Intelligently collaborative enterprise rapid access and product selection and performance integration method and system

Abstract

The invention relates to the technical field of cross-border e-commerce, in particular to an intelligent collaborative enterprise rapid access and product selection and performance integration method and system, and the method comprises the steps: screening out a high-potential product from multi-source data through a multi-dimensional analysis model, and building an authorization data channel with an enterprise; the system monitors commodity information in real time, completes price negotiation through intelligent price analysis, automatically matches commodities with SKUs, and achieves intelligent racking. In addition, the system monitors sales orders, intelligently dispatches an optimal warehouse, and completes warehouse-out performance through an API interface. Order fulfillment data is fed back to an article selection system to form closed-loop optimization; according to the system, through efficient data processing and intelligent cooperation, the product selection accuracy is remarkably improved, the risk is reduced, the cooperation period is shortened, the information communication efficiency is improved, and comprehensive technical support is provided for cross-border e-commerce operation.

Description

Technical Field

[0001] The present invention relates to the field of cross-border e-commerce technology, and in particular to an intelligent and collaborative method and system for integrating rapid enterprise access and product selection and fulfillment, which is used to achieve integrated management of rapid enterprise access, product selection, price negotiation, product matching and binding, automatic shelving, order delivery, and fulfillment and distribution. Background Art

[0002] With the rapid expansion of cross-border e-commerce, companies face significant challenges in product selection, fulfillment, and supply chain management. Traditional product selection methods rely on manual screening, which is extremely inefficient in the face of massive amounts of product data. This makes it difficult to accurately analyze market trends and shifts in consumer preferences, resulting in unscientific product selection decisions and increased operational risks. Regarding enterprise partnerships, the process from business negotiations to system integration is cumbersome and complex, with information asymmetry and discrepancies in data standards leading to inefficient collaboration. Inventory management and delivery fulfillment lack real-time monitoring and intelligent scheduling, leading to frequent inventory backlogs and out-of-stock issues, as well as low distribution efficiency and high transportation costs.

[0003] In existing technologies, most systems are single-point applications, lacking effective coordination and integration across various links. For example, real-time data sharing and intelligent linkage between product selection and inventory management are impossible. This results in inadequate consideration of inventory levels during product selection, and inventory management cannot be dynamically adjusted based on selection strategies. Furthermore, traditional systems have significant shortcomings in data silos, enterprise collaboration processes, price negotiation and product matching, order fulfillment, and logistics scheduling, lacking an end-to-end, integrated, full-process solution.

[0004] With the development of large-scale artificial intelligence model applications, technologies such as intelligent product selection, price analysis, and product matching have gradually matured, providing the possibility of building a full-process integrated solution. However, there is currently a lack of complete solutions on the market that can fully integrate the advantages of these technologies. Summary of the Invention

[0005] The present invention aims to solve the problems existing in the prior art and provide an intelligent collaborative method and system for rapid enterprise access and integrated product selection and fulfillment. By building an intelligent collaborative data microservice system, it can realize the full-process automated management of enterprises from product selection to fulfillment, improve operational efficiency, and optimize supply chain collaboration.

[0006] The present invention proposes an intelligent collaborative method for integrating enterprise rapid access and product selection and fulfillment, including: Obtain multi-source data from cross-border e-commerce platforms and store it in a database; including: Based on a multi-dimensional analysis model, intelligently screen the multi-source data to generate a list of high-potential products; Based on the list of high-potential products, we will initiate cooperation invitations with target companies and establish authorized data channels, including: Obtain enterprise product information through the authorized data channel, and conduct real-time monitoring and negotiation based on the intelligent price analysis system to obtain final price confirmation data; Based on the final price confirmation data, the intelligent product matching system is called to automatically match and bind the seller's products with the supplier's SKUs to generate product binding data; Based on the product binding data, the intelligent listing system is called to automatically assemble product details according to channel rules and complete the listing operation, generating a listing product record; Monitor the sales orders generated by the listed product records, and automatically select the optimal warehouse based on the multi-dimensional intelligent scheduling system to generate shipping work orders; Based on the delivery work order, the warehouse management system is called through the standardized API interface to complete the outbound operation and realize order fulfillment; The order fulfillment data is fed back to the product selection system to form a closed-loop optimization mechanism for product selection and fulfillment.

[0007] Preferably, the obtaining of multi-source data from the cross-border e-commerce platform includes: At preset time intervals, obtain product data from cross-border e-commerce platforms that have scores above a preset threshold and have high sales rankings; The product data is structured and stored in a unified database.

[0008] Preferably, the intelligent screening of multi-source data based on the multi-dimensional analysis model includes: Analyze product descriptions and review texts through the big model API to extract consumer sentiment and demand characteristics; Build a demand forecasting model based on historical sales data and calculate the product's future sales potential index; Based on the comprehensive evaluation results of product ratings, sales volume, profit margins, and potential index, a list of high-potential products is screened and generated.

[0009] Preferably, initiating a cooperation invitation to the target enterprise and establishing an authorized data channel includes: Generate a cooperation invitation including a personalized product potential analysis report; Guide target companies to complete store authorization binding through the OAuth authorization framework; Establish a secure cross-enterprise data transmission channel based on JWT tokens.

[0010] Preferably, the real-time monitoring and negotiation based on the intelligent price analysis system includes: Capture competitor price data at preset time intervals; Automatically generate initial quotes and concession strategies based on price elasticity models and profit margin analysis; The negotiation results will be fed back to the client through a secure data channel and finally confirmed by the business personnel.

[0011] Preferably, the automatic matching and binding performed by the intelligent product matching system includes: Analyze product images and extract visual features through image recognition technology; Analyze product descriptions based on semantic understanding models and create semantic vectors; Build a SKU feature matrix to achieve accurate cross-platform product matching; Generate a confidence score for the matching result, and trigger a manual review mechanism when it is lower than the preset threshold.

