Aero-engine multi-terminal teaching method and system based on mechanism driving

Abstract

The invention discloses an aero-engine multi-terminal teaching method and system based on mechanism driving, and relates to the field of electrical digital data process.The method comprises the steps that a thermodynamic state equation is established, physical parameters and working condition information are substituted into the thermodynamic state equation, and a state transition matrix is obtained; decomposing the state transition matrix to obtain coupling characteristics; constructing a regular equation of the aero-engine based on the stability criterion and the coupling characteristics, and solving the regular equation to obtain a teaching parameter evolution path of each terminal; establishing a Lagrange function of the aero-engine according to the evolution path, and constructing a variational equation set representing coupling characteristics between terminals based on the Lagrange function; solving the variational equation set to obtain a collaborative optimization criterion of the terminal teaching process; determining a teaching compensation mechanism among the plurality of terminals by adopting a hierarchical iteration mode based on a collaborative optimization criterion; and according to the teaching compensation mechanism, teaching parameters of each terminal are adjusted in real time. According to the invention, the teaching accuracy and reliability of the aero-engine terminal are improved.

Description

Technical Field

[0001] This application belongs to the field of electronic digital data processing, and particularly relates to a mechanism-driven multi-terminal teaching method and system for aero-engines. Background Technology

[0002] In the process of mining resource extraction, accurate measurement of the volume of mined material is of great significance for production planning and cost accounting. Traditional methods of measuring the volume of mined material mainly rely on manual ground surveying or total station surveying. These methods not only require surveyors to enter the mining site, posing safety hazards, but are also inefficient and unable to obtain timely data on the mining area. As the core power plant of modern aircraft, the accuracy and reliability of the teaching process of aero-engines directly affect the engine's service life and flight safety. Traditional single-terminal teaching methods, lacking a multi-system coordination mechanism, struggle to accurately grasp the coupling relationships between various engine systems, easily leading to deviations in teaching parameters and even system failures. This is unacceptable in the field of high-performance, high-reliability aero-engines.

[0003] In related technologies, a deep neural network model can be constructed and trained using operational data from various engine systems, enabling intelligent collaboration between different engine terminals. Furthermore, this method integrates online learning algorithms, allowing for dynamic updates to the network model based on real-time operational data, thus improving the accuracy and adaptability of the teaching process.

[0004] However, since neural networks are essentially "black box" models, their internal decision-making mechanisms lack interpretability. This makes it difficult to accurately track and interpret the evolution of teaching parameters under rapidly changing or unconventional engine operating conditions. This lack of interpretability makes it difficult for engineers to judge the reliability of teaching results and to predict and prevent potential teaching failure risks. This is a critical problem that needs to be solved in the field of aero-engines, which requires a high degree of safety and controllability. Summary of the Invention

[0005] This application provides a mechanism-driven multi-terminal teaching method and system for aero-engines, which can improve the accuracy and reliability of aero-engine terminal teaching.

[0006] In the first aspect, this application provides a mechanism-driven multi-terminal teaching method for aero-engines to obtain physical parameters and operating condition information of multiple terminals of the aero-engine; Thermodynamic state equations are established based on the physical mechanism of aero-engines, and physical parameters and operating condition information are substituted into the thermodynamic state equations to obtain the state transition matrix of aero-engines. Decompose the state transition matrix to obtain the stability criterion and the coupling characteristics between terminals; Based on stability criteria and coupling characteristics, the canonical equations of the aero-engine are constructed, and the evolution path of the teaching parameters of each terminal is obtained by solving the canonical equations. The Lagrangian function of the aero-engine is established based on the evolution path, and a set of variational equations characterizing the coupling characteristics between terminals is constructed based on the Lagrangian function. Solving the system of variational equations yields the collaborative optimization criteria for the terminal teaching process; Based on the collaborative optimization criterion, a hierarchical iterative approach is adopted to determine the teaching compensation mechanism among multiple terminals. The compensation mechanism includes transient condition compensation and steady-state condition compensation. The teaching parameters of each terminal are adjusted in real time according to the teaching compensation mechanism.

[0007] By employing the aforementioned technical solution, and acquiring the physical parameters and operating condition information of multiple terminals of the aero-engine, a state transition matrix can be established in conjunction with the thermodynamic equation of state, accurately characterizing the physical correlation between the various terminals of the engine. Decomposition of the state transition matrix yields stability criteria and coupling characteristics. Based on the evolution path obtained from solving the canonical equations, a Lagrangian function is established, and subsequently, a set of variational equations is constructed, enabling the system to comprehensively describe the dynamic coupling relationships between terminals. The collaborative optimization criteria obtained by solving the set of variational equations guide the hierarchical iterative process, simultaneously addressing the compensation requirements of both transient and steady-state operating conditions. This physics-based teaching method organically combines the physical characteristics, operating condition features, and compensation mechanisms of the engine terminals, ensuring that the adjustment of teaching parameters conforms to physical laws and adapts to the requirements of different operating conditions, thereby improving the accuracy and reliability of the teaching process.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the thermodynamic equation of state is: ; In the above equation, This is the engine state vector. For time, Here is the state transition matrix. For the control matrix, For control vectors, Indicates the characteristics of the reverse compressor. Indicates the effect of pressure ratio. Indicates the combustion process. Representing the equation of state for a gas, Indicates turbine characteristics, Indicates the air intake process. Indicates heat transfer. Indicates compressor power. Indicates mechanical properties.

[0009] By employing the aforementioned technical solution and using a thermodynamic equation of state comprising a state vector, a state transition matrix, and a control matrix, a systematic model was developed for key physical parameters of the engine, including back compressor characteristics, pressure ratio influence, combustion process, gas state, turbine characteristics, intake process, heat transfer, compressor power, and mechanical characteristics. Each element in the state transition matrix corresponds to a characteristic parameter of a different physical process within the engine, enabling the model to comprehensively describe the interactions between various engine components. This mathematical modeling method based on detailed physical processes allows the system to accurately reflect the energy conversion and transfer processes within the engine, improving the adaptability of the teaching process to the engine's physical characteristics and reducing the deviation between the taught parameters and the actual physical processes.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, solving the canonical equations yields the teaching parameter evolution path for each terminal, specifically including: The constraints of the regular equations are determined based on the coupling characteristics between terminals. The characteristic solutions of the canonical equations are solved using the variational method. Construct the evolution equation of the teaching parameters based on the characteristic solutions; Solving the evolution equations yields the evolution path of the teaching parameters for each terminal.

