Artificial Intelligence-Based Cross-Border E-Commerce Supply Chain Data Analysis Method and System

Abstract

The present invention relates to the technical field of cross-border e-commerce supply chain, and discloses a cross-border e-commerce supply chain data analysis method and system based on artificial intelligence. The method has the following effects: The embodiment of the present invention provides a cross-border e-commerce supply chain data analysis method and system based on artificial intelligence. The method is executed by a processor and includes obtaining cross-border e-commerce supply chain data; analyzing through artificial intelligence algorithms to obtain supply chain analysis results for each type of product; obtaining a product number set, matching the product number set with the supply chain analysis results to obtain product number supply chain analysis results; establishing a product supply visualization network model; determining main product nodes in the product supply network model according to the product supply visualization network model; and determining the product supply route between the target product node and the main product node. This method provides an effective high-efficiency transportation method for cross-border e-commerce supply chain through artificial intelligence analysis methods.

Description

Technical Field

[0001] The present invention relates to the technical field of cross-border e-commerce supply chains, and particularly to a data analysis method and system for cross-border e-commerce supply chains based on artificial intelligence.

[0002] e-commerce supply chain data analysis method and system. Background Art

[0003] A cross-border e-commerce supply chain refers to a supply chain system formed by cross-border e-commerce enterprises in conducting business activities such as global commodity procurement, logistics transportation, warehousing storage, sales, etc. It involves different countries and regions and is a complex value chain network.

[0004] Currently, after the user identity information is verified, the e-commerce establishes a communication connection with the user, the e-commerce obtains the target products of the user, generates temporary product information by combining the user identity information, then obtains the user's payment method, generates a temporary order based on the temporary product information and the payment method, after the temporary order is paid, generates order process information according to the order number, and then sends the order process information and the user identity information to the logistics system, and receives the distribution information and the estimated delivery time. Finally, the order process information is tracked at regular intervals.

[0005] In the prior art, due to the increasingly diverse and changing consumer demands, during the export process, the shortest distance cannot be selected for domestic transportation and foreign transportation according to different export destinations, resulting in low transportation efficiency. Summary of the Invention

[0006] The present invention provides a data analysis method for cross-border e-commerce supply chains based on artificial intelligence to achieve the function of high-efficiency transportation of cross-border e-commerce supply chains.

[0007] In a first aspect, to solve the above technical problems, the present invention provides a data analysis method for cross-border e-commerce supply chains based on artificial intelligence, including:

[0008] Obtain cross-border e-commerce supply chain data;

[0009] According to the cross-border e-commerce supply chain, through artificial intelligence algorithm analysis, obtain the supply chain analysis results of each type of product;

[0010] According to the mapping relationship between the product name and the product number, obtain the product number set, match the product number set with the supply chain analysis results, and obtain the product number supply chain analysis results;

[0011] According to the product number supply chain analysis results, establish a product supply visualization network model;

[0012] According to the product supply visualization network model, determine the main product nodes in the product supply visualization network model;

[0013] Determine the product supply route between the target product node and the main product node according to the main product node and the product supply visualization network model.

[0014] Preferably, the cross-border e-commerce supply chain data includes:

[0015] Historical order data, including the order delivery time and the order delivery quantity;

[0016] Inventory data, including the quantity and status information of various products in the warehouse;

[0017] Transportation data, including the time, location, transportation mode, and transportation route information of various products during transportation;

[0018] Supplier data, including the location of the supplier and the product production information.

[0019] Preferably, according to the cross-border e-commerce supply chain, through artificial intelligence algorithm analysis, the supply chain analysis results of each type of product are obtained, including:

[0020] Arrange the cross-border e-commerce product data corresponding to each product to form a cross-border e-commerce product data matrix;

[0021] Input the cross-border e-commerce product data matrix into the K-means clustering algorithm for clustering operation to complete product classification;

[0022] Input the cross-border e-commerce product data of the same type of products into the linear regression model to obtain the supply chain analysis results of each type of product.

[0023] Preferably, the objective function of the K-means clustering algorithm is: Where, is an index value for measuring the product clustering effect, represents the number of categories into which the product is divided, represents the th data feature vector of the product, represents the th central feature vector of the product category group, represents the th product category group.

[0024] Preferably, according to the mapping relationship between the product name and the product number, obtain the product number set, and match the product number set with the supply chain analysis results to obtain the product number supply chain analysis results, including:

[0025] Establish a mapping table between the product name and the product number, and obtain the product numbers of all products by querying the mapping table to form a product number set;

[0026] Traverse each piece of data in the supply chain analysis results to find the product information corresponding to the product number set;

[0027] For the successfully matched product numbers and supply chain analysis results, extract relevant data for integration to form the specific content in the product number supply chain analysis results;

[0028] Perform data cleaning and standardization processing on the product number supply chain analysis results.

