CN110751501A - Commodity shopping guide method, device, equipment and storage medium in new retail mode - Google Patents
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Abstract
The invention relates to the technical field of artificial intelligence, and discloses a commodity shopping guide method, a device, equipment and a storage medium in a new retail mode, wherein the method comprises the following steps: when the customer terminal is determined to be located in the target entity store according to the position information reported by the customer terminal, obtaining the customer information reported by the customer terminal; calculating the similarity between the customer information and each transacted customer information stored in the historical transaction record to obtain a plurality of similarity calculation results; determining a maximum similarity calculation result from the multiple similarity calculation results, and inputting the customer information into a preset prediction model when the maximum similarity calculation result is smaller than a preset threshold value to obtain a product recommendation result aiming at the customer information; the face image reported by the customer terminal is obtained, and the face image and the product recommendation result are sent to the sales terminal corresponding to the target entity store. By the invention, sales personnel can provide more accurate shopping guide service for customers, and sales efficiency and success rate are improved.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a commodity shopping guide method, a commodity shopping guide device, commodity shopping guide equipment and a storage medium in a new retail mode.
Background
At present, the sales mode in the brick-and-mortar store is generally that a user enters the brick-and-mortar store, and then a sales consultant judges whether the user is a potential purchasing user according to experience, and if the user is judged to be a potential purchasing user, the sales consultant follows up with the sales guide. This approach relies on the expertise of the sales advisor, requires a higher capacity on the sales advisor, and when there are more customers, the sales advisor is limited in the number of people who are available to the sales advisor, causing potential consumer customers to lose their stock because they are not available to the sales advisor, or the sales advisor is required to have excessive conversations with the customers without first knowing any of the customer's preferences and characteristics, causing customer discomfort, and resulting in a lower success rate of sales.
Disclosure of Invention
The invention mainly aims to provide a commodity shopping guide method, a commodity shopping guide device, commodity shopping guide equipment and a storage medium in a new retail mode, and aims to solve the technical problem of low sales success rate in the prior art.
In order to achieve the above object, the present invention provides a method for guiding shopping of commodities in a new retail mode, comprising:
acquiring position information reported by a customer terminal, and determining whether the customer terminal is located in a target entity store or not according to the position information;
if the customer terminal is located in a target entity store, obtaining customer information reported by the customer terminal;
calculating the similarity between the customer information and each transacted customer information stored in a historical transaction record to obtain a plurality of similarity calculation results;
determining a maximum similarity calculation result from the plurality of similarity calculation results, and judging whether the maximum similarity calculation result is smaller than a preset threshold value;
if the maximum similarity calculation result is smaller than a preset threshold value, inputting the customer information into a preset prediction model to obtain a product recommendation result aiming at the customer information;
and acquiring the face image reported by the customer terminal, and sending the face image and the product recommendation result to a sales terminal corresponding to the target entity store.
In addition, to achieve the above object, the present invention provides a merchandise shopping guide apparatus in a new retail mode, including:
the system comprises a position judgment module, a position judgment module and a display module, wherein the position judgment module is used for acquiring position information reported by a customer terminal and determining whether the customer terminal is located in a target entity store or not according to the position information;
the customer information acquisition module is used for acquiring the customer information reported by the customer terminal if the customer terminal is positioned in a target entity store;
the calculation module is used for calculating the similarity between the customer information and each transacted customer information stored in the historical transaction record to obtain a plurality of similarity calculation results;
the detection module is used for determining a maximum similarity calculation result from the similarity calculation results and judging whether the maximum similarity calculation result is smaller than a preset threshold value or not;
the prediction module is used for inputting the customer information into a preset prediction model if the maximum similarity calculation result is smaller than a preset threshold value, so as to obtain a product recommendation result aiming at the customer information;
and the pushing module is used for acquiring the face image reported by the customer terminal and sending the face image and the product recommendation result to the sales terminal corresponding to the target entity store.
In addition, to achieve the above object, the present invention also provides a computer apparatus including a memory and a processor;
the memory is used for storing a computer program;
the processor is configured to execute the computer program and implement the method for shopping guide in the new retail mode as described above when executing the computer program.
Further, to achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a product shopping guide program in a new retail mode, the product shopping guide program in the new retail mode implementing the steps of the product shopping guide method in the new retail mode as described above when executed by a processor.
In the invention, when the customer is determined to be located in the target physical store, the commodity which the customer is interested in is determined according to the information of the customer, and the salesperson in the target physical store is informed, so that the salesperson can provide more accurate shopping guide service for the customer, and the sales efficiency and the success rate are improved.
