CN110008970A - A kind of address information generation method and device - Google Patents

Abstract

The invention discloses a method and a device for generating address information, and relates to the technical field of computers. A specific implementation of the method includes: acquiring online object demand data and offline geographic environment data; using a clustering algorithm to respectively aggregate the online object demand data and the offline geographic environment data to obtain an initial Aggregate data points; determine final aggregated data points based on the initial aggregated data points. This embodiment can solve the problems of inaccurate addresses and low efficiency in the prior art selected by offline stores.

Description

A method and device for generating address information

technical field

The present invention relates to the technical field of computers, and in particular, to a method and device for generating address information.

Background technique

With the development of e-commerce technology, large-scale e-commerce enterprises have accumulated a lot of technical strength, and at the same time, they have been loved by consumers with relatively low prices and fast and convenient services, and have accumulated a large amount of online data. However, offline stores also have their incomparable advantages. For some commodities with low prices, high purchase frequency or urgent daily needs, consumers prefer to buy in offline stores, which can save freight and get what they need faster. Products of.

For e-commerce companies, through a large amount of offline data accumulated in the early stage, through site selection technology, the development of offline experience stores has increased the sales channels of products and filled the gap of low online sales of some items. At the same time, for brands Communication is also very important.

The location of the store is the primary problem faced by e-commerce companies to develop offline smart stores. If the location is not appropriate, it will cause problems such as high management costs and insufficient passenger flow. E-commerce companies can use the large amount of accumulated online data, through the geographical distribution of commodity demand, combined with geographical environment data, to select areas with relatively concentrated demand, dense traffic and convenient transportation, and select the location of offline smart stores.

In the process of realizing the present invention, the inventor found that there are at least the following problems in the prior art:

At present, there are two main types of mainstream technologies for site selection of e-commerce companies: First, by collecting geographic environment data (offline data), estimating the possible average daily turnover after the opening of offline smart stores (average daily traffic * store entry rate) *Customer unit price), minus the corresponding operating costs, and select the site based on the principle of maximizing revenue. The first is to use the previous online data of e-commerce companies, estimate the average daily turnover through online data, and then select the site based on the principle of maximizing revenue.

However, pure online data or offline data do not fully reflect the needs of consumers: only using offline data can not take advantage of the online data advantages that e-commerce companies have accumulated. When offline, there may be "acclimatization" in offline operations. Only using online data, because online data cannot completely record all relevant geographic environment data, it may lead to selected stores that do not meet offline needs.

SUMMARY OF THE INVENTION

In view of this, the embodiments of the present invention provide a method and device for generating address information, which can solve the problems of inaccurate and low efficiency of addresses selected by offline stores in the prior art.

In order to achieve the above object, according to an aspect of the embodiments of the present invention, a method for generating address information is provided, which includes acquiring online object demand data and offline geographic environment data; Aggregate with the offline geographic environment data to obtain initial aggregated data points; and determine final aggregated data points according to the initial aggregated data points.

Optionally, the determining of the final aggregated data points according to the initial aggregated data points includes:

Calculate the distance between the online object demand data point and the initial aggregated data point to determine the classification of the online object demand data point; and calculate the distance between the offline geographic environment demand data point and the aggregated data point to determine the line The classification of data points of geographical environment requirements; wherein, online object demand data includes online object demand data points;

According to the classification of online object demand data points and the offline geographical environment demand data point classification, the final aggregated data points are obtained.

Optionally, calculating the distance between the online object demand data point and the initial aggregated data point to determine the classification of the online object demand data point, including:

Calculate the distance between each online object demand data point and the initial aggregated data point;

According to the distance, the weighted distance is calculated to determine the aggregation data point closest to the online object demand data point as the classification of the online object demand data point;

In addition, the distance between the offline geographic environment demand data points and the aggregated data points is calculated to determine the classification of the offline geographic environment demand data points, including:

Calculate the distance between each offline geographic environment demand data point and the initial aggregated data point;

According to the distance, a weighted distance is calculated to determine an aggregated data point closest to the offline geographic environment demand data point as a classification of the offline geographic environment demand data point.

Optionally, the classification according to online object demand data points and offline geographic environment demand data point classification to obtain final aggregated data points includes:

For the location information of each online object demand data point classification, calculate the centroid of all online object demand data points within the preset radius; similarly, for the location information of each offline geographic environment demand data point classification, calculate the preset The centroid of all offline geographic environment demand data points within the radius;

Based on the centroid of all online object demand data points and the centroid of all offline geographic environment demand data points, the final aggregated data points are obtained.

Optionally, according to the classification of online object demand data points and the classification of offline geographical environment demand data points, the aggregated data points are iteratively updated until a preset condition is reached, so as to obtain the final aggregated data points.

