140 results about "Modelica" patented technology

The invention discloses a conversion method and system based on SysML and Modelica model semantic mapping. The method comprises the following steps that S1, a Modelica standard model library is imported into a SysML modeling tool; s2, completing the definition of behavior elements, and forming an SysML model instance; s3, exporting a Modelica model which can be executed by the Modelica simulator; s4, establishing a mapping relation table between SysML model elements and Modelica model elements; s5, when a change event occurs in the SysML model, updating the content of the corresponding Modelica model; s6, the Modelica model is imported into the SysML model, and a corresponding SysML model instance is generated; s7, simulation result mapping and behavior modeling generation are executed, a state transition diagram structure is formed, and structure updating of internal block diagrams in the system model is completed. According to the method, bidirectional conversion and joint simulation optimization between models are realized, and the method is suitable for cross-platform modeling, collaborative design and functional verification scenes of a complex system.

The invention discloses a large model driven automobile model modeling method based on a Modelica language, and belongs to the technical field of intelligent modeling and automobile simulation. The method comprises the following steps: firstly, accurately analyzing a natural language demand into a structured triple by adopting a BERT-CRF (domain knowledge enhanced) multi-task model; matching an optimal component combination through a multi-objective optimization algorithm driven by a graph neural network, and cooperatively predicting an interdisciplinary parameter feasible region in combination with symbolic mathematical derivation and machine learning; a topological connection matrix is innovatively optimized by using a graph attention network, and intelligent generation and dynamic verification of simulation codes are realized by fusing a template engine and syntax tree analysis; and finally, constructing a multi-target reward function optimization control strategy through reinforcement learning, and establishing a closed-loop knowledge iteration mechanism. Compared with a traditional modeling method, through deep combination of the large model and Modelica, the technical difficulty of automobile system modeling is remarkably reduced while the preciseness of physical modeling is kept, and the method is particularly suitable for complex scenes such as new energy vehicle model development and intelligent driving system integration.

The invention discloses an integrated coupling calculation method and system for a floating type wind wave combined power generation device based on Modelica-AeroDyn-MoorDyn, and relates to the technical field of offshore wind power generation, and the method comprises the steps: constructing a floating type fan model and a wave energy floater model which are highly matched with the actual operation of the floating type wind wave combined power generation device through Modelica; the aerodynamic load can be accurately loaded to the floating fan in an omnibearing mode, meanwhile, the attachment mode between the wave energy floater and the floating fan is established, the freedom degree of the floater can be freely released, enough precision is provided for reliability and stability evaluation of mooring based on the concentrated mass method by combining MoorDyn, and based on collaborative simulation of Modelica-AeroDyn-MoorDyn, the reliability and stability of the floating fan can be evaluated. The simulation result of the multi-floating-body coupling response obtained on the whole can truly reflect the operation state of the floating type wind and wave combined power generation device, and the coupling reliability is improved.

The invention provides a metro station air-conditioning water system energy saving method based on a Modelica model and a genetic algorithm, and relates to the technical field of rail transit building energy optimization and intelligent control, the method comprises the following steps: establishing a simulation model of a metro station air-conditioning water system in Dymola software based on a Modelica language; performing population initialization on the established simulation model, realizing a genetic algorithm by utilizing MATLAB, and taking minimization of the total energy consumption of the air-conditioning water system as a target function to obtain an optimization control strategy; according to the optimization control strategy, a Dymola-MATLAB joint simulation interface is constructed through Python; and according to the joint simulation interface, an optimal control strategy of the subway station air conditioner water system is obtained through genetic algorithm iterative optimization. According to the method, through the technical path of'precise modeling-intelligent optimization-closed-loop control ', the industrial problems of'high energy consumption and difficult regulation and control' of the subway station air conditioning water system are systematically solved, and an innovative solution is provided for intelligent rail transit construction.

