Store operation method and device based on data analysis, equipment, storage medium and product

Abstract

The invention discloses a store operation method and device based on data analysis, equipment, a storage medium and a product, and relates to the technical field of data analysis, and the method comprises the steps: obtaining the current passenger flow data and current sales data of a current store; performing data preprocessing on the current passenger flow data and the current sales data to obtain processed current passenger flow data and processed current sales data; inputting the processed current passenger flow data into a value prediction model to obtain a predicted comprehensive value; and carrying out operation analysis on the current store based on the processed current sales data and the predicted comprehensive value. According to the invention, operation analysis is carried out on the current store based on the predicted comprehensive value output by the processed current sales data and the processed current passenger flow data in the value prediction model, so that store operation is objectively and efficiently realized without relying on complex market research results and subjective personnel experience; and the operation effect of the store is improved.

Description

Technical Field

[0001] The present application relates to the field of data analysis technology, and in particular to a store operation method, device, equipment, storage medium and product based on data analysis. Background Art

[0002] Store operation refers to a series of activities for the comprehensive management and operation of physical stores or retail stores. Existing store operation methods often rely on complex market research results and subjective personnel experience, which leads to low execution efficiency of existing store operation methods and the inability to guarantee the operation effect. Therefore, the industry is in urgent need of an objective and efficient store operation method. Summary of the invention

[0003] The main purpose of this application is to provide a store operation method, device, equipment, storage medium and product based on data analysis, aiming to solve the technical problems that the execution efficiency of existing store operation methods is low and the operation effect cannot be guaranteed.

[0004] To achieve the above objectives, the present application provides a store operation method based on data analysis, the method comprising the following steps:

[0005] Obtain the current customer flow data and current sales data of the current store, the current customer flow data includes the customer flow in front of the store, the customer flow into the store and the collection time, and the current sales data includes the total sales, the total number of sales and the sales date;

[0006] Performing data preprocessing on the current passenger flow data and the current sales data to obtain processed current passenger flow data and processed current sales data;

[0007] Inputting the processed current customer flow data into a value prediction model to obtain a predicted comprehensive value, wherein the predicted comprehensive value is used to represent the predicted economic value and predicted social value of the current store;

[0008] An operation analysis of the current store is performed based on the processed current sales data and the predicted comprehensive value.

[0009] In one embodiment, the step of obtaining the current customer flow data and current sales data of the current store includes:

[0010] Collecting the behavior information of the current customer to construct a historical cumulative heat map corresponding to the current customer, and obtaining the current customer flow data of the current store according to the historical cumulative heat map;

[0011] A log retrieval request including identity authentication information is sent to the current store, and after identity authentication is passed, the store log is retrieved from the current store, and current sales data is obtained from the store log, wherein the identity authentication information includes a digital signature and a digital certificate.

[0012] In one embodiment, the step of preprocessing the current passenger flow data and the current sales data to obtain processed current passenger flow data and processed current sales data includes:

[0013] Performing data cleaning, data screening and data profiling on the current passenger flow data to obtain processed current passenger flow data;

[0014] The current sales data is subjected to data cleaning processing, data screening processing and data fuzzification processing to obtain processed current sales data.

[0015] In one embodiment, the step of performing operation analysis on the current store based on the processed current sales data and the predicted comprehensive value includes:

[0016] Calculating the actual comprehensive value corresponding to the current store based on the processed current sales data, and comparing the actual comprehensive value with the predicted comprehensive value;

[0017] If the actual comprehensive value is greater than or equal to the predicted comprehensive value, it is determined that the current store operation is normal and the existing operation strategy is maintained;

[0018] If the actual comprehensive value is less than the predicted comprehensive value, the current store operation is determined to be abnormal and the management personnel are notified to update the existing operation strategy.

[0019] In one embodiment, the step of calculating the actual comprehensive value corresponding to the current store based on the processed current sales data includes:

[0020] Determine a first weight corresponding to the total sales amount, a second weight corresponding to the total number of sales, and a third weight corresponding to the sales date in the processed current sales data;

[0021] The actual comprehensive value corresponding to the current store is calculated based on the processed current sales data, the first weight, the second weight and the third weight.

