CN110008414B - Method and device for determining geographic information point - Google Patents
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Abstract
The application discloses a method and a device for determining geographic information points. One embodiment of the method comprises: acquiring positioning information of a user, wherein the positioning information comprises user positioning coordinates; the user positioning coordinates are used as input values of a pre-trained Bayesian prediction model, and the probability value of the user at each geographic information point in at least one geographic information point is obtained according to the Bayesian prediction model, wherein the Bayesian prediction model is obtained by training by using basic information of the geographic information points as sample data, and the basic information comprises the positioning coordinates of the geographic information points and historical positioning information of a historical visiting user; and determining the geographic information point corresponding to the maximum probability value as the geographic information point where the user is located. The embodiment realizes the determination of the geographic information point where the user is located.
Description
The application is a divisional application of Chinese patent application with the application number of CN201610196304.9, the application date of 2016, 3 and 31, and the name of the invention of a method and a device for determining geographic information points.
Technical Field
The application relates to the technical field of computers, in particular to the technical field of internet, and particularly relates to a method and a device for determining geographic information points.
Background
A Point Of geographic information (POI), also known as "information Point" or "Point Of Interest", refers to a place Of some significance, such as a restaurant, a school, a parking lot. The positioning in the prior art, especially the positioning of the user, is studied with respect to the absolute position of the user.
However, the prior art lacks of mining and calculating data of the geographic information point where the user is located, and cannot determine the geographic information point where the user is located.
Disclosure of Invention
The present application aims to provide an improved method and apparatus for determining geographical information points, so as to solve the technical problems mentioned in the above background section.
In a first aspect, the present application provides a method for determining geographic information points, where the method includes: acquiring positioning information of a user, wherein the positioning information comprises user positioning coordinates; the user positioning coordinates are used as input values of a pre-trained Bayesian prediction model, and the probability value of the user at each geographic information point in at least one geographic information point is obtained according to the Bayesian prediction model, wherein the Bayesian prediction model is obtained by training by using basic information of the geographic information points as sample data, and the basic information comprises the positioning coordinates of the geographic information points and historical positioning information of a historical visiting user; and determining the geographic information point corresponding to the maximum probability value as the geographic information point where the user is located.
In some embodiments, the bayesian prediction model parameters include a historical visiting probability of each geographic information point of the at least one geographic information point, wherein the historical visiting probability is obtained according to the location coordinates of the geographic information point and the historical location information, wherein: obtaining the historical visiting probability according to the positioning coordinate of the geographic information point and the historical positioning information comprises the following steps: selecting at least one geographic information point according to a preset rule, and establishing a geographic information point set; obtaining the number of times of each historical visit in the geographic information point set according to the positioning coordinate of each geographic information point in the geographic information point set and the positioning information of each historical visiting user in the geographic information point set; calculating the sum of the historical visiting times of each piece of geographic information in the geographic information point set, and taking the sum as the total historical visiting times of the geographic information point set; and obtaining the historical visiting probability of each geographic information point in the geographic information point set according to the historical visiting total times and the historical visiting times of the geographic information points in the geographic information point set.
In some embodiments, the obtaining the historical visiting times of the geographic information point according to the positioning coordinate of the geographic information point and the historical visiting user positioning information of the geographic information point includes: selecting historical users in a preset range of geographic information points; acquiring historical positioning information and historical search records of historical users corresponding to the historical positioning coordinates, wherein the historical positioning information comprises historical positioning coordinates and historical positioning time when the historical positioning coordinates are collected; if the historical search record comprises the identification information of the geographic information point; calculating a time interval between the historical locating time and a time point of searching the geographic information point; in response to the time interval being less than a predetermined threshold, determining the historical user as a historical visited user for the geographic information point.
In some embodiments, the historical location information of the historical visited user includes historical location coordinates and a historical location time when the historical visited user was located at the historical location coordinates; and the Bayesian prediction model parameters include: and time probability distribution of the geographic information points, wherein the time probability distribution is obtained according to the historical positioning time of the historical visiting users of the geographic information points.
In some embodiments, the location information of the user further includes a user location time when the user is located at the user location coordinates; and the step of taking the user positioning coordinates as an input value of a pre-trained Bayesian prediction model and obtaining a probability value of the user at each geographic information point in at least one geographic information point according to the Bayesian prediction model comprises the steps of: and taking the user positioning time and the user positioning coordinates as input values of a pre-trained Bayesian prediction model, and obtaining a probability value of the user at each geographic information point in at least one geographic information point according to the Bayesian prediction model.
In some embodiments, the bayesian prediction model parameters comprise a location probability of a geographic information point, wherein the location probability is derived from a distance between the geographic information point and a cluster center, wherein the cluster center is derived from at least one geographic information point cluster.
In some embodiments, the cluster center is clustered by at least one geographic information point, including: and clustering by using a K-means algorithm to obtain a clustering center of at least one geographic information point, wherein: selecting at least one geographic information point and establishing a clustering geographic information point set; determining the clustering number according to the total times of the clustering geographic information point set visits; selecting the clustering number of positioning coordinates as an initial clustering center; and setting the cluster number, the coordinates corresponding to the initial cluster centers and the positioning coordinates of the geographic information points in the cluster geographic information point set as input values of a K-means algorithm to obtain the cluster number cluster centers.
