WO2019000468A1 - 用户位置识别方法、装置、存储介质及电子设备 - Google Patents

用户位置识别方法、装置、存储介质及电子设备 Download PDF

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Publication number
WO2019000468A1
WO2019000468A1 PCT/CN2017/091380 CN2017091380W WO2019000468A1 WO 2019000468 A1 WO2019000468 A1 WO 2019000468A1 CN 2017091380 W CN2017091380 W CN 2017091380W WO 2019000468 A1 WO2019000468 A1 WO 2019000468A1
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Prior art keywords
location
user
cluster center
electronic device
geographic location
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PCT/CN2017/091380
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English (en)
French (fr)
Inventor
梁昆
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广东欧珀移动通信有限公司
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Application filed by 广东欧珀移动通信有限公司 filed Critical 广东欧珀移动通信有限公司
Priority to CN201780090276.5A priority Critical patent/CN110832918B/zh
Priority to EP17915273.1A priority patent/EP3627339A4/en
Priority to PCT/CN2017/091380 priority patent/WO2019000468A1/zh
Publication of WO2019000468A1 publication Critical patent/WO2019000468A1/zh
Priority to US16/725,850 priority patent/US11082806B2/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases
    • 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
    • H04W4/029—Location-based management or tracking services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic
    • 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

Definitions

  • the present invention relates to the field of communications technologies, and in particular, to a user location identification method, apparatus, storage medium, and electronic device.
  • one of the key points is to identify the location of the user and provide location-based services for the user. For example, by identifying the location, the user is informed at home or at the office, so that some services such as terminal mode intelligent switching can be provided.
  • the embodiment of the invention provides a user location identification method, device, storage medium and electronic device, which can accurately identify the location of the user and provide an effective reference for the intelligent service decision.
  • an embodiment of the present invention provides a user location identification method, including the following steps:
  • the cluster center that matches the current geographic location is determined, and the common location corresponding to the matched cluster center is used as the current location of the user.
  • an embodiment of the present invention provides a user location identification apparatus, including: a clustering module, a first identification module, a first obtaining module, and a first determining module;
  • the clustering module is configured to cluster a plurality of geographical locations of pre-acquired users to obtain a clustering center of each type of geographic location;
  • the first identification module is configured to identify a common location corresponding to each cluster center
  • the first acquiring module is configured to acquire a current geographic location of the user
  • the first determining module is configured to determine a cluster center that matches the current geographic location, and use a common location corresponding to the matched cluster center as the current location of the user.
  • an embodiment of the present invention provides a storage medium, where the storage medium stores a plurality of instructions, the instructions being adapted to be loaded by a processor to perform the method as described in the first aspect above.
  • an embodiment of the present invention provides an electronic device, including: a processor, a memory, a display screen, and a control circuit;
  • the processor is electrically connected to the memory, a display screen and a control circuit, wherein the memory is used for storing instructions and data, the display screen is for displaying information, and the control circuit is configured to control the display screen to display information
  • the processor is configured to perform the following steps:
  • the cluster center that matches the current geographic location is determined, and the common location corresponding to the matched cluster center is used as the current location of the user.
  • FIG. 1 is a schematic diagram of a scenario of a user location identification method according to an embodiment of the present invention.
  • FIG. 1b is a schematic flowchart of a user location identification method according to an embodiment of the present invention.
  • FIG. 2 is another schematic flowchart of a user location identification method according to an embodiment of the present invention.
  • FIG. 3 is another schematic flowchart of a user location identification method according to an embodiment of the present invention.
  • FIG. 4 is a schematic structural diagram of a user location identification apparatus according to an embodiment of the present invention.
  • FIG. 5 is another schematic structural diagram of a user location identification apparatus according to an embodiment of the present invention.
  • FIG. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
  • FIG. 7 is another schematic structural diagram of an electronic device according to an embodiment of the present invention.
  • the existing methods of learning the user's living habits to identify the user's usual location mostly need to be realized through network interaction.
  • the collected habit data of the user needs to be uploaded to the server through the network, and the server performs learning analysis to identify the location of the user.
  • the unauthorized uploading of sensitive information of the user is often involved, which brings a certain degree of security concern to the user, and the user may refuse to use some location-based services.
  • An embodiment of the present invention provides a user location identification method, including the following steps:
  • the cluster center that matches the current geographic location is determined, and the common location corresponding to the matched cluster center is used as the current location of the user.
  • the plurality of geographical locations of the pre-acquired users are clustered, and the cluster centers of each type of geographic location are obtained:
  • the K-means algorithm is used to take the plurality of geographical locations as samples, and a preset number of geographical locations are randomly selected from the samples as clusters for initial clustering, and a clustering center of each type of geographic location is obtained.
  • identifying common locations corresponding to each cluster center includes:
  • the electronic device of the user is counted, and the number of sleeps of the preset duration is met at a position matching each cluster center;
  • the common location corresponding to each cluster center is identified according to the statistical number of sleeps.
  • determining the cluster center where the current geographic location matches includes:
  • the method further includes:
  • the basic service set identifier is correspondingly added to the location set corresponding to the matched cluster center.
  • the method further includes:
  • the common location corresponding to each location set is identified based on the number of sleeps counted.
  • the method further includes:
  • an electronic device may cluster multiple geographical locations (ie, samples) of a user that is pre-acquired, obtain a clustering center of each type of geographic location, identify a common location corresponding to each cluster center, and acquire a user's common location. After the current geographic location, the cluster center that matches the current geographic location may be determined, and the common location corresponding to the matched cluster center is used as the current location of the user.
  • the current geographic location of the acquired user is the geographic location A, and the common location that the geographic location A finally matches is “home”, then the current location of the user is determined to be the home; or the current geographic location of the acquired user is the geographic location.
  • Location B where the common location that the geographic location B eventually matches is "office”, then the current location of the user is determined to be the office.
  • the electronic device can perform some mode of intelligent switching, for example, when the user is identified in the office, the electronic device is in the normal mode, and when the user is recognized at home, the electronic device switches to the vibration or silent mode. So as not to disturb the rest of the user.
  • FIG. 1b is a schematic flowchart of a user location identification method according to an embodiment of the present invention.
  • Step S101 Clustering a plurality of geographical locations of the user that are collected in advance to obtain a clustering center of each type of geographic location.
  • the geographic location may refer to a location represented by latitude and longitude, spatial coordinates, and the like.
  • the user's geographic location can be obtained through the electronic device used by the user, for example, through a map installed on the electronic device.
  • the software development kit (SDK) of the application can be obtained by using the application programming interface (API) of the location-related application (such as social application, life service application, etc.) provided by the system. Make specific limits.
  • API application programming interface
  • the clustering center is the feature information common to the samples corresponding to the information of a certain category.
  • the plurality of geographical locations of the pre-acquired users may be considered as the sample geographic location, for example, the sample geographic location may include l 1 , l 2 ... l n , and n is a positive integer.
  • the geographical position of the sample is latitude and longitude
  • any one of the sample geographic locations l i is composed of a longitude value and a latitude value
  • the geographical position of the sample is the spatial position coordinate
  • the K-means algorithm can be used to cluster the geographical locations of the samples, as follows:
  • j position data from the geographical position of the sample as the initial cluster center u 1 , u 2 ... u j , j is a positive integer, j is smaller than n; the value of j is determined by the desired cluster
  • the number of categories is determined. For example, if the geographical location of the sample needs to be clustered into 2 categories, then j is taken as 2, and if the geographical location of the sample needs to be aggregated into 3 categories, then j is taken as 3;
  • Formula 1 calculates the distance from each sample's geographic location to each cluster center, and divides each sample's geographic location into clusters of cluster centers closest to that point.
  • Formula 2 calculates the coordinate average of all the points (sample geographical points) included in each cluster, and uses this average as the new cluster center of the cluster;
  • the geographical distance of the sample with the distance from the cluster center exceeding the preset distance threshold is repeated, and the second, third and fourth steps are repeated, and the final j cluster centers u 1 are calculated.
  • the preset distance threshold can be customized according to the requirements.
  • K-means clustering algorithm to cluster the geographical locations of the samples as an example.
  • other clustering algorithms may also be used to cluster the geographical locations of the samples, for example, using K-medoids, Clustering algorithms such as Clara are not specifically limited herein.
  • Step S102 identifying a common location corresponding to each cluster center.
  • the common location may be a location where the user often lives and works, such as a home, an office, and the like.
  • the common location corresponding to each cluster center may be identified according to the user's living habits (for example, the habit of using an electronic device).
  • Each cluster center corresponds to a common location. For example, if there are two common locations, the value of j above is 2, and in step 101, two cluster centers u 1 , u 2 are obtained .
