Linkage Mechanism of Employee Performance Optimization and Functional Textile Quality Control Based on Improved DDQN

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To address the challenge of jointly optimizing employee performance and functional textile quality in intelligent manufacturing environments, this study proposes a multi-objective collaborative optimization framework based on an improved Double Deep Q-Network (DDQN). A multidimensional state-space model is constructed by integrating employee behavioral indicators, production process parameters, and real-time quality inspection data, enabling coupled representation of human–machine interactions and manufacturing performance. The proposed method incorporates a dynamic reward function, prioritized experience replay, and multi-step temporal difference prediction to improve policy learning efficiency and long-term decision stability. Considering that modern textile manufacturing increasingly relies on heterogeneous sensing devices and distributed industrial information acquisition, the proposed state modeling and decision-making framework provides a scalable architecture for integrating multimodal production data and supporting intelligent monitoring systems. Experimental evaluations demonstrate that the improved DDQN reduces the average defect rate to 2.6% with a standard deviation of 0.4 while achieving a comprehensive performance score of 89.4, significantly outperforming conventional DDQN and static scheduling strategies. The framework effectively balances production efficiency, operational consistency, and quality stability, offering a practical solution for intelligent textile manufacturing and providing methodological insights for future interdisciplinary research on advanced sensing, industrial information fusion, and smart manufacturing infrastructures.

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