Intelligent multi-target parameter optimization method and system for analog machine equipment

Abstract

The invention relates to the technical field of simulation, in particular to an intelligent multi-target parameter optimization method and system for analog machine equipment. The method comprises the following steps of: constructing a decoupling architecture of a component model algorithm and a debugging algorithm of a simulation platform, and respectively compiling the component model algorithm and the debugging algorithm into a dynamic link library; a standardized debugging parameter input framework is established, and flexible input of multi-working-condition debugging parameters is achieved; automatically loading a corresponding dynamic link library file, and converting multiple groups of working condition data configured by a user into a memory structure body; the structural body transmits to the currently loaded debugging algorithm for execution; the debugging algorithm is operated, and monitoring and feedback are implemented in the debugging algorithm execution process; when the debugging algorithm is terminated, the simulation platform receives a state code returned by the debugging algorithm, and converts the state code into error information readable by a user through a predefined error code mapping table; and the simulation platform extracts the optimized parameter result from the debugging algorithm output, and the result data is verified and automatically updated to the corresponding equipment parameters. According to the invention, multi-working-condition automatic debugging of the simulator equipment is realized, and the debugging efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of simulation, in particular to a simulation machine equipment intelligent multi-target parameter optimization method and system. BACKGROUND

[0002] In the development process of a simulation machine, modeling and debugging work as a core link, the core goal of which is to make the running characteristics of the simulation model accurately match the actual working conditions of the real unit, while meeting the simulation requirements under multiple working conditions, so as to ultimately ensure that the simulation machine system meets the technical indicators and application standards specified by the project, and the implementation effect directly determines the simulation accuracy and operation reliability of the simulation machine. Under the traditional development mode of the simulation machine, model engineers often highly rely on their long-term accumulated debugging experience and professional intuition, and need to spend a lot of time and effort to repeatedly adjust and numerically fit the model parameters, which is low in efficiency and limited in accuracy.

[0003] From the perspective of technical requirements, the parameter debugging of the simulation machine equipment essentially belongs to a typical multi-target optimization problem, and the core challenge lies in meeting multiple performance indicators under different working condition scenarios. The running working conditions of the real unit have significant dynamics and diversity, covering multiple typical scenarios such as start-up, shutdown, rated load operation, variable load regulation, and fault disturbance. The traditional experience-driven debugging method cannot achieve global optimal matching of various indicators under multiple working conditions, and often results in the problem of "single working condition meeting the standard but multiple working conditions being unbalanced".

[0004] As the core carrier of modeling and debugging work of the simulation machine, the rationality of the architecture directly affects the debugging efficiency and simulation quality. Under the traditional architecture, the deep coupling between the debugging algorithm and the platform software leads to the dependence of algorithm iteration on the overall upgrade of the software, which seriously affects the stability of the platform software and the flexibility of the debugging algorithm. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a simulation machine equipment intelligent multi-target parameter optimization method and system, which realizes automatic debugging of the simulation machine equipment under multiple working conditions and improves the debugging efficiency.

[0006] The present application provides a simulation machine equipment intelligent multi-target parameter optimization method, which comprises the following steps:

[0007] Step 1: constructing a decoupling architecture of the component model algorithm and the debugging algorithm of the simulation platform, and compiling the component model algorithm and the debugging algorithm to dynamic link libraries respectively;

[0008] Step 2: establishing a standardized debugging parameter input framework to realize flexible input of multi-working condition debugging parameters;

[0009] Step 3: automatically loading corresponding dynamic link library files, converting user-configured multiple sets of working condition data into memory structure bodies; the structure bodies are passed to the currently loaded debugging algorithm for execution; the debugging algorithm is run, and monitoring and feedback are implemented during the execution of the debugging algorithm;

[0010] Step 4: when the debugging algorithm terminates, the simulation platform receives a state code returned by the debugging algorithm, and converts the state code into user-readable error information through a predefined error code mapping table; the simulation platform extracts optimized parameter results from the output of the debugging algorithm, and the result data is automatically updated into corresponding device parameters after being verified.

