CN112651575A - Training method for making artificial neural network have shop site selection capability, shop site selection method, system and storage medium - Google Patents
Training method for making artificial neural network have shop site selection capability, shop site selection method, system and storage medium Download PDFInfo
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Abstract
The invention provides a training method, a shop location method, a system and a storage medium for enabling an artificial neural network to have shop location capability, wherein the training method acquires business data of a branch, commercial interest point data in a peripheral preset range and the number of other related shops under the condition that a plurality of related shops with preset influence degrees exist and the related shop with the highest influence degree is provided with a plurality of branches; using commercial interest point data and other related shop quantities as input signals, and using whether business data of the branch shop reaches a preset value corresponding to a shop location standard as an output signal to form a group of learning samples for the artificial neural network to carry out shop location training; and carrying out shop location training on the artificial neural network by adopting a plurality of groups of learning samples until the artificial neural network has the capability of predicting whether business data of the shop to be located reaches a preset value according to the business interest point data in the peripheral preset range of the shop location and the number of other related shops.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a training method for enabling an artificial neural network to have shop location selection capability, a shop location selection method, a shop location selection system and a storage medium.
Background
Before the shop is opened, the shop needs to be properly located to ensure the good business condition of the shop. Currently, a commonly used shop address selection method is that an experimenter inspects all candidate addresses in the field, so as to obtain the surrounding environment and the pedestrian volume of all the candidate addresses, and then shop address selection is performed according to the surrounding environment and the pedestrian volume. However, this shop location method is performed based on the personal experience of the experimenter, which makes the result of shop location highly subjective and reduces the rationality of the result of shop location.
The Point of Interest (POI) is used as a new spatial data source, the distribution mode and the distribution density of the POI have important significance in infrastructure planning and urban space analysis, wherein the commercial POI data comprises spatial position information and commercial attribute information of different business state shops, has the characteristics of abundant data volume and strong situational property, and is beneficial to improving the accuracy of urban commercial space hotspot judgment. Before a shop is opened, the peripheral environment and the peripheral pedestrian volume of each shop location can be obtained based on the commercial interest point data, so that shop location selection is facilitated, the shop location selection result is reasonable, but the peripheral environment and the peripheral pedestrian volume are only partial main factors influencing the shop business condition, even if the peripheral environment of the shop is good and the peripheral pedestrian volume is large, if a plurality of competitive shops are arranged around the shop, the shop is difficult to attract enough consumers to enter the shop for consumption, the shop business condition is poor, and the shop location selection based on the commercial interest point data is limited in reasonableness.
Disclosure of Invention
The invention aims to solve the technical problem of how to improve the rationality of shop location selection.
In order to solve the technical problem, the invention provides a training method for enabling an artificial neural network to have store address selection capability, which comprises the following steps:
p. in the case that there are a plurality of relevant stores whose influence degrees on the store to be addressed reach a preset degree, and a plurality of branch stores are provided in the relevant store having the highest influence degree, performing a sample acquisition step for each of the plurality of branch stores to obtain a plurality of sets of learning samples, wherein the sample acquisition step performed for each branch store includes A, B, C, D:
-a. obtaining business data of the branch;
b, obtaining the commercial interest point data in a preset range around the branch;
c, acquiring the number of other related stores with the influence degree on the store to be selected reaching the preset degree in the peripheral preset range of the branch store;
d, using the commercial interest point data and the other related stores as input signals, and using whether the business data of the branch reaches a preset value corresponding to a store address selection standard as an output signal to form a group of learning samples for the artificial neural network to carry out store address selection training;
and Q, carrying out shop location training on the artificial neural network by adopting the multiple groups of learning samples until the artificial neural network has the capability of predicting whether business data of the shop to be selected reaches the preset value according to the commercial interest point data in the peripheral preset range of the shop to be selected and the quantity of other related shops with the influence degree reaching the preset degree in the peripheral preset range of the shop to be selected, so that the artificial neural network can select the shop to be selected at the shop with the business data reaching the preset value.
Preferably, in the step C, the obtained other related stores number includes a positive related store number which positively affects business data of the branch and a negative related store number which negatively affects business data of the branch; the more the positive correlation shop is, the higher the business data of the branch is; the greater the number of the relevant stores, the lower the business data of the branch.
