249 results about "Domain model" patented technology

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 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 provides a construction log generation method based on an intelligent agent and a large model. The method comprises the following steps: 1, constructing a multi-source heterogeneous data acquisition layer: automatically acquiring required original data from a built-in interface, an external application programming interface, Internet of Things equipment and a manual input channel through the intelligent agent; 2, establishing a data preprocessing and structuring layer: processing unstructured data by using an AI technology, extracting key information, and packaging the key information into a standardized data unit; 3, constructing a domain model of precise construction terms and log styles; 4, generating a log first draft by the large model; 5, man-machine collaboration auditing and optimizing, wherein a man-machine collaboration interface is provided for engineers to audit, correct and confirm the generated log first draft; and 6, based on manual feedback, establishing an optimization closed loop, and carrying out continuous iterative optimization on data acquisition, a preprocessing rule, a cue word template or a large model. According to the method, full-automatic acquisition is realized, various data sources are automatically docked through the intelligent agent, and manpower is thoroughly liberated.

The invention relates to the cross technical field of computer-aided engineering and audio equipment design, discloses a circuit design simulation-based disc player design optimization method and system, and aims to solve the problem that the existing disc player design depends on physical prototype trial-manufacture and subjective listening evaluation and lacks multi-physical domain co-simulation capability. The method comprises the following steps: constructing a unified multi-physical domain model comprising a mechanical transducer, an electromechanical transducer and an electronic circuit sub-model; bidirectional coupling co-simulation based on the time sequence is executed, and an output electric signal is generated; extracting objective audio performance indexes such as total harmonic distortion and signal-to-noise ratio; and carrying out multi-objective optimization on the key design parameters by utilizing a genetic algorithm, and outputting a Pareto optimal scheme. The system comprises a model construction module, a co-simulation module, a performance index extraction module and an optimization module. Through high-fidelity full-link simulation and automatic optimization, the design efficiency and the audio fidelity are remarkably improved, and the research and development cost is reduced.

The invention provides a domain reasoning model training method and device, a domain reasoning model using method and device, equipment and a medium, and relates to the artificial intelligence technology. The domain inference model training method comprises the steps that an original domain model is finely adjusted based on a preset inference data set, and an intermediate model with the inference capability is obtained; the preset reasoning data set is generated by reasoning general open source data through a reasoning model and comprises reasoning process data and reasoning conclusion data which conform to the output form of the target domain; based on the current inference data set, performing iterative training on the basis of the original domain model to obtain a domain inference model; wherein in each iteration process, the current reasoning data set is updated based on the field problem data set and a current intermediate model obtained based on the current reasoning data set in the current iteration process; the domain problem data set comprises task feature information of the target domain. By means of the method, the domain model reasoning effect can be effectively improved.

The invention provides a vehicle body heterogeneous data alignment and vertical domain model fine tuning method, relates to the technical field of industrial data processing and vehicle artificial intelligence, and aims to solve the problem that the incubation period quality risk induced by complex physical causes is difficult to early warn in the prior art. The method comprises the following steps: acquiring a logic difference between an actual process sequence and a reference sequence as first data; responding to a specific event, synchronously acquiring a multi-modal physical signal, and calculating physical characteristics of the multi-modal physical signal as second data; fusing the first data and the second data through a preset causal association probability model to generate an indication signal representing the risk, such as a causal inertia index CII; and finally, according to whether the indication signal satisfies a dynamic risk condition, a differentiated linkage fine tuning instruction is generated. According to the method, dynamic and self-adaptive early warning of potential quality risks is realized through deep fusion of process logic and physical processes, and the refined management and control level of the manufacturing process is improved.

