AI marketing and production collaboration system driven by industrial agent-driven dynamic resource allocation

Abstract

This invention discloses an AI-driven marketing and production collaboration system for dynamic resource allocation driven by an industrial intelligent agent. The system includes a multi-role cognitive interaction module, a product modeling and value grading module, a multi-scenario digital twin module, a transaction data assetization module, an omnichannel sales attribution module, an industrial cluster intelligent agent module, a big data prediction model module, a cloud-based central scheduling module, an edge node computing module, and a three-brain fusion decision-making module. The system integrates a multi-dimensional data acquisition module, combines it with the industrial cluster intelligent agent module to achieve data interoperability among multiple entities within the cluster, completes full data fusion in the cloud-based central system, and uses the big data prediction model module to accurately predict market and production trends, providing quantitative basis for decision-making. This forms a closed-loop system encompassing data acquisition, fusion, prediction, decision-making, execution, and feedback, improving resource allocation efficiency and decision-making scientific rigor, and solving problems such as fragmented production and sales data and delayed collaborative response.

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence technology, specifically relating to an AI marketing and production collaboration system driven by an industrial intelligent agent and featuring dynamic resource allocation. Background Technology

[0002] Currently, artificial intelligence, digital twins, and big data technologies have been gradually applied to industrial marketing and production processes. Individual intelligent systems in each process have achieved local data collection and processing, but there are still many technical bottlenecks in the full-chain collaboration of marketing and production at the industrial cluster level, and an integrated dynamic resource allocation system has not yet been formed.

[0003] In existing technologies, the production, marketing, and inventory data of enterprises within an industrial cluster are fragmented, lacking dedicated modules to enable data exchange among multiple entities and cross-enterprise resource scheduling, resulting in low overall resource allocation efficiency. Furthermore, multi-dimensional data is processed in a dispersed manner, lacking a unified cloud-based fusion mechanism, making it impossible to form a global industry dataset and resulting in incomplete decision-making basis. Simultaneously, the lack of professional big data prediction modules makes it difficult to quantitatively predict market demand, production load, and other trends through algorithms, leading to marketing and production decisions relying heavily on experience, which easily results in mismatches between production scheduling and demand, and inefficient resource allocation.

[0004] Furthermore, the existing system's marketing-to-production process lacks a closed loop, and the results of command execution cannot be accurately fed back to the data acquisition module. The modeling and acquisition logic of each module cannot be dynamically optimized. Moreover, the collaboration between the cloud and the edge is insufficient. The direct uploading of massive amounts of raw data to the cloud results in high computational pressure and response delays. Local production and marketing operations lack rapid execution capabilities, ultimately leading to slow response and low scientific decision-making in industrial marketing and production collaboration, and an inability to achieve dynamic adaptation across the entire resource chain. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes an AI-driven marketing and production collaboration system with dynamic resource allocation driven by industrial intelligent agents. This system possesses full-dimensional data fusion and cluster intelligent scheduling capabilities, and can achieve a collaborative solution with closed-loop linkage across the entire marketing-to-production chain. Specifically, this is the AI-driven marketing and production collaboration system and method with dynamic resource allocation driven by industrial intelligent agents, which aims to overcome existing technological bottlenecks and improve the collaborative efficiency and dynamic resource allocation level of industrial cluster marketing and production.

[0006] In the first aspect, the present invention proposes an AI marketing and production collaboration system driven by an industrial intelligent agent for dynamic resource allocation. The system includes a multi-role cognitive interaction module, a product modeling and value grading module, a multi-scenario digital twin module, a transaction data assetization module, an omni-channel sales attribution module, an industrial cluster intelligent agent module, a big data prediction model module, a cloud central scheduling module, an edge node computing module, and a three-brain fusion decision-making module.

[0007] Specifically, the system includes:

[0008] The multi-role cognitive interaction module collects behavioral characteristic data and interaction data of internal enterprise roles, customer groups, ecosystem partners and competitors, constructs an AI digital human model, and outputs cognitive intent data and collaborative instruction data.

[0009] The product modeling and value grading module digitally models product design parameters, production process data, and market performance data, classifies product value types, and outputs product attribute-capacity mapping data and value tag data.

[0010] The multi-scenario digital twin module performs three-dimensional mapping of physical entity fields, value proof fields, and digital virtual fields, collects production status data, sales scenario data, and ecological collaboration data, and outputs multi-scenario fusion data.

[0011] The transaction data assetization module transforms transaction behaviors related to payment settlement, supply chain finance, capital management, and risk management into digital asset data, establishes a linkage model of capital flow, production flow, and order flow, and outputs transaction asset data and risk warning data.

[0012] The omnichannel sales attribution module collects full-link sales behavior data from all types of sales channels and outputs traffic attribution data, conversion path data, and fulfillment data.

[0013] The industrial cluster intelligent agent module is configured to connect with the production, marketing, and inventory systems of various enterprises within the industrial cluster, enabling data exchange among multiple entities within the cluster, resource pooling, and cross-enterprise task scheduling and collaboration. It also collects and outputs cluster resource distribution data, enterprise collaboration demand data, and cluster capacity load data.

[0014] The big data prediction model module is configured to use multi-dimensional industry data to perform prediction calculations on market demand trends, product sales trends, peak production loads, and order increments through time series analysis, regression analysis, and clustering algorithms, and output demand forecast data, load prediction data, and order trend data.

[0015] The cloud-based central scheduling module is configured to receive and merge data output from each module.

[0016] The three-brain fusion decision-making module is configured to generate collaborative scheduling instructions by combining prediction data, and the edge node computing module is configured to execute the scheduling instructions and provide feedback on the execution results.

[0017] Among them, the multi-role cognitive interaction module, product modeling and value classification module, multi-scenario digital twin module, transaction data assetization module, omni-channel sales attribution module, and industrial cluster intelligent agent module transmit their respective output data to the cloud central scheduling module. The cloud central scheduling module distributes the integrated global industrial dataset to the big data prediction model module and the three-brain fusion decision module. The three-brain fusion decision module combines the prediction data of the big data prediction model module to generate collaborative scheduling instructions. These instructions are then sent to the edge node computing module via the cloud central scheduling module. The edge node computing module feeds back the execution results to the corresponding modules in each scenario.

[0018] This application constructs a multi-module collaborative industrial intelligent body architecture to achieve dynamic resource allocation across the entire marketing and production chain of an industrial cluster, demonstrating significant technical effects and advantages. The system integrates multi-dimensional data acquisition modules, combined with the industrial cluster intelligent body module to achieve data interoperability among multiple entities within the cluster. A cloud-based central hub completes full data fusion, and a big data prediction model module enables accurate prediction of market and production trends, providing quantitative basis for decision-making. A three-brain fusion decision-making module generates scientific scheduling instructions based on predictive data, which are rapidly executed and results fed back through edge nodes, forming a closed-loop chain of data acquisition, fusion, prediction, decision-making, execution, and feedback. This application breaks down data barriers across various dimensions of people, goods, place, money, and sales, achieving deep linkage between marketing and production, cross-enterprise resource collaborative scheduling, effectively improving the overall resource allocation efficiency of the industrial cluster, solving problems such as fragmented production and sales data, lack of forward-looking decision-making, and delayed collaborative response, and significantly enhancing the intelligence and precision of production and sales collaboration.

[0019] Preferably, the multi-role cognitive interaction module includes: an internal role sub-module, a competition perception sub-module, a customer profile sub-module, and an ecosystem collaboration sub-module; wherein:

[0020] Internal role submodule: Configured to collect internal management instruction data and business process data based on knowledge graph and dialogue system, and output instruction distribution data and execution status data;

[0021] Competition Awareness Submodule: Configured to collect competitor product parameter data, pricing strategy data, and market activity data based on web crawlers and competitor analysis algorithms, and output competitive situation data;

[0022] Customer profiling submodule: Configured to collect customer browsing data, order data and interaction data based on behavior clustering and tag construction technology, and output customer demand data and preference prediction data;

[0023] Ecosystem Collaboration Submodule: Configured to connect to the inventory, logistics and order data of suppliers and distributors based on API interfaces and message queue technology, and output supply chain collaboration data;

[0024] The output data from each submodule is aggregated and transmitted to the cloud-based central scheduling module.

[0025] Preferably, the product modeling and value grading module includes: a life cycle submodule and a value tagging submodule;

[0026] Specifically:

[0027] Lifecycle submodule: Configured to model based on process parameters, link product design data, prototyping data and production data, and output process-cost mapping data;

[0028] Value Tag Submodule: Configured to classify product value types based on classification and clustering algorithms, according to sales data and profit data, and output production scheduling priority data and marketing resource allocation data;

[0029] The lifecycle submodule and the value tag submodule transmit product feature data bidirectionally and output it to the cloud central scheduling module.

[0030] The multi-scenario digital twin module includes a physical entity field sub-module, a value proof field module, and a digital virtual field module; wherein:

[0031] Physical Entity Field Submodule: Configured to collect equipment operation data and customer flow data from R&D centers, production lines, and stores based on IoT sensors and digital twin technology, and output physical scene mapping data;

[0032] Value Proof Field Submodule: Configured to collect case data, standard certification data, and summit data based on document digitization and knowledge graph technology, and output brand value data;

[0033] Digital Virtual Field Submodule: Configured to collect online transaction data and supply chain collaboration data from e-commerce platforms and industrial internet platforms based on API integration and data synchronization technology, and output virtual scene fusion data;

[0034] The output data of each submodule is preprocessed by the edge node computing module and then transmitted to the cloud central scheduling module.

[0035] Preferably, the transaction data assetization module includes: a payment and settlement submodule, a supply chain finance submodule, and a risk management submodule; wherein:

[0036] Payment and settlement submodule: Configured to collect payment transaction data and settlement data based on payment interface and reconciliation algorithm, and output fund flow data;

[0037] Supply chain finance sub-module: configured to use blockchain and smart contract technology to transform accounts receivable and order data into digital asset certificates and output financing matching data;

[0038] Risk management submodule: configured to collect exchange rate, interest rate and credit data based on risk control algorithms, and output risk warning data;

[0039] The payment and settlement submodule, supply chain finance submodule, and risk management submodule transmit transaction data bidirectionally and jointly output it to the cloud central scheduling module.

[0040] The omnichannel sales attribution module includes: offline channel sub-module, online channel sub-module, B2B channel sub-module, and innovative channel sub-module;

[0041] Offline channel submodule: Configured to collect customer behavior data and sales data in offline scenarios based on sensor and customer flow statistics technology, and output offline conversion data;

[0042] Online channel submodule: Configured to collect traffic data, click data and order data from online platforms based on tracking and user behavior analysis technology, and output online conversion data;

[0043] B-end channel submodule: configured to collect large customer demand data and customized order data based on enterprise-level API and demand modeling technology, and output B-end fulfillment data;

[0044] Innovative Channels Submodule: Configured to collect behavioral data from scenarios such as subscription, experiential, and metaverse sales based on new consumption scenario data collection technology, and output innovative scenario conversion data;

[0045] The output data from each submodule is aggregated and transmitted to the cloud-based central scheduling module.

[0046] Preferably, the cloud-based central scheduling module is configured to receive input data from the multi-role cognitive interaction module, the product modeling and value grading module, the multi-scenario digital twin module, the transaction data assetization module, and the omnichannel sales attribution module;

[0047] The input data is cleaned, formatted, and integrated to generate a global industry dataset.

[0048] Distribute the global industry dataset to the big data prediction model module and the three-brain fusion decision-making module;

[0049] It receives the collaborative scheduling instructions output by the three-brain fusion decision-making module and sends the instructions to the edge node computing module.

[0050] Preferably, the edge node computing module is configured as follows:

[0051] Deployed in various physical and digital scenarios, it collects local device operation data and scenario interaction data;

[0052] The collected data undergoes lightweight processing and feature extraction, and the processed data is then transmitted to the cloud-based central scheduling module.

[0053] Receive collaborative scheduling instructions from the cloud-based central scheduling module and execute local production control, marketing triggering, or resource allocation operations;

[0054] The operation execution results are fed back to the corresponding scenario module.

[0055] Preferably, the three-brain fusion decision-making module includes: a cognitive brain module, a collaborative brain module, and an evolutionary brain module; wherein:

[0056] Cognitive Brain Module: Configured to perform situational awareness and demand identification based on a global industry dataset, and output preliminary decision-making direction data;

[0057] Collaborative Brain Module: Configured to coordinate cross-module and cross-scenario task conflicts based on preliminary decision-making direction data, and output collaborative task allocation data;

[0058] Evolutionary Brain Module: Configured to iteratively optimize model parameters based on historical collaborative data and simulation inference, and output knowledge accumulation data and strategy optimization data;

[0059] Data is transmitted sequentially between the cognitive brain module, the collaborative brain module, and the evolutionary brain module, ultimately generating collaborative scheduling instructions and outputting them to the cloud-based central scheduling module.