In some embodiments, the location information of the user further includes a user location time when the user is located at the user location coordinates; and, the acquiring the positioning information of the user comprises: screening out user positioning coordinates of user positioning time in a preset time period, and establishing an original user positioning coordinate set; rejecting abnormal points in the original user positioning coordinate set to obtain a user positioning coordinate set, wherein the abnormal points refer to coordinate points moving within a second preset time period and having a distance larger than a preset distance threshold; aggregating at least one user positioning coordinate in the user positioning coordinate set into a track center coordinate through a track clustering algorithm; taking the average time point of the time points corresponding to at least one user positioning time in the user positioning coordinate set as the track center time; and the step of taking the track center coordinates and the track center time as input values of a pre-trained Bayesian prediction model, and obtaining a probability value of each geographic information point of the user in at least one geographic information point according to the Bayesian prediction model comprises the steps of: and taking the track center coordinates as an input value of a pre-trained Bayesian prediction model, and obtaining a probability value of each geographic information point of the user in at least one geographic information point according to the Bayesian prediction model.
In some embodiments, after determining the geographic information point corresponding to the maximum probability value as the geographic information point where the user is located, the method further includes: adding historical visited user marks of the geographic information points corresponding to the maximum probability values to the positioning information of the users; adding the positioning information of the user with the historical visiting user mark into the sample data set of the Bayesian prediction model; and training and generating a new Bayesian prediction model by using the sample data in the sample data set.
In a second aspect, the present application provides an apparatus for determining geographic information points, the apparatus comprising: the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is configured to acquire positioning information of a user, and the positioning information comprises user positioning coordinates; the calculation module is configured to use the user positioning coordinates as an input value of a pre-trained Bayesian prediction model, and obtain a probability value of each geographic information point of the user in at least one geographic information point according to the Bayesian prediction model, wherein the Bayesian prediction model is obtained by training by using basic information of the geographic information points as sample data, and the basic information comprises positioning coordinates of the geographic information points and historical positioning information of a historical visiting user; and the determining module is configured to determine the geographic information point corresponding to the maximum probability value as the geographic information point where the user is located.
In some embodiments, the bayesian prediction model parameters include a historical visiting probability of each geographic information point of the at least one geographic information point, wherein the historical visiting probability is obtained according to the location coordinates of the geographic information point and the historical location information, wherein: obtaining the historical visiting probability according to the positioning coordinate of the geographic information point and the historical positioning information comprises the following steps: selecting at least one geographic information point according to a preset rule, and establishing a geographic information point set; obtaining the number of times of each historical visit in the geographic information point set according to the positioning coordinate of each geographic information point in the geographic information point set and the positioning information of each historical visiting user in the geographic information point set; calculating the sum of the historical visiting times of each piece of geographic information in the geographic information point set, and taking the sum as the total historical visiting times of the geographic information point set; and obtaining the historical visiting probability of each geographic information point in the geographic information point set according to the historical visiting total times and the historical visiting times of the geographic information points in the geographic information point set.
In some embodiments, the obtaining the historical visiting times of the geographic information point according to the positioning coordinate of the geographic information point and the historical visiting user positioning information of the geographic information point includes: selecting historical users in a preset range of geographic information points; acquiring historical positioning information and historical search records of historical users corresponding to the historical positioning coordinates, wherein the historical positioning information comprises historical positioning coordinates and historical positioning time when the historical positioning coordinates are collected; if the historical search record comprises the identification information of the geographic information point; calculating a time interval between the historical locating time and a time point of searching the geographic information point; in response to the time interval being less than a predetermined threshold, determining the historical user as a historical visited user for the geographic information point.
In some embodiments, the historical location information of the historical visited user includes historical location coordinates and a historical location time when the historical visited user was located at the historical location coordinates; and the Bayesian prediction model parameters include: and time probability distribution of the geographic information points, wherein the time probability distribution is obtained according to the historical positioning time of the historical visiting users of the geographic information points.
In some embodiments, the location information of the user further includes a user location time when the user is located at the user location coordinates; and, the computing module is further to: and taking the user positioning time and the user positioning coordinates as input values of a pre-trained Bayesian prediction model, and obtaining a probability value of the user at each geographic information point in at least one geographic information point according to the Bayesian prediction model.
In some embodiments, the bayesian prediction model parameters comprise a location probability of a geographic information point, wherein the location probability is derived from a distance between the geographic information point and a cluster center, wherein the cluster center is derived from at least one geographic information point cluster.
In some embodiments, the cluster center is clustered by at least one geographic information point, including: and clustering by using a K-means algorithm to obtain a clustering center of at least one geographic information point, wherein: selecting at least one geographic information point and establishing a clustering geographic information point set; determining the clustering number according to the total times of the clustering geographic information point set visits; selecting the clustering number of positioning coordinates as an initial clustering center; and setting the cluster number, the coordinates corresponding to the initial cluster centers and the positioning coordinates of the geographic information points in the cluster geographic information point set as input values of a K-means algorithm to obtain the cluster number cluster centers.
