Measurement Quality Of Underground Pipeline Based On Cloud Model Analysis And Evaluation

With the continuous acceleration of urbanization,projects such as census and construction of underground pipelines have developed extremely rapidly,and underground pipeline data has also accumulated a lot.How to reasonably excavate and fully utilize these data has become an increasingly important issue.Related research on this issue is extremely rare.Because the quality analysis and evaluation problems of underground pipeline measurement data are often accompanied by strong ambiguity,the classic mathematical "hard calculation" has been insufficient in handling such problems.With the clustering theory based on fuzzy mathematics and the extensive use of cloud theory,which is an extension of fuzzy mathematics,there is a novel and effective solution to big data mining analysis and fuzzy evaluation problems.Therefore,this paper has conducted related research around the above problems and corresponding solutions,and the main work is as follows:(1)Introduce the fuzzy clustering theory and cloud model theory systematically,and analyze its feasibility in pipeline measurement data mining.After analysis,it is found that the factors that affect the detection quality of each pipeline have a certain degree of ambiguity,and the detection quality evaluation also has strong ambiguity and randomness.Therefore,the clustering theory based on fuzzy mathematics and the continuation and extension of fuzzy mathematics The research and application of cloud model theory has become the focus of this article.(2)According to the similarity and difference of the influencing factors of the detection quality of each pipeline and its strong ambiguity,a fuzzy clustering model of influencing factor analysis was established,and the characteristics and importance of the influencing factor indicators were thoroughly studied in combination with the detection experiment.For the pipeline detection quality under the influence of the above factors,a cloud model analysis and cloud fuzzy evaluation method based on fuzzy theory is proposed,and a reverse cloud algorithm suitable for pipeline geophysical data analysis and mining is researched and improved,and a method for determining evaluation index weights.(3)The above research results and models have been applied to a certain area detection project in Dalian.Through application,it is found that the fuzzy clustering analysis method has a unique advantage in finding the main factors that affect the quality of pipeline detection.It uses the clustering method to obtain the differences and similarities of various professional pipelines in influencing factors.Clustering effectively reduces the workload of analysis,makes more and better use of pipeline achievement data,and provides theoretical basis and decision support for improving pipeline detection quality.Using the cloud model to analyze and evaluate the detection results under the influence of the above influencing factors,it is found that the cloud model can provide multi-angle and multi-level decision-making knowledge based on the cloud digital characteristics in analyzing and mining geophysical data;in terms of measurement quality evaluation,the cloud model solves The inherent incompleteness and maximum membership of fuzzy comprehensive evaluation are provided,which provides a novel and more scientific and reasonable method for pipeline quality evaluation.