[0012] Preferably, the intelligent listing system automatically assembles product details according to channel rules, including: Extract target platform listing rules from the maintained platform rule library; Automatically adjust product information format and parameters according to the listing rules; Through a unified API scheduling layer, the target platform interface is called to complete the listing operation.

[0013] Preferably, the multi-dimensional intelligent scheduling system automatically selects the optimal warehouse including: A comprehensive scoring model including distance factor, inventory factor, cost factor and timeliness factor is constructed. The calculation formula of the scoring model is: Final score = ×Distance Score+ ×Inventory Score+ ×Cost Score+ × Timeliness score; in, 、 is the weight coefficient, and + + + =1; The weight coefficients are automatically optimized based on historical delivery data.

[0014] Preferably, the product selection-performance closed-loop optimization mechanism includes: Use fulfillment data such as order completion rate, customer satisfaction, and return rate as optimization parameters for product selection algorithms; Dynamically adjust product selection strategies based on product sales performance; Optimize SKU management strategy based on inventory turnover rate indicators.

[0015] Intelligent and collaborative enterprise rapid access and integrated product selection and fulfillment system, including: Multi-source data access module, used to obtain multi-source data from cross-border e-commerce platforms and store them in the database; An intelligent product selection module, configured to intelligently screen the multi-source data based on a multi-dimensional analysis model to generate a list of high-potential products; An enterprise cooperation management module is used to initiate cooperation invitations to target enterprises based on the high-potential product list and establish an authorized data channel; An intelligent price analysis module is used to obtain enterprise product information through the authorized data channel, conduct real-time monitoring and negotiation, and obtain final price confirmation data; An intelligent product matching module, configured to automatically match and bind the seller's products with the supplier's SKUs based on the final price confirmation data, and generate product binding data; An intelligent listing module is used to automatically assemble product details based on the product binding data and channel rules, complete the listing operation, and generate a listing product record; The order fulfillment module is used to monitor the sales orders generated by the listed product records, automatically select the optimal warehouse based on the multi-dimensional intelligent scheduling system, and generate a shipping work order; The fulfillment and delivery module is used to call the warehouse management system through the standardized API interface based on the delivery work order to complete the outbound operation and realize order fulfillment; The data closed-loop module is used to return order fulfillment data to the product selection system, forming a product selection-fulfillment closed-loop optimization mechanism.

[0016] The beneficial effects of the present invention include: improving product selection accuracy and reducing product selection risks through multi-source data acquisition and intelligent screening technology; significantly shortening the enterprise cooperation cycle and improving information communication efficiency through the enterprise rapid cooperation management mechanism; realizing scientific price decision-making based on data analysis through intelligent price analysis and negotiation mechanism; improving product matching accuracy and shelving efficiency through intelligent product matching and automatic shelving; optimizing inventory management and distribution efficiency and reducing operating costs through intelligent order fulfillment and multi-warehouse collaboration; most importantly, by building a complete closed-loop system from product selection to fulfillment, realizing real-time data sharing and seamless business connection, and significantly improving the overall operational efficiency and market competitiveness of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a system architecture diagram of the present invention; Figure 2 This is a flow chart of the system of the present invention; Figure 3 This is the intelligent product selection design diagram of the present invention; Figure 4 Quickly collaborate on design drawings for the present invention enterprise; Figure 5 This is the intelligent price analysis design diagram of the present invention; Figure 6 Matching design drawings for the smart products of the present invention; Figure 7 Assign a warehouse design drawing to the present invention. DETAILED DESCRIPTION

[0018] Please refer to the attached Figure 1-7 The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, but the embodiments of the present invention are not limited thereto.

[0019] Example 1: Overview of the overall process of the method like Figure 2 As shown, the intelligent collaborative enterprise rapid access and product selection and fulfillment integrated method provided by the present invention includes the following main steps: First, the multi-source data access module dynamically acquires product data from cross-border e-commerce platforms. Following pre-set rules, the system periodically captures product information from platforms like Amazon, eBay, and TikTok, focusing on key data such as ratings, sales volume, price, and reviews. This data is standardized through a unified data protocol conversion module, ensuring that data from diverse sources and structures can be uniformly processed by the system. Data transmission is encrypted to ensure data security.

[0020] Next, the acquired data is fed into the intelligent product selection module. This module utilizes large-scale model APIs (such as the Dify platform's intelligent analysis service) to conduct in-depth data analysis. Through multi-dimensional evaluation, it automatically identifies products with high market potential and stores the qualifying product information in the product selection database. The intelligent product selection module not only considers basic metrics such as ratings and sales volume but also uses large-scale model analysis of product reviews and market trends to provide a more comprehensive product evaluation.

[0021] Then, based on the product selection results, the Enterprise Partnership Management module initiates partnership invitations to the companies that own the target products. By quickly establishing authorization channels, efficient data sharing between companies is achieved. This step utilizes intelligent customer service robots to assist in answering sellers' questions, significantly improving partnership efficiency and shortening the time between initial contact and formal partnership.

[0022] Next, the system accesses the partner's product listings through an established data channel, and the intelligent price analysis module conducts real-time monitoring and comparison. Based on market data and pre-set strategies, this module automatically initiates price negotiations and transmits the results back to the client through a secure data channel for confirmation by sales personnel.

[0023] After the price is confirmed, the product management module uses the intelligent product matching system to accurately match and bind the seller's product with the supplier's SKU. This system uses image recognition and text analysis technologies to achieve precise matching, while also supporting manual correction to ensure data consistency.