The application discloses a meteorological data processing and storage method and system, and relates to the technical field of intelligent meteorological data processing. The method comprises the following steps: collecting multi-source meteorological observation data, radar data and numerical prediction data, performing access determination to obtain access data sets, performing standardization and elevation correction on the access data sets, calculating comprehensive quality scores to obtain quality marked data sets; filtering samples based on the quality marked data sets to obtain aligned data sets, obtaining grid fusion data through the aligned data sets and performing smoothing processing to obtain a fusion data set; obtaining a sealing partition based on the fusion data set, and revising the sealing partition by using a late sample to obtain a versioned sealing partition; obtaining a fact table and a dimension table according to the versioned sealing partition, establishing a subject domain model and a multi-version query interface, and calculating a prediction deviation and a hit rate. Through unit standardization, spatial elevation correction and comprehensive quality scoring on the access data sets, dynamic evaluation and reliable marking of meteorological data quality are realized.

This invention provides a medical ultrasound image recognition method based on generator domain alignment, relating to the field of image classification technology. The method includes: creating labeled generated medical ultrasound image samples for fine-tuning using a conditional diffusion model, where labels characterize the category corresponding to the generated medical ultrasound image sample; aligning each labeled generated medical ultrasound image sample to the generator domain using an unconditional diffusion model of the generator domain, obtaining a generator domain medical ultrasound image set; fine-tuning the source domain model using the generator domain medical ultrasound image set, obtaining a generator domain model; aligning a target medical ultrasound image to the generator domain using the unconditional diffusion model of the generator domain, obtaining a generator domain target medical ultrasound image; and inputting the generator domain target medical ultrasound image into the generator domain model to obtain a first predicted classification result for the target medical ultrasound image. This transforms a cross-domain task into an intra-domain prediction task, aligning both the source domain model and the target data to the same generator domain, thereby achieving accurate predicted classification results.

This application discloses a dynamic form construction method, device, and medium, relating to the fields of software engineering and enterprise application development technology. The method includes: receiving a form request containing a view model identifier and request type based on a preset view parsing and execution engine; parsing a predefined view model according to the identifier to obtain its metadata, which defines the target business entity, view data object structure, field set, and filtering conditions; for data query requests, dynamically constructing and executing SQL statements based on the aforementioned metadata, obtaining raw data, performing aggregation and transformation, and generating structured view data that meets the front-end rendering requirements for return; for data submission requests, converting the data submitted from the front-end back into domain model data according to the same view model, and calling the business entity engine service to complete business logic verification and persistent storage, thereby achieving dynamic generation of the form view, decoupling of front-end and back-end logic, and ensuring business consistency.

Systems and methods of automated task planning in a remotely operated device system are provided. The method includes storing a domain model for modeling a domain of the remotely operated device system and a task library including a plurality of subgoals each mapped to a corresponding subtask or set of subtasks that achieves the subgoal, the task library being filterable by subgoal, and receiving, through a human-machine interface, a user input including at least one goal and initial conditions. The method further includes performing, with a task planner module: automatically decomposing the at least one goal into subgoals; querying the task library for matching subtasks using the subgoals; generating a sequence of actions using the initial conditions, the domain model, and the matching subtasks returned by the task library; returning the sequence of actions as a task to the human-machine interface; and displaying the task in the human-machine interface.

The invention discloses an authority management method and device, a storage medium and electronic equipment, and belongs to the technical field of resource management. The resource initialization request carries user information of a target user, resource information of a target resource, and resource information and association relationships of all associated resources associated with the target resource; data in domain models are updated according to the resource initialization request, the domain models comprise a user domain, a resource domain, an action domain and an authority domain, the user domain is used for maintaining users, the resource domain is used for maintaining resources and association relationships thereof, and the action domain is used for maintaining access operation, access authority and association relationships thereof; the permission field is used for maintaining the incidence relation among resources, access permissions and users; and performing authority management on the target resource based on the updated domain model, thereby realizing refined authority management on each resource in the MLOps platform, and realizing high flexibility.

