CN111797181B - Positioning method, device, control equipment and storage medium for user location - Google Patents
Positioning method, device, control equipment and storage medium for user location Download PDFInfo
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- Y02D30/00—Reducing energy consumption in communication networks
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Abstract
According to the method, the device, the control equipment and the storage medium for positioning the user location, the positioning data of the user in the preset time period are obtained, and the positioning data are used for indicating the positions of the user at different times; performing spatial aggregation on the positioning data of the user to generate an aggregation point, wherein the aggregation point has an aggregation point occurrence time; determining that the polymerization point with the occurrence time meeting the preset time condition is an effective polymerization point; positioning and outputting the information of the user location according to the effective aggregation point; that is, the application analyzes the positioning data of the user in two dimensions of space and time, thereby positioning more accurate user job sites.
Description
Technical Field
The present application relates to information processing technologies, and in particular, to a method and apparatus for locating a user location, a control device, and a storage medium.
Background
The method effectively obtains the distribution situation of the liveplace of urban residents, and has important reference value for urban and traffic planning.
In the prior art, spatial clustering analysis is performed on acquired user positioning data through a traditional clustering algorithm, such as a Density-based noisy spatial clustering (Density-Based Spatial Clustering of Applications with Noise, DBACAN for short) algorithm, so as to acquire the distribution situation of the user residence.
However, in the prior art, the traditional clustering algorithm ignores the time characteristic of the user positioning data, so that the finally acquired user location places may have deviation, and the error between the positioned user location places and the actual user location places is larger, so that the positioning accuracy is poor.
Disclosure of Invention
The application provides a method, a device, control equipment and a storage medium for positioning a user location.
In a first aspect, the present application provides a method for positioning a user location, including: acquiring positioning data of a user within a preset duration, wherein the positioning data are used for indicating positions of the user at different times; performing spatial aggregation on the positioning data of the user to generate an aggregation point, wherein the aggregation point has an aggregation point occurrence time; determining the polymerization point with the occurrence time meeting the preset time condition as an effective polymerization point; and positioning and outputting the user job site information according to the effective aggregation point.
In other alternative embodiments, the preset duration includes a plurality of identical time periods; the determining that the polymerization point with the occurrence time of the polymerization point meeting the preset time condition is an effective polymerization point comprises: if the occurrence time of the aggregation point in the time period is larger than a first threshold value, determining the time period as a valid time period; if the number of times of occurrence of the effective time period is determined to be larger than the second threshold value and the time span of the effective time period is determined to be larger than the third threshold value, determining the corresponding aggregation point as an effective aggregation point.
In other optional embodiments, the acquiring the positioning data of the user within the preset time period includes: acquiring log data of an application program on a user terminal within a preset duration; and determining the positioning data of the user according to the log data.
In other optional embodiments, the spatial aggregation processing of the positioning data of the user includes: performing thinning treatment of preset time granularity on the positioning data of the user; and carrying out space aggregation treatment on the positioning data of the user after the thinning treatment.
In other optional embodiments, the location data of the user includes a set of location points of the user at different times; the spatial aggregation processing of the positioning data of the user comprises the following steps: performing spatial aggregation processing of the preset distance on the position point set to generate an aggregation point; correspondingly, the positioning and outputting the information of the user location according to the effective aggregation point comprises the following steps: restoring a position point set corresponding to the effective aggregation point according to the preset distance; and determining the position point with the largest occurrence number in the position point set corresponding to the effective aggregation point as the information of the user location.
In other optional embodiments, after the obtaining the positioning data of the user within the preset time period, the method further includes: dividing the positioning data of the user into daytime positioning data and nighttime positioning data; the spatial aggregation processing is performed on the positioning data of the user to generate an aggregation point, wherein the aggregation point has an aggregation point occurrence time, and the spatial aggregation processing comprises the following steps: space aggregation processing is carried out on the daytime positioning data of the user, and a daytime aggregation point is generated, wherein the daytime aggregation point has the occurrence time of the daytime aggregation point; performing space aggregation processing on night positioning data of a user to generate a night aggregation point, wherein the night aggregation point has the occurrence time of the night aggregation point; the determining that the polymerization point with the occurrence time of the polymerization point meeting the preset time condition is an effective polymerization point comprises: and determining the daytime polymerization point of which the occurrence time meets a preset time condition. Is an effective daytime polymerization point; determining a night polymerization point, the occurrence time of which meets the preset time condition, as an effective night polymerization point; the step of positioning and outputting the user job site information according to the effective aggregation point comprises the following steps: positioning and outputting user work place information according to the effective daytime aggregation point; and positioning and outputting the residence information of the user according to the effective night aggregation point.
