The three-dimensional visualization of coal seam structure in coal mines is the foundation for coal enterprises to implement transparent mining. Existing three-dimensional modeling systems generally have problems such as poor portability and single interpolation methods. Therefore, this paper adopts the development mode of front and rear separation, and designs and realizes a three-dimensional visualization system of no plug-in and strong portable field coal seam structure based on Springboot+Vue+Mysql. The system interpolates the seam elevation in unknown areas using the optimized Crekin interpolation method, uses the Delaunay rule to generate an irregular triangle network on the coal seam surface, and extends the triangle network along the coal seam elevation to generate a three-dimensional seam model based on generalized triprism prism; finally, with the help of Three.js three-dimensional engine, the browser side displays the three-dimensional effect of the coal seam structure of the minefield. The application of this system has certain guiding significance and reference value for coal enterprises to make reasonable preparations.
To solve of the traditional collaborative filtering recommendation algorithm has some problems, such as sparse data and difficult cold start, we proposed a collaborative filtering (TCF) recommendation algorithm based on trust relation and item preference. The algorithm performs triple processing on user ratings. First it introduces a correction factor to optimize the traditional similarity calculation. Then it uses user similarity to mine potential trust relationships between users. And taking into account the complex real-world relationship between users, using distrust information to filter users and get new ratings. Finally, a new scoring matrix is constructed on the basis of this score, and the improved Tanimoto coefficient is used to calculate the similarity, and the recommendation results are obtained by integrating user trust and item preferences. Experimental results show that the algorithm can effectively improve the quality of recommendations.
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