CN114245242B - User offline detection method and device and electronic equipment - Google Patents
User offline detection method and device and electronic equipment Download PDFInfo
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Abstract
The invention discloses a user offline detection method, a user offline detection device and electronic equipment. The method comprises the following steps: receiving an uplink and downlink log message of a whole network user, and determining a time slice and network element equipment corresponding to the uplink and downlink log message; performing group offline feature detection on the offline log messages with the same time slices and belonging to the same network element equipment; if the group offline feature detection is carried out, acquiring an offline user list, wherein the offline user list at least comprises offline time of each offline user; and generating a group offline event of the network element equipment under the time slice according to the offline user list. The technical scheme of the invention improves the early warning mechanism by generating the user offline list which is caused by the accurate identification of the fault by the group offline event auxiliary network operation and maintenance platform.
Description
Technical Field
The invention relates to the technical field of internet access management, in particular to a user offline detection method, a user offline detection device and electronic equipment.
Background
With the continuous development and expansion of broadband services and the more complex networking structure, the wide-home users caused by network faults and engineering operations are declared to stay high for a long time. At present, fault early warning and dispatch mechanisms formed based on traditional equipment monitoring and alarming are still not perfect, faults of an optical line terminal (Optical Line Terminal, OLT for short) and specific users influenced by the faults cannot be accurately identified, so that the condition of untimely early warning possibly occurs, false sending of faults is caused, blocking and shortening of messages are caused to clients, reporting of the users is caused, and the satisfaction degree of the family width is not high.
Disclosure of Invention
Therefore, the main purpose of the present invention is to provide a method, an apparatus and an electronic device for detecting user offline, which assist a network operation and maintenance platform to accurately identify a user offline list caused by a fault, and improve an early warning mechanism.
According to a first aspect of the present invention, there is provided a method for detecting user offline, including: receiving an uplink and downlink log message of a whole network user, and determining a time slice and network element equipment corresponding to the uplink and downlink log message; performing group offline feature detection on the offline log messages with the same time slices and belonging to the same network element equipment; if the group offline feature detection is carried out, acquiring an offline user list, wherein the offline user list at least comprises offline time of each offline user; and generating a group offline event of the network element equipment under the time slice according to the offline user list.
According to a second aspect of the present invention, there is provided a user offline detection device, comprising: the receiving unit is used for receiving the uplink and downlink log messages of the whole network user and determining time slices and network element equipment corresponding to the uplink and downlink log messages; the detection unit is used for carrying out group offline feature detection on the offline log messages with the same time slices and belonging to the same network element equipment; the statistics unit is used for acquiring a offline user list if the group offline characteristics are detected, wherein the offline user list at least comprises offline time of each offline user; and the generation unit is used for generating a group offline event of the network element equipment under the time slice according to the offline user list.
According to a third aspect of the present invention, there is provided an electronic device comprising: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform a user offline detection method.
According to a fourth aspect of the present invention, there is provided a computer-readable storage medium storing one or more programs that, when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform a user-offline detection method.
The at least one technical scheme adopted by the invention can achieve the following beneficial effects: the user offline detection method and device of the embodiment detect the group offline characteristics of the online and offline log messages according to the time slices and the network element equipment by collecting the online and offline log messages of the whole network users, determine the actually affected offline user list and the offline time of each offline user according to the offline log messages with the group offline characteristics, generate the group offline event of the network element equipment under the time slices based on the offline user list, and assist the operation and maintenance platform to accurately identify the actually affected users and fault duration based on the group offline event provided by the embodiment, so that the operation and maintenance platform can accurately, rapidly and comprehensively monitor and analyze the group offline event.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 shows a flowchart of a user offline detection method according to one embodiment of the invention;
FIG. 2 illustrates a user offline detection flow diagram based on an AAA system according to an embodiment of the invention;
fig. 3 shows an application scenario diagram of a user offline detection method according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a subscriber line-down detection apparatus according to an embodiment of the present invention;
fig. 5 shows a schematic structural diagram of an electronic device according to an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. These embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein.