[0022] In addition, to achieve the above purpose, the present application also provides a method for constructing a value prediction model, the method comprising the following steps:

[0023] Obtain sample customer flow data and sample sales data of sample stores, wherein the sample customer flow data includes sample store front customer flow, sample store entrance customer flow and sample collection time, and the sample sales data includes sample total sales amount, total number of sample sales and sample sales date;

[0024] Performing data preprocessing on the sample passenger flow data and the sample sales data to obtain processed sample passenger flow data and processed sample sales data;

[0025] Constructing a model training set, a model validation set, and a model test set according to the processed sample passenger flow data and the processed sample sales data;

[0026] The initial model is trained and parameterized based on the model training set, the model validation set, and the model test set to obtain a value prediction model, and the value prediction model is used to predict the comprehensive value of the store.

[0027] In addition, to achieve the above purpose, the present application also proposes a store operation device based on data analysis, and the store operation device based on data analysis includes:

[0028] The data acquisition module is used to acquire the current customer flow data and current sales data of the current store. The current customer flow data includes the customer flow in front of the store, the customer flow into the store and the collection time. The current sales data includes the total sales, the total number of sales and the sales date.

[0029] A data processing module, used for performing data preprocessing on the current passenger flow data and the current sales data to obtain processed current passenger flow data and processed current sales data;

[0030] A value prediction module, used for inputting the processed current customer flow data into a value prediction model to obtain a predicted comprehensive value, wherein the predicted comprehensive value is used to represent the predicted economic value and predicted social value of the current store;

[0031] An operation analysis module is used to perform operation analysis on the current store based on the processed current sales data and the predicted comprehensive value.

[0032] In addition, to achieve the above-mentioned objectives, the present application also proposes a store operation device based on data analysis, which includes: a memory, a processor, and a store operation program based on data analysis stored on the memory and executable on the processor, and the store operation program based on data analysis is configured to implement the steps of the store operation method based on data analysis as described above.

[0033] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a store operation program based on data analysis is stored on the storage medium. When the store operation program based on data analysis is executed by a processor, the steps of the store operation method based on data analysis as described above are implemented.

[0034] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer program product, which includes a store operation program based on data analysis, and when the store operation program based on data analysis is executed by a processor, it implements the steps of the store operation method based on data analysis as described above.

[0035] This application obtains the current customer flow data and current sales data of the current store, the current customer flow data includes the customer flow flow in front of the door, the customer flow flow into the store and the collection time, and the current sales data includes the total sales, the total number of sales and the sales date; pre-processes the current customer flow data and the current sales data to obtain the processed current customer flow data and the processed current sales data; inputs the processed current customer flow data into the value prediction model to obtain the predicted comprehensive value, which is used to represent the predicted economic value and predicted social value of the current store; and performs an operation analysis on the current store based on the processed current sales data and the predicted comprehensive value. Compared with the traditional store operation method, the above method of this application obtains the current customer flow data and the current sales data of the current store and performs data pre-processing, and then performs an operation analysis on the current store based on the predicted comprehensive value output by the processed current sales data and the processed current customer flow data in the value prediction model, thereby objectively and efficiently realizing store operation without relying on complex market research results and subjective personnel experience, thereby improving the store operation effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic diagram of the structure of a store operation device based on data analysis in the hardware operating environment involved in the embodiment of the present application;

[0037] Figure 2 This is a flow chart of the first embodiment of the store operation method based on data analysis of the present application;

[0038] Figure 3 This is a flow chart of a second embodiment of the store operation method based on data analysis of the present application;

[0039] Figure 4 This is a flow chart of the third embodiment of the store operation method based on data analysis of the present application;

[0040] Figure 5This is a structural block diagram of the first embodiment of the store operation device based on data analysis of the present application.

[0041] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0042] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0043] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of a store operation device based on data analysis in the hardware operating environment involved in the embodiment of the present application.