  • Step S103 Acquire a current geographic location of the user.
  • the user's current geographic location may be the user's real-time geographic location, which may be obtained through the user's electronic device.
  • Step S104 Determine a cluster center that matches the current geographic location, and use a common location corresponding to the matched cluster center as the current location of the user.
  • the method may be specifically: calculating a distance between the current geographic location and each cluster center, and using a cluster center having the smallest distance from the current geographic location as a cluster center that matches the current geographic location. After the matching cluster center is obtained, the common location corresponding to the matched cluster center may be used as the current location of the user.
  • the electronic device multiple geographical locations of the pre-acquired users are clustered, cluster centers of each type of geographic location are obtained, and then common locations corresponding to each cluster center are identified. After obtaining the current geographic location of the user, determining the cluster center that matches the current geographic location, and using the common location corresponding to the matching cluster center as the current location of the user; the solution is based on the geographic location of the sample.
  • the unauthorized uploading problem involving sensitive user information eliminates the user's security concerns and identifies the location of the user, which can provide an effective reference for location-based intelligent service decision-making.
  • the current location of the user is identified according to the current geographic location of the user.
  • the method in this embodiment includes:
  • Step S201 clustering a plurality of geographical locations of the pre-acquired users to obtain each type of geographic location. Set the cluster center.
  • the specific clustering algorithm may adopt any clustering algorithm such as K-means, K-medoids, Clara, etc., and is not specifically limited herein.
  • the home location and the office of the user are taken as an example.
  • two cluster centers u 1 , u 2 are obtained .
  • Step S202 In the preset sampling period, the electronic device of the user is counted, and the number of sleeps of the preset duration is met at a position matching each cluster center.
  • the preset sampling period can be customized according to requirements, for example, it can be defined as one week, one month, and the like. Based on the habit statistics of many users using electronic devices, when a user is at home, there will be a long period of time (when the user is at rest) not using the electronic device, and the electronic device will be in a dormant state during this time; while the user is in the office Due to work needs, electronic devices will be frequently used, and it is difficult for electronic devices to have a long sleep time. For example, when a user is at home, the electronic device has 6 to 7 hours of continuous sleep time per day, while when the user is in the office, it is difficult for the electronic device to have more than 6 hours of sleep time.
  • the sampleDays is a sampling period, and the sampling period can be, for example, 7 days, 10 days, one month, etc., and is not specifically limited herein.
  • the electronic device In the i-day of the sampling period, at the position matching the cluster center u j , the electronic device has a continuous sleep duration on the current day (can be counted by the continuous screen time of the electronic device), if it matches the cluster center u j At the position of the electronic device, the continuous sleep duration of the electronic device is greater than or equal to the preset duration T thresh , and the counter J j is incremented by 1.
  • Formula 3 is to accumulate the number of times that the electronic device matches the clustering center u j and satisfy the preset duration of sleep, and obtain J j .
  • J j is maximum, the common position corresponding to the cluster center u j is home.
  • the preset duration can be based on the user's actual resting habits, for example, 6 hours, 8 hours, and the like.
  • Step S203 identifying common positions corresponding to each cluster center according to the counted number of sleeps.
  • the sampling period is 7 days
  • the statistically obtained J 1 is 7 (ie, the user's electronic device is matched with u 1
  • the statistically obtained J 2 is 0 (ie, the user's electronic device is at the position matching u 2 . If the number of sleeps greater than or equal to the preset duration is 0, it is known that the common location corresponding to u 1 is home, and the common location corresponding to u 2 is the office.
  • Step S204 Acquire a current geographic location of the user.
  • Step S205 Determine a cluster center where the current geographical location matches, and use the common location corresponding to the matched cluster center as the current location of the user.
  • the method may be specifically: calculating a distance between the current geographic location and each cluster center, and using a cluster center having the smallest distance from the current geographic location as a cluster center that matches the current geographic location. After the matching cluster center is obtained, the common location corresponding to the matched cluster center may be used as the current location of the user.
  • the electronic device multiple geographical locations of the pre-acquired users are clustered, cluster centers of each type of geographic location are obtained, and then common locations corresponding to each cluster center are identified. After obtaining the current geographic location of the user, determining the cluster center that matches the current geographic location, and using the common location corresponding to the matching cluster center as the current location of the user; the solution is based on the geographic location of the sample.
  • the unauthorized uploading problem involving sensitive user information eliminates the user's security concerns and identifies the location of the user, which can provide an effective reference for location-based intelligent service decision-making.
  • the above embodiment describes in detail the method for identifying the common location where the user is currently located according to the current geographic location of the user.
  • This embodiment will describe a method for identifying the common location where the user is currently located according to the basic service set identifier of the wireless network currently connected by the user. Please refer to FIG. 3, the method of this embodiment includes:
  • Step S301 clustering a plurality of geographical locations of the pre-acquired users to obtain a clustering center of each type of geographic location.
  • the specific clustering algorithm may adopt any clustering algorithm such as K-means, K-medoids, Clara, etc., and is not specifically limited herein.
  • the user's home and office are taken as an example for the common location to be identified.
  • two cluster centers u 1 , u 2 are obtained .
  • Step S302 Acquire the basic service set identifier of the wireless network of the user connected to the electronic device at each geographical location.
  • BSSID Basic Service Set IDentity of a wireless network, that is, a Media Access Control (MAC) address of a wireless network, different wireless networks
  • MAC Media Access Control
  • Step S303 determining cluster centers that are matched by the respective geographical locations.
  • the distance between each of the above geographical locations and each cluster center is determined, and the cluster center closest to the geographic location is used as the clustering center of the geographical location matching.
  • Step S304 the foregoing basic service set identifier is correspondingly added to the location set corresponding to the matched cluster center.
  • a cluster center corresponds to a set of locations. Initially, each set of locations is empty. Steps S302 to S304, that is, the BSSID of the wireless network is merged with the cluster center.
  • the geographic location of the user may be obtained. If the geographic location of the user matches a certain cluster center, Adding the BSSID of the wireless network to the location set corresponding to the cluster center.
  • BSSID pm ⁇ , w 2 ⁇ BSSID q1 , BSSID q2 ... BSSID qm ⁇ , m is a positive integer. As the user uses it, the location sets w 1 , w 2 will be continuously expanded.
  • Step S305 In the preset sampling period, the electronic device of the user is counted, and the number of sleeps of the preset duration is met at a position matching the set of each location.
  • the preset sampling period can be customized according to requirements, for example, it can be defined as one week, one month, and the like. Based on the habit statistics of many users using electronic devices, when a user is at home, there will be a long period of time (when the user is at rest) not using the electronic device, and the electronic device will be in a dormant state during this time; while the user is in the office Due to work needs, electronic devices will be frequently used, and it is difficult for electronic devices to have a long sleep time. For example, when a user is at home, the electronic device has 6 to 7 hours of continuous sleep time per day, while when the user is in the office, it is difficult for the electronic device to have more than 6 hours of sleep time.
  • the sampleDays is a sampling period, and the sampling period can be, for example, 7 days, 10 days, one month, etc., and is not specifically limited herein.
  • the electronic device continuously sleeps for the current day (can be counted by the continuous screen time of the electronic device), if it matches the position set w j
  • the duration of the continuous sleep of the electronic device on the current day is greater than or equal to the preset duration T thresh , and the counter J j is incremented by 1.
  • Formula 4 is to accumulate the number of times that the electronic device meets the position set w j and the number of sleeps satisfying the preset duration to obtain J j .
  • J j is maximum
  • the common position corresponding to the position set w j is home, preset
  • the duration can be based on the user's actual resting habits, for example, 6 hours, 8 hours, etc.
  • Step S306 identifying a common location corresponding to each location set according to the counted number of sleeps.
  • the statistically obtained J 1 is 7 (ie, the position of the user's electronic device in the match with w 1 )
  • the statistically obtained J 2 is 0 (ie, the user's electronic device is at the position matching w 2 ) If the number of sleeps greater than or equal to the preset duration is 0, it is known that the common location corresponding to the location set w 1 is the home, and the common location corresponding to the location set w 2 is the office.
  • Step S307 Acquire a basic service set identifier of the wireless network to which the user is currently connected.
  • Step S308 determining a location set matching the basic service set identifier of the wireless network currently connected by the user, and using the common location corresponding to the matched location set as the current location of the user.
  • a plurality of geographical locations of users collected in advance are clustered, a clustering center of each type of geographic location is obtained, and then a location set corresponding to each cluster center is established, and the identification is performed.
