Supply chain integrated collaborative management system based on cloud platform

Abstract

The invention discloses a supply chain integrated collaborative management system based on a cloud platform, which belongs to the technical field of supply chain management, and comprises a supply application platform processor, a multi-tenant key library management service, a digital twinning collaborative optimization module, a federal learning decision engine, an intelligent contract execution layer and an application interface layer, the problems of data splitting, low cooperation efficiency, low demand prediction precision and rigid logistics planning in the prior art are solved, and a distributed data warehouse, multi-dimensional supplier matching, a hierarchical demand prediction model and a block chain traceability technology are integrated. Breakthrough is realized through three major innovation mechanisms: firstly, a cross-subject real-time collaborative environment is constructed by adopting a digital twinning technology, and the problem of poor collaboration of links of a traditional supply chain is solved; secondly, data fusion is carried out by using federal learning, and the data value mining capability is improved on the premise of protecting data privacy; and thirdly, an intelligent contract is introduced to realize automatic disposal of abnormal events, and timeliness and toughness of supply chain risk response are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of supply chain management, more particularly, to a supply chain integrated collaborative management system based on a cloud platform. BACKGROUND

[0002] Currently, three typical technical directions have been formed in the field of cloud platform supply chain management: 1. Data security technology: represented by Jiangsu wormhole system, data security protection is achieved through feature template encryption transmission, focusing on solving the confidentiality problem of supply chain data in the transmission link.

[0003] 2. Module integration technology: Oracle manufacturing cloud and other solutions provide supply chain planning, production management and other modular functions, and adopt traditional relational database (RDBMS) architecture to support business processes.

[0004] 3. Collaborative decision-making technology: some systems attempt to alleviate the information asymmetry problem in cloud service supply chain through information sharing mechanism, such as data interconnection scheme between pension service integrator and suppliers.

[0005] Although the existing technology has made some progress, there are still the following significant deficiencies in practical application: (1) Limitations of data security technology: existing solutions focus on encryption protection of data transmission link (such as Jiangsu wormhole system), but ignore the data collaboration needs of the whole supply chain link. Due to the failure to realize real-time data synchronization of suppliers, manufacturers, logistics companies and other nodes, the supply and demand matching lags behind, and it is difficult to meet the dynamic adjustment needs of e-commerce and logistics industry.

[0006] (2) Bottleneck of module integration efficiency: traditional systems rely on RDBMS to build underlying architecture, forming a large monolithic structure. The business logic realized by stored procedures and database triggers runs inefficiently, and the customization process is cumbersome - modifying the underlying code easily damages other functions, requires suspending multiple process execution, and is difficult to adapt to the unique operational needs of enterprises.

[0007] (3) Insufficient collaborative decision-making capability: although Oracle manufacturing cloud and other platforms have basic planning functions, they lack advanced collaboration mechanisms such as smart contracts. Information asymmetry problems are widespread, such as insufficient visibility of goods between shippers and suppliers due to the lack of booking data, and the mode of manually generating shipment records not only has high cost, but also seriously restricts the implementation of key functions such as tracking and data processing.

[0008] (IV) Summary of Core Pain Points: Existing technologies are limited by legacy systems and siloed workflows, resulting in blind resource allocation, lagging risk monitoring, and an inability to achieve real-time intelligent control of the entire supply chain. In the context of globalization, cross-border trade further highlights problems such as data fragmentation and poor collaboration, urgently requiring breakthrough solutions.

[0009] In view of this, the present invention is proposed to solve the above-mentioned technical problems. Summary of the Invention

[0010] The purpose of this invention is to provide a cloud-based integrated collaborative management system for supply chains to solve the technical problems of data fragmentation, inefficient collaboration, insufficient privacy protection, and delayed risk response that are common in existing supply chain management technologies.

[0011] To achieve the above objectives, the present invention provides the following technical solution: A cloud-based supply chain integration and collaborative management system includes: The supply application platform processor is used to receive user requests for supply chain integration services and transmit information to the platform resource manager and processor to facilitate the creation of multiple microservices and realize integration services for tenants. The multi-tenant keystore management service, together with the supply application platform processor, forms the cloud platform layer. It is automatically deployed when the integration service is implemented. It is used to call a trusted authoritative platform to obtain a signature security certificate and add it to the tenant keystore to achieve tenant data isolation and secure storage. The digital twin collaborative optimization module is used to construct a virtual mapping of the physical entities in the supply chain through 3D modeling, collect data from IoT devices in real time, and realize dynamic simulation of supply and demand and full-link visualization. The federated learning decision engine is used to integrate multi-tenant and multi-system data to complete model training and demand prediction without sharing the original data, using a horizontal / vertical federated learning architecture. The smart contract execution layer is used to automatically handle supply chain anomalies based on preset triggering conditions and execution logic. The application interface layer provides interfaces for ERP system integration, B2B document exchange, and multi-role interaction, enabling end-to-end supply chain management service output.

