1345 results about "Domain adaptation" patented technology

The invention provides an improved YOLOv11s safety helmet wearing detection model and an optimization method thereof, and relates to the technical field of computer vision target detection. According to the improved YOLOv11s safety helmet wearing detection model and the optimization method thereof, the improved YOLOv11s safety helmet wearing detection model comprises the following modules: a multi-modal fusion module, a space-time analysis module, a domain adaptation module, a topological optimization module and a dynamic architecture module; and the multi-modal fusion module is used for realizing feature decoupling by adopting channel separation convolution based on input RGB and near-infrared images, fusing visible light and thermal radiation features through a dynamic weight distribution algorithm, implementing affine transformation alignment on multi-scale features by utilizing a spatial transformation network, and generating a multi-modal feature graph. Through fusion of visible light and near infrared spectrum features and implementation of dynamic weight distribution, complementarity of target texture and thermal radiation features under a complex illumination condition is enhanced, and the problem of feature distortion of single-mode data in a strong backlight or low-illumination scene is solved.

An intelligent analysis method based on medical document structure perception and multi-modal fusion comprises the following steps: carrying out structure topology modeling on a medical document, extracting visual layout, text meta-information, space coordinates and semantic keyword features, constructing a semantic topological graph and dynamically shielding irrelevant contents; selecting an extraction path according to a document type, performing deep semantic analysis and entity recognition on a text-type document, and performing visual enhancement OCR recognition on a scanning-type document; the features are injected into a medical knowledge graph, and feature fusion, semantic verification, relation reasoning and information completion are achieved through a graph neural network; a three-stage strategy optimization model of basic pre-training, domain adaptation and online reinforcement learning is adopted; and large-scale processing is realized through a dynamically aggregated distributed architecture. The method is used for intelligent analysis and structured conversion of documents of hospitals, medical insurance and medical scientific research. The problems that heterogeneous medical document analysis adaptability is poor, multi-modal fusion is difficult, medical knowledge utilization is insufficient, and large-scale processing efficiency is low are solved.

The invention provides an underwater target detection method based on multi-modal features and domain adaptation. The method comprises the following steps: S11, acquiring a sonar image, an optical image and environmental data; s12, extracting a sonar feature and an optical feature, encoding the environment data into an environment channel weight, and dynamically adjusting a fusion proportion of the sonar feature and the optical feature through the environment channel weight to obtain a fusion feature; s13, performing spatial attention calculation on the sonar features to obtain a spatial weight map, enhancing the optical features by using the spatial weight map, and performing forced alignment with the sonar features at the target edge; and S14, decoupling the fusion feature into a synthetic domain feature, decoupling the fusion feature and the environment data into a real domain feature, and gradually aligning the synthetic domain feature and the real domain feature through asymptotic domain alignment to complete construction of a target detection model. According to the invention, multi-modal data acquisition, dynamic feature fusion and decoupling and embedded real-time detection are combined, so that the precision of underwater target monitoring is remarkably improved.

The invention discloses a multi-modal medical image fusion diagnosis system based on artificial intelligence, and the system comprises the following steps: extracting shared features of CT and MRI images through a convolutional neural network, and mapping the shared features to the same feature space; a two-way step-by-step alignment strategy is adopted, a three-dimensional deformation field matrix is generated, and cross-modal image anatomical structure alignment is achieved; calculating modal feature weights and eliminating distribution differences through an attention mechanism and an adversarial domain adaptation layer; constructing a CT-MRI image block contrast learning task, and optimizing a shared feature encoder; a conditional generative adversarial network is used for generating a false image of a missing mode according to the semantic segmentation map, and data distribution is constrained through a Wasserstein distance; uniform feature extraction of multi-modal medical images is realized through a shared feature encoder, the cross-modal image alignment accuracy is improved in combination with a bidirectional deformation field prediction module, and the comprehensiveness and accuracy of fusion features are enhanced by using a multi-modal feature fusion module.

