61 results about "Feature synthesis" patented technology

The invention relates to the technical field of defect detection, and particularly discloses an electronic component defect detection method, system and device, and the method comprises the steps: collecting a multiband original image set based on multispectral imaging, inhibiting batch material difference and surface reflection through orthogonal complementary space projection, and obtaining a de-noised image set; performing feature extraction on the de-noised image set, and identifying and marking all fragmented defect areas; based on a graph neural network space attention mechanism, establishing an association relationship between fragmented defect areas to form a complete defect feature chain; gray frequency value sequences of the to-be-detected image and the standard sample image are constructed respectively, and a first fitting curve and a second fitting curve are obtained through fitting of a skewed distribution function; and finally, comprehensively judging whether the electronic component has defects or not by combining the defect characteristic chain and the difference characteristics of the two fitting curves. According to the method, interference can be effectively suppressed, fragmented defects can be accurately identified, and the detection accuracy and reliability are remarkably improved through multi-dimensional feature fusion.

The invention discloses a rice disease and insect pest image real-time identification and decision-making system based on deep learning, and particularly relates to the technical field of agriculture. A multi-modal fusion identification module; a resistance decision engine module; a dynamic prevention and control execution module; and a feedback optimization module. According to the invention, through a new intelligent plant protection normal form of intelligent perception-collaborative decision-precise execution-closed loop evolution, a multi-spectral imaging and microscopic feature synthesis technology is utilized, the bottleneck of traditional diagnosis is broken through, early-stage precise recognition of diseases and insect pests is realized, a medicament-pathogenic bacteria-natural enemy tripartite game model and a multi-objective optimization mechanism are innovatively constructed, and the method has a wide application prospect. The ecological regulation and control capability of the rice field is activated while the risk of drug resistance is avoided, it is ensured that prevention and control measures accurately reach a target by means of the three-dimensional profiling spraying and natural enemy synergistic release technology, residual feedback and model self-iteration are fused in the whole process, a prevention and control closed loop which is more intelligent is formed, and an evolvable digital solution is provided for rice green production.

The invention relates to the technical field of information retrieval, in particular to an image retrieval method and device based on depth difficulty perceptual hash and a storage medium. In the method, in order to comprehensively model a global geometric structure of an embedded space, self-adaptive difficulty perception enhancement is carried out on an original data sample, and the difficulty level is dynamically adjusted by utilizing linear interpolation for embedding; difficulty perception feature synthesis is carried out to generate enhanced data samples which are consistent in semantics and reserved in labels, and loop training is carried out on the enhanced data samples; the strategy not only makes full use of potential information in all training samples, but also systematically challenges learned metrics with the difficulty of adaptive calibration, ensures continuous optimization on the whole feature manifold, and promotes generation of similarity-preserving hash codes.

The invention relates to the technical field of artificial intelligence, and discloses an artificial intelligence-driven multi-modal data analysis method and system, and the system comprises a data collection module which is used for setting collection nodes and terminals, collecting multi-modal data and equipment operation parameters in real time, and carrying out the format conversion; the multi-modal fusion module is used for constructing a cross-modal semantic association model, processing space-time characteristics, fusing characterization characteristics and performing association simulation; the feature extraction module is used for constructing image, text and voice related extraction algorithms to obtain corresponding features; the comprehensive analysis module is used for carrying out correlation calculation on the features after standardization processing, and comparing and analyzing semantic consistency with a preset threshold value; and the grading processing module is used for combining context calculation analysis when semantics are inconsistent, and analyzing target scene performance after secondary comparison. The system and the method can effectively process multi-modal data, improve semantic analysis accuracy and efficiency, and are suitable for various scenes.

The invention discloses a bearing fault diagnosis method based on feature mode decomposition and heterogeneous model fusion, and belongs to the technical field of mechanical fault diagnosis. An original vibration signal of the rolling bearing is collected; independent of any bearing geometric parameter or fault characteristic frequency information, a finite impulse response (FIR) filter is iteratively optimized to maximize a correlation kurtosis (CK) value, and a plurality of intrinsic mode functions (IMF) are adaptively decomposed; selecting first M IMFs with the highest CK value, extracting time domain, frequency domain and nonlinear complexity features of the IMFs, generating high-order derivative features by adopting depth feature synthesis DFS, and constructing an enhanced fusion feature vector; and finally, inputting a stacked integrated classifier consisting of six types of heterogeneous basic learners and an XGBoost element learner, and outputting a fault diagnosis result. According to the method, high-precision and high-robustness diagnosis can still be realized under the working conditions of speed change, strong noise and weak faults.

