8results about How to "Implement automatic classification" patented technology

The invention discloses an automatic garbage classification device for a smart city and a using method thereof, relates to the technical field of garbage classification, and solves the problems that in the existing garbage throwing and classification process, classified collection of a garbage truck is inconvenient, solid-liquid separation of garbage cannot be achieved, electric quantity supplementation of the garbage can is inconvenient, and environmental protection is not facilitated. The automatic garbage classification device for the smart city and the using method of the automatic garbage classification device for the smart city comprise a mounting and conveying mechanism, and two classification and storage mechanisms are mounted on the inner side of the mounting and conveying mechanism; and the classified storage mechanism comprises a classified storage box, a movable baffle, a fan box, a liquid injection plate, a disinfectant spraying head, a garbage guide inclined plate, a garbage temporary storage seat, a garbage compression plate and a hydraulic rod. According to the garbage disposal device, garbage which is thrown in a classified mode is automatically compressed and subjected to solid-liquid separation, garbage transfer and disposal are facilitated, cleanliness of a throwing point can be kept, and electric energy supplementation is facilitated by adjusting the angle of the solar cell panel.

This invention discloses a Yangtze River waterway classification method based on ship feature clustering, comprising: dividing the Yangtze River waterway into multiple segments using an angle bisector method; assigning geographical semantic knowledge such as ports, prefecture-level cities, and provincial-level administrative regions to each segment using overlay analysis; calculating the navigation time of each type of ship within a square grid by dividing the grid, and calculating static traffic characteristics using a summation-based waterway mapping method; calculating relatively discrete traffic characteristics such as speed, acceleration, and turning rate using the mean method, and mapping them to waterway segments using the maximum value method; calculating flow characteristics using a summation method; selecting traffic characteristics with appropriate similarity based on hierarchical clustering analysis; and classifying the Yangtze River waterway using a combination of K-means clustering and sliding window methods. This invention can automatically classify waterway segments in the Yangtze River waterway based on ship traffic characteristics.

This invention discloses a method, apparatus, and device for field extraction based on multi-task deep learning. The method includes: acquiring remote sensing time-series images of the phenological stages of agricultural crops within a region to be identified; inputting the remote sensing time-series images of the phenological stages of agricultural crops within the region to be identified into a trained farmland field extraction framework for prediction, thereby obtaining farmland field identification results; wherein, the farmland field extraction framework is trained based on a remote sensing time-series image training set, which is a dataset within the study area after data preprocessing. This invention solves the problems of poor accuracy and low efficiency in field identification.

The present application relates to a method for automatically generating wall, column and beam components in Revit based on 3D scanning point cloud data. The method comprises: S1, point cloud data acquisition and preprocessing, denoising, simplifying and aligning the original point cloud; S2, point cloud feature extraction and component classification, point cloud segmentation is performed by region growing algorithm, component type identification is performed by deep learning model and attribute parameters are given; S3, Revit component mapping and model generation, mapping rules are set through Revit plug-in, corresponding building components are automatically created in Revit and parameters are set; S4, model verification and optimization, the deviation between the model and the point cloud is automatically calculated and manual correction and lightweight processing are performed. The present application realizes the automatic conversion of point cloud data to Revit model, the modeling efficiency is improved by more than 80%, the model accuracy can reach ±2cm, and can be widely applied in old building reconstruction and existing building digitization fields.

The utility model discloses a hardware stamping part processing discharge device relates to hardware stamping part discharge device technical field, include: support, the lateral wall fixed mounting of support has distribution box, the top surface rotationally connected with first transmission belt of support, one side rotationally connected with second transmission belt of first transmission belt, and the lateral wall of first transmission belt is provided with the fender, and the lateral wall of fender is provided with visual screening machine, overturns spare, overturns spare and is arranged on fender, and is used to overturn the hardware stamping part of needing detection, shunt spare, shunt spare and is arranged on fender, and is used to shunt after the hardware stamping part of detection, be equipped with overturning spare, can carry out comprehensive detection to hardware stamping part, solved the problem that traditional equipment can not detect hardware stamping part bottom surface. The cooperation of electromagnet and drive motor no.

This application proposes an image classification method, apparatus, and device. The method includes: identifying whether an image to be processed is a beautified image using a pre-trained face detection model and a beautification recognition model; classifying the image to be processed using a pre-trained multi-class classification model to obtain the probability of the image belonging to each image category; and identifying whether the image to be processed contains an image of a target person using a pre-trained person classification model. This application integrates a beautification recognition model for identifying beautified images, a multi-class classification model for classifying multiple image categories, a person classification model for identifying whether an image contains a target person, and so on. By using these multiple models to automatically classify the image to be processed, the method offers strong real-time performance, enabling automatic classification of a large number of images and improving the efficiency of image data structured processing.

This invention discloses a method for analyzing cold seep methane activity by combining benthic foraminifera isotope analysis. This invention relates to the field of Earth science and technology. It involves collecting and preparing sediment columns from cold seep activity areas, covering different activity periods; meticulously separating individual benthic foraminifera from the sediment columns and treating them to eliminate secondary influences; using mass spectrometry to determine the oxygen and carbon isotope values ​​of the benthic foraminifera, constructing continuous isotope time series data; and analyzing the degree of shift in the oxygen and carbon isotope values ​​of the benthic foraminifera based on the continuous isotope time series data. This invention achieves precise quantification of cold seep activity by measuring the oxygen and carbon isotope values ​​of the benthic foraminifera shells and combining sliding window technology and K-means clustering algorithm. It can identify short-term anomalous shift events and, combined with indicators such as isotope range and the number of zero crossover points, accurately classify cold seep activity types, providing a basis for understanding the dynamic changes of cold seep methane leakage.

This invention discloses an automated device and method for cleaning multiple types of vegetables, belonging to the technical field of vegetable cleaning equipment. It includes a feeding and conveying assembly, cleaning tanks for leafy vegetables, fruits, and root vegetables, as well as a conveying assembly for each type of vegetable. The cleaning tanks for leafy vegetables, fruits, and root vegetables are all located outside the feeding and conveying assembly. Through the coordinated operation of a first, second, and third cleaning mechanism, this invention employs specific cleaning modes tailored to the physical characteristics of leafy vegetables, fruits, and root vegetables: gentle, non-contact bubble cleaning; general cleaning with soft brushes; and powerful cleaning with alternating scrubbing using hard brushes. This ensures that leafy vegetables remain undamaged, fruits are scratch-free, and root vegetables are efficiently cleaned, achieving a balance between food integrity and thorough cleaning.