[0060] Furthermore, the system also includes a metaverse scene interaction module, configured to: construct an immersive metaverse interaction space based on multi-scene fusion data output by the multi-scene digital twin module; receive AI digital human data output by the multi-role cognitive interaction module, product data output by the product modeling and value grading module, and sales data output by the omnichannel sales attribution module;

[0061] Provide users with interactive interfaces for product previews, order negotiation, and production progress inquiries, and feed back user interaction data to the multi-role cognitive interaction module and the omnichannel sales attribution module.

[0062] Secondly, this application also proposes an AI marketing and production collaboration method driven by industrial intelligent agents and based on dynamic resource allocation, applied to the AI ​​marketing and production collaboration system driven by industrial intelligent agents and based on dynamic resource allocation as described in the first aspect. The method includes the following steps:

[0063] S1. Multi-dimensional Industry Data Collection and Module Output: The multi-role cognitive interaction module collects multi-role behavior and interaction data, constructs an AI digital human model, and outputs cognitive intent and collaborative instruction data; the product modeling and value grading module completes product digital modeling, classifies value types, and outputs product attribute-capacity mapping and value tag data; the multi-scenario digital twin module completes 3D scene mapping, collects and outputs multi-scenario fusion data; the transaction data assetization module transforms transaction behavior, establishes a linkage model, and outputs transaction asset and risk warning data; the omnichannel sales attribution module collects omnichannel sales behavior data and outputs traffic attribution and fulfillment data; the industry cluster intelligent agent module connects to the systems of various enterprises within the industry cluster, collects and outputs cluster resource distribution, enterprise collaboration needs, and cluster capacity load data. After each module independently completes data collection and processing, it synchronously transmits the output data to the cloud central scheduling module.

[0064] S2. Global Industry Data Aggregation and Integration: The cloud-based central scheduling module receives the output data from all modules in step S1, performs feature fusion processing on the data, and generates a global industry dataset that includes all dimensions of marketing, production, clusters, transactions, and sales.

[0065] S3. Data Distribution and Big Data Predictive Analysis: The cloud-based central scheduling module synchronously distributes the global industry dataset generated in step S2 to the big data prediction model module and the three-brain fusion decision module. Based on the global industry dataset, the big data prediction model module uses time series analysis and regression analysis algorithms to perform prediction calculations of market demand trends, peak production load, and order increments, outputting demand forecast data, load prediction data, and order trend data, and transmits all prediction data to the three-brain fusion decision module.

[0066] S4. Generation of Three-Brain Hierarchical Decision-Making and Collaborative Scheduling Instructions: The three-brain fusion decision-making module receives the global industry dataset and the prediction data from step S3. It then sequentially completes situational awareness and demand identification through the cognitive brain module, outputting preliminary decision direction data. Based on the preliminary decision direction, the collaborative brain module coordinates cross-module and cross-scenario marketing-production task conflicts, outputting collaborative task allocation data. Finally, the evolutionary brain module combines historical collaborative data and simulation deduction to iteratively optimize decision parameters, generating marketing-production collaborative scheduling instructions, and sending the instructions back to the cloud-based central scheduling module.

[0067] S5. Command Issuance and Local Execution at Edge Nodes: The cloud-based central scheduling module will send the collaborative scheduling command generated in step S4 to the edge node computing modules deployed in various physical and digital scenarios. The edge node computing modules will execute the corresponding local production control, marketing resource triggering, or cluster resource allocation operations according to the command type.

[0068] S6. Execution result feedback and full-link data closed loop: The edge node computing module feeds back the operation execution result of step S5 to the source data acquisition module corresponding to each operation. Each source data acquisition module updates the data acquisition dimension, modeling logic and parameter configuration based on the feedback result, and at the same time synchronizes the result to the cloud central scheduling module and big data prediction model module to realize the full-link data closed loop.

[0069] Furthermore, the method also includes: constructing an immersive metaverse interactive space based on multi-scene fusion data output by the multi-scene digital twin module; receiving all user interaction operation data within the metaverse space; and feeding back the interaction operation data to the multi-role cognitive interaction module and the omnichannel sales attribution module, respectively. The multi-role cognitive interaction module updates the interaction logic and behavioral characteristics of the AI ​​digital human model based on the interaction operation data, and the omnichannel sales attribution module optimizes the collection dimensions and attribution analysis algorithms of sales behavior data based on the interaction operation data. Simultaneously, the metaverse interactive data is transmitted to the cloud central scheduling module, integrated into the global industry dataset, and the linkage between the metaverse interactive data and the industry's full-link data is completed.

[0070] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0071] This application breaks down data barriers between production, marketing, and inventory of enterprises within an industrial cluster through an intelligent agent module, achieving unified collection and scheduling of cluster resources and collaborative needs, and improving the overall resource allocation efficiency of the industrial cluster. By constructing a comprehensive data fusion system encompassing people, goods, place, money, and sales, and through a cloud-based central hub, multi-module data is cleaned and integrated to generate a global industrial dataset, providing complete and accurate data support for collaborative decision-making and solving the problems of fragmented data and insufficient decision-making basis in traditional methods. Simultaneously, relying on a big data prediction model module, quantitative trend prediction is achieved, outputting predicted data such as market demand and production load through professional algorithms, making marketing-production decisions more forward-looking and effectively avoiding problems such as production-sales mismatch and inefficient resource allocation. Combined with a three-brain fusion decision-making module, scientific scheduling instructions are generated based on the predicted data, with edge nodes quickly executing and providing feedback, achieving dynamic optimization of each module and improving the intelligence level of collaborative decision-making and execution; a full-link linkage channel between marketing and production is established, enabling two-way data interaction and precise instruction issuance at each stage, solving the pain points of slow response and fragmented links in traditional production-sales collaboration, and significantly improving the accuracy and efficiency of industrial marketing and production collaboration. Attached Figure Description

[0072] Figure 1 This is a diagram of an AI marketing and production collaboration system driven by an industrial intelligent agent and dynamically allocating resources, as illustrated in an embodiment of the present invention. Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0074] Example 1: The AI ​​marketing and production collaboration system driven by an industrial intelligent agent of the present invention, with dynamic resource allocation, is based on a five-dimensional data collection and processing system encompassing people, goods, place, money, and sales. This system is combined with an industrial cluster intelligent agent module, a big data prediction model module, a cloud-based central scheduling module, an edge node computing module, and a three-brain fusion decision-making module to form a full-link collaborative architecture. It also includes a metaverse scene interaction module to achieve immersive collaborative interaction. All modules of the system adopt a distributed deployment approach. The cloud-based central scheduling module is deployed on the industrial cluster's cloud server, while the edge node computing modules are deployed as needed in physical / virtual scenarios such as production lines, marketing stores, and digital scene terminals within the cluster. All modules achieve data interoperability through standardized API interfaces and encrypted transmission protocols, ensuring the real-time performance and security of data transmission.

[0075] like Figure 1 As shown, this invention proposes an AI marketing and production collaboration system driven by an industrial intelligent agent for dynamic resource allocation. The system includes a multi-role cognitive interaction module, a product modeling and value grading module, a multi-scenario digital twin module, a transaction data assetization module, an omni-channel sales attribution module, an industrial cluster intelligent agent module, a big data prediction model module, a cloud central scheduling module, an edge node computing module, and a three-brain fusion decision-making module.

[0076] Specifically, the system includes:

[0077] The multi-role cognitive interaction module is configured to collect comprehensive data on internal roles, customer groups, ecosystem partners, and competitors. Based on computer vision, natural language processing, and knowledge graph technologies, it constructs AI digital human models for each role, ultimately outputting cognitive intent data and collaborative instruction data. In this embodiment, the multi-role cognitive interaction module collects workflow, instruction issuance, and business communication data from internal roles such as the boss, production scheduler, and customer service through the enterprise's internal management system; it collects browsing, order placement, interaction, and after-sales data from customer groups such as new customers, returning customers, and repeat customers through e-commerce platforms and offline stores; it connects with inventory, logistics, and order data from ecosystem partners such as suppliers and distributors through the supply chain system; and it collects product parameters, pricing strategies, and marketing activity data from competitor companies through web crawling technology. After feature extraction and tagging of the above data, it constructs knowledge graphs and behavioral models for each role, forming an AI digital human capable of real-time interaction. Through intent recognition algorithms, it extracts the behavioral intent and collaborative needs of each role, generates standardized collaborative instruction data, and transmits it to the cloud-based central scheduling module.

[0078] All roles are built on a unified knowledge graph architecture, which consists of three layers: entity layer, attribute layer, and relationship layer.

[0079] Entity layer: This consists of the core entities of each role, such as "production scheduler (position), customer group A (user), supplier B (company), competitor C (company)", etc.

[0080] Attribute layer: This layer represents the core characteristics of each entity and corresponds to the knowledge dimensions of each role mentioned above. For example, "Production scheduler - attributes: production line allocation authority, process knowledge reserves";

[0081] Relationship layer: This refers to the business relationships between various entities, such as "production scheduler - collaboration - purchasing specialist, customer group - purchase - high-interest products, supplier - supply - production enterprise", etc.

[0082] Using the ontology modeling tool Protégé, based on the "entity layer - attribute layer - relation layer" architecture of the first step, knowledge ontology is constructed for the four types of roles, defining the type, attributes, and relation rules of each entity. For example, the type of the "customer group" entity is defined as "user", the attributes include "consumption preference, purchase frequency", the entity types that can be associated are "product, sales channel", and the relations are "purchase, browse".

[0083] From the fused structured / semi-structured data, three core knowledge categories—entities, attributes, and relationships—are extracted through a combination of rule extraction and machine learning extraction.

[0084] Structured data (such as customer order tables and supplier capacity tables): Entities, attributes, and relationships can be directly extracted through rule extraction (writing SQL query statements);

[0085] Semi-structured / unstructured data (such as customer service dialogues and competitor marketing copy): Entities are extracted using the BERT+CRF named entity recognition model, and the relationship between attributes and entities is extracted using semantic similarity algorithms (such as cosine similarity).

[0086] The completed role knowledge graph is deeply mapped to the AI ​​digital human model, transforming the entities, attributes, and relationships in the graph into the behavioral characteristics, intent recognition logic, interactive dialogue rules, and business decision-making capabilities of the AI ​​digital human model. The model is then optimized through sample training and reinforcement learning, enabling the AI ​​digital human model to accurately simulate the corresponding role.

[0087] Entity Layer: AI Digital Human Model Main Body: Maps each role entity in the graph to the corresponding AI digital human main body, such as mapping the "Supplier X" entity to "Supplier X AI Digital Human", and the "High-Value Customer Group Y" entity to "High-Value Customer Group Y Simulation AI Digital Human";

[0088] Attribute Layer: AI Digital Human Model Features: Map the attributes of entities in the graph to the appearance, personality, and ability features of the AI ​​digital human model. For example, "Customer Service Specialist - Attributes: Patient, Familiar with Product Knowledge, Skilled at Solving After-Sales Problems" is mapped to the interactive personality and business capabilities of the customer service AI digital human; "Customer Group - Attributes: Prefers Low-Sugar Tea Drinks, High Average Order Value, Average Monthly Repurchase 2 Times" is mapped to the consumption behavior features of the customer group simulation AI digital human.

[0089] Relationship Layer: AI Digital Human Model Interaction / Decision Logic: Maps the relationships between entities in the graph to the interaction rules and business decision logic of the AI ​​digital human model. For example, mapping "Production Scheduler - Responsible for - Production Line 2, Production Scheduler - Collaboration - Purchasing Specialist" to the production scheduler AI digital human's production line allocation decision logic and cross-position interaction rules; mapping "Customer Group - Purchase - Low Sugar Tea Beverage - Association - Online Channel" to the customer group AI digital human's purchase decision and channel selection logic.

[0090] After completing the construction and training of the AI ​​digital human model, the model is deployed to the multi-role cognitive interaction module. Based on a real-time updated knowledge graph, the model performs intent recognition, behavior reasoning, and collaborative demand analysis on the real-time behavioral data of each role, and finally outputs cognitive intent data and collaborative instruction data.

