CN113762683A - Method and device for site selection of store - Google Patents
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Abstract
The invention discloses a method and a device for selecting a site of a store, and relates to the technical field of computers. One embodiment of the method comprises: calculating the behavior probability of different types of users at candidate points based on a pre-fitted discrete selection model; calculating the user flow of the candidate points based on a pre-fitted multiple regression model; calculating expected values of the candidate points according to the behavior probabilities of the different types of users at the candidate points, the user flow of the candidate points and the operation information of each article; and calculating a risk evaluation result of the candidate point according to the user flow and the expected value of the candidate point, so as to select the shop address based on the risk evaluation result. The implementation method can solve the technical problem that the site selection result of the store is not accurate enough.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for selecting a site of an store.
Background
At present, a large amount of software or algorithms provide an addressing scheme in both the industrial and academic circles, but the addressing scheme is limited by the digital level or information isolated island of part of client systems, parameters in an store addressing model are difficult to accurately quantify and describe, and the modeling difficulty is high.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
in the current site selection operation model, factors of surrounding users are difficult to add into the model for modeling, and most of the modeling influence factors are factors on physical properties, so that the site selection result of stores is not accurate enough.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for store site selection to solve the technical problem that a result of the store site selection is not accurate enough.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a method for site selection of a store, including:
calculating the behavior probability of different types of users at candidate points based on a pre-fitted discrete selection model;
calculating the user flow of the candidate points based on a pre-fitted multiple regression model;
calculating expected values of the candidate points according to the behavior probabilities of the different types of users at the candidate points, the user flow of the candidate points and the operation information of each article;
and calculating a risk evaluation result of the candidate point according to the user flow and the expected value of the candidate point, so that the shop site selection is carried out based on the risk evaluation result.
Optionally, calculating the probability of behavior of different types of users at the candidate point based on the pre-fitted discrete selection model includes:
fitting the mixed polynomial logic model by adopting the historical behavior data of each store and the attribute information of the articles;
calculating the behavior probability of different types of users at candidate points based on the fitted mixed polynomial logic model;
wherein the historical behavior data comprises at least one of: probability distribution of different types of users entering stores, distance between stores and users, and behavior results of users.
Optionally, calculating the user flow of the candidate points based on a pre-fitted multiple regression model includes:
fitting the multiple regression model by adopting store position information and user flow of each store;
calculating the user flow of the candidate points based on the fitted multiple regression model;
wherein the store location information comprises at least one of: the floor where the store is located, whether the store is close to the street, the number of other stores around the store, and the degree of density of the staff flow.
Optionally, fitting the multiple regression model by using store location information and user traffic of each store includes:
constructing a multiple regression model;
and performing distribution fitting on the fitting residual of the multiple regression model by adopting store position information and user flow of each store to obtain a residual distribution function.
Optionally, calculating the user flow of the candidate point based on the fitted multiple regression model includes:
generating a random number through the residual distribution function, wherein the random number is larger than zero and smaller than one;
and calculating the user flow of the candidate points based on the fitted multiple regression model and the random number.
Optionally, the operation information of the article includes at least one of: selling price, cost, amount of goods in stock, cost price lost after unsold.
Optionally, calculating a risk assessment result of the candidate point according to the user traffic and the expected value of the candidate point, including:
calculating the number of days and the probability that the user traffic of the candidate point is smaller than the user traffic threshold according to the user traffic of the candidate point and the user traffic threshold;
and calculating the days and the probability that the expected value of the candidate point is smaller than the expected value threshold according to the expected value and the expected value threshold of the candidate point.
In addition, according to another aspect of the embodiments of the present invention, there is provided an store site selection apparatus including:
the first calculation module is used for calculating the behavior probability of different types of users at candidate points based on a pre-fitted discrete selection model;
the second calculation module is used for calculating the user flow of the candidate points based on a pre-fitted multiple regression model;
the evaluation module is used for calculating expected values of the candidate points according to the behavior probabilities of the different types of users at the candidate points, the user flow of the candidate points and the operation information of each article;
and the site selection module is used for calculating a risk evaluation result of the candidate point according to the user flow and the expected value of the candidate point, so that the site selection of the store is carried out based on the risk evaluation result.
