33results about How to "Reduce misclassification" patented technology

The invention relates to the technical field of data security, in particular to an unstructured video data automatic classification method, which comprises the following steps: S1, video data preprocessing: standardizing video data to a uniform format and size, and carrying out noise processing; s2, video data feature extraction and multi-mode feature fusion: feature extraction is performed on the preprocessed video data from three modes of audio, video and text, time sequence processing is performed, and multi-mode feature fusion is performed; s3, video data same-category matching: classifying the fused multi-modal features to complete video classification; according to the invention, multi-modal information in a video can be captured more accurately, and automatic processing of video data is realized; high processing efficiency and classification accuracy are kept in large-scale data, meanwhile, the degree of automation is improved, the necessity of manual participation is reduced, and the overall process is optimized.

The invention relates to a tree painting projection data collection and analysis system and method, and the system comprises a data collection module, a data preprocessing module, a feature extraction module, a quantitative analysis module, a classification and prediction module, and a result evaluation module. The modules cooperate with each other to realize standardized processing, feature extraction, quantitative analysis, intelligent classification and prediction and result evaluation of data. According to the method, the holographic theory and the Ama pothole theory are combined, the drawing features are finely analyzed through the cross-modal comparative learning and graph neural architecture search combined algorithm, a scientific and objective psychological data acquisition and analysis method is provided, the diagnosis sensitivity and specificity are improved, a powerful tool is provided for clinical psychological assessment, and the method is suitable for clinical psychological assessment. The method has important clinical and research values, and has wide application prospects in the fields of psychological problem screening, social psychological services, judicial, public security, marriage matching, talent selection and the like.

The invention discloses a wind power blade unmanned aerial vehicle inspection method and device, and belongs to the technical field of wind driven generators, and the wind power blade unmanned aerial vehicle inspection method comprises the steps: carrying out the unmanned aerial vehicle inspection of the front surface of a wind power blade to form first inspection information, and carrying out the unmanned aerial vehicle inspection of the back surface of the wind power blade to form second inspection information, the first inspection information comprises a first track point data set and a blade front image set, and the second inspection information comprises a second track point data set and a blade back image set; according to the first inspection information and the second inspection information, in combination with a wind power blade defect judgment condition, obtaining a wind power blade unmanned aerial vehicle inspection result; according to the wind power blade unmanned aerial vehicle inspection result, whether an unmanned aerial vehicle re-inspection mode is started is judged, if yes, the unmanned aerial vehicle re-inspection mode is started, and the wind power blade unmanned aerial vehicle inspection result is updated; and if not, the re-inspection mode is not started. The method can reduce the influence on the wind power plant operation and power generation efficiency, and improves the accuracy of the wind power blade unmanned aerial vehicle inspection result.

The human cost data storage method for the power grid engineering system comprises the steps that human cost data related to cost data in the power grid engineering system are acquired, and the human cost data comprise work type data, man-hour data and salary data of constructors; determining a DBSCAN clustering radius range based on the average man-hour salary of each type of work; based on a DBSCAN clustering radius range, performing clustering analysis in a man-hour-salary two-dimensional plane formed by the human cost data to obtain a preliminary classification result; when it is determined that the isolated data points exist based on the preliminary classification result, secondary classification is carried out on the isolated data points based on the distance between the isolated data points and the known clustering cluster, and a final classification result is obtained; and realizing partition storage of the cost data based on a final classification result. By implementing the method, optimized storage of the cost data of the power grid engineering system can be realized.

The invention discloses a kitchen waste pretreatment device, system and method, and relates to the technical field of kitchen waste treatment.The kitchen waste pretreatment device comprises a vibration conveying platform, under cooperation of a transmission coherence assembly, a multi-stage bevel gear transmission structure and a speed regulator are adopted, the stability and reliability of equipment operation are guaranteed, the transmission efficiency is improved, and energy consumption is reduced; and through linkage of an external PLC, parameters of crushing and drying links are automatically adjusted, efficient treatment of different garbage types is ensured, energy use is optimized, preliminary screening can be performed through an external pretreatment screening machine according to natural differences of garbage sizes and densities, the pressure of subsequent treatment links is relieved, the efficiency and precision of the whole process are improved, and the production cost is reduced. Further, under the cooperation of a high-definition camera shooting end and a visual inspection sensor, the grabbing mechanical arm is assisted by spectral analysis of a top puncture detection structure, the garbage type is effectively judged, misclassification is reduced, automatic operation is achieved, manpower dependence is reduced, and operation safety and continuity are improved.

