WO2020258951A1 - 用户常驻位置的获取方法、装置以及计算机可读存储介质 - Google Patents

用户常驻位置的获取方法、装置以及计算机可读存储介质 Download PDF

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Publication number
WO2020258951A1
WO2020258951A1 PCT/CN2020/081242 CN2020081242W WO2020258951A1 WO 2020258951 A1 WO2020258951 A1 WO 2020258951A1 CN 2020081242 W CN2020081242 W CN 2020081242W WO 2020258951 A1 WO2020258951 A1 WO 2020258951A1
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Prior art keywords
information
wifi
sampling point
point information
cluster
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PCT/CN2020/081242
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English (en)
French (fr)
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黎聪
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中兴通讯股份有限公司
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Publication of WO2020258951A1 publication Critical patent/WO2020258951A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Definitions

  • the embodiments of the present application relate to the field of communication technologies, and in particular, refer to a method, device, and computer-readable storage medium for obtaining a user's resident location.
  • the resident location is the location where the user to which the terminal belongs frequently during a period of time.
  • the acquisition of the user's resident location helps operators provide users with more targeted services, and at the same time makes use of the user to make personalized settings.
  • the embodiments of the present application provide a method, a device, and a computer-readable storage medium for obtaining a user's resident location, which can achieve the user's resident location.
  • the embodiments of the present application provide a method for obtaining a user's resident location, including: a terminal obtains information of a number of sampling points within a preset time period; wherein, each of the sampling point information It is obtained by the terminal according to the sampling period and includes: sampling time and sampling object information, each of the sampling object information includes at least one of the following: wireless fidelity (wifi) information and base station information: the terminal according to The connection between the sampling object information included in the different sampling point information clusters the obtained several sampling point information to obtain the resident location information of the user in the preset time period.
  • a terminal obtains information of a number of sampling points within a preset time period; wherein, each of the sampling point information It is obtained by the terminal according to the sampling period and includes: sampling time and sampling object information, each of the sampling object information includes at least one of the following: wireless fidelity (wifi) information and base station information: the terminal according to The connection between the sampling object information included in the different sampling point information clusters the obtained several sampling point information to obtain
  • the embodiment of the present application also provides a terminal, including: an acquisition module, configured to acquire information of a number of sampling points within a preset time period; wherein each of the sampling point information is obtained by the terminal according to a sampling period and Including: sampling time and sampling object information, each of the sampling object information includes at least one of the following: wifi information and base station information; a processing module for obtaining information based on the connection pair between the sampling object information included in different sampling point information The information of several sampling points is clustered to obtain the resident location information of the user in the preset time period.
  • An embodiment of the present application also provides a device for acquiring a user's resident location, including: a memory and a processor, wherein the memory stores the following instructions that can be executed by the processor: acquiring several sampling points in a preset time period Information; wherein, each of the sampling point information is obtained by the terminal according to the sampling period and includes: sampling time and sampling object information, each of the sampling object information includes at least one of the following: wifi information and base station information; The connection between the sampling object information included in the different sampling point information clusters the obtained several sampling point information to obtain the resident location information of the user in the preset time period.
  • the embodiment of the present application also provides a computer-readable storage medium with computer-executable instructions stored on the storage medium, and the computer-executable instructions are used to perform the following steps: obtaining information of several sampling points within a preset time period; wherein, Each of the sampling point information is obtained by the terminal according to the sampling period and includes: sampling time and sampling object information, each of the sampling object information includes at least one of the following: wifi information and base station information; according to different sampling point information The included connection between the sampling object information clusters the obtained several sampling point information to obtain the resident location information of the user within the preset time period.
  • wifi information and/or base station information can be used to characterize the location, when the terminal obtains several sampling point information within a preset time period, and according to the sampling object information included in the different sampling point information (the sampling object information includes wifi information and base station).
  • the sampling object information includes wifi information and base station
  • the connection between at least one of the information clusters the obtained several sampling point information, and the user's resident location information within a preset time period can be obtained, thereby realizing the acquisition of the user's resident location.
  • FIG. 1 is a schematic flowchart of a method for acquiring a user's resident location according to an embodiment of the application
  • FIG. 2 is a schematic structural diagram of a terminal provided by an embodiment of the application.
  • FIG. 3 is a schematic structural diagram of an apparatus for acquiring a user's resident location provided by an embodiment of the application.
  • the embodiment of the present application provides a method for obtaining the permanent location of a user. As shown in FIG. 1, the method includes:
  • Step 101 The terminal obtains information of several sampling points within a preset time period.
  • each sampling point information is obtained by the terminal according to the sampling period and includes: sampling time and sampling object information.
  • Each sampling object information includes at least one of the following: wireless fidelity wifi information and base station information.
  • Step 102 The terminal clusters the obtained several sampling point information according to the connection between the sampling object information included in the different sampling point information, and obtains the resident location information of the user within a preset time period.
  • the preset time period is M days
  • the sampling period is N minutes
  • M and N are both positive integers
  • the terminal obtains several pieces of information according to the connection pair between the sampling object information included in different sampling point information.
  • the sampling point information is clustered to obtain the user's resident location information within a preset time period, including:
  • Step 201 The terminal divides the obtained several sampling point information according to the date of the sampling time to obtain several single-day sampling point information.
  • Step 202 The terminal performs the following operations on the sampling point information of each single day: clustering the sampling point information of the single day according to the relationship between the sampling object information included in the different sampling point information, to obtain the resident location of the single day Clustering results.
  • Step 203 The terminal merges the resident location clustering results of all single days to obtain the resident location clustering results within a preset time period.
  • Step 204 The terminal obtains the resident location information of the user in the preset time period according to the obtained resident location clustering result in the preset time period.
  • the obtained clustering result of the resident location within the preset time period includes several clusters, each cluster includes several sampling point information, the sampling point information includes sampling object information, and the sampling object information includes One of the following: Wi-Fi information and base station information, and whether it is Wi-Fi information, base station information, or Wi-Fi information + base station information can be used as location information to characterize a location, so a cluster of resident locations within a preset time period is obtained
  • Wi-Fi information and base station information and whether it is Wi-Fi information, base station information, or Wi-Fi information + base station information can be used as location information to characterize a location, so a cluster of resident locations within a preset time period is obtained
  • Wi-Fi information and base station information can be used as location information to characterize a location, so a cluster of resident locations within a preset time period is obtained.
  • the terminal clusters the sampling point information of a single day according to the connection between the sampling object information included in the different sampling point information to obtain the resident location clustering result of the single day, including:
  • Step 301 The terminal clusters the sampling point information including the wifi information in the sampling point information of a single day according to the connection between the wifi information included in the different sampling point information, and obtains a resident location clustering result based on the wifi information in a single day.
  • Step 302 Based on the clustering result of the resident location of the terminal based on the Wi-Fi information in a single day, and according to the connection between the base station information included in the different sampling point information, the sampling point information of the single day only includes the base station information. Clustering, the clustering result of the resident location of a single day is obtained.
  • the wifi information includes: a wifi list including at least one wifi that can be scanned by the terminal, and the terminal includes wifi information in the sampling point information of a single day according to the connection between the wifi information included in different sampling point information
  • the sampling point information is clustered, and the resident location clustering results based on wifi information in a single day are obtained, including:
  • Step 401 The terminal clusters the sampling point information of a single day according to the rule that the same wifi in the two sampling point information is included in a cluster, and obtains a pre-clustering result including several clusters.
  • Step 402 The terminal sequentially obtains each cluster in the pre-clustering result, and performs the following operations on the obtained cluster each time a cluster is obtained: map the wifi list included in each sampling point information in the obtained cluster to the dimension and obtain The wifi lists of the clusters in the same space to obtain the space coordinates of the wifi list, and according to the Euclidean distance between the space coordinates of the different wifi lists, the obtained cluster information is re-clustered to obtain several sub-clusters .
  • Step 403 The terminal obtains a sub-cluster whose number of sampling points is greater than a preset value among the several sub-clusters obtained from each cluster, and obtains a resident location clustering result based on wifi information in a single day.
  • the terminal clusters the sampling point information of a single day according to the rule that the same wifi in the two sampling point information is included in one cluster, and obtains the pre-clustering result including several clusters.
  • Step 501 The terminal obtains the first sampling point information in the sampling point information of a single day as a cluster, and uses the wifi list of the first sampling point information as the wifi list of the cluster, and the terminal sequentially obtains the sampling point information of the single day Sampling point information other than the first sampling point information, and each time a sampling point information is obtained, the following operations are performed on the obtained sampling point information:
  • Step 501a The terminal judges whether at least one wifi in the obtained sampling point information exists in the wifi list of any existing cluster.
  • Step 501b When at least one wifi in the obtained sampling point information exists in the wifi list of an existing cluster, add the obtained sampling point information to the cluster, and add the wifi list of the obtained sampling information to the wifi list of the cluster List.
  • Step 501c When all the wifis in the obtained sampling point information do not exist in the wifi lists of all existing clusters, the obtained sampling point information is taken as a new cluster, and the wifi list of the obtained sampling point information is taken as The wifi list of the new cluster.
  • the wifi list of the sampling point information is a list of wifi whose wifi signal strength is arranged before the first preset digit from strong to weak.
  • the sampling point information of a single day includes: the sampling point information that has undergone noise reduction processing in a single day. Before the terminal clusters the single-day sampling point information including the wifi information according to the connection between the wifi information included in the different sampling point information, it also includes:
  • the terminal performs noise reduction processing on the sampling point information of a single day, and obtains the sampling point information that has undergone noise reduction processing in a single day.
  • the terminal performing noise reduction processing on the sampling point information of a single day includes:
  • Step 601 The terminal counts the number of occurrences of sampling object information included in all sampling point information in the sampling point information of a single day.
  • Step 602 The terminal obtains sampling object information whose occurrence times are less than a preset number of times.
  • Step 603 The terminal removes the sampling point information to which the obtained sampling object information belongs.
