A method and system for operating an online shopping mall based on mobile Internet

Abstract

The present invention relates to the field of mall operation technology, and discloses an online mall operation method and system based on mobile Internet. The method tracks and monitors the inventory status of commodities in real time through an integrated inventory management database, and sets upper and lower warning thresholds of the inventory, so as to generate a trigger instruction. When the trigger instruction is received, the mall operation related data is collected, and the user's purchase behavior path is analyzed through the user behavior data. By analyzing the sales data, logistics data and market data respectively, the future sales trend of the commodities is comprehensively analyzed and predicted according to the analysis results combined with the historical mall operation related data. The inventory status of the current commodities is analyzed by combining the user behavior data with the sales prediction data, so as to schedule and replenish the inventory status of the current commodities, which is conducive to predicting the inventory demand of commodities, and realizing intelligent replenishment and scheduling, and further improving the purchasing desire and shopping satisfaction of users.

Description

Technical Field

[0001] The present invention relates to the field of mall operation technology, and more specifically to an online mall operation method and system based on mobile Internet. Background Art

[0002] With the rapid development and popularization of mobile Internet technology, people's shopping habits have changed significantly. Traditional physical store shopping has gradually been replaced by online shopping. The successful operation of online shopping malls is inseparable from effective product management and supply chain support. Merchants should establish a complete product information management and classification system to ensure the accuracy and completeness of product information. Therefore, online shopping mall operations through the Internet came into being. By real-time tracking of product inventory, automatic generation of reports and data analysis, and by analyzing historical sales data, market trends and seasonal changes, future demand can be more accurately predicted, thereby optimizing inventory levels;

[0003] However, the above process still has the following disadvantages:

[0004] First, the existing online mall operation system can only replenish the inventory of goods. The inventory management method is relatively simple. It is unable to comprehensively analyze the inventory demand data from multiple aspects to further analyze the inventory demand trends of different goods, so as to achieve intelligent replenishment and scheduling, which may lead to excessive stockpiling and insufficient storage of goods.

[0005] Second, the existing online shopping mall operation system lacks the ability to schedule and replenish product inventory from the user's perspective. It is unable to analyze the user's demand for product inventory based on the user's shopping behavior data, thereby scheduling the product inventory, resulting in a decrease in the user's desire to purchase a certain product due to insufficient inventory. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides an online mall operation method and system based on mobile Internet to solve the problems existing in the above-mentioned background technology.

[0007] The present invention provides the following technical solution: a method for operating an online mall based on mobile Internet, comprising:

[0008] S1: Track and monitor the inventory of goods in real time through the integrated inventory management database, automatically record the information of goods entering and leaving the warehouse, and set the upper and lower limit warning thresholds of inventory. When the inventory reaches the warning threshold, a trigger instruction is immediately generated and transmitted to S2;

[0009] S2: used to collect data related to mall operations. When receiving a trigger instruction, it collects data related to mall operations, including user behavior data, sales data, logistics data, and market data, and transmits the collected data related to mall operations to S3 and S4;

[0010] S3: Analyze the user's purchase behavior path through user behavior data to obtain the user behavior path monitoring coefficient, monitor the user's behavior path on the website in real time according to the user behavior path monitoring coefficient, and transmit the user behavior path monitoring coefficient to S4;

[0011] S4: used to analyze and monitor the sales status of commodities, obtain the sales status monitoring index by analyzing the sales data, obtain the logistics status monitoring index by analyzing the logistics data, obtain the market status evaluation index by analyzing the market data, and transmit the analysis results to S5;

[0012] S5: Obtain historical mall operation-related data from the database, combine the historical mall operation-related data with the analysis results for comprehensive analysis, obtain sales forecast coefficients, predict future sales trends of the products based on the sales forecast coefficients, and transmit the sales forecast coefficients to S6;

[0013] S6: Analyze the inventory status of the current goods through the user behavior path monitoring coefficient and the sales prediction coefficient, and calculate the commodity scheduling coefficient. Use the commodity scheduling coefficient to schedule and replenish the inventory status of the current goods;

[0014] S7: is used to automatically generate prompt information of inventory status and feed it back to the user, and display the scheduling and replenishment results of the inventory status to the user through the user terminal.

