A network traffic management method based on data analysis

Abstract

The present invention relates to the technical field of network traffic management, and in particular to a network traffic management method based on data analysis, the method comprising: obtaining historical traffic data of all network user terminals and traffic usage data and user terminal information of each network user terminal from a server; obtaining the geographical location distribution of all network user terminals, and determining the traffic type of each network user terminal; determining the traffic usage user type of each network user terminal according to the user terminal information and the traffic type; determining usage-related characterization parameters of the network user terminal, and determining a traffic management area in combination with the traffic usage user type; determining a traffic management mode within the next traffic management cycle according to the traffic management area; the present invention analyzes the traffic characteristics of the user terminal, and performs corresponding traffic management according to traffic usage requirements, thereby improving the rationality of traffic management and ensuring the traffic requirements of different users.

Description

Technical Field

[0001] The present invention relates to the technical field of network traffic management, and in particular to a network traffic management method based on data analysis. Background Art

[0002] With the development and popularization of the Internet, more and more people are beginning to use various network services. In order to meet users' Internet access needs, operators need to manage and optimize the network. Among them, traffic management is an important part of network management, which involves how to reasonably allocate resources and improve network performance. At present, in the process of traffic management, manual analysis and experience judgment are mainly used to determine the traffic management plan. This method is not only inefficient but also prone to errors, resulting in poor traffic management results. Therefore, there is an urgent need for a technical means to automatically and accurately manage traffic.

[0003] Chinese Patent Publication No.: CN103458466B discloses a flow control device and method and a network flow management system and method, wherein the flow control device comprises: a receiving unit, receiving network status information and network congestion information, wherein the network status information comprises multiple network types between a base station and a terminal and a service type requested by a user, and the network congestion information indicates whether the network between the base station and the terminal is congested; a judging unit, judging whether the service type requested by the user is suitable for transmission in other networks in the user's area when the network congestion information indicates that the current network of the service requested by the user is congested; and an allocating unit, selecting a network from other networks judged suitable for transmission by the judging unit as an alternative target network to replace the current network; it can be seen that the flow control device and method and the network flow management system and method have the following problems: the flow characteristics of the user end are not analyzed, and the corresponding flow management is not performed according to the flow usage requirements, and there is unreasonable flow allocation, resulting in poor network quality or excess network resources. Summary of the invention

[0004] To this end, the present invention provides a network traffic management method based on data analysis to overcome the problem in the prior art that there is no analysis of the traffic characteristics of the user end, and corresponding traffic management is performed according to the traffic usage demand, and there is an unreasonable traffic distribution, resulting in poor network quality or excess network resources.

[0005] To achieve the above object, the present invention provides a network traffic management method based on data analysis, comprising:

[0006] Obtain historical traffic data of all network users and traffic usage data and user information of each network user;

[0007] Obtaining the geographical location distribution of all the network users, and determining the traffic type of each of the network users;

[0008] Determine the traffic usage user type of each of the network user terminals according to the user terminal information and the traffic type;

[0009] Determining usage-related characterization parameters of the network user terminals according to the geographical location distribution of each network user terminal and the traffic usage data of each network user terminal, and determining the traffic management area in combination with the traffic usage user type;

[0010] Determining a traffic management method in a next traffic management cycle according to the traffic management area includes:

[0011] Directly predict the expected stable traffic demand in the next traffic management cycle based on historical traffic data;

[0012] Determine the expected fluctuating traffic demand for the next traffic management cycle based on historical traffic data and the total number of traffic influencing factors, determine the number of user migrations in the next traffic management cycle, and determine the number and method of traffic migrations within the traffic management area;

[0013] The total estimated traffic demand for the next traffic management cycle is determined based on the estimated traffic demand in each traffic management area.

[0014] Furthermore, the traffic type of the network user terminal includes a personal user traffic type and a non-personal user traffic type;

[0015] Traffic usage user types include individual stable traffic user type, individual fluctuating traffic user type, group stable traffic user type and group fluctuating traffic user type;

[0016] Each of the network user terminals corresponds to a traffic usage user type.

[0017] Furthermore, the usage-related characterization parameter of the network user terminal is determined according to the following formula:

[0018]

[0019] In the formula, K is the relevant characterization parameter, r is the traffic correlation coefficient, l0 is the standard geographical distance, and l1 is the distance between the current network user end and the reference user end.