[0011] By adopting the above technical solution, determining constraints based on terminal coupling characteristics, and using variational methods to solve the characteristic solutions of the canonical equations, the evolution equations of the teaching parameters are constructed, and the evolution paths of each terminal are solved, thus achieving theoretical optimization of the teaching parameter adjustment process. Introducing the coupling relationship between terminals as a constraint into the calculation process ensures that the solution results meet the requirements of engine physical characteristics. Using variational methods to solve the characteristic solutions yields the globally optimal solution. By constructing and solving the evolution equations, the system can obtain the complete trajectory of the teaching parameters of each terminal over time, improving the accuracy and predictability of the teaching parameter adjustment.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, a hierarchical iterative approach is used to determine the teaching compensation mechanism among multiple terminals based on a collaborative optimization criterion, specifically including: The collaborative optimization criteria are converted into quantitative evaluation indicators, and the coupling strength matrix and response characteristic matrix between terminals are calculated based on the quantitative evaluation indicators. A hierarchical compensation structure is established based on the coupling strength matrix and the response characteristic matrix. The hierarchical compensation structure includes a control layer for determining the compensation strategy, a coordination layer for coordinating terminal coupling, and an execution layer for performing compensation. The dynamic response characteristics are calculated based on the physical parameters of the terminal, such as pressure, temperature and speed, when the load changes, and the parameter deviation under transient conditions is obtained. The transient compensation amount is determined by using parameter deviations, and transient compensation commands are generated by combining the compensation strategy of the control layer. Calculate static characteristic deviations based on the physical parameters of the terminal during stable operation; The steady-state compensation amount is determined based on the static characteristic deviation, and a steady-state compensation command is generated in conjunction with the compensation strategy of the control layer. Transient and steady-state compensation commands are input into the coordination layer, and the compensation parameters are initially iterated based on the terminal coupling relationship. The compensation parameters from the initial iteration are input into the execution layer, and the compensation parameters are adjusted based on real-time feedback to obtain the optimal compensation parameters. A teaching compensation mechanism is constructed based on the optimal compensation parameters to achieve coordinated compensation for transient and steady-state operating conditions.

[0013] By adopting the above technical solution, and converting the collaborative optimization criteria into quantitative evaluation indicators, calculating the coupling strength matrix and response characteristic matrix between terminals, and establishing a hierarchical compensation structure including a control layer, coordination layer, and execution layer, the system achieves collaborative control of the multi-terminal teaching process. The transient operating condition compensation amount is calculated based on the dynamic response characteristics of the terminals, and the steady-state operating condition compensation amount is determined by combining the static characteristic deviation. Compensation parameters are optimized through a hierarchical iterative approach, ensuring that the compensation process simultaneously meets the requirements of both transient and steady-state operating conditions. This multi-level compensation mechanism organically combines the coupling relationship, dynamic response characteristics, and static characteristics between terminals. Iterative optimization improves the accuracy of compensation parameters, enhances the adaptability of the teaching process to changes in operating conditions, and achieves collaborative optimization of the multi-terminal teaching process.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, after adjusting the teaching parameters of each terminal in real time according to the teaching compensation mechanism, the method further includes: The terminal is divided into driving end, transmission end and response end according to the physical characteristics of the thermodynamic process, and the operating data of driving end, transmission end and response end are acquired; Analyze the impact of the driving end's operating data on the transmitting and responding ends to determine the dynamic transmission characteristics; A cascade compensation mechanism is established based on the dynamic transmission characteristics, and the cascade compensation mechanism is integrated with the teaching compensation mechanism to obtain a hierarchical compensation strategy. Adjust the terminal's teaching parameters according to the tiered compensation strategy.

[0015] By adopting the above technical solution, and dividing the terminals into driving, transmission, and response ends according to the physical characteristics of the thermodynamic process, the role and positioning of each end in the entire thermodynamic system can be clearly defined. The obtained operational data can accurately reflect the physical correlation between different types of terminals. Analysis of the influence of driving end operational data on the transmission and response ends allows for understanding the dynamic transmission characteristics of the system. Constructing a cascaded compensation mechanism based on these dynamic transmission characteristics and integrating it with the teaching compensation mechanism to form a graded compensation strategy ensures that the compensation process considers both the correction of deviations in the teaching parameters and the physical transmission laws of the thermodynamic process. This makes the compensation effect more consistent with the actual operating characteristics of the engine system, improving the system's dynamic response performance and steady-state accuracy.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, a cascaded compensation mechanism is established based on dynamic transmission characteristics, specifically including: Analyze the primary impact characteristics of the driving end's operational data on the transmitting end; Analyze the secondary impact characteristics of the operational data at the transmission end on the response end; A physical transfer compensation strategy is constructed based on the first and second impact characteristics. A cascaded compensation mechanism is generated based on the physical transfer compensation strategy.

[0017] By adopting the above technical solution and analyzing the first influence characteristics of the driving end operating data on the transmission end and the second influence characteristics of the transmission end operating data on the response end, the complete link characteristics of energy transfer in the thermodynamic system can be obtained. These characteristics reflect the cascade coupling relationship between various engine components. A physical transfer compensation strategy is constructed based on these two levels of influence characteristics to match the compensation process with the actual physical transfer process, thereby reducing compensation deviation.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, the cascading compensation mechanism and the teaching compensation mechanism are integrated to obtain a hierarchical compensation strategy, specifically including: Calculate the difference between the teaching compensation parameter in the teaching compensation mechanism and the cascading compensation parameter in the cascading compensation mechanism; A compensation parameter mapping table is established based on the difference. The mapping table includes the compensation coefficients corresponding to the driving end, the transmitting end, and the response end. The teaching compensation parameters of the teaching compensation mechanism are linearly weighted using compensation coefficients to obtain compensation parameters that take into account the transmission characteristics. The compensation parameters that take into account the transmission characteristics are substituted into the physical transmission compensation strategy for verification, and a hierarchical compensation strategy that takes into account the transmission of physical characteristics is generated.

