CN112884514B - Method, device, equipment and medium for analyzing activity data based on polygon algorithm - Google Patents
Method, device, equipment and medium for analyzing activity data based on polygon algorithm Download PDFInfo
- Publication number
- CN112884514B CN112884514B CN202110191578.XA CN202110191578A CN112884514B CN 112884514 B CN112884514 B CN 112884514B CN 202110191578 A CN202110191578 A CN 202110191578A CN 112884514 B CN112884514 B CN 112884514B
- Authority
- CN
- China
- Prior art keywords
- user
- activity
- information
- target
- longitude
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/067—Enterprise or organisation modelling
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Game Theory and Decision Science (AREA)
- Finance (AREA)
- Marketing (AREA)
- Educational Administration (AREA)
- Operations Research (AREA)
- Data Mining & Analysis (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Databases & Information Systems (AREA)
- Remote Sensing (AREA)
- General Engineering & Computer Science (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The embodiment of the application provides an activity data analysis method, device, equipment and medium 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 the activity tracks 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 the 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 plurality of preset interest points and the user performance based on the target interest points corresponding to the activity tracks 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
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
Along with the development of network and communication technology, various information systems currently access a large amount of target continuous activity data, but the data are not collected and utilized, so that great data waste and effective information is not mined, and the information utilization rate is low. For example: in insurance and other related industries, agents act as the main body of insurance sales, and their activity tracks, and the range of motion, living habits, and personal attributes derived from the activity tracks may have a crucial influence on the management of their personnel. However, the prior art lacks attention to agents and activities of managing the overall activities of the level of secondary institutions, tertiary institutions, business areas and the like, and also lacks automatic collection, analysis, processing and deriving of the activity track information of agents or the overall activity track of institutions/business units, so that the problems of losing a large amount of data and not mining effective information and the like are caused, and a closed loop system for collecting user data and improving the overall performance by analyzing the user data cannot be realized.
Therefore, how to reasonably utilize and analyze the activity data of individuals is a need for solving the problem.
Disclosure of Invention
The present application has been made in view of the above problems, and it is an object of the present application to provide a method, apparatus, device and medium for analyzing activity data based on a polygon algorithm, which overcomes or at least partially solves the above 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 the activity tracks 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 plurality of preset interest points and user performance based on a target interest point corresponding to the activity track corresponding to each user and user information corresponding to each user, wherein the user information comprises the user performance.
By the method of the first aspect, the embodiment of the application describes an activity data analysis method based on a polygon algorithm. According to the method, the mapping relation between each interest point in the preset interest points in daily life of the plurality of users and the user performance can be analyzed through the activity tracks of the plurality of users in the past, and therefore the user performance can be improved better. The mapping relationship may include a degree of influence of the interest point on the performance of the user, for example: the interest point 1 has beneficial effects on the user and is larger, the interest point 2 has beneficial effects on the user but is smaller, the interest point 3 has adverse effects on the user and is larger, and the like. In addition, the method can reasonably utilize and analyze the activity data of the individual, and can collect and store the data difficult to obtain in an intelligent mode while improving the individual, thereby laying a foundation for the internal staff and the manager to know the first-line market condition, adjust the internal business strategy, adjust the regional personnel arrangement, adjust the cultivation scheme and the assessment scheme of the staff.
In one possible implementation manner, before determining, by using a polygon algorithm, the target interest point corresponding to the activity track corresponding to each user based on the latitude and longitude information of the activity track corresponding to each user, the method further includes: acquiring polygon information of a polygon corresponding to each interest point in a plurality of interest points, wherein the polygon information comprises POI activity names and POI activity longitude and latitude ranges; the determining, based on longitude and latitude information of the activity track corresponding to each user and through a polygon algorithm, a target interest point corresponding to the activity track corresponding to each user includes: judging whether longitude and latitude information corresponding to the moving track corresponding to the target user is in the longitude and latitude range of the polygon corresponding to the target interest point through the polygon algorithm; if yes, 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 one possible implementation manner, the determining whether the latitude and longitude information corresponding to the activity track corresponding to the target user is within the latitude and longitude range of the polygon corresponding to the target interest point includes: and judging whether the longitude and latitude information corresponding to the moving 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, based on longitude and latitude information of the activity track corresponding to each user and through a polygon algorithm, a target interest point corresponding to the activity track corresponding to each user includes: and determining target interest points corresponding to the activity tracks corresponding to the users through a polygon algorithm based on the activity information of the users and the longitude and latitude information corresponding to the activity tracks.
