636 results about "Binary classification" patented technology

The invention discloses an industrial equipment fault detection method fusing a complex relation and space-time dependence, and belongs to the technical field of industrial anomaly detection, and the method comprises the steps: constructing a plurality of adjacent matrixes, carrying out the weighted fusion to form an enhanced adjacent matrix, and comprehensively and accurately describing the complex multi-dimensional relation between industrial equipment; designing a spatial-temporal feature extraction module, extracting spatial features in parallel by using a graph convolutional neural network and a random graph attention network, extracting time features through time convolution and a multi-head attention mechanism, and dynamically fusing the spatial-temporal features by means of a gating mechanism to generate graph-level features; a state judgment layer composed of a plurality of node-level binary classifiers and a voting mechanism are adopted to comprehensively judge classification results of all nodes, so that the stability and reliability of judgment of the overall state of the industrial control system are enhanced, and the risk of misjudgment is reduced; the problems of equipment relation modeling and multi-dimensional information fusion are effectively solved, features are extracted and fused more accurately, and the accuracy and adaptability of anomaly detection are improved.

A GNSS positioning slowly-varying fault detection method based on residual-SVR regression comprises the steps that an observation information sequence is acquired based on a Kalman filter, and a covariance matrix of the observation information sequence is calculated; accumulating multi-step information through a sliding window, and constructing chi-square statistics; based on the fault-free data, constructing a training set by taking an innovation sequence as input and chi-square statistics as output, and generating an innovation-statistics mapping function; and fitting a normal slope threshold value based on an SVR predicted value, carrying out least square fitting on an observation statistic curve by sliding a window in real time, and judging whether to start a slow change fault alarm or not. According to the method, the residual error sequence is directly used as model input, and the dynamic chi-square statistical magnitude is used for replacing a traditional dichotomy label, so that the detection delay is reduced; an SVR detection model based on grid search and cross validation collaborative optimization is utilized, and an optimal parameter combination of a minimum mean square error (MSE) is screened through logarithm uniform sampling, interval linear sampling and five-fold cross validation, so that the average absolute error of slowly varying fault detection is reduced.

The invention discloses an emotion recognition method and system based on cross-modal alignment and matching enhancement. According to the method, firstly, feature extraction is carried out on text, audio and video modalities in a data set, and then a text and audio cross-modal emotion alignment module and a text and video cross-modal emotion alignment module are constructed respectively, so that cross-modal semantic alignment is realized. Constructing an emotion label matching module based on an alignment result, generating modal pairs with similar emotions but different labels by using a difficult negative sample mining strategy, and paying attention to cross-modal emotion consistency through a dichotomy task guide model; performing modal feature fusion on the three modals through a six-layer attention crossing mechanism, finally splicing feature vectors, inputting the spliced feature vectors into a long-sequence context fusion modeling module for deep modal fusion, and capturing cross-modal interaction information; and the fused features are sent to an emotion classification module, and a final emotion category recognition result is output.

The invention discloses a data collaborative directory management method and system, and relates to the technical field of government affair informatization, and the method comprises the steps: generating a directory snapshot containing a global hash value; calculating a directory node hash value of each edge directory node based on the directory snapshot identifier; eliminating clock drift interference through time sequence alignment and dynamic tolerance filtering; inputting the Hash difference time sequence into an isolated forest model to judge a substantial change node; based on the difference entry number and the historical calling weight, combining a dual-threshold rule and an online dichotomy model to hierarchically synchronize requirements; according to a grading result, matching an incremental push mode or a full pull mode, and constructing a synchronous transaction context containing an exponential backoff retry mechanism; synchronous operation is executed through the two-stage state model, and compensation rollback is triggered when the synchronous operation fails; and calculating a health index of the substantially changed node, dynamically selecting a self-healing action and optimizing system parameters. The problem of misjudgment caused by time sequence drift is effectively solved, the synchronization efficiency is improved, and the consistency of directory versions is guaranteed.

