Intelligent operation planning method and system for tea sales network platform

Abstract

The invention discloses an intelligent operation planning method and system for a tea sales network platform, and relates to the field of tea sales planning, and the method comprises the steps: obtaining the existing total amount of current to-be-sold tea from a storage database; counting sales nodes in the current sales network; historical sales data of each sales node is called from a database; performing analysis based on the historical sales data of the sales node to obtain a sales prediction basic value of the sales node; making a sales operation strategy of each tea sales node based on the sales prediction basic values of all the sales nodes and the existing total amount of the current to-be-sold tea; and summarizing the sales operation strategies of all the tea sales nodes and recording the summarized sales operation strategies as an optimal operation planning scheme of the tea sales network. The method has the advantages that the unsalable amount of tea leaves can be effectively reduced, the tea leaf storage cost is reduced, meanwhile, intelligent operation planning of a tea leaf sales network platform can be effectively achieved, and it is guaranteed that the tea leaf sales network maintains optimal operation.

Description

Technical Field

[0001] The present invention relates to the field of tea sales planning, and in particular to an intelligent operation planning method and system for a tea sales network platform. Background Art

[0002] In the prior art, there is a lack of an effective overall integrated operational planning scheme for the tea sales network. It is difficult to formulate tea sales strategies for each sales node based on a comprehensive analysis of the existing total amount of tea to be sold and the sales data of each tea sales node. It is difficult to ensure the rationality of the tea sales strategies of each sales node in the tea sales network, and it is impossible to achieve the optimal formulation of tea sales plans. Summary of the invention

[0003] In order to solve the above-mentioned technical problems, a technical solution is provided to solve the problems that the existing technology proposed in the above-mentioned background technology lacks a set of effective overall comprehensive operation planning scheme for the tea sales network, it is difficult to formulate tea sales strategies for each sales node based on a comprehensive analysis of the existing total amount of tea to be sold and the sales data of each tea sales node, it is difficult to ensure the rationality of the tea sales strategies of each sales node in the tea sales network, and it is impossible to achieve the optimal formulation of tea sales plans.

[0004] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0005] A method for intelligent operation planning of a tea sales network platform, comprising:

[0006] Obtain the current total amount of tea to be sold from the warehouse database;

[0007] Count the sales nodes in the current sales network;

[0008] Retrieve historical sales data of each sales node from the database;

[0009] Analyze the historical sales data of the sales node to obtain the basic sales forecast value of the sales node;

[0010] Formulate sales operation strategies for each tea sales node based on the sales forecast base value of all sales nodes and the current total amount of tea to be sold;

[0011] The sales operation strategies of all tea sales nodes are summarized and recorded as the optimal operation planning scheme of the tea sales network.

[0012] Preferably, the analyzing the historical sales data of the sales node to obtain the sales forecast basic value of the sales node specifically includes:

[0013] Set tea sales cycle;

[0014] Get the tea sales data within the n historical sales cycles closest to the current period;

[0015] Number the tea sales data in n historical sales cycles from small to large according to the time sequence from far to near, and obtain the time sequence numbers corresponding to the historical sales cycles one by one;

[0016] The sales forecast basic value of the sales node within the sales cycle corresponding to the current period is calculated based on the sales forecast formula.

[0017] Preferably, the sales forecast formula is specifically:

[0018]

[0019] Where L is the sales forecast base value of the sales node in the sales cycle corresponding to the current period, j is the time series number of the sales cycle, and V j is the tea sales volume in the jth historical sales cycle.

[0020] Preferably, the sales operation strategy for each tea sales node is formulated based on the sales forecast base value of all sales nodes and the current total amount of tea to be sold, specifically including:

[0021] Determine the impact of each tea sales strategy on tea sales volume;

[0022] Determine the impact of each tea sales strategy on tea sales profits;

[0023] Establishing tea sales quantity restrictions based on the current total amount of tea to be sold;

[0024] Calculate the tea sales strategy at each tea sales node that maximizes the total profit of tea sales under the tea sales volume restriction condition, and record it as the optimal tea sales strategy;

[0025] Based on the impact of each tea sales strategy on tea sales volume, the tea sales volume at each tea sales node under the optimal tea sales strategy is obtained and recorded as the optimal tea sales volume;

[0026] Allocate tea to each tea sales node according to the optimal tea sales volume.

