1213 results about "Characteristic space" patented technology

The invention discloses a semi-supervised identification method and system for a small target of a container, and belongs to the technical field of artificial intelligence and computer vision, and the method comprises the steps: carrying out the target detection of a container image through a pre-trained target detection model, intercepting a sub-image, and inputting the sub-image into an initial classification model, and obtaining a classification confidence coefficient; the uncertainty of the model on a sample classification result is quantified through a Monte Carlo Dropout method; a feature space distance filtering and dynamic threshold adjusting mechanism is combined, and samples with high confidence, low uncertainty and consistent feature space are screened out to serve as pseudo label data; pseudo label data and initial synthesis data are mixed, and the generalization ability of the model is gradually improved through semi-supervised iterative training. According to the method, the dependence on manual annotation can be remarkably reduced, meanwhile, the distribution difference between synthetic data and real scene data is gradually reduced, and finally, high-precision recognition and strong generalization ability of a classification model in a real scene are achieved.

The invention relates to the technical field of aluminum alloy cold stamping quality control, in particular to a multi-source data fusion and real-time optimization-based cold stamping quality control method, which comprises the following steps of: inputting a fusion feature space into a pre-trained LSTM (Long Short Term Memory) model, and associating a real-time stamping speed with a dwell time parameter through an attention mechanism; combined prediction of springback value deviation and defect probability is realized for the first time, a process parameter correction value is dynamically generated based on a prediction result, and a thermal expansion compensation algorithm and micron-sized mold compensation are combined, so that the quality regulation response speed reaches a millisecond level, the compensation precision is improved to + / -2 [mu] m, and the error is reduced by more than 50% compared with that of traditional PID (Proportion Integration Differentiation) control.

The invention discloses a cloud platform auditing method based on multi-modal data processing, and relates to the technical field of cloud platform auditing. Independent features of text, image, audio, video and log data are extracted, each piece of modal information can be subjected to refined analysis in a corresponding feature space, and in a cross-modal feature fusion stage, a multi-modal feature fusion model is obtained. The correlation between different modal data is calculated by using a cross-modal consistency comparison network CMCN, and feature alignment is optimized through an attention mechanism, so that multi-modal information can be mutually verified, and misjudgment and missed judgment caused by independent auditing are reduced; in the auditing decision-making link, an auditing model based on deep learning is constructed, and end-to-end illegal content judgment is realized in combination with anomaly detection and a self-adaptive risk control threshold value, so that the intelligent level of an auditing system is improved; in addition, the suspicious areas of the image and the video are visually marked, and an auditing conclusion is generated by utilizing a natural language to generate an NLG model, so that the auditing result is more transparent.

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 provides a financial document automatic analysis and question-answering system based on a large model, and relates to the field of data intelligent analysis, and the method comprises the steps: firstly carrying out the multi-granularity segmentation and semantic embedding coding of a financial document, so as to map the document data to a vector space; then dynamically matching user questions and candidate document fragments in a feature space by adopting a multi-layer retrieval mechanism so as to accurately position related contexts, and then carrying out cross-paragraph semantic association modeling on the candidate fragments by introducing dynamic propagation of feature space constraint so as to break through the information fragmentation limitation of traditional paragraph matching; and finally, a model is generated in combination with thinking chain reasoning, a traceable reasoning path is synchronously constructed when an answer is output, and the illusion risk of a large model is remarkably reduced. In this way, the analysis depth of the complex financial problem can be improved, and intelligent analysis services with both accuracy and credibility are provided for users.

The invention provides a man-machine cooperation method and system based on robot process automation, and relates to the technical field of automation, and the method comprises the steps: building a data perception processing network, collecting a user operation instruction, a system operation state and task environment data, obtaining a task feature vector through semantic analysis, and matching a business model to generate a task processing strategy. A man-machine cooperative processing framework is constructed based on the strategy, a robot unit processes a structured task, a manual operation unit processes an unstructured task and is connected through an information interaction channel, and transmission of a data analysis result and decision suggestions and updating of processing regulation information when deviation exceeds a threshold value are achieved. Besides, a knowledge migration processing network is constructed, business rules and expert experience data features are extracted, a scene migration model is trained, business feature vectors are mapped to a preset scene feature space, and after validity verification, parameters of the data perception processing network and the man-machine cooperative processing framework are updated.

