Data optimization management system and method for network distribution model

Abstract

The invention discloses a data optimization management system and method for a network distribution model, and belongs to the technical field of artificial intelligence, and the method comprises the following steps: analyzing a comprehensive trend difference value and a traffic fluctuation value of a to-be-managed network at the same time point; establishing a traffic prediction model, and training the traffic prediction model; acquiring a predicted flow value of the network to be managed at the next time point; analyzing the server demand quantity of the network to be managed at the next time point; adjusting the number of online servers of the network to be managed at the next time point; after adjusting the number of the online servers of the network to be managed at the next time point, pre-distributing the load of each online server of the network to be managed at the next time point; according to the invention, the historical traffic data of the to-be-managed network is analyzed, so that the load of the server is balanced, the waste and overload phenomena of server resources are avoided, and the stability and operation efficiency of the network are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a data optimization management system and method for a network distribution model. Background Art

[0002] The network distribution model refers to a method used to manage and optimize data flow, transmission and resource allocation in computer networks. Its main purpose is to ensure efficient distribution of data in the network, avoid network congestion, and improve overall transmission efficiency.

[0003] In the modern Internet environment, as user demands and service complexity continue to increase, network traffic has become increasingly volatile. Traditional resource allocation methods for servers often have difficulty coping with such fluctuations, resulting in insufficient resources during peak traffic periods, causing server overloads, and insufficient resources during low traffic periods, resulting in resource waste, which reduces network service quality and resource utilization.

[0004] Therefore, people are in urgent need of a data optimization management system for network distribution models to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a data optimization management system for a network distribution model to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] A data optimization management method for a network distribution model, the method comprising the following steps:

[0008] S1. Set a cycle to collect traffic values ​​at each time point in different cycles of the network to be managed; set a sliding window to calculate the traffic trend and trend difference at each time point in different cycles of the network to be managed, and analyze the comprehensive trend difference and traffic fluctuation value at the same time point of the network to be managed;

[0009] S2. Establish a traffic prediction model and train the traffic prediction model; collect the traffic value of the network to be managed at the current time point, and obtain the predicted traffic value of the network to be managed at the next time point through the trained traffic prediction model;

[0010] S3, collecting the traffic processing capacity and maximum load utilization of the servers in the managed network, combining the predicted traffic value of the managed network at the next time point, and analyzing the server demand of the managed network at the next time point;

[0011] S4, collecting the number of online servers at the current time point of the network to be managed, and adjusting the number of online servers at the next time point of the network to be managed;

[0012] S5. After adjusting the number of online servers of the network to be managed at the next time point, pre-allocate the load of each online server of the network to be managed at the next time point.

[0013] According to the above technical solution, the specific process of step S1 is as follows:

[0014] S1-1, set the period to T; record the i-th time point as t i ; The traffic value at the i-th time point in the j-th cycle of the managed network is recorded as A j (t i );

[0015] S1-2, set the sliding window, denoted as B k ; Where k represents the size of the sliding window; the traffic trend at each time point in different cycles of the managed network is calculated according to the following formula:

[0016]

[0017] Among them, C j (t i ) represents the traffic trend at the i-th time point in the j-th cycle of the managed network; A j (t a ) indicates that the i-th time point in the j-th cycle of the managed network is located in the sliding window B k The flow value within; a represents the sliding window B k In is used to traverse the index of time points, a takes values ​​from i-k+1 to i in sequence;

[0018] The sliding window is a fixed-size window used for local analysis of time series data. The window moves gradually on the time series, one time step at a time. It captures short-term trends and eliminates noise through local time point data, while smoothing traffic changes to help identify trends more accurately.

[0019] Flow trends are used to identify the overall direction of change in a time series. The trend can be used to understand whether the flow is increasing, decreasing, or remaining stable;

[0020] Calculate the trend difference at each time point in different cycles of the managed network. The calculation formula is: ΔC j (t i )=C j (t i )-C j (t i-1 ), where ΔC j (t i ) represents the trend difference at the i-th time point in the j-th cycle of the network to be managed; C j (t i-1) represents the traffic trend at the i-th time point in the j-th cycle of the managed network; t i-1 represents the i-1th time point;

[0021] The trend difference is used to measure the rate of change of the flow trend. A positive difference indicates that the flow trend is increasing, and a negative difference indicates that the flow trend is decreasing.

