Modelica system reduced-order model generation method, device, equipment and medium

Abstract

The present invention discloses a method, apparatus, device, and medium for generating a Modelica system reduced-order model. The method comprises: obtaining computer-aided engineering (CAE) three-dimensional simulation result data; generating a reduced-order model based on the CAE three-dimensional simulation result data; obtaining a reduction method for generating the reduced-order model, environmental information on which the reduced-order model relies, and input and output information of the reduced-order model; and generating a Modelica system reduced-order model based on the reduced-order model, the reduction method, the environmental information, and the input and output information. This method ensures good compatibility and scalability for modeling across different disciplines within the same simulation software, reducing the difficulty of multi-domain and multi-disciplinary joint simulation.

Description

Technical Field

[0001] The present invention relates to the field of simulation technology, and in particular to a method, device, equipment and medium for generating a reduced-order model of a Modelica system. Background Art

[0002] The design, operation, and maintenance of complex equipment such as aircraft, automobiles, ships, and nuclear power equipment involve unified modeling and simulation across multiple domains of the entire system. This critical step is described by two types of control equations in multi-domain physical phenomena. One type, represented by CAE simulation technology, takes a long time to solve and requires a large amount of data, and is commonly used to solve problems in fluids, flexible structures, thermodynamics, and electromagnetics. The other type, represented by system simulation, describes large systems of ordinary differential equations, including linear and nonlinear equations. These algorithms offer fast computational speeds and large equation sizes, and are commonly used in control, hydraulics, multi-body systems, one-dimensional thermodynamics, one-dimensional fluids, and other fields.

[0003] At present, different disciplines have their own applicable order reduction algorithms and system simulation software, which leads to the lack of universal applicability of the same system simulation software in different disciplines. Therefore, multi-domain and multi-disciplinary joint simulation is of great significance to simplifying system simulation software. Summary of the Invention

[0004] The present invention provides a method, device, equipment and medium for generating a Modelica system reduced-order model, so as to solve the problem that modeling in different disciplines is not universally applicable on the same simulation software.

[0005] According to one aspect of the present invention, a method for generating a Modelica system reduced-order model is provided, the method comprising:

[0006] Obtain computer-aided engineering (CAE) 3D simulation result data;

[0007] generating a reduced-order model based on the CAE three-dimensional simulation result data;

[0008] Acquiring a reduction method for generating the reduced-order model, environmental information on which the reduced-order model depends, and input and output information of the reduced-order model;

[0009] A Modelica system reduced-order model is generated according to the reduced-order model, the reduced-order method, the environmental information, and the input and output information.

[0010] According to another aspect of the present invention, a device for generating a Modelica system reduced-order model is provided, the device comprising:

[0011] Result data acquisition module, used to obtain computer-aided engineering (CAE) three-dimensional simulation result data;

[0012] A reduced-order model generation module, configured to generate a reduced-order model based on the CAE three-dimensional simulation result data;

[0013] A method and information acquisition module, configured to acquire a reduction method for generating the reduced-order model, environmental information on which the reduced-order model depends, and input and output information of the reduced-order model;

[0014] The system model generation module is used to generate a Modelica system reduced-order model according to the reduced-order model, the reduced-order method, the environmental information and the input and output information.

[0015] According to another aspect of the present invention, an electronic device is provided, comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can execute the method for generating a Modelica system reduced-order model according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and wherein the computer instructions are used to enable a processor to implement the method for generating a Modelica system reduced-order model according to any embodiment of the present invention when executed.

[0020] The technical solution of an embodiment of the present invention obtains computer-aided engineering (CAE) three-dimensional simulation result data, extracts feature data from the CAE three-dimensional simulation result data, processes the feature data based on a pre-set reduced-order model algorithm and model parameters, and generates a reduced-order model. The reduced-order model's reduction method, the environmental information on which the reduced-order model depends, and the input and output information of the reduced-order model are input into a Modelica automatic generation module, which can automatically assemble and generate a Modelica system reduced-order model. This method enables modeling in different disciplines to have good compatibility and scalability on the same simulation software, reducing the difficulty of multi-domain and multi-disciplinary joint simulation.

