19results about How to "Improve classification efficiency" patented technology

The utility model discloses a kind of high-efficiency catalyst material grading equipment, it is related to catalyst material processing technical field.The utility model includes equipment frame, and grading box is movably connected in the equipment frame inner chamber, and grading box one side is equipped with discharge port.The utility model is equipped with discharge assembly, using the linkage structure of slide bar, receiving block, sealing plate and extruding block, the automatic switching of grading box between screening and discharging state is realized, when screening, spring pushes receiving block to make sealing plate tightly clamped discharge port, ensure sealing, after screening is completed, cylinder drives grading box to rotate, extruding block compreses receiving block to drive sealing plate to separate discharge port, realize automatic discharge, this design cleverly uses equipment self-motion as power source, without additional driving device, both solve the problem of material leakage in traditional vibrating screening, and also ensure the accurate opening and closing of discharge port through mechanical linkage, significantly improve material recovery rate and equipment reliability.

The utility model discloses a food weighing and grading equipment, including first chain plate conveyer, first chain plate conveyer one side is provided with platform scale, and platform scale one side is provided with material grabbing manipulator, and the both ends of platform scale are provided with qualified grading assembly and substandard product grading assembly respectively, and substandard product grading assembly and qualified grading assembly structure are same, qualified grading assembly includes second chain plate conveyer at first chain plate conveyer one end, and one end of second chain plate conveyer is fixedly connected with fixed frame, and the bottom of fixed frame is installed with visual inspection camera, and the outer wall of visual inspection camera is provided with illuminating lamp, and the bottom of visual inspection camera is provided with placing frame, and the bottom of placing frame swing joint has fixed seat, and the inside fixed seat is installed with third motor, and one end of third motor axle is connected with second connecting frame, and one end of second connecting frame is installed with second motor. The device can realize automatic weighing and ketone body classification after livestock slaughtering, thereby improving classification precision and classification efficiency.

The utility model relates to the technical field of food processing equipment, and discloses drying, sterilizing and classifying equipment for food processing, which comprises a drying mechanism, a sterilizing mechanism is arranged on the right side of the drying mechanism, and a classifying mechanism is arranged on the right side of the sterilizing mechanism; the drying mechanism comprises a first mesh belt conveyor, a heating frame is fixedly connected to the top of the first mesh belt conveyor, a heating pipe is installed in the heating frame, an infrared heater is installed at the front end of the heating frame, a hollow ventilation hole is formed in the top of the heating frame, and a filter screen and an air inlet pipe are fixedly connected to the inner wall of the ventilation hole; and a fan is mounted at the top of the heating frame. According to the drying, sterilizing and classifying equipment for food processing, the three functions of drying, sterilizing and classifying are integrated, food sequentially passes through the sterilizing mechanism and the classifying mechanism from entering the drying mechanism, transfer between different equipment is not needed, intermediate links are reduced, and the production efficiency of food processing is greatly improved.

This application belongs to the fields of artificial intelligence and finance, and relates to a semantic-based text classification method. The method includes inputting a training sample set and a knowledge graph into a knowledge-enhanced language model to obtain knowledge-enhanced text semantic feature vectors; inputting the text semantic feature vectors into a capsule network model to output classification prediction results; calculating the loss value between the predicted classification result and the classification label; adjusting the model parameters based on the loss value to output a model to be validated; validating the model to be validated using a test sample set to obtain a text semantic classification model; and inputting the text to be classified into the text semantic classification model for classification. This application also provides a semantic-based text classification device, computer equipment, and storage medium. Furthermore, this application relates to blockchain technology, allowing the classified text dataset to be stored in the blockchain. This application can effectively identify multi-labeled text, improving the efficiency and accuracy of text classification.

The present application relates to the technical field of grading machine, propose a kind of carbon powder special grading machine, including grading cylinder, the outer surface of the grading cylinder is sequentially installed with exhaust port and air inlet from top to bottom, and tangential with the outer surface of the grading cylinder, the front of the air inlet is installed with fan, the top of the grading cylinder is fixedly installed with support cylinder, the side of the support cylinder is installed with feed inlet, the top of the support cylinder is installed with motor.The device can produce up and down vibration when the distribution disc rotates, the material in the "distribution area" is vibrated and scattered, the material with smaller quality is gradually moved to the upper layer, and the material moved to the upper layer can be horizontally moved to the outside of the distribution disc through the support plate and the vertical plate, while the material with larger quality is directly vertically downward through the discharge port, increasing the gap between the two materials, so that the material with smaller quality can be quickly separated, thereby realizing the function of improving the grading efficiency of the device.

