Supply chain toughness enhancing system based on federal learning and multi-agent cooperation

Abstract

The invention relates to the technical field of artificial intelligence and supply chain management crossing, in particular to a federated learning and multi-agent cooperation-based supply chain toughness enhancement system, which comprises a federated learning framework, a multi-agent cooperation module, a router, a data processing and analysis module, a trimmer and a data pool, the federal learning framework is used for cooperatively training a model among a plurality of supply chain participants in a privacy protection mode, each participant participates in training only through model parameter updating, and the trimmer is used for carrying out dynamic parameter adjustment on a federal large model and comprises three functional units, namely, a target management unit, a process management unit and a quality control management unit; according to the method, cross-enterprise data collaboration under privacy protection is realized through a federated learning framework, real-time decision and toughness optimization of the whole chain of the supply chain are realized in combination with a multi-agent framework, and stable operation, rapid recovery and adaptive optimization capabilities of the supply chain when the supply chain faces external impact or internal disturbance are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of interdisciplinary technology of artificial intelligence and supply chain management, and in particular to a supply chain resilience enhancement system based on federated learning and multi-agent collaboration. Background Technology

[0002] Traditional supply chain management relies primarily on manual decision-making or centralized information systems. However, with the increasing globalization and complexity of supply chains and growing uncertainty in the external environment, the limitations of existing technologies are as follows: Data silos and the challenges of collaborative decision-making: Due to business competition and data security concerns, various participants in the supply chain (such as suppliers, manufacturers, logistics providers, and retailers) form "data silos." For example, in the food supply chain, planting data from farms, production data from processing plants, and sales data from supermarkets cannot be shared, resulting in demand forecasting errors as high as 20%-30% and a 15%-20% reduction in inventory turnover. Although centralized data platforms attempt to integrate data, they require each participant to upload raw data, which can easily lead to the risk of leakage of trade secrets. Limited computing power and lagging edge decision-making: Supply chain terminal devices (such as factory sensors and logistics vehicle terminals) have limited computing power and cannot run large-scale decision-making models, while centralized cloud-based model inference suffers from network latency (usually 2-5 seconds). In scenarios such as real-time optimization of logistics routes and emergency scheduling of production lines, this latency leads to a 25%-30% increase in decision error rate. Existing distributed systems (such as supply chain optimization solutions based on edge computing) only optimize data transmission and do not solve the fundamental problems of privacy protection and collaborative training. Risk warning and insufficient resilience: Traditional risk warning relies on historical experience or local data, lacks full-chain data support, and the warning time for sudden risks (such as raw material supply disruptions and port congestion) is less than 48 hours, resulting in a delayed emergency response.

[0003] Therefore, to address the aforementioned issues, we propose a supply chain resilience enhancement system based on federated learning and multi-agent collaboration. This system addresses the problems of data silos, limited computing power, and delayed risk response in traditional supply chain systems. It achieves cross-enterprise data collaboration under privacy protection through a federated learning framework and enables real-time decision-making and resilience optimization across the entire supply chain through a multi-agent architecture. This significantly improves the supply chain's stable operation, rapid recovery, and adaptive optimization capabilities when facing external shocks or internal disturbances. Summary of the Invention

[0004] In order to overcome the problems of data silos, limited computing power, and delayed risk response in the use of existing supply chain management systems.

[0005] The technical solution of this invention is: a supply chain resilience enhancement system based on federated learning and multi-agent collaboration, comprising: The federated learning framework is used to collaboratively train models among multiple supply chain participants in a privacy-preserving manner, with each participant only participating in training through model parameter updates; The multi-agent collaboration module includes multiple agents deployed at different levels of the supply chain, including industrial park agents, supply chain agents, enterprise agents, job agents, and event agents, which are used to perform localized decisions at their respective levels and interact with the federated learning framework. Routers, as the fusion hub of multiple agents, are used to schedule the collaborative operation between agents and dynamically allocate computing resources. The data processing and analysis module is used to clean, standardize, and integrate data across the entire supply chain, and to perform risk warning and emergency optimization analysis. The fine-tuner is used to dynamically adjust the parameters of the federated model, and includes three functional units: target management, process management and quality control management. The data pool is used to achieve closed-loop management of data from collection to assetization, including seven stages: data collection and sorting, integration and governance, compliance and rights confirmation, accounting, audit and disclosure, data trading and data dividends.

