Network line dynamic optimization method based on gateway equipment user behavior analysis

Abstract

The invention relates to the technical field of network communication, in particular to a network line dynamic optimization method based on gateway equipment user behavior analysis, which comprises the following steps of: acquiring network access data of a user; the network access data comprises application types accessed by a user at each time point and corresponding flow consumption; according to the network access data, analyzing the internet behavior preference of the user, and judging the user type corresponding to the user; and distributing a network line for the corresponding user according to the user type. By adopting the scheme, network lines better matched with network use requirements of different users can be provided for the different users, so that the network use experience of the users is improved.

Description

Technical Field

[0001] The present invention relates to the field of network communication technology, and in particular to a network line dynamic optimization method based on gateway device user behavior analysis. Background Art

[0002] In today's digital age, internet technology is developing at an unprecedented pace, and network application scenarios are becoming increasingly diverse. From high-definition video viewing and real-time online gaming to large-scale data transmission, a wide range of applications are emerging, greatly enriching people's lives and work styles. At the same time, user demands for network service quality are becoming increasingly diverse and differing. In actual network usage, different users have distinct requirements for key network performance indicators, such as bandwidth, latency, and stability, based on their specific application scenarios.

[0003] However, existing operator networks still generally adopt static resource allocation strategies, which are unable to adjust network resources according to the actual needs of users, resulting in a mismatch between service quality and demand, affecting user experience. Specifically, existing methods mostly allocate resources based on shallow indicators such as total traffic volume and access frequency, which makes it difficult to accurately identify the real needs of users. As a result, the network lines allocated to users are often mismatched with their actual network needs. As a result, when users actually use the network, they frequently encounter problems such as freezes, excessive latency, and unstable connections, which seriously affects the user's network experience. Therefore, there is an urgent need to provide a dynamic optimization method for network lines based on user behavior analysis of gateway devices to provide different users with network lines that better match their network needs, thereby improving the user's network experience. Summary of the Invention

[0004] The present invention provides a method for dynamic optimization of network lines based on gateway device user behavior analysis, which can provide different users with network lines that better match their network usage needs, thereby improving the user's network usage experience.

[0005] In order to achieve the above objectives, this application provides the following technical solutions:

[0006] A method for dynamically optimizing network lines based on gateway device user behavior analysis includes the following steps:

[0007] Obtaining the user's network access data; the network access data includes the types of applications accessed by the user at each time point and the corresponding traffic consumption;

[0008] Analyzing the user's online behavior preferences based on the network access data to determine the user type corresponding to the user;

[0009] According to the user type, a network line is allocated to the corresponding user.

[0010] Furthermore, the application types include game software, film and television software, and office software; and the user types include game users, film and television users, and office users.

[0011] Furthermore, analyzing the user's online behavior preferences based on the network access data and determining the user type corresponding to the user includes:

[0012] If the time a user spends accessing the game software during the preset idle time period is greater than the preset game access time, and the traffic consumption corresponding to each time point of accessing the game software is greater than the preset game traffic value, then the user type corresponding to the user is determined to be a game user.

[0013] Furthermore, analyzing the user's online behavior preferences based on the network access data and determining the user type corresponding to the user includes:

[0014] If the duration of a user's access to the film and television software is greater than the preset film and television access duration, and the traffic consumption corresponding to each time point of accessing the film and television software is within the preset viewing traffic range, then the user type corresponding to the user is determined to be a film and television user.

[0015] Furthermore, analyzing the user's online behavior preferences based on the network access data and determining the user type corresponding to the user includes:

[0016] If the time a user spends accessing office software during the preset working time period is greater than the preset office access time, and the traffic consumption corresponding to each time point of accessing the office software is within the preset office traffic range, then the user type corresponding to the user is determined to be an office user.

[0017] Furthermore, the user's network access data is obtained, including:

[0018] Obtain the user's access data packet through the network interface of the gateway device;

[0019] Parse the timestamp, data packet size, feature code and domain name of the access data packet, and analyze the application type accessed by the user at each time point and the corresponding traffic consumption.

[0020] Furthermore, allocating a network line to the corresponding user according to the user type includes:

[0021] Obtaining real-time performance data for each network line; the real-time performance data includes latency, bandwidth, and packet loss rate;

[0022] A network line is allocated to the corresponding user according to the user type and the real-time performance data of each network line.

[0023] The principles and advantages of the present invention are:

[0024] This solution categorizes users based on their online behavior preferences and selects the most suitable network line for them based on user type and real-time performance data of each network line. This addresses the inherent flaws of traditional static allocation of network resources, achieves efficient utilization of network resources, and comprehensively improves user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a flow chart of an embodiment of a method for dynamic optimization of network lines based on gateway device user behavior analysis according to the present invention. DETAILED DESCRIPTION

[0026] The following is further described in detail through specific implementation methods:

[0027] Example 1:

[0028] A network line dynamic optimization method based on gateway device user behavior analysis, such as Figure 1 As shown, the following steps are included:

[0029] S100, obtaining the user's network access data; the network access data includes the types of applications accessed by the user at each time point and the corresponding traffic consumption; in this embodiment, the application types include game software, film and television software, and office software. S100 includes the following steps:

[0030] S101, obtaining a user's access data packet through a network interface of a gateway device. Specifically, the user's access data packet is captured through the network interface of the gateway device using a zero-copy packet capture technology.

[0031] S102, parse the timestamp, data packet size, feature code (application layer feature code, such as game heartbeat packet, video stream fragment identifier, specifically, extract the application layer feature code through deep packet inspection technology) and domain name of the access data packet, and cache them in the memory database; analyze the application type accessed by the user at each time point and the corresponding traffic consumption; in this embodiment, a comparison table is pre-stored, which records the application type, feature code and domain name corresponding to each application software, and compares the feature code and domain name obtained by the analysis with the feature code and domain name in the comparison table to analyze the application type accessed by the user.