[0027] Preferably, the determining the influence of each tea sales strategy on tea sales volume or tea sales profit specifically includes:

[0028] The tea sales strategy currently analyzed is recorded as the main tea sales strategy;

[0029] From the historical sales data of tea, select several tea sales volumes / tea sales profits when the main tea sales strategy is implemented to different degrees, and record them as initial training data;

[0030] Obtain all other tea sales strategies executed at the time corresponding to the initial training data of the main tea sales strategy at each execution level, and record them as comparative tea sales strategies;

[0031] From the historical sales data of tea, select a number of comparison moments when only the comparison tea sales strategy is implemented, and determine the tea sales volume / tea sales profit data at the comparison moments;

[0032] The tea sales volume / tea sales profit data at several comparison moments are averaged to obtain comparison standard data;

[0033] Based on the initial training data and the comparison standard data, the change rate of tea sales volume / tea sales profit data is calculated according to the standardized calculation formula;

[0034] Based on the tea sales data volume / tea sales profit change rate and the implementation degree of the main tea sales strategy, a linear regression equation between the tea sales volume data / tea sales profit change rate and the implementation degree of the main tea sales strategy is established;

[0035] The standardized calculation formula is as follows:

[0036]

[0037] Where p is the change rate of tea sales volume / tea sales profit data, x is the initial training data, and x0 is the comparison standard data.

[0038] Preferably, the tea sales volume restriction condition expression is:

[0039]

[0040] Where Q is the current total amount of tea to be sold, m is the total number of sales nodes, and L i is the sales forecast base value of the i-th sales node, K is the total number of sales strategies, is the linear regression function of the execution degree of the k-th sales strategy for the change rate of the tea sales volume data at the i-th sales node, b k is the execution degree of the k-th sales strategy.

[0041] Preferably, the calculation formula for the total profit from tea sales is:

[0042]

[0043] Where M is the total profit from tea sales, W0 is the basic profit rate of tea sales, is the linear regression equation function of the execution degree of the kth sales strategy with respect to the rate of change of tea sales profit.

[0044] Furthermore, a tea sales network platform intelligent operation planning system is proposed, which is used to implement the above-mentioned tea sales network platform intelligent operation planning method, including:

[0045] A tea quantity acquisition module, which is used to obtain the current total quantity of tea to be sold from the storage database;

[0046] A data analysis module, which is used to retrieve historical sales data of each sales node from a database and perform analysis based on the historical sales data of the sales node to obtain a basic sales forecast value of the sales node;

[0047] An operation planning module is electrically connected to the tea quantity acquisition module and the data analysis module. The operation planning module is used to formulate sales operation strategies for each tea sales node based on the sales forecast basic values ​​of all sales nodes and the current total amount of tea to be sold, and to summarize the sales operation strategies of all tea sales nodes as the optimal operation planning plan for the tea sales network.

[0048] Optionally, the operation planning module internally integrates:

[0049] A regression prediction unit, the regression prediction unit is used to determine the impact of each tea sales strategy on tea sales volume and determine the impact of each tea sales strategy on tea sales profit;

[0050] A restriction condition formulation unit, the restriction condition formulation unit is used to establish a tea sales quantity restriction condition based on the current total amount of tea to be sold;

