CN111061691A - Method and device for determining fixed member - Google Patents
Method and device for determining fixed member Download PDFInfo
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- CN111061691A CN111061691A CN201911359038.7A CN201911359038A CN111061691A CN 111061691 A CN111061691 A CN 111061691A CN 201911359038 A CN201911359038 A CN 201911359038A CN 111061691 A CN111061691 A CN 111061691A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/17—Details of further file system functions
- G06F16/1734—Details of monitoring file system events, e.g. by the use of hooks, filter drivers, logs
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/18—File system types
- G06F16/1805—Append-only file systems, e.g. using logs or journals to store data
- G06F16/1815—Journaling file systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
Abstract
The application provides a method and a device for determining a fixed member, wherein the method comprises the steps of firstly, acquiring a monitoring log of user equipment; then, based on the network identification information in the monitoring log, determining equipment identification information corresponding to the network identification information and the occurrence frequency of each piece of equipment identification information in a preset time period; and finally, when the occurrence frequency corresponding to any one piece of equipment identification information meets a preset number condition, determining the member corresponding to the any one piece of equipment identification information as a fixed member. In the process, whether the corresponding member is a fixed member or not can be judged by monitoring the occurrence frequency of the network identification information in the log, so that the labor cost is reduced, and the accuracy of determining the fixed member is improved.
Description
Technical Field
The present application relates to the field of big data, and in particular, to a method and an apparatus for determining a fixed member.
Background
The family is one of the most basic social settings and is the most basic and important system and group form of human beings. The number of the family members and the composition of the members are determined, and the method has important significance for all walks of life.
At present, the determination of the number of people in each family and the composition of members is mostly carried out by depending on living and committee or some house property intermediaries and adopting a mode of inquiring about each family, so that the information of the number of people in each family and the composition of members is acquired, the great labor cost is required to be consumed, and the accuracy of the investigation information is low.
Disclosure of Invention
In view of this, an object of the present application is to provide a method and an apparatus for determining a fixed member, so as to improve the accuracy of determining the fixed member and reduce the labor cost.
In a first aspect, an embodiment of the present application provides a method for determining a fixed member, including:
acquiring a monitoring log of user equipment;
determining equipment identification information corresponding to the network identification information and the occurrence frequency of each piece of equipment identification information in a preset time period based on the network identification information in the monitoring log;
and when the occurrence frequency corresponding to any one piece of equipment identification information meets a preset number condition, determining that the member corresponding to the any one piece of equipment identification information is a fixed member.
In an optional embodiment, the network identification information includes one or more of the following:
an internet protocol, IP, address of the device, a wireless local area network, WLAN, address of the wireless network, and a media access control, Mac, address of the router.
In an alternative embodiment, the preset number condition includes:
the occurrence frequency of the equipment identification information in the preset time period is greater than a preset frequency threshold value.
In an optional embodiment, the method for determining the fixed member further includes:
acquiring characteristic information of user equipment corresponding to each fixed member based on the monitoring log; the characteristic information comprises one or more of the following: clicking and browsing information of different media, commodities and application programs by a user;
feature information of the user equipment is obtained; inputting the information into a machine learning model trained in advance to obtain the information of the fixed member; the information of the fixed member includes one or more of the following:
age, gender, and occupation.
In an optional embodiment, the method for determining the fixed member further includes:
and generating information to be pushed corresponding to the information of the fixed member based on the information of the fixed member.
In a second aspect, an embodiment of the present application further provides an apparatus for determining a fixed member, where the apparatus for determining a fixed member includes: the device comprises an acquisition module, a first determination module and a second determination module, wherein:
the acquisition module is used for acquiring a monitoring log of the user equipment;
the first determining module is configured to determine, based on the network identification information in the monitoring log, device identification information corresponding to the network identification information and the occurrence frequency of each device identification information within a preset time period;
the second determining module is configured to determine that the member corresponding to any one of the device identification information is a fixed member when the occurrence frequency corresponding to the device identification information satisfies a preset number condition.
In an optional embodiment, the network identification information includes one or more of the following:
an internet protocol, IP, address of the device, a wireless local area network, WLAN, address of the wireless network, and a media access control, Mac, address of the router.
In an alternative embodiment, the preset number condition includes:
the occurrence frequency of the equipment identification information in the preset time period is greater than a preset frequency threshold value.
