CN112884514A - Activity data analysis method, device, equipment and medium based on polygon algorithm - Google Patents

Activity data analysis method, device, equipment and medium based on polygon algorithm Download PDF

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CN112884514A
CN112884514A CN202110191578.XA CN202110191578A CN112884514A CN 112884514 A CN112884514 A CN 112884514A CN 202110191578 A CN202110191578 A CN 202110191578A CN 112884514 A CN112884514 A CN 112884514A
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activity
information
target
interest point
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CN112884514B (en
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王蕾
张茜
金璐
曾宪镇
胡立波
段建长
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Ping An Technology Shenzhen Co Ltd
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Abstract

The embodiment of the application provides a method, a device, equipment and a medium for analyzing activity data based on a polygon algorithm. The activity data analysis method based on the polygon algorithm comprises the following steps: acquiring longitude and latitude information of an activity track corresponding to a plurality of users respectively; determining a target interest point corresponding to the activity track corresponding to each user through a polygon algorithm based on longitude and latitude information of the activity track corresponding to each user in a plurality of users, wherein the target interest point belongs to one or more of a plurality of preset interest points; and determining a mapping relation between each interest point in the preset interest points and the user performance based on the target interest point corresponding to the activity track corresponding to each user and the user information corresponding to each user, wherein the user information comprises the user performance. The embodiment of the application can reasonably utilize and analyze the activity data of the individual.

Description

Activity data analysis method, device, equipment and medium based on polygon algorithm
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a medium for analyzing activity data based on a polygon algorithm.
Background
With the development of networks and communication technologies, various information systems access a large amount of target continuous activity data, but the data are not collected and utilized, so that great data waste is caused, effective information is not mined, and the information utilization rate is low. For example: in insurance and other related industries, agents are the main body of insurance sales, and the activity track and the activity range, living habits and personal attributes derived from the activity track may have a crucial influence on the management of the personnel. However, the prior art lacks attention to the agents and activities of the whole activity management activities of the whole hierarchy such as the second-level organization, the third-level organization, the business area and the like, and also lacks problems of losing a large amount of data and not mining effective information and the like caused by the fact that the activity track information of the agents or the whole activity track of the organization/business unit is collected, analyzed, processed and derived automatically, and cannot realize a closed-loop system for collecting user data, analyzing and improving the whole by utilizing the user data.
Therefore, how to reasonably utilize and analyze the activity data of the individual is a problem which needs to be solved urgently.
Disclosure of Invention
In view of the above, the present application is proposed to provide a method, apparatus, device and medium for activity data analysis based on a polygon algorithm that overcomes or at least partially solves the above mentioned problems.
In a first aspect, an embodiment of the present application provides an activity data analysis method based on a polygon algorithm, which may include:
acquiring longitude and latitude information of an activity track corresponding to a plurality of users respectively;
determining a target interest point POI corresponding to the activity track corresponding to each user through a polygon algorithm based on longitude and latitude information of the activity track corresponding to each user, wherein the target interest point belongs to one or more of a plurality of preset interest points;
and determining a mapping relation between each interest point in the preset interest points and the performance of the user based on the target interest point corresponding to the activity track corresponding to each user and the user information corresponding to each user, wherein the user information comprises the performance of the user.
By the method of the first aspect, the embodiment of the present application describes an activity data analysis method based on a polygon algorithm. By the method, the mapping relation between each interest point in the preset interest points and the performance of the user in daily life of the users can be analyzed through the activity tracks of the users in the past, and the performance of the users can be improved better. Wherein the mapping relationship may include the degree of influence of the interest point on the user performance, such as: the point of interest 1 has a beneficial effect and is large for the user, the point of interest 2 has a beneficial effect but is small for the user, and the point of interest 3 has an adverse effect and is large for the user. In addition, the method can reasonably utilize and analyze the activity data of the individuals, improve the individuals, and simultaneously can collect and store the data which are difficult to obtain in an intelligent mode, thereby laying a foundation for the housekeeper and the manager to know the first-line market condition, adjust the internal business strategy, adjust the regional personnel arrangement and adjust the culture scheme and the assessment scheme of the staff.
