Method, apparatus and device for user identification, and computer storage medium
By using clustering and search algorithms within the target area to identify resident users and other users, the problems of inaccurate cell boundary delineation and inaccurate user location are solved, achieving higher user identification accuracy.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- XIANGYANG BRANCH CHINA MOBILE GRP HUBEI CO LTD
- Filing Date
- 2021-04-06
- Publication Date
- 2026-05-15
AI Technical Summary
In existing technologies, inaccurate cell boundary delineation and inaccurate user location information make it impossible to accurately match and identify users within the cell, resulting in inaccurate user identification.
By acquiring user test report data in the target area, using preset clustering algorithms such as DBSCAN density clustering algorithm, resident users are identified. Based on the latitude and longitude data of resident users and preset extension parameters, the target area is determined. A binary search algorithm is used to identify other users, thereby improving the accuracy of user identification.
It improves the accuracy of user identification within the target area, avoids the problem of inaccurate user identification and matching caused by inaccurate user positioning or cell delineation, and enhances the precision of user identification.
Smart Images
Figure CN115186724B_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of communication technology, and in particular relates to a method, apparatus, device and computer storage medium for user identification. Background Technology
[0002] Typically, data mining and analysis of mobile users can help operators with business management and user maintenance.
[0003] In related technologies, there are still some problems when locating users in various communities or units, such as inaccurate community boundary delineation or inaccurate user location information, which makes it impossible to accurately match and identify users within the community. Summary of the Invention
[0004] This application provides a method, apparatus, device, and computer storage medium for user identification, which can improve the accuracy of user identification in a target area.
[0005] In a first aspect, embodiments of this application provide a user identification method, the method comprising:
[0006] Obtain user measurement report (MR) data for the target area to be identified within a preset time period, wherein the user MR data includes the user's latitude and longitude data;
[0007] Using a preset clustering algorithm and the latitude and longitude data of the user, the first user in the target area is determined, and the first user is a resident user in the target area;
[0008] Based on the latitude and longitude data of the first user and preset extension parameters, a first region within the target area is determined;
[0009] Based on the latitude and longitude data of users in the first region and a preset distance threshold, a preset search algorithm is used to determine the second user in the first region;
[0010] The first user and the second user are designated as target users in the target region.
[0011] Optionally, determining the first user in the target area using a preset clustering algorithm and the user's latitude and longitude data includes:
[0012] The latitude and longitude data of the user are clustered using a preset clustering algorithm to obtain the latitude and longitude clusters corresponding to the latitude and longitude data;
[0013] The users corresponding to the clustered latitude and longitude clusters are designated as the first users in the target region.
[0014] Optionally, the step of clustering the user's latitude and longitude data using a preset clustering algorithm to obtain the clustered latitude and longitude clusters corresponding to the latitude and longitude data includes:
[0015] Based on the user's latitude and longitude data and preset density parameters, a preset density clustering algorithm is used to perform iterative clustering.
[0016] When the clustering result is a single latitude and longitude cluster, the single latitude and longitude cluster is taken as the latitude and longitude cluster corresponding to the latitude and longitude data.
[0017] Optionally, the preset density parameters include a neighborhood radius parameter and a neighborhood density threshold parameter.
[0018] Optionally, determining the first region within the target region based on the latitude and longitude data of the first user and preset extension parameters includes:
[0019] Based on the latitude and longitude data of the first user, the latitude and longitude data of the center location of the target area are calculated;
[0020] Based on the latitude and longitude data of the center position, the area determined by extending the preset extension parameter length in both the longitude and latitude directions, with the position corresponding to the latitude and longitude data of the center position as the center, is the first area.
[0021] Optionally, determining the second user in the first area based on the latitude and longitude data of the user in the first area and a preset distance threshold using a preset search algorithm includes:
[0022] Obtain the latitude and longitude data of a third user in the first region, wherein the third user is a user to be identified other than the first user in the first region;
[0023] Based on the latitude and longitude data of the third user, the latitude and longitude data of the first user, and a preset distance threshold, a binary search algorithm is used to determine the second user in the first region.