Drawings
FIG. 1 is a schematic diagram of a computer device architecture of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a method for shopping guide in a new retail mode according to an embodiment of the present invention;
FIG. 3 is a schematic view of a scenario of an embodiment of a method for shopping guide in a new retail mode according to the present invention;
FIG. 4 is a schematic diagram illustrating a detailed process of FIG. 2 for determining that a customer terminal is located at a target brick-and-mortar store;
FIG. 5 is a schematic view of a detailed flow chart of the step of sending the face image and the product recommendation result to the sales terminal corresponding to the target brick-and-mortar store in FIG. 2;
fig. 6 is a functional block diagram of an embodiment of the merchandise shopping guide apparatus in the new retail mode of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The flow diagrams depicted in the figures are merely illustrative and do not necessarily include all of the elements and operations/steps, nor do they necessarily have to be performed in the order depicted. For example, some operations/steps may be decomposed, combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.
Fig. 1 is a schematic diagram of a computer device in a hardware operating environment according to an embodiment of the present invention.
The computer equipment in the embodiment of the invention can be a PC, and can also be terminal equipment with data processing capacity, such as a smart phone, a server and the like.
As shown in fig. 1, the computer apparatus may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the computer device architecture depicted in FIG. 1 is not intended to be limiting of computer devices and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and a network operation control application program.
In the computer device shown in fig. 1, the network interface 1004 is mainly used for connecting to a backend server and communicating with the backend server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and the processor 1001 may be configured to invoke the computer program stored in the memory 1005 and perform the following operations:
acquiring position information reported by a customer terminal, and determining whether the customer terminal is located in a target entity store or not according to the position information;
if the customer terminal is located in a target entity store, obtaining customer information reported by the customer terminal;
calculating the similarity between the customer information and each transacted customer information stored in a historical transaction record to obtain a plurality of similarity calculation results;
determining a maximum similarity calculation result from the plurality of similarity calculation results, and judging whether the maximum similarity calculation result is smaller than a preset threshold value;
if the maximum similarity calculation result is smaller than a preset threshold value, inputting the customer information into a preset prediction model to obtain a product recommendation result aiming at the customer information;
and acquiring the face image reported by the customer terminal, and sending the face image and the product recommendation result to a sales terminal corresponding to the target entity store.
In one embodiment, the processor 1001 may invoke a computer program stored in the memory 1005 to also perform the following operations:
acquiring position information reported by a customer terminal, and calculating the distance between the position information and the position information of each entity store to obtain a plurality of distance calculation results;
determining whether a target calculation result smaller than or equal to a preset distance exists in the plurality of distance calculation results;
and if one target calculation result which is smaller than or equal to a preset distance exists in the plurality of distance calculation results, determining that the customer terminal is located in a target entity store corresponding to the target calculation result.
In one embodiment, the processor 1001 may invoke a computer program stored in the memory 1005 to also perform the following operations:
acquiring information of each transacted customer stored in a historical transaction record;
calculating the similarity between the customer information and each transacted customer information through a cosine similarity formula to obtain a plurality of similarity calculation results, wherein the cosine similarity formula is as follows:
where n (a) indicates the number of types of information included in the customer information, n (B) indicates the number of types of information included in the transacted customer information, n (a ∩ B) indicates the number of the same information in the customer information and the transacted customer information, and K is the similarity calculation result.
In one embodiment, the processor 1001 may invoke a computer program stored in the memory 1005 to also perform the following operations:
acquiring a historical transaction record, wherein the historical transaction record comprises a product name and information of a transacted customer corresponding to the product name;
calculating a characteristic value corresponding to the information of the transacted customers, and substituting the characteristic value into a prediction function formula to obtain a plurality of prediction functions;
performing iterative solution on the plurality of prediction functions to obtain a prediction model corresponding to the product name;
the prediction function is formulated as follows:
wherein,
θiweighted value, x, of information i of transacted customeriA characteristic value theta corresponding to the information i of the transacted customerT＝[θ1,θ2,...,θn]，x＝[x1,x2,...,xn]And e is a natural constant.
In one embodiment, the processor 1001 may invoke a computer program stored in the memory 1005 to also perform the following operations:
if the maximum identification degree calculation result is smaller than a preset threshold value, calculating a characteristic value corresponding to the customer information, and respectively inputting the characteristic value into a prediction model corresponding to each product to obtain a plurality of output values;
and selecting a target output value which is greater than or equal to a preset probability value, and obtaining a product recommendation result aiming at the customer information based on the target output value.
