No abstract

Through the recognition and analysis of human motion information, the actual motion state of human body can be obtained. However, the multifeature fusion of human behavior has limitations in recognition accuracy and robustness. Combined with deep reinforcement learning, multifeature fusion human behavior recognition is studied and we proposed a multifeature fusion human behavior recognition algorithm using deep reinforcement learning. Firstly, several typical human behavior data sets are selected as the research data in the benchmark data set. In the selected data sets, the behavior category contained in each video is the same behavior, and there are category tags. Secondly, the attention model is constructed. In the deep reinforcement learning network, the small sampling area is used as the model input. Finally, the corresponding position of the next visual area is estimated according to the time series information obtained after the input. The human behavior recognition algorithm based on deep reinforcement learning multifeature fusion is completed. The results show that the average accuracy of multifeature fusion of the algorithm is about 95%, the human behavior recognition effect is good, the identification accuracy rate is as high as about 98% and passed the camera movement impact performance test and the algorithm robustness, and the average time consumption of the algorithm is only 12.7 s, which shows that the algorithm has very broad application prospects.

Support pattern is the most important factor affecting the stability of foundation pit. In order to study the stable state of deep foundation pit, this paper selects pile pillared support and pile-anchor retaining which are high accident rate for comparison, and optimizes the construction scheme combined with the actual deep foundation pit project. The deformation of the supporting structure and the settlement of the foundation pit of Huiquan Square are used to analyze by FLAC3D. The variation range and trend of the internal force of the steel support and the axial force of the anchor cable are analyzed under the different values of the soil layer parameters, such as elastic modulus, cohesion and internal friction angle. The results show that the internal force of pile pillared support is greatly affected by the change of cohesion and the anchor axial force is greatly affected by the change of elastic modulus and internal friction angle. Meanwhile, the influence degree of each soil layer parameter on the internal force of support structure is different, which provides reference suggestions for the selection of support pattern of deep foundation pit.