The invention discloses a focus auxiliary recognition method and device based on deep learning, and relates to the technical field of image recognition. The method comprises the following steps: acquiring a CT image of a target area, taking the CT image as a target image, and executing an image enhancement operation according to the target image to obtain a first feature map; performing image reconstruction operation according to the first feature map to obtain a first reconstructed image, and inputting the first feature map into a decoder to obtain a first continuous vector; the first continuous vector is a continuous vector obtained by decoding a first feature map; performing an abnormal feature synthesis operation according to the first continuous vector to obtain a discrete feature vector; inputting the discrete feature vector into an encoder to obtain a second reconstructed image; fusing the first reconstructed image and the second reconstructed image to obtain a final reconstructed image; performing feature classification network operation on the discrete feature vector to obtain an abnormal feature vector; and integrating the abnormal feature vector and the final reconstructed image to obtain a second abnormal feature graph. The method improves the definition of the image and the accuracy of diagnosis.

The invention discloses a sewer pipe network defect detection method based on multi-label zero sample learning. The method comprises the following steps: generating defect description corresponding to each pipeline defect category through a large language model; performing feature extraction and domain adaptation on the pipeline inner wall image and the defect description by using a representation guide module to obtain global and local image features and defect detailed description text features; synthesizing global and local image features of the image, respectively calculating global and local matching scores of the global and local image features and detailed description text features, and fusing to obtain a defect initial prediction score; constructing a semantic relationship adjacency matrix for displaying the relationship between different defect categories; and correcting the initial prediction score by using the semantic relationship adjacency matrix between the categories to obtain a final prediction score of each defect category. According to the method provided by the invention, knowledge migration from known defects to unknown defects is realized by constructing a guidance-fusion-correction network, and the difficulty in identifying types of unseen defects is effectively solved.

This application relates to the field of computer technology, providing an intent prediction method, apparatus, electronic device, and storage medium. The intent prediction method includes: first, acquiring target user data, which includes data associated with a target product; then, determining comprehensive characteristics corresponding to the target user based on the target user data, including user profile features, sentiment features, and behavioral features; finally, obtaining a target probability based on the comprehensive characteristics of the target user and a target prediction strategy, whereby the target probability represents the probability that the target user will purchase the target product. The target prediction strategy is correlated with the amount of historical user data corresponding to the target product, and the amount of historical user data is positively correlated with the product's shelf life. This allows the target prediction strategy to evolve gradually over time, enabling it to match prediction needs at different stages and improving prediction accuracy.

The application discloses a kind of neurogenetic disease identification model construction method and system, it is related to neurogenetic disease identification technical field.The method includes: obtaining multiple identification results and multiple user feature sets of neurogenetic disease identification, and multiple inter-class difference degrees are obtained by analysis;Based on multiple identification results and multiple user feature sets, train first identification model;According to multiple inter-class difference degrees, configure user feature synthesis strategy, generate synthetic user feature set and synthetic identification result, carry out the reinforcement learning of first identification model, obtain second identification model;Test the combined identification error rate of second identification model, combined with multiple inter-class difference degrees again configure synthetic strategy, continue to generate synthetic user feature set and synthetic identification result, carry out reinforcement learning, obtain third identification model.The application solves the problem that neurogenetic disease identification model is poor in generalization performance, easily confused class and low in recognition accuracy under small sample condition.

The application discloses an automatic feature engineering method based on Monte Carlo tree search and a large language model, which comprises inputting feature data such as a table data set into a computer system; the computer system performs feature initialization, evaluates the score of each initial feature input, and constructs an initial feature tree structure; the steps of candidate feature selection, expansion based on a large language model, feature score evaluation and score parameter updating are iteratively performed until the termination condition is met; based on the selected features, the large language model is called in combination with the prompt word engineering to expand and generate new feature nodes; the new feature nodes are substituted into a predefined end-to-end machine learning pipeline for evaluation, and the feature score is comprehensively calculated; the related parameters of all ancestor nodes in the feature tree are updated from bottom to top along the feature synthesis path; and after the iteration is terminated, the optimal feature set capable of improving the prediction performance of a basic machine learning model is output. The application can significantly improve the accuracy of a machine learning model in a table data prediction task.

