Parameter determination method and device of reactor control system and electronic equipment

Abstract

The embodiment of the application provides a parameter determination method and device of a reactor control system and electronic equipment, and belongs to the technical field of nuclear power. The method comprises the following steps: sampling operation data of the reactor control system under a preset condition in a first preset time period to obtain a plurality of groups of measurement data, each group of measurement data comprising at least one control parameter of the reactor control system for controlling a target component; determining performance indexes of the reactor control system under the condition of adopting the control parameters in each group of measurement data; selecting a target performance index with the minimum absolute value difference between the performance index corresponding to each group of control parameters and the index corresponding to the preset condition; and taking the group of control parameters corresponding to the target performance index as the control parameters of the reactor control system for controlling the target component under the preset condition. The embodiment of the application can realize control parameter debugging and optimization of the reactor under different application scenarios and different constraint conditions.

Description

Technical Field

[0001] This application relates to the field of nuclear power technology, and in particular to a method, apparatus and electronic equipment for determining parameters of a reactor control system. Background Technology

[0002] Reactors have shown broad application prospects in various scenarios such as urban heating, power supply in remote areas, replacement of coal-fired cogeneration units, oil extraction, and seawater desalination. However, reactors often have characteristics such as uncertainty, nonlinearity, correlation between variables, incomplete information, and large time lag. Ensuring the stability and safety of reactors under different application scenarios and operating conditions is an important goal of control system design.

[0003] Currently, reactor control system parameter adjustments primarily focus on stable unit operation and grid-connected power generation, with insufficient consideration given to the specific needs of other application scenarios. When new application scenarios are introduced, the existing control system parameter adjustment methods struggle to simultaneously meet the constraints of different scenarios, resulting in poor control performance. Summary of the Invention

[0004] The main objective of this application is to provide a method, apparatus, electronic device, and storage medium for determining parameters of a reactor control system, aiming to improve the control effect of adjusting the reactor control system under preset conditions.

[0005] To achieve the above objectives, a first aspect of this application provides a method for determining parameters of a reactor control system, wherein the reactor control system is used to control target components of the reactor, the method comprising:

[0006] The operating data of the reactor control system under preset conditions within a first preset time period are sampled to obtain multiple sets of measurement data. Each set of measurement data includes at least one control parameter of the reactor control system for controlling the target component.

[0007] Determine the performance indicators of the reactor control system under the condition of using the control parameters in each set of measurement data;

[0008] Select the target performance index with the smallest absolute value of the difference between the performance index corresponding to each set of control parameters and the index corresponding to the preset conditions.

[0009] The set of control parameters corresponding to the target performance index is used as the control parameters when the reactor control system controls the target component under preset conditions.

[0010] In some embodiments, determining the performance indicators of the reactor control system under each set of control parameters includes:

[0011] The multiple sets of control parameters are processed sequentially according to the sampling order. For the Mth set of control parameters, the performance index of the reactor control system under the condition of using the Mth set of control parameters is obtained based on the Mth set of control parameters and the pre-acquired performance index objective function of the reactor control system. The performance index objective function is used to calculate the performance of the reactor control system, where M is a positive integer.

[0012] In some embodiments, the performance indicator objective function includes multiple indicator parameters and a weighting coefficient for each indicator parameter, and each set of measurement data includes a first value range for each indicator parameter;

[0013] Determining the performance indicators of the reactor control system under the condition of using the control parameters in each set of measurement data includes:

[0014] By randomly perturbing the coefficients of the transfer function of the target component's operating model, multiple sets of simulation control parameters are obtained. The target component's operating model is used to simulate the operating state of the target component.

[0015] Based on multiple sets of simulation control parameters, determine the second value range for each of the aforementioned index parameters;

[0016] For each indicator parameter in each set of measurement data, the following is performed: assuming the minimum value is taken within the target range of the indicator parameter, the weight coefficient of each indicator parameter in each set of measurement data and the parameter value of the indicator parameter are calculated, wherein the target range of the indicator parameter is determined based on the first value range and the second value range of the indicator parameter in the measurement data.

[0017] Based on the weighting coefficient of each indicator parameter in each set of measurement data and the parameter value of the indicator parameter, the performance index corresponding to each set of measurement data is obtained.