[0019] By adopting the above technical solution, and calculating the difference between the teaching compensation parameters and the cascade compensation parameters, a mapping table containing corresponding compensation coefficients for the driving end, the transmitting end, and the responding end can be established. This allows for the quantification of the role weights of different types of terminals in the compensation process. Linearly weighting the teaching compensation parameters using the compensation coefficients yields compensation parameters that retain the correction effect of teaching compensation while incorporating the influence of transmission characteristics, making the compensation process more comprehensive and accurate. Substituting the compensation parameters considering transmission characteristics into the physical transmission compensation strategy for verification ensures that the generated hierarchical compensation strategy meets both teaching requirements and physical laws, improving the accuracy and reliability of the compensation.

[0020] Secondly, embodiments of this application provide a mechanism-driven multi-terminal teaching system for aero-engines. The mechanism-driven multi-terminal teaching system for aero-engines includes: one or more processors and a memory; the memory is coupled to one or more processors, and the memory is used to store computer program code, which includes computer instructions. One or more processors call the computer instructions to cause the system to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a system, cause the system to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer program product that, when run on a system, causes the system to execute the method described in any possible implementation of the first aspect.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. This application provides a mechanism-driven multi-terminal teaching method for aero-engines. By acquiring the physical parameters and operating condition information of multiple aero-engine terminals, and combining this with the thermodynamic equation of state to establish a state transition matrix, the physical correlation between the various terminals of the engine can be accurately characterized. Decomposition of the state transition matrix yields stability criteria and coupling characteristics. Based on the evolution path obtained from solving the canonical equations, a Lagrangian function is established, and then a set of variational equations is constructed, enabling the system to comprehensively describe the dynamic coupling relationships between terminals. The collaborative optimization criteria obtained by solving the set of variational equations guide the hierarchical iterative process, simultaneously addressing the compensation requirements of both transient and steady-state operating conditions. This mechanism-based teaching method organically combines the physical characteristics, operating condition features, and compensation mechanisms of the engine terminals, ensuring that the adjustment of teaching parameters conforms to physical laws and adapts to the requirements of different operating conditions, thereby improving the accuracy and reliability of the teaching process.

[0024] 2. This application provides a mechanism-driven multi-terminal teaching method for aero-engines. By converting collaborative optimization criteria into quantitative evaluation indicators, calculating the coupling strength matrix and response characteristic matrix between terminals, and establishing a hierarchical compensation structure including a control layer, a coordination layer, and an execution layer, the system achieves collaborative control of the multi-terminal teaching process. Transient operating condition compensation is calculated based on the dynamic response characteristics of the terminals, and steady-state operating condition compensation is determined by combining static characteristic deviations. Compensation parameters are optimized through hierarchical iteration, enabling the compensation process to simultaneously meet the requirements of transient and steady-state operating conditions. This multi-level compensation mechanism organically combines the coupling relationship, dynamic response characteristics, and static characteristics between terminals. Iterative optimization improves the accuracy of compensation parameters, enhances the adaptability of the teaching process to changes in operating conditions, and achieves collaborative optimization of the multi-terminal teaching process.

[0025] 3. This application provides a mechanism-driven multi-terminal teaching method for aero-engines. By dividing the terminals into driving, transmission, and response terminals according to the physical characteristics of the thermodynamic process, the role and positioning of each terminal in the entire thermodynamic system can be clearly defined. The obtained operational data can accurately reflect the physical correlation between different types of terminals. Based on the analysis of the influence of driving terminal operational data on the transmission and response terminals, the dynamic transmission characteristics in the system can be understood. The dynamic transmission characteristics are constructed into a cascaded compensation mechanism and integrated with the teaching compensation mechanism to form a hierarchical compensation strategy. This ensures that the compensation process considers both the deviation correction of the teaching parameters and the physical transmission laws in the thermodynamic process, thereby making the compensation effect more consistent with the actual operating characteristics of the engine system and improving the dynamic response performance and steady-state accuracy of the system. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating a mechanism-driven multi-terminal teaching method for aero-engines in an embodiment of this application.

[0027] Figure 2 This is a flowchart illustrating a hierarchical compensation method based on the physical characteristics of a terminal in an embodiment of this application.

[0028] Figure 3 This is a schematic diagram of the physical device structure of a mechanism-driven multi-terminal teaching system for aero-engines provided in an embodiment of this application. Detailed Implementation

[0029] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0030] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0031] The following example is used in conjunction with Figure 1 The present application describes a mechanism-driven multi-terminal teaching method for aero-engines. Please see Figure 1 This is a flowchart illustrating a mechanism-driven multi-terminal teaching method for aero-engines in an embodiment of this application.

[0032] S101. Obtain physical parameters and operating condition information from multiple terminals of the aero-engine; This step involves the system acquiring physical parameters and operating condition information from multiple terminals of the aero-engine. Physical parameters can include pressure, temperature, flow rate, and rotational speed at each terminal; these parameters can be obtained directly through sensors or estimated theoretically or empirically. Operating condition information mainly refers to the engine's operating status, such as startup, acceleration, deceleration, cruise, and landing. Under different operating conditions, the physical parameters of each engine terminal differ. The physical parameters and operating condition information acquired in this step form the foundational data for subsequent analysis and calculations.

[0033] The system can acquire physical parameters and operating condition information from multiple terminals of an aero-engine through various methods. One specific implementation involves installing sensors, such as pressure sensors, temperature sensors, and flow sensors, at each terminal of the engine to collect physical parameters in real time; simultaneously, the engine's operating condition information is obtained through the engine control system. The system then aggregates the collected physical parameters and operating condition information to form complete engine operating data. Another specific implementation involves the system calculating the physical parameters of each terminal based on the engine's performance model, according to the engine's control commands and operating condition information. This method does not require the installation of a large number of sensors, but the accuracy of the calculated physical parameters is relatively low.