The invention belongs to the field of target tracking, and particularly relates to an infrared weak and small target tracking method based on attention guidance proposal comparison, which comprises the following steps of: 1, preparing an infrared weak and small target tracking data set; 2, constructing a network model, wherein the model is composed of a feature extraction module, a hierarchical cross attention feature enhancement module, a global search proposal generation module, a comparison enhancement perception module and a dynamic template updating module; according to the method, cross-feature and cross-hierarchy information association is realized through a hierarchy cross attention module, and dependency between features is enhanced; a candidate proposal is generated by the global search module and then input into the contrast enhancement perception module, so that the gap between positive and negative samples is effectively enlarged, and the positive samples are closer to a real target; and finally, the output of the comparison module, the template features and the old dynamic template features are integrated through a dynamic template updating module to complete template updating, so that the robustness and the accuracy in the tracking process are improved, and the method is suitable for stable tracking of the infrared weak and small target under the complex background.

A method includes obtaining a first dataset including a first feature set, generating a first set of feature values by providing the first dataset to a set of feature primitive stacks, and determining a reduced set of feature values based on the first set of feature values by dimensionally reducing features of the first set of feature values. The method further includes generating an intermediate set of feature values by providing a value of the first dataset and a value of the reduced set of feature values to at least one feature primitive of the set of feature primitive stacks. The method further includes updating the reduced set of feature values by dimensionally reducing features of the intermediate set of feature values and storing a second dataset including features of the intermediate set of feature values in association with the first feature set.

At least a method and an apparatus are presented for efficiently encoding or decoding video using neural networks wherein the bitstream is adapted to hybrid machine / human vision applications. For example, the scalable decoding comprises applying to a tensor of reconstructed data a neural network-based feature synthesis processing to generate a tensor of input feature representative of a feature of image data samples, resizing the tensor of input feature to generate a tensor of output feature intended to be fed a neural network-based vision inference processing to generate a collection of inference results. Advantageously, resizing the tensor of input feature adapt at least a dimension of the tensor of input feature to the neural network-based vision inference processing.

The invention relates to the technical field of brain-eye information sensing management systems, and discloses a chip-level encryption-based brain-eye information sensing management system, which comprises a data acquisition module, an encryption processing module, a feature extraction module, a comprehensive management module and a security verification module. The data acquisition module acquires electroencephalogram signals and eye movement characteristic data in real time through sensing nodes; the encryption processing module carries out chip-level encryption on data by adopting symmetric and asymmetric encryption technologies; the feature extraction module extracts electroencephalogram feature parameters and eye movement track features through a multi-modal fusion algorithm; the comprehensive management module carries out standardization processing and threshold comparison analysis; and the security verification module performs secondary dynamic verification in combination with the identity information. Through a hardware encryption engine and a multi-modal fusion technology, the data security and analysis precision are improved, and the method is suitable for the fields of identity authentication and the like.

The invention discloses a power grid fault rapid diagnosis method and system based on artificial intelligence, relates to the technical field of power grid detection, and aims to analyze a power grid topological structure and historical fault data by using a convolutional neural network through a deep learning model and a positioning recovery prediction model so as to accurately position a fault source. Particularly, the performance is outstanding in a complex power grid environment, and the problem of low positioning precision of a traditional method is solved; according to the system, efficient processing of data such as voltage, current and frequency is achieved through an optimized data preprocessing module, the fault diagnosis response time is remarkably shortened by combining a fast training deep learning model, and the problem that the data processing speed is low is solved; besides, the system accurately identifies single, multiple and composite fault types through the fault feature comprehensive evaluation value Fce and a deep learning model, provides response measures for different fault types, and generates a real-time feedback report, thereby effectively solving the problems of misjudgment and missed judgment when an existing system processes complex faults.