[0011] In a specific embodiment of the present application, the step 1 specifically comprises:

[0012] Step 1-1: defining a joint version identifier for each component model algorithm and a matching debugging algorithm;

[0013] Step 1-2: developing the component model algorithm and the debugging algorithm, and configuring a debugging parameter template;

[0014] Step 1-3: after the development of the component model algorithm and the debugging algorithm is completed, compiling the component model algorithm and the debugging algorithm matching a specific joint version into dynamic link libraries, and naming the files with the joint version.

[0015] In a specific embodiment of the present application, after the input and output parameters of the debugging algorithm are determined, the debugging template configuration is performed synchronously with the development of the debugging algorithm;

[0016] A standardized and configurable parameter template is constructed for different algorithm version management, optimized parameter definition and abnormality processing definition, and data persistent storage is completed to form configuration data corresponding to the debugging algorithm;

[0017] The parameter template is stored in a standardized data format, and precise retrieval and calling of different device and version parameter templates are achieved through hierarchical directories and unified naming.

[0018] In a specific embodiment of the present application, the step 2 specifically comprises:

[0019] Step 2-1: starting a version selection function, dynamically loading a list of currently available algorithm versions, automatically verifying the compatibility with the component model, and loading the debugging parameter template of the corresponding version after the verification is passed;

[0020] Step 2-2: configuring for multiple working conditions: supporting tabular single-group parameter input for simple debugging, and supporting manual addition or file import in the form of a function for loading working condition data sets for batch debugging of multiple working conditions; the input working conditions are displayed in the form of a paginated table in the interface, and a unique working condition index identifier is automatically generated;

[0021] Step 2-3: customizing a set of working conditions as priority working conditions;

[0022] Step 2-4: setting a debugging running time;

[0023] Step 2-5: integrating four configuration items of version, working condition, priority index and running time, saving a parameter set as structured data, and supporting export as a reusable template.

[0024] In a specific embodiment of the application, in step 3:

[0025] The output information of the debugging algorithm is transmitted to the user interface in real time through multiple channels; and the output information is presented to the user in a visual manner after intelligent filtering and formatting processing.

[0026] Detailed logs of the execution process are also provided, and the user can view complete calculation process details by expanding the log panel.

[0027] In a specific embodiment of the application, in step 3, different debugging algorithms are dynamically loaded by using a variable-length parameter loading technology, and data conversion is performed according to a preset parameter mapping relationship.

[0028] Specifically, the configured multiple sets of working condition data are directly converted into continuous memory blocks, a structure body containing the total number of working conditions, an index array and a data pointer is constructed, and the structure body is passed to the currently loaded debugging algorithm for execution through a specific calling interface defined by the platform.

[0029] In a specific embodiment of the application, the debugging result automatically generates a debugging report containing comprehensive information such as key parameter changes and abnormal situation statistics.

[0030] The application provides an intelligent multi-target parameter optimization system for a simulation machine device, comprising:

[0031] A simulation platform, comprising a data persistence layer, a simulation engine layer, a simulation data processing layer and a user interaction layer;

[0032] The data persistence layer serves as a basic support layer of the architecture, and undertakes the storage function of device, algorithm and working condition related configuration data;

[0033] The simulation engine layer processes algorithm scheduling and data interaction;

[0034] The simulation data processing layer is used to drive the execution of the debugging algorithm, manage multi-element working condition configuration and close-loop optimize device parameters, and realize intelligent tuning of device performance;

[0035] The debugging algorithm module interacts with the data of the simulation engine layer to realize algorithm calling.

[0036] In a specific embodiment of the present application, the data persistence layer includes a device library, an algorithm configuration library and a working condition configuration library.