[0029] Preferably, based on the product number supply chain analysis results, establishing a product supply visualization network model includes:

[0030] Taking the product numbers as nodes and the various product association relationships in the product number supply chain analysis results as edges;

[0031] Assign attributes to each node and edge; among them, the attributes of the node include the basic information, supply situation, and transparency of the product, and the attributes of the edge include the strength and direction of the supply relationship;

[0032] Using a visualization tool, display the nodes and edges according to the preset layout rules to form a product supply visualization network model.

[0033] Preferably, the steps of the algorithm of the product supply visualization network model include:

[0034] Construct a product node set and an edge set ; where is the product node set, is the edge set; construct a road edge weight function: ; where is the delivery time range, expressed as , is the delivery time, is the number of segments of the delivery time range; construct a piecewise assignment function for the country where the consumer is located: where represents the country where the consumer is located, represents the number of country segments; according to the road edge weight function and the piecewise assignment function, obtain the product supply visualization network model: where represents the final weight.

[0035] Preferably, determining the main product nodes in the product supply visualization network model according to the product supply visualization network model includes: calculating the degree of each node in the product supply visualization network model, where the degree is defined as the number of edges connected to the node; setting a degree threshold, and marking the nodes with a degree greater than the degree threshold as candidate main product nodes;

[0036] Analyzing the importance indicators of the products corresponding to the candidate main product nodes in the supply chain; among them, the importance indicators include the proportion of product sales, profit contribution, and the impact degree of out-of-stock on the supply chain;

[0037] Determining the main product nodes from the candidate main product nodes according to the importance indicators.

[0038] Preferably, determining the product supply route between the target product node and the main product node according to the main product node and the product supply visualization network model includes:

[0039] Taking the main product node as the starting point, traversing all paths in the product supply visualization network model to find the paths connected to the target product node;

[0040] For each path, calculating its path distance;

[0041] Setting a distance threshold, and screening out the paths with a path distance lower than the distance threshold as candidate supply routes;

[0042] Conducting a reliability assessment on the candidate supply routes to obtain a reliability assessment result; among them, the indicators for reliability assessment include transportation stability and supplier reputation;

[0043] Determining the product supply route between the target product node and the main product node from the candidate supply routes according to the reliability assessment result.

[0044] In a second aspect, the present invention provides an artificial intelligence-based cross-border e-commerce supply chain data analysis method, including:

[0045] A data acquisition module for acquiring cross-border e-commerce supply chain data;

[0046] An intelligent algorithm module for analyzing the cross-border e-commerce supply chain through artificial intelligence algorithms to obtain the supply chain analysis results of each type of product;

[0047] A product numbering module for obtaining a product number set according to the mapping relationship between the product name and the product number, and matching the product number set with the supply chain analysis results to obtain the product number supply chain analysis results;

[0048] A model establishment module for establishing a product supply visualization network model according to the product number supply chain analysis results;

[0049] The main node determination module determines the main product nodes in the product supply visualization network model according to the product supply visualization network model;

[0050] The supply route planning module determines the product supply route between the target product node and the main product node according to the main product node and the product supply visualization network model.

[0051] In a third aspect, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for analyzing cross-border e-commerce supply chain data based on artificial intelligence described in any one of the above is implemented.

[0052] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the method for analyzing cross-border e-commerce supply chain data based on artificial intelligence described in any one of the above.

[0053] Compared with the prior art, the present invention has the following beneficial effects: The embodiments of the present invention provide a method and a system for analyzing cross-border e-commerce supply chain data based on artificial intelligence. The method is executed by a processor and includes obtaining cross-border e-commerce supply chain data; analyzing according to the cross-border e-commerce supply chain through an artificial intelligence algorithm to obtain the supply chain analysis results of each type of product; obtaining a product number set according to the mapping relationship between the product name and the product number, and matching the product number set with the supply chain analysis results to obtain the product number supply chain analysis results; establishing a product supply visualization network model according to the product number supply chain analysis results; determining the main product nodes in the product supply visualization network model according to the product supply visualization network model; and determining the product supply route between the target product node and the main product node according to the main product node and the product supply visualization network model.