In some embodiments, the location information of the user further includes a user location time when the user is located at the user location coordinates; and the obtaining module is further configured to: screening out user positioning coordinates of user positioning time in a preset time period, and establishing an original user positioning coordinate set; rejecting abnormal points in the original user positioning coordinate set to obtain a user positioning coordinate set, wherein the abnormal points refer to coordinate points moving within a second preset time period and having a distance larger than a preset distance threshold; aggregating at least one user positioning coordinate in the user positioning coordinate set into a track center coordinate through a track clustering algorithm; taking the average time point of the time points corresponding to at least one user positioning time in the user positioning coordinate set as the track center time; and the step of taking the track center coordinates and the track center time as input values of a pre-trained Bayesian prediction model, and obtaining a probability value of each geographic information point of the user in at least one geographic information point according to the Bayesian prediction model comprises the steps of: and taking the track center coordinates as an input value of a pre-trained Bayesian prediction model, and obtaining a probability value of each geographic information point of the user in at least one geographic information point according to the Bayesian prediction model.
In some embodiments, the apparatus further comprises an update module configured to: adding historical visited user marks of the geographic information points corresponding to the maximum probability values to the positioning information of the users; adding the positioning information of the user with the historical visiting user mark into the sample data set of the Bayesian prediction model; and training and generating a new Bayesian prediction model by using the sample data in the sample data set.
According to the method and the device for determining the geographic information point, the positioning information of the user is obtained, wherein the positioning information comprises the positioning coordinate of the user; the user positioning coordinates are used as input values of a pre-trained Bayesian prediction model, and the probability value of the user at each geographic information point in at least one geographic information point is obtained according to the Bayesian prediction model, wherein the Bayesian prediction model is obtained by training by using basic information of the geographic information points as sample data, and the basic information comprises the positioning coordinates of the geographic information points and historical positioning information of a historical visiting user; and determining the geographic information point corresponding to the maximum probability value as the geographic information point where the user is located, so that the geographic information point where the user is located is determined.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a method for geographic information point determination according to the present application;
fig. 3 is a flowchart of yet another embodiment of a geographic information point determination method according to the present application;
fig. 4 is a time probability distribution of geographical information points according to the determination method of geographical information points of the present application;
fig. 5 is a schematic structural diagram of an embodiment of a geographic information point determination apparatus according to the present application;
fig. 6 is a schematic structural diagram of a computer system suitable for implementing the terminal device or the server according to the embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows an exemplary system architecture 100 to which an embodiment of the method for determining geographical information points or the device for determining geographical information points of the present application can be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. Various communication client applications, such as a map application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture Experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a location service server providing support for location services of the terminal devices 101, 102, 103. The positioning service server can analyze and process the received data such as the positioning data.
It should be noted that the method for determining the geographic information point provided in the embodiment of the present application is generally executed by the server 105, and accordingly, the device for determining the geographic information point is generally disposed in the server 105.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to fig. 2, a flow 200 of one embodiment of a method of determining geographic information points in accordance with the present application is shown. The method for determining the geographic information point comprises the following steps:
In this embodiment, an electronic device (for example, a server shown in fig. 1) on which the method for determining a geographical information point operates may acquire the positioning information of the user based on a mobile terminal used by the user. It should be noted that the above-mentioned obtaining of the Positioning information of the user based on the mobile terminal used by the user can be implemented in various ways, and the implementation ways include, but are not limited to, Positioning based on a Global Positioning System (GPS), Positioning based on a base station of a mobile operating network, Positioning based on an Assisted Global Positioning System (AGPS), Positioning based on a WiFi, and other Positioning ways of the mobile terminal that are known now or developed in the future.
In this embodiment, the positioning information of the user includes user positioning coordinates. Here, the user location coordinates may be longitude and latitude coordinates.
In this embodiment, based on the user positioning coordinates obtained in step 201, the electronic device (e.g., the server shown in fig. 1) may first use the positioning coordinates as input values of a pre-trained bayesian prediction model; then, obtaining the probability of the user at a certain geographic information point by using a Bayesian prediction model; the probability that the user is located at each geographic information point in at least one geographic information point is obtained by one or more times of utilization of the Bayesian prediction model, for example, the probability that the user is located at the first place can be obtained by taking the user positioning coordinates of the user as the input value of the Bayesian prediction model, and then the probability that the user is located at the second place is obtained by utilizing the Bayesian prediction model to predict the probability that the user is located at the second place is b.
In this embodiment, the bayesian prediction model is obtained by training using basic information of the geographic information point as sample data, where the basic information includes a positioning coordinate of the geographic information point and historical positioning information of a historical visiting user. Here, the bayesian prediction model is a prediction model based on bayesian formula, and the bayesian formula applied to the present embodiment can be expressed by the following equation as an example:
P(U|poi)＝A*B
the poi represents a certain geographic information point, U represents positioning information of a user, P (U | poi) represents a probability that the user is located at the certain geographic information point, A, B are bayesian prediction model parameters, and x represents an operational relationship between the parameters bayesian prediction model a and bayesian prediction model parameters B, wherein the operational relationship includes but is not limited to a product relationship and an addition relationship.
In some optional implementation manners of this embodiment, the bayesian prediction model parameter includes a historical visiting probability of each geographic information point of the at least one geographic information point, where the historical visiting probability is obtained according to the location coordinate of the geographic information point and the historical location information, and where the location coordinate according to the geographic information and the historical visiting probability to the bottom history of the historical location information may be obtained through the following steps: selecting at least one geographic information point according to a preset rule, and establishing a geographic information point set; obtaining the number of times of each historical visit in the geographic information point set according to the positioning coordinate of each geographic information point in the geographic information point set and the positioning information of each historical visited user in the geographic information point set; calculating the sum of the historical visiting times of each piece of geographic information in the geographic information point set, and taking the sum as the total historical visiting times of the geographic information point set; and obtaining the historical visiting probability of each geographic information point in the geographic information point set according to the total historical visiting times and the historical visiting times of the geographic information points in the geographic information point set.