[0024] Optionally, based on the latitude and longitude data of the third user, the latitude and longitude data of the first user, and a preset distance threshold, a binary search algorithm is used to determine the second user in the first region, including:
[0025] Based on the latitude and longitude data of the third user and the latitude and longitude data of the first user, the distance between the third user and each of the first users is calculated using a binary search algorithm.
[0026] When the distance between the third user and any of the first users is less than a preset distance threshold, the third user is determined to be the second user.
[0027] Optionally, the preset clustering algorithm is the DBSCAN density clustering algorithm.
[0028] Secondly, embodiments of this application provide a user identification device, the device comprising:
[0029] The acquisition module is used to acquire user test report (MR) data of the target area to be identified within a preset time period, wherein the user MR data includes the latitude and longitude data of the user;
[0030] The first determining module is used to determine the first user in the target area using a preset clustering algorithm and the latitude and longitude data of the user, wherein the first user is a resident user in the target area;
[0031] The second determining module is used to determine the first region in the target region based on the latitude and longitude data of the first user and preset extension parameters;
[0032] The third determining module is used to determine the second user in the first area based on the latitude and longitude data of the user in the first area and a preset distance threshold, using a preset search algorithm.
[0033] The setting module is used to designate the first user and the second user as target users of the target area.
[0034] Thirdly, embodiments of this application provide a user identification device, the device comprising:
[0035] Processor and memory storing computer program instructions;
[0036] When the processor executes the computer program instructions, it implements the user identification method as described in the first aspect and optionally in the first aspect.
[0037] Fourthly, embodiments of this application provide a computer storage medium storing computer program instructions, which, when executed by a processor, implement the user identification method as described in the first aspect and optionally in the first aspect.
[0038] The user identification method, apparatus, device, and computer storage medium of this application embodiment can use a preset clustering algorithm to cluster user MR data within a predetermined time period in a target area, and determine the first user who is a resident user in the target area. The target area can be an area such as a residential community. Based on the determined first user, other users who can be target users are searched in the target area to obtain the corresponding target users in the target area. Therefore, by performing cluster analysis on users in the target area to identify the actual resident users in the target area, problems such as inaccurate matching between users and their communities due to inaccurate user positioning or community delineation can be avoided, thereby improving the accuracy of user identification in the target area. Attached Figure Description
[0039] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0040] Figure 1 This is a flowchart illustrating a user identification method provided in some embodiments of this application;
[0041] Figure 2 This is a flowchart illustrating a user identification method provided in some embodiments of this application;
[0042] Figure 3 These are schematic diagrams illustrating user identification in application scenarios provided by some embodiments of this application;
[0043] Figure 4 This is a schematic diagram of the structure of a user identification device provided in some embodiments of this application;
[0044] Figure 5 This is a schematic diagram of the hardware structure of a user identification device provided in some embodiments of this application. Detailed Implementation
[0045] The features and exemplary embodiments of various aspects of this application will now be described in detail. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only configured to explain this application and are not configured to limit this application. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples of this application.
[0046] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.
[0047] Typically, existing spatial-level technologies for identifying target users measure the boundary coordinates of each user's residential area or workplace using instruments. Then, they locate target users within the measured boundary range to facilitate targeted services. The physical coverage area of a user's residential area is characterized by manually measuring the coordinates of several cell boundary vertices. The coordinates of the user's residence are represented by the latitude and longitude coordinates of the communication base station. By finding the cell vertex closest to the base station in terms of latitude and longitude coordinates, all users under the base station are assigned to the cell containing that vertex, thereby identifying the target users residing within that cell.
[0048] However, the relevant technologies still have some problems. For example, the boundary coordinates of the community or unit are uploaded manually, and human factors will lead to a large amount of false data and error data; the latitude and longitude coordinates of the residents in the community may be incorrect, which may cause residents to be unable to be classified into the community where they actually live, etc.
[0049] To address the problems of the prior art, embodiments of this application provide a method, apparatus, device, and computer storage medium for user identification, which can improve the accuracy of user identification in a target area.
[0050] The following description, with reference to the accompanying drawings, describes a user identification method, apparatus, device, and computer storage medium according to embodiments of this application. It should be noted that these embodiments are not intended to limit the scope of this application.