[0021] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 This is a flowchart of a method for generating a Modelica system reduced-order model according to the first embodiment of the present invention;

[0024] Figure 2 is a flowchart of generation and application of a Modelica system reduced-order model applicable to an embodiment of the present invention;

[0025] Figure 3 This is a flowchart of a method for generating a Modelica system reduced-order model according to a second embodiment of the present invention;

[0026] Figure 4 2 is a schematic structural diagram of a device for generating a reduced-order model of a Modelica system according to a third embodiment of the present invention;

[0027] Figure 5 The present invention is a schematic diagram of the structure of an electronic device for implementing the method for generating a Modelica system reduced-order model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] Example 1

[0031] Figure 1 A flowchart of a method for generating a Modelica system reduced-order model is provided for the first embodiment of the present invention. This embodiment is applicable to the generation of Modelica system reduced-order models in different fields and systems. The method can be executed by a Modelica system reduced-order model generation device. The Modelica system reduced-order model generation device can be implemented in the form of hardware and / or software. The Modelica system reduced-order model generation device can be configured in any electronic device with network communication and receiving functions. Figure 1 As shown, the method includes:

[0032] S110, obtaining computer-aided engineering (CAE) three-dimensional simulation result data.

[0033] Computer-aided engineering (CAE) involves discretizing an actual structure into discrete bodies composed of regular unit cells and analyzing the discrete bodies to obtain three-dimensional simulation data of the actual structure. The three-dimensional simulation data can be three-dimensional model data of the actual structure obtained through simulation technology.

[0034] Specifically, computer-aided engineering (CAE) technology is used to simulate the multi-domain physical phenomena involved in the simulation target, and obtain three-dimensional simulation result data of the multi-domain physical phenomena of the simulation target, where the multi-domain physical phenomena may include: solving fluid, flexible body structure, thermodynamics, and electromagnetic fields.

[0035] S120: Generate a reduced-order model based on the CAE three-dimensional simulation result data.

[0036] The reduced-order model may be a model generated by reducing the order of a three-dimensional model based on a deep learning method or other data processing method.

[0037] As an optional but not limiting implementation, generating a reduced-order model based on the CAE 3D simulation result data may include steps A1-A2:

[0038] Step A1: extracting feature data from the CAE three-dimensional simulation result data.

[0039] Specifically, feature data is extracted from the CAE three-dimensional simulation result data. The feature data requires data processing. The data processing includes data cleaning and data reduction operations. Data processing operations are performed based on the feature data extracted from the CAE three-dimensional simulation result data.

[0040] Step A2: Process the characteristic data based on a preset reduced-order model algorithm and corresponding model parameters to generate a reduced-order model.

[0041] Specifically, according to a pre-set reduced-order model algorithm, a suitable reduced-order model algorithm and corresponding model parameters are selected to process the feature data, thereby generating a reduced-order model. This process may include the following steps:

[0042] Step A21: Select input variables and output variables, and divide the data into training set and validation set.

[0043] Step A22: Select a reduced-order model algorithm, set the parameters of the reduced-order model algorithm, solve the reduced-order model, and automatically add a model identifier to the initially generated reduced-order model. The model identifier includes copyright information, input and output dimensions, model category, and dependent environment information.

[0044] Step A23: Select the validation set data and substitute it into the reduced-order model to verify the accuracy of the reduced-order model. If the accuracy of the reduced-order model meets expectations, generate the reduced-order model. If the accuracy of the reduced-order model does not meet expectations, re-divide the validation set or set different reduced-order model algorithm parameters, and repeat steps A21 to A23 until the accuracy of the reduced-order model meets expectations and a reduced-order model is generated.

[0045] S130: Acquire a reduction method for generating the reduced-order model, environmental information on which the reduced-order model depends, and input and output information of the reduced-order model.

[0046] Specifically, the reduced-order model for which the Modelica system reduced-order model needs to be generated and the storage location after the model is generated are selected, and the input and output information of the reduced-order model, the reduced-order model method, and the environment on which the reduced-order model depends can be obtained through an automatic retrieval method.