[0015] Preferably, the S1 first classifies and manages the goods by category, sales speed and value, and then connects with the warehousing system to track the inventory changes of various goods in real time, record the goods entering the warehouse information, and update the inventory information data in real time. By setting the inventory upper limit warning threshold Q1 and the inventory lower limit warning threshold Q2 respectively, and Q2<Q1, when the commodity inventory L={q∈I|q≤Q2} or L={q∈I|q≥Q1}, a trigger instruction for data collection is automatically generated immediately, q represents the current inventory of a specific commodity, and I represents a set of commodities whose inventory will change with sales and replenishment activities.

[0016] Preferably, the S2 collects mall operation-related data in real time through the API interface of the e-commerce platform, and automatically classifies and stores the collected mall operation-related data in the database according to the collection time. The user behavior data includes the user's visited pages, the number of times the goods are browsed, the dwelling time, the search frequency, the purchase time, the purchase amount, and the user's return visit frequency; the sales data includes sales volume, sales revenue, sales growth rate and return rate; the logistics data includes commodity inventory, delivery time and delivery cost; the market data includes the market share of the goods, the market sales volume share of the goods and the average market selling price of the goods.

[0017] Preferably, the specific formula for calculating the user behavior path monitoring coefficient by analyzing the user behavior data in S3 is: P1 represents the number of users who perform a specific behavior path, P 总 represents the total number of users, C1 represents the conversion rate of a specific behavior path, and C avg represents the average conversion rate, Z j represents the browsing time of the user for the jth product, W j It represents the purchase frequency of the user for the jth product, and the specific behavior path includes browsing—adding to shopping cart—purchasing.

[0018] Preferably, the S4 analyzes and monitors the sales status of the commodity through sales data, logistics data and market data;

[0019] The specific calculation formula of the sales status monitoring index is: G v represents the sales growth rate, G r represents the sales growth rate, and R represents the return rate;

[0020] The specific calculation formula of the logistics status monitoring index is: T1 represents the average delivery time, T2 represents the target delivery time, O1 represents the actual delivery cost, O2 represents the target delivery cost, and L represents the inventory of the product;

[0021] The specific calculation formula of the market status assessment index is: MS represents the market share of the product, SVR represents the market sales volume share of the product, ASP represents the average market selling price of the product, and α1, α2, and α3 are weight coefficients.

[0022] Preferably, S5 analyzes the future sales trend of the product by simultaneously collecting historical sales data, historical logistics data and historical market data over a period of time, and calculates the sales forecast coefficient as follows: S i represents the sales status monitoring index at the i-th historical time point, M irepresents the logistics status monitoring index at the i-th historical time point, F i It represents the market status evaluation index at the i-th historical time point, t represents the current time point, n represents the number of periods of the moving average, and β1, β2, and β3 are weight coefficients.

[0023] Preferably, S6 analyzes the inventory status of the current goods, thereby performing real-time scheduling and replenishment of the inventory status of the current goods according to the analysis results;

[0024] The specific calculation formula of the commodity scheduling coefficient is: D represents the sales prediction coefficient, A represents the user behavior path monitoring coefficient, and a represents a constant;

[0025] By setting the commodity scheduling threshold X, the commodity scheduling coefficient K is compared with the commodity scheduling threshold X. If the commodity scheduling coefficient K is greater than the commodity scheduling threshold X, it indicates that the demand for the commodity is high, and it is necessary to increase inventory or speed up replenishment. If the commodity scheduling coefficient K is less than the commodity scheduling threshold X, it indicates that the demand for the commodity is low, and it is necessary to reduce inventory or replenishment.

[0026] Preferably, the S7 displays the current inventory status on the product details page and prompts the user terminal of the inventory status. When the inventory status changes, the information displayed on the user terminal is immediately updated.