In one possible implementation, the plurality of preset points of interest belong to a plurality of restaurant services, shopping services, literature 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, user interests.
In one possible implementation manner, 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 of the plurality of preset interest points and a user performance includes: determining the residence 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 residence 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 movable tracks corresponding to the users respectively;
the first determining unit is used for determining target interest points POIs corresponding to the activity tracks corresponding to the users through a polygon algorithm based on longitude and latitude information of the activity tracks corresponding to the users, wherein the target interest points belong to one or more of a plurality of preset interest points;
and the second determining unit is used for determining a mapping relation between each interest point in the plurality of preset interest points and user performance based on the target interest point corresponding to the activity track corresponding to each user and 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 of a plurality of interest points, wherein the polygon information comprises POI activity names and POI activity longitude and latitude ranges, and the polygon information is based on longitude and latitude information of an activity track corresponding to each user and before determining a target interest point corresponding to the activity track corresponding to each user through a polygon algorithm; the first determining unit is specifically configured to: judging whether longitude and latitude information corresponding to the moving track corresponding to the target user is in the longitude and latitude range of the polygon corresponding to the target interest point through the polygon algorithm; if yes, 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 moving 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 acquisition 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 target interest points corresponding to the activity tracks corresponding to the users through a polygon algorithm based on the activity information of the users and the longitude and latitude information corresponding to the activity tracks.
In one possible implementation, the plurality of preset points of interest belong to a plurality of restaurant services, shopping services, literature 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, user interests.
In a possible implementation manner, the second determining unit is specifically configured to: determining the residence time of each target interest point corresponding to each user; and determining the mapping relation between each interest point in the plurality of preset interest points and user performance according to the stay 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 is used to store a computer program, and the communication component is used to interact information with an external device; the processing component is configured to invoke a computer program to perform the method according to the first aspect, which is not described in detail here
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method of the first aspect.
Drawings
In order to more clearly describe the embodiments of the present application or the technical solutions in the background art, the following description will describe the drawings that are required to be used in the embodiments of the present application or the background art.
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 polygonal boundary point according to an embodiment of the present application.
Fig. 4 is a schematic polygon diagram of a preset interest point according to an embodiment of the present application.
Fig. 5 is a schematic structural diagram of an activity data analysis device 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 device based on a polygon algorithm according to an embodiment of the present application.
Detailed Description
Embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application.
The terms first, second, third and the like in the description and in the claims and in the drawings are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may 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 may be included in at least one embodiment of the application. The appearances of such phrases 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. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The terms "second device," "unit," "system," and the like are used in the present application 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 being, a processor, a data processing platform, a computing device, a computer, two or more computers, or the like.
First, some terms in the present application will be explained in order to be understood by those skilled in the art.
(1) The location-based service (Location Based Service, LBS) is a value-added service for providing corresponding service for users under the support of a geographic information system platform by acquiring the location information of mobile terminal users through a radio communication network of a telecom mobile operator or an external positioning mode (such as a global positioning system).
(2) The Cisco Internet Operating System (IOS), which is an operating system optimized for internetworking, is a software architecture separate from hardware, and can be dynamically upgraded with the development of network technology to accommodate changing technologies (hardware and software), with modularity, flexibility, scalability, and manageability.
(3) Windows Phone (WP for short) is a mobile Phone operating system formally released by Microsoft in 10/21 2010, and has a series of front guard operating experiences of desktop customization, icon dragging, sliding control and the like. Its home screen displays new emails, text messages, missed calls, calendar appointments, etc., by providing a dashboard-like experience. It also includes an enhanced touch screen interface to facilitate finger manipulation.
(3) POIs are abbreviations of "Point of Interest" and can be translated into "points of interest" or "information points", each POI containing four aspects of information, name, category, longitude, latitude. POIs (information points) are classified into a first class and a second class, and each class corresponds to a code and a name of a corresponding industry. And recording and distinguishing of information acquisition are facilitated.
