55results about How to "Improve classification efficiency" patented technology

The application discloses a foreground target migration method for unsupervised domain adaptation, and the method comprises the following steps: obtaining labeled source domain samples and unlabeled target domain samples as training samples, inputting the source domain samples and the target domain samples into a deep neural network for training to obtain a classification model, inputting data to be classified in the target domain into the classification model, and obtaining a classification result. The application further provides a foreground target migration device for unsupervised domain adaptation. The foreground target migration method and device for unsupervised domain adaptation provided by the application can improve classification precision and classification efficiency.

The utility model discloses a rapid screening device for automatic production line products, which comprises a device body, the device body comprises a four-class sorting device, a sorting auxiliary device, a first discharging device and a second discharging device, the first discharging device is arranged at the left end of the four-class sorting device, and the second discharging device is arranged at the right end of the four-class sorting device; the four-class sorting device comprises a sorting main body frame, two groups of bidirectional sorting rollers and two groups of positive and negative sorting motors, the two groups of bidirectional sorting rollers are transversely mounted in the sorting main body frame at equal intervals, and the two groups of positive and negative sorting motors are transversely mounted at the rear end of the sorting main body frame at equal intervals; driving ends of the two groups of positive and negative sorting motors are respectively connected with one ends of the two groups of bidirectional sorting rollers; the sorting auxiliary device is mounted at the front end in the sorting main body frame; and a protective gasket made of rubber is arranged on the surface of the pushing plate. According to the device, different objects can be classified to the four discharging openings to be discharged, so that the applicability of the device is improved, and the overall classification efficiency is improved.

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 relates to the technical field of new energy automobile production equipment, in particular to a waste treatment device for new energy automobile battery production, which comprises a sorting treatment box and a negative pressure suction pump, and two obliquely-arranged second conveying rollers are rotationally connected to the positions, located below the first conveying rollers, in the sorting treatment box, a second conveying belt of a strong magnetic structure is in transmission connection between the exteriors of the two second conveying rollers, and a sorting baffle used in cooperation with the diaphragm discharging groove and the plastic discharging groove is fixedly connected to the interior of the sorting treatment box. The waste material sorting device has the advantages that battery waste materials generated in production are collected through the negative pressure suction pump, comprehensive collection of the waste materials is achieved, then different types of waste materials can be accurately separated through the sorting unit composed of the first conveying belt, the second conveying belt and the sorting baffle in a matched mode, the waste material sorting efficiency and accuracy are improved, and the production cost is reduced. And a foundation is laid for subsequent treatment and resource recovery.

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.

This application relates to a vulnerability classification model training method, vulnerability classification method, apparatus, and device. The method includes: extracting features from sample source code and target language code using a feature extraction network to obtain sample features of the sample source code and target features of the target language code; aligning the sample features and target features using a feature alignment network and determining a feature alignment loss based on the alignment result; classifying the feature alignment result using a classification network to obtain sample classification results and determining a classification loss based on the classification results and corresponding sample classification labels; and training a vulnerability classification model based on the feature alignment loss and classification loss. This method enables the trained vulnerability classification model to adapt to multi-language ecosystems, improving vulnerability classification efficiency and accuracy.

The application discloses an image classification method and device, a storage medium and a computer device. The method is applied to the computer device and comprises the following steps: in a self-defined image classification mode of an electronic album, in response to a triggering operation of a user on an update control of an image corresponding to a to-be-managed image category, updating the image in the to-be-managed image category, calling a training interface, obtaining a first image classification model corresponding to the self-defined image mode through the training interface, the model being pre-trained, and performing transfer training on all images of the to-be-managed image category according to the first image classification model, so as to obtain a first image classification model after transfer training. The classification of the updated image can be matched by using the first image classification model after transfer training, the classification accuracy is improved, finally, the to-be-classified images of the electronic album are classified by using the trained first image classification model, the image classification efficiency and accuracy are improved, and the image searching efficiency is further improved.

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.