Optionally, according to the classification of online object demand data points and the classification of offline geographical environment demand data points, iteratively update the aggregated data points, including:

For the classified location information of each online object demand data point, calculate the centroids A ₁ ′(x _a1 ′,y _a1 ′), B ₁ ′(x b1 ′, B 1 ′(x _b1 ′, y _b1 ′), C ₁ ′ (x _c1 ′, y _c1 ′), D ₁ ′ (x _d1 ′, y _d1 ′)...; Similarly, for the location information of each offline geographic environment demand data point classification, Calculate the centroids A ₁ ″(x _a1 ″,y _a1 ″), B ₁ ″(x _b1 ″,y _b1 ″), C ₁ ″(x _c1 ″), C 1 ″(x c1 ″) of all offline geographic environment demand data points within the preset radius, y _c1 ″), D ₁ ″(x _d1 ″, y _d1 ″)…;

Determine the aggregated data points for the next iteration:

N ₁ (x,y)＝[N′ ₁ (x′ _n1 ,y′ _n1 )+β×N″ ₁ (x″ _n1 ,y″ _n1 )]÷2

in:

N∈{A,B,C,D…}

When β>1: the influence of offline geographical environment demand data points is greater than the influence of online object demand data points;

When β<1: the influence of offline geographical environment demand data points is less than the influence of online object demand data points;

When β=1: the influence of offline geographical environment demand data points is equal to the influence of online object demand data points.

Optionally, the preset condition is: the distance between the aggregated data points after the update and the aggregated data points before the update is less than the error distance, or the number of cycles is greater than or equal to a set threshold for the maximum number of cycles.

Optionally, the clustering algorithm adopts a k-means clustering algorithm.

In addition, according to an aspect of the embodiments of the present invention, an apparatus for generating address information is provided, including an acquisition module for acquiring online object demand data and offline geographic environment data; a calculation module for using a clustering algorithm to The online object demand data and the offline geographic environment data are aggregated to obtain initial aggregated data points; a selection module is configured to determine final aggregated data points according to the initial aggregated data points.

Optionally, the selection module determines the final aggregated data points according to the initial aggregated data points, including:

Calculate the distance between the online object demand data point and the initial aggregated data point to determine the classification of the online object demand data point; and calculate the distance between the offline geographic environment demand data point and the aggregated data point to determine the line The classification of data points of geographical environment requirements; wherein, online object demand data includes online object demand data points;

According to the classification of online object demand data points and the offline geographical environment demand data point classification, the final aggregated data points are obtained.

Optionally, the selection module calculates the distance between the online object demand data point and the initial aggregated data point to determine the classification of the online object demand data point, including:

Calculate the distance between each online object demand data point and the initial aggregated data point;

According to the distance, the weighted distance is calculated to determine the aggregation data point closest to the online object demand data point as the classification of the online object demand data point;

In addition, the computing module calculates the distance between the offline geographic environment demand data point and the initial aggregated data point to determine the classification of the offline geographic environment demand data point, including:

Calculate the distance between each offline geographic environment demand data point and the initial aggregated data point;

According to the distance, a weighted distance is calculated to determine an aggregated data point closest to the offline geographic environment demand data point as a classification of the offline geographic environment demand data point.

Optionally, the selection module obtains the final aggregated data points according to the classification of online object demand data points and offline geographical environment demand data points, including:

For the location information of each online object demand data point classification, calculate the centroid of all online object demand data points within the preset radius; similarly, for the location information of each offline geographic environment demand data point classification, calculate the preset The centroid of all offline geographic environment demand data points within the radius;

Based on the centroid of all online object demand data points and the centroid of all offline geographic environment demand data points, the final aggregated data points are obtained.

Optionally, the selection module iteratively updates the aggregated data points according to the classification of the online object demand data points and the offline geographical environment demand data points until a preset condition is reached, so as to obtain the final aggregated data points.

Optionally, the selection module iteratively updates the aggregated data points according to the classification of online object demand data points and the offline geographical environment demand data point classification, including:

For the classified location information of each online object demand data point, calculate the centroids A ₁ ′(x _a1 ′,y _a1 ′), B ₁ ′(x b1 ′, B 1 ′(x _b1 ′, y _b1 ′), C ₁ ′ (x _c1 ′, y _c1 ′), D ₁ ′ (x _d1 ′, y _d1 ′)...; Similarly, for the location information of each offline geographic environment demand data point classification, Calculate the centroids A ₁ ″(x _a1 ″,y _a1 ″), B ₁ ″(x _b1 ″,y _b1 ″), C ₁ ″(x _c1 ″), C 1 ″(x c1 ″) of all offline geographic environment demand data points within the preset radius, y _c1 ″), D ₁ ″(x _d1 ″, y _d1 ″)…;

Determine the aggregated data points for the next iteration:

N ₁ (x,y)＝[N′ ₁ (x′ _n1 ,y′ _n1 )+β×N″ ₁ (x″ _n1 ,y″ _n1 )]÷2

in:

N∈{A,B,C,D…}

When β>1: the influence of offline geographical environment demand data points is greater than the influence of online object demand data points;

When β<1: the influence of offline geographical environment demand data points is less than the influence of online object demand data points;

When β=1: the influence of offline geographical environment demand data points is equal to the influence of online object demand data points.

Optionally, the preset condition is: the distance between the aggregated data points after the update and the aggregated data points before the update is less than the error distance, or the number of cycles is greater than or equal to a set threshold for the maximum number of cycles.

