Analog simulation calculation method based on multiple modules

Abstract

The invention discloses a multi-module-based analog simulation calculation method, and relates to the technical field of analog simulation calculation, and the method comprises the following steps: S1, system demand analysis and module division; s2, establishing a module model and setting parameters; s3, constructing a parallel computing environment; s4, module parallel computing task allocation; s5, data exchange and synchronization; s6, summarizing results; and S7, feedback optimization. According to the analog simulation calculation method based on the multiple modules, through modular design, a complex system is decomposed into the multiple relatively simple modules, each module can independently conduct simulation and calculation, and the maintainability and expandability of the system are improved; data and information exchange and cooperative work can be carried out among different modules through interfaces, so that comprehensive simulation and optimization of the whole system are realized; in addition, parallel computing resources are fully utilized, parallel computing is carried out on the multiple modules, the computing time is greatly shortened, and the computing efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of simulation computing, and in particular to a method of simulation computing based on multiple modules. Background Art

[0002] Simulation computing is a technology that simulates and simulates a specific system or process in a virtual environment through computer software. It is based on physical, chemical or other scientific principles, combined with mathematical models and algorithms, to predict and analyze the behavior, performance, and changes of the system. Simulation computing plays an important role in many fields such as materials science, engineering design, medical research, and financial analysis.

[0003] With the continuous advancement of computer technology, especially the rapid development of computer hardware and software technology, the computing power, storage capacity and graphics processing capabilities of computers have been significantly improved, which provides strong technical support for simulation computing. The emergence of high-performance computers and supercomputers enables simulation computing to be carried out with higher accuracy and efficiency, and to handle more complex and larger-scale models and problems. In addition, the continuous development and improvement of various simulation software has also made simulation computing more convenient and efficient. These software usually provide a rich model library, algorithm library and visualization tools, and users can choose appropriate models and algorithms for simulation computing according to specific needs. With the continuous development and innovation of technology, simulation computing will play an important role in more fields and make greater contributions to the progress and development of human society.

[0004] However, since existing simulation calculations usually lack multi-module design, the system complexity is limited. Single-module simulation calculations often find it difficult to handle the interactions and mutual influences between the various components in a complex system, resulting in limited accuracy and reliability of the simulation results, and poor scalability and maintainability of the simulation system. It is difficult to expand and modify according to actual needs, which increases the difficulty and cost of system maintenance. In addition, for large-scale or highly complex systems, single-module simulation calculations are difficult to achieve parallel computing and cannot fully utilize the multi-core parallel processing capabilities of modern computers. They require a large amount of computing resources and time, resulting in low computing efficiency.

[0005] Therefore, it is urgent to improve this shortcoming. The present invention studies and improves the existing technology and its shortcomings, and provides a method for simulation calculation based on multiple modules. Summary of the invention

[0006] The purpose of the present invention is to provide a method for simulation calculation based on multiple modules to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: a method for simulation calculation based on multiple modules, comprising the following steps:

[0008] S1. System requirements analysis and module division:

[0009] Clarify the goals and requirements of simulation calculations as well as the main functions and characteristics of the system, and divide the system into several relatively independent modules according to the complexity and functional requirements of the system;

[0010] S2. Module model establishment and parameter setting:

[0011] For each module, establish the corresponding mathematical model and simulation model, and set the parameters of the model according to actual needs and known conditions, including physical parameters, control parameters, etc.;

[0012] S3, parallel computing environment construction:

[0013] It includes two parts: hardware resource preparation and software environment configuration. The hardware resource preparation is used to select suitable parallel computing hardware resources, and the software environment configuration is used to install and configure parallel computing software.

[0014] S4. Module parallel computing task allocation:

[0015] Decompose the overall simulation task into several subtasks, each of which corresponds to the computing task of one or more modules, and assign the subtasks to different computing nodes or processing cores for parallel computing according to the performance and load of computing resources;

[0016] S5. Data exchange and synchronization:

[0017] During the parallel computing process, data is exchanged between modules to coordinate the computing results and progress of each module, and a synchronization mechanism is used to ensure the coordination and consistency of each module during data exchange and computing to avoid data conflicts and computing errors.