Next, an active data analysis system architecture based on a polygon algorithm according to an embodiment of the present application will be described. Referring to fig. 1, 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, including: an activity data analysis means 001 and a terminal device 002 based on a polygon algorithm. Wherein:
The polygon algorithm-based activity data analysis device 001 may include, but is not limited to, a background server, a component server, a data processing server, etc., where the polygon algorithm-based activity data analysis device 001 is a server, the server may communicate with a plurality of terminals through the internet, and a corresponding server-side program is required to be run on the server to provide corresponding automatically generated document services, such as database services, data calculation, decision execution, etc. For example, the server may: acquiring longitude and latitude information of the activity tracks 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 plurality of preset interest points and user performance based on a target interest point corresponding to the activity track corresponding to each user and user information corresponding to each user, wherein the user information comprises the user performance.
The terminal device 002 can install and run the relevant applications. An application refers to a program corresponding to a server that provides local services to clients. Here, the local service may include, but is not limited to: and acquiring the activity track of the 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 may also accept user information, user activity information, etc. entered by the user. The terminal in this embodiment of the present application may include, but is not limited to, any electronic product based on an intelligent operating system, which may perform man-machine 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, a personal computer, and so on. Wherein the intelligent operating system includes, but is not limited to, any device power enrichment by providing various mobile applications to the mobile deviceAn enabled operating system such as: android (Android) TM )、iOS TM 、Windows Phone TM Etc.
It should also be understood that the active data analysis system architecture based on the polygon algorithm of fig. 1 is only a part of exemplary implementation of the embodiment of the present application, and the active data analysis system architecture based on the polygon algorithm of the embodiment of the present application includes, but is not limited to, the active data analysis system architecture based on the polygon algorithm.
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 in fig. 1 described above, the interaction between the activity data analysis means 001 and the terminal device 002 based on the polygon algorithm will be described below in connection with fig. 2. Wherein the method may comprise the following step S201-step S205.
Step S201, longitude and latitude information corresponding to the activity track of each user is obtained.
Specifically, the activity data analysis device based on the polygon algorithm may obtain longitude and latitude information corresponding to the activity track of each user in the plurality of users. The method comprises the steps of acquiring longitude and latitude information corresponding to an activity track of each user in a plurality of users through a positioning system in related terminal equipment in real time, and sending the longitude and latitude information to an activity data analysis device based on a polygon algorithm.
Step S202, activity information of each user is acquired.
Specifically, the activity data analysis device based on the polygon algorithm may acquire activity information of each of the plurality of users, where the activity information includes one or more of an activity place, an activity range, and an activity name. It will be appreciated that the activity information of each of the plurality of users may be counted and calculated based on the activity trajectories described above. For example: the collected geographic position change information is automatically processed by an LBS system to generate an active range of the geographic position change information, and finally the geographic position change information is stored in an LBS1.0 mode to form a huge database containing information of the active ranges, the active periods, the active radiuses and the like 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, by using the polygon algorithm, a target interest point corresponding to the activity track corresponding to each user, where the target interest point belongs to one or more of multiple interest points, based on longitude and latitude information of the activity track corresponding to each user. 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, so as to obtain the target interest points corresponding to the corresponding activity tracks of the user. It can be further understood that the target interest points corresponding to the multiple users respectively can be determined based on longitude and latitude information corresponding to the activity track of each user, so that analysis of each individual data in the whole is improved. For example: and finally, the daily activity arrangement and working modes of different agents are known and 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 the user activity tracks.
In one possible implementation, the plurality of points of interest belong to one or more of a dining service, a shopping service, a literature activity, a financial service, an educational activity, a point of interest POI activity in a medical service. It should be noted that, the interest points may be obtained according to a classification of activity information obtained by obtaining a plurality of users in advance, and the interest points are used for indicating a series of activities possibly attended by the users in daily life. It should be further noted that the point of interest division manner is not only the above-listed related content, but also other kinds of activities, such as: public welfare services, recreational entertainment, etc., and embodiments of the present application are not particularly limited thereto. Wherein, the interest point is equivalent to the interest point POI activity in the application.
In one possible implementation manner, based on the activity information of each user and the longitude and latitude information corresponding to the activity track, determining the target interest point corresponding to the activity track corresponding to each user through a polygon algorithm.