[0091] Cognitive intent data: This represents the core behavioral intent and demand tendency of each role. For example, the customer AI digital human model infers "the customer's purchase intent for low-sugar osmanthus tea has increased by 80%" based on real-time browsing / interaction data; the production scheduler AI digital human model infers "the production capacity of production line 3 needs to be adjusted to meet the demand for high-profit products" based on production line data.

[0092] Collaborative instruction data: These are collaborative request instructions proposed by various roles based on business relationships. For example, a supplier's AI digital human model generates a collaborative supply instruction based on inventory data to "supply 50,000 boxes of low-sugar tea beverage packaging to manufacturer A"; a competitor's AI digital human model generates a collaborative warning instruction based on marketing data to "the competitor will increase its online live streaming investment, and our company's marketing strategy needs to be adjusted".

[0093] It should be noted that the core technology tools and algorithms selected include:

[0094] Knowledge graph construction: Protégé (ontology modeling), Neo4j (graph database), BERT+CRF (named entity recognition), cosine similarity (semantic relation extraction); data processing: Python (Pandas / Numpy), NLP natural language processing tools (Jieba segmentation, HanLP); AI digital human model training: Transformer+RLHF (deep learning training), reinforcement learning (PPO algorithm), but not limited to these.

[0095] The product modeling and value grading module is configured to digitally model the entire product lifecycle data, classify product value types, and output product attribute-capacity mapping data and value tag data. In this embodiment, by collecting production-end data such as product design parameters, prototyping process data, and production line processing parameters, as well as market performance data such as product sales, profits, market share, and customer reviews, a product design-production process-cost correlation model is established using process parameter modeling technology. Clustering and classification algorithms are used to grade the product value, classifying it into types such as lead-generation products, high-profit products, repeat purchase products, and flagship products. Corresponding production line capacity parameters are matched for each product type, generating a product attribute-capacity mapping data table. Simultaneously, value tags and production scheduling priority tags are added to the products. The above data is synchronously transmitted to the cloud-based central scheduling module, providing data support for production scheduling and marketing resource allocation.

[0096] Preferably, this module employs digital twin and knowledge graph technologies to construct a comprehensive digital model of the product. Using the product ID as the core entity, a three-layer digital model structure is built:

[0097] Attribute entities: basic product attributes, functional attributes, and quality attributes;

[0098] Production entity: Adapted to production lines, processes, capacity, materials, and process parameters;

[0099] Business entity: sales channels, pricing range, value level, market status;

[0100] Establish relationships between entities:

[0101] Product: Adaptable to production lines;

[0102] Product: Required materials;

[0103] Product: Optimal sales channels;

[0104] Product: Competitors in the same category;

[0105] Product: Target customer group;

[0106] Based on a multi-dimensional value assessment model, products are automatically classified into different value types, providing a basis for dynamic resource allocation.

[0107] We have constructed 14 core indicators across 5 categories:

[0108] Profitability metrics: gross profit margin, net profit margin, sales revenue as a percentage of total revenue, and profit contribution.

[0109] Sales metrics: sales volume, sales growth rate, order volume;

[0110] Operational metrics: inventory turnover rate, stockout rate, production costs;

[0111] Market metrics: user engagement, repurchase rate, channel compatibility;

[0112] Strategic indicators: strategic position of the industrial cluster, ability to attract customers, and brand value;

[0113] The entropy weight method combined with the TOPSIS comprehensive evaluation algorithm is adopted.

[0114] Calculate the objective weights of each indicator;

[0115] Assess the overall value of the product (0-100 points).

[0116] Automatically categorize values ​​based on ratings;

[0117] Based on the comprehensive score, the products are divided into 4 standard value types:

[0118] High-margin products: high gross profit, high profit margin, high value;

[0119] Lead generation products: low profit margin, high traffic, high exposure;

[0120] Main product: stable sales volume, stable contribution, and moderate gross profit;

[0121] Long-tail / declining products: low sales volume, low gross profit, and low turnover;

[0122] Create a product value classification table, which includes: product ID, product name, value score, value type, core advantages, and core disadvantages.

[0123] Establish a mapping between product attributes and production capacity factors, where the mapping relationship includes:

[0124] Product Attributes: Compatible Production Lines (Production Line ID, Production Line Name);

[0125] Product attributes: Unit hourly capacity / daily capacity / monthly capacity;

[0126] Product attributes: manufacturing process and procedures;

[0127] Product Attributes: Required Bill of Materials (BOM);

[0128] Product attributes: production changeover cost, changeover time;

[0129] Preferably, the module has built-in constraint rules:

[0130] Products using similar processes can be produced on the same production line;

[0131] Products with special processes can only be produced on dedicated production lines;

[0132] High-profit products are prioritized for production lines with high capacity and low switchover costs.

[0133] Lead-generation products can share idle production capacity;

[0134] The final output is structured mapping data: product ID, product attribute, adapted production line ID, standard capacity, maximum capacity, minimum batch size, material configuration, and process requirements.

[0135] The module automatically generates standardized value tags based on value type and product comprehensive score, which are used for global data integration, marketing decisions, production scheduling, and resource allocation.

[0136] Value tags include:

[0137] Value rating labels: S / A / B / C;

[0138] Value type tags: High-profit product / Traffic driver product / Main product / Long-tail product;

[0139] Marketing tags: Featured product, Promotional product, Profitable product, Image product;

[0140] Production labels: Priority production, Regular production, Restricted production;

[0141] Channel tags: Online exclusive, Offline exclusive, Universal across all channels.

[0142] The multi-scenario digital twin module is configured to perform 3D digital twin mapping of physical entities, value proof fields, and digital virtual fields, collect data from each scenario, and output multi-scenario fused data. In this embodiment, IoT sensors are deployed in physical entities such as R&D centers, production lines, and offline stores to collect equipment operating parameters, production progress, and customer flow data. Data from value proof fields such as industry standard certifications, benchmark cases, and industry summits are transformed into structured data using document digitization technology to construct a value proof knowledge graph. Data from digital virtual fields such as e-commerce platforms, industrial internet platforms, and metaverse marketing scenarios are collected via API connections to collect online transaction, traffic, and supply chain collaboration data. Digital twin technology is used to create 1:1 3D models of the three scenarios, mapping the data collected from each scenario to the virtual model in real time. Multi-scenario data is then fused, redundant data is removed, and multi-scenario fused data is generated. After lightweight processing by the edge node computing module, the data is transmitted to the cloud central scheduling module.

[0143] The transaction data assetization module is configured to transform the entire industrial transaction chain into digital asset data, establish a linkage model of capital flow, production flow, and order flow, and output transaction asset data and risk warning data. In this embodiment, it connects to the enterprise payment and settlement system, supply chain finance platform, and risk management system to collect data such as payment records, settlement vouchers, accounts receivable, order financing, exchange rates / interest rates, and enterprise credit. Based on blockchain and distributed ledger technology, each transaction is bound to the corresponding production order and logistics information to generate tamper-proof digital asset vouchers, establish a correlation mapping model of capital flow, production flow, and order flow, and achieve real-time linkage among the three. Through risk control algorithms, the transaction data is analyzed in real time. When credit risk, abnormal capital flow, or order fulfillment risk is detected, risk warning data is automatically generated and transmitted to the cloud central scheduling module along with the transaction asset data.

[0144] The omnichannel sales attribution module is configured to collect end-to-end sales behavior data from all types of sales channels, outputting traffic attribution data, conversion path data, and fulfillment data. In practice, it covers traditional offline channels such as physical stores, trade shows, and door-to-door sales; online digital channels such as e-commerce platforms, independent websites, and cross-border e-commerce; B2B / enterprise-level channels such as bidding and solution-based sales; and innovative cross-industry channels such as metaverse sales, subscription models, and experiential consumption. It collects customer behavior and sales data from offline channels through sensors and customer flow statistics technology; collects traffic, click, and conversion data from online channels through tracking technology; collects B2B channel customer demand and customized order data through enterprise-level APIs; and collects consumer behavior data from innovative channels through new scenario data collection technology. Based on a multi-touchpoint attribution algorithm, it analyzes the omnichannel data to determine the traffic contribution and conversion path of each channel and marketing touchpoint, generating traffic attribution data and conversion path data. Simultaneously, it collects fulfillment data such as order delivery, logistics, and receipt from each channel, and transmits the aggregated data to the cloud-based central scheduling module.

[0145] The industrial cluster intelligent agent module: This module is one of the core innovative modules of this invention. It is configured to connect with the production, marketing, and inventory systems of various enterprises within the industrial cluster, enabling multi-entity data interoperability, resource pooling, and cross-enterprise task scheduling and collaboration within the cluster. It collects and outputs cluster resource distribution data, enterprise collaboration demand data, and cluster capacity load data. In actual implementation, it connects to the core business systems of upstream and downstream enterprises within the industrial cluster through standardized interfaces, establishing a cluster-level resource sharing pool. It uniformly collects data such as production line capacity, equipment load, product inventory, marketing resources, and order demand from each enterprise, generating a cluster resource distribution data table; it collects cross-enterprise collaboration needs from each enterprise in real time, such as raw material procurement needs, capacity sharing needs, and order subcontracting needs, forming enterprise collaboration demand data; it analyzes the operating status and capacity utilization of all production lines within the cluster in real time through a capacity load algorithm, generating cluster capacity load data; and it transmits the above three types of data to the cloud-based central scheduling module, providing a basis for cluster-level resource scheduling and collaborative decision-making, and achieving optimal allocation of overall resources for the industrial cluster.

[0146] The big data prediction model module, one of the core innovative modules of this invention, is configured to perform trend prediction based on multi-dimensional industry data and professional algorithms, outputting demand prediction data, load forecast data, and order trend data. In actual implementation, it receives a global industry dataset distributed by the cloud-based central scheduling module, uses time series analysis algorithms to predict product market sales and customer demand trends, generating market demand prediction data; it uses regression analysis algorithms to analyze the capacity utilization rate and equipment operating status of each production line within the cluster, predicting peak and trough production loads, generating production load forecast data; and it combines market demand and enterprise production capacity to predict order increments and order types within a certain future period, generating order trend data. All prediction data are output to the three-brain fusion decision-making module in the form of quantitative reports and trend curves, providing quantitative basis for collaborative decision-making, and the model parameters can be continuously iterated and optimized based on historical data to improve prediction accuracy.

[0147] The cloud-based central scheduling module serves as the core data processing and scheduling hub of the system. It is configured to receive, integrate, and distribute data from various modules, and issue collaborative scheduling commands. In actual implementation, it receives all data output from the five-dimensional data acquisition module and the industrial cluster intelligent agent module via an encrypted transmission protocol. The data undergoes preprocessing such as cleaning, deduplication, and format unification. Then, a multimodal data fusion algorithm deeply integrates the data from various dimensions to generate a global industry dataset encompassing marketing, production, clustering, transactions, and sales. This global industry dataset is simultaneously distributed to the big data prediction model module and the three-brain fusion decision-making module. Simultaneously, it receives collaborative scheduling commands generated by the three-brain fusion decision-making module and, based on the command type and execution scenario, directs the commands to the corresponding edge node computing modules, achieving centralized scheduling and distribution of data and commands.

[0148] The edge node computing module is configured to be deployed at various physical / digital scenarios to complete local data preprocessing, instruction execution, and result feedback. In actual implementation, the edge node computing module adopts a lightweight server deployment, arranged as needed at enterprise production lines, offline stores, e-commerce platform servers, and other scenarios within the cluster. It collects local device operation data and scenario interaction data in real time, performs lightweight processing and feature extraction on the data, and then transmits it to the cloud central scheduling module to reduce the data processing pressure on the cloud. It receives collaborative scheduling instructions issued by the cloud central scheduling module and executes corresponding local operations according to the instruction type, such as production line scheduling adjustment, marketing resource allocation, inventory allocation, and store activity triggering. After the operation is completed, it collects the execution result data in real time and accurately feeds it back to the corresponding source data acquisition module and the cloud central scheduling module to achieve closed-loop management of instruction execution.

[0149] The three-brain fusion decision-making module is the core decision-making module of the system. It is configured to generate collaborative scheduling instructions by combining global data and predictive data. It includes a cognitive brain module, a collaborative brain module, and an evolutionary brain module. In actual implementation, the cognitive brain module, based on the global industry dataset and predictive data from the big data prediction model module, performs situational awareness and demand identification, analyzes the overall production and sales status of the industrial cluster, market demand trends, and capacity load, and outputs preliminary decision-making direction data. For example, if market demand for a certain type of product surges, the corresponding production line capacity needs to be increased and marketing resources should be allocated more. Based on the preliminary decision-making direction, the collaborative brain module coordinates cross-module, cross-scenario, and cross-enterprise task conflicts, such as resolving capacity competition and marketing resource allocation conflicts between different enterprises, and outputs collaborative task allocation data, clarifying the specific work tasks and execution priorities of each enterprise and module. The evolutionary brain module, combining historical collaborative data and simulation technology, iteratively optimizes decision parameters, while accumulating decision-making experience and knowledge, outputting knowledge accumulation data and strategy optimization data. Finally, the three modules collaboratively generate standardized and implementable marketing-production collaborative scheduling instructions, which are transmitted to the cloud-based central scheduling module.