The invention relates to a nonlinear damping element based on a Modelica language, and relates to the field of mechanical system modeling and simulation. According to the technical scheme, a novel damping element is designed based on the Modelica language, corresponding damping coefficients can be defined according to different movement speeds of the element, and the limitation of the prior art is effectively broken through; specifically, according to the technical scheme, the element types of the Modelica mechanical library are enriched, the blank of nonlinear damping elements in the library is filled up, and the application range of the library is expanded; moreover, simulation requirements of a nonlinear damping system can be accurately matched, so that the modeling process better fits nonlinear characteristics of a damping coefficient in an actual working condition; besides, free switching between non-linear damping and linear damping can be flexibly realized depending on a speed-damping force two-dimensional number table, the adaptability of the element to different modeling scenes is greatly enhanced, and powerful support is provided for precise modeling of a mechanical system based on Modelica.

The invention provides a pipe network model parameter inversion method and device based on an FMU standard and an optimization algorithm, and relates to the technical field of fluid simulation and parameter calibration. A pipe network physical model is established, and a to-be-inverted parameter is defined as an adjustable parameter and compiled and exported as an FMU format file; an FMU file is loaded in an optimization environment, a parameter sample set is generated through uniform random sampling, and a neural network agent model is trained to establish a mapping relation from parameters to system response; and a weighted error loss function is designed, deviation of measured data and simulation output is taken as an optimization target, optimal parameters of the neural network agent model are solved by adopting an optimization algorithm, and the pipe network system is operated according to the optimal parameters. According to the method, the combination of the Modelica modeling advantage and the Python optimization algorithm is realized through the FMU standard; optimization iteration is accelerated through a neural network agent model, and the parameter calibration efficiency is greatly improved; and high-precision automatic inversion of the key parameters of the pipe network is realized.

The invention discloses a digital vibration isolation system design optimization method based on Modelica, and the method sequentially comprises the steps: determining the overall layout of each vibration isolation element of a vibration isolation system, constructing the dynamic performance mapping from the rigidity k of the vibration isolation element and the damping c of a damping element to the vibration isolation system, constructing a vibration isolation element rigidity and damping and structural parameter mapping model, and carrying out the design optimization of the vibration isolation system. Structural parameters of the vibration isolation element are optimized based on methods such as sensitivity analysis, a particle swarm optimization algorithm and a genetic algorithm, and structural finite element simulation verification and vibration isolation system performance simulation verification of a three-dimensional model are carried out according to the structural parameters of the vibration isolation element. According to the method, a digital forward design method is adopted, mapping among the vibration isolation system, rigidity and damping of the vibration isolation elements and structural parameters of the vibration isolation elements can be accurately established, and index decomposition completeness and accuracy of the vibration isolation system can be guaranteed.

The invention discloses a ship electric propulsion system reliability dynamic deduction method based on multi-parameter parallel simulation, and the method comprises the following steps: S1, analyzing the equipment composition of a ship electric propulsion system, and determining the connection relation of all equipment; s2, constructing a mathematical model of the ship electric propulsion system equipment, and defining parameters and behaviors of the equipment; s3, realizing system unified modeling and multi-parameter parallel simulation in combination with a Modelica language, and analyzing system performance; s4, equipment function FMECA analysis is carried out, a performance analysis model with faults as the core is constructed, and ship electric propulsion system multi-fault parallel simulation analysis is achieved; and S5, carrying out simulation analysis based on the performance analysis model taking the fault as the core, and dynamically deducing the reliability of the ship electric propulsion system. The method effectively solves the problems that the incidence relation and interaction between devices are difficult to express and the model accuracy is insufficient, and the problem that the system only has a binary state and is mutually independent from faults.