[0006] Preferably, the federated learning framework allows multiple supply chain participants to collaboratively train a model by updating model parameters without sharing the original data. After each participant trains the model locally, they only upload the parameters to the aggregation server. The server aggregates the parameters using a federated averaging algorithm, specifically: ; in, For the first Local model parameters for each participant For the number of participants, These are the aggregated global parameters; this mechanism can reduce the risk of data leakage and integrate data from the entire chain, thereby improving the accuracy of demand forecasting and inventory turnover. The multi-agent collaboration module includes agents deployed at different levels of the supply chain; each agent acts as a federated learning node, executes localized decisions and interacts with the federated framework; for example, the enterprise agent is deployed at the manufacturer level to use local data to infer production scheduling strategies in real time; the event agent is deployed at the device end to detect anomalies and trigger autonomous responses; this module achieves a closed loop from global optimization to local execution through hierarchical collaboration. The router employs a real-value fusion mechanism to schedule inter-agent collaboration; the router receives parameter updates from each agent and aggregates the parameters using a weighted average algorithm, specifically: ; in, For the first The weights of each agent are dynamically adjusted based on its data quality and computing power; the router also optimizes the network transmission path, reducing inference latency and improving decision response speed. The data processing and analysis module includes evidence chain, data chain, and smart chain units; the evidence chain ensures data traceability through timestamps and digital signatures; the data chain uses asymmetric encryption and distributed message queues to achieve secure transmission; the smart chain collects data in real time through sensors and combines it with machine learning algorithms for risk prediction; this module extends the risk warning time. The fine-tuner includes a no-code user interface, a fine-tuner gateway, and a knowledge base. Parameters are defined through JSON Schema, and hot reloading and version rollback are supported. Business users can adjust parameters through sliders or drop-down menus to adapt the model to different scenarios, thereby shortening the interruption recovery time. Data pools are used to realize a closed loop from data collection to assetization. By integrating federated learning, cross-entity data collaboration is achieved under the premise of privacy protection, thereby improving the utilization rate of data value.

[0007] Preferably, the federated learning framework employs a hierarchical federated distillation mechanism, replacing full data transmission with lightweight parameter interaction, specifically including: At the device layer, event-based and job-specific intelligent agents are deployed to be responsible for local data collection and preliminary processing, and to reduce data transmission volume through lightweight parameter interaction. At the edge layer, enterprise intelligent agents are deployed, responsible for regional data aggregation and model fine-tuning. Knowledge distillation technology is used to migrate knowledge from large cloud models to small edge models. The cloud layer deploys intelligent agents for industrial parks and supply chains, responsible for global model training and distribution. This mechanism is used to reduce cross-enterprise computing power consumption and solve the problem of limited computing power for edge devices.

[0008] Preferably, in the multi-agent cooperation module: The industrial park intelligent agent is deployed on the park platform to coordinate regional production capacity allocation, carbon emission monitoring and cross-enterprise resource scheduling. The supply chain intelligence agent is deployed in the chain leader enterprise to build a supply chain knowledge graph and perform full-chain risk prediction. The risk prediction is performed using a graph neural network. The enterprise intelligent agent is deployed at the manufacturer or supplier level to perform local model inference and real-time production scheduling, participate in federated parameter uploading, and support inventory management. The intelligent agents for specific tasks are deployed in factory workshops or work sections to enable human-machine collaborative operation and equipment control, thereby improving work efficiency through automated control. The event intelligence agent is deployed on edge devices to detect abnormal events in real time and trigger local autonomous responses. This deployment enables full-level collaboration across the supply chain and reduces the error rate in decision-making.

