An asphalt mixture compaction degree internet of things detection method and system

Abstract

The application relates to the technical field of road engineering quality detection, and discloses an asphalt mixture compactness Internet of Things detection method and system, wherein the asphalt mixture compactness Internet of Things detection method comprises the following steps: acquiring a road roller working state signal and driving multiple types of sensors to cooperatively collect; intelligently pre-processing original data streams of multiple-source heterogeneous sensors in an edge computing unit; intelligently identifying and compensating and correcting a multi-dimensional comprehensive feature vector in terms of subgrade stiffness, mixture temperature state and rolling stages; constructing and training a light-weight space-time convolutional neural network model, performing real-time prediction of compactness through migration learning and online optimization; performing three-dimensional visual processing and intelligent analysis; and performing safe and reliable transmission and cloud-side deep analysis; through construction of an intelligent detection system based on Internet of Things technology and multi-sensor fusion, the application realizes real-time, continuous and full-coverage monitoring of an asphalt mixture compaction process.

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