As an example, three geographical information points with close distances are selected to establish a geographical information point set, the three geographical information points are named as a geographical information point a, a geographical information point b and a geographical information point c respectively, the total number of visits of the three geographical information points in a day is 100, wherein the number of visits of the geographical information point a is 20, the number of visits of the geographical information point b is 30, the number of visits of the geographical information point c is 50, then the historical visit probability of the geographical information point a is 20/100 twenty percent, then the historical visit probability of the geographical information point a is 30/100 thirty percent, and then the historical visit probability of the geographical information point a is 50/100 fifty percent.
Optionally, the historical visiting times of the geographic information point is obtained according to the positioning coordinate of the geographic information point and the historical visiting user positioning information of the geographic information point, and the historical visiting times of the geographic information point can be obtained through the following steps: selecting historical users in a preset range of geographic information points; acquiring historical positioning information and historical search records of historical users corresponding to the historical positioning coordinates, wherein the historical positioning information comprises historical positioning coordinates and historical positioning time when the historical positioning coordinates are acquired; if the historical search record comprises the identification information of the geographic information point; calculating the time interval between the historical positioning time and the time point of searching the geographic information point; and in response to the time interval being smaller than a preset threshold value, determining the historical user as a historical visiting user of the geographic information point.
As an example, a historical user of the geographic information point a at 10 am to 11 am within a radius of 100 meters is selected, for example, the user opens one in the range of 10 am 30 pm, then a search record is obtained, if the search record includes identification information of the geographic information point a, such as the name of the geographic information point a and a name similar to the name of the geographic information point a, then a time point of searching the identification information of the geographic information point a, such as 10 am 15 am, is obtained, and a time interval between the search time point 10 am 15 and the historical positioning time 10 am 30 is smaller than a predetermined threshold, then the historical user is determined as a historical visiting user of the geographic information point, and it is understood that the predetermined threshold may be half an hour, may be one day, or may be one month.
Optionally, the historical visiting times of the geographic information point is obtained according to the positioning coordinate of the geographic information point and the historical visiting user positioning information of the geographic information point, and may be obtained by: and acquiring data of the historical user connection WiFi, and if the WiFi is determined to belong to a certain geographic information point, determining that the historical user is a historical visited user of the geographic information point.
Optionally, at least one geographic information point is selected according to a preset rule, and a geographic information point set is established, wherein the preset rule for selecting at least one geographic information point may be selecting a plurality of geographic information points within a predetermined range centered on a user. The geographic information points in a certain area may be divided into a plurality of geographic information point sets according to the density of the geographic information points, for example, a college campus is respectively centered on a main teaching building, a sports hall and a library, and three geographic information point sets are established, wherein the geographic information point set centered on the sports hall may include an equipment room and a playground.
In some optional implementation manners of this embodiment, the bayesian prediction model parameter includes a location probability of a geographic information point, where the location probability is obtained according to a distance between the geographic information point and a cluster center, where the cluster center is obtained by clustering at least one geographic information point. For example, the inverse ratio of the distance between the geographic information point and the cluster center is used as the positioning probability of the geographic information point. Of course, the positioning probability of the geographic information point can also be obtained by multiplying the inverse ratio of the distance between the geographic information point and the clustering center by a coefficient.
Alternatively, the cluster center may be used as a bridge between the user and the geographic information point, and a user-cluster center probability may be first calculated, for example, an inverse ratio of a distance between the user and the cluster center may be used as the user-cluster center probability, and then a product of the user-cluster center probability and the positioning probability may be used as the probability of the user at the geographic information point. As an example, user a is near cluster center a and cluster center B, the distance between user a and cluster center a is 10 meters, and the distance between user a and cluster center a is 20 meters; a geographical information point c and a geographical information point d are arranged in a clustering geographical information point set of the clustering center A, wherein the distance between the geographical information point c and the clustering center A is 1 meter, and the distance between the geographical information point c and the clustering center A is 2 meters; a geographic information point e is arranged in a clustering geographic information point set of the clustering center B, wherein the distance between the geographic information point e and the clustering center B is 4 m; then, if the inverse ratio of the distance between the geographic center and the cluster center is taken as the positioning probability, the positioning probability of the geographic information point a is 1/1, the positioning probability of the geographic information point B is 1/2, the positioning probability of the geographic information point c is 1/4, the user-cluster center probability between the user and the cluster center a is 1/10, and the user-cluster center probability between the user and the cluster center B is 1/20; finally, it is found that the probability of the user at the geographic information point a is (1/1) × (1/10), the probability of the user at the geographic information point b is (1/2) × (1/10), and the probability of the user at the geographic information point c is (1/4) × (1/20). Here, "/" denotes a division sign, "" denotes an operation, and preferably, "" denotes a multiplication sign.
Optionally, the clustering center obtained by clustering at least one geographic information point may be divided into regions at random, and may be clustered by using a clustering algorithm, where the clustering algorithm includes, but is not limited to: k-means clustering algorithm, hierarchical clustering algorithm, SOM clustering algorithm and FCM clustering algorithm. It should be understood that the calculation process of the above-mentioned clustering algorithm itself is well known to those skilled in the art, and will not be described herein.