The invention relates to the technical field of cranial neuroscience, and provides a model construction method, an image processing method, equipment, a medium and a product. The method comprises the following steps: acquiring representation data corresponding to a brain medical image and a classification label of the brain medical image for an Alzheimer's disease scene; sequentially constructing a plurality of processing layers under the scene of the Alzheimer's disease based on the representation data and the classification labels; constructing a Laplacian matrix corresponding to the initial classification model, constructing an objective function of the initial classification model based on the initial classification model and the Laplacian matrix, and solving through the objective function to obtain an output weight of the initial classification model; and updating the initial classification model based on the output weight to obtain a target classification model for the Alzheimer's disease scene. Through the technical scheme of the invention, efficient modeling in a small sample scene is realized, the problems that a deep learning model depends on a large number of samples and iteration time is long are avoided, and the classification efficiency is 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 invention provides an image processing method, electronic equipment and a storage medium, and the method comprises the steps: carrying out the gray processing of an eye fundus image, and obtaining a gray image, the retina in the eye fundus image comprises an optic disc; determining a relationship between a corresponding brightness value and a pixel point based on the grayscale image, and determining a target position of the optic disk; and determining a first classification result of the fundus image according to the target position, wherein the first classification result comprises a left eye image and a right eye image. The method can reduce the classification cost of the eye fundus image and improve the classification precision.

The application relates to a business data classification method and device, computer equipment, a readable storage medium and a program product, and relates to the field of big data. The application comprises the following steps: acquiring each business data, each basic data category and each classification index; each basic data category corresponds to a set of classification weights, and the classification weights are in one-to-one correspondence with the classification indexes; performing vector conversion on each business data; the vector data comprises vector components corresponding to each classification index of the business data; for each basic data category, the classification weight corresponding to the classification index under the basic data category is fused with the vector component corresponding to the classification index to obtain a matching degree between the business data and the basic data category; when the matching degree meets a matching condition, the basic data category is determined as a basic data category to which the business data belongs; and each vector data belonging to the same basic data category is clustered to obtain a classification result of each business data. The method can improve the classification efficiency.

The invention relates to the technical field of quartz sand desliming, in particular to a graded desliming treatment method for quartz sand, which comprises the following steps: acquiring a second particle size of the quartz sand at an underflow port of a cyclone to determine a second site diameter, judging the qualification of the quartz sand according to the second site diameter and a second target particle size, and responding to the disqualification of the quartz sand. The effective height of a cylindrical barrel of the cyclone is determined, the extending length of an overflow pipe extending into the barrel is determined on the basis that a third bit diameter determined on the basis that the third particle size of sand-removed mud at an overflow opening is larger than a third target particle size, and the feeding pressure is determined and regulated on the basis that the third bit diameter is larger than the third target particle size in response to the fact that quartz sand is unqualified; according to the invention, the separation effect can be checked according to the obtained particle size and bit diameter of the separated target substance so as to feed back the working parameters of the cyclone, and the separation process is accurately regulated and controlled, so that high-purity quartz sand is obtained by high-precision separation of quartz sand and mud.

The present disclosure relates to a wafer classification method and device, electronic equipment and computer readable storage medium, and belongs to the technical field of semiconductor production and manufacturing, and can be applied to the scenario of classifying wafers. The method comprises: obtaining associated information of a wafer to be classified; the associated information comprises repair circuit information of the wafer to be classified and historical reference wafer information corresponding to a historical reference wafer; and based on the repair circuit information and the historical reference wafer information, the wafer to be classified is compared with the historical reference wafer one by one to obtain a current wafer category; the current wafer category comprises a current reference wafer and a current abnormal wafer. The present disclosure can quickly classify wafers according to the repair circuit information of the wafers to provide subsequent problem tracking of failed wafers.

This application relates to a video classification method, apparatus, computer device, and storage medium. The method includes: extracting multiple video frames from a video sequence corresponding to the video to be classified, generating a sampling sequence. The sampling sequence includes frame vectors corresponding to the multiple video frames. Frame extraction reduces the amount of computational data, saves computational resources, and improves video classification efficiency. Based on the vector differences between pairs of adjacent video frames in the sampling sequence, each vector difference corresponds to a difference frame. The frame vectors corresponding to multiple difference frames form a difference sequence, which represents the action information in the video to be classified through the vector differences between adjacent video frames. The difference sequence is combined with the sampling sequence to generate a splicing sequence. Based on the first encoding result corresponding to the splicing sequence and the second encoding result corresponding to the difference sequence, the classification result of the video to be classified is determined. Combining the encoding results corresponding to the two sequences to determine the classification result of the video to be classified improves the accuracy of the video classification result.

