Intelligent store order sending control method and system based on big data

Abstract

The invention discloses an intelligent store order distribution control method and system based on big data, and particularly relates to the field of e-commerce, and the method comprises the steps: S1, store order distribution information acquisition, S2, store information text mining, S3, data standardization processing, S4, store order distribution comprehensive analysis, S5, store order distribution comprehensive judgment, and S6, screening information output. Through deep analysis of mined detailed data, a comprehensive and accurate mathematical model is creatively constructed, and the model ingeniously fuses four core dimensions of store order processing information, store sales information, store employee management information and store operation information. According to the method, all-around and comprehensive measurement of the shop order distribution quality is realized, through establishment of the comprehensive model, the accuracy and comprehensiveness of evaluation are remarkably improved, and an intuitive and quantitative tool is provided for a user, so that the user can more accurately grasp the influence of a shop order distribution control system on the shop order distribution quality.

Description

Technical Field

[0001] The present invention relates to the field of e-commerce technology, and more specifically, to a smart store dispatching control method and system based on big data. Background Art

[0002] The intelligent store dispatch control system is a comprehensive software platform that integrates product information management, inventory monitoring, order processing, logistics distribution, data analysis and other functions. It uses digital means to achieve seamless connection between stores, suppliers and headquarters, optimize the dispatch process, and improve the efficiency of supply chain collaboration.

[0003] Big data technology provides powerful data processing and analysis capabilities for smart store dispatch. By collecting and analyzing customer purchase history, consumption habits, geographic location and other data information, it can more accurately predict customer order needs and service preferences. At the same time, big data technology can also monitor the store's operating status, inventory status, and service staff's work status in real time, providing real-time and accurate data support for dispatch decisions.

[0004] However, there are still some shortcomings in its actual use. For example, the existing technology lacks an indicator system specifically adapted to the store order dispatch control system for comprehensive monitoring of the store order dispatch quality. The traditional store order dispatch quality monitoring means are limited by the limitations of their technology and methods, and it is difficult to achieve a comprehensive and accurate evaluation of the store order dispatch quality. This deficiency directly restricts the actual application of the store order dispatch control system, making it impossible to make a more comprehensive and accurate comprehensive evaluation of the impact of the store order dispatch control system on the store order dispatch quality. Therefore, the development of a new monitoring indicator system that can accurately reflect the store order dispatch quality is of great significance to improving the practicability of the store order dispatch control system. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a smart store dispatch control method and system based on big data, and solves the problems raised in the above-mentioned background technology through the following scheme.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a method and system for intelligent store dispatch control based on big data, comprising: S1: acquiring store dispatch information: numbering the stores and recording them as 1, 2, 3…i…n in sequence, filtering store dispatch feature texts according to different stores, and obtaining store dispatch feature text data; S2: Store information text mining: including store order processing information collection steps, store sales information collection steps, store employee management information collection steps and store operation information collection steps, used to select store dispatch key texts from store dispatch feature text data to obtain store dispatch key texts; S3: Data standardization processing: used to perform standardization processing and analysis on the key text data of the store dispatch order to obtain the key text of the second store dispatch order; S4: Comprehensive analysis of store dispatch: used to import the key text data of the second store dispatch into the mathematical model of the store order processing influence coefficient, the mathematical model of the store sales management influence coefficient, the mathematical model of the store employee management influence coefficient, the mathematical model of the store operation influence coefficient and the mathematical model of the store dispatch comprehensive evaluation coefficient, and process and analyze them to obtain a text output value based on the key text of the second store dispatch; S5: Comprehensive judgment of store dispatch: used to screen out the text output value of the second store dispatch key text whose comprehensive evaluation coefficient of store dispatch is less than a preset value; S6: Output filtering information: used to import the filtered store information into the user information terminal.