[0037] The device library is used to record simulation parameters of various devices, which is an operation object for subsequent debugging algorithm; the algorithm configuration library stores debugging algorithm parameter templates suitable for different devices, including optimization parameter list and abnormal coding; the working condition configuration library stores multiple sets of working condition conditions and selected debugging algorithm versions of each device instance, which is used as an input of multi-objective optimization debugging algorithm;

[0038] The simulation engine layer includes an algorithm scheduling module and a data interface, the algorithm scheduling module dynamically loads debugging algorithms according to device debugging versions, and structurally organizes and arranges multi-working condition data according to a preset parameter list, to ensure that parameter sequences under different working conditions and algorithm logic form effective matching. The data interface builds a bidirectional interactive information transmission mechanism: on the one hand, it delivers dynamic data such as optimization intermediate results generated in the debugging algorithm running process to the simulation data processing layer in real time, to ensure accurate control of the debugging process by the upper layer module; on the other hand, it receives user input information such as parameter adjustment transmitted by the user interaction layer, and standardizes the transmission of the information to the debugging algorithm layer, to realize efficient connection between user demand and algorithm execution.

[0039] The simulation data processing layer includes an algorithm management module, a working condition management module and a device parameter optimization module; the algorithm management module is responsible for debugging algorithm template management; the working condition management module manages multiple sets of working condition configurations of device instances; the device parameter optimization module iteratively optimizes device parameters based on debugging algorithms, and adjusts device data according to the best running parameters;

[0040] The user interaction layer includes an algorithm configuration interface, a working condition configuration interface and a process and result display interface.

[0041] In a specific embodiment of the present application, the debugging algorithm module classifies and stores multiple versions of debugging algorithms for different devices;

[0042] Through the control and data interface with the simulation engine layer, the debugging algorithm module provides algorithm resources for the simulation data processing layer on demand, adapts to differentiated and complex debugging requirements of devices, and supports flexible calling of algorithms and coverage of debugging scenarios.

[0043] Compared with the prior art, the simulation machine device intelligent multi-objective parameter optimization method and system has the following beneficial effects.

[0044] (1) The debugging algorithm and the simulation platform are completely decoupled, and the debugging algorithm is independently developed and optimized; updating and replacing the debugging algorithm does not affect the stability of the simulation platform, and at the same time, reduces the dependence of the simulation platform on the debugging algorithm, and enhances the adaptability of the simulation platform to different debugging requirements;

[0045] (2) Realize dynamic loading of debugging algorithm, efficient mapping and transmission of parameters, and real-time monitoring and feedback of debugging process, ensure automation, precision and transparency of debugging work, and improve model machine precision and development efficiency;

[0046] (3) Formulate a complete intelligent multi-target parameter debugging process for equipment, covering algorithm development (including version management and naming rules, dynamic library decoupling), debugging parameter template configuration (supporting independent creation and inheritance and derivation mode), equipment instance multi-working condition debugging (involving version selection, working condition configuration, etc.), debugging execution and process output based on hot loading, and debugging result and equipment parameter optimization, and standardize the whole process of debugging work.

[0047] Compared with the traditional method, the method has obvious advantages in development efficiency, debugging efficiency and system maintenance cost, provides strong technical support for the debugging work of the simulation machine equipment, lays a solid foundation for improving the simulation precision and development efficiency of the simulation machine, and has important significance for the development of the simulation machine development field. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 The figure shows a schematic diagram of an intelligent multi-target parameter optimization system for simulation machine equipment. DETAILED DESCRIPTION

[0049] In order to further understand the present application, the embodiments of the present application are described below in combination with examples, but it should be understood that these descriptions are only for further illustrating the features and advantages of the present application, but not for limiting the present application.

[0050] The embodiments of the present application disclose an intelligent multi-target parameter optimization method for simulation machine equipment, comprising the following steps:

[0051] Step 1: Construct the decoupling architecture of the component model algorithm and the debugging algorithm of the simulation platform, and unify the version management of the two;

[0052] The simulation platform provides a standardized framework.