[0032] S200, analyzing the user's online behavior preferences based on the network access data, and determining the user type corresponding to the user; the user types include game users, film and television users, office users, and comprehensive users.

[0033] In this embodiment, if the current time is within the preset working time period, the user is first analyzed to determine whether it is an office user. If not, the user is then analyzed to determine whether it is a film and television user. If not, the user is determined to be a comprehensive user. Specifically, if the duration of the user's access to office software within the preset working time period is greater than the preset office access duration, and the traffic consumption corresponding to each time point of accessing the office software is within the preset office traffic range, the user type corresponding to the user is determined to be an office user. If the duration of the user's access to film and television software within the preset working time period is greater than the preset film and television access duration, and the traffic consumption corresponding to each time point of accessing the film and television software is within the preset viewing traffic range, the user type corresponding to the user is determined to be a film and television user.

[0034] If the current time is within the preset idle time period, the user is first analyzed to see if it is a game user. If not, the user is then analyzed to see if it is a film and television user. If not, the user is determined to be a comprehensive user. Specifically, if the duration of the user's access to game software during the preset idle time period is greater than the preset game access duration, and the traffic consumption corresponding to each time point of accessing the game software is greater than the preset game traffic value, the user type corresponding to the user is determined to be a game user. If the duration of the user's access to film and television software during the preset idle time period is greater than the preset film and television access duration, and the traffic consumption corresponding to each time point of accessing the film and television software is within the preset viewing traffic range, the user type corresponding to the user is determined to be a film and television user.

[0035] In this embodiment, the preset working time period is 9:00-16:00 on weekdays, and the rest of the time is the preset off-peak time period.

[0036] S300, allocating a network line to a corresponding user according to the user type, including the following steps:

[0037] S301, obtaining real-time performance data of each network line; the real-time performance data includes latency, bandwidth and packet loss rate.

[0038] S302: Allocate a network line to the corresponding user based on the user type and the real-time performance data of each network line. Specifically, if the user type is a game user, a network line with a latency lower than a preset latency is allocated to the corresponding user, prioritizing connection stability. If the user type is a movie user, a network line with a bandwidth higher than a preset bandwidth is allocated to the corresponding user to support high-volume transmission. If the user type is an office user, a network line with a packet loss rate lower than a preset packet loss rate is allocated to the corresponding user to ensure the reliability of document synchronization and video conferencing.

[0039] In this embodiment, the user type of the user is identified and analyzed in real time, and the allocation of network lines is dynamically adjusted according to the changes in the user type, which solves the inherent defects of the traditional static allocation of network resources and achieves efficient utilization of network resources and comprehensive improvement of user experience.

[0040] The above are only embodiments of the present invention. Common knowledge such as the known specific structures and characteristics in the scheme are not described in detail here. Ordinary technicians in the field are aware of all common technical knowledge in the technical field of the invention before the application date or priority date, can obtain all existing technologies in the field, and have the ability to apply conventional experimental means before that date. Ordinary technicians in the field can improve and implement this scheme in combination with their own abilities under the inspiration given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several variations and improvements can be made, which should also be regarded as the scope of protection of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for dynamic network line optimization based on gateway device user behavior analysis, characterized by: The following steps are involved: Obtaining the user's network access data; the network access data includes the types of applications accessed by the user at each time point and the corresponding traffic consumption; Analyzing the user's online behavior preferences based on the network access data to determine the user type corresponding to the user; According to the user type, a network line is allocated to the corresponding user.

2. The method for dynamic network line optimization based on gateway device user behavior analysis according to claim 1, characterized in that: The application types include game software, film and television software, and office software; the user types include game users, film and television users, and office users.

3. The method for dynamic network line optimization based on gateway device user behavior analysis according to claim 2, characterized in that: Analyzing the user's online behavior preferences based on the network access data and determining the user type corresponding to the user, including: If the time a user spends accessing the game software during the preset idle time period is greater than the preset game access time, and the traffic consumption corresponding to each time point of accessing the game software is greater than the preset game traffic value, then the user type corresponding to the user is determined to be a game user.

4. The method for dynamic network line optimization based on gateway device user behavior analysis according to claim 2, characterized in that: Analyzing the user's online behavior preferences based on the network access data and determining the user type corresponding to the user, including: If the duration of a user's access to the film and television software is greater than the preset film and television access duration, and the traffic consumption corresponding to each time point of accessing the film and television software is within the preset viewing traffic range, then the user type corresponding to the user is determined to be a film and television user.

5. The method for dynamic network line optimization based on gateway device user behavior analysis according to claim 2, characterized in that: Analyzing the user's online behavior preferences based on the network access data and determining the user type corresponding to the user, including: If the time a user spends accessing office software during the preset working time period is greater than the preset office access time, and the traffic consumption corresponding to each time point of accessing the office software is within the preset office traffic range, then the user type corresponding to the user is determined to be an office user.

6. The method for dynamic network line optimization based on gateway device user behavior analysis according to claim 1, characterized in that: Get the user's network access data, including: Obtain the user's access data packet through the network interface of the gateway device; Parse the timestamp, data packet size, feature code and domain name of the access data packet, and analyze the application type accessed by the user at each time point and the corresponding traffic consumption.

7. The method for dynamic network line optimization based on gateway device user behavior analysis according to claim 2, characterized in that: Allocate a network line to the corresponding user according to the user type, including: Obtaining real-time performance data for each network line; the real-time performance data includes latency, bandwidth, and packet loss rate; A network line is allocated to the corresponding user according to the user type and the real-time performance data of each network line.

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