In a possible implementation manner, before determining, by a polygon algorithm, a target interest point corresponding to an activity track corresponding to each user based on latitude and longitude information of the activity track corresponding to each user, the method further includes: obtaining polygon information of a polygon corresponding to each interest point in a plurality of interest points, wherein the polygon information comprises a POI activity name and a POI activity longitude and latitude range; the determining, by a polygon algorithm, a target interest point corresponding to the activity track corresponding to each user based on the longitude and latitude information of the activity track corresponding to each user includes: judging whether longitude and latitude information corresponding to the activity track corresponding to the target user is in a longitude and latitude range of a polygon corresponding to the target interest point through the polygon algorithm; and if so, associating the POI activity name corresponding to the target interest point with the target user, and determining that the activity track corresponding to the target user corresponds to the target interest point, wherein the target user is one of the multiple users.
In a possible implementation manner, the determining whether longitude and latitude information corresponding to an activity track corresponding to a target user is within a longitude and latitude range of a polygon corresponding to a target interest point includes: and judging whether the longitude and latitude information corresponding to the activity track corresponding to the target user passes through the longitude and latitude range of the polygon corresponding to the target interest point and stays in the longitude and latitude range of the polygon corresponding to the target interest point for more than a preset time.
In one possible implementation, the method further includes: acquiring activity information of each user, wherein the activity information comprises one or more of an activity place, an activity range and an activity name; the determining, by a polygon algorithm, a target interest point corresponding to the activity track corresponding to each user based on the longitude and latitude information of the activity track corresponding to each user includes: and determining a target interest point corresponding to the activity track corresponding to each user through a polygon algorithm based on the activity information of each user and the longitude and latitude information corresponding to the activity track.
In one possible implementation, the plurality of preset points of interest belong to a plurality of catering services, shopping services, literary activities, financial services, educational activities, medical services.
In one possible implementation, the user information further includes one or more of user age, user family information, user position information, and user hobbies and interests.
In one possible implementation manner, the determining, based on the target interest point corresponding to the activity track corresponding to each of the users and the user information corresponding to each of the users, a mapping relationship between each of the plurality of preset interest points and a user performance includes: determining the dwell time of each target interest point corresponding to each user; and determining each interest point in the plurality of preset interest points and the mapping relation between the user information and user performance according to the staying time and the user information corresponding to each user.
In a second aspect, an embodiment of the present application provides an activity data analysis apparatus based on a polygon algorithm, which may include:
the first acquisition unit is used for acquiring longitude and latitude information of the activity track corresponding to a plurality of users respectively;
the first determining unit is used for determining a target interest point POI corresponding to the activity track corresponding to each user through a polygon algorithm based on longitude and latitude information of the activity track corresponding to each user, wherein the target interest point belongs to one or more of a plurality of preset interest points;
and the second determining unit is used for determining the mapping relation between each interest point in the preset interest points and the user performance based on the target interest point corresponding to the activity track corresponding to each user and the user information corresponding to each user, wherein the user information comprises the user performance.
In one possible implementation, the apparatus further includes: a second acquisition unit, configured to: acquiring polygon information of a polygon corresponding to each interest point in a plurality of interest points before determining a target interest point corresponding to an activity track corresponding to each user through a polygon algorithm based on longitude and latitude information of the activity track corresponding to each user, wherein the polygon information comprises a POI activity name and a POI activity longitude and latitude range; the first determining unit is specifically configured to: judging whether longitude and latitude information corresponding to the activity track corresponding to the target user is in a longitude and latitude range of a polygon corresponding to the target interest point through the polygon algorithm; and if so, associating the POI activity name corresponding to the target interest point with the target user, and determining that the activity track corresponding to the target user corresponds to the target interest point, wherein the target user is one of the multiple users.
In a possible implementation manner, the first determining unit is specifically configured to: and judging whether the longitude and latitude information corresponding to the activity track corresponding to the target user passes through the longitude and latitude range of the polygon corresponding to the target interest point and stays in the longitude and latitude range of the polygon corresponding to the target interest point for more than a preset time.
In one possible implementation, the apparatus further includes: a third obtaining unit configured to: acquiring activity information of each user, wherein the activity information comprises one or more of an activity place, an activity range and an activity name; the first determining unit is specifically configured to: and determining a target interest point corresponding to the activity track corresponding to each user through a polygon algorithm based on the activity information of each user and the longitude and latitude information corresponding to the activity track.
In one possible implementation, the plurality of preset points of interest belong to a plurality of catering services, shopping services, literary activities, financial services, educational activities, medical services.
In one possible implementation, the user information further includes one or more of user age, user family information, user position information, and user hobbies and interests.
In a possible implementation manner, the second determining unit is specifically configured to: determining the dwell time of each target interest point corresponding to each user; and determining the mapping relation between each interest point in the preset interest points and the user performance according to the staying time and the user information corresponding to each user.