Optionally, the clustering algorithm adopts a k-means clustering algorithm.

According to another aspect of the embodiments of the present invention, an electronic device is also provided, including:

one or more processors;

storage means for storing one or more programs,

When the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any one of the above address information generation embodiments.

According to another aspect of the embodiments of the present invention, there is also provided a computer-readable medium on which a computer program is stored, and when the program is executed by a processor, implements the method described in any of the foregoing address information generation embodiments.

An embodiment of the above invention has the following advantages or beneficial effects: because the technical means of generating offline store address information in combination with the online order demand data and offline geographic environment data of the e-commerce enterprise are adopted, the offline store address information generation is realized. The benefits of the store are maximized, and at the same time, the brand effect of the e-commerce enterprise can be enhanced.

Further effects of the above non-conventional alternatives will be described below in conjunction with specific embodiments.

Description of drawings

The accompanying drawings are used for better understanding of the present invention and do not constitute an improper limitation of the present invention. in:

1 is a schematic diagram of a main flow of a method for generating address information according to an embodiment of the present invention;

2 is a schematic diagram of the main flow of a method for generating address information according to a referenced embodiment of the present invention;

3 is a schematic diagram of main modules of an apparatus for generating address information according to an embodiment of the present invention;

4 is an exemplary system architecture diagram to which an embodiment of the present invention may be applied;

FIG. 5 is a schematic structural diagram of a computer system suitable for implementing a terminal device or a server according to an embodiment of the present invention.

Detailed ways

Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, which include various details of the embodiments of the present invention to facilitate understanding and should be considered as exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted from the following description for clarity and conciseness.

FIG. 1 is a method for generating address information according to an embodiment of the present invention. As shown in FIG. 1 , the method for generating address information includes:

Step S101, acquiring online object demand data and offline geographic environment data.

The online object demand data includes the order data of the online object, and the latitude and longitude coordinate information corresponding to the order data, that is, the online object demand data point.

Step S102, using a clustering algorithm to respectively aggregate the online object demand data and the offline geographic environment data to obtain initial aggregated data points.

Among them, the clustering algorithm is initialized to obtain the initial clustered data points.

Step S103: Determine a final aggregated data point according to the initial aggregated data point. The specific implementation process includes:

The online object demand data includes online object demand data points.

Step 1: Calculate the distance between the online object demand data point and the initial aggregated data point to determine the classification of the online object demand data point; and calculate the distance between the offline geographic environment demand data point and the aggregated data point to determine Classification of the offline geographic environment demand data points.

Preferably, when step 1 is performed, the distance between each online object demand data point and the initial aggregated data point can be calculated; according to the distance, the weighted distance is calculated to determine the closest distance to the online object demand data point. Aggregate data points as a classification of the online object demand data points.

Similarly, the distance between each offline geographic environment demand data point and the initial aggregated data point can be calculated; according to the distance, the weighted distance can be calculated to determine the aggregated data point closest to the offline geographic environment demand data point as Classification of the offline geographic environment demand data points.

Step 2: According to the classification of online object demand data points and the offline geographical environment demand data point classification, to obtain final aggregated data points.

Preferably, for the classified location information of each online object demand data point, the centroid of all online object demand data points within a preset radius is calculated; similarly, for each offline geographic environment demand data point classified location information , calculate the centroid of all offline geographic environment demand data points within the preset radius;

Then according to the centroid of all online object demand data points and the centroid of all offline geographic environment demand data points, the final aggregated data points are obtained.

In another preferred embodiment, according to the classification of online object demand data points and the classification of offline geographical environment demand data points, the aggregated data points are iteratively updated until a preset condition is reached, so as to obtain the final aggregated data points. The specific implementation process includes:

When step 3 is performed, the centroids A ₁ ′(x _a1 ′, y _a1 ′), B ₁ ′(x _b1 ′,y _b1 ′), C ₁ ′(x _c1 ′,y _c1 ′), D ₁ ′(x _d1 ′,y _d1 ′)…; Similarly, for each offline geographical environment requirement The location information of the data point classification, calculate the centroids of all offline geographic environment demand data points within the preset radius A ₁ ″(x _a1 ″,y _a1 ″), B ₁ ″(x _b1 ″,y _b1 ″), C ₁ ″(x _c1 ″,y _c1 ″), D ₁ ″(x _d1 ″,y _d1 ″)…;

Determine the aggregated data points for the next iteration:

N ₁ (x,y)＝[N′ ₁ (x′ _n1 ,y′ _n1 )+β×N″ ₁ (x″ _n1 ,y″ _n1 )]÷2

in:

N∈{A,B,C,D…}

When β>1: the influence of offline geographical environment demand data points is greater than the influence of online object demand data points;

When β<1: the influence of offline geographical environment demand data points is less than the influence of online object demand data points;

When β=1: the influence of offline geographical environment demand data points is equal to the influence of online object demand data points.

Further, the preset condition is: the distance between the aggregated data points after the update and the aggregated data points before the update is less than the error distance, or the number of cycles is greater than or equal to the set threshold for the maximum number of cycles.