With the construction of broadband service diagnosis supporting capability, operation and maintenance platforms of network operators are widely applied, and operation and maintenance personnel can find and solve the requirements of offline events of part of user groups through the operation and maintenance platforms, but the following problems are found in the practical process:
firstly, through the existing analysis logic of the online and offline logs, only the frequent offline problem of a single user can be found, the offline event of the user group can not be accurately identified and positioned, so that the offline event of the group can not be found in time by operation and maintenance personnel, the user influenced by the offline event of the group can not normally use broadband service, and the user experience is poor.
Secondly, the active group obstacle data analysis or the manual group obstacle data analysis at the present stage achieves the purpose of identifying the user group offline event by gathering the group obstacle problems and pointing to a network element of a certain area or a certain area. However, the identification capability of the method is weak, and a large number of events in the group fault data are discovered through the declaration of users, so that the satisfaction degree of the users is seriously influenced.
Thirdly, the related art also combines and analyzes the group fault data and the frequent offline of a single user, which can discover the problems of offline and flashing of the user to a certain extent, but can not accurately identify the user list and the fault duration actually affected by the group offline event, and brings inconvenience to the subsequent user care and centralized improvement.
In view of the above problems, an embodiment of the present invention provides a method for detecting user offline, which includes collecting single user offline log messages of a network security system (AAA), performing detailed analysis and operation on offline and offline behavior features of a whole network user, extracting offline log messages with offline features in a time slice, generating an offline event of the group based on the extracted offline log messages, so that an operation and maintenance platform accurately identifies a user and a fault duration actually affected by the offline event of the group based on an offline user list in the offline event of the group.
The broadband access network is an access part between the user terminal and the backbone network, and the devices in the broadband access network form a network in a tree-shaped architecture. Therefore, after a node with a relatively core fails, other devices and user terminals connected down to the node are all affected, so that large-area faults are caused, few tens of users and tens of thousands of users cannot normally use the network.
Fig. 1 shows a flowchart of a method for detecting user offline according to an embodiment of the present invention, as shown in fig. 1, the method of this embodiment at least includes the following steps S110 to S140:
step S110, receiving the log message of the whole network user, and determining the time slice and the network element equipment corresponding to the log message.
In practical application, the AAA system server records the user on-line and off-line request condition in real time, and forms an on-line and off-line log message at fixed time, where the on-line and off-line log message includes information such as user identifier, user name, on-line end time of on-line start time, and resource home network element.
In this embodiment, the analysis server collects the uplink and downlink log messages generated by the server of the AAA system, and determines the network element equipment corresponding to the uplink and downlink log messages according to the resource home network element in the uplink and downlink log messages, where the network element equipment includes, but is not limited to, OLT equipment information, passive optical network (Passive Optical Network, PON) equipment information, and the analysis server may collect the uplink and downlink log messages in a near real-time manner or in real-time manner, which can be flexibly selected by a person skilled in the art according to the application requirement and the computing capability of the analysis server.
In this embodiment, the time slice refers to a time slice of a monitoring time, for example, in a monitoring scene of 24 hours throughout the day, if the time granularity is 5 minutes, 288 time intervals can be divided from 24 hours, and each time interval corresponds to one time slice, so that the time slice corresponding to the uplink and downlink log message can be determined according to the uplink end time included in the uplink and downlink log message.
Step S120, the group offline feature detection is performed on the offline log messages with the same time slices and belonging to the same network element equipment.
Step S130, if the group offline feature detection is performed, an offline user list is obtained, wherein the offline user list at least comprises offline time periods of all offline users.
Step S140, generating a group offline event of the network element equipment under the time slice according to the offline user list.
In practical application, due to the reasons of network element equipment failure, network element equipment outage and the like, part/all of users are sequentially offline within a certain time range, the user offline events caused by the same reasons have the same group offline characteristics, the embodiment analyzes the uplink and downlink log messages based on the group offline characteristics, and when the uplink and downlink log messages which have the same time slice and belong to the same network element equipment have the group offline characteristics, the actually affected offline user list and the offline duration of each offline user are determined based on the uplink and downlink log messages, and the group offline events of the network element equipment under the time slice are generated based on the offline user list.
It should be noted that, in this embodiment, the steps S110 to S140 may be executed by the analysis server, and in practical application, may be executed by other devices, which may be flexibly selected by a person skilled in the art.