[0053] The component model algorithm and the debugging algorithm use a parallel development mode to ensure high coordination in terms of function, interface, and performance. This synchronous development strategy can effectively avoid compatibility problems caused by algorithm version mismatch and improve overall development efficiency. The development process follows the following procedures: 1) requirement alignment: the development teams of the component model algorithm and the debugging algorithm synchronize requirements to ensure consistency in functions. 2) parallel coding: using the interface contract method, first define the data interaction protocol (such as input / output structure, error code, etc.), and then implement the functions respectively. The new version of the debugging algorithm needs to be compatible with the old version of the model algorithm (such as `PumpV2.3_DA1.2` still supports the data format of `PumpV2.3_DA1.1`).

[0054] Specifically,

[0055] Step 1-1: Define the joint version identifier for each component model algorithm and the corresponding debugging algorithm;

[0056] The naming format of the joint version identifier can be [model algorithm name] [model version number] _ [debugging algorithm type] [debugging version number]; for example: `PumpV2.3_DA1.1`, where: PumpV2.3 represents the model algorithm name (Pump) and its version number (2.3), and DA1.1 represents the debugging algorithm type (DA, i.e. Debug Algorithm) and its version number (1.1).

[0057] When the core calculation logic, input / output interface, and key parameter structure of the component model algorithm are modified, the component model version number must be upgraded;

[0058] When the component model version number is updated, the debugging algorithm version number is forcibly updated to ensure compatibility between algorithms;

[0059] If only the optimization of the debugging algorithm is involved, and the component model algorithm has not changed, then the debugging algorithm version number is upgraded independently.

[0060] Step 1-2: Development of component model algorithm and debugging algorithm, configuration of debugging parameter template;

[0061] Specifically, it includes:

[0062] After the input / output parameters of the debugging algorithm are determined, the debugging template configuration is synchronized with the development of the debugging algorithm. For different algorithm version management, optimization parameter definition, and exception handling definition, standardize and configure the parameter template, and complete the data persistent storage to form the configuration data corresponding to the debugging algorithm.

[0063] Users can create parameter templates based on the following two modes:

[0064] Create mode: create a new template, automatically generate the basic parameter framework;

[0065] Inheritance mode: copy the existing template. Automatically inherit the core parameter configuration of the selected version, reduce repeated input.

[0066] The core function of the parameter template is to define the input parameters and abnormal coding system of the corresponding version debugging algorithm. The input parameter interface adopts tabular design, which clearly shows the variable name, data type, value range, default value and detailed description. The abnormal coding system is an important support for the debugging process, which adopts the design principle of "standardization + extensibility":

[0067] System pre-defined coding: common abnormal scenarios, ensure the unified processing standard of basic problems;

[0068] User-defined coding: reserved for special business scenarios.

[0069] Each error code is associated with detailed error description, possible causes and processing suggestions, forming a complete abnormal knowledge base to assist the debugging personnel to quickly locate and solve the problem. Key information forms a structured parameter configuration view.

[0070] The parameter template adopts a standardized data format for storage, and the naming follows the Da_[component library].xml specification. Through hierarchical directory and unified naming, the parameter templates of different devices and versions can be accurately searched and called, which is convenient for centralized management and version iteration. The standardized data format is an XML file;

[0071] Steps 1-3: After the development of the component model algorithm and the debugging algorithm, compile the component model algorithm and the debugging algorithm matching the specific joint version into the same dynamic link library, and name the file with the joint version;

[0072] The model algorithm and the debugging algorithm can be compiled into a dynamic link library respectively, which improves the development efficiency and iteration speed; isolates the component model algorithm-debugging algorithm to enhance the stability and compatibility of the system; reduces unnecessary running resource consumption; strengthens the function expansion and customization ability, adapts to the optimization needs of different types of units and different application scenarios; and the standardized interface design enhances the openness of the simulation platform.

[0073] Step 2: Establish a standardized debugging parameter input framework to realize flexible input of multi-working-condition debugging parameters;

[0074] The core task of this step is to establish a standardized parameter input framework so that users can complete the parameter settings required by algorithm calling according to actual needs.

