CN110717006A - User school location analysis method and system, storage medium and electronic equipment - Google Patents

User school location analysis method and system, storage medium and electronic equipment Download PDF

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CN110717006A
CN110717006A CN201910969010.9A CN201910969010A CN110717006A CN 110717006 A CN110717006 A CN 110717006A CN 201910969010 A CN201910969010 A CN 201910969010A CN 110717006 A CN110717006 A CN 110717006A
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王宁君
马胡双
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Guangdong Genius Technology Co Ltd
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Abstract

The invention provides a method, a system, a storage medium and an electronic device for analyzing school positions of users, wherein the method comprises the following steps: acquiring daily positioning data of a target user, wherein the target user is a user not marked with a position of a school; cleaning the daily positioning data to obtain a plurality of target positioning points; acquiring all school positioning points within a preset range of each target positioning point; respectively calculating probability values of the schools serving as target schools of the target users according to the target positioning points and the school positioning points; and determining the target school according to the probability value. The invention realizes the identification of the target school of the user without marking the position of the school based on the daily positioning data of the user, and establishes a foundation for realizing the guard of the student during the school.

Description

User school location analysis method and system, storage medium and electronic equipment
Technical Field
The invention relates to the technical field of intelligent terminals, in particular to a method and a system for analyzing school positions of users, a storage medium and electronic equipment.
Background
The guarding function based on the location service is one of the important basic functions of the child watch, and particularly, parents are very concerned about guarding the child during school, school and school. Therefore, the child watch provides accurate learning-stage position service for the child watch user, and becomes one of the most competitive technical highs of the child watch industry. The child watch realizes the guard of students during school based on the school data labeled by users, but there are a large number of users who do not provide school information, and the functional accuracy of the child watch is affected. The problem to be solved urgently is to improve the guard ability of the unmarked user during school.
Disclosure of Invention
The invention aims to provide an analysis method, an analysis system, a storage medium and electronic equipment for positions of a user school, daily positioning data based on the user is realized, identification of a target school where the user school is located is realized, and a foundation is established for realizing guard of students during school.
The technical scheme provided by the invention is as follows:
the invention provides a method for analyzing school positions of users, which comprises the following steps:
acquiring daily positioning data of a target user, wherein the target user is a user not marked with a position of a school;
cleaning the daily positioning data to obtain a plurality of target positioning points;
acquiring all school positioning points within a preset range of each target positioning point;
respectively calculating probability values of the schools serving as target schools of the target users according to the target positioning points and the school positioning points;
and determining the target school according to the probability value.
Further, the step of cleaning the daily positioning data to obtain a plurality of target positioning points specifically comprises:
selecting positioning point data in any preset time period in the daily positioning data, wherein the positioning point data in the preset time period comprises m positioning points;
calculating the active value beta of the user at the mth positioning point according to the positioning point data,
Figure BDA0002231454810000021
m is more than 1, wherein the active value beta is the mean value of the distances between the last positioning point and all the other positioning points in a preset time period, diThe distance from the mth positioning point to the ith positioning point in a preset time period is obtained;
if the active value of the user at the mth positioning point is smaller than the standard active value, marking the mth positioning point as a target positioning point;
and obtaining a plurality of target positioning points according to all the positioning point data in the daily positioning data.
Further, respectively calculating probability values of schools as the schools of the target user according to the target positioning point and the school positioning points specifically includes:
selecting any one target positioning point, and calculating a target distance value from the any one target positioning point to each school positioning point;
based on any one target positioning point, calculating a target probability value of the target school of the school corresponding to each school positioning point according to the target distance value,
Figure BDA0002231454810000022
wherein p issBased on the arbitrary target positioning point, the s-th school positioning point is the target probability value of the target school, k is the number of the school positioning points, and DsThe target distance value from any one target locating point to the s-th school locating point is obtained, and e is a natural constant;
calculating the probability value of the target school of the target user corresponding to each school positioning point according to the target probability value obtained based on all the target positioning points,
Figure BDA0002231454810000031
wherein,
Figure BDA0002231454810000032
based on all target positioning points, the s-th school positioning point is the probability value of the target school, k is the number of the school positioning points, and n is all target positioning points of a target userNumber of target anchor points, psjThe s-th school positioning point is the target probability value of the target school based on the j-th target positioning point.
Further, determining the target school according to the probability value specifically includes:
selecting the school positioning point with the probability value larger than the preset probability as the target school; or
And comparing all the probability values, and selecting the school positioning point corresponding to the maximum probability value as the target school.
The invention also provides an analysis system for the school location of the user, which comprises the following components:
the data acquisition module is used for acquiring daily positioning data of a target user, wherein the target user is a user not marked with the position of the school;
the data cleaning module is used for cleaning the daily positioning data acquired by the data acquisition module to obtain a plurality of target positioning points;
the locating point acquisition module is used for acquiring all school locating points within the preset range of each target locating point determined by the data cleaning module;
the probability calculation module is used for calculating probability values of all schools as target schools of the target user according to the target positioning points determined by the data cleaning module and the school positioning points acquired by the positioning point acquisition module;
and the analysis module is used for determining the target school according to the probability value obtained by the probability calculation module.