[0018] In some embodiments, the process of obtaining multiple sets of simulation control parameters by randomly perturbing the coefficients of the transfer function of the target component's running model includes:

[0019] The target component's operational model is obtained by performing model identification on the controlled object model and the internal and external disturbance model using historical measurement data of the target component.

[0020] A set of initial simulation control parameters is obtained based on the preset gain margin and preset phase margin;

[0021] Based on the initial simulation control parameters, the transfer function coefficients of the target component's operating model are randomly perturbed a preset number of times within the target preset range to obtain multiple sets of simulation control parameters.

[0022] In some embodiments, the step of randomly perturbing the transfer function coefficients of the target component's operating model a preset number of times within a target preset range according to the initial simulation control parameters to obtain multiple sets of simulation control parameters includes:

[0023] Based on the initial simulation control parameters, the transfer function coefficients of the target component's operating model are subjected to N random perturbations within a preset target range, where N is a positive integer. For each random perturbation, the following is executed:

[0024] The target component's operating model is controlled to randomly perturb within the target's preset range;

[0025] If the target component operation model satisfies the preset conditions after random perturbation within the target preset range, then a set of simulation control parameters corresponding to this random perturbation will be output.

[0026] If the target component operating model does not meet the preset conditions after this random perturbation, the value range of the target preset range is reduced to obtain the preset range corresponding to the next random perturbation. The preset range corresponding to the next random perturbation is then used as the target preset range, and the step of controlling the target component operating model to randomly perturb within the target preset range is executed.

[0027] In some embodiments, determining the second value range of each of the index parameters based on multiple sets of simulation control parameters includes:

[0028] For each set of simulated control parameters, the parameter value of each index parameter corresponding to each set of simulated control parameters is obtained by performing step signal simulation on the target component running model.

[0029] Based on the maximum and minimum values ​​of each index parameter corresponding to each set of simulation control parameters in the multiple sets of simulation control parameters, a second value range for each index parameter is determined.

[0030] To achieve the above objectives, a second aspect of this application provides a parameter determination apparatus for a reactor control system, the apparatus comprising:

[0031] The sampling module is used to sample the operating data of the reactor control system under preset conditions within a first preset time period to obtain multiple sets of measurement data. Each set of measurement data includes at least one control parameter used by the reactor control system to control the target component. The reactor control system is used to control the target component of the reactor.

[0032] The calculation module is used to determine the performance indicators of the reactor control system under the condition of using the control parameters in each set of measurement data;

[0033] The selection module is used to select the target performance index with the smallest absolute value of the difference between the performance index corresponding to the preset conditions and the performance index corresponding to each group of control parameters.

[0034] The parameter determination module is used to use a set of control parameters corresponding to the target performance index as the control parameters when the reactor control system controls the target component under preset conditions.

[0035] To achieve the above objectives, a third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect.

[0036] To achieve the above objectives, a fourth aspect of this application provides a computer program product, wherein the instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the method described in the first aspect.

[0037] To achieve the above objectives, a fifth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0038] The reactor control system parameter determination method, apparatus, electronic device, and storage medium proposed in this application sample the operating data of the reactor control system under preset conditions within a first preset time period to obtain multiple sets of measurement data. Each set of measurement data includes at least one control parameter for the reactor control system to control a target component. Based on the control parameters in each set of measurement data, the corresponding performance index is calculated or simulated. Then, the performance index with the smallest absolute value of the difference between the performance index corresponding to each set of control parameters and the index corresponding to the preset conditions is selected as the target performance index. The set of control parameters corresponding to the target performance index is used as the optimized control parameters for the reactor control system to control the target component under preset conditions. The embodiments of this application analyze the system performance under different control parameters and select the control parameters that make the system performance closest to the index corresponding to the preset conditions, thereby realizing the parameter debugging and optimization of the reactor control system, and further realizing the debugging and optimization of control parameters of the reactor under different application scenarios and different constraints. Attached Figure Description

[0039] Figure 1This is a flowchart illustrating the parameter determination method for a reactor control system provided in an embodiment of this application;

[0040] Figure 2 This is a schematic flowchart illustrating the process of obtaining simulated control parameters in the method for determining parameters of a reactor control system provided in this application embodiment;