[0022] Take the trend difference ΔC corresponding to the i-th time point in all cycles of the network to be managed j (t i ) is taken as the comprehensive trend difference of the network to be managed at the i-th time point, and is recorded as ΔC1(t i );

[0023] S1-3. Get the traffic value A corresponding to all time points in the jth cycle of the network to be managed j (t i ) is taken as the average flow value of the jth period of the network to be managed, and is recorded as A1 j ;

[0024] The traffic fluctuation value of the network to be managed at the same time point is calculated according to the following formula:

[0025]

[0026] Among them, D(t i ) represents the traffic fluctuation value of the network to be managed at the i-th time point;

[0027] The flow fluctuation value is used to evaluate the stability of the flow at the same time point. A high fluctuation value indicates unstable flow, and a low fluctuation value indicates relatively stable flow.

[0028] According to the above technical solution, the specific process of step S2 is as follows:

[0029] S2-1. Establish a traffic prediction model, as follows:

[0030] Y = X1 + α1 × X2 + α2 × X3 + α3;

[0031] Among them, Y represents the dependent variable, X1, X2 and X3 all represent the dependent variables, α1 and α2 both represent the adjustment parameters; α3 represents the correction parameter;

[0032] S2-2, let the i+1th time point be t i+1 ; The traffic value at the i+1th time point in the jth cycle of the managed network is recorded as A j (t i+1 );

[0033] S2-3, the flow value A corresponding to the i+1th time point in each cycle of the network to be managedj (t i+1 ), as the corresponding Y in the training set; the flow value A corresponding to the i-th time point in each cycle of the network to be managed j (t i ), as the corresponding X1 in the training set; the comprehensive trend difference ΔC1(t i ) as the corresponding X2 in the training set; the traffic fluctuation value D(t i ), as the corresponding X3 in the training set; the traffic prediction model is trained through the training set;

[0034] S2-4, record the current time point as t; record the next time point as t+1; record the flow value of the network to be managed at the current time point as F t ; F t , through the trained traffic prediction model, obtain the predicted traffic value of the network to be managed at the next time point, denoted as F t+1 .

[0035] According to the above technical solution, the specific process of step S3 is as follows:

[0036] The traffic processing capacity of the server in the managed network is recorded as G; the maximum load utilization rate of the server in the managed network is recorded as H; the server demand of the managed network at the next time point is calculated according to the following formula:

[0037]

[0038] Among them, N t+1 Indicates the server demand at the next time point of the network to be managed; Indicates F t+1 The value of / (G·H) is rounded up;

[0039] The traffic processing capacity refers to the maximum amount of network traffic that a server can process per unit time, and can be directly measured using a network performance testing tool.

[0040] According to the above technical solution, the specific process of step S4 is as follows:

[0041] S4-1. The number of online servers at the current time point in the managed network is recorded as N t ;

[0042] S4-2, if N t+1 ≥N t , indicating that the number of online servers is insufficient to handle the traffic at the next time point, and additional online servers are needed at the next time point. The number of additional servers is (N t+1 -N t);

[0043] If If N t+1 <N t , indicating that the number of online servers is sufficient to handle the traffic at the next time point, and the number of online servers to be reduced at the next time point is (N t -N t+1 ).

[0044] According to the above technical solution, the specific process of step S5 is as follows:

[0045] After adjusting the number of online servers in the managed network at the next time point, the number of online servers in the managed network at the next time point is N t+1 , pre-allocate the load of each online server at the next time point in the managed network, so that the load of each online server should meet the following conditions: I b，t+1 ≤I=F t+1 / N t+1 ; Among them, I b，t+1 represents the load of the bth online server of the managed network at the next time point; I represents the predicted average traffic value of the managed network at the next time point.

[0046] A data optimization management system for a network distribution model, the system comprising an information collection module, an information processing module and an execution module;

[0047] The information acquisition module is used to acquire the information required by the system; the information processing module is used to store, analyze and transmit the information of each module; and the execution module is used to execute the information of the information processing module.

[0048] According to the above technical solution, the information collection module includes a historical information unit and a real-time information unit;

[0049] The historical information unit is used to collect historical data information of traffic and servers in the network to be managed, and the real-time information unit is used to collect real-time data information of traffic and servers in the network to be managed.