[0009] Preferably, the router achieves real-valued fusion of multiple AI agents, specifically through the following methods: Receive local model parameter updates from each agent; The parameters are aggregated using a weighted average algorithm to generate a global model update. The weights are dynamically calculated based on the data contribution and reliability of the agents. The updated parameters are distributed to each agent, and the network transmission path is optimized to reduce latency; Allocate computing resources and dynamically adjust the computing power allocation of agents according to task priorities to improve the overall efficiency of the system.

[0010] Preferably, the data processing and analysis module includes: The chain of evidence unit is used to attach timestamps and digital signatures to the collected data to ensure data traceability; The data link unit employs asymmetric encryption algorithms and distributed message queues to achieve secure data transmission and status monitoring. The intelligent chain unit collects physical world data in real time through external sensors and combines it with machine learning algorithms to make automated decisions; The evidence chain unit records the data generation time using timestamp technology and verifies the data source using digital signature technology based on asymmetric encryption, ensuring that the data is tamper-proof. The data chain unit uses TLS / SSL protocol to encrypt data transmission, monitors the transmission status through a distributed message queue, and uses JSON Schema to verify the data format. The smart chain unit connects to multiple types of sensors, preprocesses data through an edge computing gateway, and uses machine learning models for automated decision-making. This module reduces the risk of data forgery.

[0011] Preferably, the fine-tuner includes: A no-code user interface that receives parameter adjustment commands via sliders, drop-down menus, and gear buttons; The fine-tuner gateway is used to validate parameter formats and generate configuration files. The knowledge base stores supply chain knowledge and large-scale model hint templates; The fine-tuner supports hot loading and version rollback, and the parameter adjustment takes effect in ≤10 seconds; Specifically, the no-code user interface is implemented based on the React framework, receiving parameter adjustment instructions through sliders, drop-down menus, and gear buttons; the fine-tuner gateway is developed in Python, validating JSON Schema and generating configuration files, and pushing parameters to the Dify platform via WebSocket; the knowledge base stores supply chain domain knowledge and prompt templates, supporting dynamic updates; the fine-tuner supports hot reloading, with parameter adjustments taking effect within 1000 milliseconds and rollback to historical versions, improving model parameter tuning efficiency.

[0012] Preferably, the parameter adjustment of the fine-tuner is based on a structured JSON Schema, including: Target management parameters: maximum token count, temperature value, and top probability; Process management parameters: file template, batch size, and number of retries; Quality control management parameters: sensitive word list, confidence threshold, and similarity limit; Specifically, the target management parameters are: maximum number of tokens (1-4096), temperature value (0-2), and top probability (0-1), which are used to control the range of model generation. Process management parameters: slot template (e.g., P, R, D-eco), batch size (1-128), number of retries (0-5), used to optimize the execution path; Quality control management parameters: sensitive word list, confidence threshold (0-1), and similarity upper limit (0-1), used to check output quality in real time; this schema ensures standardized parameter tuning and improves model stability.

[0013] As a preferred option, the system adopts a federated size model collaborative architecture: The large cloud-based model is responsible for learning global supply chain knowledge, while the small edge models are deployed on terminal devices to make real-time decisions, using the Transformer architecture to process data across the entire supply chain. The edge mini-model is deployed on the terminal device and is responsible for real-time decision-making. It realizes bidirectional knowledge transfer through the FedMKT framework. Specifically, the large model distills knowledge into the mini-model, and the mini-model updates local parameters in reverse, which reduces the inference latency of the edge device while ensuring the accuracy of the decision.

[0014] As a preferred approach, the system employs a clustering federated training strategy: The participants are grouped according to vertical industry chain type and geographical region; Customized models are trained for different groups separately; For non-independent and identically distributed data, the K-means clustering algorithm is used to group the data, reducing the model convergence time and improving generalization ability.

[0015] Preferably, the system also includes a resilience assessment module: Embed risk warning time, interruption recovery speed, and resource reconfiguration efficiency metrics into model training; By prioritizing the optimization of the supply chain's resilience through reinforcement learning algorithms and providing real-time feedback on the optimization results during the decision-making process, emergency response time can be shortened.

