951 results about "Characteristic space" patented technology

The invention belongs to the field of artificial intelligence, particularly relates to a large model illusion suppression method, system and equipment based on a dynamic knowledge base and multi-modal consistency constraint, and aims at solving the problem that factual illusion is likely to occur when an existing large language model generates content. The method comprises the steps that a knowledge base of multi-source heterogeneous data is constructed and dynamically maintained, and a dynamic credibility weight fusing data source authority, knowledge timeliness and multi-modal consistency is calculated for each piece of knowledge in the knowledge base; when the content is generated by the model, high-credibility related knowledge is retrieved from the knowledge base according to the current context; in the decoding stage of the model, a constraint loss item is designed, and the generation probability is adjusted in real time by calculating the similarity between the currently generated content and the retrieval knowledge in the feature space. According to the method, the multi-modal knowledge base for dynamic credibility evaluation is introduced, and the real-time consistency constraint is applied in the generation and decoding link, so that the accuracy and the reliability of the generated content are remarkably improved.

The invention relates to the technical field of intelligent manufacturing equipment fault diagnosis, and particularly discloses a Meta-Transform driven multi-working-condition equipment bearing fault diagnosis method and system. The method aims at bearing fatigue damage risks caused by dynamic adjustment of technological parameters of a numerical control machine tool in the aerospace manufacturing process and challenges such as feature distribution offset and fault sample scarcity caused by variable working conditions. The diagnosis system is constructed through three core modules. The method comprises the following steps: firstly, reconstructing an original bearing signal into a multi-scale time-frequency feature space by adopting continuous wavelet transform; then designing a causal Transform architecture with a strict lower triangle attention mask, and realizing feature extraction and classification according to a physical causal law of fault propagation; and finally, integrating the mechanisms into a model-independent element learning framework, and realizing cross-working-condition rapid self-adaption through a self-adaption gradient pruning strategy. The bearing fault diagnosis accuracy under the condition of few samples is improved, the interpretability and generalization ability of the model are enhanced, and the industrial application practicability of bearing fault diagnosis is improved.

The invention provides a dynamic game difficulty self-adaptive adjustment method and system based on user behavior feedback, and relates to the technical field of game design, the method comprises the following steps: carrying out dynamic weighting calculation based on a difficulty adaptation index, generating a dynamic difficulty correction coefficient in combination with user real-time physiological feedback data, and adjusting the dynamic game difficulty according to the dynamic difficulty correction coefficient; the dynamic difficulty correction coefficient comprises a scene complexity adjustment parameter and an interaction response threshold adjustment parameter; inputting the user feedback parameter set into an image feature weight distributor, dynamically adjusting weight distribution of image retrieval feature vectors according to user attention distribution data and operation delay parameters, and generating an optimized scene element retrieval strategy; and performing feature space mapping on the dynamic difficulty correction coefficient and the optimized scene element retrieval strategy, and generating a final game difficulty control instruction set through nonlinear superposition. Game design can be optimized, and game adaptability and flexibility are enhanced.

The invention relates to a PCB production line process parameter intelligent matching method based on feature space mapping, and the method comprises the steps: collecting and fusing the material attributes, structure parameters, sizes and historical process records of a plurality of batches of PCB products, carrying out the normalization preprocessing, removing abnormal data, and constructing a high-quality feature matrix; after multi-dimensional feature expression is realized by utilizing a multi-scale embedded network, a mapping relation between features and process parameters is dynamically learned on the basis of an adaptive space mapping network in combination with a soft constraint multi-objective loss function, and gradient cutting, step length adjustment and a disturbance elasticity pool mechanism are introduced in a parameter recommendation process to guarantee convergence and stability. After the parameters are implemented, feedback data are collected in real time, periodic iteration distillation optimization and loss function recalibration are carried out, and finally a fine-tuning parameter recommendation scheme marked with conflict indexes, confidence intervals and weight suggestions is output for an engineer. According to the scheme, the intelligence, the traceability and the field adaptation capability of parameter recommendation are improved.

