聯(lián)邦學(xué)習(xí)的社群化制造韌性能力預(yù)測建模

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中圖分類號：TP166文獻標(biāo)志碼：ADOI：10.7652/xjtuxb202508002 文章編號：0253-987X（2025）08-0011-09

Federated Learning-Based Predictive Modeling of Resilience Capability in Social Manufacturing

ZHANG Fuqiangl'2，WANG Haojiel'2，HUI Jizhuang12，DING Kai 1，2 （1.Key LaboratoryofRoad Construction Technologyand Equipmentof MOE，Chang'an University，Xi'an 71oo64，China; 2. Institute of Smart Manufacturing Systems，Chang'an University，Xi'an 71Oo64，China）

Abstract： Considering the characteristics of decentralized resource layouts in social manufacturing， as well as the issues of data privacy and information silos faced by traditional centralized modeling， this paper proposes a resilience capability prediction framework based on federated learning. The framework analyzes the impact of various factors on product processng time from multiple perspectives. First， considering different interruption scenarios in production processes， a working hour disturbance model is established with the order delivery cycle as the objective function to calculate loss time. Subsequently，a federated learning network model is constructed based on a distributed learning paradigm. Next，a federated mini-batch gradient descent （FedMBGD） algorithm is designed， detailing the algorithmic process and performing local training. Finally， the resilience capability of social manufacturing is predicted in conjunction with the working hour disturbance model and the algorithm. The feasibility and effectiveness of the proposed algorithm are validated through comparisons with other algorithms.The research results indicate that the proposed algorithm significantly enhances convergence and optimization capabilities， raising prediction accuracy to over 90% . Furthermore，it enables dynamic and precise prediction of social manufacturing resilience without sharing raw data， resolving the conflict between data privacy and collaborative modeling. This study provides theoretical references for predicting resilience capabilities in social manufacturing models and offers guidance for algorithm training with private data，parameter uploading，and information sharing.

Keywords： social manufacturing; working hour disturbance； federated mini-batch gradient descent; manufacturing resilience capacity prediction

在全球化浪潮與先進制造技術(shù)的雙重驅(qū)動下，生產(chǎn)制造業(yè)的資源布局呈分散化趨勢，這要求多個領(lǐng)域的制造資源必須實施深度重組與優(yōu)化配置，并且轉(zhuǎn)型為一個高度集成化且各環(huán)節(jié)間緊密相連、相互依存的生態(tài)系統(tǒng)，其穩(wěn)定性和抗風(fēng)險能力研究至關(guān)重要[1]。（剩余12869字）