As can be seen, in the method shown in fig. 1, by collecting the log messages of the online and offline of the whole network users, the log messages of the online and offline are subjected to group offline feature detection according to the time slices and the network element devices, the actually affected offline user list and the offline duration of each offline user are determined according to the offline log messages with the group offline features, the group offline event of the network element device under the time slices is generated based on the offline user list, and the auxiliary operation and maintenance platform accurately identifies the actually affected users and the fault duration based on the group offline event provided by the embodiment, so that the operation and maintenance platform can accurately, rapidly and comprehensively monitor and analyze the group offline event.
As shown in fig. 2, in practical application, the AAA system server records online and offline requests of the authenticated user in real time, and periodically generates an online and offline log message. The AAA system server associates user information such as an authenticated user's internet account with service operation support system (service & Operation Support System, BOSS, abbreviated as BOSS) data to form an online record and a offline record of a single user with 84 fields, where the following is one of the online record and the offline record:
dacp::Cb4JVrRInVQxhnCUxKpOJQ＝＝jtkd NULL 1 1 0 120.202.10.243 trunk 2/0/3:2653.1275 192.168.2.110/0/0/11/0/10/NBELb16f6fe5 GCOB 15 0 0 NULL NULL NULL 280 94:fe:9d:b6:61:05 100.64.44.141 94:fe:9d:b6:61:05 0 1 NULL 10 -1 2020-07-19 10:24:06 2020-07-20 00:00:00 2020-07-20 16:39:06 59946 0 NULL 0 HBEZH-M04209265301275f342bf155125 NULL NULL 1 1 0 dacp::Cb4JVrRInVQxhnCUxKpOJQ＝＝HBEZH–MC–CMNET-BAS04–CQ-ME60-15997155155@jtkd 1 -1 0 0 NULL 0NULL 19 10010 3 16 0 0 0 0 0 NULL NULL 202007 20200720
the jtkd in the above-mentioned online and offline record is a user identifier, 15997155155 is a user name, 2020-07-20:00:00 is an online start time field value, 2020-07-20:16:39:06 is an online end time field value.
After receiving the log messages of the online and offline of the whole network user, the embodiment determines the time slice corresponding to the log messages of the online and offline through the following steps:
grouping the online and offline log messages of the whole network users according to time slices according to the online end time in the online and offline log messages; and determining the time slice corresponding to the uplink and downlink log message according to the grouping information of the uplink and downlink log message.
As described above, in this embodiment, the monitoring time is divided in advance to obtain at least one time slice, where the time intervals corresponding to the time slices are not overlapped and are independent, for example, the time interval corresponding to the time slice 1 is 2021, 12, 7, 15:55, 16:00, and the time interval corresponding to the time slice 1 is 2021, 12, 7, 16:00, 16:05. And each time slice corresponds to a time interval, dividing the uplink and downlink log messages into groups corresponding to the time areas according to the time areas where the uplink end time in the uplink and downlink log messages is located, and determining the time slices corresponding to the uplink and downlink log messages according to the grouping conditions. In practical application, the log messages of the uplink and the downlink in each group can be stored so as to store the log messages of the uplink and the downlink in full quantity and provide data support for other applications.
The time length of the monitoring time in this embodiment should be not less than the time length of the time slice, and may be 24 hours, 12 hours, one week, etc., which can be flexibly set by those skilled in the art.
For convenience of description, the uplink and downlink log messages with the same time slices and belonging to the same network element equipment are marked as a first type of uplink and downlink log message.
In practical application, the method for determining the first type of uplink and downlink log message comprises the following steps: and determining the first type of the uplink and downlink log messages according to the uplink and downlink log messages in each group.
Taking the first type of uplink and downlink log message in the packet 1 as an example, assuming that the network element device corresponding to the first part of uplink and downlink log message in the packet 1 is OLT1, the network element device corresponding to the second part of uplink and downlink log message is OLT2, and the network element device corresponding to the third part of uplink and downlink log message is OLT3, it can be determined that the first part of uplink and downlink log message, the second part of uplink and downlink log message and the third part of uplink and downlink log message are all the first type of uplink and downlink log message.