[0027] To achieve the above object, the present invention provides the following technical solution: an online mall operation system based on mobile Internet, implementing the above online mall operation method based on mobile Internet, comprising:

[0028] Inventory status monitoring module: real-time tracking and monitoring of commodity inventory through the integrated inventory management database, automatic recording of commodity in-and-out information, and setting upper and lower limit warning thresholds for inventory. When the inventory reaches the warning threshold, a trigger instruction is immediately generated and transmitted to the data acquisition module;

[0029] Data collection module: used to collect data related to the mall operation. When receiving a trigger instruction, the data related to the mall operation is collected, including user behavior data, sales data, logistics data and market data, and the collected data related to the mall operation is transmitted to the user behavior analysis module and the sales analysis module;

[0030] User behavior analysis module: Analyze the user's purchase needs and purchase preferences through user behavior data, obtain the user behavior path monitoring coefficient, monitor the user's behavior path on the website in real time according to the user behavior path monitoring coefficient, and transmit the user behavior path monitoring coefficient to the inventory scheduling module;

[0031] Sales analysis module: used to analyze and monitor the sales status of commodities. By analyzing the sales data, the sales status monitoring index is obtained. By analyzing the logistics data, the logistics status monitoring index is obtained. By analyzing the market data, the market status evaluation index is obtained. The analysis results are transmitted to the sales forecasting module.

[0032] Sales forecasting module: by obtaining historical mall operation-related data from the database, combining the historical mall operation-related data with the analysis results for comprehensive analysis, the sales forecast coefficient is obtained, the future sales trend of the product is predicted based on the sales forecast coefficient, and the sales forecast coefficient is transmitted to the inventory scheduling module;

[0033] Inventory scheduling module: Analyze the inventory status of the current goods through the user behavior path monitoring coefficient and the sales forecast coefficient, and calculate the commodity scheduling coefficient. The inventory status of the current goods is scheduled and replenished through the commodity scheduling coefficient;

[0034] Information feedback module: used to automatically generate inventory status prompt information feedback to the user, and display the inventory status scheduling and replenishment results to the user through the user terminal.

[0035] Technical effects and advantages of the present invention:

[0036] The present invention tracks and monitors the inventory of goods in real time through an integrated inventory management database, automatically records the in-and-out information of goods, and sets the upper and lower limit warning thresholds of the inventory. When the inventory reaches the warning threshold, a trigger instruction is immediately generated. When the trigger instruction is received, the data related to the operation of the mall is collected, the user's purchase behavior path is analyzed through the user behavior data, and the user behavior path monitoring coefficient is obtained. The user's behavior path on the website is monitored in real time according to the user behavior path monitoring coefficient. By analyzing the sales data, logistics data and market data respectively, and then combining the historical mall operation related data according to the analysis results, the future sales trend of the goods is comprehensively analyzed and predicted. The inventory status of the current goods is analyzed by combining the user behavior data with the sales forecast data, so as to schedule and replenish the inventory status of the current goods, which is conducive to comprehensively analyzing the inventory demand related data from the sales data, logistics data, market data and historical data, and further predicting the inventory sales trend of different goods, so as to predict the inventory demand of the goods, and realize intelligent replenishment and scheduling. By combining the user's shopping behavior data to analyze the user's inventory demand for the goods, and then scheduling the inventory of the goods, it is conducive to improving the user's purchasing desire and shopping satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a diagram of the method steps of the present invention.

[0038] Figure 2 It is a system structure block diagram of the present invention. DETAILED DESCRIPTION

[0039] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. In addition, the forms of the various structures recorded in the following embodiments are only examples. The online mall operation method and system based on mobile Internet involved in the present invention are not limited to the various structures recorded in the following embodiments. All other implementations obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0040] like Figure 1 This embodiment provides an online shopping mall operation method based on mobile Internet, including:

[0041] S1: Track and monitor the inventory status of goods in real time through the integrated inventory management database, automatically record the information of goods entering and leaving the warehouse, and set the upper and lower limit warning thresholds of the inventory. When the inventory reaches the warning threshold, a trigger instruction is immediately generated and transmitted to S2.

[0042] In this embodiment, the S1 first classifies and manages the goods according to category, sales speed and value, and then connects with the warehousing system to track the inventory changes of various goods in real time, record the goods entry information, and update the inventory information data in real time. By setting the inventory upper limit warning threshold Q1 and the inventory lower limit warning threshold Q2 respectively, and Q2<Q1, when the commodity inventory L={q∈I|q≤Q2} or L={q∈I|q≥Q1}, a trigger instruction for data collection is automatically generated immediately, q represents the current inventory of a specific commodity, and I represents a set of commodities whose inventory will change with sales and replenishment activities.