[0051] The user identification method provided in the embodiments of this application will be described below.
[0052] Figure 1 This is a flowchart illustrating a user identification method provided in some embodiments of this application. For example... Figure 1 As shown in the embodiments of this application, the user identification method may include the following steps:
[0053] S101: Obtain user MR data for the target area to be identified within a preset time period.
[0054] The target area can include the user's residential community, workplace, or other areas where the user frequently resides. The preset time period can include daytime working hours (9:00 AM to 6:00 PM), nighttime hours (12:00 AM to 6:00 AM), etc. User MR data can include the user's latitude and longitude data.
[0055] For example, the target area to be identified is a residential community with a specific address, the user is a user whose registered residential address is in that community when registering for the service, and the preset time period is nighttime. The MR data for users whose registered residential address is in that community during the nighttime period is obtained.
[0056] Based on the acquired user MR data, the user's latitude and longitude data can be determined. Specifically, fingerprint feature matching is performed on the acquired MR data in an established feature fingerprint database to obtain the user's latitude and longitude coordinates, which is the user's latitude and longitude data. The generation time of this user MR data can be used to identify the time period to which the user's latitude and longitude data belongs.
[0057] S102: Using a preset clustering algorithm and the user's latitude and longitude data, determine the first user in the target area.
[0058] The first user can be a resident user in the target area. By clustering the acquired user latitude and longitude data, resident users in the target area can be obtained. Therefore, users whose registered address is in the target area but who do not actually reside there can be eliminated.
[0059] In some embodiments of this application, the preset clustering algorithm may include a density clustering algorithm. For example, the preset clustering algorithm may be the DBSCAN density clustering algorithm. It is understood that the preset clustering algorithm may include other clustering algorithms, and the corresponding clustering algorithm can be selected according to the actual application requirements.
[0060] S103: Determine the first region in the target area based on the latitude and longitude data of the first user and the preset extension parameters.
[0061] S104: Based on the latitude and longitude data of the users in the first region and the preset distance threshold, use the preset search algorithm to determine the second user in the first region.
[0062] S105: Designate the first user and the second user as target users in the target area.
[0063] The first user can be a resident user in the target area, and a first area related to the first user can be defined within the target area. Then, within the first area, a preset search algorithm is used to filter out the second user. The second user can include users in the first area who meet a preset distance condition from the first user. The second user can be a resident user in the target area other than the first user. Finally, the first user and the second user can be identified as the target users in the target area, so that relevant business services can be provided to the target users in the future. For example, the target users in the target area can be all residents in the community.
[0064] In summary, the user identification method of this application embodiment can utilize a preset clustering algorithm to cluster user MR data within a predetermined time period in a target area, identifying a first user who is a resident user in the target area, such as a residential community. Then, based on the identified first user, other users in the target area who can also be target users are searched to obtain the corresponding target users in the target area. Therefore, by performing clustering analysis on users within the target area to identify the actual resident users in the target area, problems such as inaccurate matching between users and their respective communities due to inaccurate user location or community delineation can be avoided, thus improving the accuracy of user identification in the target area.
[0065] To more clearly illustrate the user identification method of this application, in some embodiments of this application, such as... Figure 2 As shown, Figure 2 This is a flowchart illustrating a user identification method provided in some embodiments of this application. The user identification method can also be implemented using the following steps:
[0066] S201: Obtain user MR data for the target area to be identified within a preset time period.
[0067] The specific implementation process of this step is the same as that of S101, and will not be repeated here.
[0068] S202: Use a preset clustering algorithm to cluster the user's latitude and longitude data to obtain the corresponding latitude and longitude clusters.
[0069] First, based on the user's latitude and longitude data and preset density parameters, a preset density clustering algorithm is used to perform iterative clustering. Then, when the clustering result is a single latitude and longitude cluster, this single latitude and longitude cluster is taken as the latitude and longitude cluster corresponding to the latitude and longitude data.
[0070] S203: The users corresponding to the clustered latitude and longitude clusters are designated as the first users in the target area.