The invention discloses a regional expansion method and device for large-scale vibration propagation of a large-scale power machine foundation, and the method comprises the steps: firstly, building an initial finite element domain model of the large-scale power machine foundation and surrounding site soil with the size 2 times that of the foundation; then, establishing a spectral element region expansion model within a kilometer-level range around the large power machine foundation, and extracting surface node information parameters of a hollowed-out region; constructing a finite element domain expansion model of the large power machine foundation and a surrounding soil body with the size 3 times that of the foundation; selecting a foundation stress surface of the finite element domain extension model, and extracting free field equivalent node force of each surface of a soil body with two times of foundation size around the large power machine foundation; and finally, applying the free field equivalent node force to the spectrum element region expansion model according to the corresponding position for analysis. According to the method, the large-scale analysis model is decomposed into the internal fine finite element domain and the external spectral element domain, and the large-scale vibration response under the basis of a large-scale complex machine can be obtained in combination with the expanded auxiliary solution domain of the internal finite element domain.

The invention relates to the technical field of data transmission, and discloses a cross-domain model communication method, device and equipment and a computer readable storage medium. The method is applied to a cross-domain model communication system, the system comprises a user domain, a data domain and a gatekeeper, and the method comprises the following steps: sending request information of a model in the data domain to the gatekeeper through the user domain, and performing security auditing on the request information through the gatekeeper; when the security check is passed, sending the request information to a data domain through a gatekeeper, and generating response information based on the request information through a model; sending the response information to a gatekeeper through the data domain, and performing security auditing on the response information through the gatekeeper; and when the security check is passed, the response information is sent to the user domain through the gatekeeper. When the model carries out data transmission between the user domain and the data domain, the transmitted data is subjected to security auditing through the gatekeeper, and the data transmission is carried out only when the security auditing is passed, so that the security of cross-domain communication of the model is improved.

The invention discloses an intelligent operation and maintenance method, platform and equipment for a power transformer industry large model. The method comprises the following steps: receiving a natural language request of operation and maintenance personnel; based on the local vector index and the local keyword index, a semantic fragment corresponding to the natural language request is retrieved; inputting the semantic fragment into a private domain intelligent operation and maintenance model, and generating a corresponding intelligent operation and maintenance answer; the private domain intelligent operation and maintenance model is obtained by performing increment fine adjustment on a public domain model pre-trained based on public power transformer industry knowledge by using local private domain knowledge constructed by preprocessing enterprise private data; and managing, scheduling and overhauling the power transformer equipment according to the intelligent operation and maintenance answer so as to realize intelligent operation and maintenance based on the power transformer industry large model. Wherein the intelligent operation and maintenance answer carries traceability information of the reference document. According to the method, professional, accurate and efficient intelligent support can be provided for operation and maintenance of the power transformer on the premise that data security is guaranteed and compliance requirements are met.

The application provides a cross-scene cognitive ability evaluation method based on fine-grained migration, which comprises the following steps: taking cognitive data of a user in a first scene as source domain data and taking cognitive data of the user in a second scene as target domain data; taking a source domain data set as a training set, training a random forest classifier, and generating a source domain model; obtaining a test accuracy rate of the individual classifier on a target domain data set and an information gain difference from source domain features to target domain features; clustering all the individual classifiers into multiple clusters according to the test accuracy rate and the information gain difference; updating the individual classifiers in each cluster by using a corresponding growth mechanism to obtain a target domain model; and evaluating the cognitive ability of the user in the second scene through the target domain model. The application also provides a cross-scene cognitive ability evaluation system based on fine-grained migration and a data processing device.