The invention discloses a returned intelligent electric meter sorting assembly line monitoring system. The system comprises a feature fusion sorting module, a digital mapping control module, a chain type tracing management module, a state diagnosis module and an environment management and control module. The feature fusion sorting module collects ammeter data through an industrial camera array, a microphone array, a structured light scanner and an infrared thermal imager, and performs feature fusion classification; the digital mapping control module calculates sorting process control parameters by using a distributed control model and an optimization algorithm; the chain type traceability management module adopts a block chain technology to record data and realize information retrieval; the state diagnosis module performs equipment fault prediction in combination with a data prediction technology; the environment management and control module monitors key parameters of the sorting environment. According to the system, the automation and intelligence level of an electricity meter sorting assembly line is improved, and the problems that an existing system is low in sorting efficiency, lagged in equipment monitoring and the like are solved.

The utility model discloses a rice seed sorting system, and relates to the technical field of rice seed processing. The rice seed sorting system comprises a base, a shaking assembly and an adjusting assembly, a U-shaped frame is fixedly installed at the top of the base, a movable box is arranged on one side of the U-shaped frame, a sorting frame is arranged at the top of the base, the shaking assembly is arranged on the U-shaped frame, the adjusting assembly is arranged on the movable box, and the adjusting assembly comprises a threaded column, a movable plate and a limiting block. A movable plate is arranged on the threaded column, a limiting block is fixedly installed on one side of the movable plate, and the other side of the limiting block and the outer wall of one side of the sorting frame are fixedly installed. By arranging the adjusting assembly and adjusting the inclination angle of the sorting frame, the movement track and speed of the seeds on the sorting frame can be changed, so that the standing time and position of the seeds on the sorting frame are changed, the seeds and impurities can be effectively separated, the sorting accuracy can be improved, and the conditions of wrong sorting and missing sorting are reduced; the sorting efficiency is improved.

The invention belongs to the technical field of logging data processing and interpretation, and discloses a logging curve layered prediction method and system based on optimal window wavelet transform, and the method comprises the steps: carrying out the preprocessing of logging curves of a cored well and a non-cored well; carrying out artificial layering and sedimentary microfacies analysis on the natural gamma logging curve; respectively carrying out wavelet transformation on the natural gamma logging curve and the natural potential logging curve to obtain an optimal window size; layering the non-cored well according to the size of the optimal window; and merging the layering points to obtain an optimal automatic layering effect of the logging curve. According to the method, the optimal window size of the wavelet transform modulus maxima of the natural gamma GR curve and the natural potential SP curve is determined by using the loss function through the mutual comparison constraint of manual layering and automatic layering, layering is performed by using the GR curve and the SP curve, and finally, the layering post-processing is performed, so that the missing division and the wrong division are effectively reduced, and the optimal automatic layering effect is obtained.

The present invention belongs to the field of intelligent construction, and provides a construction site waste management method and system, wherein various types of Internet of Things sensors are arranged on the construction site to collect real-time data of waste; the component ratio of the current construction site waste is determined based on the first model according to the real-time data; the parameters of the second model are adjusted based on the component ratio to improve the recognition weight and processing priority of the high-proportion components; and the construction site waste is sorted according to the instructions generated by the second model. The above scheme can improve the efficiency of sorting construction site waste.

The invention discloses a text classification method and device. The method comprises the steps of obtaining a to-be-classified text set and a category set; inputting the plurality of categories into a Chinese word vector model to obtain a plurality of category vectors; for each to-be-classified text, performing data preprocessing on the to-be-classified text; converting the preprocessed to-be-classified text vector into a high-dimensional vector; for each category vector, calculating the cosine similarity between the category vector and the high-dimensional vector; screening out the category vector with the highest similarity from the similarity between the high-dimensional vector and each category vector, and marking the category vector as a target category vector; and adding the to-be-classified text corresponding to the high-dimensional vector to a category corresponding to the target category vector. In the vector conversion process, word information is fully analyzed, conversion is performed according to the word information, the cosine similarity between the category vector and the high-dimensional vector is calculated, category division is performed according to the similarity, wrong division caused by understanding deviation is reduced, and accurate text classification is realized.