  • the terminal maps the wifi list included in each sampling point information in the obtained cluster to a space with the same dimension as the length of the wifi list of the obtained cluster to obtain the spatial coordinates of the wifi list, including:
  • Step 701 The terminal obtains the obtained wifi list length of the cluster.
  • Step 702 The terminal creates a preset array with the same dimension as the length of the obtained wifi list of the cluster.
  • Step 703 The terminal determines the wifi in the wifi list of the obtained cluster corresponding to each element in the preset array and the value used to represent the wifi.
  • Step 704 The terminal sequentially obtains the obtained sampling point information in the cluster, and performs the following operations on the obtained sampling point information each time a sampling point information is obtained: according to the wifi list and the preset array included in the obtained sampling point information
  • the element in corresponds to the wifi in the wifi list of the obtained cluster and fills the preset array with the value representing the wifi to obtain the spatial coordinates of the wifi list included in the obtained sampling point information.
  • the terminal re-clusters the obtained information of several sampling points in the cluster according to the Euclidean distance between the spatial coordinates of different wifi lists to obtain several sub-clusters, including:
  • Step 801 The terminal sorts the obtained sampling point information of the cluster according to the number of occurrences of the sampling object information from large to small to obtain a descending list.
  • Step 802 The terminal obtains the first sampling point information in the descending list as a sub-cluster, and uses the spatial coordinates of the wifi list included in the sampling point information as the center of the sub-cluster, and the terminal sequentially obtains the first sampling point in the descending list except the first one.
  • Step 802a The terminal judges whether the Euclidean distance between the spatial coordinates of the wifi list included in the obtained sampling point information and the center of any existing sub-cluster is less than a preset threshold.
  • Step 802b When the Euclidean distance between the spatial coordinates of the wifi list included in the obtained sampling point information and the center of a sub-cluster is less than a preset threshold, add the sampling point information to the sub-cluster.
  • Step 802c When the Euclidean distance between the spatial coordinates of the wifi list included in the obtained sampling point information and the centers of all sub-clusters is not less than a preset threshold, the sampling point information is regarded as a new sub-cluster.
  • the resident location clustering results based on wifi information in a single day include: clustering results of resident locations based on wifi information in a single day sorted by stay time; the terminal obtains the resident location clustering results based on wifi information in a single day After the clustering result of the station location, and before re-clustering the sampling point information that only includes the base station information in the sampling point information of a single day, it also includes:
  • the terminal sorts the clustering results of the resident location based on wifi information in a single day according to the number of sampling point information contained in the sub-cluster from large to small, and obtains the resident location based on wifi information sorted by the stay time in a single day Clustering results.
  • the terminal is based on the resident location clustering result based on wifi information in a single day, and according to the connection between base station information included in different sampling point information, the sampling point information of a single day only includes base station information.
  • the sampling point information is re-clustered to obtain the clustering results of the resident location of a single day, including:
  • Step 901 The terminal sequentially obtains each sampling point information that only contains base station information from the sampling point information of a single day, and performs the following operations on the obtained sampling point information to obtain a clustering result of the resident location of a single day:
  • Step 901a The terminal sequentially compares the base station information included in the obtained sampling point information with the base station information of each sub-cluster in the clustering result of the resident location based on wifi information sorted according to the stay time in a single day.
  • Step 901b When the base station information included in the obtained sampling point information exists in a sub-cluster, the terminal merges the obtained sampling point information into the sub-cluster.
  • Step 901c When the base station information included in the obtained sampling point information does not exist in any sub-cluster, the terminal uses the obtained sampling point information as a new sub-cluster.
  • the terminal merges the resident location clustering results of all single days to obtain the resident location clustering results within a preset time period, including:
  • Step 1001 The terminal obtains the resident location clustering result of the first single day in a preset time period as the historical resident location clustering result, and the terminal sequentially obtains single days except the resident location clustering result of the first single day.
  • Step 1001a The terminal merges the obtained clustering result of the resident location of a single day with the clustering result of the historical resident location to obtain a new clustering result of the historical resident location, which can be used to compare with the resident location of the next single day.
  • the location clustering results are merged.
  • the terminal merges the obtained single-day resident location clustering result with the historical resident location clustering result to obtain a new historical resident location clustering result, including:
  • Step 1101. The terminal merges the clusters containing wifi information in the resident location clustering results of a single day and the clusters containing wifi information in the historical resident clustering results according to the connection of the wifi information to obtain a new historical wifi information-based cluster. Residential clustering results.
  • Step 1102 Based on the new historical resident location clustering result based on wifi information, the terminal determines clusters that only contain base station information in the single-day resident location clustering result based on the connection between base station information included in different sampling point information Perform re-clustering to get the new historical resident location clustering result.
  • the terminal combines the clusters containing wifi information in the resident location clustering results of a single day and the clusters containing wifi information in the historical resident clustering results according to the connection of wifi information to obtain a new
  • the clustering results of historical resident locations of wifi information include:
  • Step 1201 The terminal sequentially obtains the clusters in the historical resident location clustering result, and each time a cluster is obtained, the following operations are performed on the obtained clusters: the wifi in the wifi list of the obtained clusters is in accordance with the information contained in the sampling point The number is sorted from most to least, and the wifi sorted before the second preset digit is obtained, and the first wifi list is obtained.
  • Step 1202 The terminal sequentially obtains the clusters in the clustering results of a single daily resident location, and performs the following operations on the obtained clusters each time a cluster is obtained:
  • the wifi in the wifi list of the obtained cluster is included in the sampling point information Sort the numbers from most to least, and get the wifi sorted before the third preset digit to get the second wifi list.
  • Step 1203 The terminal sequentially obtains the second wifi list of each cluster in the single-day resident location clustering result, and performs the following operations on the obtained second wifi list each time a second wifi list is obtained:
  • Step 1203a The terminal judges whether at least one wifi in the obtained second wifi list of the cluster exists in any first wifi list.
  • Step 1203b When at least one wifi in the obtained second wifi list of the cluster exists in a first wifi list, merge the obtained clusters into a cluster corresponding to the first wifi list.
  • Step 1203c When all wifi in the second wifi list of the obtained cluster does not exist in all the first wifi lists, the obtained cluster is regarded as a new cluster.
  • the new historical resident location clustering result based on wifi information includes: a new historical resident location clustering result based on wifi information sorted by stay time. After the terminal obtains the new historical resident location clustering results based on wifi information, and before re-clustering clusters that only contain base station information in the single-day resident location clustering results, it also includes:
  • the terminal sorts the new historical resident location clustering results based on wifi information according to the number of sampling point information contained in the cluster from large to small, and obtains a new historical resident location cluster based on wifi information sorted by stay time result.
  • the terminal only has a single-day resident location clustering result based on the new historical resident location clustering result based on wifi information, and according to the connection between the base station information included in different sampling point information.
  • the clusters containing the base station information are re-clustered to obtain the new historical resident location clustering results, including:
  • Step 1301 The terminal sequentially obtains clusters containing only base station information in the cluster results of resident locations for a single day, and performs the following operations on the obtained clusters each time a cluster is obtained to obtain a new historical resident location clustering results:
  • Step 1301a The terminal judges whether the obtained cluster base station information exists in the new cluster base station information based on the historical resident location clustering result of wifi information sorted by stay time.
  • Step 1301b When the obtained base station information of the cluster exists in the base station information of a target cluster in the new historical resident location clustering result based on wifi information sorted by stay time, the terminal merges the obtained clusters into the target cluster.
  • Step 1301c When the base station information of the obtained cluster does not exist in the base station information of any cluster in the new historical resident location clustering result based on wifi information sorted by stay time, the terminal regards the obtained cluster as a new cluster .
  • the base station information includes: mobile country code (Mobile Country Code, MCC), mobile network code (Mobile Neworkt Code, MNC), location area code (Location Area Code, LAC), base station number ( CELLIDentification) CELLID, network standard, where the network standard includes: Global System for Mobile Communications (GSM), Code Division Multiple Access (CDMA), third-generation mobile communication technology (the One of the 3th Generation mobile communication technology (3G) and the 4th Generation mobile communication technology (4G).
  • GSM Global System for Mobile Communications
  • CDMA Code Division Multiple Access
  • 3G 3th Generation mobile communication technology
  • 4G 4th Generation mobile communication technology
  • the method for obtaining the user’s resident location provided by the embodiments of the present application, since wifi information and/or base station information can be used to characterize the location, when the terminal obtains the sampling time of several sampling points included in the preset time period, and according to The connection between the sampling object information included in different sampling point information (the sampling object information includes at least one of wifi information and base station information) clusters the obtained sampling point information, and the resident in the preset time period can be obtained. The location clustering results, thereby achieving the acquisition of the user's resident location.
  • the terminal 2 includes:
  • the acquiring module 21 is used to acquire the information of several sampling points within a preset time period; wherein, the information of each sampling point is obtained by the terminal according to the sampling period and includes: sampling time and sampling object information, and each sampling object information includes at least One of the following: Wi-Fi information and base station information.
  • the processing module 22 is configured to cluster the obtained several sampling point information according to the connection between the sampling object information included in the different sampling point information, and obtain the resident location information of the user within a preset time period.
  • the preset time period is M days
  • the sampling period is N minutes
  • both M and N are positive integers.
  • the processing module 22 is specifically used for:
  • the obtained sampling point information is divided to obtain the sampling point information of several single days.
  • the following operations are performed on the sampling point information of each single day: clustering the sampling point information of the single day according to the connection between the sampling object information included in the different sampling point information, and obtaining the resident location clustering result of the single day.
  • the resident location clustering results of all single days are merged to obtain the resident location clustering results within the preset time period.
  • the resident location information of the user in the preset time period is obtained according to the obtained permanent location clustering results in the preset time period.
  • processing module 22 is specifically configured to:
  • the sampling point information including the wifi information in the sampling point information of a single day is clustered, and the resident location clustering result based on the wifi information in a single day is obtained.
  • the wifi information includes: a wifi list including at least one wifi that can be scanned by the terminal.
  • the processing module 22 is specifically used for:
  • the sampling point information of a single day is clustered according to the rule that the same wifi in the two sampling point information is included in a cluster, and the pre-clustering result including several clusters is obtained.
