Store passenger flow prediction method under weather influence and related device

Abstract

The invention discloses a store passenger flow volume prediction method under weather influence and a related device. The method comprises the following steps: acquiring precipitation data and store passenger flow volume of a current day; judging whether the current day is rainfall or not according to the precipitation data, and if yes, calculating a customer flow prediction factor of the store according to the store customer flow, the time factor and the abnormal weather factor based on a prediction formula; and calculating the store passenger flow according to the passenger flow prediction factor to obtain a store passenger flow prediction value. The weather condition is analyzed and judged based on the precipitation data, if it rains, the association influence of the precipitation data, the time factor, the abnormal weather factor and the like on the store passenger flow is comprehensively considered, the accuracy can be ensured based on the predicted store passenger flow, meanwhile, the inherent association influence of the weather factor on the store passenger flow is also considered, and the prediction accuracy is improved. And the actual situation is better met. Therefore, the technical problem that the passenger flow prediction result lacks accuracy due to the fact that the inherent correlation influence of weather factors on the store passenger flow is not considered in the prior art can be solved.

Description

Technical Field

[0001] The present application relates to the technical field of customer flow analysis, and in particular to a method and related device for predicting store customer flow under the influence of weather. Background Art

[0002] In the daily operation of stores, customer flow, as an important indicator of store operation scenes, will have a great impact on all aspects of store operations. An accurate customer flow prediction method can help stores gain great foresight and high fault tolerance in terms of management, operation, supply chain, and scene layout. Therefore, how to calculate accurate customer flow is an important issue in store operations.

[0003] Existing technologies can only calculate the mutual influence factors between stores, but do not consider the impact of special weather conditions such as rainy days on customer flow. In addition, current technologies only calculate rainy day factors or multi-store customer flow influence factors separately. This will miss the intrinsic connection factors between weather factors and customer flow itself, resulting in inaccurate customer flow forecasts on rainy days. Summary of the invention

[0004] The present application provides a method and related device for predicting store customer flow under the influence of weather, which is used to solve the technical problem that the existing technology does not consider the intrinsic correlation effect of weather factors on store customer flow, resulting in a lack of accuracy in customer flow prediction results.

[0005] In view of this, the first aspect of the present application provides a method for predicting store customer flow under the influence of weather, comprising:

[0006] Get the current day’s precipitation data and store customer traffic;

[0007] Determine whether it rains on the current day according to the precipitation data, and if so, calculate the store's customer flow prediction factor based on the store's customer flow, time factor, and abnormal weather factor based on a prediction formula;

[0008] The store customer flow is estimated based on the customer flow prediction factor to obtain a store customer flow prediction value.

[0009] Preferably, the obtaining of the current day's precipitation data and store customer flow includes:

[0010] Obtain the current day's precipitation in the target area through weather forecast to obtain precipitation data;

[0011] Determine the store's current day customer flow based on historical store operating data to obtain the store's customer flow.

[0012] Preferably, the method of judging whether it rains on the current day according to the precipitation data, and if so, calculating the store's customer flow prediction factor according to the store's customer flow, time factor and abnormal weather factor based on a prediction formula, further includes:

[0013] Get the season type and month of the current day, and perform time series analysis based on temperature changes to determine the time factor;

[0014] Obtain wind force level information and disaster level information based on weather forecast;

[0015] An abnormal weather analysis is performed based on the precipitation data, the wind force level information and the disaster level information to determine abnormal weather factors.

[0016] Preferably, the method further comprises: determining whether it rains on the current day according to the precipitation data, and if so, calculating the store's customer flow prediction factor according to the store's customer flow, time factor and abnormal weather factor based on a prediction formula, and then further comprising:

[0017] A linear regression method is used to perform a linear analysis on the precipitation of all stores in the target area based on the precipitation data, and a store precipitation distribution map is abstracted.