Optionally, the clustering center of at least one geographic information point may be obtained through K-means algorithm clustering, and optionally, the specific process is as follows: selecting at least one geographic information point and establishing a clustering geographic information point set; determining the clustering number according to the total times of the access of the clustering geographic information point set; selecting the positioning coordinates of the clustering number as an initial clustering center; and setting the cluster number, the coordinates corresponding to the initial cluster centers and the positioning coordinates of the geographic information points in the cluster geographic information point set as input values of a K-means algorithm to obtain the cluster number cluster centers. It should be understood that the K-means algorithm is a clustering algorithm well known to those skilled in the art and will not be described herein.
Optionally, the clustering number may be determined according to the number of geographic information points in the clustered geographic information set, or the number of geographic information points may be determined according to whether the geographic information points in the clustered geographic information point set have an obvious dense partition phenomenon.
Optionally, the location coordinates of the number of clusters may be randomly selected as an initial cluster center, or the location coordinates of the geographic information points whose historical visiting probability is greater than a predetermined threshold may be used as the initial cluster center.
In this embodiment, based on the probability values of the user at each geographic information point of the at least one geographic information point obtained in step 202, the maximum probability value is selected from the probability values as the maximum probability value, and the geographic information point corresponding to the maximum probability value is determined as the geographic information point where the user is located.
The method provided by the embodiment of the application realizes the determination of the geographic information point where the user is located by utilizing the historical visiting probability and the positioning probability of the geographic information point.
With further reference to fig. 3, a flow 300 of yet another embodiment of a method of determining a geographic information point is shown. The process 300 of the method for determining geographic information points includes the following steps:
In this embodiment, an electronic device (for example, a server shown in fig. 1) on which the method for determining a geographical information point operates may acquire the positioning information of the user based on a mobile terminal used by the user.
In this embodiment, the positioning information of the user includes a user positioning coordinate and a user positioning time when the user is located in the user positioning coordinate. The positioning information of the user can be obtained by the following steps: screening out user positioning coordinates of user positioning time in a preset time period, and establishing an original user positioning coordinate set; rejecting abnormal points in the original user positioning coordinate set to obtain a user positioning coordinate set, wherein the abnormal points refer to coordinate points moving within a second preset time period and having a distance larger than a preset distance threshold; aggregating at least one user positioning coordinate in the user positioning coordinate set into a track center coordinate through a track clustering algorithm; taking the average time point of the time points corresponding to at least one user positioning time in the user positioning coordinate set as the track center time; and taking the track center coordinates and the track center time as input values of a pre-trained Bayesian prediction model, and obtaining a probability value of each geographic information point of the user in at least one geographic information point according to the Bayesian prediction model.
And 302, taking the user positioning coordinates and the user positioning time as input values of a pre-trained Bayes prediction model, and obtaining a probability value of each geographic information point of the user in at least one geographic information point according to the Bayes prediction model.
In this embodiment, based on the user positioning coordinates obtained in step 301, the electronic device (e.g., the server shown in fig. 1) may first use the positioning coordinates as input values of a pre-trained bayesian prediction model; then, obtaining the probability of the user at a certain geographic information point by using a Bayesian prediction model; and obtaining the probability that the user is at least one geographic information point by utilizing the Bayesian prediction model for one or more times.
In this embodiment, the bayesian prediction model is obtained by training using basic information of the geographic information point as sample data, where the basic information includes a positioning coordinate of the geographic information point and historical positioning information of a historical visiting user. Here, the bayesian prediction model is a prediction model based on bayesian formula, and the bayesian formula applied to the present embodiment can be expressed by the following equation as an example:
P(U|poi)＝A*B
the poi represents a certain geographic information point, U represents positioning information of a user, P (U | poi) represents a probability that the user is located at the certain geographic information point, A, B are bayesian prediction model parameters, and x represents an operational relationship between the parameters bayesian prediction model a and bayesian prediction model parameters B, wherein the operational relationship includes but is not limited to a product relationship and an addition relationship.
In this embodiment, the bayesian prediction model parameter includes a historical visiting probability of each geographic information point in at least one geographic information point, where the historical visiting probability is obtained according to the location coordinate of the geographic information point and the historical location information.
In this embodiment, the bayesian prediction model parameter includes a location probability of a geographic information point, where the location probability is obtained according to a distance between the geographic information point and a cluster center, where the cluster center is obtained by clustering at least one geographic information point.
In this embodiment, the bayesian prediction model parameter includes a time probability distribution of a geographic information point, where the time probability distribution is obtained according to a historical positioning time of a historical visited user of the geographic information point, where the historical positioning time is a time point when the historical visited user is located in a historical positioning coordinate, the historical positioning coordinate and the historical positioning time belong to historical positioning information of the historical visited user, and the historical positioning coordinate and the historical positioning time are obtained by collecting historical positioning information of the historical visited user. As an example, reference may be made to fig. 4, which shows a time probability distribution of geographical information points in terms of a week. The geographic information point A has 100 historical visiting users in total in seven days of a week, 10 historical visiting users are available every day from Monday to Friday, 20 historical visiting users are available every Saturday, and 30 historical visiting users are available every day, and here, it is assumed that each historical visiting user visits the geographic information point A once. A time probability distribution over a period of weeks can be established.