[0016] The beneficial effects of this invention are: This invention initializes the local models of each participant through a federated learning framework and achieves collaborative training under privacy protection through parameter aggregation; it implements hierarchical decision-making through a multi-agent module and dynamically schedules resources through a router to ensure real-time response; the data processing module ensures data trustworthiness and security through a three-chain structure; it allows for no-code parameter adjustment through a fine-tuner to quickly adapt to different scenarios; it manages data assets in a closed loop through a data pool; and it continuously optimizes the supply chain's resilience by embedding reinforcement learning through a resilience assessment module. The entire process forms an intelligent closed loop from data collection to decision execution, improving the stability and recovery of the supply chain. It achieves a unified improvement in the supply chain in terms of privacy protection, collaborative decision-making, and resilience enhancement, and significantly improves the supply chain's stable operation, rapid recovery, and adaptive optimization capabilities when facing external shocks or internal disturbances. Attached Figure Description

[0017] Figure 1 The diagram shown is a schematic representation of the supply chain resilience enhancement system based on federated learning and multi-agent collaboration of the present invention. Detailed Implementation

[0018] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] Please see Figure 1 This invention provides an embodiment: a supply chain resilience enhancement system based on federated learning and multi-agent collaboration, comprising: The federated learning framework is used to collaboratively train models among multiple supply chain participants in a privacy-preserving manner, with each participant only participating in training through model parameter updates; The multi-agent collaboration module includes multiple agents deployed at different levels of the supply chain. These agents include industrial park agents, supply chain agents, enterprise agents, job agents, and event agents, which are used to execute localized decisions at their respective levels and interact with the federated learning framework. Routers, as the fusion hub of multiple agents, are used to schedule the collaborative operation between agents and dynamically allocate computing resources. The data processing and analysis module is used to clean, standardize, and integrate data across the entire supply chain, and to perform risk warning and emergency optimization analysis. The fine-tuner is used to dynamically adjust the parameters of the federated model, and includes three functional units: target management, process management and quality control management. The data pool is used to achieve closed-loop management of data from collection to assetization, including seven stages: data collection and sorting, integration and governance, compliance and rights confirmation, accounting, audit and disclosure, data trading and data dividends.

[0020] The federated learning framework allows multiple supply chain participants to collaboratively train a model by updating model parameters without sharing raw data. After each participant trains the model locally, they only upload the parameters to the aggregation server. The server then aggregates the parameters using a federated averaging algorithm. ; in, For the first Local model parameters for each participant For the number of participants, These are the aggregated global parameters; this mechanism can reduce the risk of data leakage and integrate data from the entire chain, thereby improving the accuracy of demand forecasting and inventory turnover. The multi-agent collaboration module includes agents deployed at different levels of the supply chain; each agent acts as a federated learning node, executes localized decisions and interacts with the federated framework; for example, the enterprise agent is deployed at the manufacturer level to use local data to infer production scheduling strategies in real time; the event agent is deployed at the device end to detect anomalies and trigger autonomous responses; this module achieves a closed loop from global optimization to local execution through hierarchical collaboration. The router employs a real-value fusion mechanism to schedule inter-agent collaboration; the router receives parameter updates from each agent and aggregates the parameters using a weighted average algorithm, specifically: ; in, For the first The weights of each agent are dynamically adjusted based on its data quality and computing power; the router also optimizes the network transmission path, reducing inference latency and improving decision response speed. The data processing and analysis module includes evidence chain, data chain, and smart chain units; the evidence chain ensures data traceability through timestamps and digital signatures; the data chain uses asymmetric encryption and distributed message queues to achieve secure transmission; the smart chain collects data in real time through sensors and combines it with machine learning algorithms for risk prediction; this module extends the risk warning time. The fine-tuner includes a no-code user interface, a fine-tuner gateway, and a knowledge base. Parameters are defined through JSON Schema, and hot reloading and version rollback are supported. Business users can adjust parameters through sliders or drop-down menus to adapt the model to different scenarios, thereby shortening the interruption recovery time. Data pools are used to realize a closed loop from data collection to assetization. By integrating federated learning, cross-entity data collaboration is achieved under the premise of privacy protection, thereby improving the utilization rate of data value.

