Tunnel operation state division method based on big data clustering analysis

Abstract

The present invention relates to the tunnel engineering field, especially to a tunnel operation state division method based on big data clustering analysis. The tunnel operation state division method based on big data clustering analysis is suitable for the dynamic changing requirements of a tunnel state operation state and provides accurate prediction. The method comprises the steps: 1) performing a tunnel operation monitoring experiment, and obtaining the multi-dimensional space of a road tunnel operation state; 2) performing preprocessing o a monitoring sample data set, determining whether the monitoring sample data set comprises missing values and noise data or not, and performing rejection of all the abnormal data; 3) calculating the outline coefficient of any one sample xi of a sample set comprising k clusters through division to calculate the average outline coefficient Sk of all the samples n in different clusters and identify that the corresponding k value when the Sk is maximized is the number of optimal state classification of the tunnel operation state; 4) presetting the number of the clusters as k, applying the FCM algorithm to perform clustering analysis of the tunnel operation monitoring sample data set, and obtaining the degree of membership of the k clusters and k cluster centers of all the samples; and 5) according to the maximum principle of the degree of membership, determining the operation state category corresponding to each tunnel operation monitoring sample.

Description

technical field [0001] The invention relates to the field of tunnel engineering, in particular to a tunnel operation status division method based on big data cluster analysis. Background technique [0002] In recent years, a large number of extra-long highway tunnels have been built and put into operation, and the highway tunnel has gradually shifted from the peak period of construction to the peak period of operation. However, due to the influence of traffic composition and traffic volume, pollutants in the tunnel continue to accumulate, and it is relatively difficult to discharge or dilute them, making ventilation the primary problem during operation, which brings difficulties to tunnel operation and management. Therefore, it is necessary to analyze the traffic flow data and environmental monitoring data in the tunnel, and formulate corresponding operation control measures after determining the operation status of the tunnel. Among them, the rationality and scientificity ...

AI Technical Summary

Problems solved by technology

However, since the operation status of extra-long highway tunnels is generated by the mutual influence and superposition of traffic elements such as people, vehicles, roads, and the environment in the tunnel, there are many influencing factors and complex evolution rules. At present, there is no scientific division method and unified division standard.

In the published literature and patent documents, there is no research invention that comprehensively utilizes real-time traffic flow information and ventilation environment information in the tunnel to analyze the operating status

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