The invention also provides a shop address selecting method, which comprises the following steps:
a. obtaining a shop place to be selected;
b. obtaining commercial interest point data in a peripheral preset range of the shop location;
c. acquiring the number of other related shops with the influence degree of the shop to be addressed reaching a preset degree in a peripheral preset range of the shop location;
d. inputting the commercial interest point data and the quantity of other related shops into a trained artificial neural network, predicting whether business data of the shop to be addressed reaches a preset value corresponding to a shop addressing standard or not by the artificial neural network, and addressing the shop to be addressed at the shop location if the business data of the shop to be addressed reaches the preset value.
Preferably, in the step c, the number of other relevant stores whose influence degree on the store to be selected reaches a preset degree includes a positive relevant store number that positively influences business data of the store to be selected and a negative relevant store number that negatively influences business data of the store to be selected; the more the number of the positive correlation shops is, the higher the business data of the shop to be selected is; the more the number of the relevant shops is, the lower the business data of the shop to be selected is.
Preferably, the artificial neural network is an artificial neural network obtained after the training method is performed.
The invention also provides a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, carries out the training method and/or the store addressing method as described above.
The invention also provides a store addressing system comprising a computer readable storage medium and a processor connected to each other, the computer readable storage medium being as described above.
The invention has the following beneficial effects: in the shop addressing process, compared with the prior art that only the business interest point data in the peripheral preset range of the shop location to be selected is considered, the invention also considers the number of other related shops with the influence degree of the shop to be addressed in the peripheral preset range of the shop location to be selected reaching the preset degree, so that under the condition that the peripheral environment reflected by the business interest point data in the peripheral preset range of the shop location to be selected is good and the peripheral people flow is large, the addressing is carried out based on the number of other related shops, the business data of the shop to be addressed can reach the preset value corresponding to the addressing standard, the business condition of the shop is enabled to be better, and the shop addressing can be carried out more reasonably.
Detailed Description
Before the shop is opened, in order to enable the shop to have good business conditions in the future, the shop address system is used for predicting business data of the shop opened at different places, and then the shop address is selected at the shop place of which the business data reaches the preset value corresponding to the shop address standard according to the predicted business data of each shop place. The business data may be influenced by the surrounding environment of the shop location, the surrounding traffic of people, and the number of related shops around the shop location, which may be reflected by the data of business interest points within a predetermined range around the shop location. In this embodiment, the predetermined range of the periphery refers to within 5 kilometers of a square circle, and optionally, within 3 kilometers of a square circle, within 8 kilometers of a square circle, within 10 kilometers of a square circle, or any other settable range.
Taking the clothing stores to be addressed as an example, because the shoe stores, the backpack stores and other clothing stores sell the goods related to the clothing, the shoe stores, the backpack stores and other clothing stores attract the consumers who want to purchase the clothing to consume, and accordingly, the shoe stores, the backpack stores and other clothing stores are preset as the relevant stores whose influence degree on the clothing stores to be addressed reaches a preset degree, wherein: the shoe store and the backpack store do not compete with the clothing store to be addressed for consumers, so under the condition that the shoe store and the backpack store can attract the consumers, the shoe store and the backpack store are positively correlated stores which have positive influence on business data of the clothing store to be addressed, namely, the more the shoe store and the backpack store are, the higher the business data of the clothing store to be addressed is; the other clothing stores compete with the clothing stores to be addressed for consumers, so that the customer flow of the clothing stores to be addressed is reduced, the other clothing stores are negative related stores which negatively affect the business data of the clothing stores to be addressed, namely, the business data of the clothing stores to be addressed are lower as the number of the other clothing stores is larger.