Further, the data cleansing module specifically includes:
the data selection unit is used for selecting positioning point data in any preset time period in the daily positioning data, and the positioning point data in the preset time period comprises m positioning points;
a calculating unit for calculating the active value beta of the user at the mth positioning point according to the positioning point data selected by the data selecting unit,
Figure BDA0002231454810000041
m is greater than 1, whichIn the method, the active value beta is the mean value of the distances between the last positioning point and all the other positioning points in a preset time period, and diThe distance from the mth positioning point to the ith positioning point in a preset time period is obtained;
the processing unit is used for marking the mth positioning point as a target positioning point if the active value of the user at the mth positioning point calculated by the calculating unit is smaller than a standard active value;
and the processing unit is used for obtaining a plurality of target positioning points according to all the positioning point data in the daily positioning data.
Further, the probability calculation module specifically includes:
the distance calculation unit is used for selecting any one target positioning point and calculating a target distance value from the any one target positioning point to each school positioning point;
a target probability calculating unit for calculating a target probability value of the target school corresponding to each school positioning point according to the target distance value obtained by the distance calculating unit based on any one target positioning point,
Figure BDA0002231454810000042
wherein p issBased on the arbitrary target positioning point, the s-th school positioning point is the target probability value of the target school, k is the number of the school positioning points, and DsThe target distance value from any one target locating point to the s-th school locating point is obtained, and e is a natural constant;
a probability calculation unit for calculating probability values of schools corresponding to the school location points as target schools of the target users according to the target probability values obtained by the target probability calculation unit based on all the target location points,
Figure BDA0002231454810000043
wherein,
Figure BDA0002231454810000044
based on all target positioning points, the s-th school positioning point is the probability value of the target school, and k is schoolThe number of calibration sites, n is the number of target positioning points of a target user, psjThe s-th school positioning point is the target probability value of the target school based on the j-th target positioning point.
Further, the analysis module specifically includes:
the analysis unit selects the school positioning point with the probability value larger than the preset probability as the target school; or
And the analysis unit compares all the probability values and selects the school positioning point corresponding to the maximum probability value as the target school.
The invention also provides a storage medium having stored thereon a computer program which, when executed by a processor, implements any of the methods described above.
The invention also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program running on the processor, and the processor implements any one of the methods described above when executing the computer program.
By the method and the system for analyzing the position of the user school, the storage medium and the electronic equipment, the identification of the target school where the user school is located is realized on the basis of the daily positioning data of the user, and a foundation is established for realizing the guard of the student during the school.
Drawings
The above features, technical features, advantages and implementations of the method, system, storage medium and electronic device for analyzing a location of a user school will be further described in the following detailed description of preferred embodiments with reference to the accompanying drawings.
FIG. 1 is a flow diagram of one embodiment of a method for analyzing a user school location of the present invention;
FIG. 2 is a flow chart of another embodiment of a method for analyzing school locations of a user of the present invention;
FIG. 3 is a flow chart of another embodiment of a method for analyzing school locations of a user in accordance with the present invention;
FIG. 4 is a flow chart of another embodiment of a method for analyzing school locations of a user in accordance with the present invention;
FIG. 5 is a schematic diagram of an embodiment of a system for analyzing school locations of users according to the present invention;
fig. 6 is a schematic structural diagram of another embodiment of the system for analyzing school locations of users according to the present invention.
The reference numbers illustrate:
100 user school location analysis system
110 data acquisition module
120 data selection unit 121 data selection unit 122 calculation unit 123 processing unit
130 positioning point acquisition module
140 probability calculation module 141 distance calculation unit 142 target probability calculation unit 143 probability calculation unit
150 analysis module 151 analysis unit
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
For the sake of simplicity, the drawings only schematically show the parts relevant to the present invention, and they do not represent the actual structure as a product. In addition, in order to make the drawings concise and understandable, components having the same structure or function in some of the drawings are only schematically depicted, or only one of them is labeled. In this document, "one" means not only "only one" but also a case of "more than one".
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
In particular implementations, the terminal devices described in embodiments of the present application include, but are not limited to, other portable devices such as mobile phones, laptop computers, family computers, or tablet computers having touch sensitive surfaces (e.g., touch screen displays and/or touch pads). It should also be understood that in some embodiments the terminal device is not a portable communication device, but is a desktop computer having a touch-sensitive surface (e.g., a touch screen display and/or touchpad).
In the discussion that follows, a terminal device that includes a display and a touch-sensitive surface is described. However, it should be understood that the terminal device may include one or more other physical user interface devices such as a physical keyboard, mouse, and/or joystick.
