562 results about "Domain model" patented technology

The invention relates to a ship safety monitoring method, system and device based on data fusion, and relates to the technical field of ship navigation safety, and the ship safety monitoring method comprises the steps: collecting multi-source heterogeneous data; preprocessing the multi-source heterogeneous data; carrying out fusion processing on the multi-source heterogeneous data based on an information entropy theory; a ship safety domain model is constructed, trajectory prediction is executed, a collision risk index is calculated, and the ship collision risk is evaluated; and performing anomaly detection on the ship equipment operation data based on manifold learning and a thermodynamic model. According to the invention, a multi-source heterogeneous data fusion technology is adopted, multi-dimensional data sources are integrated, and omnibearing ship safety situation awareness is constructed. The early warning time is prolonged to 8-10 minutes, which is far better than the early warning time of 3-5 minutes in the prior art. The system thoroughly solves the problem that subsystems in a traditional ship monitoring system operate independently and do not communicate with one another, the crew information acquisition cost is reduced, and the number of times of interactive operation is reduced by 42%.

The invention discloses an adaptive medical image segmentation method during testing based on prototype alignment. The method comprises the following steps: acquiring target domain medical image data; obtaining a source domain model, wherein the source domain model comprises a feature extractor and a classifier; extracting a class prototype of each class by using a classifier, and initializing two models with the same structure as the source domain model by using a feature extractor and the classifier of the source domain model; the two initialization models are respectively used as a teacher model and a student model; the target domain image is input into a teacher model and a student model, the teacher model outputs a pseudo tag and an entropy graph of a target domain, the student model carries out supervised training according to the pseudo tag, and a target domain prototype output by the student model is aligned with a source domain prototype; the teacher model is updated through the index moving average value of the student model parameters, and finally the teacher model outputs a segmentation result. According to the method, the relation between the source domain and the target domain can be effectively established, and the cross-modal image segmentation precision is improved.

The systems and methods disclosed herein relate to querying data using artificial intelligence models. A generalized model receives an output generation request and partitions it into segments mapped to specific domains, where each domain indicates associated databases and guidelines. The segments are routed to domain-specific models trained on domain-specific data, which generate query fragments by comparing performance metrics and system resource usage metrics. The query fragments are aggregated into an overall query that satisfies guidelines across domains. The systems and methods can include a feedback loop to adjust the domain-specific models using user interactions and performance metrics to dynamically adapt to a skill level or experience of the user.

The invention discloses a consultation method and system based on natural language processing and a legal knowledge graph, and relates to the field of data processing, and the method comprises the steps: receiving a multi-format legal consultation demand of a user, converting the multi-format legal consultation demand into a text, inputting the text into a BERT law NLP model, and analyzing key information through word segmentation, intention recognition and entity extraction; based on a pre-constructed multi-level legal knowledge graph, carrying out accurate and fuzzy retrieval and domain filtering in combination with an analysis result, and obtaining an associated law article, a case and a legal relationship; screening conflict law articles and similar cases, and inputting the conflict law articles and the similar cases into a graph neural network reasoning model to generate a preliminary conclusion; the conclusion is converted into a spoken consultation report through a natural language generation module, and output is customized according to a user scene; and if the user feedback satisfaction degree is less than the threshold value, iteratively optimizing the storage data to the historical library. The method has the advantages that accurate retrieval is realized based on the BERT model and the multi-level knowledge graph in the legal field, the oral personalized conclusion combined with the user scene is generated through GNN reasoning, and iterative optimization is performed through user feedback.

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.

Systems and methods for domain-specific model-generated content item generation, evaluation, and selection can include generating a plurality of candidate model-generated content items that can then be evaluated based on one or more signals, which can then be leveraged for candidate model-generated content item selection. The plurality of candidate model-generated content items can be generated with a generative model that was tuned for domain-specific content item generation. The selected model-generated content item can be processed to generate an outline that may then be provided to a user for user interaction to generate an augmented outline. The augmented outline may then be processed to generate an updated model-generated content item.