The invention discloses a big data analysis-oriented edge artificial intelligence calculation method, which relates to the technical field of edge artificial intelligence, and comprises the following steps of: after a boundary aggregation condition is identified, calculating a data distribution compression ratio on the basis of a numerical mapping overlapping degree before and after truncation, a sample clustering overlapping rate of a boundary interval and an upper and lower adjacent section thereof, and a data distribution compression ratio; constructing a feature space overlapping ratio model for identifying whether a distribution folding trend exists or not; and on the basis of the boundary aggregation condition and the identification result of the distribution folding trend, constructing a credibility discrimination evaluation function, generating a credibility discrimination evaluation coefficient, and dividing the current input data into a high credibility area, a middle credibility area or a low credibility area. According to the method, the problems of data distribution folding and misjudgment caused by truncation processing in industrial edge calculation are solved, and dynamic evaluation of input credibility and adaptive regulation and control of a reasoning path are realized, so that the model discrimination accuracy and robustness are improved.

The invention relates to the technical field of mode recognition and data recognition, and discloses an intelligent visual detection method for non-standard precision part surface microdefects, which comprises the following steps: acquiring surface gray level image data of a to-be-detected part, physically abandoning low-frequency components through discrete wavelet transform, and reserving high-frequency detail components to construct a frequency domain input tensor; constructing a double-flow reconstruction model containing a space domain coding network and a frequency domain coding network, and minimizing the distribution difference of the same feature between double-domain characterization through potential feature space consistency constraint joint optimization; the method comprises the following steps of: calculating a spatial domain residual image and a frequency domain residual image, combining a texture topological residual image extracted by structural tensor characteristic decomposition, and generating a comprehensive abnormal response image through weighted fusion to judge the defect, and effectively inhibiting macroscopic geometric contour interference through frequency domain decoupling and a topological check mechanism on the premise of not needing a standard geometric template. And sensitive perception and accurate identification of weak texture defects on the surface of the non-standard part are realized.

The invention relates to the technical field of machine vision, in particular to a precision part size automatic detection method and system based on machine vision, precision part images are collected through a high-precision industrial camera, part positioning is carried out, sub-pixel-level topological feature mapping is carried out on interested area images, and precision part size automatic detection is carried out. Comprising the steps of gray histogram equalization, gray morphological processing, edge detection and edge chain code tracking, construction of an edge point topological feature space, execution of sub-pixel subdivision, obtaining of an edge line through contour analysis of a feature distance and a feature angle, and double-constraint geometric reconstruction based on the edge line. The characteristic distance and the characteristic angle are used for rotation matrix conversion and geometric dimension calculation, a relation model of the geometric dimension and the actual dimension of the part is established, precise part dimension measurement is achieved through dynamic error analysis and compensation, the measurement precision is remarkably improved, the risk caused by unreliability of a single characteristic is effectively reduced, and the measurement accuracy is improved. And the measurement stability is improved.

The invention provides a project cost big data management and analysis system, and relates to the technical field of data management, and the system comprises a data collection and preprocessing module which is used for collecting original cost data from a heterogeneous data source, and carrying out the preprocessing of the original cost data, and obtaining the preprocessed cost data; the semantic feature extraction module is used for converting the preprocessed cost data into a multi-dimensional feature vector based on a multi-level feature extraction system; the similarity calculation module is used for calculating similarities among different cost data based on the multi-dimensional feature vectors to obtain a similarity matrix; and the data storage and management module is used for storing the cost data, the multi-dimensional feature vector and the similarity matrix by adopting a mixed storage architecture, and providing retrieval and recommendation functions of cost projects based on a multi-level feature space index structure. According to the method, the limitation that a traditional method only depends on keyword matching is solved, and the system can recognize the deep incidence relation between the items.

The invention relates to the technical field of image analysis, and discloses a multi-modal emotion recognition method and system based on hypergraph diffusion and evidence fusion, a terminal and a storage medium, and the method comprises the steps: carrying out the random shielding of a data set through a randomly generated mask, thereby simulating the random data missing condition, and carrying out inverse sampling on the preprocessed simulation data by using a trained conditional diffusion model to obtain a training set in which missing modals are complemented so as to train an emotion classification network, and finally carrying out emotion recognition. According to the method, through dual-channel evidence fusion, uncertainty is estimated at a feature source level and a discrimination level at the same time, so that adaptive evidence fusion is realized, the condition of performance reduction caused by modal loss is reduced, potential features of the lost modal are explicitly recovered in a feature space, and the accuracy of final emotion recognition is improved.

