CN112101999A - User identification method, device, electronic equipment and computer readable storage medium - Google Patents
User identification method, device, electronic equipment and computer readable storage medium Download PDFInfo
- Publication number
- CN112101999A CN112101999A CN202010971485.4A CN202010971485A CN112101999A CN 112101999 A CN112101999 A CN 112101999A CN 202010971485 A CN202010971485 A CN 202010971485A CN 112101999 A CN112101999 A CN 112101999A
- Authority
- CN
- China
- Prior art keywords
- terminal
- determined
- preset time
- clustering
- domain name
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Finance (AREA)
- Development Economics (AREA)
- Accounting & Taxation (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Bioinformatics & Computational Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Computation (AREA)
- Game Theory and Decision Science (AREA)
- Economics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Telephonic Communication Services (AREA)
- Telephone Function (AREA)
Abstract
The application provides a user identification method, a user identification device, electronic equipment and a computer readable storage medium, and relates to the field of network data processing. The method comprises the following steps: marking the user terminal which generates the target service domain name as a terminal to be determined; acquiring a position signaling data set of a plurality of terminals to be determined within a first preset time; clustering the position signaling data set to obtain a plurality of clustering results; and acquiring a target clustering result matched with a preset condition in the plurality of clustering results, and marking the terminal to be determined corresponding to the target clustering result as a service providing user of the target service. By acquiring the terminal to be determined, and then acquiring the service providing user through clustering according to the position signaling data set of the terminal to be determined, the defect that the user identification is carried out by simply analyzing the user signaling domain name is overcome, and the service providing user is accurately identified.
Description
Technical Field
The present application relates to the field of network data processing, and in particular, to a user identification method, apparatus, electronic device, and computer-readable storage medium.
Background
With the progress of society and the development of internet, the car appointment and take-out businesses gradually take the lead of public transportation and catering industry.
For the takeaway business, in order to determine the position of the takeaway rider, the Application program (APP) using condition of a mobile phone user is analyzed so as to capture the mark of the user using the takeaway rider APP as the takeaway rider. However, the method of APP resolution is to packet APP records and perform domain name resolution. However, the takeaway platform operates the takeaway rider platform and the takeaway ordering platform simultaneously, so that domain name resolution results of different APPs are the same (for example, both the takeaway and the rider under the same manufacturer finally point to the manufacturer), and therefore, the situation that the crowd who take out on demand is judged as the takeaway rider by mistake is caused; moreover, uncertainty exists in the use of the APP by the user; for example, false touch when a user operates a mobile phone or continuous running of an APP in a background of the mobile phone can cause false records, and then a takeaway rider and a food ordering person generate false identification; the same problems exist with respect to network appointment vehicles as with takeaway services. Therefore, how to provide accurate identification services to users is a problem that needs to be solved at present.
Disclosure of Invention
The present application provides a user identification method, apparatus, electronic device and computer readable storage medium, which can solve the drawback of performing user identification by simply analyzing a user signaling domain name by acquiring a terminal to be determined, and then obtaining a service providing user by clustering according to a location signaling data set of the terminal to be determined, thereby accurately identifying the service providing user.
The embodiment of the application can be realized as follows:
in a first aspect, an embodiment of the present application provides a user identification method, where the method includes: marking the user terminal which generates the target service domain name as a terminal to be determined; acquiring a position signaling data set of a plurality of terminals to be determined within a first preset time; clustering the position signaling data set to obtain a plurality of clustering results; and acquiring a target clustering result matched with a preset condition in the plurality of clustering results, and marking the terminal to be determined corresponding to the target clustering result as a service providing user of the target service.
In an optional embodiment, the marking the user terminal that has generated the target service domain name as the terminal to be determined includes: acquiring a domain name list comprising a plurality of service domain names; taking the service domain name matched with the target service in the domain name list as the target service domain name; acquiring a plurality of historical use signaling of the target service; and marking the user terminal which comprises the usage signaling of the target service domain name in the plurality of historical usage signaling as the terminal to be determined.
In an optional embodiment, obtaining a location signaling data set of a plurality of terminals to be determined within a first preset time includes: acquiring a residence point position of the terminal to be determined in the first preset time; the residence time position is position information that the residence time of the terminal to be determined is greater than or equal to a second preset time; acquiring the residence times of the terminal to be determined at the residence point position within a third preset time; the first preset time is longer than the third preset time, and the third preset time is longer than the second preset time; and taking the residence point position of each terminal to be determined and the residence times corresponding to the residence point position as the position signaling data set.
