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Health tourism is an important issue in humanistic geography. In this paper, the NVivo11 algorithm, questionnaire survey, field investigation, literature, interview, and other methods were used to analyze the dilemma in the implementation of the health and wellness tourism industry policy in Heilongjiang Province under the background of big data from the two dimensions of the policy itself and the target group of the policy and put forward countermeasures. It is expected to provide the corresponding theoretical support and practical suggestions for the healthy and rapid development of the health tourism industry in Heilongjiang Province.

Abstract. In order to solve the problem that the identification method of wind turbine is not suitable for large data environment. In this paper, we propose FSIQUE. Firstly, the data space is divided into dense grid cells and sparse grid cells. Based on the FS-Tree storage structure proposed in this paper, a dense grid cell is stored and the subspace is traversed by this storage structure. Secondly, traversing the connected grid cells in the subspace to find the clusters. Finally, the maximum and minimum coverage of the cluster is calculated. According to the parameter, the clustering is divided into normal data and abnormal data to realize the anomaly recognition. The proposed method is run on the Spark platform and compared with the WPMCLU and DBSCAN methods, has the highest abnormal recognition rate and the runtime is shortest.