  • a common location corresponding to each location set after obtaining the basic service set identifier of the wireless network currently connected by the user, determining a location set matching the basic service set identifier of the wireless network currently connected by the user, and matching the common location corresponding to the location set As the current location of the user; the solution is based on the clustering of the geographical location of the sample, by learning the basic service set identifier and the common location in the electronic device, and identifying the common location of the user according to the learning result, the whole learning
  • the identification process is performed locally on the electronic device and does not require network interaction. Therefore, the unauthorized uploading of sensitive information of the user is not involved, the security concern of the user is dispelled, and the location of the identified user can be location-based. Intelligent service decision making provides an effective reference.
  • the above two embodiments respectively describe the process of identifying the common location where the user is located according to the current geographic location of the user, and identifying the common location where the user is located according to the basic service set identifier of the wireless network currently connected by the user.
  • the two are combined, that is, the common location of the user is identified according to the current geographic location of the user and the basic service set identifier of the wireless network that the user is currently connected to.
  • the specific identification method may refer to the corresponding description in the foregoing, and details are not described herein again.
  • the embodiment of the present invention further provides a user location identifying apparatus, including: a clustering module, a first identifying module, a first acquiring module, and a first determining module;
  • the clustering module is configured to cluster a plurality of geographical locations of pre-acquired users to obtain a clustering center of each type of geographic location;
  • the first identification module is configured to identify a common location corresponding to each cluster center
  • the first acquiring module is configured to acquire a current geographic location of the user
  • the first determining module is configured to determine a cluster center that matches the current geographic location, and use a common location corresponding to the matched cluster center as the current location of the user.
  • the first identification module is configured to: in a preset sampling period, count the electronic devices of the user, and meet the preset number of sleep times at a position matching each cluster center; The common location corresponding to each cluster center is identified according to the statistical number of sleeps.
  • the apparatus further includes: a second obtaining module, a second determining module, and a set establishing module;
  • the second obtaining module is configured to acquire, by the electronic device of the user, a basic service set identifier of a wireless network connected at each geographic location;
  • the second determining module is configured to determine a cluster center that is matched by each geographical location
  • the set establishing module is configured to add the basic service set identifier to the location set corresponding to the matched cluster center.
  • the apparatus further includes a second identification module
  • the second identification module is configured to: during the preset sampling period, the electronic device of the user is counted, and the number of sleeps of the preset duration is met at a position matching each location set; according to statistics The number of sleeps identifies the common location corresponding to each location set.
  • the apparatus further includes: a third obtaining module and a third determining module;
  • the third obtaining module is configured to acquire a basic service set identifier of a wireless network currently connected by the user;
  • the third determining module is configured to determine a location set that matches a basic service set identifier of the wireless network that the user is currently connected to, and uses a common location corresponding to the matched location set as a current location of the user.
  • a user location identification device is also provided, which can be integrated in an electronic device, which can be a mobile internet device (such as a smart phone or a tablet). Intelligent electronic devices such as smart wearable devices (such as smart watches).
  • the user location identifying apparatus may include: a clustering module 401, a first identifying module 402, a first obtaining module 403, and a first determining module 404;
  • the clustering module 401 is configured to cluster the geographical locations of the users collected in advance to obtain a clustering center of each type of geographic location;
  • a first identification module 402 configured to identify a common location corresponding to each cluster center
  • a first obtaining module 403, configured to acquire a current geographic location of the user
  • the first determining module 404 is configured to determine a cluster center that matches the current geographic location, and use a common location corresponding to the matched cluster center as the current location of the user.
  • the first identification module 402 is configured to: in a preset sampling period, count the electronic devices of the user, and meet the preset number of sleep times at a position matching each cluster center; The common location corresponding to each cluster center is identified according to the statistical number of sleeps.
  • the identification apparatus may further include: a second obtaining module 405, a second determining module 406, and a set establishing module 407;
  • a second obtaining module 405, configured to acquire, by the electronic device of the user, a basic service set identifier of a wireless network connected at each geographic location;
  • a second determining module 406 configured to determine a cluster center that is matched by each geographic location
  • the set establishing module 407 is configured to add the basic service set identifier to the location set corresponding to the matched cluster center.
  • the identification device provided by the embodiment of the present invention may further include: a second identification module 408;
  • the second identification module 408 is configured to: during the preset sampling period, the electronic device of the user is counted, and the number of sleeps of the preset duration is satisfied at a position matching each location set; The number of times identifies the common location corresponding to each location set.
  • the present disclosure may further include: a third obtaining module 409 and a third determining module 410;
  • the third obtaining module 409 is configured to obtain a basic service set identifier of the wireless network that the user is currently connected to;
  • a third determining module 410 configured to determine a location set that matches a basic service set identifier of the wireless network that the user is currently connected to, and uses a common location corresponding to the matched location set as the current user The location.
  • the clustering module 401 clusters a plurality of geographical locations of the pre-acquired users to obtain a clustering center of each type of geographic location, and then the first identifying module 402 identifies each
  • the first determining module 404 determines the cluster center that matches the current geographic location, and the common location corresponding to the matching cluster center.
  • the solution learns the geographic location and common location of the sample in the electronic device, and identifies the common location where the user is currently located according to the learning result, and the entire learning and identification process is local to the electronic device.
  • the network interaction is not required. Therefore, the unauthorized uploading of sensitive information of the user is not involved, the security concerns of the user are eliminated, and the location of the identified user can provide an effective reference for the location-based intelligent service decision.
  • the foregoing modules may be implemented as a separate entity, or may be implemented in any combination, and may be implemented as the same or a plurality of entities.
  • the foregoing modules refer to the foregoing method embodiments, and details are not described herein again.
  • An embodiment of the present invention further provides an electronic device, including: a processor, a memory, a display screen, and a control circuit;
  • the processor is electrically connected to the memory, a display screen and a control circuit, wherein the memory is used for storing instructions and data, the display screen is for displaying information, and the control circuit is configured to control the display screen to display information
  • the processor is configured to perform the following steps:
  • the cluster center that matches the current geographic location is determined, and the common location corresponding to the matched cluster center is used as the current location of the user.
  • an electronic device which may be a device such as a smart phone or a tablet.
  • the electronic device 500 includes a processor 501, a memory 502, a display screen 503, and a control circuit 504.
  • the processor 501 is electrically connected to the memory 502, the display screen 503, and the control circuit 504, respectively.
  • the processor 501 is a control center of the electronic device 500, and connects the entire electricity by using various interfaces and lines.
  • the various portions of the child device perform overall monitoring of the electronic device by running or loading an application stored in the memory 502, and invoking data stored in the memory 502, performing various functions and processing data of the electronic device.
  • the processor 501 in the electronic device 500 loads the instructions corresponding to the process of one or more applications into the memory 502 according to the following steps, and is stored and stored in the memory 502 by the processor 501.
  • the application thus implementing various functions:
  • Clustering the geographical locations of pre-acquired users to obtain a clustering center for each type of geographic location;
  • the cluster center that matches the current geographic location is determined, and the common location corresponding to the matched cluster center is used as the current location of the user.
  • the processor 501 when clustering a plurality of geographical locations of the pre-acquired users to obtain cluster centers of each type of geographic location, is configured to perform the following steps: using a K-means algorithm, The geographical location is a sample, and a predetermined number of geographical locations are randomly selected from the sample as clusters for initial clustering, and cluster centers of each type of geographic location are obtained.
  • the processor 501 when identifying a common location corresponding to each cluster center, is configured to perform the following steps: during the preset sampling period, the electronic device of the user is counted and matched with each cluster center. At the position of the device, the number of sleeps of the preset duration is met; and the common position corresponding to each cluster center is identified according to the counted number of sleeps.
  • the processor 501 when determining the current geographic location matching cluster center, is configured to perform the following steps: determining a distance between the current geographic location and each cluster center, and the current geographic location The smallest cluster center is the cluster center that matches the current geographic location.
  • the processor 501 is further configured to perform the following steps: acquiring the electronic device of the user, the basic service set identifier of the wireless network connected at each geographical location Determining the cluster centers that match the respective geographic locations; and correspondingly adding the basic service set identifiers to the location set corresponding to the matching cluster centers.
  • the processor 501 is further configured to perform the following steps: during the preset sampling period, the statistics are performed.
  • the user's electronic device satisfies the preset number of sleep times at a position matching each location set; and identifies a common location corresponding to each location set according to the statistical number of sleeps.
  • the processor 501 is further configured to: obtain a basic service set identifier of the wireless network to which the user is currently connected; The location set of the basic service set identifier of the wireless network currently connected by the user is matched, and the common location corresponding to the matched location set is used as the current location of the user.
  • Memory 502 can be used to store applications and data.
  • the application stored in the memory 502 contains instructions executable in the processor.
  • Applications can form various functional modules.
  • the processor 501 executes various functional applications and data processing by running an application stored in the memory 502.