[0034] S102. Establish a thermodynamic state equation based on the physical mechanism of the aero-engine, and substitute the physical parameters and operating condition information into the thermodynamic state equation to obtain the state transition matrix of the aero-engine. The system establishes a thermodynamic equation of state based on the physical mechanism of the aero-engine, and substitutes physical parameters and operating condition information into the thermodynamic equation of state to obtain the state transition matrix of the aero-engine. The thermodynamic equation of state is as follows: ; In the above equation, This is the engine state vector. For time, Here is the state transition matrix. For the control matrix, For control vectors, Indicates the characteristics of the reverse compressor. Indicates the effect of pressure ratio. Indicates the combustion process. Representing the equation of state for a gas, Indicates turbine characteristics, Indicates the air intake process. Indicates heat transfer. Indicates compressor power. Indicates mechanical properties.

[0035] The function for calculating each element in the state transition matrix is: ; ; ; ; ; ; ; ; .

[0036] In the above function, and This is a dimensional correction factor used to ensure unit consistency; The compressor efficiency represents the aerodynamic efficiency of the compressor. Turbine efficiency, representing the aerodynamic efficiency of the turbine; Mechanical efficiency represents the efficiency of mechanical transmission. Combustion efficiency represents the completeness of combustion in the combustion chamber; This refers to the compressor power, the power consumed by the compressor. Turbine power, the power output by the turbine; Mechanical power, shaft power; The temperature of the working medium; The inlet temperature; The outlet temperature; To alleviate import pressure; To alleviate export pressure; The characteristic time constant of the compressor; The characteristic time constant of the turbine; The characteristic time constant of the mechanical system; It is the gas constant; Specific heat ratio; Specific heat capacity at constant pressure; Specific heat capacity at constant volume; The density of the gas; This refers to the gas mass flow rate; Fuel flow rate; Airflow rate; Characteristic volume; It is the moment of inertia; This is the compressor characteristic correction factor; This is a correction factor for turbine characteristics; This is a correction factor for flow characteristics; The lower heating value of the fuel; For inputting heat; This is the heat loss coefficient; The value is the rotational speed.

[0037] This step involves establishing the thermodynamic equation of state for the aero-engine and solving for the state transition matrix. The thermodynamic equation of state is a mathematical model describing the energy conversion and transfer processes within the engine, allowing for the analysis of the engine's dynamic characteristics. The state transition matrix reflects the interrelationships between the engine's state variables. The state transition matrix obtained in this step is a crucial basis for subsequent analyses of engine stability and terminal coupling characteristics.

[0038] S103. Decompose the state transition matrix to obtain the stability criterion and the coupling characteristics between terminals; This step involves analyzing the state transition matrix to obtain key information reflecting engine stability and terminal coupling characteristics. Stability criteria indicate whether the engine can maintain stable operation under given conditions and are crucial for engine control. The coupling characteristics between terminals reflect the mutual influence relationships between various engine components and form the basis for multi-terminal coordinated control. The stability criteria and coupling characteristics obtained in this step can guide subsequent optimization of teaching parameters and design of compensation mechanisms.

[0039] The system can employ various matrix decomposition methods to obtain stability criteria and coupling characteristics. Common matrix decomposition methods include eigenvalue decomposition, singular value decomposition, and QR decomposition. Eigenvalue decomposition yields the eigenvalues ​​and eigenvectors of the state transition matrix. Negative real parts of the eigenvalues ​​indicate system stability, while imaginary parts reflect the system's oscillatory characteristics. Analyzing the eigenvectors determines the contribution of each state variable to system stability. Furthermore, singular value decomposition of the state transition matrix can be used to extract coupling characteristics between terminals. The magnitude of the singular values ​​reflects the correlation between state variables; larger singular values ​​indicate stronger coupling. Analyzing the right singular vectors determines the contribution of each terminal to the coupling mode.

[0040] S104. Based on stability criteria and coupling characteristics, construct the canonical equations of the aero-engine and solve the canonical equations to obtain the teaching parameter evolution path of each terminal. Based on stability criteria and coupling characteristics, the canonical equations of the aero-engine are constructed, and the evolution path of the teaching parameters of each terminal is obtained by solving the canonical equations. Specifically: the constraints of the canonical equations are determined based on the coupling characteristics between terminals; the characteristic solutions of the canonical equations are solved by variational method; the evolution equations of the teaching parameters are constructed based on the characteristic solutions; and the evolution path of the teaching parameters of each terminal is obtained by solving the evolution equations.

[0041] This step involves utilizing the engine's stability and coupling characteristics to construct and solve canonical equations, thereby obtaining the optimized trajectory of the multi-terminal taught parameters. The canonical equations are an optimized control model formed by adding objective functions and constraints reflecting engine performance indicators to the state equations. By solving the canonical equations, the evolution trajectory of the taught parameters that optimizes engine performance indicators while satisfying stability and coupling constraints can be obtained. The taught parameter evolution path obtained in this step provides an optimization objective for the multi-terminal cooperative control of the engine.

[0042] The system can employ various methods to construct the canonical equations for an aero-engine. A common approach is to combine the engine's state equations and performance index functions to form an extended optimization control problem. Performance index functions typically include thrust, fuel consumption, and stability margin, reflecting the engine's overall performance. When constructing the canonical equations, the weighting coefficients of the objective function need to be appropriately set according to the engine's mission requirements. Furthermore, constraints on the canonical equations must be set based on the engine's physical constraints, such as structural strength and temperature limitations. After the canonical equations are constructed, they can be solved using variational methods and the maximum principle from optimal control theory to obtain the optimal evolution trajectory of the taught parameters.

[0043] S105. Establish the Lagrangian function of the aero-engine according to the evolution path, and construct a set of variational equations characterizing the coupling characteristics between terminals based on the Lagrangian function. This step involves analyzing the multi-terminal coupling characteristics of the system based on the evolution trajectory of the taught parameters by constructing a Lagrangian function and a system of variational equations. The Lagrangian function is a functional formed by introducing the constraints of the state equations into the objective function in an optimization control problem. By taking the variational form of the Lagrangian function, the optimality conditions satisfied by the control and state variables can be obtained, i.e., the system of variational equations. This system of variational equations reflects the cooperative optimization criteria of the multi-terminal coupled system. The system of variational equations constructed in this step provides a theoretical basis for the design of the multi-terminal teaching compensation mechanism.