[0024] Subsequently, the intelligent listing module automatically assembles detailed product information (including descriptions, pictures, keywords, etc.) according to the specific rules of each channel, and calls the corresponding platform API to complete the automatic listing operation.

[0025] After products are listed, the system monitors sales orders in real time. The order fulfillment module comprehensively analyzes factors such as warehouse inventory, geographic location, and freight costs. Using intelligent scheduling algorithms, it automatically selects the optimal warehouse and generates a work order. Based on the work order information, the fulfillment and delivery module calls the warehouse management system's standard API to automatically dispatch orders and schedule logistics, completing order fulfillment.

[0026] Finally, the system will flow the order fulfillment data back to the product selection system, forming a complete data closed loop and achieving continuous optimization.

[0027] This approach is built on Java, Spring Boot, Spring Cloud, and Spring AI frameworks, and integrates large-model workflow platforms (such as Dify) to achieve intelligent data processing. It effectively solves the challenges faced by cross-border e-commerce companies in product selection, corporate cooperation, price negotiation, product matching, and fulfillment, significantly improving operational efficiency and competitiveness.

[0028] Example 2: Multi-source data acquisition and storage This embodiment details the implementation of multi-source data acquisition and storage. At preset intervals, the system retrieves product data from cross-border e-commerce platforms that have scores above a preset threshold and high sales rankings. The system then structures the product data and stores it in a unified database.

[0029] Specifically, the system uses distributed crawler technology to regularly collect product data from major cross-border e-commerce platforms like Amazon, eBay, and TikTok. Following pre-set rules (e.g., starting at 2:00 AM daily), the crawler program focuses on obtaining product records with a score of 4.5 or higher and the top 100 sales rankings. It also screens out products from companies that ship their own products.

[0030] Collected raw data is cleaned and converted through a unified data processing pipeline. The system first standardizes data formats across different platforms, mapping key fields to a unified data model. It then performs a quality check on the data, addressing missing values and outliers. Finally, the processed data is encrypted, transmitted, and stored in the system database.

[0031] The data is stored in the MongoDB document database. Each product record contains the following main fields: {product_id: "unique identifier", source_platform: "Source Platform", product_name: "Product Name", category: "Product Category", rating: "rating", sales_rank: "Sales ranking", price: "price", seller_id: "Seller ID", is_fba: "Whether self-operated delivery", images: ["image URL array"], description: "Product Description", reviews: ["User review sample"], collection_timestamp: "data collection timestamp"} The system has a data update policy that updates information on popular products every 12 hours to ensure data timeliness. At the same time, a data backup mechanism is implemented, with daily incremental backups of the database and weekly full backups to ensure data security and recoverability.

[0032] This multi-source data acquisition and storage method provides a rich, timely, and structured data foundation for subsequent intelligent product selection analysis, while ensuring data quality and security through unified data standards and security mechanisms.

[0033] Example 3: Multi-dimensional analysis model and intelligent screening This embodiment explains in detail how the system intelligently filters multi-source data based on a multi-dimensional analysis model to generate a list of high-potential products.

[0034] The intelligent product selection module builds a three-tiered evaluation framework, combining large-scale model analysis capabilities to comprehensively evaluate products: Basic-level analysis: The system first performs a preliminary screening of products based on preset thresholds, such as a score ≥ 4.5 and a top 100 sales ranking, to establish a basic product pool. This step is implemented using a traditional rules engine, effectively narrowing the scope of analysis.

[0035] Deep Semantic Analysis: For products that pass the initial screening, the system uses the Dify platform's large-scale API model to analyze product descriptions and user reviews. Specifically, the system extracts consumer sentiment (the proportion of positive, negative, and neutral reviews), key demand characteristics (such as specific evaluations of performance, appearance, and user experience), and quality feedback to generate a product quality score. The Dify platform's API supports batch text analysis, and the system interacts with the Dify platform through a unified interface specification to ensure efficient and accurate analysis.

[0036] Predictive Analysis Layer: The system combines historical sales data and market trend information to build a demand forecast model and calculate the product's sales potential index for the next 30 / 60 / 90 days. This model uses an LSTM (Long Short-Term Memory) architecture and comprehensively considers variables such as seasonality, recent growth trends, and market popularity. The forecast formula is: , in: is the potential index of the product, Score for sales, is the growth trend score, Ti is the market heat score, is the quality score. α, β, γ, and δ are weight coefficients, and α + β + γ + δ = 1. The system initially sets α = 0.3, β = 0.25, γ = 0.25, and δ = 0.2, and automatically adjusts the weights periodically based on historical data to optimize prediction accuracy.

[0037] Comprehensive Scoring Mechanism: Based on the results of the multi-dimensional analysis above, the system calculates a comprehensive potential score for each product, ranging from 0 to 100. Products with scores exceeding a preset threshold (typically 75 points) are identified as high-potential and stored in the product selection database. The system regularly backtests and optimizes the model (weekly), continuously improving its accuracy by comparing predicted results with actual sales performance.

[0038] This multi-dimensional analysis model significantly improves product selection accuracy and reduces the risks associated with subjective judgment. By analyzing user review sentiment and demand characteristics through a large-scale API, the system can gain insights into product potential factors that are difficult to capture using traditional methods, enabling more intelligent and accurate product screening. In practice, this approach has increased the product selection success rate by approximately 35% compared to traditional methods, significantly reducing the risk of inventory overstock.

[0039] Example 4: Enterprise Quick Access and Authorization Mechanism This embodiment describes in detail how the system initiates cooperation invitations to target enterprises and establishes authorized data channels based on a list of high-potential products.

[0040] The Enterprise Partnership Management module first extracts high-potential product information from the product selection database, including product ID, seller ID, basic product information, and potential analysis results. The system then links the seller ID with their contact information and generates a personalized partnership invitation.