  • the display screen 503 can be used to display information entered by the user or information provided to the user as well as various graphical user interfaces of the electronic device, which can be composed of images, text, icons, video, and any combination thereof.
  • the control circuit 504 is electrically connected to the display screen 503 for controlling the display screen 503 to display information.
  • the electronic device 500 further includes a radio frequency circuit 505, an input unit 506, an audio circuit 507, a sensor 508, and a power source 509.
  • the processor 501 is electrically connected to the radio frequency circuit 505, the input unit 506, the audio circuit 507, the sensor 508, and the power source 509, respectively.
  • the radio frequency circuit 505 is used for transmitting and receiving radio frequency signals to establish wireless communication with network devices or other electronic devices through wireless communication, and to transmit and receive signals with network devices or other electronic devices.
  • the input unit 506 can be configured to receive input digits, character information, or user characteristic information (eg, fingerprints), and to generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function controls.
  • the input unit 506 can include a fingerprint identification module.
  • the audio circuit 507 can provide an audio interface between the user and the electronic device through a speaker and a microphone.
  • Sensor 508 is used to collect external environmental information.
  • Sensor 308 can include one or more of ambient brightness sensors, acceleration sensors, gyroscopes, and the like.
  • Power source 509 is used to power various components of electronic device 500.
  • the power supply 509 can be logically coupled to the processor 501 through a power management system to enable functions such as managing charging, discharging, and power management through the power management system.
  • the electronic device 500 may further include a camera, a Bluetooth module, and the like, and details are not described herein again.
  • the electronic device clusters a plurality of geographical locations of the user that are collected in advance, obtains a clustering center of each type of geographic location, and then identifies a common location corresponding to each cluster center, and obtains the current user's current location. After the geographic location, the cluster center corresponding to the current geographic location is determined, and the common location corresponding to the matched cluster center is used as the current location of the user; the solution is based on clustering the geographic location of the sample, The electronic device searches the geographical location of the sample and the common location, and identifies the common location where the user is currently located according to the learning result. The entire learning and identification process is performed locally on the electronic device, and does not require network interaction, and therefore does not involve user sensitive information.
  • the unauthorized uploading problem eliminates the user's security concerns and identifies the location of the user, which can provide an effective reference for location-based intelligent service decision-making.
  • the storage medium may include: a read only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.
  • the user location identification method, apparatus, storage medium, and electronic device provided by the embodiments of the present invention are described in detail.
  • the functional modules may be integrated into one processing chip, or each module may exist physically separately, or may be two or More than two modules are integrated in one module.
  • the above integrated modules can be implemented in the form of hardware or in the form of software functional modules.
  • the principles and embodiments of the present invention are described herein with reference to specific examples. The description of the above embodiments is only for the purpose of understanding the method of the present invention and the core idea thereof. Also, those skilled in the art according to the present invention The present invention is not limited by the scope of the present invention.