The invention relates to a talent matching and intelligent recruitment method and system based on an AI large model. The method comprises the steps of obtaining multi-modal data of candidates and post demand data of recruiters; performing semantic extraction and feature fusion on the multi-modal data through a multi-modal fusion encoder to generate a comprehensive feature vector containing semantic association features and behavior features; performing field adaptation processing on the comprehensive feature vector based on a dynamically updated industry knowledge graph to generate a skill label set of candidates; performing dynamic matching degree calculation on the skill label set and post demand data according to historical recruitment feedback data by adopting a reinforcement learning model to generate a recommendation list; and outputting the recommendation list to a recruiter terminal, and performing online optimization on the reinforcement learning model based on operation behavior data of a recruiter. According to the invention, intelligent matching of the job seeker and the recruitment demand is realized, and the recruitment efficiency and accuracy are improved.

The invention discloses a network security situation analysis method and device based on multi-agent collaboration, and the method comprises the steps: firstly constructing an agent architecture and a route for network security situation assessment analysis based on an open source large language model, carrying out the role definition of each agent in the agent architecture through combining with a Prompt prompt word, and stipulating the communication format of the agent; based on domain knowledge, a dynamic rank learning LoRA algorithm is adopted to carry out fine adjustment on each agent in the agent architecture, based on a natural language rule base, a commander agent is utilized to coordinate a collusion agent and a simulated blue army agent, multi-dimensional analysis is carried out through reflection, inquiry and debate mechanisms, and a network security situation assessment result and trend prediction are generated. Through combination of dynamic LoRA fine tuning and a multi-agent debate mechanism, the problems that universality and field adaptability are difficult to balance and complex attack intention analysis is insufficient in a traditional method are solved, and the accuracy and prediction reliability of network security situation assessment are remarkably improved.

The invention relates to a micro-expression recognition method based on a cross-source double-branch dynamic space-time diagram convolutional network model, and belongs to the technical field of deep learning and pattern recognition. According to the method, a cross-source double-branch twin space-time diagram structure network is provided to mine subtle motion features of a facial structure when expressions change, and domain invariant features are learned through a twin structure. According to the design, facial global information is modeled through a global information and dynamic spatio-temporal feature extraction network, fine motion information of a facial key structure is extracted through an attention enhancement twin spatio-temporal diagram fusion network, facial structure association when expressions occur is fully modeled, and domain adaptation is performed by constructing cross-domain joint loss.

The invention provides an intelligent path planning method and system based on a large language model, and relates to the field of path planning. According to the invention, an LLM-based multi-level collaborative optimization framework optimizes an original mathematical model and an algorithm strategy for a new demand described by a natural language by integrating the semantic understanding ability of the LLM, so that the algorithm strategy is optimized on the basis of a constructed first mathematical model and a constructed first genetic algorithm. The optimized second mathematical model and the optimized second genetic algorithm are generated in combination with changing new requirements, so that the problems of expressive limitation of engineering modeling by directly using LLM cue words and insufficient field adaptability of generating a meta-heuristic algorithm can be solved, and the scene adaptability of a traditional framework can be improved. Besides, the code proxy based on the MCP is constructed, and a hierarchical progressive self-correction mechanism of the code proxy is combined with standardized scheduling of the MCP, so that omnibearing automatic processing from a surface code error to a deep mathematical model problem is realized.

The invention discloses a machine tool thermal error prediction method based on multi-modal deep learning and domain adaptation, and the method comprises the steps: constructing an infrared image feature extraction module, and extracting the low-dimensional thermal features of infrared image data; constructing a spatio-temporal feature extraction module, and extracting spatio-temporal features in the current and power data; constructing a multi-modal feature fusion module, and fusing the low-dimensional thermal features and the spatial-temporal features to obtain fused features; a thermal error predictor is constructed, thermal error prediction is carried out according to the fusion features, and prediction loss is calculated; machine tool operation data under different working conditions are collected as a source domain and a target domain; through processing of the steps, fusion features of the source domain and the target domain and prediction loss of the source domain are obtained. Inputting the fusion features of the source domain and the target domain into a deep transfer learning module, calculating domain alignment loss, constructing a total loss function in combination with the prediction loss of the source domain, and performing joint optimization on the model; and predicting the thermal error of the target domain after optimization is completed. According to the invention, the prediction accuracy can be improved.