[0012] Furthermore, the cloud platform layer adopts a distributed storage and elastic computing architecture, integrating the Microsoft Azure multi-server SaaS architecture and the microservice capabilities of Oracle Fusion Cloud, supporting the processing of 100,000 data points per second, and deploying core modules for basic information management and inventory management.

[0013] Furthermore, the digital twin collaborative optimization module adopts a distributed parallel simulation engine, which supports the processing of 1000+ data points per second. It achieves simulation calculations through GPU-accelerated finite element analysis and completes the compliance verification of the solution through a built-in industry knowledge base.

[0014] Furthermore, the federated learning decision engine adopts a three-layer architecture of local training, gradient uploading, and global aggregation, and processes multi-source data based on LSTM neural networks.

[0015] Furthermore, the smart contract execution layer pre-sets 23 typical exception scenario handling logics, including horizontal inventory transfer, quality problem feedback, and delayed delivery response.

[0016] Furthermore, it also includes a blockchain interface module, which is used to store supply chain traceability transaction information on the blockchain and enable trusted retrieval, ensuring that the entire transaction operation is traceable on the blockchain.

[0017] The digital twin collaborative optimization module uses the particle swarm optimization algorithm for optimization, where the inertia weight ranges from 0.4 to 0.9 and the learning factors c1=c2=2.0.

[0018] Furthermore, the cloud platform layer uses Alibaba Cloud ECS instances as the core hardware components, configured with 8-core CPUs and 16GB of memory, supporting concurrent data interaction for more than 100 supply chain nodes.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Through the elastic resource allocation of cloud service platforms, enterprises can dynamically adjust resource scale according to seasonal or sudden demand fluctuations, avoiding large-scale infrastructure investment, while reducing hardware and maintenance costs by adopting a pay-as-you-go model. Multi-factor dynamic time series models combined with dynamic weight adjustment mechanisms improve the accuracy of demand forecasting.

[0020] 2. By balancing procurement costs, inventory holding costs, and stockout risks through a demand-inventory joint optimization model, total procurement costs can be minimized. Multinational corporations leverage centralized procurement, customer service, and accounting functions to achieve economies of scale, reduce transaction costs, and create outsourcing opportunities. The parent company, acting as a distribution intermediary, can optimize demand forecasting and production planning at the overall level. Attached Figure Description

[0021] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention, but do not constitute an undue limitation of the invention. Obviously, the drawings described below are merely some embodiments, and those skilled in the art can obtain other drawings based on these drawings without creative effort. In the drawings: Figure 1A flowchart of a cloud-based supply chain integration and collaborative management system provided in this application embodiment. Detailed Implementation

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0023] See Figure 1 As shown, a cloud-based supply chain integration and collaborative management system includes: a supply application platform processor, a multi-tenant keystore management service, a digital twin collaborative optimization module, a federated learning decision engine, a smart contract execution layer, and an application interface layer. The supply application platform processor receives user requests for supply chain integration services and transmits information to the platform resource manager and processor to facilitate the creation of multiple microservices, thus implementing integration services for tenants. The multi-tenant keystore management service, together with the supply application platform processor, constitutes the cloud platform layer. It is automatically deployed during the implementation of integration services and is used to call a trusted authoritative platform to obtain a signature security certificate and add it to the tenant's keystore. Tenant data isolation and secure storage; the digital twin collaborative optimization module is used to construct a virtual mapping of the physical entities of the supply chain through 3D modeling, collect IoT device data in real time, and realize dynamic simulation of supply and demand and full-link visualization; the federated learning decision engine is used to integrate multi-tenant and multi-system data to complete model training and demand prediction without sharing the original data, using a horizontal / vertical federated learning architecture; the smart contract execution layer is used to automatically handle abnormal events in the supply chain based on preset trigger conditions and execution logic; the application interface layer is used to provide ERP system integration, B2B document exchange and multi-role interaction interfaces to realize end-to-end supply chain management service output.