This invention relates to the field of intelligent surveillance technology, and more particularly to an image data retrieval method and system based on surveillance big data. The method includes: extracting the visibility and color features of pedestrian key points from surveillance trajectory segments; calculating viewpoint similarity using the cosine similarity of visibility vectors combined with the ratio of frequency standard deviations; selecting comparable key points and weighting them by the minimum visible frequency; calculating appearance similarity by combining color differences; introducing time constraint parameters to optimize the initial clustering distance to obtain a significant clustering distance; clustering matching for the same pedestrian trajectory; and finally establishing a retrieval index to complete image data retrieval. This invention improves pedestrian information retrieval performance by extracting key point visibility and color information, fusing optimized viewpoint and appearance similarity to construct an initial clustering distance, and optimizing with time constraint parameters to avoid interference from multi-camera scenes.

This invention discloses a multi-view few-sample Android malware classification method based on optimal guidance matching, belonging to the field of information security technology. The invention includes constructing multi-view grayscale images, training a backbone network, optimal guidance matching classification, and dynamic fusion of multiple views. First, the method extracts permissions, APIs, components, and intent features of the Android application to construct multi-view grayscale images. Then, it trains a backbone network that integrates attention mechanisms and self-supervised rotation prediction to extract discriminative features with geometric structure awareness. Valid guidance samples are identified through optimal guidance matching, and category similarity scores are calculated. Finally, adaptive weights are generated based on view confidence, and the multi-view scores are dynamically fused to complete the classification. This invention improves the accuracy and robustness of malware family classification in few-sample scenarios, reduces noise interference, and is suitable for rapid and accurate identification of malware.

The present invention provides a method for accurate tracking of three-dimensional objects based on the fusion of regions and point clouds, belonging to the field of computer vision technology, and comprising: (1) establishing a sparse viewpoint model; (2) establishing a color model on the object projection contour; (3) using a search line to sample candidate contour points on the projection contour image; (4) proposing a new step function to improve the discrimination between the foreground and background areas near the contour; (5) fusing the color and distance information of the object contour to determine the matching object contour points; (6) weighting the energy function with a weight function, solving the pose according to the energy equation of all matching contour points; (7) after obtaining the transformation matrix of inter-frame motion, using it to initialize the model point cloud, and then solving the optimal pose by performing ICP precise registration on the model point cloud and the object point cloud, thereby achieving accurate tracking of the three-dimensional object. The method of the present invention can solve the problems of tracking offset and tracking failure of low-texture three-dimensional objects under complex backgrounds and occlusion.

The present invention relates to a slag waste treatment method based on image recognition, comprising the following steps: S1: arranging a plurality of high-resolution image acquisition devices above the slag waste conveyor belt, pre-processing the acquired images, splicing the images at different angles, and obtaining a real-time monitoring splicing image; S2: constructing a slag waste classification model based on a machine learning algorithm; S3: identifying different types of slags according to the slag waste classification model and the real-time monitoring splicing image, and performing preliminary sorting; S4: providing an image acquisition device at each classification collection point, obtaining a real-time image of the classification collection point, and performing classification confirmation based on a slag classification confirmation model; S5: performing secondary fine sorting based on the classification confirmation result, and correcting the recognition error through a feedback loop, and adjusting the sorting. The present invention can not only improve the accuracy and efficiency of slag waste identification and sorting, but also enhance the flexibility and adaptability of the sorting system, and ensure stable operation under different environments and conditions.