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Abstract

一种用户位置识别方法，包括：对预先采集的用户的多个地理位置进行聚类，得到每类地理位置的聚类中心，识别每个聚类中心对应的常用位置，获取所述用户的当前地理位置，确定所述当前地理位置匹配的聚类中心，将匹配的聚类中心对应的常用位置，作为所述用户当前所处的位置。本发明还提供一种用户位置识别装置、存储介质及电子设备。

Description

用户位置识别方法、装置、存储介质及电子设备 技术领域
本发明涉及通信技术领域，具体涉及一种用户位置识别方法、装置、存储介质及电子设备。
背景技术
随着智能终端硬件的不断发展，硬件已经不再是瓶颈，如今越来越多的智能终端厂商，从追求高配置的硬件，转向提升软件服务水平。其中，重点之一就是识别用户所处的位置，为用户提供基于位置的服务。例如，通过位置的识别，获知用户在家还是在办公室，从而可以提供一些诸如终端模式智能切换的服务。
发明内容
本发明实施例提供一种用户位置识别方法、装置、存储介质及电子设备，能够准确识别出用户所处位置，为智能服务决策提供有效的参考依据。
第一方面，本发明实施例提供一种用户位置识别方法，包括以下步骤：
对预先采集的用户的多个地理位置进行聚类，得到每类地理位置的聚类中心；
识别每个聚类中心对应的常用位置；
获取所述用户的当前地理位置；
确定所述当前地理位置匹配的聚类中心，将匹配的聚类中心对应的常用位置，作为所述用户当前所处的位置。
第二方面，本发明实施例提供一种用户位置识别装置，包括：聚类模块、第一识别模块、第一获取模块以及第一确定模块；
所述聚类模块，用于对预先采集的用户的多个地理位置进行聚类，得到每类地理位置的聚类中心；
所述第一识别模块，用于识别每个聚类中心对应的常用位置；
所述第一获取模块，用于获取所述用户的当前地理位置；
所述第一确定模块，用于确定所述当前地理位置匹配的聚类中心，将匹配的聚类中心对应的常用位置，作为所述用户当前所处的位置。
第三方面，本发明实施例提供一种存储介质，所述存储介质中存储有多条指令，所述指令适于由处理器加载以执行如上述第一方面所述的方法。
第四方面，本发明实施例提供一种电子设备，包括：处理器、存储器、显示屏以及控制电路；
所述处理器与所述存储器、显示屏以及控制电路电性连接，所述存储器用于存储指令和数据，所述显示屏用于显示信息，所述控制电路用于控制所述显示屏显示信息；所述处理器用于执行以下步骤：
对预先采集的用户的多个地理位置进行聚类，得到每类地理位置的聚类中心；
识别每个聚类中心对应的常用位置；
获取所述用户的当前地理位置；
确定所述当前地理位置匹配的聚类中心，将匹配的聚类中心对应的常用位置，作为所述用户当前所处的位置。
附图说明
为了更清楚地说明本发明实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1a是本发明实施例提供的用户位置识别方法的场景示意图。
图1b为本发明实施例提供的用户位置识别方法的流程示意图。
图2为本发明实施例提供的用户位置识别方法的另一流程示意图。
图3为本发明实施例提供的用户位置识别方法的另一流程示意图。
图4为本发明实施例提供的用户位置识别装置的结构示意图。
图5为本发明实施例提供的用户位置识别装置的另一结构示意图。
图6为本发明实施例提供的电子设备的结构示意图。
图7为本发明实施例提供的电子设备的另一结构示意图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是 全部的实施例。基于本发明中的实施例，本领域技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
现有的对用户的生活习惯进行学习，从而识别用户常处位置(例如家、办公室等)的方法，大多需要通过网络交互实现。例如，需要将采集的用户的生活习惯数据，通过网络上传至服务器，由服务器进行学习分析，从而识别出用户所处的位置。网络交互的过程中，常常会涉及用户敏感信息的非授权上传问题，这会给用户带来一定程度的安全顾虑，导致用户可能拒绝使用一些基于位置提供的服务。
本发明实施例提供了一种用户位置识别方法，包括以下步骤：
对预先采集的用户的多个地理位置进行聚类，得到每类地理位置的聚类中心；
识别每个聚类中心对应的常用位置；
获取所述用户的当前地理位置；
确定所述当前地理位置匹配的聚类中心，将匹配的聚类中心对应的常用位置，作为所述用户当前所处的位置。
一实施例中，对预先采集的用户的多个地理位置进行聚类，得到每类地理位置的聚类中心包括：
采用K均值算法，以所述多个地理位置为样本，从样本中随机选取预设数目个地理位置作为初始的聚类中心进行聚类，得到每类地理位置的聚类中心。
一实施例中，识别每个聚类中心对应的常用位置包括：
在预设采样周期内，统计所述用户的电子设备，在与每个聚类中心匹配的位置处，满足预设时长的休眠次数；
根据统计的休眠次数识别每个聚类中心对应的常用位置。
一实施例中，确定所述当前地理位置匹配的聚类中心包括：
确定所述当前地理位置与每个聚类中心的距离，将与所述当前地理位置距离最小的聚类中心，作为所述当前地理位置匹配的聚类中心。
一实施例中，在得到每类地理位置的聚类中心之后，所述方法还包括：
获取所述用户的电子设备，在各个地理位置处连接的无线网络的基本服务集标识；
确定所述各个地理位置匹配的聚类中心；
将所述基本服务集标识对应加入至匹配的聚类中心对应的位置集合。
一实施例中，在将所述基本服务集标识对应加入至匹配的聚类中心对应的位置集合之后，所述方法还包括：
在所述预设采样周期内，统计所述用户的电子设备，在与每个位置集合匹配的位置处，满足所述预设时长的休眠次数；
根据统计的休眠次数识别每个位置集合对应的常用位置。
一实施例中，在根据统计的休眠次数确定每个位置集合对应的常用位置之后，所述方法还包括：
获取所述用户当前连接的无线网络的基本服务集标识；
确定所述用户当前连接的无线网络的基本服务集标识匹配的位置集合，将匹配的位置集合对应的常用位置，作为所述用户当前所处的位置。
本实施例将从用户位置识别装置的角度进行描述，该用户位置识别装置具体可以集成在电子设备中，例如，该电子设备可以为智能手机、平板电脑等。参见图1a，电子设备可以对预先采集的用户的多个地理位置(即样本)进行聚类，得到每类地理位置的聚类中心，识别每个聚类中心对应的常用位置，在获取用户的当前地理位置之后，可以确定所述当前地理位置匹配的聚类中心，将匹配的聚类中心对应的常用位置，作为所述用户当前所处的位置。例如：所获取的用户的当前地理位置为地理位置A，地理位置A最终匹配到的常用位置为“家”，则确定用户当前所处位置为家；或者所获取的用户的当前地理位置为地理位置B，地理位置B最终匹配到的常用位置为“办公室”，则确定用户当前所处位置为办公室。在确定用户所处位置之后，电子设备可以进行一些模式的智能切换，例如在识别到用户在办公室时，电子设备为正常模式，而当识别到用户在家时，电子设备切换为震动或静音模式，以免打扰用户休息。
在一优选实施例中，提供了一种用户位置识别方法，请参阅图1b，图1b为本发明实施例提供的用户位置识别方法的流程示意图，本实施例的用户位置识别方法包括：
步骤S101，对预先采集的用户的多个地理位置进行聚类，得到每类地理位置的聚类中心。
本实施例中，地理位置可以指经纬度、空间坐标等表示的位置。用户的地理位置可以通过用户使用的电子设备来获取，例如通过电子设备上安装的地图 应用的软件开发工具包(Software Development Kit，SDK)获取，也可以采用系统提供的位置相关应用(例如社交应用、生活服务应用等)程序编程接口(Application Programming Interface，API)来获取，此处不做具体限定。
聚类中心，是用于表征某一类别的信息对应的样本所共有的特征信息。
预先采集的用户的多个地理位置，可以认为是样本地理位置，例如样本地理位置可以包括l1，l2...ln，n为正整数。当样本地理位置为经纬度时，任意一个样本地理位置li由经度值与纬度值构成，i为正整数，例如li＝(loni，lati)；当样本地理位置为空间位置坐标时，任意一个样本地理位置li由横轴x值，纵轴y值及竖轴z值构成，例如li＝(xi，yi，zi)。
具体地，可以采用K均值(k-means)算法对样本地理位置进行聚类，如下：
第一，随机从样本地理位置中选取j个位置数据作为初始的聚类中心u1，u2...uj，j为正整数，j小于n；j的取值，由所需聚类的类别的数量决定，例如需要将样本地理位置聚为2类，则j取2，若需要将样本地理位置聚为3类，则j取3；
第二，按照公式一将每个样本地理位置划分到其所属的聚类中；
Figure PCTCN2017091380-appb-000001
公式一即计算每个样本地理位置到每个聚类中心的距离，将每个样本地理位置划分到离该点最近的聚类中心的聚类中去。
第三，对于每个聚类，按照公式二重新计算该聚类的聚类中心；
Figure PCTCN2017091380-appb-000002
公式二即计算每个聚类中包括的所有点(样本地理位置点)的坐标平均值，并将这个平均值作为该聚类的新的聚类中心；
第四，重复第二、第三步，直至聚类中心不再变化，得到j个聚类中心，将样本地理位置划分到这j个聚类中心的聚类中；
第五，剔除j个聚类中，与聚类中心的距离超过预设距离阈值的样本地理位置，并重复第二、第三和第四步，计算得到最终的j个聚类中心u1，u2...uj，预设距离阈值可视需求自定义。
需要说明的是，以上仅以采用K-means聚类算法对样本地理位置进行聚类为例进行说明，实际中还可以采用其他聚类算法对样本地理位置进行聚类，例如采用K-medoids、Clara等聚类算法，此处不作具体限定。
