This paper proposes a real-time computational method for multidimensional dynamic data fusion (VIO-SLAM) for intelligent monitoring of seat belts in the grid construction environment. In this paper, the optical flow method is first used to process and track point features, and the geometrically constrained line matching algorithm is utilized to improve the accuracy of feature matching. Combined with IMU modeling and pre-integration techniques, it effectively reduces the computation of high frequency IMU data and improves the system efficiency. At the same time, a real-time lightweight semantic segmentation system is constructed to achieve fast semantic understanding of the construction scene. The real-time and accuracy of data processing is further improved by sliding window method with BA optimization. On this basis, a VIO-SLAM algorithm based on EKF fusion of multidimensional dynamic data is proposed to realize real-time monitoring and localization of seat belt status. The results show that when a dangerous collision occurs in a complex power grid construction environment, the protection performance of shoulder belt, neck bending moment force and head acceleration of the construction personnel under the method of this paper is much higher than that of the traditional seat belt. In the process of emergency collision avoidance, the VIO-SLAM algorithm is able to tighten the seat belt in advance for the construction personnel, which has better protection performance and can achieve the purpose of “collision avoidance and damage reduction”. The pre-tensioning force for eliminating the gap in the webbing of seat belts and the pre-tensioning force for somatosensory warning reminders are also determined to improve the protection performance of construction workers.