[0044] like Figure 1 As shown, the store operation device based on data analysis may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and 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 and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (Wireless-Fidelity, Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM), or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk storage. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0045] Those skilled in the art will understand that Figure 1 The structure shown in does not constitute a limitation on the store operation equipment based on data analysis, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0046] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a store operation program based on data analysis.

[0047] exist Figure 1In the store operation device based on data analysis shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the store operation device based on data analysis of the present application can be set in the store operation device based on data analysis, and the store operation device based on data analysis calls the store operation program based on data analysis stored in the memory 1005 through the processor 1001, and executes the store operation method based on data analysis provided in the embodiment of the present application.

[0048] The present application embodiment provides a store operation method based on data analysis, referring to Figure 2 , Figure 2 This is a flow chart of the first embodiment of the store operation method based on data analysis of the present application.

[0049] In this embodiment, the store operation method based on data analysis includes the following steps:

[0050] Step S10: Obtain the current customer flow data and current sales data of the current store, wherein the current customer flow data includes the customer flow in front of the door, the customer flow into the store and the collection time, and the current sales data includes the total sales amount, the total number of sales and the sales date.

[0051] It should be noted that the execution subject of the method of this embodiment can be a terminal device with data processing, data analysis and program running functions, such as a smart phone, a computer, etc., or an electronic device with the same or similar functions, such as the above-mentioned store operation equipment based on data analysis. The following takes the store operation equipment based on data analysis (hereinafter referred to as the operation equipment) as an example to illustrate this embodiment and the following embodiments.

[0052] In a specific implementation, the current customer flow data and current sales data of the current store can be obtained from the server cloud.

[0053] Step S20: performing data preprocessing on the current passenger flow data and the current sales data to obtain processed current passenger flow data and processed current sales data.

[0054] It should be understood that the data preprocessing projects for the above-mentioned current passenger flow data and the above-mentioned current sales data may include but are not limited to data cleaning, data screening, data profiling, data fuzzification, data conversion, data aggregation, etc.

[0055] Step S30: input the processed current customer flow data into a value prediction model to obtain a predicted comprehensive value, where the predicted comprehensive value is used to represent the predicted economic value and predicted social value of the current store.

[0056] It should be noted that the above-mentioned value prediction model can be a model for comprehensive value of a store, which can be obtained by training and optimizing the initial model. In particular, the above-mentioned initial model can be a linear regression model, or a logistic regression model, a decision tree model, a random forest model, or other models, which are not limited in this embodiment.

[0057] It should be understood that the above-mentioned predicted economic value may refer to the predicted economic value that the current store can generate in the future operation process, and the above-mentioned predicted social value may refer to the predicted social value that the current store can generate in the future operation process.

[0058] Step S40: performing an operation analysis on the current store based on the processed current sales data and the predicted comprehensive value.

[0059] In a specific implementation, the processed current sales data can be matched with the predicted comprehensive value, and the operation analysis of the current store can be realized based on whether the processed current sales data can match the predicted comprehensive value.

[0060] This embodiment obtains the current customer flow data and current sales data of the current store, the current customer flow data includes the customer flow flow in front of the door, the customer flow flow into the store and the collection time, and the current sales data includes the total sales, the total number of sales and the sales date; pre-processes the current customer flow data and the current sales data to obtain the processed current customer flow data and the processed current sales data; inputs the processed current customer flow data into the value prediction model to obtain the predicted comprehensive value, which is used to represent the predicted economic value and predicted social value of the current store; and performs operation analysis on the current store based on the processed current sales data and the predicted comprehensive value. Compared with the traditional store operation method, the above method of this embodiment performs data pre-processing on the current customer flow data and the current sales data of the current store, and then performs operation analysis on the current store based on the predicted comprehensive value output by the processed current sales data and the processed current customer flow data in the value prediction model, thereby objectively and efficiently realizing store operation without relying on complex market research results and subjective personnel experience, thereby improving the store operation effect.

[0061] refer to Figure 3 , Figure 3 This is a flow chart of the second embodiment of the store operation method based on data analysis of the present application.

