CN-121981643-A - E-commerce inventory management method and system based on artificial intelligence

CN121981643ACN 121981643 ACN121981643 ACN 121981643ACN-121981643-A

Abstract

The invention discloses an e-commerce inventory management method and system based on artificial intelligence, which improves the intelligent level of inventory management by comprehensively utilizing the layout of an e-commerce warehouse, commodity attributes and sales data. Firstly, warehouse layout and storage state information are acquired, and warehouse efficiency is estimated. And analyzing the similarity of the commodities by using a graph algorithm, capturing a topological structure, dividing commodity storage areas and optimizing the warehouse-out efficiency. And combining sales data to predict commodity sales and obtain sales prediction data. Finally, the inventory cycle and the quantity of the stock of each commodity are determined based on the sales prediction and the storage area data. The invention realizes more efficient inventory management by comprehensively considering warehouse layout, commodity relation and sales trend.

Inventors

HUANG XUDONG
HUANG XUXING
ZHONG TING

Assignees

广州中厨信息产业有限公司

Dates

Publication Date: 20260505
Application Date: 20240116

Claims (10)

1. An electronic commerce inventory management method based on artificial intelligence is characterized by comprising the following steps: Acquiring warehouse layout information, inventory commodity attribute information and current inventory commodity storage state information of a target electronic commerce, and evaluating the ex-warehouse efficiency of the target electronic commerce according to the warehouse layout information and the current inventory commodity storage information of the electronic commerce; Based on a graph algorithm, carrying out inventory commodity similarity analysis on the warehouse layout information, the inventory commodity attribute information and the current inventory commodity storage state information so as to capture a relationship topological structure among inventory commodities; Dividing commodity areas of the warehouse of the target electronic commerce according to the topological structure to obtain commodity storage area data, and optimizing the warehouse-out efficiency of the target electronic commerce according to the commodity storage area data; Acquiring sales volume data of a target electronic commerce in a preset time period, and predicting sales volume conditions of commodities in a future preset time period according to the sales volume data to obtain commodity sales volume prediction data; And determining the stock period and the stock quantity of each commodity in the target electronic commerce according to the sales volume prediction data and the commodity storage area data.
2. The method for managing the e-commerce inventory based on the artificial intelligence of claim 1, wherein the acquiring the warehouse layout information, the inventory commodity attribute information and the current inventory commodity storage state information of the target e-commerce comprises evaluating the ex-warehouse efficiency of the target e-commerce according to the warehouse layout information and the current inventory commodity storage information of the e-commerce, specifically: Acquiring a warehouse plan of a target e-commerce, and acquiring the shelf layout, the warehouse area and the shelf volume of a warehouse according to the warehouse plan to obtain warehouse layout information; collecting inventory commodity attribute information of a target electronic commerce, wherein the inventory commodity attribute information comprises commodity names, sizes, volumes, categories and weights; Acquiring the storage position, the storage quantity and the storage time data of the inventory goods of the warehouse of the target electronic commerce to obtain the current inventory goods storage information of the target electronic commerce; Acquiring sales volume data of the inventory commodity, and evaluating the ex-warehouse frequency of the inventory commodity according to the sales volume data of the inventory commodity; Evaluating the ex-warehouse route and the ex-warehouse difficulty of the inventory commodity according to the warehouse plan and the current inventory commodity storage information; and evaluating the delivery efficiency of the target E-commerce inventory commodity according to the delivery frequency, the delivery route and the delivery difficulty.
3. The method for e-commerce inventory management based on artificial intelligence according to claim 1, wherein the graph algorithm is used for performing inventory commodity similarity analysis on warehouse layout information, inventory commodity attribute information and current inventory commodity storage state information to capture a relationship topological structure among inventory commodities, specifically comprising: Introducing a graph algorithm, acquiring a data processing format of the graph algorithm, and converting warehouse layout information and inventory commodity attribute information into a digital label according to the data processing format; Constructing a graph model based on a graph algorithm, defining each inventory commodity as a node in the graph model according to the inventory commodity attribute information to obtain graph nodes, and endowing each graph node with corresponding inventory commodity attribute information to obtain graph node attribute information; Acquiring physical positions of commodities based on warehouse layout information and current inventory commodity storage state information, and establishing edges of graph nodes of adjacent commodities according to the physical positions; Selecting a community monitoring algorithm to detect graph nodes of the graph model, and identifying commodities with similarity in delivery time, delivery frequency, inventory positions and commodity categories according to graph node attribute information to obtain commodity similarity data; forming a graph node community according to the commodity similarity data; And constructing a topological structure of the graph model according to the graph node communities, and mapping the commodity similarity data into the topological structure to obtain a relationship topological structure among the inventory commodities.
4. The method for managing e-commerce inventory based on artificial intelligence according to claim 3, wherein the method for classifying the commodity area of the warehouse of the target e-commerce according to the topology structure to obtain commodity storage area data, and optimizing the ex-warehouse efficiency of the target e-commerce according to the commodity storage area data comprises the following steps: Extracting features of each graph node community according to the relationship topological structure to obtain commodity similarity features of each graph node community; Determining an inventory commodity storage plan according to similarity characteristics of commodities and warehouse layout information, and carrying out regional division on a target electronic commerce warehouse according to the inventory commodity storage plan to obtain a preliminary division region; Calculating the capacity of the preliminary divided areas, and judging whether the storage capacity of the inventory commodity can be met according to the inventory commodity attribute information in the relation topological structure to obtain storage capacity suitability information; Performing secondary region division on the target e-commerce warehouse according to the storage suitability to obtain commodity storage region data; And optimizing the delivery efficiency of the target electronic commerce according to the commodity storage area data.