In an alternative embodiment, the device for determining a fixed member further includes:
acquiring characteristic information of user equipment corresponding to each fixed member based on the monitoring log; the characteristic information comprises one or more of the following: clicking and browsing information of different media, commodities and application programs by a user;
feature information of the user equipment is obtained; inputting the information into a machine learning model trained in advance to obtain the information of the fixed member; the information of the fixed member includes one or more of the following:
age, gender, and occupation.
In an alternative embodiment, the device for determining a fixed member further includes:
and generating information to be pushed corresponding to the information of the fixed member based on the information of the fixed member.
In a third aspect, an embodiment of the present application further provides a computer device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating via the bus when the computer device is running, the machine-readable instructions when executed by the processor performing the steps of the first aspect or any possible implementation of the first aspect.
In a fourth aspect, this application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and the computer program is executed by a processor to perform the steps in the first aspect or any one of the possible implementation manners of the first aspect.
The method and the device for determining the fixed members are used for determining the number and the composition of the fixed members. In the application, firstly, a monitoring log of user equipment is obtained; then, based on the network identification information in the monitoring log, determining equipment identification information corresponding to the network identification information and the occurrence frequency of each piece of equipment identification information in a preset time period; and finally, when the occurrence frequency corresponding to any one piece of equipment identification information meets a preset number condition, determining the member corresponding to the any one piece of equipment identification information as a fixed member. In the process, whether the corresponding member is a fixed member or not can be judged by monitoring the occurrence frequency of the network identification information in the log, so that the labor cost is reduced, and the accuracy of determining the fixed member is improved.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a flowchart illustrating a method for determining a fixed member according to an embodiment of the present application;
FIG. 2 is a flow chart illustrating another method for determining a fixed member provided by an embodiment of the present application;
fig. 3 is a schematic structural diagram illustrating a name of a device for determining a fixed member according to an embodiment of the present application;
fig. 4 shows a schematic structural diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
Considering the prior art determination methods for fixed members, methods relying on artificial statistics are mostly needed, for example: the general population survey, the residence committee statistics or some house property intermediary surveys and the like need to consume a large amount of manpower, material resources and financial resources, and have long time consumption and low accuracy.
Based on the above research, the method and the device for determining the number and the composition of the fixed members provided by the embodiment of the application are used for determining the number and the composition of the fixed members. In the application, firstly, a monitoring log of user equipment is obtained; then, based on the network identification information in the monitoring log, determining equipment identification information corresponding to the network identification information and the occurrence frequency of each piece of equipment identification information in a preset time period; and finally, when the occurrence frequency corresponding to any one piece of equipment identification information meets a preset number condition, determining the member corresponding to the any one piece of equipment identification information as a fixed member. In the process, whether the corresponding member is a fixed member or not can be judged by monitoring the occurrence frequency of the network identification information in the log, so that the labor cost is reduced, and the accuracy of determining the fixed member is improved.
The above-mentioned drawbacks are the results of the inventor after practical and careful study, and therefore, the discovery process of the above-mentioned problems and the solution proposed by the present application to the above-mentioned problems in the following should be the contribution of the inventor to the present application in the process of the present application.
The technical solutions in the present application will be described clearly and completely with reference to the drawings in the present application, and it should be understood that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the present application, as generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The execution subject of the fixed member determination method provided by the embodiment of the present disclosure is generally a computer device with certain computing capability, and the computer device includes: a terminal device, which may be a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, a vehicle mounted device, a wearable device, or a server or other processing device. In some possible implementations, the method of fixed member determination may be implemented by a processor calling computer readable instructions stored in a memory.
The following describes a method for determining a fixed member provided in the embodiment of the present disclosure, taking an execution subject as a computer device as an example.
Example one
Referring to fig. 1, a flowchart of a method for determining a fixed member according to an embodiment of the present application is shown, where the method includes steps S101 to S103, where:
s101: and acquiring a monitoring log of the user equipment.
S102: and determining equipment identification information corresponding to the network identification information and the occurrence frequency of each piece of equipment identification information in a preset time period based on the network identification information in the monitoring log.
S103: and when the occurrence frequency corresponding to any one piece of equipment identification information meets a preset number condition, determining that the member corresponding to the any one piece of equipment identification information is a fixed member.
The following describes each of the above-mentioned steps S101 to S103 in detail.
Firstly, the method comprises the following steps: in the above S101, a monitoring log corresponding to the user equipment is acquired.
Illustratively, the user device may be a mobile device, a user terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, an in-vehicle device, a wearable device, or the like.