In a third aspect, an embodiment of the present application provides a computer device, including a storage component, a processing component and a communication component, where the storage component, the processing component and the communication component are connected to each other, where the storage component is used to store a computer program, and the communication component is used to perform information interaction with an external device; the processing component is configured to invoke the computer program to execute the method according to the first aspect, which is not described herein again
In a fourth aspect, the present application provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and the computer program is executed by a processor to implement the method of the first aspect.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments or the background art of the present application, the drawings required to be used in the embodiments or the background art of the present application will be described below.
Fig. 1 is a schematic diagram of an activity data analysis system architecture based on a polygon algorithm according to an embodiment of the present application.
Fig. 2 is a schematic diagram of a flow of an activity data analysis method based on a polygon algorithm according to an embodiment of the present application.
Fig. 3 is a schematic diagram of a polygon intersection point according to an embodiment of the present disclosure.
Fig. 4 is a schematic polygon diagram of a preset point of interest according to an embodiment of the present disclosure.
Fig. 5 is a schematic structural diagram of an activity data analysis apparatus based on a polygon algorithm according to an embodiment of the present application.
Fig. 6 is a schematic structural diagram of another activity data analysis apparatus based on a polygon algorithm according to an embodiment of the present application.
Detailed Description
The embodiments of the present application will be described below with reference to the drawings.
The terms "first," "second," and "third," etc. in the description and claims of this application and in the accompanying drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, "include" and "have" and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
As used in this application, the terms "second device," "unit," "system," and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, or software in execution. For example, the second device may be, but is not limited to, a processor, a data processing platform, a computing device, a computer, two or more computers, and the like.
First, some terms in the present application are explained so as to be easily understood by those skilled in the art.
(1) Location Based Service (LBS) is a value-added Service that obtains the Location information of the mobile terminal user through the radio communication network of the telecom mobile operator or the external positioning mode (such as global positioning system), and provides the corresponding Service for the user under the support of the geographic information system platform.
(2) Cisco's Internet Operating System (IOS), an operating system optimized for internetworking, is a software architecture separated from hardware, and with the continuous development of network technology, can be dynamically upgraded to adapt to the changing technology (hardware and software), and has modularity, flexibility, scalability and controllability.
(3) Windows Phone (abbreviated as WP) is a mobile Phone operating system formally released by Microsoft in 2010 at 10/21.M, and has a series of defensive operation experiences such as desktop customization, icon dragging, sliding control and the like. Its home screen displays new emails, short messages, missed calls, calendar appointments, etc. by providing a dashboard-like experience. It also includes an enhanced touch screen interface for more convenient finger operation.
(3) POIs are an abbreviation for "Point of Interest" that can be translated into "points of Interest" or "points of information," each POI containing four directions of information, name, category, longitude, latitude. POI (information point) is classified, and has a primary class and a secondary class, and each classification is corresponding to the code and name of the corresponding industry. The recording and distinguishing of information acquisition are facilitated.
Next, a description is given of an activity data analysis system architecture based on a polygon algorithm according to an embodiment of the present application. Referring to fig. 1, fig. 1 is a schematic diagram of an activity data analysis system based on a polygon algorithm according to an embodiment of the present application, including: activity data analysis means 001 and terminal device 002 based on the polygon algorithm. Wherein:
the active data analysis device 001 based on the polygon algorithm may include, but is not limited to, a backend server, a component server, a data processing server, and the like, and when the active data analysis device 001 based on the polygon algorithm is a server, the server may communicate with a plurality of terminals through the internet, and a corresponding server-side program also needs to be run on the server to provide corresponding automatically generated document services, such as database services, data calculation, decision execution, and the like. For example, the server may: acquiring longitude and latitude information of an activity track corresponding to a plurality of users respectively; determining a target interest point corresponding to the activity track corresponding to each user through a polygon algorithm based on longitude and latitude information of the activity track corresponding to each user, wherein the target interest point belongs to one or more of a plurality of preset interest points; and determining a mapping relation between each interest point in the preset interest points and the performance of the user based on the target interest point corresponding to the activity track corresponding to each user and the user information corresponding to each user, wherein the user information comprises the performance of the user.