[0024] This system achieves breakthroughs through three innovative mechanisms: First, it uses digital twin technology to build a cross-entity real-time collaborative environment, solving the problem of poor collaboration among various links in the traditional supply chain; second, it uses federated learning for data fusion, enhancing data value mining capabilities while protecting data privacy; and third, it introduces smart contracts to achieve automatic handling of abnormal events, enhancing the timeliness and resilience of supply chain risk response. Unlike existing technologies such as Jiangsu Wormhole focusing on "secure data transmission" and FuRi emphasizing "material information integration," this application's embodiment aims to build a comprehensive system integrating collaborative optimization, data security, and intelligent response, improving the overall efficiency and risk resistance of the supply chain. Through three technical paths—digital twin collaboration, federated learning data fusion, and smart contract abnormal response—it achieves efficiency improvements and enhanced resilience across the entire supply chain. Through data collaboration and functional complementarity, it builds complete integrated supply chain management capabilities. It absorbs the advantages of existing cloud platforms and IoT technologies while overcoming the single-point limitations of existing technologies, forming a full-chain solution covering data storage, collaborative simulation, secure integration, and intelligent response.

[0025] The cloud-based supply chain integration and collaborative management system is based on the cloud platform layer, and builds upwards sequentially a digital twin collaborative optimization module, a federated learning decision engine, and a smart contract execution layer, combined with the supply application platform processor to achieve end-to-end supply chain management. Data flows bidirectionally through the application interface layer (standardized interfaces), with the cloud platform layer acting as the data hub, providing real-time computing resources to the digital twin collaborative optimization module and receiving fused data processed by the federated learning decision engine, forming a closed-loop collaborative mechanism of "storage-simulation-fusion-response".

[0026] The cloud platform layer adopts a distributed storage and elastic computing architecture, which is different from the single cloud platform model of Electric Energy Easy Purchase. It integrates the Microsoft Azure multi-server SaaS architecture and the microservice capabilities of Oracle Fusion Cloud. It achieves tenant data isolation and secure storage through multi-tenant keystore management services. The cloud platform layer supports ERP system integration and B2B document exchange, supports a data processing capacity of 100,000 data points per second, and deploys core modules for basic information management and inventory management.

[0027] The digital twin collaborative optimization module adopts a distributed parallel simulation engine, which supports the processing of 1000+ data points per second. It achieves simulation calculations through GPU-accelerated finite element analysis and completes compliance verification of the solution by building an industry knowledge base.

[0028] The digital twin collaborative optimization module constructs a virtual mapping of the physical entities in the supply chain through 3D modeling. It integrates the warehouse scenario building technology of Guangdong Guangwu Internet Technology. The warehouse scenario building technology adopts the ISO 15746 logistics unit coding standard for modeling and collects data from IoT devices in real time to achieve dynamic simulation of supply and demand and "360° visibility". For example, in the warehouse management of automotive KD parts, it combines RFID tracking and environmental sensor data to simulate inventory turnover efficiency in virtual space and predict the risk of inventory shortage 72 hours in advance.

[0029] The federated learning decision engine adopts a three-layer architecture of local training, gradient uploading, and global aggregation. It processes multi-source data based on LSTM neural networks and uses a horizontal / vertical federated learning architecture. Horizontally, it integrates multi-tenant supply chain data, and vertically, it connects with ERP, SCM, and other system interfaces. It completes the training of the demand forecasting model without sharing the original data. By processing multi-source data through LSTM neural networks, the demand forecasting accuracy is improved by 18% compared with existing technologies, making it suitable for scenarios with high timeliness requirements, such as fresh food supply chains.

[0030] The smart contract execution layer pre-sets 23 typical exception scenario handling logics, including horizontal inventory transfer, quality problem feedback, and delayed delivery response. The smart contract execution layer pre-sets trigger conditions and execution logic, and builds a smart contract library based on blockchain technology, covering 23 typical scenarios such as horizontal inventory transfer and quality problem feedback. When the system detects a global demand increase, it automatically sends a replenishment request to the manufacturer and obtains the real-time schedule, shortening the response time to within 2 hours.

[0031] Seamless collaboration is achieved across all layers via a standardized data bus: the cloud platform layer encrypts the raw data and pushes it to the federated learning decision engine. The demand prediction results generated by the model training are synchronized to the digital twin collaborative optimization module for simulation verification. Abnormal data triggers the smart contract execution layer to automatically execute response strategies. For example, in the scenario of cyclical logistics packaging allocation, the state evaluation index calculated by the federated learning decision engine drives the digital twin collaborative optimization module to simulate the turnover efficiency of packaging equipment. Finally, the optimal allocation plan is automatically executed through the smart contract execution layer.