Optionally, the first computing module is further configured to:
fitting the mixed polynomial logic model by adopting the historical behavior data of each store and the attribute information of the articles;
calculating the behavior probability of different types of users at candidate points based on the fitted mixed polynomial logic model;
wherein the historical behavior data comprises at least one of: probability distribution of different types of users entering stores, distance between stores and users, and behavior results of users.
Optionally, the second computing module is further configured to:
fitting the multiple regression model by adopting store position information and user flow of each store;
calculating the user flow of the candidate points based on the fitted multiple regression model;
wherein the store location information comprises at least one of: the floor where the store is located, whether the store is close to the street, the number of other stores around the store, and the degree of density of the staff flow.
Optionally, the second computing module is further configured to:
constructing a multiple regression model;
and performing distribution fitting on the fitting residual of the multiple regression model by adopting store position information and user flow of each store to obtain a residual distribution function.
Optionally, the second computing module is further configured to:
generating a random number through the residual distribution function, wherein the random number is larger than zero and smaller than one;
and calculating the user flow of the candidate points based on the fitted multiple regression model and the random number.
Optionally, the operation information of the article includes at least one of: selling price, cost, amount of goods in stock, cost price lost after unsold.
Optionally, the addressing module is further configured to:
calculating the number of days and the probability that the user traffic of the candidate point is smaller than the user traffic threshold according to the user traffic of the candidate point and the user traffic threshold;
and calculating the days and the probability that the expected value of the candidate point is smaller than the expected value threshold according to the expected value and the expected value threshold of the candidate point.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including:
one or more processors;
a storage device 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 methods of any of the embodiments described above.
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, which when executed by a processor implements the method of any of the above embodiments.
One embodiment of the above invention has the following advantages or benefits: the technical means of shop site selection based on the risk assessment result is achieved by adopting the technical means of calculating the expected value of the candidate point according to the behavior probability of different types of users at the candidate point, the user flow of the candidate point and the operation information of each article and calculating the risk assessment result of the candidate point according to the user flow and the expected value of the candidate point, so that the technical problem that the shop site selection result is not accurate in the prior art is solved. According to the method and the device, the expected value of the candidate point is calculated according to the behavior probability of different types of users at the candidate point, the user flow of the candidate point and the operation information of each article, and the risk assessment result of the candidate point is further calculated according to the user flow and the expected value of the candidate point, so that whether the candidate point or even the area meets the requirement of setting up the store in the future period of time is observed, and therefore the site selection efficiency and the site selection accuracy of the store are improved.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with specific embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of a main flow of a store location method according to an embodiment of the invention;
FIG. 2 is a schematic view of a main flow of a store site selection method according to a referential embodiment of the present invention;
FIG. 3 is a schematic diagram of a store site selection simulation system according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the main modules of a store location apparatus according to an embodiment of the present invention;
FIG. 5 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 6 is a schematic block diagram of a computer system suitable for use with a terminal device or server implementing an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered 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 in the following description for clarity and conciseness.
Since the location of the offline convenience store generally occurs in a situation where a store is to be newly established in a certain cell or business district, a decision is made in a limited number of candidate points. Therefore, the embodiment of the invention carries out simulation on each candidate point, and constructs the user flow and the expected value of the store during normal operation if the store is established at the candidate point. Meanwhile, a decision maker can add continuous adverse influence factors in the simulation process at any time to influence the user flow and the expected value of the store so as to observe whether the candidate point and even the area meet the requirement of opening the store in a future period of time.
Fig. 1 is a schematic diagram of a main flow of a store site selection method according to an embodiment of the present invention. As an embodiment of the present invention, as shown in fig. 1, the store site selection method may include:
and step 101, calculating the behavior probability of different types of users at the candidate points based on the pre-fitted discrete selection model.
In the embodiment of the invention, the behavior effectiveness of the user on the property and the self-property of the article can be fitted by using a discrete selection model, the discrete selection model needs to be fitted in advance, and the data related to the fitting model can be derived from the existing historical behavior data of each store.