[0075] Specifically, the parameter setting includes four dimensions: version selection, working condition configuration, definition of priority working condition, and running time setting, which together constitute the basic parameter set for component debugging.

[0076] Step 2-1: Start the version selection function, dynamically load the list of currently available algorithm versions, automatically verify compatibility with the component model, load the debugging parameter template of the corresponding version after verification, and lay the foundation for configuration;

[0077] The debugging parameter template includes a parameter list, exception code definition, and template description.

[0078] Step 2-2: Focus on core working condition configuration, the system provides dual-path input: for simple debugging, support tabular single-group parameter entry (real-time data type / range verification), and for multi-working condition batch debugging, support manual addition or selection of file import form to load working condition data set. The entered working conditions are clearly displayed in the interface in the form of a paginated table, and a unique working condition index identifier is automatically generated.

[0079] The file import interface provides data preview, format verification, and mapping configuration functions to ensure that the imported working condition data accurately corresponds to each parameter item.

[0080] At the same time, the system automatically generates a unique index identifier for each working condition (such as CASE_023), and users can add custom labels (such as "full load test"). The index and parameter set are bidirectionally associated, supporting quick retrieval and filtering, thereby achieving efficient management of multiple working conditions;

[0081] Step 2-3: Define priority working condition: the user selects a set of working conditions as the priority working condition. Under the premise of meeting all working condition constraints, the priority working condition converges to the optimal solution, ensuring that its performance indicators are prioritized, while considering the parameter matching degree of other working conditions;

[0082] Step 2-4: Set the debugging running time: after the user inputs the estimated duration, the system recommends a reasonable interval based on historical data and starts the out-of-range check to prevent problems such as resource waste or insufficient debugging due to improper time setting. For long-time tasks, additional support for timed start / automatic termination is provided;

[0083] Step 2-5: Integrate version, working condition, priority index, and running time configuration items, save the parameter set as structured data (such as JSON), support export as reusable templates, provide standardized configuration reference for similar components, and form a "configuration-management-reuse" closed loop.

[0084] Step 3: Debugging execution and parameter optimization based on hot loading;

[0085] In the algorithm debugging execution phase, the system will fully use all the parameter information configured in the early stage to realize the full-process automation from parameter preparation to actual debugging. The system will automatically load the corresponding dynamic link library file and perform strict version checking. The output information of the debugging algorithm will be transmitted to the user interface in real time through multiple channels and presented to the user in a visual manner.

[0086] In the debugging execution phase, the system starts the full-automation closed-loop process according to the configured parameter set:

[0087] Step 3-1: According to the currently selected component type and version information, automatically load the corresponding dynamic link library file. This loading process uses a dynamic loading mechanism, and for recently used dynamic libraries, version reading and loading errors due to memory caching are avoided.

[0088] Step 3-2: Convert the user-configured multiple sets of working condition data into memory structure bodies;

[0089] Different debugging algorithms are dynamically loaded using variable-length parameter loading technology. In order to efficiently convert the user-configured multiple sets of debugging working condition data into the input format required by the algorithm, the platform performs data conversion based on the pre-set parameter mapping relationship. This process fully utilizes memory mapping technology, avoiding the unnecessary data copying operations involved in traditional methods. The system automatically constructs a complete parameter structure body, which contains the total number of working conditions, the currently selected working condition index, and a pointer to the converted parameter data, among other core information. Finally, this structure body is passed to the currently loaded debugging algorithm for execution through a specific calling interface defined by the platform.

[0090] The structure body is passed to the currently loaded debugging algorithm for execution through a specific calling interface defined by the platform, avoiding data copying performance loss;

[0091] Step 3-3: Run the debugging algorithm, and implement monitoring and feedback during the execution process of the debugging algorithm;

[0092] Specifically, the output information of the debugging algorithm will be transmitted to the user interface in real time through multiple channels, including numerical results, calculation progress, warning information, and other key data. After intelligent filtering and formatting, these information are presented to the user in a visual manner. The system also provides detailed log records of the execution process, and the user can view the complete calculation process details by expanding the log panel.

