7536 results about "Dependability" patented technology

The invention discloses an equipment anomaly detection method and system based on multi-source heterogeneous data, and the method comprises the steps: obtaining a real-time multi-source monitoring data flow of production equipment in a smart park, carrying out the cross-modal data alignment processing of the real-time multi-source monitoring data flow, and generating a target monitoring data set, and executing a dynamic feature extraction operation in each edge computing node, generating a multi-dimensional equipment state feature set based on the target monitoring data set, inputting the multi-dimensional equipment state feature set into the trained anomaly detection integrated model, generating equipment anomaly probability distribution through a multi-level feature fusion strategy, and outputting the equipment anomaly probability distribution. Determining the abnormity type and abnormity confidence of the target equipment according to the equipment abnormity probability distribution, generating an equipment maintenance instruction set based on the abnormity type and the abnormity confidence, and sending the equipment maintenance instruction set to an equipment management terminal of the smart park to trigger abnormity processing operation; therefore, the automation degree and decision reliability of the equipment maintenance response of the smart park are improved.

The invention discloses a system and a method for task planning of an intelligent robot with a body based on multi-dimensional situation awareness, and particularly relates to the technical field of task planning of the intelligent robot with the body, space-time alignment is carried out on asynchronous heterogeneous data generated by a multi-modal sensor channel, and cross-modal space-time features are extracted through a cross-modal feature fusion network; a fusion situation matrix is generated, dynamic causal modeling is used to update association strength among the multi-modal data, an anti-factual reasoning engine is used to identify and trace abnormities, and an abnormities traceability result is output; dynamically adjusting the reliability weight of each sensing channel through a multi-modal credibility evaluation model by using the fusion situation matrix and an abnormal traceability result; and on the basis of the reliability weight, inputting the fusion situation matrix into a real robot dynamic model and a digital twin virtual model, executing collaborative predictive control, and starting an adaptive rule evolution mechanism when a safety score is lower than a threshold value, thereby solving the problem of fusion matrix distortion in dynamic obstacle avoidance and precise grabbing tasks.

The invention discloses an intelligent scheduling method for a comprehensive virtual power plant, and relates to the technical field of power plant intelligent scheduling, and the method comprises the steps: carrying out the redundancy elimination filtering and unified time sequence calibration of multi-node sensing data of a cold and hot energy station, energy storage equipment and the like through the deployment of an edge side high-concurrency collection module, generating a high-quality time sequence fusion sequence, and screening schedulable resources; on-line model correction is realized through an error dynamic evaluation mechanism, a multi-stage scheduling strategy is generated by adopting an improved particle swarm algorithm aiming at the targets of economy, reliability and environmental protection, and an optimal instruction is output in combination with an equipment constraint condition. Through priority control and real-time monitoring index evaluation deviation, a rolling correction and standby strategy is triggered, equipment-level fault risk quantification and self-healing control are realized, abnormity is rapidly isolated, and redundant resources are started. Through data-prediction-scheduling-execution closed-loop cooperation, the resource utilization rate, the scheduling precision and the system fault-tolerant capability are significantly improved, and the scheduling capability of source heterogeneous resources is enhanced.

The invention relates to the technical field of computer programming languages, and particularly discloses a coal-fired power plant safety monitoring system and method. The system comprises a multi-modal data fusion platform, a federal learning agent network and a digital twinborn simulation engine. The edge computing node carries out unified acquisition and feature extraction on multi-source heterogeneous data through multi-protocol conversion, quantum noise suppression and a preprocessing chipset; the multi-modal data fusion platform realizes semantic mapping based on a knowledge graph, and fuses time-space correlation characteristics of data such as an infrared image and gas concentration by adopting a time-space encoder and a cross-modal attention mechanism. According to the method, the problems of early warning delay and high false alarm rate caused by low multi-source data decentralized processing efficiency and insufficient nonlinear correlation analysis in a traditional scheme are solved, efficient data fusion, complex risk accurate prediction and automatic safety response are realized, and the real-time performance and reliability of a coal-fired power plant monitoring system are remarkably improved.

