184 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 real-time simulation platform of a floating production storage and offloading (FPSO) riser support system based on Modelica, and belongs to the field of intelligent simulation. Comprising a demand definition module, a data loading module, an environment simulation module, a riser support modeling module, a system verification module, a simulation calculation module, an optimization scheduling module, an evaluation analysis module, a control suggestion module, a real-time visualization module, a storage backtracking module and a feedback adjustment module. According to the method, a high-dimensional nonlinear space can be globally searched, a better solution can be found, premature convergence is avoided, the stability of an optimization result is improved, it can be ensured that parameter configuration always keeps the optimal performance under variable working conditions, the intelligence and adaptability of an operation strategy can be improved, and it is ensured that a riser supporting system is always in an optimal control state under different working conditions; according to the method, high efficiency of multi-objective optimization is achieved, the best balance point among vibration reduction, abrasion reduction and equipment service life prolonging can be found, and therefore double improvement of economical efficiency and safety is achieved.

The invention discloses a fuel cell system modeling and control-oriented joint simulation design method, which comprises the following steps of: based on an electrochemical and heat transfer mechanism, dividing an electric pile into a plurality of nodes along a reaction gas flowing direction by adopting a distributed parameter method, and establishing a one-dimensional solid oxide fuel cell dynamic model by applying a multi-field unified modeling language Modelica; establishing a dynamic model of each auxiliary machine part of the solid oxide fuel cell system; all the components are connected according to a unified interface to form a system overall dynamic model, and dynamic simulation is carried out; carrying out multi-output multi-input state space model identification in SIMULINK by applying dynamic simulation data of the solid oxide fuel cell system; a model prediction controller KF-MPC based on Kalman filtering is developed; and establishing a joint simulation platform. According to the invention, rapid and accurate load tracking and temperature control are realized, and the operation safety of the system is maintained at the same time.

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 a modeling method for a wind wave flow coupling environment of an FPSO system based on modelica, and relates to the technical field of model convergence, and the method comprises the steps: setting different time steps, carrying out simulation, recording the response of a key physical quantity, and calculating a time step sensitivity index; performing different grid division and simulation on the model, and calculating a grid independence index; and recording the dynamic response of the FPSO platform, and calculating the instability index of the simulation result. A time step sensitivity index, a grid independence index and a simulation result instability index are combined to calculate a convergence anomaly coefficient, the convergence anomaly coefficient is compared with a preset threshold value, whether the convergence of the model is qualified or not is judged, the standardized convergence of the FPSO system wind wave flow coupling model based on Modelica can be verified in the process, it is ensured that the convergence performance of the model is detected in time, and the reliability of the model is improved. Error accumulation is avoided, the accuracy and reliability of a simulation result are improved, and therefore potential influences on engineering decision and safety evaluation are reduced.

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 discloses an SKTC task model data interaction association mapping transmission method, and relates to the technical field of data interaction and association mapping of a SysML design model and a Modelica simulation model. The method comprises the steps that a unified data interaction format is defined, the format and the protocol follow the FMI standard, and interoperability of the model is ensured; a mapping rule is designed, and data read-write operation between the models is achieved through an interface provided by the FMI; a data interaction interface is realized, and setting of model parameters and triggering of calculation steps are carried out through an FMI; and developing a co-simulation environment. By defining a unified data interaction format and a mapping rule, a design model and a simulation model are exported into an FMU format through a format conforming to an FMI interface standard, and are effectively integrated in a co-simulation environment, so that the two models can be seamlessly jointed, accurate transmission of data and co-simulation of the models are realized, and the simulation efficiency is improved. And the interactivity and reusability of the model are greatly improved.

The invention discloses a coal-fired unit twinning mechanism model boiler parameter self-correction method and device, and the method comprises the steps: building an object unit twinning mechanism model and an offline correction mechanism model through employing a Modelica language according to design parameters; the data platform collects data from the DCS, and provides data services for off-line correction of heat exchange coefficients of all heat exchange surfaces of the boiler, on-line correction of heat exchange scaling factors of all the heat exchange surfaces of the boiler and twinning application; setting heat exchange scaling factors of each heat exchange surface of a unit twinning mechanism model and an off-line correction mechanism model as operating state intermediate values, and determining optimal heat exchange coefficients of each heat exchange surface of the boiler by the off-line correction mechanism model by adopting a differential evolution algorithm based on historical data; and based on the optimal heat exchange coefficient of each heat exchange surface of the boiler, a fuzzy control algorithm is adopted, and heat exchange scaling factors of each heat exchange surface of the unit twinning mechanism model boiler are circularly corrected in sequence on line. The invention further provides a corresponding device and equipment.

The invention discloses a No.0 high-pressure heater dynamic modeling and depth flexible peak regulation performance evaluation method based on Modelica. The method comprises the following steps: establishing a dynamic coupling model of a subcritical coal-fired unit; parameters of the model are set, different load working conditions are simulated, and key operation data are obtained; real-time dynamic simulation is carried out, and the influence of commissioning and removal of the No.0 high-pressure heater under different load working conditions on unit performance is evaluated; and the response performance of the No.0 high-pressure heater in the frequency modulation process is deeply analyzed, simulation results are integrated, the operation effect is evaluated, and an optimized control strategy is provided. According to the method, through dynamic simulation analysis, the frequency modulation capability and the heat economy of the unit are optimized, and the flexibility of the unit under the deep peak regulation working condition is improved. Performance evaluation indexes provide scientific optimization basis and control strategy suggestions for deep peak regulation of the coal-fired unit. The dynamic modeling and evaluation method can comprehensively analyze various performance numbers of the unit in the flexible peak regulation process, and has good theoretical value and practical application prospect.

