Method, device, medium and equipment for detecting abnormal offline access to the Internet

Abstract

The present invention discloses a method, device, medium and equipment for detecting abnormal offline Internet access, which relates to the field of communication technology and solves the technical problem of low efficiency and high cost in detecting abnormal offline Internet access for users. The key points of the technical solution are to periodically collect Internet access index data of users, analyze the Internet access index data, query the Internet access status of users in each cycle, and obtain whether there are users whose online status changes to offline status. If there are users whose online status changes to offline status, the method and device determine whether the user has abnormal offline Internet access based on the user's Internet habit profile or the status of the device associated with the user. The method and device can quickly and accurately locate whether the user is in an abnormal offline state when surfing the Internet, and after analyzing and predicting the abnormal offline state of the user, there is reliable data as a basis, which can predict and solve the fault in advance, improve user satisfaction, and help retain old users on the Internet and develop new users.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a method, device, medium and equipment for detecting abnormal Internet offline. Background Art

[0002] Currently, international standards organizations including ETSI (European Telecommunications Standards Institute), operators and equipment manufacturers are aware of the importance of network optimization based on user perception. With the rapid development of the Internet, the home broadband market is becoming increasingly saturated, and market competition among operators in the broadband field is becoming increasingly fierce. Price wars are no longer the only means of competition. Users' actual experience and broadband quality have become key factors in determining market competitiveness.

[0003] While operators are leaders in optical network deployment and installation and maintenance informationization, operational support still faces challenges such as incomplete end-to-end system support and a need to improve process informationization. Due to the complexity and numerous issues of home networks, operators face persistently high user complaint rates and low user satisfaction, but with limited solutions, high costs, and low efficiency. Preliminary operator analysis shows that 70% of network complaints are caused by users being unable to access the internet. Therefore, effectively detecting abnormal user offline activity and reducing the complaint rate has become a pressing issue for operators. Some operators use offline metrics, such as identifying users as abnormally offline if they are offline for more than two times per day, and proactively implementing maintenance. However, this approach is inaccurate and costly. Summary of the Invention

[0004] The present invention provides a method, device, medium and equipment for detecting abnormal offline Internet access, the technical purpose of which is to quickly and accurately detect abnormal offline Internet access of users and reduce detection costs.

[0005] The above technical objectives of the present disclosure are achieved through the following technical solutions:

[0006] A method for detecting abnormal Internet offline conditions, comprising:

[0007] Periodically collect user Internet access indicator data, including online status, offline status, uplink RTT, and downlink RTT;

[0008] Analyze the Internet access indicator data, query the Internet access status of users in each cycle, and determine whether there are users whose online status changes to offline status;

[0009] If there is a user whose online status changes to offline status, it is determined whether the user has abnormally gone offline based on the user's online habit profile or the status of the device associated with the user.

[0010] Further, judging whether the user is abnormally offline in terms of Internet access according to the status of the device associated with the user includes: querying the Internet access status of all users associated with the device;

[0011] If the Internet access status of all users is offline, it is determined that the device is abnormal and the user is abnormally offline.

[0012] Furthermore, based on the user's online habits profile, determining whether the user has abnormal online offline behavior includes:

[0013] Classifying the user's online time, wherein the types of online time include idle time and non-idle time, and the non-idle time includes normal online time and critical online time;

[0014] According to the type of the online time, it is determined whether the user has abnormal online offline time.

[0015] Furthermore, judging whether the user is abnormally offline based on the type of the online time includes:

[0016] When the user is not idle, check whether the user has any online behavior before, during, and after going offline.

[0017] If the user has online behavior before going offline but has no online behavior during the offline period, it is determined that there is abnormal online offline behavior; if the user has online behavior during the offline period, it is determined that there is no abnormal online offline behavior.

[0018] A device for detecting abnormal offline Internet access, comprising:

[0019] A collection device for periodically collecting user Internet access indicator data, wherein the Internet access indicator data includes online status, offline status, uplink RTT and downlink RTT;

[0020] An analysis device is used to analyze the online index data, query the online status of users in each cycle, and determine whether there are users whose online status changes to offline status;

[0021] The judging device is used to judge whether the user has abnormally gone offline when the online state changes to the offline state according to the user's online habit profile or the status of the device associated with the user.

[0022] Furthermore, the judging device further includes:

[0023] A query unit, configured to query the online status of all users associated with the device;

[0024] The first judgment unit is configured to judge that if the Internet access status of all users is offline, then the device is abnormal and a user is abnormally offline.

