Abstract: Abstract In the high-risk petrochemical industry, operational environments involve complex factors such as high temperature, high pressure, flammable, explosive, and toxic substances. Even minor deviations in worker behavior can lead to severe accidents, resulting in irreversible casualties and property losses. Traditional supervision methods, which rely heavily on manual inspection, are not only inefficient but also struggle to cover multi-worker and multi-equipment collaborative scenarios, and are highly susceptible to subjective interference. The core challenges in recognizing group collaborative behaviors lie in modeling complex human–object interactions, capturing dynamic features of multiple targets, and bridging the ambiguous mapping between macroscopic group intentions and microscopic individual actions. To address these issues, this paper proposes a graph neural network-based method for group collaborative behavior recognition. By constructing a unified interactive graph structure, entities such as workers and equipment are encoded as nodes, and multimodal perceptual features are integrated to enable end-to-end reasoning of interpersonal and human–object interactions. A hierarchical graph network architecture is further designed to model the correlation and evolution from individual actions to group behaviors, achieving accurate recognition and understanding of multi-target group behaviors in complex operational scenarios. Comparative experimental results show that the proposed method improves the MCA/MPCA metrics by 3.91% and 2.86%, respectively, over the next best method on a self-built dataset. On the public open-source Volleyball dataset, the MCA/MPCA metrics are improved by 0.26% and 0.21% compared to the next best method, fully verifying the method's advancement and robustness.

摘要： 摘　要 在石化作业这一典型高危场景中，作业环境涉及高温高压、易燃易爆及有毒有害介质，人员操作行为稍有不慎即可能引发严重事故。传统依赖人工巡查的监管方式不仅效率低下，且难以有效覆盖多人员、多机具协同作业场景，极易受主观因素干扰。面向群体协同作业的行为识别，核心挑战在于复杂的人-物交互关系建模难、多目标动态特征捕捉弱，以及宏观群体意图与微观个体动作之间的映射关系模糊。为此，本文提出一种基于图神经网络的群体协同行为识别方法。该方法通过构建统一的交互图结构，将人员、设备等实体统一编码为节点，融合多模态感知特征，在逻辑上实现对人际、人-物交互关系的端到端推理；进一步设计分层图网络架构，建立从个体动作到群体行为的关联演化模型，从而在复杂作业场景下实现对多目标群体行为的精准识别与理解。对比实验结果表明，所提方法在自建数据集上，MCA/MPCA指标较次优方法分别提升3.91%与2.86%；在公共开源数据集Volleyball上，MCA/MPCA 指标较次优方法分别提升0.26%和0.21%，充分验证了该方法的先进性与鲁棒性。