15 results about "Form classification" patented technology

The invention discloses a composite structure damage form monitoring method and system based on deep learning, and the method comprises the following steps: collecting multi-source monitoring data of a composite structure in a loaded state, and carrying out the preprocessing; reconstructing a damage evolution trajectory in a high-dimensional phase space by adopting a delay coordinate embedding method, and executing dimension reduction to generate a chaotic dynamics low-dimensional trajectory; extracting singular attractor features, and generating a singular attractor feature set; carrying out sequence modeling through an improved Linformer damage identification network, and generating a prediction vector; training an improved Linformer damage identification network based on the prediction vector, and introducing nonlinear dynamic constraints to generate a damage identification network of the nonlinear dynamic constraints; and performing damage form classification and damage evolution prediction. According to the method, dynamics and deep learning are fused, composite structure damage monitoring is achieved, and the method has the advantages of being high in accuracy, high in stability and reliable in early warning.

The invention discloses a water pollution cross-domain traceability method based on multi-chain collaboration and stable isotope source analysis, and relates to the technical field of water environment monitoring, the method comprises the following steps: pollution source chemical fingerprint construction: collecting an environmental water sample and a potential pollution source sample in a drainage basin; adopting a mass spectrometer to detect stable isotope compositions such as delta 13C, delta 15N, delta 18O and the like in the pollutants; and performing morphological division on the pollutants by using a morphological grading extraction technology. Homology judgment precision is greatly improved through isotope and morphological analysis; cross-chain credible collaboration is achieved, multi-party data interaction is achieved, and timeliness and accuracy of pollution tracing are guaranteed; the space-time model traceability is combined with an evolution path and gridding mapping, so that the pollution inversion accuracy is improved; and ecological compensation support provides a quantitative basis for pollution intensity and diffusion range so as to support a compensation model.

The invention relates to the technical field of big data management, in particular to a bioinformatics data classification method and system based on big data, and the method comprises the following steps: obtaining time sequence recognition trend reversal and positioning fragments, extracting recognition difference positions inside and outside a frequency band data division region, screening samples with consistent features, and rearranging path labels; connecting nodes are cut off to generate fracture indexes, and label states are updated and written into sample fields to form a classification result set. According to the method, a labeling area is constructed by extracting trend inversion points in a time sequence, sample fragments are divided by combining data fluctuation positions in a disturbance frequency band, label numbers are arranged according to the fluctuation sequence of samples in a path, a corresponding sequence of a label chain connection relation and the sample positions is established, and label section boundaries are positioned and limited by fracture nodes. And the updated label state is synchronously written into a sample field, and the path label is bound according to a chain sequence, so that the sample identifier is kept coherent in sequence change, and the continuous coverage capability of the path information in classified output is improved.

The invention discloses an intelligent form tool for data management. The intelligent form tool comprises the following steps: step 1, collecting historical form data and preprocessing the historical form data; 2, calculating the preprocessed historical form data to obtain a feature vector of each lexical item in the form; 3, performing clustering analysis on the feature vectors based on a clustering method to obtain a form classification result, and establishing a classification label library; 4, according to a keyword input by a user, through a similarity calculation method, performing matching to obtain candidate target classifications; 5, recommending an optimal form template from the target classification in combination with user information; and step 6, carrying out dynamic optimization on the recommended optimal form template. According to the method, the problems that in an existing form system, classification depends on manual annotation, and template recommendation accuracy is low are solved, form creation efficiency and data standardization degree are remarkably improved, and the method is suitable for data governance requirements in a multi-service scene.

The molecular cloud block mass form classification method based on geometric moment parameters comprises the following steps: preprocessing original three-dimensional PPV data so as to accurately extract and analyze block masses in molecular cloud; performing block mass detection to obtain block mass parameters; obtaining intensity data of each block mass area by using the original three-dimensional PPV data matrix; obtaining a two-dimensional integral intensity graph of each block mass; calculating a second-order central moment of each block mass; constructing a covariance matrix for each block mass; constructing the deviation degree of each block mass; calculating a classification parameter L of each block mass; according to the classification parameter L value, classifying the forms of the block masses; and outputting the category and morphological parameters of each block mass. According to the method, human intervention and subjective deviation can be reduced, accurate and efficient automatic form classification of the molecular cloud mass is realized, and molecular cloud mass samples in different forms are provided for researching initial conditions of fixed star formation.