The invention relates to the technical field of power transmission and distribution production, and discloses a data integration and multi-modal diagnosis method based on a power transmission and distribution scene, and the method comprises the following steps: S1, enhanced integration of multi-source heterogeneous data: collecting time sequence monitoring data, text procedures and image data of power transmission and distribution equipment, constructing an equipment topological correlation graph through a graph attention neural network, and carrying out the enhanced integration of the multi-source heterogeneous data; node feature embedding is optimized through self-supervised comparative learning, an adversarial variational auto-encoder is designed for edge data, and an enhanced sample is generated in combination with physical constraints of equipment. According to the data integration and multi-modal diagnosis method based on the power transmission and distribution scene, the field adaptability and reliability of a diagnosis result are improved while the model fine tuning cost is reduced, and the knowledge migration problem of a general model in the power transmission and distribution scene is solved; the introduction of a dynamic knowledge graph and a multi-dimensional evaluation system realizes the real-time integration of new regulation knowledge and the comprehensive evaluation of model performance, and ensures the sustainable evolution ability and decision transparency of the diagnosis model.

The invention relates to the technical field of image processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing method, device and equipment based on cross-domain feature alignment and a medium. The method comprises the following steps: generating potential space representation based on linear projection and position coding, inputting a pre-trained time sequence processing unit to extract and decompose target domain features, realizing alignment of different scale feature distributions by using domain adaptation parameters, fusing the scale features through a hierarchical attention weight mechanism, and generating target image data. According to the method, potential spatial features are extracted by performing blocking and coding on the image data, feature extraction and multi-scale decomposition are realized in combination with a time sequence processing unit, alignment processing of different scale features is completed by using domain adaptation parameters, and the accuracy of local detail recovery and global structure maintenance in a decryption process is effectively improved.

The invention discloses an intelligent question answering system based on a large language model and a knowledge base, and particularly relates to the technical field of artificial intelligence application, comprising a large language model module, a knowledge base module, a retrieval and matching module, an answer generation module and a user interaction module; according to the method, on the basis of a pre-trained large language model and a structured knowledge base, key entities and semantic intentions in user questions are extracted through a multi-level text analysis technology, and preliminary answers are generated; the system combines domain knowledge in a knowledge base, uses vector retrieval and a graph database to efficiently retrieve related information, dynamically fuses retrieval results and generates answers through an attention mechanism and a gating mechanism, and ensures logic consistency and fact accuracy. The system supports multi-modal input and output, and realizes continuity of multiple rounds of dialogues through a memory network; user feedback data optimizes system performance through strategy gradient, and continuously improves answer quality and field adaptability.

Provided is a method for perceiving and eliminating an abnormal state of active distribution network based on data enhancement, including: acquiring, by synchrophasor measurement device, data of each node of active distribution network in target domain in real-time and transmitting to processor; inputting the acquired data into a classification model, and outputting abnormal detection and classification results in real time; and analyzing the abnormal detection and classification results, and transmitting an abnormal state eliminating instruction to a distribution terminal to eliminate the abnormal state. Wherein, hidden distribution features in node data of active distribution network are mined through dynamic clustering, a large amount of unlabeled data are clustered, a data label is generated through self-coding and label correction rule, training samples with balanced category distribution is generated through data enhancement and is used to train the classification model based on dynamic graph attention network by domain adaption method.

