Intelligent stockpiling decision-making method based on multi-source data fusion and artificial intelligence analysis

Abstract

The invention relates to the technical field of AI technology collaboration, in particular to an intelligent stockpiling decision-making method based on multi-source data fusion and artificial intelligence analysis. An intelligent stockpiling decision-making method based on multi-source data fusion and artificial intelligence analysis comprises a billion collection cloud chain platform, and is characterized in that the method comprises the following steps: S1, in the billion collection cloud chain platform, constructing a data system supporting AI decision-making; s2, performing AI intelligent analysis and decision suggestion generation; and S3, intelligent order generation and execution. Compared with the prior art, the intelligent goods storage decision-making method based on multi-source data fusion and artificial intelligence analysis is provided, the technology takes a billion collection cloud chain platform as a base, integrates internal and external data through a data middle platform and deploys an AI decision-making service, and intelligent identification and decision-making support for goods storage opportunities are achieved.

Description

Technical Field

[0001] This invention relates to the field of AI technology collaboration technology, specifically to an intelligent inventory decision-making method based on multi-source data fusion and artificial intelligence analysis. Background Technology

[0002] In the field of bulk commodity procurement, strategic stockpiling decisions are a core element affecting enterprise costs and operational efficiency. Currently, supply chain management systems, represented by the YiCaiYunChain platform, have achieved online business processes, but they still face various technical bottlenecks in complex decision-making scenarios such as stockpiling management, making it difficult to support the platform's intelligent upgrades and the exploitation of commercial value.

[0003] As a platform that supports enterprises' core supply chain operations, YiCaiYunChain faces the following technical bottlenecks in inventory storage scenarios: 1. The platform cannot automatically acquire and integrate key data affecting stockpiling decisions (such as real-time market prices and stockpiled inventory). Business personnel need to manually log in to multiple external websites to query real-time prices and manually compile inventory consumption data through cross-departmental inquiries. This results in the YiCaiYunChain platform being unable to provide management with real-time, unified decision-making data.

[0004] 2. For critical decisions such as whether and when to stockpile inventory, the YiCaiYunChain platform only provides basic data query functions and lacks the ability to conduct collaborative analysis of multi-dimensional factors such as price trends, inventory levels, and supplier conditions. Decision-makers primarily rely on subjective judgment based on personal experience, which limits the platform's business intelligence potential.

[0005] 3. After making the decision to stockpile inventory, sales staff still need to manually enter stockpile orders containing numerous estimated fields (specifications, quantity, online price, etc.) on the YiCaiYunChain platform. This requires frequent modifications, making the process cumbersome and prone to errors. This inefficient user experience hinders the platform's operational efficiency in high-frequency business scenarios.

[0006] The core issue is that the existing YiCaiYunChain platform architecture lacks the ability to intelligently perceive, integrate, and make cognitive decisions about multi-source heterogeneous data. To overcome this bottleneck, it is urgent to introduce artificial intelligence (AI) technology to empower it, aiming to achieve two major improvements: first, by providing scientific inventory recommendations through AI analysis models, enhancing the platform's irreplaceability; and second, by using AI agents to automate order creation, greatly improving operational efficiency and user experience. Summary of the Invention

[0007] To overcome the shortcomings of existing technologies, this invention provides an intelligent stockpiling decision-making method based on multi-source data fusion and artificial intelligence analysis. This technology uses the YiCaiYunChain platform as its foundation, integrates internal and external data through a data middle platform, and deploys AI decision-making services to achieve intelligent identification and decision support for stockpiling timing.

[0008] To achieve the above objectives, an intelligent inventory management decision-making method based on multi-source data fusion and artificial intelligence analysis is designed, including the YiCaiYunChain platform. The method is characterized by the following: S1, within the YiCaiYunChain platform, constructs a data system to support AI decision-making; S2 is generated through AI-powered intelligent analysis and decision-making suggestions. S3, intelligent order generation and execution.

