Multi-level collaborative verification method and platform for aircraft power generation system controller, and storage medium

Abstract

The invention relates to the field of aviation electrical engineering and real-time simulation testing, and provides a multi-level collaborative verification method and platform for an aircraft power generation system control strategy and a storage medium, and the method comprises the steps: an offline simulation stage: carrying out the preliminary design and verification of the control strategy; in the real-time simulation stage, closed-loop real-time verification and parameter optimization are carried out on the control strategy; in the rapid prototyping stage, optimized control strategy codes are deployed to a rapid prototyping controller and are connected with real or high-precision simulated power generation system hardware, and hardware-in-loop-entity hybrid verification is carried out; wherein a unified performance reference and error feedback chain penetrating three stages of off-line simulation, real-time simulation and rapid prototyping is established, a verification result of each stage is used as an expected reference of the next stage, and error monitoring and backtracking correction are carried out. According to the method, through offline-real-time-prototype three-layer progressive verification and a model precision progressive calibration mechanism, the confidence coefficient and the one-time success rate of final physical verification are greatly improved.

Description

Technical Field

[0001] This invention relates to the fields of aviation electrical engineering and real-time simulation testing technology, and in particular to a multi-level collaborative verification method, platform and storage medium for aircraft power generation system control strategies, which is applicable to rapid prototyping and high-confidence verification of aircraft three-level brushless AC power generation system controllers. Background Technology

[0002] The modern aircraft power generation system is the core of the aviation electrical system, and the reliability of its control strategy is directly related to flight safety. Traditional control strategy development and verification mainly rely on pure digital simulation and final physical bench testing. Pure digital simulation cannot fully consider practical factors such as real-time performance, hardware interfaces, and electromagnetic compatibility, resulting in low confidence levels; while direct physical bench testing is risky, costly, and time-consuming, and it is difficult to cover all critical and fault conditions.

[0003] In existing technologies, Hardware-in-the-Loop (HIL) simulation and Rapid Control Prototyping (RCP) techniques have been introduced into the development process of control systems. For example, some patents disclose aircraft system testing methods based on hardware-in-the-loop simulation. However, most of these solutions focus on a single simulation level (such as pure HIL or pure RCP), or a simple combination of two levels, lacking a verification system that seamlessly connects the entire process from offline design to physical integration and has closed-loop quality control. Specifically, the following problems exist: 1. Poor model consistency: There is a disconnect between the offline design model, the real-time simulation model and the actual hardware behavior. The model accuracy is inconsistent at different stages, which means that problems found in later testing require a lot of time to backtrack and locate.

[0004] 2. Disjointed verification process: Each verification stage is relatively independent, and data and conclusions cannot be effectively transmitted and mutually verified, making it difficult to form a continuous and progressive evaluation of the performance of the control strategy.

[0005] 3. Complex interface configuration: When interfacing with real hardware during the RCP phase, tedious manual configuration and debugging are required for specific interface protocols, resulting in low adaptation efficiency.

[0006] 4. Limited testing scenarios: There is a lack of a systematic library of automated testing scenarios, resulting in insufficient verification of the robustness of control strategies under complex and extreme load disturbances.

[0007] Therefore, there is an urgent need for an integrated verification method and platform that can integrate offline design, real-time simulation and rapid prototyping, and realize the collaboration of models, data and processes, in order to improve the efficiency, quality and safety of aircraft power generation system controller development. Summary of the Invention

[0008] To address the shortcomings of existing technologies, the present invention aims to provide a multi-level collaborative verification method, platform, and storage medium for aircraft power generation system control strategies. Its core lies in constructing a collaborative verification system that includes three levels: "offline simulation - real-time simulation - rapid prototyping," and possesses progressive calibration of model accuracy, cross-level consistency benchmarks and error feedback chains, adaptive interface simulation, and intelligent load disturbance injection capabilities.