The application discloses an account classification method and device, electronic equipment and a storage medium; the method relates to the field of big data in cloud technology, at least one candidate account in a first application, grade data of the candidate account, publishing content of the candidate account and interaction data can be obtained, the interaction data is the interaction data of a user account in the first application for the publishing content; a reference account associated with the candidate account is determined, the reference account is a user account in a second application, and the reference account and the candidate account belong to the same user; according to the grade data, the publishing content, the interaction data of the candidate account and the grade data of the reference account, the probability that the candidate account belongs to a preset account type is predicted; according to the probability, the candidate account is classified to obtain a target account of the preset account type. The application does not need manual annotation of account sample data, can reduce the cost of manpower and material resources, and improves the efficiency of account classification.

The application relates to the technical field of image processing, and provides a model training method and a contract text classification method and device. The model training method comprises the following steps: pre-training a text extraction model and an image extraction model of a feature extraction model according to each contract training sample, until a loss function of the feature extraction model converges, and a target feature extraction model is obtained; training the target feature extraction model according to a contract training sample set obtained after data enhancement of each contract training sample, and obtaining a trained target feature extraction model. The model training method provided in the application can more comprehensively extract contract features of a contract text, and improves the classification efficiency of the contract text.

The application provides a vehicle detection data classification method and system, a computer and a readable storage medium. The method comprises the following steps: preprocessing vehicle detection data to generate input text; inputting an input vector into an ERNIE model to convert the input vector into a first word vector, and performing sequence feature processing on the input text to generate a second word vector; performing splicing processing on the first word vector and the second word vector to generate a word vector matrix, and inputting the word vector matrix into a DPCNN model; optimizing the DPCNN model through an equal-length convolution function, performing maximum pooling processing on the equal-length convolution function through the optimized DPCNN model to generate a maximum feature value; and outputting a predicted classification label corresponding to the input text according to the maximum feature value. Through the above method, the classification of vehicle detection data can be quickly completed, thereby greatly shortening the time consumed for classifying vehicle detection data.

The present application relates to the emerging software technology field about processing classification, and especially relates to a hull part intelligent classification method and system based on internal and external contour features. Based on the hull part drawing, a structured part object list is generated, the internal structure features and the external contour features of the part are extracted, and a multi-dimensional feature vector set is constructed; based on the preset weight configuration of each dimension, the multi-dimensional feature vector is processed by the K-means clustering algorithm based on weighted Euclidean distance, and the clustering allocation result is output; the contour coefficient of the part is calculated, the CH index is calculated, and the classification effect is output according to the contour coefficient and the CH index. By extracting the internal and external contour features of the part, constructing the multi-dimensional feature vector, and classifying based on the K-means clustering algorithm based on weighted Euclidean distance and evaluating the classification by the contour coefficient and the CH index, high-precision and high-process-fitting-degree classification is realized, and the classification efficiency and consistency are improved.

The application discloses an airborne Lidar point cloud building extraction method and system based on space profile sampling guidance, a medium and equipment, and belongs to the technical field of three-dimensional point cloud segmentation. Airborne radar point clouds are acquired, a segmentation network model is trained, three-dimensional point cloud segmentation is performed based on the trained segmentation network model, and buildings are extracted. In the encoding layer, random sampling and feature aggregation are performed, the point cloud scale is compressed, and the point cloud features are enhanced. In the feature aggregation, the nearest neighbor points are searched based on the KNN algorithm and the space profile algorithm respectively, then, spatial position coding and feature coding based on the self-attention mechanism are performed in sequence, finally, the enhanced features are obtained through attention pooling, and the point cloud scale is compressed to 1 / 256 of the original. In the decoding layer, the compressed point cloud is up-sampled and jump-connected using the nearest neighbor interpolation method, the point cloud scale is restored, and finally each point is classified. The application can significantly improve the classification accuracy of buildings in large-scale airborne point clouds, has a low time complexity, and has high classification accuracy and efficiency.

This invention discloses a method, apparatus, and system for power equipment fault diagnosis based on the Storm platform. The method includes receiving power equipment data from the power grid in real time and sending it to a Spout source component to form a data stream to be processed; encapsulating the data stream into multiple tuples and generating unique IDs; preprocessing the dataset in the received tuples using the PreBolt component to obtain standardized samples to be classified; and processing the standardized samples to be classified using a fault diagnosis model to obtain the fault diagnosis results for the power equipment. This invention determines authoritative labeled samples through an authoritative labeled sample confirmation module in the fault diagnosis model, and performs online learning and fault diagnosis of a deep model through an online learning and fault diagnosis module in the fault diagnosis model, forming a deep online diagnostic model for the Storm platform. This improves classification efficiency while maintaining data stream classification accuracy.