In some embodiments, performing group offline feature detection on an offline log message with the same time slice and belonging to the same network element device includes:
calculating the total number of off-line users corresponding to each network element device in the corresponding time slices according to the first type of off-line log messages; here, the total number of the offline users corresponding to each network element device in the corresponding time slice may be calculated according to the number of the user identifiers corresponding to the first type of the offline log messages and the offline end time corresponding to the first type of the offline log messages.
In practical application, the log messages of the uplink and the downlink under the same time slice include a plurality of log messages of the uplink and the downlink corresponding to the same user, and also include log messages of the uplink and the downlink corresponding to the passive downlink caused by system timeout, for example, in some scenes, at 0 point of each day, namely at 00:00.00 would have all online users collectively come off-line. In order to solve the problem, in this embodiment, when the total number of offline users is calculated, a plurality of uplink and downlink log messages generated by one user in a plurality of offline are determined as one offline user, and the uplink and downlink log messages caused by the timeout of the system are not counted into the total number of offline users.
If the total number of the offline users corresponding to a certain network element device in the time slice is smaller than the threshold value of the offline users, determining that the offline log message belonging to the network element device in the time slice does not pass the group offline feature detection; if the total number of the offline users corresponding to a certain network element device in the time slice is not less than the threshold value of the offline users, determining that the offline log message belonging to the network element device in the time slice passes the group offline feature detection.
For example, the total number of offline users corresponding to the network element equipment OLT1 in the time slice 1 is smaller than 200, and it is determined that the first type of online and offline log message does not have a group offline feature and does not pass detection; if the total number of the offline users corresponding to the network element equipment OLT1 in the time slice 1 is greater than 200, determining that the first type of offline log message has the group offline feature, and detecting the group offline feature, wherein the time slice 1 is the offline time slice.
In some embodiments, before performing group offline feature detection on the offline log messages with the same time slice and belonging to the same network element device, the method further includes:
and (3) user screening is carried out on the uplink and downlink log messages with the same time slices, and the uplink and downlink log messages of the target user are screened out. For example, the user identification field in the log message of the online and offline is truncated, the data content of the @ domain name is intercepted, the intercepted data content is matched with the online account information in the user data (Customer Identification, abbreviated as CID) in fig. 2, and the log message of the online and offline passing the matching describes the online/offline event of the concerned target user.
Correspondingly, the group offline feature detection is performed on the offline log messages with the same time slice and belonging to the same network element equipment, including: and performing group offline feature detection on the online and offline log messages of the target users with the same time slices and belonging to the same network element equipment, wherein the detection method refers to the group offline feature detection on the first type of online and offline log messages and is not repeated herein.
When the group offline feature detection is passed, acquiring offline user information corresponding to the user identification according to the user identification corresponding to each online and offline log message detected by the group offline feature detection; determining the offline time length of the offline user according to the offline end time and the next offline starting time corresponding to each offline log message detected by the group offline characteristics, wherein the next offline starting time is extracted from the next offline log message according to the user identification; and generating a list of the offline users according to the offline user information and the offline time of the offline users.
Taking the first type of uplink and downlink log message in the packet 1 as an example, assuming that the first part of uplink and downlink log message in the packet 1 is detected by the group downlink feature, the downlink user information may be obtained according to the user identifier of the first part of uplink and downlink log message, where the downlink user information includes, but is not limited to, personal information such as a user name, an authentication account number, a registered phone number, a registered mailbox, and the like.
Searching from the up-down log messages stored in the analysis server group according to the user identification, searching the up-down starting time closest to the current down-line ending time, and determining the time length between the current down-line ending time and the searched up-line starting time as the down-line time length. Here, the current end time of the downlink refers to the end time of the downlink in the first type of uplink and downlink log message in the packet 1.
According to the embodiment, the group offline event in the embodiment is to use each network element device as a target object, and the offline user list affected by the downlink of each target object is determined through the group offline event, and the group offline time in the embodiment is uploaded to the operation and maintenance platform because the offline user list comprises personal user information and offline time, so that the operation and maintenance platform can be assisted to accurately identify the user affected by the fault and the duration of the fault, and support is provided for overcoming the platform. In practical application, the group offline event may further include an offline start time, an offline end time, and an auxiliary operation and maintenance platform for evaluating an event influence range and an influence duration of each offline user. Here, the offline start time is the offline end time of the offline log message, and the offline end time is the next offline start time described above.