The invention discloses a multi-scale and multi-field digital twin model construction method for complex equipment, and belongs to the technical field of intelligent manufacturing and engineering. The implementation method comprises the following steps of: aiming at the characteristic that machine tool equipment comprises multi-scale layers and multi-disciplinary fields, displaying the multi-scale layers and the multi-disciplinary fields of physical entity equipment in a unified modeling mode by adopting a Modelica modeling language through non-causal modeling and connector modeling, and breaking through the limitation of complex coupling characteristics of the equipment; taking the minimum error between the output of the machine tool digital twin model and the test of a machine tool test bed as an optimization target, considering the main shaft rigidity, the main shaft size, the processing temperature, the thermal conductivity and the thermal expansion coefficient correction variables, and constructing a machine tool digital twin model correction optimization problem model; an optimal parameter combination is searched by adopting an improved particle swarm algorithm of a model-elite learning strategy, correction of the machine tool digital twinborn model is realized, the consistency of the twinborn model and physical equipment is improved, and the accuracy of the machine tool equipment digital twinborn model is ensured.

The invention discloses an LLM (Logical Language Modelica) domain method for Modelica intelligent modeling optimization, and belongs to the field of computer-aided modeling. Structured information is converted and extracted by collecting field data of the new energy automobile; then, based on the RAG technology, intelligent blocking and vectorization processing is carried out on the structured information to form a structured knowledge base, and then a recall test is carried out by calculating a comprehensive weighted score to judge whether the structured knowledge base is available or not; then, the LLM is integrated into an AI-Agent framework, an AI-Agent is formed, and LLM parameters are configured; integrating the structured knowledge base which passes the test into the AI-Agent by utilizing an API (Application Program Interface) and an access protocol; and meanwhile, a specialized cue word template is designed and integrated into the AI-Agent, and a Modelica code generation template and related constraints are standardized. And finally, the user asks a question to the AI-Agent, the LLM works according to a specialized cue word instruction, an API interface is called to access the knowledge base, and Modelica codes which conform to language specifications and can be operated by the user are automatically output through the LLM. According to the invention, end-to-end conversion from unstructured input to high-fidelity model output is realized.

The invention relates to the field of computers, and provides a Modelica model state curing method, device, equipment, medium and product, and the method comprises the steps: responding to a translation simulation request of a to-be-cured model, translating the to-be-cured model, and obtaining model uncertainty representation, the model uncertainty representation comprises a state variable selected by translation, an initial value of an initial supplement variable, a nonlinear iteration variable and an iteration initial value of the nonlinear iteration variable, and the initial supplement variable refers to a variable needing to be supplemented with the initial value; and executing at least one of the following steps and finally writing into the model so as to carry out state solidification on the translated model: determining the choice of the state variable, locking the certainty of the initial value, providing the iteration initial value of the nonlinear iteration variable and marking the nonlinear iteration variable by utilizing the manufacturer annotation. According to the method, the device, the equipment, the medium and the product provided by the invention, ambiguity in a model translation process is eliminated, and high consistency and reliability of cross-platform simulation results are realized.

The invention discloses a Modelica-based intelligent coal-fired boiler hearth coupling modeling method and system, and belongs to the field of hearth design, and the modeling method comprises the following specific steps: I, collecting boiler operation data and historical documents, constructing a boiler knowledge graph, extracting a relationship between input and output variables, and establishing a hearth three-dimensional model through a Modelica language; iI, comparing a hearth three-dimensional model simulation experiment result with actual boiler data, verifying the reliability of the model, and globally optimizing the hearth three-dimensional model; according to the method, the complex working condition exploration and optimization capability can be improved, the operation efficiency and environmental protection requirements can be considered, comprehensive optimization of the working condition is realized, computing resources in the working condition optimization process are saved, and the model development period is shortened; the adaptability and operation efficiency of the boiler under the unsteady state condition are improved, local optimal blind adjustment is avoided, a scientific basis is provided for comprehensive performance improvement, calculation and debugging of complex working conditions are simplified, and dependence on experimental verification is reduced.