Terminal device 002 can install and run related applications. An application is a program that corresponds to a server and provides local services to a client. Here, the local service may include, but is not limited to: the method comprises the steps of collecting an activity track of a user and longitude and latitude information corresponding to the activity track through a positioning system, and sending the activity track and the longitude and latitude information of the activity track to a server. The terminal device 002 can also accept user information, user activity information, and the like input by the user. The terminal in this embodiment may include, but is not limited to, any electronic product based on an intelligent operating system, which may perform human-computer interaction with a user through an input device such as a keyboard, a virtual keyboard, a touch pad, a touch screen, and a voice control device, such as a smart phone, a tablet computer, and a personal computer. Smart operating systems include, but are not limited to, any operating system that enriches device functionality by providing various mobile applications to a mobile device, such as: android (Android)TM)、iOSTM、Windows PhoneTMAnd the like.
It is further understood that the polygon algorithm based activity data analysis system architecture of fig. 1 is only a partial exemplary implementation manner in the embodiments of the present application, and the polygon algorithm based activity data analysis system architecture in the embodiments of the present application includes, but is not limited to, the above polygon algorithm based activity data analysis system architecture.
Referring to fig. 2, fig. 2 is a schematic diagram of a flow of an activity data analysis method based on a polygon algorithm according to an embodiment of the present application. Applicable to the system of fig. 1 described above, the interaction between the activity data analysis device 001 and the terminal device 002 based on the polygon algorithm will be described below with reference to fig. 2. The method may include the following steps S201 to S205.
Step S201, obtaining longitude and latitude information corresponding to the activity track of each user.
Specifically, the activity data analysis device based on the polygon algorithm may obtain longitude and latitude information corresponding to an activity track of each of the plurality of users. The longitude and latitude information corresponding to the activity track of each user in the plurality of users can be acquired in real time through a positioning system in the related terminal equipment, and is sent to the activity data analysis device based on the polygon algorithm.
Step S202, activity information of each user is acquired.
Specifically, the activity data analysis device based on the polygon algorithm may obtain activity information of each of the plurality of users, where the activity information includes one or more of an activity location, an activity range, and an activity name. It will be appreciated that activity information for each of the plurality of users may be counted and calculated based on the activity traces described above. For example: the collected geographical position change information is automatically processed by an LBS system to generate the range of motion of the geographical position change information, and finally the geographical position change information is stored in an LBS1.0 mode to form a huge database containing the range of motion, the active time period, the active radius and other information of a plurality of users.
Step S203, determining a target interest point corresponding to the activity track corresponding to each user through a polygon algorithm based on the longitude and latitude information of the activity track corresponding to each user.
Specifically, the activity data analysis device based on the polygon algorithm may determine, based on the longitude and latitude information of the activity track corresponding to each of the users, a target interest point corresponding to the activity track corresponding to each of the users through the polygon algorithm, where the target interest point belongs to one or more of the interest points. It can be understood that the activity data analysis device based on the polygon algorithm can associate a plurality of interest points divided in advance with the activities of the user through the polygon algorithm, and further obtain target interest points corresponding to the activity tracks corresponding to the user. It can also be understood that, based on the longitude and latitude information corresponding to the activity track of each user, the target interest points corresponding to the multiple users respectively can be determined, so as to improve the analysis of each individual data in the whole. For example: the daily activity arrangement and the working mode of different agents are known and finally stored in an LBS2.0 form to form a database containing a plurality of agent behavior characteristics, wherein the behavior characteristics comprise target interest points corresponding to user activity tracks.
In one possible implementation, the plurality of points of interest belong to one or more of a point of interest (POI) activity in a catering service, a shopping service, a cultural and sports activity, a financial service, an educational activity, a medical service. The interest points may be obtained by classifying according to activity information of a plurality of users acquired in advance, and are used for indicating a series of activities that the users are likely to participate in daily life. It should be further noted that the point of interest division manner may be not only the related contents listed above, but also other kinds of activities, such as: public service, entertainment, etc., which are not specifically limited in the embodiments of the present application. The point of interest corresponds to a point of interest POI activity in the present application.
In a possible implementation manner, based on the activity information of each user and the longitude and latitude information corresponding to the activity track, a target interest point corresponding to the activity track corresponding to each user is determined through a polygon algorithm.
In a possible implementation manner, before determining, by a polygon algorithm, a target interest point corresponding to an activity track corresponding to each user based on latitude and longitude information of the activity track corresponding to each user, the method further includes: obtaining polygon information of a polygon corresponding to each interest point in a plurality of interest points, wherein the polygon information comprises a POI activity name and a POI activity longitude and latitude range; the determining, by a polygon algorithm, a target interest point corresponding to the activity track corresponding to each user based on the longitude and latitude information of the activity track corresponding to each user includes: judging whether longitude and latitude information corresponding to the activity track corresponding to the target user is in a longitude and latitude range of a polygon corresponding to the target interest point through the polygon algorithm; and if so, associating the POI activity name corresponding to the target interest point with the target user, and determining that the activity track corresponding to the target user corresponds to the target interest point, wherein the target user is one of the multiple users.