The invention discloses a server cooperative control method, a storage medium and electronic equipment, and relates to the technical field of firmware, and the method comprises the steps: employing a programmable logic device to actively take over the control authority of target equipment based on the operation parameters of a sensing device in a server starting stage, stable operation of equipment before initialization of the substrate management control device is not completed is ensured, a mechanism that a preset communication link and the substrate management control device are subjected to interactive verification is adopted, and the functional integrity and communication reliability of the substrate management control device during permission transfer are ensured through bidirectional communication verification. A dynamic permission recovery strategy based on real-time anomaly detection is adopted, control right reverse switching is triggered through a sensing device and communication link state dual monitoring mechanism, and the purpose of actively taking over equipment management responsibilities when a substrate management control device runs abnormally is achieved. Therefore, double improvement of system fault self-healing capability and equipment control continuity is realized, and the problem of difficulty in cooperative control among heterogeneous units is solved.

The invention relates to the technical field of factory management. The intelligent factory management method based on digital twinning comprises the following steps: performing dynamic identification and analysis processing on a multi-source heterogeneous communication protocol of factory equipment to obtain a standardized data stream; constructing a three-dimensional digital twinborn model comprising an equipment state and a technological process; generating a virtual control instruction according to an analog control strategy in the three-dimensional digital twin model; sandbox security verification processing is carried out on the virtual control instruction, and a compliance instruction set is screened out; converting the compliance instruction set into a protocol format supported by the target equipment, generating a reverse control instruction and issuing the reverse control instruction to the physical equipment; performing closed-loop feedback optimization processing according to the difference between the execution result of the physical equipment and the prediction result of the three-dimensional digital twinborn model, so as to solve the problem that the digital twinborn model lacks real-time sensor data injection and dynamic behavior constraint embedding, ensure that the model can reflect the real state of the equipment, and improve the real-time performance of the equipment. And the reliability and the execution security of the virtual control instruction are improved.

The invention relates to the technical field of data analysis, and discloses a business data analysis method, device and equipment based on big data and a storage medium, the method comprises the following steps: collecting and preprocessing original log data to obtain standardized log data, and performing adaptive multi-modal feature extraction to obtain comprehensive feature representation; inputting a heterogeneous graph neural network to carry out anomaly detection and multi-level root cause analysis to obtain a hierarchical anomaly event; executing task scheduling and resource allocation of reinforcement learning to obtain a task allocation scheme; performing deep learning enhanced root cause reasoning and knowledge graph construction on the task allocation scheme to obtain a system state diagram and a causal relationship; and generating a multi-dimensional task data analysis report. According to the method, comprehensive analysis, anomaly detection, root cause reasoning and intelligent scheduling of log data are realized. The reliability and performance of the system can be improved, and more intuitive and accurate decision support can be provided for operation and maintenance personnel.

The invention relates to the technical field of data processing, and discloses a bridle machine fault monitoring method and system based on the Internet of Things. The method comprises the following steps: collecting multi-source operation data through a sensor; performing filtering segmentation processing on the data to obtain feature data; analyzing the feature data to obtain an abnormal identifier and a fault type; establishing a component association map to position a fault source and predict a development trend; calculating a maintenance priority to generate a maintenance scheme; and recording the actual maintenance result and the prediction deviation to update the fault library and the knowledge parameters. Through multi-source data fusion, intelligent fault diagnosis, predictive maintenance and knowledge self-adaptive updating mechanisms, early recognition and accurate positioning of faults of the bridle machine are realized, the non-planned downtime is effectively shortened, and the equipment reliability and the production efficiency are improved.