Optionally, step 101 may comprise: fitting the mixed polynomial logic model by adopting the historical behavior data of each store and the attribute information of the articles; calculating the behavior probability of different types of users at candidate points based on the fitted mixed polynomial logic model; wherein the historical behavioral data includes at least one of: probability distribution of different types of users entering stores, distance between stores and users, and behavior results of users. According to the embodiment of the invention, a mixed multinomial logic model (mixed multinomial logic model) is selected as a discrete selection model, the mixed multinomial logic model is fitted by adopting the existing historical behavior data of each store and the attribute information of an article, and then the behavior probability (such as purchase probability) of different types of users at candidate points is calculated based on the fitted mixed multinomial logic model, so that the behavior probability of different types of users at the candidate points can be accurately calculated.
Optionally, taking purchasing behavior as an example, the mixed polynomial logic model is as follows, and its purchasing probability expression for a certain item i is:
PC(i|x,θ)＝∫LC(i；x,α)·G(dα；θ)
wherein the content of the first and second substances,
the purchase efficiency of the user under the item set C; c is an article set available for the user to select; x is the number ofiK-dimensional vectors representing k attribute information of the articles; theta is a hyper-parameter of the mixed distribution G, G distribution is probability distribution of different types of users entering stores, and alpha is a parameter needing model fitting.
It is noted that in xiThe attribute information of each dimension (2) includes an attribute of a distance between the store and the user in addition to an attribute of an object having a certain dimension.
Then, by utilizing maximum likelihood estimation, a parameter alpha can be obtained, and the alpha is substituted into a purchase probability expression formula, namely, the purchase probability P of different types of users aiming at each article under the article combination C can be obtainedC(i | x, θ). The embodiment of the invention not calculates the purchase probability of each user, but calculates the purchase probability of each type of user, such as calculating the purchase probability of male users, users in 30-40 years old users or users in white collar and the like, thereby being beneficial to accurately calculating the expected value of the candidate point.
In embodiments of the present invention, the user history data may originate from two sources, such as online purchase data, offline purchase data. If the user history data is online purchase data, the distance between the store and the user can be calculated by using the longitude and latitude of the online ordering address of the user and the longitude and latitude of the store. If the user history data is offline purchase data, the distance between the store and the user can be calculated by using the distance between the community or the office building where the user is located and the store.
Optionally, the attribute information of the article may include an article code, an article weight, an article selling price, an article packaging material, a popularity rating of a brand to which the article belongs, and the like. The distance between the store and the user can be calculated through user data, and the user data can comprise user codes, longitude and latitude of a user address, user consumption grades, user sexes, user age groups, object codes of objects purchased by the user, user occupation and the like.
And 102, calculating the user flow of the candidate points based on a pre-fitted multiple regression model.
The embodiment of the invention takes a multiple regression model as a flow model, and calculates the user flow of the candidate points based on the pre-fitted multiple regression model. Optionally, step 102 may comprise: fitting the multiple regression model by using store position information and user flow of each store; calculating the user flow of the candidate points based on the fitted multiple regression model; wherein the store location information comprises at least one of: the floor where the store is located, whether the store is close to the street, the number of other stores around the store, and the degree of density of the staff flow. In the embodiment of the invention, the multivariate regression model is fitted by adopting the existing store position information of each store and the user flow of the stores, and then the user flow of the candidate point is calculated based on the fitted multivariate regression model, so that the user flow of the candidate point can be accurately calculated.
Optionally, fitting the multiple regression model by using store location information and user traffic of each store includes: constructing a multiple regression model; and performing distribution fitting on the fitting residual error of the multiple regression model by adopting store position information and user flow of each store to obtain a residual error distribution function ResidualDistribution. Optionally, calculating the user flow of the candidate points based on the fitted multiple regression model includes: generating a random number through the residual distribution function, wherein the random number is larger than zero and smaller than one; and calculating the user flow of the candidate points based on the fitted multiple regression model and the random number. The embodiment of the invention generates the random number through the residual error distribution function, thereby generating the randomized user flow.