In other optional embodiments, if positioning data of a plurality of users is acquired, the method further includes: and processing the positioning data of the plurality of users by adopting a distributed computing model to acquire the job site information of the plurality of users.
In a second aspect, the present application provides a positioning device for a user's job site, comprising: the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring positioning data of a user in preset duration, and the positioning data are used for indicating positions of the user at different times; the aggregation module is used for carrying out space aggregation processing on the positioning data of the user to generate an aggregation point, wherein the aggregation point has the occurrence time of the aggregation point; the determining module is used for determining the polymerization point with the occurrence time meeting the preset time condition as an effective polymerization point; and the output module is used for positioning and outputting the information of the user location according to the effective aggregation point.
In a third aspect, the present application provides a control apparatus comprising: at least one processor and memory; the memory stores computer-executable instructions; the at least one processor executing computer-executable instructions stored in the memory causes the at least one processor to perform the method of locating a user's place of employment as set forth in any preceding claim.
In a fourth aspect, the present application provides a readable storage medium having stored therein computer-executable instructions which, when executed by a processor, implement the method of locating a user location as defined in any preceding claim.
According to the method, the device, the control equipment and the storage medium for positioning the user location, the positioning data of the user in the preset time period are obtained, and the positioning data are used for indicating the positions of the user at different times; performing spatial aggregation on the positioning data of the user to generate an aggregation point, wherein the aggregation point has an aggregation point occurrence time; determining the polymerization point with the occurrence time meeting the preset time condition as an effective polymerization point; positioning and outputting the information of the user location according to the effective aggregation point; the application analyzes the positioning data of the user in two dimensions of space and time, thereby positioning more accurate distribution situation of the user's job place.
Drawings
FIG. 1 is a schematic diagram of a network architecture on which the present application is based;
FIG. 2 is a flow chart of a method for locating a user location according to the present application;
FIG. 3 is a flow chart of another method for locating a user location according to the present application;
FIG. 4 is a flow chart of another method for locating a user location according to the present application;
FIG. 5 is a block diagram of a method for locating a user location according to the present application;
FIG. 6 is a schematic diagram of a positioning device for a user's job site according to the present application;
fig. 7 is a schematic hardware structure of a control device according to the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the present application will be clearly and completely described below with reference to the accompanying drawings in the present application examples.
The method effectively obtains the distribution situation of the liveplace of urban residents, and has important reference value for urban and traffic planning.
In the prior art, a traditional clustering algorithm, such as Density-based spatial clustering with noise (Density-Based Spatial Clustering of Applications with Noise, abbreviated as DBACAN) algorithm, is mostly adopted to perform spatial clustering analysis on acquired user positioning data, so as to acquire the job place distribution situation of the user. However, the user positioning data has unique space-time characteristics, and the traditional clustering algorithm ignores the time characteristics of the user positioning data, so that the finally acquired user job place information may have deviation, and the error between the positioned user job place and the actual user job place is larger, so that the positioning accuracy is poor.
Aiming at the problems, the technical conception of the application is that the positioning data of the user is analyzed from two dimensions of space and time to obtain the aggregation point which simultaneously meets the conditions of high density in space and high frequency in time, thereby positioning more accurate information of the job location of the user.
Fig. 1 is a schematic diagram of a network architecture according to the present application, as shown in fig. 1, where one network architecture according to the present application may include a plurality of terminals 1 and a server 2, where the plurality of terminals 1 report positioning data to the server 2, and after the server 2 receives the positioning data, the method described in the following embodiments is executed to obtain the distribution situation of the job sites of the user.
It should be noted that, the terminal 1 may be a mobile phone, a tablet computer, a vehicle-mounted terminal, or the like with a positioning function.
In a first aspect, an example of the present application provides a method for locating a user location, and fig. 2 is a schematic flow chart of a method for locating a user location provided by the present application.
As shown in fig. 2, the method for positioning the user location includes:
step 101, acquiring positioning data of a user within a preset duration.
Wherein the positioning data is used for indicating the positions of the users at different times.
Specifically, mobile phone signaling data, mobile phone application program data and the like of a preset duration can be obtained, and then positioning data of a user is determined according to the mobile phone signaling data, the mobile phone application program data and the like. In order to ensure the validity of the positioning data, optionally, the preset duration may be a preset duration that starts from the current time, for example, a month, a quarter, a year, or the like; the number of users may be plural, which is not limited in this embodiment.