In one possible implementation manner, before determining, by using a polygon algorithm, the target interest point corresponding to the activity track corresponding to each user based on the latitude and longitude information of the activity track corresponding to each user, the method further includes: acquiring polygon information of a polygon corresponding to each interest point in a plurality of interest points, wherein the polygon information comprises POI activity names and POI activity longitude and latitude ranges; the determining, based on longitude and latitude information of the activity track corresponding to each user and through a polygon algorithm, a target interest point corresponding to the activity track corresponding to each user includes: judging whether longitude and latitude information corresponding to the moving track corresponding to the target user is in the longitude and latitude range of the polygon corresponding to the target interest point through the polygon algorithm; if yes, 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 one possible implementation manner, the determining whether the latitude and longitude information corresponding to the activity track corresponding to the target user is within the latitude and longitude range of the polygon corresponding to the target interest point includes: and judging whether the longitude and latitude information corresponding to the moving 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 determining whether the longitude and latitude information corresponding to the track point on the moving track is within the longitude and latitude range of the polygon corresponding to the target interest point, a polygon PNPOLY algorithm may be used, and the specific contents are as follows:
(1) Processing relating to interface points
PNPOLY divides a plane into points inside a polygon and points outside the polygon. That is, points of interest correspond to points within the polygon and points of interest correspond to points outside the polygon, with points on the boundary also being classified as inside or outside. Any particular point is always classified in the same way. Referring to fig. 3, fig. 3 is a schematic diagram of a polygonal junction according to an embodiment of the application. As shown in fig. 3, the test point P is considered to be at the boundary common to both triangles TL (one point of interest corresponding to the polygon) and TR (the other point of interest corresponding to the polygon). PNPOLY may say that test point P is in TL or TR, depending on the internal rounding error, in determining which triangle test point P corresponds to. However, before for those triangle test points P, it will always give the same answer. That is, if PNPOLY finds that test point P is in TL before test point P is at the common boundary, then test point P will be found not to be TR when test point P is at the common boundary. If PNPOLY finds that test point P is not in TL before test point P is in common boundary, then test point P will be found in TR when test point P is in common boundary.
(2) Processing of convex polygons
If the polygon is convex, then an equation containing an infinite line for each edge can be found, each equation expressed as an expression: d=ax+by+c. The point on the line takes zero. Each equation is normalized so that if replaced with a point inside the polygon, the result is positive, or equivalently, the external point is negative. Now, a test is performed for the track points on each active track. If inside each line, it is inside the corresponding polygon of interest point.
For example: if 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 length, 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 moving track can be determined by judging whether the track point existing on the moving track or the latitude and longitude information corresponding to more than a certain number of track points are within the latitude and longitude range of the polygon corresponding to the target interest point, and the stay time of the target user within the latitude and longitude range of the polygon corresponding to the target interest point. Referring to fig. 4, fig. 4 is a schematic polygon diagram of a preset interest point according to an embodiment of the present application. As shown in fig. 4, there are three points of interest corresponding to the polygon and the activity track of the target user, where the latitude and longitude range corresponding to the point of interest corresponding to the polygon 2 includes the track point on the activity track, so that the target point of interest corresponding to the activity track corresponding to the user is the polygon 2.
Step S204, user information of each user is acquired.
Specifically, the activity data analysis device based on the polygon algorithm may acquire user information of each user of the plurality of users, where the user information includes the user performance.
In one possible implementation, the user information further includes one or more of user age, user family information, user position information, user interests. For example: based on the LBS2.0, basic information such as age, identity, family information, income and the like of the agent are automatically supplemented from the inside of the system, the agent information is classified and stored in a labeled and scene mode, and finally the agent information is stored in the form of LBS3.0 containing data such as professional characteristics, consumption level, hobbies, family information and the like of the agent, so that a database containing personal characteristics and working modes of the agent is formed.
Step S205, determining a mapping relationship between each of a plurality of 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.
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 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. The mapping relationship may include a degree of influence of the interest point on the performance of the user, for example: the interest point 1 has beneficial effects on the user and is larger, the interest point 2 has beneficial effects on the user but is smaller, the interest point 3 has adverse effects on the user and is larger, and the like. Also for example: based on the LBS3.0, the performance completion conditions of the agents associated with the business departments are automatically compared with the different personal characteristics and working modes of the agents, especially the data of the excellent performance agents and the non-excellent performance agents, and the comparison results are added into the agent classification model to provide an intuitive scheme for improving related work. Meanwhile, 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, generating a user portrait of the target user based on the target interest points corresponding to the activity tracks corresponding to the users and the user information of the target user; and analyzing user portraits respectively corresponding to the plurality of target users, and determining the mapping relation between each interest point in the plurality of interest points and the user performance of the plurality of target users. The activity data analysis device based on the polygon algorithm can analyze user portraits respectively corresponding to a plurality of target users and determine the mapping relation of each interest point in the plurality of interest points to the user performance of the plurality of target users. The LBS-based activity data acquisition and data analysis tool can avoid the problems of data and data dislocation to the greatest extent, and complete closed loop of data acquisition is realized in the process that the concerned basic-level agent is continuously added up to the individual institutions and the areas. Meanwhile, the self-contained data processing system automatically processes the underlying data into stratum layers, presents the stratum layers to the internal staff in the forms of labels, scenes and the like, collects and stores the data which are difficult to acquire in the most intelligent mode, and lays a foundation for the internal staff and management staff 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 the user performance includes: determining the residence 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 residence time and the user information corresponding to each user. It will be appreciated that the longer the user stays within the range of points of interest, the greater the degree to which his points of interest will affect. But also because the user information is different for each user (e.g., age, hobbies, etc.). Thus, the mapping relationship between each point of interest and the user performance can be determined based on the stay time and the user information.