[0021] Furthermore, the federated learning framework employs a hierarchical federated distillation mechanism, replacing full data transmission with lightweight parameter interaction, specifically including: At the device layer, event-based and job-specific intelligent agents are deployed to be responsible for local data collection and preliminary processing, and to reduce data transmission volume through lightweight parameter interaction. At the edge layer, enterprise intelligent agents are deployed, responsible for regional data aggregation and model fine-tuning. Knowledge distillation technology is used to migrate knowledge from large cloud models to small edge models. The cloud layer deploys intelligent agents for industrial parks and supply chains, responsible for global model training and distribution. This mechanism is used to reduce cross-enterprise computing power consumption and solve the problem of limited computing power for edge devices.

[0022] Furthermore, in the multi-agent collaboration module: The industrial park's intelligent agent is deployed on the park's platform to coordinate regional production capacity allocation, carbon emission monitoring, and cross-enterprise resource scheduling. The supply chain intelligence agent is deployed in the chain leader enterprise to build a supply chain knowledge graph and perform full-chain risk prediction. The risk prediction is carried out using a graph neural network. Enterprise intelligent agents are deployed at the manufacturer or supplier level to perform local model inference and real-time production scheduling, participate in federated parameter uploading, and support inventory management; The intelligent agents for specific tasks are deployed in factory workshops or work sections to enable human-machine collaborative operation and equipment control, thereby improving work efficiency through automated control. The event intelligence agent is deployed on edge devices to detect abnormal events in real time and trigger local autonomous responses. This deployment enables full-level collaboration across the supply chain and reduces the error rate in decision-making.

[0023] Furthermore, the router achieves real-data fusion of multiple AI agents, specifically through the following methods: Receive local model parameter updates from each agent; The parameters are aggregated using a weighted average algorithm to generate a global model update. The weights are dynamically calculated based on the data contribution and reliability of the agents. The updated parameters are distributed to each agent, and the network transmission path is optimized to reduce latency; Allocate computing resources and dynamically adjust the computing power allocation of agents according to task priorities to improve the overall efficiency of the system.

[0024] Furthermore, the data processing and analysis module includes: The chain of evidence unit is used to attach timestamps and digital signatures to the collected data to ensure data traceability; The data link unit employs asymmetric encryption algorithms and distributed message queues to achieve secure data transmission and status monitoring. The intelligent chain unit collects physical world data in real time through external sensors and combines it with machine learning algorithms to make automated decisions; The evidence chain unit records the data generation time using timestamp technology and verifies the data source using digital signature technology based on asymmetric encryption, ensuring that the data is tamper-proof. The data chain unit uses TLS / SSL protocol to encrypt data transmission, monitors the transmission status through a distributed message queue, and uses JSON Schema to verify the data format. The smart chain unit connects to multiple types of sensors, preprocesses data through an edge computing gateway, and uses machine learning models for automated decision-making. This module reduces the risk of data forgery.

[0025] Furthermore, the fine-tuner includes: A no-code user interface that receives parameter adjustment commands via sliders, drop-down menus, and gear buttons; The fine-tuner gateway is used to validate parameter formats and generate configuration files. The knowledge base stores supply chain knowledge and large-scale model hint templates; Among them, the fine-tuner supports hot reloading and version rollback, and the parameter adjustment takes effect in ≤10 seconds; Specifically, the no-code user interface is implemented based on the React framework, receiving parameter adjustment instructions through sliders, drop-down menus, and gear buttons; the fine-tuner gateway is developed in Python, validating JSON Schema and generating configuration files, and pushing parameters to the Dify platform via WebSocket; the knowledge base stores supply chain domain knowledge and prompt templates, supporting dynamic updates; the fine-tuner supports hot reloading, with parameter adjustments taking effect within 1000 milliseconds and rollback to historical versions, improving model parameter tuning efficiency.