[0018] S6. Summary of results:

[0019] The calculation results of each module are summarized to form the overall simulation results.

[0020] Furthermore, in step S1, the module division principle is as follows:

[0021] Functional independence: Each module can independently complete a specific function or a group of related functions, reducing the mutual dependence between modules;

[0022] Interface clarity: Each module has clearly defined input and output interfaces to facilitate data exchange and communication with other modules;

[0023] High cohesion and low coupling: The functions within the modules are highly related (high cohesion), and the dependencies between modules are as few as possible (low coupling) to improve the maintainability and scalability of the system;

[0024] Parallel processing capability: Consider the parallelism between modules and assign computing tasks that can be processed in parallel to different modules in order to utilize parallel computing resources.

[0025] Furthermore, in step S2, the mathematical model and the simulation model must be able to accurately reflect the working principle and input-output relationship within the module.

[0026] Furthermore, the establishment process of the mathematical model is as follows:

[0027] Problem definition: clarify the problem or function to be solved by each module, as well as its interaction with other modules;

[0028] Assumptions and simplifications: Based on the basic principles of physics, chemistry, engineering and other fields, necessary assumptions and simplifications are made for practical problems, and the accuracy and practicality of the model are maintained as much as possible;

[0029] Equation establishment: Based on the working principle of the module, mathematical equations describing the module behavior are established using mathematical languages ​​(such as differential equations, difference equations, algebraic equations, etc.), and these equations should be able to fully reflect the physical, chemical or engineering processes within the module;

[0030] Parameter determination: Determine the parameters in the model through experimental data, empirical formulas or theoretical derivation;

[0031] Model verification: After the model is established, it is verified through experimental data or known results to ensure that the model can accurately reflect the actual behavior of the module.

[0032] Furthermore, the establishment process of the simulation model is as follows:

[0033] Model conversion: select programming languages ​​(such as MATLAB, Python, C++, etc.) and simulation tools (such as Simulink, Modelica, etc.) to convert the established mathematical model into a computer-executable simulation model;

[0034] Interface design: Design clear input and output interfaces for the simulation model, and the interfaces follow a unified data format and exchange protocol;

[0035] Module integration: Integrate the simulation models of each module into the overall simulation system, and during the integration process, handle data transfer, synchronization control, and possible conflicts and dependencies between modules;

[0036] Simulation operation and debugging: Run the entire system in a simulation environment to observe whether the output results of each module meet expectations. If problems occur, debug and optimize.

[0037] Result analysis and evaluation: Analyze the simulation results, evaluate the accuracy and reliability of the simulation model, and further optimize and improve the model based on the analysis results.

[0038] Furthermore, in step S3, the hardware resource preparation specifically includes:

[0039] Computing node selection: Select computing nodes based on the task scale and computing requirements of simulation computing, including but not limited to multi-core processor (CPU) servers, graphics processing unit (GPU) clusters, or other high-performance computing (HPC) devices;

[0040] Network configuration: Design the network topology, allocate network bandwidth, and optimize network latency in order to ensure that the network connection between computing nodes is stable and high-speed, so that data exchange and communication can be carried out efficiently.

[0041] Storage solution: Provide sufficient storage space for simulation calculations and ensure that the data read and write speeds can meet the computing requirements. The storage space options include but are not limited to high-speed solid-state drives (SSDs), network-attached storage (NAS) or distributed storage systems.

[0042] Furthermore, in step S3, the software environment configuration specifically includes:

[0043] Operating system selection: Select an operating system based on hardware resources and application requirements. For high-performance computing environments, Linux operating system is preferred.