[0007] Preferably, the key text of the store dispatch specifically refers to: store order quantity parameter, total order amount parameter, average order processing time parameter, order completion rate parameter, order delivery time parameter, delivery on-time rate parameter, average delivery cost parameter, store sales parameter, store out-of-stock times parameter, store return rate parameter, store gross profit rate parameter, store unsalable rate parameter, employee daily average order quantity parameter, employee error rate parameter, store employee cost parameter, store average customer flow parameter, store customer flow conversion rate parameter; The store order volume parameter refers to the store order volume of the i-th store. ; The order total amount parameter refers to the total order amount of the i-th store ; The order completion rate parameter refers to the order completion rate of the i-th store ; The average order processing time parameter refers to the average order processing time of the i-th store ; The order delivery time parameter refers to the order delivery time of the i-th store ; The delivery on-time rate parameter refers to the on-time delivery rate of the order of the i-th store ; The average delivery cost parameter refers to the average delivery cost of orders from the i-th store ; The store sales parameter refers to the store sales of the i-th store ; The store out-of-stock times parameter refers to the store out-of-stock times of the i-th store ; The store return rate parameter refers to the store return rate of the i-th store ; The store gross profit margin parameter refers to the store gross profit margin of the i-th store ; The store unsalable rate parameter refers to the store unsalable rate of the i-th store The parameter of the average number of orders received by employees per day refers to the average number of orders received by employees of the i-th store per day. ; The employee error rate parameter refers to the employee error rate of the i-th store ; The store staff cost parameter refers to the store staff cost of the i-th store ; The store average customer flow parameter refers to the store average customer flow of the i-th store ; The store customer flow conversion rate parameter refers to the store customer flow conversion rate of the i-th store .

[0008] Preferably, the average order processing time specifically refers to the average time from order receipt to order processing completion; the store unsalable rate specifically refers to the ratio of the number of unsold goods in the store within a certain period of time to the total inventory; the average store customer flow specifically refers to the average number of customers entering the store every day; the store gross profit margin specifically refers to the ratio of the difference between store sales and costs to sales.

[0009] Preferably, the standardization process specifically refers to Z-score standardization process, which converts the original data into a standardized Z-Score value by calculating the mean and standard deviation of the original data.

[0010] Preferably, the text output value includes a store order processing influence coefficient, a store sales management influence coefficient, a store employee management influence coefficient, a store operation influence coefficient and a store dispatch comprehensive evaluation coefficient.

[0011] Preferably, the mathematical model of the store order processing influence coefficient is as follows: = ,in refers to the store order processing influence coefficient of the i-th store, refers to the store order volume of the i-th store, Refers to the total order amount of the i-th store, refers to the order completion rate of the i-th store, refers to the average order processing time of the i-th store, refers to the order delivery time of the i-th store, refers to the on-time delivery rate of orders from the i-th store, Refers to the average delivery cost of orders from the i-th store; The mathematical model of the store sales management influence coefficient is as follows: = ,in refers to the store sales management influence coefficient of the i-th store, refers to the store sales of the ith store, refers to the number of out-of-stock times of the i-th store, refers to the store return rate of the i-th store, refers to the store gross profit margin of the i-th store, refers to the store unsalable rate of the i-th store; The mathematical model of the store staff management influence coefficient is as follows: = ,in refers to the store staff management influence coefficient of the i-th store, refers to the average number of orders received by employees of the i-th store per day, refers to the error rate of employees in the i-th store, Refers to the store staff cost of the i-th store; The mathematical model of the store operation impact coefficient is as follows: = ,in refers to the store operation impact coefficient of the i-th store, refers to the average customer flow of the i-th store, Refers to the store customer flow conversion rate of the i-th store.

[0012] Preferably, the mathematical model of the comprehensive evaluation coefficient of store dispatch is as follows: = + ,in Refers to the comprehensive evaluation coefficient of store dispatch. refers to the store order processing influence coefficient of the i-th store, refers to the store sales management influence coefficient of the i-th store, refers to the store staff management influence coefficient of the i-th store, refers to the store operation impact coefficient of the i-th store, , , , Refers to the weight coefficient.

[0013] Preferably, the preset value is obtained based on an in-depth analysis of daily operational practices, and is intended to evaluate the critical thresholds of the store's order processing efficiency, inventory holdings, store profitability, employee management effectiveness and overall operational capabilities under a given order dispatch mode. When the comprehensive evaluation coefficient of the store's order dispatch output by the system is lower than this preset value, it means that the store's order processing efficiency is low, inventory levels are low, profitability is poor, employee management effectiveness is insufficient, and overall operational performance is poor under the current order dispatch mechanism, and the store's order dispatch method and quantity need to be adjusted.