The invention relates to the technical field of performance testing, and discloses a multi-parameter insulating sleeve aging performance testing method and system, and the method comprises the steps: carrying out the multi-parameter detection signal collection of an insulating sleeve, and obtaining standardized signal data; inputting the standardized signal data into a coupling recognition network for feature analysis to obtain a classification recognition result; performing aging factor threshold adjustment on the rising edge slope of the discharge pulse of the insulating sleeve based on the classification identification result to obtain a dynamic separation parameter; performing feature space matching and differential identification on the partial discharge signal according to the dynamic separation parameter to obtain feature classification data; phase clustering analysis and step response characteristic analysis are carried out based on the characteristic classification data to obtain the aging state variable coefficient of the insulating sleeve, the problem that the recognition precision of a traditional fixed parameter method is reduced in different aging states is solved, and high recognition accuracy and anti-interference robustness can still be kept in a complex electromagnetic environment.

The application discloses a feature editing method for large model content security, which compares and analyzes the sparse coding features of a chat assistant constructed based on a large language model under positive user input and negative user input, extracts the internal response differences of the model to different semantic directions, and the mechanism can automatically and accurately identify the key feature dimensions highly related to the semantic direction of the target attribute. The model activation is mapped to a sparse feature space by using a sparse autoencoder, and each dimension of the feature has independent and interpretable semantic meaning. By injecting a feature guide vector in the space, the interference of the control process on the text grammar, fluency and information density is significantly reduced. The sparse representation mechanism is introduced to structure the intermediate activation features in the reasoning process of the large language model and to intervene in a targeted manner, so that the reply of the chat assistant to the user input conforms to the preset safety specification, and the safety and controllability of the chat assistant in the interaction with the user are improved.

The invention discloses an open set domain adaptive image classification method based on a difference prompt learning technology of a pre-training vision-language model. According to the method, high-quality pseudo-open class images are generated, and de-noising text embedding and de-noising visual embedding are obtained by using a differential prompt learning technology, so that class characteristics of a source domain, a target domain and pseudo-open class samples are effectively extracted, and irrelevant noise is inhibited. According to the method, a vision-text comparison loss mechanism, a triple distance comparison loss mechanism and a negative sample penalty mechanism are further designed, a known category and an unknown category are effectively distinguished in a feature space, and the semantic alignment capability of cross-domain similar samples is enhanced. The method can significantly improve the classification accuracy and model robustness in an open set domain adaptation task, has the advantages of simple structure, high calculation efficiency, good generalization performance and the like, and is suitable for image classification, cross-domain transfer learning and other related application scenes.

The invention discloses a self-adaptive visual admittance control method fusing fluid characteristics and multi-modal perception, which comprises the following steps: designing a self-adaptive Bingham-shear thickening fluid virtual damping coefficient through nonlinear mapping based on sigmoid, and combining a threshold triggering behavior of a Bingham fluid and a sudden stiffening characteristic under the impact of the shear thickening fluid; the flexibility is enhanced under the action of small force, and the anti-interference capability is improved under impact. Besides, a force auxiliary function based on force amplitude is introduced, an anisotropic compliance strategy is combined, rigidity and damping are dynamically adjusted by identifying the main force direction, and the mechanism can reduce sensitivity to noise of a micro sensor and ensure stability and accuracy in the task execution process. Meanwhile, an environment attraction domain model is established in a feature space, Lyapunov analysis shows that the system has consistent final boundaries, stable convergence is ensured, and secondary correction is supported.

The invention discloses a bearing variable working condition fault diagnosis method fusing model migration and feature migration learning, and the method comprises the steps: processing bearing vibration signals of a source domain and a target domain through wavelet transform, and extracting a time-frequency diagram; expanding the two-dimensional time-frequency graph data set by using DCGAN, and balancing the number of the two-dimensional time-frequency graph data set; then, model parameter migration is adopted, AlexNet network parameters pre-trained in a source domain are migrated, a migrated AlexNet network is constructed, and depth features are extracted; then, a domain adaptation method based on improved migration joint matching is provided, multiple strategies are fused, and a low-dimensional feature space with small distribution difference and good discrimination performance is obtained; and finally, on the basis of a labeled source domain feature data training model after domain adaptation, realizing identification and classification of unlabeled target domain feature data. The method is ideal in diagnosis performance and high in accuracy under variable working conditions and data imbalance, domain data distribution difference can be reduced by improving the migration joint matching method, and feature discrimination performance and fault diagnosis accuracy are improved.