[0041] Personalized partnership invitations include the following core content: a product potential analysis report (including market demand forecasts, competitive product analysis, and expected revenue estimates), a demonstration of the value of the partnership (including platform advantages, service content, and success cases), and an explanation of the partnership process. The system supports multiple invitation delivery channels, including in-platform messaging, email, and API push, allowing sellers to choose the best contact method based on their preferences.

[0042] Once the seller responds to the invitation, the system will guide the seller into the quick authorization process. This process is based on the OAuth 2.0 protocol and the specific steps are as follows: The system generates an authorization request link, which includes the requested scope (such as store data reading permissions, inventory management permissions, etc.); The seller clicks the link and jumps to the authorization page, where they can select the authorization scope and duration; After the seller confirms the authorization, the system obtains the access token and refresh token; The system stores the token securely in the key management service and sets up an automatic refresh mechanism to ensure long-term validity.

[0043] After authorization is completed, the system builds a secure data channel between the seller and the platform. This channel is implemented based on the following technologies: JWT (JSON Web Token) as an authentication mechanism to ensure secure cross-system communication; Kafka message queues enable real-time data synchronization between enterprises and support high-concurrency data exchange; Unify the data mapping layer to solve the problem of inconsistent data structures between different enterprise systems.

[0044] To improve the efficiency of the cooperation process, the system integrates an intelligent customer service robot with the following functions: Access to the Dify platform provides real-time question answers and 24 / 7 uninterrupted service support; A pre-loaded cooperation process knowledge base can answer common questions about authorization scope, data security, cooperation model, etc. Optimize answer strategies based on interaction history to improve problem solving rates; Supports seamless intervention of manual customer service to ensure that complex problems are resolved in a timely manner.

[0045] Through this rapid access and authorization mechanism, the system significantly shortens the enterprise collaboration cycle from the traditional average of 2-4 weeks to an average of 3 days, increasing the success rate of collaboration by 60%. Furthermore, the establishment of standardized data channels breaks down data barriers between enterprises, laying the foundation for subsequent product information acquisition and price negotiations.

[0046] Example 5: Intelligent Price Analysis and Negotiation System This embodiment describes in detail how the system obtains enterprise product information through authorized data channels and performs real-time monitoring and negotiation based on the intelligent price analysis system.

[0047] The intelligent price analysis module first pulls product listings from the company's store through an established authorized data channel at a preset frequency (usually every 15 minutes). This data includes key information such as product ID, price, inventory status, and sales history. The system also captures pricing information for competing products as reference data.

[0048] The price monitoring and analysis engine builds a real-time data processing pipeline based on Spring Cloud Stream to perform multi-dimensional analysis on the acquired price data: Market positioning analysis: The system collects statistics on the price range of similar products to determine the price positioning of the current product in the market (low-end, mid-end or high-end); Price trend analysis: Identify product price fluctuation patterns and long-term trends through time series analysis; Price elasticity analysis: Based on historical sales data, calculate the impact of price changes on sales volume and generate a price elasticity coefficient.

[0049] The formula for calculating the price elasticity coefficient is: , in: is the price elasticity coefficient, is the percentage change in sales volume, is the price change percentage. Based on the calculation results, the system divides the products into high elasticity ( , medium elasticity ( and low elasticity ( Three categories, and adjust negotiation strategies accordingly.

[0050] The automated negotiation decision-making mechanism is based on a three-tier negotiation strategy framework: Initial quotation strategy: The system generates an initial price suggestion based on the market average price, competitor prices, and product positioning; Concession strategy: Based on marginal profit analysis, determine the scope and pace of price concessions. High-elasticity products adopt a more flexible concession strategy. Bottom line price: Set a price bottom line based on cost structure and minimum profit requirements.

[0051] At the same time, the system introduces a time pressure factor and automatically adjusts the negotiation strategy according to the product life cycle stage: the new product stage emphasizes market penetration, the mature stage emphasizes profit maximization, and the decline stage makes flexible adjustments to clear inventory.

[0052] The negotiation process is conducted through a secure data channel. The system uses end-to-end encryption to ensure the secure transmission of price information. The negotiation process is also recorded using blockchain technology to ensure transaction transparency. The negotiation results are presented to sales personnel through the client, who then provide final confirmation, ensuring the accuracy of human-machine collaborative decision-making.

[0053] This intelligent price analysis and negotiation system enables businesses to make more informed pricing decisions based on data analysis, increasing profits by an average of 15-20% while maintaining market competitiveness. The system's high level of automation significantly reduces manual intervention, shortening price negotiation time from an average of three days to just four hours, significantly improving operational efficiency.

[0054] Example 6: Intelligent Product Matching and Binding System This embodiment describes in detail how the system calls the intelligent product matching system to automatically match and bind the seller's products with the supplier's SKU based on the final price confirmation data.

[0055] The intelligent product matching module receives product information determined after price negotiation and intelligently matches it with the company's internal SKU library. This module uses multimodal matching technology, combining image recognition, text semantic understanding, and attribute mapping to achieve accurate matching: Image recognition technology: The system uses deep learning models (such as ResNet50 or EfficientNet) to analyze product images and extract visual features, including key visual elements such as shape, color, and texture. These features are converted into feature vectors for image similarity calculation.

[0056] Text Semantic Understanding: The system uses the BERT (or its variants) model to analyze product titles and descriptions and generate semantic vectors. This method can understand synonyms, near-synonyms, and the same meaning across different expressions, effectively addressing textual discrepancies.

[0057] Attribute mapping technology: The system constructs a SKU feature matrix that includes key attributes such as size, weight, material, and function, and achieves precise matching at the specification parameter level through attribute comparison algorithms.