[0051] The profit calculation unit is used to calculate the tea sales strategy of each tea sales node when the total profit of tea sales reaches the maximum value under the condition of tea sales volume restriction, which is recorded as the optimal tea sales strategy.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] This scheme proposes an intelligent operation planning scheme for the tea sales network platform. Based on the historical sales data of each tea sales node, a comprehensive analysis is conducted to obtain the basic sales volume of each tea sales node, and the impact of each sales strategy on the tea sales volume and tea sales profit is combined. On the basis of ensuring that the total sales volume of all tea sales nodes is equal to the existing total amount of tea to be sold, the sales profit is maximized. In this way, the unsold tea can be effectively reduced, the storage cost of tea can be reduced, and at the same time, the intelligent operation planning of the tea sales network platform can be effectively realized to ensure that the tea sales network maintains the most optimized operation, thereby ensuring the sales rate of tea. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A flow chart of the intelligent operation planning method for the tea sales network platform proposed by the present invention;

[0055] Figure 2 A flow chart of a method for obtaining a sales forecast base value of a sales node according to the present invention;

[0056] Figure 3 A flow chart of a method for formulating a sales operation strategy for each tea sales node of the present invention;

[0057] Figure 4 This is a flow chart of the method for determining the impact of tea sales strategy on tea sales volume or tea sales profit of the present invention. DETAILED DESCRIPTION

[0058] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0059] Reference Figure 1 As shown, a method for intelligent operation planning of a tea sales network platform includes:

[0060] Obtain the current total amount of tea to be sold from the warehouse database;

[0061] Count the sales nodes in the current sales network;

[0062] Retrieve historical sales data of each sales node from the database;

[0063] Analyze the historical sales data of the sales node to obtain the basic sales forecast value of the sales node;

[0064] Formulate sales operation strategies for each tea sales node based on the sales forecast base value of all sales nodes and the current total amount of tea to be sold;

[0065] The sales operation strategies of all tea sales nodes are summarized and recorded as the optimal operation planning scheme of the tea sales network.

[0066] Based on the historical sales data of each tea sales node, a comprehensive analysis is conducted to determine the basic sales volume of each tea sales node, and combined with the impact of each sales strategy on tea sales volume and tea sales profit, the sales profit is maximized while ensuring that the total sales volume of all tea sales nodes is equal to the existing total amount of tea to be sold.

[0067] Reference Figure 2 As shown, based on the analysis of the historical sales data of the sales node, the sales forecast basic value of the sales node is obtained, which specifically includes:

[0068] Set tea sales cycle;

[0069] Get the tea sales data within the n historical sales cycles closest to the current period;

[0070] Number the tea sales data in n historical sales cycles from small to large according to the time sequence from far to near, and obtain the time sequence numbers corresponding to the historical sales cycles one by one;

[0071] The sales forecast basic value of the sales node within the sales cycle corresponding to the current period is calculated based on the sales forecast formula.

[0072] The sales forecast formula is as follows:

[0073]

[0074] Where L is the sales forecast base value of the sales node in the sales cycle corresponding to the current period, j is the time series number of the sales cycle, and V j is the tea sales volume in the jth historical sales cycle.

[0075] It is understandable that tea sales have distinct cyclical fluctuations. The production properties of tea determine that the spring and autumn tea seasons are the peak sales seasons. In different sales cycles, the sales data of tea show obvious differences. Based on this, this solution conducts targeted predictions on the sales data characteristics of different sales cycles to calculate the sales forecast base value of the sales cycle corresponding to the current period;

[0076] In this solution, linear regression is used to predict the basic sales forecast value of the sales node within the sales cycle corresponding to the current period, which fully considers the changing trend of sales data over time and can effectively predict the basic sales volume of tea at the current sales node.

[0077] Reference Figure 3As shown in the figure, based on the sales forecast base values ​​of all sales nodes and the current total amount of tea to be sold, the sales operation strategy for each tea sales node is formulated, including:

[0078] Determine the impact of each tea sales strategy on tea sales volume;

[0079] Determine the impact of each tea sales strategy on tea sales profits;

[0080] Establishing tea sales quantity restrictions based on the current total amount of tea to be sold;

[0081] Calculate the tea sales strategy at each tea sales node that maximizes the total profit of tea sales under the tea sales volume restriction condition, and record it as the optimal tea sales strategy;

[0082] Based on the impact of each tea sales strategy on tea sales, the tea sales volume at each tea sales node under the optimal tea sales strategy is obtained and recorded as the optimal tea sales volume;

[0083] Allocate tea to each tea sales node according to the optimal tea sales volume.