[0014] Preferably, the user information terminal is used to receive a text output value of a store dispatching comprehensive evaluation coefficient lower than a preset value, to help users filter out stores that do not meet the requirements.

[0015] Preferably, an intelligent store dispatch control system based on big data comprises: a system central processor module, a system operation database and a user information terminal, a store dispatch information acquisition module, a store information text mining module, a data standardization processing module, a store dispatch comprehensive analysis module, a store dispatch comprehensive judgment module and a human-computer interaction module; The system operation database includes all data texts of an intelligent store dispatch control system based on big data, and collects information texts output by each module in real time. The system central processor module is used to centrally control the information text instructions output by each module, and the user information terminal is an information output device for receiving an intelligent store dispatch control system based on big data; Store dispatch information acquisition module: number the stores in sequence as 1, 2, 3…i…n, filter the store dispatch feature text according to different stores, obtain the store dispatch feature text data, and output the store dispatch feature text data to the store information text mining module; Store information text mining module: including store order processing information collection unit, store sales information collection unit, store employee management information collection unit and store operation information collection unit, used to select store dispatch key text from store dispatch feature text data to obtain store dispatch key text, and output the store dispatch key text to the data standardization processing module; Data standardization processing module: used for standardizing and analyzing the key text data of store dispatch orders, obtaining the key text of the second store dispatch order, and outputting the key text of the second store dispatch order to the comprehensive analysis module of store dispatch orders; Store dispatch comprehensive analysis module: used for importing the second store dispatch key text data into the mathematical model of the store order processing influence coefficient, the mathematical model of the store sales management influence coefficient, the mathematical model of the store employee management influence coefficient, the mathematical model of the store operation influence coefficient and the mathematical model of the store dispatch comprehensive evaluation coefficient, and processing and analyzing, obtaining a text output value based on the second store dispatch key text, and outputting the text output value to the store dispatch comprehensive judgment module; Store dispatch comprehensive judgment module: used to filter out the text output value of the second store dispatch key text whose store dispatch comprehensive evaluation coefficient is less than a preset value, and output the screening result to the human-computer interaction module; Human-computer interaction module: used to import filtered store information into the user information terminal.

[0016] Technical effects and advantages of the present invention: 1. The present invention screens the store information database, deeply mines the basic information elements therein, obtains key indicator information for evaluating the quality of store dispatch, and integrates and comprehensively analyzes these multi-dimensional indicator information. The present invention can monitor and evaluate the quality of store dispatch in real time with unprecedented accuracy and comprehensiveness, and can timely discover potential risks, which provides a scientific basis for optimizing the dispatch process of stores, improving operational efficiency and service quality, thereby improving the practicality and accuracy of the store dispatch control system; 2. The present invention creatively constructs a comprehensive and accurate mathematical model by deeply analyzing the mined detailed data. The model cleverly integrates the four core dimensions of store order processing information, store sales information, store employee management information and store operation information, and realizes a comprehensive and integrated measurement of the quality of store dispatch. Through the establishment of this comprehensive model, the present invention significantly improves the accuracy and comprehensiveness of the evaluation, and provides users with an intuitive and quantitative tool, enabling users to more accurately grasp the impact of the store dispatch control system on the quality of store dispatch. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the overall structure of the present invention; Figure 2 It is a schematic diagram of the system structure of the present invention; Figure 3 It is a schematic diagram of the structure of the store information text mining module of the present invention. DETAILED DESCRIPTION

[0018] 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.