The terminal device supports various applications, such as one or more of the following: a drawing application, a presentation application, a network creation application, a word processing application, a disc burning application, a spreadsheet application, a gaming application, a telephone application, a video conferencing application, an email application, an instant messaging application, an exercise support application, a photo management application, a digital camera application, a digital video camera application, a Web browsing application, a digital music player application, and/or a digital video player application.
Various applications that may be executed on the terminal device may use at least one common physical user interface device, such as a touch-sensitive surface. One or more functions of the touch-sensitive surface and corresponding information displayed on the terminal can be adjusted and/or changed between applications and/or within respective applications. In this way, a common physical architecture (e.g., touch-sensitive surface) of the terminal can support various applications with user interfaces that are intuitive and transparent to the user.
In addition, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not intended to indicate or imply relative importance.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will be made with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.
In an embodiment of the present invention, as shown in fig. 1, a method for analyzing a school location of a user includes:
s100, daily positioning data of a target user is obtained, wherein the target user is a user not marked with the position of a school.
Specifically, daily positioning data of a target user is obtained, and the target user is a user who does not mark the position of the school and sits on the target user. Because the target school where the target user is located is estimated and classified according to the daily positioning data of the user in the follow-up process, when the target user is a student, particularly a child, the target user is usually waiting in the school in a working day, and therefore the daily positioning data of the working day can be further screened and obtained to serve as initial data for follow-up analysis.
S200, cleaning the daily positioning data to obtain a plurality of target positioning points.
Specifically, wash daily locating data and obtain a plurality of target setpoint, if the target user is the activity in the campus, because the school is the confined garden to the area of inclusion is limited, show that the user is limited in the activity range of the time quantum of school of keeping somewhere, relatively speaking the change of target user's locating data is not active enough, consequently select a plurality of target setpoint of deeming as target user's campus of keeping somewhere from daily locating data.
S300, all school positioning points within the preset range of each target positioning point are obtained.
Specifically, all school locating points within a preset range of all target locating points are obtained, schools corresponding to the obtained school locating points are likely to be target schools of target users, possible probability values of all schools are calculated respectively subsequently, and then selection is performed according to the probability values.
The preset range is set by a system, can be set according to the statistical campus area size, and can also be calculated according to the distribution condition of each regional school.
S400, respectively calculating probability values of the schools as the target schools of the target users according to the target positioning points and the school positioning points.
Specifically, the distance between the target positioning points and the school positioning points is calculated according to the target positioning points, the probability that the school corresponding to each school positioning point is the target school is calculated by taking any one of the target positioning points as a research object, and then the probability mean value calculated by corresponding all the target positioning points is taken as the actual probability value that the school corresponding to each school positioning point is the target school.
S500, determining the target school according to the probability value.
Specifically, the target schools in the target schools are selected according to the probability values corresponding to the schools, wherein the selection rules can be set to different rules through system selection, for example, the schools corresponding to the maximum probability values can be directly determined as the target schools, the corresponding schools with the probability values arranged in the front can be located at the schools to be determined temporarily, then, the identification and the judgment are continued according to the new location data of the target users, or the location data are directly pushed to the target users for selection, or all the schools to be determined are used as monitoring objects, and the guard of the students during the schools is achieved.
In this embodiment, the target location point and the school location point are analyzed according to the daily location data of the target user on the working day, and probability values of possible schools as the target school of the target user are respectively calculated, so that the target school of the target user is analyzed and presumed to establish a basis for realizing the guard of the student during the school.
Another embodiment of the present invention is a preferable embodiment of the above-mentioned embodiment, as shown in fig. 2, including:
s100, daily positioning data of a target user is obtained, wherein the target user is a user not marked with the position of a school.
S200, cleaning the daily positioning data to obtain a plurality of target positioning points.
S210, positioning point data in any preset time period in the daily positioning data is selected, wherein the positioning point data in the preset time period comprises m positioning points.
Specifically, positioning point data in any preset time period in the daily positioning data is selected, for convenience of description, only data in one preset time period is selected here for analysis, and practically all data in the daily positioning data are cleaned in the same manner.
The preset time interval comprises m positioning points, m is an integer larger than 1, m is 1 fixed value, and the movement mode of the user is not fixed, so the preset time intervals corresponding to the m positioning points can be the same or different. For example, the daily positioning data includes 10 positioning point data in total, 5 positioning point data are fixedly selected in a preset time period, the 5 positioning point data in the first study preset time period are 1-5 positioning point data, the 5 positioning point data in the second study preset time period are 2-6 positioning point data, and so on.
S220, calculating the active value beta of the user at the mth positioning point according to the positioning point data,
Figure BDA0002231454810000101
m is more than 1, wherein the active value beta is the mean value of the distances between the last positioning point and all the other positioning points in a preset time period, diThe distance from the mth positioning point to the ith positioning point in the preset time period is obtained.
