Comprehensive management system for store dairy products based on big data

Abstract

The invention relates to the technical field of dairy product management, and discloses a comprehensive management system for store dairy products based on big data. The sales volume of different categories of dairy products in a store is counted through a dairy product sales data acquisition module, and then a data analysis module calculates a time adaptation coefficient of each time period of each day and a category adaptation coefficient of each dairy product according to a statistical result; according to the time adaptation coefficient of each time period and the category adaptation coefficient of each dairy product, the dairy product making time and making quantity of the store are adjusted, and through big data analysis and intelligent decision support, efficient management of the dairy product store is realized, the operation cost is reduced, and the customer satisfaction and loyalty are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of dairy product management, and in particular to a comprehensive store dairy product management system based on big data. Background Art

[0002] Dairy products refer to various foods made from cow's milk or goat's milk and their processed products as the main raw materials, with or without appropriate amounts of vitamins, minerals and other auxiliary materials, under the conditions required by laws, regulations and standards. They are also called cream products.

[0003] With the continuous expansion of the dairy market and the intensification of competition, store management faces many challenges, especially the short shelf life of dairy products. Many dairy products need to be made on the same day. Traditional store management systems often have problems such as data silos, information opacity, and delayed decision-making. They are difficult to meet the management needs of the modern dairy industry. Therefore, it is particularly important to develop a comprehensive store dairy management system based on big data. Summary of the invention

[0004] The purpose of the present invention is to provide a comprehensive store dairy product management system based on big data to solve the above technical problems.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A comprehensive store dairy product management system based on big data, characterized by comprising a store traffic collection module, a dairy product sales data collection module, a data analysis module, and a dairy product adjustment module;

[0007] The store traffic collection module is used to collect store traffic data at various time periods every day;

[0008] The dairy product sales data collection module is used to collect sales data of various dairy products in the store;

[0009] The data analysis module is used to analyze the data collected by the store traffic collection module and the dairy product sales data collection module;

[0010] The dairy product adjustment module is used to adjust the production quantity and production time of dairy products in the store according to the analysis results of the data analysis module.

[0011] As a further description of the solution of the present invention, the working process of the data acquisition module includes:

[0012] Divide each day into m time periods, and collect the average number of customers in the store and the average number of customers spending money in the store in each time period;

[0013] According to the average number of customers in the store in each time period and the average number of customers who consume in the store in each time period, the function of the average number of customers changing with time P(t) and the function of the average number of customers who consume with time S(t) are fitted respectively.

[0014] As a further description of the scheme of the present invention, the work of the dairy product sales data collection module includes:

[0015] The dairy products sold in the store are numbered by type, and the numbers are: 1, 2, ..., n;

[0016] Collect the sales volume change data of the i-th category of dairy products in the historical time period in real time, and construct the sales volume change function f of the i-th category of dairy products in the historical time period according to the sales volume change data of the i-th category of dairy products in the historical time period. i (t), where i belongs to n.

[0017] As a further description of the solution of the present invention, the working process of the data analysis module includes:

[0018] Get historical data on store traffic;

[0019] According to the characteristics of the average number of customers changing over time function P(t) and the average number of consuming customers changing over time function S(t), a mathematical model of the time adaptation coefficient is constructed, and the expression is:

[0020]

[0021] In the formula, α and β are weight coefficients, Δt j1 is the initial time period of the jth time period, Δt j2 is the end time period of the jth time period, ρ j is the time adaptation coefficient of the jth time period, where j belongs to m.

[0022] As a further description of the solution of the present invention, the working process of the data analysis module also includes:

[0023] Get the standard function f of the sales volume of the i-th type of dairy products over time during the historical event period i0 (t);

[0024] According to the time-varying function f of the sales volume of the i-th type of dairy products in the historical period i (t) and the standard function f of the time variation of the sales volume of the i-th type of dairy products during the historical event period i0 (t) characteristics, and construct a mathematical model of category adaptation coefficient, which is expressed as:

[0025]

[0026] In the formula, fth is the preset sales volume standard reference value, σ i is the category adaptation coefficient of the i-th category of dairy products.