In a possible implementation manner, the determining whether longitude and latitude information corresponding to an activity track corresponding to a target user is within a longitude and latitude range of a polygon corresponding to a target interest point includes: and judging whether the longitude and latitude information corresponding to the activity track corresponding to the target user passes through the longitude and latitude range of the polygon corresponding to the target interest point and stays in the longitude and latitude range of the polygon corresponding to the target interest point for more than a preset time.
Wherein, whether the longitude and latitude information corresponding to the track point on the active track is in the longitude and latitude range of the polygon corresponding to the target interest point can be judged by using a polygon PNPOLY algorithm, and the specific contents are as follows:
(1) processing relating to intersections
PNPOLY divides the plane into points inside the polygon and points outside the polygon. That is, the interest point corresponds to a point inside the polygon and the interest point corresponds to a point outside the polygon, and meanwhile, points on the boundary are also classified as inside or outside. Any particular point is always classified in the same way. Referring to fig. 3, fig. 3 is a schematic view of a polygon intersection point according to an embodiment of the present disclosure. As shown in fig. 3, when considering two triangles TL (one interest point corresponding polygon) and TR (another interest point corresponding polygon), the test point P is at a common boundary of the two triangles. In determining which triangle the test point P corresponds to, PNPOLY may say that the test point P is in TL or TR, depending on the internal rounding error. However, before the test point P for those triangles it will always give the same answer. That is, if PNPOLY finds that the test point P is in TL before the test point P is at the common boundary, it will be found that the test point P is not TR when the test point P is at the common boundary. If PNPOLY finds that the test point P is not in TL before the test point P is at the common boundary, the test point P will be found to be in TR when the test point P is at the common boundary.
(2) Treatment of convex polygons
If the polygon is convex, then an equation can be found that contains an infinite line for each edge, representing each equation as an expression: d is ax + by + c. The point on the line takes zero. Each equation is normalized so that if a point inside the polygon is substituted, the result is positive, or equivalently, the outside point is negative. Now, the test is performed for the trace points on each active trace. If inside each line, it is inside the point of interest corresponding polygon.
For example: and if the longitude and latitude information corresponding to the activity track of the target user passes through the longitude and latitude range of the polygon corresponding to the target interest point and stays in the longitude and latitude range of the polygon corresponding to the target interest point for more than a preset time, the activity track of the target user can be considered to correspond to the target interest point.
It can be understood that the target interest point corresponding to the activity track corresponding to the user can be determined by judging whether the track points existing on the activity track or the longitude and latitude information corresponding to the track points with the number exceeding a certain number are in the longitude and latitude range of the polygon corresponding to the target interest point, and the stay time of the target user in the longitude and latitude range of the polygon corresponding to the target interest point. Referring to fig. 4, fig. 4 is a schematic view of a polygon with a preset interest point according to an embodiment of the present disclosure. As shown in fig. 4, there are three interest points corresponding to the polygon and the activity track of the target user, where the longitude and latitude range corresponding to the interest point corresponding to the polygon 2 includes the track point on the activity track, and therefore, the target interest point corresponding to the activity track corresponding to the user is the polygon 2.
Step S204, user information of each user is obtained.
In particular, the polygon algorithm based activity data analysis means may obtain user information for each of a plurality of users, the user information including the user performance.
In one possible implementation, the user information further includes one or more of user age, user family information, user position information, and user hobbies and interests. For example: on the basis of the LBS2.0, basic information of the agent, such as age, identity, family information, income and the like, is automatically supplemented from the inside of the system, the information of the agent is classified and stored in a labeling and scene mode, and finally the information of the agent is stored in a LBS3.0 mode containing the occupational characteristics, consumption level, interest, family information and other data of the agent, so that a database containing the personal characteristics and the working mode of the agent is formed.
Step S205, determining a mapping relationship between each interest point of the plurality of preset interest points and the performance of the user based on the target interest point corresponding to the activity track corresponding to each user and the user information corresponding to each user.