5. The method for managing the inventory of the e-commerce based on the artificial intelligence according to claim 1, wherein the acquiring sales volume data of the target e-commerce in the preset time period predicts sales volume conditions of the commodity in the preset time period in the future according to the sales volume data to obtain commodity sales volume prediction data, specifically comprises: acquiring historical sales volume data of a target electronic commerce in a historical preset time period, wherein the historical sales volume data comprises commodity IDs, sales data and sales time; Identifying trend characteristics, price change characteristics and promotion activity characteristics of sales volume data according to the historical sales volume data to obtain sales characteristic data; Analyzing the correlation between the sales volume characteristic data and the sales volume according to a correlation analysis method to obtain correlation sales volume influence characteristics; constructing a sales volume prediction model based on an ARIMA algorithm, and training the sales volume prediction model by using the historical sales volume data and the correlation sales volume characteristics; Acquiring sales volume data and correlation sales volume influence characteristic data of a target e-commerce in the current time, importing the sales volume data and the correlation sales volume influence characteristic data of the current time into a sales volume prediction model, and predicting sales volume in a future preset time period to obtain commodity sales volume prediction data.
6. The method for managing the inventory of the electronic commerce according to claim 1, wherein the determining the inventory period and the quantity of the incoming goods of each commodity in the target electronic commerce according to the sales volume prediction data and the commodity storage area data comprises the following steps: analyzing market trend according to the sales volume forecast data, and analyzing the demand change of the commodity according to the market trend to obtain commodity demand change data in a preset time period in the future; Calculating the capacity of each commodity storage area according to the commodity storage area data, and determining the commodity-in quantity of each commodity according to the capacity and commodity demand change data; acquiring the quality guarantee period of each commodity of a target electronic commerce and the geographic position of a warehouse, calculating commodity transportation time according to the geographic position of the warehouse, and determining a commodity arrival period according to the commodity transportation time; And determining the inventory period of each commodity according to the shelf life and the arrival period of the commodity.
7. An electronic commerce inventory management system based on artificial intelligence, which is characterized by comprising a storage and a processor, wherein the storage comprises an electronic commerce inventory management method program based on artificial intelligence, and the electronic commerce inventory management method program based on artificial intelligence realizes the following steps when being executed by the processor: Acquiring warehouse layout information, inventory commodity attribute information and current inventory commodity storage state information of a target electronic commerce, and evaluating the ex-warehouse efficiency of the target electronic commerce according to the warehouse layout information and the current inventory commodity storage information of the electronic commerce; Based on a graph algorithm, carrying out inventory commodity similarity analysis on the warehouse layout information, the inventory commodity attribute information and the current inventory commodity storage state information so as to capture a relationship topological structure among inventory commodities; Dividing commodity areas of the warehouse of the target electronic commerce according to the topological structure to obtain commodity storage area data, and optimizing the warehouse-out efficiency of the target electronic commerce according to the commodity storage area data; Acquiring sales volume data of a target electronic commerce in a preset time period, and predicting sales volume conditions of commodities in a future preset time period according to the sales volume data to obtain commodity sales volume prediction data; And determining the stock period and the stock quantity of each commodity in the target electronic commerce according to the sales volume prediction data and the commodity storage area data.
8. The e-commerce inventory management system based on artificial intelligence of claim 7, wherein the commodity area division is performed on the warehouse of the target e-commerce according to the topology structure to obtain commodity storage area data, and the ex-warehouse efficiency of the target e-commerce is optimized according to the commodity storage area data, specifically: Extracting features of each graph node community according to the relationship topological structure to obtain commodity similarity features of each graph node community; Determining an inventory commodity storage plan according to similarity characteristics of commodities and warehouse layout information, and carrying out regional division on a target electronic commerce warehouse according to the inventory commodity storage plan to obtain a preliminary division region; Calculating the capacity of the preliminary divided areas, and judging whether the storage capacity of the inventory commodity can be met according to the inventory commodity attribute information in the relation topological structure to obtain storage capacity suitability information; Performing secondary region division on the target e-commerce warehouse according to the storage suitability to obtain commodity storage region data; And optimizing the delivery efficiency of the target electronic commerce according to the commodity storage area data.
9. The system for managing inventory of electronic commerce based on artificial intelligence according to claim 7, wherein the acquiring sales volume data of the target electronic commerce in the preset time period predicts sales volume conditions of the commodity in the preset time period in the future according to the sales volume data to obtain commodity sales volume prediction data, specifically comprises: acquiring historical sales volume data of a target electronic commerce in a historical preset time period, wherein the historical sales volume data comprises commodity IDs, sales data and sales time; Identifying trend characteristics, price change characteristics and promotion activity characteristics of sales volume data according to the historical sales volume data to obtain sales characteristic data; Analyzing the correlation between the sales volume characteristic data and the sales volume according to a correlation analysis method to obtain correlation sales volume influence characteristics; constructing a sales volume prediction model based on an ARIMA algorithm, and training the sales volume prediction model by using the historical sales volume data and the correlation sales volume characteristics; Acquiring sales volume data and correlation sales volume influence characteristic data of a target e-commerce in the current time, importing the sales volume data and the correlation sales volume influence characteristic data of the current time into a sales volume prediction model, and predicting sales volume in a future preset time period to obtain commodity sales volume prediction data.
10. The system for managing the inventory of electronic commerce on the basis of artificial intelligence according to claim 7, wherein the determining the inventory period and the quantity of goods in the target electronic commerce according to the sales volume prediction data and the goods storage area data is specifically as follows: analyzing market trend according to the sales volume forecast data, and analyzing the demand change of the commodity according to the market trend to obtain commodity demand change data in a preset time period in the future; Calculating the capacity of each commodity storage area according to the commodity storage area data, and determining the commodity-in quantity of each commodity according to the capacity and commodity demand change data; acquiring the quality guarantee period of each commodity of a target electronic commerce and the geographic position of a warehouse, calculating commodity transportation time according to the geographic position of the warehouse, and determining a commodity arrival period according to the commodity transportation time; And determining the inventory period of each commodity according to the shelf life and the arrival period of the commodity.