[0062] In a feasible implementation manner, the step S10 may include:

[0063] Step S101: collect the behavior information of the current customer to construct a historical cumulative heat map corresponding to the current customer, and obtain the current customer flow data of the current store according to the historical cumulative heat map.

[0064] It is understandable that the above-mentioned current customer behavior information may include the current user's browsing behavior, stay behavior, transaction behavior, etc.

[0065] In the specific implementation, the historical cumulative heat map corresponding to the current customer can be constructed based on the following steps. The first step is to sort the behavior information of the current customer in chronological order to ensure that the customer's behavior data can be accumulated according to the timeline. The second step is to perform historical accumulation of the behavior data of each customer to form a corresponding historical behavior data set. The third step is to aggregate the historical behavior data set according to dimensions such as time, location, and product type to obtain aggregated data. The fourth step is to input the aggregated data into a data visualization tool (such as Tableau, Power BI, etc.) to output the historical cumulative heat map corresponding to the current customer.

[0066] Step S102: Send a log retrieval request containing identity authentication information to the current store, retrieve the store log from the current store after identity authentication, and obtain current sales data from the store log, wherein the identity authentication information includes a digital signature and a digital certificate.

[0067] It should be understood that in order to prevent malicious users from stealing or tampering with the above-mentioned store logs, the identity authentication information of the operation device needs to be verified before the operation device retrieves the store logs.

[0068] In a feasible implementation manner, the step S20 may include:

[0069] Step S201: performing data cleaning processing, data screening processing and data profiling processing on the current passenger flow data to obtain processed current passenger flow data.

[0070] Step S202: performing data cleaning processing, data screening processing and data fuzzification processing on the current sales data to obtain processed current sales data.

[0071] In a feasible implementation manner, the step S40 may include:

[0072] Step S401: Calculate the actual comprehensive value corresponding to the current store based on the processed current sales data, and compare the actual comprehensive value with the predicted comprehensive value.

[0073] It should be noted that the above-mentioned actual comprehensive value may refer to the actual economic value and actual social value corresponding to the current store. The above-mentioned actual economic value may refer to the actual value of the economic value generated by the current store during its operation, and the above-mentioned actual social value may refer to the actual value of the social value generated by the current store during its future operation.

[0074] Step S402: If the actual comprehensive value is greater than or equal to the predicted comprehensive value, it is determined that the current store operation is normal and the existing operation strategy is maintained.

[0075] Step S403: If the actual comprehensive value is less than the predicted comprehensive value, the current store operation is determined to be abnormal and the management personnel are notified to update the existing operation strategy.

[0076] In a feasible implementation manner, the step S401 may include:

[0077] Step S4011: Determine a first weight corresponding to the total sales amount, a second weight corresponding to the total number of sales, and a third weight corresponding to the sales date in the processed current sales data.

[0078] In a specific implementation, the first weight, the second weight and the third weight can be set by a management personnel according to a specific usage scenario, and the default values ​​are all 1.

[0079] Step S4012: Calculate the actual comprehensive value corresponding to the current store based on the processed current sales data, the first weight, the second weight and the third weight.

[0080] This embodiment collects the behavior information of the current customer to construct a historical cumulative heat map corresponding to the current customer, and obtains the current customer flow data of the current store based on the historical cumulative heat map; sends a log retrieval request containing identity authentication information to the current store, retrieves the store log from the current store after passing the identity authentication, and obtains the current sales data from the store log, wherein the identity authentication information includes a digital signature and a digital certificate; performs data cleaning, data screening and data profiling on the current customer flow data to obtain the processed current customer flow data; performs data cleaning, data screening and data fuzzification on the current sales data to obtain the processed current previous sales data; determine the first weight corresponding to the total sales amount, the second weight corresponding to the total number of sales, and the third weight corresponding to the sales date in the processed current sales data; calculate the actual comprehensive value corresponding to the current store based on the processed current sales data, the first weight, the second weight, and the third weight, and compare the actual comprehensive value with the predicted comprehensive value; if the actual comprehensive value is greater than or equal to the predicted comprehensive value, determine that the current store is operating normally and maintain the existing operating strategy; if the actual comprehensive value is less than the predicted comprehensive value, determine that the current store is operating abnormally and notify the management personnel to update the existing operating strategy. Compared with the traditional store operation method, the above method of this embodiment compares the actual comprehensive value corresponding to the current store with the predicted comprehensive value output by the value prediction model, and determines whether to update the operating strategy of the current store based on the comparison result, thereby realizing timely detection of the effectiveness of the store operation strategy.