Specifically, the activity data analysis device based on the polygon algorithm may determine a mapping relationship between each of the plurality of preset interest points and the performance of the user based on the target interest point corresponding to the activity track corresponding to each of the users and the user information corresponding to each of the users. Wherein the mapping relationship may include the degree of influence of the interest point on the user performance, such as: the point of interest 1 has a beneficial effect and is large for the user, the point of interest 2 has a beneficial effect but is small for the user, and the point of interest 3 has an adverse effect and is large for the user. Another example is: on the basis of the LBS3.0, the performance completion condition of the agents of each business department is automatically related, different personal characteristics and working modes of the agents are compared, particularly data of the performance agents and the non-performance agents are compared, and then the comparison result is entered into the agent classification model, so that an intuitive scheme is provided for improvement of related work. Meanwhile, the main indexes in a plurality of preset interest points found in the artificial weight comparison result improve the culture scheme and the assessment scheme of the agent.
Optionally, a user representation of the target user is generated based on a target interest point corresponding to the activity track corresponding to each user and user information of the target user; and analyzing user figures corresponding to a plurality of target users respectively, and determining the mapping relation between each interest point in the interest points and the user performances of the target users. The activity data analysis device based on the polygon algorithm can analyze user figures corresponding to a plurality of target users respectively and determine the mapping relation of each interest point in the interest points to the user performances of the target users. The LBS-based activity data acquisition and data analysis tool can avoid the problems of data loss and data disjointing to the maximum extent, and realize the complete closed loop of data acquisition in the process that the basic-level agent is concerned about and the basic-level agent is added to each mechanism and area continuously. Meanwhile, the data processing system of the system automatically processes the basic bottom data layer by layer, presents the data to the housekeeper in the forms of labels, scenes and the like, collects and stores the data which are most difficult to obtain in the most intelligent way, and lays a foundation for the housekeeper and the manager to know the first-line market condition, adjust the internal business strategy, adjust the regional personnel arrangement, adjust the agent culture scheme and the assessment scheme.
Optionally, the determining, based on the target interest point corresponding to the activity track corresponding to each user and the user information corresponding to each user, a mapping relationship between each interest point in the plurality of preset interest points and a user performance includes: determining the dwell time of each target interest point corresponding to each user; and determining each interest point in the plurality of preset interest points and the mapping relation between the user information and user performance according to the staying time and the user information corresponding to each user. It can be understood that the longer the user stays in the interest point range, the greater the influence of the interest point on the user is. And since user information (such as age, hobbies, etc.) is different for each user. Thus, the mapping between each point of interest and the user's performance may be determined based on the length of stay and the user information.
The embodiment of the application describes an activity data analysis method based on a polygon algorithm. By the method, the mapping relation between each interest point in the preset interest points and the performance of the user in daily life of the users can be analyzed through the activity tracks of the users in the past, and the performance of the users can be improved better. In addition, the method can reasonably utilize and analyze the activity data of the individuals, improve the individuals, and simultaneously can collect and store the data which are difficult to obtain in an intelligent mode, thereby laying a foundation for the housekeeper and the manager to know the first-line market condition, adjust the internal business strategy, adjust the regional personnel arrangement and adjust the culture scheme and the assessment scheme of the staff.
The method of the embodiment of the present application is explained in detail above, and the following provides an activity data analysis apparatus based on the polygon algorithm related to the embodiment of the present application, and the activity data analysis apparatus 10 based on the polygon algorithm may be a service device that brings various conveniences to the use of a third party based on the interactive data by rapidly acquiring, processing, analyzing and extracting valuable data. Referring to fig. 5, fig. 5 is a schematic structural diagram of an activity data analysis apparatus based on a polygon algorithm according to an embodiment of the present application. The polygon algorithm based activity data analysis apparatus 10 may include a first obtaining unit 101, a first determining unit 102, and a second determining unit 103, and may further include a second obtaining unit 104, a third obtaining unit 105.
A first obtaining unit 101, configured to obtain longitude and latitude information of an activity track corresponding to each of a plurality of users;
a first determining unit 102, configured to determine, based on latitude and longitude information of an activity track corresponding to each user, a target interest point corresponding to the activity track corresponding to each user through a polygon algorithm, where the target interest point belongs to one or more of a plurality of preset interest points;
a second determining unit 103, configured to determine, based on a target interest point corresponding to an activity track corresponding to each of the users and user information corresponding to each of the users, a mapping relationship between each of the plurality of preset interest points and user performance, where the user information includes the user performance.