[0084] Reference Figure 4 As shown in the figure, the impact of each tea sales strategy on tea sales volume or tea sales profit is determined as follows:

[0085] The tea sales strategy currently analyzed is recorded as the main tea sales strategy;

[0086] From the historical sales data of tea, select several tea sales volumes / tea sales profits when the main tea sales strategy is implemented to different degrees, and record them as initial training data;

[0087] Obtain all other tea sales strategies executed at the time corresponding to the initial training data of the main tea sales strategy at each execution level, and record them as comparative tea sales strategies;

[0088] From the historical sales data of tea, select a number of comparison moments when only the comparison tea sales strategy is implemented, and determine the tea sales volume / tea sales profit data at the comparison moments;

[0089] The tea sales volume / tea sales profit data at several comparison moments are averaged to obtain comparison standard data;

[0090] Based on the initial training data and the comparison standard data, the change rate of tea sales volume / tea sales profit data is calculated according to the standardized calculation formula;

[0091] Based on the tea sales data volume / tea sales profit change rate and the implementation degree of the main tea sales strategy, a linear regression equation between the tea sales volume data / tea sales profit change rate and the implementation degree of the main tea sales strategy is established;

[0092] The standardized calculation formula is as follows:

[0093]

[0094] Where p is the change rate of tea sales volume / tea sales profit data, x is the initial training data, and x0 is the comparison standard data.

[0095] In this scheme, the linear regression equation between the rate of change as the dependent variable calculation and the degree of execution of the tea sales strategy can effectively reduce the error impact of the gap in the basic sales data of each sales node, and thus effectively ensure that the fitted linear regression equation can more accurately reflect the impact of the degree of execution of the tea sales strategy on the tea sales data volume / tea sales profit change rate.

[0096] The tea sales volume restriction expression is:

[0097]

[0098] Where Q is the current total amount of tea to be sold, m is the total number of sales nodes, and L i is the sales forecast base value of the i-th sales node, K is the total number of sales strategies, is the linear regression function of the execution degree of the k-th sales strategy for the change rate of the tea sales volume data at the i-th sales node, b k is the execution degree of the k-th sales strategy.

[0099] The formula for calculating the total profit from tea sales is:

[0100]

[0101] Where M is the total profit from tea sales, W0 is the basic profit rate of tea sales, is the linear regression equation function of the execution degree of the kth sales strategy with respect to the rate of change of tea sales profit.

[0102] It is understandable that the execution of different strategies will have a linkage effect on the sales volume and profit of tea sales. For example, discount promotions will lead to an increase in tea sales and a decrease in tea sales profits. Based on this, this solution calculates the linear regression equations of the tea sales strategy for tea sales volume and each tea sales strategy for tea sales profits, and establishes tea sales volume restriction conditions to ensure that the total sales volume of all tea sales nodes is equal to the existing total amount of tea to be sold. This ensures that the existing total amount of tea can be fully sold, maximizes the total profit of tea sales, effectively reduces unsold tea, reduces tea storage costs, and maintains the maximum profit of tea sales.

[0103] Furthermore, based on the same inventive concept as the above-mentioned intelligent operation plan of the tea sales network platform, this solution also proposes an intelligent operation plan of the tea sales network platform, including:

[0104] The total amount of tea obtaining module is used to obtain the current total amount of tea to be sold from the storage database;

[0105] The data analysis module is used to retrieve the historical sales data of each sales node from the database and analyze the historical sales data of the sales node to obtain the basic value of sales forecast of the sales node;

[0106] The operation planning module is electrically connected to the tea quantity acquisition module and the data analysis module. The operation planning module is used to formulate the sales operation strategy of each tea sales node based on the sales forecast basic value of all sales nodes and the current total amount of tea to be sold, and to summarize the sales operation strategies of all tea sales nodes as the optimal operation planning plan for the tea sales network.