[0150] Among them, the multi-role cognitive interaction module, product modeling and value classification module, multi-scenario digital twin module, transaction data assetization module, omni-channel sales attribution module, and industrial cluster intelligent agent module transmit their respective output data to the cloud central scheduling module. The cloud central scheduling module distributes the integrated global industrial dataset to the big data prediction model module and the three-brain fusion decision module. The three-brain fusion decision module combines the prediction data of the big data prediction model module to generate collaborative scheduling instructions. These instructions are then sent to the edge node computing module via the cloud central scheduling module. The edge node computing module feeds back the execution results to the corresponding modules in each scenario.

[0151] This application constructs a multi-module collaborative industrial intelligent body architecture to achieve dynamic resource allocation across the entire marketing and production chain of an industrial cluster, demonstrating significant technical effects and advantages. The system integrates multi-dimensional data acquisition modules, combined with the industrial cluster intelligent body module to achieve data interoperability among multiple entities within the cluster. A cloud-based central hub completes full data fusion, and a big data prediction model module enables accurate prediction of market and production trends, providing quantitative basis for decision-making. A three-brain fusion decision-making module generates scientific scheduling instructions based on predictive data, which are rapidly executed and results fed back through edge nodes, forming a closed-loop chain of data acquisition, fusion, prediction, decision-making, execution, and feedback. This application breaks down data barriers across various dimensions of people, goods, place, money, and sales, achieving deep linkage between marketing and production, cross-enterprise resource collaborative scheduling, effectively improving the overall resource allocation efficiency of the industrial cluster, solving problems such as fragmented production and sales data, lack of forward-looking decision-making, and delayed collaborative response, and significantly enhancing the intelligence and precision of production and sales collaboration.

[0152] Preferably, the multi-role cognitive interaction module includes: an internal role sub-module, a competition perception sub-module, a customer profile sub-module, and an ecosystem collaboration sub-module; wherein:

[0153] Internal Role Submodule: This submodule is implemented based on knowledge graph construction technology and an intelligent dialogue system framework. First, it analyzes the enterprise's internal organizational structure, job responsibilities, and business processes, constructing a job knowledge graph containing core roles such as the boss, production scheduler, customer service representative, and purchasing specialist. The management instruction specifications, business operation processes, and cross-job collaboration rules for each role are used as core nodes and related edges of the graph. Then, by connecting to the enterprise's OA system, production management system, and customer service dialogue platform, it collects real-time data on management instruction issuance, business process execution, and cross-job communication interactions from each internal role. Natural language processing technology is used to perform structured parsing of the instruction and process data. Finally, based on the job knowledge graph, it intelligently distributes instructions, generates instruction distribution data, tracks instruction execution progress in real-time, collects execution node data at each stage, generates execution status data, and synchronously outputs it to the main module.

[0154] Competition Awareness Submodule: This submodule is implemented based on web crawling technology and competitor analysis algorithms. By configuring a targeted crawler program, it crawls data from competitor websites, e-commerce platform stores, industry information platforms, social media marketing accounts, and other channels to accurately collect data such as competitors' core product parameters, pricing strategies, promotional activity plans, market placement channels, and user feedback. Then, the competitor analysis algorithm performs feature extraction and quantitative analysis on the collected data, comparing the company's and competitors' strengths and weaknesses in product, price, marketing, and other dimensions to identify the patterns and trends of competitor market behavior. Finally, it generates competitive landscape data containing core competitor data, market behavior analysis, and a comparison of competitive advantages and disadvantages, which is then output to the main module.

[0155] Customer profiling submodule: This submodule is implemented based on user behavior clustering algorithms and tag system construction technology. By connecting with enterprise online stores, offline store POS systems, private domain operation platforms, and other channels, it collects behavioral data from all dimensions, including customer browsing history, product clicks, order records, payment amounts, repurchase frequency, and interactive comments. Then, using the K-means clustering algorithm, it divides the customer base into different groups such as new customers, repeat customers, high-value loyal fans, and potential churned customers. At the same time, it constructs a multi-level customer tag system that includes demographic attributes, consumption preferences, purchasing power, and behavioral habits, matching corresponding tag features to each customer group. Finally, combining customer behavior data and tag features, it analyzes the product demand tendencies of different customer groups, generates customer demand data and preference prediction data, and outputs them to the main module.

[0156] Ecosystem Collaboration Submodule: This submodule is implemented based on standardized API interfaces and message queue (MQ) technology. First, it establishes a collaborative data connection channel for the industrial supply chain. Through RESTful API interfaces, it connects with the business systems of ecosystem partners such as suppliers, distributors, and logistics partners. Simultaneously, it deploys message queue components to achieve asynchronous data transmission and peak / valley smoothing, ensuring the stability and real-time performance of data transmission. Then, through this connection channel, it collects targeted data on suppliers' raw material inventory and delivery cycles, distributors' product distribution and terminal sales, logistics partners' order collection, transportation, and delivery data, and order connection and fulfillment data from various ecosystem partners. Finally, it integrates and analyzes the above data to generate supply chain collaboration data containing the status of each link in the supply chain and the collaborative needs of partners, which is then output to the main module.

[0157] After each submodule completes the collection and output of its corresponding data, the multi-role cognitive interaction main module uniformly summarizes and integrates the instruction distribution data, execution status data, competitive situation data, customer demand data, preference prediction data, and supply chain collaboration data, removes redundant data, and fills in missing data to form a comprehensive dataset with multiple role dimensions. This dataset is then transmitted to the cloud central scheduling module as an important component of the global industry dataset.

[0158] Preferably, the product modeling and value grading module includes: a life cycle submodule and a value tagging submodule;

[0159] Specifically:

[0160] Lifecycle Submodule: This submodule is implemented based on process parameter modeling technology, and its core function is to realize the correlation mapping of data and process-cost quantitative modeling of the entire process of product design, prototyping and production. In practical applications, a data acquisition channel for the entire product lifecycle is first established, connecting to the enterprise's product design system, prototyping laboratory management system, and production line MES system. This accurately collects product design parameters (including indicators such as structure, material, and precision), prototyping process data (including prototyping procedures, consumable usage, equipment parameters, and labor costs), and production data (including mass production process layout, production line adaptation parameters, unit production time, batch consumable costs, and equipment depreciation allocation). Then, through process parameter modeling technology, a correlation mapping model between product design parameters and prototyping and mass production processes is established. This transforms the design-end indicator requirements into implementable process execution standards. Simultaneously, based on the cost data collected at each stage, the entire process cost breakdown and calculation from prototyping to mass production is completed, ultimately generating process-cost mapping data. This data includes core information such as parameter requirements for each process stage, process connection logic and corresponding cost proportions, and unit product comprehensive cost. On one hand, this data is synchronized to the value tag submodule to provide a cost basis for product value classification; on the other hand, it is output to the module aggregation end.

[0161] Value Tagging Submodule: This submodule is based on classification and clustering algorithms, with the preferred K-means clustering + decision tree classification. Its core function is to classify product value types and prioritize the allocation of production and marketing resources. In practical applications, the system first connects to the enterprise's sales management system and financial accounting system to collect omnichannel sales data (including sales volume, sales revenue, sales region, repurchase rate, etc.) and profit data (including unit product profit, gross profit margin, net profit margin, profit contribution, etc.). Simultaneously, it receives process-cost mapping data from the lifecycle submodule, incorporating cost indicators into the product value assessment system. Next, it uses a K-means clustering algorithm to initially cluster products, combining industry standards and enterprise business objectives to classify products into value types such as lead generation products, high-profit products, repurchase products, and flagship products. Then, a decision tree classification algorithm is used to further calibrate each value type, clarifying the core positioning and resource matching rules for each product type. Finally, based on the product value type and the enterprise's current production capacity and marketing resources, it generates production scheduling priority data and marketing resource allocation data. The production scheduling priority data clarifies the production line adaptation sequence and capacity allocation ratio for each product, while the marketing resource allocation data clarifies the promotion channels, budget, and activity priorities for each product. This data is synchronized to the lifecycle submodule to provide market value guidance for process optimization and cost control, and is also output to the module's aggregation end.

[0162] A two-way, real-time data transmission channel for product characteristics is established between the lifecycle submodule and the value tag submodule. The process-cost data transmitted from the lifecycle submodule to the value tag submodule provides a core cost dimension for product value grading, preventing value classification from solely relying on sales and profits while ignoring actual production costs. Conversely, the product value type and resource allocation data transmitted from the value tag submodule to the lifecycle submodule provides market guidance for process optimization and cost control. For example, for high-profit products, feedback can be provided to the lifecycle submodule to optimize processes and improve product quality; for lead-generation products, processes can be optimized to further control costs. After completing their respective data processing and two-way interaction, the module aggregation end integrates the process-cost mapping data, production scheduling priority data, and marketing resource allocation data to form a comprehensive product data system encompassing product process, cost, value type, and resource matching rules. This data system is then uniformly transmitted to the cloud-based central scheduling module as a core component of the global industry dataset's product dimensions, providing precise data for subsequent production scheduling and marketing decisions.

[0163] The multi-scenario digital twin module includes a physical entity field sub-module, a value proof field module, and a digital virtual field module; wherein:

[0164] Physical Entity Field Submodule: This submodule is implemented based on IoT sensor networking technology and digital twin 3D modeling technology. Its core function is to digitally replicate and collect real-time data from the physical scenarios of R&D, production, and sales. In actual implementation, experimental equipment condition sensors and environmental monitoring sensors are deployed in the R&D center to collect equipment operating parameters and R&D experimental environment data. Production line IoT sensor networks are deployed on each production line to collect core equipment operation data such as equipment start / stop status, production cycle time, capacity load, and material consumption. Offline stores are equipped with customer flow statistics sensors, movement tracking sensors, and product touchpoint sensors to collect real-time customer flow, customer consumption patterns, and product scanning / trial purchase data. Based on the collected physical scenario data, digital twin technology is used to create a 1:1 3D digital model of the R&D center, production line, and stores, achieving real-time linkage mapping between the physical scenario state and the virtual model. This ultimately generates physical scenario mapping data containing 3D physical scenario model data, real-time equipment operating data, and dynamic scenario operation data, which is directly output to the edge node computing module.

[0165] The Value Proof Field Submodule: This submodule is implemented using document digitization technology and knowledge graph construction technology. Its core function is to structure and extract value dimension data from scenario data related to industry brand value. In actual implementation, it first collects case data such as benchmark cooperation cases and project implementation cases from enterprises and industry clusters, standard certification data such as product quality certification, technology patents, and industry system certifications, as well as summit data such as participation in industry summits, technology achievement releases, and industry forum exchanges. Then, using document digitization technologies such as OCR optical character recognition and natural language processing, the aforementioned unstructured document and image data are transformed into standardized structured data, extracting key information such as core indicators of the cases, certification levels, and summit results. Based on this structured data, a brand value knowledge graph is constructed, using cases, certifications, and summits as core nodes. With technical support, value association, and achievement transformation as node association edges, the logical connections and value integration between various data are completed. Finally, brand value data containing core brand value points, technical certification systems, and industry case results is generated and output to the edge node computing module.

[0166] Digital Virtual Scene Submodule: This submodule is implemented based on standardized API interface technology and real-time data synchronization technology. Its core function is to complete the multi-source data collection and fusion processing of online digital scenarios. In actual implementation, standardized interfaces such as RESTful APIs and platform SDKs are used to connect with core digital virtual scenarios such as e-commerce platforms and industrial internet platforms, establishing stable online data collection channels. This includes collecting online transaction data such as product exposure, clicks, and sales from e-commerce platforms, as well as operational data such as customer reviews and store traffic; and collecting supply chain collaboration data such as cross-enterprise order matching, material allocation, and production capacity coordination within industrial internet platforms. Real-time data synchronization technology achieves millisecond-level synchronization of data across various digital platforms. The collected multi-source online data is cleaned, deduplicated, and dimensionally completed, eliminating invalid and redundant information. Finally, it generates virtual scene fusion data integrating online transactions, supply chain collaboration, and digital scene operation dimensions, which is then output to the edge node computing module.