[0009] The present invention adopts the following technical solution: On one hand, the present invention provides a multi-level collaborative verification method for an aircraft power generation system controller, comprising: Offline simulation: A high-precision offline model is established based on the physical parameters of the aircraft power generation system to conduct preliminary design and verification of the control strategy; Real-time simulation: The offline model is reduced to a real-time model to obtain a real-time reduced model. The real-time reduced model is deployed to a real-time simulator to form a hardware-in-the-loop simulation environment for closed-loop real-time verification and parameter optimization of the control strategy. Rapid Prototyping: Deploy the optimized control strategy code to the rapid prototyping controller and connect it to real or high-precision simulated power generation system hardware to perform hardware-in-the-loop-physical hybrid verification; Simultaneously, a unified performance benchmark and error feedback chain is established that runs through the offline simulation stage, the real-time simulation stage, and the rapid prototyping stage. The verification results of each stage are used as the reference benchmark for the next stage, and real-time error monitoring and backtracking correction are performed.

[0010] In addition to any of the possible implementations described above, another implementation is provided, wherein the offline model is an aircraft three-stage synchronous generator system model, including: Auxiliary exciter based on permanent magnet synchronous generator model; An excitation power supply employing an asymmetric H-bridge topology; Main exciter and main generator based on rotating rectifier; The control strategy includes a dual closed-loop control structure for voltage regulation, wherein the outer loop is the generator output voltage loop and the inner loop is the excitation current loop.

[0011] In addition to any of the possible implementations described above, a further implementation is provided in which, during the real-time simulation, the offline model is progressively calibrated to ensure that the real-time reduced-order model remains consistent with the offline model in key dynamic characteristics, thereby guaranteeing the reliability of the unified performance benchmark transfer between stages. The progressive model accuracy calibration specifically includes: (1) Parametric calibration: Using the high-precision dynamic output data generated under standard test excitation in the offline simulation as a reference benchmark, one or more key parameters in the real-time reduced-order model are adjusted by using parameter identification or optimization algorithms to minimize the error between the output response of the real-time reduced-order model under the same excitation and the reference benchmark. (2) Error Triggering Backtracking: During calibration or subsequent real-time simulation, if the difference between the output response of the real-time reduced-order model and the reference benchmark exceeds a preset threshold, the error feedback chain is triggered. The error feedback chain guides the correction of the parameters of the preceding offline model, the parameters of the current real-time reduced-order model, or the parameters of the control strategy.

[0012] In addition to any of the possible implementations described above, another implementation is provided in which the rapid prototyping phase further includes: Adaptive Interface Simulation: Automatically identifies or configures the physical interface protocol between the rapid prototyping controller and the real power generation system hardware, wherein the interface protocol includes at least one of CAN, ARINC429, and discrete I / O; The adaptive interface simulation supports online modification of interface parameters without affecting the operation of the deployed control strategy code.

[0013] In addition to any of the possible implementations described above, a further implementation is provided in which a load perturbation and evaluation step is included during both the real-time simulation and rapid prototyping verification, specifically as follows: Select or program a disturbance sequence from a predefined load disturbance scenario library, which includes load step change, nonlinear load access, short circuit fault and parallel switching; The perturbation sequence is loaded into the offline model or the real-time reduced-order model, the perturbation test is automatically executed, and the robustness of the control strategy is evaluated based on preset performance indicators, and an evaluation report is generated.

[0014] On the other hand, the present invention also provides a verification platform for aircraft power generation system control strategy for implementing the above method, comprising: The host computer subsystem is used for offline modeling, simulation monitoring, and code generation. The real-time simulation subsystem includes a first target machine and a second target machine, which are used to run the controlled object model and the control strategy model, respectively. The rapid prototyping subsystem includes a rapid prototyping controller and real power generation system hardware; The collaborative verification management unit is used to manage the unified performance benchmark and error feedback chain, and coordinate the data transfer and verification process between the subsystems.

[0015] In addition to any of the possible implementations described above, another implementation is provided in which the collaborative verification management unit includes: The benchmark management module is used to define and maintain key performance indicator benchmarks for each verification phase. The error analysis module is used to calculate the deviation between the output of each stage and the corresponding benchmark in real time, and to determine whether to trigger backtracking correction. The process scheduling module is used to automatically schedule re-execution or parameter adjustment of the verification phase based on error analysis results.

[0016] In addition to any of the possible implementations described above, another implementation is provided in which the rapid prototyping subsystem includes the adaptive interface simulation module, which is used to automatically identify or configure the physical interface protocol between the rapid prototyping controller and the real power generation system hardware. The adaptive interface simulation module is a pluggable hardware board or a configurable FPGA logic unit.

[0017] On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0018] On the other hand, the present invention also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the program to implement the above-described method.