This invention discloses a method and system for labeling prompt words in a large-scale communication domain model based on knowledge graphs. Belonging to the fields of communication technology and artificial intelligence, it addresses the following technical problems: low efficiency in labeling specialized terms in communication domain prompt word annotation, low accuracy and efficiency of automatic recognition, limited prompt word dimensions, and lagging domain knowledge. The method includes: sequence labeling and entity extraction using a trained combined model to output entities; generating relation triples as candidate relations using a trained generator, and scoring and ranking the selected relation triples using a trained discriminator; semantic parsing of user input to extract key entities and intents; multi-hop reasoning from a dynamic knowledge graph to retrieve relevant entities and relations; eliminating illusory content using a trained GAN network; generating entity prompt words from three levels: basic, extended, and application dimensions; and introducing a triple quality verification mechanism.

Existing multi-agent system are employed collaboratively, where each agent specialized in extracting entities, relations, or events from documents which is a more generalized collaborative framework without specific customizations. The disclosed method presents a collaborative multi-agent knowledge graph Retrieval-Augmented Generation (RAG) framework using a plurality of collaborative multi-agent LLMs for extracting information from a plurality of multimodal documents and constructing the unified knowledge graph to facilitate accurate and efficient query-based retrieval. A query generator agent generates a plurality of queries that aim to uncover all possible information present in the plurality of multimodal documents. A domain model is generated by a domain model generator agent based on the plurality of queries. The domain model populator agent populates the domain model with data extracted from the plurality of multimodal documents. A unified knowledge graph is constructed from ta plurality of populated domain models and organizes the extracted information for accurate retrieval.

The invention discloses an ancient masonry bridge floater impact analysis method based on fluid-solid coupling, which comprises the following steps of: firstly, creating and correcting a finite element model of a bridge structure by combining three-dimensional scanning and field vibration test; secondly, according to the water flow monitoring data, a matched fluid domain model is created, and the motion characteristics and boundary conditions of fluid are determined; then, a bidirectional information exchange mechanism between the structure and the fluid is established, and bidirectional transmission of key parameters such as load and displacement under the fluid-solid coupling effect is achieved; and finally, setting the size and material parameters of the floating object, executing the whole process simulation of the floating object impacting the ancient masonry bridge, obtaining the core data of the bridge structure, and completing the impact influence analysis. The evaluation method has the advantages of being complete in process, accurate in modeling and rich in result, the influence of floating object impact on the ancient masonry bridge can be effectively quantified, the blank of floating object impact analysis of the ancient masonry bridge is filled up, and a scientific basis is provided for preventive protection of bridge heritage.

The invention discloses a code generation method and device based on a domain model, equipment and a medium, and the method comprises the steps: obtaining a target domain engineering model selected by a target user, and the target domain engineering model comprises an engineering structure model, an engineering component model and an engineering configuration model; analyzing the target domain engineering model based on a code generation engine, and generating a target engineering code corresponding to a target domain; and determining a target code warehouse corresponding to the target user, and pushing the target engineering code to the target code warehouse. Based on the technical scheme, the target domain engineering model is analyzed through the code generation engine, and the target engineering code is automatically generated, so that the preparation time required in the development process is shortened, the project starting efficiency is improved, and the initialization cost is reduced.

This invention provides a method and system for predicting the combined wear and fatigue failure of turbine runner blades, relating to the field of hydropower equipment operation and maintenance technology. The method includes: establishing a fluid computational domain model and mesh generation; constructing a flow field based on single-phase flow characteristics; constructing a two-phase flow system using a discrete phase model; constructing a material wear model based on the characteristics of suspended matter passing through the machine; obtaining a geometrically varying wear prediction model and calculating the model; calculating the fluid-structure interaction strength characteristics of the initial structural field, and based on the calculation results, calculating the fatigue characteristics of the initial structural field; reconstructing the flow channel geometrically varying model based on fatigue characteristic data, and calculating the flow and fatigue characteristics during the reconstruction process; and evaluating the fatigue failure prediction of the runner under the influence of wear. This invention utilizes dynamic mesh smoothing technology to achieve real-time reconstruction of the geometric model and iterative coupling of the flow field and structural field during the wear process, improving the accuracy and scientific rigor of failure prediction.