[0018] A second aspect of the present application provides a store customer flow prediction device under the influence of weather, comprising:

[0019] A data acquisition unit, used to obtain the current day's precipitation data and store customer flow;

[0020] a parameter calculation unit, configured to determine whether it rains on the current day according to the precipitation data, and if so, calculate a store customer flow prediction factor according to the store customer flow, time factor and abnormal weather factor based on a prediction formula;

[0021] The flow prediction unit is used to calculate the store customer flow based on the customer flow prediction factor to obtain the store customer flow prediction value.

[0022] Preferably, the data acquisition unit is specifically used for:

[0023] Obtain the current day's precipitation in the target area through weather forecast to obtain precipitation data;

[0024] Determine the store's current day customer flow based on historical store operating data to obtain the store's customer flow.

[0025] Preferably, it also includes:

[0026] The time analysis unit is used to obtain the season type and month of the current day, and to perform time series analysis based on temperature changes to determine the time factor;

[0027] An information extraction unit, used to obtain wind force level information and disaster level information according to weather forecast;

[0028] The weather analysis unit is used to perform abnormal weather analysis based on the precipitation data, the wind force level information and the disaster level information to determine abnormal weather factors.

[0029] Preferably, it also includes:

[0030] The precipitation distribution unit is used to perform linear analysis on the precipitation of all stores in the target area according to the precipitation data using a linear regression method, and abstract a store precipitation distribution map.

[0031] A third aspect of the present application provides a device for predicting store customer flow under weather conditions, the device comprising a processor and a memory;

[0032] The memory is used to store program code and transmit the program code to the processor;

[0033] The processor is used to execute the store customer flow prediction method under the influence of weather described in the first aspect according to the instructions in the program code.

[0034] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program code, and the program code is used to execute the store customer flow prediction method under the influence of weather described in the first aspect.

[0035] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0036] In the present application, a method for predicting store customer flow under the influence of weather is provided, including: obtaining the current day's precipitation data and store customer flow; judging whether it rains on the current day based on the precipitation data, and if so, calculating the store's customer flow prediction factor based on the prediction formula according to the store customer flow, time factor and abnormal weather factor; and calculating the store's customer flow based on the customer flow prediction factor to obtain a store customer flow prediction value.

[0037] The store customer flow prediction method under the influence of weather provided by the present application determines the weather conditions based on precipitation data analysis, that is, whether it will rain, because rainy weather will directly affect the store customer flow; if so, the correlation effects of precipitation data, time factors, and abnormal weather factors on the store customer flow are comprehensively considered, so as to calculate an accurate and reliable customer flow prediction factor, and the store customer flow predicted based on this factor can ensure accuracy while also considering the inherent correlation effect of weather factors on store customer flow, which is more in line with the actual situation. Therefore, the present application can solve the technical problem that the prior art does not consider the inherent correlation effect of weather factors on store customer flow, resulting in the lack of accuracy of customer flow prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A flow chart of a method for predicting store customer flow under the influence of weather provided in an embodiment of the present application;

[0039] Figure 2 A schematic diagram of the structure of a device for predicting store customer flow under weather conditions provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0041] For easier understanding, see Figure 1 , the present application provides an embodiment of a method for predicting store customer flow under weather conditions, including:

[0042] Step 101: Obtain the current day's precipitation data and store customer flow.

[0043] Furthermore, step 101 includes:

[0044] Obtain the current day's precipitation in the target area through weather forecast to obtain precipitation data;

[0045] Determine the store's current day customer flow based on historical store operating data to obtain the store's customer flow.

[0046] Precipitation data is mainly obtained through public weather forecasts, as long as the precipitation conditions in the target area on the current day can be estimated. The store customer flow is determined based on the prediction of historical store operating data. The historical store operating data is internal data. By integrating historical data, the correlation between operating data and store customer flow can be analyzed, and then the store customer flow on the current day can be predicted. If the customer flow on the current day has been counted, it can be determined directly without relying on historical data. The specific determination method is not limited here and is only used as an example. In addition, if historical store operating data is obtained, and the relationship between store customer flow and store operating data is analyzed, the optimization of store operating strategies can be guided based on this analysis result. The specific process is described here and can be designed according to actual conditions.