[0081] refer to Figure 4 , Figure 4 This is a flow chart of the first embodiment of the method for constructing a value prediction model of the present application.

[0082] In this embodiment, the method for constructing the value prediction model includes the following steps:

[0083] Step S1: Obtain sample customer flow data and sample sales data of sample stores, wherein the sample customer flow data includes sample door-to-door customer flow, sample store-entry customer flow and sample collection time, and the sample sales data includes sample total sales amount, total number of sample sales and sample sales date.

[0084] Step S2: performing data preprocessing on the sample passenger flow data and the sample sales data to obtain processed sample passenger flow data and processed sample sales data.

[0085] It should be understood that the data preprocessing projects for the above-mentioned sample passenger flow data and the above-mentioned sample sales data may include but are not limited to data cleaning, data screening, data profiling, data fuzzification, data conversion, data aggregation, etc.

[0086] Step S3: construct a model training set, a model verification set and a model test set according to the processed sample passenger flow data and the processed sample sales data.

[0087] Step S4: Based on the model training set, the model verification set and the model test set, the initial model is trained and adjusted to obtain a value prediction model, and the value prediction model is used to predict the comprehensive value of the store.

[0088] It is understandable that the above initial model can be a linear regression model, or a logistic regression model, a decision tree model, a random forest model or other models, and this embodiment does not limit this.

[0089] This embodiment obtains sample customer flow data and sample sales data of sample stores, wherein the sample customer flow data includes sample front-door customer flow flow, sample in-store customer flow flow and sample collection time, and the sample sales data includes sample total sales, total number of sample sales and sample sales date; performs data preprocessing on the sample customer flow data and the sample sales data to obtain processed sample customer flow data and processed sample sales data; constructs a model training set, a model verification set and a model test set based on the processed sample customer flow data and the processed sample sales data; trains and adjusts the initial model based on the model training set, the model verification set and the model test set to obtain a value prediction model, which is used to predict the comprehensive value of the store. The above method of this embodiment trains and adjusts the initial model based on the model training set, the model verification set and the model test set constructed by the sample front-door customer flow flow, the sample in-store customer flow flow, the sample collection time, the sample sales data including the sample total sales, the total number of sample sales and the sample sales date, thereby improving the accuracy of the model in predicting the comprehensive value of the store.

[0090] In addition, an embodiment of the present application also proposes a storage medium, on which a store operation program based on data analysis is stored. When the store operation program based on data analysis is executed by a processor, the steps of the store operation method based on data analysis as described above are implemented.

[0091] Reference Figure 5 , Figure 5 This is a structural block diagram of the first embodiment of the store operation device based on data analysis of the present application.

[0092] like Figure 5As shown, the store operation device based on data analysis proposed in the embodiment of the present application includes:

[0093] The data acquisition module 501 is used to acquire the current customer flow data and current sales data of the current store, wherein the current customer flow data includes the customer flow in front of the store, the customer flow into the store and the collection time, and the current sales data includes the total sales amount, the total number of sales and the sales date;

[0094] A data processing module 502 is used to perform data preprocessing on the current passenger flow data and the current sales data to obtain processed current passenger flow data and processed current sales data;

[0095] The value prediction module 503 is used to input the processed current customer flow data into the value prediction model to obtain a predicted comprehensive value, where the predicted comprehensive value is used to represent the predicted economic value and predicted social value of the current store;

[0096] The operation analysis module 504 is used to perform operation analysis on the current store based on the processed current sales data and the predicted comprehensive value.