In one embodiment, the processor 1001 may invoke a computer program stored in the memory 1005 to also perform the following operations:
acquiring a product list of the target physical store;
detecting whether the product recommendation result exists in the product list;
and if the product recommendation result exists in the product list, executing the step of acquiring the face image reported by the customer terminal and sending the face image and the product recommendation result to a sales terminal corresponding to the target entity store.
In one embodiment, the processor 1001 may invoke a computer program stored in the memory 1005 to also perform the following operations:
acquiring state information of each sales terminal corresponding to the target entity store;
determining a target sales terminal with an idle state based on the state information;
and sending the face image and the product recommendation result to the target sales terminal.
Referring to fig. 2, fig. 2 is a schematic flow chart of an embodiment of a method for shopping guide in the new retail mode of the present invention.
As shown in fig. 2, the method for shopping guide of goods in new retail mode includes:
and step S10, obtaining the position information reported by the customer terminal, and determining whether the customer terminal is located in the target entity store according to the position information.
In this embodiment, the message pushing method is applied to a computer device, and the computer device may specifically be a server. The server stores location information of a plurality of brick-and-mortar stores, for example, location information of brick-and-mortar stores 1 to 5, which are W1, W2, W3, W4, and W5, respectively. And respectively calculating the distances between the position information W reported by the customer terminal and W1-W5 to obtain five calculation results, namely L1-L5. Then, it is further determined whether or not there is a result of L1 to L5 being less than or equal to a preset distance, where the preset distance is set according to actual conditions, for example, to 50 meters. Because of the large physical distance between stores, generally speaking, there is only one result of less than or equal to 50 meters in L1-L5. For example, if L3 is less than or equal to 50 meters, it is determined that the customer terminal is currently at brick-and-mortar store 3.
And step S20, if the customer terminal is located in the target entity store, obtaining the customer information reported by the customer terminal.
In this embodiment, when a client installed in a client terminal is used by a client, the client needs to register in advance, in the registration process, some personal information, for example, information such as gender, age, occupation, income status, health status, family medical history, and the like (the type of the personal information may be expanded or reduced according to actual needs, and is not limited herein), and then the client terminal reports the personal information to a server, and the server stores the personal information in association with a terminal identifier of the client terminal. When it is detected that the customer terminal is located in the target brick and mortar store, the personal information (i.e., customer information) corresponding to the terminal identifier of the customer terminal can be acquired from the stored data.
Step S30, calculating the similarity between the customer information and each transacted customer information stored in the historical transaction record, and obtaining a plurality of similarity calculation results.
In an alternative embodiment, step S30 includes:
acquiring information of each transacted customer stored in a historical transaction record; calculating the similarity between the customer information and each transacted customer information through a cosine similarity formula to obtain a plurality of similarity calculation results, wherein the cosine similarity formula is as follows:
where n (a) indicates the number of types of information included in the customer information, n (B) indicates the number of types of information included in the transacted customer information, n (a ∩ B) indicates the number of the same information in the customer information and the transacted customer information, and K is the similarity calculation result.
In this embodiment, the transaction details are recorded into the historical transaction record each time a sales transaction is completed. The transaction details include: product name, customer information. As shown in table 1, table 1 is a data table storing historical transaction records.
TABLE 1
| Transacted customer information 1 | Product name 1 |
| Transacted customer information 2 | Product name 2 |
| Transacted customer information 3 | Product name 3 |
| Transacted customer information 4 | Product name 4 |
And respectively calculating the similarity between the customer information and the transacted customer information 1 to the transacted customer information 4 through a cosine similarity formula. The cosine similarity formula is as follows:
wherein n (a) indicates the type and quantity of information included in the customer information, and if the customer information includes gender, age, occupation, income status, health status, and family medical history, n (a) is 6, n (B) indicates the type and quantity of information included in the transacted customer information, and if the transacted customer information includes gender, age, occupation, income status, health status, and family medical history, n (B) is 6, n (a ∩ B) indicates the quantity of the same information in the customer information and the transacted customer information, and K indicates the similarity calculation result between the customer information and the transacted customer information.
Step S40, determining a maximum similarity calculation result from the plurality of similarity calculation results, and determining whether the maximum similarity calculation result is smaller than a preset threshold.
In this embodiment, if the transacted customer information includes the transacted customer information 1 to the transacted customer information 4, four similarity calculation results are calculated, which are K1 to K4, and the largest value is selected from K1 to K4, for example, the largest calculation result is K2, and it is determined whether K2 is greater than or equal to a preset threshold. The setting of the preset threshold is set according to actual needs, for example, to 0.8.