[0019] The beneficial effects of this invention are as follows: 1. High-confidence verification throughout the entire process: Through a three-layer progressive verification process of "offline-real-time-prototype" and a progressive calibration mechanism for model accuracy, a smooth transition from virtual to physical is ensured, which greatly improves the confidence and first-time success rate of the final physical verification.

[0020] 2. Efficient error tracing and iteration: Cross-level consistency benchmarks and error feedback chains enable performance deviations discovered later to be quickly and accurately located in the early design or modeling stages, greatly shortening the debugging cycle.

[0021] 3. Strong versatility and adaptability: The adaptive interface simulation module simplifies the integration of the RCP system with diverse real hardware, and improves the platform's testing and adaptation efficiency for power generation systems with different models and interface standards.

[0022] 4. Systematic robustness assessment: The intelligent load disturbance injection and assessment subsystem provides standardized and automated extreme condition testing capabilities, enabling a more comprehensive and objective assessment of the robustness and reliability of control strategies, and reducing the risk of human error during testing.

[0023] 5. Significantly reduce development costs and risks: This integrated verification process can identify and resolve as many problems as possible before investing in high-cost physical test benches, effectively reducing total development costs, timelines, and safety risks. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the multi-level collaborative verification method for an aircraft power generation system provided in an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram of the multi-level collaborative verification platform architecture provided in the embodiments of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0027] The accompanying drawings illustrate a layer structure according to an embodiment of the present invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0028] Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0029] In the description of this invention, it should be noted that the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0030] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0031] like Figure 1 As shown, an embodiment of the present invention provides a multi-level collaborative verification method for an aircraft power generation system controller, comprising the following steps: S1: Offline Simulation: Based on the accurate physical parameters of the aircraft power generation system, a high-fidelity offline model is built in an advanced modeling environment (such as MATLAB / Simulink), including a three-stage generator, excitation system, load, and control strategy model. Preliminary algorithm verification and functional testing are conducted.

[0032] S2: Real-time Simulation: The offline model is downgraded and made real-time, then deployed to a real-time simulator (such as NI PXI or dSPACE) to build a hardware-in-the-loop simulation environment. In this stage, progressive model accuracy calibration is introduced: the high-precision simulation data from S1 is used to calibrate key parameters of the real-time model, ensuring that the real-time model maintains operational efficiency while its key dynamic characteristics are consistent with the offline model. Simultaneously, closed-loop real-time testing of the control strategy is performed.

[0033] Downsizing: Transforming the high-fidelity, non-real-time model used for offline design and analysis into a simplified model that can run on a real-time simulator, with a lighter computational burden, while still maintaining the key dynamic behaviors of the system. This allows for the construction of an effective hardware-in-the-loop simulation environment, enabling reliable and efficient closed-loop testing and optimization of the actual controller code.

[0034] S3: Rapid Prototyping Verification: The control strategy optimized in the S2 stage is automatically generated into embedded code and downloaded to the rapid prototyping controller (such as another real-time simulator or a dedicated prototype board). Through the adaptive interface simulation module, the physical interface (such as CAN, ARINC429) between the prototype controller and the real power generation system hardware (or a high-precision simulator) is quickly configured. A hardware-in-the-loop / physical hybrid verification environment is built to conduct the final-level closed-loop control test.

[0035] S4: Cross-level collaborative management. Establish a unified performance benchmark and error feedback chain spanning S1 to S3. Define a unified set of key performance indicators for each stage. Use the verification output of the previous stage as the reference benchmark for the next stage. Calculate the error between the output and the benchmark in real time during S2 and S3 stages. If the error exceeds the limit, an alarm is triggered through the error feedback chain, and the system can automatically or manually backtrack to the previous stage for model or strategy correction. Furthermore, during S2 and S3 stages, the intelligent load disturbance injection and evaluation subsystem can be invoked to automatically execute predefined extreme or fault load scenario tests and generate robustness evaluation reports.

[0036] In one specific embodiment, during the real-time simulation, the offline model is progressively calibrated to ensure that the real-time reduced-order model remains consistent with the offline model in key dynamic characteristics, thereby guaranteeing the reliability of the unified performance benchmark transfer between stages. The progressive model accuracy calibration specifically includes: (1) Parametric calibration: Using the high-precision dynamic output data generated under standard test excitation in the offline simulation as a reference benchmark, one or more key parameters in the real-time reduced-order model are adjusted by using parameter identification or optimization algorithms to minimize the error between the output response of the real-time reduced-order model under the same excitation and the reference benchmark. (2) Error Triggering Backtracking: During calibration or subsequent real-time simulation, if the difference between the output response of the real-time reduced-order model and the reference benchmark exceeds a preset threshold, the error feedback chain is triggered. The error feedback chain guides the correction of the parameters of the preceding offline model, the parameters of the current real-time reduced-order model, or the parameters of the control strategy.