[0050] According to the above technical solution, the information processing module includes an information storage unit, an information analysis unit and an information transmission unit;

[0051] The information storage unit is used to store the information acquired by the information acquisition module; the information analysis unit is used to analyze the information acquired by the information acquisition module; and the information transmission unit is used for information transmission of each module in the system.

[0052] According to the above technical solution, the execution module includes a suggestion display unit and a control unit;

[0053] The display unit is used to display the data information of the traffic and online servers in the network to be managed; the control unit is used to control the servers in the network to be managed.

[0054] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0055] The present invention analyzes the historical traffic data of the managed network and obtains the trend and difference of the historical traffic data, so as to more accurately capture the traffic fluctuation law and improve the accuracy of traffic prediction. On this basis, the traffic of the managed network is predicted to ensure the accurate analysis of server demand and more effectively cope with the changes in network traffic. By dynamically adjusting the number of online servers and pre-allocating the load of the online servers, the load of the servers is balanced, thus avoiding the waste and overload of server resources and greatly improving the stability and operation efficiency of the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0057] Figure 1 It is a flow chart of a data optimization management method for a network distribution model of the present invention;

[0058] Figure 2 It is a structural schematic diagram of a data optimization management system for a network distribution model of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] See also Figure 1 , the present invention provides a technical solution:

[0061] A data optimization management method for a network distribution model, the method comprising the following steps:

[0062] S1. Set a cycle to collect traffic values ​​at each time point in different cycles of the network to be managed; set a sliding window to calculate the traffic trend and trend difference at each time point in different cycles of the network to be managed, and analyze the comprehensive trend difference and traffic fluctuation value at the same time point of the network to be managed;

[0063] According to the above technical solution, the specific process of step S1 is as follows:

[0064] S1-1, set the period to T; record the i-th time point as t i ; The traffic value at the i-th time point in the j-th cycle of the managed network is recorded as A j (t i );

[0065] For example:

[0066] The set period is T = 1 day, and the time points are 24 time points in 1 day;

[0067] S1-2, set the sliding window, denoted as B k ; Where k represents the size of the sliding window; the traffic trend at each time point in different cycles of the managed network is calculated according to the following formula:

[0068]

[0069] Among them, C j (t i ) represents the traffic trend at the i-th time point in the j-th cycle of the managed network; A j (t a ) indicates that the i-th time point in the j-th cycle of the managed network is located in the sliding window B k The flow value within; a represents the sliding window B k In is used to traverse the index of time points, a takes values ​​from i-k+1 to i in sequence;

[0070] For example:

[0071] Given that k = 3, A j (t i＝14 )=140,A j (t i＝15 )=150,A j (t i＝16 )=160, according to the formula:

[0072]

[0073] Obtain the traffic trend C at the i=16th time point in the jth cycle of the network to be managed j (t i＝16 )=150;

[0074] The sliding window is a fixed-size window used for local analysis of time series data. The window moves gradually on the time series, one time step at a time. It captures short-term trends and eliminates noise through local time point data, while smoothing traffic changes to help identify trends more accurately.

[0075] Flow trends are used to identify the overall direction of change in a time series. The trend can be used to understand whether the flow is increasing, decreasing, or remaining stable;

[0076] Calculate the trend difference at each time point in different cycles of the managed network. The calculation formula is: ΔC j (t i )=C j (t i )-C j (t i-1 ), where ΔC j (t i ) represents the trend difference at the i-th time point in the j-th cycle of the network to be managed; C j (t i-1 ) represents the traffic trend at the i-th time point in the j-th cycle of the managed network; t i-1 represents the i-1th time point;

[0077] The trend difference is used to measure the rate of change of the flow trend. A positive difference indicates that the flow trend is increasing, and a negative difference indicates that the flow trend is decreasing.

[0078] Take the trend difference ΔC corresponding to the i-th time point in all cycles of the network to be managed j (t i ) is taken as the comprehensive trend difference of the network to be managed at the i-th time point, and is recorded as ΔC1(t i );

[0079] S1-3. Get the traffic value A corresponding to all time points in the jth cycle of the network to be managed j (t i ) is taken as the average flow value of the jth period of the network to be managed, and is recorded as A1 j ;

[0080] The traffic fluctuation value of the network to be managed at the same time point is calculated according to the following formula:

[0081]

[0082] Among them, D(t i ) represents the traffic fluctuation value of the network to be managed at the i-th time point;

[0083] The flow fluctuation value is used to evaluate the stability of the flow at the same time point. A high fluctuation value indicates unstable flow, and a low fluctuation value indicates relatively stable flow.