[0044] The system can employ various methods to establish the Lagrangian function of an aero-engine. A common approach is to linearly combine the engine's state equations and objective function to form an extended functional. In the Lagrangian function, constraints on the state equations are implemented by introducing Lagrange multipliers. This method can transform complex constrained optimization problems into unconstrained optimization problems for solution. After obtaining the Lagrangian function, it can be differentiated using variational methods to obtain a set of variational equations containing state variables, control variables, and costate variables. The solution to this set of variational equations is the optimal control law for the multi-terminal coupled system.

[0045] S106. Solve the system of variational equations to obtain the collaborative optimization criteria for the terminal teaching process; This step involves solving a system of variational equations to obtain the optimization criteria for multi-terminal coordinated control of the engine. The coordinated optimization criteria, based on the coupling characteristics of the terminals, aim to achieve the optimal control strategy for the overall engine performance. This criterion not only ensures that the state variables of each terminal satisfy physical constraints but also coordinates the control variables of each terminal to maximize the engine's overall performance. The coordinated optimization criteria obtained in this step provide a decision-making basis for the multi-terminal teaching compensation mechanism.

[0046] The system can employ various numerical methods to solve the variational equations and obtain the co-optimization criteria. Common numerical methods include the target-shooting method, gradient method, and sequential quadratic programming. These methods continuously update the state and control variables through iterative search to ensure they satisfy the optimality conditions of the variational equations. During the solution process, it is necessary to reasonably set the initial values ​​and boundary conditions to ensure the convergence and stability of the optimization process. Furthermore, the optimization results need to be verified and corrected based on the actual operating conditions of the engine to ensure its physical realizability.

[0047] S107. Based on the collaborative optimization criterion, a hierarchical iterative approach is adopted to determine the teaching compensation mechanism among multiple terminals, and the teaching parameters of each terminal are adjusted in real time according to the teaching compensation mechanism.

[0048] Based on the collaborative optimization criterion, a hierarchical iterative approach is adopted to determine the teaching compensation mechanism among multiple terminals. The compensation mechanism includes transient condition compensation and steady-state condition compensation, and the teaching parameters of each terminal are adjusted in real time according to the teaching compensation mechanism. The teaching compensation mechanism among multiple terminals is determined using a hierarchical iterative approach based on the collaborative optimization criterion. Specifically, this involves: converting the collaborative optimization criterion into quantitative evaluation indicators; calculating the coupling strength matrix and response characteristic matrix between terminals based on these indicators; establishing a hierarchical compensation structure based on the coupling strength matrix and response characteristic matrix, comprising a control layer for determining compensation strategies, a coordination layer for coordinating terminal coupling, and an execution layer for implementing compensation; calculating dynamic response characteristics based on physical parameters such as pressure, temperature, and speed of the terminals under load changes to obtain parameter deviations under transient conditions; determining the transient compensation amount using the parameter deviations and generating transient compensation commands based on the control layer's compensation strategy; calculating static characteristic deviations based on the physical parameters of the terminals during stable operation; determining the steady-state compensation amount based on the static characteristic deviations and generating steady-state compensation commands based on the control layer's compensation strategy; inputting the transient and steady-state compensation commands into the coordination layer for preliminary iteration of compensation parameters based on terminal coupling relationships; inputting the preliminary iteration's compensation parameters into the execution layer for adjustment based on real-time feedback to obtain optimal compensation parameters; and constructing a teaching compensation mechanism based on the optimal compensation parameters to achieve collaborative compensation between transient and steady-state conditions.

[0049] Based on the aforementioned collaborative optimization criteria, the system employs a hierarchical iterative approach to determine the teaching compensation mechanism among multiple terminals, and adjusts the teaching parameters of each terminal in real time according to this mechanism. This step aims to establish a teaching compensation mechanism that can dynamically adapt to changes in the operating state of the aero-engine through a hierarchical iterative method, thereby achieving collaborative optimization control among multiple terminals. This step is not limited to a specific hierarchical iterative algorithm or compensation mechanism construction method; appropriate technical approaches can be flexibly selected based on actual application requirements and system characteristics.

[0050] In one possible implementation, the system first converts the collaborative optimization criteria into quantitative evaluation indicators, and then calculates the coupling strength matrix and response characteristic matrix between terminals based on these indicators. The quantitative evaluation indicators can be mathematical expressions reflecting the degree of collaborative optimization between terminals, such as minimum mean square error or maximum mutual information. The coupling strength matrix characterizes the degree of mutual influence between terminals and can be obtained by calculating the correlation coefficient or cross-spectral density between terminal state variables. The response characteristic matrix describes the dynamic response capability of the terminals to external inputs and can be obtained through frequency domain analysis or time domain simulation.

[0051] Then, the system establishes a hierarchical compensation structure based on the coupling strength matrix and response characteristic matrix. This hierarchical compensation structure includes a control layer for determining the compensation strategy, a coordination layer for coordinating terminal coupling, and an execution layer for executing the compensation. The control layer determines the system-level compensation strategy and objectives based on collaborative optimization criteria and evaluation indicators, combined with global information. The coordination layer receives instructions from the control layer, comprehensively considers the coupling characteristics of each terminal, calculates compensation parameters through optimization algorithms, and coordinates the interaction between terminals. The execution layer is responsible for adjusting the control instructions of each terminal based on the compensation parameters issued by the coordination layer, thus realizing the compensation process. The introduction of this hierarchical structure helps reduce system complexity and improve optimization efficiency.