In an optional implementation manner, taking the dwell position of each terminal to be determined and the dwell number corresponding to the dwell position as the position signaling data set includes: performing maximum pooling on the residence point position and the residence times corresponding to the residence point position to obtain residence data of the terminal to be determined in fourth preset time; the first preset time is longer than the fourth preset time, and the fourth preset time is longer than the third preset time; and aggregating the plurality of resident data to obtain the position signaling data set.
In an optional embodiment, clustering the position signaling data set to obtain a plurality of clustering results includes: determining a number of categories of the position signaling data set using elbow rules; clustering the position signaling data set according to the category number to obtain a plurality of clustering results; and the number of the clustering results is the number of the categories.
In a second aspect, an embodiment of the present application provides a user identification apparatus, where the apparatus includes: the marking module is used for marking the user terminal which generates the target service domain name as a terminal to be determined; the acquiring module is used for acquiring a position signaling data set of a plurality of terminals to be determined within a first preset time; the clustering module is used for clustering the position signaling data set to obtain a plurality of clustering results; and the processing module is used for acquiring a target clustering result matched with a preset condition in the plurality of clustering results and marking the terminal to be determined corresponding to the target clustering result as a service providing user of the target service.
In an optional embodiment, the marking module is further configured to obtain a domain name list including a plurality of service domain names; the marking module is also used for taking a service domain name matched with a target service in the domain name list as the target service domain name; the marking module is also used for acquiring a plurality of historical use signaling of the target service; the marking module is further configured to mark the user terminal that includes the usage signaling of the target service domain name in the multiple historical usage signaling as the terminal to be determined.
In an optional embodiment, the obtaining module is further configured to obtain a location of a residence point of the terminal to be determined within the first preset time; the residence time position is position information that the residence time of the terminal to be determined is greater than or equal to a second preset time;
the acquisition module is further configured to acquire the residence time of the terminal to be determined at the residence point position within a third preset time; the first preset time is longer than the third preset time, and the third preset time is longer than the second preset time;
the obtaining module is further configured to use the location of the residence point of each terminal to be determined and the residence times corresponding to the location of the residence point as the location signaling data set.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor and a memory, where the memory stores machine executable instructions that can be executed by the processor, and the processor can execute the machine executable instructions to implement the method described in any one of the foregoing embodiments.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the method of any one of the foregoing embodiments.
Compared with the prior art, the application provides a user identification method, a user identification device, electronic equipment and a computer-readable storage medium, and relates to the field of network data processing. The method comprises the following steps: marking the user terminal which generates the target service domain name as a terminal to be determined; acquiring a position signaling data set of a plurality of terminals to be determined within a first preset time; clustering the position signaling data set to obtain a plurality of clustering results; and acquiring a target clustering result matched with a preset condition in the plurality of clustering results, and marking the terminal to be determined corresponding to the target clustering result as a service providing user of the target service. By acquiring the terminal to be determined, and then acquiring the service providing user through clustering according to the position signaling data set of the terminal to be determined, the defect that the user identification is carried out by simply analyzing the user signaling domain name is overcome, and the service providing user is accurately identified.
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 schematic block diagram of an electronic device according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a user identification method according to an embodiment of the present application;
fig. 3 is a schematic flowchart of another user identification method according to an embodiment of the present application;
fig. 4 is a schematic flowchart of another user identification method according to an embodiment of the present application;
fig. 5 is a schematic flowchart of another user identification method according to an embodiment of the present application;
fig. 6 is a line drawing of a location signaling data set according to an embodiment of the present application;
fig. 7 is a line drawing of another set of location signaling data provided by an embodiment of the present application;
fig. 8 is a schematic diagram of a clustering result provided in an embodiment of the present application;
fig. 9 is a block diagram illustrating a user identification device according to 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 some embodiments of the present application, but not all 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 given herein without making any creative effort, shall fall within the protection scope of the present application. Furthermore, the appearances of the terms "first," "second," and the like, if any, are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance. It should be noted that the features of the embodiments of the present application may be combined with each other without conflict.