步骤S102，识别每个聚类中心对应的常用位置。
常用位置可以是用户经常生活、工作的位置，例如家、办公室等。具体实现中，可以根据用户的生活习惯(例如使用电子设备的习惯)识别每个聚类中心对应的常用位置。每个聚类中心对应一个常用位置，例如，常用位置有两个，则上述j的取值就为2，步骤101就会得到2个聚类中心u1，u2。
步骤S103，获取所述用户的当前地理位置。
用户的当前地理位置可以是用户的实时地理位置，具体可以通过用户的电子设备获取。
步骤S104，确定所述当前地理位置匹配的聚类中心，将匹配的聚类中心对应的常用位置，作为所述用户当前所处的位置。
具体确定的方法可以是：计算所述当前地理位置与每个聚类中心的距离，将与当前地理位置距离最小的聚类中心，作为所述当前地理位置匹配的聚类中心。得到匹配的聚类中心之后，可以将匹配的聚类中心对应的常用位置，作为所述用户当前所处的位置。
由上可知，本实施例采用在电子设备中，对预先采集的用户的多个地理位置进行聚类，得到每类地理位置的聚类中心，然后识别每个聚类中心对应的常用位置，在获取用户的当前地理位置之后，确定所述当前地理位置匹配的聚类中心，将匹配的聚类中心对应的常用位置，作为所述用户当前所处的位置；本方案基于对样本地理位置的聚类，通过在电子设备中对样本地理位置及常用位置进行学习，根据学习结果识别用户当前所在的常用位置，整个学习、识别的过程均在电子设备本地进行，不需要网络交互，因此，不会涉及用户敏感信息的非授权上传问题，打消了用户的安全顾虑，识别出的用户所处位置，可以为基于位置的智能服务决策提供有效的参考依据。
下面两个实施例将在上述实施例描述的方法基础上，对本发明的识别方法做进一步介绍。参考图2，本实施例将根据用户的当前地理位置识别用户的当前所处的常用位置，本实施例的方法包括：
步骤S201，对预先采集的用户的多个地理位置进行聚类，得到每类地理位 置的聚类中心。
具体的聚类算法可以采用K-means、K-medoids、Clara等任意一种聚类算法，此处不做具体限定。本实施例将以所要识别的常用位置为用户的家和办公室为例进行说明，则经过步骤S201，将得到两个聚类中心u1，u2。
步骤S202，在预设采样周期内，统计用户的电子设备，在与每个聚类中心匹配的位置处，满足预设时长的休眠次数。
预设采样周期可视需求自定义，例如可以定义为一周，一个月等。基于对众多用户使用电子设备的习惯统计可知，用户在家的时候，每天会有较长的一段时间(用户休息时)不使用电子设备，电子设备这段时间会处于休眠状态；而用户在办公室时，由于工作需要，电子设备将被频繁使用，电子设备很难有较长的休眠时间。例如，用户在家的时候，电子设备每天会有6至7个小时的连续休眠时间，而用户在办公室的时候，电子设备则很难有超过6个小时的休眠时间。
因此，可以统计在不同位置，用户的电子设备满足预设时长的休眠次数，据此来识别不同位置对应的常用位置。具体可按照公式三进行统计：
Figure PCTCN2017091380-appb-000003
其中，j＝1，2。sampleDays为采样周期，采样周期例如可以取7天、10天、一个月等，此处不做具体限定。
Figure PCTCN2017091380-appb-000004
为在采样周期内的第i天，在与聚类中心uj匹配的位置处，电子设备当日连续休眠时长(可通过电子设备的持续息屏时间统计)，如果在与聚类中心uj匹配的位置处，电子设备当日连续休眠时长大于或等于预设时长Tthresh，则计数器Jj加1。公式三即将电子设备在与聚类中心uj匹配的位置处，满足预设时长的休眠的次数累加起来，得到Jj，当Jj最大时，聚类中心uj对应的常用位置为家，预设时长可根据用户的实际休息习惯取值，例如可以取6小时、8小时等。
步骤S203，根据统计的休眠次数识别每个聚类中心对应的常用位置。
在以家和办公室为常用位置的例子中，如果采样周期为7天，在与聚类中心u1匹配的位置处，统计得到的J1为7(即用户的电子设备在与u1匹配的位置处，大于或等于预设时长的休眠次数，累积为7次)，在与u2匹配的位置处，统计得到的J2为0(即用户的电子设备在与u2匹配的位置处，大于或等于预设时长的休眠次数，为0次)，则可知u1对应的常用位置为家，u2对应的常用位置为办公 室。
步骤S204，获取用户的当前地理位置。
即获取用经纬度、空间坐标等表示的位置。
步骤S205，确定当前地理位置匹配的聚类中心，将匹配的聚类中心对应的常用位置，作为用户当前所处的位置。
具体确定的方法可以是：计算所述当前地理位置与每个聚类中心的距离，将与当前地理位置距离最小的聚类中心，作为所述当前地理位置匹配的聚类中心。得到匹配的聚类中心之后，可以将匹配的聚类中心对应的常用位置，作为所述用户当前所处的位置。
由上可知，本实施例采用在电子设备中，对预先采集的用户的多个地理位置进行聚类，得到每类地理位置的聚类中心，然后识别每个聚类中心对应的常用位置，在获取用户的当前地理位置之后，确定所述当前地理位置匹配的聚类中心，将匹配的聚类中心对应的常用位置，作为所述用户当前所处的位置；本方案基于对样本地理位置的聚类，通过在电子设备中对样本地理位置及常用位置进行学习，根据学习结果识别用户当前所在的常用位置，整个学习、识别的过程均在电子设备本地进行，不需要网络交互，因此，不会涉及用户敏感信息的非授权上传问题，打消了用户的安全顾虑，识别出的用户所处位置，可以为基于位置的智能服务决策提供有效的参考依据。
上面实施例详细描述了根据用户的当前地理位置识别用户当前所处的常用位置的方法，本实施例将描述根据用户当前连接的无线网络的基本服务集标识识别用户当前所处的常用位置的方法，请参考图3，本实施例的方法包括：
步骤S301，对预先采集的用户的多个地理位置进行聚类，得到每类地理位置的聚类中心。
具体的聚类算法可以采用K-means、K-medoids、Clara等任意一种聚类算法，此处不做具体限定。本实施例仍以所要识别的常用位置为用户的家和办公室为例进行说明，则经过步骤S301，将得到两个聚类中心u1，u2。
步骤S302，获取用户的电子设备，在各个地理位置处连接的无线网络的基本服务集标识。
无线网络的基本服务集标识(Basic Service Set IDentity，BSSID)，即无线网络的路由媒体访问控制(Media Access Control，MAC)地址，不同无线网 络的BSSID不同。
步骤S303，确定上述各个地理位置匹配的聚类中心。
即确定上述每个地理位置与每个聚类中心的距离，将距离该地理位置最近的聚类中心，作为该地理位置匹配的聚类中心。
步骤S304，将上述基本服务集标识对应加入至匹配的聚类中心对应的位置集合。
一个聚类中心对应一个位置集合，初始时，各个位置集合均为空。步骤S302至S304，即将无线网络的BSSID与聚类中心融合，当用户的电子设备连接上某个无线网络时，可以获取用户的地理位置，如果用户的地理位置与某个聚类中心匹配，则将该无线网络的BSSID加入该聚类中心对应的位置集合。经过步骤S304，将得到与聚类中心u1对应的位置集合w1，与聚类中心u2对应的位置集合w2，w1＝{BSSIDp1，BSSIDp2...BSSIDpm}，w2＝{BSSIDq1，BSSIDq2...BSSIDqm}，m为正整数。随着用户的使用，位置集合w1、w2将被不断的扩充。
步骤S305，在预设采样周期内，统计用户的电子设备，在与每个位置集合匹配的位置处，满足预设时长的休眠次数。
预设采样周期可视需求自定义，例如可以定义为一周，一个月等。基于对众多用户使用电子设备的习惯统计可知，用户在家的时候，每天会有较长的一段时间(用户休息时)不使用电子设备，电子设备这段时间会处于休眠状态；而用户在办公室时，由于工作需要，电子设备将被频繁使用，电子设备很难有较长的休眠时间。例如，用户在家的时候，电子设备每天会有6至7个小时的连续休眠时间，而用户在办公室的时候，电子设备则很难有超过6个小时的休眠时间。
因此，可以统计在不同位置，用户的电子设备满足预设时长的休眠次数，据此来识别不同位置对应的常用位置。具体可按照公式四进行统计：
Figure PCTCN2017091380-appb-000005
其中，j＝1，2。sampleDays为采样周期，采样周期例如可以取7天、10天、一个月等，此处不做具体限定。
Figure PCTCN2017091380-appb-000006
为在采样周期内的第i天，在与位置集合wj匹配的位置处，电子设备当日连续休眠时长(可通过电子设备的持续息屏时间统计)，如果在与位置集合wj匹配的位置处，电子设备当日连续休眠时长大于或等于预设时长Tthresh，则计数器Jj加1。公式四即将电子设备在与位置集合wj匹配的 位置处，满足预设时长的休眠的次数累加起来，得到Jj，当Jj最大时，位置集合wj对应的常用位置为家，预设时长可根据用户的实际休息习惯取值，例如可以取6小时、8小时等。
步骤S306，根据统计的休眠次数识别每个位置集合对应的常用位置。
在以家和办公室为常用位置的例子中，如果采样周期为7天，在与位置集合w1匹配的位置处，统计得到的J1为7(即用户的电子设备在与w1匹配的位置处，大于或等于预设时长的休眠次数，累积为7次)，在与位置集合w2匹配的位置处，统计得到的J2为0(即用户的电子设备在与w2匹配的位置处，大于或等于预设时长的休眠次数，为0次)，则可知位置集合w1对应的常用位置为家，位置集合w2对应的常用位置为办公室。
步骤S307，获取用户当前连接的无线网络的基本服务集标识。
步骤S308，确定用户当前连接的无线网络的基本服务集标识匹配的位置集合，将匹配的位置集合对应的常用位置，作为用户当前所处的位置。
即确定用户当前连接的无线网络的基本服务集标识属于哪个位置集合，将所属的位置集合对应的常用位置，作为用户当前所处的位置。