  • each cluster in the pre-clustering result in turn, and perform the following operations on the obtained cluster each time a cluster is obtained: map the wifi list included in the information of each sampling point in the obtained cluster to the dimension and the wifi of the obtained cluster The space coordinates of the wifi list are obtained in the space with the same list length, and the obtained information of several sampling points in the cluster is re-clustered according to the Euclidean distance between the space coordinates of different wifi lists to obtain several sub-clusters.
  • the terminal obtains a sub-cluster whose number of sampling points is greater than a preset value among the several sub-clusters obtained from each cluster, and obtains a resident location clustering result based on wifi information in a single day.
  • processing module 22 is specifically configured to:
  • the terminal sequentially obtains the sampling point information of a single day except the first one Sampling point information other than the sampling point information, and each time a sampling point information is obtained, perform the following operations on the obtained sampling point information:
  • the obtained sampling point information is added to the cluster, and the wifi list of the obtained sampling information is added to the wifi list of the cluster.
  • the obtained sampling point information is regarded as a new cluster, and the wifi list of the obtained sampling point information is regarded as the new cluster Wifi list.
  • the wifi list of the sampling point information is a list of wifi whose wifi signal strength is arranged before the first preset digit from strong to weak.
  • the sampling point information of a single day includes: the sampling point information that has undergone noise reduction processing in a single day.
  • the processing module 22 is also used to perform noise reduction processing on the sampling point information of a single day to obtain the sampling point information that has undergone noise reduction processing in a single day.
  • processing module 22 is specifically configured to:
  • processing module 22 is specifically configured to:
  • each element in the preset array corresponds to the wifi in the wifi list of the obtained cluster and the value used to represent the wifi.
  • each sampling point information in the obtained cluster in turn, and perform the following operations on the obtained sampling point information each time a sampling point information is obtained:
  • the wifi list included and the element in the preset array correspond to The wifi in the wifi list of the obtained cluster and the value used to represent the wifi are filled in the preset array, and the spatial coordinates of the wifi list included in the obtained sampling point information are obtained.
  • processing module 22 is specifically configured to:
  • the terminal sequentially obtains the information except the first sampling point in the descending list Sampling point information, and whenever a sampling point information is obtained, perform the following operations on the obtained sampling point information:
  • the sampling point information is added to the subcluster.
  • the sampling point information is regarded as a new sub-cluster.
  • the resident location clustering result based on wifi information in a single day includes: a clustering result of resident location based on wifi information sorted according to stay time in a single day.
  • the processing module 22 is also used to sort the clustering results of the resident location based on the wifi information in a single day according to the number of sampling point information contained in the sub-clusters from large to small, to obtain a wifi-based sorted day according to the stay time The clustering result of the permanent location of the information.
  • processing module 22 is specifically configured to:
  • sampling point information of a single day obtain each sampling point information that only contains base station information in turn, and perform the following operations on the obtained sampling point information to obtain a single-day clustering result of the resident location:
  • the base station information included in the obtained sampling point information is sequentially compared with the base station information of each sub-cluster in the clustering result of the resident location based on wifi information sorted according to the stay time in a single day.
  • the obtained sampling point information is merged into the sub-cluster.
  • the obtained sampling point information is regarded as a new sub-cluster.
  • processing module 22 is specifically configured to:
  • processing module 22 is specifically configured to:
  • clusters that only contain base station information in the single-day resident location clustering results are re-clustered according to the connection between the base station information included in different sampling point information , Get the new historical resident location clustering result.
  • processing module 22 is specifically configured to:
  • the wifi in the wifi list of the obtained clusters is as large as the number of information contained in the sampling point. Sort at least, get the wifi sorted before the second preset digit, and get the first wifi list.
  • the wifi in the wifi list of the obtained clusters is selected from the number of information contained in the sampling point Sort from more to less, and get the wifi sorted before the third preset digit, and get the second wifi list.
  • the obtained clusters are merged into a cluster corresponding to the first wifi list.
  • the obtained cluster is regarded as a new cluster.
  • the new historical resident location clustering result based on wifi information includes: a new historical resident location clustering result based on wifi information sorted by stay time.
  • the processing module 22 is also used to sort the new historical resident location clustering results based on wifi information according to the number of sampling point information contained in the cluster, from large to small, to obtain a new wifi information-based sorted by stay time Clustering results of historical resident locations.
  • processing module 22 is specifically configured to:
  • clusters that only contain base station information in the clustering results of resident locations for a single day in sequence, and perform the following operations on the obtained clusters each time a cluster is obtained to obtain new clustering results of historical resident locations:
  • the obtained cluster When the base station information of the obtained cluster exists in the base station information of a target cluster in the new historical resident location clustering result based on wifi information sorted by stay time, the obtained clusters are merged into the target cluster.
  • the obtained cluster When the base station information of the obtained cluster does not exist in the base station information of any cluster in the new historical resident location clustering results based on wifi information sorted by stay time, the obtained cluster is regarded as a new cluster.
  • the base station information includes: MCC, MNC, LAC, CELLID, and network standard, where the network standard includes one of GSM, CDMA, 3G, and 4G.
  • the wifi information and/or base station information can be used to characterize the location
  • the terminal obtains the sampling time included in the preset time period of several sampling point information, and according to the different sampling point information included
  • the connection between the sampling object information (the sampling object information includes at least one of wifi information and base station information) clusters the obtained sampling point information, and the clustering result of the resident location within the preset time period can be obtained. In this way, the user's permanent location is acquired.
  • the acquisition module 21 and the processing module 22 are both located in a central processing unit (CPU), a microprocessor (Micro Processor Unit, MPU), and a digital signal processor (Digital Signal Processor) in the terminal. DSP) or Field Programmable Gate Array (Field Programmable Gate Array, FPGA).
  • CPU central processing unit
  • MPU Micro Processor Unit
  • DSP Digital Signal Processor
  • FPGA Field Programmable Gate Array
  • An embodiment of the present application also provides a device for acquiring a user's resident location. As shown in FIG. 3, the device 3 includes:
  • the data collection module 31 is used to collect data every five minutes.
  • the collected content includes: base station information: MCC, MNC, LAC, CELLID, network standard (GSM, CDMA, 3G, 4G), WiFi information: scan list The first three digits of the WiFi MAC address.
  • the data storage module 32 is used to store data.
  • the stored data includes two parts: one is the effective raw data collected by the data collection module 31, and the other is the clustering result data obtained by the clustering algorithm module 34.
  • the data preprocessing module 33 is used for data preprocessing to run only before the algorithm is updated, and the algorithm is updated once a day.
  • all the original data collected in a day are read from the database, and the number of occurrences of each WiFi and base station is counted. Data with cumulative times less than 5 times is considered as noise data and removed (the data that only contains noise data will be eliminated).
  • the sampling points are deleted from the original data).
  • WiFi uses its Mac address as the unique identification; the base station is characterized by a set of data such as MCC, MNC, LAC, CELLID, and network standard. Only when the five parameters are consistent can the same base station.
  • the clustering algorithm module 34 is used for clustering the remaining valid data, clustering the WiFi and base station data of a day into multiple clusters, and the WiFi and base stations in each cluster represent a specific location.
  • the algorithm is designed and implemented based on the top-down splitting method in the hierarchical clustering algorithm according to the characteristics of the application scenario.
  • the specific process of the algorithm is as follows:
  • sampling points during the user's exercise have been deleted, and only the sampling data corresponding to the positions where the user stays for more than 25 minutes can be retained. So in most cases the sampled data at different locations are independent of each other.
  • Step 1 perform the first round of splitting all sampled data including WiFi. This process includes the following steps:
  • Step 1.1 Read the sampling points in sequence according to the sampling time, and use the WiFi list in the sampling points as its characterization;
  • Step 1.2 Read the first point to form the first cluster, which is characterized by the union of the WiFi list and base station list of all sampling points in it;
  • Step 1.3 Read the subsequent sampling points and calculate the Jaccard distance between the sampling point and each existing cluster. If the Jaccard distance between a certain cluster and the point is less than 1, then merge the point into this cluster and update The WiFi list and base station list of the cluster; if both are not less than 1, the sampling point will be formed into a new cluster.
  • the Jaccard distance calculation formula is shown in Equation 1
  • J represents the Jaccard distance
  • a and B represent the WiFi collection lists of sampling points or clusters.
  • the Jaccard distance between all clusters is calculated, and the clusters whose distance is less than 1 are merged.
  • Step 2 The first split has got a relatively rough location division, and the second split is needed. Do the following processing for each cluster obtained above, including the following steps:
  • Step 2.1 Calculate the length N of the WiFi list of the cluster
  • Step 2.2 Count the number of occurrences of each sampling point and arrange them in descending order to form a list Lp;
  • Step 2.3 Establish an N-dimensional space.
  • One WiFi represents one dimension. If the sampling point contains the WiFi corresponding to a certain axis, the sampling point will take the value of 1 on the axis, otherwise it will be 0; this way, all the sampling points can be mapped to In this N-dimensional space, the Euclidean distance between points is then calculated;
  • Step 2.4 Use the first sampling point of the list Lp as the cluster center to form the first cluster, and the center of the cluster is the coordinate of the sampling point in the aforementioned N-dimensional space;
  • Step 2.5 Extract the sampling points in the list Lp in turn, calculate the Euclidean distance between the sampling point and the center point of the existing cluster; if there is a cluster with a distance less than 2, then merge the point into this cluster, and combine all the sampling points The average value is used as the new cluster center point; if it does not exist, the sampling point will be formed into a new cluster;
  • Step 2.6 Repeat step 2.5 to complete the clustering of all sampling points;
  • Step 2.7 Remove clusters with less than 5 sampling points in the new cluster, and arrange the formed clusters from large to small to generate a list Lw.