[0027] As a further description of the scheme of the present invention, the working process of the dairy product adjustment module includes:

[0028] Calculate the time adaptation coefficients of m time periods in sequence, and sort the time adaptation coefficients of each time period in descending order;

[0029] Get the top-ranked time period q, and suggest that the store complete the day's production before the qth time period, where q belongs to m.

[0030] As a further description of the solution of the present invention, the working process of the dairy product adjustment module also includes:

[0031] The category adaptation coefficient σ of the i-th category of dairy products i The threshold value of the category adaptation coefficient σ for the preset i-th category of dairy products ith Interval comparison;

[0032] If the category adaptation coefficient σ of the i-th category of dairy products i Category adaptation coefficient threshold σ greater than or equal to the i-th category of dairy products ith interval, it is recommended that the store maintain or increase the production quantity of the i-th category dairy products;

[0033] If the category adaptation coefficient σ of the i-th category of dairy products i Less than the category adaptation coefficient threshold σ of the i-th category of dairy products ith interval, it is recommended that the store reduce the amount of dairy products in category i;

[0034] The category adaptation coefficients of n types of dairy products are calculated in turn, and compared with the corresponding preset adaptation coefficient thresholds, and the production quantities of different dairy products are adjusted according to the comparison results.

[0035] As a further description of the solution of the present invention, the working method of the management system includes:

[0036] Step S1, divide each day into m time periods, and collect the average number of customers in the store in each time period and the average number of customers who consume in the store in each time period;

[0037] Step S2, calculating the time adaptation coefficients of m time periods;

[0038] Step S3: sort the time adaptation coefficients of the m time periods in descending order, and suggest that the store complete the production of the day before the time period with the highest time adaptation coefficient;

[0039] Step S4: number the dairy products sold in the store by type, and the numbers are: 1, 2, ..., n;

[0040] Step S5, calculating the category adaptation coefficient of n types of dairy products;

[0041] Step S6, comparing the category adaptation coefficients of the n types of dairy products with the corresponding preset adaptation coefficient thresholds;

[0042] Step S7: If the category adaptation coefficient σ of the i-th category of dairy products i Category adaptation coefficient threshold σ greater than or equal to the i-th category of dairy products ith interval, it is recommended that the store maintain or increase the production quantity of the i-th category dairy products;

[0043] If the category adaptation coefficient σ of the i-th category of dairy products i Less than the category adaptation coefficient threshold σ of the i-th category of dairy products ith interval, it is recommended that stores reduce the production quantity of category i dairy products.

[0044] Beneficial effects of the present invention:

[0045] The present invention uses a store traffic collection module to count the average traffic of a store in each time period of the day, and uses a dairy product sales data collection module to count the sales volume of different categories of dairy products in the store. Then, a data analysis module calculates the time adaptation coefficient of each time period of the day and the category adaptation coefficient of each dairy product according to the statistical results, and adjusts the dairy product production time and production quantity of the store according to the time adaptation coefficient of each time period and the category adaptation coefficient of each dairy product. Through big data analysis and intelligent decision support, efficient management of dairy product stores is achieved, operating costs are reduced, and customer satisfaction and loyalty are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present invention will be further described below in conjunction with the accompanying drawings.

[0047] Figure 1 It is a structural schematic diagram of the store dairy product comprehensive management system based on big data provided by the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] See also Figure 1,The present invention is a comprehensive store dairy product management system based on big data, characterized by comprising a store traffic collection module, a dairy product sales data collection module, a data analysis module, and a dairy product adjustment module;

[0050] The store traffic collection module is used to collect store traffic data at various time periods every day;

[0051] The dairy product sales data collection module is used to collect sales data of various dairy products in the store;

[0052] The data analysis module is used to analyze the data collected by the store traffic collection module and the dairy product sales data collection module;

[0053] The dairy product adjustment module is used to adjust the production quantity and production time of dairy products in the store according to the analysis results of the data analysis module.