In order to solve at least the drawbacks of the background art, an embodiment of the present application provides a user identification method applied to an electronic device, please refer to fig. 1, where fig. 1 is a block diagram of an electronic device provided in an embodiment of the present application. The electronic device 20 comprises a memory 21, a processor 22 and a communication interface 23. The memory 21, processor 22 and communication interface 23 are electrically connected to each other, directly or indirectly, to enable transmission or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 21 may be used for storing software programs and modules, such as program instructions/modules corresponding to the user identification method provided in the embodiment of the present application, and the processor 22 executes the software programs and modules stored in the memory 21, so as to execute various functional applications and data processing. The communication interface 23 may be used for communication of signaling or data with other node devices. The electronic device 20 may have a plurality of communication interfaces 23 in this application.
The Memory 21 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The processor 22 may be an integrated circuit chip having signal processing capabilities. The Processor may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), etc.; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc.
The electronic device 20 may implement any of the user identification methods provided herein. The electronic device 20 may be, but is not limited to, a Mobile phone, a tablet Computer, a wearable device, an in-vehicle device, an Augmented Reality (AR)/Virtual Reality (VR) device, a notebook Computer, an Ultra-Mobile Personal Computer (UMPC), a netbook, a Personal Digital Assistant (PDA), a server, and the like, and the embodiment of the present application does not set any limitation to the specific type of the electronic device.
To solve at least the deficiencies of the background art, an embodiment of the present application provides a user identification method applied to an electronic device on the basis of the electronic device 20 shown in fig. 1, please refer to fig. 2, and fig. 2 is a flowchart illustrating the user identification method provided by the embodiment of the present application, where the user identification method may include the following steps:
and S31, marking the user terminal which generates the target service domain name as the terminal to be determined.
For example, the target service domain name may be, but is not limited to, "meituan," "eleme," "ali," and the like. The user terminal may be, but is not limited to, a mobile phone, a tablet computer, a notebook computer, a wearable device, a vehicle-mounted device, an AR/VR device, a notebook computer, a UMPC, a netbook, a PDA, etc.
S32, acquiring a plurality of position signaling data sets of the terminal to be determined within a first preset time.
The first preset time may be 30 days, 15 days, etc., and the position signaling data set may include, but is not limited to, information of a staying point (position coordinates of a staying) of the terminal to be determined, a staying number of the terminal at the staying point to be determined, a staying time, etc.
And S33, clustering the position signaling data set to obtain a plurality of clustering results.
It should be understood that a variety of clustering methods may be used for clustering, such as unsupervised K-means clustering. In one possible case, the above S33 may include: determining the number of categories of the position signaling data set by using elbow rules; and clustering the position signaling data set according to the category number to obtain a plurality of clustering results, wherein the number of the clustering results is the category number of the clustering.
It should be understood that the principle of the elbow rule is a cost function, which is the sum of the class distortion degrees, the distortion degree of each class being equal to the sum of the squared distances of the positions of each variable point from its class center, the distortion degree of a class being smaller if the members within a class are more compact to each other, and conversely, the distortion degree of a class being larger if the members within a class are more dispersed to each other. If the unsupervised K-means cluster is selected for clustering, the K value of the cluster is determined by using an elbow rule, for example, K is 4.
And S34, obtaining a target clustering result matched with the preset conditions from the plurality of clustering results, and marking the terminal to be determined corresponding to the target clustering result as a service providing user of the target service.
The preset condition can be adjusted according to different target services, and the service providing users also change along with the change of the target services. For example, if the target service is a takeaway meal delivery service, the preset condition may be that the positions of 12 am and 6 pm points in the peak of a meal are frequently changed, and the service providing user may be a takeaway rider; if the target service is a network car booking service, the preset condition can be that the positions of 17 pm and 22 pm in the peak period of the car usage frequently change, and the service providing user can be a network car booking driver.
It can be understood that the service providing users are obtained by acquiring the terminal to be determined, clustering according to the position signaling data set of the terminal to be determined, so that the defect that the user identification is carried out by simply analyzing the user signaling domain name is overcome, and the service providing users are accurately identified.
In an alternative embodiment, in order to determine a terminal to be determined, a possible implementation is provided on the basis of fig. 2, please refer to fig. 3, and fig. 3 is a flowchart illustrating another user identification method provided in an embodiment of the present application, where the foregoing S31 may include:
s311, a domain name list including a plurality of service domain names is obtained.
For example, service domain names of a plurality of services may be collected and stored, and for the same service, since different providers and different APPs are used, they may all be included in the domain name list.
S312, the service domain name matched with the target service in the domain name list is used as the target service domain name.