The application relates to a driving risk identification method based on an automatic timing hyperparameter optimization model, aiming to construct an automatic timing hyperparameter optimization model based on space-time trajectory data to identify driving risk behaviors, reduce training costs and optimize model precision on the basis of an existing model. The method constructs an automatic timing hyperparameter optimization model, builds an automatic machine learning framework based on vehicle space-time trajectory data, realizes automatic optimization of sliding window parameters and model hyperparameters, first synchronizes the phase coupling relationship between multiple feature data based on a dynamic time warping algorithm; second, automatically generates and reduces the dimension of features based on a deep feature synthesis-sliding window algorithm and a t-distributed stochastic neighbor embedding algorithm; finally, automatic model selection and hyperparameter adjustment are realized through Bayesian optimization, and model integration is carried out; the method considers the time sequence characteristics of data in the automatic machine learning framework, realizes the reduction of training costs and the optimization of the precision of the driving risk identification model.

The application provides a digital prototype lightweight system based on feature extraction and adaptive grid optimization, and relates to the technical field of computers, comprising a model access and preprocessing module and a file analysis unit; the file analysis unit is used for receiving an original digital prototype model, and analyzing geometric topology data and design tree information of the model; a feature collaborative decision unit receives output results of the three units, realizes screening and classification of multi-dimensional features through feature influence weight calculation, and generates a feature mapping diagram containing key feature areas and non-key feature areas; through multi-dimensional collaborative extraction of geometric analysis, topology analysis and semantic analysis, and in combination with weight coefficient calculation of feature comprehensive influence degree determined according to downstream CAE analysis sensitivity, key areas having influences on product performance and assembly precision can be accurately identified, compared with an existing single-dimensional feature extraction scheme, loss of functional key features is avoided, and a basis guarantee for subsequent CAE analysis precision is provided.

The present invention provides a fine-grained method, medium, and system for optimizing cultivated land conservation potential, belonging to the field of cultivated land data processing technology. The method first acquires multi-source remote sensing, soil, and climate data, extracting a basic feature matrix through multi-dimensional decomposition. A pyramid neural network model is then used to perform multi-scale analysis of soil quality characteristics, and adaptively integrates them through a feature integration module. A contribution optimization equation system is then constructed, comprehensively considering four aspects: benchmark evaluation, spatial correlation, temporal evolution, and weight balance. This allows for accurate evaluation of cultivated land conservation potential and generates an optimized conservation zoning scheme. The pyramid neural network model overcomes the problem of insufficient feature extraction in traditional methods through multi-scale feature extraction and adaptive integration. Furthermore, the contribution optimization equation system, through multi-dimensional information fusion and dynamic optimization, addresses the difficulty in accurately evaluating cultivated land conservation potential in existing technologies.

The invention discloses an automatic feature engineering method based on Monte Carlo tree search and a large language model. The method comprises the following steps: inputting feature data such as a table data set into a computer system; the computer system executes feature initialization, evaluates the score of each input initial feature, and constructs an initial feature tree structure; iteratively executing the steps of candidate feature selection, expansion based on a large language model, feature score evaluation and score parameter updating until a termination condition is met; on the basis of the selected features, calling a large language model in combination with cue word engineering, and expanding to generate new feature nodes; substituting the new feature nodes into predefined end-to-end machine learning pipeline evaluation, and comprehensively calculating feature scores; related parameters of all ancestor nodes in the feature tree are updated from bottom to top along the feature synthesis path; and after iteration is terminated, outputting an optimal feature set capable of improving the prediction performance of the basic machine learning model. According to the method, the accuracy of the machine learning model in the table data prediction task can be remarkably improved.

The invention discloses a storage environment intelligent regulation and control method and system based on deep learning driving, and relates to the field of artificial intelligence, and the method comprises the steps: extracting the operation effect characteristics of a to-be-executed environment regulation and control operation through constructing a first storage region state association map; carrying out interactive calculation on the parameter characteristics of different characteristic domains of the driving unit to obtain multi-dimensional parameter characteristics of the driving unit; comprehensively generating a regulation and control instruction set for each storage area category (the area category is determined by storage state parameters and / or historical environment parameters) in combination with the operation parameter characteristics; and finally, the corresponding area is regulated and controlled. According to the method, accurate and intelligent regulation and control of the storage environment are realized through multi-dimensional feature fusion and a regional differentiation strategy, the regulation and control effect and the energy efficiency ratio are improved, and the method is suitable for intelligent management of complex storage scenes.