[0047] Step 102: determine whether it rains on the current day based on the precipitation data. If so, calculate the store's customer flow prediction factor based on the prediction formula according to the store's customer flow, time factor and abnormal weather factor.

[0048] Furthermore, step 102, before that, also includes:

[0049] Get the season type and month of the current day, and perform time series analysis based on temperature changes to determine the time factor;

[0050] Obtain wind force level information and disaster level information based on weather forecast;

[0051] Abnormal weather analysis is conducted based on precipitation data, wind force level information and disaster level information to determine abnormal weather factors.

[0052] It should be noted that the precipitation data can reflect the precipitation of the current day, so based on the precipitation data, it can be judged whether it rains on the current day. If it does not rain, it can be inferred based on the prediction factor calculation method of the prior art to obtain the store customer flow prediction value; the specific process is not repeated, and it can be implemented with reference to the prior art. If it is judged that it will rain on the current day, it is necessary to calculate a customer flow prediction factor that is more in line with the actual situation based on the comprehensive calculation method proposed in this embodiment.

[0053] The rainfall judgment mechanism based on precipitation data in this embodiment is to judge the relationship between the rainfall and the threshold value 0. If the rainfall is 0, it is judged that it is not raining; if the rainfall is greater than 0, it is judged that it is raining. Different prediction factor calculation methods can be selected according to different judgment results. Similarly, other rainfall judgment mechanisms can be designed according to actual conditions. This embodiment only provides an example and is not limited.

[0054] In order to improve the accuracy and reliability of the passenger flow prediction factor, in addition to considering the influence of precipitation, this embodiment also adds the influence of time factor and abnormal weather factor in the process of calculating the passenger flow prediction factor. Among them, the time factor mainly refers to the current season and month, because different seasons have different temperatures in different months, and the temperature difference will affect the weather and passenger flow, such as affecting the rainfall situation, and the temperature affects the passenger flow size. Therefore, this embodiment needs to perform a temperature-based time analysis for the current season and month of the target area store to determine the time factor; the average temperature of multiple time points can be used for time series analysis. The time factor can be a quantitative value of the climate, which can include but is not limited to early spring, mid-spring, late spring, summer, etc.; the quantification process is not described in detail, and can be quantified based on actual conditions. The abnormal weather factor is a parameter determined according to the weather forecast; specifically, the information such as rainfall, wind force level and disaster level is obtained according to the weather forecast; then the abnormal weather analysis is performed based on this information to determine the abnormal weather conditions, which specifically include extremely bad weather, bad weather, slightly bad weather, normal weather and comfortable weather, etc.; and the abnormal weather factor is a quantitative value, and the specific quantification process is not described in detail, and can be set according to actual conditions.

[0055] In this embodiment, based on the prediction formula, the store customer flow prediction factor can be calculated according to the store customer flow, time factor and abnormal weather factor. The prediction formula is expressed as:

[0056]

[0057] in, Indicates the time day number, including the number of all rainy days in the three months before the rain is forecast. is the passenger flow on the i-th day, that is, the passenger flow on the current day, It indicates the sum of customer traffic of other stores in the area except this store. It represents the common prediction factor of passenger flow, which is obtained based on the passenger flow time series prediction method. is the rainfall for the day, i.e. rainfall data, is the time factor, Abnormal weather factors.

[0058] Step 103: Calculate the store customer flow based on the customer flow prediction factor to obtain a store customer flow prediction value.