For example, if the target service is a takeaway service, the service domain name that matches the takeaway service may include: "meituan", "eleme", and the like; if the target service is a network car booking service, the service domain name matched with the network car booking service may include: "didi", "uber", and the like.
S313, a plurality of historical usage signaling of the target service is obtained.
The historical usage signaling may be a service domain name signaling generated by the user terminal for the target service, for example, if the target service is a takeaway service and the user terminal is a mobile phone, the historical usage signaling may be an APP usage signaling generated by the user terminal within a certain time (e.g., 7 days).
S314, marking the user terminal of the usage signaling of the target service domain name in the plurality of historical usage signaling as the terminal to be determined.
For example, capturing an APP usage signaling generated by a user using a mobile phone by a manufacturer (such as an operator provided by a takeaway APP), analyzing a domain name therein, and screening a crowd generating the takeaway domain name signaling, wherein the crowd is marked as a suspected crowd of a takeaway rider (a terminal to be determined).
In an alternative embodiment, in order to acquire the location signaling data set, on the basis of fig. 2, a possible implementation is given, please refer to fig. 4, where fig. 4 is a flowchart illustrating another user identification method provided in this embodiment, where the above S32 may include:
s321, obtaining a residence point position of the terminal to be determined in a first preset time.
The residence time is the position information of the terminal to be determined, wherein the residence time is greater than or equal to the second preset time. For example, the second preset time may be 1 minute, 3 minutes, etc.; the location information may be determined by a navigation system of the user terminal and stored in the form of geographical coordinates.
And S322, obtaining the residence times of the terminal to be determined at the residence point position within the third preset time.
The first preset time is longer than the third preset time, and the third preset time is longer than the second preset time. For example, the first preset time may be 30 days, the second preset time may be 3 minutes, and the third preset time may be 1 hour.
And S323, taking the residence point position of each terminal to be determined and the residence times corresponding to the residence point position as a position signaling data set.
For example, the location of the residence point of each terminal to be determined and the residence times corresponding to the location of the residence point may be stored and retained in a table form (e.g., a line graph).
It should be understood that, by acquiring the residence time position of the terminal to be determined in the first preset time and the residence time number of the terminal to be determined in the residence time position in the third preset time, the position signaling data of the terminal to be determined may be acquired, so that the electronic device clusters the position signaling data, and uses the terminal to be determined that meets the preset conditions as a service providing user.
In an alternative embodiment, in order to further acquire the location signaling data, a possible implementation is provided on the basis of fig. 4, please refer to fig. 5, where fig. 5 is a flowchart of another user identification method provided in this embodiment, and the above S323 may include:
and S323a, performing maximum pooling on the residence point position and the residence times corresponding to the residence point position to obtain residence data of the terminal to be determined in the fourth preset time.
The first preset time is longer than the fourth preset time, and the fourth preset time is longer than the third preset time. For example, the first preset time may be 30 days, the fourth preset time may be 24 hours, and the third preset time may be 1 hour.
S323b, aggregating the plurality of resident data to obtain a location signaling data set.
It should be understood that the time of aggregation may be stored in the form of a table or graph.
In order to facilitate understanding of the user identification method provided in the foregoing embodiment, a possible specific embodiment is provided in the embodiments of the present application, and taking an example in which a target service is a takeout service and a user terminal is a mobile phone, the user identification method is divided into three stages:
in the first stage, suspected people (terminals to be determined) of takeaway riders are searched through APP use conditions of the mobile phone: firstly, establishing a domain name list with takeout services, and marking as a takeout domain name (target service domain name); secondly, capturing APP (application) use signaling generated by an operator user using a mobile phone, analyzing a domain name in the APP use signaling, screening people who generate takeout domain name signaling, and marking the people as suspected takeout rider people (terminals to be determined).
In the second stage, a standardized data set (position signaling data set) of suspected people is established: firstly, selecting resident point data in all position signaling data of each suspected user (terminal to be determined) in a target time window (first preset time), and dividing the data according to the degree of day.
In the position signaling data set, each user should have a dwell position at each moment, and the data of each user is cut again according to the dwell position. And counting the times of residence of the user in the residence point in the hour according to different residence points. For example, if a small plum goes to sleep at 1 am every day of 6 months in 2020, the number of times the small plum is at one point for a residential building where it lives is 30 times. Referring to fig. 6, fig. 6 is a line graph of a position signaling data set provided in the embodiment of the present application, in which an abscissa is a distribution of "0 to 24" hours according to time, an ordinate is a number of times (0 to 30 times) of staying at a dwell point, and different broken lines represent sub data sets of different dwell points.