A disease and pest recognition and prediction algorithm based on deep learning, the algorithm comprising the following steps: step 1, selecting a material; step 2, constructing a disease and pest species data set; step 3, constructing a disease and pest recognition model; step 4, establishing a binary classification model; step 5, extracting image features from a multi-classification deep learning model, i.e., constructing a feature library; step 6, building an automatic disease and pest recognition system; step 7, constructing a disease and pest occurrence rule data set; step 8, constructing a disease and pest prediction model; step 9, correcting and updating a model; step 10, constructing a disease and pest recognition and prediction system; and step 11, constructing a disease and pest early-warning model.

The invention provides a multi-receptive-field network abnormal flow detection method and system based on endogenous security attributes, and relates to the technical field of network security and abnormal flow intelligent detection. The method comprises the following steps: firstly, performing multi-scale flow representation, preprocessing and data enhancement on network flow data to obtain enhanced input flow data; local features are extracted through basic convolution, and local and global fusion features are obtained based on a double-branch network comprising a multi-receptive field convolution branch and a Mama-self-attention branch; deep fusion representation is formed through multi-round feature extraction and tensor fusion, and finally binary classification and fine-grained classification results are output through global pooling and a linear classification layer. According to the method, high-precision, high-robustness and high-real-time detection of the abnormal traffic of the complex network is realized under low calculation overhead.

The invention provides a user credit scoring method and system based on multi-source behavior data, and the method comprises the steps: obtaining user behavior data, so as to construct a standardized multi-module behavior event record; taking a user as a unit, using the behavior event record as a node to construct a behavior graph, and constructing a directed edge according to a timestamp of event occurrence; based on the behavior map, constructing a user behavior path by using a constrained maximum edge weight path search algorithm; performing structure matching on the high-credit user behavior path template set and each user behavior path, calculating a path deviation degree of each user behavior path, and obtaining a minimum deviation score; and in combination with the minimum deviation score and the matched optimal matching path, predicting a risk tag of the user through a credit scoring model based on dichotomy learning, and outputting a customer credit score.

The invention relates to the technical field of image detection, in particular to a satellite and unmanned aerial vehicle cooperative remote sensing image change detection system and method, and the method comprises the steps: obtaining a dual-time-phase satellite remote sensing image and an unmanned aerial vehicle remote sensing image of the same geographic region; the satellite branches are subjected to four-stage convolution-pooling operation, multi-stage wide-area features are output, and the unmanned aerial vehicle branches output high-resolution local features aligned with the satellite branches in space through the backbone network; performing channel interaction operation on each stage of double-time-phase features, and generating a spatial weight map to strengthen a change region; aligning adjacent level resolutions through transposition convolution, dynamically integrating multi-scale features, and outputting optimized fusion features in combination with residual connection; and performing convolution dichotomy on the fused features to generate a change detection result graph. According to the method, the problem of signal attenuation caused by heterogeneous data feature mismatch is effectively solved, the false alarm rate caused by environmental interference is remarkably reduced, and the complete detection capability of a micro-to-macro full-scale dynamic target is synchronously improved.

The invention discloses an alarm analysis method, device and equipment, and the method comprises the steps: determining a target DAG matched with a target alarm analysis scene from a preset directed acyclic graph DAG; the DAG comprises a plurality of rule judgment nodes; the rule judgment nodes comprise at least one natural language rule judgment node, and each natural language rule judgment node is used for calling a large language model based on a preset prompt text to perform binary classification judgment on the input alarm data; inputting the alarm data into the target DAG, and generating a node hit track of the alarm data; the node hit track is used for recording a rule judgment node triggered by the alarm data and a corresponding judgment result; and obtaining an alarm analysis result based on the node hit trajectory. According to the invention, the automatic, interpretable and traceable research and judgment process of the alarm can be realized, and the accuracy, interpretability and maintenance convenience of alarm analysis are improved.