[0009] The specific process of step S1 is as follows: S11, the YiCaiYunChain platform builds a hybrid data collection solution based on APIs and empowered by AI; S12 automatically obtains price data from mainstream websites through a standardized API interface; S13 introduces an AI intelligent analysis engine, which ensures that all market data can be automatically, accurately, and structurally collected and stored in the data platform through a unified data cleaning and fusion process; S14 utilizes the YiCaiYunChain platform to develop a dedicated inventory reporting module to record information such as inventory order inbound and outbound, and inventory quantity. S15, Establish data entry standards and assessment system, and ensure that sales staff complete data entry in a timely manner after a business transaction occurs; S16, the AI ​​intelligent analysis engine, utilizes the YiCaiYunChain platform and QiXinBao's existing interfaces to obtain supplier information. It comprehensively identifies and analyzes textual information such as the supplier's latest developments, corporate risks, operating conditions, and evaluation reports to quantify the supplier's stability.

[0010] The specific process of step S2 is as follows: S21 deploys an AI analysis model within the YiCaiYunChain platform, continuously monitoring external price fluctuation trends, internal inventory levels and consumption rates, and supplier operating conditions based on real-time data from the data platform. S22, The AI ​​analysis model compares and analyzes the multidimensional data in step S21 with the preset stockpiling strategy rule base based on the stockpiling strategy rule base; S23, when the analysis results meet the preset stockpiling conditions, a decision recommendation with data support will be automatically generated through intelligent agent technology and actively pushed to the decision-maker's workbench for approval.

[0011] The specific process of step S3 is as follows: S31, After the decision is approved, the salesperson enters key parameters on the interactive interface; S32, the AI ​​intelligent order generation engine, automatically fills in and generates a complete, structured, and submitable stock order based on the key input parameters; In step S33, the generated order will undergo final review and adjustments by the salesperson. Once the salesperson confirms that everything is correct, they can submit the order with a single click, and the YiCaiYunChain platform will automatically complete the order creation process.

[0012] The key parameters include contract number, category of goods purchased, specifications, quantity, etc.

[0013] Compared with existing technologies, this invention provides an intelligent stockpiling decision-making method based on multi-source data fusion and artificial intelligence analysis. This technology uses the YiCaiYunChain platform as its foundation, integrates internal and external data through a data middle platform, and deploys AI decision-making services to achieve intelligent identification and decision support for stockpiling timing.

[0014] This invention not only generates decision-making suggestions when the system determines it is the optimal time to stockpile inventory, but also intelligently generates purchase orders by inputting key parameters. This solution will help the YiCaiYunChain platform achieve a strategic upgrade from a process management tool to an intelligent decision-making and operation platform, significantly enhancing its core technological competitiveness and commercial value. Attached Figure Description

[0015] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0016] The present invention will now be further described with reference to the accompanying drawings.

[0017] Currently, supply chain management systems, represented by the YiCaiYunChain platform, have achieved online business processes, but they still face various technical bottlenecks in complex decision-making scenarios such as inventory management, making it difficult to support the platform's intelligent upgrade and commercial value mining.

[0018] By offering products and services such as cloud procurement, cloud trade, cloud logistics, cloud business opportunities, and cloud traceability, and through the deep application of core technologies such as big data, artificial intelligence, and blockchain, we provide the entire industry with comprehensive and integrated precision supply chain optimization technology solutions, helping companies in the industry chain reduce costs, improve management, and control risks. Essentially, it transforms or indirectly transforms the needs of various stakeholders in the modern construction industry chain into digital assets (these digital assets correspond to physical entities or the demand for physical entities), and then uses the platform to circulate and trade these digital assets, thereby monetizing them.

[0019] Internally: The trading company aggregates the procurement needs of various subsidiaries or project departments, and then centrally procures and selects the best building material suppliers through various methods such as bidding, price inquiry, and point-to-point procurement. The building material suppliers then supply resources to various construction sites in a timely manner based on the progress and order requirements reported by each construction project. This can minimize the large amount of manpower required for order placement and reconciliation in the trade execution process. At the same time, based on the timely and accurate collection of "four flows in one" trade data through online centralized procurement, a reliable transaction credit record is formed, which serves as a basis for risk control and assists the group in carrying out supply chain finance business. This achieves goals such as cost reduction, risk management, and supply coordination, thereby optimizing the allocation of corporate resources and improving efficiency.