Alternatively, the period of the temporal probability distribution may be twenty-four hours a day, seven days a week, days of a month, twelve months of a year, or four quarters of a year. Of course, combinations of the above described periodic patterns are also possible. As an example, a combination of twenty-four hours a day and seven days a week may be established, a probability that a certain user visits the geographic information point a at 19 o ' clock of friday, and a probability that friday corresponds to the time of week and a probability that 19 o ' clock corresponds to the time of day of the geographic information point a may be used as parameter values to calculate a probability that the user visits the geographic information point a at 19 o ' clock of friday.
In some optional implementation manners of this embodiment, the location information of the user further includes user location time when the user is located in the user location coordinate, the user location time and the user location coordinate may be used as input values of a pre-trained bayesian prediction model, and a probability value of the user at each geographic information point in at least one geographic information point is obtained according to the bayesian prediction model.
In this embodiment, based on the probability values of the user at each geographic information point of the at least one geographic information point obtained in step 302, the maximum probability value is selected from the probability values as the maximum probability value, and the geographic information point corresponding to the maximum probability value is determined as the geographic information point where the user is located.
In this embodiment, based on step 303, a history visited user tag of the geographic information point corresponding to the maximum probability value is added to the positioning information of the user, the positioning information of the user with the history visited user tag is added to the sample data set of the bayesian prediction model, and a new bayesian prediction model is generated by training sample data in the sample data set.
As can be seen from fig. 3, compared with the embodiment corresponding to fig. 2, the flow 300 of the method for determining geographic information points in this embodiment highlights the step of introducing the user location time of the user, and utilizes the time probability distribution of the geographic information points, thereby achieving more accurate determination of the geographic information points where the user is located.
With further reference to fig. 5, as an implementation of the method shown in the above-mentioned figures, the present application provides an embodiment of a geographic information point determining apparatus, which corresponds to the method embodiment shown in fig. 2, and which is specifically applicable to various electronic devices.
As shown in fig. 5, the above-mentioned geographic information point determining apparatus 500 of the present embodiment includes: the device comprises an acquisition module 501, a calculation module 502 and a determination module 503. The system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is configured to acquire positioning information of a user, and the positioning information comprises user positioning coordinates; the calculation module is configured to use the user positioning coordinates as an input value of a pre-trained Bayesian prediction model, and obtain a probability value of each geographic information point of the user in at least one geographic information point according to the Bayesian prediction model, wherein the Bayesian prediction model is obtained by training by using basic information of the geographic information points as sample data, and the basic information comprises positioning coordinates of the geographic information points and historical positioning information of a historical visiting user; and the determining module is configured to determine the geographic information point corresponding to the maximum probability value as the geographic information point where the user is located.
In this embodiment, the obtaining module 501 of the geographic information point determining apparatus 500 may obtain the positioning information of the user based on the mobile terminal used by the user. It should be noted that, the above-mentioned obtaining of the positioning information of the user based on the mobile terminal used by the user can be implemented in various ways.
In this embodiment, based on the positioning information of the user obtained by the obtaining module 501, the calculating module 502 may obtain the user positioning coordinates of the user obtained by the obtaining module 501, and the electronic device (for example, the server shown in fig. 1) may first use the positioning coordinates as an input value of a pre-trained bayesian prediction model; then, obtaining the probability of the user at a certain geographic information point by using a Bayesian prediction model; and obtaining the probability of the user at each geographic information point in at least one geographic information point by utilizing the Bayesian prediction model for one or more times.
In this embodiment, based on the probability value of each geographic information point of the user in at least one geographic information point obtained by the calculating module 502, the determining module 503 selects the maximum probability value from the probability values as the maximum probability value, and determines the geographic information point corresponding to the maximum probability value as the geographic information point where the user is located.
In some optional implementations of this embodiment, the apparatus 500 for determining a geographic information point further includes an updating module 504, configured to add a history visited user tag of the geographic information point corresponding to the maximum probability value to the location information of the user, add the location information of the user with the history visited user tag to a sample data set of the bayesian prediction model, and train and generate a new bayesian prediction model by using sample data in the sample data set.
It will be appreciated by those skilled in the art that the above-described geographic information point determining apparatus 500 also includes some other well-known structure, such as a processor, memory, etc., which is not shown in fig. 5 in order to unnecessarily obscure embodiments of the present disclosure.
Referring now to FIG. 6, shown is a block diagram of a computer system 600 suitable for use in implementing a server according to embodiments of the present application.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present application may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes an acquisition module, a calculation module, and a determination module. The names of these modules do not in some cases constitute a limitation to the module itself, and for example, the acquiring module may also be described as a "module that acquires positioning information of a user".