This invention relates to a remote sensing extraction method for pear orchard areas based on the Re-UNet model, which overcomes the shortcomings of inaccurate classification results and low efficiency in pear orchard area extraction from remote sensing images compared with existing technologies. The invention includes the following steps: acquiring a remote sensing image dataset; constructing the Re-UNet pear orchard area extraction model; training the Re-UNet pear orchard area extraction model; acquiring and preprocessing the remote sensing images of the pear orchard areas to be segmented; and obtaining the remote sensing extraction results for the pear orchard areas. Based on the UNet semantic segmentation model, this invention solves the overfitting problem that easily occurs in small datasets. It also incorporates spatial and channel attention mechanisms and a residual module, further enhancing the feature transfer and cumulative integration characteristics of pear orchard areas in high-resolution remote sensing images, effectively reducing the "salt and pepper" phenomenon and misclassification, and improving the overall segmentation accuracy.

The invention provides a self-attention website fingerprint identification method based on position target guidance, and the method comprises the steps: generating a fingerprint feature vector of a to-be-classified website through a neural network according to the network flow generated by a network user and based on an attention mechanism; for a traffic data set to be classified, firstly, the traffic is preprocessed by using a two-dimensional convolution model, then extracted features are recombined into a feature matrix with the position as the target orientation, and in combination with processing of convolution calculation and a self-attention mechanism, obtained new website fingerprint feature vectors are input into a classifier to be classified. In the processing process based on the attention mechanism, the generated fingerprint feature vector simultaneously has features in time and space, and the two features have relevance, so that the classifier also improves the classification accuracy. According to the method, intelligent identification of website traffic can be realized, and the method is of great significance to monitoring and management of anonymous network website access.

The present application relates to remote sensing image processing and geographic information system technical field, disclose a kind of based on lithology background and land use preprocessing Karst rocky desertification remote sensing interpretation method, comprising: S1: based on the logical constraint preprocessing of lithology background and land use, generate three types of output area, including: effective interpretation area, exclusion area, and non-karst area;S2: adaptive spatial preprocessing is carried out to effective interpretation area, using multi-scale fusion filtering and through the closed-loop adjustment driven by image quality index, output final preprocessing image;S3: in the effective interpretation area of final preprocessing image, using improved CART decision tree is classified;S4: the classified result is post-processed, and then the classified result after post-processing is spliced with the exclusion area and the non-karst area that have been assigned, to generate the final rocky desertification spatial distribution map.The present application significantly improves the accuracy and efficiency of Karst rocky desertification interpretation.

The invention discloses a data governance method based on big data classification, which comprises the following steps: identifying keywords and phrases in data, carrying out self-learning on the data by utilizing a machine learning algorithm, classifying the data, and adding corresponding labels to the data according to a classification result; setting access authority and login authentication for each user, and recording access behaviors and operation logs of the users; carrying out encryption processing on the sensitive data; carrying out desensitization processing on the sensitive data, and adopting a data integrity verification mechanism in a data transmission process; the abnormal condition in the data is monitored, the data flow is monitored, and the machine learning technology is utilized to prevent data leakage. According to the method, sensitive data can be more effectively protected, and the safety and efficiency of data management are improved.

The invention belongs to the technical field of text information monitoring, and discloses an information monitoring management system based on an intensive platform, which comprises a text information preprocessing module, a text information word segmentation module, a text information anomaly recognition module, a text information error correction module, a text information error correction quality analysis module and a text information error correction quality anomaly recognition module. According to the method, the forward word segmentation operation and the reverse word segmentation operation are respectively carried out when the information word segmentation operation is carried out, the appropriate text information is selected as the target text information word segmentation based on the word segmentation result, the correct combination mode of the vocabularies in the text can be more accurately recognized through the analysis mode, and the overall word segmentation accuracy is high. According to the method, after text information error correction is carried out, analysis and anomaly recognition are further carried out on the text information error correction quality, errors in the error correction process can be found and corrected in time through the analysis mode, and therefore the error correction accuracy is improved. And the accuracy and the reliability of the text information can be ensured.