  • Step 3 The WiFi set and base station set of all sampling points in the cluster are the characteristic parameters of the cluster. At this point, the clustering results based on WiFi are obtained. The next step is to process the sampling points that do not contain WiFi (only base station data):
  • the sampling points that do not include WiFi are compared with each cluster in Lw in turn. If the base station of the sampling point is consistent with the base station in a certain cluster, the point is merged into this cluster; if it is a base station that has never appeared in all clusters in Lw, the sampling point is formed into a new cluster. Finally, the final result list Lo, which is clustered by one day's data, is obtained. Each cluster in Lo represents a location.
  • Step 4 Combine the clustering results Lo of one day with the historical clustering results, and arrange the clustering results in ascending order of non-arrival time. Only the first one hundred cluster points are retained, and the rest are deleted to obtain the final cluster list La.
  • An embodiment of the present application also provides a device for obtaining a user's resident location, including a memory, a processor, and a computer program stored on the memory and running on the processor.
  • the processor implements any of the above embodiments when the computer program is executed. How to obtain the user's resident location.
  • the embodiments of the present application also provide a computer-readable storage medium, and the storage medium stores computer-executable commands, and the computer-executable commands are used to execute any method for obtaining the user's resident location in the above-mentioned embodiments.

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Abstract

本文公开了一种用户常驻位置的获取方法、装置以及计算机可读存储介质，包括：终端获取预设时间段内的若干个采样点信息；其中，每个采样点信息是终端根据采样周期获得的且包括：采样时间和采样对象信息，每个采样对象信息至少包括以下一种：无线保真wifi信息和基站信息；终端根据不同采样点信息包括的采样对象信息之间的联系对获得的若干个采样点信息进行聚类，得到预设时间段内用户的常驻位置信息。

Description

用户常驻位置的获取方法、装置以及计算机可读存储介质
相关申请的交叉引用
本申请基于申请号为201910550212.X、申请日为2019年6月24日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此以引入方式并入本申请。
技术领域
本申请实施例涉及通信技术领域，尤指一种用户常驻位置的获取方法、装置以及计算机可读存储介质。
背景技术
常驻位置是终端所属用户在一段时间内经常所处的位置，用户常驻位置的获取有利于帮助运营商为用户提供更具针对性的服务，并同时有利用用户自身进行个性化的设置。
相关技术中，存在多种获取用户所在实时位置的获取方法，但缺乏用户常驻位置的获取方法，因此，如何获取用户的常驻位置成为一个亟待解决的问题。
发明内容
本申请实施例提供了一种用户常驻位置的获取方法、装置以及计算机可读存储介质，能够实现用户常驻位置的获取。
为了达到本申请实施例的目的，本申请实施例提供了一种用户常驻位置的获取方法，包括：终端获取预设时间段内的若干个采样点信息；其中，每个所述采样点信息是所述终端根据采样周期获得的且包括：采样时间和采样 对象信息，每个所述采样对象信息至少包括以下一种：无线保真(wireless fidelity，wifi)信息和基站信息：所述终端根据不同采样点信息包括的采样对象信息之间的联系对获得的若干个采样点信息进行聚类，得到所述预设时间段内用户的常驻位置信息。
本申请实施例还提供了一种终端，包括：获取模块，用于获取预设时间段内的若干个采样点信息；其中，每个所述采样点信息是所述终端根据采样周期获得的且包括：采样时间和采样对象信息，每个所述采样对象信息至少包括以下一种：wifi信息和基站信息；处理模块，用于根据不同采样点信息包括的采样对象信息之间的联系对获得的若干个采样点信息进行聚类，得到所述预设时间段内用户的常驻位置信息。
本申请实施例还提供了一种用户常驻位置的获取装置，包括：存储器和处理器，其中，存储器中存储有以下可被处理器执行的指令：获取预设时间段内的若干个采样点信息；其中，每个所述采样点信息是所述终端根据采样周期获得的且包括：采样时间和采样对象信息，每个所述采样对象信息至少包括以下一种：wifi信息和基站信息；根据不同采样点信息包括的采样对象信息之间的联系对获得的若干个采样点信息进行聚类，得到所述预设时间段内用户的常驻位置信息。
本申请实施例还提供了一种计算机可读存储介质，储介质上存储有计算机可执行指令，计算机可执行指令用于执行以下步骤：获取预设时间段内的若干个采样点信息；其中，每个所述采样点信息是所述终端根据采样周期获得的且包括：采样时间和采样对象信息，每个所述采样对象信息至少包括以下一种：wifi信息和基站信息；根据不同采样点信息包括的采样对象信息之间的联系对获得的若干个采样点信息进行聚类，得到所述预设时间段内用户的常驻位置信息。
由于wifi信息和/或基站信息能够用于表征位置，因此当终端获取预设时间段内的若干个采样点信息，并根据不同采样点信息包括的采样对象信息(采样对象信息包括wifi信息和基站信息中至少一种)之间的联系对获得的若干个采样点信息进行聚类，就能够得到预设时间段内用户的常驻位置信息，从而实现了用户常驻位置的获取。
本申请实施例的其它特征和优点将在随后的说明书中阐述，并且，部分地从说明书中变得显而易见，或者通过实施本申请实施例而了解。本申请实施例的目的和其他优点可通过在说明书、权利要求书以及附图中所特别指出的结构来实现和获得。
附图说明
附图用来提供对本申请实施例技术方案的进一步理解，并且构成说明书的一部分，与本申请的实施例一起用于解释本申请实施例的技术方案，并不构成对本申请实施例技术方案的限制。
图1为本申请实施例提供的一种用户常驻位置的获取方法的流程示意图；
图2为本申请实施例提供的一种终端的结构示意图；
图3为本申请实施例提供的一种用户常驻位置的获取装置的结构示意图。
具体实施方式
为使本申请实施例的目的、技术方案和优点更加清楚明白，下文中将结合附图对本申请的实施例进行详细说明。需要说明的是，在不冲突的情况下，本申请中的实施例及实施例中的特征可以相互任意组合。
在附图的流程图示出的步骤可以在诸如一组计算机可执行指令的计算机系统中执行。并且，虽然在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于此处的顺序执行所示出或描述的步骤。
本申请实施例提供一种用户常驻位置的获取方法，如图1所示，该方法包括：
步骤101、终端获取预设时间段内的若干个采样点信息。
其中，每个采样点信息是终端根据采样周期获得的且包括：采样时间和采样对象信息，每个采样对象信息至少包括以下一种：无线保真wifi信息和基站信息。
步骤102、终端根据不同采样点信息包括的采样对象信息之间的联系对获得的若干个采样点信息进行聚类，得到预设时间段内用户的常驻位置信息。
在一种示例性实例中，预设时间段为M天，采样周期为N分钟，M、N均为正整数，终端根据不同采样点信息包括的采样对象信息之间的联系对获得的若干个采样点信息进行聚类，得到预设时间段内用户的常驻位置信息，包括：
步骤201、终端根据采样时间的所属日期对获得的若干个采样点信息进行划分，得到若干个单日的采样点信息。
步骤202、终端对每一个单日的采样点信息都进行如下操作：根据不同采样点信息包括的采样对象信息之间的联系对单日的采样点信息进行聚类，得到单日的常驻位置聚类结果。
步骤203、终端对所有单日的常驻位置聚类结果进行合并，得到预设时间段内的常驻位置聚类结果。