[0047] S140 , generating a Modelica system reduced-order model according to the reduced-order model, the reduced-order method, the environmental information, and the input and output information.

[0048] Specifically, based on the Modelica semantic syntax, the environment required for the reduced-order model is automatically configured. According to the reduced-order model, reduction method, environmental information, and input and output information, the Modelica system reduced-order model and the Modelica system reduced-order model interface suitable for the Modelica modeling and simulation software are automatically generated. The Modelica system reduced-order model interface is connected by a simple drag-and-drop method, which improves the adaptability of the reduced-order model and the system simulation software.

[0049] As an optional but not limiting implementation, generating a Modelica system reduced-order model according to the reduced-order model, the reduction method, the environment information, and the input and output information may include:

[0050] Based on the Modelica semantic grammar, the reduced-order model is processed according to a preset template to generate a Modelica system reduced-order model; wherein the reduction method is used to determine a calculation method of the Modelica system reduced-order model, the environmental information is integrated into the Modelica system reduced-order model, and the input and output information is used to generate an interface in the Modelica system reduced-order model.

[0051] Specifically, based on the Modelica semantic grammar, the steps of processing the reduced-order model according to a preset template to generate the Modelica system reduced-order model may include:

[0052] Step B1: Select a reduced-order model for which a Modelica system reduced-order model needs to be generated, and select a generation location for the Modelica system reduced-order model.

[0053] Step B2: The functional module responsible for generating the Modelica system reduced-order model identifies the identifier of the reduced-order model, retrieves the dependent environment of the reduced-order model based on the correspondence between the environmental information of the reduced-order model and the Modelica system reduced-order model, automatically configures and generates the environmental information required by the Modelica system reduced-order model, and integrates the environmental information into the Modelica system reduced-order model.

[0054] Step B3: Obtain the identification of the reduced-order model according to step B2, retrieve the input and output information of the reduced-order model according to the automatic retrieval method, and generate the interface of the Modelica system reduced-order model.

[0055] Step B4: Based on steps B2 and B3, the functional module responsible for generating the Modelica system reduced-order model automatically configures the environment information required to generate the Modelica system reduced-order model, generates the interface of the Modelica system reduced-order model, and automatically generates the Modelica system reduced-order model based on the Modelica semantic grammar. The Modelica system reduced-order model is embedded with functions that call the external environment.

[0056] Step B5: Store the Modelica system reduced-order model generated by the reduced-order model and the environment on which the reduced-order model depends in the generation location specified in step B1.

[0057] In the embodiment of the present application, the process of establishing the Modelica system reduced-order model and the process of using the Modelica system reduced-order model and the Modelica component model to perform multi-domain and multi-system joint simulation can be referred to Figure 2 ,like Figure 2As shown, according to the obtained and processed CAE three-dimensional simulation result data, a suitable reduced order model algorithm is selected, a reduced order model is generated and the reduced order model has a model identifier, and a Modelica system reduced order model is automatically generated according to the reduced order model, the reduction method, the environmental information and the input and output information, and the functions for calling the external environment required for assembling the Modelica system reduced order model, the environmental information required by the Modelica system reduced order model and the interface of the Modelica system reduced order model are generated, and the Modelica system reduced order model is automatically assembled and generated.

[0058] An embodiment of the present invention provides a method for generating a Modelica system reduced-order model. The method obtains computer-aided engineering (CAE) three-dimensional simulation result data, extracts feature data from the CAE three-dimensional simulation result data, processes the feature data based on a pre-set reduced-order model algorithm and model parameters, and generates a reduced-order model. The reduced-order model's reduction method, the environmental information on which the reduced-order model depends, and the input and output information of the reduced-order model are input into a Modelica automatic generation module, and the Modelica system reduced-order model can be automatically assembled and generated. This method enables modeling in different disciplines to have good compatibility and scalability on the same simulation software, reducing the difficulty of multi-domain and multi-disciplinary joint simulation.