Optionally, the embodiment of the invention uses a multiple regression model to fit the floor where the store is located, whether the store is close to the street, the number of other stores around the store, and the degree of density of the flow of people on the flow of people. Optionally, the multivariate regression model is as follows:
StoreTraffic＝α1*floor+α2*frontage+α3*competitor+α4*density+ε
distribution fitting can be carried out on fitting residual errors of the multiple regression model according to historical user flow StoreTraffic of different stores, floor where the stores are located, whether the stores are street front or not, the number of other stores around the stores, and personnel flow intensity degree grade intensity, so that all parameters and residual error distribution function are obtained in a fitting mode, and random user flow can be generated. Where ε is the random error term, i.e., the random number, for the model.
And performing distribution fitting on the fitting residual errors of the multiple regression model, and finding out a residual error distribution function ResidualDistribution which accords with the scene so as to generate random passenger flow in the following fitting process. Assuming that the residual distribution function is f (x), a random number epsilon between 0 and 1 may be randomly generated, for example, the random number may be obtained by using the inverse function of the residual distribution function. The multiple regression model and the residual error distribution function have high universality and can be constructed at one time. And generating randomized user traffic Storettraffic according to the multiple regression model and the random number.
Step 103, calculating expected values of the candidate points according to the behavior probabilities of the different types of users at the candidate points, the user flow of the candidate points and the operation information of each article.
Obtaining the behavior probability P of different types of users at the candidate points according to the two stepsCAnd (i | x, theta) and the user flow Storettraffic of the candidate point, and further calculating the expected value of the candidate point according to the operation information of each article. Optionally, the operation information of the article includes at least one of: selling price PiCost CiAnd the amount of shipment PuriUnsold loss price Li。
Alternatively, the expected value may be calculated using the following formula:
wherein, PiFor selling the item i, CiIs the cost of item i, PC(i | x, θ) is the probability of behavior of different types of users at the candidate point, PuriIs the quantity of articles i taken in, LiFor the loss price per unsold item of item i, StoreTraffic is the user traffic for the candidate point, and e (r) is the expected value.
Since a plurality of user traffic may be randomly generated in step 102, repeating step 103 may generate a plurality of expected values accordingly.
And 104, calculating a risk evaluation result of the candidate point according to the user flow and the expected value of the candidate point, and selecting an address of an store based on the risk evaluation result.
In this step, a risk assessment result of the candidate point in a certain period is calculated according to the user traffic generated in step 102 and the expected values generated in step 103, so that a decision maker can choose or not in combination with other information, and the decision maker can decide whether to open a store at the candidate point based on the risk assessment result. Therefore, decision-making personnel can carry out decision-making according to mutual verification of the current surrounding environment information and the risk assessment result calculated by the embodiment of the invention, so that the decision-making time of the decision-making personnel is saved, the working efficiency is improved, and meanwhile, the decision-making information of the decision-making personnel can be enhanced by combining big data information.
Optionally, calculating a risk assessment result of the candidate point according to the user traffic and the expected value of the candidate point, including: calculating the number of days and the probability that the user traffic of the candidate point is smaller than the user traffic threshold according to the user traffic of the candidate point and the user traffic threshold; and calculating the days and the probability that the expected value of the candidate point is smaller than the expected value threshold according to the expected value of the candidate point and the expected value threshold. The user traffic threshold and the user traffic threshold can be configured in advance, the number of days that the user traffic of the candidate point is smaller than the user traffic threshold can be calculated first, and then the probability that the user traffic is smaller than the user traffic threshold can be obtained by dividing the number of days by the total number of days; the number of days that the expected value of the candidate point is smaller than the expected value of the user can be calculated, and then the probability that the expected value is smaller than the expected value threshold value can be obtained by dividing the number of days by the total number of days.
According to the various embodiments described above, it can be seen that the embodiment of the present invention calculates the expected value of the candidate point according to the behavior probability of the users of different types at the candidate point, the user traffic of the candidate point, and the operation information of each item, and calculates the risk assessment result of the candidate point according to the user traffic and the expected value of the candidate point, so as to perform the technical means of portal site selection based on the risk assessment result, thereby solving the technical problem in the prior art that the portal site selection result is not accurate enough. According to the embodiment of the invention, the expected value of the candidate point is calculated according to the behavior probability of different types of users at the candidate point, the user flow of the candidate point and the operation information of each article, and the risk assessment result of the candidate point is further calculated according to the user flow and the expected value of the candidate point, so that whether the candidate point and even the area meet the requirement of setting up the store in the future period of time is observed, and therefore, the efficiency and the accuracy of the site selection of the store are improved.