The invention relates to a power equipment fault prediction method based on multi-source heterogeneous data and a twinborn model, and the method comprises the following steps: S1, obtaining multi-modal data related to the operation of power equipment, constructing a joint feature encoder through an attention mechanism in deep learning, and carrying out the joint modeling of multi-modal information, and obtaining a joint feature representation; s2, based on joint feature representation, adopting a dynamic twin model of self-supervised learning and fusion reinforcement learning, and dynamically updating the equipment health state; s3, constructing a complete-cycle prediction framework based on Transform, capturing long-term dependency and dynamic change characteristics in equipment operation, and identifying trigger factors of faults in combination with causal relationship analysis; and S4, based on the equipment health state and the fault prediction result, optimizing equipment operation parameters by adopting an intelligent operation and maintenance optimization engine, and dynamically generating an optimal operation and maintenance plan in combination with a genetic algorithm. The reliability and the operation efficiency of the power equipment are remarkably improved.

Examples of the present disclosure describe systems and methods for automatically detecting and repairing reliability issues in operating systems and applications using a generative artificial intelligence (“AI”) system. In examples, a generative AI system receives a request to evaluate a detected issue in a software service or application. In response, the system analyzes error information associated with the detected issue to build an error context for the software code that caused the detected issue. The error context is used to identify the location of the software code file that comprises the software code. The error context and the software code file are used to identify a prompt. The prompt, the error context, and / or the identified software code are provided as input to a language model. The language model provides an output that is responsive to the user request and may perform actions to further evaluate or repair the detected issue.

The invention belongs to the technical field of circuit breaker management, and discloses a control system for managing a primary and secondary fusion circuit breaker, and the system collects the electrical parameters, mechanical states and environmental conditions of the circuit breaker through multiple channels, and constructs a standardized operation data set; establishing a circuit breaker health characteristic spectrum based on deep characteristic learning; establishing an environmental adaptability control parameter library through environmental factor correlation analysis and multi-scene simulation; performing fault mode identification and predictive diagnosis in combination with the health characteristic spectrum, and generating a fault risk early warning matrix; optimizing a multi-circuit-breaker cooperative control strategy based on the early warning matrix, and generating an optimal control instruction sequence; and reliable execution and effect feedback of the control instruction are realized through security encryption verification and a hierarchical execution mechanism. The problems that a traditional circuit breaker control system is difficult in data integration, insufficient in environment adaptability, weak in fault prediction capacity, incomplete in cooperative control and the like are solved, and the safety and reliability of power grid operation are remarkably improved.

The invention discloses an intelligent operation and maintenance system and method for a high-voltage switch cabinet based on digital twinning, relates to the technical field of intelligent power grids, and solves the problems of nonlinear effect modeling distortion, cross-spatio-temporal scale coupling deviation accumulation, time sequence real-time contradiction and insufficient extreme working condition adaptation in the prior art. Hysteresis parameters of the ferromagnetic material are dynamically calibrated through a quantum annealing optimization algorithm, and electromagnetic-thermal field strong coupling synchronous calculation is realized by combining multi-scale mesh generation and an implicit thermal field iterative algorithm; constructing an incremental transfer learning framework to fuse aging features and real-time data, and correcting boundary conditions of the model by adopting four-dimensional variational assimilation; establishing a hybrid verification platform to dynamically feed back extreme working condition parameters, and generating a credible operation and maintenance instruction in combination with a block chain; according to the method, the contact temperature rise prediction precision, the residual life evaluation reliability and the circuit breaker transient response real-time performance are remarkably improved, and active immune type intelligent operation and maintenance of the high-voltage switch cabinet under the extreme working condition are achieved.

The invention discloses a task integrity judgment method for multi-device cooperative work under complex constraint conditions. The method comprises the steps that a comprehensive data collection system is established, multi-dimensional data of cooperative devices are collected in real time and preprocessed, constraint modeling is combined, and the complex constraint conditions are quantified into a computable constraint condition model; a multi-agent collaborative evaluation model is constructed, each device participating in collaborative work is regarded as an agent by the multi-agent collaborative evaluation model, and evaluation of the integrity of the whole task is achieved by simulating the collaborative process between the devices; automatically extracting advanced features from the multi-dimensional data by using a deep learning technology; and a distributed real-time analysis system is designed, and large-scale data processing and continuous model optimization are realized in combination with an adaptive optimization mechanism. According to the method, the reliability and efficiency of the cooperative task can be improved, powerful support can be provided for equipment task decision making, and the method plays an important role in improving the intelligent level of application guarantee and the decision making accuracy.