[0037] Example 1: Verification of a voltage regulator controller for a three-stage brushless AC power generation system for aircraft This verification platform adopts a layered distributed architecture: The host computer layer includes high-performance workstations (running MATLAB / Simulink for offline modeling and simulation) and host computer monitoring software for real-time simulation systems (such as RT-LAB host computer).

[0038] Mid-level machine layer (real-time simulation and rapid prototyping layer): This layer consists of two or more real-time simulation target machines. Target machine A (lower-level machine role) primarily runs the real-time model of the controlled object (power generation system and load) or connects to the physical hardware. Target machine B (mid-level machine core) runs the control strategy model or rapid prototyping code.

[0039] Lower-level machine layer (physical layer): Includes a real aircraft-type three-stage brushless AC generator, excitation controller, load cabinet, and related power interfaces, sensors, etc. It connects to the middle-level machine layer via power amplifiers and signal conditioning boxes.

[0040] Collaborative Verification Management Unit: As the core software, it is deployed on the host computer and is responsible for managing the verification process, maintaining performance benchmarks, analyzing cross-level errors, and scheduling.

[0041] In the final physical experiment verification stage, the real-time simulation part was replaced with a real controller and aircraft generator, and connected to the corresponding host computer to complete the physical experiment verification.

[0042] The verification platform adopts a layered distributed architecture, such as Figure 2 As shown.

[0043] Step 1: Offline simulation modeling and preliminary verification Build a detailed offline model of a three-stage generator system in Simulink.

[0044] Auxiliary exciter: adopts a permanent magnet synchronous generator (PMSG) model.

[0045] Excitation system: Includes a power stage model based on an asymmetric H-bridge topology to simulate the excitation power supply.

[0046] Main exciter and main generator: They adopt an electrically excited synchronous motor model, connected in the middle by a rotating rectifier model.

[0047] Control strategy model: A dual-closed-loop voltage regulator model is established. The outer loop is the generator output voltage loop, which generates the excitation current reference value; the inner loop is the excitation current loop, which generates the PWM duty cycle to drive the H-bridge.

[0048] Load models include resistive, inductive, rectifier loads, and sudden load addition / removal models.

[0049] At this stage, preliminary functional verification of the control strategy is performed, such as no-load voltage build-up, load regulation, and dynamic response, and high-precision reference data is generated for subsequent calibration.

[0050] Step 2: Real-time simulation verification and model calibration Model Conversion and Deployment: The offline model in Simulink is divided into a "Controlled Object Model" (power generation system + load) and a "Control Strategy Model." These are compiled separately using a code generation tool (such as Simulink Coder) and downloaded to target machine A and target machine B. Target machines A and B exchange data in real time via high-speed reflective memory or Ethernet.

[0051] Model accuracy progressive calibration: The real-time model running in target machine A is a downgraded version of the offline model. After starting the real-time simulation, the system automatically injects a set of standard test signals (such as a step load). The collaborative verification management unit compares the output response of the real-time model with the "baseline response" of the offline model under the same excitation in step 1. Through preset algorithms (such as parameter identification or gain scheduling), the parameters of key components in the real-time model (such as the exciter time constant and rectifier voltage drop) are dynamically fine-tuned to ensure that the error between the two in key dynamic indicators is less than a preset threshold (such as 5%). This process is called model accuracy progressive calibration.

[0052] Closed-loop real-time testing and optimization: Closed-loop real-time testing of the control strategy is conducted in a high-confidence environment after model calibration. The controller's PID parameters can be initially adjusted, and the steady-state and dynamic performance of the system can be observed.

[0053] Intelligent Disturbance Testing (Optional): Calls the intelligent load disturbance injection and evaluation subsystem to automatically execute test cases in the scenario library, such as "sudden cut-in of 75% rated load" or "simulated single-phase short circuit", and automatically records indicators such as voltage recovery time and maximum transient deviation, generating a preliminary robustness report.