The invention belongs to the technical field of new energy power generation and grid connection, discloses a grid-side converter fault ride-through control structure modeling method, system and equipment and a medium, and aims to solve the problems of difficult modeling, high cost, poor extrapolation and lack of universality caused by dependence on manufacturer internal logic or massive experimental data in the prior art. According to the method, disturbance input and current output data under a limited working condition are acquired, a dimension raising mapping function is constructed to map the input to a high-dimensional feature space, a Koopman operator is solved based on ridge regression to establish a global linear mapping model, active and reactive current reference components are predicted, and the current reference components are optimized. And finally, inputting the current inner loop time domain model to generate a current transient response curve during the fault period. According to the method, high-precision modeling and prediction can be realized only by a small number of samples, and the method has good extrapolation and universality and can be adapted to various fault ride-through strategies.

This invention provides a flexible production line control method and system based on a large vertical domain model. The method includes: constructing a pruning index combining temporal stability and spatial similarity for the original large vertical domain model to evaluate the redundancy of the activation tensors in the intermediate layers, and performing structured pruning accordingly to obtain a sparse structure model; performing mixed-precision quantization on the pruned model: allocating quantization bits according to the sensitivity of each network layer weight to task loss, and calculating scaling factors and zeros based on the range of non-zero weights to represent sparse weights; calculating the output deviation between the quantized pruned model and the original model under the same input, and training a feedforward network with the former's output as input to fit and compensate for the output deviation; combining the quantized pruned model and the feedforward network to form a composite control model; after deployment, adding the output command of the quantized pruned model to the compensation vector during inference to generate control commands.

This invention discloses a large-scale reliable fault diagnosis method for aviation maintenance scenarios, comprising: constructing a domain model for aviation troubleshooting tasks; standardizing and retrieving the input fault descriptions, normalizing and weighting the retrieval results to generate a unified-ranked candidate document list; selecting several high-scoring documents to perform dynamic context expansion and semantic relevance verification to obtain a structured evidence set; using this as input to the domain model for aviation troubleshooting tasks to output a troubleshooting solution; structurally decomposing the troubleshooting solution into several sub-steps and quantifying the uncertainty of each sub-step; combining fuzzy representation recognition and uncertainty classification to label the risk level of each sub-step, and forming a credible aviation maintenance diagnosis solution after manual review. This invention also discloses a large-scale reliable fault diagnosis system for aviation maintenance scenarios. This method and system can achieve stable generation, reliable verification, and risk-controlled output of aviation maintenance diagnostic content.

This application relates to a method, system, device, and medium for real-time evaluation of business rules based on a large language model. The method includes: invoking a large language model to convert natural language business rules into structured description data containing entity identifiers, attribute identifiers, and rule constraints; mapping entity identifiers and attribute identifiers to the location information of corresponding program elements in a domain model based on a pre-stored mapping library, generating detailed mapping data; generating source code files implementing a preset evaluator interface based on the structured description data and the detailed mapping data; converting the source code files into an executable form and executing them in an isolated execution environment; invoking the evaluator interface, evaluating the execution results using generated test cases, and generating a verification report. This application achieves fully automated conversion of business rules to executable code, reducing maintenance costs and improving the efficiency of rule deployment.

The invention provides a design drawing auditing method and device and electronic equipment, and belongs to the technical field of artificial intelligence. The method comprises the steps that a to-be-audited design drawing is acquired; processing the design drawing to obtain structured bill of materials (BOM) data corresponding to the design drawing; inputting the structured BOM data corresponding to the design drawing into a pre-constructed large vertical domain model, auditing the structured BOM data by the large vertical domain model based on a pre-constructed knowledge base, and outputting an auditing result of the design drawing; the vertical domain large model is obtained by training based on sample structured BOM data corresponding to a sample design drawing and an audit result label of the sample design drawing; and generating a structured auditing report based on the auditing result. According to the design drawing auditing method provided by the invention, the structured BOM data corresponding to the design drawing is audited on the basis of the large vertical domain model in combination with the knowledge base, so that the drawing auditing efficiency and accuracy are improved.