As another aspect, the present application also provides a non-volatile computer storage medium, which may be the non-volatile computer storage medium included in the apparatus in the above embodiment; or it may be a non-volatile computer storage medium that exists separately and is not incorporated into the terminal. The non-volatile computer storage medium stores one or more programs that, when executed by a device, cause the device to: acquiring positioning information of a user, wherein the positioning information comprises user positioning coordinates; the user positioning coordinates are used as an input value of a pre-trained Bayesian prediction model, and a probability value of the user at each geographic information point in at least one geographic information point is obtained according to the Bayesian prediction model, wherein the Bayesian prediction model is obtained by training by using basic information of the geographic information points as sample data, and the basic information comprises positioning coordinates of the geographic information points and historical positioning information of a historical visiting user; and determining the geographic information point corresponding to the maximum probability value as the geographic information point where the user is located.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (16)
1. A method for determining geographic information points, the method comprising:
acquiring positioning information of a user, wherein the positioning information comprises a user positioning coordinate and user positioning time when the user is positioned in the user positioning coordinate;
the user positioning coordinates are used as input values of a pre-trained Bayesian prediction model, and the probability value of the user at each geographic information point in at least one geographic information point is obtained according to the Bayesian prediction model, wherein the Bayesian prediction model is obtained by training by using basic information of the geographic information points as sample data, and the basic information comprises the positioning coordinates of the geographic information points and historical positioning information of a historical visiting user;
determining the geographic information point corresponding to the maximum probability value as the geographic information point where the user is located; wherein
The acquiring of the positioning information of the user comprises:
screening out user positioning coordinates of user positioning time in a preset time period, and establishing an original user positioning coordinate set;
rejecting abnormal points in the original user positioning coordinate set to obtain a user positioning coordinate set, wherein the abnormal points refer to coordinate points moving within a second preset time period and having a distance larger than a preset distance threshold;
aggregating at least one user positioning coordinate in the user positioning coordinate set into a track center coordinate through a track clustering algorithm;
taking the average time point of the time points corresponding to at least one user positioning time in the user positioning coordinate set as the track center time; and
the step of taking the user positioning coordinates as an input value of a pre-trained Bayesian prediction model and obtaining a probability value of the user at each geographic information point in at least one geographic information point according to the Bayesian prediction model comprises the steps of:
and taking the track center coordinates and the track center time as input values of a pre-trained Bayesian prediction model, and obtaining a probability value of each geographic information point of the user in at least one geographic information point according to the Bayesian prediction model.
2. The method of claim 1, wherein the bayesian prediction model parameters comprise a historical visiting probability of each of the at least one geographic information point, wherein the historical visiting probability is derived from location coordinates of the geographic information point and the historical location information, and wherein:
obtaining the historical visiting probability according to the positioning coordinate of the geographic information point and the historical positioning information comprises the following steps:
selecting at least one geographic information point according to a preset rule, and establishing a geographic information point set;
obtaining the number of times of each historical visit in the geographic information point set according to the positioning coordinate of each geographic information point in the geographic information point set and the positioning information of each historical visiting user in the geographic information point set;
calculating the sum of the historical visiting times of each piece of geographic information in the geographic information point set, and taking the sum as the total historical visiting times of the geographic information point set;
and obtaining the historical visiting probability of each geographic information point in the geographic information point set according to the historical visiting total times and the historical visiting times of the geographic information points in the geographic information point set.
3. The method of claim 2, wherein obtaining the historical visiting times of the geographic information point according to the positioning coordinates of the geographic information point and the historical visiting user positioning information of the geographic information point comprises:
selecting historical users in a preset range of geographic information points;
acquiring historical positioning information and historical search records of historical users corresponding to the historical positioning coordinates, wherein the historical positioning information comprises historical positioning coordinates and historical positioning time when the historical positioning coordinates are collected;
if the historical search record comprises the identification information of the geographic information point;
calculating a time interval between the historical locating time and a time point of searching the geographic information point;
in response to the time interval being less than a predetermined threshold, determining the historical user as a historical visited user for the geographic information point.
4. The method of claim 1, wherein the historical location information of the historical visited user comprises historical location coordinates and a historical location time when the historical visited user was located at the historical location coordinates; and the number of the first and second groups,
the Bayesian prediction model parameters include:
and time probability distribution of the geographic information points, wherein the time probability distribution is obtained according to the historical positioning time of the historical visiting users of the geographic information points.
5. The method of claim 4, wherein the location information of the user further comprises a user location time when the user is located at the user location coordinate; and the number of the first and second groups,
the step of taking the user positioning coordinates as an input value of a pre-trained Bayesian prediction model and obtaining a probability value of the user at each geographic information point in at least one geographic information point according to the Bayesian prediction model comprises the steps of:
and taking the user positioning time and the user positioning coordinates as input values of a pre-trained Bayesian prediction model, and obtaining a probability value of the user at each geographic information point in at least one geographic information point according to the Bayesian prediction model.
6. The method of claim 1, wherein the Bayesian prediction model parameters comprise a location probability of a geographic information point, wherein the location probability is derived from a distance between the geographic information point and a cluster center, wherein the cluster center is derived from at least one cluster of geographic information points.
7. The method of claim 6, wherein the cluster center is clustered by at least one geographic information point, comprising:
and clustering by using a K-means algorithm to obtain a clustering center of at least one geographic information point, wherein:
selecting at least one geographic information point and establishing a clustering geographic information point set;
determining the clustering number according to the total times of the clustering geographic information point set visits;
selecting the clustering number of positioning coordinates as an initial clustering center;
and setting the cluster number, the coordinates corresponding to the initial cluster centers and the positioning coordinates of the geographic information points in the cluster geographic information point set as input values of a K-means algorithm to obtain the cluster number cluster centers.
8. The method according to any one of claims 1 and 4 to 7, wherein after determining the geographic information point corresponding to the maximum probability value as the geographic information point where the user is located, the method further comprises:
adding historical visited user marks of the geographic information points corresponding to the maximum probability values to the positioning information of the users;
adding the positioning information of the user with the historical visiting user mark into the sample data set of the Bayesian prediction model;
and training and generating a new Bayesian prediction model by using the sample data in the sample data set.
