5results about How to "Simplify computational problems" patented technology

A method for generating typical watershed hydro-wind-solar hybrid scenarios based on an improved C-vine Copula model is proposed. First, based on long-term multi-energy complementarity requirements, multi-year runoff data from a power station and power output data from wind and solar power stations within the watershed are selected, outliers are removed, and missing values ​​are filled, completing data preprocessing. Next, runoff and wind / solar output are set as random variables, and nonparametric kernel density estimation is used to obtain the marginal distribution functions of each variable. Then, the improved C-vine Copula model is used to accurately characterize the spatiotemporal correlation between water, wind, and solar resources, deriving the joint probability distribution. Then, Latin hypercube sampling is used to collect uniformly random samples stratified, and K-means clustering is used to generate typical scenarios. Finally, the effectiveness of the scenarios is evaluated from the perspectives of temporal and spatial correlation and randomness. This technology can effectively address the randomness and complexity of water, wind, and solar resources, generating realistic typical scenarios, providing valuable reference for the planning and scheduling of watershed hydro-wind-solar hybrid systems.

The invention discloses an angular point acquisition method, a camera calibration method, a chip and a robot, and the angular point acquisition method comprises the steps: setting a target plane to be parallel to the optical axis of a camera, and controlling the field angle of the camera to cover the target plane; performing transmission transformation on the target plane to obtain pixel coordinates of a target angular point in the imaging plane; wherein the transmission transformation enables the camera to capture more feature points in the target plane in the imaging plane, and the feature points comprise target angular points. In conclusion, based on the cooperative design of the field angle of the camera and the target plane, transmission transformation is carried out on the target plane, the angular point coordinate data are obtained in the imaging plane, and the angular point coordinate extraction number and the extraction success rate can be guaranteed.

The application belongs to the field of electric power data processing, and particularly relates to a power short-term load prediction method based on deep reinforcement learning and transfer learning; the method comprises the following steps: obtaining multiple groups of different source domain power load data and performing preprocessing; training multiple power load source domain prediction models based on GRU network respectively by using the multiple groups of preprocessed source domain power load data and corresponding power fusion features; taking target domain power load data as input, selecting a source domain prediction model based on a time series double-depth Q network; performing dynamic distribution alignment on the source domain power load data and the target domain power load data based on a multi-core maximum mean difference algorithm, and adjusting model parameters of the selected source domain prediction model; migrating the adjusted model parameters of the source domain prediction model to a power load target domain prediction model based on GRU network; inputting the target domain power load data into the target domain prediction model, and outputting a prediction result of power load in a short term.

The utility model belongs to spline connection detection technical field relates to a device for detecting spline connection disc end surface tooth symmetry, include: work piece placing base, tensioning cover mandrel, tensioning cover, detection reference column, detection block, tensioning cover mandrel rotation connects work piece placing base, tensioning cover is covered and is established on the outer circumference of tensioning cover mandrel, and tensioning cover outer cover is equipped with spline connection disc, the boss of detection block is matched with spline connection disc end surface tooth groove, the top of detection reference column is equipped with detection reference groove, the utility model discloses through detection block and leads out the detection interface of spline connection disc end surface tooth, provides the contrast basis of detection device through detection reference column and blocks detection block, and the symmetry error of spline connection disc end surface tooth can be detected through the contrast of before and after detection value, therefore the utility model can effectively eliminate the clearance between spline mandrel and spline connection disc, and the accuracy of detection has been improved.

The invention discloses a camera calibration method based on depth information, a chip and a robot, and the camera calibration method comprises the steps: 1, controlling a camera to collect the pixel coordinates of a target corner point from a target plane, and converting the pixel coordinates of the target corner point into the normalized coordinates of the target corner point through the internal reference of the camera; 2, converting the normalized coordinates of the camera center and the target angular point through the external parameters of the camera to obtain the converted camera center and the target angular point after the normalized coordinates are converted, and then drawing a ray to a target plane in a mode of connecting the converted camera center and the target angular point after the normalized coordinates are converted, the world coordinate of the intersection point of the ray and the target plane is marked as the world coordinate of the angular point to be calibrated; and 3, matching the world coordinates of the to-be-calibrated angular point with the world coordinates of the target angular point, and correcting the depth of the normalized plane according to a matching result until the matching result meets preset matching precision.