Fig. 2 is a schematic diagram of a main flow of a store site selection method according to a referential embodiment of the present invention. As still another embodiment of the present invention, as shown in fig. 2, the store site selection method may include:
step 201, fitting the mixed polynomial logic model by using the historical behavior data of each store and the attribute information of the articles.
The embodiment of the invention selects the mixed polynomial logic model as a discrete selection model, and adopts the historical behavior data of each store and the attribute information of the articles to fit the mixed polynomial logic model; wherein the historical behavior data comprises at least one of: probability distribution of different types of users entering stores, distance between the stores and the users and behavior results of the users; the attribute information of the article can comprise an article code, an article weight, an article selling price, an article packaging material, a popularity level of a brand to which the article belongs and the like.
Optionally, taking purchasing behavior as an example, the mixed polynomial logic model is as follows, and its purchasing probability expression for a certain item i is:
PC(i|x,θ)＝∫LC(i；x,α)·G(dα；θ)
wherein the content of the first and second substances,
the purchase efficiency of the user under the item set C; c is an article set available for the user to select; x is the number ofiThe k-dimensional vector represents k attribute information of the article and also contains a distance between a store and a user; theta is a hyper-parameter of the mixed distribution G, the distribution G is a probability distribution of different types of users entering the store, and alpha is a parameter needing model fitting. Using maximum likelihood estimation, the parameter α can be obtained.
And 202, calculating the behavior probability of different types of users at the candidate points based on the fitted mixed polynomial logic model.
Taking the purchase probability expression in step 201 as an example, substituting α into the purchase probability expression, that is, obtaining the purchase probability P of different types of users for each item under the item combination CC(ix | x, θ), such as calculating the probability of purchase for a user of the type female, 50-60 years old, or student, which helps to accurately calculate the expected value of the candidate point.
And 203, constructing a multiple regression model, and performing distribution fitting on fitting residual errors of the multiple regression model by adopting store position information and user flow of each store to obtain a residual error distribution function.
Optionally, the multivariate regression model is as follows:
StoreTraffic＝α1*floor+α2*frontage+α3*competitor+α4*density+ε
distribution fitting can be carried out on fitting residual errors of the multiple regression model according to historical user flow StoreTraffic of different stores, floor where the stores are located, whether the stores are street front or not, the number of other stores around the stores, and personnel flow intensity degree grade intensity, so that all parameters and residual error distribution function are obtained in a fitting mode, and random user flow can be generated. Where ε is the random error term, i.e., the random number, for the model.
And step 204, generating a random number through the residual error distribution function, wherein the random number is greater than zero and smaller than one.
Assuming that the residual distribution function is f (x), a random number epsilon between 0 and 1 may be randomly generated, for example, the random number may be obtained by using the inverse function of the residual distribution function.
And step 205, calculating the user flow of the candidate point based on the fitted multiple regression model and the random number.
The multiple regression model and the residual error distribution function have high universality and can be constructed at one time. And generating randomized user traffic Storettraffic according to the multiple regression model and the random number. Repeating steps 204 and 205 results in a plurality of random user traffic.
And step 206, calculating expected values of the candidate points according to the behavior probabilities of the different types of users at the candidate points, the user flow of the candidate points and the operation information of each article.
Alternatively, the expected value may be calculated using the following formula:
wherein, PiFor selling the item i, CiIs the cost of item i, PC(i | x, θ) is the probability of behavior of different types of users at the candidate point, PuriIs the quantity of articles i taken in, LiFor the loss price per unsold item of item i, StoreTraffic is the user traffic for the candidate point, and e (r) is the expected value.
Since a plurality of user traffic may be randomly generated through steps 204 and 205, repeating step 206 may generate a plurality of expected values accordingly.
And step 207, calculating a risk assessment result of the candidate point according to the user flow and the expected value of the candidate point, and accordingly performing store site selection based on the risk assessment result.
