Optimizing Enterprise Office Efficiency through Multimodal Employee Behavior Analysis

This paper proposes a model for optimizing enterprise office efficiency through multimodal employee behavior analysis. The model integrates computer vision, sensor data, workflow data, and text information in a four step process: data acquisition, feature extraction, behavior identification, and optimization strategy formulation, to identify task, collaborative, non-work, and organizational behaviors. It dynamically optimizes the office environment, workflow, and human resource allocation. Key innovations include: first, the “in-depth fusion of four-modal data” via the “temporal-semantic dual alignment” technique, to address data heterogeneity. Second, the “behavior-efficiency dynamic mapping” mechanism, which quantifies negative correlation (r=-0.72) between non-work behaviors and task efficiency, provides a quantitative basis for optimization. Experiments involving 50 technology company employees demonstrate the model’s superiority, achieving an office efficiency improvement rate of 0.85, which surpasses both traditional methods (0.65) and common multimodal methods (0.72). The model also attained a behavior identification accuracy rate of 0.90, outperforming comparison methods (0.80 and 0.85). The model also excels in optimizing the office environment, workflow, and human resources, with improvements of up to 30%. Core contributions include: 1) a multimodal data fusion framework that increases behavior recognition accuracy to 90%; 2) an integrated “three-in-one” optimization strategy for the environment, processes, and human resources, which increased employee satisfaction by 30% and reduced task time by 25%; and 3) empirical validation that demonstrates an 85% efficiency improvement rate, significantly outperforming traditional and standard multimodal methods. The study highlights the model’s practicality and potential for widespread adoption in enhancing office efficiency.