[0019] As attached Figure 1 The method for controlling smart store dispatching orders based on big data includes: S1: Acquisition of store dispatch information: Number the stores and record them as 1, 2, 3…i…n in sequence, filter the store dispatch feature texts according to different stores, and obtain the store dispatch feature text data; S2: Store information text mining: including store order processing information collection steps, store sales information collection steps, store employee management information collection steps and store operation information collection steps, used to select store dispatch key texts from store dispatch feature text data to obtain store dispatch key texts; The key texts of store dispatch orders in the store information text mining step in the preferred technical solution of this application are as follows: It should be specifically explained in this embodiment that the key text of the store dispatch specifically refers to: store order quantity parameter, order total amount parameter, order average processing time parameter, order completion rate parameter, order delivery time parameter, delivery on-time rate parameter, average delivery cost parameter, store sales parameter, store out-of-stock times parameter, store return rate parameter, store gross profit margin parameter, store unsalable rate parameter, employee daily average order quantity parameter, employee error rate parameter, store employee cost parameter, store average customer flow parameter, store customer flow conversion rate parameter; The store order volume parameter refers to the store order volume of the i-th store. ; The order total amount parameter refers to the total order amount of the i-th store ; The order completion rate parameter refers to the order completion rate of the i-th store ; The average order processing time parameter refers to the average order processing time of the i-th store ; The order delivery time parameter refers to the order delivery time of the i-th store ; The delivery on-time rate parameter refers to the on-time delivery rate of the order of the i-th store ; The average delivery cost parameter refers to the average delivery cost of orders from the i-th store ; The store sales parameter refers to the store sales of the i-th store ; The store out-of-stock times parameter refers to the store out-of-stock times of the i-th store ; The store return rate parameter refers to the store return rate of the i-th store ; The store gross profit margin parameter refers to the store gross profit margin of the i-th store ; The store unsalable rate parameter refers to the store unsalable rate of the i-th store The parameter of the average number of orders received by employees per day refers to the average number of orders received by employees of the i-th store per day. ; The employee error rate parameter refers to the employee error rate of the i-th store ; The store staff cost parameter refers to the store staff cost of the i-th store ; The store average customer flow parameter refers to the store average customer flow of the i-th store ; The store customer flow conversion rate parameter refers to the store customer flow conversion rate of the i-th store .

[0020] It should be specifically explained in this embodiment that the average order processing time specifically refers to the average time from order receipt to order processing completion; the store unsalable rate specifically refers to the ratio of the number of unsold goods in a store within a certain period of time to the total inventory; the average store customer flow specifically refers to the average number of customers entering the store every day; the store gross profit margin specifically refers to the ratio of the difference between the store sales and costs to the sales; S3: Data standardization processing: used to perform standardization processing and analysis on the key text data of the store dispatch order to obtain the key text of the second store dispatch order; It should be specifically explained in this embodiment that the standardization process specifically refers to Z-score standardization process, which converts the original data into a standardized Z-Score value by calculating the mean and standard deviation of the original data.