[0107] The operations planning module integrates:

[0108] A regression prediction unit, which is used to determine the impact of each tea sales strategy on tea sales volume and the impact of each tea sales strategy on tea sales profit;

[0109] A restriction condition formulation unit, which is used to establish a tea sales quantity restriction condition based on the current total amount of tea to be sold;

[0110] The profit calculation unit is used to calculate the tea sales strategy of each tea sales node when the total profit of tea sales reaches the maximum value under the condition of tea sales volume restriction, which is recorded as the optimal tea sales strategy.

[0111] The working process of the above-mentioned intelligent operation planning system of the tea sales network platform is as follows:

[0112] Step 1: The tea quantity acquisition module obtains the current total quantity of tea to be sold from the storage database;

[0113] Step 2: The data analysis module retrieves the historical sales data of each sales node from the database; sets the tea sales cycle; obtains the tea sales data of the n historical sales cycles closest to the current period; numbers the tea sales data of the n historical sales cycles from small to large according to the time sequence from far to near, and obtains the time sequence number corresponding to the historical sales cycle one by one; calculates the sales forecast basic value of the sales node in the sales cycle corresponding to the current period based on the sales forecast formula;

[0114] Step 3: The regression prediction unit determines the impact of each tea sales strategy on tea sales volume and the impact of each tea sales strategy on tea sales profit;

[0115] Step 4: The restriction condition formulation unit establishes the tea sales quantity restriction condition based on the current total amount of tea to be sold;

[0116] Step 5: The profit calculation unit calculates the tea sales strategy for each tea sales node when the total profit of tea sales reaches the maximum value under the tea sales volume restriction condition, and records it as the optimal tea sales strategy.

[0117] In summary, the advantages of the present invention are: it can effectively reduce unsold tea and reduce tea storage costs, and at the same time can effectively realize the intelligent operation planning of the tea sales network platform to ensure that the tea sales network maintains the most optimized operation.

[0118] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for intelligent operation planning of a tea sales network platform, characterized in that: include: Obtain the current total amount of tea to be sold from the warehouse database; Count the sales nodes in the current sales network; Retrieve historical sales data of each sales node from the database; Analyze the historical sales data of the sales node to obtain the basic sales forecast value of the sales node; Formulate sales operation strategies for each tea sales node based on the sales forecast base value of all sales nodes and the current total amount of tea to be sold; The sales operation strategies of all tea sales nodes are summarized and recorded as the optimal operation planning scheme of the tea sales network.

2. The method for intelligent operation planning of a tea sales network platform according to claim 1, characterized in that: The step of analyzing the historical sales data of the sales node to obtain the sales forecast basic value of the sales node specifically includes: Set tea sales cycle; Get the tea sales data within the n historical sales cycles closest to the current period; Number the tea sales data in n historical sales cycles from small to large according to the time sequence from far to near, and obtain the time sequence numbers corresponding to the historical sales cycles one by one; The sales forecast basic value of the sales node within the sales cycle corresponding to the current period is calculated based on the sales forecast formula.

3. The method for intelligent operation planning of a tea sales network platform according to claim 2, characterized in that: The sales forecast formula is specifically: Where L is the sales forecast base value of the sales node in the sales cycle corresponding to the current period, j is the time series number of the sales cycle, and V j is the tea sales volume in the jth historical sales cycle.