In this embodiment, if the K2 is greater than or equal to the preset threshold, it indicates that the personal information of the customer is very similar to that of the traded customer 2, and the commodity purchased by the traded customer 2 is likely to be the commodity of interest of the customer, so the commodity purchased by the traded customer 2 can be directly used as the commodity to be recommended. For example, the commodities purchased by transacted customer 2 include: and products 1 and 3, the product recommendation results are the products 1 and 3.
And step S50, if the maximum similarity calculation result is smaller than a preset threshold value, inputting the customer information into a preset prediction model to obtain a product recommendation result aiming at the customer information.
In this embodiment, if the maximum similarity calculation result is smaller than the preset threshold, it indicates that the current customer information is different from the stored transacted customer information, and the product interested in the current customer information cannot be predicted according to the historical transaction record, and then the customer information is input into the prediction model, so as to obtain a product recommendation result for the customer information.
In this embodiment, the prediction model may be a plurality of models, for example, an LR model whose prediction information is product 1, an LR model whose prediction information is product 2, and an LR model whose prediction information is product 3. The feature values corresponding to the customer information are respectively input into the three prediction models, and three probability values P1, P2 and P3 are obtained. A target output value greater than a preset probability value (e.g., 0.85) is selected from P1, P2, and P3. If the P2 and the P3 are greater than or equal to the preset probability values, the P2 and the P3 are target output values, which indicate that the products 2 and 3 conform to the requirements of the customers to which the current customer information belongs, and therefore, the determined product recommendation results are the products 2 and 3.
And step S60, obtaining the face image reported by the customer terminal, and sending the face image and the product recommendation result to the sales terminal corresponding to the target entity store.
In this embodiment, after the product recommendation result is obtained, the face image reported by the customer terminal is further obtained, and the face image and the product recommendation result are sent to the sales terminal corresponding to the target entity store, so that the salesperson belonging to the sales terminal finds the customer according to the face image, and conducts shopping guide for the customer according to the product recommendation result. In this embodiment, when a customer registers to use a customer terminal, the customer is required to upload a face image, the customer terminal reports the face image to the server after receiving the face image, and the server stores the face image and a terminal identifier of the customer terminal in an associated manner.
Referring to fig. 3, fig. 3 is a schematic view of a scenario of an embodiment of a method for shopping guide in a new retail mode according to the present invention. As shown in fig. 3, the server establishes communication connections with the client terminal and the sales terminal, respectively, determines a target entity store where the server is located according to the position information reported by the client terminal, and determines a product recommendation result according to the client information corresponding to the client terminal, so as to push the product recommendation result and the face image reported by the client terminal to the sales terminal corresponding to the target entity store.
In the embodiment, position information reported by a customer terminal is obtained, and whether the customer terminal is located in a target entity store is determined according to the position information; if the customer terminal is located in a target entity store, obtaining customer information reported by the customer terminal; calculating the similarity between the customer information and each transacted customer information stored in a historical transaction record to obtain a plurality of similarity calculation results; determining a maximum similarity calculation result from the plurality of similarity calculation results, and judging whether the maximum similarity calculation result is smaller than a preset threshold value; if the maximum similarity calculation result is smaller than a preset threshold value, inputting the customer information into a preset prediction model to obtain a product recommendation result aiming at the customer information; and acquiring the face image reported by the customer terminal, and sending the face image and the product recommendation result to a sales terminal corresponding to the target entity store. Through this embodiment, confirm the commodity that the customer is interested in according to customer's information to inform sales force, make sales force can provide more accurate shopping guide service for customer, improved sales efficiency and success rate.
Referring to fig. 4, fig. 4 is a schematic diagram illustrating a detailed flow of fig. 2 for determining that the customer terminal is located at the target brick-and-mortar store. In an embodiment of the method for shopping guide in the new retail mode of the present invention, step S10 includes:
step S101, position information reported by a customer terminal is obtained, the distance between the position information and the position information of each entity store is calculated, and a plurality of distance calculation results are obtained.