The invention discloses a multi-modal data enhancement method and system based on feature space alignment. The method comprises the following steps: collecting modal original data streams of multiple unmanned aerial vehicles; reversely deducing an internal generation rule from a data final state, and constructing a sparse coding feature representation library through causal pilot signal extraction and singular trajectory analysis; constructing an adaptive weight map according to the feature representation library, and identifying a space alignment path through dynamic calibration processing to generate an alignment imbalance index; performing cross information entropy analysis to establish a collaborative enhancement chain, and identifying a collaborative enhancement mode based on the information coupling degree to generate a complementary enhancement vector; performing multi-dimensional projection reconstruction on the complementary enhancement vector, and determining feature mapping probability distribution through diffusion time inversion to obtain a multi-dimensional projection feature map; carrying out nonlinear enhancement processing to generate an enhancement expansion mode, and identifying an enhancement key node to construct an enhancement regulation and control sequence; and outputting hierarchical enhancement data based on the regulation and control sequence and the projection feature map, and realizing accurate space alignment and collaborative enhancement of multi-sensor data.

Systems and methods for detecting and diagnosing machine faults are discussed. An exemplary system includes at least one sensor node to sense a signal indicative of an operation status of a machine part, and a machine health analyzer circuit to generate a computational machine fault model comprising an autoencoder (AE) network and an associative module. The AE network encodes the sensed signal into signal features in a latent feature space, and decodes the signal features to produce a reconstructed signal. The associative module transforms the encoded signal features into an associative output using a dynamically updatable codebook. The machine health analyzer circuit detects a presence or absence of fault in the machine part based on reconstruction losses determined respectively from the reconstructed signal and the associative output. The detected fault can be presented to a user or to a process such as fault diagnosis or fault correction.

The invention discloses a poisoning defense method based on characteristic difference analysis and model layer purification. The method comprises the following steps: S1, constructing a poisoning classification model according to an electromagnetic signal sample; adversarial disturbance is introduced, cross entropy loss of disturbed samples is calculated and sorted, and a threshold value is set to distinguish clean samples from poisoned samples; s2, after the poisoning samples are separated out, the poisoning score of each layer of the model is calculated through quantification, and the higher the score is, the stronger the influence of the back door neurons of the layer on the poisoning samples is; s3, generating a pseudo-poisoning sample based on an inversion trigger, analyzing the feature distribution difference between the pseudo-poisoning sample and a clean sample, aligning a feature space and purifying a model layer by optimizing an objective function, and enhancing the distinguishing ability of the model to the sample; s4, repeating the step S3 until the model classification precision converges, and storing the optimal network parameters; and the classification precision of the model on a normal sample and the attack success rate on a poisoning sample before and after defense are evaluated. According to the method, the robustness and the safety of the model are improved, and the method has relatively high universality.

The invention provides a jujube variety discrimination method and system based on artificial intelligence. The method belongs to the technical field of agricultural artificial intelligence and computer vision crossing. The method comprises the following steps: synchronously collecting multi-modal original data of jujube samples, and constructing a multi-modal original data set; preprocessing the multi-modal original data set to obtain standardized multi-modal feature data; a super-dimensional feature space is constructed based on the standardized multi-modal feature data, and a jujube super-dimensional feature vector set is generated through feature cross fusion. By synchronously acquiring the macroscopic visual image, the hyperspectral data, the three-dimensional form scanning data and the microscopic texture image, various feature information of jujube samples can be comprehensively and accurately captured, a super-dimensional feature space is constructed, and deep fusion of cross-modal features is realized, so that the precision and robustness of variety discrimination are improved.