According to the user traffic generated in step 205 and the expected values generated in step 206, a risk assessment result of the candidate point in a certain period is calculated, so that a decision maker can take a trade off in combination with other information, and the decision maker can decide whether to set a store at the candidate point based on the risk assessment result. Specifically, according to the user traffic of the candidate point and a user traffic threshold, calculating the number of days and the probability that the user traffic of the candidate point is smaller than the user traffic threshold; and calculating the days and the probability that the expected value of the candidate point is smaller than the expected value threshold according to the expected value of the candidate point and the expected value threshold. The user traffic threshold value and the user traffic threshold value can be configured in advance, the number of days that the user traffic of the candidate point is smaller than the user traffic threshold value can be calculated firstly, and then the probability that the user traffic is smaller than the user traffic threshold value can be obtained by dividing the number of days by the total number of days; the number of days that the expected value of the candidate point is smaller than the expected value of the user can be calculated, and then the probability that the expected value is smaller than the expected value threshold value can be obtained by dividing the number of days by the total number of days.
In addition, in one embodiment of the present invention, the detailed implementation contents of the store site selection method are described in detail above, and therefore, the repeated contents are not described herein.
Fig. 3 is a schematic structural diagram of a store site selection simulation system according to an embodiment of the present invention. As shown in fig. 3, the store site selection simulation system comprises a front-end interaction module, a model building module, a simulation module and a risk assessment module.
The front-end interaction module is responsible for transmitting input or configuration of a user to the simulation module, and mainly informs the simulation module of what parameters of the model building module are, the total simulation execution time, simulation stopping conditions (such as the maximum number of days of simulation, the number of days that user flow is less than a user flow threshold value, the number of days that an expected value is less than an expected value threshold value), and the like.
And the simulation module calls the model construction module and receives the expected value and the user flow of each candidate point fed back by the model construction module under the current environment. The simulation module is also responsible for controlling the whole simulation process, determining to stop simulation in due time according to the requirements of users, and outputting simulation results. And the output of the simulation result is that the simulation module outputs the result fed back by the multiple model building module to the risk evaluation module. The analog simulation module in fig. 2 is mainly used for regulating and controlling each link in the simulation process:
the risk evaluation module calculates according to the received simulation result to obtain a risk evaluation result (such as the number of days and the probability that the user flow is smaller than the user flow threshold value, and the number of days and the probability that the expected value is smaller than the expected value threshold value) of each candidate point, and displays the result on the front-end interaction module. The user can adjust the parameters and configuration according to the front-end display content and start the next task.
For example, the input parameters of the store site selection simulation system are as follows:
article attribute information table
| SKU coding | SKU weight | SKU selling price | SKU packaging material | Grade of popularity of brand to which SKU belongs | … |
| 123 | 500g | 20.5 yuan | Plastic material | Level 1 |
Article operation information table
| SKU coding | Cost of SKU | SKU inventory | Loss of SKU unsold |
| 123 | 15 Yuan | 100 pieces/week | 10 yuan/month |
Candidate point information table
Information tables of other stores
Other model parameter information table (header)
User consumption record table
The output of the store site selection simulation system is as follows
And calculating the number of days that the user traffic is less than the threshold value and the number of days that the expected value is less than the threshold value in the period according to the random user traffic Storettraffic generated for multiple times and the expected value, and the probability of the occurrence of the situation. And feeding back the information to decision-making personnel to assist the decision-making personnel in judging the loss possibility and the degree of the loss.
Fig. 4 is a schematic diagram of main modules of a store site selection apparatus according to an embodiment of the present invention, and as shown in fig. 4, the store site selection apparatus 400 includes a first calculation module 401, a second calculation module 402, an evaluation module 403, and a site selection module 404; the first calculation module 401 is configured to calculate behavior probabilities of different types of users at candidate points based on a pre-fitted discrete selection model; the second calculation module 402 calculates the user flow of the candidate points based on a pre-fitted multiple regression model; the evaluation module 403 calculates an expected value of the candidate point according to the behavior probability of the different types of users at the candidate point, the user traffic of the candidate point, and the operation information of each item; the addressing module 404 is configured to calculate a risk assessment result of the candidate point according to the user traffic and the expected value of the candidate point, so as to perform store addressing based on the risk assessment result.
