425 results about "Domain model" patented technology

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.

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 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 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 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 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 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.

The invention discloses a flood storage and detention area flood diversion process simulation method and device based on a hydrodynamic model, and the method comprises the steps: obtaining the landform, river section, dike engineering parameters, and real-time hydrological and meteorological data of a flood storage and detention area, constructing a basic parameter library, and storing the basic parameter library in a data input module; constructing a hydrodynamic model calculation module by taking a hydrodynamic equation as a theoretical basis on the basis of the basic database, and combining a finite element volume method; receiving a numerical calculation result, analyzing real-time parameters through a dynamic simulation module so as to match actual hydrological parameters, and performing comparison under multiple scenes; and transmitting the hydrological parameters and the comparison data to a result output module, and outputting key parameters of the flood diversion process and a risk assessment result. The method has the advantages that high-frequency coupling of a hydrodynamic model, multi-source dynamic data, a cross-domain model and an engineering control system is achieved, and the problems of data lag, model isolation and control disjunction in traditional simulation are solved.

A system and method for automated domain advertising utilizing artificial intelligence is provided. The system includes a processor and memory in communication with the processor. The memory includes a user interface module, a domain advertising (DA) module, an artificial intelligence (AI) module, a posting module, and a domain parking module. The system receives the domain name from a user. The AI module generates the domain advertising suggestion based on the domain name. The domain advertising suggestion is provided to the user and may be posted to social media or a domain parking website. The AI module includes a large language model (LLM) and a retrieval-augmented generation (RAG) module. The RAG module includes a knowledge base to retrieve a knowledge-based response from an external knowledge source, an expert module to retrieve an expert response from a specialized domain model, and a ranker to rank the knowledge-based response and the expert response.

The invention relates to the technical field of superconducting magnet manufacturing and insulation impregnation process optimization, and discloses a vacuum pressure impregnation process optimization method, device, equipment, medium and product, and the method comprises the steps: inputting a target porosity and a target permeability into an insulation layer homogenization fluid domain model to obtain a macroscopic homogeneous model; according to the macroscopic homogeneous model, a first corresponding relation between multiple technological parameters and the impregnation performance parameters under the boundary condition is determined through simulation, and the multiple technological parameters comprise at least two of the temperature, the glue injection parameter, the vacuum pressure, the resin viscosity and the resin curing kinetic parameter; and according to the plurality of first corresponding relations, optimizing the plurality of process parameters by taking the shortest filling time, the highest uniformity and the lowest residual bubble rate as optimization objectives to obtain an optimal process parameter combination of the vacuum pressure impregnation process. According to the method, the multiple process parameters are optimized, the multi-parameter optimization database can be established, and the optimal process combination scheme is obtained.

A UDA encrypted traffic detection method based on LLM enhancement comprises the following steps: under a UDA framework, using a target domain model as a detection model of TLS encrypted traffic, and extracting network attack features from the encrypted traffic; the UDA framework is an LLM (Large Language Model) enhanced UDA framework, and comprises a Prompt-based cross-domain translator which is used for guiding LLM to carry out domain feature conversion between source domain label data and target domain label-free data; according to the cross-domain alignment network, original data and a cross-domain translation result serve as input, and a UDA detection model is trained based on task classification loss and cross-domain consistency loss; the UDA detection model comprises a source domain model and a target domain model; and the LLM iterative optimizer is used for pushing the LLM translation capability to be aligned to a training target of the UDA detection model under the guidance of the loss value. Experiments on a real encrypted traffic data set show that the accuracy and the F1 score of the method in a feature offset scene are improved by 2.45% compared with those of an advanced reference method.

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 present disclosure relates to the technical field of deep learning, and provides a model deployment method and an electronic device. The model deployment method comprises: on the basis of the computing time required to be occupied by each of a plurality of neural network layers of a target data processing model, sequentially allocating the plurality of neural network layers to N computing devices, so that the difference value between the total number of computing times corresponding to the neural network layers on the computing devices is not greater than a preset value, wherein N≥2; and running the target data processing model by means of the N computing devices so as to perform pipelining processing on a plurality of requests to be processed. The embodiments of the present disclosure can improve the balance of pipelining models.

The invention relates to an MBSE multi-field-based virtual-real fusion simulation platform and method, and the method comprises the steps: an intelligent matching module is used for receiving a to-be-detected algorithm model according to a user interface module, determining an MBSE multi-field model from a model management module, and achieving the MBSE multi-field model through the MBSE multi-field model; the virtual-real fusion test module is used for determining a plurality of test scenes constructed according to real scene data according to the MBSE multi-field model, performing full-life-cycle verification on the MBSE multi-field model and the to-be-detected algorithm model according to each test scene to obtain a corresponding verification result, and when the verification result meets a preset condition, executing the virtual-real fusion test on the to-be-detected algorithm model. The to-be-detected algorithm model is stored in the model management module, the full-life-cycle automatic verification process is achieved through the virtual-real fusion test module, the problems that virtual simulation and real object test are separated, and evaluation standards are not uniform are solved, and the method has the advantages that the test efficiency is improved, and the virtual-real fusion test capability is enhanced.