[0054] Through the above technical scheme, the present invention counts the average traffic volume of the store in each time period of the day through the store traffic collection module, counts the sales volume of different categories of dairy products in the store through the dairy product sales data collection module, and then the data analysis module calculates the time adaptation coefficient of each time period of the day and the category adaptation coefficient of each dairy product according to the statistical results, and adjusts the dairy product production time and production quantity of the store according to the time adaptation coefficient of each time period and the category adaptation coefficient of each dairy product. Through big data analysis and intelligent decision support, efficient management of dairy stores is achieved, operating costs are reduced, and customer satisfaction and loyalty are improved.

[0055] The working process of the data acquisition module includes:

[0056] Divide each day into m time periods, and collect the average number of customers in the store and the average number of customers spending money in the store in each time period;

[0057] According to the average number of customers in the store in each time period and the average number of customers who consume in the store in each time period, the function of the average number of customers changing with time P(t) and the function of the average number of customers who consume with time S(t) are fitted respectively.

[0058] The working process of the data analysis module includes:

[0059] Get historical data on store traffic;

[0060] According to the characteristics of the average number of customers changing over time function P(t) and the average number of consuming customers changing over time function S(t), a mathematical model of the time adaptation coefficient is constructed, and the expression is:

[0061]

[0062] In the formula, α and β are weight coefficients, Δt j1 is the initial time period of the jth time period, Δt j2 is the end time period of the jth time period, ρ j is the time adaptation coefficient of the jth time period, where j belongs to m.

[0063] The working process of the dairy product adjustment module includes:

[0064] Calculate the time adaptation coefficients of m time periods in sequence, and sort the time adaptation coefficients of each time period in descending order;

[0065] Get the top-ranked time period q, and suggest that the store complete the day's production before the qth time period, where q belongs to m.

[0066] Through the above technical solution, this embodiment substitutes the function data of the average number of customers changing over time and the function data of the average number of consuming customers changing over time into the formula Calculate the time adaptation coefficient of the store's j-th time period, where: represents the cumulative number of customers in the jth time period, Represents the cumulative number of consuming customers in the j-th time period, then calculates the time adaptation coefficients of m time periods in turn, and sorts the time adaptation coefficients of each time period in descending order; obtains the first-ranked time period q, and recommends that the store complete the day's production before the q-th time period, where q belongs to m.

[0067] The work of the dairy product sales data collection module includes:

[0068] The dairy products sold in the store are numbered by type, and the numbers are: 1, 2, ..., n;

[0069] Collect the sales volume change data of the i-th category of dairy products in the historical time period in real time, and construct the sales volume change function f of the i-th category of dairy products in the historical time period according to the sales volume change data of the i-th category of dairy products in the historical time period. i (t), where i belongs to n.

[0070] The working process of the data analysis module also includes:

[0071] Get the standard function f of the sales volume of the i-th type of dairy products over time during the historical event period i0 (t);

[0072] According to the time-varying function f of the sales volume of the i-th type of dairy products in the historical period i(t) and the standard function f of the time variation of the sales volume of the i-th type of dairy products during the historical event period i0 (t) characteristics, and construct a mathematical model of category adaptation coefficient, which is expressed as:

[0073]

[0074] In the formula, f th is the preset sales volume standard reference value, σ i is the category adaptation coefficient of the i-th category of dairy products.