[0044] Parallel computing framework installation: Install and configure parallel computing frameworks such as MPI (Message Passing Interface), OpenMP (Open Multiprocessing) or CUDA (Compute Unified Device Architecture), which provide the ability to execute tasks in parallel on multiple computing nodes or processing cores;

[0045] Simulation software deployment: Deploy the simulation software to the computing nodes and ensure that it can run normally. At the same time, configure the software to support parallel computing, including setting the parameters of parallel computing, optimizing the allocation of computing tasks, etc.

[0046] Furthermore, the step S4 specifically includes the following sub-steps:

[0047] S41, Simulation task decomposition: Conduct an in-depth analysis of the overall simulation task to clarify its various components and the dependencies between them; then, based on these analysis results, decompose the overall task into several relatively independent subtasks, and each subtask should contain clear input, processing logic and output to facilitate subsequent parallel processing;

[0048] S42, parallel computing planning: According to the performance and load of computing resources, reasonably allocate subtasks to balance the computing load of each node, determine the dependencies between subtasks, and ensure that these dependencies are not destroyed during parallel execution. At the same time, combine the communication overhead between modules during parallel execution to formulate a parallel computing plan;

[0049] S43. Parallel computing implementation: After determining the parallel computing plan, deploy each module to the corresponding computing node or processing core, and start the parallel computing process; during the calculation process, monitor the computing status and load of each node in real time, and make adjustments as needed to ensure the smooth progress of the overall calculation.

[0050] Furthermore, in step S5, the synchronization mechanism specifically includes:

[0051] 1) Message passing mechanism: MPI (Message Passing Interface) implements inter-process communication by sending and receiving messages. In MPI, each module (or process) can send data to each other, and the reception and sending of this data are managed by message queues. This method can effectively realize data exchange and synchronization between modules.

[0052] 2) Shared memory mechanism: OpenMP allows multiple threads to access the same memory address space. In OpenMP, shared data can be protected by synchronization mechanisms such as locks and semaphores to avoid data conflicts. In addition to OpenMP, there are some other shared memory models, such as Pthreads (POSIX thread library), which also provide similar synchronization mechanisms.

[0053] 3) Barrier synchronization: All modules will continue to execute after reaching a certain synchronization point. This is applicable to some parallel algorithms, where it is necessary to wait for all modules to complete a certain stage of calculation before proceeding to the next stage of calculation.

[0054] 4) Event-driven synchronization: The event-based synchronization mechanism achieves synchronization between modules by listening to and responding to events. When an event occurs (such as data arrival, calculation completion, etc.), the relevant modules will be triggered and continue to execute.

[0055] 5) Locks and semaphores:

[0056] The lock is used to protect access to shared resources (such as shared memory) to prevent data conflicts caused by simultaneous access by multiple modules, including but not limited to mutex and spinlock;

[0057] The semaphore is a counter used to control access to shared resources. It allows a certain number of modules to access a resource at the same time. When the counter reaches zero, subsequent modules requesting the resource will be blocked.

[0058] 6) Timestamp and sequence number: Synchronize data between modules through timestamp or sequence number; for example, when exchanging data, attach a timestamp or sequence number to each data packet so that the receiver can sort and reassemble according to this information;

[0059] 7) Distributed locks and atomic operations: In distributed systems, distributed locks are used to ensure the synchronization of multiple nodes when accessing shared resources, or atomic operations (such as CAS, Compare-And-Swap) are used to implement a lock-free synchronization mechanism.

[0060] Furthermore, the method further comprises the steps of:

[0061] S7. Feedback optimization:

[0062] Analyze the simulation results, evaluate the system's performance, stability, reliability and other indicators, and establish a feedback mechanism based on the comparison between the simulation results and actual needs to correct and optimize the model; in subsequent simulation calculations, continuously iterate and optimize the model and parameter settings to improve the accuracy and efficiency of simulation calculations.