[0059] The customer flow prediction factor obtained based on comprehensive calculation can be used to estimate the store customer flow in the future time period and obtain the store customer flow prediction value; in addition, the process of predicting store customer flow based on the prediction factor can be implemented based on the existing technology, which will not be elaborated here. The core of this application is how to determine an accurate and reliable customer flow prediction factor. Since the customer flow prediction factor is calculated based on multiple influencing factors, it is more in line with the actual situation and is accurate and reliable. Therefore, the store customer flow prediction value predicted based on this factor also has a certain theoretical guiding significance and can help optimize store operation and management.

[0060] Furthermore, step 103 further includes:

[0061] The linear regression method was used to conduct a linear analysis of the precipitation of all stores in the target area based on the precipitation data, and the store precipitation distribution map was abstracted.

[0062] Linear regression is a statistical analysis method that uses regression analysis in mathematical statistics to determine the quantitative relationship between two or more variables that are interdependent. Specifically, it is a regression analysis that uses the least square function called the linear regression equation to model the relationship between one or more independent variables and the dependent variable. This function is a linear combination of one or more model parameters called regression coefficients. The case with only one independent variable is called simple regression, and the case with more than one independent variable is called multiple regression. This embodiment performs a linear analysis of store precipitation based on linear regression technology, and then abstracts the precipitation in the target area into a dynamic graph in combination with the weather forecast.

[0063] Since there may be multiple stores in the same target area, the precipitation data of the area within 24 hours can be obtained based on the weather forecast, and then a linear regression analysis of the store precipitation can be performed, and even the rainfall in the future time period of the area can be estimated. In addition, for the convenience of observation, all stores in the target area can be abstracted into a graph, and new stores can be added to the graph. It can be understood that the rainfall in the future time period can be substituted into the above formula, and the customer flow forecast value of each store on rainy days can be calculated in combination with other factors and store operating data, thereby predicting the store customer flow.

[0064] The method for predicting store customer flow under the influence of weather provided in the embodiment of the present application determines the weather conditions based on precipitation data analysis, that is, whether it will rain, because rainy weather will directly affect the store customer flow; if so, the correlation effects of precipitation data, time factors, and abnormal weather factors on store customer flow are comprehensively considered, so as to calculate an accurate and reliable customer flow prediction factor, and the store customer flow predicted based on this factor can ensure accuracy while also considering the intrinsic correlation effect of weather factors on store customer flow, which is more in line with the actual situation. Therefore, the embodiment of the present application can solve the technical problem that the prior art does not consider the intrinsic correlation effect of weather factors on store customer flow, resulting in the lack of accuracy of customer flow prediction results.

[0065] For easier understanding, see Figure 2 The present application provides an embodiment of a store customer flow prediction device under the influence of weather, comprising:

[0066] The data acquisition unit 201 is used to acquire the current day's precipitation data and store customer flow;

[0067] The parameter calculation unit 202 is used to determine whether it rains on the current day according to the precipitation data, and if so, calculate the store's customer flow prediction factor based on the store's customer flow, time factor and abnormal weather factor based on the prediction formula;

[0068] The flow prediction unit 203 is used to calculate the store customer flow according to the customer flow prediction factor to obtain the store customer flow prediction value.

[0069] Furthermore, the data acquisition unit 201 is specifically used for:

[0070] Obtain the current day's precipitation in the target area through weather forecast to obtain precipitation data;

[0071] Determine the store's current day customer flow based on historical store operating data to obtain the store's customer flow.

[0072] Furthermore, it also includes:

[0073] The time analysis unit 204 is used to obtain the season type and month of the current day, and perform time series analysis based on temperature changes to determine the time factor;

[0074] An information extraction unit 205 is used to obtain wind force level information and disaster level information according to the weather forecast;

[0075] The weather analysis unit 206 is used to perform abnormal weather analysis based on precipitation data, wind force level information and disaster level information to determine abnormal weather factors.

[0076] Furthermore, it also includes:

[0077] The precipitation distribution unit 207 is used to perform linear analysis on the precipitation of all stores in the target area according to the precipitation data using a linear regression method, and abstract a store precipitation distribution map.