The invention discloses a cross-domain model parameter security aggregation processing system based on federated learning, and the system comprises a decentralized identity and reputation management module which is used for registering nodes and initializing reputation; the dynamic aggregation node election module is used for electing aggregation nodes from candidates based on reputation; the local security processing and proof generation module is used for generating encrypted model update verifiable proof at each node; the encryption transmission and on-chain verification module is used for verifying, proving and confirming the legal contribution; the trusted adaptive aggregation and execution proof module is used for safely aggregating the legal contribution in the trusted environment of the aggregation node, and generating a new model and execution proof; and the global state updating and reputation feedback module updates the reputation of each node according to the execution proof to complete a closed loop. According to the method, a decentralized model parameter aggregation framework is constructed by adopting a distributed consistency account book management system rule and combining with a dynamic aggregation node formed by a trusted execution environment, so that periodic rotation and hardware-level security isolation of an aggregation task executor are realized.

The invention relates to an uncertainty perception passive multi-target field adaptive image classification method, which is used for image recognition of autism spectrum disorder patients. According to the method, firstly, source domain model parameters are obtained and used for initializing a target model, then resting state functional magnetic resonance images of a plurality of imaging centers are preprocessed, and a plurality of target domains are constructed. On this basis, a current most representative target domain is selected through a minimum inter-domain difference strategy, an uncertainty modeling method based on evidence deep learning is adopted to train a target model, and class feature consistency is improved through domain contrast learning based on a class prototype in combination with a dynamically expanded auxiliary data set; and generating a pseudo tag to relieve the influence caused by tag noise. And finally, a trained target model is obtained through fine tuning optimization, and accurate classification of unknown images is realized. The method does not need to access source domain data, has the advantages of high robustness, high generalization ability and the like, and is suitable for actual cross-center medical image analysis 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 provides a transformer area distributed photovoltaic power prediction method and system, and the method comprises the steps: carrying out the clustering according to meteorological feature vectors in meteorological data through employing a clustering algorithm, dividing a source domain shared data set into similar data sets of different weather prediction scenes, training an Informer basic source domain model through employing a key representative sample in each prediction scene, and carrying out the prediction of the power of a transformer area. In the prediction process, the similarity between the meteorological characteristics of the day to be predicted and the meteorological characteristics of the typical weather scene is calculated, and the weather prediction scene and the corresponding basic source domain model are dynamically matched for each transformer area; and screening a key historical similar day of a day to be predicted of each transformer area as a micro training set, and dynamically updating decoder parameters of the adaptive Informer basic source domain model based on a model fine tuning thought of transfer learning to obtain a power prediction result of each transformer area. According to the method, the pertinence of a prediction result is improved, so that rapid modeling of distributed photovoltaic power prediction of massive transformer areas is realized.

The invention relates to the field of data annotation, and particularly discloses a data annotation method and system based on a large model, a terminal and a medium. Loading the fine-tuned domain model to carry out batch pre-labeling on the standardized data set to obtain a pre-labeling result of the standardized data set; calculating the prediction uncertainty of each sample in the standardized data set, and selecting a plurality of samples according to the prediction uncertainty to form a first to-be-audited data set; predicting the contribution degree of each sample in the standardized data set to the improvement of the domain model, and selecting a plurality of samples according to the improvement contribution degree to form a second to-be-audited data set; taking a union set of the first to-be-audited data set and the second to-be-audited data set to generate a to-be-audited target data set, and manually auditing the pre-labeling result of each sample in the to-be-audited target data set; and obtaining a labeling result of the standardized data set according to a manual auditing result. According to the invention, the data labeling efficiency and precision are improved.