[0043] It should be specifically noted that real-time tracking and monitoring of inventory status can ensure the timeliness and accuracy of data, reduce inventory errors caused by human errors or information lags, and set upper and lower inventory warning thresholds to facilitate timely analysis of commodity inventory demand and avoid inventory backlogs or out-of-stock situations.

[0044] S2: Used to collect data related to mall operations. When a trigger instruction is received, the data related to mall operations is collected, including user behavior data, sales data, logistics data and market data, and the collected data related to mall operations is transmitted to S3 and S4.

[0045] In this embodiment, the S2 collects mall operation-related data in real time through the API interface of the e-commerce platform, and automatically classifies and stores the collected mall operation-related data in the database according to the collection time. The user behavior data includes the user's visited pages, the number of times the products are browsed, the dwelling time, the search frequency, the purchase time, the purchase amount, and the user's return visit frequency; the sales data includes sales volume, sales revenue, sales growth rate and return rate; the logistics data includes product inventory, delivery time and delivery cost; the market data includes the market share of the product, the market sales volume share of the product and the average market selling price of the product.

[0046] S3: Analyze the user's purchase behavior path through user behavior data to obtain the user behavior path monitoring coefficient, monitor the user's behavior path on the website in real time according to the user behavior path monitoring coefficient, and transmit the user behavior path monitoring coefficient to S4.

[0047] In this embodiment, the specific formula for calculating the user behavior path monitoring coefficient by S3 through user behavior data analysis is: P1 represents the number of users who perform a specific behavior path, P 总 represents the total number of users, C1 represents the conversion rate of a specific behavior path, and C avg represents the average conversion rate, Z j represents the browsing time of the user for the jth product, W j It represents the purchase frequency of the user for the jth product, and the specific behavior path includes browsing—adding to shopping cart—purchasing.

[0048] It should be specifically noted that by analyzing user behavior data, it is helpful to optimize inventory scheduling strategies according to user needs, ensure adequate supply of popular products, reduce out-of-stock phenomena, and through real-time monitoring of user behavior paths, promptly discover and solve problems in the inventory scheduling and replenishment process, improve inventory turnover and operational efficiency, reduce operating costs, and ensure that users can purchase the required goods in a timely manner, thereby improving user satisfaction and loyalty and enhancing the market competitiveness of the mall.

[0049] S4: used to analyze and monitor the sales status of commodities. By analyzing the sales data, a sales status monitoring index is obtained. By analyzing the logistics data, a logistics status monitoring index is obtained. By analyzing the market data, a market status evaluation index is obtained. The analysis results are then transmitted to S5.

[0050] In this embodiment, the S4 analyzes and monitors the sales status of the commodity through sales data, logistics data and market data;

[0051] The specific calculation formula of the sales status monitoring index is: G vrepresents the sales growth rate, G r represents the sales growth rate, and R represents the return rate;

[0052] The specific calculation formula of the logistics status monitoring index is: T1 represents the average delivery time, T2 represents the target delivery time, O1 represents the actual delivery cost, O2 represents the target delivery cost, and L represents the inventory of the product;

[0053] The specific calculation formula of the market status assessment index is: MS represents the market share of the product, SVR represents the market sales volume share of the product, ASP represents the average market selling price of the product, and α1, α2, and α3 are weight coefficients.

[0054] It should be specifically noted that by analyzing logistics data, sales data and market data, the mall can respond to changes in market demand more quickly, which is conducive to timely adjustment of supply chain resources and ensuring the timely supply and delivery of goods.

[0055] S5: Obtain historical mall operation related data from the database, combine the historical mall operation related data with the analysis results for comprehensive analysis, obtain the sales forecast coefficient, predict the future sales trend of the product based on the sales forecast coefficient, and transmit the sales forecast coefficient to S6.

[0056] In this embodiment, S5 simultaneously collects historical sales data, historical logistics data, and historical market data over a period of time to analyze the future sales trend of the product and calculates the sales forecast coefficient as follows: S i represents the sales status monitoring index at the i-th historical time point, M i represents the logistics status monitoring index at the i-th historical time point, F i It represents the market status evaluation index at the i-th historical time point, t represents the current time point, n represents the number of periods of the moving average, and β1, β2, and β3 are weight coefficients.