In some embodiments, after generating the group offline event, further comprising: and determining the fault network element according to the group offline event generated in the set time. For example, if N group offline events of the same network element device are generated within a set time, determining that the network element device corresponding to the N group offline events is a faulty network element, where N is a natural number not less than 1.
For example, a timing task may be set, and the group offline event generated by the analysis server is monitored and analyzed by using the timing task, and if more than two group offline events exist in the same network element device are monitored, the group offline event existing in the same network element device is determined to be an abnormal group offline event, and the network element device is a faulty network element.
Through experiments, if the analysis server analyzes 760 about ten-thousand online and offline log records every day, the number of the offline event records of the group is about 11 ten-thousand, which indicates that the proportion of the offline event records of the whole network group is about 1.64%. The method has the advantages that the number statistics is carried out on the same network element equipment of the group offline event, the high-frequency fault network element equipment can be effectively determined, centralized improvement and reinsurance are carried out on the high-frequency fault network element equipment, and the equipment fault probability is reduced.
Based on the user offline detection method of the above embodiment, at least the following advantages can be obtained:
first, assist in fault localization. The method comprises the steps of carrying out detailed analysis and operation on the online and offline behavior characteristics of the whole network user by collecting and analyzing the online and offline log messages of the whole network user, extracting the online and offline log messages with the same time slices and the same network element equipment with the group offline characteristics, and generating group offline events based on the extracted online and offline log messages, so that an operation and maintenance platform monitors and analyzes the group offline events and timely discovers hidden faults of the network element equipment.
Secondly, the fault influences the user to perform accurate early warning. Based on monitoring and analyzing the group offline event of the whole network, accurately acquiring a user list hung down by the network element equipment when the hidden fault occurs and the actual influence condition of the fault on the user; through interfaces with online companies and BOSS service platforms, fault early warning and group barrier pushing of the offline users of the whole network group are realized; the method and the system accurately identify the fault influence user list, determine whether the fault truly influences the condition of user service interruption, and accurately, rapidly and comprehensively monitor and analyze the group offline events of the whole network users.
Third, user reporting is effectively intercepted. The operation and maintenance platform can timely and accurately acquire group obstacle information and user service interruption conditions in a full-province home-wide scene by pushing the group obstacle information and the condition that the group obstacle influences a user, and can judge whether the offline reasons of the user reporting are caused by fault interruption or not at the first time when the user reporting is received, so that the user is given more accurate response and reply, the user is calmed timely, and the perception of the fault interruption to user experience is reduced.
Fig. 3 shows an application of the user offline detection method in this embodiment in an operation and maintenance platform, where the operation and maintenance platform collects an uplink and downlink log message of an AAA system through a preset interface in a group fault processing process, processes the uplink and downlink log message by using the user offline detection logic in this embodiment, and sends a group offline event and a fault network element device obtained by logic processing to an upper data service layer, so that the operation and maintenance platform performs fault real-time analysis processing, real-time early warning release processing, batch processing convergence analysis and the like by combining the group offline event and the fault network element device.
The embodiment of the invention realizes the monitoring of the off-line events of the whole network group and can discover the occurrence and the influence range of the off-line events of the group in time; the problem that abnormal group offline events influence user perception is solved, fault users are early warned in time, user satisfaction is improved, and user loss is reduced. If the tariff per user is 100/month, the lost user can be reduced by 1 kilowatt by the method of the embodiment, and the revenue loss of 100 x 12 x 1000=120 kiloyuan is estimated to be reduced. The method of the embodiment of the invention has obvious market competitiveness.
The user off-line detection method in the foregoing embodiment belongs to the same technical concept, and the embodiment of the present invention further provides a user off-line detection device, where the user off-line detection device is used to implement the user off-line detection method in the foregoing embodiment.