The present invention relates to the field of information retrieval technology, and more particularly to an image retrieval method, device, and storage medium based on deep difficulty-aware hashing. In the present invention, adaptive difficulty-aware enhancement is performed on original data samples to comprehensively model the global geometric structure of the embedding space, dynamically adjusting the difficulty level by linear interpolation of the embedding. Difficulty-aware feature synthesis is also performed to generate semantically consistent and label-preserving enhanced data samples for cyclic training. This strategy not only fully utilizes the potential information in all training samples, but also systematically challenges the learned metric with an adaptively calibrated difficulty, ensuring continuous optimization across the entire feature manifold and promoting the generation of similarity-preserving hash codes.

The application discloses a sewer network defect detection method based on multi-label zero-shot learning, and the method comprises the following steps: generating a defect description corresponding to each pipeline defect category through a large language model; using a representation guide module to perform feature extraction and field adaptation on a pipeline inner wall image and the defect description, so as to obtain global and local image features and defect detailed description text features; comprehensively considering the global and local image features of the image, respectively calculating global and local matching scores of the image and the detailed description text features, and fusing the global and local matching scores to obtain an initial defect prediction score; constructing a semantic relation adjacency matrix for displaying the relation between different defect categories; and using the semantic relation adjacency matrix between the categories to correct the initial prediction score, so as to obtain a final prediction score of each defect category. The method disclosed by the application realizes knowledge transfer from known defects to unknown defects by constructing a guide-fusion-correction network, and effectively solves the problem of recognizing unknown defect types.

The present invention provides a multi-scale motion rehabilitation assessment method, system, computer device, and storage medium. The method includes the following steps: collecting limb rehabilitation exercise videos during the rehabilitation training of the object to be evaluated; for each frame image in the limb rehabilitation exercise videos, extracting feature information of the limb parts, including equivalent shape information, vector information, and joint point information; performing feature encoding on each limb part to generate weights corresponding to each limb part and action sequence encoding; based on the feature encoding results, constructing an assessment model, which is a sequential model for establishing the internal connection of actions, and outputting the assessment result of the rehabilitation training of the object to be evaluated. The present invention can comprehensively evaluate the training actions by integrating the body part information, limb vector features, and joint point features in the actions, so as to more accurately evaluate the training actions.

The invention belongs to the technical field of artificial intelligence, and relates to a business processing intelligent recommendation method and device, equipment and a storage medium thereof. Preprocessing the to-be-processed business data to obtain preprocessed data; inputting the preprocessed data into the intelligent recommendation model after learning training; taking the preprocessed data as a service type calculation basis, and calculating the service type of the to-be-processed service data by adopting a feature comprehensive calculation network in the intelligent recommendation model; and processing scheme recommendation is carried out on the to-be-processed business data by combining recommendation nodes which are respectively learned and trained based on different business types in the intelligent recommendation model. The method is applied to an insurance claim settlement service classification intelligent processing recommendation scene, intelligent processing scheme recommendation can be carried out for claim settlement service types corresponding to different insurance types, and an insurance institution is assisted to better carry out claim settlement service classification shunting processing.

According to the asynchronous federated prediction method for the integrated energy system fusing the multi-time scale characteristics, the federated learning can construct a higher-precision load prediction model under the condition of guaranteeing data privacy for a plurality of integrated energy systems with similar energy consumption characteristics. For the situation that the processing speeds of different nodes have significant differences, the execution efficiency of the existing federated learning method is generally low due to the influence of an adopted global synchronization mechanism. For two types of load data of a long time scale and a short time scale, the framework respectively constructs an asynchronous federated learning model, and each model can better adapt to the fluctuation rule of the emphasized load while the coupling relationship between different loads is fully used, so that the accuracy of the model is improved. According to the local model parameter weighted aggregation mechanism based on the comprehensive contribution rate, the appropriate aggregation weight can be autonomously given according to the importance degree of the local model, and then the performance of a global prediction model on a server side is improved.