[0057] It should be specifically noted that by analyzing historical sales data, the mall can more accurately predict future demand for goods, which will help the mall to reasonably arrange purchasing and procurement plans and avoid excessive or insufficient inventory. At the same time, through sales forecast analysis of goods, it is helpful to accurately understand the inventory status and demand trends of different goods.

[0058] S6: Analyze the current inventory status of the goods through the user behavior path monitoring coefficient and the sales forecast coefficient, and calculate the product scheduling coefficient. Use the product scheduling coefficient to schedule and replenish the current inventory status of the goods.

[0059] In this embodiment, S6 analyzes the inventory status of the current goods, and performs real-time scheduling and replenishment of the inventory status of the current goods according to the analysis results;

[0060] The specific calculation formula of the commodity scheduling coefficient is: D represents the sales prediction coefficient, A represents the user behavior path monitoring coefficient, and a represents a constant;

[0061] By setting the commodity scheduling threshold X, the commodity scheduling coefficient K is compared with the commodity scheduling threshold X. If the commodity scheduling coefficient K is greater than the commodity scheduling threshold X, it indicates that the demand for the commodity is high, and it is necessary to increase inventory or speed up replenishment. If the commodity scheduling coefficient K is less than the commodity scheduling threshold X, it indicates that the demand for the commodity is low, and it is necessary to reduce inventory or replenishment.

[0062] It should be specifically noted that by comprehensively considering the sales forecast coefficient and the user behavior path monitoring coefficient, the inventory status and demand trend of the goods can be evaluated more accurately, which is conducive to more reasonable inventory scheduling and replenishment strategies, and improves the accuracy and efficiency of inventory management. According to the level of the commodity scheduling coefficient, the company can adjust the inventory structure more accurately, which helps to ensure sufficient supply of popular goods, reduce inventory backlogs of unsalable goods, improve inventory turnover, and facilitate the reasonable scheduling and replenishment of inventory, thereby reducing the additional costs caused by inventory backlogs and out-of-stocks, and further improving users' purchasing desire and shopping satisfaction.

[0063] S7: is used to automatically generate prompt information of inventory status and feed it back to the user, and display the scheduling and replenishment results of the inventory status to the user through the user terminal.

[0064] In this embodiment, S7 displays the current inventory status on the product details page and prompts the user terminal of the inventory status. When the inventory status changes, the information displayed on the user terminal is immediately updated.

[0065] It should be specifically stated that

[0066] like Figure 2 The embodiment shown provides an implementation system corresponding to the online mall operation method based on mobile Internet, including an inventory status monitoring module, a data acquisition module, a user behavior analysis module, a sales analysis module, a sales forecasting module, an inventory scheduling module and an information feedback module. The inventory status monitoring module is connected to the data acquisition module, the data acquisition module is connected to the user behavior analysis module, the data acquisition module is connected to the sales analysis module, the sales analysis module is connected to the sales forecasting module, the sales forecasting module is connected to the inventory scheduling module, and the inventory scheduling module is connected to the information feedback module.

[0067] The inventory status monitoring module tracks and monitors the inventory status of goods in real time through the integrated inventory management database, automatically records the information of goods entering and leaving the warehouse, and sets the upper and lower limit warning thresholds of the inventory. When the inventory reaches the warning threshold, a trigger instruction is immediately generated and transmitted to the data acquisition module;

[0068] The data collection module is used to collect data related to the operation of the mall. When a trigger instruction is received, the data related to the operation of the mall is collected, including user behavior data, sales data, logistics data and market data, and the collected data related to the operation of the mall is transmitted to the user behavior analysis module and the sales analysis module;

[0069] The user behavior analysis module analyzes the user's purchase needs and purchase preferences through user behavior data to obtain a user behavior path monitoring coefficient, monitors the user's behavior path on the website in real time according to the user behavior path monitoring coefficient, and transmits the user behavior path monitoring coefficient to the inventory scheduling module;

[0070] The sales analysis module is used to analyze and monitor the sales status of commodities, obtain a sales status monitoring index by analyzing sales data, obtain a logistics status monitoring index by analyzing logistics data, obtain a market status evaluation index by analyzing market data, and transmit the analysis results to the sales forecasting module;