The systems and methods disclosed herein relate to querying data using artificial intelligence models. A generalized model receives an output generation request and partitions it into segments mapped to specific domains, where each domain indicates associated databases and guidelines. The segments are routed to domain-specific models trained on domain-specific data, which generate query fragments by comparing performance metrics and system resource usage metrics. The query fragments are aggregated into an overall query that satisfies guidelines across domains. The systems and methods can include a feedback loop to adjust the domain-specific models using user interactions and performance metrics to dynamically adapt to a skill level or experience of the user.

The invention belongs to the technical field of knowledge Token generation and traceability, and discloses a knowledge Token generation and traceability method based on structured metadata, which is characterized in that a current knowledge scene is quickly matched and a core field is dynamically adjusted through few-sample learning in combination with various technical models and a preset industry field model library; a mixed semantic segmentation mode is adopted, knowledge fragments are completely segmented according to business logic, then a refined element extraction strategy is matched, three types of key elements including entities, relationships and clauses are accurately extracted, and errors are reduced through multi-dimensional verification; originally dispersed PDF, audio and video and other unstructured knowledge can be converted into standardized assets with anchoring information. Meanwhile, a multi-dimensional verification process is designed, comprehensive verification is carried out from a data source, access permission and logic consistency to a health state, it is ensured that a knowledge source can be checked, authenticity can be distinguished, and the information distortion risk during large model calling is reduced.

The invention relates to the technical field of model transfer learning, and discloses a model adaptive optimization method based on transfer learning. The method comprises the steps that source domain model structure parameters and target domain task initial data distribution are obtained, the feature mapping relation of all levels of a source domain model is extracted, and a cross-domain feature migration reference topological framework is generated; dividing a migratable feature layer and a to-be-reconstructed feature layer according to a target domain data distribution difference, and dynamically adjusting a migration priority in combination with a sample distribution density; freezing and unfreezing the transferable feature layer layer by layer based on the priority, synchronously constructing a local feature reconstructor, and optimizing domain offset through iterative feature alignment; collecting a feature reconstruction error and a migration feature retention degree in each iteration, and calculating a dynamic balance coefficient to adjust a freezing proportion and reconstruction intensity; and fusing the two types of features through a global model integrator, and generating mixed feature representation to drive end-to-end training of a target domain task.

The invention discloses a robot operation and maintenance long thinking chain corpus generation method based on a knowledge graph. The method comprises the steps of establishing an original corpus, generating a cleaning corpus, generating a deduplication corpus, performing intelligent blocking, generating question and answer pairs, performing entity recognition, performing entity mapping, determining a target reasoning path and generating a long thinking chain corpus. According to the method, a knowledge graph reasoning path is introduced as a generation constraint, so that model illusion is effectively inhibited, and explicit reasoning, logic completeness and full-link traceability of the reasoning process are realized; and meanwhile, in combination with puzzle-driven partitioning and domain model fine tuning, the semantic coherence and professional accuracy of the corpus are remarkably improved, and low-cost, large-scale and high-quality operation and maintenance corpus automatic production is realized.

The invention discloses a real-time production data acquisition and intelligent scheduling method and system based on the Internet of Things, and relates to the technical field of artificial intelligence, an abnormal result is calculated based on a fusion weight and a predicted abnormal probability by constructing an abnormal detection model composed of sub-models, and when the abnormal result is greater than an abnormal judgment threshold, an abnormal state is judged; a threshold value self-adaption module is embedded in the anomaly detection model, and an anomaly judgment threshold value is output by taking minimization of the total misjudgment rate of anomaly detection as an optimization target; selecting a device and taking an anomaly detection model as a source domain model, migrating to a target device to obtain a fine-tuned target model, and predicting an anomaly probability value; identifying the key index, calculating the comprehensive health degree score of the key index based on the evaluation rule base, and judging the health state grade of the target equipment; and on the basis of the abnormal probability value and the health state level on the key index, the overall health score of the target equipment is judged, and intelligence and self-adaptability of equipment anomaly detection and health management are realized.