由上可知，本实施例采用在电子设备中，对预先采集的用户的多个地理位置进行聚类，得到每类地理位置的聚类中心，然后建立每个聚类中心对应的位置集合，识别每个位置集合对应的常用位置，在获取用户当前连接的无线网络的基本服务集标识之后，确定用户当前连接的无线网络的基本服务集标识匹配的位置集合，将匹配的位置集合对应的常用位置，作为用户当前所处的位置；本方案基于对样本地理位置的聚类，通过在电子设备中对样本基本服务集标识及常用位置进行学习，根据学习结果识别用户当前所在的常用位置，整个学习、识别的过程均在电子设备本地进行，不需要网络交互，因此，不会涉及用户敏感信息的非授权上传问题，打消了用户的安全顾虑，识别出的用户所处位置，可以为基于位置的智能服务决策提供有效的参考依据。
以上两个实施例分别描述了根据用户的当前地理位置识别用户所处的常用位置，以及根据用户当前连接的无线网络的基本服务集标识识别用户所处的常用位置的过程，实际中，还可以将二者结合起来，即同时根据用户的当前地理位置及用户当前连接的无线网络的基本服务集标识识别用户所处的常用位置，具体识别方法可可参阅前文对应描述，此处不再赘述。
本发明实施例还提供了一种用户位置识别装置，包括：聚类模块、第一识别模块、第一获取模块以及第一确定模块；
所述聚类模块，用于对预先采集的用户的多个地理位置进行聚类，得到每类地理位置的聚类中心；
所述第一识别模块，用于识别每个聚类中心对应的常用位置；
所述第一获取模块，用于获取所述用户的当前地理位置；
所述第一确定模块，用于确定所述当前地理位置匹配的聚类中心，将匹配的聚类中心对应的常用位置，作为所述用户当前所处的位置。
一实施例中，所述第一识别模块具体用于，在预设采样周期内，统计所述用户的电子设备，在与每个聚类中心匹配的位置处，满足预设时长的休眠次数；根据统计的休眠次数识别每个聚类中心对应的常用位置。
一实施例中，所述装置还包括：第二获取模块、第二确定模块以及集合建立模块；
所述第二获取模块，用于获取所述用户的电子设备，在各个地理位置处连接的无线网络的基本服务集标识；
所述第二确定模块，用于确定所述各个地理位置匹配的聚类中心；
所述集合建立模块，用于将所述基本服务集标识对应加入至匹配的聚类中心对应的位置集合。
一实施例中，所述装置还包括第二识别模块；
所述第二识别模块，用于在所述预设采样周期内，统计所述用户的电子设备，在与每个位置集合匹配的位置处，满足所述预设时长的休眠次数；根据统计的休眠次数识别每个位置集合对应的常用位置。
一实施例中，所述装置还包括：第三获取模块和第三确定模块；
所述第三获取模块，用于获取所述用户当前连接的无线网络的基本服务集标识；
所述第三确定模块，用于确定所述用户当前连接的无线网络的基本服务集标识匹配的位置集合，将匹配的位置集合对应的常用位置，作为所述用户当前所处的位置。
在一优选实施例中，还提供了一种用户位置识别装置，该装置可以集成在电子设备中，该电子设备可以为移动互联网设备(如智能手机、平板电脑)、 智能穿戴设备(如智能手表)等各类智能电子设备。
如图4所示，该用户位置识别装置可以包括：聚类模块401、第一识别模块402、第一获取模块403以及第一确定模块404；
聚类模块401，用于对预先采集的用户的地理位置进行聚类，得到每类地理位置的聚类中心；
第一识别模块402，用于识别每个聚类中心对应的常用位置；
第一获取模块403，用于获取所述用户的当前地理位置；
第一确定模块404，用于确定所述当前地理位置匹配的聚类中心，将匹配的聚类中心对应的常用位置，作为所述用户当前所处的位置。
在一些实施例中，第一识别模块402具体用于，在预设采样周期内，统计所述用户的电子设备，在与每个聚类中心匹配的位置处，满足预设时长的休眠次数；根据统计的休眠次数识别每个聚类中心对应的常用位置。
在一些实施例中，如图5所示，本发明实施例提供识别装置还可以包括：第二获取模块405、第二确定模块406以及集合建立模块407；
第二获取模块405，用于获取所述用户的电子设备，在各个地理位置处连接的无线网络的基本服务集标识；
第二确定模块406，用于确定所述各个地理位置匹配的聚类中心；
集合建立模块407，用于将所述基本服务集标识对应加入至匹配的聚类中心对应的位置集合。
在一些实施例中，本发明实施例提供识别装置还可以包括：第二识别模块408；
第二识别模块408，用于在所述预设采样周期内，统计所述用户的电子设备，在与每个位置集合匹配的位置处，满足所述预设时长的休眠次数；根据统计的休眠次数识别每个位置集合对应的常用位置。
在一些实施例中，本发明实施例提供识别装置还可以包括：第三获取模块409和第三确定模块410；
第三获取模块409，用于获取所述用户当前连接的无线网络的基本服务集标识；
第三确定模块410，用于确定所述用户当前连接的无线网络的基本服务集标识匹配的位置集合，将匹配的位置集合对应的常用位置，作为所述用户当前 所处的位置。
由上可知，本实施例采用在电子设备中，由聚类模块401对预先采集的用户的多个地理位置进行聚类，得到每类地理位置的聚类中心，然后第一识别模块402识别每个聚类中心对应的常用位置，在第一获取模块403获取用户的当前地理位置之后，第一确定模块404确定所述当前地理位置匹配的聚类中心，将匹配的聚类中心对应的常用位置，作为所述用户当前所处的位置；本方案通过在电子设备中对样本地理位置及常用位置进行学习，根据学习结果识别用户当前所在的常用位置，整个学习、识别的过程均在电子设备本地进行，不需要网络交互，因此，不会涉及用户敏感信息的非授权上传问题，打消了用户的安全顾虑，识别出的用户所处位置，可以为基于位置的智能服务决策提供有效的参考依据。
具体实施时，以上各个模块可以作为独立的实体来实现，也可以进行任意组合，作为同一或若干个实体来实现，以上各个模块的具体实施可参见前面的方法实施例，在此不再赘述。
本发明实施例还提供了一种电子设备，包括：处理器、存储器、显示屏以及控制电路；
所述处理器与所述存储器、显示屏以及控制电路电性连接，所述存储器用于存储指令和数据，所述显示屏用于显示信息，所述控制电路用于控制所述显示屏显示信息；所述处理器用于执行以下步骤：
对预先采集的用户的多个地理位置进行聚类，得到每类地理位置的聚类中心；
识别每个聚类中心对应的常用位置；
获取所述用户的当前地理位置；
确定所述当前地理位置匹配的聚类中心，将匹配的聚类中心对应的常用位置，作为所述用户当前所处的位置。
在一优选实施例中，还提供一种电子设备，该电子设备可以是智能手机、平板电脑等设备。如图6所示，电子设备500包括处理器501、存储器502、显示屏503以及控制电路504。其中，处理器501分别与存储器502、显示屏503、控制电路504电性连接。
处理器501是电子设备500的控制中心，利用各种接口和线路连接整个电 子设备的各个部分，通过运行或加载存储在存储器502内的应用程序，以及调用存储在存储器502内的数据，执行电子设备的各种功能和处理数据，从而对电子设备进行整体监控。
在本实施例中，电子设备500中的处理器501会按照如下的步骤，将一个或一个以上的应用程序的进程对应的指令加载到存储器502中，并由处理器501来运行存储在存储器502中的应用程序，从而实现各种功能：
对预先采集的用户的地理位置进行聚类，得到每类地理位置的聚类中心；
识别每个聚类中心对应的常用位置；
获取所述用户的当前地理位置；
确定所述当前地理位置匹配的聚类中心，将匹配的聚类中心对应的常用位置，作为所述用户当前所处的位置。
在一些实施例中，对预先采集的用户的多个地理位置进行聚类，得到每类地理位置的聚类中心时，处理器501用于执行以下步骤：采用K均值算法，以所述多个地理位置为样本，从样本中随机选取预设数目个地理位置作为初始的聚类中心进行聚类，得到每类地理位置的聚类中心。
在一些实施例中，识别每个聚类中心对应的常用位置时，处理器501用于执行以下步骤：在预设采样周期内，统计所述用户的电子设备，在与每个聚类中心匹配的位置处，满足预设时长的休眠次数；根据统计的休眠次数识别每个聚类中心对应的常用位置。
在一些实施例中，确定所述当前地理位置匹配的聚类中心时，处理器501用于执行以下步骤：确定所述当前地理位置与每个聚类中心的距离，将与所述当前地理位置距离最小的聚类中心，作为所述当前地理位置匹配的聚类中心。
在一些实施例中，在得到每类地理位置的聚类中心之后，处理器501还用于执行以下步骤：获取所述用户的电子设备，在各个地理位置处连接的无线网络的基本服务集标识；确定所述各个地理位置匹配的聚类中心；将所述基本服务集标识对应加入至匹配的聚类中心对应的位置集合。
在一些实施例中，在将所述基本服务集标识对应加入至匹配的聚类中心对应的位置集合之后，处理器501还用于执行以下步骤：在所述预设采样周期内，统计所述用户的电子设备，在与每个位置集合匹配的位置处，满足所述预设时长的休眠次数；根据统计的休眠次数识别每个位置集合对应的常用位置。
在一些实施例中，在根据统计的休眠次数确定每个位置集合对应的常用位置之后，处理器501还用于执行以下步骤：获取所述用户当前连接的无线网络的基本服务集标识；确定所述用户当前连接的无线网络的基本服务集标识匹配的位置集合，将匹配的位置集合对应的常用位置，作为所述用户当前所处的位置。
存储器502可用于存储应用程序和数据。存储器502存储的应用程序中包含有可在处理器中执行的指令。应用程序可以组成各种功能模块。处理器501通过运行存储在存储器502的应用程序，从而执行各种功能应用以及数据处理。
显示屏503可用于显示由用户输入的信息或提供给用户的信息以及电子设备的各种图形用户接口，这些图形用户接口可以由图像、文本、图标、视频和其任意组合来构成。
控制电路504与显示屏503电性连接，用于控制显示屏503显示信息。
在一些实施例中，如图7所示，电子设备500还包括：射频电路505、输入单元506、音频电路507、传感器508以及电源509。其中，处理器501分别与射频电路505、输入单元506、音频电路507、传感器508以及电源509电性连接。
射频电路505用于收发射频信号，以通过无线通信与网络设备或其他电子设备建立无线通讯，与网络设备或其他电子设备之间收发信号。
输入单元506可用于接收输入的数字、字符信息或用户特征信息(例如指纹)，以及产生与用户设置以及功能控制有关的键盘、鼠标、操作杆、光学或者轨迹球信号输入。其中，输入单元506可以包括指纹识别模组。