Specifically, the set of all the positioning points in the preset time period, i.e. the positioning point data, is set as { a }1,a2,…,am-1,amComputing the mth positioning point (namely positioning point a) according to the positioning point datam) The activity value beta of the user is used,m is more than 1, and the active value beta is the last positioning point (namely positioning point a)m) And all the other anchor points (i.e. anchor point a)1,a2,…,am-1) Mean value of distances, diFor the distance from the mth positioning point to the ith positioning point in the preset time period, firstly, respectively calculating the last positioning point (namely positioning point a) in the preset time periodm) With all the other anchor points (i.e. anchor point a)1,a2,…,am-1) Obtaining m-1 distance values by the distance values between the distances, and then calculating the mean value of the m-1 distance values to obtain the mth positioning point (namely positioning point a)m) Is active value of.
S230, if the user activity value at the mth positioning point is smaller than the standard activity value, marking the mth positioning point as a target positioning point.
Specifically, if the activity value is smaller than the standard activity value, it indicates that the amount of change of the position of the target user at the corresponding time of the mth location point with respect to the previous time is small, that is, the user is in a certain relatively fixed area range within the preset time period, that is, the target user may be in school, and therefore, the mth location point (i.e., the location point a) is determinedm) And marking as a target positioning point.
S240, obtaining a plurality of target positioning points according to all the positioning point data in the daily positioning data.
Specifically, all positioning point data in the daily positioning data are subjected to identification and judgment of the target positioning points according to the analysis process, and finally a plurality of target positioning points are obtained. Generating a target positioning point set C according to all target positioning points, wherein C { (x)1，y1)，...，(xi，yi)，...，(xn，yn) Wherein (x)i，yi) The number of all target positioning points is n.
S300, all school positioning points within the preset range of each target positioning point are obtained.
S400, respectively calculating probability values of the schools as the target schools of the target users according to the target positioning points and the school positioning points.
S500, determining the target school according to the probability value.
In this embodiment, an active value of the target user at a certain positioning point is defined to reflect an activity condition of the target user within a certain time, then the target positioning point is selected from the daily positioning data, and the target school of the target user is analyzed subsequently according to the target positioning point, so that partial interference data is reduced, the calculation amount is reduced, and the calculation reliability is improved.
Another embodiment of the present invention is a preferable embodiment of the above-mentioned embodiment, as shown in fig. 3, including:
s100, daily positioning data of a target user is obtained, wherein the target user is a user not marked with the position of a school.
S200, cleaning the daily positioning data to obtain a plurality of target positioning points.
S300, all school positioning points within the preset range of each target positioning point are obtained.
S400, respectively calculating probability values of the schools as the target school of the target user according to the target location point and the school location points, specifically includes:
s410, any one target positioning point is selected, and the target distance value from the any one target positioning point to each school positioning point is calculated.
Specifically, any one target positioning point is selected, a target distance value from the target positioning point to each school positioning point is calculated, and a target positioning point set C { (x)1，y1)，…，(xi，yi)，…，(xn，yn) Wherein (x)i，yi) The number of all target positioning points is n. The set of all school positioning points is U, U { (X)1，Y1)，...，(Xi，Yi)，…，(Xk，Yk) In which (X)i，Yi) The number of all target positioning points is k, wherein the number is the coordinates of the ith school positioning point. And calculating the target distance value from each target positioning point to each school positioning point by combining the target positioning point set C and the school positioning point set U to obtain n multiplied by k target distance values.
S420, based on any one target positioning point, calculating a target probability value of the target school corresponding to each school positioning point according to the target distance value,
Figure BDA0002231454810000121
wherein p issBased on the arbitrary target positioning point, the s-th school positioning point is the target probability value of the target school, k is the number of the school positioning points, and DsAnd e is a natural constant, wherein the target distance value from any one target positioning point to the s-th school positioning point is the target distance value.
Specifically, firstly, a target probability value of the target school is calculated for the school corresponding to each school location point based on each target location point. For convenience of understanding, any one target positioning point is selected for explanation, and the calculation modes of the target probability values of all the target positioning points corresponding to the schools are the same.
And selecting any one target positioning point, and calculating a corresponding target probability value according to the target distance value between the target positioning point and one school positioning point, so that each school positioning point corresponds to each target positioning point and has one target probability value. For example, if there are n target anchor points, there are n target probability values for each school anchor point.
S430, calculating probability values of schools corresponding to the school positioning points as target schools of the target users according to the target probability values obtained based on all the target positioning points,
Figure BDA0002231454810000122
wherein,
Figure BDA0002231454810000123
based on all target positioning points, the s-th school positioning point is the probability value of the target school, k is the number of the school positioning points, n is the number of all the target positioning points of a target user, and p issjThe s-th school positioning point is the target probability value of the target school based on the j-th target positioning point.
Specifically, based on all the target location points, the probability value of the target school corresponding to each school location point is calculated according to the target probability value, and the mean value of all the target probability values corresponding to each school location point is calculated as the probability value of the target school corresponding to the school location point, for example, if there are n target location points, n target probability values corresponding to each school location point are calculated according to step S420, and then the mean value of the n target probability values is the probability value of the target school corresponding to the school location point.