The invention relates to a multi-modal sparse fusion three-dimensional target detection method in an unstructured environment, and the method is based on a complexity perception candidate region dynamic sampling strategy, and through the cross-domain self-compensation of point cloud geometric features and image spectral features, and the fusion of multi-view sparse features, the detection of a multi-modal sparse fusion three-dimensional target is realized. The perception capability and the recognition precision of the obstacle area, and the remote dynamic target detection performance and the real-time performance are improved; an anti-deformation convolution group and a terrain gradient perception attention module are introduced into a ParScaleNet image backbone network, so that the extraction of edge features of a complex scene is enhanced, and the cross-scale characterization capability of the features is improved; according to the method, a ContraSpaceOpt module with semantic maintenance capability is deployed behind a Transform, excessive smoothness of obstacle features is inhibited through class comparison loss functions and feature space anisotropy constraints, the space separability of obstacles is maintained, and the problems of point cloud fracture and texture misalignment caused by the obstacles are effectively solved.

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 discloses a surface defect detection method based on metric element learning, which belongs to the technical field of industrial visual monitoring, and comprises the following steps: acquiring defect images and non-defect images, and constructing a data set; a high-resolution image is generated based on a cascaded generative adversarial network fused with a multi-dimensional attention mechanism, and the local detail and global feature expression ability of the image is improved through the multi-dimensional attention mechanism; and constructing a defect detection model, and carrying out weak supervision detection on the surface defects based on metric learning and meta learning. According to the method, confidence coefficient screening and structural consistency constraint are performed on unlabeled samples through metric learning and feature space constraint, so that the representation capability and generalization performance of the model are effectively improved, and the problems of few defect samples, high labeling cost and difficult representation of small defects in an industrial scene are emphatically solved; and the defect detection accuracy and the fine-grained discrimination performance are 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 discloses a small sample radar target identification method based on multi-feature fusion. According to the invention, the natural resonant frequency characteristics of radar echo signals are introduced into a small sample learning framework, and the advantages of certain stability and simple and rapid extraction under the condition of attitude and observation angle change are fully utilized; attention multi-feature weighted fusion based on energy guidance is carried out on natural resonant frequency features and time frequency features, scattering features are described from different feature mechanisms, and the completeness of the features under the small sample condition is improved; in addition, a loss function based on combination of feature similarity measurement and classification loss is also designed, so that the model can learn feature representation with higher distinction degree while the feature space discrimination is optimized, and the performance is improved in a feature fusion-classification network. According to the method, on the premise that the reasoning time and the calculation complexity are not remarkably increased, the recognition accuracy under the condition that the observation angle changes is effectively improved.

The invention discloses a noise text open intention classification method and system based on a large model, and belongs to the technical field of natural language processing and artificial intelligence, and the method mainly comprises the steps: extracting text features through a pre-trained large language model, carrying out the clustering of the text features through unsupervised granular ball clustering, and constructing intra-class structure representation in a feature space; dividing the training sample into a clean sample, an internal distribution noise sample, an external distribution noise sample and an uncertain sample based on a global particle-ball-level OOD tendency index and a sample-level consistency index; constructing a joint loss function guided by beneficial noise, and performing differentiation training on the model by using a sample division result; and constructing a multi-granularity decision boundary based on a granular ball structure, wherein the multi-granularity decision boundary is used for judging the category of the input sample. According to the method, multi-granularity structure information, a noise pattern recognition mechanism and a joint representation learning strategy are fused, and the recognition robustness and generalization ability of the model in a complex open environment are effectively improved.

The invention discloses a software project development method based on a low-code platform, which belongs to the technical field of software development, and specifically comprises the following steps: receiving original demand description through an embedded natural language parser, and establishing a semantic incidence matrix to extract demand elements; encoding the demand elements into three-dimensional structured demand vectors; mapping the demand vector to a three-dimensional orthogonal feature space, calculating the coordinates of a functional module, and generating an initial topological structure based on a Delou inner triangulation algorithm; rendering an interactive three-dimensional network map on a visual interface; monitoring user operation, and recalculating the adjacency relation of the affected module when the displacement of the module exceeds a threshold value or the connection edge is reconstructed; compared with a historical version, when a key service path is interrupted or a data flow ring is broken, a compensatory connection bridge is activated; a deployable architecture with a version traceability identifier is output, and a traceability code is built in the module to reversely map an original demand vector.

The invention provides a few-sample fine-grained target detection method for an SAR (Synthetic Aperture Radar) image, and aims to solve the problems of insufficient detection precision and weak generalization ability caused by the problems of sparse texture, large fine-grained difference of target categories, limited labeled samples and the like in the SAR image. According to the method, ARS-DETR is used as a trunk detection model, d key point prediction branches are introduced on the basis of the trunk detection model, and the model is guided to pay attention to a target discriminative region through OKS loss; and meanwhile, a feature prototype set is constructed, class center representation is obtained through confidence screening and k-means clustering, and dynamic updating is performed in combination with an EMA mechanism. In the few-sample training stage, prototype regularization constraint is introduced to keep the feature space stable; in the test stage, through training test prototype distance and classification confidence coefficient weighted fusion, the final discrimination accuracy is improved. The method can effectively improve the structure modeling capability and the few-sample adaptive capability of the target detection task in the SAR image, and is suitable for fine recognition application in a complex remote sensing scene.