[0020] Further, determining the flow management area according to the usage-related characterization parameter and the flow usage user type includes:

[0021] If the traffic usage user type belongs to a stable traffic user type and the usage-related characterization parameter is greater than or equal to the standard usage-related characterization parameter, the traffic management area is a stable traffic management area;

[0022] If the traffic usage user type belongs to the fluctuating traffic user type and the usage-related characterization parameter is greater than or equal to the standard usage-related characterization parameter, the traffic management area is the fluctuating traffic management area;

[0023] Among them, the stable traffic user type includes individual stable traffic user type and group stable traffic user type, and the fluctuating traffic user type includes individual fluctuating traffic user type and group fluctuating traffic user type.

[0024] Furthermore, determining the traffic management area also includes:

[0025] If the traffic usage user type belongs to a stable traffic user type and the usage-related characterization parameter is less than the standard usage-related characterization parameter, the traffic management area is a fluctuating traffic management area;

[0026] If the traffic usage user type belongs to the fluctuating traffic user type and the usage-related characterization parameter is less than the standard usage-related characterization parameter, the traffic management area is a fluctuating traffic management area.

[0027] Further, determining a traffic management mode in a next traffic management cycle according to the traffic management area includes:

[0028] If the traffic management area is a stable traffic management area, the traffic management method is to directly predict the expected stable traffic demand in the next traffic management period based on historical traffic data;

[0029] If the traffic management area is a fluctuating traffic management area, the traffic management method is to determine the expected fluctuating traffic demand for the next traffic management cycle based on historical traffic data and the total number of traffic influencing factors, and determine the number of user migrations in the next traffic management cycle, and determine the number of traffic migrations and the migration method within the traffic management area.

[0030] Furthermore, the expected stable traffic demand for the next traffic management cycle is directly predicted based on historical traffic data, including:

[0031] If the traffic usage data of the stable traffic management area is within the corresponding stable interval, the estimated stable traffic corresponding to the individual stable traffic user type and the group stable traffic user type in the next traffic management period is set to the maximum value of the corresponding stable interval;

[0032] If the traffic usage data in the stable traffic management area have the same changing trend, the estimated stable traffic corresponding to the individual stable traffic user type and the group stable traffic user type in the next traffic management period is calculated based on the traffic usage data and the change rate at the end of the current traffic management period.

[0033] Furthermore, the estimated fluctuating flow demand for the next flow management cycle is determined based on the historical flow data and the total number of flow influencing factors, including:

[0034] Determine the total number of flow influencing factors for the next flow management cycle;

[0035] The expected fluctuating flow demand is determined based on the historical flow data of the fluctuating flow management area and the difference between the total number of flow influencing factors in the next flow management cycle and the total number of historical flow influencing factors.

[0036] Further, determining the number of user migrations in the next traffic management cycle includes:

[0037] Determine the number of times the geographical location of each network user terminal in the fluctuating traffic management area changes according to historical traffic data and traffic influencing factors in the next traffic management cycle;

[0038] Determine a time point when the number of geographical location changes of a network user terminal is greater than a preset number of geographical location changes as a user migration;

[0039] The number of user migrations is determined based on the total number of user migrations in the next traffic management cycle.

[0040] Furthermore, the total estimated flow demand of the next flow management cycle is determined according to the sum of the estimated stable flow corresponding to the stable flow management area and the estimated fluctuating flow corresponding to the fluctuating flow management area.

[0041] Compared with the prior art, the beneficial effect of the present invention is that by collecting and analyzing the historical traffic data, traffic usage data and user terminal information of the network user terminal, the present invention can determine the geographical location distribution and traffic type of the user, and then divide different traffic usage user types. In combination with the traffic usage user type and geographical location distribution, the present invention further determines the usage-related characterization parameters of the network user terminal, and divides the traffic management area accordingly. On the basis of these areas, the stable traffic demand of the expected traffic level in the next traffic management cycle can be predicted, the fluctuating traffic demand can be predicted by analyzing the historical traffic data and traffic influencing factors, and more accurate traffic allocation control can be performed considering the impact of user migration. The present method can more accurately predict and manage network traffic, thereby improving the utilization efficiency of network resources, optimizing network performance, and ensuring the stability of network services.