To provide a method for predicting a strain distribution map and determining damage based on deep learning.SOLUTION: The present invention comprises: a step S1 of establishing an image dataset of finite element analysis results for strain distribution map prediction; a step S2 of building a deep learning model for strain distribution map prediction based on a DeepLabv3+ network and performing learning and validation; a step S3 of establishing a dataset for damage determination of a structural analysis model and performing preliminary processing and data enhancement operations; a step S4 of building a binary classification deep learning model for damage determination based on a convolutional neural network and performing learning and validation for transition learning; and a step S5 of performing an interpretability analysis on the learned binary classification structural analysis model and outputting a region in an image that more contributes to classification.SELECTED DRAWING: Figure 1

The invention discloses a text classification method, electronic equipment and a storage medium, and relates to the technical field of data processing, which comprises the steps of improving the input quality through noise filtering and standardization processing in a word segmentation stage, realizing accurate numerical mapping of semantics by means of an embedded matrix in a vector conversion stage, and improving the input quality. The semantic association between word segmentation units is analyzed and quantified into a weight matrix through interactive operation and normalization processing of query vectors and key vectors, value vectors are subjected to weighted fusion through the weight matrix, comprehensive features containing global contexts are obtained, and a result is output through pooling compression and a dichotomy model. By optimizing matrix operation logic and reducing redundant information processing, the technical problems of high calculation complexity, insufficient expandability and insufficient real-time performance caused by dense matrix operation in a large-scale text classification task are solved, and the purposes of improving the semantic comprehension accuracy and improving the text classification efficiency are achieved while the semantic comprehension accuracy is guaranteed. The text classification efficiency is obviously improved, and the applicability of the model in a large-scale scene is enhanced.

The invention relates to the technical field of message recovery, and discloses an AI-based iOS device message recovery method and system, and the method comprises the steps: connecting an iOS device, positioning a database file of a message application, and obtaining a database file path list; reading an equipment storage sector, creating a physical mirror image, and analyzing a free block position mark in an APFS container super block; binary classification identification is carried out through an AI data block classification model, an SQLite page type label and a page mapping relation are obtained, deleted messages of a recombined database and a device end are determined, differential comparison is carried out on all historical backup snapshot records of an iCloud account, and cloud end deleted messages are obtained; according to the method and the system, the reorganization success rate of the database and the extraction integrity of the deleted messages are improved, and a technical solution is provided for comprehensive, accurate and efficient recovery of the iOS device messages.

A system and method for facilitating training of large language model based recommender systems are provided. The system may utilize one or more LLMs to create probability distributions for binary classification tasks associated with specific user-item pairs. The probabilities may be utilized to rank one or more tasks directly. The training of the one or more LLMs may involve the use of Knowledge Distillation methods and may be based on incorporating a dual-label system such as, for example, hard labels and soft labels. The one or more LLMs training data may consist of user-item pairs and their corresponding features. The labels used in the training process may include binary classification labels and their respective probabilities. The system may further implement the trained one or more LLMs to determine rankings or recommendations associated with user engagement of one or more content items.

The invention discloses a model training method and device, a storage medium and a computer program product, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a weight positive sample training set and a weight negative sample training set, the weight positive sample training set and the weight negative sample training set are obtained by labeling and screening a preset unlabeled sample set; based on a preset unbiased positive sample and unmarked sample learning algorithm, according to the weight positive sample training set and the weight negative sample training set, training to obtain a dichotomy model; iterating the dichotomy model through a loss function and a regular function corresponding to the dichotomy model based on a preset expectation maximization algorithm until the dichotomy model converges, and obtaining a converged dichotomy model; and taking the converged dichotomy model as a prediction model. The problems that an existing method is poor in universality and generalization and needs to label samples manually are solved, and the universality and generalization of model training are improved.

The invention belongs to the technical field of medical image intelligent diagnosis, and provides a breast cancer focus benign and malignant discrimination method based on a gated multi-expert mechanism. The method comprises the following steps: firstly, carrying out standardization and semantic preprocessing on a mammary gland X-ray image, a BI-RADS imaging report and structured clinical data, embedding age, mammary gland density and focus position information into a text template in a natural language form, and realizing unified expression of multi-modal input; secondly, extracting image features by utilizing a ResNet network and a simplified CLIP model, obtaining a text semantic vector by adopting a Bio-ClinicalBERT model, and establishing two sub-paths of a lump expert and a calcification expert in a Transform structure; further, an expert weight is dynamically generated through a gating routing mechanism, and soft routing fusion is executed; and finally, outputting benign and malignant results of the breast cancer focus by the binary classification module. According to the method, deep fusion and dynamic collaboration of the mammary gland X-ray image, the BI-RADS text and the clinical information are realized, and the accuracy and interpretability of breast cancer discrimination can be remarkably improved.