The earth is a nearly standard ellipsoid with an equatorial radius of 6378.140 km, a polar radius of 6356.755 km, and an average radius of 6371.004 km. If we assume that the earth is a perfect sphere, its radius is the average radius of the earth, denoted as R. If the meridian of 0 degree is taken as a reference, the earth surface distance between any two points on the earth surface can be calculated according to the longitude and latitude of the two points (the error of the earth surface topography on the calculation is ignored, and is only a theoretical estimation value). And if the Longitude and Latitude of the first point A is (lonA, LatA), the Longitude and Latitude of the second point B is (LonB, LatB), according to the reference of 0-degree Longitude, the east Longitude takes a positive Longitude value (Longitude), the west Longitude takes a negative Longitude value (Longitude), the north Latitude takes a 90-Latitude value (90-Latitude), and the south Latitude takes a 90+ Latitude value (90+ Latitude), the two processed points are (MLonA, MLataA) and (MLonB, MLatB). Then from the trigonometric derivation, the following formula can be derived for calculating the distance between two points:
equation 1:
C＝sin(MLatA)*sin(MLatB)*cos(MLonA-MLonB)+cos(MLatA)*cos(MLatB)
equation 2:
distance R arccos (c) Pi/180
In this embodiment, the location information (the location information reported by the client terminal and the location information of each physical store pre-stored by the server) may be latitude and longitude, and the distance between the client terminal and each physical store may be obtained according to the above formula 1 and formula 2.
And step S102. And determining whether a target calculation result which is smaller than or equal to a preset distance exists in the plurality of distance calculation results.
In this embodiment, a plurality of distance calculation results may be obtained according to step S101, and then it is further determined whether there is a target calculation result smaller than or equal to the preset distance in the plurality of distance calculation results. Wherein the preset distance is set according to actual needs, for example, set to 50 meters.
Step S103, if one target calculation result which is smaller than or equal to a preset distance exists in the plurality of distance calculation results, it is determined that the customer terminal is located in a target entity store corresponding to the target calculation result.
In this embodiment, if the calculation result of the distance between the customer terminal and the brick-and-mortar store 7 is less than or equal to the preset distance, it is determined that the customer terminal is located in the brick-and-mortar store 7.
In an embodiment, in an embodiment of the method for guiding shopping for goods in the new retail mode, before step S10, the method further includes:
acquiring a historical transaction record, wherein the historical transaction record comprises a product name and information of a transacted customer corresponding to the product name; calculating a characteristic value corresponding to the information of the transacted customers, and substituting the characteristic value into a prediction function formula to obtain a plurality of prediction functions; performing iterative solution on the plurality of prediction functions to obtain a prediction model corresponding to the product name; the prediction function is formulated as follows:
wherein,
θiweighted value, x, of information i of transacted customeriA characteristic value theta corresponding to the information i of the transacted customerT＝[θ1,θ2,...,θn]，x＝[x1,x2,...,xn]And e is a natural constant.
In this embodiment, if customer 1, customer 2, customer 3 … …, and customer 10 purchased product 1, the transacted customer information corresponding to product 1 in the historical transaction record includes the customer information of customer 1 to customer 10; if customer 11, customer 12, customer 13 … … purchased product 2, then the transacted customer information for product 2 in the historical transaction record includes customer information from customer 11 to customer 20; when customer 21, customer 22, customer 23 … … and customer 30 have purchased product 3, the transacted customer information for product 3 in the historical transaction record includes the customer information from customer 21 to customer 30. Wherein the customer information includes: gender, age, income, personal health (the type of information can be expanded or reduced according to actual needs). Calculating a characteristic value corresponding to the customer information according to a preset characteristic value conversion rule, for example: if the sex is male, 1 is selected, 0 is selected for female, 1 is selected when the age is greater than or equal to 50, 0 is selected when the age is less than 50, 1 is selected when the income is greater than or equal to 20 ten thousand, 0 is selected when the income is less than 20 ten thousand, 1 is selected when there is a disease, and 0 is selected when there is no disease. Based on the conversion rule, the characteristic value corresponding to the information of the transacted customer can be obtained. And substituting the characteristic value into a formula:
wherein,
θiweighted value, x, of information i of transacted customeriA characteristic value theta corresponding to the information i of the transacted customerT＝[θ1,θ2,...,θn]，x＝[x1,x2,...,xn]And e is a natural constant.
In this embodiment, the nature of the prediction model is a specific algorithm. The specific algorithm may include: a logistic regression algorithm or a neural network algorithm. The following description is given by using a logistic regression algorithm, and the prediction model is an lr (logistic regression) model. Logistic regression is a classification method, mainly used to solve two classification problems (i.e. only two kinds of outputs are output, and each represents two classifications), and the Logistic regression algorithm uses Logistic function (or Sigmoid function), the curve form of the function is S-shaped curve, and the function form is:
for the case of linear boundaries, the boundary form is as follows:
constructing a prediction function using equation (1) and equation (2):
θiweighted value, x, of information i of transacted customeriA characteristic value theta corresponding to the information i of the transacted customerT＝[θ1,θ2,...,θn]，x＝[x1,x2,...,xn]And e is a natural constant.