[0054] This method provides an effective high-efficiency transportation method for cross-border e-commerce supply chains through an artificial intelligence analysis method. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a schematic flowchart of a method for analyzing cross-border e-commerce supply chain data based on artificial intelligence provided by the first embodiment of the present invention;

[0056] Figure 2 is a schematic structural diagram of a system for analyzing cross-border e-commerce supply chain data based on artificial intelligence provided by the second embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] Next, in combination with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0058] Referring to Figure 1 , the first embodiment of the present invention provides a cross-border e-commerce supply chain data analysis method based on artificial intelligence, including the following steps:

[0059] S11, obtaining cross-border e-commerce supply chain data;

[0060] S12, according to the cross-border e-commerce supply chain, through artificial intelligence algorithm analysis, obtaining the supply chain analysis results of each type of product;

[0061] S13, according to the mapping relationship between the product name and the product number, obtaining the product number set, and matching the product number set with the supply chain analysis results to obtain the product number supply chain analysis results;

[0062] S14, according to the product number supply chain analysis results, establishing a product supply visualization network model;

[0063] S15, according to the product supply visualization network model, determining the main product nodes in the product supply visualization network model;

[0064] S16, according to the main product nodes and the product supply visualization network model, determining the product supply route between the target product node and the main product node.

[0065] This cross-border e-commerce supply chain data analysis method based on artificial intelligence has brought innovation and change to the supply chain management in the cross-border e-commerce field.

[0066] This method obtains cross-border e-commerce supply chain data from multiple channels, including historical order data, inventory data, transportation data, supplier data, etc. These rich data sources provide a solid foundation for comprehensively analyzing the cross-border e-commerce supply chain. When analyzing through artificial intelligence algorithms, first, the cross-border e-commerce product data corresponding to each product is arranged into a matrix, and then the K-means clustering algorithm is used for product classification, enabling similar products to be accurately classified. Next, the data of the same type of products is input into a linear regression model to obtain detailed supply chain analysis results for each type of product. This process fully utilizes the high efficiency and accuracy of artificial intelligence algorithms and can deeply explore various characteristics and trends of products in the supply chain. This method obtains a product number set according to the mapping relationship between the product name and the product number and matches it with the supply chain analysis results; this step ensures the accurate correspondence of product information and provides accurate data support for establishing a visual network model subsequently. After establishing the product supply visual network model, the structure of the supply chain is presented intuitively; users can clearly see key information such as the network relationship of product supply, main product nodes, and supply routes; this not only facilitates the decision-making process of supply chain management but also helps enterprises quickly discover potential problems and optimization points; determining the main product nodes and the product supply routes between the target product nodes and the main product nodes helps enterprises focus on key products and key links, optimize resource allocation, and improve the overall efficiency of the supply chain. In terms of accurate analysis, the application of artificial intelligence algorithms makes the analysis results of each type of product more accurate and reliable, providing strong data support for the strategic decision-making of enterprises. The establishment of the visual network model greatly improves the intuitiveness and convenience of supply chain management, making complex supply chain relationships clear at a glance; at the same time, the efficient matching and positioning process can quickly and accurately determine the position and status of products in the supply chain, greatly improving the efficiency and accuracy of supply chain management and winning advantages for cross-border e-commerce enterprises in the fierce market competition.

[0067] In step S11, cross-border e-commerce supply chain data is obtained.

[0068] It should be noted that the cross-border e-commerce supply chain data includes:

[0069] Historical order data, including the order delivery time and the order delivery quantity;

[0070] Inventory data, including the quantity and status information of various products in the warehouse;

[0071] Transportation data, including the time, location, transportation method, and transportation route information of various products during transportation;

[0072] Supplier data, including the location of the supplier and the product production situation information.

[0073] The purchase time in historical order data can help analyze consumers' purchase behavior patterns, predict sales trends, arrange marketing activities and customer service duty hours, and make good inventory and logistics plans; the purchased products can help understand consumers' preferences and needs, and adjust the product portfolio and purchasing strategies; the purchase quantity reflects the scale of market demand, and can reasonably arrange production and inventory, predict demand changes and adjust business strategies; the purchase price helps understand the market price sensitivity and product price positioning, formulate reasonable pricing strategies, evaluate costs and profits, optimize the supply chain cost structure, and the quantity of products in the warehouse in inventory data allows enterprises to understand the inventory level, avoid out-of-stock or overstocking, and reasonably arrange procurement and production; the status information can timely master the product quality, damage, etc., and facilitate taking corresponding measures. The transportation time in transportation data can be used to evaluate logistics efficiency, reasonably arrange the shipping time and estimated delivery time, and improve customer satisfaction; the location can track the location of goods to ensure the safety and timely delivery of goods; the transportation method affects logistics costs and speed, and enterprises can choose appropriate transportation methods according to different needs; the transportation route information helps optimize the logistics route, the supplier location in supplier data is related to logistics costs and delivery times, and enterprises can choose suppliers that are closer or have convenient transportation; the product production situation information allows enterprises to understand the production capacity and supply stability of suppliers, ensure timely supply, and ensure the smooth operation of the supply chain.

[0074] In step S12, according to the cross-border e-commerce supply chain, through artificial intelligence algorithm analysis, the supply chain analysis results of each type of product are obtained.