[0052] Next, the system calculates the dynamic response characteristics of the terminal based on physical parameters such as pressure, temperature, and speed under load changes, obtaining the parameter deviation under transient conditions. This parameter deviation is then used to determine the transient compensation amount, and combined with the compensation strategy of the control layer, transient compensation commands are generated. Transient conditions are usually accompanied by rapid changes in system state, requiring timely capture of the dynamic response characteristics of the terminal's physical parameters, calculation of deviations from nominal values, and timely generation of compensation commands based on the magnitude and trend of the deviations, enabling the system to quickly return to a stable state. Commonly used dynamic response characteristics include overshoot, settling time, rise time, and oscillation frequency, which can be described using mathematical models such as differential equations or transfer functions. Deviation calculation can be based on criteria such as mean square error and absolute error, while the compensation amount has a certain functional relationship with the deviation and can be determined through methods such as PID control and adaptive control.

[0053] Simultaneously, the system calculates the static characteristic deviation based on the physical parameters of the terminal during stable operation, determines the steady-state compensation amount based on the static characteristic deviation, and generates steady-state compensation commands in conjunction with the compensation strategy of the control layer. Unlike transient conditions, the system state changes slowly under steady-state conditions, focusing primarily on the static characteristics of the terminal's physical parameters, such as steady-state error, linearity, and repeatability. The system calculates the terminal's static characteristic deviation to obtain the steady-state compensation amount and generates corresponding steady-state compensation commands. The static characteristic deviation can be calculated using statistical methods such as data fitting and confidence interval estimation, while the compensation amount can be determined using methods such as table lookup and analytical calculation.

[0054] Then, the system inputs transient and steady-state compensation commands into the coordination layer, performs preliminary iterations of the compensation parameters based on the terminal coupling relationship, and inputs the preliminary iterated compensation parameters into the execution layer. The compensation parameters are adjusted based on real-time feedback to obtain the optimal compensation parameters. After receiving the compensation commands from the control layer, the coordination layer comprehensively considers the coupling characteristics between the terminals and uses optimization algorithms to perform preliminary calculations of the compensation parameters, such as Newton's iteration method or the conjugate gradient method, and inputs the calculation results into the execution layer. The execution layer performs secondary adjustments to the compensation parameters based on real-time information fed back from the terminals to eliminate the influence of modeling errors and environmental disturbances, obtaining the optimal compensation parameters.

[0055] Finally, the system constructs a teaching compensation mechanism based on optimal compensation parameters to achieve coordinated compensation for transient and steady-state operating conditions. The teaching compensation mechanism essentially establishes a mapping relationship between teaching parameters and compensation parameters, enabling the system to automatically calculate compensation amounts and generate compensation commands based on the current operating conditions, maintaining coordinated operation between terminals. This mechanism needs to comprehensively consider both transient and steady-state operating conditions and can be constructed using mathematical methods such as piecewise functions and interpolation fitting.

[0056] In the above embodiments, by acquiring the physical parameters and operating condition information of multiple terminals of the aero-engine and establishing a state transition matrix in conjunction with the thermodynamic equation of state, the physical correlation between the various terminals of the engine can be accurately characterized. Decomposition of the state transition matrix yields stability criteria and coupling characteristics. Based on the evolution path obtained from solving the canonical equations, a Lagrangian function is established, and then a set of variational equations is constructed, enabling the system to comprehensively describe the dynamic coupling relationships between terminals. The collaborative optimization criteria obtained by solving the set of variational equations guide the hierarchical iterative process, simultaneously addressing the compensation requirements of both transient and steady-state operating conditions. This teaching method based on physical mechanisms organically combines the physical characteristics, operating condition features, and compensation mechanisms of the engine terminals, ensuring that the adjustment of teaching parameters conforms to physical laws and adapts to the requirements of different operating conditions, thereby improving the accuracy and reliability of the teaching process.

[0057] To further improve the teaching effect of the aforementioned mechanism-driven teaching method, this application also proposes a hierarchical compensation method based on the physical characteristics of the terminal. After constructing the teaching compensation mechanism, this method establishes a cascaded compensation mechanism integrated with the teaching compensation mechanism by classifying the physical characteristics and analyzing the transfer characteristics of the terminal. This introduces a hierarchical compensation strategy that considers the laws of physical transfer on the basis of the original teaching method. This improved scheme not only retains the advantages of the mechanism-driven teaching method in parameter adjustment, but also enhances the adaptability of the teaching process to the transfer characteristics of the thermodynamic system through physical characteristic analysis and cascaded compensation. The following is a detailed explanation... Figure 2 The following describes a hierarchical compensation method based on terminal physical characteristics in an embodiment of this application: Please see Figure 2 This is a flowchart illustrating a hierarchical compensation method based on the physical characteristics of a terminal in an embodiment of this application.

[0058] S201. Divide the terminal into driving end, transmission end and response end according to the physical characteristics of the thermodynamic process, and acquire the operating data of driving end, transmission end and response end; The system categorizes terminals into driving, transmission, and response ends based on the physical characteristics of the thermodynamic process, and acquires operational data for each end. This step classifies the terminals according to their physical properties, dividing them into three parts—driving, transmission, and response—based on the energy transfer laws of the thermodynamic system. The driving end is the energy input end of the thermodynamic system, typically including heat sources and fuel supply systems; the transmission end is the energy transfer channel within the system, such as heat exchangers and pipelines; and the response end is the energy output end of the system, such as heat-using equipment and heat-to-work conversion devices. In practical applications, the system can further refine the division of these three parts based on the specific characteristics of the process to improve the accuracy of physical characteristic analysis.

[0059] After port partitioning is completed, the system acquires operational data from the drive, transmission, and response ends, including process parameters such as temperature, pressure, flow rate, and composition, as well as equipment status signals and control commands. Data acquisition can be achieved through various methods, such as field instruments, DCS systems, and MES systems. The system can preprocess the acquired data, such as noise reduction, smoothing, and normalization, to improve data quality. Simultaneously, the system can select appropriate storage methods based on the characteristics of the data, such as relational databases, time-series databases, and big data platforms, to facilitate subsequent analysis and utilization.

[0060] S202. Analyze the impact of the driving end's operating data on the transmission and response ends to determine the dynamic transmission characteristics; The system analyzes the impact of the driving-end's operational data on the transmission and response ends to determine the dynamic transfer characteristics. This step, based on the acquired operational data from each end, focuses on analyzing the driving-end's influence on the system. As the entry point to the thermodynamic system, the driving-end's operating state directly determines the energy transfer and conversion process within the system. Data-driven modeling methods, such as statistical learning and machine learning, can be used to establish a correlation model between the driving-end's operational data and the states of the transmission and response ends, characterizing the dynamic characteristics of energy transfer within the system.