步骤204、终端根据获得的预设时间段内的常驻位置聚类结果得到预设时间段内用户的常驻位置信息。
在一种示例性实例中，获得的预设时间段内的常驻位置聚类结果中包含若干个簇，每个簇包括若干个采样点信息，采样点信息包括采样对象信息，采样对象信息包括以下一种：wifi信息和基站信息，而无论是wifi信息，还是基站信息，还是wifi信息+基站信息均能作为位置信息以表征一个位置，因此得到了预设时间段内的常驻位置聚类结果即得到了设时间段内用户的常驻位置信息。
在一种示例性实例中，终端根据不同采样点信息包括的采样对象信息之间的联系对单日的采样点信息进行聚类，得到单日的常驻位置聚类结果，包括：
步骤301、终端根据不同采样点信息包括的wifi信息之间的联系对单日的采样点信息中包括wifi信息的采样点信息进行聚类，得到单日基于wifi信息的常驻位置聚类结果。
步骤302、终端基于单日基于wifi信息的常驻位置聚类结果、并根据不同采样点信息包括的基站信息之间的联系对单日的采样点信息中只包括基站信息的采样点信息进行再聚类，得到单日的常驻位置聚类结果。
在一种示例性实例中，wifi信息包括：终端能够扫描到的包括至少一个wifi的wifi列表，终端根据不同采样点信息包括的wifi信息之间的联系对单日的采样点信息中包括wifi信息的采样点信息进行聚类，得到单日基于wifi信息的常驻位置聚类结果，包括：
步骤401、终端按照两个采样点信息中存在相同的wifi就包含在一个簇中的规则对单日的采样点信息进行聚类，得到包含若干个簇的预聚类结果。
步骤402、终端依次获取预聚类结果中的每个簇，并每当获得一个簇时对获得的簇进行如下操作：将获得的簇中每个采样点信息包括的wifi列表映射到维度与获得的簇的wifi列表长度相同的空间中以得到wifi列表的空间坐标，并根据不同wifi列表的空间坐标之间的欧式距离对获得的簇中若干个采样点信息进行再聚类以得到若干个子簇。
步骤403、终端在由每个簇获得的若干个子簇中获取采样点个数大于预设数值的子簇，得到单日基于wifi信息的常驻位置聚类结果。
在一种示例性实例中，终端按照两个采样点信息中存在相同的wifi就包含在一个簇中的规则对单日的采样点信息进行聚类，得到包含若干个簇的预聚类结果，包括：
步骤501、终端获取单日的采样点信息中第一个采样点信息作为一个簇，并将第一个采样点信息的wifi列表作为所在簇的wifi列表，终端依次获取单日的采样点信息中除第一个采样点信息以外的采样点信息，并每当获得一个采样点信息时对获得的采样点信息进行如下操作：
步骤501a、终端判断获得的采样点信息中的至少一个wifi是否存在于已存在的任意一个簇的wifi列表中。
步骤501b、当获得的采样点信息中的至少一个wifi存在于已存在的一个簇的wifi列表中，将获得的采样点信息加入该簇，并将获得的采样信息的wifi列表加入该簇的wifi列表中。
步骤501c、当获得的采样点信息中的所有wifi均不存在于已存在的所有簇的wifi列表中，将获得的采样点信息作为一个新的簇，并将获得的采样点信息的wifi列表作为新的簇的wifi列表。
在一种示例性实例中，采样点信息的wifi列表为wifi信号强度从强到弱排列在第一预设位数前的wifi的列表。
在一种示例性实例中，单日的采样点信息包括：单日经过降噪处理的采样点信息。终端根据不同采样点信息包括的wifi信息之间的联系对包括wifi信息的单日的采样点信息进行聚类之前，还包括：
终端对单日的采样点信息进行降噪处理，得到单日经过降噪处理的采样点信息。
在一种示例性实例中，述终端对单日的采样点信息进行降噪处理，包括：
步骤601、终端统计单日的采样点信息中所有采样点信息包括的采样对象信息出现的次数。
步骤602、终端获取出现的次数小于预设次数的采样对象信息。
步骤603、终端剔除获得的采样对象信息所属的采样点信息。
在一种示例性实例中，终端将获得的簇中每个采样点信息包括的wifi列表映射到维度与获得的簇的wifi列表长度相同的空间中以得到wifi列表的空间坐标，包括：
步骤701、终端获取获得的簇的wifi列表长度。
步骤702、终端创建维度与获得的簇的wifi列表的长度相同的预设数组。
步骤703、终端确定预设数组中的每个元素对应获得的簇的wifi列表中的wifi以及用于表示该wifi的值。
步骤704、终端依次获取获得的簇中的每个采样点信息，并每当获得一个采样点信息时对获得的采样点信息进行如下操作：根据获得的采样点信息包括的wifi列表、预设数组中的元素对应获得的簇的wifi列表中的wifi以及用于表示该wifi的值填充预设数组，得到获得的采样点信息包括的wifi列表的空间坐标。
在一种示例性实例中，终端根据不同wifi列表的空间坐标之间的欧式距离对获得的簇中若干个采样点信息进行再聚类以得到若干个子簇，包括：
步骤801、终端将获得的簇的采样点信息按照采样对象信息的出现次数由 大到小排序，得到降序列表。
步骤802、终端获取降序列表中的第一个采样点信息作为一个子簇，并将该采样点信息包括的wifi列表的空间坐标作为该子簇的中心，终端依次获取降序列表中除第一个采样点信息以外的采样点信息，并每当获得一个采样点信息时对获得的采样点信息进行如下操作：
步骤802a、终端判断获得的采样点信息包括的wifi列表的空间坐标与已存在的任意一个子簇的中心的欧式距离是否小于预设阈值。
步骤802b、当获得的采样点信息包括的wifi列表的空间坐标与一个子簇的中心的欧式距离小于预设阈值，将该采样点信息加入该子簇。
步骤802c、当获得的采样点信息包括的wifi列表的空间坐标与所有子簇的中心的欧式距离均不小于预设阈值，将该采样点信息作为一个新的子簇。
在一种示例性实例中，单日基于wifi信息的常驻位置聚类结果包括：单日按照停留时间排序的基于wifi信息的常驻位置的聚类结果；终端得到单日基于wifi信息的常驻位置聚类结果之后，且对单日的采样点信息中只包括基站信息的采样点信息进行再聚类之前，还包括：
终端将单日基于wifi信息的常驻位置的聚类结果按照子簇所包含的的采样点信息的个数从大到小排序，得到单日按照停留时间排序的基于wifi信息的常驻位置的聚类结果。
在一种示例性实例中，终端基于单日基于wifi信息的常驻位置聚类结果、并根据不同采样点信息包括的基站信息之间的联系对单日的采样点信息中只包括基站信息的采样点信息进行再聚类，得到单日的常驻位置聚类结果，包括：
步骤901、终端在单日的采样点信息中依次获取每一个只包含基站信息的采样点信息，并对获得的采样点信息进行如下操作，以得到单日的常驻位置聚类结果：
步骤901a、终端将获得的采样点信息包括的基站信息依次与单日按照停留时间排序的基于wifi信息的常驻位置的聚类结果中每个子簇的基站信息进行比较。
步骤901b、当获得的采样点信息包括的基站信息存在于一个子簇中，终端将获得的采样点信息合并至该子簇中。
步骤901c、当获得的采样点信息包括的基站信息不存在于任意一个子簇中，终端将获得的采样点信息作为一个新的子簇。
在一种示例性实例中，终端对所有单日的常驻位置聚类结果进行合并，得到预设时间段内的常驻位置聚类结果，包括：
步骤1001、终端获取预设时间段内第一个单日的常驻位置聚类结果作为历史常驻位置聚类结果，终端依次获取除第一个单日的常驻位置聚类结果以外单日的常驻位置聚类结果，并每当获得一个单日的常驻位置聚类结果时对获得的单日的常驻位置聚类结果进行如下操作：
步骤1001a、终端将获得的单日的常驻位置聚类结果与历史常驻位置聚类结果合并，得到新的历史常驻位置聚类结果，以用于与下一个获得的单日的常驻位置聚类结果合并。
在一种示例性实例中，终端将获得的单日的常驻位置聚类结果与历史常驻位置聚类结果合并，得到新的历史常驻位置聚类结果，包括：
步骤1101、终端根据wifi信息的联系对单日的常驻位置聚类结果中包含wifi信息的簇和历史常驻聚类结果中包括wifi信息的簇进行合并，得到新的基于wifi信息的历史常驻位置聚类结果。
步骤1102、终端在新的基于wifi信息的历史常驻位置聚类结果上、根据不同采样点信息包括的基站信息之间的联系对单日的常驻位置聚类结果中只包含基站信息的簇进行再聚类，得到新的历史常驻位置聚类结果。
在一种示例性实例中，终端根据wifi信息的联系对单日的常驻位置聚类结果中包含wifi信息的簇和历史常驻聚类结果中包括wifi信息的簇进行合并，得到新的基于wifi信息的历史常驻位置聚类结果，包括：
步骤1201、终端依次获取历史常驻位置聚类结果中的簇，并每当获得一个簇时对获得的簇进行以下操作：将获得的簇的wifi列表中的wifi按照所包含在采样点信息的个数从多到少进行排序，并获取排序在第二预设位数前的wifi，得到第一wifi列表。
步骤1202、终端依次获取单日常驻位置聚类结果中的簇，并每当获得一个簇时对获得的簇进行以下操作：将获得的簇的wifi列表中的wifi按照所包含在采样点信息的个数从多到少进行排序，并获取排序在第三预设位数前的wifi，得到第二wifi列表。
步骤1203、终端依次获取单日的常驻位置聚类结果中每个簇的第二wifi列表，并每当获得一个第二wifi列表时对获得的第二wifi列表进行如下操作：
步骤1203a、终端判断获得的簇的第二wifi列表中至少一个wifi是否存在于任意一个第一wifi列表中。
步骤1203b、当获得的簇的第二wifi列表中至少一个wifi存在于一个第一wifi列表中，将获得的簇合并至与该第一wifi列表对应的簇中。
步骤1203c、当获得的簇的第二wifi列表中的所有wifi均不存在于所有第一wifi列表中，将获得的簇作为一个新的簇。
在一种示例性实例中，新的基于wifi信息的历史常驻位置聚类结果包括：新的按照停留时间排序的基于wifi信息的历史常驻位置聚类结果。终端得到新的基于wifi信息的历史常驻位置聚类结果之后，且对单日的常驻位置聚类结果中只包含基站信息的簇进行再聚类之前，还包括：
终端将新的基于wifi信息的历史常驻位置聚类结果按照簇所包含的采样点信息的个数从大到小排序，得到新的按照停留时间排序的基于wifi信息的历史常驻位置聚类结果。
在一种示例性实例中，终端在新的基于wifi信息的历史常驻位置聚类结果上、根据不同采样点信息包括的基站信息之间的联系对单日的常驻位置聚类结果中只包含基站信息的簇进行再聚类，得到新的历史常驻位置聚类结果，包括：