The invention relates to the technical field of clinical diagnosis, in particular to a cervical cancer risk diagnosis system. The image processing module is used for extracting image features based on a preprocessed cervical cell pathological image; the text processing module is used for extracting text features based on the preprocessed medical record book data; the feature alignment module is used for aligning the image features and the text features to obtain the aligned image features and text features; the feature fusion module is used for fusing the aligned image features and text features by using a mutual attention mechanism to obtain fused features; and the prediction module is used for predicting the cervical cancer onset risk of the target patient by using a preset tumor risk prediction model based on the fusion features to obtain a dichotomy prediction result. Therefore, through the cervical cancer risk diagnosis system, the problem of low screening accuracy caused by manual limitation or difficulty in multi-modal data fusion in the existing diagnosis technology is solved, and the accuracy of early screening of cervical cancer is improved.

The embodiment of the invention relates to a processing method and device for predicting ejection fraction retention type heart failure. The method comprises the steps that a deep learning model used for conducting dichotomy prediction on the ejection fraction retention type heart failure is constructed and recorded as a first prediction model; constructing a model data set and recording the model data set as a first data set; training the first prediction model based on the first data set; after model training is finished, heart structure feature analysis, heart hemodynamic feature analysis and left ventricle vortex time sequence feature analysis are conducted on four-dimensional blood flow heart magnetic resonance imaging data input by a user to obtain a corresponding structure feature set, and the hemodynamic feature set and the vortex time sequence feature form a corresponding first feature data set; and inputting the first feature data set into a first prediction model for prediction to obtain a corresponding first classification vector which is fed back to the current user. According to the invention, the prediction accuracy and the prediction efficiency can be improved.

The invention discloses an improved hemodialysis data generation method and system based on a generative adversarial network, and relates to the technical field of medical data generation and artificial intelligence, and the method comprises the steps: 1, collecting hemodialysis multivariable time series data; 2, mapping the original time sequence data to a low-dimensional potential space through an embedded network; original data are reconstructed from the low-dimensional potential space through the recovery network; step 3, a multi-scale attention mechanism is introduced into the generator, weights are dynamically distributed by calculating the similarity of query vectors and key vectors, and weighted context vectors are generated in combination with value vectors; the discriminator executes a dichotomy task and introduces a dynamic gradient clipping strategy: if the norm of the gradient L2 exceeds a preset threshold value c, scaling the gradient according to a proportion, otherwise, keeping the original value; 4, introducing a supervision loss function, and learning a time sequence dynamic rule through an autoregressive prediction task constraint generator; and 5, jointly optimizing the reconstruction loss, the confrontation loss and the supervision loss, and outputting synthetic hemodialysis data.

A bot detection service associated with an overlay network operates to score traffic as a probability of being a bot, as opposed to returning a binary classification (i.e., bot or human). According to the approach herein, scoring is determined through probability estimates, wherein a score (the probability) is based on considering a set of detections concurrently. In one embodiment, all (or substantially all) triggered (current) threat detections contribute to the score. The preferred approach penalizes requests that fail all (or substantially all) combinations of detection algorithms. According to a further feature, an automated tuning (autotuning) is also applied, e.g., using real-time empirical statistical models, to adapt the measurement of false positive probability for one or more threat detection algorithms to suit customer traffic trends. The approach herein is also extensible to include any number of future threat detection algorithms.

The invention relates to a tumor classification model training and using method and device, equipment, a medium and a product. The method comprises the following steps: acquiring an organ description sample of a sample user; based on a feature extraction network in the tumor classification model, extracting sample feature data of the organ description sample; based on a dichotomy network in the tumor classification model, according to the sample feature data, determining prediction probability distribution of the sample user under the dichotomy network; and based on a three-classification network in the tumor classification model, according to the sample feature data, determining prediction probability distribution of the sample user under the three-classification network; adjusting network parameters of the tumor classification model according to the prediction probability distribution under the binary classification network and the prediction probability distribution under the ternary classification network; wherein the dichotomy network is used for classifying whether MPR is reached or not; and the three-classification network is used for carrying out three classifications of not reaching MPR, reaching MPR but not reaching PCR, or reaching PCR.