[0020] Externally: Driven by digital technology, oriented towards platformization, and based on the B2B operation service capabilities that combine online and offline channels, we provide professional B2B supply chain integrated services to participants in the upstream and downstream of the construction industry.

[0021] For construction companies: By providing services that combine transparency, process tracking, and data archiving, we significantly improve the level of refined management in areas such as material procurement, labor, and equipment leasing, reducing reliance on the professional skills of staff in various positions and helping companies achieve their development goals of cost reduction and efficiency improvement. Based on the accumulation of a large amount of procurement transaction data, and through a closed-loop data system encompassing tender documents, contracts, warehouse receipts, and invoices, we create credible transaction credit records. These records serve as a basis for risk control. Through cloud-based traceability and cooperation with banks and other financial institutions, we establish channels connecting various types of innovative financial products, further helping companies solve problems such as difficulty and high cost of financing.

[0022] For suppliers: Helping platform suppliers efficiently increase their corporate exposure and interactivity, helping to meet the diverse needs of supplier users, achieving brand exposure, improving corporate awareness, and thus obtaining more orders.

[0023] For financial institutions: Procurement information between construction companies and suppliers is indispensable data support for banks to carry out supply chain finance business. Data from the platform can serve as evidence for scenario-based finance, thereby forming a complete closed loop in transactions between construction companies and suppliers to prove the authenticity of transactions. Through supply chain finance services, an innovative "scenario + data + technology" supply chain finance service model is created, directly aggregating the needs of financial institutions for users, transactions, and evidence storage into the bank's traffic pool. This allows for a deeper understanding of industry scenarios, a clearer view of the true situation of procurement transactions in the construction industry, and promotes financial institutions to truly implement inclusive finance for SMEs, providing faster and better benefits to the real economy.

[0024] Once the aforementioned model matures, the plan is to share and empower the platform's business model (data, capabilities, platform) with participants across the upstream and downstream of the industry. This will allow other partners to directly access readily available advantages in products, services, talent, funding, research, and overall solutions, becoming part of each other's efforts without incurring additional costs, jointly empowering the traditional construction industry and sharing the benefits of the modern construction industry internet.

[0025] like Figure 1 As shown, the intelligent stockpiling decision-making method based on multi-source data fusion and artificial intelligence analysis of the present invention has the following specific process: Step S1: Build a data system to support AI decision-making.

[0026] It aims to address the issues of missing external data and fragmented internal data on the platform, providing a real-time, integrated data foundation for AI decision-making.

[0027] S11, External Market Data Acquisition: This invention enhances the YiCaiYunChain platform's ability to cope with complex external data environments by constructing a hybrid acquisition solution that is "API-based and AI-enabled." Building upon the automatic acquisition of price data from mainstream websites through standardized API interfaces, it specifically addresses unstructured challenges such as specifications being ranges (e.g., φ10-12) or prices hidden in remarks fields by introducing an AI intelligent parsing engine. Based on natural language processing technology, this engine can understand and decompose specification ranges into platform-recognizable standard specifications and accurately extract key price information from remarks text. Finally, through a unified data cleaning and fusion process, it ensures that all market data is automatically, accurately, and structurally collected and stored in the data platform.

[0028] S12, Internal Business Data Preparation: The YiCaiYunChain platform has developed a dedicated inventory reporting module to record information such as inventory order inflows and outflows, and inventory quantities, providing reliable key indicators such as inventory quantity and order consumption rate for AI data analysis. To ensure the timeliness and accuracy of the data, strict data entry standards and assessment systems have been established, requiring sales staff to complete data entry promptly after a business transaction occurs.

[0029] S13, Supplier Data Analysis and Quantification: AI utilizes the YiCaiYunChain platform and QiXinBao's existing interfaces to obtain supplier information, comprehensively identify and analyze textual information such as the supplier's latest developments, corporate risks, operating conditions, and evaluation reports, and quantify the supplier's stability.

[0030] Step S2: AI intelligent analysis and decision suggestion generation.

[0031] This transforms individual experience-based decisions into data-driven, scientifically sound decisions. The key to this is deploying an AI-powered decision-making service within the platform.

