Edge Computing （EC） and Simultaneous Wireless Information and Power Transfer （SWIPT） technologies can improve the performance of traditional networks， but they also increase the difficulty and complexity of system decision-making. The system decisions designed by optimization methods often have high computational complexity and are difficult to meet the real-time requirements of the system. Therefore， aiming at Wireless Sensor Network （WSN） assisted by EC and SWIPT， a mathematical model of system energy efficiency optimization was proposed by jointly considering beamforming， computing offloading and power control problems in the network. Then， concerning the non-convex and parameter coupling characteristics of this model， a joint optimization method based on deep reinforcement learning was proposed by designing information interchange process of the system. This method did not need to build an environmental model and adopted a reward function instead of the Critic network for action evaluation， which could reduce the difficulty of decision-making and improve the system real-time performance. Finally， based on the joint optimization method， an Improved Deep Deterministic Policy Gradient （IDDPG） algorithm was designed. Simulation comparisons were made with a variety of optimization algorithms and machine learning algorithms to verify the advantages of the joint optimization method in reducing the computational complexity and improving real-time performance of decision-making.

With the rapid development of the mobile Internet industry， user data and browsing data have increased significantly， so it is extremely important to accurately grasp the potential needs of users and improve the effect of advertisement recommendation. As a relatively advanced recommendation method at present， DeepFM model can extract various complexity features from the original features， but the model does not protect the data. In order to realize the privacy protection in DeepFM model， a new DeepFM model based on Differential Privacy （DP） was proposed， namely DP-DeepFM. The Gaussian noise was added to Adam optimization algorithm in the training process of DP-DeepFM and the gradient clipping was performed to prevent the addition of excessive noise causing poor model performance. Experimental results on advertising dataset Criteo show that compared with DeepFM， DP-DeepFM only has the accuracy decreased by 0.44 percentage points， but it provides differential privacy protection and is more secure.

The supply chain faces many challenges in the development process， including how to ensure the authenticity and reliability of information as well as the security of the traceability system in the process of product traceability， the security of products in the process of logistics， and the trust management in the financing process of small and medium enterprises. With characteristics of decentralization， immutability and traceability， blockchain provides efficient solutions to supply chain management， but there are some technical challenges in the actual implementation process. To study the applications of blockchain technology in the supply chain， some typical applications were discussed and analyzed. Firstly， the concept of supply chain and the current challenges were briefly introduced. Secondly， problems faced by blockchain in three different supply chain fields of information flow， logistics flow and capital flow were described， and a comparative analysis of related solutions was given. Finally， the technical challenges faced by blockchain in the practical applications of supply chain were summarized， and future applications were prospected.