S500, determining the target school according to the probability value.
In this embodiment, the probability value of each school being the target school is calculated according to the target distance value from each target locating point to each school locating point, the relationship between the location of the target user and the school locating point is comprehensively considered, and the probability value corresponding to the school with the closer distance is larger, so that the target school is quickly determined.
Another embodiment of the present invention is a preferable embodiment of the above-mentioned embodiment, as shown in fig. 4, including:
s100, daily positioning data of a target user is obtained, wherein the target user is a user not marked with the position of a school.
S200, cleaning the daily positioning data to obtain a plurality of target positioning points.
S300, all school positioning points within the preset range of each target positioning point are obtained.
S400, respectively calculating probability values of the schools as the target schools of the target users according to the target positioning points and the school positioning points.
S500, determining the target school according to the probability value.
S510, selecting the school locating point with the probability value larger than the preset probability as the target school; or
S520, all the probability values are compared, and the school positioning point corresponding to the maximum probability value is selected as the target school.
Specifically, the target schools in the target schools are selected according to the probability values corresponding to the schools, wherein the selection rules can be set to different rules through system selection, for example, a preset probability can be set, the school locating points with the probability values larger than the preset probability are the corresponding target schools, the schools corresponding to the maximum probability values can be directly determined as the target schools, the corresponding schools with the probability values arranged in the front can be located to be the schools to be determined temporarily, then, the identification and judgment are continued according to new locating data of the target users, or the target users are directly pushed to select, or all the schools to be determined are used as monitoring objects, and the guard of the students during the school period is achieved.
In this embodiment, the target school is selected according to the probability values corresponding to all the school location points and according to different setting modes, where the setting modes may be analyzed based on data of the user whose location is marked with the school, for example, the location data of the user whose location is marked with the school is obtained according to the above calculation method, the probability value of the school location point is calculated according to the location data, and then the probability value is compared with the actual school of the user, so as to determine the selection rule of the target school.
In one embodiment of the present invention, as shown in fig. 5, the system 100 for analyzing school locations of users includes:
the data acquisition module 110 is used for acquiring daily positioning data of a target user, wherein the target user is a user without marking the position of the school;
specifically, daily positioning data of a target user is obtained, and the target user is a user who does not mark the position of the school and sits on the target user. Because the target school where the target user is located is estimated and classified according to the daily positioning data of the user in the follow-up process, when the target user is a student, particularly a child, the target user is usually waiting in the school in a working day, and therefore the daily positioning data of the working day can be further screened and obtained to serve as initial data for follow-up analysis.
The data cleaning module 120 is used for cleaning the daily positioning data acquired by the data acquisition module 110 to obtain a plurality of target positioning points;
specifically, wash daily locating data and obtain a plurality of target setpoint, if the target user is the activity in the campus, because the school is the confined garden to the area of inclusion is limited, show that the user is limited in the activity range of the time quantum of school of keeping somewhere, relatively speaking the change of target user's locating data is not active enough, consequently select a plurality of target setpoint of deeming as target user's campus of keeping somewhere from daily locating data.
A positioning point obtaining module 130, configured to obtain all school positioning points within a preset range of each target positioning point determined by the data cleaning module 120;
specifically, all school locating points within a preset range of all target locating points are obtained, schools corresponding to the obtained school locating points are likely to be target schools of target users, possible probability values of all schools are calculated respectively subsequently, and then selection is performed according to the probability values.
The preset range is set by a system, can be set according to the statistical campus area size, and can also be calculated according to the distribution condition of each regional school.
A probability calculation module 140, configured to calculate probability values of the schools as the target schools of the target user according to the target positioning point determined by the data cleaning module 120 and the school positioning points acquired by the positioning point acquisition module 130;
specifically, the distance between the target positioning points and the school positioning points is calculated according to the target positioning points, the probability that the school corresponding to each school positioning point is the target school is calculated by taking any one of the target positioning points as a research object, and then the probability mean value calculated by corresponding all the target positioning points is taken as the actual probability value that the school corresponding to each school positioning point is the target school.
The analysis module 150 determines the target school according to the probability value obtained by the probability calculation module 140.
Specifically, the target schools in the target schools are selected according to the probability values corresponding to the schools, wherein the selection rules can be set to different rules through system selection, for example, the schools corresponding to the maximum probability values can be directly determined as the target schools, the corresponding schools with the probability values arranged in the front can be located at the schools to be determined temporarily, then, the identification and the judgment are continued according to the new location data of the target users, or the location data are directly pushed to the target users for selection, or all the schools to be determined are used as monitoring objects, and the guard of the students during the schools is achieved.