The embodiment of the application describes an activity data analysis method based on a polygonal algorithm. According to the method, the mapping relation between each interest point in the preset interest points in daily life of the plurality of users and the user performance can be analyzed through the activity tracks of the plurality of users in the past, and therefore the user performance can be improved better. In addition, the method can reasonably utilize and analyze the activity data of the individual, and can collect and store the data difficult to obtain in an intelligent mode while improving the individual, thereby laying a foundation for the internal staff and the manager to know the first-line market condition, adjust the internal business strategy, adjust the regional personnel arrangement, adjust the cultivation scheme and the assessment scheme of the staff.
Having described the method of the embodiments of the present application in detail, the activity data analysis apparatus based on the polygon algorithm according to the embodiments of the present application is provided below, and the activity data analysis apparatus 10 based on the polygon algorithm may be a service device that provides various convenience for third parties to use by rapidly acquiring, processing, analyzing and extracting valuable data based on interactive data. Referring to fig. 5, fig. 5 is a schematic structural diagram of an activity data analysis device based on a polygon algorithm according to an embodiment of the present application. The activity data analysis apparatus 10 based on the polygon algorithm may include a first acquisition unit 101, a first determination unit 102, and a second determination unit 103, and may further include a second acquisition unit 104, and a third acquisition unit 105.
A first obtaining unit 101, configured to obtain latitude and longitude information of activity tracks corresponding to a plurality of users respectively;
a first determining unit 102, configured to determine, according to 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, 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 a mapping relationship between each of the plurality of preset interest points and a user performance based on a target interest point corresponding to an activity track corresponding to each user and user information corresponding to each user, where the user information includes the user performance.
In one possible implementation, the apparatus further includes: a second acquisition unit 104, where the second acquisition unit 104 is configured to: acquiring polygon information of a polygon corresponding to each of a plurality of interest points, wherein the polygon information comprises POI activity names and POI activity longitude and latitude ranges, and the polygon information is based on longitude and latitude information of an activity track corresponding to each user and before determining a target interest point corresponding to the activity track corresponding to each user through a polygon algorithm; the first determining unit 102 is specifically configured to: judging whether longitude and latitude information corresponding to the moving track corresponding to the target user is in the longitude and latitude range of the polygon corresponding to the target interest point through the polygon algorithm; if yes, 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 one possible implementation manner, the first determining unit 102 is specifically configured to: and judging whether the longitude and latitude information corresponding to the moving 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 acquiring unit 105, the third acquiring unit 105 being 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 target interest points corresponding to the activity tracks corresponding to the users through a polygon algorithm based on the activity information of the users and the longitude and latitude information corresponding to the activity tracks.
In one possible implementation, the plurality of preset points of interest belong to a plurality of restaurant services, shopping services, literature 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, user interests.
In a possible implementation manner, the second determining unit 103 is specifically configured to: determining the residence 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 residence time and the user information corresponding to each user.
It should be noted that the implementation of each operation may also correspond to the corresponding description of the method embodiment shown in fig. 2 to fig. 4, which is not repeated here.
As shown in fig. 6, fig. 6 is a schematic structural diagram of still another activity data analysis apparatus based on a polygon algorithm according to an embodiment of the present application, where 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 include common components such as an antenna, which are not described in detail herein.
The processor 201 may be a general purpose Central Processing Unit (CPU), microprocessor, application Specific Integrated Circuit (ASIC), or one or more integrated circuits for controlling the execution of the above program schemes.