Optionally, the first computing module 401 is further configured to:
fitting the mixed polynomial logic model by adopting the historical behavior data of each store and the attribute information of the articles;
calculating the behavior probability of different types of users at candidate points based on the fitted mixed polynomial logic model;
wherein the historical behavior data comprises at least one of: probability distribution of different types of users entering stores, distance between stores and users, and behavior results of users.
Optionally, the second computing module 402 is further configured to:
fitting the multiple regression model by adopting store position information and user flow of each store;
calculating the user flow of the candidate points based on the fitted multiple regression model;
wherein the store location information comprises at least one of: the floor where the store is located, whether the store is close to the street, the number of other stores around the store, and the degree of density of the staff flow.
Optionally, the second computing module 402 is further configured to:
constructing a multiple regression model;
and performing distribution fitting on the fitting residual of the multiple regression model by adopting store position information and user flow of each store to obtain a residual distribution function.
Optionally, the second computing module 402 is further configured to:
generating a random number through the residual distribution function, wherein the random number is larger than zero and smaller than one;
and calculating the user flow of the candidate points based on the fitted multiple regression model and the random number.
Optionally, the operation information of the article includes at least one of: selling price, cost, amount of goods in stock, cost price lost after unsold.
Optionally, the addressing module 404 is further configured to:
calculating the number of days and the probability that the user traffic of the candidate point is smaller than the user traffic threshold according to the user traffic of the candidate point and the user traffic threshold;
and calculating the days and the probability that the expected value of the candidate point is smaller than the expected value threshold according to the expected value and the expected value threshold of the candidate point.
According to the various embodiments described above, it can be seen that the embodiment of the present invention calculates the expected value of the candidate point according to the behavior probability of the users of different types at the candidate point, the user traffic of the candidate point, and the operation information of each item, and calculates the risk assessment result of the candidate point according to the user traffic and the expected value of the candidate point, so as to perform the technical means of portal site selection based on the risk assessment result, thereby solving the technical problem in the prior art that the portal site selection result is not accurate enough. According to the embodiment of the invention, the expected value of the candidate point is calculated according to the behavior probability of different types of users at the candidate point, the user flow of the candidate point and the operation information of each article, and the risk assessment result of the candidate point is further calculated according to the user flow and the expected value of the candidate point, so that whether the candidate point and even the area meet the requirement of setting up the store in the future period of time is observed, and therefore, the efficiency and the accuracy of the site selection of the store are improved.
It should be noted that, in the embodiment of the store site selection apparatus according to the present invention, the store site selection method has been described in detail above, and therefore, the repeated description is omitted here.
Fig. 5 illustrates an exemplary system architecture 500 of a store location method or store location apparatus to which embodiments of the invention may be applied.
As shown in fig. 5, the system architecture 500 may include terminal devices 501, 502, 503, a network 504, and a server 505. The network 504 serves to provide a medium for communication links between the terminal devices 501, 502, 503 and the server 505. Network 504 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 501, 502, 503 to interact with a server 505 over a network 504 to receive or send messages or the like. The terminal devices 501, 502, 503 may have installed thereon various communication client applications, such as shopping applications, web browser applications, search applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 501, 502, 503 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 505 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the terminal devices 501, 502, 503. The background management server may analyze and otherwise process the received data such as the item information query request, and feed back a processing result (for example, target push information, item information — just an example) to the terminal device.
It should be noted that the store location selection method provided by the embodiment of the present invention is generally executed by the server 505, and accordingly, the store location selection apparatus is generally disposed in the server 505. The store address selecting method provided by the embodiment of the invention can also be executed by the terminal equipment 501, 502 and 503, and correspondingly, the store address selecting device can be arranged in the terminal equipment 501, 502 and 503.
It should be understood that the number of terminal devices, networks, and servers in fig. 5 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 6, a block diagram of a computer system 600 suitable for use with a terminal device implementing an embodiment of the invention is shown. The terminal device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as an internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 601.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic 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 programs 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, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block 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 will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes a first computation module, a second computation module, an evaluation module, and an addressing module, where the names of the modules do not in some cases constitute a limitation on the modules themselves.
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, implement the method of: calculating the behavior probability of different types of users at candidate points based on a pre-fitted discrete selection model; calculating the user flow of the candidate points based on a pre-fitted multiple regression model; calculating expected values of the candidate points according to the behavior probabilities of the different types of users at the candidate points, the user flow of the candidate points and the operation information of each article; and calculating a risk evaluation result of the candidate point according to the user flow and the expected value of the candidate point, so that the shop site selection is carried out based on the risk evaluation result.