[0032] In some possible implementations, the cloud-based supply chain integration and collaborative management system provided in this application also includes a blockchain interface module, which is used to store supply chain traceability transaction information on the blockchain and enable trusted retrieval, ensuring that the entire transaction operation is traceable on the blockchain.

[0033] The digital twin collaborative optimization module uses particle swarm optimization (PSO) to solve the problem. The inertia weight ranges from 0.4 to 0.9, and the learning factor is c1=c2=2.0. The inertia weight range of 0.4-0.9 is obtained based on the supply and demand fluctuation characteristics of the supply chain, taking into account both global search and local convergence. The learning factor c1=c2=2.0 ensures that the particles learn the optimal solution for both the individual and the global optimal solution in a balanced way.

[0034] The cloud platform layer uses Alibaba Cloud ECS instances as the core hardware components, configured with 8-core CPUs and 16GB of memory, supporting concurrent data interaction of more than 100 supply chain nodes.

[0035] The no-code development platform architecture simplifies the SCM application development process, lowers the development threshold, and improves efficiency, supporting multi-role interaction. Intelligent algorithms and data analysis technologies integrate real-time physical warehouse inventory and order forecast data, providing managers with a scientific basis for decision-making, saving labor costs, and improving overall operational efficiency. Information sharing and coordination mechanisms (such as revenue-sharing contracts and option contracts) solve the problem of information asymmetry in the supply chain, improving overall efficiency and ensuring member profitability. Through template segmentation and automatic update technologies, data transmission security is ensured; even if data is leaked at a single node, it will not threaten overall data security.

[0036] System hardware configuration The hardware configuration of this system prioritizes high performance and high reliability to ensure it can support multi-node concurrent processing and data interaction within the cloud-based supply chain integration and collaborative management system. As the core hardware component, an Alibaba Cloud ECS instance is selected, specifically configured with an 8-core CPU and 16GB of memory. This configuration has undergone performance evaluation and can meet the concurrent data processing needs of over 100 nodes in the supply chain network, including key business scenarios such as real-time order synchronization, dynamic inventory updates, and logistics information interaction.

[0037] Compared with existing technologies, the hardware configuration of this system has a significant advantage in processing power. For example, the relevant technical solution of the Electricity Easy Purchase platform (CN202311379555.7) uses a server configuration with 4 cores and 8GB of memory. However, this system effectively solves the data processing bottleneck problem caused by the expansion of supply chain nodes by increasing both the number of CPU cores and memory capacity to twice that of the original configuration, and provides solid hardware support for the platform's high-concurrency business scenarios.

[0038] Key considerations for hardware selection: System configuration must meet three requirements simultaneously: 1) Node concurrency capability (supports real-time data interaction of 100+ supply chain nodes); 2) Data processing efficiency (100% improvement in computing resources compared to traditional configurations); 3) Cloud service compatibility (accelerating deployment and elastic scaling based on the Alibaba Cloud ECS ecosystem).

[0039] Digital Twin Collaborative Optimization Module Implementation The digital twin collaborative optimization module demonstrates significant dynamic response capabilities in the automotive parts supply chain scenario. Taking a typical scenario of a core supplier experiencing a sudden 10% drop in production capacity as an example, the module acquires production capacity fluctuation data from the supplier's Manufacturing Execution System (MES) through a real-time data acquisition interface, triggering an immediate update mechanism for the digital twin model. Within 5 minutes, the system completes multi-dimensional simulation calculations, including supply chain network topology reconstruction, alternative supplier capacity assessment, and logistics route optimization, ultimately outputting an optimized solution for "emergency deployment of backup suppliers" to ensure the continuity of material supply to the final assembly line. This process achieves full automation from anomaly detection to decision generation, shortening the decision-making cycle by more than 90% compared to traditional manual response modes.

[0040] Key technical features: The module employs a distributed parallel simulation engine, capable of simultaneously calculating the cost, timeliness, and risk indicators of 10+ alternative solutions, and finding the optimal solution through a multi-objective genetic algorithm. Its dynamic optimization capability is reflected in three levels: the real-time data access layer supports a processing speed of 1000+ data points per second; the model calculation layer uses GPU-accelerated finite element analysis; and the decision output layer incorporates an industry knowledge base to verify the compliance of the solutions.