[0054] Step 3: Rapid Prototyping Code generation and deployment: The optimized control strategy model from step 2 is automatically used to generate embedded C code suitable for target machine B (which serves as a rapid prototyping controller at this time), and then downloaded.

[0055] Hardware connectivity and adaptive interface configuration: Disconnect the virtual signal connection between target machine A and target machine B.

[0056] Connect a real generator (and its original excitation controller) to the system, replacing the generator system model in target machine A. The load cabinet can still be connected via the load model in target machine A or the physical load.

[0057] The target machine B (prototype controller) outputs PWM signals to the real excitation controller through its IO board and receives voltage and current sensor signals from the real generator.

[0058] The adaptive interface simulation module (integrated as an FPGA board within the chassis of target aircraft B) automatically detects the type of the connected signal lines (e.g., analog voltage signals of 0-5V, and digital PWM signals). The operator selects the communication protocol (e.g., CAN bus for communication with the aircraft's electrical system management unit) in the host computer software. This module automatically handles signal scaling, electrical isolation, and protocol stack configuration, eliminating the need for manual low-level driver development.

[0059] Closed-loop physical control test: Start the system. The prototype control strategy code in target machine B, based on feedback from real sensors, calculates and outputs PWM drive signals in real time to control the operation of the real generator. Perform performance and disturbance tests similar to those in step 2.

[0060] Cross-level error feedback: The collaborative verification management unit continuously monitors key performance indicators (such as steady-state voltage accuracy) in this stage and compares them with the "benchmark" established in the real-time simulation stage of step 2.

[0061] Suppose that at a certain load point, the steady-state voltage error of the physical test is +1.5%, while the reference error in the real-time simulation stage is +0.3%, which exceeds the preset ±0.5% consistency threshold.

[0062] The error feedback chain was triggered, and the system issued an alarm on the host computer interface: "The steady-state error of the physical test exceeds the limit. It is recommended to backtrack and check the 'main generator voltage drop parameter' or 'sensor conditioning circuit model' in the real-time simulation model."

[0063] Following the prompts, the engineer returned to the real-time simulation model from step 2, corrected the relevant parameters, and re-performed real-time simulation calibration and testing. After calibration, rapid prototype testing was performed again until the error met the requirements.

[0064] Through the progressive verification and closed-loop feedback of the above three stages, a prototype of the power generation control strategy and its embedded code, which are fully verified in a hybrid simulation system that closely approximates the real environment, are finally obtained, providing a solid and reliable foundation for the final full physical bench integration test.

[0065] The above description of the embodiments is only for the purpose of helping to understand the method and core idea of ​​this application; at the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​this application. Therefore, the content of this specification should not be construed as a limitation of this application.

[0066] Certain terms are used in the specification and claims to refer to specific components. Those skilled in the art will understand that hardware manufacturers may use different names to refer to the same component. This specification and claims do not distinguish components based on differences in name, but rather on differences in function. The terms "comprising" and "including" used throughout the specification and claims are open-ended and should be interpreted as "comprising / including but not limited to". "Approximately" means that within an acceptable margin of error, those skilled in the art can solve the technical problem and substantially achieve the technical effect within a certain margin of error. The following descriptions in the specification are preferred embodiments for carrying out this application; however, these descriptions are for the purpose of illustrating the general principles of this application and are not intended to limit the scope of this application. The scope of protection of this application shall be determined by the appended claims.

[0067] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a product or system comprising a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a product or system. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the product or system that includes said element.

[0068] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0069] The foregoing description illustrates and describes several preferred embodiments of this application. However, as previously stated, it should be understood that this application is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the application concept described herein through the foregoing teachings or techniques or knowledge in related fields. Any modifications and variations made by those skilled in the art that do not depart from the spirit and scope of this application should be within the protection scope of the appended claims.

Claims

1. A multi-level collaborative verification method for an aircraft power generation system controller, characterized in that, include: Offline simulation: A high-precision offline model is established based on the physical parameters of the aircraft power generation system to conduct preliminary design and verification of the control strategy; Real-time simulation: The offline model is reduced to a real-time model to obtain a real-time reduced model. The real-time reduced model is deployed to a real-time simulator to form a hardware-in-the-loop simulation environment for closed-loop real-time verification and parameter optimization of the control strategy. Rapid Prototyping: Deploy the optimized control strategy code to the rapid prototyping controller and connect it to real or high-precision simulated power generation system hardware to perform hardware-in-the-loop-physical hybrid verification; Simultaneously, a unified performance benchmark and error feedback chain is established that runs through the three stages of offline simulation, real-time simulation, and rapid prototype verification. The verification results of each stage are used as the reference benchmark for the next stage, and real-time error monitoring and backtracking correction are performed.