In one possible implementation, the apparatus further includes: a second obtaining unit 104, where the second obtaining unit 104 is configured to: acquiring polygon information of a polygon corresponding to each interest point in a plurality of interest points before determining a target interest point corresponding to an activity track corresponding to each user through a polygon algorithm based on longitude and latitude information of the activity track corresponding to each user, wherein the polygon information comprises a POI activity name and a POI activity longitude and latitude range; the first determining unit 102 is specifically configured to: judging whether longitude and latitude information corresponding to the activity track corresponding to the target user is in a longitude and latitude range of a polygon corresponding to the target interest point through the polygon algorithm; and if so, associating the POI activity name corresponding to the target interest point with the target user, and determining that the activity track corresponding to the target user corresponds to the target interest point, wherein the target user is one of the multiple users.
In a possible implementation manner, the first determining unit 102 is specifically configured to: and judging whether the longitude and latitude information corresponding to the activity track corresponding to the target user passes through the longitude and latitude range of the polygon corresponding to the target interest point and stays in the longitude and latitude range of the polygon corresponding to the target interest point for more than a preset time.
In one possible implementation, the apparatus further includes: a third obtaining unit 105, wherein the third obtaining unit 105 is configured to: acquiring activity information of each user, wherein the activity information comprises one or more of an activity place, an activity range and an activity name; the first determining unit 102 is specifically configured to: and determining a target interest point corresponding to the activity track corresponding to each user through a polygon algorithm based on the activity information of each user and the longitude and latitude information corresponding to the activity track.
In one possible implementation, the plurality of preset points of interest belong to a plurality of catering services, shopping services, literary activities, financial services, educational activities, medical services.
In one possible implementation, the user information further includes one or more of user age, user family information, user position information, and user hobbies and interests.
In a possible implementation manner, the second determining unit 103 is specifically configured to: determining the dwell time of each target interest point corresponding to each user; and determining each interest point in the plurality of preset interest points and the mapping relation between the user information and user performance according to the staying time and the user information corresponding to each user.
It should be noted that implementation of each operation may also correspond to corresponding description of the method embodiments shown in fig. 2 to fig. 4, and details are not described here again.
As shown in fig. 6, fig. 6 is a schematic structural diagram of another activity data analysis apparatus based on polygon algorithm according to an embodiment of the present application, and the apparatus 20 is applied to a second device and includes at least one processor 201, at least one memory 202, and at least one communication interface 203. In addition, the device may also include common components such as an antenna, which will not be described in detail herein.
The processor 201 may be a general purpose Central Processing Unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of programs according to the above schemes.
Communication interface 203 for communicating with other devices or communication Networks, such as ethernet, Radio Access Network (RAN), core network, Wireless Local Area Networks (WLAN), etc.
The Memory 202 may be a Read-Only Memory (ROM) or other type of static storage device that can store static information and instructions, a Random Access Memory (RAM) or other type of dynamic storage device that can store information and instructions, an Electrically Erasable Programmable Read-Only Memory (EEPROM), a Compact Disc Read-Only Memory (CD-ROM) or other optical Disc storage, optical Disc storage (including Compact Disc, laser Disc, optical Disc, digital versatile Disc, blu-ray Disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to these. The memory may be self-contained and coupled to the processor via a bus. The memory may also be integral to the processor.
The memory 202 is used for storing application program codes for executing the above scheme, and is controlled by the processor 201 to execute. The processor 201 is configured to execute application program code stored in the memory 202.
The code stored in the memory 202 may execute the activity data analysis method based on the polygon algorithm provided in fig. 2, for example, when the apparatus 20 is an activity data analysis apparatus based on the polygon algorithm, the latitude and longitude information of the activity track corresponding to each of the plurality of users may be obtained; determining a target interest point corresponding to the activity track corresponding to each user through a polygon algorithm based on longitude and latitude information of the activity track corresponding to each user, wherein the target interest point belongs to one or more of a plurality of preset interest points; and determining a mapping relation between each interest point in the preset interest points and the performance of the user based on the target interest point corresponding to the activity track corresponding to each user and the user information corresponding to each user, wherein the user information comprises the performance of the user.
It should be noted that, the functions of the functional units in the activity data analysis apparatus based on the polygon algorithm described in the embodiment of the present application may refer to the corresponding descriptions of the method embodiments shown in fig. 2 to fig. 4, and are not described again here.
In this application, the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present application.
In addition, functional components in the embodiments of the present application may be integrated into one component, or each component may exist alone physically, or two or more components may be integrated into one component. The integrated components can be realized in a form of hardware or a form of software functional units.