[0071] In some embodiments of this application, the preset density parameters may include a domain radius parameter and a domain density threshold parameter. The domain density threshold parameter may be the minimum number of user latitude and longitude coordinates within the domain radius parameter.
[0072] For example, the preset density clustering algorithm can be the DBSCAN density clustering algorithm. Based on the DBSCAN density clustering algorithm, cluster analysis is performed on the latitude and longitude data of users in the target area to output the latitude and longitude of resident users within the target area, thus determining the first user in the target area. The specific algorithm process may include:
[0073] Input data: MR latitude and longitude data of users belonging to the target area.
[0074] Algorithm input parameters: neighborhood radius parameter (eps), neighborhood density threshold parameter (min_samples).
[0075] Output: Latitude and longitude of the first user in the target area, and the identified resident users in the target area.
[0076] For example, first, set the initial values for the neighborhood radius parameter `eps` and the neighborhood density threshold parameter `min_samples`. Depending on the accuracy requirements, the value of `eps` can range from 0.0001 to 0.01; based on the actual number of residents handling business in the community, the initial value of `min_samples` ranges from 2 to 500. Keeping `eps` fixed at 0.0001 (approximately 100 meters), start clustering the model with `min_samples` from 200. If the clustering result of the user latitude and longitude data for the target area is multiple clusters, increment `min_samples` by 1. If the clustering result of the user latitude and longitude data for the target area is empty, decrement `min_samples` by 1, and re-cluster. This process is repeated until the clustering result is a single cluster, at which point the loop ends. The output clustering result then represents the latitude and longitude of the core users residing in the target area, and the set of circles representing the neighborhood radii of these users represents the actual geographical range of the target area.
[0077] Here, the DBSCAN density clustering algorithm is a density-based noise-adaptive spatial clustering algorithm, and it is a representative density-based clustering algorithm. DBSCAN can divide sufficiently high-density core points into clusters and can discover clusters of arbitrary shapes in noisy spatial databases. The core idea of this algorithm is to start from a core point and continuously expand to a density-accessible region, thereby obtaining a maximum region containing both core and boundary points, where any two points are density-connected. This algorithm can discover clusters of arbitrary shapes, and the clustering results are almost independent of the node traversal order, effectively removing noisy points.
[0078] S204: Calculate the latitude and longitude data of the center location of the target area based on the latitude and longitude data of the first user.
[0079] S205: Based on the latitude and longitude data of the center position, take the position corresponding to the latitude and longitude data of the center position as the center, and extend the area determined by the preset extension parameter length in the longitude direction and latitude direction respectively as the first area.
[0080] S206: Obtain the latitude and longitude data of the third user in the first region.
[0081] The third user can be a user to be identified other than the first user in the first region.
[0082] S207: Based on the latitude and longitude data of the third user, the latitude and longitude data of the first user, and a preset distance threshold, a binary search algorithm is used to determine the second user in the first region.
[0083] In some embodiments of this application, firstly, based on the latitude and longitude data of the third user and the first user, a binary search algorithm is used to calculate the distance between the third user and each of the first users. Then, each calculated distance is compared with a preset distance threshold. When the distance between the third user and any of the first users is less than the preset distance threshold, the third user is determined to be the second user.
[0084] In some embodiments of this application, the preset extension parameter can be set according to actual needs. For example, the preset extension parameter can be 0.015. The preset distance threshold can be the domain radius parameter value.
[0085] In some embodiments of this application, for each target area, among the first users already calculated for that target area, the latitude and longitude data of the center location point are calculated based on the longitude and latitude of the first user's MR data. Then, based on the center location point, a first area is delineated by extending 0.015 latitude and longitude outward in both directions. Users other than the first users within this area are identified as users to be identified, i.e., third users.
[0086] Using a binary search algorithm, the distance between the latitude and longitude data of each third user and the latitude and longitude data of each first user is calculated sequentially. When the distance between a third user and any first user is less than a preset distance threshold, i.e., less than the neighborhood radius parameter eps, the third user is marked as another resident in the target area, i.e., a second user. Second users and first users together constitute all residents of the target area. Figure 3 As shown, Figure 3 This is a schematic diagram illustrating user identification in application scenarios provided by some embodiments of this application. Light-colored dots can represent the first user, circles can represent target areas, and dark-colored dots inside the circles can represent the second user.