The invention discloses an intelligent system failure analysis method based on fault recognition, and relates to the technical field of intelligent system reliability analysis, and the analysis method comprises the specific steps: employing Modelica to construct a multi-physical domain digital twinborn model, combing historical data, and building a structured fault mode library; developing a fault injection algorithm to acquire data to form a standardized data set; preprocessing data to construct a causal link and a probability flow network; carding task scenes to construct a reliability evaluation model and training the reliability evaluation model; inputting actual parameters to trace faults, generating an optimization plan, and outputting an analysis report and a plan after verification; according to the method, the problem of insufficient fault correlation analysis caused by modeling scale splitting in a traditional method is solved by constructing a multi-physical domain digital twinborn model covering microcosmic, mesoscopic and macroscopic and establishing a structured fault mode library of a standardized feature description system containing three types of faults of hardware, software and environment interaction.

The invention discloses a construction method of a Modelica-based mechanism-data hybrid urban sewage treatment simulation model. The construction method comprises the following steps: constructing six unit equipment models of a primary sedimentation tank, a nitrification tank, a denitrification tank, a secondary sedimentation tank, a two-flow-diverter and a two-flow-confluence device; constructing an urban sewage treatment system simulation model according to actual working conditions, and outputting water quality and flow; determining model mechanism parameters needing to be adjusted in the system simulation model as input; constructing an optimization module, and outputting an optimal parameter by taking the simulation result and the actual data deviation as evaluation indexes; the optimization module and the system simulation model are combined for calculation to conduct parameter identification and correction, and therefore the urban sewage treatment system simulation model is constructed. Modelica is used as a basis for modeling, the technical problems that a traditional modeling method is not comprehensive, causality is considered, and modification and reuse are not easy are solved, and then the accuracy and reusability of the urban sewage treatment simulation model are improved.

The invention discloses an engine model modeling method and system based on a Modelica language. The method comprises the steps that external engine ECU instruction information is read; the current average effective pressure and ideal output torque of the engine are calculated according to the engine dynamic parameter characteristics and the ECU instruction information; calculating an external torque output value of the engine by combining the ideal output torque of the engine, the temperature and the dynamic correction coefficient; an external interaction interface is established, dynamic balance and thermodynamic balance calculation is conducted on the external torque output value in combination with the engine, an engine model is obtained, the external interaction interface comprises a thermal interface and a flange interface, the thermal interface is used for connecting the engine with a cooling component, and the flange interface is used for connecting the engine with a load component. According to the method, the engine model is established based on the Modelica language, the dynamic rotating speed and torque conditions in the working process of the engine are comprehensively analyzed, and meanwhile the economical and environment-friendly characteristics of the engine in the process are analyzed.

The present invention discloses a method and system for modeling an engine model based on the Modelica language. The method includes: reading external engine ECU instruction information; calculating the current engine mean effective pressure and the ideal output torque according to the engine power parameter characteristics and the ECU instruction information; calculating the engine external torque output value by combining the engine ideal output torque, temperature and dynamic correction coefficient; establishing an external interaction interface, and performing dynamic balance and thermodynamic balance calculations in combination with the engine external torque output value to obtain the engine model. Among them, the external interaction interface includes a thermal interface and a flange interface. The thermal interface is used for connecting the engine to the cooling components, and the flange interface is used for connecting the engine to the load components. The present invention establishes an engine model based on the Modelica language, realizes comprehensive analysis of the dynamic speed and torque conditions during the engine operation process, and simultaneously analyzes the economic, environmental protection and other characteristics of the engine during this process.

The application relates to a model version automatic conversion method and system for a Modelica language, and the method comprises the following steps: in response to receiving a conversion request for a user model dependency, the conversion request comprising version information of model libraries relied on by the user model before and after conversion; loading at least one version conversion script according to the version information of the model libraries relied on by the user model before and after conversion, the at least one version conversion script being used for recording version conversion information when a current version model library relied on by the user model is upgraded to a target version model library; parsing the at least one version conversion script to obtain at least one corresponding version difference mapping table; and converting the dependency of the user model according to the at least one version difference mapping table, so that the converted user model is compatible with the target version model library. The application can automatically convert the dependency of the user model, adapt to a new version model library, and reduce the complexity of manual operation.