The invention provides an evaluation method for gastric cancer peritoneal metastasis state recognition and PCI score estimation, and the method comprises the steps: receiving a preoperative abdominal enhancement CT image, and carrying out the automatic positioning preprocessing of the peritoneum through resampling, normalization, image enhancement and a region attention mechanism; image omics features are extracted from the region of interest and fused with the depth features of the multi-scale convolutional neural network, and a binary classification model based on a residual network / Transform is constructed to output transition state probability and confidence; pCI scores of all the areas are quantitatively evaluated synchronously through the peritoneal thirteen subareas, and finally a transfer prediction result, a PCI spatial distribution map, a visual heat map and a model interpretation report are integrated to form structured diagnosis output. According to the method, transition state identification and PCI score quantification can be realized, and the problems of single function, weak generalization, low interpretability and the like of a traditional model are solved.

The invention relates to a Parkinson's disease walking state recognition method based on tensor singular value decomposition and automatic hyper-parameter optimization. The Parkinson's disease walking state recognition method comprises the following steps: acquiring a sensor signal of a target person; constructing a tensor according to the sensor signal; performing t-SVD decomposition processing on the tensor to obtain a frequency domain core tensor; the frequency domain core tensor and the sensor signal are subjected to feature extraction, the extracted features are input into a trained classification model, the binary classification recognition result of the Parkinson's disease patient and the healthy person is obtained, and the trained classification model is obtained through training of a training set marked with a patient and health contrast label. According to the method, efficient dimension reduction and noise suppression are realized by using t-SVD, and adaptive optimization is performed on the key hyper-parameters of the classification model in combination with an automatic machine learning technology, so that the dichotomy recognition accuracy and robustness of the Parkinson's disease patient and the health control are improved.

The invention relates to a cross-domain three-dimensional feature collaborative dynamic gating fusion network system and a detection method, belongs to the technical field of generative model detection, solves the problems of insufficient utilization of multi-dimensional features and lack of cross-domain feature fusion in the prior art, and comprises the following steps: S1, obtaining a to-be-detected image and adjusting the to-be-detected image into a preset model input size, performing normalization processing to obtain a preprocessed image; s2, performing three-branch feature extraction to obtain gradient features, frequency features and spatial domain features; s3, performing cross-domain feature alignment and fusion to obtain features after channel compression; s4, performing dynamic gating fusion to obtain a final fusion feature; and S5, inputting the obtained final fusion features into a classifier, carrying out AI generated graph dichotomy detection to obtain the probability that the to-be-detected image is the AI generated graph, comparing the probability with a discrimination threshold, if the probability is greater than the discrimination threshold, determining that the to-be-detected image is the AI generated graph, otherwise, determining that the to-be-detected image is not the AI generated graph, and outputting a determination result as a detection result.

The invention provides an attack technology and tactical identification method oriented to honey array linkage. The method comprises the following steps: coding according to malicious sample data and associated TTP labels, and constructing a training data set; constructing an interpretable discrimination model for binary classification modeling to obtain a behavior triggering weight vector and an offset item of the malicious sample; extracting dominant trigger rules to construct a mapping inference rule base; obtaining alarm data, extracting a behavior clue based on a preset standard behavior dimension set, converting the behavior clue to obtain a trigger identifier, and mapping the alarm data into an alarm behavior vector; constructing a prediction TTP label set of the alarm behavior vector and recording a trigger path; and encapsulating the behavior semantic data, constructing an execution strategy response rule set, performing mapping in the execution strategy response rule set according to the behavior semantic data to obtain an execution strategy, and adjusting honey array deployment according to the execution strategy. By applying the method, accurate identification and transparent reasoning of the potential TTP in the attack activity can be constructed and realized, and linkage scheduling of a defense system is supported.

The present invention relates to the technical field of computer vision, and provides a pseudo-label filtering-based online domain change continual learning method and system. The method comprises: acquiring a pre-trained model, using the pre-trained model to predict changing target-domain data, and generating a pseudo-label for online adaptation; deriving a lemma for threshold-based pseudo-label filtering in online domain change continual learning on the basis of binary classification, and designing a threshold setting principle in the online domain change continual learning on the basis of the lemma; using the designed threshold setting principle to filter a pseudo-label having a low confidence level in model prediction, and introducing a class prior alignment method to encourage the model to perform fair prediction on an unknown-domain sample; and using the filtered pseudo-label to update and optimize the model to obtain a classification prediction result in the online domain change continual learning. In the present invention, an adaptive threshold capable of adapting to a CTTA process is established, thereby ensuring the quality of pseudo-labels.