Alternatively, the user's positioning data may comprise a plurality of data records, each data record comprising: the user identification, the time and the position are in one-to-one correspondence, and the positions of the user at different times are described. The user identifier is used for uniquely distinguishing different users, and the implementation manner of the user identifier is not limited in this embodiment, for example, the user identifier may be an identification card number of the user, or a name plus an identification card number, or the like.
Step 102, performing spatial aggregation processing on the positioning data of the user to generate an aggregation point, wherein the aggregation point has an aggregation point occurrence time.
Specifically, the clustering algorithm may be used to perform spatial aggregation processing on the positioning data of the user, so as to generate a plurality of aggregation points, and obtain the occurrence time corresponding to each aggregation point. It should be noted that, the type of the clustering algorithm is not limited in this embodiment, and alternatively, the clustering algorithm may be a DBSCAN algorithm.
Step 103, determining the polymerization point with the occurrence time meeting the preset time condition as an effective polymerization point.
Specifically, each user may have multiple aggregation points, and only the aggregation points meeting the preset time condition can be used as effective aggregation points.
Optionally, the preset duration includes a plurality of identical time periods, and determining, in step 103, that the polymerization point whose occurrence time meets the preset time condition is an effective polymerization point includes: if the occurrence time of the aggregation point in the time period is larger than a first threshold value, determining the time period as a valid time period; if the number of times of occurrence of the effective time period is determined to be larger than the second threshold value and the time span of the effective time period is determined to be larger than the third threshold value, determining the corresponding aggregation point as an effective aggregation point.
Specifically, according to the work and rest habits of most users, it is known that the time frequency of the user at the work place and the residence place is high and relatively stable, but special situations such as business trip and the like of the user are not excluded, so as to reduce the interference of the special situations such as business trip and the like of the user on the residence place of the positioning user, in this example, the aggregation point is limited on the time frequency, so as to obtain an effective aggregation point.
For example, assuming that 90 days of positioning data of a user is acquired, first, a plurality of aggregation points of the user may be acquired according to a DBSCAN algorithm, and occurrence times of aggregation points corresponding to the plurality of aggregation points are recorded, then the 90 days of time may be divided into 90 time periods averagely, that is, a time period of one day is taken as a time period, then it is determined whether a time (hours) covered by the occurrence times of each aggregation point in the day is greater than a first threshold (for example, 3 hours), if the time is greater than the first threshold, the user is considered as an effective day, then it is determined whether a number of days of the effective day is greater than a second threshold (for example, 7 days), and if the time span of the effective day is greater than a third threshold (for example, 15 days), the aggregation point is determined as an effective aggregation point.
Assuming that the user goes to the place of business trip for 2 days, in the method of the embodiment, the aggregation point corresponding to the place of business trip can be obtained through space aggregation processing, but the place of business trip is not the place of job of the user, so that the aggregation point corresponding to the place of business trip needs to be screened out, at this time, screening can be performed through a preset time condition, whether the residence time of the user on one day of the place of business trip is greater than a first threshold value or not can be judged, if so, the user can be determined to be an effective day, then the effective day of the aggregation point corresponding to the place of business trip can be determined to be 2 days, and obviously, the effective day of the aggregation point is smaller than a second threshold value (such as 7 days) and can be determined to be invalid because the preset time condition is not met, and the method cannot be used as the analysis basis of the place of job of the user.
Further, if the user continuously goes on business 10 days to the foreign area, the number of days of effective days can be determined to be 10 days, but the time span of the effective days is 10 days and is smaller than the third threshold (15 days), and if the preset time condition is not met, the aggregation point can be determined to be invalid and cannot be used as an analysis basis of the user's residence.
In addition, the user may have multiple worksites and multiple habitable locations. Preferably, the days of effective days when the polymerization points meet the preset time conditions are present may be counted separately and arranged in descending order. If the first aggregation point corresponds to the first working place of the user in the daytime period, the second aggregation point corresponds to the second working place of the user, and so on; similarly, during the night time period, the first aggregation point is ranked corresponding to the first residence of the user, the second aggregation point is ranked corresponding to the second residence of the user, and so on.
And 104, positioning and outputting the user job site information according to the effective aggregation point.