[0167] The physical scene mapping data, brand value data, and virtual scene fusion data output by the physical entity field submodule, value proof field submodule, and digital virtual field submodule are all uniformly transmitted to the edge node computing modules deployed at each scene end for lightweight preprocessing: data compression algorithms are used to reduce data volume, feature extraction algorithms are used to extract the core features of each scene's data, and all data formats are standardized to match the data receiving standards of the cloud central scheduling module, effectively reducing the cloud data processing pressure and improving data transmission efficiency. The full-scene data, after preprocessing by the edge node computing modules, is synchronously uploaded to the cloud central scheduling module via an encrypted transmission protocol. As a core component of the scene dimension of the global industry dataset, it provides full-scene, high-fidelity digital twin data support for subsequent marketing-production collaborative decision-making and industrial cluster resource scheduling.

[0168] Preferably, the transaction data assetization module includes: a payment and settlement sub-module, a supply chain finance sub-module, and a risk management sub-module; each sub-module focuses on the entire industrial transaction chain to realize capital flow collection, transaction asset digitization, and real-time risk monitoring, and establishes a two-way transaction data transmission channel between the three sub-modules to achieve data sharing and logical linkage. Finally, the core data processed by each sub-module is integrated and aggregated, and uniformly transmitted to the cloud central scheduling module. Wherein:

[0169] Payment and Settlement Submodule: This submodule is implemented based on standardized payment interface technology and automated reconciliation algorithms. Its core function is to collect, verify, and standardize the cash flow data across the entire industrial transaction chain. In practical application, it first establishes a multi-channel payment data collection channel by connecting to standardized payment interfaces of UnionPay, third-party payment platforms, and corporate payment systems. This channel collects real-time online and offline payment transaction data between enterprises, customers, suppliers, and distributors within the industrial cluster, including payment amount, payment time, transaction entity, payment channel, and order association information. Simultaneously, it connects to the enterprise's financial settlement system to collect transaction settlement data, covering core information such as settlement cycle, settlement amount, outstanding payments, and settlement vouchers. Based on automated reconciliation algorithms, the payment transaction data is intelligently matched and verified with corresponding order and settlement data. Abnormal payment data is eliminated, and missing association information is supplemented. This completes the structured sorting and standardization of cash flow data, ultimately generating cash flow data containing transaction entities, fund flows, payment settlement status, and order associations. This data is transmitted bidirectionally to the supply chain finance and risk management submodules, providing core financial data for asset digitization and risk monitoring. Simultaneously, it is output to the module aggregation end.

[0170] Supply Chain Finance Submodule: This submodule is implemented based on blockchain and smart contract technologies. Its core function is to complete the digital asset transformation of transaction targets such as accounts receivable and orders, and to match financing needs. In practical application, it first receives cash flow data transmitted from the payment and settlement submodule, and simultaneously connects to the enterprise's sales and procurement systems to collect authentic and valid accounts receivable data (including creditors, debtors, outstanding amounts, payment terms, and corresponding contracts) and order data (including order entities, order amounts, performance progress, and payment methods). Based on blockchain technology, a distributed transaction asset notarization platform is built, storing accounts receivable, order data, and related data such as corresponding contracts and payment vouchers on the blockchain to generate tamper-proof and traceable digital asset certificates, thus realizing the assetization and confirmation of rights for the transaction targets. Simultaneously, it connects with the supply chain finance service platform to collect data such as financial institutions' financing products, credit lines, and financing interest rates. Through smart contract technology, it presets financing matching rules to intelligently match the enterprise's digital asset certificates with financial institutions' financing products, selects financing solutions that meet the enterprise's needs, and finally generates financing matching data that includes digital asset ownership information, financing product matching results, and financing application channels. On the one hand, it transmits the data bidirectionally to the payment and settlement submodule and the risk management submodule to provide a basis for fund flow verification and financing risk monitoring. On the other hand, it outputs the data to the module aggregation terminal.

[0171] Risk Management Submodule: This submodule is implemented based on a multi-dimensional risk control algorithm, and its core function is to collect, analyze in real time, and issue risk warnings for the entire industrial transaction chain. In practical applications, it first receives cash flow data from the payment and settlement submodule and financing matching data from the supply chain finance submodule. Simultaneously, it connects to the foreign exchange management system, bank interest rate system, and enterprise credit reporting platform to collect core risk data such as exchange rate fluctuation data, market interest rate changes, credit ratings of trading entities and financing entities, and credit records. Based on the risk control algorithm, a multi-dimensional risk assessment model is constructed to perform real-time quantitative analysis of the entire transaction chain data from dimensions such as abnormal cash flow, exchange rate and interest rate fluctuations, entity credit risk, and financing performance risk. When data indicators are detected to exceed preset safety thresholds, such as a decline in the credit rating of the trading entity, significant exchange rate fluctuations, accounts receivable overdue risk, or increased financing performance risk, the system automatically triggers a risk warning mechanism. This generates risk warning data including risk type, risk level, risk entity, and risk warning suggestions. This data is transmitted bidirectionally to both the payment and settlement submodule and the supply chain finance submodule, providing a risk basis for cash flow management and financing plan adjustments. Simultaneously, it is output to the module aggregation end.

[0172] A two-way, real-time, multi-dimensional data transmission channel is established between the payment and settlement submodule, the supply chain finance submodule, and the risk management submodule to achieve mutual sharing and logical linkage of fund flow data, assetization data, and risk data. The payment and settlement submodule provides the supply chain finance submodule with real fund flow and transaction target data to ensure the authenticity of digital asset conversion, and provides the risk management submodule with basic fund flow data to support quantitative risk analysis. The supply chain finance submodule provides the payment and settlement submodule with assetization and financing matching information, and provides the risk management submodule with financing-related data to support financing risk monitoring. The risk management submodule outputs real-time risk warning data to the first two submodules to achieve proactive risk control throughout the transaction chain and ensure the security of fund flow and assetization operations. After completing their respective data processing and two-way interaction, the module aggregation end integrates and merges the capital flow data, financing matching data, and risk warning data to form a comprehensive data system for transaction assetization that includes transaction capital flow, digital asset ownership confirmation, financing matching, and risk monitoring. Finally, this data system is uniformly transmitted to the cloud central scheduling module as a core component of the transaction dimension of the global industry dataset, providing accurate data support for the system to build a capital flow-production flow-order flow linkage model and achieve deep integration of industrial transactions and production and sales collaboration.

[0173] The omnichannel sales attribution module includes: offline channel sub-modules, online channel sub-modules, B2B channel sub-modules, and innovative channel sub-modules. Each sub-module, tailored to the characteristics of different sales channels in industry marketing, utilizes specialized technologies to accurately collect and output full-link behavioral and transaction data, as well as feature data. The output data from each sub-module is then uniformly aggregated and integrated at the module level before being transmitted to the cloud-based central scheduling module. This provides comprehensive sales-dimensional data support for the system to achieve omnichannel sales data attribution analysis and precise allocation of marketing resources, specifically including:

[0174] Offline Channel Submodule: This submodule is implemented based on IoT sensor technology and intelligent customer flow statistics technology. Its core function is to collect customer behavior and sales data in offline physical sales scenarios and output conversion data. In practical applications, infrared customer flow statistics sensors, customer movement tracking sensors, product contact sensors, and offline POS system interfaces are deployed in offline stores, exhibition booths, and offline experience centers. The customer flow statistics sensors collect real-time data on in-store traffic, dwell time, and peak traffic periods. The movement and contact sensors collect behavioral data such as customer movement within the store, product touch frequency, and trial purchase experience. Simultaneously, it interfaces with the offline POS system to collect sales data such as transaction amount, quantity, average order value, and payment method. The collected behavioral and sales data are correlated and matched to analyze key indicators such as offline customer flow conversion efficiency and contact conversion ratios for different regions / products. Finally, it generates offline conversion data including offline customer flow data, customer behavior characteristics, sales transaction data, and customer flow-sales conversion indicators, which is then output to the module's aggregation terminal.

[0175] Online Channel Submodule: This submodule is implemented based on full-link tracking technology and user behavior analysis technology. Its core function is to collect traffic and transaction data and output conversion data for online sales scenarios such as e-commerce platforms, corporate websites, and online mini-programs. In practical applications, full-link tracking nodes are deployed on core pages such as product detail pages, homepages, checkout pages, and activity pages of various online platforms to collect traffic and user behavior data, including visitor volume, traffic source channels, page dwell time, product clicks, add-to-cart counts, and favorites. Simultaneously, it connects to the transaction backends of various online platforms to collect order creation data, payment data, transaction amount, and order cancellation data. Through user behavior analysis technology, traffic and order data are deeply correlated to analyze the conversion efficiency of different traffic sources and the funnel conversion situation at each stage of the behavior. Finally, it generates online conversion data including online traffic characteristics, user behavior trajectories, order transaction data, and traffic-to-order conversion metrics, which is then output to the module's aggregation end.

[0176] The B2B channel submodule: This submodule is implemented based on enterprise-level API interface technology and large customer demand modeling technology. Its core function is to collect demand and order data for B2B large customer cooperation scenarios and output fulfillment data. In practical applications, through standardized enterprise-level API interfaces, it connects to the enterprise's B2B sales management system, large customer cooperation platform, and order fulfillment system. It collects demand data such as enterprise attributes, customized product requirements, purchase volume, and cooperation cycle from large customers, as well as order data such as order amount, order delivery cycle, and product customization parameters for B2B customized orders. Simultaneously, it collects fulfillment data for the entire process, including order production scheduling, material allocation, logistics transportation, customer receipt, and acceptance feedback. Based on demand modeling technology, it structures and organizes large customer demand and order data, and combines fulfillment data analysis to evaluate indicators such as B2B order fulfillment progress, fulfillment qualification rate, and large customer demand matching degree. Finally, it generates B2B fulfillment data containing B2B large customer demand characteristics, customized order data, order fulfillment status throughout the entire process, and fulfillment assessment indicators, which is then output to the module's aggregation end.

[0177] Innovative Channel Submodule: This submodule is implemented based on new consumption scenario data collection technology. Its core function is to collect behavioral and transaction data and output conversion data for innovative sales scenarios such as subscription-based consumption, experiential consumption, and metaverse sales. In practical applications, for subscription-based consumption scenarios, it connects to enterprise subscription service platforms to collect data such as user subscription duration, subscription package selection, renewal rate, and subscription spending. For experiential consumption scenarios, it deploys scenario behavior collection terminals to collect user experience appointment data, on-site experience behavior, and post-experience conversion and transaction data. For metaverse sales scenarios, it connects to the metaverse virtual mall backend to collect data such as user virtual avatar behavior trajectory, virtual product reach, and order transactions within the metaverse.

[0178] The collected data from various innovation scenarios are integrated and analyzed to calculate indicators such as user engagement, behavior conversion efficiency, and transaction completion rate for different innovation scenarios. Finally, innovation scenario conversion data containing user behavior data, transaction data, and scenario conversion indicators are generated and output to the module aggregation end.

[0179] After the offline channel submodule, online channel submodule, B2B channel submodule, and innovative channel submodule complete their respective data collection and feature data output, the main end of the omnichannel sales attribution module performs unified aggregation and fusion processing on offline conversion data, online conversion data, B2B fulfillment data, and innovative scenario conversion data. This involves standardizing the format of data from each channel, supplementing the correlation dimensions of cross-channel data, eliminating redundant and invalid data, and retaining the unique characteristics and core indicators of each channel's data. This results in a comprehensive omnichannel sales dataset covering offline, online, B2B, and innovative channels. This comprehensive dataset is ultimately transmitted to the cloud-based central scheduling module via an encrypted transmission protocol. As a core component of the sales dimension of the global industry dataset, it provides complete and accurate omnichannel sales data support for subsequent omnichannel sales attribution analysis, cross-channel optimization of marketing resource allocation, and dynamic adjustment of sales strategies.