For example, the monitoring log may be a monitoring log generated in an advertisement monitoring process, a monitoring log of a user browsing different webpages, a monitoring log of a user using a mobile device application program, or the like.
II, secondly: in the above S102, based on the monitoring log obtained in the step S101, network identification information in the monitoring log is extracted, and device identification information corresponding to the network identification information and the occurrence frequency of each device identification information in a preset time period are determined.
Wherein the network identification information includes one or more of the following: an Internet Protocol (IP) Address of the device, a Wireless Local Area Network (WLAN) Address, a Wireless Network name, and a Media Access Control (Mac) Address of the router.
Illustratively, the network identification information refers to identification information of a user equipment in a network. For example: an Internet Protocol (IP) address of a Personal Computer (PC) connected to a network can be regarded as network identification information of the PC; for some mobile devices such as mobile phones, tablet personal computers (PADs), Smart watches (Smart Watch), and the like, a Wireless Local Area Network (WLAN) address connected to the mobile device may also be considered as Network identification information of the mobile device; the names of some routers and the corresponding Media Access control (Mac) addresses may also be considered as network identification information corresponding to the routers.
Illustratively, the device identification information includes: international Mobile Equipment Identity (IMEI), Android Identity (Android ID), advertisement Identifier (IDFA), and other identification information. The device identification information further includes identification information such as a Software Development Kit (SDK) with development assistance function, which is embedded in the target application.
Illustratively, in view of the determined device identification information corresponding to the network identification information, the number of times that the device identification information appears in a certain preset time period is counted in a monitoring log of the user equipment.
Thirdly, the method comprises the following steps: in step S103, based on the device identification information corresponding to the network identification information determined in step S102 and the occurrence frequency of each piece of device identification information in a preset time period, it is determined whether the occurrence frequency corresponding to any piece of device identification information meets a preset number condition, so that a member corresponding to any piece of device identification information is determined to be a fixed member.
Wherein the preset quantity condition includes: the occurrence frequency of the equipment identification information in the preset time period is greater than a preset frequency threshold value.
And if the occurrence frequency of any piece of equipment identification information in a preset time period is greater than a preset frequency threshold value, determining that the member corresponding to any piece of equipment identification information is a fixed member.
And if the occurrence frequency of any piece of equipment identification information in a preset time period is smaller than a preset frequency threshold value, determining that the member corresponding to any piece of equipment identification information is a non-fixed member.
Illustratively, for the same internet protocol IP address, wireless local area network WLAN address, wireless network name, and Mac address of the router, the occurrence number of the device identification information corresponding thereto within a preset time period, for example: if the identification information of a certain device appears for 20 times or more in five days, the member corresponding to the device can be determined to be a fixed member; if the identification information of a certain device fails to appear 20 times within five days, the corresponding member of the device can be determined to be a non-fixed member or a guest member.
The method for determining the fixed members is used for determining the number and the composition of the fixed members. In the application, firstly, a monitoring log of user equipment is obtained; then, based on the network identification information in the monitoring log, determining equipment identification information corresponding to the network identification information and the occurrence frequency of each piece of equipment identification information in a preset time period; and finally, when the occurrence frequency corresponding to any one piece of equipment identification information meets a preset number condition, determining the member corresponding to the any one piece of equipment identification information as a fixed member. In the process, whether the corresponding member is a fixed member or not can be judged by monitoring the occurrence frequency of the network identification information in the log, so that the labor cost is reduced, and the accuracy of determining the fixed member is improved.
Referring to fig. 2, a flowchart of another method for determining a fixed member provided in the first embodiment of the present application is shown, where the method includes steps S201 to S202, where:
s201: acquiring characteristic information of user equipment corresponding to each fixed member based on the monitoring log; the characteristic information comprises one or more of the following: and clicking and browsing information of different media, commodities and application programs by the user.
S202: feature information of the user equipment is obtained; inputting the information into a machine learning model trained in advance to obtain the information of the fixed member; the information of the fixed member includes one or more of the following: age, gender, and occupation.
In the above S201, based on the monitoring log of the fixed member user device, the feature information of the device of each fixed member in the monitoring log is obtained. The characteristic information can be click and browse information of different media, commodities and application programs.
For example, in the monitoring log, characteristic information such as media websites frequently browsed by the fixed member, clicks and browses of different commercial advertisements, and application information frequently used by the fixed member is displayed.
In step S201, the feature information of the user equipment of the fixed member acquired in step S201 is input into a machine learning model trained in advance, so as to obtain the information of the fixed member.
