2results about How to "Inhibition range" patented technology

The present application belongs to the technical field of automobile steering system, especially relates to a method for suppressing instantaneous high current under mechanical impact working condition of electric power steering system, comprising obtaining first, second, third and fourth voltage limits, and performing weighted summation to obtain target voltage limit; obtaining rated assist motor maximum terminal voltage of electric power steering system. The present application combines the four voltage limits to calculate the target voltage limit, obtains the real-time allowed maximum motor voltage through calculation, and obtains the PWM waveform output to the motor after inverse Park transformation and inverse Clarke, which can limit the motor terminal voltage, thereby reducing the risk of instantaneous high current and power supply voltage drop in the steering process, and preventing damage to the electric power steering system.

This invention discloses a spatiotemporal traffic flow prediction method based on dual-flow decoupling. The method first decomposes the temporal data of road network traffic flow into trend and periodic components using the exponential moving average method. Then, features are extracted and fused using a deep linear network and a local feature hybrid network with temporal block embedding to obtain the global temporal prediction component. After the traffic flow temporal data is encoded in the temporal domain by gated dilated convolution, multi-view spatial aggregation is performed using forward, backward, and normalized graph convolutions with adaptive adjacency matrices to obtain the local spatiotemporal prediction component. Finally, dynamic weights are generated by a gated network, and the two types of components are weighted and fused to obtain the final prediction result. This invention accurately separates long-term and short-term traffic flow features, adaptively balances global patterns and local details, improves the accuracy and robustness of long-term temporal prediction, and reduces computational complexity, making it suitable for intelligent traffic flow management scenarios.