[0075] It should be noted that the objective function of the K-means clustering algorithm is: Where is the index value for measuring the clustering effect of products, represents the number of categories into which the products are divided, represents the th data feature vector of the product, represents the th central feature vector of the product category group, represents the th product category group.

[0076] In an optional embodiment, first, the cross-border e-commerce product data is arranged to obtain a feature matrix, and the feature matrix includes product attributes, sales data, and supplier information; determine the number of categories ; classify according to the distance between the product and the central vector; update the central vector until the objective function is stable; input the data of the same type of products into a linear regression model to obtain the supply chain analysis results of each type of product.

[0077] The cross-border e-commerce product data corresponding to each product are arranged to form a matrix, realizing the structured collation of data and providing an orderly data basis for subsequent analysis. By inputting the cross-border e-commerce product data matrix into the K-means clustering algorithm for clustering operations, different products can be classified according to their inherent characteristics. This classification helps to better understand the similarities and differences between products, enabling products with similar attributes to be grouped together, facilitating targeted management and analysis of different types of products. After completing the product classification, the cross-border e-commerce product data of the same type of products are input into a linear regression model to obtain the supply chain analysis results for each type of product. This process can deeply explore the specific performance and trends of each type of product in the supply chain. Through the analysis results, various information such as the demand pattern of each type of product, the relationship between inventory levels and sales, and the impact of transportation efficiency on product supply can be understood, providing a strong basis for enterprises to make scientific decisions in all aspects of supply chain management. For product categories with longer transportation times, production and shipment can be arranged in advance to ensure timely meeting of market demand. At the same time, by comprehensively evaluating the supply chain analysis results of each type of product, enterprises can optimize the operation efficiency of the entire cross-border e-commerce supply chain, improve customer satisfaction, and enhance market competitiveness.

[0078] In step S13, according to the mapping relationship between the product name and the product number, a set of product numbers is obtained, and the set of product numbers is matched with the supply chain analysis results to obtain the supply chain analysis results of the product numbers.

[0079] In an alternative embodiment, the obtaining a set of product numbers according to the mapping relationship between the product name and the product number, matching the set of product numbers with the supply chain analysis results, and obtaining the supply chain analysis results of the product numbers includes: establishing a mapping table between the product name and the product number, obtaining the product numbers of all products by querying the mapping table, and forming a set of product numbers; traversing each piece of data in the supply chain analysis results to find the product information corresponding to the set of product numbers; for the successfully matched product numbers and supply chain analysis results, extracting relevant data for integration to form the specific content in the supply chain analysis results of the product numbers; and performing data cleaning and normalization processing on the supply chain analysis results of the product numbers.

[0080] Establishing a mapping table between product names and product numbers and obtaining a set of product numbers provides a standardized way for the accurate identification and management of products. In the cross-border e-commerce supply chain, there is a wide variety of products with different naming methods. Through product numbers, unified and accurate identification can be achieved, avoiding confusion caused by non-standard or ambiguous names; matching the set of product numbers with the supply chain analysis results can associate specific products with corresponding supply chain analysis results; by traversing the supply chain analysis results to find the corresponding product information, it ensures that each product has a clear position and interpretation in the supply chain analysis; integrating the data of the successfully matched product numbers and supply chain analysis results makes the supply chain performance of the products clearer and more specific. In this way, the situation of each product in each link of the supply chain can be understood, providing a basis for precise supply chain management; cleaning and standardizing the data of the product number supply chain analysis results helps to improve the quality and usability of the data; removing redundant, incorrect or non-standard data makes the analysis results more accurate and reliable, facilitating subsequent decision-making and operations. At the same time, the standardized results are also convenient for information sharing and collaborative work among different departments, improving the management efficiency and collaboration of the entire cross-border e-commerce supply chain.

[0081] In step S14, according to the product number supply chain analysis results, a product supply visualization network model is established.

[0082] In an optional embodiment, the establishing of a product supply visualization network model according to the product number supply chain analysis results includes: using the product numbers as nodes and the various product association relationships in the product number supply chain analysis results as edges; assigning attributes to each node and edge; wherein, the attributes of the nodes include the basic information, supply situation and transparency of the products, and the attributes of the edges include the strength and direction of the supply relationship; using a visualization tool to display the nodes and edges according to the preset layout rules to form a product supply visualization network model.