The invention provides an ethical examination method and device based on a large language model, and the method is characterized in that the method comprises the following steps: S1, carrying out the secondary pre-training, secondary instruction fine tuning and human feedback reinforcement learning of an existing large language model according to the existing training data, and obtaining a trained large language model as a domain model; s2, inputting the project document into an existing general model, and obtaining a preliminary review result corresponding to the project document in combination with a retrieval enhancement method and a review rule base; and S3, inputting the preliminary review result into the domain model, and obtaining a review result in combination with the review rule base. In a word, the method can assist the reviewer in rapid and efficient ethical review.

The invention discloses a textile workshop optimization algorithm recommendation method and system based on a large language model, and relates to the technical field of textile workshop scheduling, and the method comprises the steps: S1, constructing a textile knowledge vector database comprising a first-stage textile modeling knowledge base and a second-stage algorithm code knowledge base; s2, taking the spinning knowledge vector database as a fine tuning data set, and performing spinning field adaptive fine tuning on the large language model to obtain a spinning field model; and S3, based on the textile knowledge vector database, taking the obtained textile workshop scheduling demand as input, and using a retrieval enhancement generation method to generate a recommendation algorithm code for textile workshop scheduling. The textile field model is obtained by establishing the textile knowledge vector database and performing efficient fine tuning in the textile field on the large language model, the executable code is automatically generated through the retrieval enhancement generation method, the scheduling requirements and constraint conditions of different textile workshops can be adapted, the manual intervention cost is reduced, and the scheduling efficiency is improved. And the reusability and the scheduling scheme solving efficiency are improved.

The invention discloses a source domain irrelevant cross-domain cardiac beat recognition method and system for pseudo-label mining, and relates to the technical field of pseudo-label learning, and the method comprises the steps: obtaining electrocardiogram data with cardiac beat labels in a source domain, inputting a pre-established source domain model for pre-training, and obtaining a pre-trained source domain model; acquiring unlabeled electrocardiogram data of a target domain, and screening and classifying the data of the target domain based on a pre-trained source domain model and a preset category threshold to obtain data with high false label confidence and data with low false label confidence; using the source domain model to initialize target domain model parameters, revising a strategy based on a pseudo label of local and global semantic perception, updating the pseudo label of the low-confidence data, and combining the high-confidence data to obtain updated pseudo-labeled target domain data; target domain data subjected to data augmentation pseudo labeling are input into a target domain model, a target domain model optimization total loss function is calculated, and cross-domain cardiac beat intelligent recognition irrelevant to a source domain is achieved.

The invention discloses a multi-modal map data-oriented network architecture search synchronous transfer learning method, and the method comprises the steps: carrying out the preprocessing operation of a collected near infrared spectrum, a collected Raman spectrum and a collected microscopic image, carrying out the feature extraction, and carrying out the fusion of the extracted features through a cross attention mechanism, and forming a new optimal fusion feature; in the constructed SSLNST module, a fused data set is divided into a source domain and a target domain through a transfer learning technology, and neural network architecture search NAS adopts a three-stage optimization strategy including network search, fine tuning and feedback; a loss weighting optimization strategy is introduced into the obtained target domain model, the proportion between the classification loss value and the sample weight is adjusted, and the training process of the fusion data model is optimized. The technical problems that in the prior art, the map fusion efficiency is low, the method is poor, and an effective model cannot be constructed under the condition that labeled sample data is insufficient are solved.

The invention discloses an efficient optimization selection and model adaptation method and system based on LoRA and MoE technologies, and the method comprises the steps: firstly, constructing a LoRA module pool for various models and vertical field data sets according to different field models and data set features, so as to rapidly screen and call adaptation models in similar tasks; secondly, based on downstream task description, hidden layer features and confusion, performing coarse-grained LoRA selection from a LoRA module pool; and then, by using the performance characteristics and the single LoRA weight distance, further screening a fine-grained LoRA module from the LoRA modules selected from the coarse-grained LoRA modules based on diversity. And finally, activating the selected LoRA module by adopting a Top-k strategy based on the MoE architecture, and finally determining the LoRA module which is most adaptive to the downstream task in combination with performance feedback after integration with the basic model. According to the method, a large number of LoRA modules can be effectively constructed, the module most adaptive to the downstream task is selected, and the accuracy of the model in processing tasks such as mathematical reasoning is remarkably improved.