The integrated components, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented as part of or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product stored in a storage medium, and including instructions for causing a computer device (which may be a personal computer, a second device, or a network device) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
While the invention has been described with reference to specific embodiments, the scope of the invention is not limited thereto, and those skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application. While the present application has been described herein in conjunction with various embodiments, other variations to the disclosed embodiments may be understood and effected by those skilled in the art in practicing the present application as claimed herein.

Claims (10)

1. A method for analyzing activity data based on a polygon algorithm is characterized by comprising the following steps:
acquiring longitude and latitude information of an activity track corresponding to a plurality of users respectively;
determining a target interest point POI corresponding to the activity track corresponding to each user through a polygon algorithm based on longitude and latitude information of the activity track corresponding to each user, wherein the target interest point belongs to one or more of a plurality of preset interest points;
and determining a mapping relation between each interest point in the preset interest points and the performance of the user based on the target interest point corresponding to the activity track corresponding to each user and the user information corresponding to each user, wherein the user information comprises the performance of the user.
2. The method of claim 1, wherein before determining the target interest point corresponding to the activity track corresponding to each of the users through a polygon algorithm based on the longitude and latitude information of the activity track corresponding to each of the users, the method further comprises:
obtaining polygon information of a polygon corresponding to each interest point in a plurality of interest points, wherein the polygon information comprises a POI activity name and a POI activity longitude and latitude range;
the determining, by a polygon algorithm, a target interest point corresponding to the activity track corresponding to each user based on the longitude and latitude information of the activity track corresponding to each user includes:
judging whether longitude and latitude information corresponding to the activity track corresponding to the target user is in a longitude and latitude range of a polygon corresponding to the target interest point through the polygon algorithm;
and if so, associating the POI activity name corresponding to the target interest point with the target user, and determining that the activity track corresponding to the target user corresponds to the target interest point, wherein the target user is one of the multiple users.
3. The method of claim 1, wherein the determining whether the longitude and latitude information corresponding to the activity track corresponding to the target user is within the longitude and latitude range of the polygon corresponding to the target interest point comprises:
and judging whether the longitude and latitude information corresponding to the activity track corresponding to the target user passes through the longitude and latitude range of the polygon corresponding to the target interest point and stays in the longitude and latitude range of the polygon corresponding to the target interest point for more than a preset time.
4. The method of claim 1, further comprising:
acquiring activity information of each user, wherein the activity information comprises one or more of an activity place, an activity range and an activity name;
the determining, by a polygon algorithm, a target interest point corresponding to the activity track corresponding to each user based on the longitude and latitude information of the activity track corresponding to each user includes:
and determining a target interest point corresponding to the activity track corresponding to each user through a polygon algorithm based on the activity information of each user and the longitude and latitude information corresponding to the activity track.
5. The method of claim 1, wherein the plurality of predetermined points of interest belong to one or more of a catering service, a shopping service, a cultural and sports activity, a financial service, an educational activity, and a medical service.
6. The method of claim 1, wherein the user information further comprises one or more of user age, user family information, user job information, and user hobbies and interests.
7. The method of claim 6, wherein the determining a mapping relationship between each of the plurality of preset points of interest and the performance of the user based on the target point of interest corresponding to the activity track corresponding to each of the users and the user information corresponding to each of the users comprises:
determining the dwell time of each target interest point corresponding to each user;
and determining each interest point in the plurality of preset interest points and the mapping relation between the user information and user performance according to the staying time and the user information corresponding to each user.
8. An activity data analysis apparatus based on a polygon algorithm, comprising:
the first acquisition unit is used for acquiring longitude and latitude information of the activity track corresponding to a plurality of users respectively;
the first determining unit is used for determining a target interest point POI corresponding to the activity track corresponding to each user through a polygon algorithm based on longitude and latitude information of the activity track corresponding to each user, wherein the target interest point belongs to one or more of a plurality of preset interest points;
and the second determining unit is used for determining the mapping relation between each interest point in the preset interest points and the user performance based on the target interest point corresponding to the activity track corresponding to each user and the user information corresponding to each user, wherein the user information comprises the user performance.
9. The computer equipment is characterized by comprising a processing component, a storage component and a communication module component, wherein the processing component, the storage component and the communication module component are connected with each other, the storage component is used for storing a computer program, and the communication component is used for carrying out information interaction with external equipment; the processing component is configured to invoke a computer program to perform the method of any of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which is executed by a processor to implement the method of any one of claims 1-7.
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