[0180] Preferably, the cloud-based central scheduling module is configured as follows: the cloud-based central scheduling module is deployed on a dedicated cloud server cluster of the industrial cluster, adopts a highly available and highly concurrent distributed computing architecture design, and realizes four core functions: multi-source data reception, data fusion processing, global dataset distribution, and scheduling instruction relay, with seamless connection between each functional link; it receives input data from the multi-role cognitive interaction module, product modeling and value classification module, multi-scenario digital twin module, transaction data assetization module, and omni-channel sales attribution module;

[0181] Furthermore, the input data is cleaned, formatted, and fused to generate a global industry dataset; this dataset is then distributed to the big data prediction model module and the three-brain fusion decision-making module; collaborative scheduling instructions output by the three-brain fusion decision-making module are received and sent to the edge node computing module, specifically including:

[0182] (1) Unified reception of multi-source data

[0183] The module establishes a standardized and highly compatible data receiving interface, supporting multi-protocol and multi-format data access. It receives multi-dimensional industry data in real time from multi-role cognitive interaction modules, product modeling and value classification modules, multi-scenario digital twin modules, transaction data assetization modules, and omni-channel sales attribution modules. At the same time, it receives local pre-processed data from various scenarios uploaded by edge node computing modules. It automatically identifies and marks the data source, data type, and data format of all input data, establishes a data receiving ledger, and ensures data traceability.

[0184] (2) Data cleaning, format unification and fusion processing

[0185] The module incorporates a multi-dimensional data processing engine. First, it cleans all input data, filtering out invalid, erroneous, and redundant data through outlier detection, missing value completion, and duplicate value removal algorithms to ensure data validity. Next, it unifies the format, transforming structured / semi-structured data from different modules and formats into the system's standard data format, unifying data fields, encoding rules, and timestamp formats to achieve data format normalization. Finally, it performs deep fusion processing, mapping data from various dimensions—people, goods, places, money, and sales—according to business logic based on industry data association rules. This constructs a relationship graph between data, generating a global industry dataset encompassing all dimensions of marketing, production, transactions, supply chain, and sales within the industry cluster. This dataset is categorized and stored according to data dimensions, supporting rapid retrieval and analysis.

[0186] (3) Precise distribution of global industry datasets

[0187] According to the system's preset distribution rules, the module synchronously and accurately distributes the generated global industry dataset to the big data prediction model module and the three-brain fusion decision-making module, providing complete basic data support for the big data prediction model module and providing full-dimensional data basis for the situational awareness, task coordination, and strategy optimization of the three-brain fusion decision-making module; at the same time, it reserves a dataset interface to support each source data module to retrieve relevant dimensional data as needed, so as to realize data sharing.

[0188] (4) Coordinated scheduling instruction relay and issuance

[0189] The module receives marketing-production collaborative scheduling instructions from the three-brain fusion decision-making module in real time. First, it parses the instructions to identify the instruction type (production control, marketing trigger, resource allocation, etc.), the instruction execution scenario (production line, offline store, e-commerce platform, etc.), the instruction execution subject, and the instruction execution requirements. Then, based on the instruction parsing results, it sends the instructions to the edge node computing modules deployed in the corresponding scenario through an encrypted transmission channel to ensure that the instructions reach the execution end accurately and quickly. At the same time, it marks the instruction issuance status and execution node in real time to achieve full lifecycle tracking of the instructions.

[0190] Preferably, the edge node computing module is configured as follows: the edge node computing module adopts lightweight hardware deployment and is deployed in all physical and digital scenarios such as R&D centers, production lines, offline stores, and e-commerce platform servers according to scenario type. At least one edge computing node is deployed in each scenario. The nodes are independent of each other and maintain real-time communication with the cloud central scheduling module. Specifically, it includes:

[0191] (1) Perform lightweight processing and feature extraction on the collected data, and transmit the processed data to the cloud central scheduling module;

[0192] Preferably, the edge node computing modules in each scenario are equipped with dedicated data acquisition components to collect local device operation data and scenario interaction data tailored to the characteristics of each scenario: production line nodes collect equipment operation data such as production line equipment start-up and shutdown, production cycle time, and capacity load; offline store nodes collect data such as store equipment operation, customer flow interaction, and product reach; e-commerce platform nodes collect data such as online platform traffic interaction and transaction node operation; and R&D center nodes collect data such as experimental equipment operation and R&D scenario interaction. The collection process is localized and real-time, without the need for cloud relay, effectively reducing data transmission latency.

[0193] The module performs lightweight processing on locally collected raw data, reducing data volume through data compression algorithms to minimize cloud transmission bandwidth usage; it extracts core features from the data using feature extraction algorithms, eliminating invalid and redundant information while retaining key data value; and it standardizes the format of the processed data to match the data reception standards of the cloud central scheduling module. After processing, the local feature data is uploaded to the cloud central scheduling module in real time via an encrypted transmission channel, serving as an important supplement to the global industry dataset, while a local backup is maintained to ensure no data loss.

[0194] (2) Receive collaborative scheduling instructions issued by the cloud central scheduling module and execute local production control, marketing triggering or resource allocation operations;

[0195] Preferably, the module receives collaborative scheduling instructions from the cloud-based central scheduling module in real time. First, it performs localized parsing of the instructions, combining the actual equipment status, resource configuration, and operational conditions at the scenario end to transform the cloud-based general instructions into localized operation instructions that can be directly executed at the scenario end. Then, it executes the corresponding operation based on the instruction type: production control instructions include production line scheduling adjustments, equipment start / stop control, and capacity allocation optimization; marketing trigger instructions include launching offline store marketing activities, online platform traffic allocation, and triggering innovative scenario marketing actions; resource allocation instructions include allocating store merchandise inventory, allocating production line materials, and distributing marketing resources across channels. The entire instruction execution process is localized, with millisecond-level response speeds, significantly improving instruction execution efficiency.

[0196] (3) Feedback the operation execution results to the corresponding scenario module. Preferably, after the module completes the instruction execution, it collects the operation execution result data in real time, including the instruction execution status, execution completion degree, execution effect indicators, and changes in the status of scenario-end devices / resources. After the execution results are structured and organized, they are accurately fed back to the corresponding scenario source data module and cloud central scheduling module through an encrypted transmission channel: Feedback to the source data module supports each module in updating the data collection logic and optimizing the modeling parameters based on the execution results; Feedback to the cloud central scheduling module allows the cloud to integrate the execution results into the global industry dataset, and simultaneously synchronize them to the big data prediction model module and the three-brain fusion decision module, providing actual execution basis for subsequent model optimization and decision adjustment, and finally forming a full-link execution closed loop of cloud-based instruction issuance, edge execution, and result feedback to the cloud and source modules.

[0197] The cloud-based central scheduling module and the edge node computing module achieve deep collaboration through a model of centralized cloud management and distributed edge execution: the cloud is responsible for the centralized integration of all-dimensional data, the relay scheduling of global decisions, and the overall coordination of system operation; the edge is responsible for localized data collection and rapid command execution, adapting to the real-time operation needs of various scenarios. Through an encrypted, low-latency bidirectional data transmission channel, the two achieve real-time data exchange, precise command relay, and rapid result feedback. This solves both the problems of high computational pressure and high response latency in directly processing massive amounts of raw data in the cloud, and the lack of global decision support for localized operations at the edge. Ultimately, it achieves efficient collaboration in system data processing, decision scheduling, and command execution, improving the overall response speed and execution efficiency of marketing and production collaboration within the industrial cluster.

[0198] Preferably, the three-brain fusion decision-making module includes: a cognitive brain module, a collaborative brain module, and an evolutionary brain module; wherein:

[0199] Cognitive Brain Module:

[0200] This submodule is based on a global industry dataset distributed by a cloud-based central scheduling module. It performs comprehensive situational awareness and demand identification for the industry cluster, outputting preliminary decision-making data to lay the foundation for subsequent tiered decision-making. In actual implementation, this submodule incorporates a situational awareness algorithm and a demand identification model. First, it performs a comprehensive analysis of the global industry dataset, extracting core data indicators from various dimensions such as multi-role cognitive interaction, product modeling and value grading, multi-scenario digital twins, transaction data assetization, and omnichannel sales attribution. This covers key information such as cluster capacity load, market demand trends, product value types, sales conversion rates across channels, supply chain collaboration status, transaction cash flow, and risks. Then, the situational awareness algorithm quantitatively analyzes the overall production and sales operation status of the industry cluster, clarifying the current operational characteristics, strengths, and weaknesses of the cluster in production, marketing, transaction, and supply chain. Subsequently, the demand identification model, combined with market, sales, and client data, accurately identifies production-side demands (such as production line scheduling and capacity allocation demands), marketing-side demands (such as marketing resource allocation demands and event planning demands across channels), and collaboration-side demands (such as cross-enterprise supply chain collaboration demands and cluster resource sharing demands). Based on the above situational awareness and demand identification results, this submodule initially prioritizes various demands and, combined with the overall operational goals of the industrial cluster, generates preliminary decision-making direction data, which includes cluster operational situation analysis, core demand sorting, demand priority marking, and preliminary decision-making direction. For example, if the market demand for high-interest products is surging, it is necessary to increase the production capacity of the corresponding production line and increase the investment of online channel marketing resources; if the conversion rate of offline store traffic is low, it is necessary to optimize the store flow and trigger offline promotional activities. Then, this data is transmitted unidirectionally to the Collaborative Brain module.

[0201] Collaborative Brain Module:

[0202] This submodule, based on the initial decision-making direction data transmitted by the cognitive brain module, coordinates cross-module and cross-scenario task conflicts and allocates collaborative tasks, outputting collaborative task allocation data to visualize and task-orientedize the decision-making direction. In actual implementation, this submodule incorporates a task conflict coordination algorithm and a collaborative task allocation model. First, it breaks down the various core requirements and decision-making directions in the initial decision-making direction data, transforming them into specific execution tasks for production, marketing, supply chain, and transaction processes, clarifying the execution objectives, involved modules, covered scenarios, and execution requirements of each task. Then, through the task conflict coordination algorithm, it cross-analyzes the decomposed tasks, identifying and resolving cross-module and cross-scenario task conflict issues, such as capacity competition conflicts (overlapping capacity allocation needs of multiple product production lines), resource allocation conflicts (multiple channel marketing resource allocation needs exceeding total resources), and scenario collaboration conflicts (inconsistent execution rhythms of online and offline sales tasks). For different types of conflicts, it employs methods such as priority matching, balanced resource allocation, and task rhythm synchronization to coordinate and resolve them. After conflict coordination is completed, this submodule uses a collaborative task allocation model to accurately allocate concrete execution tasks to the corresponding modules, scenarios, and execution entities, taking into account the functional positioning, resource configuration, and operational status of each module and scenario. It clarifies the execution module / scenario, execution entity, execution content, execution time limit, and assessment indicators for each task, and generates collaborative task allocation data. For example, the task of increasing the production capacity of high-profit product A by 30% is allocated to production lines 1 and 2; the task of increasing the online channel marketing resource investment of this product by 50% is allocated to the online channel submodule and the e-commerce platform edge node. Then, this data is transmitted unidirectionally to the Evolutionary Brain module.

[0203] Evolutionary Brain Module:

[0204] This submodule is based on the collaborative task allocation data transmitted by the collaborative brain module. It combines the historical collaborative data accumulated by the system with simulation and deduction technology to complete the iterative optimization of model parameters and the upgrading of decision-making strategies. Finally, it integrates all the preceding decision data to generate collaborative scheduling instructions, which are then output to the cloud central scheduling module to realize the optimization and implementation of decisions. In actual implementation, this submodule incorporates a historical data mining engine, a simulation model, and a parameter optimization algorithm. First, it retrieves locally stored historical collaborative data, including execution data, decision-making effectiveness data, and problem feedback data from similar past tasks. The historical data mining engine analyzes the strengths and weaknesses of past decisions, extracting reusable decision-making experience. Next, collaborative task allocation data is imported into the simulation model to simulate the entire task execution process, predict potential problems, and optimize task execution paths and resource allocation schemes. Then, through parameter optimization algorithms, combined with historical experience and simulation results, the core parameters of the situational awareness algorithm and demand identification model in the cognitive brain module, and the conflict coordination algorithm and task allocation model in the collaborative brain module are iteratively optimized. Simultaneously, optimization experience, decision rules, and core parameters are extracted as knowledge accumulation data, and the optimized task execution schemes, resource allocation strategies, and path adjustment suggestions are generated as strategy optimization data. Finally, this submodule integrates collaborative task allocation data, knowledge accumulation data, and strategy optimization data. Combined with the optimal execution plan derived from simulation, it transforms all decision content into standardized, implementable, and recognizable collaborative scheduling instructions that can be executed by edge node computing modules. The instructions contain core information such as task execution details, resource configuration standards, execution rhythm requirements, and exception handling rules. Subsequently, the collaborative scheduling instructions are uniformly output to the cloud central scheduling module, whereby the cloud completes the targeted distribution of the instructions.