[0078] The present application also provides a device for predicting store customer flow under the influence of weather, the device comprising a processor and a memory;

[0079] The memory is used to store the program code and transmit the program code to the processor;

[0080] The processor is used to execute the store customer flow prediction method under the influence of weather in the above method embodiment according to the instructions in the program code.

[0081] The present application also provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the store customer flow prediction method under the influence of weather in the above method embodiment.

[0082] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0083] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0084] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0085] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions for executing all or part of the steps of the method described in each embodiment of the present application through a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[0086] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for predicting store customer flow under weather conditions, characterized in that: include: Get the current day’s precipitation data and store customer traffic; Determine whether it rains on the current day according to the precipitation data, and if so, calculate the store's customer flow prediction factor based on the store's customer flow, time factor, and abnormal weather factor based on a prediction formula; The store customer flow is estimated based on the customer flow prediction factor to obtain a store customer flow prediction value.

2. The method for predicting store customer flow under weather conditions according to claim 1, characterized in that: The acquisition of the current day's precipitation data and store customer flow includes: Obtain the current day's precipitation in the target area through weather forecast to obtain precipitation data; Determine the store's current day customer flow based on historical store operating data to obtain the store's customer flow.

3. The method for predicting store customer flow under weather conditions according to claim 1, characterized in that: The method further comprises: determining whether it rains on the current day according to the precipitation data, and if so, calculating the store's customer flow prediction factor based on the store's customer flow, time factor and abnormal weather factor based on a prediction formula, and the method also comprises: Get the season type and month of the current day, and perform time series analysis based on temperature changes to determine the time factor; Obtain wind force level information and disaster level information based on weather forecast; An abnormal weather analysis is performed based on the precipitation data, the wind force level information and the disaster level information to determine abnormal weather factors.

4. The method for predicting store customer flow under weather conditions according to claim 1, characterized in that: The method further comprises: determining whether it rains on the current day according to the precipitation data, and if so, calculating the store's customer flow prediction factor according to the store's customer flow, time factor and abnormal weather factor based on a prediction formula, and then further comprising: A linear regression method is used to perform a linear analysis on the precipitation of all stores in the target area based on the precipitation data, and a store precipitation distribution map is abstracted.

5. A device for predicting store customer flow under weather conditions, characterized in that: include: A data acquisition unit, used to obtain the current day's precipitation data and store customer flow; a parameter calculation unit, configured to determine whether it rains on the current day according to the precipitation data, and if so, calculate a store customer flow prediction factor according to the store customer flow, time factor and abnormal weather factor based on a prediction formula; The flow prediction unit is used to calculate the store customer flow based on the customer flow prediction factor to obtain the store customer flow prediction value.

6. The device for predicting store customer flow under weather conditions according to claim 5, characterized in that: The data acquisition unit is specifically used for: Obtain the current day's precipitation in the target area through weather forecast to obtain precipitation data; Determine the store's current day customer flow based on historical store operating data to obtain the store's customer flow.

7. The device for predicting store customer flow under weather conditions according to claim 5, characterized in that: Also includes: The time analysis unit is used to obtain the season type and month of the current day, and to perform time series analysis based on temperature changes to determine the time factor; An information extraction unit, used to obtain wind force level information and disaster level information according to weather forecast; The weather analysis unit is used to perform abnormal weather analysis based on the precipitation data, the wind force level information and the disaster level information to determine abnormal weather factors.

8. The device for predicting store customer flow under weather conditions according to claim 5, characterized in that: Also includes: The precipitation distribution unit is used to perform linear analysis on the precipitation of all stores in the target area according to the precipitation data using a linear regression method, and abstract a store precipitation distribution map.

9. A device for predicting store customer flow under weather conditions, characterized in that: The device comprises a processor and a memory; The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the store customer flow prediction method under the influence of weather as described in any one of claims 1-4 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program code, and the program code is used to execute the store customer flow prediction method under the influence of weather as described in any one of claims 1-4.

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