The position signaling data set shown in fig. 6 is subjected to maximum pooling, for example, for each hour, a maximum ordinate value under the abscissa is selected to obtain a vector with a length of 24, and the position signaling data set shown in fig. 6 is subjected to maximum pooling, so that the obtained result is fig. 7, fig. 7 is a line graph of another position signaling data set provided in the embodiment of the present application, the abscissa is distributed for "0 to 24" hours according to time, and the ordinate is the number of times (0 to 30 times) of staying at a dwell point. It will be appreciated that in the manner of figures 6 and 7, a standardized data set of length 24, i.e. a position signalling data set for a plurality of terminals to be determined, may be generated for the entire suspected group of takeaway riders.
And a third stage, clustering and generating corresponding labels: using K-Means clustering to the data set obtained in the second stage, and obtaining an optimal K value by utilizing an elbow rule; for example, for data of a month in first city, the optimal K is selected to be 4, the class centers of the four subclasses are shown in fig. 8, fig. 8 is a schematic diagram of a clustering result provided in the embodiment of the present application, the abscissa is distribution for "0 to 24" hours according to time, and the ordinate is the number of times (0 to 30 times) of staying at a residence point. For the curves 1 to 4 shown in fig. 8, since the dependence of the curve 3 on a single working place is weak in the normal working time period, and the characteristic is that 12 am and 6 pm in the peak dining period are most obvious, the terminal to be determined corresponding to the curve 3 is a real takeout rider group (service providing user), and then the terminal to be determined corresponding to the curve 3 is marked with a takeout rider label.
In order to implement the user identification method provided in any one of the above embodiments, an embodiment of the present application provides a user identification device, please refer to fig. 9, where fig. 9 is a schematic block diagram of a user identification device provided in an embodiment of the present application, and the user identification device includes: a labeling module 41, an acquisition module 42, a clustering module 43, and a processing module 44.
The marking module 41 is configured to mark the user terminal that has generated the target service domain name as a terminal to be determined; the obtaining module 42 is configured to obtain a position signaling data set of a plurality of terminals to be determined within a first preset time; the clustering module 43 is configured to cluster the position signaling data sets to obtain a plurality of clustering results; the processing module 44 is configured to obtain a target clustering result matching with a preset condition from the multiple clustering results, and mark a terminal to be determined corresponding to the target clustering result as a service providing user of a target service.
In an alternative embodiment, the marking module 41 is further configured to obtain a domain name list including a plurality of service domain names; the marking module 41 is further configured to use a service domain name matched with the target service in the domain name list as the target service domain name; the marking module 41 is further configured to obtain a plurality of historical usage signaling of the target service; the marking module 41 is further configured to mark the user terminal including the usage signaling of the target service domain name in the multiple historical usage signaling as the terminal to be determined.
In an optional embodiment, the obtaining module 42 is further configured to obtain a location of a residence point of the terminal to be determined within a first preset time; the residence point position is position information that the residence time of the terminal to be determined is greater than or equal to a second preset time; the obtaining module 42 is further configured to obtain the residence time of the terminal to be determined at the residence point position within the third preset time; the first preset time is longer than the third preset time, and the third preset time is longer than the second preset time; the obtaining module 42 is further configured to use the location of the residence point of each terminal to be determined and the residence times corresponding to the location of the residence point as a location signaling data set.
It should be understood that the labeling module 41, the obtaining module 42, the clustering module 43 and the processing module 44 may cooperatively implement the user identification method and possible sub-steps thereof provided in any of the above embodiments. By acquiring the terminal to be determined, and then acquiring the service providing user through clustering according to the position signaling data set of the terminal to be determined, the defect that the user identification is carried out by simply analyzing the user signaling domain name is overcome, and the service providing user is accurately identified.
The embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the user identification method provided in any one of the above embodiments. The computer readable storage medium may be, but is not limited to, various media that can store program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a PROM, an EPROM, an EEPROM, a magnetic or optical disk, etc.