[0071] The sales forecasting module obtains historical mall operation-related data from the database, combines the historical mall operation-related data with the analysis results for comprehensive analysis, obtains a sales forecast coefficient, predicts the future sales trend of the product based on the sales forecast coefficient, and transmits the sales forecast coefficient to the inventory scheduling module;

[0072] The inventory scheduling module analyzes the inventory status of the current goods through the user behavior path monitoring coefficient and the sales prediction coefficient, and calculates the commodity scheduling coefficient, and schedules and replenishes the inventory status of the current goods through the commodity scheduling coefficient;

[0073] The information feedback module is used to automatically generate prompt information of the inventory status and feed it back to the user, and display the scheduling and replenishment results of the inventory status to the user through the user terminal.

[0074] 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.

[0075] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for operating an online mall based on mobile Internet, characterized in that: include: S1: Track and monitor the inventory of goods in real time through the integrated inventory management database, automatically record the information of goods entering and leaving the warehouse, and set the upper and lower limit warning thresholds of inventory. When the inventory reaches the warning threshold, a trigger instruction is immediately generated and transmitted to S2; S2: used to collect data related to mall operations. When receiving a trigger instruction, it collects data related to mall operations, including user behavior data, sales data, logistics data, and market data, and transmits the collected data related to mall operations to S3 and S4; S3: Analyze the user's purchase behavior path through user behavior data to obtain the user behavior path monitoring coefficient, monitor the user's behavior path on the website in real time according to the user behavior path monitoring coefficient, and transmit the user behavior path monitoring coefficient to S4; S4: used to analyze and monitor the sales status of commodities, obtain the sales status monitoring index by analyzing the sales data, obtain the logistics status monitoring index by analyzing the logistics data, obtain the market status evaluation index by analyzing the market data, and transmit the analysis results to S5; S5: Obtain historical mall operation-related data from the database, combine the historical mall operation-related data with the analysis results for comprehensive analysis, obtain sales forecast coefficients, predict future sales trends of the products based on the sales forecast coefficients, and transmit the sales forecast coefficients to S6; S6: Analyze the inventory status of the current goods through the user behavior path monitoring coefficient and the sales prediction coefficient, and calculate the commodity scheduling coefficient. Use the commodity scheduling coefficient to schedule and replenish the inventory status of the current goods; S7: is used to automatically generate prompt information of inventory status and feed it back to the user, and display the scheduling and replenishment results of the inventory status to the user through the user terminal.

2. The method for operating an online shopping mall based on mobile Internet according to claim 1, characterized in that: The S1 first classifies and manages the goods according to category, sales speed and value, and then connects with the warehousing system to track the inventory changes of various goods in real time, record the information of goods entering the warehouse, and update the inventory information data in real time. By setting the upper and lower warning thresholds of inventory Q1 and Q2 respectively, and Q2<Q1, when the inventory of the goods L={q∈I|q≤Q2} or L={q∈I|q≥Q1}, a trigger instruction for data collection is automatically generated immediately, q represents the current inventory of a specific product, and I represents a set of goods whose inventory will change with sales and replenishment activities.

3. The method for operating an online shopping mall based on mobile Internet according to claim 1, characterized in that: The S2 collects mall operation-related data in real time through the API interface of the e-commerce platform, and automatically classifies and stores the collected mall operation-related data in the database according to the collection time. The user behavior data includes the user's visited pages, the number of times the goods are browsed, the dwelling time, the search frequency, the purchase time, the purchase amount, and the user's return visit frequency; the sales data includes sales volume, sales revenue, sales growth rate and return rate; the logistics data includes commodity inventory, delivery time and delivery cost; the market data includes the market share of the goods, the market sales volume share of the goods and the average market selling price of the goods.

4. The method for operating an online shopping mall based on mobile Internet according to claim 1, characterized in that: The specific formula for calculating the user behavior path monitoring coefficient by S3 through user behavior data analysis is as follows: P1 represents the number of users who perform a specific behavior path, P 总 represents the total number of users, C1 represents the conversion rate of a specific behavior path, and C avg represents the average conversion rate, Z j represents the browsing time of the user for the jth product, W j It represents the purchase frequency of the user for the jth product, and the specific behavior path includes browsing—adding to shopping cart—purchasing.