[0042] Furthermore, in the present invention, network users are divided into four types according to their traffic types. When performing traffic control on users of different types, the server can more effectively plan network traffic distribution, thereby improving system efficiency.

[0043] Furthermore, in the present invention, the type of traffic management area is determined according to the user type and the size of the relevant characterization parameter. The method can flexibly adapt to traffic coverage areas of different sizes and effectively distinguish between stable and fluctuating traffic user types. In addition, the present invention also takes into account the situation where the traffic usage user type and the characterization parameter do not match, and further refines the division of traffic management areas, ensuring that in different areas, the traffic management strategy can better fit the user's actual traffic usage pattern.

[0044] Furthermore, in the present invention, the accuracy of network traffic prediction and management efficiency are significantly improved through refined data analysis methods. By setting a stable interval and using the maximum value method to predict the stable traffic of the next cycle when the traffic usage data conforms to this interval, this method ensures that accurate guidance can be provided for network resource allocation when the traffic pattern is foreseeable. At the same time, for situations where traffic usage data shows similar change trends, the present invention uses a calculation method based on historical data and change rate to predict future traffic. This method not only takes into account the dynamic changes of the time series, but also ensures the scientificity and accuracy of the prediction model by using linear regression and least squares method. In addition, the present invention particularly emphasizes the importance of the determination coefficient of the regression equation. By setting a higher regression standard, the reliability of the prediction is further improved, ensuring the accuracy of the traffic management decision.

[0045] Furthermore, the present invention achieves accurate prediction and management of network traffic fluctuations by comprehensively considering historical traffic data and various traffic influencing factors. By quantitatively analyzing the impact of special dates such as holidays, solar terms, anniversaries and other key events on traffic demand, the method can foresee and adapt to traffic fluctuations caused by these factors, thereby more effectively planning and allocating network resources. In addition, the present invention takes into account the impact of user migration on traffic distribution. By analyzing the changing trends of user geographic locations, the method can dynamically adjust the network traffic supply strategy to meet traffic demands in different time periods and locations. This traffic management method not only improves the efficiency of network resource utilization, but also ensures the stability and quality of network services when users migrate and traffic demands change. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A flowchart of a network traffic management method based on data analysis according to an embodiment of the present invention;

[0047] Figure 2 A logical diagram for determining traffic management areas for an embodiment of the present invention;

[0048] Figure 3 A logic diagram for directly predicting the estimated stable traffic demand for the next traffic management cycle according to an embodiment of the present invention;

[0049] Figure 4 The present invention is a flowchart of determining the number of user migrations in the next traffic management cycle according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0051] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0052] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0053] See also Figure 1 As shown, it is a flow chart of a network traffic management method based on data analysis according to an embodiment of the present invention; the present invention provides a network traffic management method based on data analysis, comprising:

[0054] Step S1, obtaining historical traffic data of all network user terminals and traffic usage data and user terminal information of each network user terminal;

[0055] Step S2, obtaining the geographical location distribution of all network users and determining the traffic type of each network user;

[0056] Step S3, determining the traffic usage user type of each of the network user terminals according to the user terminal information and the traffic type;

[0057] Step S4, determining the usage-related characterization parameters of the network user terminals according to the geographical location distribution of each network user terminal and the traffic usage data of each network user terminal, and determining the traffic management area in combination with the traffic usage user type;

[0058] Step S5, determining the traffic management mode in the next traffic management cycle according to the traffic management area, including:

[0059] Directly predict the expected stable traffic demand in the next traffic management cycle based on historical traffic data;

[0060] Determine the expected fluctuating traffic demand for the next traffic management cycle based on historical traffic data and the total number of traffic influencing factors, determine the number of user migrations in the next traffic management cycle, and determine the number and method of traffic migrations within the traffic management area;

[0061] Step S6, determining the total estimated traffic demand for the next traffic management period according to the estimated traffic demand of each traffic management area.

[0062] In implementation, the user-side information includes the user's age and the user-side device. It is understandable that the user-side device can be a mobile device, such as a mobile phone, a tablet computer, etc., or a non-mobile device, such as a smart TV, a desktop computer, etc.; users aged 55 and above are recorded as low-activity users, and users aged under 55 are recorded as high-activity users.

[0063] The traffic management period may be one week, two weeks or one month, preferably two weeks.