[0084] S2. Establish a traffic prediction model and train the traffic prediction model; collect the traffic value of the network to be managed at the current time point, and obtain the predicted traffic value of the network to be managed at the next time point through the trained traffic prediction model;

[0085] According to the above technical solution, the specific process of step S2 is as follows:

[0086] S2-1. Establish a traffic prediction model, as follows:

[0087] Y = X1 + α1 × X2 + α2 × X3 + α3;

[0088] Among them, Y represents the dependent variable, X1, X2 and X3 all represent the dependent variables, α1 and α2 both represent the adjustment parameters; α3 represents the correction parameter;

[0089] S2-2, let the i+1th time point be t i+1 ; The traffic value at the i+1th time point in the jth cycle of the managed network is recorded as A j (t i+1 );

[0090] S2-3, the flow value A corresponding to the i+1th time point in each cycle of the network to be managed j (t i+1 ), as the corresponding Y in the training set; the flow value A corresponding to the i-th time point in each cycle of the network to be managed j (t i ), as the corresponding X1 in the training set; the comprehensive trend difference ΔC1(t i ) as the corresponding X2 in the training set; the traffic fluctuation value D(t i ), as the corresponding X3 in the training set; the traffic prediction model is trained through the training set;

[0091] S2-4, record the current time point as t; record the next time point as t+1; record the flow value of the network to be managed at the current time point as F t ; F t , through the trained traffic prediction model, obtain the predicted traffic value of the network to be managed at the next time point, denoted as F t+1 ;

[0092] For example:

[0093] It is known that the current time point t = t i＝16 , the traffic value F of the network to be managed at the current time point t =160, the trend difference ΔC1(t i＝16 )=5, the traffic fluctuation value D(t i＝16)=10, α1=0.5, α2=0.3, α3=-0.2 in the traffic prediction model; according to the traffic prediction model, obtain the predicted traffic value F of the network to be managed at the next time point t+1 =(F t =160)+(α1=0.5)×(ΔC1(t i＝16 )=5)+(α2=0.3)×(D(t i＝16 )=10)+(α3=-0.2)=163.8.

[0094] S3, collecting the traffic processing capacity and maximum load utilization of the servers in the managed network, combining the predicted traffic value of the managed network at the next time point, and analyzing the server demand of the managed network at the next time point;

[0095] According to the above technical solution, the specific process of step S3 is as follows:

[0096] The traffic processing capacity of the server in the managed network is recorded as G; the maximum load utilization rate of the server in the managed network is recorded as H; the server demand of the managed network at the next time point is calculated according to the following formula:

[0097]

[0098] Among them, N t+1 Indicates the server demand at the next time point of the network to be managed; Indicates F t+1 The value of / (G·H) is rounded up;

[0099] For example:

[0100] It is known that the traffic processing capacity of the server in the managed network is G = 50; the maximum load utilization rate of the server in the managed network is H = 0.8; the predicted traffic value F of the managed network at the next time point is t+1 =163.8; Calculate the server demand of the managed network at the next time point according to the following formula:

[0101]

[0102] Get the server demand N of the network to be managed at the next time point t+1 =5;

[0103] The traffic processing capacity refers to the maximum amount of network traffic that a server can process per unit time, and can be directly measured using a network performance testing tool.

[0104] S4, collecting the number of online servers at the current time point of the network to be managed, and adjusting the number of online servers at the next time point of the network to be managed;

[0105] According to the above technical solution, the specific process of step S4 is as follows:

[0106] S4-1. The number of online servers at the current time point in the managed network is recorded as N t ;

[0107] S4-2, if N t+1 ≥N t , indicating that the number of online servers is insufficient to handle the traffic at the next time point, and additional online servers are needed at the next time point. The number of additional servers is (N t+1 -N t );

[0108] If If N t+1 <N t , indicating that the number of online servers is sufficient to handle the traffic at the next time point, and the number of online servers to be reduced at the next time point is (N t -N t+1 ).