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.

This invention discloses an automated equipment for low-temperature pulverization of Panax notoginseng powder, relating to the field of pulverization processing technology. The automated equipment includes a front-end component for crushing Panax notoginseng powder and a rear-end component for screening the finished product. The front-end component includes a coarse crusher for coarse crushing of the Panax notoginseng powder and a pulverizing host for pulverizing the Panax notoginseng powder. The pulverizing host includes a base, and an air inlet assembly is mounted above the base. The air inlet assembly includes a first chamber fixedly connected to the base, with a first chamber pipe fixedly connected to one side of the first chamber. A pulverizing chamber assembly is mounted above the air inlet assembly, including a second chamber fixedly connected to the first chamber, with a second chamber pipe fixedly connected to one side of the second chamber. A pulverizing component is located in the lower part of the pulverizing chamber assembly. This automated equipment for low-temperature pulverization of Panax notoginseng powder has high grading efficiency, effectively avoids over-pulverization of materials, and saves energy.

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 invention discloses a micro negative pressure separation system for a graphite material super-crushing process, which comprises the following steps: firstly, carrying out ultrasonic pre-dispersion on super-crushed graphite powder under the protection of inert gas, so that agglomerated particles are dissociated to form uniform suspended powder cloud; and then sealed feeding between the normal-pressure area and the negative-pressure grading area is achieved through a spiral airlock mechanism. In a negative pressure environment, powder is sequentially subjected to coarse separation of the speed increasing channel, grading separation of the rotational flow field and dispersion conditioning under the action of the electrostatic field, and a stable gas-solid two-phase system suitable for fine screening is formed. In the fine grading part, a multi-layer ceramic membrane filtering structure is adopted to cut off the particle size of submicron-scale and nano-scale powder, and periodic regeneration of a membrane surface is realized through a pulse back flushing device, so that continuous and stable filtering flux is maintained. The system realizes closed-loop control of atmosphere through negative-pressure air exhaust and oxygen content monitoring, so that the whole process is operated under a low-oxygen condition, and graphite powder with different particle size grades, such as coarse powder, medium powder, fine powder and nano powder, is obtained.

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.

The invention relates to the technical field of ensemble learning, in particular to a production identification screening method and system based on a machine-made sand dry method, and the method comprises the steps: obtaining a first vector of each placer particle sample; obtaining a hierarchical clustering tree, and obtaining an integrated weight of a base classifier of each dimension in a dimension set of each class cluster of each hierarchy of the hierarchical clustering tree; obtaining a base classifier of each level of the hierarchical clustering tree in each dimension; obtaining an integrated classifier of each level of the hierarchical clustering tree; obtaining a classification error rate of an integrated classifier of each level of the hierarchical clustering tree; obtaining the comprehensive weight of the integrated classifier of each level; obtaining a total classifier of the training set; and obtaining the production quality of the ore sand particles to be analyzed. According to the method, the difference influence of different characteristics of the ore sand particles is reduced, and the efficiency and accuracy of production quality evaluation of the to-be-analyzed ore sand particles are improved.

The present application relates to the technical field of agricultural product origin identification and traceability, and particularly discloses an agricultural product origin identification and traceability system and method based on characteristic fingerprints, which comprises the following steps: collecting agricultural product samples to obtain a target sample set; marking the target sample set with dominant characteristic fingerprints according to the dominant characteristics of the agricultural products, and performing one-class partitioning on the target sample set according to the dominant marking result to obtain a plurality of one-class sample clusters; marking the target sample set with recessive characteristic fingerprints according to the recessive characteristics of the agricultural products, and performing two-class partitioning on the target sample set according to the recessive marking result to obtain a plurality of two-class sample clusters; comparing the one-class partitioning result and the two-class partitioning result to determine whether to start a successive verification mode or a separate verification mode; based on the successive verification mode, obtaining each verification result, and determining whether to output an actual origin traceability result according to the verification results.