In summary, the present application provides a user identification method, an apparatus, an electronic device, and a computer-readable storage medium, and relates to the field of network data processing. The method comprises the following steps: marking the user terminal which generates the target service domain name as a terminal to be determined; acquiring a position signaling data set of a plurality of terminals to be determined within a first preset time; clustering the position signaling data set to obtain a plurality of clustering results; and acquiring a target clustering result matched with a preset condition in the plurality of clustering results, and marking the terminal to be determined corresponding to the target clustering result as a service providing user of the target service. By acquiring the terminal to be determined, and then acquiring the service providing user through clustering according to the position signaling data set of the terminal to be determined, the defect that the user identification is carried out by simply analyzing the user signaling domain name is overcome, and the service providing user is accurately identified.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within 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 (10)
1. A method for identifying a user, the method comprising:
marking the user terminal which generates the target service domain name as a terminal to be determined;
acquiring a position signaling data set of a plurality of terminals to be determined within a first preset time;
clustering the position signaling data set to obtain a plurality of clustering results;
and acquiring a target clustering result matched with a preset condition in the plurality of clustering results, and marking the terminal to be determined corresponding to the target clustering result as a service providing user of the target service.
2. The method of claim 1, wherein marking the ue that has generated the target service domain name as the ue to be determined comprises:
acquiring a domain name list comprising a plurality of service domain names;
taking the service domain name matched with the target service in the domain name list as the target service domain name;
acquiring a plurality of historical use signaling of the target service;
and marking the user terminal which comprises the usage signaling of the target service domain name in the plurality of historical usage signaling as the terminal to be determined.
3. The method of claim 1, wherein obtaining a plurality of location signaling data sets of the terminal to be determined within a first preset time comprises:
acquiring a residence point position of the terminal to be determined in the first preset time; the residence time position is position information that the residence time of the terminal to be determined is greater than or equal to a second preset time;
acquiring the residence times of the terminal to be determined at the residence point position within a third preset time; the first preset time is longer than the third preset time, and the third preset time is longer than the second preset time;
and taking the residence point position of each terminal to be determined and the residence times corresponding to the residence point position as the position signaling data set.
4. The method according to claim 3, wherein the step of using the dwell position and the dwell number corresponding to the dwell position of each terminal to be determined as the position signaling data set comprises:
performing maximum pooling on the residence point position and the residence times corresponding to the residence point position to obtain residence data of the terminal to be determined in fourth preset time; the first preset time is longer than the fourth preset time, and the fourth preset time is longer than the third preset time;
and aggregating the plurality of resident data to obtain the position signaling data set.
5. The method of claim 1, wherein clustering the set of location signaling data to obtain a plurality of clustering results comprises:
determining a number of categories of the position signaling data set using elbow rules;
clustering the position signaling data set according to the category number to obtain a plurality of clustering results; and the number of the clustering results is the number of the categories.
6. A user identification device, the device comprising:
the marking module is used for marking the user terminal which generates the target service domain name as a terminal to be determined;
the acquiring module is used for acquiring a position signaling data set of a plurality of terminals to be determined within a first preset time;
the clustering module is used for clustering the position signaling data set to obtain a plurality of clustering results;
and the processing module is used for acquiring a target clustering result matched with a preset condition in the plurality of clustering results and marking the terminal to be determined corresponding to the target clustering result as a service providing user of the target service.
7. The apparatus of claim 6, wherein the tagging module is further configured to obtain a domain name list comprising a plurality of service domain names;
the marking module is also used for taking a service domain name matched with a target service in the domain name list as the target service domain name;
the marking module is also used for acquiring a plurality of historical use signaling of the target service;
the marking module is further configured to mark the user terminal that includes the usage signaling of the target service domain name in the multiple historical usage signaling as the terminal to be determined.
8. The apparatus according to claim 6, wherein the obtaining module is further configured to obtain a location of a residence point of the terminal to be determined within the first preset time; the residence time position is position information that the residence time of the terminal to be determined is greater than or equal to a second preset time;
the acquisition module is further configured to acquire the residence time of the terminal to be determined at the residence point position within a third preset time; the first preset time is longer than the third preset time, and the third preset time is longer than the second preset time;
the obtaining module is further configured to use the location of the residence point of each terminal to be determined and the residence times corresponding to the location of the residence point as the location signaling data set.