[0097] This embodiment obtains the current customer flow data and current sales data of the current store, the current customer flow data includes the customer flow flow in front of the door, the customer flow flow into the store and the collection time, and the current sales data includes the total sales, the total number of sales and the sales date; pre-processes the current customer flow data and the current sales data to obtain the processed current customer flow data and the processed current sales data; inputs the processed current customer flow data into the value prediction model to obtain the predicted comprehensive value, which is used to represent the predicted economic value and predicted social value of the current store; and performs operation analysis on the current store based on the processed current sales data and the predicted comprehensive value. Compared with the traditional store operation method, the above method of this embodiment performs data pre-processing on the current customer flow data and the current sales data of the current store, and then performs operation analysis on the current store based on the predicted comprehensive value output by the processed current sales data and the processed current customer flow data in the value prediction model, thereby objectively and efficiently realizing store operation without relying on complex market research results and subjective personnel experience, thereby improving the store operation effect.

[0098] Based on the above-mentioned first embodiment of the store operation device based on data analysis of the present application, a second embodiment of the store operation device based on data analysis of the present application is proposed.

[0099] In this embodiment, the data acquisition module 501 is also used to collect the behavior information of the current customer to construct a historical cumulative heat map corresponding to the current customer, and obtain the current customer flow data of the current store based on the historical cumulative heat map; send a log retrieval request containing identity authentication information to the current store, retrieve the store log from the current store after passing the identity authentication, and obtain the current sales data from the store log, wherein the identity authentication information includes a digital signature and a digital certificate.

[0100] Furthermore, the data processing module 502 is also used to perform data cleaning processing, data screening processing and data profiling processing on the current passenger flow data to obtain processed current passenger flow data; and to perform data cleaning processing, data screening processing and data fuzzification processing on the current sales data to obtain processed current sales data.

[0101] Furthermore, the operation analysis module 504 is also used to calculate the actual comprehensive value corresponding to the current store based on the processed current sales data, and compare the actual comprehensive value with the predicted comprehensive value; if the actual comprehensive value is greater than or equal to the predicted comprehensive value, it is determined that the current store operation is normal and the existing operation strategy is maintained; if the actual comprehensive value is less than the predicted comprehensive value, it is determined that the current store operation is abnormal and the management personnel are notified to update the existing operation strategy.

[0102] Furthermore, the operation analysis module 504 is also used to determine a first weight corresponding to the total sales amount, a second weight corresponding to the total number of sales, and a third weight corresponding to the sales date in the processed current sales data; and calculate the actual comprehensive value corresponding to the current store based on the processed current sales data, the first weight, the second weight, and the third weight.

[0103] Furthermore, the value prediction module 503 is also used to obtain sample customer flow data and sample sales data of sample stores, the sample customer flow data including sample door-to-door customer flow, sample store-entry customer flow and sample collection time, and the sample sales data including sample total sales amount, total number of sample sales and sample sales date; perform data preprocessing on the sample customer flow data and the sample sales data to obtain processed sample customer flow data and processed sample sales data; construct a model training set, a model verification set and a model test set based on the processed sample customer flow data and the processed sample sales data; train and adjust the initial model based on the model training set, the model verification set and the model test set to obtain a value prediction model, and the value prediction model is used to predict the comprehensive value of the store.

[0104] Other embodiments or specific implementation methods of the store operation device based on data analysis of the present application can refer to the above-mentioned method embodiments, which will not be repeated here.

[0105] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0106] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0107] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0108] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A store operation method based on data analysis, characterized in that: The method comprises the following steps: Obtain the current customer flow data and current sales data of the current store, the current customer flow data includes the customer flow in front of the store, the customer flow into the store and the collection time, and the current sales data includes the total sales, the total number of sales and the sales date; Performing data preprocessing on the current passenger flow data and the current sales data to obtain processed current passenger flow data and processed current sales data; Inputting the processed current customer flow data into a value prediction model to obtain a predicted comprehensive value, wherein the predicted comprehensive value is used to represent the predicted economic value and predicted social value of the current store; An operation analysis of the current store is performed based on the processed current sales data and the predicted comprehensive value.