The invention provides a method and a system for evaluating the packaging tightness of an integrated circuit in a high-temperature environment, and relates to the technical field of circuit testing, and the method comprises the steps: obtaining the temperature, tracer gas concentration and gas pressure data of the integrated circuit, and extracting temperature nonlinear features; non-local weighted adaptive anisotropic diffusion filtering and mixed tensor decomposition are utilized to obtain concentration multi-dimensional distribution features, air pressure frequency domain features are extracted, and feature fusion and space-time dependency analysis are performed through a quantum depth auto-encoder, a multi-head cross attention network and a heterogeneous space-time diagram neural network. Time sequence features are extracted through waveform dynamic time warping and a bidirectional gating circulation attention network, sealing defect diagnosis is performed based on knowledge distillation, a failure trend is predicted by using a causal long-short term memory network, and a failure mode is matched in combination with a dynamic heterogeneous knowledge graph. And a multi-target Pareto differential evolution algorithm is adopted to evaluate the packaging reliability and generate an evaluation report.

According to the multi-energy complementary power distribution system data analysis method based on deep learning, firstly, preprocessing and feature extraction are carried out on multi-source heterogeneous sensor data, and key indexes reflecting the health state of equipment are obtained; and obtaining globally consistent equipment state representation through a spatio-temporal data fusion technology. And then a multi-fault mode knowledge base is constructed, and a multi-label classification algorithm of deep learning is adopted to identify fault types. And finally, establishing a mapping relation between an equipment topological structure and a fault propagation path by using an attention mechanism and a graph convolutional network, and reasoning a specific position of equipment damage through fault traceability and influence analysis. The robustness and generalization ability of the model are improved by adopting methods of data enhancement, transfer learning and the like, and real-time and reliable fault location is realized through edge calculation. According to the method, the equipment fault in the multi-energy complementary power distribution system can be accurately positioned, and the reliability and safety of the system are improved.

The invention relates to the technical field of industrial Internet of Things, in particular to a predictive maintenance method for intelligent factory Internet of Things equipment, which comprises the following steps of: acquiring equipment operation parameters, environment monitoring data and historical maintenance records, constructing a multi-dimensional feature data set, extracting equipment degradation features by adopting a topological graph attention mechanism and a Bayesian network, and establishing a multi-dimensional feature data set; the method realizes equipment health state modeling and fault probability prediction, combines a dynamic adjacency matrix and a multi-objective optimization algorithm, comprehensively optimizes maintenance cost, equipment fault risk and associated equipment influence, dynamically generates an optimal maintenance plan, carries out constraint optimization based on a mixed integer programming method, automatically generates a maintenance instruction sequence, and achieves the optimal maintenance of the equipment. Tasks are issued through the computerized maintenance management system, the PLC control system and the industrial Internet of Things gateway, and the execution state is monitored and maintained in real time. The intelligent level of equipment maintenance is effectively improved, non-planned shutdown is reduced, and the equipment reliability and the production efficiency are improved.

The invention relates to the technical field of data processing, in particular to an intelligent document updating processing method and system.The intelligent document updating processing method comprises the steps that part parameters and coding rules are extracted from engineering design file metadata, a part list and a PDM and PLM system, the semantic association relation between parts is analyzed through a knowledge graph, and the mapping relation between logic identifiers and physical files is constructed; generating an initial version file library; and monitoring file names and version changes in real time based on a micro-service architecture, calling a simulation service in combination with a knowledge graph to verify parameter compatibility, screening an optimal version, updating the optimal version to a main version library, optimizing historical conflict data in a block chain evidence storage index by using a genetic algorithm to generate a standardized code, and outputting a cross-platform parameter mapping table. According to the method, the problems of file name change, non-standard coding conflict, version control missing and cross-platform adaptation incoherence are solved, and the data tracing efficiency, the version management reliability and the system compatibility are improved.