The invention relates to the technical field of artificial intelligence, and particularly provides a low-sample NL2SQL intelligent generation method and device, and the method comprises the following steps: S1, enabling a dynamic sample extension module to solve a training data sparse problem in a small sample scene through an intention clarification and data enhancement technology; s2, the clause dependency chain type generation framework converts natural language query into structured query language (SQL) statements with clear structures through semantic analysis, clause generation and dependency modeling; s3, the multi-agent cooperation platform performs iterative optimization through intention recognition, SQL generation and code execution; s4, enabling an automatic evaluation and iteration mechanism to pass a standardized test and continuous optimization; and S5, carrying out field adaptation and security enhancement. Compared with the prior art, the complex query processing capacity can be improved, and the stability and safety of the system are guaranteed through automatic evaluation and a safety mechanism.

The invention relates to a domain adaptive semantic segmentation method and system based on deep learning, and the method comprises the steps: obtaining a to-be-predicted image, inputting the to-be-predicted image into an improved MIC domain adaptive semantic segmentation model for semantic segmentation, and obtaining a prediction segmentation image; the method specifically comprises the following steps: inputting a source domain image into an improved Mamba-based multi-layer feature segmentation network for training to obtain a first student network; inputting the source domain image and the target domain image into a first student network for training, obtaining a second student network, adjusting parameters of the second student network, obtaining a teacher network, inputting the target domain image into the teacher network, and obtaining a target domain pseudo tag; obtaining a high-quality pseudo tag to train the second student network, and obtaining a third student network; and masking the target domain image, and inputting the target domain image into a third student network for training to obtain an improved MIC domain adaptive semantic segmentation model. According to the method, the unsupervised domain adaptive semantic segmentation task can be more efficiently and accurately adapted.

The invention discloses an aircraft intelligent trajectory planning and cooperative control method based on offline optimization-intelligent learning-online guidance, and the method comprises the steps: firstly building an aircraft motion model and an aircraft-target relative motion model, and constructing a full-airspace and full-feature-point optimal trajectory data set; secondly, parameterized representation is carried out on the three-dimensional optimal trajectory, and a proportional guidance coefficient data set is constructed; then neural network training fitting is carried out on the proportional guidance coefficient data set, and a multi-constraint guidance method based on a neural network is designed; and finally, designing a three-dimensional collaborative guidance law by combining a multi-constraint guidance method and a residual time accurate estimation technology. According to the invention, the problems of insufficient real-time performance, incapability of meeting index optimality, incapability of coping with multiple constraints, difficulty in realizing multiple flight modes and the like of the existing trajectory planning and collaborative guidance technology are solved; the multi-constraint and optimality of the trajectory are realized through offline optimization, the global adaptability and small calculation amount of the trajectory are realized through intelligent learning, and a multi-cooperative flight mode is realized through online cooperative guidance.

The invention discloses an artificial intelligence speech recognition system, and the system comprises a multi-modal feature extraction module which employs an improved Conformer architecture to synchronously extract the time-frequency features and text embedding vectors of speech signals; the joint training module is used for performing joint optimization on ASR and NMT loss functions through an adversarial training strategy, learning voice recognition and machine translation tasks at the same time through joint training, and completing direct mapping from voice features to a target language; the context perception translation engine is used for integrating an attention mechanism of a pre-training language model, carrying out deep coding on the extracted speech features and generating cross-language semantic representation; the self-adaptive post-processing module is used for dynamically optimizing an output result by adopting a reinforcement learning framework, dynamically adjusting the output result according to a reward function, and optimizing translation quality and a speech synthesis effect; the dynamic language recognition module is a real-time language classifier based on a Wave2Vec 2.0 framework and is used for recognizing the language of the input voice in real time; and the incremental field adaptation module is used for quickly updating a field term library by using a LoRA fine tuning technology.