In this embodiment, the target location point and the school location point are analyzed according to the daily location data of the target user on the working day, and probability values of possible schools as the target school of the target user are respectively calculated, so that the target school of the target user is analyzed and presumed to establish a basis for realizing the guard of the student during the school.
Another embodiment of the present invention is a preferable embodiment of the above-mentioned embodiment, as shown in fig. 6, including:
the data acquisition module 110 is used for acquiring daily positioning data of a target user, wherein the target user is a user without marking the position of the school;
the data cleaning module 120 is used for cleaning the daily positioning data acquired by the data acquisition module 110 to obtain a plurality of target positioning points;
the data cleansing module 120 specifically includes:
the data selecting unit 121 is configured to select positioning point data in any preset time period in the daily positioning data, where the positioning point data in the preset time period includes m positioning points;
specifically, positioning point data in any preset time period in the daily positioning data is selected, for convenience of description, only data in one preset time period is selected here for analysis, and practically all data in the daily positioning data are cleaned in the same manner.
The preset time interval comprises m positioning points, m is an integer larger than 1, m is 1 fixed value, and the movement mode of the user is not fixed, so the preset time intervals corresponding to the m positioning points can be the same or different. For example, the daily positioning data includes 10 positioning point data in total, 5 positioning point data are fixedly selected in a preset time period, the 5 positioning point data in the first study preset time period are 1-5 positioning point data, the 5 positioning point data in the second study preset time period are 2-6 positioning point data, and so on.
A calculating unit 122, which calculates the active value β of the user at the mth positioning point according to the positioning point data selected by the data selecting unit 121,
Figure BDA0002231454810000161
m is more than 1, wherein the active value beta is the mean value of the distances between the last positioning point and all the other positioning points in a preset time period, diThe distance from the mth positioning point to the ith positioning point in a preset time period is obtained;
specifically, the set of all the positioning points in the preset time period, i.e. the positioning point data, is set as { a }1,a2,…,am-1,amComputing the mth positioning point (namely positioning point a) according to the positioning point datam) The activity value beta of the user is used,
Figure BDA0002231454810000171
m is more than 1, and the active value beta is the last positioning point (namely positioning point a)m) And all the other anchor points (i.e. anchor point a)1,a2,…,am-1) Mean value of distances, diFor the distance from the mth positioning point to the ith positioning point in the preset time period, firstly, respectively calculating the last positioning point (namely positioning point a) in the preset time periodm) With all the other anchor points (i.e. anchor point a)1,a2,…,am-1) Obtaining m-1 distance values by the distance values between the distances, and then calculating the mean value of the m-1 distance values to obtain the mth positioning point (namely positioning point a)m) Is active value of.
A processing unit 123, configured to mark the mth locating point as a target locating point if the user active value at the mth locating point calculated by the calculating unit 122 is smaller than a standard active value;
specifically, if the activity value is smaller than the standard activity value, it indicates that the amount of change of the position of the target user at the corresponding time of the mth location point with respect to the previous time is small, that is, the user is in a certain relatively fixed area range within the preset time period, that is, the target user may be in school, and therefore, the mth location point (i.e., the location point a) is determinedm) And marking as a target positioning point.
The processing unit 123 obtains a plurality of target positioning points according to all the positioning point data in the daily positioning data.
Specifically, all positioning point data in the daily positioning data are subjected to identification and judgment of the target positioning points according to the analysis process, and finally a plurality of target positioning points are obtained. Generating a target positioning point set C according to all target positioning points, wherein C { (x)1，y1)，...，(xi，yi)，...，(xn，yn) Wherein (x)i，yi) The number of all target positioning points is n.
In this embodiment, an active value of the target user at a certain positioning point is defined to reflect an activity condition of the target user within a certain time, then the target positioning point is selected from the daily positioning data, and the target school of the target user is analyzed subsequently according to the target positioning point, so that partial interference data is reduced, the calculation amount is reduced, and the calculation reliability is improved.
A positioning point obtaining module 130, configured to obtain all school positioning points within a preset range of each target positioning point determined by the data cleaning module 120;
a probability calculation module 140, configured to calculate probability values of the schools as the target schools of the target user according to the target positioning point determined by the data cleaning module 120 and the school positioning points acquired by the positioning point acquisition module 130;
the probability calculation module 140 specifically includes:
the distance calculation unit 141 selects any one target positioning point, and calculates a target distance value from the any one target positioning point to each school positioning point;
specifically, any one target positioning point is selected, a target distance value from the target positioning point to each school positioning point is calculated, and a target positioning point set C { (x)1，y1)，...，(xi，yi)，...，(xn，yn) Wherein (x)i，yi) The number of all target positioning points is n. The set of all school positioning points is U, U { (X)1，Y1)，...，(Xi，Yi)，...，(Xk，Yk) In which (X)i，Yi) The number of all target positioning points is k, wherein the number is the coordinates of the ith school positioning point. And calculating the target distance value from each target positioning point to each school positioning point by combining the target positioning point set C and the school positioning point set U to obtain n multiplied by k target distance values.