This invention provides a clinical blood specimen classification and reception confirmation device, belonging to the field of blood specimen classification. It includes a support frame, a first sliding device, a second sliding device, a first placement tray, a second placement tray, a turntable, test tube chucks, a classification scanner, a first gripper, and a second gripper. The first sliding device, second sliding device, first placement tray, second placement tray, turntable, and classification scanner are all mounted on the support frame. The turntable is located between the first and second sliding devices. The first placement tray is located below the first sliding device, and the second placement tray is located below the second sliding device. The classification scanner is located on one side of the turntable. Multiple test tube chucks are rotatably connected to the turntable in a circumferential array. The first gripper is mounted on the first sliding device, and the second gripper is mounted on the second sliding device. The purpose is to solve the problem of low classification efficiency for blood collection tube specimens. The achieved technical effect is: effectively improving the classification efficiency of blood collection tube specimens.

This application provides a training method for a classification model, a text classification method, and related equipment to address the problem of low classification flexibility in classification models. The method includes at least the following steps: based on preset multiple feature dimensions, extracting corresponding multidimensional text features, multidimensional associated label features, and at least one multidimensional other label feature for selected sample text, associated classification labels, and at least one other classification label from among multiple classification labels; determining the negative sample similarity between each of the at least one other classification label feature and the multidimensional text feature based on the hierarchical distance between each of the at least one other classification label and the associated classification label; and adjusting model parameters based on the obtained positive sample similarity and at least one negative sample similarity between the multidimensional text feature and the multidimensional associated label feature. This enables a target classification model to have feature extraction capabilities of different granularities, improving classification flexibility.

This application provides an adaptive integrated land surface classification method and system based on multi-scale spatiotemporal partitioning, belonging to the field of remote sensing image processing technology. The method includes: acquiring multi-source remote sensing image data, digital elevation model (DEM) data, and historical land use data; partitioning the target area into spatial units based on the DEM data; identifying the phenological change process of each spatial unit using time-series data; determining spatiotemporal partitions based on the change process, historical land use data, and each spatial unit; constructing a feature set based on the spectral, textural, and index features of the topography and phenological characteristics of each spatiotemporal partition; selecting and applying a corresponding pre-trained land surface classification model based on the feature set and spatial units to obtain the classification result; and establishing a feedback update mechanism based on the classification result, multi-source remote sensing image data, and DEM data. This application improves classification efficiency and accuracy.

The embodiment of the application discloses a method and device for obtaining a cell classification model, the method comprising: obtaining a trained cell classification model; obtaining M feature maps of a target to be classified according to test data and the cell classification model; M is a positive integer greater than or equal to 1; the test data comprises one or more targets to be classified; obtaining the weights of the M feature maps; inputting the M feature maps with weights into the cell classification model, optimizing the cell classification model, and thus obtaining an optimal cell classification model. Through the embodiment, the classification of a target with a small inter-class gap is realized, and the classification efficiency and accuracy are improved.

The invention relates to an alarm processing method and device. The method comprises the following steps: grouping a plurality of alarm condition texts in an alarm comprehensive database, and respectively screening high-risk important alarm condition texts from each group; after the multiple important alarm texts are classified through a large language model, the incidence relation among the multiple important alarm texts of each class is mined, and a corresponding knowledge graph is constructed for at least two important alarm texts with the incidence relation in each class; when a query request described by a natural language is received, performing mixed retrieval in a police comprehensive database to obtain a target alarm text matched with the query request; the mixed retrieval comprises keyword retrieval and semantic retrieval; and querying an associated alarm text associated with the target alarm text in the knowledge graph to perform case series-parallel analysis on the target alarm text and the associated alarm text. According to the scheme provided by the invention, the technical defects in the aspects of classification efficiency, correlation analysis and semantic retrieval in related technologies can be solved.

This application provides a method, electronic device, and medium for classifying yellow-spotted cigarettes. The method includes: acquiring a plurality of camera images, wherein each camera image is captured by a corresponding preset camera, and all preset cameras are arranged along the circumference of the cigarette to be tested; obtaining a first contour of the cigarette to be tested in the camera images based on a first preset strategy; extracting a target pixel region within the first contour using a first preset algorithm; determining a first detection result of the target pixel region based on a preset model, wherein the first detection result is used to characterize whether the target pixel region is a first yellow-spotted region; and determining whether the cigarette to be tested is a yellow-spotted cigarette based on the first detection result. This solution can adapt to the detection needs of cigarettes of different batches and different background colors, and can quickly complete the yellow-spot detection and qualification judgment of a large number of cigarettes, meeting the real-time requirements of high-speed production line flow and ensuring the consistency of yellow-spotted cigarette detection.