步骤1301、终端依次获取单日的常驻位置聚类结果中只包含基站信息的簇，并每当获得一个簇时对获得的簇进行如下操作，以得到新的历史常驻位置聚类结果：
步骤1301a、终端判断获得的簇的基站信息是否存在于新的按照停留时间排序的基于wifi信息的历史常驻位置聚类结果中的簇的基站信息中。
步骤1301b、当获得的簇的基站信息存在于新的按照停留时间排序的基于wifi信息的历史常驻位置聚类结果中一个目标簇的基站信息中，终端将获得的簇合并至目标簇中。
步骤1301c、当获得的簇的基站信息不存在于新的按照停留时间排序的基于wifi信息的历史常驻位置聚类结果中任意一个簇的基站信息中，终端将获得的簇作为一个新的簇。
在一种示例性实例中，所述基站信息包括：移动国家代码(Mobile Country Code，MCC)、移动网络代码(Mobile Neworkt Code，MNC)、位置区域码(Location Area Code，LAC)、基站编号(CELLIDentification)CELLID、网络制式，其中，所述网络制式包括：全球移动通信系统(Global System for Mobile Communications，GSM)、码分多址(Code Division Multiple Access，CDMA)、第三代移动通信技术(the 3th Generation mobile communication technology，3G)、第四代移动通信技术(the 4th Generation mobile communication technology，4G)中的一种。
本申请实施例提供的用户常驻位置的获取方法，由于wifi信息和/或基站信息能够用于表征位置，因此当终端获取采样时间包含在预设时间段内的若干个采样点信息，并根据不同采样点信息包括的采样对象信息(采样对象信息包括wifi信息和基站信息中至少一种)之间的联系对获得的若干个采样点信息进行聚类，就能够得到预设时间段内常驻位置的聚类结果，从而实现了用户常驻位置的获取。
本申请实施例还提供一种终端，如图2所示，该终端2包括：
获取模块21，用于获取预设时间段内的若干个采样点信息；其中，每个采样点信息是终端根据采样周期获得的且包括：采样时间和采样对象信息，每个采样对象信息至少包括以下一种：无线保真wifi信息和基站信息。
处理模块22，用于根据不同采样点信息包括的采样对象信息之间的联系对获得的若干个采样点信息进行聚类，得到预设时间段内用户的常驻位置信息。
在一种示例性实例中，预设时间段为M天，采样周期为N分钟，M、N均为正整数。处理模块22具体用于：
根据采样时间的所属日期对获得的若干个采样点信息进行划分，得到若干个单日的采样点信息。
对每一个单日的采样点信息都进行如下操作：根据不同采样点信息包括的采样对象信息之间的联系对单日的采样点信息进行聚类，得到单日的常驻位置聚类结果。
对所有单日的常驻位置聚类结果进行合并，得到预设时间段内的常驻位置聚类结果。
根据获得的预设时间段内的常驻位置聚类结果得到所述预设时间段内用户的常驻位置信息。
在一种示例性实例中，处理模块22具体用于：
根据不同采样点信息包括的wifi信息之间的联系对单日的采样点信息中包括wifi信息的采样点信息进行聚类，得到单日基于wifi信息的常驻位置聚类结果。
基于单日基于wifi信息的常驻位置聚类结果、并根据不同采样点信息包括的基站信息之间的联系对单日的采样点信息中只包括基站信息的采样点信息进行再聚类，得到单日的常驻位置聚类结果。
在一种示例性实例中，wifi信息包括：终端能够扫描到的包括至少一个wifi的wifi列表。处理模块22具体用于：
按照两个采样点信息中存在相同的wifi就包含在一个簇中的规则对单日的采样点信息进行聚类，得到包含若干个簇的预聚类结果。
依次获取预聚类结果中的每个簇，并每当获得一个簇时对获得的簇进行如下操作：将获得的簇中每个采样点信息包括的wifi列表映射到维度与获得的簇的wifi列表长度相同的空间中以得到wifi列表的空间坐标，并根据不同wifi列表的空间坐标之间的欧式距离对获得的簇中若干个采样点信息进行再聚类以得到若干个子簇。
终端在由每个簇获得的若干个子簇中获取采样点个数大于预设数值的子簇，得到单日基于wifi信息的常驻位置聚类结果。
在一种示例性实例中，处理模块22具体用于：
获取单日的采样点信息中第一个采样点信息作为一个簇，并将第一个采样点信息的wifi列表作为所在簇的wifi列表，终端依次获取单日的采样点信息中除第一个采样点信息以外的采样点信息，并每当获得一个采样点信息时对获得的采样点信息进行如下操作：
判断获得的采样点信息中的至少一个wifi是否存在于已存在的任意一个簇的wifi列表中。
当获得的采样点信息中的至少一个wifi存在于已存在的一个簇的wifi列表中，将获得的采样点信息加入该簇，并将获得的采样信息的wifi列表加入该簇的wifi列表中。
当获得的采样点信息中的所有wifi均不存在于已存在的所有簇的wifi列表中，将获得的采样点信息作为一个新的簇，并将获得的采样点信息的wifi列表作为新的簇的wifi列表。
在一种示例性实例中，采样点信息的wifi列表为wifi信号强度从强到弱排列在第一预设位数前的wifi的列表。
在一种示例性实例中，单日的采样点信息包括：单日经过降噪处理的采样点信息。处理模块22，还用于对单日的采样点信息进行降噪处理，得到单日经过降噪处理的采样点信息。
在一种示例性实例中，处理模块22具体用于：
计单日的采样点信息中所有采样点信息包括的采样对象信息出现的次数。
获取出现的次数小于预设次数的采样对象信息。
剔除获得的采样对象信息所属的采样点信息。
在一种示例性实例中，处理模块22具体用于：
获取获得的簇的wifi列表长度。
创建维度与获得的簇的wifi列表的长度相同的预设数组。
确定预设数组中的每个元素对应获得的簇的wifi列表中的wifi以及用于表示该wifi的值。
依次获取获得的簇中的每个采样点信息，并每当获得一个采样点信息时 对获得的采样点信息进行如下操作：根据获得的采样点信息包括的wifi列表、预设数组中的元素对应获得的簇的wifi列表中的wifi以及用于表示该wifi的值填充预设数组，得到获得的采样点信息包括的wifi列表的空间坐标。
在一种示例性实例中，处理模块22具体用于：
将获得的簇的采样点信息按照采样对象信息的出现次数由大到小排序，得到降序列表。
获取降序列表中的第一个采样点信息作为一个子簇，并将该采样点信息包括的wifi列表的空间坐标作为该子簇的中心，终端依次获取降序列表中除第一个采样点信息以外的采样点信息，并每当获得一个采样点信息时对获得的采样点信息进行如下操作：
判断获得的采样点信息包括的wifi列表的空间坐标与已存在的任意一个子簇的中心的欧式距离是否小于预设阈值。
当获得的采样点信息包括的wifi列表的空间坐标与一个子簇的中心的欧式距离小于预设阈值，将该采样点信息加入该子簇。
当获得的采样点信息包括的wifi列表的空间坐标与所有子簇的中心的欧式距离均不小于预设阈值，将该采样点信息作为一个新的子簇。
在一种示例性实例中，单日基于wifi信息的常驻位置聚类结果包括：单日按照停留时间排序的基于wifi信息的常驻位置的聚类结果。处理模块22，还用于将单日基于wifi信息的常驻位置的聚类结果按照子簇所包含的的采样点信息的个数从大到小排序，得到单日按照停留时间排序的基于wifi信息的常驻位置的聚类结果。
在一种示例性实例中，处理模块22具体用于：
在单日的采样点信息中依次获取每一个只包含基站信息的采样点信息，并对获得的采样点信息进行如下操作，以得到单日的常驻位置聚类结果：
将获得的采样点信息包括的基站信息依次与单日按照停留时间排序的基于wifi信息的常驻位置的聚类结果中每个子簇的基站信息进行比较。
当获得的采样点信息包括的基站信息存在于一个子簇中，将获得的采样点信息合并至该子簇中。
当获得的采样点信息包括的基站信息不存在于任意一个子簇中，将获得的采样点信息作为一个新的子簇。
在一种示例性实例中，处理模块22具体用于：
获取预设时间段内第一个单日的常驻位置聚类结果作为历史常驻位置聚类结果，终端依次获取除第一个单日的常驻位置聚类结果以外单日的常驻位置聚类结果，并每当获得一个单日的常驻位置聚类结果时对获得的单日的常驻位置聚类结果进行如下操作：
将获得的单日的常驻位置聚类结果与历史常驻位置聚类结果合并，得到新的历史常驻位置聚类结果，以用于与下一个获得的单日的常驻位置聚类结果合并。
在一种示例性实例中，处理模块22具体用于：
根据wifi信息的联系对单日的常驻位置聚类结果中包含wifi信息的簇和历史常驻聚类结果中包括wifi信息的簇进行合并，得到新的基于wifi信息的历史常驻位置聚类结果。
在新的基于wifi信息的历史常驻位置聚类结果上、根据不同采样点信息包括的基站信息之间的联系对单日的常驻位置聚类结果中只包含基站信息的簇进行再聚类，得到新的历史常驻位置聚类结果。
在一种示例性实例中，处理模块22具体用于：
依次获取历史常驻位置聚类结果中的簇，并每当获得一个簇时对获得的簇进行以下操作：将获得的簇的wifi列表中的wifi按照所包含在采样点信息的个数从多到少进行排序，并获取排序在第二预设位数前的wifi，得到第一wifi列表。
依次获取单日常驻位置聚类结果中的簇，并每当获得一个簇时对获得的簇进行以下操作：将获得的簇的wifi列表中的wifi按照所包含在采样点信息的个数从多到少进行排序，并获取排序在第三预设位数前的wifi，得到第二wifi列表。
依次获取单日的常驻位置聚类结果中每个簇的第二wifi列表，并每当获得一个第二wifi列表时对获得的第二wifi列表进行如下操作：
判断获得的簇的第二wifi列表中至少一个wifi是否存在于任意一个第一wifi列表中。
当获得的簇的第二wifi列表中至少一个wifi存在于一个第一wifi列表中，将获得的簇合并至与该第一wifi列表对应的簇中。
当获得的簇的第二wifi列表中的所有wifi均不存在于所有第一wifi列表中，将获得的簇作为一个新的簇。
在一种示例性实例中，新的基于wifi信息的历史常驻位置聚类结果包括：新的按照停留时间排序的基于wifi信息的历史常驻位置聚类结果。处理模块22，还用于将新的基于wifi信息的历史常驻位置聚类结果按照簇所包含的采样点信息的个数从大到小排序，得到新的按照停留时间排序的基于wifi信息的历史常驻位置聚类结果。