9. An apparatus for determining geographic information points, the apparatus comprising:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is configured to acquire positioning information of a user, and the positioning information comprises user positioning coordinates;
the calculation module is configured to use the user positioning coordinates as an input value of a pre-trained Bayesian prediction model, and obtain a probability value of each geographic information point of the user in at least one geographic information point according to the Bayesian prediction model, wherein the Bayesian prediction model is obtained by training by using basic information of the geographic information points as sample data, and the basic information comprises positioning coordinates of the geographic information points and historical positioning information of a historical visiting user;
the determining module is configured to determine the geographic information point corresponding to the maximum probability value as the geographic information point where the user is located; wherein
The obtaining module is further configured to: screening out user positioning coordinates of user positioning time in a preset time period, and establishing an original user positioning coordinate set; rejecting abnormal points in the original user positioning coordinate set to obtain a user positioning coordinate set, wherein the abnormal points refer to coordinate points moving within a second preset time period and having a distance larger than a preset distance threshold; aggregating at least one user positioning coordinate in the user positioning coordinate set into a track center coordinate through a track clustering algorithm; taking the average time point of the time points corresponding to at least one user positioning time in the user positioning coordinate set as the track center time;
the computing module is further configured to: and taking the track center coordinates and the track center time as input values of a pre-trained Bayesian prediction model, and obtaining a probability value of each geographic information point of the user in at least one geographic information point according to the Bayesian prediction model.
10. The apparatus of claim 9, wherein the bayesian prediction model parameters comprise a historical visiting probability for each of the at least one geographic information point, wherein the historical visiting probability is derived from location coordinates of the geographic information point and the historical location information, and wherein:
obtaining the historical visiting probability according to the positioning coordinate of the geographic information point and the historical positioning information comprises the following steps:
selecting at least one geographic information point according to a preset rule, and establishing a geographic information point set;
obtaining the number of times of each historical visit in the geographic information point set according to the positioning coordinate of each geographic information point in the geographic information point set and the positioning information of each historical visiting user in the geographic information point set;
calculating the sum of the historical visiting times of each piece of geographic information in the geographic information point set, and taking the sum as the total historical visiting times of the geographic information point set;
and obtaining the historical visiting probability of each geographic information point in the geographic information point set according to the historical visiting total times and the historical visiting times of the geographic information points in the geographic information point set.
11. The apparatus of claim 10, wherein obtaining the historical visiting times of the geographic information point according to the positioning coordinates of the geographic information point and the historical visiting user positioning information of the geographic information point comprises:
selecting historical users in a preset range of geographic information points;
acquiring historical positioning information and historical search records of historical users corresponding to the historical positioning coordinates, wherein the historical positioning information comprises historical positioning coordinates and historical positioning time when the historical positioning coordinates are collected;
if the historical search record comprises the identification information of the geographic information point;
calculating a time interval between the historical locating time and a time point of searching the geographic information point;
in response to the time interval being less than a predetermined threshold, determining the historical user as a historical visited user for the geographic information point.
12. The apparatus of claim 9, wherein the historical location information of the historical visited user comprises historical location coordinates and a historical location time when the historical visited user was located at the historical location coordinates; and the number of the first and second groups,
the Bayesian prediction model parameters include:
and time probability distribution of the geographic information points, wherein the time probability distribution is obtained according to the historical positioning time of the historical visiting users of the geographic information points.
13. The apparatus of claim 12, wherein the location information of the user further comprises a user location time when the user is located at the user location coordinate; and the number of the first and second groups,
the computing module is further configured to:
and taking the user positioning time and the user positioning coordinates as input values of a pre-trained Bayesian prediction model, and obtaining a probability value of the user at each geographic information point in at least one geographic information point according to the Bayesian prediction model.
14. The apparatus of claim 9, wherein the bayesian prediction model parameters comprise a location probability of a geographic information point, wherein the location probability is derived from a distance between the geographic information point and a cluster center, wherein the cluster center is derived from at least one cluster of geographic information points.
15. The apparatus of claim 14, wherein the cluster center is clustered by at least one geographic information point, comprising:
and clustering by using a K-means algorithm to obtain a clustering center of at least one geographic information point, wherein:
selecting at least one geographic information point and establishing a clustering geographic information point set;
determining the clustering number according to the total times of the clustering geographic information point set visits;
selecting the clustering number of positioning coordinates as an initial clustering center;
and setting the cluster number, the coordinates corresponding to the initial cluster centers and the positioning coordinates of the geographic information points in the cluster geographic information point set as input values of a K-means algorithm to obtain the cluster number cluster centers.
16. The apparatus of claim 15, further comprising an update module configured to:
adding historical visited user marks of the geographic information points corresponding to the maximum probability values to the positioning information of the users;
adding the positioning information of the user with the historical visiting user mark into the sample data set of the Bayesian prediction model;
and training and generating a new Bayesian prediction model by using the sample data in the sample data set.
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| CN110740418A (en) * | 2018-07-03 | 2020-01-31 | 百度在线网络技术（北京）有限公司 | Method and device for generating user visit information |
| CN109041218B (en) * | 2018-09-25 | 2020-08-11 | 广东小天才科技有限公司 | Method for predicting user position and intelligent hardware |
| CN111460057B (en) * | 2019-01-22 | 2023-06-27 | 阿里巴巴集团控股有限公司 | POI (Point of interest) coordinate determining method, device and equipment |
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