The invention relates to the technical field of data processing, and discloses a real-time data processing analysis method and system of an industrial PLC. The method comprises the following steps: transmitting temperature, pressure, current, vibration and acoustic parameters acquired by multiple sensors to an industrial PLC (Programmable Logic Controller) in real time to obtain multi-source heterogeneous original data; preprocessing the multi-source heterogeneous original data to obtain standardized fusion data; correlation calculation and anomaly recognition are carried out through the multivariate analysis model, and an abnormal state classification result is obtained; dynamically adjusting data interaction frequency and sampling rate between the edge nodes and the central PLC, and generating a real-time control decision instruction; and matching the real-time control decision instruction with the current motor load fluctuation state, and outputting the optimal frequency conversion control parameter. According to the invention, the response delay of the system is reduced, the control precision and reliability are improved, the dynamic balance between the safety and the energy efficiency is realized, and the system can intelligently adjust the control strategy according to the real-time safety situation.

The invention discloses an intelligent question answering system based on a large language model, and relates to the technical field of natural language processing and information retrieval. According to the system, a retrieval enhancement generation (RAG) and knowledge reordering (Re-rankig) technology is utilized, and the knowledge processing capacity for mechanical manufacturing process design, industrial standards and related specifications is improved. The system is mainly composed of four parts, wherein the first part is used for constructing a knowledge base covering machine manufacturing knowledge; in the second part, a LoRA + fine-tuned Qwen2-7B model is used for guaranteeing high-precision requirements such as process parameter calculation; in the third part, the RAG technology is integrated to effectively retrieve and process domain knowledge; and 4, introducing a reordering model to optimize answers. The system is remarkably superior to a traditional large language model LLM in BLEU-4 and ROUGE text matching evaluation indexes, the reliability and speciality of application of the system in the mechanical manufacturing field are verified, an effective scheme is provided for intelligent question answering in the field, and the prospect is wide.

The invention provides a distributed system fault positioning diagnosis method and system based on log analysis, and relates to the technical field of fault diagnosis, and the method comprises the steps: collecting call data between micro-service nodes, constructing a service dependency graph, and calculating the influence weight of each node as a fault source; and modeling the service dependency relationship graph by using a graph convolutional neural network, and constructing a fault propagation prediction model. And when an abnormal node is detected, inputting the node into the prediction model, calculating a fault propagation path and an influence range, outputting an influenced service node list and an influence degree thereof, carrying out risk grade division and alarm, and meanwhile, visually displaying the fault propagation path. According to the method, the fault source can be quickly and accurately positioned, the fault propagation influence is predicted, and the reliability and stability of the distributed system are improved.

The invention relates to a code generation method and system based on heterogeneous multi-agent collaboration and electronic equipment. According to the method, a special commentator agent instantiated by a heterogeneous large language model is added into a multi-agent collaboration process; according to the method, consistency and feasibility verification is carried out on a demand document given by an analyst agent by adopting a commentator agent, a modification suggestion is proposed in an early stage, deviation and problems are found in time in the early stage of a task, the cost and complexity of later correction are reduced, the robustness and reliability of codes are improved, and the method is suitable for large-scale popularization and application. And the generated result can still maintain high-level correctness under extreme conditions or specific boundary conditions. And the method has practical significance in improving software development efficiency, reducing project risks and improving the quality of an intelligent automatic solution.