[0021] S4: Comprehensive analysis of store dispatch: used to import the key text data of the second store dispatch into the mathematical model of the store order processing influence coefficient, the mathematical model of the store sales management influence coefficient, the mathematical model of the store employee management influence coefficient, the mathematical model of the store operation influence coefficient and the mathematical model of the store dispatch comprehensive evaluation coefficient, and process and analyze them to obtain a text output value based on the key text of the second store dispatch; It should be specifically explained in this embodiment that the text output value includes the store order processing influence coefficient, the store sales management influence coefficient, the store employee management influence coefficient, the store operation influence coefficient and the store dispatch comprehensive evaluation coefficient; It should be specifically explained in this embodiment that the mathematical model of the store order processing influence coefficient is as follows: = ,in refers to the store order processing influence coefficient of the i-th store, refers to the store order volume of the i-th store, Refers to the total order amount of the i-th store, refers to the order completion rate of the i-th store, refers to the average order processing time of the i-th store, refers to the order delivery time of the i-th store, refers to the on-time delivery rate of orders from the i-th store, Refers to the average delivery cost of orders from the i-th store; It should be specifically explained in this embodiment that the mathematical model of the store sales management influence coefficient is as follows: = ,in refers to the store sales management influence coefficient of the i-th store, refers to the store sales of the ith store, refers to the number of out-of-stock times of the i-th store, refers to the store return rate of the i-th store, refers to the store gross profit margin of the i-th store, refers to the store unsalable rate of the i-th store; It should be specifically explained in this embodiment that the mathematical model of the store employee management influence coefficient is as follows: = ,in refers to the store staff management influence coefficient of the i-th store, refers to the average number of orders received by employees of the i-th store per day, refers to the error rate of employees in the i-th store, Refers to the store staff cost of the i-th store; It should be specifically explained in this embodiment that the mathematical model of the store operation impact coefficient is as follows: = ,in refers to the store operation impact coefficient of the i-th store, refers to the average customer flow of the i-th store, Refers to the store customer flow conversion rate of the i-th store; It should be specifically explained in this embodiment that the mathematical model of the comprehensive evaluation coefficient of store dispatch is as follows: = + ,in Refers to the comprehensive evaluation coefficient of store dispatch. refers to the store order processing influence coefficient of the i-th store, refers to the store sales management influence coefficient of the i-th store, refers to the store staff management influence coefficient of the i-th store, refers to the store operation impact coefficient of the i-th store, , , , Refers to the weight coefficient; It should be specifically explained in this embodiment that the weight coefficient , , , It is calculated by the entropy method, and the specific calculation steps of the entropy method are as follows: Data standardization: Since the measurement units of various indicators may be different, in order to eliminate the impact of the dimension, the collected data needs to be standardized. Commonly used standardization methods include Z-score standardization and range method standardization; Calculate the proportion of indicators: Calculate the proportion of the zth sample value under the jth indicator to the sum of all sample values ​​of the indicator. This step is the basis for the subsequent calculation of information entropy; Calculate information entropy: According to the definition and calculation formula of information entropy, calculate the information entropy ej of the jth indicator. The smaller the information entropy, the greater the degree of variation of the indicator and the greater the amount of information provided; Calculate information entropy redundancy: Information entropy redundancy dj is the complement of information entropy, that is, dj=1-ej. The greater the information entropy redundancy, the more information the indicator contains, and the greater the impact on the comprehensive evaluation; Calculate weight: Calculate the weight of each indicator in the comprehensive evaluation according to its information entropy redundancy. The weight coefficient is the ratio of the information entropy redundancy of each indicator to the sum of the information entropy redundancy of all indicators.

[0022] S5: Comprehensive judgment of store dispatch: used to screen out the text output value of the second store dispatch key text whose comprehensive evaluation coefficient of store dispatch is less than a preset value; What needs to be specifically explained in this embodiment is that the preset value is obtained based on an in-depth analysis of daily operational practices, and is intended to evaluate the critical threshold of the store's order processing efficiency, inventory holdings, store profitability, employee management effectiveness and overall operational capabilities under a given order dispatch mode. When the comprehensive evaluation coefficient of the store's order dispatch output by the system is lower than this preset value, it means that the store's order processing efficiency is low, inventory levels are low, profitability is poor, employee management effectiveness is insufficient, and overall operational performance is poor under the current order dispatch mechanism, and it is necessary to adjust the store's order dispatch method and quantity.

[0023] S6: Output filtering information: used to import the filtered store information into the user information terminal.

[0024] What needs to be specifically explained in this embodiment is that the user information terminal is used to receive a text output value of a store dispatch comprehensive evaluation coefficient lower than a preset value, so as to help the user filter out stores that do not meet the requirements.