[0083] This process takes the product numbers as nodes and the relationships between various products as edges, which is a very effective way to construct the network structure of the supply chain. In this way, each product is abstracted as a node, and the supply relationships between products are represented as the edges connecting these nodes. Such a graph structure can clearly show the positions of different products in the supply chain and their mutual relationships. For example, if there is an edge between two product nodes, it means that there is a direct supply relationship between these two products, which could be an upstream-downstream relationship or a complementary product relationship; The attribute assignment technology also plays a key role in this process. For node attributes, the basic information of the product can be obtained through database queries or data preprocessing, including descriptive information such as the product name, specifications, and categories. These information can help users quickly identify different products; The supply situation comes from the inventory management system and the production planning system. For example, the inventory level can reflect the current inventory quantity of the product, the production progress can show the production status of the product, and the delivery time can let users know when the product can be delivered to the next link. The transparency attribute can be determined by implementing the whole-process traceability of the product through blockchain technology, etc. If a product can be clearly traced and recorded at all links in the supply chain, then its transparency is relatively high, and users can have more trust in the source and quality of this product; For the attributes of the edges, the intensity of the supply relationship can be determined according to factors such as the transaction frequency and supply volume between products. If two products have frequent transactions and a large supply volume, then the intensity of the supply relationship of the edge between them is relatively high. This means that the connection between these two products in the supply chain is very close and they have a greater impact on each other. The direction of the supply relationship clarifies the supply flow direction between products, such as from the supplier to the manufacturer, from the manufacturer to the distributor. Such a direction attribute can let users clearly understand the flow path of products in the supply chain, which helps to analyze the efficiency and optimization direction of the supply chain. Using visualization tools to display the nodes and edges according to the preset layout rules to form a product supply visualization network model. The visualization tools can use various graphic drawing software or the visualization functions provided by data analysis platforms. Through reasonable layout rules, such as placing related product nodes in close positions and representing the edges with high supply relationship intensity with thicker lines, etc., the visualization network model can be made clearer and easier to read. Users can intuitively understand the structure and relationships of the entire product supply through this visualization network model, so as to better conduct supply chain management and decision-making.

[0084] It should be noted that the steps of the algorithm of the product supply visualization network model include: constructing a set of product nodes and a set of edges ; where is the set of product nodes, is the set of edges; constructing a highway edge weight function: Among them, is the delivery time range, expressed as , is the delivery time, is the number of segments of the delivery time range;

[0085] Construct a piecewise assignment function for the country where the consumer is located: Among them, represents the country where the consumer is located, represents the number of country segments; According to the roadside weight function and the piecewise assignment function, a product supply visualization network model is obtained: Among them, represents the final weight.

[0086] Product node set represents different products, each product is a node in the network, and the edge set represents the association relationship between products. If two products have a supply relationship or other connections, an edge is formed between the corresponding nodes. Roadside weight function In, X is the delivery time range, divided into multiple segments , is the delivery time. According to the segment to which the delivery time belongs, different values are assigned. For example, when , the function value is 1; when , the function value is 2, and so on. This function reflects the influence of delivery time on the network edge weight. Piecewise assignment function for the country where the consumer is located In, represents the country where the consumer is located. Different countries are divided into segments , and different values are assigned according to the segment to which the country where the consumer is located belongs. For example, when , the function value is 1; when , the function value is 2, and so on. This function considers the influence of the country where the consumer is located on the network edge weight. Final weight , integrating the two factors of delivery time and the country where the consumer is located, determines the weight of the edge between nodes in the product supply visualization network through the product of these two factors, so as to more comprehensively construct a visualization network model for better analyzing and understanding the product supply situation.

[0087] By constructing the product node set V and the edge set E, the basic architecture of the product supply visualization network model is clarified. Using the product number as the node and the product association relationship as the edge can intuitively present the positions and interrelationships of different products in the supply chain. This makes the structure of the supply chain clearer, facilitating managers to quickly understand the roles and functions of each product in the entire supply system; endowing attributes to the nodes and edges further enriches the information. The attributes of the nodes include the basic information, supply situation, and transparency of the products, enabling people to quickly understand the specific situation of each product. The attributes of the edges include the strength and direction of the supply relationship, which helps analyze the tightness and flow direction of the supply between different products, providing a specific direction for optimizing the supply chain; using the highway edge weight function and the piecewise assignment function of the country where the consumer is located to construct the final weight , taking into account the two key factors of delivery time and the country where the consumer is located, the piecewise function of the delivery time range reflects the impact of logistics efficiency on the supply chain. A shorter delivery time means higher customer satisfaction and more efficient supply chain operation, while a longer delivery time requires measures to be taken for optimization. The piecewise assignment function of the country where the consumer is located considers the impact of factors such as market demand and regulatory policies in different countries on the supply chain. Different countries have different consumption habits, import policies, etc. By making piecewise assignments to countries, it is possible to better plan and adjust the supply chain according to the situations of different countries, making the product supply visualization network model more comprehensively and accurately reflect the actual situation of the cross-border e-commerce supply chain, providing a powerful tool and decision-making basis for the optimization and management of the supply chain.

[0088] In step S15, according to the product supply visualization network model, the main product nodes in the product supply visualization network model are determined.