The utility model discloses a cooking frame of a firewood rice dumpling cooking box, which relates to the technical field of rice dumpling production and processing and comprises a frame body, a cooking box and a steaming box, the frame body is provided with a first frame, a second frame, a first steaming partition plate, a second steaming partition plate and a third partition plate, the frame body is connected with the box body, and the steaming box is detachably mounted on the first frame or the second frame. Through the structural design of the cooking frame, the box body is divided into two spaces by the third partition plate, and then the box body can be divided into four spaces by combining the first steaming partition plate and the second steaming partition plate, so that rice dumplings can be placed in each space according to actual requirements, extrusion caused by excessive accumulation of the rice dumplings is reduced, and the rice dumplings can be conveniently cooked. Different kinds of zongzi can be placed in each space, wrong classification caused by the fact that different kinds of zongzi are stacked and cooked together is reduced, working efficiency is improved, the zongzi which is cooked in a waterproof mode and stewed in a water soaking mode can be provided at the same time, and the zongzi is subjected to waterproof heat preservation while being cooked.

The invention belongs to the technical field of image classification, and particularly relates to an open world image classification method based on adaptive contrast learning. The method comprises the following steps: firstly, training a pre-training classification model by using source domain data to obtain a source domain training classification model; clustering the feature vectors extracted from the source domain samples by the pre-training classification model to obtain a clustering center of each category; then, training the source domain training classification model during testing by using target domain data, enhancing the target domain data in the training process, and optimizing the model through comparative learning of a sample pair; meanwhile, training and optimizing the model through a category pair, a strong OOD category clustering center and an alignment pair of feature distribution of a source domain and a target domain; and finally, the cosine similarity between the feature vector extracted from the target domain sample by the source domain training classification model and each target domain category clustering center is calculated, and the target domain category corresponding to the maximum cosine similarity is the prediction category label of the target domain sample. According to the method, the adaptability and the recognition capability of the model to unknown categories are improved, the classification stability can be kept when domain transfer and unknown categories of samples are encountered, and misclassification is effectively reduced.

The invention provides a branch and leaf separation method and system for vegetation point cloud around a power transmission line, and relates to the technical field of data processing. According to the implementation scheme, based on the curvature of each point cloud in surrounding vegetation point clouds of a target power transmission line, the surrounding vegetation point clouds are divided into a first branch point cloud set and a first leaf point cloud set; performing connected domain segmentation on the first branch point cloud set to obtain a plurality of connected components; dividing the first branch point cloud set into a second branch point cloud set and a branch and leaf mixed point cloud set based on the linear feature significance degree of the geometric distribution feature of each connected component; taking each branch point in the second branch point cloud set as a seed point, and dividing the branch and leaf mixed point cloud set into a regional growing point cloud set and a non-regional growing point cloud set through regional growing; determining a target branch point cloud set based on a union set of the second branch point cloud set and the regional growing point cloud set; and determining a target leaf point cloud set based on a union set of the first leaf point cloud set and the non-regional growing point cloud set.

The present invention relates to the field of neural network technology, and in particular to a method and system for classifying marine organisms based on graph neural networks. The method comprises acquiring point cloud data of marine organisms; constructing a three-dimensional grid model based on the acquired point cloud data, and extracting key morphological features using the three-dimensional grid model; training the graph neural network using the extracted key morphological features; and performing classification prediction based on the trained graph neural network. The present invention can capture the fine three-dimensional structural features of marine organisms by combining high-precision three-dimensional modeling and advanced graph neural network technology, thereby significantly improving the accuracy of classification.

The application provides an image recognition method and device, electronic equipment and storage medium, wherein the method comprises: acquiring an image to be recognized; performing local feature extraction on the image to be recognized to obtain local features; constructing a global feature representation of the image based on the correlation between each block in the local features and the position information of each block to obtain the global feature representation; and performing recognition on the global feature representation to obtain an image recognition result. The image recognition method and device, electronic equipment and storage medium provided by the application can construct a global feature representation of the image based on the correlation between each block in the local features and the position information of each block, and the global feature representation obtained thereby has the representation ability of local subtle features and the representation ability of global semantic information. Image recognition based on the global feature representation can reduce misclassification and improve the accuracy of recognition.

