With the development of Big Data technology and Internet, the surge of data in the network will cause network congestion and untimely task processing. Additionally, caching content in the core network may cause redundant access of content and backhaul bottlenecks. Due to the increasing requirements of users for task processing efficiency, the centralized maintenance system based on traditional cloud computing cannot meet the current computing requirements. In view of these problems, we propose a content collaborative caching mechanism based on joint decision of download delay and energy consumption. By integrating network coding and content caching technology, the work content maintained in the communication network is deployed near the edge of the network in the form of coding to reduce the redundant transmission of content and acquisition time of content. This article establishes a user QoE satisfaction model, which consists of two indexes that measure time delay and energy consumption. This article proposes a$\varepsilon$-hybrid Q-learning algorithm to optimize the placement of cache files, and made the cache action selection based on the combination of improved heuristic greedy algorithm and simulated annealing algorithm. The experimental results show that the proposed cache strategy can reduce the delay of users downloading content and the energy consumption of content cache, so as to improve the quality of field maintenance work in communication network.

Various problems arise in the maintenance of communication networks. For example, on-site maintenance personnel have insufficient work experience. Devices used for maintenance work have limited computing resources and battery life. Moreover, most maintenance systems still use the centralized single processing mode of traditional cloud computing, which increases the data center computing pressure and slows the data flow. To overcome these problems, we propose a communication network edge maintenance system based on smart wearable technology and introduce computation offloading technology for mobile edge computing (MEC). Before offloading, we propose a multimerged computing sorting segmentation (MCSS) algorithm to divide a part of the task to offload. When making an offloading decision, we access a suitable MEC service node for each user with the lowest transmission cost and establish a related model. We use an improved Kuhn-Munkras (KM) algorithm that considers fairness among users to solve this model. After that, we propose a dynamic energy-efficiency awareness strategy. When tasks are processed locally, we optimize the CPU clock frequency. When tasks are offloaded, we adaptively allocate the transmission power. Finally, we conduct a simulation experiment. The results demonstrate that the proposed scheme can reduce the transmission cost and improve the performance, thereby increasing the level of on-site maintenance work.