The invention discloses a garbage classification and carbon emission reduction collaborative optimization method based on deep learning, and the method comprises the following steps: S1, collecting garbage images and weight data, generating image tensor and quality data, and setting carbon emission parameters; s2, constructing an image recognition model comprising a convolutional coding module, a self-attention module, a gating unit module, a residual learning module and a graph structure fusion module; s3, inputting an image tensor to generate a feature tensor; s4, generating an initial classification label and confidence, and forming classification quality data in combination with the quality data; s5, classification quality data and carbon emission parameters are input, and a datum line emission amount is calculated; s6, calculating the project discharge amount; s7, calculating carbon emission reduction, and generating a disturbance vector; s8, feeding back the disturbance vector to update the image recognition model, and generating updated classification quality data; and S9, circularly executing optimization, and outputting a final classification label, a processing path and a carbon emission reduction amount. According to the invention, linkage optimization of garbage classification and carbon emission is realized.

The invention discloses a cable channel entrance and exit positioning safety detection method and system, and relates to the technical field of underground facility intelligent operation and maintenance, and the method comprises the steps: employing MGWR to position an entrance and exit, obtaining a feature vector through convolution dimensionality reduction, employing MSFD decomposition to combine SA-Center Net and TAFM fusion output form classification, building a space-time path through SGS-HS optical flow and an attention mechanism, and carrying out the detection of the entrance and exit positioning safety. The state is inferred through GraphSAGE-CRF, the risk is evaluated by using STBM, and an alarm is triggered. According to the method, the MGWR model is used for predicting the entrance and exit coordinates, multi-scale feature decoupling and fusion are combined, the space-time heterogeneity and adaptability of coordinate prediction are improved, the risk index is calculated through space-time path space construction, GraphSAGE optimization state sequence and the STBM model, the accuracy of opening state marking is improved, and multi-channel risk assessment and immediate intervention are achieved.

The application discloses a road cavity morphology classification method based on GPR data, which comprises the following steps: performing road cavity three-dimensional modeling simulation and obtaining three-dimensional GPR data; extracting and jointly representing GPR morphology information; constructing a cavity morphology classification initial model based on a relation network and training to obtain a cavity morphology classification model; and using the cavity morphology classification model to perform road cavity morphology classification on an actual road. The application deeply mines the internal connection and imaging rules between the cavity morphology and the GPR detection data, explores the cavity morphology interpretation mechanism, uses the relation network as the main architecture of the few-sample learning network, and improves the relation network. The method has the advantages of high reliability and good accuracy in realizing the accurate classification of the road cavity morphology under the premise of less data support.

The application discloses a composite structure damage form monitoring method and system based on deep learning, comprising the following steps: collecting multi-source monitoring data of the composite structure under the loaded state, and performing pretreatment; adopting a delay coordinate embedding method to reconstruct a damage evolution trajectory in a high-dimensional phase space, and performing dimension reduction to generate a low-dimensional trajectory of chaotic dynamics; extracting a singular attractor feature to generate a singular attractor feature set; performing sequence modeling through an improved Linformer damage identification network to generate a prediction vector; training the improved Linformer damage identification network based on the prediction vector, and introducing a nonlinear dynamics constraint to generate a damage identification network with the nonlinear dynamics constraint; and performing damage form classification and damage evolution prediction. The application combines dynamics and deep learning, realizes composite structure damage monitoring, and has the advantages of high accuracy, strong stability and reliable early warning.

The invention discloses a fluidity grading method and system based on morphological characteristics of milk powder. The method comprises the following steps: obtaining fluidity indexes of training samples, constructing a PCA model, and forming grading labels in a principal component score space; the method comprises the following steps: acquiring a microscopic image of to-be-detected milk powder, preprocessing, extracting nine types of two-dimensional shape factors, and summarizing sample layers by using a median; respectively training a random forest and XGBoost to carry out regression prediction on the mobility index; modeling is carried out on the PCA hierarchical labels, and synthetic minority oversampling is introduced to improve coverage and robustness; in the inference stage, good / medium / difference grading is completed only based on images, compared with a process depending on a single instrument and subjective interpretation, the method integrates multi-index information, is objective and reproducible, has the advantages of being non-destructive, rapid, available for small samples and the like, can be suitable for online quality control and inter-batch consistency evaluation of large-scale production, and improves the quality control efficiency and stability.