在一种示例性实例中，处理模块22具体用于：
依次获取单日的常驻位置聚类结果中只包含基站信息的簇，并每当获得一个簇时对获得的簇进行如下操作，以得到新的历史常驻位置聚类结果：
判断获得的簇的基站信息是否存在于新的按照停留时间排序的基于wifi信息的历史常驻位置聚类结果中的簇的基站信息中。
当获得的簇的基站信息存在于新的按照停留时间排序的基于wifi信息的历史常驻位置聚类结果中一个目标簇的基站信息中，将获得的簇合并至目标簇中。
当获得的簇的基站信息不存在于新的按照停留时间排序的基于wifi信息的历史常驻位置聚类结果中任意一个簇的基站信息中，将获得的簇作为一个新的簇。
在一种示例性实例中，基站信息包括：MCC、MNC、LAC、CELLID、网络制式，其中，网络制式包括：GSM、CDMA、3G和4G中的一种。
本申请实施例提供的终端，由于wifi信息和/或基站信息能够用于表征位置，因此当终端获取采样时间包含在预设时间段内的若干个采样点信息，并根据不同采样点信息包括的采样对象信息(采样对象信息包括wifi信息和基站信息中至少一种)之间的联系对获得的若干个采样点信息进行聚类，就能 够得到预设时间段内常驻位置的聚类结果，从而实现了用户常驻位置的获取。
在实际应用中，所述获取模块21和处理模块22均位于终端中的中央处理器(Central Processing Unit，CPU)、微处理器(Micro Processor Unit，MPU)、数字信号处理器(Digital Signal Processor，DSP)或现场可编程门阵列(Field Programmable Gate Array，FPGA)等实现。
本申请实施例还提供一种用户常驻位置的获取装置，如图3所示，该装置3包括：
数据采集模块31，用于每五分钟进行一次数据采集，采集的内容包括：基站信息：MCC、MNC、LAC、CELLID、网络制式(GSM、CDMA、3G、4G)，WiFi信息：扫描列表中排前三位的WiFi的MAC地址。
数据存储模块32，用于进行存储数据，所存储的数据包括两部分内容：一是数据采集模块31采集到的有效原始数据，二是聚类算法模块34得到的聚类结果数据。
数据预处理模块33，用于数据预处理仅在算法更新前运行，算法每天更新一次。预处理时，从数据库中将一天内采集到的所有原始数据读出，并统计各WiFi和基站出现的次数，累积次数少于5次的数据认为是噪声数据予以剔除(将只包含噪声数据的采样点从原始数据中删除)。其中，WiFi以其Mac地址作为唯一标识；基站则由MCC、MNC、LAC、CELLID和网络制式这一组数据共同表征，五个参数均一致才表示同一个基站。
聚类算法模块34，用于对剩余的有效数据进行聚类，将一天的WiFi和基站数据聚类到多个簇中，每个簇中的WiFi和基站即代表一个具体位置。算法根据应用场景的特性，以层次聚类算法中的自顶向下分裂法思想为基础设计实现。算法具体流程如下：
经过数据预处理后，用户运动过程中的采样点已经被删除，只有用户停留时间超过25分钟的位置所对应的采样数据能被保留。所以大部分情况下不同位置的采样数据是相互独立的。
步骤1、首先对所有包含WiFi的采样数据进行第一轮分裂，该过程包括以下步骤：
步骤1.1、将采样点按采样时间顺序依次读取，采样点中的WiFi列表作为其表征；
步骤1.2、读取出第一个点形成第一个簇，簇由其内所有采样点的WiFi列表和基站列表的并集表征；
步骤1.3、读取后续采样点，计算该采样点与各已有簇之间的Jaccard距离，如果其中某簇与该点的Jaccard距离小于1，则将该点合入到这个簇中，并更新簇的WiFi列表和基站列表；如果均不小于1，则将该采样点形成一个新簇。Jaccard距离计算公式见式1
Figure PCTCN2020081242-appb-000001
其中，J表示Jaccard距离，A和B表示采样点或簇的WiFi集合列表。
所有采样点提取完后，计算所有簇之间的Jaccard距离，将距离小于1的簇合并。
步骤2、第一次分裂得到了一个相对较粗略的位置划分，还需要进行第二次分裂。对上面得到的每一个簇做下列处理，包括以下步骤：
步骤2.1、计算出簇的WiFi列表的长度N；
步骤2.2、统计出各采样点的出现次数，降序排列形成列表Lp；
步骤2.3、建立N维空间，一个WiFi代表一个维度，采样点若包含某轴对应的WiFi，则该采样点在该轴上取值为1，否则为0；这样就可以将所有采样点映射到此N维空间中，进而计算出各点之间的欧式距离；
步骤2.4、将列表Lp的第一个采样点作为聚类中心，形成第一个簇，簇的中心为该采样点在上述N维空间的坐标；
步骤2.5、依次提取列表Lp中的采样点，计算采样点与已有簇中心点的欧式距离；若存在距离小于2的簇，则将该点合入到这个簇中，并将所有采样点的平均值作为新的簇中心点；若不存在，则将该采样点形成一个新簇；
步骤2.6、重复步骤2.5完成所有采样点的聚类；
步骤2.7、将新聚类簇中采样点个数小于5个的簇去掉，并将形成的簇从大到小排列生成列表Lw。
步骤3、簇中所有采样点的WiFi集合和基站集合就是该簇的表征参数。至此得到以WiFi为基准的聚类结果，下一步对未包含WiFi(只有基站数据)的采样点进行处理：
将不包含WiFi的采样点，依次与Lw中的各簇进行比较。如果该采样点的基站与某簇中的基站一致，则将该点合入到这个簇中；如果是Lw中所有簇里从未出现的基站，则将该采样点形成一个新簇。最终得到一天数据聚类出的最终结果列表Lo，Lo中每一个簇代表一个位置地点。
步骤4、将一天的聚类结果Lo与历史聚类结果进行合并，将聚类结果以未到达时间升序排列，只保留前一百个聚类点，其余删除，得到最终聚类列表La。
本申请实施例还提供一种用户常驻位置的获取装置，包括存储器，处理器及存储在存储器上并可在处理器运行的计算机程序，处理器执行计算机程序时实现上述实施例中任意一种用户常驻位置的获取方法。
本申请实施例还提供一种计算机可读存储介质，存储介质上存储有计算机可执行命令，计算机可执行命令用于执行如上述实施例中任意一种用户常驻位置的获取方法。
虽然本申请实施例所揭露的实施方式如上，但所述的内容仅为便于理解本申请实施例而采用的实施方式，并非用以限定本申请实施例。任何本申请实施例所属领域内的技术人员，在不脱离本申请实施例所揭露的精神和范围的前提下，可以在实施的形式及细节上进行任何的修改与变化，但本申请实施例的专利保护范围，仍须以所附的权利要求书所界定的范围为准。

Claims (20)

  1. 一种用户常驻位置的获取方法，包括：
    终端获取预设时间段内的若干个采样点信息；其中，每个所述采样点信息是所述终端根据采样周期获得的且包括：采样时间和采样对象信息，每个所述采样对象信息至少包括以下一种：无线保真wifi信息和基站信息：
    所述终端根据不同采样点信息包括的采样对象信息之间的联系对获得的若干个采样点信息进行聚类，得到所述预设时间段内用户的常驻位置信息。
  2. 根据权利要求1所述的获取方法，其中，所述预设时间段为M天，所述采样周期为N分钟，所述M、N均为正整数，所述终端根据不同采样点信息包括的采样对象信息之间的联系对获得的若干个采样点信息进行聚类，得到预设时间段内用户的常驻位置信息，包括：
    所述终端根据采样时间的所属日期对获得的若干个采样点信息进行划分，得到若干个单日的采样点信息；
    所述终端对每一个单日的采样点信息都进行如下操作：根据不同采样点信息包括的采样对象信息之间的联系对单日的采样点信息进行聚类，得到单日的常驻位置聚类结果；
    所述终端对所有单日的常驻位置聚类结果进行合并，得到所述预设时间段内的常驻位置聚类结果；
    所述终端根据获得的预设时间段内的常驻位置聚类结果得到所述预设时间段内用户的常驻位置信息。
  3. 根据权利要求2所述的获取方法，其中，所述终端根据不同采样点信息包括的采样对象信息之间的联系对单日的采样点信息进行聚类，得到单日的常驻位置聚类结果，包括：
    所述终端根据不同采样点信息包括的wifi信息之间的联系对单日的采样点信息中包括wifi信息的采样点信息进行聚类，得到单日基于wifi信息的常驻位置聚类结果；
    所述终端基于所述单日基于wifi信息的常驻位置聚类结果、并根据不同 采样点信息包括的基站信息之间的联系对单日的采样点信息中只包括基站信息的采样点信息进行再聚类，得到单日的常驻位置聚类结果。
  4. 根据权利要求3所述的获取方法，其中，所述wifi信息包括：所述终端能够扫描到的包括至少一个wifi的wifi列表，所述终端根据不同采样点信息包括的wifi信息之间的联系对单日的采样点信息中包括wifi信息的采样点信息进行聚类，得到单日基于wifi信息的常驻位置聚类结果，包括：
    所述终端按照两个采样点信息中存在相同的wifi就包含在一个簇中的规则对单日的采样点信息进行聚类，得到包含若干个簇的预聚类结果；
    所述终端依次获取所述预聚类结果中的每个簇，并每当获得一个簇时对获得的簇进行如下操作：将获得的簇中每个采样点信息包括的wifi列表映射到维度与获得的簇的wifi列表长度相同的空间中以得到wifi列表的空间坐标，并根据不同wifi列表的空间坐标之间的欧式距离对获得的簇中若干个采样点信息进行再聚类以得到若干个子簇；
    所述终端在由每个簇获得的若干个子簇中获取采样点个数大于预设数值的子簇，得到所述单日基于wifi信息的常驻位置聚类结果。
  5. 根据权利要求4所述的获取方法，其中，所述终端按照两个采样点信息中存在相同的wifi就包含在一个簇中的规则对单日的采样点信息进行聚类，得到包含若干个簇的预聚类结果，包括：
    所述终端获取单日的采样点信息中第一个采样点信息作为一个簇，并将第一个采样点信息的wifi列表作为所在簇的wifi列表，所述终端依次获取单日的采样点信息中除第一个采样点信息以外的采样点信息，并每当获得一个采样点信息时对获得的采样点信息进行如下操作：
    所述终端判断获得的采样点信息中的至少一个wifi是否存在于已存在的任意一个簇的wifi列表中；
    当获得的采样点信息中的至少一个wifi存在于已存在的一个簇的wifi列表中，将获得的采样点信息加入该簇，并将获得的采样信息的wifi列表加入该簇的wifi列表中；
