Supply Chain Node Reputation Evaluation And Resource Allocation Method Based On Federated Learning

As the economy and e-commerce continue to develop rapidly,more and more companies are choosing to establish supply chain cooperation to cope with fierce market competition.However,information asymmetry and incomplete data in the supply chain have led to a crisis of trust and fraudulent behavior among companies,which in turn hinders the healthy development of the supply chain ecosystem.Due to the privacy attributes of enterprise data,a single enterprise cannot use supply chain data to design effective reputation evaluation and resource allocation methods,hindering the financing efficiency of small enterprises and increasing the cost of data sharing among companies.Federated learning,as a distributed training architecture of "data not moving,model moving,"is beneficial to break the data silos between enterprises and further unleash the potential value of supply chain data.In order to break through the privacy data restrictions between enterprises and achieve cost reduction and efficiency improvement of supply chain enterprises,this paper studies the supply chain network node reputation evaluation and resource allocation method based on federated learning,constructs a supply chain data sharing architecture integrating blockchain and federated learning,proposes an enterprise reputation evaluation model based on federated ensemble learning,and a resource allocation optimization mechanism based on multi-level federated learning,further enhancing the credibility of enterprise reputation,reducing inventory costs of supply chain enterprises,and improving supply chain production efficiency.Firstly,this paper proposes a supply chain data sharing architecture integrating blockchain and federated learning,and constructs an enterprise reputation evaluation model for the supply chain.Combining the characteristics of supply chain business,a business process covering the whole process data interaction of supply chain logistics,fund flow,and information flow is designed.In view of the problem that multi-source heterogeneous data leads to difficulty in local model fusion of federated learning,this paper combines the characteristics of ensemble learning,proposes an enterprise reputation evaluation scheme based on stacking fusion model,and designs a federated learning classification algorithm for heterogeneous model fusion.Experimental results show that the proposed algorithm improves the accuracy of credit evaluation by about 7%compared with the single-model credit evaluation on the credit dataset,providing effective support for supply chain enterprise financing and further unleashing the potential value of supply chain data.Secondly,based on the proposed architecture,this paper constructs a supply chain production and sales network optimization model based on federated learning,which is divided into two stages:resource prediction and resource allocation.In the supply chain resource prediction stage,a multi-level federated learning demand prediction algorithm based on attention mechanism is proposed using the supply chain multi-level data,obtaining more stable and accurate time series prediction results.In the resource allocation stage,a production and inventory configuration strategy for production and sales balance is proposed in combination with the prediction results,and more reasonable safety stock configuration is carried out to alleviate enterprise warehousing pressure.Through simulation verification,the proposed solution has lower prediction error and more efficient resource allocation capability than traditional solutions,and can quickly reach safety inventory to meet the actual requirements of enterprises.