[0075] The working process of the dairy product adjustment module also includes:

[0076] The category adaptation coefficient σ of the i-th category of dairy products i The threshold value of the category adaptation coefficient σ for the preset i-th category of dairy products ith Interval comparison;

[0077] If the category adaptation coefficient σ of the i-th category of dairy products i Category adaptation coefficient threshold σ greater than or equal to the i-th category of dairy products ith interval, it is recommended that the store maintain or increase the production quantity of the i-th category dairy products;

[0078] If the category adaptation coefficient σ of the i-th category of dairy products i Less than the category adaptation coefficient threshold σ of the i-th category of dairy products ith interval, it is recommended that the store reduce the amount of dairy products in category i;

[0079] The category adaptation coefficients of n types of dairy products are calculated in turn, and compared with the corresponding preset adaptation coefficient thresholds, and the production quantities of different dairy products are adjusted according to the comparison results.

[0080] Through the above technical solution, the actual data of sales volume of the i-th category of dairy products in the historical event period and the standard data of sales volume of the i-th category of dairy products in the preset historical event period are substituted into the formula Calculate the category adaptation coefficient of the i-th category of dairy products, and then calculate the category adaptation coefficients of n-th category of dairy products in turn, and compare them with the corresponding preset adaptation coefficient thresholds respectively; if the category adaptation coefficient σ of the i-th category of dairy products i Category adaptation coefficient threshold σ greater than or equal to the i-th category of dairy products ith interval, it is recommended that the store maintain or increase the production quantity of the i-th category of dairy products; if the category adaptation coefficient σ of the i-th category of dairy products i Less than the category adaptation coefficient threshold σ of the i-th category of dairy products ith interval, it is recommended that stores reduce the production quantity of category i dairy products.

[0081] The working method of the management system includes:

[0082] Step S1, divide each day into m time periods, and collect the average number of customers in the store in each time period and the average number of customers who consume in the store in each time period;

[0083] Step S2, calculating the time adaptation coefficients of m time periods;

[0084] Step S3: sort the time adaptation coefficients of the m time periods in descending order, and suggest that the store complete the production of the day before the time period with the highest time adaptation coefficient;

[0085] Step S4: number the dairy products sold in the store by type, and the numbers are: 1, 2, ..., n;

[0086] Step S5, calculating the category adaptation coefficient of n types of dairy products;

[0087] Step S6, comparing the category adaptation coefficients of the n types of dairy products with the corresponding preset adaptation coefficient thresholds;

[0088] Step S7: If the category adaptation coefficient σ of the i-th category of dairy products i Category adaptation coefficient threshold σ greater than or equal to the i-th category of dairy products ith interval, it is recommended that the store maintain or increase the production quantity of the i-th category dairy products;

[0089] If the category adaptation coefficient σ of the i-th category of dairy products i Less than the category adaptation coefficient threshold σ of the i-th category of dairy products ith interval, it is recommended that stores reduce the production quantity of category i dairy products.

[0090] It should be noted that all weight coefficients, thresholds and threshold intervals in the present invention are obtained based on empirical data and do not require detailed description.

[0091] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A comprehensive store dairy product management system based on big data, characterized in that: It includes store traffic collection module, dairy product sales data collection module, data analysis module, and dairy product adjustment module; The store traffic collection module is used to collect store traffic data at various time periods every day; The dairy product sales data collection module is used to collect sales data of various dairy products in the store; The data analysis module is used to analyze the data collected by the store traffic collection module and the dairy product sales data collection module; The dairy product adjustment module is used to adjust the production quantity and production time of dairy products in the store according to the analysis results of the data analysis module.

2. According to the big data-based integrated store dairy product management system of claim 1, it is characterized in that: The working process of the data acquisition module includes: Divide each day into m time periods, and collect the average number of customers in the store and the average number of customers spending money in the store in each time period; According to the average number of customers in the store in each time period and the average number of customers who consume in the store in each time period, the function of the average number of customers changing with time P(t) and the function of the average number of customers who consume with time S(t) are fitted respectively.