According to the above various embodiments, it can be seen that the method for generating address information can find the best product aggregation point through the aggregation of online product demand data. And, combined with offline geographic environment information, adjust the best store location. In addition, the present invention also provides a margin for manual location selection. By adjusting the parameters, the total number of stores finally determined by the address information generation technology can be slightly more than the final plan, and finally the final store selection is performed by considering social factors.

2 is a schematic diagram of the main flow of a method for generating address information according to a referenced embodiment of the present invention, and the method for generating address information may include:

Step S201, acquiring online object demand data and offline geographic environment data.

In an embodiment, the order data of the online object may be collected, so as to reflect the demand situation of the object through the online order data. Preferably, online objects can also be selected, and the selected objects can be used as objects that can be sold in offline stores.

Further, obtain the latitude and longitude coordinates of the object in the selected store location area (that is, the longitude and latitude coordinates corresponding to the total sales volume of the object in the store location area), and simultaneously count the total online sales volume of each object in this area within the preset period. . Wherein, the latitude and longitude coordinates of the store location area are accurate to three decimal places. For example, see Table 1, where each object (in this embodiment the object is represented as a commodity) is represented as a data row. Taking each row as a data point, for the i-th data point, where the longitude is x _i , the latitude is y _i , and the total profit of the product (which can be the unit profit multiplied by the total number of sales) is _wi , preferably, the total profit can be removed. A data point with a profit of 0.

In addition, the commodity is represented by SKU, and the full name of SKU is Stock Keeping Unit (stock keeping unit), that is, the unit of stock in and out measurement, which can be in units, boxes, trays, etc. SKU is a necessary method for logistics management of large supermarket chains or distribution centers. It has now been extended to the abbreviation of the unified product number, and each product has a unique SKU number.

For example, it is necessary to select the location of smart stores in Beijing area. Through historical data query, the order data of all related objects in Beijing area in the past two years can be counted, and the following historical commodity demand statistics table can be obtained.

It should be noted that the higher the accuracy of latitude and longitude during statistics, the less easy it is for the same type of objects to be aggregated, and the larger the amount of order data used to generate auxiliary address information; the lower the accuracy of latitude and longitude, the smaller the amount of data. However, it is not necessarily that the result of the final algorithm will be good if the amount of data is large. Preferably, the latitude and longitude coordinates are selected with three decimal places, that is, the order data with a radius of about 0.15KM will be regarded as the data of the same location.

Table 1 Statistics of historical commodity demand data

Product SKU Longitude (x_i) Latitude (y_i) Total Profit (w_i) 10000001 116.353 39.983 5 10000001 116.345 40.662 3 10000002 116.125 39.112 8 … … … … 89245922 116.349 39.024 1

In addition, the obtained offline geographic environment data is information data with relatively large passenger flow in the store address area, wherein the information data includes longitude, latitude and the passenger flow weight of each coordinate set according to the passenger flow.

For example, subway information, business district information, residential area information and other areas with large passenger flow, more detailed such as subway stations (as shown in Table 2: for each subway station, the latitude and longitude coordinates are obtained through the online map API, and according to each subway station) The passenger flow of the subway station, set different weights v _j .), bus station, community and so on.

Table 2 Statistics of subway distribution data

Subway station serial number Subway station name Longitude (x_j) Latitude (y_j) Passenger flow weight (v_j) 1 via sea 116.569 39.789 5 2 Xiaocun 116.454 39.840 1 3 Wudaokou 116.344 39.998 10 … … … … …

Step S202, initialize the k-means algorithm to obtain initial aggregated data points.

Among them, the k-means algorithm, that is, the k-means clustering algorithm is a representative of a typical prototype-based objective function clustering method, which is a certain distance from the data point to the prototype as the optimized objective function, and uses the function to find the extreme. The value method gets the adjustment rules for the iterative operation.

As an embodiment, randomly select k initial aggregation data points, that is, select k data points from offline geographical environment data points as initial aggregation data points, and further, can set:

kThe number of smart stores to be selected mAdjust parameter u

Among them: u is the adjustment parameter, the purpose is to select m+u smart offline stores, and in all the selected stores, various external factors are considered comprehensively (for example: popular business districts, popular shopping centers can increase corporate branding effect). When u=0, it means that no adjustment is made, and the k-means algorithm is completely trusted to generate address information.

In the above example, suppose you need to select 2 stores, set u=2, then select A ₀ (x _a0 , y _a0 ), B ₀ (x _b0 , y _b0 ), C ₀ (x _c0 , y _c0 ), D The four points ₀ (x _d0 , y _d0 ) are the initial aggregated data points.

Preferably, in order to make the algorithm converge quickly, it is also possible to first draw the demand points of historical objects, and select the initial aggregation data points in the area where the demand points are densely clustered.

Step S203: Calculate the distance between the online object demand data point and the initial aggregated data point to determine the classification of the online object demand data point.