[0093] Step 4: When the debugging algorithm terminates, the simulation platform receives the status code returned by the debugging algorithm and converts it into user-readable error information through a pre-defined error code mapping table. All error information is accompanied by context-related help document links, and the user can directly obtain targeted solutions.

[0094] The simulation platform extracts optimized parameter results from the output of the debugging algorithm. The result data undergoes verification, including range checks and rationality verification, and is automatically updated to the corresponding device parameters. The debugging results automatically generate a debugging report, which includes comprehensive information such as changes in key parameters and statistics on abnormal situations.

[0095] The core of this invention's intelligent automatic debugging lies in completely decoupling the debugging algorithm from the platform, allowing the algorithm to be developed and optimized independently. This decoupling consists of two parts: standardizing the debugging algorithm interface and framing the debugging process graphically. The simulation platform only provides standardized interfaces and support. Updating and replacing the debugging algorithm does not affect the stability of the simulation platform, while also reducing the platform's dependence on the algorithm and enhancing its adaptability to different debugging needs.

[0096] Embodiments of the present invention disclose an intelligent multi-objective parameter optimization system for simulator equipment, such as... Figure 1 As shown, it includes:

[0097] The simulation platform includes a data persistence layer, a simulation engine layer, a simulation data processing layer, and a user interaction layer; the data persistence layer includes a device library, an algorithm configuration library, and a working condition configuration library.

[0098] The data persistence layer, as the foundational support layer of the architecture, is responsible for storing configuration data related to devices, algorithms, and operating conditions. Specifically, the device library records simulation parameters for various devices, serving as the "operation object" for subsequent algorithm debugging; the algorithm configuration library stores debugging algorithm parameter templates applicable to different devices, including optimization parameter lists and exception codes; and the operating condition configuration library stores multiple sets of operating conditions and selected debugging algorithm versions for each device instance, serving as input for multi-objective optimization debugging algorithms. Through standardized storage formats and management mechanisms, the data persistence layer ensures data access for upper-layer modules.

[0099] The simulation engine layer includes an algorithm scheduling module and a data interface;

[0100] The simulation engine layer handles algorithm scheduling and data interaction. The algorithm scheduling module dynamically loads debugging algorithms based on the device's debugging version and structures and arranges multi-condition data according to a preset parameter list, ensuring an effective match between parameter sequences and algorithm logic under different conditions. The data interface establishes a two-way interactive information transmission mechanism: on the one hand, it provides the simulation data processing layer with dynamic data such as optimization intermediate results generated during the debugging algorithm's operation in real time, ensuring the upper-layer module's precise control over the debugging process; on the other hand, it receives user input information such as parameter adjustments from the user interaction layer and transmits it in a standardized manner to the debugging algorithm layer, achieving efficient connection between user needs and algorithm execution.

[0101] The simulation data processing layer comprises an algorithm management module, a working condition management module and a device parameter optimization module.

[0102] The algorithm management module is responsible for debugging algorithm template management; the working condition management module manages multiple working condition configurations of device instances; and the device parameter optimization module iteratively optimizes device parameters based on a debugging algorithm, and uses the best operation parameters as the adjustment device data.

[0103] The user interaction layer comprises an algorithm configuration interface, a working condition configuration interface and a process and result display interface.

[0104] The user interaction layer provides a convenient graphical interface for users to participate in the whole process of device debugging. The algorithm configuration interface supports user configuration of debugging versions, custom algorithm parameters and exception coding; the working condition configuration interface facilitates user selection of versions, setting and modifying working condition parameters; and the process and result display interface presents the algorithm running state, data change trend and final debugging result in real time, so that the user can intuitively control the debugging process and timely adjust the debugging strategy, thereby improving the interactive experience and efficiency of device debugging.

[0105] The debugging algorithm module interacts with the data of the simulation engine layer to realize algorithm calling.