Specifically, the position corresponding to the effective aggregation point can be positioned as the occupancy zone of the user, the occupancy zone of the user is output, and preferably, the distribution situation of the occupancy zone of the user can be visually displayed on a map, so that staff can intuitively know the distribution situation of the occupancy zone of the user, and further, reasonable planning is made for cities and traffic planning.
As an alternative example, the location data of the user includes a set of location points of the user at different times; in step 102, spatial aggregation processing is performed on the positioning data of the user, including: performing spatial aggregation processing of the preset distance on the position point set to generate an aggregation point; accordingly, in step 104, positioning and outputting the location information of the user according to the effective aggregation point, including: restoring a position point set corresponding to the effective aggregation point according to the preset distance; and determining the position point with the largest occurrence number in the position point set corresponding to the effective aggregation point as the information of the user location.
Specifically, the positioning data of the user may be represented as a set of location points of the user at different times, e.g., ID, time, location, where ID represents a user identification, time represents a time, location represents a location point, and may be geographic coordinates. The method comprises the steps of carrying out space aggregation on all the position points in a position point set according to a preset distance L, namely, aggregating all the position points within the preset distance L to the same place, namely, aggregating the position points, wherein a proper value, such as 100 meters, can be set for the preset distance L in consideration of the normal activity range of a user around a residence or a work place, after space aggregation treatment is carried out, each user possibly corresponds to a plurality of aggregation points, and effective aggregation points can be screened out according to preset time conditions, and the aggregation points are not real places where the user is located according to the space aggregation treatment process of step 102, so that in order to improve the accuracy of the acquired places where the user is located, in step 104, the position point set corresponding to the effective aggregation points needs to be restored according to the space aggregation treatment process of step 102, and the position point with the largest occurrence times is taken as the place where the user is located.
According to the positioning method for the user job site, the positioning data of the user in the preset time period are obtained, and the positioning data are used for indicating the positions of the user at different times; performing spatial aggregation on the positioning data of the user to generate an aggregation point, wherein the aggregation point has an aggregation point occurrence time; determining the polymerization point with the occurrence time meeting the preset time condition as an effective polymerization point; positioning and outputting the information of the user location according to the effective aggregation point; the embodiment of the application analyzes the positioning data of the user in two dimensions of space and time to obtain the aggregation point which simultaneously meets the conditions of high density in space and high frequency in time, thereby positioning the information of the job place of the user and improving the accuracy of the obtained job place of the user.
With reference to the foregoing implementations, fig. 3 is a flow chart of another method for locating a user location, as shown in fig. 3, provided by the present application, where the method for locating a user location includes:
step 201, acquiring log data of an application program on a user terminal within a preset duration.
And 202, determining positioning data of the user according to the log data.
Wherein the positioning data is used for indicating the positions of the users at different times.
And 203, performing spatial aggregation processing on the positioning data of the user to generate an aggregation point, wherein the aggregation point has an aggregation point occurrence time.
Step 204, determining that the occurrence time of the polymerization point meets the preset time condition as an effective polymerization point.
Step 205, positioning and outputting the information of the user job place according to the effective aggregation point.
Step 203, step 204 and step 205 in this embodiment are similar to the implementation manners of step 102, step 103 and step 104 in the foregoing embodiment, respectively, and are not described here again.
Unlike the foregoing embodiment, in this embodiment, log data of an Application (App) on a user terminal within a preset duration is obtained in order to obtain a more accurate distribution of the locations of the users; and determining the positioning data of the user according to the log data.
Generally, there are many ways to source positioning data, such as mobile phone signaling data, mobile phone application program data, etc., but the positioning of mobile phone signaling data depends on the density of the base stations of the telecom operator, and the positioning error is often several tens of meters to several thousands of meters, and the error is large, so that the accuracy of the finally obtained location of the user is not high. Therefore, in order to acquire more accurate user location information, it is defined in the present embodiment that the positioning data of the user is determined from the log data by acquiring the log data of the application App installed on the user terminal.
Specifically, a plurality of apps are installed on a mobile terminal held by a user, and the apps can acquire position information of the mobile terminal according to global positioning system (Global Positioning System, abbreviated as GPS) positioning and wireless local area network (Wireless Fidelity, abbreviated as WiFi) positioning started by the mobile terminal and record the position information in log data of the apps, so that positioning data of the user can be determined according to the log data. It should be noted that, the position error obtained by GPS positioning and WiFi positioning is small, and is only a few meters to tens of meters, that is, more accurate positioning data can be obtained by mobile phone App data, and more accurate user location information can be obtained.