As an example, for each object demand data point i, the initial distances RA _i0 , RB _i0 , RC _i0 , RD _i0 . And according to the initial distance, calculate the initial weighted distance RA _i1 , RB _i1 , RC _i1 , RD _{i1 ......} :

RA _i1 =RA _i0 /1+α× _wi )

The parameter α can be adjusted. The larger the α, the more sensitive the address information is to the profit. The range of α is (0, +∞). Preferably, α can be selected to be slightly larger than 1. w _i is the total profit.

According to the calculated initial weighted distances RA _i1 , RB _i1 , RC _i1 , RD _i1 . Demand data point i, calculated that RB _i1 is the minimum value, then this commodity demand data point is classified as B.

Step S204: Calculate the distance between the offline geographic environment demand data points and the initial aggregated data points to determine the classification of the offline geographic environment demand data points.

As an example, using the algorithm of step S203, for each offline geographic environment demand data point j, the distance between the offline geographic environment demand data point j and the selected initial aggregation point is calculated through the latitude and longitude, and the passenger flow weight (v _i ). Initial weighted distances RA _j1 , RB _j1 , RC _j1 , RD _j1 . . . And according to the initial weighted distance, also for each offline geographic environment demand point j, according to the classification method of step S203, the initial classification of each offline geographic environment demand point j is calculated.

Step S205 , according to the classification of online object demand data points and the classification of offline geographical environment demand data points, iteratively update the aggregated data points until a preset condition is reached, so as to obtain the final aggregated data points.

As an example, for the classified position information of each online object demand data point, the centroid of all online object demand data points within the radius R is calculated (the average value is taken according to the coordinates), and it is determined as A ₁ ′(x _a1 ′ , y _a1 ′), B ₁ ′ (x _b1 ′, y _b1 ′), C ₁ ′ (x _c1 ′, y _c1 ′), D ₁ ′ (x _d1 ′, y _d1 ′). Similarly, for the location information of each offline geographic environment demand data point classification, the centroid A ₁ ″(x _a1 ″,y _a1 ″), B ₁ ″(x _b1 ″,y _b1 ″), C ₁ ″( x _c1 ″, y _c1 ″), D ₁ ″ (x _d1 ″, y _d1 ″).

Among them, for the selection of R: you can first draw the online object demand data points or the offline geographical environment demand data points, and then determine the value of R in combination with the drawing and the actual situation. Preferably, it can be initially selected as 5km, that is, the audience range of this store is set to be within 5km of customers. Subsequently, R can be adjusted based on the data accumulated after the opening of the physical store, and then the address information of the new store can be calculated.

Then, determine the aggregated data points for the next iteration:

N ₁ (x,y)＝[N′ ₁ (x′ _n1 ,y′ _n1 )+β×N″ ₁ (x″ _n1 ,y″ _n1 )]÷2

in:

N∈{A,B,C,D…}

β can be equal to 1, slightly greater than 1, or slightly less than 1, where:

When β>1: the influence of offline geographic environment demand data points is greater than that of online object demand data points.

When β<1: the influence of offline geographic environment demand data points is smaller than that of online object demand data points.

When β=1: the influence of offline geographical environment demand data points is equal to the influence of online object demand data points.

Finally, according to the calculated new _aggregated data _{points A1} , _B1 , _{C1 , D1 ...} When the loop is iteratively optimized and the preset condition is reached, the loop can be ended, and the finalized aggregated data point, that is, the last selected offline store location, can be obtained. Wherein, the optimization condition can be when the distance between the newly calculated aggregated data point and the old aggregated data point (the data aggregated point from the previous iteration) is less than the error distance S, or the number of cycles is greater than or equal to the set cycle The maximum number of times threshold T.

Specifically, the loop stop condition is:

distance(N _q , N _q-1 )<S or q≥T

q: number of cycles

S: Error distance set in advance

T: The maximum cycle number threshold set in advance

Step S206, obtain the selected address according to the finally determined aggregated data points.

In the embodiment, k offline stores are finally obtained through step S205, that is, the finally determined aggregated data points, and then the number m of smart stores to be selected is finally determined according to the adjustment parameter u. In the above specific embodiment, from the four store addresses finally obtained by the k-means algorithm, various factors are combined, and two stores are finally selected.

In addition, in the present invention, reference may be made to the specific implementation content of the address information generation method described in the embodiments, which has been described in detail in the address information generation method described above, so the repeated content will not be described here.

FIG. 3 is an apparatus for generating address information according to an embodiment of the present invention. As shown in FIG. 3 , the apparatus 300 for generating address information includes an acquisition module 301 , a calculation module 302 , and a selection module 303 . The acquisition module 301 acquires online object demand data and offline geographic environment data. Then, the computing module 302 uses a clustering algorithm to respectively aggregate the online object demand data and the offline geographic environment data to obtain initial aggregated data points. Finally, the selection module 303 determines the final aggregated data points according to the initial aggregated data points.

As a preferred embodiment, the selection module 303 determines the final aggregated data point according to the initial aggregated data point to obtain the selected address. The specific implementation process includes:

Step 1: Calculate the distance between the online object demand data point and the initial aggregated data point to determine the classification of the online object demand data point; and calculate the distance between the offline geographic environment demand data point and the aggregated data point to determine Classification of the offline geographic environment demand data points.