Before the garment shop to be located is opened, the shop scale and the commodity average price of the garment shop to be located and other garment shops are analyzed, the other garment shop with the closest shop scale and the closest commodity average price are taken as the related shop with the highest influence degree on the garment shop to be located, and the business data of the related shop with the highest influence degree is very close to the business data of the garment shop to be located, for example, the garment shop to be located is a good clothing library, and according to the comprehensive analysis of the shop scale and the commodity average price of the good clothing library and other garment shops (such as a good product without printing, H & M, DIOR and the like), the similarity degree of the good product without printing and the good clothing library is 90%, the similarity degree of the H & M and the good clothing library is 85%, and the similarity degree of the DIOR and the good clothing library is 60%, therefore, the other clothing stores which are closest to the excellent clothing warehouse in combination of store scale and commodity average price are non-printed good products, so the non-printed good products are used as related stores which have the highest influence degree on the excellent clothing warehouse. In a case where a plurality of clothing branches are located in the relevant store with the highest influence degree, the store addressing system may implement a training method for enabling the artificial neural network to have a store addressing capability based on the business interest point data in a predetermined range around the plurality of clothing branches of the relevant store with the highest influence degree and the number of other relevant stores in a predetermined range around the plurality of clothing branches of the relevant store with the highest influence degree, the training method performing a sample acquisition step on each of the plurality of clothing branches of the relevant store with the highest influence degree to obtain a plurality of sets of learning samples, wherein the sample acquisition step performed on each clothing branch is as follows A, B, C, D:
A. and acquiring business data of the clothing branch.
B. And acquiring the business interest point data in the peripheral preset range of the clothing branch, and analyzing the environment and the pedestrian volume in the peripheral preset range of the clothing branch based on the business interest point data.
C. The number of shoe stores, the number of back-pack stores and the number of other clothing stores in a predetermined range around the clothing branch are obtained, wherein the number of shoe stores and the number of back-pack stores are positive correlation stores which positively affect business data of the clothing store to be located, and the number of other clothing stores is negative correlation stores which negatively affect business data of the clothing store to be located.
D. And a group of learning samples for the artificial neural network to train the shop location are formed by taking the commercial interest point data, the number of shoe shops, the number of backpack shops and the number of other clothing shops as input signals and taking whether business data reach a preset value corresponding to the shop location standard as output signals. The preset value corresponding to the store address selection standard is the annual turnover number of the clothing store to be addressed, for example, 100 ten thousand yuan, if the business data of the clothing branch of the relevant store with the highest influence degree reaches the preset value of 100 ten thousand yuan corresponding to the store address selection standard, the output signal in the omic learning sample is yes, and if the business data of the clothing branch of the relevant store with the highest influence degree does not reach the preset value of 100 ten thousand yuan corresponding to the store address selection standard, the output signal in the omic learning sample is no. Alternatively, the preset value corresponding to the store address selection criteria may be 80 ten thousand yuan, 150 ten thousand yuan, 200 ten thousand yuan or any other settable value.
After the above-described sample acquisition step is performed for each of the plurality of clothing branch stores of the relevant store having the highest degree of influence to obtain a plurality of sets of learning samples, the shop location selection ability training of the artificial neural network is carried out by adopting a plurality of groups of learning samples until the artificial neural network has commercial interest point data in a peripheral preset range according to the shop location to be selected, and the ability of predicting whether the business data of the clothing shop to be selected reaches the preset value corresponding to the shop location standard according to the number of shoe shops, the number of backpack shops and the number of other clothing shops in the peripheral preset range of the shop location to be selected, the artificial neural network can select the clothing shop to be selected at the shop location of which the business data reaches the preset value, therefore, the shop addressing method can be realized by using the shop addressing system, and the following steps a, b, c and d are detailed:
a. and obtaining the shop place to be selected.
b. Obtaining the business interest point data in the peripheral preset range of the shop location, and analyzing the environment and the pedestrian volume in the peripheral preset range of the shop location based on the business interest point data;
c. acquiring the number of shoe stores, the number of backpack stores and the number of other clothing stores in a peripheral preset range of the store site, wherein the number of shoe stores and the number of backpack stores are positive correlation stores which positively influence business data of the clothing stores to be addressed, and the number of other clothing stores is negative correlation stores which negatively influence business data of the clothing stores to be addressed;
d. inputting the obtained commercial interest point data, the number of shoe stores, the number of backpack stores and the number of other clothing stores into the artificial neural network trained by the training method, predicting whether business data of the clothing store to be addressed reaches a preset value corresponding to a store addressing standard by the artificial neural network, addressing the clothing store to be addressed at the store location if the business data of the clothing store to be addressed reaches the preset value, and not addressing the clothing store to be addressed at the store location if the business data of the clothing store to be addressed does not reach the preset value.