The invention provides an industrial anomaly detection and root localization method and system based on data driving, and the method comprises the steps: carrying out the cleaning, feature extraction and normalization processing of original data collected in an industrial production process, and constructing a feature space; based on a local anomaly factor LOF and a mahalanobis distance MD method, jointly detecting local anomaly and global anomaly, and identifying an abnormal working condition; extracting space and time correlation characteristics of the abnormal variables through Pearson correlation weighting and Granger causal test to form a space-time correlation matrix; constructing an abnormal causal network based on the matrix, and tracing an abnormal root and a propagation path through depth-first search and abnormal propagation intensity evaluation; and finally, dynamic optimization of the anomaly detection and diagnosis method is realized based on parameter self-adaption and model incremental learning. According to the method, the anomaly detection accuracy and the anomaly traceability interpretation capability can be effectively improved, and the intelligent level and the self-adaptive capability of data processing are enhanced.

The invention provides an industrial process fault detection method based on a space-time causal diagram autoencoder, and the method comprises the steps: 1, carrying out the data preprocessing of the space-time process data of all process variables collected in the operation process of a target industrial process for the target industrial process; step 2, establishing a causal graph space-time auto-encoder CGSTAE; 3, executing a three-step causal graph structure learning algorithm to realize training of a causal graph space-time auto-encoder CGSTAE, wherein the training comprises three steps of pre-training, causal extraction and fine tuning; and step 4, obtaining a fault detection result based on hidden layer features of the causal graph space-time auto-encoder CGSTAE and residual data output by reconstruction. According to the method, effective process monitoring and fault detection are realized by constructing two statistical magnitudes in a feature space and a residual space. Compared with other methods, the fault detection method provided by the invention can improve the reliability and interpretability of industrial process monitoring.

The invention discloses a dynamic planning method and system for an intelligent patrol point location of power transformation equipment, and belongs to the technical field of intelligent patrol of power systems, the dynamic planning method for the intelligent patrol point location of the power transformation equipment comprises the following steps: mapping dynamic features and static attributes to the same feature space and carrying out cross-modal association; constructing a defect severity model, and introducing defect severity in risk quantification calculation to generate a point location priority list; a transformer substation three-dimensional point cloud model is built, path nodes are initialized according to a point location priority list, and a greedy algorithm is utilized. A closed-loop decision-making system of multi-modal data fusion is constructed; according to the method, automatic planning driven by a risk quantification model based on defect history, generation of a three-dimensional space non-blind area coverage path, dynamic adjustment triggered by two factors of equipment change and inspection effect, deep collaborative analysis of machine account-defect-real-time data and continuous inspection of a complex scene are guaranteed by a semantic compensation mechanism.

The embodiment of the invention discloses an air conditioner maintenance data classification method and system based on machine learning, and the method comprises the steps: integrating multi-source heterogeneous maintenance records generated in the maintenance process of air conditioner equipment, building a correlation index through a common identification field, and fusing dispersed data into a maintenance data set in a uniform format; performing hierarchical semantic analysis on unstructured texts in the set to generate structured semantic features, and performing time sequence feature extraction on structured data; then constructing a hybrid classification model training framework fusing semantic and time sequence features, and generating a maintenance data classification model through feature space alignment, dynamic weight distribution, hyper-parameter optimization and an early stop strategy; and finally, classifying newly-added maintenance records by applying the model, checking by combining an expert knowledge base, manually rechecking conflict results, and returning corrected data as an incremental sample back to the model to realize continuous optimization.

The invention relates to the technical field of deep counterfeit image detection, and discloses a deep counterfeit detection method and system, a storage medium and computer equipment. The method comprises the following steps: firstly, constructing a reference data set containing a forged image and an original real image; secondly, through an integrated model, generating antagonistic samples for the reference data set, and integrating the successfully attacked antagonistic samples into an antagonistic sample set; and finally, merging the reference data set and the adversarial sample set, and constructing a robustness enhanced data set containing four types of samples. In the model training stage, multi-classification cross entropy loss and comparative learning loss are combined, and expression of the model in a feature space is optimized through comparative learning constraint, so that the model learns discriminative features with more compact intra-class features and more dispersed inter-class features. The model trained by the method not only can effectively defend against attack and improve robustness, but also surpasses original detection performance on clean samples, and has remarkable technical advantages and application value.