3. The integrated store dairy product management system based on big data according to claim 1 is characterized in that: The work of the dairy product sales data collection module includes: The dairy products sold in the store are numbered by type, and the numbers are: 1, 2, ..., n; The sales volume variation data of the i-th category dairy product in the historical time period is collected in real time, and the sales volume variation function fi(t) of the i-th category dairy product in the historical time period is constructed according to the sales volume variation data of the i-th category dairy product in the historical time period, where i belongs to n.

4. The integrated store dairy product management system based on big data according to claim 2 is characterized in that: The working process of the data analysis module includes: Get historical data on store traffic; According to the characteristics of the average number of customers changing over time function P(t) and the average number of consuming customers changing over time function S(t), a mathematical model of the time adaptation coefficient is constructed, and the expression is: In the formula, α and β are weight coefficients, Δt j1 is the initial time period of the jth time period, Δt j2 is the end time period of the j-th time period, ρj is the time adaptation coefficient of the j-th time period, where j belongs to m.

5. The integrated store dairy product management system based on big data according to claim 3 is characterized in that: The working process of the data analysis module also includes: Get the standard function f of the sales volume of the i-th type of dairy products over time during the historical event period i0 (t); According to the characteristics of the sales volume change function fi(t) of the i-th category of dairy products in the historical time period and the standard function fi0(t) of the sales volume change of the i-th category of dairy products in the historical event period, a mathematical model of the category adaptation coefficient is constructed, and the expression is: In the formula, f th is the preset sales volume standard reference value, σ i is the category adaptation coefficient of the i-th category of dairy products.

6. The integrated store dairy product management system based on big data according to claim 4 is characterized in that: The working process of the dairy product adjustment module includes: Calculate the time adaptation coefficients of m time periods in sequence, and sort the time adaptation coefficients of each time period in descending order; Get the top-ranked time period q, and suggest that the store complete the day's production before the qth time period, where q belongs to m.

7. The integrated store dairy product management system based on big data according to claim 5 is characterized in that: The working process of the dairy product adjustment module also includes: The category adaptation coefficient σ of the i-th category of dairy products i The threshold value of the category adaptation coefficient σ for the preset i-th category of dairy products ith Interval comparison; If the category adaptation coefficient σ of the i-th category of dairy products i Category adaptation coefficient threshold σ greater than or equal to the i-th category of dairy products ith interval, it is recommended that the store maintain or increase the production quantity of the i-th category dairy products; If the category adaptation coefficient σ of the i-th category of dairy products i Less than the category adaptation coefficient threshold σ of the i-th category of dairy products ith interval, it is recommended that the store reduce the amount of dairy products in category i; The category adaptation coefficients of n types of dairy products are calculated in turn, and compared with the corresponding preset adaptation coefficient thresholds, and the production quantities of different dairy products are adjusted according to the comparison results.

8. The integrated store dairy product management system based on big data according to claim 7 is characterized in that: The working method of the management system includes: Step S1, divide each day into m time periods, and collect the average number of customers in the store in each time period and the average number of customers who consume in the store in each time period; Step S2, calculating the time adaptation coefficients of m time periods; Step S3: sort the time adaptation coefficients of the m time periods in descending order, and suggest that the store complete the production of the day before the time period with the highest time adaptation coefficient; Step S4: number the dairy products sold in the store by type, and the numbers are: 1, 2, ..., n; Step S5, calculating the category adaptation coefficient of n types of dairy products; Step S6, comparing the category adaptation coefficients of the n types of dairy products with the corresponding preset adaptation coefficient thresholds; Step S7: If the category adaptation coefficient σ of the i-th category of dairy products i Category adaptation coefficient threshold σ greater than or equal to the i-th category of dairy products ith interval, it is recommended that the store maintain or increase the production quantity of the i-th category dairy products; If the category adaptation coefficient σ of the i-th category of dairy products i Less than the category adaptation coefficient threshold σ of the i-th category of dairy products ith interval, it is recommended that stores reduce the production quantity of category i dairy products.

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