This invention discloses a method, apparatus, and device for intelligent identification and classification of microplastics based on multi-dimensional collaboration. The method includes: acquiring optical microscopic images and auxiliary modal data of the water sample to be identified; performing multi-modal encoding and fusion processing to obtain a fused feature vector; obtaining global contextual features through global attention sequence encoding; obtaining a microplastic segmentation probability map through network decoding; generating a morphological topology map based on the segmentation probability map; extracting graph structure information to obtain global topological features; calculating geometric statistical features; and fusing global contextual features, global topological features, and geometric statistical features for prototype matching to obtain a morphological classification result. Because this invention utilizes optical and auxiliary modal information uniformly through multi-modal fusion, explicitly models the skeleton and contour relationships based on the topology map to overcome local texture misjudgment defects, and combines multi-feature prototype matching, it effectively improves the accuracy of microplastic identification and classification under complex morphological conditions and uneven sample distribution.

The invention discloses a DTW and SVM fused daily runoff process form classification method. The method comprises the steps of obtaining a hydrological year historical daily runoff sequence and a corresponding hydrological year type; smoothing the historical daily runoff sequence of the hydrological year to obtain a daily runoff smooth sequence; performing normalization processing on the daily runoff smooth sequence to obtain a daily runoff normalized sequence; calculating a correlation coefficient matrix of the daily runoff normalization sequence by using a Pearson correlation coefficient, and calculating a distance matrix of the daily runoff normalization sequence by using DTW; converting the distance matrix into a kernel matrix based on an improved Gaussian kernel and a correlation coefficient matrix; training an SVM model based on the kernel matrix and the hydrological year type to obtain a trained SVM model; and through the trained SVM model, determining a hydrological year type to which the runoff sequence in the future day belongs. The problem of insufficient classification identification degree in the prior art can be solved.

The invention relates to the technical field of image processing, in particular to an intestinal polyp recognition method, system and product based on recognition model assistance, and the method comprises the following steps: S1, obtaining an image of the inner wall of an intestinal tract; s2, identifying and marking a target polyp range on the image through a polyp detection model; s3, inputting the image into a depth map prediction model to obtain a depth map of the target polyp; s4, obtaining edge information and real size information of the target polyp based on the target polyp range and the depth map of the target polyp; and S5, inputting the target polyp image information in the plurality of preset directions into the polyp form prediction model, outputting the form type of the polyp form, and calculating and outputting the volume of the polyp according to the form type of the polyp form. According to the application, accurate recognition, form classification and volume estimation of the polyp can be realized based on an artificial intelligence neural network computer-aided detection system through the intestinal endoscope image.

The invention discloses a microplastic multi-dimensional feature high-throughput rapid detection method, which is characterized in that microplastic multi-dimensional feature synchronous detection is carried out by coupling microimaging, Raman spectrum, deep learning and machine learning, so that full-process automatic processing, multi-modal data fusion synchronous characterization, standardized data integration and report generation are realized; and a structured report containing morphological characteristic parameters can be automatically output, cross-research data comparison is supported, and detection standardization is promoted. Meanwhile, the detection efficiency is remarkably improved; the detection time of a single sample is shortened to 15 minutes; the detection precision is comprehensively guaranteed: the material identification accuracy is greater than or equal to 95%; the form classification accuracy is greater than or equal to 92%; a volume prediction average error lt; 40%; and multi-dimensional synchronous analysis capability: synchronously outputting a plurality of core morphological characteristics in a single process for the first time, and avoiding sample loss and data deviation caused by step-by-step detection. And a reliable tool is provided for micro-plastic pollution mechanism research and environment monitoring, so that the method has a wide application prospect.

The invention relates to the technical field of biology, and discloses a sperm morphological analysis method and system based on visual enhancement and knowledge reasoning, and the method comprises the steps: obtaining a sperm microscopic image, extracting a global feature map and generating a candidate region through a sperm region detection and feature extraction module, extracting independent features, and obtaining a sperm microscopic image; the visual relation enhancement module constructs a visual relation matrix to capture recessive relevance between candidate areas, and generates enhanced features through neighborhood feature weighted fusion; the knowledge guidance analysis module performs semantic reasoning on enhanced features in combination with visual features and a knowledge graph based on sperm morphology, performs reasoning on attribute categories, and optimizes reasoning results through a neighborhood propagation mechanism to obtain semantic attribute features; and the refined classification module of the fusion features performs global form classification and fine-grained attribute classification of each sperm part on the sperm instances according to the enhanced features and the semantic attribute features. According to the method, overlapped sperms in a complex background can be processed, and the capability of analyzing and explaining sperm morphology is improved.