Compared with the Unmanned Aerial Vehicle （UAV） without manipulator， the UAV with manipulator has large deviation in the flight trajectory and is more difficult to control stably. In order to solve the precise trajectory control problem of UAV with manipulator， a control method of quadrotor UAV with manipulator based on expert PID was proposed. Firstly， the manipulator was equipped to the UAV and the two was considered as a whole， and the kinematics and dynamics system models of UAV with manipulator was established through Lagrange equation. Secondly， an expert PID controller was designed to control the stability of the system. Thirdly， the trajectory planning of the manipulator of UAV with manipulator was carried out by using quintic polynomial. Finally， the effectiveness of expert PID control method for the stability control of UAV with manipulator is verified by simulation. The experimental results show that compared with conventional PID control， the proposed control method based on expert PID improves the response speed of the system and can effectively suppress external disturbances. This method can track the trajectory of the manipulator stably under the action， and has good immunity and robustness.

Aiming at the problems of strong interference and low detection precision of the existing safety helmet wearing detection， an algorithm of safety helmet detection based on improved YOLOv5 （You Only Look Once version 5） model was proposed. Firstly， for the problem of different sizes of safety helmets， the K-Means++ algorithm was used to redesign the size of the anchor box and match it to the corresponding feature layer. Secondly， the multi-spectral channel attention module was embedded in the feature extraction network to ensure that the network was able to learn the weight of each channel autonomously and enhance the information dissemination between the features， thereby strengthening the network ability to distinguish foreground and background. Finally， images of different sizes were input randomly during the training iteration process to enhance the generalization ability of the algorithm. Experimental results show as follows： on the self-built safety helmet wearing detection dataset， the proposed algorithm has the mean Average Precision （mAP） reached 96.0%， the the Average Precision （AP） of workers wearing safety helmet reached 96.7%， and AP of workers without safety helmet reached 95.2%. Compared with the YOLOv5 algorithm， the proposed algorithm has the mAP of helmet safety-wearing detection increased by 3.4 percentage points， and it meets the accuracy requirement of helmet safety-wearing detection in construction scenarios.

When using Immersed Boundary-Lattice Boltzmann Method （IB-LBM） to solve the flow field， in order to obtain more accurate results， a larger and denser flow field grid is often required， which results in a long time of simulation process. In order to improve the efficiency of the simulation， according to the characteristics of IB-LBM local calculation， combined with three different task scheduling methods in OpenMP， a parallel optimization method of IB-LBM was proposed. In the parallel optimization， three task scheduling modes were mixed to solve the load imbalance problem caused by single task scheduling. The structural decomposition was performed on IB-LBM， and the optimal scheduling mode of each structure part was tested. Based on the experimental results， the optimal scheduling combination mode was selected. At the same time， it could be concluded that the optimal combination is different under different thread counts. The optimization results were verified by speedup， and it could be concluded that when the number of threads is small， the speedup approaches the ideal state； when the number of threads is large， although the additional time consumption of developing and destroying threads affects the optimization of performance， the parallel performance of the model is still greatly improved. The flow field simulation results show that the accuracy of IB-LBM simulation of fluid-solid coupling problems is not affected after parallel optimization.

According to the mechanism of speech signal, a super resolution pitch detection algorithm, which combined Linear Predictive Coding (LPC) with Average Magnitude Difference Function (AMDF), was proposed. Firstly, residual of LPC was extracted by linear predictive analysis. Then, cumulative mean normalized difference function and difference signal revision were used to make pitch valley sharper. At last, parabolic interpolation and pitch multiple check were taken to select real pitch period. The experimental results indicate that the pitch detection effect of the algorithm is superior to that of the conventional algorithms. The proposed algorithm conquers half frequency errors, and has good accuracy and robustness under the condition of high Signal-to-Noise Ratio (SNR).