[0025] Furthermore, the judging device further includes:

[0026] A classification unit, configured to classify the user's online time, wherein the types of online time include idle time and non-idle time, and the non-idle time includes normal online time and critical online time;

[0027] The second judgment unit is used to judge whether the user has abnormal Internet offline time according to the type of the Internet access time.

[0028] Furthermore, the second judgment unit is further configured to:

[0029] When the user is not idle, check whether the user has any online behavior before, during, and after going offline.

[0030] If the user has online behavior before going offline but has no online behavior during the offline period, it is determined that there is abnormal online offline behavior; if the user has online behavior during the offline period, it is determined that there is no abnormal online offline behavior.

[0031] A computer medium stores a computer program, which, when executed by a processor, implements any of the above-mentioned methods for detecting abnormal Internet access offline.

[0032] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above-mentioned methods for detecting abnormal Internet access offline when executing the program.

[0033] The beneficial effects of the present disclosure are as follows: the method, apparatus, medium, and device for detecting abnormal internet offline conditions described herein periodically collect user internet index data, analyze the data, query the user's internet status within each period, and determine whether there are users whose online status has changed to an offline state. If there are users whose online status has changed to an offline state, the method and apparatus determine whether the user has been abnormally offline based on a profile of the user's internet habits or the status of the device associated with the user. This method and apparatus can quickly and accurately determine whether a user is in an abnormally offline state, and after analyzing and predicting abnormal user offline conditions, reliable data is used as a basis, enabling the early detection and resolution of faults, improving user satisfaction, and facilitating the retention of existing users and the development of new ones. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1This is a flow chart of the method for detecting abnormal offline Internet access provided by the present disclosure;

[0035] Figure 2 This is a schematic diagram of a device for detecting abnormal Internet offline conditions provided by the present disclosure;

[0036] Figure 3 This is a schematic diagram of a method for detecting abnormal Internet offline conditions provided in the first embodiment of the present disclosure;

[0037] Figure 4 This is a schematic diagram of a method for detecting abnormal Internet offline conditions provided in the second embodiment of the present disclosure;

[0038] Figure 5 This is a schematic diagram of a device for detecting abnormal Internet offline conditions provided in the third embodiment of the present disclosure;

[0039] Figure 6 This is a flow chart of a method for detecting abnormal Internet offline provided in the fourth embodiment of the present disclosure. DETAILED DESCRIPTION

[0040] The technical solution of the present disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that this application is used for offline analysis of abnormal users in a PON (Passive Optical Network) network, and can also be used in other similar networks, such as copper access networks. The PON network is a passive optical network, which is a network used in access networks, where the central office equipment and multiple user-end devices (ONU / ONT) are connected by an optical distribution network (ODN) composed of passive optical cables, splitters / combiners, etc. There is no active electronic equipment in the ODN (Optical Distribution Network) before the OLT (Optical Line Terminal) and the ONU (Optical Network Unit).

[0041] Figure 1The flowchart of the detection method for abnormal offline Internet access provided by the present invention is as follows: 100: Periodically collect user Internet access indicator data, including online status, offline status, uplink RTT and downlink RTT, etc. Specifically: deploy DPI (Deep Packet Inspection) probes in the local network to periodically collect user Internet access indicator data, such as online status, offline status, uplink RTT (Round-Trip Time) and downlink RTT, etc. Generally, the DPI probe divides the broadband end-to-end network into two parts: the upper network above the BRAS (Broadband Remote Access Server) and the lower network below the BRAS, and calculates the uplink network RTT, downlink network RTT and service indicators respectively through bidirectional service traffic, thereby having a certain quality difference demarcation and positioning capability while perceiving the user service experience and identifying users with poor quality.

[0042] 101: Analyze the Internet access indicator data, query the Internet access status of users in each cycle, and determine whether there are users whose online status changes to offline status.

[0043] 102: If there is a user whose online status changes to offline status, determine whether the user has abnormally gone offline based on the user's online habit profile or the status of the device associated with the user.

[0044] Figure 3 Schematic diagram of a method for detecting abnormal offline Internet access provided by the first embodiment of the present disclosure. Figure 3 As shown, 200: There are users whose Internet access status changes in each cycle; 201: Query the user and its associated devices, query all users associated with the device, and query the Internet access status of all users. Generally, the user and device information recorded in the authentication information is used to check the user's device information, and then all related users are queried based on this device information. For example, for FTTH (Fiber (Fiber) To The Home) users, it can be determined in turn whether there are common problems among users under the same PON, OLT, and BRAS. 202: If all users connected to the entire device are offline, it can be determined that there is a problem with the device (such as a failure in the trunk line, uplink, etc.), and it can be directly concluded that the user is passively abnormally offline.