[0063] The present invention provides a method for simulation calculation based on multiple modules, which has the following beneficial effects:

[0064] The present invention decomposes a complex system into multiple relatively simple modules through modular design. Each module can be simulated and calculated independently, thereby improving the maintainability and scalability of the system; and different modules can exchange data and information and work together through interfaces, thereby realizing comprehensive simulation and optimization of the entire system; in addition, full use is made of parallel computing resources, and multiple modules are calculated in parallel, which greatly shortens the calculation time and improves the calculation efficiency; in addition, the modular design also allows users to select appropriate modules for combination and customization according to specific problems and needs, which helps to build a simulation system that meets actual needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 The present invention is a flowchart of a method for simulation calculation based on multiple modules. DETAILED DESCRIPTION

[0066] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0067] like Figure 1 As shown, a method for simulation calculation based on multiple modules includes the following steps:

[0068] S1. System requirements analysis and module division: clarify the goals, requirements, and main functions and characteristics of the simulation calculation, and divide the system into several relatively independent modules according to the complexity and functional requirements of the system; the principles of module division are as follows:

[0069] Functional independence: Each module can independently complete a specific function or a group of related functions, reducing the mutual dependence between modules;

[0070] Interface clarity: Each module has clearly defined input and output interfaces to facilitate data exchange and communication with other modules;

[0071] High cohesion and low coupling: The functions within the modules are highly related (high cohesion), and the dependencies between modules are as few as possible (low coupling) to improve the maintainability and scalability of the system;

[0072] Parallel processing capability: Consider the parallelism between modules and assign computing tasks that can be processed in parallel to different modules in order to utilize parallel computing resources;

[0073] S2. Module model establishment and parameter setting: For each module, establish the corresponding mathematical model and simulation model, and set the parameters of the model according to actual needs and known conditions, including physical parameters, control parameters, etc.; the mathematical model and simulation model must be able to accurately reflect the working principle and input-output relationship inside the module;

[0074] The process of establishing the mathematical model is as follows:

[0075] Problem definition: clarify the problem or function to be solved by each module, as well as its interaction with other modules;

[0076] Assumptions and simplifications: Based on the basic principles of physics, chemistry, engineering and other fields, necessary assumptions and simplifications are made for practical problems, and the accuracy and practicality of the model are maintained as much as possible;

[0077] Equation establishment: Based on the working principle of the module, mathematical equations describing the module behavior are established using mathematical languages ​​(such as differential equations, difference equations, algebraic equations, etc.), and these equations should be able to fully reflect the physical, chemical or engineering processes within the module;

[0078] Parameter determination: Determine the parameters in the model through experimental data, empirical formulas or theoretical derivation;

[0079] Model verification: After the model is established, it is verified through experimental data or known results to ensure that the model can accurately reflect the actual behavior of the module;

[0080] The establishment process of the simulation model is as follows:

[0081] Model conversion: select programming languages ​​(such as MATLAB, Python, C++, etc.) and simulation tools (such as Simulink, Modelica, etc.) to convert the established mathematical model into a computer-executable simulation model;

[0082] Interface design: Design clear input and output interfaces for the simulation model, and the interfaces follow a unified data format and exchange protocol;

[0083] Module integration: Integrate the simulation models of each module into the overall simulation system, and during the integration process, handle data transfer, synchronization control, and possible conflicts and dependencies between modules;

[0084] Simulation operation and debugging: Run the entire system in a simulation environment to observe whether the output results of each module meet expectations. If problems occur, debug and optimize.

[0085] Result analysis and evaluation: Analyze the simulation results, evaluate the accuracy and reliability of the simulation model, and further optimize and improve the model based on the analysis results;

[0086] S3, parallel computing environment construction: including hardware resource preparation and software environment configuration;

[0087] Hardware resource preparation is used to select appropriate parallel computing hardware resources, including:

[0088] Computing node selection: Select computing nodes based on the task scale and computing requirements of simulation computing. Computing nodes include but are not limited to multi-core processor (CPU) servers, graphics processing unit (GPU) clusters, or other high-performance computing (HPC) devices.