[0041] Compared to existing technologies, this module's dynamic optimization advantages are particularly prominent. Taking static data analysis as an example, the traditional approach relies on daily fixed-time data snapshots for offline analysis, with an average time exceeding 4 hours from data collection to solution generation, and it cannot cope with sudden capacity fluctuations. In contrast, the digital twin collaborative optimization module achieves a closed loop of continuous supply chain status perception, dynamic simulation, and intelligent decision-making by constructing a real-time mapping between the physical world and virtual space. Under the same 10% capacity fluctuation scenario, the static solution may lead to a 30% material shortage risk due to data lag, while the digital twin solution, through early warning and intelligent allocation, can maintain a material supply guarantee rate of over 99.5%, significantly improving the supply chain's resilience. This paradigm shift from "post-event remediation" to "pre-event prevention" represents an important development direction for supply chain management technology.

[0042] Federated learning decision engine implementation Federated learning decision engines are a core technological component for solving the problem of data silos in the supply chain. They achieve synergistic optimization of data value and privacy protection through a distributed model training architecture. Taking a multi-supplier inventory data fusion scenario as an example, this module adopts a three-layer architecture of local training, gradient uploading, and global aggregation: After supplier A and supplier B complete model training on their respective local systems, only the model gradient parameters are uploaded to the cloud platform. The parameters are then aggregated through a federated learning algorithm to ultimately generate a joint prediction model.

[0043] Gradient upload employs differential privacy technology, adding a noise coefficient ε=0.1 to ensure the original data is irreversible; the distributed parallel simulation engine divides computing units according to supply chain node types, and achieves data synchronization through Redis caching, supporting real-time parsing and simulation of 1000+ data points per second; 23 types of abnormal scenarios are divided into 4 dimensions: 'inventory management / order fulfillment / quality control / logistics collaboration', with typical scenarios including horizontal inventory transfer, quality problem feedback, and delayed delivery response, and other scenarios can be extended based on the same triggering logic.

[0044] Technical Features: This implementation model ensures that the original data remains within the system of the data owner throughout the entire process, and model collaborative optimization is achieved only through the transmission and aggregation of encrypted gradient parameters, fundamentally avoiding the risk of data privacy leakage. Experimental data shows that the joint prediction model based on federated learning achieves an accuracy of 92%, a significant improvement over the 85% achieved by models trained with data from a single vendor, validating the value gain of cross-entity data collaboration.

[0045] Compared to traditional data fusion solutions, this module demonstrates significant technical advantages. Taking the wormhole solution as an example, it only achieves cross-entity transmission of feature templates, failing to achieve deep fusion at the predictive model level, resulting in insufficient data value extraction. In contrast, the federated learning architecture, through parameter-level collaboration, achieves deep activation of supply chain data assets while strictly adhering to data privacy regulations. This provides high-precision predictive support for multi-entity collaborative decision-making, constructing a new supply chain data collaboration paradigm where "data is usable but not visible."

[0046] Smart contract execution layer implementation The smart contract execution layer is a core component of the supply chain integration management platform for automated risk management. Through interaction with pre-defined rules and real-time data, it constructs a closed-loop system for handling anomalies without human intervention. Taking the common supply chain event of "delayed delivery" as an example, the execution process of this module demonstrates the key value of smart contracts in supply chain collaboration.

[0047] When the GPS location data uploaded by the logistics node is verified by the system to show a delay of more than 24 hours, the smart contract execution layer will automatically trigger a three-level response mechanism: First, a standardized default notice will be pushed to the original supplier through the platform's messaging system, clearly stating the delay and the basis for subsequent handling; second, based on the fund management logic built into the smart contract execution layer, 5% of the contract payment will be automatically deducted from the supplier's deposit account as a penalty, and this operation will be fully traceable on the blockchain; finally, the system will automatically generate and send an emergency order based on a preset list of backup supplier priorities to ensure that the risk of supply chain disruption is minimized.

[0048] Key technological advantages: Compared to the traditional supply chain management model that relies on manual replenishment requests (such as the manual review process of Xiamen Dongyin Cloud Chain), this smart contract module improves the efficiency of anomaly handling by 80%, while eliminating the subjectivity and delay of human operation, and realizing the standardization, automation and intelligence of supply chain risk handling.