Further, since the log data generally includes private data of the user, it is preferable to desensitize the log data in order to improve the privacy security of the user.
Optionally, in step 203, spatial aggregation processing is performed on the positioning data of the user, including: performing thinning treatment of preset time granularity on the positioning data of the user; and carrying out space aggregation treatment on the positioning data of the user after the thinning treatment.
Specifically, instead of acquiring the positioning data of the App in each time period, for example, the user may turn on the map navigation App before or after going to work, and may not run the map navigation App at other times, that is, the acquired App positioning data is not uniformly distributed in time, and the finally acquired user position may deviate due to the non-uniform distribution, so in order to reduce the deviation, in this embodiment, the positioning data is uniformly processed in time density, that is, the thinning process is performed in a preset granularity time (for example, in a minute level), so that the difference in time density of the positioning data is within a certain range, for example, the number of positioning times of a single user terminal in each minute is not greater than one. And then carrying out space aggregation, preset time condition judgment and other treatments on the positioning data after the thinning treatment, and finally positioning more accurate user positions.
On the basis of the above example, performing thinning processing of a preset time granularity on the positioning data of the user; and the spatial aggregation processing is carried out on the positioning data of the user after the thinning processing, namely, the thinning processing is carried out on the positioning data, so that the result deviation caused by the uniform distribution of the positioning data in time is reduced, and the accuracy of the positioning user location information is improved.
With reference to the foregoing implementations, fig. 4 is a schematic flow chart of another method for locating a user location, as shown in fig. 4, where the method for locating a user location includes:
step 301, obtaining positioning data of a user within a preset duration.
Wherein the positioning data is used for indicating the positions of the users at different times.
Step 302, dividing the positioning data of the user into daytime positioning data and nighttime positioning data.
Steps 303-305 are performed for daytime positioning data; steps 306-308 are performed for night time positioning data.
And 303, performing spatial aggregation processing on the daytime positioning data of the user to generate a daytime aggregation point, wherein the daytime aggregation point has the occurrence time of the daytime aggregation point.
Step 304, determining the daytime polymerization point with the occurrence time meeting the preset time condition as an effective daytime polymerization point.
And 305, positioning and outputting the user work place information according to the effective daytime aggregation point. And (5) ending.
Step 306, performing spatial aggregation processing on the night positioning data of the user to generate a night aggregation point, wherein the night aggregation point has the occurrence time of the night aggregation point.
Step 307, determining that the occurrence time of the night polymerization point meets the preset time condition as an effective night polymerization point.
And 308, positioning and outputting the residence information of the user according to the effective night aggregation point. And (5) ending.
Step 301 in this embodiment is similar to the implementation of step 201 in the foregoing embodiment, and will not be described here.
Unlike the previous embodiments, this embodiment defines a specific implementation of how the user's workplace and residence is obtained. In the present embodiment, the user's positioning data is divided into daytime positioning data and nighttime positioning data; aiming at the daytime positioning data, spatial aggregation processing is carried out on the daytime positioning data of the user, and a daytime aggregation point is generated, wherein the daytime aggregation point has the occurrence time of the daytime aggregation point; determining that the daytime polymerization point with the occurrence time meeting the preset time condition is an effective daytime polymerization point; positioning and outputting user work place information according to the effective daytime aggregation point; aiming at night positioning data, carrying out space aggregation processing on the night positioning data of a user to generate a night aggregation point, wherein the night aggregation point has the occurrence time of the night aggregation point; determining that the night polymerization point with the occurrence time of the night polymerization point meeting the preset time condition is an effective night polymerization point; and positioning and outputting the residence information of the user according to the effective night aggregation point.
Specifically, after user positioning data of a preset duration is obtained, the user positioning data needs to be divided into daytime positioning data and nighttime positioning data. For example, the positioning data may be extracted according to a daytime period (e.g., 9 am to 5 pm) and a nighttime period (e.g., 22 pm to 7 am) respectively, to obtain the daytime positioning data and the nighttime positioning data.
The following description will take the processing of daytime positioning data as an example:
firstly, spatial aggregation processing is carried out on daytime positioning data according to a clustering algorithm, and a plurality of daytime aggregation points are obtained, wherein the daytime aggregation points have corresponding daytime aggregation point occurrence time.