[0061] In practical implementation, the system can employ algorithms such as multiple regression, time series analysis, and neural networks to construct nonlinear mapping relationships between the driving end, transmission end, and response end. Through training on historical operational data, the model can predict the response trends of the transmission and response ends based on changes in the state of the driving end. Building upon this, the system can further analyze the model's parameters and structure to uncover key factors influencing the transfer process, such as heat transfer coefficient, specific heat capacity, and flow resistance. By correlating these physical quantities with the model parameters, the system can transform the black-box data model into a gray-box physical model, enhancing the model's interpretability and generalization ability.

[0062] S203. Based on the dynamic transmission characteristics, a cascade compensation mechanism is established, and the cascade compensation mechanism is integrated with the teaching compensation mechanism to obtain a hierarchical compensation strategy. The system establishes a cascaded compensation mechanism based on dynamic transmission characteristics. Specifically, this includes: analyzing the first impact characteristics of the driving end's operational data on the transmission end; analyzing the second impact characteristics of the transmission end's operational data on the response end; constructing a physical transmission compensation strategy based on the first and second impact characteristics; and generating a cascaded compensation mechanism based on the physical transmission compensation strategy. The cascaded compensation mechanism is then integrated with a teaching compensation mechanism to obtain a hierarchical compensation strategy. This involves: calculating the difference between the teaching compensation parameters in the teaching compensation mechanism and the cascade compensation parameters in the cascaded compensation mechanism; establishing a compensation parameter mapping table based on the difference, including compensation coefficients corresponding to the driving end, transmission end, and response end; linearly weighting the teaching compensation parameters of the teaching compensation mechanism using the compensation coefficients to obtain compensation parameters considering transmission characteristics; and substituting the compensation parameters considering transmission characteristics into the physical transmission compensation strategy for verification, thereby generating a hierarchical compensation strategy considering physical transmission characteristics.

[0063] The system establishes a cascaded compensation mechanism based on dynamic transfer characteristics and integrates it with a teaching compensation mechanism to obtain a hierarchical compensation strategy. This step utilizes the results of physical characteristic analysis to construct an adaptive compensation scheme and optimize the teaching compensation process. Traditional teaching compensation methods are typically based on feedback control theory, adjusting control parameters according to the deviation between the system output and the target value. However, this method does not consider the internal physical transfer mechanism of the system, resulting in a lagging and blind adjustment process. To address this issue, this step proposes a physically driven cascaded compensation mechanism.

[0064] The system first analyzes the impact of the driving end on the transmitting end and establishes a cascaded forward compensation channel. When the state of the driving end changes, the control parameters of the transmitting end are adjusted in advance through this channel to reduce the propagation and amplification of disturbances in the system. Then, the system analyzes the impact of the transmitting end on the response end and establishes a cascaded feedback correction channel. By monitoring the state changes of the transmitting end in real time, the system predicts the output deviation of the response end and performs compensation adjustments in advance. Based on the two compensation channels, and combined with the self-learning capability of the teaching compensation mechanism, the system generates a hierarchical compensation strategy that integrates physical characteristics and data-driven approaches. During the compensation process, the system comprehensively utilizes model prediction, feedforward control, and feedback correction to achieve compensation optimization across the entire process, multiple stages, and across scales.

[0065] S204. Adjust the terminal's teaching parameters according to the hierarchical compensation strategy.

[0066] The system adjusts the terminal's teaching parameters according to a hierarchical compensation strategy. This step applies the compensation strategy to parameter optimization during the teaching process, improving the system's adaptability to physical characteristics. Traditional teaching parameter tuning typically relies on manual experience and repeated trial and error, which is cumbersome and has limited accuracy. This step, however, utilizes a hierarchical compensation strategy to achieve adaptive optimization of the teaching parameters.

[0067] The system first calculates the parameter correction amount for each compensation channel based on the compensation strategy. The correction amount reflects the weight of the physical characteristics' influence on the control parameters. Then, the system adds the correction amount to the original teaching parameters to obtain new teaching parameters driven by the physical characteristics. During parameter adjustment, the system adopts a progressive optimization strategy, smoothing the adjustment process through small, multi-step parameter corrections to avoid system oscillations caused by large fluctuations. Simultaneously, the system introduces a parameter constraint mechanism to ensure that the adjusted teaching parameters are within a reasonable range, meeting the requirements of system stability and safety. In the final stage of teaching optimization, the system uses a hierarchical compensation strategy to perform online verification of the optimization results. By collecting system response data in real time, the system evaluates the compensation effect and performs parameter fine-tuning when necessary to ensure the reliability and stability of the teaching performance.

[0068] In the above embodiments, by dividing the terminals into driving, transmission, and response ends according to the physical characteristics of the thermodynamic process, the role and positioning of each end in the entire thermodynamic system can be clearly defined, and the obtained operational data can accurately reflect the physical correlation between different types of terminals. Based on the analysis of the influence of driving end operational data on the transmission and response ends, the dynamic transmission characteristics in the system can be understood. Constructing the dynamic transmission characteristics into a cascaded compensation mechanism and integrating it with the teaching compensation mechanism to form a hierarchical compensation strategy ensures that the compensation process considers both the correction of deviations in the teaching parameters and the physical transmission laws in the thermodynamic process. This makes the compensation effect more consistent with the actual operating characteristics of the engine system, improving the system's dynamic response performance and steady-state accuracy.

[0069] The system in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference needed]. Figure 3 This is a schematic diagram of the physical device structure of a mechanism-driven multi-terminal teaching system for aero-engines provided in an embodiment of this application.