[0059] Example 2

[0060] Figure 3 This is a flowchart of a method for generating a Modelica system reduced-order model provided by the second embodiment of the present invention. This embodiment describes the generation process of the Modelica system reduced-order model and the application process of the Modelica system reduced-order model based on the above embodiment. Figure 3 As shown, the method includes:

[0061] S210: Obtain computer-aided engineering (CAE) three-dimensional simulation result data.

[0062] Specifically, computer-aided engineering (CAE) technology is used to simulate multi-domain physical phenomena involved in the simulation target, and three-dimensional simulation result data is obtained.

[0063] S220: Generate a reduced-order model based on the CAE three-dimensional simulation result data.

[0064] Specifically, a suitable order reduction algorithm is selected according to a preset order reduction algorithm selection range, and a reduced-order model is generated according to CAE three-dimensional simulation result data.

[0065] S230. Add identification information to the reduced-order model; wherein the identification information includes at least one of the reduction method for generating the reduced-order model, the environment information on which the reduced-order model depends, the input and output information of the reduced-order model, the type of the reduced-order model, the parameter information of the reduced-order model, and the version information of the reduced-order model.

[0066] Specifically, identification information is added to the reduced-order model generated above, and the identification information includes at least one of the information such as the reduction method of the reduced-order model, the environment information on which the reduced-order model depends, the input and output information of the reduced-order model, the type of the reduced-order model, the parameter information of the reduced-order model, and the version information of the reduced-order model.

[0067] S240: Acquire a reduction method for generating the reduced-order model, environmental information on which the reduced-order model depends, and input and output information of the reduced-order model.

[0068] Specifically, the input and output information, the reduction method, the dependent environment information and the input and output information required to generate the Modelica system reduced-order model are obtained through an automatic retrieval method.

[0069] As an optional but not limiting implementation, obtaining the reduction method for generating the reduced-order model, the environment information on which the reduced-order model depends, and the input and output information of the reduced-order model may include:

[0070] The identification information is parsed to determine a reduction method for generating the reduced-order model, environmental information on which the reduced-order model depends, and input and output information of the reduced-order model.

[0071] Specifically, according to step 230, identification information is added to the reduced order model, and by parsing the representation information of the reduced order model, the reduction method for generating the reduced order model, the environmental information on which the reduced order model depends, and the input and output information of the reduced order model are determined.

[0072] S250: Obtain a preset storage path.

[0073] Specifically, the functional module responsible for generating the Modelica system reduced-order model selects a generation location of the Modelica system reduced-order model. When the Modelica system reduced-order model is successfully built, it is necessary to obtain a storage location of the Modelica system reduced-order model generated according to the reduced-order model.

[0074] S260 : Generate a Modelica system reduced-order model according to the reduced-order model, the reduced-order method, the environmental information, and the input and output information.

[0075] Specifically, based on the Modelica semantic syntax, the environment required for converting the reduced-order model into the Modelica system reduced-order model is automatically configured, and the Modelica system reduced-order model is automatically generated according to the reduced-order model, reduction method, environment information, and input and output information.

[0076] S270 : In response to a modeling and simulation event being triggered, reading the Modelica system reduced-order model, and performing modeling and simulation based on the Modelica system reduced-order model.

[0077] Specifically, when a modeling and simulation event is triggered, the automatically generated Modelica system reduction model is read. The Modelica system reduction model has a system model interface for unified modeling and simulation of multiple fields and multiple systems. The Modelica system reduction model interface connects component modules of different fields and systems in a simple drag-and-drop manner. The modeling languages ​​of different disciplines uniformly apply the Modelica system reduction model. In this way, modeling applications of different disciplines can be applied on the same Modelica modeling and simulation software, which has good compatibility and scalability.

[0078] In the embodiment of the present application, the process of establishing the Modelica system reduced-order model and the process of using the Modelica system reduced-order model and the Modelica component model to perform multi-domain and multi-system joint simulation can be referred to Figure 2 ,like Figure 2 As shown, according to the obtained and processed CAE three-dimensional simulation result data, a suitable reduced-order model algorithm is selected, a reduced-order model is generated and the reduced-order model has a model identifier, and a Modelica system reduced-order model is automatically generated according to the reduced-order model, the reduction method, the environmental information and the input and output information, and the functions of the external environment required to be called for assembling the Modelica system reduced-order model, the environmental information required by the Modelica system reduced-order model and the interface of the Modelica model are generated, and the Modelica system reduced-order model is automatically assembled and generated. When the Modelica modeling and simulation software triggers a modeling and simulation event, the Modelica system reduced-order model is read, and a joint simulation of multiple fields and systems is performed based on the Modelica system reduced-order model and the Modelica component modules.

