CN109948070A - The analysis of a kind of family and company position determines method, storage medium and terminal - Google Patents
The analysis of a kind of family and company position determines method, storage medium and terminal Download PDFInfo
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Abstract
The invention discloses the analyses of a kind of family and company position to determine method, storage medium and terminal, the method includes the steps: extract the driving trace in the recent preset time range of user, all location points cluster of driving trace is divided into several clusters, wherein, the location point in different clusters is no more than pre-determined distance threshold value relative to the distance of the center of each cluster；Operation is distinguished to each cluster clustered out, the location point traversed in each cluster is given a mark, and the morning on working day, which sets out or reaches afternoon, then adds preset fraction to family, and the morning on working day reaches or sets out afternoon, adds preset fraction to company；The marking situation for comparing each cluster selects the highest position as family of family's score, the highest position as company of company's score.The present invention can facilitate user's intelligent travel, improve the usage experience of intelligent travel according to the driving trace of user, the position of automatical and efficient, accurate determining analysis user company and family.
Description
Technical Field
The invention relates to the technical field of address analysis and determination, in particular to a method, a storage medium and a terminal for analyzing and determining positions of a home and a company.
Background
When the intelligent travel function is used, the traditional method depends on data items set by a user, and due to the reasons of client software design, user use habits and the like, a plurality of users do not set data such as positions of homes and companies, so that a plurality of intelligent functions depending on the data cannot work or have poor effects. However, a method capable of intelligently and automatically determining the positions of the home and the company according to the driving track is absent in the prior art, so that certain intelligent travel functions are inconvenient to use, and the user experience needs to be improved.
Accordingly, the prior art is yet to be improved and developed.
Disclosure of Invention
In view of the above-mentioned shortcomings in the prior art, the present invention aims to provide a method, a storage medium and a terminal for analyzing and determining the location of a home and a company, so as to overcome the problem of the prior art that a method capable of intelligently and automatically determining the location of the home and the company according to a driving track is lacked.
The technical scheme adopted by the invention for solving the technical problem is as follows:
the invention provides a method for analyzing and determining the positions of a home and a company, which comprises the following steps:
extracting a driving track within a recent preset time range of a user, and clustering all position points of the driving track into a plurality of clusters, wherein the distance between the position points in different clusters and the center position of each cluster does not exceed a preset distance threshold;
respectively calculating the clustered clusters, traversing position points in each cluster to score, adding a preset score to a house when a working day starts in the morning or in the afternoon, and adding a preset score to a company when the working day starts in the morning or in the afternoon;
and comparing the scoring conditions of the clusters, selecting the position with the highest home score as the home position and the position with the highest company score as the company position.
The method for analyzing and determining the positions of the home and the company, wherein the step of respectively calculating each clustered cluster and scoring the position points in each cluster by traversing comprises the following steps:
selecting 3 clusters with the largest number of position points and the number not less than the preset member number;
and respectively operating the selected 3 clusters, and traversing the position points in each cluster to score.
The method for analyzing and determining the positions of the home and the company comprises the following steps of extracting the travel track of the user within the recent preset time range, and clustering all position points of the travel track into a plurality of clusters:
only one cluster is initially selected, the distance between the remaining position points and the generated cluster is calculated, each position point is added into the cluster with the minimum distance and not exceeding a preset distance threshold, and if no such cluster exists, a new cluster is created until all the position points are completely divided into a plurality of clusters.
The method for analyzing and determining the positions of the home and the company comprises the following steps of extracting the travel track of the user within the recent preset time range, and clustering all position points of the travel track into a plurality of clusters:
randomly selecting 2 position points to create 2 clusters; and adding each remaining position point into the closest cluster, and if the minimum distance is greater than a preset distance threshold, creating a new cluster by using the position point until all the position points are completely divided into a plurality of clusters.
The method for analyzing and determining the positions of the home and the company, wherein the step of respectively calculating the clustered clusters and scoring the position points in the clusters, further comprises the following steps:
calculating and updating the central point of each cluster, and then judging whether the central point of each cluster is changed compared with the initially selected position point;
and when the central point of each cluster is not changed, traversing the position points in each cluster for scoring, and returning to reestablish the cluster when the central point is changed.
The method for analyzing and determining the positions of the home and the company comprises the following steps of:
judging whether the score of each cluster is lower than a preset score threshold value, if so, excluding the cluster, and if not, reserving the cluster;
and comparing the scores of the reserved clusters, and selecting the position with the highest home score as the position of the home and the position with the highest company score as the position of the company.
The method for analyzing and determining the home and the company location, wherein the step of comparing the scores of the reserved clusters and selecting the location with the highest home score as the home location comprises the following steps:
comparing the scores of the reserved clusters, and judging whether the cluster with the highest score of the house and the company is the same cluster or not;
if the highest scores of the home and the company are not the same cluster, selecting the position with the highest home score as the home position and the position with the highest company score as the company position; if the highest scores of the home and the company are the same cluster, the analysis is judged to fail.
The analysis and determination method for the positions of the homes and the companies comprises the step of presetting and adjusting the number of the preset members according to the recent preset time range, wherein the preset distance threshold is 20-80 m.
The present invention also provides a storage medium, wherein the storage medium stores a computer program executable to implement the method for analytical determination of home and business locations as described in any one of the above.
The present invention also provides a terminal, comprising: a processor, a memory communicatively connected to the processor, the memory storing a computer program for, when executed, implementing a method of analytical determination of home and business locations as described in any of the above; the processor is adapted to invoke a computer program in the memory to implement the method of analytical determination of home and business locations as described in any of the above.
Has the advantages that: the invention relates to a method for analyzing and determining the positions of a home and a company, which is characterized in that all position points of a driving track are clustered into a plurality of clusters, the clustered clusters are respectively calculated, the position points in the clusters are traversed and scored, a preset score is added to the home when the position points start in the morning or in the afternoon on a working day, and a preset score is added to the company when the position points start in the morning or in the afternoon on the working day; the scoring conditions of each cluster are compared, the position with the highest home score is selected as the position of the home, and the position with the highest company score is selected as the position of the company, so that the positions of the user company and the home can be automatically, efficiently and accurately determined and analyzed according to the traveling track of the user, the intelligent trip of the user is facilitated, and the use experience of the intelligent trip is improved.
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FIG. 1 is a flow chart of a method for analyzing and determining home and business locations in accordance with a preferred embodiment of the present invention.
Fig. 2 is a functional block diagram of the terminal according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a flow chart of a method for analyzing and determining locations of homes and companies according to a preferred embodiment of the present invention. Referring to fig. 1, the method for analyzing and determining the locations of the home and the company includes:
step S100, extracting a driving track within a recent preset time range of a user, and clustering all position points of the driving track into a plurality of clusters, wherein the distance between the position points in different clusters and the center position of each cluster does not exceed a preset distance threshold;
step S200, respectively calculating the clustered clusters, traversing position points in each cluster to score, adding a preset score to a house when a workday starts in the morning or in the afternoon, and adding a preset score to a company when the workday starts in the morning or in the afternoon;
and step S300, comparing the scoring conditions of the clusters, and selecting the position with the highest home score as the home position and the position with the highest company score as the company position.
According to the method for analyzing and determining the positions of the companies and the families of the users, the positions of the companies and the families of the users can be automatically, efficiently and accurately determined and analyzed according to the driving tracks of the users, the users can conveniently and intelligently go out, and the use experience of the intelligent going out is improved.
Clustering as described herein refers to the process of dividing a collection of physical or abstract objects into classes composed of similar objects. The cluster generated by clustering is a collection of a set of data objects that are similar to objects in the same cluster and distinct from objects in other clusters. The invention clusters all position points of the driving track into a plurality of clusters, and is realized by adopting a K-means algorithm. The basic idea of the K-means algorithm is as follows: clustering is performed with k points in space as centroids, and the objects closest to them are classified. And gradually updating the centroid value of each cluster through an iterative method until the best clustering result is obtained. The conventional kmeans algorithm is sensitive to dirty data, and the mean deviation of results is large when dirty data appears in the fixed K ranges.
The method mainly comprises the steps of clustering, extracting and analyzing data. Data clustering: the locations that the user has gone are clustered into several limited ranges. Extraction and analysis: according to the result of data clustering, from detailed data in the range of bitter stems, analysis is carried out to extract which most likely is the position of a home and which most likely is the address of a company.
Further, in this embodiment, the preset distance threshold should not be too large, and may be selected from 20 to 80 meters, for example, 50 meters, that is, the radius of a point in the same range cannot exceed 50 meters. The preset score may be specifically set according to actual needs, and may be set to 1 score, for example. In specific implementation, for example, several clusters clustered in the previous step are respectively operated, member points are traversed and scored, a home is added with one point when a worker starts in the morning, a company is added with one point when the worker starts in the afternoon, and a home is added with one point when the worker starts in the afternoon.
Further, in this embodiment, in step S200, the step of separately operating each clustered cluster and scoring by traversing position points in each cluster specifically includes:
step S210, selecting 3 clusters (namely a cluster set) with the largest number of position points and the number not less than the preset member number;
and step S220, respectively operating the selected 3 clusters, and respectively scoring by traversing position points in each cluster.
In a specific implementation, the preset membership number is preset and adjusted according to the recent preset time range, for example, the recent preset time range may be 1 month, and then the preset membership number may be 30, that is, the lowest number of times of going home or company within one month (departure and arrival separate count), and then the data of one month uses 15 × 2 — 30 as a reference number.
Further, in this embodiment, two different methods may be adopted to cluster all the position points of the driving track into a plurality of clusters, and when the first method is adopted, the step S100 specifically includes:
step S110, only one cluster is initially selected, and the distance between the remaining position points and the generated cluster is calculated;
and step S120, adding each position point into a cluster with the minimum distance not exceeding a preset distance threshold, and if no such cluster exists, newly creating one cluster until all the position points are completely divided into a plurality of clusters.
Further, in this embodiment, when the second method is adopted to cluster all the position points of the driving track into a plurality of clusters, the step S100 specifically includes:
s111, randomly selecting 2 position points to create 2 clusters;
and step S121, adding each remaining position point into the closest cluster, and if the minimum distance is greater than a preset distance threshold, creating a new cluster by using the position point until all the position points are completely divided into a plurality of clusters.
Further, in this embodiment, before the step S200, the method further includes:
step S180, calculating and updating the central point of each cluster, and then judging whether the central point of each cluster is changed compared with the initially selected position point;
and step S190, when the center point of each cluster is not changed, traversing the position points in each cluster for scoring, and returning to reestablish the cluster when the center point of each cluster is changed.
Further, in step S300 of this embodiment, the step of comparing the scoring condition of each cluster specifically includes:
step S310, judging whether the score of each cluster is lower than a preset score threshold value, if so, excluding the cluster, and if not, reserving the cluster;
and step S320, comparing the scores of the reserved clusters, selecting the position with the highest home score as the home position, and selecting the position with the highest company score as the company position.
Further, in this embodiment, the step S320 specifically includes:
s321, comparing the scores of the reserved clusters, and judging whether the cluster with the highest score of the house and the company is the same cluster;
step S322, if the highest scores of the family and the company are not the same cluster, selecting the position with the highest family score as the position of the family and the position with the highest company score as the position of the company; if the highest scores of the home and the company are the same cluster, the analysis is judged to fail.
In specific implementation, the scoring conditions of the clusters are compared, the position with the highest home score is selected as the position of the home, the position with the highest company score is selected as the position of the company, and the exclusion with the lowest score is excluded, such as the exclusion with the score lower than 15. Traversing each cluster score, selecting locations of homes and companies, or failing the analysis.
According to the invention, the user does not need to set the positions of the house and the company by himself, and the program automatically analyzes and predicts the positions of the company and the house of the user according to the travel track data of the user, so that the intelligent travel function is automatically provided. The method can improve the influence of the dirty data on the result deviation in the common address analysis scene of the traditional kmeans algorithm, improves the accuracy and reduces the influence of the dirty data on the mean value.
According to the invention, the position where the user live and work can be analyzed and predicted according to the driving track of the user, so that the functions of intelligent travel and the like when the user does not set the position of a house and a company by himself are optimized.
An embodiment of the present invention further provides a terminal, as shown in fig. 2, where the terminal includes: a processor (processor)10, a memory (memory)20, a communication Interface (Communications Interface)30, and a communication bus 40; wherein,
the processor 10, the memory 20 and the communication interface 30 complete mutual communication through the communication bus 40;
the communication interface 30 is used for information transmission between communication devices of the terminal;
the processor 10 is configured to call the computer program in the memory 20 to execute the method provided by the above method embodiments, for example, including: extracting a driving track within a recent preset time range of a user, and clustering all position points of the driving track into a plurality of clusters, wherein the distance between the position points in different clusters and the center position of each cluster does not exceed a preset distance threshold; respectively calculating the clustered clusters, traversing position points in each cluster to score, adding a preset score to a house when a working day starts in the morning or in the afternoon, and adding a preset score to a company when the working day starts in the morning or in the afternoon; and comparing the scoring conditions of the clusters, selecting the position with the highest home score as the home position and the position with the highest company score as the company position.
The embodiment of the invention also provides a storage medium, wherein the storage medium stores a computer program which can be executed to realize the method for analyzing and determining the positions of the home and the company.
Of course, it will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by a computer program instructing relevant hardware (such as a processor, a controller, etc.), and the program may be stored in a computer readable storage medium, and when executed, the program may include the processes of the above method embodiments. The storage medium may be a memory, a magnetic disk, an optical disk, etc.
It should be understood that equivalents and modifications of the technical solution and inventive concept thereof may occur to those skilled in the art, and all such modifications and alterations should fall within the scope of the appended claims.
Claims (10)
1. A method for analytical determination of home and business locations, comprising the steps of:
extracting a driving track within a recent preset time range of a user, and clustering all position points of the driving track into a plurality of clusters, wherein the distance between the position points in different clusters and the center position of each cluster does not exceed a preset distance threshold;
respectively calculating the clustered clusters, traversing position points in each cluster to score, adding a preset score to a house when a working day starts in the morning or in the afternoon, and adding a preset score to a company when the working day starts in the morning or in the afternoon;
and comparing the scoring conditions of the clusters, selecting the position with the highest home score as the home position and the position with the highest company score as the company position.
2. The method for analyzing and determining locations of homes and companies according to claim 1, wherein the step of respectively operating the clustered clusters and traversing the location points in the clusters to score the locations specifically comprises:
selecting 3 clusters with the largest number of position points and the number not less than the preset member number;
and respectively operating the selected 3 clusters, and traversing the position points in each cluster to score.
3. The method for analyzing and determining locations of home and business as claimed in claim 1, wherein the step of extracting the travel track of the user within a recent preset time range and dividing all location points of the travel track into a plurality of clusters specifically comprises:
only one cluster is initially selected, the distance between the remaining position points and the generated cluster is calculated, each position point is added into the cluster with the minimum distance and not exceeding a preset distance threshold, and if no such cluster exists, a new cluster is created until all the position points are completely divided into a plurality of clusters.
4. The method for analyzing and determining locations of home and business as claimed in claim 1, wherein the step of extracting the travel track of the user within a recent preset time range and dividing all location points of the travel track into a plurality of clusters specifically comprises:
randomly selecting 2 position points to create 2 clusters; and adding each remaining position point into the closest cluster, and if the minimum distance is greater than a preset distance threshold, creating a new cluster by using the position point until all the position points are completely divided into a plurality of clusters.
5. The method according to claim 3 or 4, wherein the step of calculating the clustered clusters respectively and scoring the position points in the clusters further comprises:
calculating and updating the central point of each cluster, and then judging whether the central point of each cluster is changed compared with the initially selected position point;
and when the central point of each cluster is not changed, traversing the position points in each cluster for scoring, and returning to reestablish the cluster when the central point is changed.
6. The method according to claim 1, wherein the step of comparing the scoring of each cluster specifically comprises:
judging whether the score of each cluster is lower than a preset score threshold value, if so, excluding the cluster, and if not, reserving the cluster;
and comparing the scores of the reserved clusters, and selecting the position with the highest home score as the position of the home and the position with the highest company score as the position of the company.
7. The method according to claim 6, wherein comparing the scores of the remaining clusters, selecting the location with the highest home score as the home location, and the step of selecting the location with the highest company score as the company location specifically comprises:
comparing the scores of the reserved clusters, and judging whether the cluster with the highest score of the house and the company is the same cluster or not;
if the highest scores of the home and the company are not the same cluster, selecting the position with the highest home score as the home position and the position with the highest company score as the company position; if the highest scores of the home and the company are the same cluster, the analysis is judged to fail.
8. The method as claimed in claim 1, wherein the preset number of members is preset and adjusted according to the recent preset time range, and the preset distance threshold is 20-80 m.
9. A storage medium storing a computer program executable to implement the method of analytically determining home and business locations of any one of claims 1 to 8.
10. A terminal, comprising: a processor, a memory communicatively connected to the processor, the memory storing a computer program for, when executed, implementing a method of analytically determining home and business locations according to any of claims 1 to 8; the processor is used for calling a computer program in the memory to realize the method for analyzing and determining the positions of the home and the company according to any one of claims 1 to 8.
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| CN109948070B (en) | 2022-08-09 |
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