[0058] The system integrates the above three matching results into the final matching score through a weighted fusion algorithm. The calculation formula is: , in: is the final matching score, is the image matching score, Score the text match, Score the attribute match. is the corresponding weight, and The initial weights are set to , ,The system will regularly optimize the weight configuration based on the matching effect.

[0059] The matching results generate a confidence score (0-100 points), and the system performs different actions based on the score: Score ≥85: The system determines it as a high-confidence match and automatically completes the binding; 70 points ≤ Rating < 85 points: The system determines it as a medium confidence match and automatically completes the binding but marks it as requiring review; Score <70 points: The system triggers the manual review mechanism, which will be confirmed or adjusted by professionals.

[0060] To improve matching accuracy, the system also implements intelligent information completion function: For products with incomplete information, the system automatically completes the missing information through the big model API; Automatically generate standardized product descriptions based on the feature database of similar products; Intelligently identify keywords and optimize product search exposure.

[0061] After the match is complete, the system establishes a mapping relationship between the seller's product and the supplier's SKU in the database, generating standardized product binding data containing the following key information: external product ID, internal SKU code, matching confidence, attribute mapping relationship, price information, etc. This data will serve as the basis for subsequent automatic listing.

[0062] This intelligent product matching system achieves a matching accuracy rate exceeding 97%, increasing processing efficiency by more than 10 times compared to manual matching. The system supports batch processing and can handle the matching needs of over 10,000 SKUs per day, significantly improving cross-border e-commerce operational efficiency.

[0063] Example 7: Intelligent Shelving System This embodiment describes in detail how the system calls the intelligent listing system based on product binding data to automatically assemble product details according to channel rules and complete the listing operation.

[0064] The intelligent listing module first extracts matched and priced product information from the product binding database, including basic product information, bound SKU data, price information, etc. The system then intelligently assembles and optimizes the product information based on the specific rules of the target sales channel.

[0065] The system maintains a platform rules engine that contains a library of listing rules for major sales platforms, such as Amazon, eBay, and TikTok. This library covers categories, title character limits, image size requirements, description format specifications, and keyword limits. The rule library implements a dynamic update mechanism, monitoring platform policy changes through an API to ensure that rules always meet the latest requirements.

[0066] The product information assembly process includes the following key steps: Product title optimization: The system generates product titles that meet the requirements based on platform rules, and combines the big model API to achieve keyword optimization to increase search exposure opportunities; Product description generation: Based on product attribute data and template library, the system automatically generates product descriptions that meet the platform format requirements, including product features, specifications, instructions, etc. Image processing: The system automatically adjusts the image size, resolution, and format to meet platform requirements, and adds watermarks or special effects when necessary; Price and inventory settings: Set the listing price and inventory quantity based on the previously negotiated price and current inventory status; Logistics option configuration: Automatically configure the optimal logistics plan based on product volume, weight and target market; Keyword setting: The system sets efficient search keywords for products through market analysis to increase product exposure.

[0067] Once product information is assembled, the system completes the listing process by invoking the target platform's API through a unified API dispatch layer. This layer encapsulates the differences between platform APIs and provides a unified listing interface, enabling "one-time configuration, multi-platform release." The dispatch layer also includes an error handling mechanism. If a listing fails, it analyzes the cause and automatically retries or prompts for manual intervention.

[0068] After successful listing, the system records product information, including platform product ID, release date, price, inventory, and other key data. It also sets up automatic monitoring tasks to track product status changes in real time. The system supports batch listings, handling the listing needs of hundreds of products simultaneously, significantly improving operational efficiency.

[0069] This intelligent listing system enables businesses to automate and standardize product listings, reducing the traditional manual listing process (average 40 minutes per item) to automated processing (average 2 minutes per item), increasing efficiency by 20 times. Furthermore, intelligently optimized product information increases product exposure and conversion rates on the platform, boosting sales by an average of over 25%.

[0070] Example 8: Intelligent Order Fulfillment and Multi-Warehouse Collaboration System This embodiment describes in detail how the system monitors the sales orders generated by the listing records of goods, automatically selects the optimal warehouse based on the multi-dimensional intelligent scheduling system, and generates a shipping work order.

[0071] The order fulfillment module monitors the APIs of various sales channels in real time to promptly capture newly generated order information. This captured order data includes complete information such as order number, product information, delivery address, payment method, and delivery requirements. The system standardizes this data and stores it in the order management database, triggering the intelligent warehouse scheduling process.

[0072] The multi-dimensional intelligent scheduling system builds a decision-making model that includes four key factors: Distance factor: The system calculates the actual delivery distance from each warehouse to the customer through geographic information services, taking into account road conditions and traffic restrictions, and generates a distance score; Inventory factor: Real-time analysis of inventory levels in each warehouse, prioritizing warehouses with sufficient inventory to avoid split orders and improve fulfillment efficiency; Cost factor: Calculate the total cost of shipping from different warehouses, taking into account factors such as freight, packaging materials, and labor costs; Time factor: Analyze the delivery time of different logistics methods, combine the urgency of the order (such as whether it is an expedited order), and balance speed and cost.

[0073] The system uses a weighted scoring mechanism to conduct a comprehensive evaluation of each warehouse. The calculation formula is: , Among them: Score is the final score of the warehouse, Score for distance, Score the inventory, Score for cost, Score for timeliness. 、 、 、 is the corresponding weight coefficient, and . System initial settings .

[0074] The system implements an adaptive weight adjustment mechanism that automatically optimizes weight parameters by analyzing historical delivery data. The adjustment algorithm periodically updates weight configurations based on key indicators such as order fulfillment efficiency, customer satisfaction, and delivery costs to continuously optimize scheduling results.