[0205] It should be noted that the cognitive brain module, collaborative brain module, and evolutionary brain module adopt a unidirectional sequential transmission and layered decision-making linkage logic, forming a complete decision-making chain of perception-coordination-optimization-implementation:

[0206] In terms of data transmission, only one-way data transmission from the cognitive brain module to the collaborative brain module and from the collaborative brain module to the evolutionary brain module is supported. This ensures the independence and progression of the decision-making logic of each sub-module and avoids subsequent decisions from interfering with the objectivity of the early situational awareness and demand identification.

[0207] In terms of decision-making logic, the Cognitive Brain module answers the question of what it accomplishes (what is the status of cluster operation and what are the core needs), the Collaboration Brain module answers the question of what it does (breaking down tasks, allocating tasks, and resolving conflicts), and the Evolution Brain module answers the question of how to do it better (optimizing models, predicting problems, and improving solutions). These three sub-modules progress step by step, realizing a full-process decision analysis from macro-level decision-making direction to concrete task allocation, and then to optimizing execution plans.

[0208] Finally, the evolutionary brain module integrates all the preceding decision data, generates standardized collaborative scheduling instructions, and outputs them to the cloud-based central scheduling module. This enables the three-brain fusion decision module to link with other modules of the system, ensuring the accurate implementation and execution of decision instructions.

[0209] Furthermore, the system also includes a metaverse scene interaction module. This module is configured to construct an immersive metaverse collaborative interaction space, enabling visual interaction between users and the system. In actual implementation, based on the multi-scene fusion data output by the multi-scene digital twin module, a metaverse virtual space integrating production and sales of the industrial cluster is constructed. The AI ​​digital human model of the multi-role cognitive interaction module, the product digital model of the product modeling and value grading module, and the sales data of the omnichannel sales attribution module are mapped to the metaverse space in real time. It provides users with interactive interfaces such as product preview, order negotiation, production progress query, and resource scheduling visualization. Users can complete real-time interaction with the AI ​​digital human, place orders, and query production progress through virtual terminals in the metaverse space. The system collects user interaction operation data in the metaverse space in real time and feeds it back to the multi-role cognitive interaction module and the omnichannel sales attribution module, realizing the linkage update of metaverse interaction data and industry full-chain data.

[0210] Example 2: This application also proposes an AI marketing and production collaboration method driven by an industrial intelligent agent for dynamic resource allocation, applied to the AI ​​marketing and production collaboration system driven by an industrial intelligent agent for dynamic resource allocation as described in the first aspect. The method includes the following steps:

[0211] S1. Multi-dimensional Industry Data Collection and Module Output: The multi-role cognitive interaction module collects multi-role behavior and interaction data, constructs an AI digital human model, and outputs cognitive intent and collaborative instruction data; the product modeling and value grading module completes product digital modeling, classifies value types, and outputs product attribute-capacity mapping and value tag data; the multi-scenario digital twin module completes 3D scene mapping, collects and outputs multi-scenario fusion data; the transaction data assetization module transforms transaction behavior, establishes a linkage model, and outputs transaction asset and risk warning data; the omnichannel sales attribution module collects omnichannel sales behavior data and outputs traffic attribution and fulfillment data; the industry cluster intelligent agent module connects to the systems of various enterprises within the industry cluster, collects and outputs cluster resource distribution, enterprise collaboration needs, and cluster capacity load data. After each module independently completes data collection and processing, it synchronously transmits the output data to the cloud central scheduling module.

[0212] S2. Global Industry Data Aggregation and Integration: The cloud-based central scheduling module receives the output data from all modules in step S1, performs feature fusion processing on the data, and generates a global industry dataset that includes all dimensions of marketing, production, clusters, transactions, and sales.

[0213] S3. Data Distribution and Big Data Predictive Analysis: The cloud-based central scheduling module synchronously distributes the global industry dataset generated in step S2 to the big data prediction model module and the three-brain fusion decision module. Based on the global industry dataset, the big data prediction model module uses time series analysis and regression analysis algorithms to perform prediction calculations of market demand trends, peak production load, and order increments, outputting demand forecast data, load prediction data, and order trend data, and transmits all prediction data to the three-brain fusion decision module.

[0214] S4. Generation of Three-Brain Hierarchical Decision-Making and Collaborative Scheduling Instructions: The three-brain fusion decision-making module receives the global industry dataset and the prediction data from step S3. It then sequentially completes situational awareness and demand identification through the cognitive brain module, outputting preliminary decision direction data. Based on the preliminary decision direction, the collaborative brain module coordinates cross-module and cross-scenario marketing-production task conflicts, outputting collaborative task allocation data. Finally, the evolutionary brain module combines historical collaborative data and simulation deduction to iteratively optimize decision parameters, generating marketing-production collaborative scheduling instructions, and sending the instructions back to the cloud-based central scheduling module.

[0215] S5. Command Issuance and Local Execution at Edge Nodes: The cloud-based central scheduling module will send the collaborative scheduling command generated in step S4 to the edge node computing modules deployed in various physical and digital scenarios. The edge node computing modules will execute the corresponding local production control, marketing resource triggering, or cluster resource allocation operations according to the command type.

[0216] S6. Execution result feedback and full-link data closed loop: The edge node computing module feeds back the operation execution result of step S5 to the source data acquisition module corresponding to each operation. Each source data acquisition module updates the data acquisition dimension, modeling logic and parameter configuration based on the feedback result, and at the same time synchronizes the result to the cloud central scheduling module and big data prediction model module to realize the full-link data closed loop.

[0217] Furthermore, the method also includes: constructing an immersive metaverse interactive space based on multi-scene fusion data output by the multi-scene digital twin module; receiving all user interaction operation data within the metaverse space; and feeding back the interaction operation data to the multi-role cognitive interaction module and the omnichannel sales attribution module, respectively. The multi-role cognitive interaction module updates the interaction logic and behavioral characteristics of the AI ​​digital human model based on the interaction operation data, and the omnichannel sales attribution module optimizes the collection dimensions and attribution analysis algorithms of sales behavior data based on the interaction operation data. Simultaneously, the metaverse interactive data is transmitted to the cloud central scheduling module, integrated into the global industry dataset, and the linkage between the metaverse interactive data and the industry's full-link data is completed.

[0218] For example, a consumer goods industry cluster includes beverage manufacturers, packaging suppliers, offline supermarkets, and online e-commerce platforms. In this step, the industry cluster intelligent agent module connects with the MES systems of each enterprise, the supermarket sales system, and the e-commerce backend, collecting cluster resource and capacity data such as the capacity utilization rate of beverage enterprise production line 1 being 80%, the capacity utilization rate of production line 2 being 60%, the packaging enterprise's inventory being able to meet the cluster's production needs for 30 days, and the weekly sales of beverage products in supermarkets increasing by 20% year-on-year. The multi-role cognitive interaction module collects customer demand data showing a 35% increase in inquiries about low-sugar beverages. All modules synchronously upload the above data to the cloud central scheduling module.

[0219] After receiving data from various modules of the aforementioned consumer goods industry cluster, the cloud-based central scheduling module integrates customer data showing a 35% increase in inquiries about low-sugar beverages, production data showing an 80% capacity utilization rate for low-sugar beverage production lines, and sales data showing a 20% increase in weekly sales of low-sugar beverages in supermarkets. This generates a low-sugar beverage production and sales correlation dataset within the global industry dataset, clarifying the inherent business logic of each dimension of the data.

[0220] The big data prediction model module is based on the global industry dataset of the consumer goods industry cluster. It uses time series analysis algorithm to predict that the market demand for low-sugar beverages will increase by 40% in the next month. It uses regression analysis algorithm to predict that the capacity utilization rate of low-sugar beverage production lines will reach a peak of 95% in 15 days. The increase in orders for low-sugar beverages within the cluster will reach 500,000 units. The predicted data is then transmitted to the three-brain fusion decision module.

[0221] Based on the predicted data for low-sugar beverages in the consumer goods industry cluster, the Cognitive Brain module identified the core needs of insufficient low-sugar beverage production capacity and the need to increase marketing resources, and output preliminary decision directions for increasing low-sugar beverage production capacity and increasing online and offline marketing resources. The Collaborative Brain module broke down the execution tasks, such as converting beverage company's production line 2 to produce low-sugar beverages, increasing the exposure of low-sugar beverages on online e-commerce platforms, and setting up dedicated low-sugar beverage booths in offline supermarkets, and coordinating and resolving conflicts such as material allocation for the conversion of production line 2 and cross-channel distribution of marketing resources. The Evolution Brain module combined historical data on past production conversions and marketing to simulate and deduce the task execution process, and optimized the strategy to control the production conversion cycle of production line 2 within 3 days and tilt online marketing resources towards Douyin and Tmall platforms, and finally generated standardized collaborative scheduling instructions to be sent back to the cloud.

[0222] The cloud-based central scheduling module distributes the aforementioned collaborative scheduling instructions to the edge node computing modules of beverage company production line 2, online e-commerce platform, and offline supermarket: the edge node of production line 2 executes production control operations such as shutting down for 3 days to transform the low-sugar beverage production line and simultaneously allocating packaging materials; the edge node of the e-commerce platform executes marketing trigger operations such as setting up a low-sugar beverage zone on the homepage and placing direct traffic advertisements; the edge node of the supermarket executes marketing trigger operations such as setting up a dedicated low-sugar beverage booth in the beverage area and arranging sales staff. All operations achieve localized millisecond-level response.

[0223] The edge node of beverage company production line 2 collected data showing that the production line transformation took only 2.5 days and the post-transformation capacity reached 105% of the expected capacity. This data was fed back to the product modeling and value classification module and the industrial cluster intelligent agent module, which updated the product capacity modeling parameters and cluster capacity data accordingly. The edge node of e-commerce platform collected data showing that the exposure of low-sugar beverages increased by 80% and the click-through rate increased by 60%. This data was fed back to the omnichannel sales attribution module, which optimized the data collection dimensions of online channels accordingly. The above results were synchronized to the cloud and big data prediction model module, providing practical execution basis for subsequent decision-making and prediction.

[0224] Based on multi-scenario integrated data, the aforementioned consumer goods industry cluster has constructed a metaverse space for beverage production and sales. Users can preview new low-sugar beverages, experience product taste simulations, and negotiate group purchase orders with AI digital avatars for customer service within this space using virtual avatars. The system collects interactive data showing that users clicked most frequently on the virtual experience of low-sugar passion fruit beverages, and that most group purchase order inquiries were for bulk orders of 500 or more items. After this data is fed back to the multi-role cognitive interaction module, the module updates the customer service AI digital avatar model, adding exclusive interactive logic for answering bulk group purchase orders. After being fed back to the omnichannel sales attribution module, the module adds a metaverse virtual experience conversion data collection dimension. At the same time, this data is integrated into the cloud-based global industry dataset, providing data support for subsequent research and development of low-sugar passion fruit beverages and the formulation of bulk group purchase policies.

[0225] When applied to industrial clusters, this method breaks down data barriers between enterprises and modules through parallel collection and deep fusion of multi-dimensional data; it enables quantitative prediction of production and sales trends through big data forecasting, making decision-making more forward-looking; it generates scientific and implementable collaborative scheduling instructions through three-brain hierarchical decision-making; it achieves rapid execution of instructions through cloud-edge collaborative linkage; and it realizes continuous system optimization and deep integration of online and offline production and sales through full-link data closed-loop and metaverse interactive data linkage. Ultimately, it effectively solves the problems of fragmented production and sales data, lack of basis for decision-making, lagging collaborative response, and inefficient resource allocation in existing technologies for industrial clusters, significantly improving the collaborative efficiency of marketing and production, the level of dynamic resource allocation, and the degree of interactive intelligence in industrial clusters, realizing intelligent and precise collaborative operation of the entire industrial cluster chain.

[0226] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of the invention. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of the invention. All such changes and modifications are intended to be included within the scope of the invention as claimed in the appended claims.

[0227] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the statement "comprising a…" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0228] Although the description of the invention has been given in conjunction with the specific embodiments described above, it will be apparent to those skilled in the art that many substitutions, modifications, and variations can be made based on the foregoing. Therefore, all such substitutions, modifications, and variations are included within the spirit and scope of the appended claims.