2. The store operation method based on data analysis according to claim 1, characterized in that: The step of obtaining the current customer flow data and current sales data of the current store includes: Collecting the behavior information of the current customer to construct a historical cumulative heat map corresponding to the current customer, and obtaining the current customer flow data of the current store according to the historical cumulative heat map; A log retrieval request including identity authentication information is sent to the current store, and after identity authentication is passed, the store log is retrieved from the current store, and current sales data is obtained from the store log, wherein the identity authentication information includes a digital signature and a digital certificate.

3. The store operation method based on data analysis according to claim 1, characterized in that: The step of preprocessing the current passenger flow data and the current sales data to obtain processed current passenger flow data and processed current sales data includes: Performing data cleaning, data screening and data profiling on the current passenger flow data to obtain processed current passenger flow data; The current sales data is subjected to data cleaning processing, data screening processing and data fuzzification processing to obtain processed current sales data.

4. The store operation method based on data analysis according to claim 1, characterized in that: The step of performing operation analysis on the current store based on the processed current sales data and the predicted comprehensive value comprises: Calculating the actual comprehensive value corresponding to the current store based on the processed current sales data, and comparing the actual comprehensive value with the predicted comprehensive value; If the actual comprehensive value is greater than or equal to the predicted comprehensive value, it is determined that the current store operation is normal and the existing operation strategy is maintained; If the actual comprehensive value is less than the predicted comprehensive value, the current store operation is determined to be abnormal and the management personnel are notified to update the existing operation strategy.

5. The store operation method based on data analysis according to claim 4, characterized in that: The step of calculating the actual comprehensive value corresponding to the current store based on the processed current sales data includes: Determine a first weight corresponding to the total sales amount, a second weight corresponding to the total number of sales, and a third weight corresponding to the sales date in the processed current sales data; The actual comprehensive value corresponding to the current store is calculated based on the processed current sales data, the first weight, the second weight and the third weight.

6. A method for constructing a value prediction model, characterized in that: The method comprises the following steps: Obtain sample customer flow data and sample sales data of sample stores, wherein the sample customer flow data includes sample store front customer flow, sample store entrance customer flow and sample collection time, and the sample sales data includes sample total sales amount, total number of sample sales and sample sales date; Performing data preprocessing on the sample passenger flow data and the sample sales data to obtain processed sample passenger flow data and processed sample sales data; Constructing a model training set, a model validation set, and a model test set according to the processed sample passenger flow data and the processed sample sales data; The initial model is trained and parameterized based on the model training set, the model validation set, and the model test set to obtain a value prediction model, and the value prediction model is used to predict the comprehensive value of the store.

7. A store operation device based on data analysis, characterized in that: The store operation device based on data analysis includes: The data acquisition module is used to acquire the current customer flow data and current sales data of the current store. The current customer flow data includes the customer flow in front of the store, the customer flow into the store and the collection time. The current sales data includes the total sales, the total number of sales and the sales date. A data processing module, used for performing data preprocessing on the current passenger flow data and the current sales data to obtain processed current passenger flow data and processed current sales data; A value prediction module, used for inputting the processed current customer flow data into a value prediction model to obtain a predicted comprehensive value, wherein the predicted comprehensive value is used to represent the predicted economic value and predicted social value of the current store; An operation analysis module is used to perform operation analysis on the current store based on the processed current sales data and the predicted comprehensive value.

8. A store operation device based on data analysis, characterized in that: The device includes: a memory, a processor, and a store operation program based on data analysis stored in the memory and executable on the processor, wherein the store operation program based on data analysis is configured to implement the steps of the store operation method based on data analysis as described in any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, on which a store operation program based on data analysis is stored. When the store operation program based on data analysis is executed by a processor, the steps of the store operation method based on data analysis as described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product includes a store operation program based on data analysis, and when the store operation program based on data analysis is executed by a processor, the steps of the store operation method based on data analysis as described in any one of claims 1 to 6 are implemented.

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