[0025] As attached Figure 2 An intelligent store dispatch control system based on big data is shown, comprising: a system central processor module, a system operation database and a user information terminal, a store dispatch information acquisition module, a store information text mining module, a data standardization processing module, a store dispatch comprehensive analysis module, a store dispatch comprehensive judgment module and a human-computer interaction module; The system operation database includes all data texts of an intelligent store dispatch control system based on big data, and collects information texts output by each module in real time. The system central processor module is used to centrally control the information text instructions output by each module, and the user information terminal is an information output device for receiving an intelligent store dispatch control system based on big data; Store dispatch information acquisition module: number the stores in sequence as 1, 2, 3…i…n, filter the store dispatch feature text according to different stores, obtain the store dispatch feature text data, and output the store dispatch feature text data to the store information text mining module; like Figure 3 The store information text mining module shown includes a store order processing information collection unit, a store sales information collection unit, a store employee management information collection unit, and a store operation information collection unit, which is used to select the store dispatch key text from the store dispatch feature text data to obtain the store dispatch key text, and output the store dispatch key text to the data standardization processing module; Data standardization processing module: used for standardizing and analyzing the key text data of store dispatch orders, obtaining the key text of the second store dispatch order, and outputting the key text of the second store dispatch order to the comprehensive analysis module of store dispatch orders; Store dispatch comprehensive analysis module: used for importing the second store dispatch key text data into the mathematical model of the store order processing influence coefficient, the mathematical model of the store sales management influence coefficient, the mathematical model of the store employee management influence coefficient, the mathematical model of the store operation influence coefficient and the mathematical model of the store dispatch comprehensive evaluation coefficient, and processing and analyzing, obtaining a text output value based on the second store dispatch key text, and outputting the text output value to the store dispatch comprehensive judgment module; Store dispatch comprehensive judgment module: used to filter out the text output value of the second store dispatch key text whose store dispatch comprehensive evaluation coefficient is less than a preset value, and output the screening result to the human-computer interaction module; Human-computer interaction module: used to import filtered store information into the user information terminal.

[0026] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other; Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A smart store dispatch control method based on big data, characterized in that: include: S1: Acquisition of store dispatch information: Number the stores and record them as 1, 2, 3…i…n in sequence, filter the store dispatch feature texts according to different stores, and obtain the store dispatch feature text data; S2: Store information text mining: including store order processing information collection steps, store sales information collection steps, store employee management information collection steps and store operation information collection steps, used to select store dispatch key texts from store dispatch feature text data to obtain store dispatch key texts; S3: Data standardization processing: used to perform standardization processing and analysis on the key text data of the store dispatch order to obtain the key text of the second store dispatch order; S4: Comprehensive analysis of store dispatch: used to import the key text data of the second store dispatch into the mathematical model of the store order processing influence coefficient, the mathematical model of the store sales management influence coefficient, the mathematical model of the store employee management influence coefficient, the mathematical model of the store operation influence coefficient and the mathematical model of the store dispatch comprehensive evaluation coefficient, and process and analyze them to obtain a text output value based on the key text of the second store dispatch; S5: Comprehensive judgment of store dispatch: used to screen out the text output value of the second store dispatch key text whose comprehensive evaluation coefficient of store dispatch is less than a preset value; S6: Output filtering information: used to import the filtered store information into the user information terminal.

2. According to the big data-based intelligent store dispatch control method of claim 1, it is characterized by: The key text of store dispatch specifically refers to: store order quantity parameter, total order amount parameter, average order processing time parameter, order completion rate parameter, order delivery time parameter, delivery on-time rate parameter, average delivery cost parameter, store sales parameter, store out-of-stock times parameter, store return rate parameter, store gross profit margin parameter, store unsalable rate parameter, employee daily average order quantity parameter, employee error rate parameter, store employee cost parameter, store average customer flow parameter, store customer flow conversion rate parameter; The store order volume parameter refers to the store order volume of the i-th store. ; The order total amount parameter refers to the total order amount of the i-th store ; The order completion rate parameter refers to the order completion rate of the i-th store. ; The average order processing time parameter refers to the average order processing time of the i-th store. ; The order delivery time parameter refers to the order delivery time of the i-th store ; The delivery on-time rate parameter refers to the on-time delivery rate of the order of the i-th store. ; The average delivery cost parameter refers to the average delivery cost of orders from the i-th store. ; The store sales parameter refers to the store sales of the i-th store ; The store out-of-stock times parameter refers to the store out-of-stock times of the i-th store ; The store return rate parameter refers to the store return rate of the i-th store ; The store gross profit margin parameter refers to the store gross profit margin of the i-th store ; The store unsalable rate parameter refers to the store unsalable rate of the i-th store ; The employee's daily average order volume parameter refers to the average daily order volume of employees in the i-th store. ; The employee error rate parameter refers to the employee error rate of the i-th store ; The store staff cost parameter refers to the store staff cost of the i-th store ; The store average customer flow parameter refers to the store average customer flow of the i-th store ; The store customer flow conversion rate parameter refers to the store customer flow conversion rate of the i-th store .