Preferably, when step 1 is performed, the distance between each online object demand data point and the initial aggregated data point can be calculated; according to the distance, the weighted distance is calculated to determine the closest distance to the online object demand data point. Aggregate data points as a classification of the online object demand data points.

Similarly, the distance between each offline geographic environment demand data point and the initial aggregated data point can be calculated; according to the distance, the weighted distance can be calculated to determine the aggregated data point closest to the offline geographic environment demand data point as Classification of the offline geographic environment demand data points.

Step 2: According to the classification of online object demand data points and the offline geographical environment demand data point classification, to obtain final aggregated data points.

Preferably, for the classified location information of each online object demand data point, the centroid of all online object demand data points within a preset radius is calculated; similarly, for each offline geographic environment demand data point classified location information , calculate the centroid of all offline geographic environment demand data points within the preset radius;

Then according to the centroid of all online object demand data points and the centroid of all offline geographic environment demand data points, the final aggregated data points are obtained.

In another preferred embodiment, according to the classification of online object demand data points and the classification of offline geographical environment demand data points, the aggregated data points are iteratively updated until a preset condition is reached, so as to obtain the final aggregated data points. The specific implementation process includes:

When step 3 is performed, the centroids A ₁ ′(x _a1 ′, y _a1 ′), B ₁ ′(x _b1 ′,y _b1 ′), C ₁ ′(x _c1 ′,y _c1 ′), D ₁ ′(x _d1 ′,y _d1 ′)…; Similarly, for each offline geographical environment requirement The location information of the data point classification, calculate the centroids of all offline geographic environment demand data points within the preset radius A ₁ ″(x _a1 ″,y _a1 ″), B ₁ ″(x _b1 ″,y _b1 ″), C ₁ ″(x _c1 ″,y _c1 ″), D ₁ ″(x _d1 ″,y _d1 ″)…;

Determine the aggregated data points for the next iteration:

N ₁ (x,y)＝[N′ ₁ (x′ _n1 ,y′ _n1 )+β×N″ ₁ (x″ _n1 ,y″ _n1 )]÷2

in:

N∈{A,B,C,D…}

When β>1: the influence of offline geographical environment demand data points is greater than the influence of online object demand data points;

When β<1: the influence of offline geographical environment demand data points is less than the influence of online object demand data points;

When β=1: the influence of offline geographical environment demand data points is equal to the influence of online object demand data points.

Further, the preset condition is: the distance between the aggregated data points after the update and the aggregated data points before the update is less than the error distance, or the number of cycles is greater than or equal to the set threshold for the maximum number of cycles.

It should be noted that, the specific implementation content of the address information generating apparatus of the present invention has been described in detail in the above-mentioned address information generating method, so the repeated content will not be described here.

FIG. 4 shows an exemplary system architecture 400 to which an address information generating method or an address information generating apparatus according to an embodiment of the present invention may be applied. Alternatively, FIG. 4 shows an exemplary system architecture 400 to which an address information generating method or an address information generating apparatus according to an embodiment of the present invention may be applied.

As shown in FIG. 4 , the system architecture 400 may include terminal devices 401 , 402 , and 403 , a network 404 and a server 405 . The network 404 is a medium used to provide a communication link between the terminal devices 401 , 402 , 403 and the server 405 . The network 404 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user can use the terminal devices 401, 402, 403 to interact with the server 405 through the network 404 to receive or send messages and the like. Various communication client applications may be installed on the terminal devices 401 , 402 and 403 , such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only examples).

The terminal devices 401, 402, 403 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

The server 405 may be a server that provides various services, for example, a background management server that provides support for shopping websites browsed by the terminal devices 401 , 402 , and 403 (just an example). The background management server can analyze and process the received product information query request and other data, and feed back the processing results (such as target push information, product information—just an example) to the terminal device.

It should be noted that the address information generating method provided by the embodiment of the present invention is generally executed by the server 405 , and accordingly, the address information generating apparatus is generally set in the server 405 .

It should be understood that the numbers of terminal devices, networks and servers in FIG. 4 are merely illustrative. There can be any number of terminal devices, networks and servers according to implementation needs.

Referring to FIG. 5 below, it shows a schematic structural diagram of a computer system 500 suitable for implementing a terminal device according to an embodiment of the present invention. The terminal device shown in FIG. 5 is only an example, and should not impose any limitations on the functions and scope of use of the embodiments of the present invention.

As shown in FIG. 5, a computer system 500 includes a central processing unit (CPU) 501 which can be loaded into a random access memory (RAM) 503 according to a program stored in a read only memory (ROM) 502 or a program from a storage section 508 Instead, various appropriate actions and processes are performed. In the RAM 503, various programs and data necessary for the operation of the system 500 are also stored. The CPU 501 , the ROM 502 and the RAM 503 are connected to each other through a bus 504 . An input/output (I/O) interface 505 is also connected to bus 504 .

The following components are connected to the I/O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc. ; and a communication section 509 including a network interface card such as a LAN card, a modem, and the like. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I/O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is mounted on the drive 510 as needed so that a computer program read therefrom is installed into the storage section 508 as needed.