[0109] S5, after adjusting the number of online servers of the managed network at the next time point, pre-allocate the load of each online server of the managed network at the next time point;

[0110] According to the above technical solution, the specific process of step S5 is as follows:

[0111] After adjusting the number of online servers in the managed network at the next time point, the number of online servers in the managed network at the next time point is N t+1 , pre-allocate the load of each online server at the next time point in the managed network, so that the load of each online server should meet the following conditions: I b，t+1 ≤I=F t+1 / N t+1 ; Among them, I b，t+1 represents the load of the bth online server of the managed network at the next time point; I represents the predicted average traffic value of the managed network at the next time point.

[0112] See also Figure 2 , a data optimization management system for a network distribution model, the system includes an information collection module, an information processing module and an execution module;

[0113] The information acquisition module is used to acquire the information required by the system; the information processing module is used to store, analyze and transmit the information of each module; and the execution module is used to execute the information of the information processing module.

[0114] According to the above technical solution, the information collection module includes a historical information unit and a real-time information unit;

[0115] The historical information unit is used to collect historical data information of traffic and servers in the network to be managed, and the real-time information unit is used to collect real-time data information of traffic and servers in the network to be managed.

[0116] According to the above technical solution, the information processing module includes an information storage unit, an information analysis unit and an information transmission unit;

[0117] The information storage unit is used to store the information acquired by the information acquisition module; the information analysis unit is used to analyze the information acquired by the information acquisition module; and the information transmission unit is used for information transmission of each module in the system.

[0118] According to the above technical solution, the execution module includes a suggestion display unit and a control unit;

[0119] The display unit is used to display the data information of the traffic and online servers in the network to be managed; the control unit is used to control the servers in the network to be managed.

[0120] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions recorded in the aforementioned embodiments, or to replace some of the technical features therein by equivalents. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A data optimization management method for a network distribution model, characterized in that: The method comprises the following steps: S1. Set a cycle to collect traffic values ​​at each time point in different cycles of the network to be managed; set a sliding window to calculate the traffic trend and trend difference at each time point in different cycles of the network to be managed, and analyze the comprehensive trend difference and traffic fluctuation value at the same time point of the network to be managed; S2. Establish a traffic prediction model and train the traffic prediction model; collect the traffic value of the network to be managed at the current time point, and obtain the predicted traffic value of the network to be managed at the next time point through the trained traffic prediction model; S3, collecting the traffic processing capacity and maximum load utilization of the servers in the managed network, combining the predicted traffic value of the managed network at the next time point, and analyzing the server demand of the managed network at the next time point; S4, collecting the number of online servers at the current time point of the network to be managed, and adjusting the number of online servers at the next time point of the network to be managed; S5. After adjusting the number of online servers of the network to be managed at the next time point, pre-allocate the load of each online server of the network to be managed at the next time point.

2. The data optimization management method for a network distribution model according to claim 1, characterized in that: The specific process of step S1 is as follows: S1-1, set the period to T; record the i-th time point as t i ; The traffic value at the i-th time point in the j-th cycle of the managed network is recorded as A j (t i ); S1-2, set the sliding window, denoted as B k ; Where k represents the size of the sliding window; the traffic trend at each time point in different cycles of the managed network is calculated according to the following formula: Among them, C j (t i ) represents the traffic trend at the i-th time point in the j-th cycle of the managed network; A j (t a ) indicates that the i-th time point in the j-th cycle of the managed network is located in the sliding window B k The flow value within; a represents the sliding window B k In is used to traverse the index of time points, a takes values ​​from i-k+1 to i in sequence; Calculate the trend difference at each time point in different cycles of the managed network. The calculation formula is: ΔC j (t i )=C j (t i )-C j (t i-1 ), where ΔC j (t i ) represents the trend difference at the i-th time point in the j-th cycle of the network to be managed; C j (t i-1 ) represents the traffic trend at the i-th time point in the j-th cycle of the managed network; t i-1 represents the i-1th time point; Take the trend difference ΔC corresponding to the i-th time point in all cycles of the network to be managed j (t i ) is taken as the comprehensive trend difference of the network to be managed at the i-th time point, and is recorded as ΔC1(t i ); S1-3. Get the traffic value A corresponding to all time points in the jth cycle of the network to be managed j (t i ) is taken as the average flow value of the jth period of the network to be managed, and is recorded as A1 j ; The traffic fluctuation value of the network to be managed at the same time point is calculated according to the following formula: Among them, D(t i ) represents the traffic fluctuation value of the network to be managed at the i-th time point.

