With the rapid development of IoT technology, sensors are widely used for monitoring environmental parameters. The data collected by sensors needs to be stored, and distributed storage systems provide an excellent platform to handle this vast amount of data. To enhance data reliability and reduce storage costs, erasure coding technology can be employed within distributed storage systems. However, the process of recovering lost or damaged data inevitably generates significant cross-rack traffic. In recent years, various batch recovery methods have been designed to improve data recovery speed and reduce cross-rack recovery traffic, but they have limitations in different aspects, such as less consideration for heterogeneous cross-rack bandwidth and insufficient handling of recovery link scheduling. This paper proposes the HBRepair recovery framework, aimed at improving data recovery speed by balancing the recovery load and reducing cross-rack recovery traffic. Firstly, HBRepair strategically selects helper blocks and nodes for storing recovery blocks to find the optimal batch recovery plan. Then, it selectively and rationally schedules recovery links to saturate idle bandwidth resources and avoid network congestion. Experimental results show that by optimizing the use of cross-rack bandwidth, HBRepair can reduce cross-rack recovery time by up to 26.74%, effectively addressing the shortcomings of existing methods.

On the basis of deep analysis on the structural features and heading features of documents, it has researched the classification method based on templates and the classification method based on statistics as well as the sequence labeling method based on CRF (Conditional Random Field), then proposed to treat document structure identification as sequential data labeling, built CRF training model with feature templates and finally realized document structure identification upon training model with existing way of supervision learning. Experimental results show that identifying paragraph roles from document sequence structure helps to ensure a higher accuracy and it also owns certain fault-tolerant ability. Besides, it is observed that using CRF for many times could further improve the accuracy of identification.

This Paper sums up the necessary conditions to discriminate undirected Hamilton graph based on the in-depth research into the graph. The heuristic search strategy corresponding to the new necessary conditions is designed to quickly solve the problem of "determination of undirected Hamilton graph". The experimental results show that the heuristic search algorithm can effectively improve computing speed and make accurate determination of "undirected Hamilton graph".