In grid resource management, the scheduling strategy based on selecting resources that are near and owns more bandwidth available has better characteristics. The notion of grid-distance is proposed in this paper, which can be used as the criteria for grid selection taking physical distance, bandwidth available and cost into consideration. A grid resource model and a correlative application model are also presented. Based on this model, the computation of grid distance and the corresponding resource selection algorithm are described. The simulation experiment validates the improvement of the communication cost, stability; job completion time, failure rate of job execution in scheduling and the throughput of the resource exchange.

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The key problem of event extraction in the medical field is that the cost of medical data labeling is too high, and the labeled samples are scarce, making medical event extraction difficult. In response to this problem, this paper proposes to perform a partial synonymous replacement of training samples to expand the data, and the data and the original data together constitute new electronic medical record data (i.e. EDA data enhancement); In addition, the unlabeled data is predicted by the medical event extraction model to generate labeled data, and then the accurate labeled data is filtered out and added to the original data to form new electronic medical record data, thereby realizing data enhancement (i.e. UDF data enhancement) , to a certain extent, to solve the problem of the scarcity of medical data samples. Based on augmented data, a medical event extraction model (i.e. TEC_MEE model) based on Transformer Encoder and CRF are constructed to extract attributes of specified events from unstructured Chinese electronic medical record text. The experimental results show that, compared with the baseline model, the TEC_MEE model proposed in this paper obtains better medical event extraction results after data enhancement.

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