The application discloses a multi-stage dynamic arrangement method for Modelica task parallel compilation load, the method designs an arrangement optimization strategy according to the granularity of each stage, the front compilation stage improves the translation speed by setting the number of parallel CPUs for single-machine compilation of the task, but if multiple tasks are compiled concurrently on the single machine, the speed will decrease exponentially, so when the execution nodes are distributed, all the tasks currently arrived are arranged globally; the number of parallel compilation sub-threads of the total splitting and the distributed compilation optimization Modelica task parallel compilation load are set in the post-compilation stage, aiming at solving the problem that the Modelica model cannot be distributed compiled, improving the overall compilation efficiency of the system, and reducing the slow simulation speed problem caused by the single-machine computing power problem.

The invention relates to the technical field of spacecraft design, in particular to an aircraft information flow analysis, recheck and recalculation system, which comprises an information large graph design module used for automatically extracting design data such as an interface data list and a cable network contact table, generating a static information large graph through data check and analysis, and supporting signal management, layered display and export; the model library construction module is used for constructing a hierarchical information professional model library based on a Modelica language and covering multi-level models from a basic library, a universal library to a model customization library; and the information flow simulation verification analysis module is used for mapping the static information large graph into a simulation model, carrying out dynamic simulation verification on a system network, a communication network and a load network, and realizing multi-dimensional visual display of a result. According to the method, the information system design is rechecked and recalculated from static state to dynamic state and from manual operation to automatic operation, and the design efficiency, accuracy and verification sufficiency are effectively improved.

The invention discloses a proxy model determination method and device, electronic equipment and a storage medium. The method is applied to a Modelica model, and the specific scheme is as follows: obtaining input variable information of a to-be-processed simulation model, sampling the input variable information, and determining sample input data corresponding to the input variable information; performing simulation processing on the sample input data based on the to-be-processed simulation model to obtain simulation data corresponding to the sample input data; and training a to-be-trained agent model based on the simulation data to obtain a target agent model, the to-be-trained agent model being determined based on the data distribution information and the input variable information of the simulation data. According to the method, automatic generation of the agent model is realized, the calculation efficiency is improved, and the consistency and accuracy of the model simplification process are ensured.

The invention discloses a modeling method of a manual transmission with a flywheel and a clutch based on a Modelica language, which comprises the following steps of: building a flywheel model, and constructing a derivative of flywheel rotating speed to time; building a clutch model based on the hyperbolic tangent coulomb friction model, and calculating clutch output torque; building a main shaft model, calculating main shaft torque according to the loss type of the gearbox, and building a derivative of main shaft rotating speed to time; building an auxiliary shaft model, and calculating the rotating speed and torque of the auxiliary shaft according to the rotating speed of the driving shaft and the hyperbolic tangent friction model; calculating a driving shaft torque according to the gearbox loss type, and establishing a driving shaft model; and establishing a power loss model of the gearbox, and calculating the power loss of the gearbox by considering the influence of the power loss. According to the external input engine torque, the driving shaft rotating speed and the inherent characteristics of all the components, an equation is established and solved to obtain the rotating speed and the torque of all the components, finally, the power loss of the whole working process of the manual transmission is calculated, and the modeling difficulty of the manual transmission can be reduced.