2. The multi-level collaborative verification method for aircraft power generation system controllers as described in claim 1, characterized in that, The offline model is an aircraft three-stage synchronous generator system model, including: Auxiliary exciter based on permanent magnet synchronous generator model; An excitation power supply employing an asymmetric H-bridge topology; Main exciter and main generator based on rotating rectifier; The control strategy includes a dual closed-loop control structure for voltage regulation, wherein the outer loop is the generator output voltage loop and the inner loop is the excitation current loop.

3. The multi-level collaborative verification method for aircraft power generation system controllers as described in claim 1, characterized in that, During the real-time simulation, the offline model is progressively calibrated to ensure that the real-time reduced-order model remains consistent with the offline model in key dynamic characteristics, thereby guaranteeing the reliability of the unified performance benchmark transfer between stages. The progressive model accuracy calibration specifically includes: (1) Parametric calibration: Using the high-precision dynamic output data generated under standard test excitation in the offline simulation as a reference benchmark, one or more key parameters in the real-time reduced-order model are adjusted by using parameter identification or optimization algorithms to minimize the error between the output response of the real-time reduced-order model under the same excitation and the reference benchmark. (2) Error Triggering Backtracking: During parameter calibration or subsequent real-time simulation, if the difference between the output response of the real-time reduced-order model and the reference benchmark exceeds a preset threshold, the error feedback chain is triggered. The error feedback chain guides the correction of the parameters of the preceding offline model, the parameters of the current real-time reduced-order model, or the parameters of the control strategy.

4. The multi-level collaborative verification method for aircraft power generation system controllers as described in claim 1, characterized in that, The rapid prototype verification also includes: Adaptive Interface Simulation: Automatically identifies or configures the physical interface protocol between the rapid prototyping controller and the real power generation system hardware, wherein the interface protocol includes at least one of CAN, ARINC429, and discrete I / O; The adaptive interface simulation supports online modification of interface parameters without affecting the operation of the deployed control strategy code.

5. The multi-level collaborative verification method for aircraft power generation system controllers as described in claim 1, characterized in that, During real-time simulation or rapid prototyping, a load perturbation and evaluation step is included, specifically: Select or program a disturbance sequence from a predefined load disturbance scenario library, which includes load step change, nonlinear load access, short circuit fault and parallel switching; The perturbation sequence is loaded into the offline model or the real-time reduced-order model, the perturbation test is automatically executed, and the robustness of the control strategy is evaluated based on preset performance indicators, and an evaluation report is generated.

6. A verification platform for an aircraft power generation system control strategy to implement the method of any one of claims 1-5, characterized in that, include: The host computer subsystem is used for offline modeling, simulation monitoring, and code generation. The real-time simulation subsystem includes a first target machine and a second target machine, which are used to run the controlled object model and the control strategy model, respectively. The rapid prototyping subsystem includes a rapid prototyping controller and real power generation system hardware; The collaborative verification management unit is used to manage the unified performance benchmark and error feedback chain, and coordinate the data transfer and verification process between various subsystems.

7. The aircraft power generation system control strategy verification platform as described in claim 6, characterized in that, The collaborative verification management unit includes: The benchmark management module is used to define and maintain key performance indicator reference benchmarks for each verification phase; The error analysis module is used to calculate the deviation between the output of each stage and the corresponding reference benchmark in real time, and to determine whether to trigger backtracking correction. The process scheduling module is used to automatically schedule re-execution or parameter adjustment of the verification phase based on error analysis results.

8. The aircraft power generation system control strategy verification platform as described in claim 6, characterized in that, The rapid prototyping subsystem includes an adaptive interface simulation module, which is used to automatically identify or configure the physical interface protocol between the rapid prototyping controller and the real power generation system hardware. The adaptive interface simulation module is a pluggable hardware board or a configurable FPGA logic unit.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-5.

10. An electronic device comprising a processor and a memory, the memory storing a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1-5.

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