The invention provides a target identification method and device for a millimeter-wave radar and the millimeter-wave radar, and the method comprises the steps: carrying out the distance calculation of detection points of the millimeter-wave radar, obtaining a detection point graph, and determining a point cloud graph structure according to the detection point graph; performing multi-layer perceptron mapping on the point cloud image structure to obtain non-context coding features, and converting the non-context coding features into high-dimensional vector representation to obtain feature space data; performing graph convolution processing on the feature space data to obtain a context point code, and generating a target candidate frame according to the context point code; performing confidence evaluation on the target candidate box to obtain target confidence data, and calculating motion features and type probabilities of the candidate targets according to the target confidence data; and performing clustering and non-maximum suppression processing on the candidate targets according to the motion features and the type probability to obtain a target recognition result.

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 discloses a federal map learning method and system based on feature space construction and sharing. The method and system are used for solving the problems of feature space isomerism, harmful knowledge integration and insufficient personalized performance in federal map learning in a cross-domain Internet of Things environment. The method comprises the following steps: constructing a globally consistent feature space, and realizing node feature alignment through random walk feature generation and standardization; designing a client hybrid feature encoder (CHFE), and combining global features (MLP encoding) and local features (GCN encoding) to generate hybrid representation; proposing an independent adaptive aggregation strategy (IAA), and dynamically calculating an aggregation weight based on the domain similarity, the model similarity and the gradient similarity; a personalized regularization mechanism (PPT) is introduced, and global collaboration and local optimization are balanced. The method significantly improves the convergence speed and generalization ability of the model under heterogeneous data, reduces the communication overhead, and is suitable for privacy sensitive scenes such as medical treatment and industry.

The invention discloses an open set hyperspectral image instance graph construction method based on a distillation diffusion model, and the method comprises the following steps: extracting the spatial and spectral features of a target domain HSI through a spatial encoder and a spectral encoder, executing forward diffusion in a diffusion classifier by taking a real label as a starting point, and carrying out the condition reverse denoising, and finally, closed set category prediction is obtained. A single sample prediction set is generated through Monte Carlo sampling, a prediction variance is calculated, uncertainty distribution is constructed, an unknown class probability is calculated by using a cumulative distribution function, and finally, a known class probability and an unknown class probability are fused to generate teacher open set prediction through a mapping module. A K neighbor graph structure of a sample is constructed in a feature space, teacher open set prediction and student open set prediction are respectively used as nodes to construct a teacher / student instance graph, a Wasserstein distance of prediction probability distribution of the teacher / student instance graph and the student open set prediction is minimized through a graph isomorphic knowledge distillation module, and knowledge distillation based on prediction manifold alignment is realized.

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 invention provides a speed reducer fault positioning method and system based on artificial intelligence, and relates to the technical field of intelligent fault detection and positioning of industrial equipment. The method comprises the following steps: synchronously acquiring vibration, temperature and acoustic emission signals by using a multi-mode sensing module, carrying out filtering, correction and improved wavelet packet decomposition processing through an edge calculation unit, extracting time domain, frequency domain and nonlinear characteristics, calculating a vibration degradation degree and a state deviation index to construct a characteristic space, and calculating a state deviation index; fault large-class identification and fault source accurate positioning are realized through deep metric learning and a graph attention network, and credibility evaluation and online incremental learning optimization are carried out in combination with a D-S evidence theory. The system comprises a multi-modal sensing module, an edge computing unit, a cloud analysis platform, a self-adaptive optimization module and a man-machine interaction terminal. The method overcomes the defects that in a traditional method, a signal source is single, feature extraction depends on experience, real-time performance is insufficient, positioning is inaccurate and the like, and fault detection accuracy and equipment operation safety are remarkably improved.

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 belongs to the field of intelligent man-machine interaction, and relates to an intelligent writing and correcting method and system.The method comprises the steps that a flexible pressure sensor matrix and a coordinate positioning system are used for sensing multi-dimensional data generated when an input pen writes, and a four-dimensional writing feature space is constructed; a three-level correction system is adopted, and logical reasoning verification is conducted on the answers through a deep semantic understanding model; the three-stage correction system sequentially comprises OCR font matching, semantic correction based on a large language model and intelligent correction based on a process reward model; a cognitive diagnosis model is introduced, and the answer hesitation degree is judged by analyzing answer indexes; fusing question answering information, utilizing a large model to identify knowledge points to which questions belong, and inferring knowledge mastering weak points according to question answering conditions; writing information is accurately collected through an intelligent writing capture technology based on multi-modal sensing fusion, an intelligent correction integrated method is built in combination with a cognitive diagnosis model, and efficient recognition and accurate correction of written content are achieved.