[0026] Furthermore, the fine-tuning of the spinner's parameters is implemented based on a structured JSON schema, including: Target management parameters: maximum token count, temperature value, and top probability; Process management parameters: file template, batch size, and number of retries; Quality control management parameters: sensitive word list, confidence threshold, and similarity limit; Specifically, the target management parameters are: maximum number of tokens (1-4096), temperature value (0-2), and top probability (0-1), which are used to control the range of model generation. Process management parameters: slot template (e.g., P, R, D-eco), batch size (1-128), number of retries (0-5), used to optimize the execution path; Quality control management parameters: sensitive word list, confidence threshold (0-1), and similarity upper limit (0-1), used to check output quality in real time; this schema ensures standardized parameter tuning and improves model stability.

[0027] Furthermore, the system adopts a federated size model collaborative architecture: The large cloud-based model is responsible for learning global supply chain knowledge, while the small edge models are deployed on terminal devices to make real-time decisions, using the Transformer architecture to process data across the entire supply chain. The edge mini-model is deployed on the terminal device and is responsible for real-time decision-making. It realizes bidirectional knowledge transfer through the FedMKT framework. Specifically, the large model distills knowledge into the mini-model, and the mini-model updates local parameters in reverse, which reduces the inference latency of the edge device while ensuring the accuracy of the decision.

[0028] Furthermore, the system employs a clustering federated training strategy: The participants are grouped according to vertical industry chain type and geographical region; Customized models are trained for different groups separately; For non-independent and identically distributed data, the K-means clustering algorithm is used to group the data, reducing the model convergence time and improving generalization ability.

[0029] Furthermore, the system also includes a resilience assessment module: Embed risk warning time, interruption recovery speed, and resource reconfiguration efficiency metrics into model training; By prioritizing the optimization of the supply chain's resilience through reinforcement learning algorithms and providing real-time feedback on the optimization results during the decision-making process, emergency response time can be shortened.

[0030] Through the above steps, the local models of each participant are initialized using a federated learning framework, and collaborative training under privacy protection is achieved through parameter aggregation. Multi-agent modules execute decisions hierarchically, and routers dynamically schedule resources to ensure real-time response. The data processing module uses a three-chain structure to ensure data trustworthiness and security. A fine-tuner adjusts parameters without code to quickly adapt to different scenarios. A data pool manages data assets in a closed loop. A resilience assessment module embeds reinforcement learning to continuously optimize the supply chain's resilience. The entire process forms an intelligent closed loop from data collection to decision execution, improving the stability and recovery of the supply chain. It achieves a unified improvement in privacy protection, collaborative decision-making, and resilience enhancement of the supply chain, and significantly enhances the supply chain's stable operation, rapid recovery, and adaptive optimization capabilities when facing external shocks or internal disturbances.

[0031] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A supply chain resilience enhancement system based on federated learning and multi-agent collaboration, characterized in that: include: The federated learning framework is used to collaboratively train models among multiple supply chain participants in a privacy-preserving manner, with each participant only participating in training through model parameter updates; The multi-agent collaboration module includes multiple agents deployed at different levels of the supply chain, including industrial park agents, supply chain agents, enterprise agents, job agents, and event agents, which are used to perform localized decisions at their respective levels and interact with the federated learning framework. Routers, as the fusion hub of multiple agents, are used to schedule the collaborative operation between agents and dynamically allocate computing resources. The data processing and analysis module is used to clean, standardize, and integrate data across the entire supply chain, and to perform risk warning and emergency optimization analysis. The fine-tuner is used to dynamically adjust the parameters of the federated model, and includes three functional units: target management, process management and quality control management. The data pool is used to achieve closed-loop management of data from collection to assetization, including seven stages: data collection and sorting, integration and governance, compliance and rights confirmation, accounting, audit and disclosure, data trading and data dividends.