In particular, the processes described above with reference to the flowcharts may be implemented as computer software programs in accordance with the disclosed embodiments of the present invention. For example, embodiments disclosed herein include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the method illustrated in the flowchart. In such an embodiment, the computer program may be downloaded and installed from the network via the communication portion 509 and/or installed from the removable medium 511 . When the computer program is executed by the central processing unit (CPU) 501, the above-described functions defined in the system of the present invention are performed.

It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or a combination of any of the above. More specific examples of computer readable storage media may include, but are not limited to, electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable Programmable read only memory (EPROM or flash memory), fiber optics, portable compact disk read only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. In the present invention, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code therein. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device . Program code embodied on a computer readable medium may be transmitted using any suitable medium including, but not limited to, wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more logical functions for implementing the specified functions executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It is also noted that each block of the block diagrams or flowchart illustrations, and combinations of blocks in the block diagrams or flowchart illustrations, can be implemented in special purpose hardware-based systems that perform the specified functions or operations, or can be implemented using A combination of dedicated hardware and computer instructions is implemented.

The modules involved in the embodiments of the present invention may be implemented in a software manner, and may also be implemented in a hardware manner. The described modules can also be set in the processor, for example, it can be described as: a processor includes an acquisition module, a calculation module and a selection module. Among them, the names of these modules do not constitute a limitation on the module itself under certain circumstances.

As another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or may exist alone without being assembled into the device. The above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by a device, the device includes: acquiring online object demand data and offline geographic environment data; using a clustering algorithm to separate Aggregate the online object demand data and the offline geographic environment data to obtain initial aggregated data points; and determine final aggregated data points according to the initial aggregated data points.

According to the technical solution of the embodiment of the present invention, because the technical means of generating offline store address information in combination with the online order demand data and offline geographical environment data of the e-commerce enterprise are adopted, the benefits of the offline stores are maximized. , and at the same time can enhance the brand effect of e-commerce enterprises. .

The above-mentioned specific embodiments do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims (18)