A target probability calculating unit 142, which calculates a target probability value of the target school corresponding to each school positioning point according to the target distance value obtained by the distance calculating unit 141 based on the any one target positioning point,
Figure BDA0002231454810000181
wherein p issBased on the arbitrary target positioning point, the s-th school positioning point is the target probability value of the target school, k is the number of the school positioning points, and DsAnd e is a natural constant, wherein the target distance value from any one target positioning point to the s-th school positioning point is the target distance value.
Specifically, firstly, a target probability value of the target school is calculated for the school corresponding to each school location point based on each target location point. For convenience of understanding, any one target positioning point is selected for explanation, and the calculation modes of the target probability values of all the target positioning points corresponding to the schools are the same.
And selecting any one target positioning point, and calculating a corresponding target probability value according to the target distance value between the target positioning point and one school positioning point, so that each school positioning point corresponds to each target positioning point and has one target probability value. For example, if there are n target anchor points, there are n target probability values for each school anchor point.
A probability calculating unit 143, which calculates probability values of schools corresponding to the school location points as target schools of the target users according to the target probability values obtained by the target probability calculating unit 142 based on all the target location points,
Figure BDA0002231454810000191
wherein,
Figure BDA0002231454810000192
based on all target positioning points, the s-th school positioning point is the probability value of the target school, k is the number of the school positioning points, n is the number of all the target positioning points of a target user, and p issjThe s-th school positioning point is the target probability value of the target school based on the j-th target positioning point.
Specifically, based on all the target location points, the probability value of the target school corresponding to each school location point is calculated according to the target probability value, and the mean value of all the target probability values corresponding to each school location point is calculated as the probability value of the target school corresponding to the school location point, for example, if there are n target location points, n target probability values corresponding to each school location point are calculated according to step S420, and then the mean value of the n target probability values is the probability value of the target school corresponding to the school location point.
In this embodiment, the probability value of each school being the target school is calculated according to the target distance value from each target locating point to each school locating point, the relationship between the location of the target user and the school locating point is comprehensively considered, and the probability value corresponding to the school with the closer distance is larger, so that the target school is quickly determined.
The analysis module 150 determines the target school according to the probability value obtained by the probability calculation module 140.
The analysis module 150 specifically includes:
the analysis unit 151 selects a school location point with the probability value greater than a preset probability as the target school; or
The analysis unit 151 compares all the probability values, and selects a school anchor point corresponding to the maximum probability value as the target school.
Specifically, the target schools in the target schools are selected according to the probability values corresponding to the schools, wherein the selection rules can be set to different rules through system selection, for example, a preset probability can be set, the school locating points with the probability values larger than the preset probability are the corresponding target schools, the schools corresponding to the maximum probability values can be directly determined as the target schools, the corresponding schools with the probability values arranged in the front can be located to be the schools to be determined temporarily, then, the identification and judgment are continued according to new locating data of the target users, or the target users are directly pushed to select, or all the schools to be determined are used as monitoring objects, and the guard of the students during the school period is achieved.
In this embodiment, the target school is selected according to the probability values corresponding to all the school location points and according to different setting modes, where the setting modes may be analyzed based on data of the user whose location is marked with the school, for example, the location data of the user whose location is marked with the school is obtained according to the above calculation method, the probability value of the school location point is calculated according to the location data, and then the probability value is compared with the actual school of the user, so as to determine the selection rule of the target school.
An embodiment of the invention provides a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out all or part of the method steps of the first embodiment.
All or part of the flow of the method according to the embodiments of the present invention may be implemented by a computer program, which may be stored in a computer-readable storage medium and used by a processor to implement the steps of the embodiments of the method. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
An embodiment of the present invention further provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program running on the processor, and the processor executes the computer program to implement all or part of the method steps in the first embodiment.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like which is the control center for the computer device and which connects the various parts of the overall computer device using various interfaces and lines.
The memory may be used to store the computer programs and/or modules, and the processor may implement various functions of the computer device by running or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, video data, etc.) created according to the use of the cellular phone, etc. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
It should be noted that the above embodiments can be freely combined as necessary. The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (10)

1. The method for analyzing the school location of the user is characterized by comprising the following steps:
acquiring daily positioning data of a target user, wherein the target user is a user not marked with a position of a school;
cleaning the daily positioning data to obtain a plurality of target positioning points;
acquiring all school positioning points within a preset range of each target positioning point;
respectively calculating probability values of the schools serving as target schools of the target users according to the target positioning points and the school positioning points;
and determining the target school according to the probability value.