In this embodiment, the prediction model includes: the prediction information is the LR model for product 1, the prediction information is the LR model for product 2, and the prediction information is the LR model for product 3.
The way to obtain the LR model with the prediction information of product 1 is: substituting the characteristic value corresponding to the customer information of the customer 1 as sample data 1 into the formula (3) to obtain a prediction function 1; similarly, the feature value corresponding to the customer information of the customer 2 is substituted into the above formula (3) as sample data 2 to obtain a prediction function2, the feature values corresponding to the customer information of the customers 3 to 10 are substituted into the above formula (3) as sample data 3 to sample data 10 to obtain prediction functions 3 to 10, and since the prediction functions 1 to 10 are obtained from the information of the customer who purchased the product 1, the output values of the prediction functions 1 to 10 are all equal to each other, so that a plurality of solving functions are constructed, that is, the prediction function 1 is the prediction function 2, the prediction function 1 is the prediction function 3, the prediction function 1 is the prediction function 4 … …, and then the solving functions are iteratively solved to calculate θT＝[θ1,θ2,...,θn]Will calculate the obtained thetaTSubstituting the prediction function into the LR model with the prediction information of the product 1, wherein the output value of the LR model is 0-1, and the larger the output value is, the larger the probability that the customer corresponding to the input customer information is willing to buy the product 1 is.
Similarly, the way of obtaining the LR model with the prediction information of product 2 is: substituting the eigenvalue corresponding to the customer information of the customer 11 as sample data 11 into the above formula (3), substituting the eigenvalue corresponding to the customer information of the customer 12 as sample data 12 into the above formula (3), substituting the eigenvalue corresponding to the customer information of the customers 13 to 20 as sample data 13 to 20 respectively into the above formula (3) to obtain 10 prediction functions, then constructing a plurality of solving functions according to the 10 prediction functions, then iteratively solving the plurality of solving functions, and calculating θT＝[θ1,θ2,...,θn]Will calculate the obtained thetaTSubstituting the prediction function into the LR model with the prediction information of the product 2, wherein the output value of the LR model is 0-1, and the larger the output value is, the larger the probability that the customer corresponding to the input customer information is willing to buy the product 2 is.
According to the training process, the LR model with the prediction information of product 3 and the LR model … … with the prediction information of product 4 as the LR model with the prediction information of product n can be obtained.
In the process of quantifying (converting) the information (sex, age, income, and personal health) of the transacted customer into a numerical value, the information (sex, age, income, and personal health) of the transacted customer may be converted into an appropriate numerical value according to actual needs by using a reasonable rule. In addition, after the LR model is obtained, when the LR model is used, the characteristic value corresponding to the current customer information is input into the LR model, the output of the LR model is a probability value, the value range of the probability value is 0-1, and the larger the probability value is, the products corresponding to the LR model are more suitable for the requirements of customers to which the customer information belongs.
Further, on the basis of the present embodiment, step S50 includes:
if the maximum identification degree calculation result is smaller than a preset threshold value, calculating a characteristic value corresponding to the customer information, and respectively inputting the characteristic value into a prediction model corresponding to each product to obtain a plurality of output values; and selecting a target output value which is greater than or equal to a preset probability value, and obtaining a product recommendation result aiming at the customer information based on the target output value.
In the present embodiment, as described above, a plurality of training models, for example, the LR model whose prediction information is product 1, the LR model whose prediction information is product 2, and the LR model whose prediction information is product 3, are obtained by training. The feature values corresponding to the customer information are respectively input into the three prediction models, and three probability values P1, P2 and P3 are obtained. A target output value greater than a preset probability value (e.g., 0.85) is selected from P1, P2, and P3. If the P2 and the P3 are greater than or equal to the preset probability values, the P2 and the P3 are target output values, which indicate that the products 2 and 3 conform to the requirements of the customers to which the current customer information belongs, and therefore, the determined product recommendation results are the products 2 and 3.
Further, in an embodiment of the method for guiding shopping for commodities in the new retail mode, before step S60, the method further includes:
acquiring a product list of the target physical store; detecting whether the product recommendation result exists in the product list; and if the product recommendation result exists in the product list, executing the step of acquiring the face image reported by the customer terminal and sending the face image and the product recommendation result to a sales terminal corresponding to the target entity store.
In this embodiment, when the product recommendation results are obtained based on step S50, for example, the product recommendation results are product 2 and product 3, it is necessary to determine whether product 2 and product 3 are sold in the target physical store, that is, it is detected whether the product recommendation result exists in the product list of the target physical store, and step S60 is executed only when the product recommendation result exists in the product list, so that waste of selling resources is avoided.