The invention discloses an optical storage charging station cross-site heterogeneous data fusion health state early warning method, which adopts transfer learning and domain adaptation technologies, measures the data distribution difference between a source site and a target site through the maximum mean value difference, realizes knowledge transfer and improves the generalization ability of a model. And on the basis of an Attention-LSTM feature fusion mechanism, feature weights are adaptively allocated, and the fault feature extraction capability is enhanced. And designing a fault mode sharing module, coding the fault mode of the source site into a knowledge base, and rapidly matching similar faults of the target site. A model compression technology is introduced, lightweight deployment is realized through knowledge distillation and a parameter quantification method, and operation of edge equipment is effectively supported. And furthermore, an online learning mechanism is adopted to dynamically update model parameters to ensure that the model adapts to the environment change of the target site. According to the method, the accuracy and robustness of cross-site fault prediction are remarkably improved, the model deployment time is shortened, and the method is widely applied to intelligent operation and maintenance of the optical storage charging and discharging integrated power station.

A system and method for cross-domain knowledge transfer in federated compression networks. The system enables efficient lossless data compression across diverse data types by intelligently sharing compression strategies between domains. A cross-domain knowledge transfer system identifies relationships between different data domains, adapts compression parameters accordingly, and optimizes learning processes to maximize knowledge reuse. The architecture may include a knowledge repository for storing domain features and compression patterns, domain mapping components that identify similarities, and transfer learning optimization that enables efficient adaptation with minimal examples. This approach significantly accelerates model training for new domains while improving compression performance. Applications include satellite telemetry systems where efficient compression is critical for transmitting large information sets between distant locations. The system may employ probability prediction driven arithmetic coding paired with long short-term memory networks, enhanced by cross-domain knowledge sharing that adapts successful compression strategies from one domain to another while preserving domain-specific optimization.

The invention discloses a multi-source federal cross-domain and source-domain enhanced millimeter wave action recognition method and system, and the method comprises the steps: generating a micro-Doppler spectrogram through a millimeter wave radar, and extracting the motion features of human body actions through a signal processing module; in the federal multi-source domain adaptation module, dynamically evaluating and fusing knowledge of a plurality of source domains by adopting a voting-based pseudo-tag method and a weighted knowledge aggregation mechanism, and optimizing the generalization ability of a target model; and through a generalization gap optimization method, the performance of the source domain model is improved, and the robustness of the system in different environments is ensured. Through combination of a federated learning framework and a multi-source domain adaptation technology, unsupervised learning under the condition that a target domain has no annotated data is realized, only a single set of millimeter wave equipment is needed, a millimeter wave communication protocol is compatible, and the method has the characteristics of privacy protection, unsupervised learning, multi-source knowledge fusion and strong generalization ability. The method is suitable for application scenes of smart home, health monitoring, man-machine interaction and the like, and has wide practical application value and research prospect.

The invention discloses a lithium battery health state estimation method based on transfer learning, and the method comprises the steps: firstly carrying out the normalization and time sequence reconstruction of a battery charging voltage-capacity curve, and constructing a unified input sequence; extracting long-time-sequence degradation characteristics based on a Mama network, and completing SOH regression prediction through a two-stage full connection layer; in the cross-domain adaptation stage, in combination with an alignment strategy of dynamic time warping and weighted maximum mean difference, time sequence matching and feature distribution alignment in the degradation stage are realized; meanwhile, on the basis of a sample weighting mechanism of a Wasserstein distance, the effectiveness of migrating source domain knowledge to a target domain is improved; through a two-stage strategy of source domain pre-training and source-target joint training, a relatively low prediction error and a relatively high fitting degree can be kept under the condition that a target domain is not labeled; the method shows good generalization ability and robustness under different battery types and different working conditions, and can provide reference for health management of the electric vehicle.

The invention relates to the technical field of large model algorithms, in particular to a knowledge distillation-based field large model lightweight adaptive method and system, and the method comprises the steps: obtaining knowledge distillation parameters and student model performance parameters, and building a nonlinear mapping relation between the knowledge distillation parameters and the student model performance parameters; the optimal parameter combination is optimized and solved based on the mapping relation, and target knowledge distillation parameters are generated; issuing the target parameters to a training engine, monitoring performance deviation in real time and triggering re-optimization; in the reasoning process, performance fluctuation is monitored, and model characteristics are managed and controlled; target domain data characteristics are collected, a mapping relation is corrected in combination with big data analysis, and the domain adaptation capacity is improved; a knowledge base and a case base of historical distillation data are constructed, a standardized adjustment scheme is formed, and self-adaptive matching is achieved. According to the scheme, through precise modeling, dynamic optimization, real-time monitoring and knowledge reuse, the knowledge distillation efficiency, model robustness and field adaptability are remarkably improved, and systematic technical support is provided for large model lightweight.