The invention relates to the technical field of computer vision, in particular to an image target detection system and method based on deep learning, and the system comprises a dynamic feature alignment unit, a motion blur compensation unit and a feature fusion control unit. A dynamic feature alignment unit generates spatial deformation parameters through a deformable convolutional layer and an offset prediction sub-network, resamples a shallow high-resolution feature map, and realizes deep and shallow feature space alignment, and a motion blur compensation unit generates a motion vector based on brightness gradient field difference, constructs a mask and weights a suppression blur region, so as to realize deep and shallow feature space alignment. The feature fusion control unit analyzes local entropy and target size distribution, dynamically distributes feature weights and feeds back and optimizes offset parameters, a closed-loop learning loop is formed by the method, and the problems of inaccurate feature alignment, fuzzy interference and poor scene adaptation are solved.

The invention relates to an animal wound multi-modal intelligent identification method based on artificial intelligence, and the method comprises the steps: carrying out the feature extraction and semantic constraint through multi-modal sample collection and metadata extraction, employing an image preprocessing and text natural language processing technology, and combining a mixed visual model of a convolutional neural network and a visual Transform, and a large language model. A cross-modal attention mechanism and a semantic trigger are utilized to realize feature reweighting, clinical standard soft boundary constraints are introduced, and fuzzy semantic rules are converted into learnable constraints in a feature space, so that the accuracy and consistency of model judgment are improved. The method has adaptive optimization and incremental learning capabilities, and is beneficial to improving generalization and clinical applicability of exposure level intelligent judgment under different animals and complex wound types.

The invention discloses an industrial defect detection method based on self-supervised pre-training and feature space generation. Firstly, features are extracted and mapped to a unified potential space; secondly, introducing a SimMIM framework, which is one of mainstream technologies in the current industrial vision pre-training field, to carry out self-supervised pre-training to improve the representation capability, and using an OfficientForme network to improve the reasoning speed and reduce the memory demand; secondly, performing abnormal synthesis in a feature space by adopting a generative adversarial network and enhanced Perlin noise, outputting a feature increment and a soft mask by a generator, and performing linkage updating with a pixel-level mask and an image-level label; thirdly, a segmentation-classification double-head framework is adopted, a segmentation head outputs a pixel-level anomaly graph, and a classification head outputs an image-level anomaly score; and finally, training optimization is carried out through strategies such as abnormal graph up-sampling smoothing, a grouping learning rate, multi-stage scheduling and the like. The method realizes accurate and controllable synthesis of the abnormal region under the scene without, with or with mixed supervision, and significantly improves the robustness and real-time performance.

The invention provides a multi-modal sentiment analysis method and system based on main modal two-stage guidance, and relates to the technical field of sentiment analysis. Inputting the multi-modal data into a multi-modal sentiment analysis model, and extracting language, visual and acoustic features from the multi-modal data through a feature extraction module; semantically decoupling the multi-modal features into modal invariant features and modal unique features through a feature space distribution alignment module, and realizing feature distribution alignment dominated by language modals through alignment reconstruction constraints; performing self-attention modeling on the modal invariant feature through an attention enhancement module to obtain a first enhanced feature, and adaptively enhancing the visual and acoustic unique features through a cross-modal attention mechanism by taking the language unique feature as a dominant feature to obtain a second enhanced feature; the first enhanced feature and the second enhanced feature are fused through the emotion prediction module, an emotion intensity prediction result is obtained through regression prediction, and the accuracy and robustness of emotion analysis in a complex scene are improved.

The invention relates to the field of target detection, and particularly discloses an electric power work target detection method based on multi-modal large model knowledge distillation, which utilizes a vision-language multi-modal large model as a teacher model, and improves the target detection efficiency by expanding prompt word guidance. A high-quality pseudo label and a region-text pair are generated for an unlabeled electric power work image as a supervision signal, and on this basis, through joint optimization of detection loss, feature distillation loss, logic distillation loss and multi-modal contrast learning loss, a lightweight YOLO student model is guided to learn positioning and classification knowledge and to learn a multi-modal contrast learning loss. And deep alignment with the open vocabulary understanding ability of the teacher model is carried out on the feature space and semantic level, so that a semantic gap between closed category detection and open world perception is effectively bridged. Through the mode, the detection precision and generalization ability of the student model on common, rare and even unseen targets in the electric power work scene are remarkably improved.