[0106] The debugging algorithm module stores multiple versions of debugging algorithms of different devices. Through the control and data interface with the simulation engine layer, the debugging algorithm module can provide algorithm resources for the simulation data processing layer as needed, adapt to the differentiated and complex debugging requirements of devices, and support flexible calling of algorithms and coverage of debugging scenarios.

[0107] Embodiments of the present application disclose a computer comprising a memory, a processor and a computer program stored on the processor, wherein the processor implements the simulation machine device intelligent multi-target parameter optimization method when executing the computer program.

[0108] Embodiments of the present application disclose a readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the simulation machine device intelligent multi-target parameter optimization method.

[0109] The present application compares the conventional way of simulation platform coding with the decoupled framework type multi-target parameter optimization method, as shown in Table 1.

[0110] Table 1

[0111]

[0112]

[0113] The above description of the embodiments is only used to help understand the method of the present application and its core idea. It should be noted that for those skilled in the art, some improvements and modifications can be made to the present application without departing from the principles of the present application, and these improvements and modifications also fall within the protection scope of the claims of the present application.

[0114] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for intelligent multi-objective parameter optimization of simulator equipment, characterized in that, Includes the following steps: Step 1: Construct a decoupled architecture for the component model algorithm and the debugging algorithm of the simulation platform, and compile the component model algorithm and the debugging algorithm into dynamic link libraries respectively; Step 2: Establish a standardized debugging parameter input framework to enable flexible input of debugging parameters under multiple operating conditions; Step 3: Automatically load the corresponding dynamic link library files, convert the multiple sets of user-configured operating condition data into memory structures; pass the structures to the currently loaded debugging algorithm for execution; run the debugging algorithm, and implement monitoring and feedback during the execution of the debugging algorithm; Step 4: When the debugging algorithm terminates, the simulation platform receives the status code returned by the debugging algorithm and converts it into user-readable error information through a predefined error code mapping table; the simulation platform extracts the optimized parameter results from the output of the debugging algorithm, and the result data is verified and automatically updated to the corresponding device parameters.

2. The intelligent multi-objective parameter optimization method for simulator equipment according to claim 1, characterized in that, Step 1 specifically includes: Step 1-1: Define a joint version identifier for each component model algorithm and its corresponding debugging algorithm; Steps 1-2: Development of component model algorithms and debugging algorithms, configuration of debugging parameter templates; Steps 1-3: After the component model algorithm and debugging algorithm are developed, the component model algorithm and debugging algorithm that match the specific joint version will be compiled into the dynamic link library respectively, and the files will be named according to the joint version.

3. The intelligent multi-objective parameter optimization method for simulator equipment according to claim 2, characterized in that, Once the input and output parameters of the debugging algorithm are determined, the configuration of the debugging template is carried out simultaneously with the development of the debugging algorithm. For functional modules that manage different algorithm versions, optimize parameter definitions, and define exception handling, we build standardized and configurable parameter templates and complete persistent data storage to form configuration data corresponding to the debugging algorithm. The parameter templates are stored in a standardized data format, and the hierarchical directory and unified naming enable accurate retrieval and retrieval of parameter templates for different devices and versions.

4. The intelligent multi-objective parameter optimization method for simulator equipment according to claim 1, characterized in that, Step 2 specifically includes: Step 2-1: Activate the version selection function, dynamically load the list of currently available algorithm versions, automatically verify the compatibility with the component model, and load the corresponding version's debugging parameter template after the verification is passed; Step 2-2: Configure for multiple working conditions: For simple debugging, support tabular single-group parameter input; for batch debugging of multiple working conditions, support manual addition or import of working condition datasets by selecting files; the input working conditions are displayed in the interface in the form of paginated tables, and a unique working condition index identifier is automatically generated. Steps 2-3: Define a set of operating conditions as priority operating conditions; Steps 2-4: Set the debugging run time; Steps 2-5: Integrate the four configuration items of version, working condition, priority index and runtime, save the parameter set as structured data, and support exporting it as a reusable template.