The invention discloses an information reasoning method, system and equipment based on knowledge enhancement and a medium. The method comprises the following steps: constructing a target knowledge graph and a target knowledge graph index according to a target entity, a target attribute and a target constraint condition in to-be-reasoned information; determining an atlas sub-graph of the target entity through the first-level entity hash index; screening an entity attribute set matched with the target attribute from the atlas sub-graph based on the secondary attribute classification index; filtering the entity attribute set matched with the target attribute according to the three-level space-time dimension index to obtain a target dynamic attribute; recalling a target retrieval fact from the target knowledge graph according to the graph sub-graph, the entity attribute set and the target dynamic attribute; the to-be-reasoned information and the target retrieval facts are input into the preset large language model to obtain the target reasoning result, knowledge enhancement reasoning can be carried out by constructing the multi-level index target knowledge graph and fusing the dynamic attribute constraints in combination with the large language model, and then the accuracy, the real-time performance and the field adaptability of the reasoning result are improved.

The invention discloses a local legislation compliance intelligent detection system and method based on deep semantic analysis and a multi-modal legal knowledge graph, and relates to the field of computer technology and law crossing technology, the method comprises the following steps: constructing a legal knowledge graph; designing a multi-dimensional rule sub-library with a dynamic weight adjustment mechanism; a large language model based on Deepseek is utilized to construct legal provisions to perform a deep semantic analysis model, so that the intelligence of generation of triads of laws and regulations in the national field is realized, and the field adaptability of triad generation is improved; executing multi-dimensional conflict detection based on the knowledge graph, a conflict detection rule base and a semantic analysis result; and a multi-dimensional law conflict report is automatically generated. According to the method, a legal knowledge graph is constructed, multi-source legal data is integrated, a multi-dimensional rule sub-library is combined, a Deepseek-based special model is used for carrying out deep semantic analysis on legal provisions, and explicit and implicit conflict judgment is carried out on contradictory point locations and contexts.

The invention discloses an image classification method for cross-domain transfer learning and related equipment, and relates to the technical field of image classification, and the method comprises the steps: obtaining a source domain image set and a target domain image set; extracting a first multi-level semantic feature of the source domain image set and a second multi-level semantic feature of the target domain image set based on a heterogeneous feature extraction network; generating a cross-domain migration weight matrix through a dynamic domain similarity measurement module based on the first multi-level semantic features and the second multi-level semantic features; based on the deformable feature pyramid, performing spatial transformation on the first multi-level semantic features to generate a migration feature map; based on the cross-domain migration weight matrix, channel recombination is carried out on the migration feature map, and target domain adaptive feature representation is constructed; and based on the target domain adaptive feature representation, outputting a classification result of the target domain image through a target domain classifier. According to the method, the feature matching precision and the classification robustness in a cross-domain scene are improved.

The invention discloses a workflow-based multi-modal water conservancy large model decision support method, which comprises the following steps of: S1, preprocessing multi-modal input data based on a local model, and converting a data format; s2, constructing a modular workflow supporting parallel processing through a specified DSL (Digital Subscriber Line) framework; s3, constructing a feature model suitable for the water conservancy field through the multi-modal data subjected to synchronous acquisition and processing, and performing field adaptation and fine adjustment on the feature model; s4, on the basis of the feature model subjected to domain adaptation and fine adjustment, a specified workflow is constructed, and multi-modal task resources are dynamically scheduled; and S5, combining a local knowledge base with networking data by using a dynamic fusion algorithm to realize dual-channel knowledge fusion for executing the multi-modal task in the S4 in parallel. According to the invention, real-time acquisition and parallel processing of multi-modal data can be realized, and the stability of a local knowledge base and the real-time performance of networking data are dynamically balanced through a dual-channel knowledge fusion mechanism.