[0089] Network configuration: Design the network topology, allocate network bandwidth, and optimize network latency in order to ensure that the network connection between computing nodes is stable and high-speed, so that data exchange and communication can be carried out efficiently.

[0090] Storage solution: Provide sufficient storage space for simulation calculations and ensure that the data read and write speeds can meet the computing requirements. Storage space options include but are not limited to high-speed solid-state drives (SSDs), network-attached storage (NAS) or distributed storage systems;

[0091] Software environment configuration is used to install and configure parallel computing software, including:

[0092] Operating system selection: Select an operating system based on hardware resources and application requirements. For high-performance computing environments, Linux operating system is preferred.

[0093] Parallel computing framework installation: Install and configure parallel computing frameworks such as MPI (Message Passing Interface), OpenMP (Open Multiprocessing) or CUDA (Compute Unified Device Architecture), which provide the ability to execute tasks in parallel on multiple computing nodes or processing cores;

[0094] Simulation software deployment: Deploy the simulation software to the computing nodes and ensure that it can run normally. At the same time, configure the software to support parallel computing, including setting the parameters of parallel computing and optimizing the allocation of computing tasks.

[0095] S4, module parallel computing task allocation: decompose the overall simulation task into several subtasks, each subtask corresponds to the computing task of one or more modules, and allocate the subtasks to different computing nodes or processing cores for parallel computing according to the performance and load of computing resources;

[0096] S41, Simulation task decomposition: Conduct an in-depth analysis of the overall simulation task to clarify its various components and the dependencies between them; then, based on these analysis results, decompose the overall task into several relatively independent subtasks, and each subtask should contain clear input, processing logic and output to facilitate subsequent parallel processing;

[0097] S42, parallel computing planning: According to the performance and load of computing resources, reasonably allocate subtasks to balance the computing load of each node, determine the dependencies between subtasks, and ensure that these dependencies are not destroyed during parallel execution. At the same time, combine the communication overhead between modules during parallel execution to formulate a parallel computing plan;

[0098] S43, parallel computing implementation: After determining the parallel computing plan, deploy each module to the corresponding computing node or processing core, and start the parallel computing process; during the computing process, monitor the computing status and load of each node in real time, and make adjustments as needed to ensure the smooth progress of the overall computing;

[0099] S5. Data exchange and synchronization: During the parallel computing process, data is exchanged between modules to coordinate the computing results and progress of each module, and a synchronization mechanism is used to ensure the coordination and consistency of each module during data exchange and computing to avoid data conflicts and computing errors. The synchronization mechanism specifically includes:

[0100] 1) Message passing mechanism: MPI (Message Passing Interface) implements inter-process communication by sending and receiving messages. In MPI, each module (or process) can send data to each other, and the reception and sending of this data are managed by message queues. This method can effectively realize data exchange and synchronization between modules.

[0101] 2) Shared memory mechanism: OpenMP allows multiple threads to access the same memory address space. In OpenMP, shared data can be protected by synchronization mechanisms such as locks and semaphores to avoid data conflicts. In addition to OpenMP, there are some other shared memory models, such as Pthreads (POSIX thread library), which also provide similar synchronization mechanisms.

[0102] 3) Barrier synchronization: All modules will continue to execute after reaching a certain synchronization point. This is applicable to some parallel algorithms, where it is necessary to wait for all modules to complete a certain stage of calculation before proceeding to the next stage of calculation.

[0103] 4) Event-driven synchronization: The event-based synchronization mechanism achieves synchronization between modules by listening to and responding to events. When an event occurs (such as data arrival, calculation completion, etc.), the relevant modules will be triggered and continue to execute.

[0104] 5) Locks and semaphores:

[0105] Locks are used to protect access to shared resources (such as shared memory) to prevent data conflicts caused by simultaneous access by multiple modules, including but not limited to mutex and spinlock.

[0106] A semaphore is a counter used to control access to shared resources. It allows a certain number of modules to access a resource at the same time. When the counter reaches zero, subsequent modules requesting the resource will be blocked.