[0087] S208: Designate the first user and the second user as target users in the target area.
[0088] In summary, the user identification method of this application embodiment can utilize a preset clustering algorithm, such as the DBSCAN density clustering algorithm, to cluster user latitude and longitude data within a predetermined time period in a target area, thereby identifying the first user as a resident user in the target area. Based on the identified first user, other users in the target area that can be considered target users are then searched to obtain the corresponding target users in the target area. Therefore, by performing cluster analysis on users within the target area to identify the actual resident users in the target area, problems such as inaccurate matching between users and their respective cells due to inaccurate user positioning or cell delineation can be avoided, thus improving the accuracy of user identification in the target area.
[0089] The user identification method in this application avoids the errors and dirty data caused by traditional manual methods that define cell boundaries. It characterizes the physical coverage area of the target area based on the user's high-precision MR coordinates. Instead of comparing the coordinates of other users within a preset time period with the coordinates of known users within the same preset time period, it determines whether other users are actual residents of the target area. This avoids the problem of inaccurate matching caused by inaccurate user coordinates or inaccurate cell boundaries.
[0090] Based on the user identification method provided in the above embodiments, this application also provides specific implementations of a user identification device. Please refer to the following embodiments.
[0091] Figure 4 This is a schematic diagram of the structure of a user identification device provided in some embodiments of this application. For example... Figure 4 As shown, the user identification device may include:
[0092] The acquisition module 401 is used to acquire user test report MR data of the target area to be identified within a preset time period. The user MR data includes the latitude and longitude data of the user.
[0093] The first determining module 402 is used to determine the first user in the target area using a preset clustering algorithm and the user's latitude and longitude data. The first user is a resident user in the target area.
[0094] The second determining module 403 is used to determine the first region in the target area based on the latitude and longitude data of the first user and the preset extension parameters;
[0095] The third determining module 404 is used to determine the second user in the first area based on the latitude and longitude data of the user in the first area and a preset distance threshold, using a preset search algorithm.
[0096] The setting module 405 is used to designate the first user and the second user as target users in the target area.
[0097] In summary, the user identification device of this application embodiment can be used to execute the user identification method in the above embodiments. It can utilize a preset clustering algorithm to cluster user latitude and longitude data within a predetermined time period in a target area, identifying a first user who is a resident user in the target area. Then, based on the identified first user, other users in the target area who can be considered target users are searched to obtain the corresponding target user in the target area. Therefore, by performing cluster analysis on users within the target area to identify the actual resident users in the target area, problems such as inaccurate matching between users and their respective cells due to inaccurate user positioning or cell delineation can be avoided, thus improving the accuracy of user identification in the target area.
[0098] The first determining module 402 is further configured to use a preset clustering algorithm to cluster the latitude and longitude data of the user to obtain the clustered latitude and longitude clusters corresponding to the latitude and longitude data; and to use the user corresponding to the clustered latitude and longitude cluster as the first user of the target area.
[0099] The first determining module 402 is also used to perform iterative clustering based on the user's latitude and longitude data and preset density parameters using a preset density clustering algorithm; when the clustering result is a cluster of latitude and longitude, the cluster of latitude and longitude is taken as the cluster of latitude and longitude corresponding to the latitude and longitude data.
[0100] In some embodiments of this application, the preset density parameters include a neighborhood radius parameter and a neighborhood density threshold parameter.
[0101] The second determining module 403 is further configured to calculate the center location latitude and longitude data of the target area based on the latitude and longitude data of the first user; and, based on the center location latitude and longitude data, extend a region with a preset extension parameter length in both the longitude and latitude directions to determine the first region, with the location corresponding to the center location latitude and longitude data as the center.
[0102] The third determining module 404 is also used to obtain the latitude and longitude data of a third user in the first region, where the third user is a user to be identified other than the first user in the first region.
[0103] Based on the latitude and longitude data of the third user, the latitude and longitude data of the first user, and a preset distance threshold, a binary search algorithm is used to determine the second user in the first region.