The invention discloses a pressurized water reactor fuel rod bundle transient nuclear thermal coupling calculation method under a two-phase flow condition. A solid domain model and a fluid domain model are established, and grids are divided; generating a few-group cross section library based on a Monte Carlo program, and parameterizing the cross section into a temperature and density function by using Python interpolation; and a grid is imported into the CFD solving platform, and a two-phase flow thermal hydraulic model is selected. A neutron diffusion equation is discretely solved by adopting a user-defined scalar equation interface, source item, diffusion item and transient item coefficients and neutron physical boundary conditions are defined by utilizing a user-defined function interface, section parameters are updated in real time according to local temperature and density, and meanwhile, a power heat source is calculated according to neutron flux and fed back to thermal hydraulic calculation; and bidirectional coupling is realized. And carrying out steady-state characteristic value calculation, applying disturbance to carry out transient calculation after convergence, and outputting power, temperature, density, void fraction and neutron flux space-time distribution after a termination criterion is met for safety analysis of a nuclear reactor.

The invention provides a multi-source data adaptive sampling method and device, and the method comprises the steps: building a target domain verification set and a training set of a plurality of source domains based on a target domain model; the method comprises the following steps of: after sampling is started, firstly, respectively carrying out one round of sampling on a training set of each source domain, and training a target domain model by utilizing a training sample obtained by sampling after each round of sampling; then, starting from the next sampling round, selecting a source domain participating in the current sampling round based on the historical contribution degree of each source domain to target domain model training, then sampling a training sample from the selected source domain, and training the current target domain model; and when the training of the target domain model reaches a preset ending condition, completing the sampling. According to the method, a performance feedback mechanism and dobby machine modeling are introduced, so that efficient, stable and interpretable multi-source data selection and field adaptation can be realized under the conditions of limited data and dynamic environment change, and the method has a remarkable practical value.

The invention relates to the technical field of electric power, in particular to a distributed energy resource missing parameter generation method based on a knowledge graph, which comprises the following steps of: acquiring existing distributed energy resource equipment and parameters, characteristics and performance data thereof, and constructing a knowledge graph of full-amount DER equipment; based on a graph attention network, learning a dependency relationship between devices in the knowledge graph of the full-amount DER devices, and outputting global features of the devices; constructing and training a missing parameter prediction model to obtain a trained missing parameter prediction model, taking the trained missing parameter prediction model as a source domain model, and performing fine tuning by adopting a transfer learning method to obtain a fine-tuned missing parameter prediction model; and inputting the global features of the equipment into the missed parameter prediction model after fine tuning to obtain predicted values and confidence coefficients of the parameters, judging whether the predicted values of the parameters are recommended or not, and judging whether the predicted values of the parameters are fed back to the knowledge graph of the full-amount DER equipment or not, thereby solving the problem of parameter missing of the newly added distributed energy resource equipment.

The invention discloses a vertical large model fine tuning method based on a proprietary business database, belongs to the technical field of artificial intelligence and databases, and particularly relates to a vertical domain model training and application method for performing large model fine tuning based on self-developed database knowledge. A multi-dimensional evaluation system is established by constructing a proprietary corpus and adopting a parameter efficient fine tuning technology LoRA, and effective conversion from a general model to a proprietary database vertical domain model is realized; according to the method, only 0.5% of parameters are trained through the LoRA technology, so that the training cost and hardware requirements are remarkably reduced; the proprietary corpus is completely constructed for a specific database system, and it is ensured that the model masters proprietary grammar and operation and maintenance knowledge; the multi-dimensional evaluation method comprehensively measures the capabilities of the model in grammar, execution, semantics and the like; the fine-tuned model can be directly deployed in a production environment to meet actual business requirements.