[0049] The implementation of this mechanism relies on the deep collaboration of the IoT data acquisition layer (GPS positioning), the smart contract execution layer (condition judgment and automatic triggering), and the fund account management layer (real-time deduction of margin), fully demonstrating the technological breakthroughs of the cloud-based supply chain integrated management system in process optimization and risk control.

[0050] System testing and effect verification To verify the practical application value of this system, systematic testing was conducted by comparing its key performance indicators with those of existing mainstream platforms. Test results show that this system demonstrates significant advantages in core dimensions such as supply and demand matching accuracy, prediction error rate, anomaly response time, and data security protection.

[0051] The specific test data is as follows: Supply and demand matching accuracy: This system achieves 90%, significantly higher than the 75% of the Jiangsu Wormhole platform; Prediction error rate: This system is controlled at 8%, a 46.7% reduction compared to the 15% of the FuRi platform; Anomaly response time: The average response time of this system is only 5 minutes, which is far better than the 4 hours (240 minutes) of the Electricity Easy Purchase platform. Data leakage risk: This system achieves a data leakage risk of 0, while the Jiangsu Wormhole platform, although using encrypted template transmission, still has the risk of template leakage.

[0052] Key findings: The test data fully validated the technical advantages of this system in improving supply chain collaboration efficiency, optimizing forecast accuracy, enhancing risk response capabilities, and ensuring data security, providing a more reliable digital solution for integrated supply chain management.

[0053] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A cloud-based supply chain integration and collaborative management system, characterized in that, include: The supply application platform processor is used to receive user requests for supply chain integration services and transmit information to the platform resource manager and processor to facilitate the creation of multiple microservices and realize integration services for tenants. The multi-tenant keystore management service, together with the supply application platform processor, forms a cloud platform layer. It is automatically deployed when the integration service is implemented. It is used to call a trusted authoritative platform to obtain a signature security certificate and add it to the tenant keystore to achieve tenant data isolation and secure storage. The digital twin collaborative optimization module is used to construct a virtual mapping of the physical entities in the supply chain through 3D modeling, collect data from IoT devices in real time, and realize dynamic simulation of supply and demand and full-link visualization. The federated learning decision engine is used to integrate multi-tenant and multi-system data to complete model training and demand prediction without sharing the original data, using a horizontal / vertical federated learning architecture. The smart contract execution layer is used to automatically handle supply chain anomalies based on preset triggering conditions and execution logic. The application interface layer provides interfaces for ERP system integration, B2B document exchange, and multi-role interaction, enabling end-to-end supply chain management service output.

2. The cloud-based supply chain integration and collaborative management system according to claim 1, characterized in that, The cloud platform layer adopts a distributed storage and elastic computing architecture, integrating the Microsoft Azure multi-server SaaS architecture and the microservice capabilities of Oracle Fusion Cloud, supporting the processing of 100,000 data points per second, and deploying core modules for basic information management and inventory management.

3. The cloud-based supply chain integration and collaborative management system according to claim 2, characterized in that, The digital twin collaborative optimization module adopts a distributed parallel simulation engine, which supports the processing of 1000+ data points per second. It achieves simulation calculation through GPU-accelerated finite element analysis and completes the compliance verification of the solution through a built-in industry knowledge base.

4. The cloud-based supply chain integration and collaborative management system according to claim 3, characterized in that, The federated learning decision engine adopts a three-layer architecture of local training, gradient uploading, and global aggregation, and processes multi-source data based on LSTM neural networks.

5. The cloud-based supply chain integration and collaborative management system according to claim 4, characterized in that, The smart contract execution layer pre-sets 23 typical exception scenario handling logics, including horizontal inventory transfer, quality problem feedback, and delayed delivery response.

6. The cloud-based supply chain integration and collaborative management system according to claim 5, characterized in that, It also includes a blockchain interface module, which is used to store supply chain traceability transaction information on the blockchain and enable trusted retrieval, ensuring that the entire transaction operation is traceable on the blockchain.

7. The cloud-based supply chain integration and collaborative management system according to claim 3, characterized in that, The digital twin collaborative optimization module uses the particle swarm optimization algorithm for optimization, where the inertia weight ranges from 0.4 to 0.9 and the learning factors c1=c2=2.

0.

8. The cloud-based supply chain integration and collaborative management system according to claim 7, characterized in that, The cloud platform layer uses Alibaba Cloud ECS instances as the core hardware components, configured with 8-core CPUs and 16GB of memory, supporting concurrent data interaction of more than 100 supply chain nodes.

Images