4. The method for intelligent operation planning of a tea sales network platform according to claim 1, characterized in that: The sales operation strategy for each tea sales node is formulated based on the sales forecast base value of all sales nodes and the current total amount of tea to be sold, and specifically includes: Determine the impact of each tea sales strategy on tea sales volume; Determine the impact of each tea sales strategy on tea sales profits; Establishing tea sales quantity restrictions based on the current total amount of tea to be sold; Calculate the tea sales strategy at each tea sales node that maximizes the total profit of tea sales under the tea sales volume restriction condition, and record it as the optimal tea sales strategy; Based on the impact of each tea sales strategy on tea sales, the tea sales volume at each tea sales node under the optimal tea sales strategy is obtained and recorded as the optimal tea sales volume; Allocate tea to each tea sales node according to the optimal tea sales volume.

5. The method for intelligent operation planning of a tea sales network platform according to claim 4, characterized in that: Determining the impact of each tea sales strategy on tea sales volume or tea sales profit specifically includes: The tea sales strategy currently analyzed is recorded as the main tea sales strategy; From the historical sales data of tea, select several tea sales volumes / tea sales profits when the main tea sales strategy is implemented to different degrees, and record them as initial training data; Obtain all other tea sales strategies executed at the time corresponding to the initial training data of the main tea sales strategy at each execution level, and record them as comparative tea sales strategies; From the historical sales data of tea, select a number of comparison moments when only the comparison tea sales strategy is implemented, and determine the tea sales volume / tea sales profit data at the comparison moments; The tea sales volume / tea sales profit data at several comparison moments are averaged to obtain comparison standard data; Based on the initial training data and the comparison standard data, the change rate of tea sales volume / tea sales profit data is calculated according to the standardized calculation formula; Based on the tea sales data volume / tea sales profit change rate and the implementation degree of the main tea sales strategy, a linear regression equation between the tea sales volume data / tea sales profit change rate and the implementation degree of the main tea sales strategy is established; The standardized calculation formula is as follows: Where p is the change rate of tea sales volume / tea sales profit data, x is the initial training data, and x0 is the comparison standard data.

6. The method for intelligent operation planning of a tea sales network platform according to claim 5, characterized in that: The tea sales volume restriction condition expression is: Where Q is the current total amount of tea to be sold, m is the total number of sales nodes, and L i is the sales forecast base value of the i-th sales node, K is the total number of sales strategies, is the linear regression function of the execution degree of the k-th sales strategy for the change rate of the tea sales volume data at the i-th sales node, b k is the execution degree of the k-th sales strategy.

7. The method for intelligent operation planning of a tea sales network platform according to claim 6, characterized in that: The calculation formula for the total profit from tea sales is: Where M is the total profit from tea sales, W0 is the basic profit rate of tea sales, is the linear regression equation function of the execution degree of the kth sales strategy with respect to the rate of change of tea sales profit.

8. An intelligent operation planning system for a tea sales network platform, characterized in that: A method for implementing the intelligent operation planning of a tea sales network platform according to any one of claims 1 to 7, comprising: A tea quantity acquisition module, which is used to obtain the current total quantity of tea to be sold from the storage database; A data analysis module, which is used to retrieve historical sales data of each sales node from a database and analyze the historical sales data of the sales node to obtain a basic sales forecast value of the sales node; An operation planning module is electrically connected to the tea quantity acquisition module and the data analysis module. The operation planning module is used to formulate sales operation strategies for each tea sales node based on the sales forecast basic values ​​of all sales nodes and the current total amount of tea to be sold, and to summarize the sales operation strategies of all tea sales nodes as the optimal operation planning plan for the tea sales network.

9. The intelligent operation planning system for a tea sales network platform according to claim 8, characterized in that: The operation planning module internally integrates: A regression prediction unit, the regression prediction unit is used to determine the impact of each tea sales strategy on tea sales volume and determine the impact of each tea sales strategy on tea sales profit; A restriction condition formulation unit, the restriction condition formulation unit is used to establish a tea sales quantity restriction condition based on the current total amount of tea to be sold; The profit calculation unit is used to calculate the tea sales strategy of each tea sales node when the total profit of tea sales reaches the maximum value under the condition of tea sales volume restriction, which is recorded as the optimal tea sales strategy.

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