The scale of medical data is growing rapidly, and these data come from different data sources. The amount of data is huge, the production speed is fast, and the format is different. Case data is very important because it contains a lot of medical knowledge about diseases, drugs, treatments, etc. It can provide important support for the development of smart medicine. Knowledge graph is a graph-based data structure, which can well represent the relationship between these medical data in reality and form a semantic network. This research uses knowledge graph technology to connect trivial and scattered knowledge in various medical information systems to assist in disease diagnosis. This research takes thyroid disease as an example, constructs a medical knowledge graph and applies it to intelligent medical diagnosis. First, extract the relationships between biomedical entities to construct a biomedical knowledge graph. Then, the entities and relationships in the knowledge graph are transformed into low-dimensional continuous vectors through the knowledge graph embedding method. Finally, the known pathological disease relationship data is used to train the disease diagnosis model of the bidirectional long short-term memory network (BSTLM). Experiments show that the thyroid disease diagnosis method that combines knowledge graphs and deep learning has a better diagnostic effect. This shows that smart medical care based on the knowledge graph will provide a solution path for alleviating the shortage of domestic high-quality medical resources.

Nowadays, many enterprises provide cloud services based on their own Hadoop clusters. Because the resources of a Hadoop cluster are limited, the Hadoop cluster must select some specific tasks to allocate limited resources in order to get the maximal profit. In this paper, we study the maximal profit problem for a given candidate task set. We describe the candidate task set with a valid sequence and propose a sequence-based scheduling strategy. In order to improve the efficiency of finding a valid sequence, we design some pruning strategies and give the corresponding scheduling algorithm. Finally, we propose a timeout handling algorithm when some task runs timeout. Experiments show that the total profit of the proposed algorithm is very close to the ideal maxima and is obviously bigger than related scheduling algorithms under different experimental settings.

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With the continuous development of TD-SCDMA(Time Division -Synchronous Code Division MultipleAccess) network, has begun a high-traffic areas; intensive traffic flow scenes, network configuration, with high frequency multiplexing, interference problems are more prominent; there will be residential uplink interference serious, KPI(Key Performance Indicator)have worsened and volatile, poor perceived by the user; how to reduce interference and optimize the KPI targets to enhance the user perception is particularly important, currently, in the interference suppression algorithms, IDCA(Intelligence Dynamic Channel Allocation) algorithm is a more sophisticated algorithms, but in the intensive traffic flow scenes, the IDCA algorithm for interference suppression aspects of the effect is less pronounced. This paper presents a sorting method, before the IDCA algorithm for carrier sorting, based on frequency multiplexing degrees and comprehensive consideration of user level. The method, optimizes the carrier sorting method of IDCA algorithm, avoids the occurrence of the same frequency at the same time gap as much as possible, and reduces interference within the system. Therefore, it is suitable for the intensive traffic flow scenes..