[0079] An embodiment of the present invention provides a method for generating a Modelica system reduced-order model. The method obtains computer-aided engineering (CAE) three-dimensional simulation result data, extracts feature data from the CAE three-dimensional simulation result data, processes the feature data based on a pre-set reduced-order model algorithm and model parameters, and generates a reduced-order model. The reduced-order model's reduction method, the environmental information on which the reduced-order model depends, and the input and output information of the reduced-order model are input into a Modelica automatic generation module, and the Modelica system reduced-order model can be automatically assembled and generated. This method enables modeling in different disciplines to have good compatibility and scalability on the same simulation software, simplifies the process of importing the Modelica system reduced-order model into the Modelica modeling and simulation software, and reduces the difficulty of multi-domain and multi-disciplinary joint simulation.

[0080] Example 3

[0081] Figure 4 This is a schematic diagram of the structure of a device for generating a Modelica system reduced-order model provided by the third embodiment of the present invention. Figure 4 As shown, the device includes:

[0082] Result data acquisition module 310, used to obtain computer-aided engineering (CAE) three-dimensional simulation result data;

[0083] A reduced-order model generating module 320 is configured to generate a reduced-order model based on the CAE 3D simulation result data;

[0084] A method and information acquisition module 330 is used to acquire the order reduction method for generating the reduced-order model, the environmental information on which the reduced-order model depends, and the input and output information of the reduced-order model;

[0085] The system model generation module 340 is configured to generate a Modelica system reduced-order model according to the reduced-order model, the reduced-order method, the environmental information, and the input and output information.

[0086] Furthermore, the reduced-order model generation module 320 includes:

[0087] A feature data acquisition unit, configured to extract feature data from the CAE three-dimensional simulation result data;

[0088] The reduced-order model generating unit is used to process the characteristic data based on a preset reduced-order model algorithm and corresponding model parameters to generate a reduced-order model.

[0089] Furthermore, the system model generation module 340 includes:

[0090] The first Modelica model generation unit is configured to process the reduced-order model according to a preset template based on the Modelica semantic grammar to generate a Modelica system reduced-order model; wherein the reduction method is used to determine a calculation method for the Modelica system reduced-order model, the environmental information is integrated into the Modelica system reduced-order model, and the input and output information is used to generate an interface in the Modelica system reduced-order model.

[0091] Furthermore, the device further comprises:

[0092] A tag information adding module is used to add identification information to the reduced-order model; wherein the identification information includes at least one of the reduction method for generating the reduced-order model, the environment information on which the reduced-order model depends, the input and output information of the reduced-order model, the type of the reduced-order model, the parameter information of the reduced-order model and the version information of the reduced-order model.

[0093] Furthermore, the system model generation module 340 includes:

[0094] The tag information parsing unit is used to parse the identification information to determine the reduction method for generating the reduced-order model, the environmental information on which the reduced-order model depends, and the input and output information of the reduced-order model.

[0095] Furthermore, the device further comprises:

[0096] A storage path acquisition module is used to obtain a preset storage path;

[0097] Furthermore, the system model generation module 340 includes:

[0098] The second Modelica model generation unit is configured to generate a Modelica system reduced-order model in the storage path according to the reduced-order model, the reduced-order method, the environment information, and the input and output information.

[0099] Furthermore, the device further comprises:

[0100] The modeling and simulation module is used to read the Modelica system reduced-order model in response to a modeling and simulation event being triggered, and perform modeling and simulation based on the Modelica system reduced-order model.

[0101] The Modelica system reduced-order model generation device provided in the embodiment of the present invention can execute the Modelica system reduced-order model generation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0102] Example 4

[0103] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0104] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0105] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0106] Processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors that run machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 executes the various methods and processes described above, such as the Modelica system reduced-order model generation method.