The invention discloses a current rotating speed mapping control method of a liquid cooling system, and relates to the technical field of liquid cooling systems, and the control method comprises the steps: constructing a double-domain model of a motor domain and a thermal load domain, carrying out the online identification of a liquid path impedance parameter in an operation process, and obtaining a current rotating speed mapping model; a target rotating speed instruction is generated in combination with a current-rotating speed mapping reference result and a thermal load flow estimation result, self-adaptive pump speed control is achieved through weight adjustment and smooth constraint driven by residual errors, and meanwhile baseline mapping and a pump family curve are dynamically updated through a memory buffering and batch reestimation mechanism; according to the invention, the self-adaptive capability and the cooling control stability of the liquid cooling system under complex working conditions are obviously improved, and the contradiction between insufficient cooling and energy consumption increase is effectively avoided.

The invention provides a multi-source heterogeneous data integrated management method based on a digital main line, a medium and a system, and belongs to the technical field of industrial digital main lines. A digital main line platform is used for collecting data of various formats and conducting standardization processing, a deep ontology fusion model is adopted for achieving cross-domain semantic understanding, and the data of various formats is obtained. Semantic understanding efficiency is improved through adaptive pooling feature dimensionality reduction and separable attention calculation, a storage space is optimized by adopting similarity aggregation de-duplication or a distributed independent storage strategy according to a data overlap ratio, and a digital principal line data consanguinity tracing relation is established to realize full-life-cycle data link construction. Based on the semantic mapping deviation value, a fine fine tuning or coarse tuning mode is adopted to optimize semantic understanding parameters, longitudinal integrated mapping from a function model to a performance model and a physical model and a transverse integrated framework of multi-domain comprehensive simulation are constructed, and the technical problem that cross-domain model semantic understanding accuracy is insufficient is solved.

The invention discloses a personalized recommendation method and system based on cooperation of a large language model and a domain model, and relates to the technical field of information recommendation. According to the method, under a target recommendation scene, unstructured data is processed through a large language model to obtain semantic pattern features, meanwhile, quantifiable operation records of a user are analyzed through a specified domain model, behavior pattern features are output, and synchronous extraction of unstructured semantic information and structured behavior information is achieved; bidirectional information supplement and knowledge transfer are carried out on the two types of features, a collaborative optimization feature mapping set is constructed, and unified conversion and synchronous scheduling of cross-modal features are realized; in combination with real-time interaction information reflecting the current intention of the user and scene demand changes, an initial recommendation list is generated through dual-model collaborative reasoning, and whether a personalized recommendation result is output or not is judged after dynamic sorting, so that accurate adaptation between the real-time demand of the cross-scene user and personalized recommendation is realized, the recommendation timeliness is improved, and the user experience is improved. And thus, the rapid adaptability of personalized recommendation is effectively improved.

The invention discloses a knowledge-intensive task-oriented thinking chain prompt optimization method and system, and belongs to the field of natural language processing. When the model is used for reasoning knowledge-intensive tasks based on thinking chain prompts, the generated thinking chain often has the problems of knowledge missing, irrelevance, even errors and the like. Therefore, a prompt optimization process is improved in a manner of combining knowledge generation and knowledge evaluation, and a thinking chain prompt which more pays attention to knowledge quality is obtained, so that the knowledge integrity, correlation and correctness of a model generated thinking chain are improved. The knowledge generation means that the guide model explicitly generates knowledge fragments in the thinking chain; according to knowledge evaluation, a loss function containing answer accuracy and multi-dimensional knowledge indexes is designed, and gradient information of the loss function is utilized to select an optimal candidate word to update and optimize prompts. The system comprises a thinking chain knowledge generation module, a knowledge evaluation module, a candidate word initialization module, a candidate word gradient calculation module and a prompt updating module.

A load prediction method and system for a constant-temperature and constant-humidity air conditioning system based on transfer learning comprises the steps that historical factory environment parameter sequences and corresponding loads under different external environment parameters of different factories are obtained, feature data of the historical factory environment parameter sequences and the corresponding loads are extracted, and all the feature data are clustered; taking the historical factory environment parameter sequence of the same cluster and the corresponding load as a training set to train a load prediction model; obtaining a current factory environment parameter sequence, calculating the sequence similarity between the current factory environment parameter sequence and the clustering center of all clusters, and taking the load prediction model with the maximum sequence similarity as a reference load prediction model; constructing a target domain model having the same network structure as the reference load prediction model, carrying out transfer learning on the target domain model, inputting the current factory environment parameter sequence into the target domain model, and outputting a predicted load; and judging the air conditioner start-stop state of the factory at the next moment to perform thermal inertia correction. The method solves the bottleneck that a single model is difficult to adapt to multiple factories and changeable environments.

The invention discloses an electro-hydrogen system dynamic safety operation domain modeling method for new energy output fluctuation, and relates to the field of power system safety analysis and control, and the method comprises the following steps: S1, electro-hydrogen system dynamic energy efficiency coupling modeling and chaos optimization; establishing a dynamic efficiency coupling model of the electro-hydrogen system; performing chaotic parameter optimization on the dynamic efficiency coupling model; s2, electro-hydrogen safety domain collaborative modeling under multi-scale fluctuation is carried out; s3, carrying out constraint modeling and parameter optimization on the safe operation domain of the electro-hydrogen system; establishing a safe operation domain model of the electro-hydrogen system; and converting parameterized optimization of the safety domain model of the electro-hydrogen system based on a parameterized KKT condition. According to the dynamic safety operation domain modeling method for the new energy output fluctuation electric hydrogen system, the problems that in the prior art, a dynamic safety boundary is fuzzy, and a fluctuation response mechanism is lacked are solved, and particularly, quantitative analysis means are lacked in the aspect of interaction between multi-time-scale fluctuation and a hydrogen storage system.