86 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 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 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 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 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 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 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.

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.

The invention discloses a Modelica language-based supercritical carbon dioxide Brayton cycle system modeling simulation method, which comprises the following steps of: constructing a system key equipment model according to a mathematical physical equation of equipment characteristics; forming a flow-pressure iterative solution scheme of the system according to the key equipment model and the volume model of the system; all the equipment models are connected according to a design scheme, and equipment structure parameters and simulation iteration initial values are set to form a system simulation model; setting a simulation interval, an output interval and a simulation algorithm on the MWORKS platform; and performing system model inspection and simulation analysis according to the set system simulation model to obtain the operation characteristics of the supercritical carbon dioxide Brayton cycle system. According to the method, rapid reconstruction of a system topological structure, convenient adjustment of equipment parameters and efficient analysis of system characteristics are realized, and reliable data support is provided for engineering design and optimization.

The invention belongs to the technical field of thermal hydraulic simulation software, and particularly relates to a steam turbine modeling method and equipment based on Modelica. According to the method, the thermal-hydraulic process of the steam turbine is efficiently and accurately expressed under the two-fluid six-equation system framework, and coupling simulation of the thermal-hydraulic specialty and the mechanical specialty of the steam turbine is realized, so that the problem of how to ensure that the thermal-hydraulic process of the steam turbine is efficiently and accurately expressed under the two-fluid six-equation system framework is solved; and meanwhile, multi-specialty coupling calculation with a mechanical specialty is realized.

The application relates to the computer field and provides a Modelica model state solidification method, device, equipment, medium and product, wherein the method comprises the following steps: in response to a translation simulation request of a to-be-solidified model, the to-be-solidified model is translated to obtain a model uncertainty representation, the model uncertainty representation comprises translation-selected state variables, initial values of complementary variables, nonlinear iteration variables and iteration initial values of the nonlinear iteration variables, and the complementary variables refer to variables that need to be supplemented with initial values; at least one of the following steps is executed and finally written into a model to solidify the state of the translated model: determining the necessity of the state variables, determining the certainty of the locked initial value, providing the iteration initial value of the nonlinear iteration variable and marking the nonlinear iteration variable by using a manufacturer annotation. The method, device, equipment, medium and product provided by the application eliminate the ambiguity in the model translation process, and realize the high consistency and reliability of cross-platform simulation results.

The application provides a kind of Modelica mechanism model privacy computing method and system based on neural network, it is related to software engineering technical field, including: judging whether the sampling interval input by user is legal, if legal, then interval sampling is carried out for sampling interval, each sampling value is obtained, and total sample is obtained by inputting empirical formula, the training sample and overfitting sample are obtained by sparsifying total sample;Neural network is fitted and trained based on training sample, to obtain trained neural network;The trained neural network is fitted and trained based on overfitting sample, to obtain overfitting neural network model;Modify Modelica mechanism model compiler, replace empirical formula with overfitting neural network model, calculate and output simulation result for user.The application obtains overfitting neural network model for the sampling interval input by user under specific working condition of user, meets the simulation demand of user, and avoids the leakage of empirical formula.

This invention discloses a Modelica modeling method, apparatus, electronic device, and product based on a Python model library. This invention utilizes the Sysplorer modeling tool's ability to support simultaneous modeling with Modelica and Python files, enabling joint modeling of Modelica and Python based on Sysplorer. Specifically, during modeling, the PythonInterface model library is first imported into the Sysplorer modeling tool, and then a Sysplorer model is generated. Next, using the imported PythonInterface model library, a Python instantiation component of the Sysplorer model is generated, allowing the calling of Python files. Then, based on the called Python file, Modelica text information of the Sysplorer model is generated. Finally, the Modelica model can be constructed based on this. Thus, this invention allows direct modeling using Python files within the Modelica environment, transforming the entire process from manual to automated. This not only reduces the workload of model developers but also improves modeling efficiency.

The invention provides a Modelica-based ground source heat pump system self-adaptive debugging method, which comprises the following steps of: acquiring operation parameter data of a ground source heat pump system, and analyzing an operation strategy of the ground source heat pump system; according to the structure and the operation strategy of the ground source heat pump system, a physical simulation agent model of the ground source heat pump system is constructed based on Modelica; performing adaptive calibration on parameters of the physical simulation agent model by using the operation parameter data to obtain a calibration model; verifying the accuracy of the calibration model by using the operation parameter data of the other independent time period to obtain a calibration model; self-adaptive virtual debugging is carried out on the calibration model, and an operation strategy is optimized and determined; and periodically collecting operation parameter data, carrying out parameter calibration on the calibration model, and updating the calibration model. According to the ground source heat pump system self-adaptive debugging method based on Modelica, a high-fidelity dynamic model is constructed, and the debugging process is converted into self-adaptive optimization from one-time verification.

The present application discloses a vehicle system simulation parameter adjustment method and device based on Modelica, which relates to the field of simulation parameter adjustment. The method includes obtaining various causal simulation equations and vehicle simulation system output variables in the simulation solution process of a vehicle simulation system built based on Modelica; determining a target output variable based on the output variable of the vehicle simulation system; determining constants and state variables that affect the target output variable in the causal simulation equation based on the target output variable and each causal simulation equation, and obtaining parameter adjustment constants and parameter adjustment state variables corresponding to the target output variable; adjusting the parameter adjustment constants and parameter adjustment state variables corresponding to the target output variable until the target output variable reaches the expected output value. The present application searches for various parameters that affect the target output variable through the causal simulation equations in the simulation solution process, and can accurately and quickly find various parameters that affect the target output variable from a large number of parameters, thereby improving the efficiency of simulation debugging.