[0064] By collecting and analyzing the historical traffic data, traffic usage data and user terminal information of the network user terminal, the present invention can determine the geographical location distribution and traffic type of the user, and then divide different traffic usage user types. In combination with the traffic usage user type and geographical location distribution, the present invention further determines the usage-related characterization parameters of the network user terminal, and divides the traffic management area accordingly. Based on these areas, the stable traffic demand of the expected traffic level in the next traffic management cycle can be predicted, the fluctuating traffic demand can be predicted by analyzing the historical traffic data and traffic influencing factors, and more accurate traffic allocation control can be performed considering the impact of user migration. The present method can more accurately predict and manage network traffic, thereby improving the utilization efficiency of network resources, optimizing network performance, and ensuring the stability of network services.

[0065] Specifically, the traffic type of the network user terminal includes a personal user traffic type and a non-personal user traffic type;

[0066] Traffic usage user types include individual stable traffic user type, individual fluctuating traffic user type, group stable traffic user type and group fluctuating traffic user type;

[0067] Each of the network user terminals corresponds to a traffic usage user type.

[0068] In practice, personal traffic refers to the network traffic generated by individual users in their daily activities, such as web browsing, video watching, social media use and online games, while non-personal traffic usually includes traffic generated by enterprises, organizations or public services, such as internal company communications, corporate website visits, online transactions and data backup.

[0069] The traffic usage user types corresponding to the personal user traffic type include the individual stable traffic user type and the individual fluctuating traffic user type, and the traffic usage user types corresponding to the non-personal user traffic type include the group stable traffic user type and the group fluctuating traffic user type.

[0070] The network user terminals corresponding to the low-activity users are determined as individual stable traffic user types, and the network user terminals corresponding to the high-activity users are determined as individual fluctuating traffic user types;

[0071] Generally, user terminals with relatively stable traffic demands, such as public utilities, traditional manufacturing, and educational institutions, are identified as the overall stable traffic user type, while online entertainment platforms and e-commerce platforms, whose traffic demands fluctuate greatly due to special events or time, are identified as the group fluctuating traffic user type.

[0072] In the present invention, network users are divided into four types according to their traffic types. When performing traffic control on users of different types, the server can more effectively plan network traffic distribution, thereby improving system efficiency.

[0073] See also Figure 2 As shown, it is a logical diagram for determining a traffic management area according to an embodiment of the present invention. The usage-related characterization parameters of the network user terminal are determined according to the following formula:

[0074]

[0075] In the formula, K is the relevant characterization parameter, r is the traffic correlation coefficient, l0 is the standard geographical distance, and l1 is the distance between the current network user end and the reference user end.

[0076] During implementation, in all areas where the server provides traffic coverage, the types of buildings are determined, such as schools, residential buildings, commercial buildings, etc., and the building areas are determined based on the building types. In each building area, a reference user terminal in the center of the building area is selected. The reference user terminal can be any user terminal in the center of the building area.

[0077] The flow correlation coefficient is determined according to the following formula:

[0078] , where x i ,y i is the traffic usage data of the two network users on the i-th day, , It is the average traffic usage of two users in the same traffic management period, i is an integer greater than 0, and n is the total number of days included in the traffic management period.

[0079] The standard geographic distance is the average distance from the reference user end to the edge of the building area.

[0080] Specifically, determining the traffic management area according to the usage-related characterization parameter and the traffic usage user type includes:

[0081] If the traffic usage user type belongs to a stable traffic user type and the usage-related characterization parameter is greater than or equal to the standard usage-related characterization parameter, the traffic management area is a stable traffic management area;

[0082] If the traffic usage user type belongs to the fluctuating traffic user type and the usage-related characterization parameter is greater than or equal to the standard usage-related characterization parameter, the traffic management area is the fluctuating traffic management area;

[0083] Among them, the stable traffic user type includes individual stable traffic user type and group stable traffic user type, and the fluctuating traffic user type includes individual fluctuating traffic user type and group fluctuating traffic user type.

[0084] In implementation, the standard usage-related characterization parameter is selected in the interval [0.9, 1.1]. The larger the total area covered by the server's traffic, the smaller the value of the standard usage-related characterization parameter.