The invention provides a non-perceptual data migration evaluation method, which comprises the following steps of: calculating a feature distribution difference by adopting a maximum average difference measurement function according to source domain and target domain image data, taking a difference measurement value as input of a resistance loss function, and realizing feature alignment through a gradient inversion layer and a conditional generative adversarial network; extracting source domain and target domain features under different receptive field scales by applying a multi-scale feature confrontation alignment method according to the form of an adversarial loss function, respectively calculating the adversarial loss, and obtaining the total adversarial loss through weighted summation; for small targets and unbalanced categories existing in target domain data, the adaptive capacity of a target domain model to hard cases is improved in a hard case mining and resampling mode, and meanwhile the migration effect of the target domain model on the hard cases is evaluated through the antagonism loss and task loss of the hard cases.

The invention discloses a meteorological data processing and storage method and system, and relates to the technical field of intelligent meteorological data processing, and the method comprises the steps: collecting multi-source meteorological observation data, radar data and numerical forecasting data, carrying out the access judgment, obtaining an access data set, carrying out the standardization and elevation correction, and obtaining a data set; calculating a comprehensive quality score to obtain a quality mark data set; screening samples based on the mass label data set to obtain an alignment data set, obtaining grid fusion data through the alignment data set, and performing smooth processing to obtain a fusion data set; obtaining a sealing partition based on the fused data set, and revising by using a late sample to obtain a versioned sealing partition; and obtaining a fact table and a dimension table according to the versioned sealing partition, establishing a topic domain model and a multi-version query interface, and calculating a prediction deviation and a hit rate. According to the method, unit standardization, spatial elevation correction and comprehensive quality scoring are performed on the accessed data set, so that dynamic evaluation and reliable marking of the meteorological data quality are realized.

The invention relates to the technical field of engineering, and provides a data access method, a program product, a storage medium and electronic equipment. The data access method comprises the steps that a meta model of a to-be-developed product is obtained, and the meta model at least comprises domain models used for designing the to-be-developed product, entities included in the domain models and the relation between the entities; determining interface configuration corresponding to the domain model according to the meta-model; according to the interface configuration, accessing model data of the domain model; according to the model data, the relation in the meta-model is instantiated under the guidance of a unified semantic framework of the to-be-developed product, a unified semantic model of the to-be-developed product is obtained, the unified semantic framework at least comprises elements, attributes and the relation forming the to-be-developed product, and the unified semantic model at least comprises a triple composed of entities, the relation and facts. According to the method, unified description and standardized management of model data of models in different fields are realized, and a solid foundation is laid for subsequent use of the data.

The invention provides a model issuing method and device, a model obtaining method and device, UE, a network side network element, a communication system and a storage medium. The model issuing method comprises the steps that a model request sent by the UE through control plane signaling is received; in response to the model request, sending first response information to the UE through the user plane network element, or sending second response information to the UE through the control plane network element; wherein the first response information comprises target model information matched with the model request; the second response information comprises model request rejected information. The security of interaction can be ensured, and the method is suitable for transmission of a large amount of data; cross-domain model transmission can be realized, the security is high, supervision on the model can be enhanced, and resources such as storage and computing power can be fully utilized.