[0075] After selecting the optimal warehouse, the system automatically generates a standardized delivery work order containing the following key information: Basic order information: order number, product details, quantity, etc.; Customer information: consignee, contact information, detailed address, etc.; Shipping instructions: specify warehouse, packaging requirements, logistics methods, etc.; Time requirements: shipping deadline, estimated delivery time, etc.

[0076] To improve inventory management efficiency, the system also integrates inventory forecasting and management functions: Demand forecasting model based on LSTM neural network to predict future sales trends; Set the inventory warning threshold: safety stock = average daily sales * (replenishment cycle + safety factor); Automatically generate replenishment suggestions to ensure dynamic inventory balance.

[0077] This intelligent order fulfillment and multi-warehouse collaboration system enables businesses to monitor and manage inventory in real time, significantly reducing overstocking and out-of-stock issues. The system's intelligent scheduling algorithms optimize warehouse selection and logistics routes, reducing logistics costs by an average of 20% while improving order fulfillment efficiency and customer satisfaction. A dynamic adjustment mechanism powered by big data analytics enables the system to continuously learn and optimize, adapting to changing market and seasonal demand.

[0078] Example 9: Order fulfillment and closed-loop feedback mechanism This embodiment describes in detail how the system calls the warehouse management system through a standardized API interface based on the delivery work order to complete the outbound operation, realize order fulfillment, and return the fulfillment data to the product selection system, forming an implementation method of the product selection-fulfillment closed-loop optimization mechanism.

[0079] After receiving system-generated delivery work orders, the fulfillment and delivery module connects to each warehouse's WMS (Warehouse Management System) through standardized APIs. The system supports seamless integration with mainstream WMS systems, including but not limited to SAP, Oracle, and proprietary WMS, enabling data exchange through predefined interface specifications.

[0080] The outbound operation process includes the following key steps: Work order parsing: The system parses the delivery work order and converts the information into an instruction format that can be recognized by WMS; Storage location allocation: intelligently allocate the optimal picking path based on product characteristics and warehouse layout; Picking instructions: Generate electronic picking orders, support multiple orders combined picking, and improve picking efficiency; Packaging instructions: Provide intelligent packaging suggestions based on product characteristics and delivery requirements; Logistics label generation: Automatically generate standardized logistics labels that meet the requirements of logistics providers; Handover confirmation: record the outbound handover information to ensure clear responsibilities.

[0081] The system uses an API to obtain real-time order fulfillment status, including shipment confirmation, logistics tracking information, and receipt status, enabling visual monitoring of the entire process. For abnormal situations (such as out-of-stock, wrong item picking, and logistics delays), the system has set up automatic early warning and processing mechanisms to ensure timely response and resolution.

[0082] After order fulfillment is completed, the system collects and analyzes fulfillment data to form a closed-loop feedback mechanism for product selection and fulfillment: Sales performance data: including product sales, repurchase rate, conversion rate and other indicators, which serve as training samples for product selection algorithms; Inventory turnover rate: Analyze the inventory turnover of different products and guide the optimization of SKU management strategies; Customer satisfaction: Collect data such as delivery time and product reviews to evaluate product and logistics quality; Return analysis: The system categorizes and counts the reasons for returns, identifying product quality or description issues.

[0083] These data are fed back to the product selection system through the data closed-loop module to achieve the following optimization functions: Dynamic adjustment of product selection strategy: Adjust product selection criteria based on sales performance and market feedback; Weight parameter optimization: Optimize the weights of each item in the multi-dimensional analysis model based on actual sales results; Blacklist mechanism: products / sellers with high return rates or low satisfaction are added to the monitoring list; Seasonal demand forecasting: Identify seasonal sales patterns of products and plan inventory in advance.

[0084] This closed-loop feedback loop utilizes an incremental learning mechanism, with the system regularly updating model parameters (typically weekly) to continuously improve product selection accuracy. Feedback data is also used to generate business analysis reports, providing data support for management decisions.

[0085] Through this comprehensive order fulfillment and closed-loop feedback mechanism, the system enables intelligent management of the entire process, from product selection to fulfillment. Data from all stages is highly interconnected and shared, forming a virtuous cycle of self-optimization. In practice, companies adopting this mechanism have seen significant improvements in key metrics such as product selection success rate, inventory turnover, and customer satisfaction, with operational efficiency increasing by an average of over 40%.

[0086] Example 10: Overall system architecture This embodiment details the overall architecture design of the intelligent collaborative enterprise rapid access and product selection and fulfillment integrated system. Figure 1 shown.

[0087] The system includes the following core functional modules: The Multi-Source Data Access Module is responsible for acquiring product data from cross-border e-commerce platforms, supporting both scheduled crawling and real-time monitoring to ensure data timeliness and integrity. This module utilizes distributed crawler technology to support parallel data collection from multiple platforms, and implements IP rotation and request throttling to mitigate data collection limitations. After unified conversion and processing, the data is stored in a database, providing the foundation for intelligent product selection.

[0088] Intelligent Product Selection Module: This module intelligently screens product data based on a multi-dimensional analysis model to generate a list of high-potential products. This module integrates the Big Model API through the Spring AI framework to perform in-depth semantic analysis of product descriptions and reviews. It also builds a predictive model based on sales data to scientifically assess a product's future sales potential. The module utilizes a microservices architecture, supporting horizontal scalability to accommodate growing data volumes.

[0089] The Enterprise Partnership Management Module initiates partnership invitations to target companies based on a list of high-potential products and establishes authorized data channels. This module integrates three key functions: invitation management, authorization processes, and data channel development. It implements secure authorization through the OAuth framework and employs JWT and Kafka technologies to build an efficient data channel. The module also integrates intelligent customer service, providing 24 / 7 real-time answers to questions and accelerating the partnership process.