3. According to the big data-based intelligent store dispatch control method of claim 1, it is characterized by: The mathematical model of the store order processing influence coefficient is as follows: = ,in refers to the store order processing influence coefficient of the i-th store, refers to the store order volume of the i-th store, refers to the total order amount of the i-th store, refers to the order completion rate of the i-th store, refers to the average order processing time of the i-th store, refers to the order delivery time of the i-th store, refers to the on-time delivery rate of orders from the i-th store, Refers to the average delivery cost of orders from the i-th store.

4. According to the big data-based intelligent store dispatch control method of claim 1, it is characterized by: The mathematical model of the store sales management influence coefficient is as follows: = ,in refers to the store sales management influence coefficient of the i-th store, refers to the store sales of the ith store, refers to the number of out-of-stock times of the i-th store, refers to the store return rate of the i-th store, refers to the store gross profit margin of the i-th store, Refers to the store unsalable rate of the i-th store.

5. The method for controlling smart store dispatch based on big data according to claim 1, characterized in that: The mathematical model of the store staff management influence coefficient is as follows: = ,in refers to the store staff management influence coefficient of the i-th store, refers to the average number of orders received by employees of the i-th store per day, refers to the error rate of employees in the i-th store, Refers to the store staff cost of the ith store.

6. The method for controlling smart store dispatch based on big data according to claim 1, characterized in that: The mathematical model of the store operation impact coefficient is as follows: = ,in refers to the store operation impact coefficient of the i-th store, refers to the average customer flow of the i-th store, Refers to the store customer flow conversion rate of the i-th store.

7. The method for controlling smart store dispatch based on big data according to claim 1, characterized in that: The mathematical model of the comprehensive evaluation coefficient of store dispatch is as follows: = + ,in Refers to the comprehensive evaluation coefficient of store dispatch. refers to the store order processing influence coefficient of the i-th store, refers to the store sales management influence coefficient of the i-th store, refers to the store staff management influence coefficient of the i-th store, refers to the store operation impact coefficient of the i-th store, , , , Refers to the weight coefficient.

8. An intelligent store dispatch control system based on big data, used to implement the intelligent store dispatch control method based on big data as described in any one of claims 1 to 7, comprising: System central processing unit module, system operation database and user information terminal, store dispatch information acquisition module, store information text mining module, data standardization processing module, store dispatch comprehensive analysis module, store dispatch comprehensive judgment module and human-computer interaction module; The system operation database includes all data texts of an intelligent store dispatch control system based on big data, and collects information texts output by each module in real time. The system central processor module is used to centrally control the information text instructions output by each module, and the user information terminal is an information output device for receiving an intelligent store dispatch control system based on big data; Store dispatch information acquisition module: number the stores and record them as 1, 2, 3…i…n in sequence, filter the store dispatch feature text according to different stores, obtain the store dispatch feature text data, and output the store dispatch feature text data to the store information text mining module; Store information text mining module: including store order processing information collection unit, store sales information collection unit, store employee management information collection unit and store operation information collection unit, used to select store dispatch key text from store dispatch feature text data to obtain store dispatch key text, and output the store dispatch key text to the data standardization processing module; Data standardization processing module: used for standardizing and analyzing the key text data of store dispatch orders, obtaining the key text of the second store dispatch order, and outputting the key text of the second store dispatch order to the comprehensive analysis module of store dispatch orders; Store dispatch comprehensive analysis module: used for importing the second store dispatch key text data into the mathematical model of the store order processing influence coefficient, the mathematical model of the store sales management influence coefficient, the mathematical model of the store employee management influence coefficient, the mathematical model of the store operation influence coefficient and the mathematical model of the store dispatch comprehensive evaluation coefficient, and processing and analyzing, obtaining a text output value based on the second store dispatch key text, and outputting the text output value to the store dispatch comprehensive judgment module; Store dispatch comprehensive judgment module: used to filter out the text output value of the second store dispatch key text whose store dispatch comprehensive evaluation coefficient is less than a preset value, and output the screening result to the human-computer interaction module; Human-computer interaction module: used to import filtered store information into the user information terminal.

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