In the embodiment, in the process of selecting the locations of the clothing stores, in addition to the business interest point data in the peripheral preset range of the store location to be selected, the related store numbers such as the number of shoe stores, the number of backpack stores and the number of other clothing stores in the peripheral preset range of the store location to be selected are also considered, so that under the condition that the peripheral environment is good and the peripheral people flow is large, which is reflected by the business interest point data in the peripheral preset range of the store location to be selected, the locations are selected based on the related store numbers, the business data of the clothing stores to be selected can reach the preset value corresponding to the location selection standard, and the business conditions of the clothing stores become better.
In this embodiment, the store addressing system includes a computer-readable storage medium and a processor connected to each other, and a computer program is stored in the computer-readable storage medium, and when executed by the processor, the computer program implements the training method for making the artificial neural network have the store addressing capability and/or the store addressing method.
Claims (7)
1. The training method for enabling the artificial neural network to have the shop address selection capability is characterized by comprising the following steps of:
p. in the case that there are a plurality of relevant stores whose influence degrees on the store to be addressed reach a preset degree, and a plurality of branch stores are provided in the relevant store having the highest influence degree, performing a sample acquisition step for each of the plurality of branch stores to obtain a plurality of sets of learning samples, wherein the sample acquisition step performed for each branch store includes A, B, C, D:
-a. obtaining business data of the branch;
b, obtaining the commercial interest point data in a preset range around the branch;
c, acquiring the number of other related stores with the influence degree on the store to be selected reaching the preset degree in the peripheral preset range of the branch store;
d, using the commercial interest point data and the other related stores as input signals, and using whether the business data of the branch reaches a preset value corresponding to a store address selection standard as an output signal to form a group of learning samples for the artificial neural network to carry out store address selection training;
and Q, carrying out shop location training on the artificial neural network by adopting the multiple groups of learning samples until the artificial neural network has the capability of predicting whether business data of the shop to be selected reaches the preset value according to the commercial interest point data in the peripheral preset range of the shop to be selected and the quantity of other related shops with the influence degree reaching the preset degree in the peripheral preset range of the shop to be selected, so that the artificial neural network can select the shop to be selected at the shop with the business data reaching the preset value.
2. The training method of claim 1, wherein: in the step C, the obtained other related stores number includes a positive related store number which positively affects business data of the branch and a negative related store number which negatively affects business data of the branch; the more the positive correlation shop is, the higher the business data of the branch is; the greater the number of the relevant stores, the lower the business data of the branch.
3. The shop address selecting method is characterized by comprising the following steps:
a. obtaining a shop place to be selected;
b. obtaining commercial interest point data in a peripheral preset range of the shop location;
c. acquiring the number of other related shops with the influence degree of the shop to be addressed reaching a preset degree in a peripheral preset range of the shop location;
d. inputting the commercial interest point data and the quantity of other related shops into a trained artificial neural network, predicting whether business data of the shop to be addressed reaches a preset value corresponding to a shop addressing standard or not by the artificial neural network, and addressing the shop to be addressed at the shop location if the business data of the shop to be addressed reaches the preset value.
4. A store addressing method according to claim 3, wherein: in the step c, the number of other relevant stores whose influence degree on the store to be selected reaches a preset degree includes the number of positive relevant stores which positively influence business data of the store to be selected and the number of negative relevant stores which negatively influence business data of the store to be selected; the more the number of the positive correlation shops is, the higher the business data of the shop to be selected is; the more the number of the relevant shops is, the lower the business data of the shop to be selected is.
5. A store addressing method according to claim 3, wherein the trained artificial neural network is an artificial neural network obtained by performing the training method according to claim 1 or 2.
6. Computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the training method according to claim 1 or 2 and/or the store addressing method according to any one of claims 3 to 5.
7. A store addressing system comprising a computer readable storage medium and a processor coupled to each other, wherein the computer readable storage medium is as claimed in claim 6.
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- 2021-01-05 CN CN202110008875.6A patent/CN112651575A/en active Pending
Patent Citations (3)
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| CN108960912A (en) * | 2018-06-27 | 2018-12-07 | 北京京东金融科技控股有限公司 | Method and apparatus for determining target position |
| CN110543999A (en) * | 2018-08-17 | 2019-12-06 | 杉数科技(北京)有限公司 | Method and device for store site selection |
| CN110648161A (en) * | 2019-08-05 | 2020-01-03 | 中移（杭州）信息技术有限公司 | Benefit evaluation method and device for commercial site selection and storage medium |
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