Wherein the information of the fixed member includes: the age, sex, occupation, etc. of the fixed member may be used to make a judgment on the composition of the fixed member.
For example, if the fixed member is judged to be a man or a woman and the age group is 20-30 years old, the family can be considered as a lover or a family with two mouths.
For example, if the fixed member is determined to be a male or a female, wherein the age range of two persons is 30 years or more and the age range of the other person is 20 years or less, the family may be considered to be a three-family.
Other conditions also include the conditions of a family with four mouths (including two children), a family with five mouths (including two old people), a family with six mouths (including two children and two old people) and other various fixed members, and the conditions can be judged according to specific conditions.
For example, if the fixed members are judged to be of the same occupation, the fixed members can be inferred to be members of a family.
The method provided by the embodiment of the application further comprises the step of generating information to be pushed corresponding to the information of the fixed member based on the information of the fixed member.
For example, for a family with fixed members including the elderly, some information such as healthcare for the elderly or insurance for the elderly can be pushed.
For families with some fixed members including children, some educational training types of pushed information can be pushed to the families.
For some lovers, some wedding photos, honey moon travel and other push information can be pushed to the lovers.
In addition, for the determination of the number and the composition of fixed members, reference information can be provided for site selection of convenient places such as supermarkets, convenience stores, dish markets and the like.
Example two
Referring to fig. 3, a schematic structural diagram of a name of a device for determining a fixed member according to a second embodiment of the present application is shown, where the device for determining a fixed member includes: an obtaining module 31, a first determining module 32, and a second determining module 33, wherein:
an obtaining module 31, configured to obtain a monitoring log of a user equipment;
a first determining module 32, configured to determine, based on the network identification information in the monitoring log, device identification information corresponding to the network identification information and occurrence times of each device identification information within a preset time period;
a second determining module 33, configured to determine that a member corresponding to any piece of device identification information is a fixed member when the number of occurrences corresponding to the any piece of device identification information satisfies a preset number condition.
Based on the above research, the device for determining the fixed member provided by the embodiment of the application is used for determining the number and the composition of the fixed member. In the application, firstly, a monitoring log of user equipment is obtained; then, based on the network identification information in the monitoring log, determining equipment identification information corresponding to the network identification information and the occurrence frequency of each piece of equipment identification information in a preset time period; and finally, when the occurrence frequency corresponding to any one piece of equipment identification information meets a preset number condition, determining the member corresponding to the any one piece of equipment identification information as a fixed member. In the process, whether the corresponding member is a fixed member or not can be judged by monitoring the occurrence frequency of the network identification information in the log, so that the labor cost is reduced, and the accuracy of determining the fixed member is improved.
In one possible implementation, the network identification information includes one or more of the following:
an internet protocol, IP, address of the device, a wireless local area network, WLAN, address of the wireless network, and a media access control, Mac, address of the router.
In a possible embodiment, the preset number condition includes:
the occurrence frequency of the equipment identification information in the preset time period is greater than a preset frequency threshold value.
In a possible implementation, the device for determining a fixed member further includes:
acquiring characteristic information of user equipment corresponding to each fixed member based on the monitoring log; the characteristic information comprises one or more of the following: clicking and browsing information of different media, commodities and application programs by a user;
feature information of the user equipment is obtained; inputting the information into a machine learning model trained in advance to obtain the information of the fixed member; the information of the fixed member includes one or more of the following:
age, gender, and occupation.
In a possible implementation, the device for determining a fixed member further includes:
and generating information to be pushed corresponding to the information of the fixed member based on the information of the fixed member.
EXAMPLE III
An embodiment of the present application further provides an electronic device 400, as shown in fig. 4, which is a schematic structural diagram of the electronic device 400 provided in the embodiment of the present application, and includes:
a processor 41, a memory 42, and a bus 43; the memory 42 is used for storing execution instructions and includes a memory 421 and an external memory 422; the memory 421 is also referred to as an internal memory, and is used for temporarily storing the operation data in the processor 41 and the data exchanged with the external memory 422 such as a hard disk, the processor 41 exchanges data with the external memory 422 through the memory 421, and when the electronic device 400 operates, the processor 41 communicates with the memory 42 through the bus 43, so that the processor 41 executes the following instructions in a user mode:
acquiring a monitoring log of user equipment;
determining equipment identification information corresponding to the network identification information and the occurrence frequency of each piece of equipment identification information in a preset time period based on the network identification information in the monitoring log;
and when the occurrence frequency corresponding to any one piece of equipment identification information meets a preset number condition, determining that the member corresponding to the any one piece of equipment identification information is a fixed member.