[0089] In an alternative embodiment, the determining the main product nodes in the product supply visualization network model according to the product supply visualization network model includes: calculating the degree of each node in the product supply visualization network model, where the degree is defined as the number of edges connected to the node; setting a degree threshold and marking the nodes with degrees greater than the degree threshold as candidate main product nodes; analyzing the importance indicators of the products corresponding to the candidate main product nodes in the supply chain; where the importance indicators include the proportion of product sales, profit contribution, and the impact degree of out-of-stock on the supply chain; determining the main product nodes from the candidate main product nodes according to the importance indicators.

[0090] By calculating the degree of each node in the product supply visualization network model (i.e., the number of edges connected to the node), the connection degree of each product in the supply chain network can be intuitively understood. A node with a higher degree means that the product has supply relationships with more other products and is in a relatively critical position in the supply chain. Setting a degree threshold and marking the nodes with degrees greater than the degree threshold as candidate main product nodes is a preliminary screening method. It can quickly focus on those products with high connectivity in the supply network and provide a scope for further determining the main product nodes. Analyze the importance indicators of the products corresponding to the candidate main product nodes in the supply chain, including the proportion of product sales, profit contribution, and the impact of out-of-stock on the supply chain. Products with a high proportion of sales usually contribute more to the enterprise's revenue. Products with a high profit contribution directly affect the enterprise's profitability. Products with a large impact of out-of-stock on the supply chain indicate that they play a key role in the stability and reliability of the supply chain. Determine the main product nodes from the candidate main product nodes based on these importance indicators. Doing so can ensure that the determined main product nodes not only have high connectivity in the supply network but also have an important position in the enterprise's operation and the stable operation of the supply chain. This method helps enterprises accurately identify key products in the supply chain, so that these main product nodes can be focused on and managed, optimize resource allocation, improve the efficiency and stability of the supply chain, reduce risks, and safeguard the economic benefits and market competitiveness of the enterprise.

[0091] In step S16, according to the main product nodes and the product supply visualization network model, determine the product supply route between the target product node and the main product nodes.

[0092] In an optional embodiment, the determining the product supply route between the target product node and the main product nodes according to the main product nodes and the product supply visualization network model includes: taking the main product nodes as the starting point, traversing all paths in the product supply visualization network model to find the paths connected to the target product node; for each path, calculate its path distance; set a distance threshold, and filter out the paths with path distances lower than the distance threshold as candidate supply routes; conduct a reliability assessment on the candidate supply routes to obtain a reliability assessment result; wherein, the indicators of the reliability assessment include transportation stability and supplier reputation; according to the reliability assessment result, determine the product supply route between the target product node and the main product nodes from the candidate supply routes.

[0093] Traverse all paths in the product supply visualization network model starting from the main product node and search for paths connected to the target product node, which helps to clarify the potential flow direction of the product in the supply chain. In this way, a set of paths connecting the target product and the main product can be screened out from the overall supply chain network, providing a basis for subsequent analysis and selection; calculate the path distance for each path, which can quantify the length or complexity of different paths. The path distance can adopt different measurement methods according to the actual situation, such as transportation time, transportation cost, etc. By calculating the path distance, relatively efficient paths can be initially screened out, reducing the scope of subsequent evaluation; set a distance threshold and screen out the paths with path distances lower than the distance threshold as candidate supply routes, further narrowing the selection range and focusing on relatively shorter or more efficient paths. These candidate supply routes meet the requirements for efficiency to a certain extent; conduct a reliability assessment of the candidate supply routes, considering indicators such as transportation stability and supplier reputation; transportation stability ensures that the product can reach the destination on time and safely during transportation, avoiding delays or losses caused by transportation problems; supplier reputation is related to the quality of the product and the reliability of supply, and suppliers with high reputation are more likely to provide stable and high-quality products; through the reliability assessment, it can be ensured that the selected supply route not only has an advantage in distance but also has high reliability in actual operation; determine the product supply route between the target product node and the main product node from the candidate supply routes according to the reliability assessment results; the route determined in this way comprehensively considers efficiency and reliability, can provide clear guidance for the enterprise's supply chain management, optimize the product supply process, improve the overall operation efficiency and stability of the supply chain, reduce risks, and ensure the smooth progress of the enterprise's production and sales activities.