The invention discloses an electric vehicle rear-end collision accident prediction method and system based on large model knowledge and trajectory data. According to the method, two vehicle types of an electric vehicle and a fuel vehicle are identified based on license plate colors, the vehicle types and vehicle tracks are bound, and the following process is divided into three states according to the combination of the front and rear vehicle types. On the basis, multi-dimensional features extracted from historical tracks are input into a large language model, expert-level rule knowledge used for distinguishing accidents and non-accidents is extracted through structured cues, and a final feature matrix is constructed according to the knowledge. And finally, based on the final feature matrix, performing differentiation processing on the three car-following states, training and calibrating a dichotomy model, and realizing real-time estimation of the rear-end collision probability at a specific moment in the future. According to the invention, by fusing the reasoning ability of the large model and the real trajectory data, the accuracy and interpretability of rear-end collision risk identification under the electric vehicle participation scene can be significantly improved, and the method has engineering deployment value.

This invention relates to the field of medical testing, specifically to a set of biomarkers, reagent kits, and their applications for diagnosing hypertension in children. This invention involves collecting tongue / intestinal samples from obese children with hypertension, obese children, and healthy individuals, performing metagenomic sequencing, and statistically analyzing the sequencing data using bioinformatics to identify disease-related tongue / intestinal flora. By integrating tongue / intestinal flora with disease information, a combination of flora biomarkers is obtained. A binary classification prediction model constructed using this combination can maximally detect hypertension in obese children.

The invention belongs to the field of general computers, and provides a hydraulic data processing method and system based on artificial intelligence, and the method comprises the steps: constructing a first time sequence and a second time sequence corresponding to nodes; calculating a time difference vector by using a variational encoder; constructing a graph structure in combination with the physical connection relationship between the monitoring points, and determining the structural representation of each node; performing image segmentation on the remote sensing image data, extracting image region features corresponding to node positions, splicing the image region features with meteorological data at the same position, and determining image-meteorological representation of each node; for each node, splicing the structure representation output by the graph convolutional neural network with the image-weather representation to form fusion representation; a disturbance vector of a preset amplitude is injected into the fusion representation, disturbance representation is obtained, and the disturbance direction is the gradient direction of the loss function relative to the fusion representation; and jointly inputting the fusion representation and the disturbance representation into a dichotomy neural network, and outputting a judgment result about whether abnormal mutation occurs or not.

The invention relates to the technical field of crossing of remote sensing image processing and computer vision, and discloses a remote sensing image change detection method which comprises the following steps: obtaining a first time phase bottom layer pixel feature and a second time phase bottom layer pixel feature according to a first time phase remote sensing image and a second time phase remote sensing image; according to the first time phase bottom layer pixel features and the second time phase bottom layer pixel features, enhanced visual features are obtained; according to the first pixel-text similarity score plot and the second pixel-text similarity score plot, semantic guidance features are obtained; according to the enhanced visual features and semantic guidance features, obtaining fusion features of multiple levels; pixel-level binary classification is carried out on the fusion features of the multiple levels, and a change detection binary image is output. According to the invention, the precision and accuracy of remote sensing image change detection are improved.

The invention discloses a power frequency waveform classification method and device, a medium and equipment, and belongs to the field of waveform classification, and the method comprises the steps: obtaining preprocessed power frequency waveform data of a power transmission line, calculating various waveform features, and inputting the features into a preset cascade classifier model. The model is composed of a plurality of XGBoost binary classifiers which are cascaded in sequence, and each classifier is responsible for executing a specific binary classification task. The output result of the previous classifier is used for triggering the starting of the next classifier, or the final classification result is directly output. The cascade structure not only improves the accuracy and efficiency of classification, but also reduces the complexity of the model, and effectively solves the problem that the fault positioning power frequency of the power transmission line cannot be accurately and efficiently classified in the prior art.