The invention provides a revocable PPG template design privacy protection method based on a CNN network, and relates to the field of safe and efficient authentication based on revocable PPG signals, and the method comprises the steps: carrying out the noise reduction and time-frequency conversion of an original PPG signal, and obtaining a two-dimensional time-frequency image; performing feature extraction on the two-dimensional time-frequency image by using a pre-trained convolutional neural network model to obtain an original depth feature vector; and performing discontinuous segmentation, random rearrangement, feature synthesis and coding on the original depth feature vector to generate an irreversible, revocable and unlinkable binary template. According to the technical scheme provided by the invention, high accuracy is realized, so that the authentication efficiency is improved, and meanwhile, the security of PPG signal authentication is enhanced.

The present application relates to the technical field of intelligent evaluation of water conservancy dam, in particular to a reservoir dam safety intelligent evaluation system based on machine learning, comprising a data acquisition module, a preprocessing module, a multi-source feature analysis module, a state fusion evolution module and a dynamic evaluation output module. The data acquisition module acquires multiple types of monitoring time series data, the preprocessing module aligns multiple dimensions of data, and generates a unified time scale record sequence containing three types of exclusive sequences; the multi-source feature analysis module extracts structural behavior deep features, comprehensive load state spectrum and abnormal event feature coding respectively; the state fusion evolution module generates a safety comprehensive state atlas through feature fusion network interaction evolution; and the dynamic evaluation output module constructs an evaluation network based on the atlas to complete dynamic scoring and form a graded safety state list. The present application realizes accurate characterization and dynamic evaluation of dam safety state through multi-dimensional data regularization and multi-feature interaction fusion.

The invention discloses a thin-wall part milling surface quality monitoring method and system based on feature adaptive filtering, and relates to the technical field of thin-wall part milling surface quality monitoring, and the method comprises the steps: sequentially calculating the feature information capacity, the maximum information number and the distance correlation coefficient between each feature and a target vector, obtaining a sequence rank of each feature after descending sorting according to the feature information capacity, the maximum information number and the distance correlation coefficient; respectively calculating the sum of three sort sequence numbers for each feature vector to obtain a feature comprehensive sequence number of each feature; sorting the features in an ascending order based on the feature comprehensive sequence numbers; selecting the first p feature vectors in comprehensive ranking and sequentially connecting in series to obtain an input feature matrix after self-adaptive filtering; and constructing a nonlinear real-time mapping relation model between the input feature matrix and the target feature vector. According to the invention, on the basis of self-adaptive filtering of single-channel cutting signal characteristics, adverse effects of unstable dynamic characteristics of a cutting system on monitoring precision are eliminated, and the monitoring precision is improved.

The invention discloses an industrial time series data prediction method and device based on multistage feature selection. The method comprises the following steps: acquiring multi-source heterogeneous time series data, and carrying out de-noising smoothing by adopting a rolling truncation mean value; constructing a displacement prediction data set, and establishing a mapping relation between a historical observation window and a future target moment; the feature comprehensive relevancy is calculated based on an entropy weight method, the features are divided into strong, medium and weak correlation subsets, and maximum correlation minimum redundancy screening, complementarity information screening and complementary screening based on gating attention are adopted respectively to form a final feature subset; and constructing a heterogeneous base learner group by using CNN-LSTM and CatBoost, performing probability fusion in combination with a Bayesian ridge regression element learner, and outputting a prediction result with a confidence interval. According to the method, the problems of noise interference, feature redundancy and lag in industrial time series data prediction are effectively solved, and the prediction precision and robustness are improved.

The embodiment of the application belongs to the technical field of voice recognition, is applied to a voice comparison recognition scene, and relates to a voiceprint comparison recognition method, device and equipment and a storage medium thereof. The voiceprint comparison recognition method comprises the following steps: obtaining to-be-compared voice; performing voiceprint feature extraction; performing linear mapping layer mapping to obtain high-dimensional feature vector representation; inputting the high-dimensional feature vector representation into a feature representation fusion model to obtain feature comprehensive representation corresponding to the to-be-compared voice; calculating a distance value between the feature comprehensive representation corresponding to the to-be-compared voice; and identifying voice segments of the same object from the to-be-compared voice based on the distance value. Compared with direct comparison of voiceprint features, comparison based on feature comprehensive representation can reduce the calculation of comparison quantities to a certain extent and improve comparison recognition efficiency. The voiceprint comparison recognition method of the application is applied to the technical field of biometric recognition, for example, a bank or financial service voice unlocking scene, and can more safely provide fund security guarantee services for customers.