[0104] The third determining module 404 is also used to calculate the distance between the third user and each first user based on the latitude and longitude data of the third user and the latitude and longitude data of the first user using a binary search algorithm; when the distance between the third user and any first user is less than a preset distance threshold, the third user is determined to be the second user.
[0105] In some embodiments of this application, the preset clustering algorithm is the DBSCAN density clustering algorithm.
[0106] Figure 4 Each module / unit in the illustrated device has the ability to implement Figure 1 and Figure 2 The functions of each step in the process and their corresponding technical effects are described briefly and will not be elaborated here.
[0107] Based on the user identification method provided in the above embodiments, this application also provides specific implementations of user identification devices. Please refer to the following embodiments.
[0108] Figure 5 A schematic diagram of the hardware structure of a user identification device provided in an embodiment of this application is shown.
[0109] The user-identifiable device may include a processor 501 and a memory 502 storing computer program instructions.
[0110] Specifically, the processor 301 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.
[0111] Memory 502 may include a large-capacity memory for data or instructions. For example, and not limitingly, memory 502 may include a hard disk drive (HDD), a floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 502 may include removable or non-removable (or fixed) media. Where appropriate, memory 502 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 502 is a non-volatile solid-state memory. In a particular embodiment, memory 502 includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.
[0112] The processor 501 implements any of the user identification methods described in the above embodiments by reading and executing computer program instructions stored in the memory 502.
[0113] In one example, the user-identified device may further include a communication interface 503 and a bus 510. For example, Figure 5 As shown, the processor 501, memory 502, and communication interface 503 are connected through bus 510 and complete communication with each other.
[0114] The communication interface 503 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.
[0115] Bus 510 includes hardware, software, or both, that couples components of a user-identified device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 510 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.
[0116] The user identification device can execute the user identification method in the embodiments of this application, thereby achieving the combination Figure 1 and Figure 2 Describes the user identification method.
[0117] Furthermore, in conjunction with the user identification methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the user identification methods in the above embodiments.
[0118] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.
[0119] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.
[0120] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.
[0121] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.
[0122] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.
Claims
1. A method for user identification, characterized in that, include: Acquire user test report (MR) data of the target area to be identified within a preset time period. The user MR data includes the latitude and longitude data of the user. The latitude and longitude data of the user is obtained by performing fingerprint feature matching on the acquired MR data in an established feature fingerprint database. Using a preset clustering algorithm and the latitude and longitude data of the user, the first user in the target area is determined, and the first user is a resident user in the target area; the preset clustering algorithm is the DBSCAN density clustering algorithm. Based on the latitude and longitude data of the first user and preset extension parameters, a first region within the target area is determined; Based on the latitude and longitude data of users in the first region and a preset distance threshold, a preset search algorithm is used to determine the second user in the first region; The first user and the second user are designated as the target users of the target area. The step of determining the first user in the target area using a preset clustering algorithm and the user's latitude and longitude data includes: Clustering is performed based on the user's latitude and longitude data and preset density parameters to obtain clustering results. The density parameters include the initial values of the neighborhood radius parameter and the neighborhood density threshold parameter. If the clustering result is multiple clusters, increment the neighborhood density threshold parameter by one; if the clustering result is empty, decrement the neighborhood density threshold parameter by one. Clustering is performed based on the user's latitude and longitude data, the neighborhood parameter radius, and the modified neighborhood density threshold parameter until the clustering result is a single cluster. The user corresponding to the latitude and longitude data in the cluster is taken as the first user in the target area; The step of determining the second user in the first area based on the latitude and longitude data of users in the first area and a preset distance threshold, using a preset search algorithm, includes: Obtain the latitude and longitude data of a third user in the first region, wherein the third user is a user to be identified other than the first user in the first region; Based on the latitude and longitude data of the third user, the latitude and longitude data of the first user, and a preset distance threshold, a binary search algorithm is used to determine the second user in the first region. Based on the latitude and longitude data of the third user, the latitude and longitude data of the first user, and a preset distance threshold, a binary search algorithm is used to determine the second user in the first region, including: Based on the latitude and longitude data of the third user and the latitude and longitude data of the first user, the distance between the third user and each of the first users is calculated using a binary search algorithm. When the distance between the third user and any of the first users is less than a preset distance threshold, the third user is determined to be the second user.