[0090] The Intelligent Price Analysis Module obtains enterprise product information through authorized data channels, enabling real-time monitoring and negotiation. This module builds a real-time data processing pipeline based on Spring Cloud Stream. It implements automated price negotiation through price elasticity analysis and a three-tier negotiation strategy framework. The module utilizes end-to-end encryption to ensure data security and supports human-machine collaborative decision-making.

[0091] Intelligent Product Matching Module: This module automatically matches and binds seller products with supplier SKUs based on final price confirmation data. This module utilizes multimodal matching technology, combining image recognition, text semantic understanding, and attribute mapping to achieve high-precision product matching. The module also supports intelligent information completion, automatically repairing incomplete product data.

[0092] Smart Listing Module: Automatically assembles product details and completes listing based on product binding data and channel rules. This module maintains the platform rules engine, supports intelligent assembly and optimization of product information, and enables multi-platform publishing through a unified API scheduling layer. The module includes an error handling mechanism to ensure the stability and success rate of the listing process.

[0093] The Order Fulfillment Module monitors sales orders generated by listed products and automatically selects the optimal warehouse based on a multi-dimensional intelligent scheduling system. This module utilizes a comprehensive scoring model based on four factors: distance, inventory, cost, and timeliness, enabling scientific warehouse selection. It also integrates inventory forecasting and management capabilities to optimize inventory allocation.

[0094] Fulfillment and Distribution Module: Based on the delivery work order, the module calls the warehouse management system through a standardized API interface to complete the outbound operation. This module supports seamless integration with mainstream WMS systems, realizes the full process of order fulfillment, and provides real-time status monitoring and exception handling mechanisms.

[0095] The data closed-loop module is responsible for returning order fulfillment data to the product selection system, forming a closed-loop optimization mechanism for product selection and fulfillment. This module collects and analyzes fulfillment data, continuously optimizes the product selection model through incremental learning, and generates business analysis reports to support management decisions.

[0096] The system utilizes a microservices architecture, built on Java, Spring Boot, Spring Cloud, and Spring AI frameworks, achieving high cohesion and low coupling among services. Modules communicate via RESTful APIs and message queues, ensuring scalability and flexibility. The system utilizes Kubernetes containerized deployment, supporting automatic service scaling. A comprehensive monitoring system based on Prometheus and Grafana ensures high availability and stability.

[0097] Example 11: Application Case This example demonstrates the effectiveness of this system in actual cross-border e-commerce operations. Before adopting this system, a cross-border e-commerce company faced numerous challenges, including inefficient product selection, long business cooperation cycles, and difficult inventory management. This led to high operating costs and slow market response.

[0098] After deploying this system, the company's operational processes have been significantly optimized: Intelligent product selection: The system automatically collects over 50,000 pieces of product data daily from platforms like Amazon. Using multi-dimensional analysis models, it selects approximately 500 high-potential products with an accuracy rate exceeding 85%. Compared to previous manual selection processes, which could only process approximately 1,000 pieces of data daily, this represents a 50-fold increase in efficiency. The success rate of product selection has also increased from 50% to 85%, significantly reducing inventory risk.

[0099] In the enterprise partnership phase, the system automatically sends personalized partnership invitations to sellers of high-potential products. Intelligent customer service robots answer frequently asked questions, shortening the enterprise partnership cycle from an average of 18 days to 3 days and increasing the partnership success rate by 60%. Through the establishment of standardized data channels, the system can obtain store data immediately after authorization is completed, significantly reducing data integration time.

[0100] Price Negotiation: The system automatically initiates price negotiations based on market data and price elasticity analysis, reducing negotiation cycles from an average of three days to four hours. This data-driven pricing decision-making also increases average profit margins by 15%. The system can handle price analysis and negotiation requests for over 1,000 SKUs daily, more than 10 times the capacity of manual processing.

[0101] Product matching and listing: Multimodal matching technology achieves a 97% product matching accuracy rate, and the automated listing function reduces listing time from an average of 40 minutes per item to 2 minutes per item. Optimized product information increases product exposure and conversion rates, resulting in an average 25% improvement in sales results. The system can handle over 500 product listing requests daily, significantly improving operational efficiency.

[0102] Order fulfillment: A multi-dimensional intelligent scheduling system automatically selects the optimal warehouse, reducing logistics costs by 20% while improving order fulfillment efficiency and accuracy. The inventory forecasting model helps companies reduce the risk of overstocking, increase inventory turnover by 30%, and reduce capital utilization by approximately 25%.

[0103] Through a closed-loop optimization mechanism for product selection and fulfillment, the system continuously learns and optimizes, resulting in monthly improvements in various indicators. One year after implementation, the company's overall operational efficiency increased by 40%, market responsiveness increased by 50%, operating costs decreased by 25%, customer satisfaction increased by 20%, and market competitiveness significantly strengthened.

[0104] This case fully demonstrates the application value of this system in actual business and proves that intelligent collaborative full-process integrated management can significantly improve the operational efficiency and market competitiveness of cross-border e-commerce companies.

[0105] This invention provides an intelligent, collaborative, integrated method and system for rapid enterprise access and product selection and fulfillment. By building an intelligent, collaborative data microservices system, it enables automated management of the entire enterprise process, from product selection to fulfillment. The system integrates functional modules such as multi-source data acquisition, intelligent product selection analysis, rapid enterprise collaboration, intelligent price negotiation, automatic product matching and listing, and intelligent order fulfillment, and achieves continuous optimization through a closed-loop data feedback mechanism.

[0106] Compared with the existing technology, the present invention has the following outstanding advantages: improving product selection accuracy through multi-dimensional analysis models; shortening the enterprise cooperation cycle through standardized authorization processes; improving negotiation efficiency through data-driven price analysis; achieving high-precision product matching through multimodal matching technology; optimizing the order fulfillment process through intelligent scheduling algorithms; and most importantly, achieving seamless collaboration and continuous optimization of all links through the construction of a complete data closed loop.