The invention relates to the technical field of information management, and comprises a distribution automation operation and maintenance work log intelligent recording platform based on a CS architecture, and the platform comprises a log field analysis module, a task chain construction module, a task logic verification module, and a log storage tracing module. According to the method, missing and abnormal fields are effectively marked and corrected through meticulous analysis and comparison of uploaded log records, the accuracy and integrity of data are improved, task scheduling is optimized and the operation and maintenance management efficiency is improved through association of device numbers and task types and adjustment of execution time in sorting and grouping of task sequences, and the task scheduling efficiency is improved. Task chains are recombined to solve conflicts and adjust priorities, smooth execution of operation and maintenance tasks is guaranteed, delay is reduced, logic verification enhances task reliability through rule verification and error marking, unassociated tasks are deduced, analyzed and corrected, the exception processing capacity is enhanced, the data tracing and storage efficiency is improved, and the tracing capacity of data management is enhanced.

The invention provides an intelligent power grid relay protection reliability evaluation and lifting control method and system, and relates to the technical field of intelligent power grid relay protection, and the method comprises the steps: collecting the operation data of a relay protection device, carrying out the preprocessing and multi-domain feature extraction, and inputting a deep hybrid neural network to obtain a comprehensive feature vector; a multi-stage fault diagnosis model is utilized to identify a fault mode, a collaborative anomaly and a reliability level at a device level, an interval level and a station level respectively, and a fault propagation path and influence are predicted based on a causal reasoning network. And finally, constructing a hierarchical cooperative control strategy, and through multi-objective optimization, distributed game optimization and hierarchical rolling optimization, realizing coordinated optimization of a protection fixed value and improving the reliability of a relay protection device, thereby reducing the fault propagation probability and ensuring safe and stable operation of a power grid.

The invention relates to the technical field of computer networks, in particular to a monitoring method and system based on industrial computer network fault data, and the method comprises the steps: collecting the heterogeneous fault data of each layer of equipment in an industrial control network in real time through distributed probe nodes; performing multi-modal normalization processing on the original fault data; constructing a fault knowledge graph, and dynamically associating an equipment topological relation, a historical fault mode and a current production task context; fault root cause analysis is carried out by adopting a hybrid inference engine, and a potential fault propagation path is predicted in combination with a rule-based expert system and an LSTM-GNN joint model; generating a grading alarm strategy, triggering a self-adaptive fault-tolerant mechanism, and dynamically adjusting network bandwidth allocation or starting redundant equipment switching according to the fault grade; according to the invention, by constructing the industrial knowledge graph and the adaptive fault-tolerant mechanism, efficient, accurate and interpretable fault diagnosis and prediction are realized, and the reliability and operation and maintenance efficiency of an industrial network are improved.

The invention relates to the technical field of loaders, in particular to a loader multi-sensor data fusion fault diagnosis and early warning method, which comprises the following steps: S1, multi-source data acquisition and preprocessing: acquiring running state data of a loader in real time; s2, multi-modal data fusion: carrying out multi-modal data fusion on the preprocessed operation state data to form comprehensive data; s3, energy flow fault propagation prediction: analyzing energy flow distribution of each component in real time, and identifying a potential fault component and a propagation path thereof; and S4, fault assessment and graded early warning: comprehensively assessing the fault risk of the loader, and generating a graded early warning signal according to a set multi-stage early warning rule. According to the invention, the operation efficiency and safety of the loader are improved, the distribution of maintenance resources is optimized, the operation cost is reduced, the overall reliability of the loader is further improved, and the service life of the loader is further prolonged.

The invention discloses a fault processing method and system for a distributed power supply, and the method comprises the steps: carrying out the real-time monitoring of the operation data of the distributed power supply in a limit operation state, and combining with the historical fault data, so as to carry out the judgment of a fault mode; according to the current fault mode of the distributed power supply, corresponding fault operation state characteristics are extracted from historical monitoring data for severity evaluation, and according to an evaluation result, a current response measure is extracted from a fault response measure library to control execution of the distributed power supply; and acquiring a second real-time monitoring parameter when the distributed power supply executes the response measures to evaluate the response effect, and when the effect does not reach the expected standard, selecting a better target response measure from the fault response measure library based on the recovery rate of the distributed power supply under the current response measure and executing the better target response measure. The reliability and the safety of the distributed power supply in the limit operation state can be remarkably improved, and the fault response speed and the fault processing efficiency are greatly improved.