1. A method for generating address information, comprising: Obtain online object demand data and offline geographic environment data; Using a clustering algorithm to respectively aggregate the online object demand data and the offline geographic environment data to obtain initial aggregated data points; Based on the initial aggregated data points, final aggregated data points are determined. 2. The method according to claim 1, wherein determining the final aggregated data point according to the initial aggregated data point comprises: Calculate the distance between the online object demand data point and the initial aggregated data point to determine the classification of the online object demand data point; and calculate the distance between the offline geographic environment demand data point and the aggregated data point to determine the line The classification of data points of geographical environment requirements; wherein, online object demand data includes online object demand data points; According to the classification of online object demand data points and the offline geographical environment demand data point classification, the final aggregated data points are obtained. 3. The method according to claim 2, wherein calculating the distance between the online object demand data points and the initial aggregated data points to determine the classification of the online object demand data points, comprising: Calculate the distance between each online object demand data point and the initial aggregated data point; According to the distance, the weighted distance is calculated to determine the aggregation data point closest to the online object demand data point as the classification of the online object demand data point; In addition, the distance between the offline geographic environment demand data points and the aggregated data points is calculated to determine the classification of the offline geographic environment demand data points, including: Calculate the distance between each offline geographic environment demand data point and the initial aggregated data point; According to the distance, a weighted distance is calculated to determine an aggregated data point closest to the offline geographic environment demand data point as a classification of the offline geographic environment demand data point. 4. The method according to claim 2, wherein the classification according to the online object demand data point and the offline geographical environment demand data point classification to obtain the final aggregated data point, comprising: For the location information of each online object demand data point classification, calculate the centroid of all online object demand data points within the preset radius; similarly, for the location information of each offline geographic environment demand data point classification, calculate the preset The centroid of all offline geographic environment demand data points within the radius; Based on the centroid of all online object demand data points and the centroid of all offline geographic environment demand data points, the final aggregated data points are obtained. 5. The method according to claim 2, characterized in that, according to the classification of online object demand data points and the classification of offline geographical environment demand data points, iteratively update the aggregated data points until a preset condition is reached, so as to obtain the final aggregated data points . 6. The method according to claim 5, wherein, according to the classification of online object demand data points and the classification of offline geographical environment demand data points, iteratively update the aggregated data points, comprising: For the classified location information of each online object demand data point, calculate the centroids A ₁ ′(x _a1 ′,y _a1 ′), B ₁ ′(x b1 ′, B 1 ′(x _b1 ′, y _b1 ′), C ₁ ′ (x _c1 ′, y _c1 ′), D ₁ ′ (x _d1 ′, y _d1 ′)...; Similarly, for the location information of each offline geographic environment demand data point classification, Calculate the centroids A ₁ ″(x _a1 ″,y _a1 ″), B ₁ ″(x _b1 ″,y _b1 ″), C ₁ ″(x _c1 ″), C 1 ″(x c1 ″) of all offline geographic environment demand data points within the preset radius, y _c1 ″), D ₁ ″(x _d1 ″, y _d1 ″)…; Determine the aggregated data points for the next iteration: N ₁ (x,y)＝[N′ ₁ (x′ _n1 ,y′ _n1 )+β×N″ ₁ (x″ _n1 ,y″ _n1 )]÷2 in: N∈{A,B,C,D…} When β>1: the influence of offline geographical environment demand data points is greater than the influence of online object demand data points; When β<1: the influence of offline geographical environment demand data points is less than the influence of online object demand data points; When β=1: the influence of offline geographical environment demand data points is equal to the influence of online object demand data points. 7. The method according to claim 5, wherein the preset condition is: the distance between the aggregated data points after the update and the aggregated data points before the update is less than the error distance, or the number of cycles is greater than or equal to the set cycle maximum number of thresholds. 8. The method according to any one of claims 1-7, wherein the clustering algorithm adopts a k-means clustering algorithm. 9. A device for generating address information, comprising: The acquisition module is used to acquire online object demand data and offline geographic environment data; a computing module, configured to use a clustering algorithm to aggregate the online object demand data and the offline geographic environment data respectively to obtain initial aggregated data points; A selection module, configured to determine final aggregated data points according to the initial aggregated data points. 10. The apparatus according to claim 9, wherein the selection module determines the final aggregated data points according to the initial aggregated data points, comprising: Calculate the distance between the online object demand data point and the initial aggregated data point to determine the classification of the online object demand data point; and calculate the distance between the offline geographic environment demand data point and the aggregated data point to determine the line The classification of data points of geographical environment requirements; wherein, online object demand data includes online object demand data points; According to the classification of online object demand data points and the offline geographical environment demand data point classification, the final aggregated data points are obtained. 11. The device according to claim 10, wherein the selection module calculates the distance between the online object demand data points and the initial aggregation data points to determine the classification of the online object demand data points, comprising: Calculate the distance between each online object demand data point and the initial aggregated data point; According to the distance, the weighted distance is calculated to determine the aggregation data point closest to the online object demand data point as the classification of the online object demand data point; In addition, the computing module calculates the distance between the offline geographic environment demand data point and the initial aggregated data point to determine the classification of the offline geographic environment demand data point, including: Calculate the distance between each offline geographic environment demand data point and the initial aggregated data point; According to the distance, a weighted distance is calculated to determine an aggregated data point closest to the offline geographic environment demand data point as a classification of the offline geographic environment demand data point. 12. The device according to claim 10, wherein the selection module obtains final aggregated data points according to the classification of online object demand data points and offline geographical environment demand data points, comprising: For the location information of each online object demand data point classification, calculate the centroid of all online object demand data points within the preset radius; similarly, for the location information of each offline geographic environment demand data point classification, calculate the preset The centroid of all offline geographic environment demand data points within the radius; Based on the centroid of all online object demand data points and the centroid of all offline geographic environment demand data points, the final aggregated data points are obtained. 13. The device according to claim 10, wherein the selection module iteratively updates the aggregated data points according to the classification of online object demand data points and offline geographical environment demand data points until a preset condition is reached, to obtain the data points. Finally gather data points. 14. The device according to claim 13, wherein the selection module iteratively updates the aggregated data points according to the classification of online object demand data points and the offline geographical environment demand data point classification, comprising: For the classified location information of each online object demand data point, calculate the centroids A ₁ ′(x _a1 ′,y _a1 ′), B ₁ ′(x b1 ′, B 1 ′(x _b1 ′, y _b1 ′), C ₁ ′ (x _c1 ′, y _c1 ′), D ₁ ′ (x _d1 ′, y _d1 ′)...; Similarly, for the location information of each offline geographic environment demand data point classification, Calculate the centroids A ₁ ″(x _a1 ″,y _a1 ″), B ₁ ″(x _b1 ″,y _b1 ″), C ₁ ″(x _c1 ″), C 1 ″(x c1 ″) of all offline geographic environment demand data points within the preset radius, y _c1 ″), D ₁ ″(x _d1 ″, y _d1 ″)…; Determine the aggregated data points for the next iteration: N ₁ (x,y)＝[N′ ₁ (x′ _n1 ,y′ _n1 )+β×N″ ₁ (x″ _n1 ,y″ _n1 )]÷2 in: N∈{A,B,C,D…} When β>1: the influence of offline geographical environment demand data points is greater than that of online object demand data points; When β<1: the influence of offline geographical environment demand data points is less than the influence of online object demand data points; When β=1: the influence of offline geographical environment demand data points is equal to the influence of online object demand data points. 15. The device according to claim 13, wherein the preset condition is: the distance between the aggregated data points after the update and the aggregated data points before the update is less than the error distance, or the number of cycles is greater than or equal to the set cycle maximum number of thresholds. 16. The apparatus according to any one of claims 9-15, wherein the clustering algorithm adopts a k-means clustering algorithm. 17. An electronic device, comprising: one or more processors; storage means for storing one or more programs, The one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-8. 18. A computer-readable medium on which a computer program is stored, characterized in that, when the program is executed by a processor, the method according to any one of claims 1-8 is implemented.