Claims

1. An AI marketing and production collaborative system driven by industrial intelligent agent-driven dynamic resource allocation, characterized in that, The system includes a multi-role cognitive interaction module, a product modeling and value grading module, a multi-scenario digital twin module, a transaction data assetization module, an omni-channel sales attribution module, an industrial cluster intelligent agent module, a big data prediction model module, a cloud central scheduling module, an edge node computing module, and a three-brain fusion decision-making module. in, The multi-role cognitive interaction module collects behavioral characteristic data and interaction data of internal enterprise roles, customer groups, ecosystem partners and competitors, constructs an AI digital human model, and outputs cognitive intent data and collaborative instruction data. The product modeling and value classification module: digitally models product design parameters, production process data, and market performance data, classifies product value types, and outputs product attribute-capacity mapping data and value tag data; The multi-scenario digital twin module performs three-dimensional mapping of physical entity fields, value proof fields, and digital virtual fields, collects production status data, sales scenario data, and ecological collaboration data, and outputs multi-scenario fusion data. The transaction data assetization module transforms transaction behaviors related to payment settlement, supply chain finance, capital management, and risk management into digital asset data, establishes a linkage model of capital flow, production flow, and order flow, and outputs transaction asset data and risk warning data. The omnichannel sales attribution module collects full-link sales behavior data from all types of sales channels and outputs traffic attribution data, conversion path data, and fulfillment data. The industrial cluster intelligent agent module is configured to connect with the production, marketing, and inventory systems of various enterprises within the industrial cluster, enabling data exchange among multiple entities within the cluster, resource pooling, and cross-enterprise task scheduling and collaboration. It also collects and outputs cluster resource distribution data, enterprise collaboration demand data, and cluster capacity load data. The big data prediction model module is configured to perform prediction calculations on market demand trends, product sales trends, peak production load, and order increments based on multi-dimensional industry data, using time series analysis, regression analysis, and clustering algorithms, and output demand forecast data, load prediction data, and order trend data. The cloud-based central scheduling module is configured to receive and merge data output from each module. The three-brain fusion decision-making module is configured to generate collaborative scheduling instructions by combining prediction data, and the edge node computing module is configured to execute the scheduling instructions and feed back the execution results. Among them, the multi-role cognitive interaction module, product modeling and value classification module, multi-scenario digital twin module, transaction data assetization module, omni-channel sales attribution module, and industrial cluster intelligent agent module transmit their respective output data to the cloud central scheduling module. The cloud central scheduling module distributes the integrated global industrial dataset to the big data prediction model module and the three-brain fusion decision module. The three-brain fusion decision module combines the prediction data of the big data prediction model module to generate collaborative scheduling instructions. These instructions are then sent to the edge node computing module via the cloud central scheduling module. The edge node computing module feeds back the execution results to the corresponding modules in each scenario.

2. The AI marketing and production collaborative system driven by dynamic resource allocation of industrial intelligent agents according to claim 1, wherein, The multi-role cognitive interaction module includes: an internal role sub-module, a competition perception sub-module, a customer profiling sub-module, and an ecosystem collaboration sub-module; wherein: Internal role submodule: Configured to collect internal management instruction data and business process data based on knowledge graph and dialogue system, and output instruction distribution data and execution status data; Competition Awareness Submodule: Configured to collect competitor product parameter data, pricing strategy data, and market activity data based on web crawlers and competitor analysis algorithms, and output competitive situation data; Customer profiling submodule: Configured to collect customer browsing data, order data and interaction data based on behavior clustering and tag construction technology, and output customer demand data and preference prediction data; Ecosystem Collaboration Submodule: Configured to connect to the inventory, logistics and order data of suppliers and distributors based on API interfaces and message queue technology, and output supply chain collaboration data; The output data from each submodule is aggregated and transmitted to the cloud-based central scheduling module.

3. The AI ​​marketing and production collaboration system driven by an industrial intelligent agent and dynamically allocated resources, as described in claim 2, is characterized in that... The product modeling and value grading module includes: a lifecycle submodule and a value tagging submodule; wherein: Lifecycle submodule: Configured to model based on process parameters, link product design data, prototyping data and production data, and output process-cost mapping data; Value Tag Submodule: Configured to classify product value types based on classification and clustering algorithms, according to sales data and profit data, and output production scheduling priority data and marketing resource allocation data; The lifecycle submodule and the value tag submodule transmit product feature data bidirectionally and output it to the cloud central scheduling module. The multi-scenario digital twin module includes a physical entity field sub-module, a value proof field module, and a digital virtual field module; wherein: Physical Entity Field Submodule: Configured to collect equipment operation data and customer flow data from R&D centers, production lines, and stores based on IoT sensors and digital twin technology, and output physical scene mapping data; Value Proof Field Submodule: Configured to collect case data, standard certification data, and summit data based on document digitization and knowledge graph technology, and output brand value data; Digital Virtual Field Submodule: Configured to collect online transaction data and supply chain collaboration data from e-commerce platforms and industrial internet platforms based on API integration and data synchronization technology, and output virtual scene fusion data; The output data of each submodule is preprocessed by the edge node computing module and then transmitted to the cloud central scheduling module.

4. The AI ​​marketing and production collaboration system driven by an industrial intelligent agent and dynamically allocated resources, as described in claim 3, is characterized in that... The transaction data assetization module includes: a payment and settlement sub-module, a supply chain finance sub-module, and a risk management sub-module; wherein: Payment and settlement submodule: Configured to collect payment transaction data and settlement data based on payment interface and reconciliation algorithm, and output fund flow data; Supply chain finance sub-module: configured to use blockchain and smart contract technology to transform accounts receivable and order data into digital asset certificates and output financing matching data; Risk management submodule: configured to collect exchange rate, interest rate and credit data based on risk control algorithms, and output risk warning data; The payment and settlement submodule, supply chain finance submodule, and risk management submodule transmit transaction data bidirectionally and jointly output it to the cloud central scheduling module. The omnichannel sales attribution module includes: offline channel sub-module, online channel sub-module, B2B channel sub-module, and innovative channel sub-module; Offline channel submodule: Configured to collect customer behavior data and sales data in offline scenarios based on sensor and customer flow statistics technology, and output offline conversion data; Online channel submodule: Configured to collect traffic data, click data and order data from online platforms based on tracking and user behavior analysis technology, and output online conversion data; B-end channel submodule: configured to collect large customer demand data and customized order data based on enterprise-level API and demand modeling technology, and output B-end fulfillment data; Innovative Channels Submodule: Configured to collect behavioral data from subscription, experiential, and metaverse sales scenarios based on new consumption scenario data collection technology, and output innovative scenario conversion data; The output data from each submodule is aggregated and transmitted to the cloud-based central scheduling module.

5. The AI ​​marketing and production collaboration system driven by an industrial intelligent agent and dynamically allocated resources according to claim 4, characterized in that, The cloud-based central scheduling module is configured to receive input data from the multi-role cognitive interaction module, the product modeling and value grading module, the multi-scenario digital twin module, the transaction data assetization module, and the omnichannel sales attribution module. The input data is cleaned, formatted, and integrated to generate a global industry dataset. Distribute the global industry dataset to the big data prediction model module and the three-brain fusion decision-making module; It receives the collaborative scheduling instructions output by the three-brain fusion decision-making module and sends the instructions to the edge node computing module.

6. The AI marketing and production collaborative system driven by dynamic resource allocation of industrial intelligent agents according to claim 5, wherein, The edge node computing module is configured as follows: Deployed in various physical and digital scenarios, it collects local device operation data and scenario interaction data; The collected data undergoes lightweight processing and feature extraction, and the processed data is then transmitted to the cloud-based central scheduling module. Receive collaborative scheduling instructions from the cloud-based central scheduling module and execute local production control, marketing triggering, or resource allocation operations; The operation execution results are fed back to the corresponding scenario module.

7. The AI marketing and production collaborative system driven by dynamic resource allocation of industry intelligent agents according to claim 6, wherein, The three-brain fusion decision-making module includes: a cognitive brain module, a collaborative brain module, and an evolutionary brain module; wherein: Cognitive Brain Module: Configured to perform situational awareness and demand identification based on a global industry dataset, and output preliminary decision-making direction data; Collaborative Brain Module: Configured to coordinate cross-module and cross-scenario task conflicts based on preliminary decision-making direction data, and output collaborative task allocation data; Evolutionary Brain Module: Configured to iteratively optimize model parameters based on historical collaborative data and simulation inference, and output knowledge accumulation data and strategy optimization data; Data is transmitted sequentially between the cognitive brain module, the collaborative brain module, and the evolutionary brain module, ultimately generating collaborative scheduling instructions and outputting them to the cloud-based central scheduling module.

8. The system of claim 7, wherein, The system also includes a metaverse scene interaction module, configured to: construct an immersive metaverse interaction space based on multi-scene fusion data output by the multi-scene digital twin module; receive AI digital human data output by the multi-role cognitive interaction module, product data output by the product modeling and value grading module, and sales data output by the omnichannel sales attribution module; Provide users with interactive interfaces for product previews, order negotiation, and production progress inquiries, and feed back user interaction data to the multi-role cognitive interaction module and the omnichannel sales attribution module.

9. An AI marketing and production collaboration method driven by an industrial intelligent agent and featuring dynamic resource allocation, applied to the AI ​​marketing and production collaboration system driven by an industrial intelligent agent and featuring dynamic resource allocation as described in any one of claims 1-8, characterized in that... The method includes the following steps: S1. Multi-dimensional Industry Data Collection and Module Output: The multi-role cognitive interaction module collects multi-role behavior and interaction data, constructs an AI digital human model, and outputs cognitive intent and collaborative instruction data; the product modeling and value grading module completes product digital modeling, classifies value types, and outputs product attribute-capacity mapping and value tag data; the multi-scenario digital twin module completes 3D scene mapping, collects and outputs multi-scenario fusion data; the transaction data assetization module transforms transaction behavior, establishes a linkage model, and outputs transaction asset and risk warning data; the omnichannel sales attribution module collects omnichannel sales behavior data and outputs traffic attribution and fulfillment data; the industry cluster intelligent agent module connects to the systems of various enterprises within the industry cluster, collects and outputs cluster resource distribution, enterprise collaboration needs, and cluster capacity load data. After each module independently completes data collection and processing, it synchronously transmits the output data to the cloud central scheduling module. S2. Global Industry Data Aggregation and Integration: The cloud-based central scheduling module receives the output data from all modules in step S1, performs feature fusion processing on the data, and generates a global industry dataset that includes all dimensions of marketing, production, clusters, transactions, and sales. S3. Data Distribution and Big Data Predictive Analysis: The cloud-based central scheduling module synchronously distributes the global industry dataset generated in step S2 to the big data prediction model module and the three-brain fusion decision module. Based on the global industry dataset, the big data prediction model module uses time series analysis and regression analysis algorithms to perform prediction calculations of market demand trends, peak production load, and order increments, outputting demand forecast data, load prediction data, and order trend data, and transmits all prediction data to the three-brain fusion decision module. S4. Generation of Three-Brain Hierarchical Decision-Making and Collaborative Scheduling Instructions: The three-brain fusion decision-making module receives the global industry dataset and the prediction data from step S3. It then sequentially completes situational awareness and demand identification through the cognitive brain module, outputting preliminary decision direction data. Based on the preliminary decision direction, the collaborative brain module coordinates cross-module and cross-scenario marketing-production task conflicts, outputting collaborative task allocation data. Finally, the evolutionary brain module combines historical collaborative data and simulation deduction to iteratively optimize decision parameters, generating marketing-production collaborative scheduling instructions, and sending the instructions back to the cloud-based central scheduling module. S5. Command Issuance and Local Execution at Edge Nodes: The cloud-based central scheduling module will send the collaborative scheduling command generated in step S4 to the edge node computing modules deployed in various physical and digital scenarios. The edge node computing modules will execute the corresponding local production control, marketing resource triggering, or cluster resource allocation operations according to the command type. S6. Execution result feedback and full-link data closed loop: The edge node computing module feeds back the operation execution result of step S5 to the source data acquisition module corresponding to each operation. Each source data acquisition module updates the data acquisition dimension, modeling logic and parameter configuration based on the feedback result, and at the same time synchronizes the result to the cloud central scheduling module and big data prediction model module to realize the full-link data closed loop.

10. The AI-driven marketing and production collaboration method for dynamic resource allocation driven by an industrial intelligent agent, as described in claim 9, is characterized in that... The method further includes: An immersive metaverse interactive space is constructed based on multi-scenario fusion data output from the multi-scenario digital twin module. This space receives all user interaction data within the metaverse and feeds it back to the multi-role cognitive interaction module and the omnichannel sales attribution module. The multi-role cognitive interaction module updates the interaction logic and behavioral characteristics of the AI ​​digital human model based on the interaction data, while the omnichannel sales attribution module optimizes the collection dimensions and attribution analysis algorithms for sales behavior data based on the interaction data. Simultaneously, the metaverse interactive data is transmitted to the cloud-based central scheduling module and integrated into the global industry dataset, thus achieving the linkage between the metaverse interactive data and the entire industry chain data.

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