9. An electronic device comprising a processor and a memory, the memory storing machine executable instructions executable by the processor to implement the method of any one of claims 1-5.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010971485.4A CN112101999A (en) | 2020-09-16 | 2020-09-16 | User identification method, device, electronic equipment and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010971485.4A CN112101999A (en) | 2020-09-16 | 2020-09-16 | User identification method, device, electronic equipment and computer readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112101999A true CN112101999A (en) | 2020-12-18 |
Family
ID=73759128
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010971485.4A Pending CN112101999A (en) | 2020-09-16 | 2020-09-16 | User identification method, device, electronic equipment and computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112101999A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113486984A (en) * | 2021-08-02 | 2021-10-08 | 智慧足迹数据科技有限公司 | Method and related device for identifying user type based on signaling data |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106096631A (en) * | 2016-06-02 | 2016-11-09 | 上海世脉信息科技有限公司 | A kind of recurrent population's Classification and Identification based on the big data of mobile phone analyze method |
| US20170105099A1 (en) * | 2015-10-13 | 2017-04-13 | Cisco Technology, Inc. | Leveraging location data from mobile devices for user classification |
| CN108650632A (en) * | 2018-04-28 | 2018-10-12 | 广州市交通规划研究院 | It is a kind of based on duty live correspondence and when space kernel clustering stationary point judgment method |
| CN111274338A (en) * | 2020-01-08 | 2020-06-12 | 重庆邮电大学 | Pre-outbound user identification method based on mobile big data |
-
2020
- 2020-09-16 CN CN202010971485.4A patent/CN112101999A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170105099A1 (en) * | 2015-10-13 | 2017-04-13 | Cisco Technology, Inc. | Leveraging location data from mobile devices for user classification |
| CN106096631A (en) * | 2016-06-02 | 2016-11-09 | 上海世脉信息科技有限公司 | A kind of recurrent population's Classification and Identification based on the big data of mobile phone analyze method |
| CN108650632A (en) * | 2018-04-28 | 2018-10-12 | 广州市交通规划研究院 | It is a kind of based on duty live correspondence and when space kernel clustering stationary point judgment method |
| CN111274338A (en) * | 2020-01-08 | 2020-06-12 | 重庆邮电大学 | Pre-outbound user identification method based on mobile big data |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113486984A (en) * | 2021-08-02 | 2021-10-08 | 智慧足迹数据科技有限公司 | Method and related device for identifying user type based on signaling data |
| CN113486984B (en) * | 2021-08-02 | 2022-05-17 | 智慧足迹数据科技有限公司 | Method and related device for identifying user type based on signaling data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107818133B (en) | Residential area network capacity analysis method and system based on big data | |
| CN104573042B (en) | The determination method and device of POI | |
| CN105824840B (en) | A kind of method and device for area label management | |
| CN110888866B (en) | Data expansion method and device, data processing equipment and storage medium | |
| CN111597279B (en) | Information prediction method based on deep learning and related equipment | |
| CN111210201B (en) | Occupational label establishing method and device, electronic equipment and storage medium | |
| CN109447689A (en) | Consumer's risk portrait generation method, device, equipment and readable storage medium storing program for executing | |
| CN106027693B (en) | IP address-based localization method and device | |
| CN112182391A (en) | User portrait drawing method and device | |
| CN112597168A (en) | Processing method, device and platform of multi-source customer data and storage medium | |
| CN110765220A (en) | Thermodynamic diagram-based geographic position determining method and electronic equipment | |
| CN111209487B (en) | User data analysis method, server, and computer-readable storage medium | |
| CN113537806A (en) | Abnormal user identification method and device, electronic equipment and readable storage medium | |
| CN110807546A (en) | Community grid population change early warning method and system | |
| CN111242723B (en) | User child and child condition judgment method, server and computer readable storage medium | |
| CN110399361B (en) | Information processing method and information processing device | |
| CN114238375B (en) | Index query method and device, electronic equipment and storage medium | |
| CN112101999A (en) | User identification method, device, electronic equipment and computer readable storage medium | |
| CN111415168B (en) | Transaction alarm method and device | |
| CN111275071B (en) | Prediction model training method, prediction device and electronic equipment | |
| CN113449002A (en) | Vehicle recommendation method and device, electronic equipment and storage medium | |
| CN112235714A (en) | POI positioning method and device based on artificial intelligence, computer equipment and medium | |
| CN113327336B (en) | Method and device for identifying people-vehicle relationship and electronic equipment | |
| CN109885710B (en) | User image depicting method based on differential evolution algorithm and server | |
| CN114723488A (en) | Course recommendation method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201218 |
|
| RJ01 | Rejection of invention patent application after publication |