The invention belongs to the technical field of spacecraft pose estimation, and particularly relates to a cross-domain spacecraft pose estimation method based on mask self-distillation domain adaptation, and the method comprises the steps: 1, inputting a complete image, and employing a Faster R-CNN algorithm to position a spacecraft bounding box; the robustness of the model is improved by applying a track environment data enhancement strategy; and extracting a target ROI region as key point regression network input based on the detection frame. 2, dividing ROI (Region of Interest) data of a source domain and a target domain; and optimizing heat map supervision loss learning key point positioning knowledge. 3, inputting random mask enhanced target domain data into the student model; inputting original target domain data into the teacher model; a learnable shared prototype space is constructed, and self-distillation is guided through heat map consistency loss and semantic consistency loss. And 4, jointly optimizing the loss of the key point regression network 3, and realizing progressive migration of source domain annotation knowledge to a target domain. And 5, solving the 6D pose of the spacecraft relative to the camera through the EPnP. According to the invention, robust six-degree-of-freedom pose estimation of the target spacecraft is realized.

The invention relates to the technical field of blood oxygen detection, in particular to a pulse blood oxygen saturation detection method based on image processing, a transfer learning strategy and a domain adaptation network are introduced, adjustment can be carried out according to personalized physiological parameters of a user, and therefore the applicability to different crowds is improved. Especially for people with deep skin color, the measuring error is obviously reduced, and the error rate is reduced by more than 30%. A set of mechanism for calculating the current signal quality index Q in real time is designed, dynamic calibration is carried out in combination with a Bayesian filtering model, and it is ensured that a reliable blood oxygen saturation degree estimation value can be obtained even under the condition of the low signal-to-noise ratio. And when the signal quality is reduced, the current detection value is compensated by using historical data through the LSTM network prediction module, so that the accuracy and continuity of the result are further ensured.

The invention is suitable for the technical field of machine learning, and provides a feature selection method and system based on domain adaptation and domain adversarial training, and the method comprises the following steps: obtaining source domain data and target domain data, constructing a source domain feature selection target function, and generating a binary feature mask vector; constructing a deep transfer learning framework based on a domain adversarial neural network, training the domain adaptive neural network by using the source domain tagged data and the target domain untagged data in a domain adversarial form, and constructing a cross-domain shared feature representation space of the source domain and the target domain; and feature selection knowledge migration from the source domain to the target domain is realized through decoding conversion. According to the method, the feature distribution difference between the source domain and the target domain is effectively eliminated through the adversarial training strategy driven by the gradient inversion layer, the method has remarkable advantages in a target domain data scarcity scene, the data annotation cost can be reduced, cross-domain potential association can be captured, and redundant features and over-fitting risks are reduced.

The invention relates to the technical field of compliance management, and particularly discloses a compliance management system dynamic evaluation system and method based on artificial intelligence, and the method comprises the steps: firstly carrying out the content segmentation of an internal compliance document of an enterprise, and obtaining all text units for describing the compliance practice of the enterprise; and performing semantic vectorization representation on each compliance text description unit and the external regulation change text segment by adopting a converter model finely adjusted by a domain adaptability model so as to capture context semantic information of the text, and further performing multi-dimensional semantic collaborative conjoint analysis on the compliance text description units and the external regulation change text segment. And the logic relationship between the two is deconstructed in a semantic space, so that the implication, contradiction or neutral relationship between the external regulation change and the current compliance system of the enterprise is intelligently identified. According to the method, the consistency of the enterprise compliance management system and the external regulation environment is efficiently and accurately evaluated, and effective decision support can be provided for adaptive iteration of the compliance system in the supervision environment.