The invention discloses a fine-grained visual classification method and system based on a self-evaluation classifier, and relates to the technical field of computer vision and pattern recognition, and the method comprises the steps: collecting image data, and carrying out the preliminary classification of the preprocessed image data through a coarse-grained classifier; constructing a joint representation generation method, and performing joint representation on an output result of the word embedding method and the preprocessed image data; and based on the joint representation result, carrying out secondary evaluation on the preliminary classification result to generate a fine-grained classification result, and removing a region rich in useless background information and fine-grained classification useless information. According to the method, through the self-evaluation classifier, wrong classification of fuzzy categories is effectively reduced, and the classification accuracy is improved; a word embedding method and a feature map are used for joint representation, and the ability of the model to distinguish fuzzy categories is enhanced; a new Droping method is provided, and the classification effect is further improved by recognizing and removing areas causing wrong classification and useless background noise through an attention map.

The application relates to the technical field of animation design material management, in particular to an animation design material library management system based on big data, which comprises a material collection module, a preprocessing standardization module, an intelligent clustering archiving module, a network construction module, an intention recommendation module and a dynamic optimization module. The system collects multi-source heterogeneous animation original data, carries out standardization processing, and automatically classifies and archives the data according to visual and semantic correlation degrees by using an improved clustering algorithm. On this basis, a multi-level semantic correlation network containing derivation, co-occurrence and style subordination relations is constructed. The network supports an intelligent recommendation process to respond to user creation intention description and output an ordered list of associated materials. The system also dynamically optimizes clustering parameters and network connection strength by analyzing user usage behavior. The application realizes deep semantic organization and intelligent recommendation of materials, and improves the management and utilization efficiency of animation materials.

The invention discloses a defect detection method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a first image; performing defect detection on the first image through a first model to obtain a first result; a first loss function is configured in the first model; the first loss function can instruct the first model to distinguish foreground feature information and background feature information of the first image. According to the method, the defect detection is performed on the first image through the first model, and the first loss function is configured in the first model, so that the foreground feature and the background feature of the first image can be aligned, and the feature consistency can be ensured, and therefore, the use of the first model can realize the automatic detection of the metal oxidation surface, and the detection efficiency is improved. And the defect identification precision of complex textures and edge areas on the metal oxidation surface can also be improved.

The invention discloses a method for automatically filing approval files, and relates to the technical field of data management. Comprising the steps that 1, a MySQL database is used for creating a workflow related table which comprises a flow historical record table, a flow application form, a flow basic framework table, a user table, a role table and a configuration table, and 2, role permission corresponding to a user is maintained, specifically, whether the user has the corresponding role permission or not is checked, the user is authorized through the role, and the user is authorized through the role; 3, initiating an approval filing process of the project file, filling related project file information according to the workflow related table, checking the accuracy and authenticity of the project file information, entering the approval filing process, filing the project file, and ending the approval filing process.

The invention discloses an unmanned parcel supply system which improves parcel supply quality and efficiency and reduces cost and energy consumption. According to the technical scheme, a narrow-band machine is combined with a light curtain to center express items, and narrow bands are adopted for distribution. Due to the fact that the surface of the narrow band is smooth, piece jumping or stall cannot be caused, the control effect is improved, the piece supply quality is improved, and wrong sorting is reduced. Only one set of single-channel stacked part separating device and one set of single-piece separating device are needed to be matched with one set of narrow band, so that the cost can be greatly reduced, the energy consumption is reduced by half, the noise is low, the efficiency is high, and the part supply quality is greatly improved.

The utility model relates to express sorting technical field, concretely relates to express sorting mechanism and express sorting device, and express sorting mechanism includes installation main part, sorting swing arm, drive assembly and blowing assembly, and installation assembly includes first mounting plate and second mounting plate who sets side by side, and sorting swing arm rotates and sets up on first mounting plate and rotates the direction along vertical direction setting, drive assembly is used for drive sorting swing arm and swings in horizontal direction, blowing assembly is used for like sorting swing arm front blows out high pressure airflow. Through drive assembly control sorting swing arm swings in horizontal direction, can realize the guidance and the operation of the dispatch of express package, cooperate blowing assembly and blow out high pressure airflow to the front, when meeting light and small express piece such as thin body piece, through blowing assembly can blow it up, cooperate sorting swing arm and turn it to dispatch, effectively reduce the reduction of the occurrence of the mistake or the leak.