[0107] 6) Timestamp and sequence number: Synchronize data between modules through timestamp or sequence number; for example, when exchanging data, attach a timestamp or sequence number to each data packet so that the receiver can sort and reassemble according to this information;

[0108] 7) Distributed locks and atomic operations: In distributed systems, distributed locks are used to ensure synchronization of multiple nodes when accessing shared resources, or atomic operations (such as CAS, Compare-And-Swap) are used to implement lock-free synchronization mechanisms;

[0109] S6. Result summary: Summarize the calculation results of each module to form the overall simulation result;

[0110] S7, Feedback Optimization: Analyze the simulation results, evaluate the system's performance, stability, reliability and other indicators, and establish a feedback mechanism based on the comparison between the simulation results and actual needs to correct and optimize the model; in subsequent simulation calculations, continuously iterate and optimize the model and parameter settings to improve the accuracy and efficiency of the simulation calculations.

[0111] The embodiments of the present invention are given for the purpose of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for the specific application specification.

Claims

1. A method for simulation calculation based on multiple modules, characterized in that: The following steps are involved: S1. System requirements analysis and module division: Clarify the goals and requirements of simulation calculations as well as the main functions and characteristics of the system, and divide the system into several relatively independent modules according to the complexity and functional requirements of the system; S2. Module model establishment and parameter setting: For each module, establish the corresponding mathematical model and simulation model, and set the parameters of the model according to actual needs and known conditions; S3, parallel computing environment construction: It includes two parts: hardware resource preparation and software environment configuration. The hardware resource preparation is used to select suitable parallel computing hardware resources, and the software environment configuration is used to install and configure parallel computing software. S4. Module parallel computing task allocation: Decompose the overall simulation task into several subtasks, each of which corresponds to the computing task of one or more modules, and assign the subtasks to different computing nodes or processing cores for parallel computing according to the performance and load of computing resources; S5. Data exchange and synchronization: During the parallel computing process, data is exchanged between modules to coordinate the computing results and progress of each module, and a synchronization mechanism is used to ensure the coordination and consistency of each module during data exchange and computing to avoid data conflicts and computing errors. S6. Summary of results: The calculation results of each module are summarized to form the overall simulation results.

2. The method of multi-module simulation calculation according to claim 1, characterized in that: In step S1, the module division principle is as follows: Functional independence: Each module can independently complete a specific function or a group of related functions, reducing the mutual dependence between modules; Interface clarity: Each module has clearly defined input and output interfaces; High cohesion and low coupling: the functions within the modules are highly related, and the dependencies between modules are as few as possible; Parallel processing capability: Consider the parallelism between modules and assign computing tasks that can be processed in parallel to different modules.

3. The method of multi-module simulation calculation according to claim 1, characterized in that: In step S2, the mathematical model and the simulation model must be able to accurately reflect the working principle and input-output relationship inside the module.

4. The method of simulation calculation based on multiple modules according to claim 3 is characterized in that: The establishment process of the mathematical model is as follows: Problem definition: clarify the problem or function to be solved by each module, as well as its interaction with other modules; Assumptions and simplifications: Based on the basic principles of physics, chemistry, and engineering, necessary assumptions and simplifications are made for practical problems, and the accuracy and practicality of the model are maintained as much as possible; Equation establishment: Based on the working principle of the module, mathematical equations describing the module behavior are established using mathematical language; Parameter determination: Determine the parameters in the model through experimental data, empirical formulas or theoretical derivation; Model verification: After the model is established, it is verified through experimental data or known results to ensure that the model can accurately reflect the actual behavior of the module.