[0045] in addition Figure 4 This is a schematic diagram of a method for detecting abnormal offline Internet access provided in the second embodiment of the present disclosure. Figure 4300: Determine whether the user has abnormal offline Internet access based on the user's Internet usage habit profile, including 301: classify each Internet access time of the user. The Internet access time can be classified into idle time and non-idle time. Non-idle time includes normal Internet access time and critical Internet access time. The specific implementation method is: label each Internet access time of the user, Internet access time without network traffic is labeled as idle time, working time is labeled as critical Internet access time, and other ordinary browsing time is labeled as normal Internet access time. Furthermore, the ISP (Internet Service Provider) visited by the user is analyzed, and these ISPs are judged as critical or non-critical, so as to more accurately judge the importance of the user's Internet access label. For example, if the user is visiting an online course, it is considered to be critical Internet access time. If the user is visiting an ordinary news web page, it is considered to be general Internet access time.

[0046] 302: Determining whether the user has abnormally offline internet access based on the type of online time includes: when the user is in non-idle time, querying whether the user has online activity during the time before going offline, the time after going offline, and the time after going offline. If the user has online activity during the time before going offline, and if no online activity occurs during the time after going offline, then abnormally offline internet access is determined. If online activity occurs during the time after going offline, then abnormally offline internet access is determined not to have occurred. A specific real-time method is: if the user's online time is non-idle, calling a data query interface to obtain the user's online activity during the time before going offline (e.g., 30 minutes), the time after going offline, and the time after going offline (e.g., 30 minutes), and then checking whether the user has online activity during these three time periods. If there is online activity before going offline but no online activity during the time after going offline, then the user is determined to be abnormally offline. If there is online activity before going offline and during the time after going offline, then the data record is determined to be abnormal and the user is a normal online user. Abnormal offline internet access during critical online hours generally results in user complaints and affects user online behavior.

[0047] Figure 2 This is a schematic diagram of a device for detecting abnormal internet offline provided by the present disclosure. The system includes a collection device 400, an analysis device 500, and a judgment device 600. The collection device 400 is used to periodically collect user internet index data, which includes online status, offline status, uplink RTT, and downlink RTT; the analysis device 500 is used to analyze the internet index data, query the user's internet status in each period, and determine whether there are any users whose online status has changed to offline status; and the judgment device 600 is used to determine whether a user has abnormal internet offline.

[0048] Figure 5This is a schematic diagram of a detection device for abnormal offline Internet access provided in Example 3 of the present disclosure. The judgment device of the detection device also includes a first query unit and a first judgment unit. The first query unit is used to query the Internet access status of all users associated with the device; if the Internet access status of all users is offline, the first judgment unit determines that the device is abnormal and the user is abnormally offline.

[0049] The judgment device of the detection device also includes a second classification unit and a second judgment unit. The second classification unit is used to classify the user's online time. The types of online time include idle time and non-idle time. Non-idle time includes normal online time and critical online time. The second judgment unit determines whether the user has abnormal online offline based on the type of online time. The second judgment unit is used to determine whether there is a user with abnormal online offline, including: when the user is in non-idle time, querying whether the user has online behavior before going offline, during the offline time, and after going offline. If the user has online behavior before going offline, if there is no online behavior during the offline time, it is determined that there is abnormal online offline. If there is online behavior during the offline time, it is determined that there is no abnormal online offline.

[0050] Specifically, the collection device 400 is generally deployed on the operator's local equipment, such as BRAS, and collects the user's Internet IP packet header information through mirroring and spectrometry, so as to obtain relevant network indicators of the user's Internet access, such as uplink RTT, downlink RTT, link establishment delay and retransmission rate, etc. On the other hand, by analyzing the messages interacting between the user and the authentication service system, the user's online status, the user's association information with the local equipment, etc. can be obtained.

[0051] The data collected by the collection device 400 is generally stored in a storage device, which generally adopts an open source distributed file system, such as HDFS (Hadoop Distributed File System), Ceph, FastDFS, etc., and integrates self-developed software to achieve persistent storage of massive files. In general, operator networks generate massive amounts of network traffic data every minute. Even if the data generated by each message is statistically analyzed, the scale is still Gb / min or even more than Gb / s. The collection device periodically (such as 5 minutes as a cycle) generates statistical data files and pushes them to the distributed storage device for persistent storage.