[0070] It should be noted that, Figure 3 The structure of the system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0071] like Figure 3 As shown, the system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on a program stored in Read-Only Memory (ROM) 302 or a program loaded from storage portion 308 into Random Access Memory (RAM) 303, such as executing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0072] The following components are connected to I / O interface 305: input section 306 including a camera, infrared sensor, etc.; output section 307 including a liquid crystal display (LCD) and speakers, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card and a modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0073] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0074] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. The transmitted data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0075] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0076] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the system described in the above embodiments; or it may exist independently and not assembled into the system. The storage medium carries one or more computer programs that, when executed by a processor of a system, cause the system to implement the methods provided in the above embodiments.

[0077] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0078] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0079] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0080] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A mechanism-driven multi-terminal teaching method for aero-engines, characterized in that, include: Acquire physical parameters and operating condition information from multiple terminals of the aero-engine; A thermodynamic state equation is established based on the physical mechanism of the aero-engine, and the physical parameters and operating condition information are substituted into the thermodynamic state equation to obtain the state transition matrix of the aero-engine. Decompose the state transition matrix to obtain the stability criterion and the coupling characteristics between the terminals; Based on the stability criterion and the coupling characteristics, the canonical equations of the aero-engine are constructed, and the evolution path of the teaching parameters of each terminal is obtained by solving the canonical equations. The Lagrangian function of the aero-engine is established based on the evolution path, and a set of variational equations characterizing the coupling characteristics between the terminals is constructed based on the Lagrangian function. Solving the system of variational equations yields the collaborative optimization criteria for the terminal teaching process; Based on the aforementioned collaborative optimization criterion, a hierarchical iterative approach is used to determine the teaching compensation mechanism among multiple terminals. The compensation mechanism includes transient condition compensation and steady-state condition compensation. The teaching parameters of each terminal are adjusted in real time according to the teaching compensation mechanism.

2. The method according to claim 1, characterized in that, The thermodynamic equation of state is: ； In the above equation, the For the engine state vector, the For time, the stated Let be the state transition matrix, the For the control matrix, the For the control vector, the Indicating the characteristics of the reverse compressor, the aforementioned Indicating the influence of pressure ratio, the aforementioned Indicating the combustion process, the stated The equation of state for a gas is given. Indicating turbine characteristics, the Indicates the intake process, the Indicating heat transfer, the Indicates the compressor power, the Indicates mechanical properties.

3. The method according to claim 1, characterized in that, The process of solving the regular equation to obtain the teaching parameter evolution path for each terminal specifically includes: The constraints of the regular equation are determined based on the coupling characteristics between the terminals; The characteristic solution of the canonical equation is solved using the variational method. The evolution equations of the teaching parameters are constructed based on the characteristic solutions. Solving the evolution equations yields the teaching parameter evolution paths for each terminal.

4. The method according to claim 1, characterized in that, The step of determining the teaching compensation mechanism among multiple terminals using a hierarchical iterative approach based on the collaborative optimization criterion specifically includes: The collaborative optimization criteria are converted into quantitative evaluation indicators, and the coupling strength matrix and response characteristic matrix between the terminals are calculated based on the quantitative evaluation indicators. A hierarchical compensation structure is established based on the coupling strength matrix and the response characteristic matrix. The hierarchical compensation structure includes a control layer for determining the compensation strategy, a coordination layer for coordinating terminal coupling, and an execution layer for performing compensation. The dynamic response characteristics of the terminal are calculated based on the physical parameters such as pressure, temperature and speed when the load changes, and the parameter deviation under transient conditions is obtained. The transient operating condition compensation amount is determined using the parameter deviation, and a transient compensation command is generated in conjunction with the compensation strategy of the control layer. Calculate the static characteristic deviation based on the physical parameters of the terminal during stable operation; The steady-state compensation amount is determined based on the static characteristic deviation, and a steady-state compensation command is generated in conjunction with the compensation strategy of the control layer. The transient compensation command and the steady-state compensation command are input into the coordination layer, and the compensation parameters are initially iterated based on the terminal coupling relationship; The compensation parameters obtained from the initial iteration are input into the execution layer, and the compensation parameters are adjusted based on real-time feedback to obtain the optimal compensation parameters. A teaching compensation mechanism is constructed based on the optimal compensation parameters to achieve coordinated compensation for the transient and steady-state operating conditions.

5. The method according to claim 1, characterized in that, After adjusting the teaching parameters of each terminal in real time according to the teaching compensation mechanism, the method further includes: The terminal is divided into a driving end, a transmission end, and a response end according to the physical characteristics of the thermodynamic process, and the operating data of the driving end, the transmission end, and the response end are acquired. Analyze the impact of the driving end's operating data on the transmitting end and the responding end to determine the dynamic transmission characteristics; A cascaded compensation mechanism is established based on the dynamic transmission characteristics, and the cascaded compensation mechanism is integrated with the teaching compensation mechanism to obtain a hierarchical compensation strategy. The teaching parameters of the terminal are adjusted according to the hierarchical compensation strategy.

6. The method according to claim 5, characterized in that, The cascade compensation mechanism established based on the dynamic transmission characteristics specifically includes: Analyze the first influence characteristics of the driving end's operating data on the transmission end; Analyze the second influence characteristics of the operational data of the transmission end on the response end; A physical transfer compensation strategy is constructed based on the first and second impact characteristics. A cascaded compensation mechanism is generated based on the physical transfer compensation strategy.

7. The method according to claim 5, characterized in that, The fusion of the cascaded compensation mechanism and the teaching compensation mechanism to obtain a hierarchical compensation strategy specifically includes: Calculate the difference between the teaching compensation parameter in the teaching compensation mechanism and the cascading compensation parameter in the cascading compensation mechanism; A compensation parameter mapping table is established based on the difference, and the mapping table includes the compensation coefficients corresponding to the driving end, the transmitting end, and the responding end; The teaching compensation parameters of the teaching compensation mechanism are linearly weighted using the compensation coefficients to obtain compensation parameters that take into account the transmission characteristics. The compensation parameters that take into account the transmission characteristics are substituted into the physical transmission compensation strategy for verification, thereby generating a hierarchical compensation strategy that takes into account the transmission of physical characteristics.

8. A mechanism-driven multi-terminal teaching system for aero-engines, characterized in that, The system includes: One or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on the system, the system performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the system, the system performs the method as described in any one of claims 1-7.

Images