[0085] Specifically, determining the traffic management area also includes:

[0086] If the traffic usage user type belongs to a stable traffic user type and the usage-related characterization parameter is less than the standard usage-related characterization parameter, the traffic management area is a fluctuating traffic management area;

[0087] If the traffic usage user type belongs to the fluctuating traffic user type and the usage-related characterization parameter is less than the standard usage-related characterization parameter, the traffic management area is a fluctuating traffic management area.

[0088] It is understandable that the fluctuating traffic management area has a larger scope. When determining the traffic management area, the types of traffic users included in each area are not the same. For the stable traffic management area, the number of stable traffic user types accounts for the largest proportion, while for the fluctuating traffic management area, the types of traffic users included can be many.

[0089] In the present invention, the type of traffic management area is determined according to the user type and the size of the relevant characterization parameters. The method can flexibly adapt to traffic coverage areas of different sizes and effectively distinguish between stable and fluctuating traffic user types. In addition, the present invention also takes into account the situation where the traffic usage user type and the characterization parameters do not match, and further refines the division of traffic management areas, ensuring that in different areas, the traffic management strategy can better fit the user's actual traffic usage pattern.

[0090] Specifically, determining the traffic management mode in the next traffic management cycle according to the traffic management area includes:

[0091] If the traffic management area is a stable traffic management area, the traffic management method is to directly predict the expected stable traffic demand in the next traffic management period based on historical traffic data;

[0092] If the traffic management area is a fluctuating traffic management area, the traffic management method is to determine the expected fluctuating traffic demand for the next traffic management cycle based on historical traffic data and the total number of traffic influencing factors, and determine the number of user migrations in the next traffic management cycle, and determine the number of traffic migrations and the migration method within the traffic management area.

[0093] See also Figure 3 As shown, it is a logic diagram of directly predicting the expected stable traffic demand of the next traffic management cycle according to an embodiment of the present invention. The directly predicting the expected stable traffic demand of the next traffic management cycle according to the historical traffic data includes:

[0094] If the traffic usage data of the stable traffic management area is within the corresponding stable interval, the estimated stable traffic corresponding to the individual stable traffic user type and the group stable traffic user type in the next traffic management period is set to the maximum value of the corresponding stable interval;

[0095] If the traffic usage data in the stable traffic management area have the same changing trend, the estimated stable traffic corresponding to the individual stable traffic user type and the group stable traffic user type in the next traffic management period is calculated based on the traffic usage data and the change rate at the end of the current traffic management period.

[0096] In implementation, the traffic usage data of the stable traffic management area is equal to the sum of the traffic usage data of all network users in the stable management area;

[0097] The upper limit of the stable interval is 1.1 times the average value of the traffic usage data of the stable traffic user type, and the lower limit of the stable interval is 0.9 times the average value of the traffic usage data of the stable traffic user type;

[0098] Traffic usage data can be obtained by using professional network monitoring tools such as Wireshark, SolarWinds, Nagios, etc. to capture and analyze network traffic data, or through user-side log files;

[0099] The change trend of the traffic usage data is determined according to the change rate of the traffic usage data in several consecutive traffic management cycles. The change rate of the traffic usage data is determined according to the coefficient of the linear regression equation between the traffic usage data at the end of several traffic management cycles and time. The regression equation is determined by the least square method. When the determination coefficient of the regression equation is greater than 0.85, it is determined that the change rate of the traffic usage data is within the allowable change rate range.

[0100] Generally, when the determination coefficient of the regression equation is greater than 0.8, the regression equation is considered to have good regression properties. The present invention determines that the rate of change of consumption data is within the allowable range when the regression coefficient is greater than 0.85, which further improves the regression requirements for traffic usage data, making the results predicted by the regression equation closer to the actual values, thereby improving the accuracy of the final prediction results and improving the quality of network traffic management.

[0101] In the present invention, the accuracy of network traffic prediction and management efficiency are significantly improved through refined data analysis methods. By setting a stable interval and using the maximum value method to predict the stable traffic of the next cycle when the traffic usage data conforms to this interval, this method ensures that accurate guidance can be provided for network resource allocation when the traffic pattern is foreseeable. At the same time, for situations where traffic usage data shows similar change trends, the present invention uses a calculation method based on historical data and change rate to predict future traffic. This method not only takes into account the dynamic changes of the time series, but also ensures the scientificity and accuracy of the prediction model by using linear regression and least squares method. In addition, the present invention particularly emphasizes the importance of the determination coefficient of the regression equation. By setting a higher regression standard, the reliability of the prediction is further improved, ensuring the accuracy of the traffic management decision.

