4results about How to "Stable estimate" patented technology

The invention relates to the technical field of anti-collision early warning, and discloses a transformer substation crane three-dimensional anti-collision early warning system and method based on a millimeter wave radar, and the system comprises a millimeter wave radar module which is used for obtaining point cloud observation data; the Beidou satellite positioning module is used for acquiring absolute geographic coordinates of the crane; the processing module is used for estimating the motion states of the crane jib and the hoisted object based on the point cloud observation data and the absolute geographic coordinates, quantifying the dynamic collision risk based on the estimated motion states of the crane jib and the hoisted object, and generating an early warning signal; and the early warning module is used for receiving the early warning signal and sending out early warning information. According to the method, the crane jib, the hoisted object and the point cloud observation data are modeled into the random finite set, the probability hypothesis density filter is adopted for processing, and meanwhile, the point cloud observation data are obtained through the millimeter wave radar, so that the motion states of the crane jib and the hoisted object can be stably and reliably estimated, and the view blind area is eliminated.

A method and system for intelligent fault monitoring in distribution networks containing distributed energy resources are disclosed. The method first acquires multi-source information data of the distribution network containing distributed energy resources and extracts features to generate a node feature matrix. Then, based on the node feature matrix, a Markov decision model for fault detection in the distribution network containing distributed energy resources is constructed. The system's main body is used as the agent, and the agent is trained using an improved D4PG algorithm based on the Markov decision model. After training, the optimal fault detection strategy for the distribution network containing distributed energy resources is derived and deployed in the fault detection task for intelligent fault monitoring. This invention, through a deep reinforcement learning framework, transforms the complex fault detection task of the distribution network containing distributed energy resources into an optimal decision problem, successfully establishing a direct, fast, and reliable logical mapping relationship from the grid fault state to the optimal diagnostic action, providing a more robust and adaptive fault detection method.

The application discloses a wheeled quadruped robot dynamic obstacle avoidance method, system and device, relates to the technical field of robot autonomous navigation and motion control, and comprises the following steps: collecting environment original point cloud data and pose information of a wheeled quadruped robot, pre-processing the point cloud data to obtain an obstacle cluster; calculating the centroid position and speed information of each obstacle cluster; predicting the position of the obstacle in a future time period; obtaining an initial predicted trajectory based on the pose information of the wheeled quadruped robot; combining the predicted position of the obstacle to construct a relative motion point stream of the obstacle; performing feature extraction to obtain a dual variable feature; constructing a model predictive control optimization problem; solving the optimization problem by using an alternating solution algorithm to obtain an optimal control sequence at the current moment; and finally generating an optimal control instruction to drive the wheeled quadruped robot to perform a dynamic obstacle avoidance action. The application can improve the motion real-time performance, stability and environmental adaptability of the wheeled quadruped robot in a complex environment.

The invention discloses a harmonic radar nonlinear phase compensation method and system based on sparse recovery, and belongs to the technical field of harmonic synthetic aperture radar imaging. The method comprises the following steps: extracting a second harmonic component from a received harmonic echo signal; performing pulse compression by adopting an ideal reference function; randomly extracting a part of pulses from all the pulses, and constructing an observation matrix; parameterized phase fitting is carried out on the extracted pulse, and a nonlinear phase coefficient is estimated; reconstructing a full-aperture time-varying nonlinear phase trajectory through sparse recovery by using the sparsity of a phase coefficient in a transform domain; and finally, constructing a time-varying nonlinear compensation function based on the reconstructed phase, performing frequency domain compensation on the signal, and performing SAR imaging processing to obtain a harmonic synthetic aperture radar image with good focusing. Full-aperture phase reconstruction can be realized only by extracting part of pulses, and the method has the advantages of strong anti-noise capability, capability of processing spectrum distortion, avoidance of segmentation error accumulation, high compensation precision and the like.