[0094] Compared with the prior art, the present invention has the following beneficial effects: The method obtains cross-border e-commerce supply chain data from multiple channels, including historical order data, inventory data, transportation data, supplier data, etc. These rich data sources provide a solid foundation for comprehensively analyzing the cross-border e-commerce supply chain. When analyzing through artificial intelligence algorithms, first, the cross-border e-commerce product data corresponding to each product is arranged into a matrix, and then the K-means clustering algorithm is used for product classification, so that similar products can be accurately classified. Then, the data of the same type of products is input into a linear regression model to obtain a detailed supply chain analysis result for each type of product. This process gives full play to the efficiency and accuracy of artificial intelligence algorithms and can deeply explore various characteristics and trends of products in the supply chain. The method obtains a product number set according to the mapping relationship between the product name and the product number, and matches it with the supply chain analysis result; this step ensures the accurate correspondence of product information and provides accurate data support for the subsequent establishment of a visualization network model. After establishing the product supply visualization network model, the structure of the supply chain is intuitively presented; users can clearly see key information such as the network relationship of product supply, main product nodes, and supply routes; this not only facilitates the decision-making process of supply chain management but also helps enterprises quickly discover potential problems and optimization points; determining the main product nodes and the product supply routes between the target product nodes and the main product nodes helps enterprises focus on key products and key links, optimize resource allocation, and improve the overall efficiency of the supply chain; in terms of accurate analysis, the application of artificial intelligence algorithms makes the analysis results of each type of product more accurate and reliable, providing strong data support for the strategic decision-making of enterprises. The establishment of the visualization network model greatly improves the intuitiveness and convenience of supply chain management, making complex supply chain relationships clear at a glance; at the same time, the efficient matching and positioning process can quickly and accurately determine the position and status of products in the supply chain, greatly improving the efficiency and accuracy of supply chain management and winning advantages for cross-border e-commerce enterprises in the fierce market competition.

[0095] Referring to Figure 2 , the second embodiment of the present invention provides an artificial intelligence-based cross-border e-commerce supply chain data analysis system, including:

[0096] A data acquisition module for acquiring cross-border e-commerce supply chain data;

[0097] An intelligent algorithm module for obtaining a supply chain analysis result for each type of product through artificial intelligence algorithm analysis according to the cross-border e-commerce supply chain;

[0098] A product number module for obtaining a product number set according to the mapping relationship between the product name and the product number, matching the product number set with the supply chain analysis result, and obtaining a product number supply chain analysis result;

[0099] A model establishment module that establishes a product supply visualization network model based on the result of the product number supply chain analysis;

[0100] A main node determination module that determines the main product nodes in the product supply visualization network model according to the product supply visualization network model;

[0101] A supply route planning module that determines the product supply route between the target product node and the main product node according to the main product node and the product supply visualization network model.

[0102] It should be noted that the cross-border e-commerce supply chain data analysis system based on artificial intelligence provided in the embodiments of the present invention is used to execute all the process steps of the cross-border e-commerce supply chain data analysis method based on artificial intelligence in the above embodiments. The working principles and beneficial effects of the two correspond one by one, so they will not be elaborated here.

[0103] The embodiments of the present invention also provide a terminal device. The terminal device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above embodiments of the cross-border e-commerce supply chain data analysis method based on artificial intelligence are implemented, such as Figure 1 the step S11 shown. Alternatively, when the processor executes the computer program, the functions of each module / unit in the above device embodiments are implemented, such as the main node determination module.

[0104] Exemplarily, the computer program can be divided into one or more modules / units. The one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0105] The terminal device can be a computing device such as a desktop computer, a notebook, a palm computer, and a smart tablet. The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above components are only examples of the terminal device, and do not constitute a limitation on the terminal device. It may include more or fewer components than the above, or combine some components, or different components. For example, the terminal device may further include input / output devices, network access devices, a bus, etc.

[0106] The so-called processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the terminal device and connects all parts of the entire terminal device through various interfaces and lines.

[0107] The memory can be used to store the computer programs and / or modules. The processor realizes various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory, and by calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0108] Among them, if the modules / units integrated in the terminal device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0109] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement without creative efforts.