5. The method of multi-module simulation calculation according to claim 3, characterized in that: The establishment process of the simulation model is as follows: Model conversion: Select programming languages ​​and simulation tools to convert the established mathematical model into a computer executable simulation model; Interface design: Design clear input and output interfaces for the simulation model, and the interfaces follow a unified data format and exchange protocol; Module integration: Integrate the simulation models of each module into the overall simulation system, and during the integration process, handle data transfer, synchronization control, and possible conflicts and dependencies between modules; Simulation operation and debugging: Run the entire system in a simulation environment to observe whether the output results of each module meet expectations. If problems occur, debug and optimize. Result analysis and evaluation: Analyze the simulation results, evaluate the accuracy and reliability of the simulation model, and further optimize and improve the model based on the analysis results.

6. The method of multi-module simulation calculation according to claim 1, characterized in that: In step S3, the hardware resource preparation specifically includes: Computing node selection: Select computing nodes based on the task scale and computing requirements of the simulation calculation, including but not limited to multi-core processor servers, graphics processor clusters, or other high-performance computing devices; Network configuration: Design the network topology, allocate network bandwidth, and optimize network latency in order to ensure stable and high-speed network connections between computing nodes. Storage solution: Provide sufficient storage space for simulation calculations and ensure that the data read and write speeds can meet the computing needs. The storage space options include but are not limited to high-speed solid-state drives, network attached storage, or distributed storage systems.

7. The method of multi-module simulation calculation according to claim 1, characterized in that: In step S3, the software environment configuration specifically includes: Operating system selection: Select an operating system based on hardware resources and application requirements. For high-performance computing environments, Linux operating system is preferred. Parallel computing framework installation: Install and configure parallel computing frameworks that provide the ability to execute tasks in parallel on multiple computing nodes or processing cores; Simulation software deployment: Deploy the simulation software to the computing nodes and ensure that it can run properly, while configuring the software to support parallel computing.

8. The method of multi-module simulation calculation according to claim 1, characterized in that: The step S4 specifically includes the following sub-steps: S41, Simulation task decomposition: Conduct an in-depth analysis of the overall simulation task to clarify its various components and the dependencies between them; then, based on these analysis results, decompose the overall task into several relatively independent subtasks, and each subtask should contain clear input, processing logic and output to facilitate subsequent parallel processing; S42, parallel computing planning: According to the performance and load of computing resources, reasonably allocate subtasks to balance the computing load of each node, determine the dependencies between subtasks, and ensure that these dependencies are not destroyed during parallel execution. At the same time, combine the communication overhead between modules during parallel execution to formulate a parallel computing plan; S43. Parallel computing implementation: After determining the parallel computing plan, deploy each module to the corresponding computing node or processing core, and start the parallel computing process.

9. The method of multi-module simulation calculation according to claim 1, characterized in that: In step S5, the synchronization mechanism specifically includes: 1) Message passing mechanism: MPI, which enables communication between processes by sending and receiving messages; 2) Shared memory mechanism: OpenMP, allowing multiple threads to access the same memory address space; 3) Barrier synchronization: All modules continue to execute after reaching a certain synchronization point; 4) Event-driven synchronization: The event-based synchronization mechanism achieves synchronization between modules by listening to and responding to events; 5) Locks and semaphores: The lock is used to protect access to shared resources and prevent data conflicts caused by simultaneous access by multiple modules, including but not limited to mutex locks and spin locks; The semaphore is a counter used to control access to shared resources. It allows a certain number of modules to access a resource at the same time. When the counter reaches zero, subsequent modules requesting the resource will be blocked. 6) Timestamp and sequence number: synchronize data between modules through timestamp or sequence number; 7) Distributed locks and atomic operations: In distributed systems, distributed locks are used to ensure synchronization of multiple nodes when accessing shared resources, or atomic operations are used to implement lock-free synchronization mechanisms.

10. The method of multi-module simulation calculation according to claim 1, characterized in that: The method further comprises the steps of: S7. Feedback optimization: Analyze the simulation results, evaluate the various indicators of the system, and establish a feedback mechanism based on the comparison between the simulation results and actual needs to correct and optimize the model; in subsequent simulation calculations, continuously iterate and optimize the model and parameter settings.

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