2. The method according to claim 1, characterized in that, The step of determining the first user in the target area using a preset clustering algorithm and the user's latitude and longitude data includes: The latitude and longitude data of the user are clustered using a preset clustering algorithm to obtain the latitude and longitude clusters corresponding to the latitude and longitude data; The users corresponding to the clustered latitude and longitude clusters are designated as the first users in the target region.
3. The method according to claim 2, characterized in that, The step of clustering the user's latitude and longitude data using a preset clustering algorithm to obtain the corresponding latitude and longitude clusters includes: Based on the user's latitude and longitude data and preset density parameters, a preset density clustering algorithm is used to perform iterative clustering. When the clustering result is a single latitude and longitude cluster, the single latitude and longitude cluster is taken as the latitude and longitude cluster corresponding to the latitude and longitude data.
4. The method according to claim 3, characterized in that, The preset density parameters include the domain radius parameter and the domain density threshold parameter.
5. The method according to claim 1, characterized in that, The step of determining the first region within the target region based on the latitude and longitude data of the first user and preset extension parameters includes: Based on the latitude and longitude data of the first user, the latitude and longitude data of the center location of the target area are calculated; Based on the latitude and longitude data of the center position, the area determined by extending the preset extension parameter length in both the longitude and latitude directions, with the position corresponding to the latitude and longitude data of the center position as the center, is the first area.
6. A user identification device, characterized in that, The device includes: The acquisition module is used to acquire user test report (MR) data of the target area to be identified within a preset time period. The user MR data includes the latitude and longitude data of the user. The latitude and longitude data of the user is obtained by fingerprint feature matching of the acquired MR data in an established feature fingerprint database. The first determining module is used to determine the first user in the target area using a preset clustering algorithm and the latitude and longitude data of the user. The first user is a resident user in the target area. The preset clustering algorithm is the DBSCAN density clustering algorithm. The second determining module is used to determine the first region in the target region based on the latitude and longitude data of the first user and preset extension parameters; The third determining module is used to determine the second user in the first area based on the latitude and longitude data of the user in the first area and a preset distance threshold, using a preset search algorithm. The setting module is used to designate the first user and the second user as target users of the target area. The step of determining the first user in the target area using a preset clustering algorithm and the user's latitude and longitude data includes: Clustering is performed based on the user's latitude and longitude data and preset density parameters to obtain clustering results. The density parameters include the initial values of the neighborhood radius parameter and the neighborhood density threshold parameter. If the clustering result is multiple clusters, increment the neighborhood density threshold parameter by one; if the clustering result is empty, decrement the neighborhood density threshold parameter by one. Clustering is performed based on the user's latitude and longitude data, the neighborhood parameter radius, and the modified neighborhood density threshold parameter until the clustering result is a single cluster. The user corresponding to the latitude and longitude data in the cluster is taken as the first user in the target area; The step of determining the second user in the first area based on the latitude and longitude data of users in the first area and a preset distance threshold, using a preset search algorithm, includes: Obtain the latitude and longitude data of a third user in the first region, wherein the third user is a user to be identified other than the first user in the first region; Based on the latitude and longitude data of the third user, the latitude and longitude data of the first user, and a preset distance threshold, a binary search algorithm is used to determine the second user in the first region. Based on the latitude and longitude data of the third user, the latitude and longitude data of the first user, and a preset distance threshold, a binary search algorithm is used to determine the second user in the first region, including: Based on the latitude and longitude data of the third user and the latitude and longitude data of the first user, the distance between the third user and each of the first users is calculated using a binary search algorithm. When the distance between the third user and any of the first users is less than a preset distance threshold, the third user is determined to be the second user.
7. A user identification device, characterized in that, The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the user identification method as described in any one of claims 1 to 5.
8. A computer storage medium, characterized in that, The computer storage medium stores computer program instructions, which, when executed by a processor, implement the user identification method as described in any one of claims 1 to 5.