音频电路507可通过扬声器、传声器提供用户与电子设备之间的音频接口。
传感器508用于采集外部环境信息。传感器308可以包括环境亮度传感器、加速度传感器、陀螺仪等传感器中的一种或多种。
电源509用于给电子设备500的各个部件供电。在一些实施例中，电源509可以通过电源管理系统与处理器501逻辑相连，从而通过电源管理系统实现管理充电、放电、以及功耗管理等功能。
尽管图7中未示出，电子设备500还可以包括摄像头、蓝牙模块等，在此不再赘述。
本发明实施例提供的电子设备，对预先采集的用户的多个地理位置进行聚类，得到每类地理位置的聚类中心，然后识别每个聚类中心对应的常用位置，在获取用户的当前地理位置之后，确定所述当前地理位置匹配的聚类中心，将匹配的聚类中心对应的常用位置，作为所述用户当前所处的位置；本方案基于对样本地理位置的聚类，通过在电子设备中对样本地理位置及常用位置进行学习，根据学习结果识别用户当前所在的常用位置，整个学习、识别的过程均在电子设备本地进行，不需要网络交互，因此，不会涉及用户敏感信息的非授权上传问题，打消了用户的安全顾虑，识别出的用户所处位置，可以为基于位置的智能服务决策提供有效的参考依据。
需要说明的是，本领域普通技术人员可以理解上述实施例的各种方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，该程序可以存储于计算机可读的存储介质中，如存储在电子设备的存储器中，并被该电子设备内的至少一个处理器执行，在执行过程中可包括如信息发布方法的实施例的流程。其中，存储介质可以包括：只读存储器(ROM，Read Only Memory)、随机存取记忆体(RAM，Random Access Memory)、磁盘或光盘等。
以上对本发明实施例提供的用户位置识别方法、装置、存储介质及电子设备进行了详细介绍，其各功能模块可以集成在一个处理芯片中，也可以是各个模块单独物理存在，也可以两个或两个以上模块集成在一个模块中。上述集成的模块既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。本文中应用了具体个例对本发明的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本发明的方法及其核心思想；同时，对于本领域的技术人员，依据本发明的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本发明的限制。

Claims (20)

  1. 一种用户位置识别方法，其特征在于，包括以下步骤：
    对预先采集的用户的多个地理位置进行聚类，得到每类地理位置的聚类中心；
    识别每个聚类中心对应的常用位置；
    获取所述用户的当前地理位置；
    确定所述当前地理位置匹配的聚类中心，将匹配的聚类中心对应的常用位置，作为所述用户当前所处的位置。
  2. 根据权利要求1所述的方法，其特征在于，所述对预先采集的用户的多个地理位置进行聚类，得到每类地理位置的聚类中心包括：
    采用K均值算法，以所述多个地理位置为样本，从样本中随机选取预设数目个地理位置作为初始的聚类中心进行聚类，得到每类地理位置的聚类中心。
  3. 根据权利要求1或2所述的方法，其特征在于，所述识别每个聚类中心对应的常用位置包括：
    在预设采样周期内，统计所述用户的电子设备，在与每个聚类中心匹配的位置处，满足预设时长的休眠次数；
    根据统计的休眠次数识别每个聚类中心对应的常用位置。
  4. 根据权利要求3所述的方法，其特征在于，所述确定所述当前地理位置匹配的聚类中心包括：
    确定所述当前地理位置与每个聚类中心的距离，将与所述当前地理位置距离最小的聚类中心，作为所述当前地理位置匹配的聚类中心。
  5. 根据权利要求4所述的方法，其特征在于，在得到每类地理位置的聚类中心之后，所述方法还包括：
    获取所述用户的电子设备，在各个地理位置处连接的无线网络的基本服务集标识；
    确定所述各个地理位置匹配的聚类中心；
    将所述基本服务集标识对应加入至匹配的聚类中心对应的位置集合。
  6. 根据权利要求5所述的方法，其特征在于，在将所述基本服务集标识对应加入至匹配的聚类中心对应的位置集合之后，所述方法还包括：
    在所述预设采样周期内，统计所述用户的电子设备，在与每个位置集合匹 配的位置处，满足所述预设时长的休眠次数；
    根据统计的休眠次数识别每个位置集合对应的常用位置。
  7. 根据权利要求6所述的方法，其特征在于，在根据统计的休眠次数确定每个位置集合对应的常用位置之后，所述方法还包括：
    获取所述用户当前连接的无线网络的基本服务集标识；
    确定所述用户当前连接的无线网络的基本服务集标识匹配的位置集合，将匹配的位置集合对应的常用位置，作为所述用户当前所处的位置。
  8. 一种用户位置识别装置，其特征在于，包括：聚类模块、第一识别模块、第一获取模块以及第一确定模块；
    所述聚类模块，用于对预先采集的用户的多个地理位置进行聚类，得到每类地理位置的聚类中心；
    所述第一识别模块，用于识别每个聚类中心对应的常用位置；
    所述第一获取模块，用于获取所述用户的当前地理位置；
    所述第一确定模块，用于确定所述当前地理位置匹配的聚类中心，将匹配的聚类中心对应的常用位置，作为所述用户当前所处的位置。
  9. 根据权利要求8所述的装置，其特征在于，所述第一识别模块具体用于，在预设采样周期内，统计所述用户的电子设备，在与每个聚类中心匹配的位置处，满足预设时长的休眠次数；根据统计的休眠次数识别每个聚类中心对应的常用位置。
  10. 根据权利要求9所述的装置，其特征在于，所述装置还包括：第二获取模块、第二确定模块以及集合建立模块；
    所述第二获取模块，用于获取所述用户的电子设备，在各个地理位置处连接的无线网络的基本服务集标识；
    所述第二确定模块，用于确定所述各个地理位置匹配的聚类中心；
    所述集合建立模块，用于将所述基本服务集标识对应加入至匹配的聚类中心对应的位置集合。
  11. 根据权利要求10所述的装置，其特征在于，所述装置还包括第二识别模块；
    所述第二识别模块，用于在所述预设采样周期内，统计所述用户的电子设备，在与每个位置集合匹配的位置处，满足所述预设时长的休眠次数；根据统 计的休眠次数识别每个位置集合对应的常用位置。
  12. 根据权利要求11所述的装置，其特征在于，所述装置还包括：第三获取模块和第三确定模块；
    所述第三获取模块，用于获取所述用户当前连接的无线网络的基本服务集标识；
    所述第三确定模块，用于确定所述用户当前连接的无线网络的基本服务集标识匹配的位置集合，将匹配的位置集合对应的常用位置，作为所述用户当前所处的位置。
  13. 一种存储介质，其特征在于，所述存储介质中存储有多条指令，所述指令适于由处理器加载以执行如权利要求1至7任一项所述的方法。
  14. 一种电子设备，其特征在于，包括：处理器、存储器、显示屏以及控制电路；
    所述处理器与所述存储器、显示屏以及控制电路电性连接，所述存储器用于存储指令和数据，所述显示屏用于显示信息，所述控制电路用于控制所述显示屏显示信息；所述处理器用于执行以下步骤：
    对预先采集的用户的多个地理位置进行聚类，得到每类地理位置的聚类中心；
    识别每个聚类中心对应的常用位置；
    获取所述用户的当前地理位置；
    确定所述当前地理位置匹配的聚类中心，将匹配的聚类中心对应的常用位置，作为所述用户当前所处的位置。
  15. 根据权利要求14所述的电子设备，其特征在于，对预先采集的用户的多个地理位置进行聚类，得到每类地理位置的聚类中心时，所述处理器用于执行以下步骤：
    采用K均值算法，以所述多个地理位置为样本，从样本中随机选取预设数目个地理位置作为初始的聚类中心进行聚类，得到每类地理位置的聚类中心。
  16. 根据权利要求14或15所述的电子设备，其特征在于，识别每个聚类中心对应的常用位置时，所述处理器用于执行以下步骤：
    在预设采样周期内，统计所述用户的电子设备，在与每个聚类中心匹配的位置处，满足预设时长的休眠次数；
    根据统计的休眠次数识别每个聚类中心对应的常用位置。
  17. 根据权利要求16所述的电子设备，其特征在于，确定所述当前地理位置匹配的聚类中心时，所述处理器用于执行以下步骤：
    确定所述当前地理位置与每个聚类中心的距离，将与所述当前地理位置距离最小的聚类中心，作为所述当前地理位置匹配的聚类中心。
  18. 根据权利要求17所述的电子设备，其特征在于，在得到每类地理位置的聚类中心之后，所述处理器还用于执行以下步骤：
    获取所述用户的电子设备，在各个地理位置处连接的无线网络的基本服务集标识；
    确定所述各个地理位置匹配的聚类中心；
    将所述基本服务集标识对应加入至匹配的聚类中心对应的位置集合。
  19. 根据权利要求18所述的电子设备，其特征在于，在将所述基本服务集标识对应加入至匹配的聚类中心对应的位置集合之后，所述处理器还用于执行以下步骤：
    在所述预设采样周期内，统计所述用户的电子设备，在与每个位置集合匹配的位置处，满足所述预设时长的休眠次数；
    根据统计的休眠次数识别每个位置集合对应的常用位置。
  20. 根据权利要求19所述的电子设备，其特征在于，在根据统计的休眠次数确定每个位置集合对应的常用位置之后，所述处理器还用于执行以下步骤：
    获取所述用户当前连接的无线网络的基本服务集标识；
    确定所述用户当前连接的无线网络的基本服务集标识匹配的位置集合，将匹配的位置集合对应的常用位置，作为所述用户当前所处的位置。
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