Description

E-commerce inventory management method and system based on artificial intelligence Technical Field The invention relates to the technical field of e-commerce inventory management, in particular to an e-commerce inventory management method and system based on artificial intelligence. Background With the rapid development of the e-commerce industry, inventory management is critical to ensure efficient operation of supply chains and quality of customer service. The traditional inventory management method has the problems of insufficient information, lag reaction and the like, and cannot adapt to the rapidly changing market demands of electronic commerce. Therefore, the invention provides an advanced e-commerce inventory management method and system based on an artificial intelligence technology so as to meet the requirements of rapid development and efficient operation of e-commerce business. First, the layout information of the e-commerce warehouse is complex and various, and the traditional manual evaluation method often has difficulty in comprehensively considering inventory characteristics and ex-warehouse frequencies of different areas, so that the inventory efficiency is low. The introduction of the graph algorithm enables us to grasp the warehouse topology structure as a whole, and more accurately evaluate the ex-warehouse efficiency of different areas, thereby guiding the layout and management of inventory goods more effectively. Secondly, the traditional inventory management method often ignores the relation and the topological structure among commodities, and is difficult to capture the actual situation of commodity storage. The invention uses the graph algorithm to analyze the similarity of the inventory commodities, more accurately captures the topological structure among the commodities, and provides a more accurate data base for the subsequent commodity region division. Comprehensively considering sales trends is also an integral part of the traditional inventory management methods. By acquiring sales data in a preset time period and combining with an artificial intelligence technology to predict sales volume, the invention predicts sales conditions of future commodities more accurately and provides more scientific basis for determining inventory period and commodity quantity. In summary, the method for managing the e-commerce inventory based on artificial intelligence comprehensively improves the intelligent level of inventory management by comprehensively utilizing advanced technologies such as graph algorithm, sales data analysis and the like, and provides powerful support for supply chain management and business development of the e-commerce industry. Disclosure of Invention In order to solve at least one technical problem, the invention provides an e-commerce inventory management method and system based on artificial intelligence. The first aspect of the invention provides an electronic commerce inventory management method based on artificial intelligence, which comprises the following steps: Acquiring warehouse layout information, inventory commodity attribute information and current inventory commodity storage state information of a target electronic commerce, and evaluating the ex-warehouse efficiency of the target electronic commerce according to the warehouse layout information and the current inventory commodity storage information of the electronic commerce; Based on a graph algorithm, carrying out inventory commodity similarity analysis on the warehouse layout information, the inventory commodity attribute information and the current inventory commodity storage state information so as to capture a relationship topological structure among inventory commodities; Dividing commodity areas of the warehouse of the target electronic commerce according to the topological structure to obtain commodity storage area data, and optimizing the warehouse-out efficiency of the target electronic commerce according to the commodity storage area data; Acquiring sales volume data of a target electronic commerce in a preset time period, and predicting sales volume conditions of commodities in a future preset time period according to the sales volume data to obtain commodity sales volume prediction data; And determining the stock period and the stock quantity of each commodity in the target electronic commerce according to the sales volume prediction data and the commodity storage area data. In this scheme, the warehouse layout information, the inventory commodity attribute information and the current inventory commodity storage state information of the target electronic commerce are obtained, and the ex-warehouse efficiency of the target electronic commerce is estimated according to the warehouse layout information and the current inventory commodity storage information of the electronic commerce, specifically: Acquiring a warehouse plan of a target e-commerce, and acquiring the shelf layout, the warehou