The invention discloses an ST code generation method based on graph alignment coding large model and multi-agent collaboration, and the method comprises the steps: receiving an ST code programming demand inputted by a user through a demand analysis module, refining and analyzing the demand based on multiple rounds of interactive conversations of the user and insight agents, and generating a standardized ST code programming demand; the retrieval module receives a standardized ST code programming requirement, and retrieves and obtains related knowledge through a retrieval agent in combination with an ST code knowledge base; and the double-agent collaborative self-correction code generation module receives standardized ST code programming requirements and retrieved related knowledge, a graph alignment coding large model constructed based on a graph neural network and a cross-modal alignment technology serves as a coding agent to cooperatively work with a review agent, ST code structure information is injected into the large model, and a final ST code is generated. According to the method, high-accuracy and high-reliability ST code automatic generation can be realized, and the development efficiency of a PLC program in the industrial control field is improved.

The invention discloses a digital twinborn model real-time linkage method driven by information physics fusion, particularly relates to the technical field of digital twinborn cooperative control of an industrial automation production line, and is used for solving the problems of instruction conflict and control failure caused by mismatching of virtual model parameters and dynamic capability of physical equipment in the prior art. According to the method, equipment state and material flow data are collected in real time, equipment dynamic degradation parameters are extracted to generate capability attenuation feature vectors, hidden process conflict path detection and resource preemption probability simulation are combined, a collaborative optimization model of equipment health and process scheduling is constructed, and a control instruction set containing dynamic capability constraints is generated. After the screening instruction is verified through physical constraint matching, parameters are corrected in a closed loop mode based on an execution result, a model is iterated, and self-adaptive matching of the virtual instruction and the physical equipment capacity is achieved; the reliability of the digital twinning control instruction and the stability of a production line are remarkably improved, and the risk of abnormal shutdown caused by an overrun instruction is avoided.

The invention discloses an industrial MBSE model generation and evaluation method based on a large language model, and the method comprises the steps: collecting and sorting an aerodynamic model, assembly design rules and related basic design parameters, and carrying out the formatted expression of the rules applied in MBSE, so as to form a special enhanced knowledge base in the industrial field; the method comprises the following steps: converting a demand described by a natural language of a user into a standard MBSE model code based on a large language model through a cue word engineering technology, selecting an optimization algorithm and a simulation algorithm through a function call technology, performing real-time optimization on the model according to a design target, and applying the generated MBSE model to propulsion device design and verification test. And determining availability and model consistency. Reconstruction and acceleration of digital design are realized through automatic configuration based on requirements and components. The method has remarkable advantages in the aspects of reliability, effectiveness and adaptability.

The invention discloses a multi-agent-based virtual power plant intelligent control method and system, and the method comprises the steps: dividing a virtual power plant into a plurality of sub-virtual power plants, deploying an agent in each sub-virtual power plant, collecting a local resource state through each agent, and predicting a load demand and the output of a distributed power supply, a hierarchical control unit is adopted to carry out collaborative optimization among the sub-virtual power plants according to a prediction result, and an upper-layer optimization control module constructs a linear programming model according to the prediction result and solves the linear programming model to obtain an initial scheduling scheme; and the lower-layer reinforcement learning control module performs local adjustment on the preliminary scheduling scheme according to a multi-agent depth deterministic strategy gradient algorithm to obtain a decision scheme. Based on a distributed control strategy of a multi-agent architecture, the fault-tolerant capability and reliability of the system are improved, a hierarchical control architecture is adopted, global optimization and local adjustment are organically combined, and efficient coordination and real-time adjustment capability of global resources are realized.