The invention discloses a simulation method for high and medium parameter cooperative heat supply dynamic characteristics of a large unit, which belongs to the field of thermodynamic system simulation and comprises the following steps of: splitting a plant-level cooperative heat supply system into an ultra-supercritical primary reheat unit subsystem, an ultra-supercritical secondary reheat unit subsystem and a heat supply pipe network subsystem; splitting each subsystem into a plurality of basic units; performing mechanism analysis on the basic unit, and establishing a differential equation set for describing the physical process of the basic unit; a Modelica language is used for programming, and compiling is based on a Dymola simulation platform; packaging the basic units, establishing a connector, and connecting the basic units; all the basic units are connected according to the transfer sequence of the flow variables, and a differential equation set of a connected system is automatically established through the flow variables and the potential variables set by the connectors; according to the method, non-causal modeling is adopted, the modeling process is simplified, input and output do not need to be considered when the equation is established, and a large number of formula derivation processes are reduced.

The invention discloses a Modelica model automatic generation Agent development method based on SysPhS, and belongs to the field of system engineering and AI based on models. Aiming at each core component in the electric vehicle system, extracting each component, a corresponding port and a connection relation through an analysis interface by utilizing a SysMLv2 system model; secondly, aiming at the analyzed component S, retrieving each component model matched with the information of the component S in a Modelica model library through an RAG algorithm; according to the RAG semantic similarity threshold value, sorting is carried out from high to low; selecting the component model with the highest sequence, mapping the component model with the highest sequence to a port consistent with the SysMLv2 system model in connection relation through a SysPhS standard specification, and forming a Modelica system model by using all the component models with the highest sequence and the mapped connection ports; finally, inputting the Modelica system model into a model checking Agent for lexical, syntactic and semantic checking, and if an error prompt exists in a checking result, automatically correcting the Modelica system model and re-checking the Modelica system model; and otherwise, outputting the final Modelica system model. According to the invention, the accuracy, consistency and efficiency of model generation are improved.

The present invention relates to the simulation of liquid rocket engine systems, and mainly relates to a method for constructing a dynamic simulation medium library of liquid rocket engines based on the Modelica language. The purpose is to solve the technical problem that in the existing dynamic simulation of liquid rocket engines, there is a lack of a method for constructing a medium library that can cover various media involved in liquid rocket engines. Only by selecting the medium on the parameter panel can the physical properties be imported with one click. This method realizes the dynamic characteristic simulation of liquid rocket engines by establishing a complete medium library in the dynamic simulation model library, covering various media involved in liquid rocket engines, and only by selecting the medium on the parameter panel to realize the one-click import of component physical properties, separating the physical property library from the component model library. It is convenient and time-saving to operate, and can make the simulation results more reliable, providing a reference for subsequent engine scheme improvement and new model development.

The invention relates to the technical field of rolling and packing workshop-shred storage building, and particularly provides a shred storage building area-level system simulation method and system based on MWORKS, a terminal and a medium, and the method comprises the steps: decomposing a shred storage building area-level system into a shred making workshop subsystem, a shred storage building area subsystem and a rolling and packing workshop subsystem; determining components contained in each subsystem and element components contained in the components; developing a meta-component based on Modelica, and defining a physical interface, parameters and a behavior equation of the meta-component; constructing a model of each part; constructing each subsystem model; establishing a control algorithm model; establishing a communication interface model; all the subsystem models are connected, and the control algorithm models are combined to construct a wire storage building area-level system model; and starting simulation of the tobacco shred storage building area-level system model, and obtaining a tobacco shred storage cabinet operation state related result, a production scheduling plan verification result and a logistics scheduling optimization result. And adaptability to complex working conditions and decision scientificity are enhanced.

This invention belongs to the field of electronic digital data processing and discloses a propulsion modeling method for FPSOs based on Modelica, comprising the following steps: S1: Modeling the propulsion coefficient model of the propeller, including calculating the following parameters: thrust coefficient, thrust torque coefficient, thrust gain coefficient, and thrust torque gain coefficient; S2: Modeling the propeller performance model, including calculating the following parameters: wake coefficient, advance number, thrust loss coefficient, and relative rotational efficiency; S3: Based on the results of steps S1 and S2, outputting the thrust and thrust torque of the propeller, thereby obtaining the propulsion efficiency of the propeller. This method can comprehensively simulate the thrust and torque variation characteristics of the propeller under different operating conditions through a discrete dynamic modeling approach, thus providing accurate simulation support for the dynamic response of FPSO systems in complex marine environments.