In this embodiment, if the product recommendation result (for example, the product 2 and the product 3) does not exist in the product list, the out-of-stock reminder may be sent to the customer terminal to remind the customer that the product 2 and the product 3 are in the out-of-stock state in the target brick-and-mortar store.
Referring to fig. 5, fig. 5 is a schematic view of a detailed flow of the step of sending the face image and the product recommendation result to the sales terminal corresponding to the target brick-and-mortar store in fig. 2.
In this embodiment, the step of sending the face image and the product recommendation result to the sales terminal corresponding to the target brick-and-mortar store includes:
step S601, obtaining status information of each sales terminal corresponding to the target brick-and-mortar store.
In this embodiment, each salesperson configures one sales terminal, and when the salesperson works, the status of the sales terminal is adjusted to be busy, and when the salesperson is idle, the status of the sales terminal is adjusted to be idle, and the status of the sales terminal is reported to the server.
Step S602, the target sales terminal with the idle state is determined based on the state information.
In this embodiment, according to the state information reported by each sales terminal, a target sales terminal whose state is idle can be determined from all sales terminals.
And step S603, sending the face image and the product recommendation result to the target sales terminal.
In the embodiment, when the face image and the product recommendation result are sent, the target sales terminal in the idle state is determined first, and the face image and the product recommendation result are sent to the target sales terminal in the idle state, so that the salespersons belonging to the target sales terminal can serve the customers immediately, and reasonable management of sales resources is realized.
Referring to fig. 6, fig. 6 is a functional module schematic diagram of an embodiment of the merchandise shopping guide device in the new retail mode of the present invention.
In this embodiment, the commodity shopping guide device in the new retail mode includes:
the system comprises a position judgment module 10, a position determination module and a display module, wherein the position judgment module is used for acquiring position information reported by a customer terminal and determining whether the customer terminal is located in a target entity store or not according to the position information;
a customer information obtaining module 20, configured to obtain customer information reported by the customer terminal if the customer terminal is located in a target physical store;
a calculating module 30, configured to calculate similarities between the customer information and each transacted customer information stored in a historical transaction record, and obtain multiple similarity calculation results;
the detection module 40 is configured to determine a maximum similarity calculation result from the multiple similarity calculation results, and determine whether the maximum similarity calculation result is smaller than a preset threshold;
the prediction module 50 is configured to, if the maximum similarity calculation result is smaller than a preset threshold, input the customer information into a preset prediction model to obtain a product recommendation result for the customer information;
the pushing module 60 is configured to obtain a face image reported by the customer terminal, and send the face image and the product recommendation result to a sales terminal corresponding to the target entity store.
The specific embodiment of the commodity shopping guide device in the new retail mode of the present invention is basically the same as each embodiment of the commodity shopping guide method in the new retail mode, and details are not repeated here.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, where an article shopping guide program in a new retail mode is stored on the computer-readable storage medium, and when executed by a processor, the article shopping guide program in the new retail mode implements the steps of the various embodiments of the article shopping guide method in the new retail mode.
The specific embodiment of the computer-readable storage medium of the present invention is substantially the same as the embodiments of the method for guiding shopping of commodities in the new retail mode, and details thereof are not repeated herein.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method for shopping guide of goods in a new retail mode, comprising:
acquiring position information reported by a customer terminal, and determining whether the customer terminal is located in a target entity store or not according to the position information;
if the customer terminal is located in a target entity store, obtaining customer information reported by the customer terminal;
calculating the similarity between the customer information and each transacted customer information stored in a historical transaction record to obtain a plurality of similarity calculation results;
determining a maximum similarity calculation result from the plurality of similarity calculation results, and judging whether the maximum similarity calculation result is smaller than a preset threshold value;
if the maximum similarity calculation result is smaller than a preset threshold value, inputting the customer information into a preset prediction model to obtain a product recommendation result aiming at the customer information;
and acquiring the face image reported by the customer terminal, and sending the face image and the product recommendation result to a sales terminal corresponding to the target entity store.
2. The method as claimed in claim 1, wherein the step of obtaining the location information reported by the customer terminal and determining whether the customer terminal is located in the target brick and mortar store according to the location information comprises:
acquiring position information reported by a customer terminal, and calculating the distance between the position information and the position information of each entity store to obtain a plurality of distance calculation results;
determining whether a target calculation result smaller than or equal to a preset distance exists in the plurality of distance calculation results;
and if one target calculation result which is smaller than or equal to a preset distance exists in the plurality of distance calculation results, determining that the customer terminal is located in a target entity store corresponding to the target calculation result.