And judging whether the occurrence time of each daytime polymerization point meets a preset time condition, specifically, counting whether the time length covered by the occurrence times of each daytime polymerization point in one day is larger than a first threshold (for example, 3 hours), if so, determining that the time is effective, judging whether the number of days of the effective day is larger than a second threshold (for example, 7 days), judging whether the time span of the effective day is larger than a third threshold (for example, 15 days), and if so, determining that the daytime polymerization point is the effective daytime polymerization point.
And finally, positioning and outputting the user work place information according to the effective daytime aggregation point. Specifically, according to most of the work and rest habits of the user, the place where the user is located in the daytime can be known as the work place of the user.
For the night positioning data, the processing procedure is similar to that of the daytime positioning data, and will not be repeated here.
As an optional implementation manner, if positioning data of a plurality of users are acquired, the method further includes: and processing the positioning data of the plurality of users by adopting a distributed computing model to acquire the information of the user location.
In particular, when it is necessary to learn the distribution of the job sites of a large number of users, it is necessary to acquire positioning data of a large number of users, typically in the TB level, in the trillion pieces of data recording scale, and when dealing with the data amount of the trillion pieces of recording, the computing power of a single computer or a single server is far from sufficient, and therefore, an algorithm running on a single machine is not applicable. Therefore, in the present embodiment, the distributed computing model is used to process the positioning data of the user. Specifically, the distributed architecture Hadoop or Spark based cluster computing architecture can be used for sending grouped positioning data to each node in the cluster to perform distributed computing, and in addition, the computing nodes can be flexibly increased, so that the computing efficiency is remarkably improved.
FIG. 5 is a flow chart of a method for positioning a user's residence, as shown in FIG. 5, by first obtaining mobile phone App positioning data; then performing thinning treatment on the App positioning data; the positioning data are then grouped according to the user identity, e.g. into n groups; then, a grouping algorithm based on a Spark framework is called, the grouped positioning data are sent to each node, and at each node, night positioning data and daytime positioning data are screened out from each group of positioning data; then, spatially polymerizing the positioning data to obtain an aggregation point; then, processing the positioning data in a time dimension, and screening out effective aggregation points; and then restoring original positioning points from the effective aggregation points, finally converging calculation results of all nodes, and storing the positioned job sites in a database. The database comprises fields such as a user ID, a first workplace, a second workplace, a first residence place, a second residence place and the like.
On the basis of the foregoing example, by dividing the positioning data of the user into daytime positioning data and nighttime positioning data; space aggregation processing is carried out on the daytime positioning data of the user, and a daytime aggregation point is generated, wherein the daytime aggregation point has the occurrence time of the daytime aggregation point; performing space aggregation processing on night positioning data of a user to generate a night aggregation point, wherein the night aggregation point has the occurrence time of the night aggregation point; determining a daytime polymerization point, the occurrence time of which meets the preset time condition, as an effective daytime polymerization point; determining a night polymerization point, the occurrence time of which meets the preset time condition, as an effective night polymerization point; positioning and outputting user work place information according to the effective daytime aggregation point; positioning and outputting residence information of the user according to the effective night aggregation point; the embodiment of the application analyzes the daytime positioning data and the night positioning data of the user in two dimensions of space and time respectively to obtain the daytime aggregation point and the night aggregation point which simultaneously meet the conditions of high density in space and high frequency in time, thereby positioning more accurate information of the workplace and residence of the user.
In a second aspect, an example of the present application provides a positioning device for a user location, and fig. 6 is a schematic structural diagram of a positioning device for a user location provided by the present application, as shown in fig. 6, where the positioning device for a user location includes:
the acquisition module 10 is configured to acquire positioning data of a user within a preset duration, where the positioning data is used to indicate positions of the user at different times; an aggregation module 20, configured to perform spatial aggregation processing on the positioning data of the user, to generate an aggregation point, where the aggregation point has an aggregation point occurrence time; a determining module 30, configured to determine that the occurrence time of the aggregation point meets a preset time condition, is an effective aggregation point; and the output module 40 is used for positioning and outputting the information of the user job place according to the effective aggregation point.
In other alternative embodiments, the preset duration includes a plurality of identical time periods; the determining module 30 is specifically configured to: if the occurrence time of the aggregation point in the time period is larger than a first threshold value, determining the time period as a valid time period; if the number of times of occurrence of the effective time period is determined to be larger than the second threshold value and the time span of the effective time period is determined to be larger than the third threshold value, determining the corresponding aggregation point as an effective aggregation point.
In other alternative embodiments, the obtaining module 10 is specifically configured to: acquiring log data of an application program on a user terminal within a preset duration; and determining the positioning data of the user according to the log data.