[0102] Specifically, the estimated fluctuating traffic demand for the next traffic management cycle is determined based on historical traffic data and the total number of traffic influencing factors, including:

[0103] Determine the total number of flow influencing factors for the next flow management cycle;

[0104] The expected fluctuating flow demand is determined based on the historical flow data of the fluctuating flow management area and the difference between the total number of flow influencing factors in the next flow management cycle and the total number of historical flow influencing factors.

[0105] The total number of traffic impact factors can be directly obtained by counting the dates included in the traffic management cycle;

[0106] Traffic influencing factors can be any dates different from regular working days, such as holidays, solar terms or national memorial days. They can also be other indicators that can cause large fluctuations in traffic demand (fluctuations of more than 20%), such as live online events or widely used software updates.

[0107] During implementation, the estimated traffic impact of a single traffic influencing factor is determined based on the historical traffic data of several historical management cycles and the corresponding total number of traffic influencing factors. When determining the estimated fluctuating traffic demand for the next traffic management cycle, the difference between the total number of traffic influencing factors in the next traffic management cycle and the total number of historical traffic influencing factors is calculated to determine the estimated fluctuating traffic change value, and then the estimated fluctuating traffic demand is obtained by combining the historical traffic data of the fluctuating traffic management area.

[0108] See also Figure 4 As shown, it is a flow chart of determining the number of user migrations in the next traffic management cycle according to an embodiment of the present invention. Determining the number of user migrations in the next traffic management cycle includes:

[0109] Determine the number of times the geographical location of each network user terminal in the fluctuating traffic management area changes according to historical traffic data and traffic influencing factors in the next traffic management cycle;

[0110] Determine a time point when the number of geographical location changes of a network user terminal is greater than a preset number of geographical location changes as a user migration;

[0111] The number of user migrations is determined based on the total number of user migrations in the next traffic management cycle.

[0112] It is understandable that because many network users are mobile users, the geographical locations of mobile users are different during working hours and non-working hours of each working day. During working hours, more traffic is required in public building areas, while during non-working hours, the network traffic required in residential areas is greater than the network traffic required during working hours. Therefore, before and after the time point of user migration, the focus of network traffic supply is determined according to the geographical location of the mobile user.

[0113] The present invention achieves accurate prediction and management of network traffic fluctuations by comprehensively considering historical traffic data and various traffic influencing factors. By quantitatively analyzing the impact of special dates such as holidays, solar terms, anniversaries and other key events on traffic demand, the method can foresee and adapt to traffic fluctuations caused by these factors, thereby more effectively planning and allocating network resources. In addition, the present invention takes into account the impact of user migration on traffic distribution. By analyzing the changing trends of user geographic locations, the method can dynamically adjust the network traffic supply strategy to meet traffic demands in different time periods and locations. This traffic management method not only improves the efficiency of network resource utilization, but also ensures the stability and quality of network services when users migrate and traffic demands change.

[0114] Specifically, the total estimated flow demand of the next flow management cycle is determined according to the sum of the estimated stable flow corresponding to the stable flow management area and the estimated fluctuating flow corresponding to the fluctuating flow management area.

[0115] If the network traffic management method based on data analysis of the present invention is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0116] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0117] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A network traffic management method based on data analysis, characterized in that: include: Obtain historical traffic data of all network users and traffic usage data and user information of each network user; Obtaining the geographical location distribution of all the network users, and determining the traffic type of each of the network users; Determine the traffic usage user type of each of the network user terminals according to the user terminal information and the traffic type; Determining usage-related characterization parameters of the network user terminals according to the geographical location distribution of each network user terminal and the traffic usage data of each network user terminal, and determining the traffic management area in combination with the traffic usage user type; Determining a traffic management method in a next traffic management cycle according to the traffic management area includes: Directly predict the expected stable traffic demand in the next traffic management cycle based on historical traffic data; Determine the expected fluctuating traffic demand for the next traffic management cycle based on historical traffic data and the total number of traffic influencing factors, determine the number of user migrations in the next traffic management cycle, and determine the number and method of traffic migrations within the traffic management area; Determine the total estimated flow demand for the next flow management cycle based on the estimated flow demand of each flow management area; The traffic type of the network user terminal includes a personal user traffic type and a non-personal user traffic type; Traffic usage user types include individual stable traffic user type, individual fluctuating traffic user type, group stable traffic user type and group fluctuating traffic user type; Wherein, each of the network user terminals corresponds to a traffic usage user type; The usage-related characterization parameter of the network user terminal is determined according to the following formula: In the formula, K is the relevant characterization parameter, r is the traffic correlation coefficient, l0 is the standard geographical distance, and l1 is the distance between the current network user end and the reference user end.