According to the technical scheme of the embodiment of the invention, the expected value of the candidate point is calculated according to the behavior probability of different types of users at the candidate point, the user flow of the candidate point and the operation information of each article, and the risk assessment result of the candidate point is calculated according to the user flow and the expected value of the candidate point, so that the technical means of shop site selection is carried out based on the risk assessment result, and the technical problem that the shop site selection result in the prior art is not accurate enough is solved. According to the embodiment of the invention, the expected value of the candidate point is calculated through the behavior probability of different types of users at the candidate point, the user flow of the candidate point and the operation information of each article, and the risk evaluation result of the candidate point is further calculated according to the user flow and the expected value of the candidate point, so that whether the candidate point and even the area meet the requirement of setting up the store in the future period of time is observed, and therefore, the efficiency and the accuracy of the store site selection are improved.
The above-described embodiments should not be construed as limiting the scope of the 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 modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A store site selection method, comprising:
calculating the behavior probability of different types of users at candidate points based on a pre-fitted discrete selection model;
calculating the user flow of the candidate points based on a pre-fitted multiple regression model;
calculating expected values of the candidate points according to the behavior probabilities of the different types of users at the candidate points, the user flow of the candidate points and the operation information of each article;
and calculating a risk evaluation result of the candidate point according to the user flow and the expected value of the candidate point, so as to select the shop address based on the risk evaluation result.
2. The method of claim 1, wherein calculating the probability of behavior of different types of users at candidate points based on a pre-fitted discrete choice model comprises:
fitting the mixed polynomial logic model by adopting the historical behavior data of each store and the attribute information of the articles;
calculating the behavior probability of different types of users at candidate points based on the fitted mixed polynomial logic model;
wherein the historical behavior data comprises at least one of: probability distribution of different types of users entering stores, distance between stores and users, and behavior results of users.
3. The method of claim 1, wherein calculating the user traffic for the candidate points based on a pre-fitted multivariate regression model comprises:
fitting the multiple regression model by adopting store position information and user flow of each store;
calculating the user flow of the candidate points based on the fitted multiple regression model;
wherein the store location information comprises at least one of: the floor where the store is located, whether the store is close to the street, the number of other stores around the store, and the degree of density of the staff flow.
4. The method of claim 3, wherein fitting the multivariate regression model using store location information and user traffic for each store comprises:
constructing a multiple regression model;
and performing distribution fitting on the fitting residual of the multiple regression model by adopting store position information and user flow of each store to obtain a residual distribution function.
5. The method of claim 4, wherein calculating the user traffic of the candidate points based on the fitted multiple regression model comprises:
generating a random number through the residual distribution function, wherein the random number is larger than zero and smaller than one;
and calculating the user flow of the candidate points based on the fitted multiple regression model and the random number.
6. The method of claim 1, wherein the operational information of the item comprises at least one of: selling price, cost, amount of goods in stock, cost price lost after unsold.
7. The method of claim 1, wherein calculating the risk assessment result of the candidate point according to the user traffic and the expected value of the candidate point comprises:
calculating the number of days and the probability that the user traffic of the candidate point is smaller than the user traffic threshold according to the user traffic of the candidate point and the user traffic threshold;
and calculating the days and the probability that the expected value of the candidate point is smaller than the expected value threshold according to the expected value of the candidate point and the expected value threshold.
8. An store site selection device, comprising:
the first calculation module is used for calculating the behavior probability of different types of users at candidate points based on a pre-fitted discrete selection model;
the second calculation module is used for calculating the user flow of the candidate points based on a pre-fitted multiple regression model;
the evaluation module is used for calculating expected values of the candidate points according to the behavior probabilities of the different types of users at the candidate points, the user flow of the candidate points and the operation information of each article;
and the site selection module is used for calculating a risk evaluation result of the candidate point according to the user flow and the expected value of the candidate point, so that the site selection of the store is carried out based on the risk evaluation result.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
the one or more programs, when executed by the one or more processors, implement the method of any of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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