In other alternative embodiments, the aggregation module 20 is specifically configured to: performing thinning treatment of preset time granularity on the positioning data of the user; and carrying out space aggregation treatment on the positioning data of the user after the thinning treatment.
In other optional embodiments, the location data of the user includes a set of location points of the user at different times; the aggregation module 20 is specifically configured to: performing spatial aggregation processing of the preset distance on the position point set to generate an aggregation point; correspondingly, the output module 40 is specifically configured to: restoring a position point set corresponding to the effective aggregation point according to the preset distance; and determining the position point with the largest occurrence number in the position point set corresponding to the effective aggregation point as the information of the user location.
In other alternative embodiments, the apparatus further comprises a grouping module 50, in particular for: dividing the positioning data of the user into daytime positioning data and nighttime positioning data; the aggregation module 20 is specifically configured to: space aggregation processing is carried out on the daytime positioning data of the user, and a daytime aggregation point is generated, wherein the daytime aggregation point has the occurrence time of the daytime aggregation point; or performing space aggregation processing on night positioning data of the user to generate a night aggregation point, wherein the night aggregation point has the occurrence time of the night aggregation point; the determining module 30 is specifically configured to: determining a daytime polymerization point, the occurrence time of which meets the preset time condition, as an effective daytime polymerization point; determining a night polymerization point, the occurrence time of which meets the preset time condition, as an effective night polymerization point; the output module 40 is specifically configured to: positioning and outputting user work place information according to the effective daytime aggregation point; and positioning and outputting the residence information of the user according to the effective night aggregation point.
In other optional embodiments, if positioning data of a plurality of users is acquired, the grouping module 50 is further configured to: and processing the positioning data of the plurality of users by adopting a distributed computing model to acquire the job site information of the plurality of users.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process and corresponding beneficial effects of the positioning device for the user's job place described above may refer to the corresponding process in the foregoing method example, and will not be described herein again.
According to the positioning device for the user job site, provided by the embodiment of the application, the positioning data of the user in the preset time period are acquired through the acquisition module, and the positioning data are used for indicating the positions of the user at different times; the aggregation module performs space aggregation processing on the positioning data of the user to generate an aggregation point, wherein the aggregation point has an aggregation point occurrence time; the determining module determines that the polymerization point with the occurrence time meeting the preset time condition is an effective polymerization point; the output module is used for positioning and outputting the information of the user job site according to the effective aggregation point; the method and the device analyze the positioning data of the user in two dimensions of space and time to acquire the aggregation points meeting the conditions of high density in space and high frequency in time at the same time, further determine the information of the job place of the user, and improve the accuracy of acquiring the job place of the user.
In a third aspect, an example of the present application provides a control device, and fig. 7 is a schematic hardware structure of the control device provided by the present application, as shown in fig. 7, including:
at least one processor 701 and a memory 702.
In a specific implementation process, at least one processor 701 executes computer-executable instructions stored in the memory 702, so that the at least one processor 701 executes the method for locating a location of a user's residence as described above, where the processor 701 and the memory 702 are connected through the bus 703.
The specific implementation process of the processor 701 can be referred to the above method embodiment, and its implementation principle and technical effects are similar, and this embodiment will not be described herein again.
In the embodiment shown in fig. 7, it should be understood that the processor may be a central processing unit (english: central Processing Unit, abbreviated as CPU), or may be other general purpose processors, digital signal processors (english: digital Signal Processor, abbreviated as DSP), application specific integrated circuits (english: application Specific Integrated Circuit, abbreviated as ASIC), or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present application may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in a processor for execution.
The memory may comprise high speed RAM memory or may further comprise non-volatile storage NVM, such as at least one disk memory.
The bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, an external device interconnect (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, the buses in the drawings of the present application are not limited to only one bus or to one type of bus.
In a fourth aspect, the present application also provides a readable storage medium having stored therein computer-executable instructions which, when executed by a processor, implement a method for locating a user location as above.
The above-described readable storage medium may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk. A readable storage medium can be any available medium that can be accessed by a general purpose or special purpose computer.
An exemplary readable storage medium is coupled to the processor such the processor can read information from, and write information to, the readable storage medium. In the alternative, the readable storage medium may be integral to the processor. The processor and the readable storage medium may reside in an application specific integrated circuit (Application Specific Integrated Circuits, ASIC for short). The processor and the readable storage medium may reside as discrete components in a device.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the method embodiments described above may be performed by hardware associated with program instructions. The foregoing program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application.
