In order to improve the real-time ability of obstacle avoidance using image processing technology for quad-rotor helicopter, an obstacle avoidance path planning algorithm was proposed based on Bayesian estimation and region division traversal. Firstly, Bayesian estimation was used to preprocess the video images collected by quad-rotor helicopter. Secondly, obstacle probability analysis was performed to obtain key frames from video images, so as to maximize the real-time performance of the helicopter. Finally, the background difference was carried out on these selected image frames to identify the obstacles, and the pixel point traversal algorithm based on region division was implemented in order to improve the accuracy of obstacle identification. Experimental results show that with the use of the proposed algorithm, the real-time performance of quad-rotor helicopter obstacle avoidance is improved with guaranteeing the obstacle avoidance identification ability, and the maximum distance between the ideal trajectory and the actual flight trajectory of the quad-rotor helicopter is 25.6 cm, while the minimum distance is 0.2 cm. The proposed obstacle avoidance path plan algorithm can provide an efficient solution for quad-rotor helicopter to avoid obstacles by using video images collected by camera.

Aiming at the problems of insufficient use of semantic information and the poor summarization precision in the current generated text summarization method, a text summarization method was proposed based on dual encoder. Firstly, the dual encoder was used to provide richer semantic information for Sequence to Sequence (Seq2Seq) architecture. And the attention mechanism with dual channel semantics and the decoder with empirical distribution were optimized. Then, position embedding and word embedding were merged in word embedding technology, and Term Frequency-Inverse Document Frequency (TF-IDF), Part Of Speech (POS), key Score (Soc) were added to word embedding, as a result, the word embedding dimension was optimized. The proposed method aims to optimize the traditional sequence mapping of Seq2Seq and word feature representation, enhance the model's semantic understanding, and improve the quality of the summarization. The experimental results show that the proposed method has the performance improved in the Rouge evaluation system by 10 to 13 percentage points compared with traditional Recurrent Neural Network method with attention (RNN+atten) and Multi-layer Bidirectional Recurrent Neural Network method with attention (Bi-MulRNN+atten). It can be seen that the proposed method has more accurate semantic understanding of text summarization and the generation effect better, and has a better application prospect.

To solve the contradiction between the estimation accuracy and the calculation amount of the RFID tag estimation method, and the instability of the estimation method performance caused by the randomness of the tag reading process in the field of aviation logistics networking information gathering. Based on the idea of complementary advantages, a method for estimating the number of RFID tags based on rough and fine estimation was proposed. By modeling and analyzing the tag reading process of framed ALOHA algorithm, the mathematical model between the average number of tags in the collision slot and the proportion of the collision slot was established. Rough number estimation based on the model was made, and then, according to the value of rough estimation, the reliability of rough estimation was evaluated. The Maximum A Posteriori (MAP) estimation algorithm based on the value of rough estimation as priori knowledge was used to improve the estimation accuracy. Compared to the original maximum posteriori probability estimation algorithm, the search range can be reduced up to 90%. The simulation results show that, the average error of the RFID tag number estimation based on rough and fine estimation is 3.8%, the stability of the estimation method is significantly improved, and the computational complexity is greatly reduced. The proposed algorithm can be effectively applied to the information collection process aviation logistics networking.