The invention relates to a neural network-based lifting load measurement method for an automobile crane, and aims to solve the technical problems of time and labor waste and inaccurate measurement result of measurement caused by the fact that the lifting load measurement process of the automobile crane is easily influenced by uncontrollable factors such as dynamic change of arm lever torque, mechanical friction at multiple positions and steel wire rope droop. According to the method, a crane boom mechanical model under the load action is established through structural characteristics and geometric parameters of a crane boom of the automobile crane, and the lifting load of the automobile crane under any environment, place and working condition can be quickly obtained through a microprocessor and a computer by utilizing a trained radial basis function neural network, so that the field actual measurement of the automobile crane is greatly saved, and the stability evaluation of the automobile crane is more convenient by obtaining the lifting load in real time; the real-time measurement of the lifting load is obtained by utilizing the pressure intensity and the elevation angle, the implementation is easy, the arrangement is convenient, and the cost is low; and the environmental interference and the system error are reduced, and the working efficiency is effectively improved.
本发明涉及一种基于神经网络的汽车起重机的起吊载荷测量方法,目的是解决汽车起重机起吊载荷测量过程易受臂杆力矩动态变化、多处机械摩擦、钢丝绳下垂等不可控因素影响,导致测量费时费力、测量结果不精确的技术问题。本发明通过汽车起重机起重臂的结构特征和几何参数,建立载荷作用下起重臂力学模型,利用训练好的径向基神经网络,通过微处理器和计算机可快速获取汽车起重机任意环境、地点、工况下的起吊载荷,从而大大节省汽车起重机现场实测,通过实时获取起吊载荷使得汽车起重机稳定性评估更为便利;利用压强和仰角获取起吊载荷的实时测量、实现容易、布置方便、成本低;减少了环境干扰和系统误差,有效提升了工作效率。
Neural network-based lifting load measurement method for automobile crane
基于神经网络的汽车起重机的起吊载荷测量方法
2020-08-28
Patent
Electronic Resource
Chinese
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