    当获得的采样点信息中的所有wifi均不存在于已存在的所有簇的wifi列 表中，将获得的采样点信息作为一个新的簇，并将获得的采样点信息的wifi列表作为新的簇的wifi列表。
  6. 根据权利要求5所述的获取方法，其中，所述采样点信息的wifi列表为wifi信号强度从强到弱排列在第一预设位数前的wifi的列表。
  7. 根据权利要求2-5任一项所述的获取方法，其中，所述单日的采样点信息包括：单日经过降噪处理的采样点信息；
    所述终端根据不同采样点信息包括的wifi信息之间的联系对包括wifi信息的单日的采样点信息进行聚类之前，还包括：
    所述终端对单日的采样点信息进行降噪处理，得到所述单日经过降噪处理的采样点信息。
  8. 根据权利要求7所述的获取方法，其中，所述终端对单日的采样点信息进行降噪处理，包括：
    所述终端统计单日的采样点信息中所有采样点信息包括的采样对象信息出现的次数；
    所述终端获取出现的次数小于预设次数的采样对象信息；
    所述终端剔除获得的采样对象信息所属的采样点信息。
  9. 根据权利要求4所述的获取方法，其中，所述终端将获得的簇中每个采样点信息包括的wifi列表映射到维度与获得的簇的wifi列表长度相同的空间中以得到wifi列表的空间坐标，包括：
    所述终端获取获得的簇的wifi列表长度；
    所述终端创建维度与获得的簇的wifi列表的长度相同的预设数组；
    所述终端确定所述预设数组中的每个元素对应获得的簇的wifi列表中的wifi以及用于表示该wifi的值；
    所述终端依次获取获得的簇中的每个采样点信息，并每当获得一个采样点信息时对获得的采样点信息进行如下操作：根据获得的采样点信息包括的wifi列表、所述预设数组中的元素对应获得的簇的wifi列表中的wifi以及用于表示该wifi的值填充所述预设数组，得到获得的采样点信息包括的wifi列 表的空间坐标。
  10. 根据权利要求9所述的获取方法，其中，所述终端根据不同wifi列表的空间坐标之间的欧式距离对获得的簇中若干个采样点信息进行再聚类以得到若干个子簇，包括：
    所述终端将获得的簇的采样点信息按照采样对象信息的出现次数由大到小排序，得到降序列表；
    所述终端获取降序列表中的第一个采样点信息作为一个子簇，并将该采样点信息包括的wifi列表的空间坐标作为该子簇的中心，所述终端依次获取降序列表中除第一个采样点信息以外的采样点信息，并每当获得一个采样点信息时对获得的采样点信息进行如下操作：
    所述终端判断获得的采样点信息包括的wifi列表的空间坐标与已存在的任意一个子簇的中心的欧式距离是否小于预设阈值；
    当获得的采样点信息包括的wifi列表的空间坐标与一个子簇的中心的欧式距离小于预设阈值，将该采样点信息加入该子簇；
    当获得的采样点信息包括的wifi列表的空间坐标与所有子簇的中心的欧式距离均不小于预设阈值，将该采样点信息作为一个新的子簇。
  11. 根据权利要求4所述的获取方法，其中，所述单日基于wifi信息的常驻位置聚类结果包括：单日按照停留时间排序的基于wifi信息的常驻位置的聚类结果；所述终端得到单日基于wifi信息的常驻位置聚类结果之后，且对单日的采样点信息中只包括基站信息的采样点信息进行再聚类之前，还包括：
    所述终端将单日基于wifi信息的常驻位置的聚类结果按照子簇所包含的的采样点信息的个数从大到小排序，得到单日按照停留时间排序的基于wifi信息的常驻位置的聚类结果。
  12. 根据权利要求11所述的获取方法，其中，所述终端基于单日基于wifi信息的常驻位置聚类结果、并根据不同采样点信息包括的基站信息之间的联系对单日的采样点信息中只包括基站信息的采样点信息进行再聚类，得到单日的常驻位置聚类结果，包括：
    所述终端在单日的采样点信息中依次获取每一个只包含基站信息的采样 点信息，并对获得的采样点信息进行如下操作，以得到单日的常驻位置聚类结果：
    所述终端将获得的采样点信息包括的基站信息依次与单日按照停留时间排序的基于wifi信息的常驻位置的聚类结果中每个子簇的基站信息进行比较；
    当获得的采样点信息包括的基站信息存在于一个子簇中，所述终端将获得的采样点信息合并至该子簇中；
    当获得的采样点信息包括的基站信息不存在于任意一个子簇中，所述终端将获得的采样点信息作为一个新的子簇。
  13. 根据权利要求4所述的获取方法，其中，所述终端对所有单日的常驻位置聚类结果进行合并，得到所述预设时间段内的常驻位置聚类结果，包括：
    所述终端获取所述预设时间段内第一个单日的常驻位置聚类结果作为历史常驻位置聚类结果，所述终端依次获取除第一个单日的常驻位置聚类结果以外单日的常驻位置聚类结果，并每当获得一个单日的常驻位置聚类结果时对获得的单日的常驻位置聚类结果进行如下操作：
    所述终端将获得的单日的常驻位置聚类结果与历史常驻位置聚类结果合并，得到新的历史常驻位置聚类结果，以用于与下一个获得的单日的常驻位置聚类结果合并。
  14. 根据权利要求13所述的获取方法，其中，所述终端将获得的单日的常驻位置聚类结果与历史常驻位置聚类结果合并，得到新的历史常驻位置聚类结果，包括：
    所述终端根据wifi信息的联系对单日的常驻位置聚类结果中包含wifi信息的簇和历史常驻聚类结果中包括wifi信息的簇进行合并，得到新的基于wifi信息的历史常驻位置聚类结果；
    所述终端在新的基于wifi信息的历史常驻位置聚类结果上、根据不同采样点信息包括的基站信息之间的联系对单日的常驻位置聚类结果中只包含基站信息的簇进行再聚类，得到新的历史常驻位置聚类结果。
  15. 根据权利要求14所述的获取方法，其中，所述终端根据wifi信息的联系对单日的常驻位置聚类结果中包含wifi信息的簇和历史常驻聚类结果中 包括wifi信息的簇进行合并，得到新的基于wifi信息的历史常驻位置聚类结果，包括：
    所述终端依次获取历史常驻位置聚类结果中的簇，并每当获得一个簇时对获得的簇进行以下操作：将获得的簇的wifi列表中的wifi按照所包含在采样点信息的个数从多到少进行排序，并获取排序在第二预设位数前的wifi，得到第一wifi列表；
    所述终端依次获取单日常驻位置聚类结果中的簇，并每当获得一个簇时对获得的簇进行以下操作：将获得的簇的wifi列表中的wifi按照所包含在采样点信息的个数从多到少进行排序，并获取排序在第三预设位数前的wifi，得到第二wifi列表；
    所述终端依次获取单日的常驻位置聚类结果中每个簇的第二wifi列表，并每当获得一个第二wifi列表时对获得的第二wifi列表进行如下操作：
    所述终端判断获得的簇的第二wifi列表中至少一个wifi是否存在于任意一个第一wifi列表中；
    当获得的簇的第二wifi列表中至少一个wifi存在于一个第一wifi列表中，将获得的簇合并至与该第一wifi列表对应的簇中；
    当获得的簇的第二wifi列表中的所有wifi均不存在于所有第一wifi列表中，将获得的簇作为一个新的簇。
  16. 根据权利要求14所述的获取方法，其中，所述新的基于wifi信息的历史常驻位置聚类结果包括：新的按照停留时间排序的基于wifi信息的历史常驻位置聚类结果；
    所述终端得到新的基于wifi信息的历史常驻位置聚类结果之后，且对单日的常驻位置聚类结果中只包含基站信息的簇进行再聚类之前，还包括：
    所述终端将新的基于wifi信息的历史常驻位置聚类结果按照簇所包含的采样点信息的个数从大到小排序，得到所述新的按照停留时间排序的基于wifi信息的历史常驻位置聚类结果。
  17. 根据权利要求16所述的获取方法，其中，所述终端在新的基于wifi信息的历史常驻位置聚类结果上、根据不同采样点信息包括的基站信息之间 的联系对单日的常驻位置聚类结果中只包含基站信息的簇进行再聚类，得到新的历史常驻位置聚类结果，包括：
    所述终端依次获取单日的常驻位置聚类结果中只包含基站信息的簇，并每当获得一个簇时对获得的簇进行如下操作，以得到所述新的历史常驻位置聚类结果：
    所述终端判断获得的簇的基站信息是否存在于所述新的按照停留时间排序的基于wifi信息的历史常驻位置聚类结果中的簇的基站信息中；
    当获得的簇的基站信息存在于所述新的按照停留时间排序的基于wifi信息的历史常驻位置聚类结果中一个目标簇的基站信息中，所述终端将获得的簇合并至所述目标簇中；
    当获得的簇的基站信息不存在于所述新的按照停留时间排序的基于wifi信息的历史常驻位置聚类结果中任意一个簇的基站信息中，所述终端将获得的簇作为一个新的簇。
  18. 根据权利要求1或3或11或12或14或16或17所述的获取方法，其中，所述基站信息包括：移动国家代码MCC、移动网络代码MNC、位置区域码LAC、基站编号CELLID、网络制式，其中，所述网络制式包括：全球移动通信系统GSM、码分多址CDMA、第三代移动通信技术3G和第四代移动通信技术4G中的一种。
  19. 一种用户常驻位置的获取装置，其中，包括：存储器和处理器，其中，存储器中存储有以下可被处理器执行的指令：
    获取预设时间段内的若干个采样点信息；其中，每个所述采样点信息是所述终端根据采样周期获得的且包括：采样时间和采样对象信息，每个所述采样对象信息至少包括以下一种：wifi信息和基站信息；
    根据不同采样点信息包括的采样对象信息之间的联系对获得的若干个采样点信息进行聚类，得到所述预设时间段内用户的常驻位置信息。
  20. 一种计算机可读存储介质，其中，存储介质上存储有计算机可执行指令，计算机可执行指令用于执行以下步骤：
    获取预设时间段内的若干个采样点信息；其中，每个所述采样点信息是 所述终端根据采样周期获得的且包括：采样时间和采样对象信息，每个所述采样对象信息至少包括以下一种：wifi信息和基站信息；
    根据不同采样点信息包括的采样对象信息之间的联系对获得的若干个采样点信息进行聚类，得到所述预设时间段内用户的常驻位置信息。
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