【摘要】 高效率地使用工程车辆是工程项目管理中节约成本的有效方法，无人监管环境下工程车辆的工况识别，是实现工程车辆高效率使用的有效手段。目前以GPS等技术为核心的车辆智能管理系统未对工程车辆进行工况识别，提出一种基于GRU循环神经网络的工程车辆工况识别方法，通过对工程车辆在不同工况下产生的音频信号进行分析，从中提取Mel倒谱系数作为主要特征，构建GRU循环神经网络模型进行训练和识别。实验结果表明，该方法可以实现对工程车辆工况的有效识别。更多还原

【Abstract】 Efficient use of construction vehicles is an effective way to save costs in engineering project management. Working condition recognition of construction vehicles in an unsupervised environment is an effective means to achieve efficient use of construction vehicles. At present, vehicle intelligence with GPS and other technologies as the core management system do not recognize the working condition of the construction vehicle. This paper proposes a working condition recognition method for construction vehicles based on GRU re-current neural network. By analyzing the audio signals generated by construction vehicles under different working conditions, the Mel Frequency Cepstral Coefficient is extracted as the main features and the GRU recurrent neural network model is constructed. Perform training and recognition. Experiments show that this method can realize effective recognition of working conditions of construction vehicles..更多还原