[0110] The specific embodiments described above further elaborate on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. It is particularly pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A cross-border e-commerce supply chain data analysis method based on artificial intelligence, characterized in that Executed by a processor, including: Obtain cross-border e-commerce supply chain data; According to the cross-border e-commerce supply chain, through artificial intelligence algorithm analysis, obtain the supply chain analysis results of each type of product; According to the mapping relationship between product names and product numbers, obtain a set of product numbers, match the set of product numbers with the supply chain analysis results, and obtain the product number supply chain analysis results; According to the product number supply chain analysis results, establish a product supply visualization network model; According to the product supply visualization network model, determine the main product nodes in the product supply visualization network model; According to the main product nodes and the product supply visualization network model, determine the product supply routes between the target product nodes and the main product nodes; Among them, the establishing of the product supply visualization network model according to the product number supply chain analysis results includes: Use the product numbers as nodes and the various product association relationships in the product number supply chain analysis results as edges; Assign attributes to each node and edge; among them, the attributes of the nodes include the basic information, supply situation and transparency of the products, and the attributes of the edges include the strength and direction of the supply relationship; Use a visualization tool to display the nodes and edges according to the preset layout rules to form a product supply visualization network model; Among them, the steps of the algorithm of the product supply visualization network model include: constructing a product node set and an edge set ; where is the product node set, is the edge set; Construct the weight function of the roadside: Among them, is the delivery time range, expressed as , is the delivery time, is the number of segments of the delivery time range; construct the piecewise assignment function of the country where the consumer is located: Among them, represents the country where the consumer is located, represents the number of country segments; according to the roadside weight function and the piecewise assignment function, obtain the product supply visualization network model: Among them, represents the final weight; among them, according to the product supply visualization network model, determine the main product nodes in the product supply visualization network model, including: Calculate the degree of each node in the product supply visualization network model, where the degree is defined as the number of edges connected to the node; Set a degree threshold, and mark the nodes with degrees greater than the degree threshold as candidate main product nodes; Analyze the importance indicators of the products corresponding to the candidate main product nodes in the supply chain; among them, the importance indicators include the sales proportion of the products, the profit contribution degree, and the impact degree of out-of-stock on the supply chain; According to the importance indicators, determine the main product nodes from the candidate main product nodes.

2. The cross-border e-commerce supply chain data analysis method based on artificial intelligence according to claim 1, wherein The cross-border e-commerce supply chain data includes: Historical order data, including order delivery time and order delivery quantity; Inventory data, including the quantity and status information of various products in the warehouse; Transportation data, including the time, location, transportation method, and transportation route information of various products during transportation; Supplier data, including the location of the supplier and the product production situation information.

3. The cross-border e-commerce supply chain data analysis method based on artificial intelligence according to claim 1, wherein The obtaining of the supply chain analysis results of each type of product by analyzing through artificial intelligence algorithm according to the cross-border e-commerce supply chain includes: Arrange the cross-border e-commerce product data corresponding to each product to form a cross-border e-commerce product data matrix; Input the cross-border e-commerce product data matrix into the K-means clustering algorithm for clustering operations to complete product classification; Input the cross-border e-commerce product data of the same type of products into a linear regression model to obtain the supply chain analysis results of each type of product.

4. The cross-border e-commerce supply chain data analysis method based on artificial intelligence according to claim 3, characterized in that The objective function of the K-means clustering algorithm is as follows: where is the index value for measuring the clustering effect of products, represents the number of categories into which the products are divided, represents the -th data feature vector of the product, represents the central feature vector of the -th product category group, represents the -th product category group.

5. The cross-border e-commerce supply chain data analysis method based on artificial intelligence according to claim 1, wherein The obtaining of the product number supply chain analysis results by matching the set of product numbers with the supply chain analysis results according to the mapping relationship between product names and product numbers includes: Establish a mapping table between product names and product numbers, and obtain the product numbers of all products by querying the mapping table to form a set of product numbers; Traverse each piece of data in the supply chain analysis results to find the product information corresponding to the set of product numbers; For the successfully matched product numbers and supply chain analysis results, extract relevant data for integration to form the specific content in the product number supply chain analysis results; Perform data cleaning and normalization processing on the product number supply chain analysis results.

6. The cross-border e-commerce supply chain data analysis method based on artificial intelligence according to claim 1, wherein According to the main product nodes and the product supply visualization network model, determine the product supply route between the target product node and the main product nodes, including: Taking the main product nodes as the starting point, traverse all paths in the product supply visualization network model to find the paths connected to the target product node; For each path, calculate its path distance; Set a distance threshold, and filter out the paths with path distances lower than the distance threshold as candidate supply routes; Conduct a reliability assessment on the candidate supply routes to obtain a reliability assessment result; among them, the indicators for reliability assessment include transportation stability and supplier reputation; According to the reliability assessment result, determine the product supply route between the target product node and the main product nodes from the candidate supply routes.

7. A cross-border e-commerce supply chain data analysis system based on artificial intelligence, characterized in that, Used to implement the cross-border e-commerce supply chain data analysis method based on artificial intelligence described in any one of claims 1 to 6, including: A data acquisition module that acquires cross-border e-commerce supply chain data; An intelligent algorithm module that, based on the cross-border e-commerce supply chain, obtains the supply chain analysis results of each type of product through artificial intelligence algorithm analysis; A product number module that, according to the mapping relationship between the product name and the product number, obtains a set of product numbers, matches the set of product numbers with the supply chain analysis results, and obtains the product number supply chain analysis results; A model establishment module that, according to the product number supply chain analysis results, establishes a product supply visualization network model; A main node determination module that, according to the product supply visualization network model, determines the main product nodes in the product supply visualization network model; A supply route planning module that, according to the main product nodes and the product supply visualization network model, determines the product supply route between the target product node and the main product nodes.

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