Fig. 4 is a schematic structural diagram of a subscriber line drop detection apparatus according to an embodiment of the present invention, and as shown in fig. 4, a subscriber line drop detection apparatus 400 includes:
a receiving unit 410, configured to receive an uplink and downlink log message of a full network user, and determine a time slice and network element equipment corresponding to the uplink and downlink log message;
the detection unit 420 is configured to perform group offline feature detection on the offline log messages with the same time slice and belonging to the same network element device;
a statistics unit 430, configured to obtain a offline user list if the group offline feature detection is performed, where the offline user list includes at least offline time durations of offline users;
and the generating unit 440 is configured to generate a group offline event of the network element device under the time slice according to the offline user list.
In some embodiments, the receiving unit 410 groups the uplink and downlink log messages of the whole network user according to time slices according to the uplink end time of the uplink and downlink log messages; and determining a time slice corresponding to the uplink and downlink log message according to the grouping information of the uplink and downlink log message.
In some embodiments, the receiving unit 410 is further configured to perform user screening on the uplink and downlink log messages with the same time slice, and screen out the uplink and downlink log messages of the target user.
Correspondingly, the detection unit 420 is configured to perform group offline feature detection on the offline log messages with the same time slice and belonging to the target users of the same network element device.
In some embodiments, the detecting unit 420 is configured to calculate, according to the uplink and downlink log message, a total number of downlink users corresponding to each network element device in the corresponding time slice; if the total number of the offline users corresponding to a certain network element device in the time slice is smaller than the threshold value of the offline users, determining that the offline log message belonging to the network element device in the time slice does not pass the group offline feature detection; if the total number of the offline users corresponding to a certain network element device in the time slice is not less than the threshold value of the offline users, determining that the offline log message belonging to the network element device in the time slice passes the group offline feature detection.
In some embodiments, the detecting unit 420 is further configured to calculate, according to the number of user identities corresponding to the uplink and downlink log messages and the uplink end time corresponding to the uplink and downlink log messages, a total number of downlink users corresponding to each network element device in the corresponding time slice.
In some embodiments, the statistics unit 430 is configured to obtain, according to a user identifier corresponding to each of the online and offline log messages detected by the group offline feature, offline user information corresponding to the user identifier; determining the offline time length of an offline user according to the offline end time and the next offline starting time corresponding to each offline log message detected by the group offline characteristics, wherein the next offline starting time is extracted from the next offline log message according to the user identification; and generating a list of the offline users according to the offline user information and the offline time of the offline users.
In some embodiments, the user offline detection device in fig. 4 further comprises an analysis unit;
an analysis unit for determining a fault network element device according to the group offline event generated in the set time
In some embodiments, the analyzing unit is specifically configured to generate N group offline events of the same network element device within a set time, and determine that the network element device corresponding to the N group offline events is a faulty network element device, where N is a natural number not less than 1.
It can be understood that the above-mentioned user offline detection device can implement each step of the user offline detection method provided in the foregoing embodiment, and the relevant explanation about the user offline detection method is applicable to the user offline detection device, which is not described herein again.
It should be noted that:
fig. 5 shows a schematic structural diagram of an electronic device according to an embodiment of the invention. Referring to fig. 5, at the hardware level, the electronic device includes a processor and a memory, and optionally an internal bus, a network interface. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, interface module, communication module, and memory may be interconnected by an internal bus, which may be an ISA (Industry Standard Architecture ) bus, a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus, or an EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 5, but not only one bus or type of bus.
And a memory for storing computer executable instructions. The memory provides computer-executable instructions to the processor via the internal bus.
A processor executing computer executable instructions stored in the memory and specifically configured to perform the following operations:
receiving an uplink and downlink log message of a whole network user, and determining a time slice and network element equipment corresponding to the uplink and downlink log message; performing group offline feature detection on the offline log messages with the same time slices and belonging to the same network element equipment; if the group offline feature detection is carried out, acquiring an offline user list, wherein the offline user list at least comprises offline time of each offline user; and generating a group offline event of the network element equipment under the time slice according to the offline user list.
The functions performed by the user offline detection method disclosed in the embodiment of fig. 1 of the present invention may be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software.
The embodiment of the invention also provides a computer readable storage medium, which stores one or more programs, and the one or more programs, when executed by a processor, implement the user offline detection method.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) containing computer-usable program code.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
It should be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the invention.