3. The method for shopping guide of commodities in new retail mode according to claim 1, wherein said step of calculating the similarity between said customer information and each of the transacted customer information stored in the historical transaction records to obtain a plurality of similarity calculation results comprises:
acquiring information of each transacted customer stored in a historical transaction record;
calculating the similarity between the customer information and each transacted customer information through a cosine similarity formula to obtain a plurality of similarity calculation results, wherein the cosine similarity formula is as follows:
where n (a) indicates the number of types of information included in the customer information, n (B) indicates the number of types of information included in the transacted customer information, n (a ∩ B) indicates the number of the same information in the customer information and the transacted customer information, and K is the similarity calculation result.
4. The method for guiding shopping for commodities in a new retail mode as claimed in claim 1, wherein before the step of obtaining the location information reported by the customer terminal and determining whether the customer terminal is located in the target physical store according to the location information, the method further comprises:
acquiring a historical transaction record, wherein the historical transaction record comprises a product name and information of a transacted customer corresponding to the product name;
calculating a characteristic value corresponding to the information of the transacted customers, and substituting the characteristic value into a prediction function formula to obtain a plurality of prediction functions;
performing iterative solution on the plurality of prediction functions to obtain a prediction model corresponding to the product name;
the prediction function is formulated as follows:
wherein,
θiweighted value, x, of information i of transacted customeriA characteristic value theta corresponding to the information i of the transacted customerT＝[θ1,θ2,...,θn]，x＝[x1,x2,...,xn]And e is a natural constant.
5. The method of claim 4, wherein the step of inputting the customer information into a preset predictive model to obtain a product recommendation for the customer information if the maximum degree of identity calculation result is less than a preset threshold comprises:
if the maximum identification degree calculation result is smaller than a preset threshold value, calculating a characteristic value corresponding to the customer information, and respectively inputting the characteristic value into a prediction model corresponding to each product to obtain a plurality of output values;
and selecting a target output value which is greater than or equal to a preset probability value, and obtaining a product recommendation result aiming at the customer information based on the target output value.
6. The method for guiding shopping for commodities in a new retail mode as claimed in claim 1, wherein before the step of obtaining the facial image reported by the customer terminal and sending the facial image and the product recommendation result to the sales terminal corresponding to the target brick-and-mortar store, the method further comprises:
acquiring a product list of the target physical store;
detecting whether the product recommendation result exists in the product list;
and if the product recommendation result exists in the product list, executing the step of acquiring the face image reported by the customer terminal and sending the face image and the product recommendation result to a sales terminal corresponding to the target entity store.
7. The method for guiding shopping of commodities in a new retail mode as claimed in any one of claims 1 to 6, wherein the step of sending the face image and the product recommendation result to the sales terminal corresponding to the target brick-and-mortar store comprises:
acquiring state information of each sales terminal corresponding to the target entity store;
determining a target sales terminal with an idle state based on the state information;
and sending the face image and the product recommendation result to the target sales terminal.
8. A merchandise shopping guide apparatus in a new retail mode, the merchandise shopping guide apparatus in the new retail mode comprising:
the system comprises a position judgment module, a position judgment module and a display module, wherein the position judgment module is used for acquiring position information reported by a customer terminal and determining whether the customer terminal is located in a target entity store or not according to the position information;
the customer information acquisition module is used for acquiring the customer information reported by the customer terminal if the customer terminal is positioned in a target entity store;
the calculation module is used for calculating the similarity between the customer information and each transacted customer information stored in the historical transaction record to obtain a plurality of similarity calculation results;
the detection module is used for determining a maximum similarity calculation result from the similarity calculation results and judging whether the maximum similarity calculation result is smaller than a preset threshold value or not;
the prediction module is used for inputting the customer information into a preset prediction model if the maximum similarity calculation result is smaller than a preset threshold value, so as to obtain a product recommendation result aiming at the customer information;
and the pushing module is used for acquiring the face image reported by the customer terminal and sending the face image and the product recommendation result to the sales terminal corresponding to the target entity store.
9. A computer device, wherein the computer device comprises a memory and a processor;
the memory is used for storing a computer program;
the processor for executing the computer program and when executing the computer program implementing the method of shopping guide for goods in new retail mode as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a program for shopping guide of goods in a new retail mode, which program, when being executed by a processor, carries out the steps of the method for shopping guide of goods in a new retail mode as claimed in any one of claims 1 to 7.
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