5. The method for operating an online shopping mall based on mobile Internet according to claim 1, characterized in that: The S4 analyzes and monitors the sales status of the goods through sales data, logistics data and market data; The specific calculation formula of the sales status monitoring index is: G v represents the sales growth rate, G r represents the sales growth rate, and R represents the return rate; The specific calculation formula of the logistics status monitoring index is: T1 represents the average delivery time, T2 represents the target delivery time, O1 represents the actual delivery cost, O2 represents the target delivery cost, and L represents the inventory of the product; The specific calculation formula of the market status assessment index is: MS represents the market share of the product, SVR represents the market sales volume share of the product, ASP represents the average market selling price of the product, and α1, α2, and α3 are weight coefficients.

6. The method for operating an online shopping mall based on mobile Internet according to claim 1, characterized in that: The S5 simultaneously collects historical sales data, historical logistics data and historical market data over a period of time to analyze the future sales trend of the product and calculates the sales forecast coefficient as follows: S i represents the sales status monitoring index at the i-th historical time point, M i represents the logistics status monitoring index at the i-th historical time point, F i It represents the market status evaluation index at the i-th historical time point, t represents the current time point, n represents the number of periods of the moving average, and β1, β2, and β3 are weight coefficients.

7. The method for operating an online shopping mall based on mobile Internet according to claim 1, characterized in that: S6 analyzes the inventory status of the current goods, and performs real-time scheduling and replenishment of the inventory status of the current goods according to the analysis results; The specific calculation formula of the commodity scheduling coefficient is: D represents the sales prediction coefficient, A represents the user behavior path monitoring coefficient, and a represents a constant; By setting the commodity scheduling threshold X, the commodity scheduling coefficient K is compared with the commodity scheduling threshold X. If the commodity scheduling coefficient K is greater than the commodity scheduling threshold X, it indicates that the demand for the commodity is high, and it is necessary to increase inventory or speed up replenishment. If the commodity scheduling coefficient K is less than the commodity scheduling threshold X, it indicates that the demand for the commodity is low, and it is necessary to reduce inventory or replenishment.

8. The method for operating an online shopping mall based on mobile Internet according to claim 1, characterized in that: The S7 displays the current inventory status on the product details page and prompts the user terminal of the inventory status. When the inventory status changes, the information displayed on the user terminal is immediately updated.

9. An online shopping mall operation system based on mobile Internet, implementing an online shopping mall operation method based on mobile Internet as claimed in any one of claims 1 to 8, characterized in that: include: Inventory status monitoring module: real-time tracking and monitoring of commodity inventory through the integrated inventory management database, automatic recording of commodity in-and-out information, and setting of upper and lower inventory warning thresholds. When the inventory reaches the warning threshold, a trigger instruction is immediately generated and transmitted to the data acquisition module; Data collection module: used to collect data related to the mall operation. When receiving a trigger instruction, the data related to the mall operation is collected, including user behavior data, sales data, logistics data and market data, and the collected data related to the mall operation is transmitted to the user behavior analysis module and the sales analysis module; User behavior analysis module: Analyze the user's purchase needs and purchase preferences through user behavior data, obtain the user behavior path monitoring coefficient, monitor the user's behavior path on the website in real time according to the user behavior path monitoring coefficient, and transmit the user behavior path monitoring coefficient to the inventory scheduling module; Sales analysis module: used to analyze and monitor the sales status of commodities. By analyzing the sales data, the sales status monitoring index is obtained. By analyzing the logistics data, the logistics status monitoring index is obtained. By analyzing the market data, the market status evaluation index is obtained. The analysis results are transmitted to the sales forecasting module. Sales forecasting module: by obtaining historical mall operation-related data from the database, combining the historical mall operation-related data with the analysis results for comprehensive analysis, the sales forecast coefficient is obtained, the future sales trend of the product is predicted based on the sales forecast coefficient, and the sales forecast coefficient is transmitted to the inventory scheduling module; Inventory scheduling module: Analyze the inventory status of the current goods through the user behavior path monitoring coefficient and the sales forecast coefficient, and calculate the commodity scheduling coefficient. The inventory status of the current goods is scheduled and replenished through the commodity scheduling coefficient; Information feedback module: used to automatically generate inventory status prompt information feedback to the user, and display the inventory status scheduling and replenishment results to the user through the user terminal.

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