Claims (9)
1. A method for locating a user's job site, comprising:
acquiring positioning data of a user within a preset duration, wherein the positioning data are used for indicating positions of the user at different times;
performing spatial aggregation on the positioning data of the user to generate an aggregation point, wherein the aggregation point has an aggregation point occurrence time;
determining the polymerization point with the occurrence time meeting the preset time condition as an effective polymerization point;
positioning and outputting the information of the user location according to the effective aggregation point;
the preset duration comprises a plurality of same time periods; the determining that the polymerization point with the occurrence time of the polymerization point meeting the preset time condition is an effective polymerization point comprises:
if the occurrence time of the aggregation point in the time period is larger than a first threshold value, determining the time period as a valid time period;
if the number of times of occurrence of the effective time period is determined to be larger than the second threshold value and the time span of the effective time period is determined to be larger than the third threshold value, determining the corresponding aggregation point as an effective aggregation point.
2. The method of claim 1, wherein the obtaining positioning data of the user for a preset duration comprises:
acquiring log data of an application program on a user terminal within a preset duration;
and determining the positioning data of the user according to the log data.
3. The method of claim 2, wherein the spatially aggregating the positioning data of the user comprises:
performing thinning treatment of preset time granularity on the positioning data of the user;
and carrying out space aggregation treatment on the positioning data of the user after the thinning treatment.
4. The method of claim 1, wherein the user's location data comprises a set of location points of the user at different times; the spatial aggregation processing of the positioning data of the user comprises the following steps:
performing spatial aggregation processing of the preset distance on the position point set to generate an aggregation point;
correspondingly, the positioning and outputting the information of the user location according to the effective aggregation point comprises the following steps:
restoring a position point set corresponding to the effective aggregation point according to the preset distance;
and determining the position point with the largest occurrence number in the position point set corresponding to the effective aggregation point as the information of the user location.
5. The method of claim 1, further comprising, after the obtaining the positioning data of the user within the preset time period:
dividing the positioning data of the user into daytime positioning data and nighttime positioning data;
the spatial aggregation processing is performed on the positioning data of the user to generate an aggregation point, wherein the aggregation point has an aggregation point occurrence time, and the spatial aggregation processing comprises the following steps:
space aggregation processing is carried out on the daytime positioning data of the user, and a daytime aggregation point is generated, wherein the daytime aggregation point has the occurrence time of the daytime aggregation point; performing space aggregation processing on night positioning data of a user to generate a night aggregation point, wherein the night aggregation point has the occurrence time of the night aggregation point;
the determining that the polymerization point with the occurrence time of the polymerization point meeting the preset time condition is an effective polymerization point comprises:
determining a daytime polymerization point, the occurrence time of which meets the preset time condition, as an effective daytime polymerization point; determining a night polymerization point, the occurrence time of which meets the preset time condition, as an effective night polymerization point;
the step of positioning and outputting the user job site information according to the effective aggregation point comprises the following steps:
positioning and outputting user work place information according to the effective daytime aggregation point; and positioning and outputting the residence information of the user according to the effective night aggregation point.
6. The method of claim 1, wherein if positioning data for a plurality of users is obtained, the method further comprises:
and processing the positioning data of the plurality of users by adopting a distributed computing model to acquire the job site information of the plurality of users.
7. A user location device for a job site, comprising:
the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring positioning data of a user in preset duration, and the positioning data are used for indicating positions of the user at different times;
the aggregation module is used for carrying out space aggregation processing on the positioning data of the user to generate an aggregation point, wherein the aggregation point has the occurrence time of the aggregation point;
the determining module is used for determining the polymerization point with the occurrence time meeting the preset time condition as an effective polymerization point;
the output module is used for positioning and outputting the information of the user location according to the effective aggregation point;
the preset duration comprises a plurality of same time periods; the determining module is specifically configured to determine that the time period is an effective time period if it is determined that the occurrence time of the aggregation point in the time period is greater than a first threshold; if the number of times of occurrence of the effective time period is determined to be larger than the second threshold value and the time span of the effective time period is determined to be larger than the third threshold value, determining the corresponding aggregation point as an effective aggregation point.
8. A control apparatus, characterized by comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing computer-executable instructions stored in the memory causes the at least one processor to perform the method of locating a user job site as claimed in any one of claims 1 to 6.
9. A readable storage medium having stored therein computer executable instructions which, when executed by a processor, implement the method of locating a user location as defined in any one of claims 1 to 6.
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