[0032] S21, Inventory Decision Analysis Model: An AI analysis model is deployed within the YiCaiYunChain platform. Based on real-time data from the data platform, it continuously monitors external price fluctuation trends, internal inventory levels and consumption rates, and the operating status of suppliers.

[0033] S22, Embedded business decision-making logic: The reasoning process of the AI ​​model is deeply embedded in the stockpiling strategy rule base defined by business experts. The above multi-dimensional data is compared and analyzed with the preset stockpiling strategy rule base (for example: "When it is identified that the price is low and the current inventory can only meet the short-term consumption in the future, it is determined to be a potential stockpiling opportunity").

[0034] S23, Suggestion Generation and Push: When the analysis results meet the preset stockpiling conditions, a decision suggestion with data support will be automatically generated through agent technology and actively pushed to the decision-maker's workbench for approval. This suggestion is only a reminder and does not include specific purchase details to ensure that the decision-making power remains with the person.

[0035] Step S3: Intelligent order generation and execution.

[0036] S31, Input Parameters: After the decision is approved, the salesperson only needs to input key parameters on the interactive interface, such as contract number, purchase category, specifications, quantity, etc.

[0037] S32, Intelligent Fill and Order Generation: The AI ​​intelligent order generation engine automatically fills in and generates a complete, structured, and submitable inventory order based on the input key parameters.

[0038] S33, Manual Review and Confirmation: The generated order will be reviewed and adjusted by the salesperson. After the salesperson confirms that everything is correct, they can submit it with one click, and the YiCaiYunChain platform will automatically complete the order creation process.

[0039] Features of the present invention: 1. Automated Inventory Management Process: Based on the business architecture of the YiCaiYunChain platform, this invention automates the entire inventory management process, from data collection, decision analysis, and suggestion delivery to order generation. Assisted by the platform's built-in intelligent engine, this process significantly shortens the decision-making and execution cycle in traditional models, forming a complete "data -> decision -> action" closed loop on the YiCaiYunChain platform.

[0040] 2. "API+AI" Hybrid Data Acquisition: This invention innovatively adopts a hybrid acquisition mode of "API first, AI second" to address the prevalent non-standard specifications (such as φ10-12) and semi-structured information in external price data, including pricing in the remarks field. By integrating Natural Language Processing (NLP) and a data parsing engine, the system can intelligently understand contextual semantics, parse remarks text, and accurately extract key price information, achieving automated and intelligent management of complex and heterogeneous price data.

[0041] 3. Rule-based AI Decision Support Model: The AI ​​decision-making model deployed within the YiCaiYunChain platform adopts a hybrid technical architecture of "business rule base + real-time data monitoring". This engine incorporates stockpiling strategy rules defined by business experts, enabling analysis of multi-dimensional indicators such as price, inventory, and supplier status. The entire decision-making process is traceable, and results are proactively pushed through the platform's message center (workbench / email) after generation, ensuring transparency, explainability, and greater acceptance and trust from business personnel.

[0042] 4. Parameter-Driven Intelligent Order Generation: The YiCaiYunChain platform introduces an intelligent order generation engine that receives key business elements input by the user (such as product name, specifications, and quantity). The engine automatically creates purchase orders based on these parameters. This technology transforms order creation from the traditional "form filling" to a "fill-in-the-blank" mode, while the system retains the authority for manual review and adjustment before final submission, ensuring the accuracy and flexibility of business operations.

[0043] Advantages of the present invention: 1. Improved Data Perception and Processing Efficiency: By establishing automated multi-source data acquisition technology on the YiCaiYunChain platform, the inefficient model of manual data collection and integration has been completely replaced. The preparation time for multi-source data has been shortened from several hours to a few minutes, greatly improving data processing efficiency and timeliness. This lays the core foundation for the platform to build commercial value-added services such as industry data services and supply chain finance risk control.

[0044] 2. Automation and intelligence of decision analysis: By deploying a hybrid AI decision model, the YiCaiYunChain platform has achieved real-time automatic monitoring and quantitative analysis of market opportunities. It overcomes the technical limitations of traditional systems that rely entirely on human experience and have slow response times. The accuracy and response speed of the analysis and decision-making are significantly better than those of the manual mode.