The application discloses a multimedia resource classification model training method and a multimedia resource classification method. The multimedia resource classification model comprises multiple expert classification networks. The method comprises the following steps: acquiring sample description information corresponding to sample multimedia resources; inputting the sample description information into the multimedia resource classification model, performing multi-level label prediction processing on the sample description information by using the expert classification networks, obtaining sample level expert prediction results output by the expert classification networks, determining sample network weights corresponding to the expert classification networks, determining sample level comprehensive classification results based on the sample network weights and the sample level expert prediction results, training the multimedia resource classification model based on differences between the level comprehensive classification results and multi-level classification labels and category level correlation relationships between multiple sample comprehensive classification results, and obtaining a target multimedia resource classification model. The accuracy and classification efficiency of multi-level classification of the multimedia resources are improved.

Embodiments of the present application provide an image data classification method and device and electronic equipment, which are applied to the technical field of intelligent auditing. The method comprises: extracting image features of image data to be classified; determining a first pre-classification result of the image data based on the image features and using a first classification layer in an image classification model; wherein the image classification model further comprises at least one second classification layer; when the first pre-classification result indicates that the image data belongs to a specified image category, determining a second pre-classification result of the image data based on the image features and using the at least one second classification layer, and determining the image category of the image data based on the first pre-classification result and the second pre-classification result; and when the first pre-classification result indicates that the image data does not belong to the specified image category, taking the image category indicated by the first pre-classification result as the image category of the image data. Through the present solution, the accuracy and the false detection rate can be improved, and the efficiency of image data classification can be improved.

This utility model discloses a size grading device for crayfish, which includes a fixed cylinder and a grading mechanism. An inlet is fixedly installed at the upper end of the fixed cylinder, penetrating through the cylinder. The grading mechanism includes multiple fixed columns fixedly installed at the bottom of the fixed cylinder, with mounting frames slidably connected to each column. The fixed columns slide against the inner wall of the mounting frames. A vibrating plate is fixedly installed between the fixed columns. An eccentric roller is sleeved on a rotating rod and fixedly connected thereto, sliding against the vibrating plate. Multiple filter plates arranged vertically opposite each other are fixedly installed inside the fixed cylinder, with multiple brush plates slidably connected to the upper ends of the filter plates and slidingly against them. This device utilizes the cooperation between a first motor, an eccentric roller, a second motor, a transmission rod, brush plates, a pull gate, and vibrating plates to improve the grading efficiency of crayfish and simultaneously improve the crayfish recovery efficiency.

The application provides a visual analysis method supporting fine subtype identification of multiple types of immune cells, and the method comprises the following steps: constructing a multi-scale visual ViT model comprising an encoder and a full connection layer; the encoder adopts a multi-scale visual network; blood cell images of each scale are input into the encoder after being divided into blocks, so that image features of each image block are obtained; the image features belonging to the same blood cell image are fused, so that unified image embeddings of each blood cell image are obtained; the unified image embeddings of each blood cell image belonging to the same blood cell image are fused, so that a final feature of the blood cell image is obtained; the final feature is input into the full connection layer for cell type classification, so that prediction probabilities of the blood cell image belonging to each cell category are obtained.

The invention discloses a polycrystalline silicon block screening device and a screening method. The device comprises a top cover, a first screening mechanism, a second screening mechanism and a material receiving mechanism at the bottom which are rigidly connected in sequence in the vertical direction, a feeding pipe is arranged on the top cover, and a discharging pipe is arranged at the bottom of the material receiving mechanism. A vibration mechanism is arranged on the material receiving mechanism, and exciting force generated by the vibration mechanism drives the whole device to vibrate. The second screening mechanism comprises a screen, a screen plate is fixed below the screen through a connecting piece, an interlayer is formed between the screen and the screen, and a plurality of vibration pieces capable of freely moving are arranged in the interlayer. In the vibration process, the vibration piece continuously impacts the screen, and meshes of the screen are effectively prevented from being blocked by polycrystalline silicon particles. The device is further provided with a discharging part used for discharging materials with different particle sizes, a supporting mechanism facilitating inclined arrangement of the device and a cleaning mechanism arranged on the top cover. Therefore, efficient, continuous and anti-blocking screening of the polycrystalline silicon blocks is achieved, and cleaning and maintenance are easy.