3. The data optimization management method for a network distribution model according to claim 2, characterized in that: The specific process of step S2 is as follows: S2-1. Establish a traffic prediction model, as follows: Y = X1 + α1 × X2 + α2 × X3 + α3; Among them, Y represents the dependent variable, X1, X2 and X3 all represent the dependent variables, α1 and α2 both represent the adjustment parameters; α3 represents the correction parameter; S2-2, let the i+1th time point be t i+1 ; The traffic value at the i+1th time point in the jth cycle of the managed network is recorded as A j (t i+1 ); S2-3, the flow value A corresponding to the i+1th time point in each cycle of the network to be managed j (t i+1 ), as the corresponding Y in the training set; the flow value A corresponding to the i-th time point in each cycle of the network to be managed j (t i ), as the corresponding X1 in the training set; the comprehensive trend difference ΔC1(t i ) as the corresponding X2 in the training set; the traffic fluctuation value D(t i ), as the corresponding X3 in the training set; the traffic prediction model is trained through the training set; S2-4, record the current time point as t; record the next time point as t+1; record the flow value of the network to be managed at the current time point as F t ; F t , through the trained traffic prediction model, the predicted traffic value of the network to be managed at the next time point is obtained, which is recorded as F t+1 .

4. The data optimization management method for a network distribution model according to claim 3, characterized in that: The specific process of step S3 is as follows: The traffic processing capacity of the server in the managed network is recorded as G; the maximum load utilization rate of the server in the managed network is recorded as H; the server demand of the managed network at the next time point is calculated according to the following formula: Among them, N t+1 Indicates the server demand at the next time point of the network to be managed; Indicates F t+1 The value of / (G·H) is rounded up.

5. The data optimization management method for a network distribution model according to claim 4, characterized in that: The specific process of step S4 is as follows: S4-1. The number of online servers at the current time point in the managed network is recorded as N t ; S4-2, if N t+1 ≥N t , indicating that the number of online servers is insufficient to handle the traffic at the next time point, and additional online servers are needed at the next time point. The number of additional servers is (N t+1 -N t ); If If N t+1 <N t , indicating that the number of online servers is sufficient to handle the traffic at the next time point, and the number of online servers to be reduced at the next time point is (N t -N t+1 ).

6. The data optimization management method for a network distribution model according to claim 5, characterized in that: The specific process of step S5 is as follows: After adjusting the number of online servers in the managed network at the next time point, the number of online servers in the managed network at the next time point is N t+1 , pre-allocate the load of each online server at the next time point in the managed network, so that the load of each online server should meet the following conditions: I b，t+1 ≤I=F t+1 / N t+1 ; Among them, I b，t+1 represents the load of the bth online server of the managed network at the next time point; I represents the predicted average traffic value of the managed network at the next time point.

7. A data optimization management system for a network distribution model, such as a data optimization management method for a network distribution model according to any one of claims 1 to 6, characterized in that: The system includes an information collection module, an information processing module and an execution module; The information acquisition module is used to acquire the information required by the system; the information processing module is used to store, analyze and transmit the information of each module; and the execution module is used to execute the information of the information processing module.

8. The data optimization management system for network distribution model according to claim 7, characterized in that: The information collection module includes a historical information unit and a real-time information unit; The historical information unit is used to collect historical data information of traffic and servers in the network to be managed, and the real-time information unit is used to collect real-time data information of traffic and servers in the network to be managed.

9. The data optimization management system for network distribution model according to claim 7, characterized in that: The information processing module includes an information storage unit, an information analysis unit and an information transmission unit; The information storage unit is used to store the information acquired by the information acquisition module; the information analysis unit is used to analyze the information acquired by the information acquisition module; and the information transmission unit is used for information transmission of each module in the system.

10. The data optimization management system for network distribution model according to claim 7, characterized in that: The execution module includes a suggestion display unit and a control unit; The display unit is used to display the data information of the traffic and online servers in the network to be managed; the control unit is used to control the servers in the network to be managed.

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