2. The supply chain resilience enhancement system based on federated learning and multi-agent collaboration according to claim 1, characterized in that: The federated learning framework employs a hierarchical federated distillation mechanism, replacing full data transmission with lightweight parameter interaction, specifically including: At the device layer, deploy event-based and job-specific intelligent agents to be responsible for local data collection and preliminary processing. At the edge layer, enterprise intelligent agents are deployed, responsible for regional data aggregation and model fine-tuning; The cloud layer deploys intelligent agents for the industrial park and supply chain, and is responsible for global model training and distribution.

3. The supply chain resilience enhancement system based on federated learning and multi-agent collaboration according to claim 1, characterized in that: In the multi-agent cooperation module: The industrial park intelligent agent is deployed on the park platform to coordinate regional production capacity allocation, carbon emission monitoring and cross-enterprise resource scheduling. The supply chain intelligence agent is deployed in the chain leader enterprise to build a supply chain knowledge graph and perform full-chain risk prediction. The enterprise intelligence agent is deployed at the manufacturer or supplier level to perform local model inference and real-time production scheduling; The intelligent agents for specific tasks are deployed in factory workshops or work sections to enable human-machine collaborative operation and equipment control; The event intelligence agent is deployed on edge devices to detect abnormal events in real time and trigger local autonomous responses.

4. The supply chain resilience enhancement system based on federated learning and multi-agent collaboration according to claim 1, characterized in that: The router achieves real-data fusion of multiple AI agents, specifically through the following methods: Receive local model parameter updates from each agent; A weighted average algorithm is used to aggregate parameters and generate a global model update. The updated parameters are distributed to each agent, and the network transmission path is optimized to reduce latency.

5. A supply chain resilience enhancement system based on federated learning and multi-agent collaboration as described in claim 1, characterized in that: The data processing and analysis module includes: The chain of evidence unit is used to attach timestamps and digital signatures to the collected data to ensure data traceability; The data link unit employs asymmetric encryption algorithms and distributed message queues to achieve secure data transmission and status monitoring. The intelligent chain unit collects physical world data in real time through external sensors and combines it with machine learning algorithms to make automated decisions.

6. A supply chain resilience enhancement system based on federated learning and multi-agent collaboration as described in claim 1, characterized in that: The fine-tuner includes: A no-code user interface that receives parameter adjustment commands via sliders, drop-down menus, and gear buttons; The fine-tuner gateway is used to validate parameter formats and generate configuration files. The knowledge base stores supply chain knowledge and large-scale model hint templates; The fine-tuner supports hot loading and version rollback, and the parameter adjustment takes effect in ≤10 seconds.

7. A supply chain resilience enhancement system based on federated learning and multi-agent collaboration as described in claim 6, characterized in that: The parameter adjustment of the fine-tuner is based on a structured JSON schema, including: Target management parameters: maximum token count, temperature value, and top probability; Process management parameters: file template, batch size, and number of retries; Quality control management parameters: sensitive word list, confidence threshold, and similarity limit.

8. A supply chain resilience enhancement system based on federated learning and multi-agent collaboration as described in claim 1, characterized in that: The system adopts a federated size model collaborative architecture: The large cloud-based model is responsible for learning global supply chain knowledge, while the small edge models deployed on terminal devices are responsible for real-time decision-making. The FedMKT bidirectional knowledge transfer framework is used for knowledge sharing between large and small models.

9. A supply chain resilience enhancement system based on federated learning and multi-agent collaboration as described in claim 1, characterized in that: The system employs a clustering federated training strategy: The participants are grouped according to vertical industry chain type and geographical region; Customized models are trained for different groups.

10. A supply chain resilience enhancement system based on federated learning and multi-agent collaboration as described in claim 1, characterized in that: The system also includes a resilience assessment module: Embed risk warning time, interruption recovery speed, and resource reconfiguration efficiency metrics into model training; The supply chain's resilience is optimized by prioritizing reinforcement learning algorithms, and the optimization results are fed back in real time during the decision-making process.

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