The invention relates to the technical field of comprehensive energy utilization, and discloses a modeling method, device, equipment and medium for a nuclear energy comprehensive utilization system, and the method comprises the steps: carrying out the local composition modeling based on Modelica simulation software, obtaining a first dynamic model, and simulating the energy response characteristics of the nuclear energy comprehensive utilization system in a second-level dynamic process; second local composition modeling is carried out based on TRNSYS simulation software, a dynamic model is obtained, and the energy utilization condition of the nuclear energy comprehensive utilization system in the annual hourly dynamic process is simulated; and performing data interaction between the first dynamic model and the second dynamic model, constructing a multi-time scale nuclear energy comprehensive utilization system model, and simulating the system performance of the nuclear energy comprehensive utilization system. According to the method, a balance point is found between model fineness and calculation efficiency, high-precision description of dynamic characteristics of internal parts of the nuclear energy comprehensive utilization system can be realized, annual operation simulation work of the whole energy system is simplified to an hour level, and efficient collaboration and optimization among multiple energy systems are realized.

This invention discloses a method for constructing multi-scale and multi-domain digital twin models of complex equipment, belonging to the field of intelligent manufacturing and engineering technology. The method involves: considering the multi-scale and multi-disciplinary nature of machine tool equipment, the Modelica modeling language is used. Through non-causal modeling and connector modeling, a unified modeling approach is adopted to represent the multi-scale and multi-disciplinary nature of the physical equipment, overcoming the limitations of the complex coupling characteristics of the equipment; with the optimization objective of minimizing the error between the output of the machine tool digital twin model and the test on the machine tool test bench, a correction and optimization problem model for the machine tool digital twin model is constructed, considering spindle stiffness, spindle dimensions, machining temperature, thermal conductivity, and coefficient of thermal expansion; an improved particle swarm optimization algorithm with a "role model-elite" learning strategy is employed to find the optimal parameter combination, thereby correcting the machine tool digital twin model, improving the consistency between the twin model and the physical equipment, and ensuring the accuracy of the machine tool equipment digital twin model.

The application discloses a component selection method, device and equipment based on Modelica, and the method comprises the following steps: obtaining a component principle model based on multiple system design schemes of a user, generating component model codes of multiple component models based on batch design of the component principle model; determining an arrangement mode included in the system design scheme, generating a system model template according to the arrangement mode; generating system model codes of multiple system models according to the system model template; calling the system model codes to perform simulation, obtaining simulation results of the multiple system models; determining key performance indicator variables of the multiple system models, drawing key performance indicator variable curves of the simulation results according to the key performance indicator variables; and determining an optimal system design scheme according to the key performance indicator variable curves. The application solves the problem that a Modelica-based modeling and simulation software currently lacks rapid verification of multiple system design schemes based on Modelica.

The invention discloses a simulation model code automatic restoration method and device based on deep learning and a medium, and the method comprises the steps: S1, carrying out the preprocessing of an original model file, and forming a structured abstract syntax tree AST and a component dependency graph; error detection and repair code generation are carried out by using an improved LLM model, the error detection stage is realized by an improved CodeBERT, and the repair code stage is generated by adopting a GPT decoder; in the error detection stage, auxiliary information is used for enriching fine adjustment, and a multi-modal fusion mechanism is introduced to integrate serialized code texts and structured auxiliary information into a model training process at the same time, so that the error detection capability of the model is enhanced, and defect positions of deep relationships such as component dependence and equation logic are captured; in the code repairing stage, reinforcement learning fine tuning is used. According to the method, an automatic detection and repair scheme for common errors of Modelica model codes is provided for a user, and the development efficiency and reliability of a complex simulation model in industrial software are improved.