In one possible embodiment, the processor 41 executes instructions that include one or more of the following network identification information:
an internet protocol, IP, address of the device, a wireless local area network, WLAN, address of the wireless network, and a media access control, Mac, address of the router.
In a possible implementation, in the instruction executed by the processor 41, the preset quantity condition includes:
the occurrence frequency of the equipment identification information in the preset time period is greater than a preset frequency threshold value.
In a possible implementation manner, in the instructions executed by the processor 41, the method for determining the fixed member further includes:
acquiring characteristic information of user equipment corresponding to each fixed member based on the monitoring log; the characteristic information comprises one or more of the following: clicking and browsing information of different media, commodities and application programs by a user;
feature information of the user equipment is obtained; inputting the information into a machine learning model trained in advance to obtain the information of the fixed member; the information of the fixed member includes one or more of the following:
age, gender, and occupation.
In a possible implementation manner, in the instructions executed by the processor 41, the method for determining the fixed member further includes:
and generating information to be pushed corresponding to the information of the fixed member based on the information of the fixed member.
The present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method for training a user classification model and the steps of the user classification method in the foregoing method embodiments are performed.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the exemplary embodiments of the present application, and are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (12)
1. A method for determining a fixed member, the method comprising:
acquiring a monitoring log of user equipment;
determining equipment identification information corresponding to the network identification information and the occurrence frequency of each piece of equipment identification information in a preset time period based on the network identification information in the monitoring log;
and when the occurrence frequency corresponding to any one piece of equipment identification information meets a preset number condition, determining that the member corresponding to the any one piece of equipment identification information is a fixed member.
2. The method of claim 1, wherein the network identification information comprises one or more of the following:
an internet protocol, IP, address of the device, a wireless local area network, WLAN, address of the wireless network, and a media access control, Mac, address of the router.
3. The method of claim 1, wherein the predetermined number of conditions comprises:
the occurrence frequency of the equipment identification information in the preset time period is greater than a preset frequency threshold value.
4. The method of claim 1, further comprising:
acquiring characteristic information of user equipment corresponding to each fixed member based on the monitoring log; the characteristic information comprises one or more of the following: clicking and browsing information of different media, commodities and application programs by a user;
feature information of the user equipment is obtained; inputting the information into a machine learning model trained in advance to obtain the information of the fixed member; the information of the fixed member includes one or more of the following:
age, gender, and occupation.
5. The method of claim 4, further comprising:
and generating information to be pushed corresponding to the information of the fixed member based on the information of the fixed member.
6. An apparatus for determining a fixed membership, the apparatus comprising:
the acquisition module is used for acquiring a monitoring log of the user equipment;
the first determining module is used for determining equipment identification information corresponding to the network identification information and the occurrence frequency of each piece of equipment identification information in a preset time period based on the network identification information in the monitoring log;
and the second determining module is used for determining that the member corresponding to any piece of equipment identification information is a fixed member when the occurrence frequency corresponding to the equipment identification information meets a preset number condition.
7. The apparatus of claim 6, wherein the network identification information comprises one or more of:
an internet protocol, IP, address of the device, a wireless local area network, WLAN, address of the wireless network, and a media access control, Mac, address of the router.
8. The apparatus of claim 6, wherein the predetermined number of conditions comprises:
the occurrence frequency of the equipment identification information in the preset time period is greater than a preset frequency threshold value.
9. The apparatus of claim 6, further comprising:
acquiring characteristic information of user equipment corresponding to each fixed member based on the monitoring log; the characteristic information comprises one or more of the following: clicking and browsing information of different media, commodities and application programs by a user;
feature information of the user equipment is obtained; inputting the information into a machine learning model trained in advance to obtain the information of the fixed member; the information of the fixed member includes one or more of the following:
age, gender, and occupation.
10. The apparatus of claim 6, further comprising:
and generating information to be pushed corresponding to the information of the fixed member based on the information of the fixed member.
11. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when the electronic device is operating, the machine-readable instructions when executed by the processor performing the steps of the method of any of claims 1 to 5.
12. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, is adapted to carry out the steps of the method according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911359038.7A CN111061691A (en) | 2019-12-25 | 2019-12-25 | Method and device for determining fixed member |
Applications Claiming Priority (1)
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