5. The intelligent multi-objective parameter optimization method for simulator equipment according to claim 1, characterized in that, In step 3: The output information of the debugging algorithm is transmitted to the user interface in real time through multiple channels; after intelligent filtering and formatting, the output information is presented to the user in a visual manner. It also provides detailed logs of the execution process, and users can view the complete calculation process details by expanding the log panel.

6. The intelligent multi-objective parameter optimization method for simulator equipment according to claim 1, characterized in that, In step 3, different debugging algorithms are dynamically loaded using variable length parameter loading technology, and data conversion is performed according to the preset parameter mapping relationship; Specifically, this includes: converting multiple sets of configured operating condition data directly into contiguous memory blocks, constructing a structure containing the total number of operating conditions, an index array, and data pointers, and passing the structure to the currently loaded debugging algorithm for execution through a specific call interface defined by the platform.

7. The intelligent multi-objective parameter optimization method for simulator equipment according to claim 1, characterized in that, The debugging results are automatically generated into a debugging report, which includes comprehensive information such as changes in key parameters and statistics on abnormal situations.

8. A smart multi-objective parameter optimization system for simulator equipment, characterized in that, include: The simulation platform includes a data persistence layer, a simulation engine layer, a simulation data processing layer, and a user interaction layer. The data persistence layer, as the basic support layer of the architecture, is responsible for storing configuration data related to devices, algorithms, and operating conditions. The simulation engine layer handles algorithm scheduling and data interaction; The simulation data processing layer is used to drive the execution of debugging algorithms, manage the configuration of various operating conditions, and optimize equipment parameters in a closed loop to achieve intelligent tuning of equipment performance. The debugging algorithm module interacts with the data in the simulation engine layer to implement algorithm calls.

9. The intelligent multi-objective parameter optimization system for simulator equipment according to claim 8, characterized in that, The data persistence layer includes a device library, an algorithm configuration library, and a working condition configuration library; The device library is used to record the simulation parameters of various devices and is the object of operation for subsequent debugging algorithms; the algorithm configuration library stores debugging algorithm parameter templates applicable to different devices, including optimization parameter lists and exception codes; The operating condition configuration library stores multiple sets of operating conditions and selected debugging algorithm versions for each device instance, which are used as inputs for multi-objective optimization debugging algorithms; The simulation engine layer includes an algorithm scheduling module and a data interface. The algorithm scheduling module dynamically loads debugging algorithms based on the device's debugging version and structures and arranges multi-condition data according to a preset parameter list, ensuring an effective match between parameter sequences and algorithm logic under different conditions. The data interface establishes a two-way interactive information transmission mechanism: on the one hand, it provides the simulation data processing layer with dynamic data such as optimization intermediate results generated during the debugging algorithm's operation in real time, ensuring the upper-layer module's precise control over the debugging process; on the other hand, it receives user input information such as parameter adjustments from the user interaction layer and transmits it in a standardized manner to the debugging algorithm layer, achieving efficient connection between user needs and algorithm execution. The simulation data processing layer includes an algorithm management module, a working condition management module, and an equipment parameter optimization module; the algorithm management module is responsible for managing debugging algorithm templates. The operating condition management module manages multiple operating condition configurations for equipment instances; the equipment parameter optimization module relies on debugging algorithms to iteratively optimize equipment parameters and uses the optimal operating parameters as the data for adjusting the equipment. The user interaction layer includes an algorithm configuration interface, a working condition configuration interface, and a process and result display interface.

10. The intelligent multi-objective parameter optimization system for simulator equipment according to claim 8, characterized in that, The debugging algorithm module stores multiple versions of debugging algorithms for different devices in a categorized manner. Through the control and data interface with the simulation engine layer, the debugging algorithm module provides algorithm resources to the simulation data processing layer on demand, adapts to the differentiated and complex debugging needs of equipment, and supports the flexible calling of algorithms and coverage of debugging scenarios.

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