2. The method of analyzing a user school location according to claim 1, wherein said cleaning of said daily positioning data to obtain a plurality of target positioning points specifically comprises:
selecting positioning point data in any preset time period in the daily positioning data, wherein the positioning point data in the preset time period comprises m positioning points;
calculating the active value beta of the user at the mth positioning point according to the positioning point data,
Figure FDA0002231454800000011
wherein, the active value beta is the mean value of the distances between the last positioning point and all other positioning points in a preset time period, diThe distance from the mth positioning point to the ith positioning point in a preset time period is obtained;
if the active value of the user at the mth positioning point is smaller than the standard active value, marking the mth positioning point as a target positioning point;
and obtaining a plurality of target positioning points according to all the positioning point data in the daily positioning data.
3. The method for analyzing the location of a user school according to claim 1, wherein calculating, according to the target location point and the school location point, probability values of schools as the target user school respectively specifically includes:
selecting any one target positioning point, and calculating a target distance value from the any one target positioning point to each school positioning point;
based on any one target positioning point, calculating a target probability value of the target school of the school corresponding to each school positioning point according to the target distance value,
Figure FDA0002231454800000021
wherein p issBased on the arbitrary target positioning point, the s-th school positioning point is the target probability value of the target school, k is the number of the school positioning points, and DsThe target distance value from any one target locating point to the s-th school locating point is obtained, and e is a natural constant;
according to the target outline obtained based on all target positioning pointsCalculating the probability value of the school corresponding to each school positioning point as the target school of the target user,
Figure FDA0002231454800000022
wherein,
Figure FDA0002231454800000023
based on all target positioning points, the s-th school positioning point is the probability value of the target school, k is the number of the school positioning points, n is the number of all the target positioning points of a target user, and p issjThe s-th school positioning point is the target probability value of the target school based on the j-th target positioning point.
4. The method for analyzing the location of a user school according to any one of claims 1 to 3, wherein determining said target school specifically according to said probability value comprises:
selecting the school positioning point with the probability value larger than the preset probability as the target school; or
And comparing all the probability values, and selecting the school positioning point corresponding to the maximum probability value as the target school.
5. An analysis system for a location of a user school, comprising:
the data acquisition module is used for acquiring daily positioning data of a target user, wherein the target user is a user not marked with the position of the school;
the data cleaning module is used for cleaning the daily positioning data acquired by the data acquisition module to obtain a plurality of target positioning points;
the locating point acquisition module is used for acquiring all school locating points within the preset range of each target locating point determined by the data cleaning module;
the probability calculation module is used for calculating probability values of all schools as target schools of the target user according to the target positioning points determined by the data cleaning module and the school positioning points acquired by the positioning point acquisition module;
and the analysis module is used for determining the target school according to the probability value obtained by the probability calculation module.
6. The system for analyzing a user school location according to claim 5, wherein said data cleansing module specifically comprises:
the data selection unit is used for selecting positioning point data in any preset time period in the daily positioning data, and the positioning point data in the preset time period comprises m positioning points;
a calculating unit for calculating the active value beta of the user at the mth positioning point according to the positioning point data selected by the data selecting unit,
Figure FDA0002231454800000031
wherein, the active value beta is the mean value of the distances between the last positioning point and all other positioning points in a preset time period, diThe distance from the mth positioning point to the ith positioning point in a preset time period is obtained;
the processing unit is used for marking the mth positioning point as a target positioning point if the active value of the user at the mth positioning point calculated by the calculating unit is smaller than a standard active value;
and the processing unit is used for obtaining a plurality of target positioning points according to all the positioning point data in the daily positioning data.
7. The system for analyzing a location of a school of users of claim 5, wherein said probability calculation module specifically comprises:
the distance calculation unit is used for selecting any one target positioning point and calculating a target distance value from the any one target positioning point to each school positioning point;
a target probability calculating unit for calculating a target probability value of the target school corresponding to each school positioning point according to the target distance value obtained by the distance calculating unit based on any one target positioning point,
Figure FDA0002231454800000041
wherein p issBased on the arbitrary target positioning point, the s-th school positioning point is the target probability value of the target school, k is the number of the school positioning points, and DsThe target distance value from any one target locating point to the s-th school locating point is obtained, and e is a natural constant;
a probability calculation unit for calculating probability values of schools corresponding to the school location points as target schools of the target users according to the target probability values obtained by the target probability calculation unit based on all the target location points,
Figure FDA0002231454800000042
wherein,
Figure FDA0002231454800000043
based on all target positioning points, the s-th school positioning point is the probability value of the target school, k is the number of the school positioning points, n is the number of all the target positioning points of a target user, and p issjThe s-th school positioning point is the target probability value of the target school based on the j-th target positioning point.
8. The system for analyzing a user school location according to any one of claims 5 to 7, wherein said analyzing module specifically comprises:
the analysis unit selects the school positioning point with the probability value larger than the preset probability as the target school; or
And the analysis unit compares all the probability values and selects the school positioning point corresponding to the maximum probability value as the target school.
9. A storage medium having a computer program stored thereon, characterized in that: the computer program, when executed by a processor, implements the method of any of claims 1 to 4.
10. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program that runs on the processor, characterized in that: the processor, when executing the computer program, implements the method of any of claims 1 to 4.
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