A communication interface 203 for communicating with other devices or communication networks, such as ethernet, radio Access Network (RAN), core network, wireless local area network (Wireless Local Area Networks, WLAN), etc.
The Memory 202 may be, but is not limited to, read-Only Memory (ROM) or other type of static storage device that can store static information and instructions, random access Memory (random access Memory, RAM) or other type of dynamic storage device that can store information and instructions, but may also be electrically erasable programmable read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), compact disc read-Only Memory (Compact Disc Read-Only Memory) 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. The memory may be stand alone and coupled to the processor via a bus. The memory may also be integrated with the processor.
Wherein the memory 202 is used for storing application program codes for executing the above schemes, and the execution is controlled by the processor 201. The processor 201 is configured to execute application code stored in the memory 202.
The codes 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, longitude and latitude information of activity tracks corresponding to multiple users respectively 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 plurality of preset interest points and user performance based on a target interest point corresponding to the activity track corresponding to each user and user information corresponding to each user, wherein the user information comprises the user performance.
It should be noted that, the functions of each functional unit in the polygon algorithm-based activity data analysis device described in the embodiments of the present application may refer to corresponding descriptions of the method embodiments shown in fig. 2 to 4, which are not repeated herein.
In the present application, the units described as separate units may or may not be physically separate, and units 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 may be selected according to actual needs to achieve the purpose of the embodiment of the present application.
In addition, each functional component in the embodiments of the present application may be integrated in one component, or each component may exist alone physically, or two or more components may be integrated in one component. The above-described integrated components may be implemented in hardware or in 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 essentially or a part contributing to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a second device, or a network device, etc.) 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: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
While the application has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application. While the application has been described herein in connection with various embodiments, other variations of the disclosed embodiments can be understood and effected by those skilled in the art in the course of the application, which is claimed in the embodiments.
Claims (8)
1. A method for analyzing activity data based on a polygon algorithm, comprising:
acquiring longitude and latitude information of the activity tracks corresponding to a plurality of users respectively;
acquiring polygon information of a polygon corresponding to each of a plurality of interest points, wherein the polygon information comprises an interest point activity name and an interest point activity longitude and latitude range; 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, wherein the method comprises the steps of judging whether the longitude and latitude information corresponding to the activity track corresponding to the target user is in the longitude and latitude range of the polygon corresponding to the target interest point through the polygon algorithm, if so, associating the interest point activity name corresponding to the target interest point with the target user, determining that the activity track corresponding to the target user corresponds to the target interest point, wherein the target user is one of a plurality of users, and the target interest point belongs to one or a plurality of preset interest points;
And determining a mapping relation between each interest point in the plurality of preset interest points and user performance based on a target interest point corresponding to the activity track corresponding to each user and user information corresponding to each user, wherein the user information comprises the user performance, and the user information further comprises one or more of user age, user family information, user position information and user interest and hobbies.
2. The method of claim 1, wherein the determining whether the latitude and longitude information corresponding to the activity track corresponding to the target user is within the latitude and longitude range of the polygon corresponding to the target point of interest comprises:
and judging whether the longitude and latitude information corresponding to the moving track corresponding to the target user passes through the longitude and latitude range of the polygon corresponding to the target interest point, and staying in the longitude and latitude range of the polygon corresponding to the target interest point for more than a preset time length.
3. The method according to claim 1, wherein the method further comprises:
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, based on longitude and latitude information of the activity track corresponding to each user and through a polygon algorithm, a target interest point corresponding to the activity track corresponding to each user includes:
And determining target interest points corresponding to the activity tracks corresponding to the users through a polygon algorithm based on the activity information of the users and the longitude and latitude information corresponding to the activity tracks.
4. The method of claim 1, wherein the plurality of preset points of interest belong to one or more of a restaurant service, a shopping service, a literature activity, a financial service, an educational activity, a medical service.
5. The method of claim 4, wherein the determining a mapping relationship between each of the plurality of preset points of interest and user performance based on the target point of interest corresponding to each of the user's corresponding activity tracks and the user information corresponding to each of the users comprises:
determining the residence 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 residence time and the user information corresponding to each user.