[0052] The first query unit included in the judgment device 600 is used to query users and their associated devices, query all users associated with the device, and query the online status of all users. If the online status of all users is offline, it is determined that the device is abnormal. The query unit generally adopts a distributed computing framework cluster, such as Spark, Hadoop3.0, etc. Massive data is stored in distributed storage devices. How to quickly perform statistical queries to obtain the desired analysis results is particularly important for the use of distributed computing frameworks. For some data queries, direct analysis is very time-consuming and requires the development of timed algorithm tasks to periodically calculate and output results, which are provided to the query interface for subsequent queries.

[0053] The judgment device 600 obtains the online status of all users in the network, and then judges whether each offline user in the cycle is abnormally offline. Finally, it outputs an abnormal offline user report for the operator to actively operate and maintain, solve problems before users complain, improve user satisfaction, and enhance its own core competitiveness.

[0054] Figure 6 This is a flow chart of a method for detecting abnormal offline Internet access provided in the fourth embodiment of the present disclosure, as shown in FIG. Figure 6 As shown, if user A goes offline at 10 a.m. (701), the analysis device 500 obtains the user's offline behavior, and the judgment device 600 further analyzes the user data to query whether other users associated with the same device as the user have similar offline behavior (702). The analysis finds that other users have almost no similar behavior, and it is judged to be the individual behavior of user A.

[0055] The judgment device 600 queries the tag of the user in this time period and finds that it is a key online time tag (703). It then continues to query the user's online status for a period of time before going offline (e.g., 30 minutes), during the offline period, and after going offline (e.g., 30 minutes) (704). It finds that the user has online traffic before going offline, no traffic during the offline period, and traffic immediately after going online. Therefore, it is determined that the offline behavior during this period is abnormal (705), which seriously affects the user's network experience and reduces user satisfaction. After obtaining the user list, the operator analyzes and finds that the user's offline behavior is caused by periodic inspection of the local equipment. After modifying the local equipment configuration rules, the problem can be quickly resolved, thereby improving user satisfaction.

[0056] The above-described implementation methods do not constitute a limitation on the scope of protection of this technical solution. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the above-mentioned implementation methods shall be included in the scope of protection of this technical solution.

Claims

1. A method for detecting abnormal offline Internet access, characterized in that: include: Periodically collect user Internet access indicator data, including online status, offline status, uplink RTT, and downlink RTT; Analyze the Internet access indicator data, query the Internet access status of users in each cycle, and determine whether there are users whose online status changes to offline status; If there is a user whose online state changes to an offline state, judging whether the offline user has abnormally gone offline based on the state of a device associated with the offline user, wherein the device includes a passive optical network device, an optical line terminal, and a broadband access server; The step of determining whether the offline user is abnormally offline based on the status of a device associated with the offline user includes: Querying the offline user and each of the passive optical network devices, the optical line terminal, and the broadband access server associated with the offline user; Querying all users associated with each of the passive optical network devices, the optical line terminals, and the broadband access servers; Determine the Internet access status of all users associated with the same passive optical network device, the same optical line terminal, and the same broadband access server in sequence; If the Internet access status of all users associated with the same device is offline, it is determined that the corresponding device is abnormal and the offline user is abnormally offline.

2. A device for detecting abnormal offline Internet access, characterized in that: include: A collection device for periodically collecting user Internet access indicator data, wherein the Internet access indicator data includes online status, offline status, uplink RTT and downlink RTT; An analysis device is used to analyze the online index data, query the online status of users in each cycle, and determine whether there are users whose online status changes to offline status; A judging device, configured to, if a user whose online status changes to an offline status, judge whether the offline user has abnormally gone offline based on the status of a device associated with the offline user, wherein the device includes a passive optical network device, an optical line terminal, and a broadband access server; The step of determining whether the offline user is abnormally offline based on the status of a device associated with the offline user includes: Querying the offline user and each of the passive optical network devices, the optical line terminal, and the broadband access server associated with the offline user; Querying all users associated with each of the passive optical network devices, the optical line terminals, and the broadband access servers; Determine the Internet access status of all users associated with the same passive optical network device, the same optical line terminal, and the same broadband access server in sequence; If the Internet access status of all users associated with the same device is offline, it is determined that the corresponding device is abnormal and the offline user is abnormally offline.

3. A computer medium, characterized in that The computer medium stores a computer program, and when the computer program is executed by the processor, the method for detecting abnormal Internet access offline according to claim 1 is implemented.

4. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for detecting abnormal Internet offline as claimed in claim 1 is implemented.

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