The invention belongs to the technical field of thermal hydraulic simulation software, and particularly relates to a pilot operated safety valve modeling method and device based on Modelica, and the method comprises the steps: obtaining a modular model library architecture after the pilot operated safety valve is decomposed; constructing a corresponding basic-level model by adopting a modeling mode matched with the model characteristics of the basic-level model; the basic-level models are combined, and two-fluid six-equation modeling is adopted to obtain a component-level model; and building a pilot operated safety valve model by adopting the component-level model, and injecting system parameters into the pilot operated safety valve model. Characteristic analysis is carried out on a modular model library framework obtained after decomposition of the pilot operated safety valve, model combination and system parameter injection are carried out by adopting two fluids and six equations, so that the problem of how to ensure an efficient and accurate numerical calculation result of two-phase flow under a multi-specialty coupling condition is solved; modularization, reusability and expansibility of pilot operated safety valve modeling are achieved so as to meet the requirement of complex engineering application.

The application relates to a kind of Modelica model-oriented incremental compilation method and system, the method comprises: the abstract syntax tree of Modelica model is obtained by keyword matching to the Modelica model text edited and modified;Abstract syntax tree includes the definition and behavior of each component in Modelica model;Determine the changed part of Modelica model editing and modification based on abstract syntax tree and the change of last time;The intermediate representation of the changed part of Modelica model is instantiated;Intermediate representation includes: the argument tree of component and component behavior list;The intermediate representation of the changed part and unchanged part of Modelica model is integrated, and the complete representation of Modelica model is obtained;Through incremental compilation, the modified part of model can be quickly identified and locally compiled, so that the whole model is avoided to be recompiled, and the compiling efficiency is significantly improved.At the same time, incremental compilation can also effectively reduce the output of compiling diagnosis information, so that users can more easily obtain the diagnosis information related to the modified part, and further improve the modeling experience.

The invention relates to high-voltage line wind vibration analysis, in particular to a high-voltage line wind vibration simulation analysis method, system and device based on the Modelica language and a medium. Constructing a parameterized sub-model of the high-voltage line system based on the physical parameters; packaging each parameterized sub-model into a model assembly by using a Modelica language, integrating the corresponding model assembly based on an actual topological structure of the high-voltage line system, defining an initial state of the system, and establishing a kinetic equation for each cable section to form a high-voltage line system model; acquiring wind vector data of the current environment in real time; inputting the wind vector data of the current environment into the high-voltage line system model for simulation analysis, solving the kinetic equation of each cable section through numerical integration, and calculating the movement speed of each cable section at a specified moment in the future and the position of the midpoint of the cable section. According to the invention, high-precision and high-efficiency multi-physical-field wind vibration simulation can be realized.

The application relates to the field of automobile dynamics simulation, and particularly discloses a Modelica-based automobile single-mass system modeling and adaptive parameter optimization method, which comprises the following steps: determining a simplified module of an automobile single-mass system and a component connection mode according to demand analysis, and building a basic simulation model based on Modelica; performing initial simulation on the basic simulation model to obtain simulation output results under initial parameters; collecting real vehicle measured data, constructing a comprehensive error function, and iteratively optimizing key physical parameters of the basic simulation model by using a parameter optimization engine until a convergence condition is met to obtain optimized parameters; and feeding back the optimized parameters to the basic simulation model. The application combines the advantages of Modelica multi-field physical modeling and data-driven intelligent optimization algorithms, realizes a leap from manual experience parameter adjustment to automatic intelligent optimization, significantly reduces the dependence on accurate initial parameters, and effectively improves the model simulation precision and engineering application efficiency.

The invention provides a gas turbine multi-physical domain simulation platform-based gas compressor non-design point calculation method, which comprises the following steps of: firstly, constructing a gas compressor model through a Modelica language by adopting a SysTides module in a domestic gas turbine multi-system integration platform; then designing a standard physical port of the gas compressor; defining user input parameters and variables required during calculation of non-design points of the compressor model; constructing a key physical equation of the model and developing a characteristic diagram interpolation algorithm with high robustness; performing data processing and storage on the gas compressor characteristic diagram and the gas exhaust characteristic diagram; and finally, solving and calculating the non-design point of the gas compressor. According to the method, the compressor model constructed by adopting the Modelica language in the SysTides module in the domestic gas turbine multi-system integration platform is adopted, so that coupling simulation and response simulation among multiple physical domains are realized, the accuracy of performance prediction of the compressor under multiple working conditions at non-design points is improved, and the method has good model reusability and expansion capability.

The invention provides a method for modifying a heat transfer coefficient of a user-defined heat transfer pipeline based on Modelica. The method for modifying the heat transfer coefficient of the user-defined heat transfer pipeline based on Modelica comprises the following steps: step 10, receiving an input instruction of a user for variable modification required for calculating a heat transfer coefficient relational expression in a heat transfer coefficient modification interaction window of the user-defined heat transfer pipeline. And the heat transfer coefficient modification interaction window of the self-defined heat transfer pipeline only supports the modification of variables required by the calculation of the heat transfer coefficient relational expression. And step 20, modifying the custom heat transfer pipeline model according to the input instruction to form a modified custom heat transfer pipeline model. According to the method, a user is allowed to customize the heat transfer coefficient relational expression of the heat transfer pipeline in a very simple mode, so that the technical problem that inconvenience is caused due to the fact that the requirement for the user code level is high in the heat transfer coefficient relational expression modification process of an existing custom heat transfer pipeline module is solved.