The embodiment of the invention discloses a Modelica model compiling method and device, equipment and a storage medium, and the method comprises the steps: determining a target model type of a target Modelica model; wherein the target model type comprises a replaceable model or an irreplaceable model; under the condition that the target Modelica model is an irreplaceable model, aiming at each component in the target Modelica model, determining a target component type of the component; wherein the target component type comprises a fixed component, a candidate component or a template component; and based on the target component type of the component, executing a corresponding component compiling operation on the component. According to the technical scheme, the corresponding compiling operation can be executed based on the component type of the component in the Modelica model, and the compiling efficiency of the Modelica model 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 discloses an initial steady-state compiling method and an initial steady-state compiling system for a Modelica model. The initial steady-state compiling method for the Modelica model comprises the following steps: putting an initial value equation / initial constraint condition and a fixed initial value constraint condition in the Modelica model into an initial value equation system set; when a user starts an initial steady-state mode, removing a corresponding redundant initial constraint condition from the initial value equation system set according to an initial steady-state option, and supplementing the initial steady-state constraint condition; putting the initial steady-state constraint condition and the compiled model state equation into an initial value equation system set; and packaging the initial value equation system set into an initialization function, and calling the initialization function to realize initial steady-state solution of the Modelica model. According to the method, by removing the redundant initial constraint conditions and automatically adding the initial steady-state constraint conditions, the initial state of the model is constructed into the steady-state value, and the modeling efficiency of the complex system model is improved.

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 provides a Modelica language-based rapid simulation method for whole-process dynamics of a carrier rocket, and aims to solve the problem of insufficient model reusability and adaptability in a traditional simulation method. By utilizing the object-oriented characteristic of the Modelica language, the method constructs a comprehensive dynamic model, and the model integrates a plurality of subsystems such as a propulsion system, a control system and an aerodynamic model. The method comprises the following specific steps: firstly, constructing an overall model of a carrier rocket, and defining time-varying parameters to accurately calculate key parameters such as propellant consumption, rotational inertia and centroid position; secondly, the influence of different design parameters on the rocket performance is analyzed through simulation, so that potential problems are recognized and solved in advance; and moreover, the simulation result can be analyzed, so that the accuracy and reliability of the model are ensured. The innovation of the invention lies in the modular design and multi-field collaborative simulation capability, which not only significantly improves the reusability and adaptability of the model, but also effectively supports the design optimization, task planning and control strategy formulation of the rocket. The method not only reduces the research and development cost, but also accelerates the progress of the spaceflight technology, and has a wide application prospect.

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 a design and simulation integration method based on SysML (System Markup Language) and Modelica. According to the method, the existing Modelica is changed into a calculation component of SysML, and complex verification calculation can be carried out in a design stage by virtue of the strong calculation simulation capability of the Modelica model. An existing Modelica model is directly converted into a Block object of the SysML for modeling, and mixed modeling of the SysML and Modelica is achieved. The method does not need to use a Modelica modeling tool to modify a model after the Modelica simulation model is generated in the SysML, and achieves the integration of design and simulation in the aspect of visual modeling. A SysML model is converted into a Modelica model, so that consistent transmission from design to simulation knowledge is realized.

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 belongs to the technical field of Modelica language grammar analysis, and aims to solve the problem that model transmission and model details are difficult to read and recognize when an existing Modelica language uses a text form storage model. The invention discloses a grammar analysis system and method for a Modelica language. The system comprises an ANTLR4 grammar analysis module, a Modelica text code generation module, a data sequence and a deserialization module. According to the method, different model elements in a Modelica object are converted into character strings in a text format, and the character strings in the text format are combined to generate a complete Modelica model file in the text format. According to the method, mutual conversion between the text format and the structured JSON format of the simulation model based on the Modelica language is achieved, on the premise that the text format necessary for simulation of the Modelica model is met, the Modelica model can be transmitted and stored in a structured form, and large-scale collaborative modeling and model version tracking management based on the Modelica language are effectively supported.

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 belongs to the technical field of supercritical carbon dioxide system simulation, and particularly relates to a Modelica-based supercritical carbon dioxide compressor modeling method. Comprising the following steps: decomposing the supercritical carbon dioxide compressor; determining interface attributes of data transmission between the compressor model and the outside; constructing a control body model and a connecting pipe model of the supercritical carbon dioxide compressor; the control volume model corresponds to a mass and energy control equation of the two-fluid six-equation; packaging into a supercritical carbon dioxide compressor model; and connecting the constructed supercritical carbon dioxide compressor model with a pipeline and a boundary. The method has the advantages that based on the Modelica language, only specific physical phenomenon equations of the model need to be given, specific solving principles and solving steps do not need to be given, a simulation platform where the Modelica model is located can automatically compile and solve existing equations during solving, and the modeling threshold is greatly reduced.

The invention discloses a Modelica proxy model generation method and device and a medium, and relates to the technical field of engineering simulation, and the method comprises the steps: obtaining an initialization parameter of a Modelica target model, and determining a value range and probability distribution of the initialization parameter; constructing a training data set according to the value range and probability distribution of the initialization parameters; constructing an LSTM neural network model, and determining network parameters of the LSTM neural network model after random initialization; performing training optimization on network parameters of the LSTM neural network model by adopting an MAML algorithm according to the training data set to obtain optimized network parameters; according to the optimized network parameters, constructing an optimized meta-model; and performing small sample training on the optimized meta-model to obtain a Modelica proxy model. According to the method, the solution speed of the Modelica model can be effectively improved, and the modeling simulation efficiency is 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.