The invention discloses a collaborative data model adaptation-based adaptive medical image segmentation method during testing, and aims to solve the problem that the image segmentation performance of an existing segmentation method needs to be improved. According to the technical scheme, a collaborative data model adaptation-based self-adaptive medical image segmentation system during testing is firstly constructed, a prompt update model is arranged in a prompt update module of the segmentation system, an image segmentation module is composed of image segmentation models, and batch normalization layers are arranged in the two models; a source domain model and a medical image are adopted to test a segmentation system, a batch normalization layer is used as a bidirectional bridge to realize collaborative self-adaption of data adaption and model adaption, and low-level distribution alignment and high-level semantic feature adaption are realized in a Fourier space; and segmenting the medical image by using the adapted segmentation system. By adopting the method, error accumulation and disastrous forgetting can be avoided, data adaptation and model adaptation are coordinated, the image segmentation effect can be improved, and the Dice coefficient is improved.

The invention provides a big data integration and distribution unit component of a hierarchical architecture and a use method, relates to the technical field of data integration and processing, and solves the problems of real-time integration, unified processing and safe distribution of multi-source heterogeneous data. The device adopts a five-layer modular architecture; a data access layer supports multi-source data access and is buffered through Kafka; the cleaning conversion layer performs data cleaning and standardization by using a FlinkSQL, an ETL engine and a rule engine; the intelligent distribution layer judges a data distribution path in combination with a rule engine and a machine learning model; the storage calculation layer adopts a cold and hot separation storage strategy and supports parallel operation of Spark batch processing and Flink stream processing; and the service and security layer provides an interface through an API gateway, and guarantees data security in combination with AES-256 encryption and RBAC access control. Besides, dynamic resource scheduling is realized through Kubernetes, cross-domain model cooperative training is supported by utilizing a federated learning technology, the intelligent shunting decision-making capability is improved, various industrial protocols such as MQTT and Modbus are compatible, and VXLAN tunnel transmission is supported.

The invention discloses a passive domain adaptive federal learning method based on self-supervised knowledge distillation, which improves the generalization ability of a model in a target domain by optimizing pseudo label generation, self-supervised learning and knowledge distillation strategies. The method comprises the following steps: a client firstly uses source domain data to train a local model, and a server aggregates to generate a global source domain model; then, on the basis of the global source domain model, the client side generates an initial pseudo label for target domain data, the quality of the pseudo label is optimized through self-supervised learning, a knowledge distillation strategy is introduced, the teacher model uses the pseudo label to guide the student model to learn, and the student model updates parameters and then sends the parameters to the server; and the server aggregates and generates globally updated target model parameters and broadcasts the globally updated target model parameters back to the client, and the client continuously trains until the model performance reaches the standard or converges. The method does not need to depend on source domain data, only uses the unmarked data of the target domain to generate the pseudo tag, combines the federated learning framework to aggregate the model parameters, significantly enhances the adaptability and accuracy of the model to the target domain, effectively protects the data privacy, and reduces the storage cost.

The embodiment of the invention discloses a model optimization method based on transfer learning. The method comprises the following steps: acquiring to-be-migrated learning data, wherein the to-be-migrated learning data comprises source domain data associated with pathological characteristics of kidney diseases; semantic alignment processing is carried out on the source domain data and target domain data, the target domain data is kidney disease pathology data, and a semantic alignment result is obtained; determining a feature distribution result corresponding to the target domain data, and determining a migration weight corresponding to the source domain data based on the feature distribution result and the semantic alignment result; and optimizing the target domain model based on the migration weight, the source domain data and the target domain data to obtain an optimized target domain model. According to the embodiment of the invention, the accuracy and robustness of the target domain model in a kidney disease pathological diagnosis task and the generalization ability of different data distributions can be remarkably improved.

Arrangements for intelligent content generation for process automation are provided. A domain model, being structured into tasks according to a defined schema, may be exported for processing by a large language model. A prompt and a context window associated with a task of the domain model may be received. A task template associated with the task may be modified. The modified task template may be enriched with data from a backend system. Content validation may be performed on content of the modified task template enriched with the data from the backend system. Schema validation may be performed for validating the modified task template enriched with the data from the backend system against the defined schema. Correction of invalid tasks may be performed in an iterative loop until the modified task template enriched with the data from the backend system is validated. Then, changes to the domain model may be applied.