[0045] 3. Automation of Business Execution Processes: This invention deeply optimizes the order creation process in the YiCaiYunChain platform. Through intelligent order generation technology, it simplifies system operations, reduces manual data entry time and the probability of input errors, and improves user stickiness and platform satisfaction.

[0046] 4. Enhance risk warning capabilities: Through real-time data monitoring and multi-factor analysis models, it can identify and automatically warn of risks such as price anomalies and inventory shortages in the early stages. This risk control function greatly enhances the strategic value of the YiCaiYunChain platform in the core supply chain management of enterprises.

[0047] This invention is directly applied to and deeply empowers the Yicaiyun Chain platform. It is one of the core technology modules for achieving intelligent upgrades in supply chain management. Specifically, it is applied to intelligent stockpiling decision-making scenarios for bulk commodities (such as aluminum) within the platform.

[0048] Based on its general technical architecture, the intelligent inventory management decision-making capability implemented in this invention is highly portable and can be widely applied to intelligent procurement and inventory optimization on trading platforms for other bulk commodities such as steel and chemicals; strategic inventory management in the self-operated supply chain management systems of various enterprises; and enterprise resource planning (ERP) systems that require scientific and automated procurement decisions. This fully demonstrates the horizontal scalability and huge commercial potential of this invention as a fundamental intelligent capability of the YiCaiYunChain platform.

Claims

1. A smart inventory management decision-making method based on multi-source data fusion and artificial intelligence analysis, including the YiCaiYunChain platform, characterized in that, The method is as follows: S1, within the YiCaiYunChain platform, constructs a data system to support AI decision-making; S2 is generated through AI-powered intelligent analysis and decision-making suggestions. S3, intelligent order generation and execution.

2. The intelligent stockpiling decision-making method based on multi-source data fusion and artificial intelligence analysis according to claim 1, characterized in that, The specific process of step S1 is as follows: S11, the YiCaiYunChain platform builds a hybrid data collection solution based on APIs and empowered by AI; S12 automatically obtains price data from mainstream websites through a standardized API interface; S13 introduces an AI intelligent analysis engine, which ensures that all market data can be automatically, accurately, and structurally collected and stored in the data platform through a unified data cleaning and fusion process; S14 utilizes the YiCaiYunChain platform to develop a dedicated inventory reporting module to record information such as inventory order inbound and outbound, and inventory quantity. S15, Establish data entry standards and assessment system, and ensure that sales staff complete data entry in a timely manner after a business transaction occurs; S16, the AI ​​intelligent analysis engine, utilizes the YiCaiYunChain platform and QiXinBao's existing interfaces to obtain supplier information. It comprehensively identifies and analyzes textual information such as the supplier's latest developments, corporate risks, operating conditions, and evaluation reports to quantify the supplier's stability.

3. The intelligent stockpiling decision-making method based on multi-source data fusion and artificial intelligence analysis according to claim 1, characterized in that, The specific process of step S2 is as follows: S21 deploys an AI analysis model within the YiCaiYunChain platform, continuously monitoring external price fluctuation trends, internal inventory levels and consumption rates, and supplier operating conditions based on real-time data from the data platform. S22, The AI ​​analysis model compares and analyzes the multidimensional data in step S21 with the preset stockpiling strategy rule base based on the stockpiling strategy rule base; S23, when the analysis results meet the preset stockpiling conditions, a decision recommendation with data support will be automatically generated through intelligent agent technology and actively pushed to the decision-maker's workbench for approval.

4. The intelligent stockpiling decision-making method based on multi-source data fusion and artificial intelligence analysis according to claim 1, characterized in that, The specific process of step S3 is as follows: S31, After the decision is approved, the salesperson enters key parameters on the interactive interface; S32, the AI ​​intelligent order generation engine, automatically fills in and generates a complete, structured, and submitable stock order based on the key input parameters; In step S33, the generated order will undergo final review and adjustments by the salesperson. Once the salesperson confirms that everything is correct, they can submit the order with a single click, and the YiCaiYunChain platform will automatically complete the order creation process.

5. The intelligent stockpiling decision-making method based on multi-source data fusion and artificial intelligence analysis according to claim 4, characterized in that, The key parameters include contract number, category of goods purchased, specifications, quantity, etc.

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