized in that: The data analysis module packages the received enterprise information and builds a traffic prediction model according to the data packet. The process of obtaining the data packet traffic prediction value of the enterprise communication terminal includes: Packing the enterprise information uploaded by the same enterprise communication terminal into a data packet, and obtaining the upload record of the corresponding enterprise communication terminal, wherein the upload record includes the upload time, the monitoring period, and the data packet flow; Count the data packet traffic at different times of several monitoring cycles of the corresponding enterprise communication terminal, build a traffic prediction model based on deep learning, and use the data packet traffic as a training set and a test set; input the training set into the traffic prediction model for training until the loss function training is stable, save the model parameters, test the traffic prediction model with the test set until it meets the preset requirements, and output the traffic prediction model; The traffic prediction module obtains the predicted value of the data packet traffic at each moment in the current monitoring period of the enterprise communication terminal, and constructs a reference curve chart showing the change of the data packet traffic of each enterprise communication terminal over time based on the predicted value of the data packet traffic at each moment in the current monitoring period of each enterprise communication terminal.

3. The cloud computing-based enterprise management system according to claim 2, characterized in that: The process of the day-ahead planning module performing cloud resource vertical expansion planning for the enterprise communication terminal according to the data packet flow prediction value of each enterprise communication terminal includes: Obtain all specification types of single virtual servers that can be loaded in the cloud server and the upper limits of data packet processing traffic corresponding to single virtual servers of each specification type, set a specification threshold interval, and divide specification sub-intervals of different specification levels into specification sub-intervals within the specification threshold interval using the upper limits of data packet processing traffic corresponding to single virtual servers of different specification types as threshold points; Compare the reference curve graph of each enterprise communication terminal with the specification threshold interval, obtain several curve segments in the reference curve graph that are within the specification threshold interval and the specification sub-interval corresponding to each curve segment, obtain the time periods corresponding to the several curve segments contained in each specification sub-interval and the specification type of the single virtual server corresponding to each specification sub-interval, and according to the time periods corresponding to the several curve segments and the specification type of the single virtual server corresponding to each specification sub-interval, obtain the specification type corresponding to the single virtual server that needs to be pre-configured in several time periods of each enterprise communication terminal within the current monitoring period.

4. The cloud computing-based enterprise management system according to claim 3, characterized in that: The process of the day-ahead planning module performing cloud resource horizontal expansion planning for the enterprise communication terminal according to the data packet flow prediction value of each enterprise communication terminal includes: The reference curve graph of each enterprise communication terminal is compared with the specification threshold interval. If there is a curve segment in the reference curve graph that is not within the specification threshold interval, the time period corresponding to the curve segment and the packet flow prediction value corresponding to the curve segment are obtained, the specification type of the virtual server corresponding to the maximum packet processing flow upper limit is obtained, and it is marked as the highest specification virtual server, and the number of highest specification virtual servers corresponding to the curve segment is obtained according to the packet flow prediction value corresponding to the curve segment and the maximum packet processing flow upper limit, and the number of highest specification virtual servers that need to be pre-configured for each enterprise communication terminal in several time periods within the current monitoring cycle is obtained according to the time period corresponding to the curve segment and the number of highest specification virtual servers corresponding to the curve segment.

5. The cloud computing-based enterprise management system according to claim 4, characterized in that: The process of the day-ahead planning module obtaining the upper limit of the data packet flow processing pre-planned by each enterprise communication terminal includes: Obtain the cloud resource expansion type pre-planned for each enterprise communication terminal. If the enterprise communication terminal is planned for vertical expansion of cloud resources, obtain the data packet processing flow upper limit of a single virtual server according to the specification type of a single virtual server that needs to be pre-configured in several time periods of the current monitoring cycle of the enterprise communication terminal, and use the data packet processing flow upper limit as the data packet flow processing upper limit pre-planned for the enterprise communication terminal; If the enterprise communication terminal is planned for vertical expansion of cloud resources, the pre-planned data packet flow processing upper limit of the enterprise communication terminal is obtained based on the maximum number of virtual servers that need to be pre-configured in the enterprise communication terminal in several time periods within the current monitoring cycle.

6. The cloud computing-based enterprise management system according to claim 5, characterized in that: The process of performing a difference significance analysis between the real-time data packet flow of each enterprise communication terminal and the corresponding data packet flow prediction value by the intraday scheduling module includes: Acquire the real-time data packet traffic uploaded by each enterprise communication terminal during the current monitoring period, set a time window, segment the real-time data packet traffic and data packet traffic prediction value of each enterprise communication terminal during the current monitoring period according to the time window, divide the real-time data packet traffic and data packet traffic prediction value into several time windows, obtain the standard deviation of the real-time data packet traffic and the standard deviation of the data packet traffic prediction value in each time window, obtain the enterprise communication terminals with significant differences in data packet traffic according to the standard deviation, mark the enterprise communication terminals as key enterprise communication terminals, and mark the enterprise communication terminals without significant differences as normal enterprise communication terminals.

7. The cloud computing-based enterprise management system according to claim 6, characterized in that: The process of smoothing the enterprise communication terminals with significant differences in data packet flow by the intraday scheduling module includes: Set the number of blocks n, divide the real-time data packets uploaded by the key enterprise communication terminal into n data blocks, each data block includes corresponding packet header information and payload, obtain the flow difference between the real-time data packet flow processed by each virtual server in the cloud server and the corresponding data packet flow processing upper limit, obtain the first n virtual servers with the largest flow difference, and obtain the size of the real-time data packet uploaded by the key enterprise communication terminal, and determine whether the sum of the flow differences of the first n virtual servers is greater than the size of the real-time data packet; If it is greater, n data blocks are allocated to n virtual servers; if it is less, the automatic expansion mechanism is started.

8. The cloud computing-based enterprise management system according to claim 7, characterized in that: The process of automatically extending the cloud resources horizontally for the enterprise communication terminals with significant differences in data packet flow by the automatic extension module includes: Obtain the maximum value of the historical data packet flow of the key enterprise communication terminal, compare the maximum value of the historical data packet flow of the key enterprise communication terminal with the upper limit of the data packet flow processing pre-planned by the key enterprise communication terminal, and obtain the flow difference between the maximum value of the historical data packet flow of the key enterprise communication terminal and the corresponding upper limit of the data packet flow processing; Compare the flow difference with the specification threshold interval. If the flow difference is within the specification threshold interval, obtain the specification level corresponding to the specification sub-interval where the flow difference is located, and allocate virtual servers of corresponding specification types to key enterprise communication terminals according to the specification level; If the traffic difference is not within the specification threshold range, the highest specification virtual server is first allocated to the key enterprise communication terminal, and the traffic difference between the real-time data packet traffic of the key enterprise communication terminal and the data packet traffic processing upper limit pre-planned for the key enterprise communication terminal is obtained. When the real-time data packet traffic is greater than the data packet traffic processing upper limit pre-planned for the key enterprise communication terminal, and the traffic difference is greater than or equal to the data packet traffic processing upper limit corresponding to the highest specification virtual server, the highest specification virtual server is again allocated to the key enterprise communication terminal, and so on, until the key enterprise communication terminal is marked as a normal enterprise communication terminal.

Images