2. The network traffic management method based on data analysis according to claim 1 is characterized in that: Determine the traffic management area according to the usage-related characterization parameter and the traffic usage user type, include, If the traffic usage user type belongs to a stable traffic user type and the usage-related characterization parameter is greater than or equal to the standard usage-related characterization parameter, the traffic management area is a stable traffic management area; If the traffic usage user type belongs to the fluctuating traffic user type and the usage-related characterization parameter is greater than or equal to the standard usage-related characterization parameter, the traffic management area is the fluctuating traffic management area; Among them, the stable traffic user type includes individual stable traffic user type and group stable traffic user type, and the fluctuating traffic user type includes individual fluctuating traffic user type and group fluctuating traffic user type.

3. The network traffic management method based on data analysis according to claim 2 is characterized in that: Determining the traffic management area also includes, If the traffic usage user type belongs to a stable traffic user type and the usage-related characterization parameter is less than the standard usage-related characterization parameter, the traffic management area is a fluctuating traffic management area; If the traffic usage user type belongs to the fluctuating traffic user type and the usage-related characterization parameter is less than the standard usage-related characterization parameter, the traffic management area is a fluctuating traffic management area.

4. The network traffic management method based on data analysis according to claim 3 is characterized in that: Determining a traffic management method in a next traffic management cycle according to the traffic management area includes: If the traffic management area is a stable traffic management area, the traffic management method is to directly predict the expected stable traffic demand in the next traffic management period based on historical traffic data; If the traffic management area is a fluctuating traffic management area, the traffic management method is to determine the expected fluctuating traffic demand for the next traffic management cycle based on historical traffic data and the total number of traffic influencing factors, and determine the number of user migrations in the next traffic management cycle, and determine the number of traffic migrations and the migration method within the traffic management area.

5. The network traffic management method based on data analysis according to claim 4 is characterized in that: The expected stable traffic demand for the next traffic management cycle is directly predicted based on historical traffic data, including: If the traffic usage data of the stable traffic management area is within the corresponding stable interval, the estimated stable traffic corresponding to the individual stable traffic user type and the group stable traffic user type in the next traffic management period is set to the maximum value of the corresponding stable interval; If the traffic usage data in the stable traffic management area have the same changing trend, the estimated stable traffic corresponding to the individual stable traffic user type and the group stable traffic user type in the next traffic management period is calculated based on the traffic usage data and the change rate at the end of the current traffic management period.

6. The network traffic management method based on data analysis according to claim 5 is characterized in that: The estimated fluctuating traffic demand for the next traffic management cycle is determined based on historical traffic data and the total number of traffic influencing factors, including: Determine the total number of flow influencing factors for the next flow management cycle; The expected fluctuating flow demand is determined based on the historical flow data of the fluctuating flow management area and the difference between the total number of flow influencing factors in the next flow management cycle and the total number of historical flow influencing factors.

7. The network traffic management method based on data analysis according to claim 6 is characterized in that: Determine the number of user migrations in the next traffic management cycle including, Determine the number of times the geographical location of each network user terminal in the fluctuating traffic management area changes according to historical traffic data and traffic influencing factors in the next traffic management cycle; Determine a time point when the number of geographical location changes of a network user terminal is greater than a preset number of geographical location changes as a user migration; The number of user migrations is determined based on the total number of user migrations in the next traffic management cycle.

8. The network traffic management method based on data analysis according to claim 7 is characterized in that: The estimated total flow demand of the next flow management cycle is determined according to the sum of the estimated stable flow corresponding to the stable flow management area and the estimated fluctuating flow corresponding to the fluctuating flow management area.

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