6. An activity data analysis device based on a polygon algorithm for implementing the method of any of claims 1-5, characterized in that the activity data analysis device comprises:
The first acquisition unit is used for acquiring longitude and latitude information of the movable tracks corresponding to the users respectively;
the first determining unit is used for determining target interest points corresponding to the activity tracks corresponding to the users through a polygon algorithm based on longitude and latitude information of the activity tracks corresponding to the users, wherein the target interest points belong to one or more of a plurality of preset interest points;
and the second determining unit is used for determining a mapping relation between each interest point in the plurality of preset interest points and user performance based on the target interest point corresponding to the activity track corresponding to each user and user information corresponding to each user, wherein the user information comprises the user performance.
7. 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 are connected with each other, the storage component is used for storing a computer program, and the communication module 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 1-5.
8. 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 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110191578.XA CN112884514B (en) | 2021-02-19 | 2021-02-19 | Method, device, equipment and medium for analyzing activity data based on polygon algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110191578.XA CN112884514B (en) | 2021-02-19 | 2021-02-19 | Method, device, equipment and medium for analyzing activity data based on polygon algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112884514A CN112884514A (en) | 2021-06-01 |
| CN112884514B true CN112884514B (en) | 2023-09-26 |
Family
ID=76056558
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110191578.XA Active CN112884514B (en) | 2021-02-19 | 2021-02-19 | Method, device, equipment and medium for analyzing activity data based on polygon algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112884514B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109948068A (en) * | 2017-09-30 | 2019-06-28 | 阿里巴巴集团控股有限公司 | A kind of recommended method and device of interest point information |
| CN109994038A (en) * | 2019-02-20 | 2019-07-09 | 北京三快在线科技有限公司 | Point of interest determines method, apparatus, electronic equipment in irregular area |
| CN110020178A (en) * | 2017-12-30 | 2019-07-16 | 中国移动通信集团辽宁有限公司 | Point of interest recognition methods, device, equipment and storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120158445A1 (en) * | 2010-12-16 | 2012-06-21 | Cox Communications, Inc. | Geo-Spatial Mapping and Service Provision Analysis |
-
2021
- 2021-02-19 CN CN202110191578.XA patent/CN112884514B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109948068A (en) * | 2017-09-30 | 2019-06-28 | 阿里巴巴集团控股有限公司 | A kind of recommended method and device of interest point information |
| CN110020178A (en) * | 2017-12-30 | 2019-07-16 | 中国移动通信集团辽宁有限公司 | Point of interest recognition methods, device, equipment and storage medium |
| CN109994038A (en) * | 2019-02-20 | 2019-07-09 | 北京三快在线科技有限公司 | Point of interest determines method, apparatus, electronic equipment in irregular area |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112884514A (en) | 2021-06-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP2820616B1 (en) | Empirical expert determination and question routing system and method | |
| CN105227445B (en) | Recommend platform using methods and applications are recommended | |
| US9208511B2 (en) | System and method for location-based recommendations | |
| EP3471374B1 (en) | Method and device for identifying type of geographic location at where user is located | |
| US20160063523A1 (en) | Feedback instrument management systems and methods | |
| Almaatouq et al. | Mobile communication signatures of unemployment | |
| WO2017019379A1 (en) | Inferring user availability for a communication and changing notifications settings based on user availability or context | |
| US20120124059A1 (en) | Address book autofilter | |
| CN106162544B (en) | A kind of generation method and equipment of geography fence | |
| CN102859967A (en) | Method and apparatus for estimating user characteristics based on user interaction data | |
| WO2011094341A2 (en) | System and method for social networking | |
| CN110909222A (en) | User portrait establishing method, device, medium and electronic equipment based on clustering | |
| CN105408894B (en) | A kind of user identity classification determines method and device | |
| KR101706474B1 (en) | Smartphone usage patterns gathering and processing system | |
| CN115017400A (en) | Application APP recommendation method and electronic equipment | |
| Ectors et al. | Developing an optimised activity type annotation method based on classification accuracy and entropy indices | |
| WO2014059059A2 (en) | Selecting user accounts in social network to answer question | |
| CN108182240A (en) | The new gaining rate prediction model training of point of interest and Forecasting Methodology, device and storage medium | |
| CN106156123B (en) | Active value calculation method and device | |
| CN108376155A (en) | A kind of geographical location information determines method and device | |
| CN112884514B (en) | Method, device, equipment and medium for analyzing activity data based on polygon algorithm | |
| Mac Aoidh et al. | Personalization in adaptive and interactive GIS | |
| CN112288436A (en) | Triggered resource quota adjusting method, device and system | |
| Weiß | Fully observed INAR (1) processes | |
| CN114925275A (en) | Product recommendation method and device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |