CN113793171A - Region division method and device based on multi-dimensional data, storage medium and equipment - Google Patents
Region division method and device based on multi-dimensional data, storage medium and equipment Download PDFInfo
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Abstract
The invention relates to a region dividing method, a device, a storage medium and equipment based on multidimensional data, wherein the method comprises the following steps: a. collecting data and dividing regions; b. calculating the recommendation index of each region; c. and dividing the grade of each region according to the recommendation index. The invention can reasonably divide the area, thereby facilitating the implementation of the advertisement promotion activity.
Description
Technical Field
The invention relates to a region division method, a region division device, a storage medium and equipment based on multi-dimensional data.
Background
The outdoor advertising spot advertisers in the community generally adopt a ground street pushing and sweeping mode, and the salespersons in a city generally adopt group-level tree structure division to form a hierarchical structure of a sales chief, each group of sales managers and sales consultants in each group. A group conference of sales managers at night usually plans aiming at the current day of expansion condition duplication and the second day of push-and-visit arrangement, so that merchants who intend to advertise on outdoor advertising spots of the community are searched to convert into deals, and the commercial change maximization of the advertising spots is realized. However, there are many problems with this push planning approach due to the asymmetry of information about work arrangements between different sales groups. For example, a situation of high and dense advertisement spots in a short period of time is easy to occur, so that repeated visits are caused, and customers feel dislike, or a part of the positions with the advertisement spots and areas with more merchants are not visited by people, so that more merchants are lost.
There are also some technologies to determine the advertisement delivery area through a data analysis method, for example, patent CN111177287B discloses an advertisement delivery method, which determines the hot road by obtaining the popularity value of each road grid, thereby finally determining the road grid for delivering the advertisement. Therefore, although the method can preliminarily determine the region with high popularity, the method is only suitable for the advertising promotion mode in the billboard mode and does not consider the ground push mode, so that the problems of repeated visits and no people visit of part of merchants can not be reasonably solved.
Disclosure of Invention
The invention aims to provide a region dividing method and device based on multi-dimensional data, a storage medium and equipment.
In order to achieve the above object, the present invention provides a method, an apparatus, a storage medium and a device for dividing an area based on multidimensional data, wherein the method comprises the following steps:
a. collecting data and dividing regions;
b. calculating the recommendation index of each region;
c. and dividing the grade of each region according to the recommendation index.
According to one aspect of the invention, the data collected in step (a) includes advertisement spot location data, merchant data, and visit data;
the advertisement point location data is service basic data comprising name, longitude and latitude position and point location number;
the merchant data is store and merchant information and is obtained through public data on a map, wherein the public data comprises names, longitude and latitude positions, industries, sales volumes and user evaluation;
the visit data is information of sales visit merchants, and the visit data is generated by visiting through a point burying technology and software, and comprises sales ID, visit time, visit longitude and latitude, visit users and whether a transaction signing form is formed.
According to one aspect of the invention, the advertisement site location data comprises site location, site number and site covered traffic;
the merchant data comprises merchant positions, merchant types and merchant values;
the visit data comprises a visit location, visit time and visit personnel.
According to an aspect of the present invention, the dividing the region in the step (a) includes the steps of:
a1 number of spots<N, if the inter-cell distance is less than d1If not, the polymerization is not carried out;
a2, regarding administrative districts with the aerial ladder number being more than n, taking each cell point as an area, and aggregating upwards continuously;
with d2The polymerization is carried out over a distance max, as follows:
dist(Ci，Cj)＝max(dist(i，j)for i in Ci for j in Cj)；
in the above formula, i is divided into regions CiJ is a divided area CjDist (i, j) represents the distance of cells i and j, max () represents the maximum function, dist (C)i，Cj) Is region CiAnd region CjThe distance of (d);
a3, calculating the center point of each region according to the following formula:
in the above formula, the first and second carbon atoms are,
is region CiLongitude of the center point of (1), lngiIs region CiIn each cellThe longitude of (a) is determined,
is region CiLatitude of center point of (a), latiIs region CiThe latitude of each cell, mean () is the mean calculation function;
a4, mapping the merchant data and the visit data into a region with the nearest distance, wherein the distance is the arc distance from the merchant position and the visit position to the center position of the region;
the arc distance L is the distance of the circular arc surface of the earth, and the calculation formula is as follows:
wherein, R is the radius of the earth, Aj and Aw are the longitude and latitude of the point A, and Bj and Bw are the longitude and latitude of the point B.
According to one aspect of the invention, the indicators for calculating the recommendation index in step (b) include a visit value, an advertisement value, and a customer value.
According to one aspect of the invention, the visit value comprises visit conditions of about 1, 3, 7, 14, 30 and 60 days, wherein the visit conditions comprise visit times/number of good-quality merchants, visit times/number of merchants and visit times;
the advertisement value comprises the number of advertisement spots and the coverage people flow;
the customer value comprises the percentage of the high-quality commercial tenant number, the high-quality commercial tenant number and the commercial tenant number.
According to one aspect of the invention, when calculating each index, an interval scoring method is adopted, the indexes are arranged from small to large, 5 quantiles of 0.02, 0.25, 0.50, 0.75 and 0.99 are taken, data are divided into 4 levels, each quantile is respectively assigned with 60, 70, 80, 90 and 100 points, and a linear difference value is used in the middle;
after the score of each index is obtained, weighting is carried out according to the weight determined in the index weight, and the formula is as follows:
wherein p is the number of indexes under a certain classification, and w is the given weight;
for visit values, the smaller the score, the larger the score.
According to an aspect of the present invention, when the regions are classified in the step (c), the classification is performed every 30% quantile;
wherein top 30% is first grade; top 60% -30% is equal to two; the remainder being three, etc.
According to one aspect of the invention, the method is completed based on a one-stop platform of hadoop big data architecture.
An apparatus for dividing a region based on multi-dimensional data, comprising:
the data acquisition module is used for acquiring data;
the region dividing module is used for dividing regions;
the region recommendation index calculation module is used for calculating the recommendation index of each region;
and the region grade type dividing module is used for dividing the grade of each region according to the recommendation index.
A storage medium having stored thereon a computer program which, when executed by a processor, implements a method for region partitioning based on multidimensional data.
An apparatus comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, the processor implementing a method for multi-dimensional data-based region partitioning when executing the program.
According to the scheme of the invention, the advertisement point location is taken as a target to carry out region division, the recommendation index of each region is calculated, and the regions such as hot spots, key points, common regions and the like are distinguished according to the recommendation index, so that the ground push scheme of a sale city can be guided.
Drawings
FIG. 1 is a flow chart of a method for partitioning a region based on multi-dimensional data according to an embodiment of the present invention;
fig. 2 is a schematic diagram showing a method of calculating each index detail score in the recommendation method according to the embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
The present invention is described in detail below with reference to the drawings and the specific embodiments, which are not repeated herein, but the embodiments of the present invention are not limited to the following embodiments.
Referring to fig. 1, the method for dividing regions based on multidimensional data of the present invention first collects relevant data for calculation, then divides the regions with advertisement spots as targets, calculates recommendation indexes of the regions, and finally divides the grades of the regions according to the recommendation indexes.
In the invention, the data collected by the data collection module comprises advertisement site location data, merchant data and visit data. The advertisement site location data comprises site location, site location quantity, the flow of people covered by the site location and the like, the merchant (or business opportunity) data comprises merchant location, merchant type, merchant value and the like, and the visiting (or card punching) data comprises visiting location, visiting time, visiting personnel and the like. The advertisement site location data is service basic data, namely advertisement site locations deployed by advertisement services, and comprises numerous attribute information, such as names, longitude and latitude positions, site location quantity and the like. Wherein the advertisement spots may include outdoor advertisements such as elevator screens and the like. The merchant data, i.e., store and business information, is mainly obtained through public data on websites such as maps, and attributes include information such as names, longitude and latitude positions, industries, sales volumes, user evaluations, and the like. The visit data is information of sales visit merchants, and by means of a point-burying technology, sales use of internally developed software tools (such as applets, APPs, and the like) to visit and generate the visit data, wherein the information comprises: sale ID, visit time, visit latitude and longitude, visit user, whether to be a transaction sign, and the like.
After data is collected, the area division module can divide the area, specifically, the number of the advertisement points is aimed at<N, if the inter-cell distance is less than d1And if not, carrying out polymerization. For the number of scaling ladders>And n administrative districts are clustered from bottom to top in a district aggregation mode, namely, each district point is taken as an area (cluster) and is continuously aggregated upwards. The final division basis is distance division, firstly using d2Kilometers are aggregated and the distance is max, i.e. region CiAnd region CjThe maximum distance in does not exceed d2Kilometers, as follows:
dist(Ci，Cj)＝max(dist(i，j)for i in Ci for j in Cj)；
in the above formula, i is divided into regions CiJ is a divided area CjDist (i, j) represents the distance of cells i and j, max () represents the maximum function, dist (C)i，Cj) Is region CiAnd region CjThe distance of (c). Therefore, the adoption of the division of the maximum distance limiting area can promote the sales to better complete the ground push plan of one area so as to accord with the working mode that the pushing distance of the sales ground of one day is limited and the multiple cross-domain customers are difficult to visit.
Subsequently, the center point of each region is calculated as follows:
in the above formula, the first and second carbon atoms are,
is region CiLongitude of the center point of (1), lngiIs region CiThe longitude of each of the cells in the cell,
is region CiLatitude of center point of (a), latiIs region CiWhere the latitude, mean () of each cell is a mean calculation function. Therefore, the central point of the area can be better reflected in the calculation speed and the calculation precision by adopting the arithmetic mean value mode.
And then mapping the merchant data into the region, wherein the specific mapping logic is that the merchant data is mapped into the region with the closest distance, and the distance is calculated in the mode of the arc distance from the merchant position to the center position of the region. Wherein, the arc distance is the real earth circular arc distance, but not the conventional Euclidean plane distance. The arc distance L can more truly reflect the real interval of two points with longitude and latitude, and the calculation formula is as follows:
wherein, R is the earth radius of 6371m, Aj and Aw are the longitude and latitude of the point A, and Bj and Bw are the longitude and latitude of the point B.
And finally, mapping the visiting data into the area, wherein the mapping logic is the same as that of the merchant data, and the merchant position is only required to be changed into the visiting position.
And then, calculating the recommendation index of each region by a region recommendation index calculation module, specifically, calculating the recommendation index mainly based on an expert model of multidimensional data, wherein the calculation indexes comprise visit value, advertisement value and customer value. The visit value comprises visit conditions of about 1, 3, 7, 14, 30 and 60 days, and the visit conditions comprise visit times/high-quality business users, visit times/business users and visit times. The advertising value includes the number of advertising spots and the coverage traffic. The customer value comprises the percentage of the high-quality commercial tenant number, the high-quality commercial tenant number and the commercial tenant number. The multidimensional indexes and weights (weight parameters are shown in brackets) constructed as above are designed as shown in the following table 1:
TABLE 1
The score calculation of each detail constructed above adopts an interval scoring method, that is, indexes are arranged from small to large, 5 quantiles of 0.02, 0.25, 0.50, 0.75 and 0.99 are taken, so that data are approximately equally divided into 4 grades, each quantile is respectively assigned with 60, 70, 80, 90 and 100 grades, and a linear difference value is used in the middle, as shown in fig. 2.
After the score of each index is obtained, weighting is carried out according to the weight determined in the index weight, and the formula is as follows:
wherein p is the number of indexes under a certain classification, w is the given weight, Score is the final recommendation index, ScoreiScores are given to the indexes of the subordinate. Therefore, the method adopts the weighting mode of the expert model, can better adaptively push the working habit, and solves the problems of piling and unmanned visiting, thereby achieving the purposes of faster and more transformed local pushing and visiting and greater input and output. In addition, since the purpose of zone partitioning is to reduce repeat visits and improve advertisement to bill conversion, the fewer visits, the greater the corresponding recommendation score should be. Therefore, the reverse is needed for the visit value, i.e. the score is larger the smaller the score.
And finally, the region grade type division module can finish the division of the region grade types according to the calculated recommendation indexes of the regions. Specifically, according to the preset division logic, the grades are divided according to the quantiles of every 30%. Thus, top 30% is equal, i.e. hot spot area; top 60% -30% is equal to two, namely the key area; the rest is three, etc., which is the common area.
According to one embodiment of the present invention, taking the elevator screen advertisement of an internet of things elevator company in beijing as an example, the parameters related to the area division correspond to: n is 3, d1＝3,d22. Region recommendation meansThe parameters in the numbers correspond to those shown in table 2 below:
TABLE 2
The final result of dividing is that the number of the cells with the internet of things elevator advertising screen is nearly 1000, and 7000+ areas are finally divided, and the statistical conditions of the related data in the areas are as shown in the following table 3:
| cell number containing Internet of things elevator advertising screen | Total number of cells | Number of dots | Number of merchants | |
| min | 1 | 2 | 0 | 1 |
| 25％ | 1 | 3 | 4 | 14 |
| 50％ | 1 | 4 | 12 | 58 |
| 75％ | 2 | 7 | 28 | 158 |
| max | 13 | 31 | 176 | 4045 |
TABLE 3
The area division device comprises a data acquisition module for acquiring data, an area division module for dividing areas, an area recommendation index calculation module for calculating recommendation indexes of the areas, and an area grade type division module for dividing the grades of the areas according to the recommendation indexes. The storage medium of the present invention has stored thereon a computer program which, when executed by a processor, implements the region dividing method of the present invention. The device of the invention comprises a storage medium, a processor and a computer program which is stored on the storage medium and can run on the processor, wherein the processor executes the program to realize the region division method of the invention. All the acquisition, storage, analysis, mining, calculation and operation and maintenance of the embodiment are carried out on a one-stop platform based on a hadoop big data architecture, and the platform can provide abundant tool sets such as ETL (extract transform load) and the like for analysts, so that the service can be conveniently, quickly and conveniently enabled.
In conclusion, the invention distinguishes hot spots, key points, common areas and the like by using the area division method, thereby guiding the ground push scheme of the sale city, optimizing the ground push resource arrangement and solving the problem of client dislike caused by the fact that personnel push and repeatedly visit the client in short time. And moreover, the method is more purposefully and pertinently pushed to the high-quality area, meanwhile, the management is more standard, and the visit conversion is higher.
The above description is only one embodiment of the present invention, and is not intended to limit the present invention, and it is apparent to those skilled in the art that various modifications and variations can be made in the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (12)
1. A region division method based on multi-dimensional data comprises the following steps:
a. collecting data and dividing regions;
b. calculating the recommendation index of each region;
c. and dividing the grade of each region according to the recommendation index.
2. The method of claim 1, wherein the data collected in step (a) includes advertisement spot data, merchant data, and visit data;
the advertisement point location data is service basic data comprising name, longitude and latitude position and point location number;
the merchant data is store and merchant information and is obtained through public data on a map, wherein the public data comprises names, longitude and latitude positions, industries, sales volumes and user evaluation;
the visit data is information of sales visit merchants, and the visit data is generated by visiting through a point burying technology and software, and comprises sales ID, visit time, visit longitude and latitude, visit users and whether a transaction signing form is formed.
3. The method of claim 2, wherein the spot location data includes spot location, number of spots, and volume of people covered by the spots;
the merchant data comprises merchant positions, merchant types and merchant values;
the visit data comprises a visit location, visit time and visit personnel.
4. The method of claim 3, wherein the step (a) of dividing the region comprises the steps of:
a1 number of spots<N, if the inter-cell distance is less than d1If not, the polymerization is not carried out;
a2, regarding administrative districts with the aerial ladder number being more than n, taking each cell point as an area, and aggregating upwards continuously;
with d2The polymerization is carried out over a distance max, as follows:
dist(Ci，Cj)＝max(dist(i，j)for i in Ci for j in Cj)；
in the above formula, i is divided into regions CiJ is a divided area CjDist (i, j) represents the distance of cells i and j, max () represents the maximum function, dist (C)i，Cj) Is region CiAnd region CjThe distance of (d);
a3, calculating the center point of each region according to the following formula:
in the above formula, the first and second carbon atoms are,
is region CiLongitude of the center point of (1), lngiIs region CiThe longitude of each of the cells in the cell,
is region CiLatitude of center point of (a), latiIs region CiThe latitude of each cell, mean () is the mean calculation function;
a4, mapping the merchant data and the visit data into a region with the nearest distance, wherein the distance is the arc distance from the merchant position and the visit position to the center position of the region;
the arc distance L is the distance of the circular arc surface of the earth, and the calculation formula is as follows:
wherein, R is the radius of the earth, Aj and Aw are the longitude and latitude of the point A, and Bj and Bw are the longitude and latitude of the point B.
5. The method of claim 1, wherein the indicators of the recommendation index calculated in step (b) include a visit value, an advertisement value, and a customer value.
6. The method of claim 5, wherein the visit value comprises visit status of about 1, 3, 7, 14, 30, 60 days, the visit status comprises number of visits/number of good merchants, number of visits/number of merchants, number of visits;
the advertisement value comprises the number of advertisement spots and the coverage people flow;
the customer value comprises the percentage of the high-quality commercial tenant number, the high-quality commercial tenant number and the commercial tenant number.
7. The method according to claim 6, characterized in that when calculating each index, an interval scoring method is adopted, the indexes are arranged from small to large, 5 quantiles of 0.02, 0.25, 0.50, 0.75 and 0.99 are taken, the data are divided into 4 grades, each quantile is respectively assigned with 60, 70, 80, 90 and 100 grades, and linear difference values are used in the middle;
after the score of each index is obtained, weighting is carried out according to the weight determined in the index weight, and the formula is as follows:
wherein p is the number of indexes under a certain classification, and w is the given weight;
for visit values, the smaller the score, the larger the score.
8. The method of claim 1, wherein in the step (c), the regions are classified by 30% quantile;
wherein top 30% is first grade; top 60% -30% is equal to two; the remainder being three, etc.
9. The method of claim 1, wherein the method is performed based on a one-stop platform of hadoop big data architecture.
10. An apparatus for carrying out the method of any one of claims 1 to 8, comprising:
the data acquisition module is used for acquiring data;
the region dividing module is used for dividing regions;
the region recommendation index calculation module is used for calculating the recommendation index of each region;
and the region grade type dividing module is used for dividing the grade of each region according to the recommendation index.
11. A storage medium on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method of any one of claims 1 to 8.
12. An apparatus comprising a storage medium, a processor and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the method of any one of claims 1 to 8 when executing the program.
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