The foregoing is merely exemplary of the present invention and is not intended to limit the present invention. Various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are to be included in the scope of the claims of the present invention.
Claims (9)
1. The user offline detection method is characterized by comprising the following steps of:
receiving an uplink and downlink log message of a whole network user, and determining a time slice and network element equipment corresponding to the uplink and downlink log message;
performing group offline feature detection on the offline log messages with the same time slices and belonging to the same network element equipment;
if the group offline feature detection is carried out, acquiring an offline user list, wherein the offline user list at least comprises offline time of each offline user;
generating a group offline event of the network element equipment under the time slice according to the offline user list;
determining the time slice corresponding to the uplink and downlink log message through the following steps:
grouping the uplink and downlink log messages of the whole network user according to time slices according to the uplink end time in the uplink and downlink log messages;
and determining a time slice corresponding to the uplink and downlink log message according to the grouping information of the uplink and downlink log message.
2. The method of claim 1, further comprising, prior to performing group offline feature detection on the offline log messages having the same time slice and belonging to the same network element device:
user screening is carried out on the uplink and downlink log messages with the same time slices, and the uplink and downlink log messages of the target user are screened out;
performing group offline feature detection on the offline log messages with the same time slices and belonging to the same network element equipment, wherein the group offline feature detection comprises the following steps:
and carrying out group offline feature detection on the offline log messages of the target users with the same time slices and belonging to the same network element equipment.
3. The method of claim 1, wherein performing group offline feature detection on an offline log message having the same time slice and belonging to the same network element device comprises:
calculating the total number of off-line users corresponding to each network element device in the corresponding time slice according to the off-line log message;
if the total number of the offline users corresponding to a certain network element device in the time slice is smaller than the threshold value of the offline users, determining that the offline log message belonging to the network element device in the time slice does not pass the group offline feature detection;
if the total number of the offline users corresponding to a certain network element device in the time slice is not less than the threshold value of the offline users, determining that the offline log message belonging to the network element device in the time slice passes the group offline feature detection.
4. A method according to claim 3, wherein calculating the total number of offline users corresponding to each network element device in the corresponding time slice according to the offline log message comprises:
and calculating the total number of off-line users corresponding to each network element device in the corresponding time slice according to the number of the user identifiers corresponding to the off-line log messages and the off-line end time corresponding to the off-line log messages.
5. The method of claim 1, wherein if the group offline feature detection is performed, obtaining the offline user list comprises:
acquiring offline user information corresponding to user identifiers according to the user identifiers corresponding to each online and offline log message detected through the group offline characteristics;
determining the offline time length of an offline user according to the offline end time and the next offline starting time corresponding to each offline log message detected by the group offline characteristics, wherein the next offline starting time is extracted from the next offline log message according to the user identification;
and generating a list of the offline users according to the offline user information and the offline time of the offline users.
6. The method of claim 1, further comprising, after generating the group offline event:
and determining the fault network element equipment according to the group offline event generated in the set time.
7. The method of claim 6, wherein determining a faulty network element device based on the group offline event generated within the set time comprises:
if N group offline events of the same network element equipment are generated within the set time, the network element equipment corresponding to the N group offline events is determined to be the fault network element equipment, wherein N is a natural number not smaller than 1.
8. A user offline detection device, comprising:
the receiving unit is used for receiving the uplink and downlink log messages of the whole network user, determining time slices and network element equipment corresponding to the uplink and downlink log messages, and particularly grouping the uplink and downlink log messages of the whole network user according to the time slices according to the uplink end time in the uplink and downlink log messages; determining a time slice corresponding to the uplink and downlink log message according to the grouping information of the uplink and downlink log message;
the detection unit is used for carrying out group offline feature detection on the offline log messages with the same time slices and belonging to the same network element equipment;
the statistics unit is used for acquiring a offline user list if the group offline characteristics are detected, wherein the offline user list at least comprises offline time of each offline user;
and the generation unit is used for generating a group offline event of the network element equipment under the time slice according to the offline user list.
9. An electronic device, characterized in that,
a processor; and
a memory arranged to store computer executable instructions which when executed cause the processor to perform the user offline detection method of any of claims 1 to 7.
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