[0107] The present invention is applicable to the rapid access, product selection and contract fulfillment management of enterprises in the cross-border e-commerce field, which can significantly improve the operational efficiency and market competitiveness of enterprises, and provide a new intelligent solution for cross-border e-commerce enterprises.

[0108] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. An intelligent collaborative enterprise rapid access and integrated product selection and fulfillment method, characterized by: include: Obtain multi-source data from cross-border e-commerce platforms and store it in a database; including: Based on a multi-dimensional analysis model, intelligently screen the multi-source data to generate a list of high-potential products; Based on the list of high-potential products, we will initiate cooperation invitations with target companies and establish authorized data channels, including: Obtain enterprise product information through the authorized data channel, and conduct real-time monitoring and negotiation based on the intelligent price analysis system to obtain final price confirmation data; Based on the final price confirmation data, the intelligent product matching system is called to automatically match and bind the seller's products with the supplier's SKUs to generate product binding data; Based on the product binding data, the intelligent listing system is called to automatically assemble product details according to channel rules and complete the listing operation, generating a listing product record; Monitor the sales orders generated by the listed product records, and automatically select the optimal warehouse based on the multi-dimensional intelligent scheduling system to generate shipping work orders; Based on the delivery work order, the warehouse management system is called through the standardized API interface to complete the outbound operation and realize order fulfillment; The order fulfillment data is fed back to the product selection system to form a closed-loop optimization mechanism for product selection and fulfillment.

2. The method according to claim 1, characterized in that The acquisition of multi-source data from the cross-border e-commerce platform includes: At preset time intervals, obtain product data from cross-border e-commerce platforms that have scores above a preset threshold and have high sales rankings; The product data is structured and stored in a unified database.

3. The method according to claim 1, characterized in that The intelligent screening of multi-source data based on the multi-dimensional analysis model includes: Analyze product descriptions and review texts through the big model API to extract consumer sentiment and demand characteristics; Build a demand forecasting model based on historical sales data and calculate the product's future sales potential index; Based on the comprehensive evaluation results of product ratings, sales volume, profit margins, and potential index, a list of high-potential products is screened and generated.

4. The method according to claim 1, wherein Initiating a cooperation invitation to the target enterprise and establishing an authorized data channel includes: Generate a cooperation invitation including a personalized product potential analysis report; Guide target companies to complete store authorization binding through the OAuth authorization framework; Establish a secure cross-enterprise data transmission channel based on JWT tokens.

5. The method according to claim 1, wherein The real-time monitoring and consultation based on the intelligent price analysis system includes: Capture competitor price data at preset time intervals; Automatically generate initial quotes and concession strategies based on price elasticity models and profit margin analysis; The negotiation results will be fed back to the client through a secure data channel and finally confirmed by the business personnel.

6. The method according to claim 1, characterized in that The automatic matching and binding of the intelligent product matching system includes: Analyze product images and extract visual features through image recognition technology; Analyze product descriptions based on semantic understanding models and create semantic vectors; Build a SKU feature matrix to achieve accurate cross-platform product matching; Generate a confidence score for the matching result, and trigger a manual review mechanism when it is lower than the preset threshold.

7. The method according to claim 1, characterized in that The intelligent listing system automatically assembles product details according to channel rules, including: Extract target platform listing rules from the maintained platform rule library; Automatically adjust product information format and parameters according to the listing rules; Through a unified API scheduling layer, the target platform interface is called to complete the listing operation.

8. The method according to claim 1, characterized in that The multi-dimensional intelligent scheduling system automatically selects the optimal warehouse including: A comprehensive scoring model including distance factor, inventory factor, cost factor and timeliness factor is constructed. The calculation formula of the scoring model is: Final score = ×Distance Score+ ×Inventory Score+ ×Cost Score+ × Timeliness score; in, 、 is the weight coefficient, and + + + =1; The weight coefficients are automatically optimized based on historical delivery data.

9. The method according to claim 1, characterized in that The product selection-performance closed-loop optimization mechanism includes: Use fulfillment data such as order completion rate, customer satisfaction, and return rate as optimization parameters for product selection algorithms; Dynamically adjust product selection strategies based on product sales performance; Optimize SKU management strategy based on inventory turnover rate indicators.

10. An intelligent collaborative enterprise rapid access and product selection and fulfillment integrated system that implements the method according to any one of claims 1 to 9, characterized in that: include: Multi-source data access module, used to obtain multi-source data from cross-border e-commerce platforms and store it in the database; An intelligent product selection module, configured to intelligently screen the multi-source data based on a multi-dimensional analysis model to generate a list of high-potential products; An enterprise cooperation management module, configured to initiate cooperation invitations to target enterprises based on the high-potential product list and establish an authorized data channel; An intelligent price analysis module is used to obtain enterprise product information through the authorized data channel, conduct real-time monitoring and negotiation, and obtain final price confirmation data; An intelligent product matching module, configured to automatically match and bind the seller's products with the supplier's SKUs based on the final price confirmation data, and generate product binding data; An intelligent listing module, configured to automatically assemble product details based on the product binding data and channel rules, complete the listing operation, and generate a listing product record; The order fulfillment module is used to monitor the sales orders generated by the listed product records, automatically select the optimal warehouse based on the multi-dimensional intelligent scheduling system, and generate a shipping work order; The fulfillment and delivery module is used to call the warehouse management system through the standardized API interface based on the delivery work order to complete the outbound operation and realize order fulfillment; The data closed-loop module is used to return order fulfillment data to the product selection system, forming a product selection-fulfillment closed-loop optimization mechanism.

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