[0107] In some embodiments, the Modelica system reduced-order model generation method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the Modelica system reduced-order model generation method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the Modelica system reduced-order model generation method by any other appropriate means (e.g., by means of firmware).

[0108] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0109] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0110] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0111] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0112] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0113] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0114] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0115] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for generating a reduced-order model of a Modelica system, characterized in that: include: Obtain computer-aided engineering (CAE) 3D simulation result data; generating a reduced-order model based on the CAE three-dimensional simulation result data; Acquiring a reduction method for generating the reduced-order model, environmental information on which the reduced-order model depends, and input and output information of the reduced-order model; Generate a Modelica system reduced-order model according to the reduced-order model, the reduced-order method, the environmental information, and the input and output information; Generating a Modelica system reduced-order model according to the reduced-order model, the reduced-order method, the environmental information, and the input and output information, including: Based on the Modelica semantic grammar, the reduced-order model is processed according to a preset template to generate a Modelica system reduced-order model; wherein the reduction method is used to determine a calculation method of the Modelica system reduced-order model, the environmental information is integrated into the Modelica system reduced-order model, and the input and output information is used to generate an interface in the Modelica system reduced-order model.

2. The method according to claim 1, characterized in that Generating a reduced-order model based on the CAE three-dimensional simulation result data, including: Extracting feature data from the CAE three-dimensional simulation result data; The characteristic data is processed based on a preset reduced-order model algorithm and corresponding model parameters to generate a reduced-order model.

3. The method according to claim 1, characterized in that After generating a reduced-order model based on the CAE three-dimensional simulation result data, the method further includes: Add identification information to the reduced-order model; wherein the identification information includes at least one of the reduction method for generating the reduced-order model, the environment information on which the reduced-order model depends, the input and output information of the reduced-order model, the type of the reduced-order model, the parameter information of the reduced-order model, and the version information of the reduced-order model.

4. The method according to claim 3, characterized in that Obtaining a reduction method for generating the reduced-order model, environmental information on which the reduced-order model depends, and input and output information of the reduced-order model, including: The identification information is parsed to determine a reduction method for generating the reduced-order model, environmental information on which the reduced-order model depends, and input and output information of the reduced-order model.

5. The method according to claim 1, wherein Before generating a Modelica system reduced-order model according to the reduced-order model, the reduced-order method, the environmental information, and the input-output information, the method further includes: Get the preset storage path; Generating a Modelica system reduced-order model according to the reduced-order model, the reduced-order method, the environmental information, and the input and output information, including: A Modelica system reduced-order model is generated in the storage path according to the reduced-order model, the reduced-order method, the environmental information, and the input-output information.

6. The method according to claim 1, wherein After generating the Modelica system reduced-order model, it also includes: In response to a modeling and simulation event being triggered, the Modelica system reduced-order model is read, and modeling and simulation is performed based on the Modelica system reduced-order model.

7. A device for generating a Modelica system reduced-order model, characterized in that: include: Result data acquisition module, used to obtain computer-aided engineering (CAE) three-dimensional simulation result data; A reduced-order model generation module, configured to generate a reduced-order model based on the CAE three-dimensional simulation result data; A method and information acquisition module, configured to acquire a reduction method for generating the reduced-order model, environmental information on which the reduced-order model depends, and input and output information of the reduced-order model; A system model generation module, configured to generate a Modelica system reduced-order model according to the reduced-order model, the reduced-order method, the environmental information, and the input and output information; The system model generation module includes: The first Modelica model generation unit is configured to process the reduced-order model according to a preset template based on the Modelica semantic grammar to generate a Modelica system reduced-order model; wherein the reduction method is used to determine a calculation method for the Modelica system reduced-order model, the environmental information is integrated into the Modelica system reduced-order model, and the input and output information is used to generate an interface in the Modelica system reduced-order model.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the Modelica system reduced-order model generation method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the Modelica system order reduction model generation method according to any one of claims 1 to 6 when executed.

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