[0041] Figure 3 This is a schematic diagram of the OTSG steam pressure cascade control structure of the reactor control system parameter determination method provided in the embodiments of this application;

[0042] Figure 4 This is a schematic diagram of the constraint interval under a step action of the controlled object in the parameter determination method of the reactor control system provided in the embodiments of this application;

[0043] Figure 5 This is a schematic diagram of the performance constraint range under the applied disturbance effect of the parameter determination method for the reactor control system provided in the embodiments of this application;

[0044] Figure 6 This is a schematic diagram of the structure of the parameter determination device for the reactor control system provided in the embodiments of this application;

[0045] Figure 7 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0047] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0049] First, let's analyze some of the terms used in this application:

[0050] Model identification refers to determining an equivalent model to the measured system from a given set of model classes based on the input and output data. Model identification has three elements: data, model, and criteria. Identification involves selecting the model that best fits the data from a set of model classes according to a given criterion. Specifically, model identification determines the mathematical structure and internal parameters of the model by analyzing the observed input and output data, including the model's equation form, order, and parameter estimates.

[0051] The transfer function is the ratio of the Laplace transform of the response (output) to the Laplace transform of the excitation (input) of a linear system under zero initial conditions. It is a mathematical model describing the input-output relationship of a linear time-invariant system. The transfer function can be obtained through the Laplace transform; it is the ratio of the output signal to the input signal. The transfer function can be used to analyze the stability, dynamic response, and frequency characteristics of a system. In control systems, the transfer function can be used to describe the relationships between the controller, the controlled object, and sensors.

[0052] Monte Carlo simulation is a numerical simulation method based on statistical sampling theory, relying on repeated random sampling to obtain numerical results. This method is named after Monte Carlo, the famous gambling city in Monaco, symbolizing its random sampling nature. The basic concept is to use randomness to solve problems that are theoretically deterministic.

[0053] Time-domain analysis refers to the analysis of a control system's stability, transient, and steady-state performance based on the time-domain expression of its output under given input conditions. Because time-domain analysis directly analyzes the system in the time domain, it offers advantages such as intuitiveness and accuracy. The time-domain representation of the system's output can be obtained from differential equations or transfer functions. Because it directly analyzes the system in the time domain, time-domain analysis provides complete information about the system's time response, offering both intuitiveness and accuracy.

[0054] Frequency domain analysis is a method of analyzing signals by converting them from the time domain to the frequency domain using Fourier transform. It is primarily used to describe the frequency structure of a signal and the amplitude of each frequency component. Frequency domain analysis has wide applications in control systems. It evaluates system performance by studying the system's frequency characteristics without directly solving the system's differential equations. This method indirectly reveals the system's time-domain performance, conveniently shows the influence of system parameters on system performance, and guides the design of corrections.

[0055] Gain margin: This refers to the magnitude of the open-loop transfer function when the closed-loop control system is in a critical stable state. Gain margin can be obtained by calculating the magnitude of the open-loop transfer function at the crossover frequency and taking its reciprocal. The crossover frequency is the frequency at which the magnitude of the open-loop transfer function equals 1. A larger gain margin indicates greater robustness of the system to parameter variations and external disturbances. A larger gain margin means the system can tolerate larger parameter errors or disturbances without losing stability. Gain margin is generally set to be greater than 1 (e.g., 6 dB) to ensure system stability and sufficient robustness.

[0056] Phase margin: This refers to the difference between the phase angle of the open-loop transfer function and -180° when the closed-loop control system is in a critical stable state. The phase margin can be obtained by calculating the phase angle of the open-loop transfer function at the crossover frequency and taking the difference between it and -180°. A larger phase margin indicates better system stability. A larger phase margin means the system can tolerate a greater phase lag before approaching instability. The phase margin is typically set between 30° and 60° to ensure system stability and a good dynamic response.

[0057] In different application scenarios and operating conditions of reactors, a good control system needs to ensure the stability and safety of the system. However, obtaining optimal control parameters for a control system often encounters many difficulties, especially since reactors often have characteristics such as uncertainty, nonlinearity, correlation between variables, incomplete information, and large time lag. It is very difficult to obtain control parameters that meet different application scenarios and operating requirements.

[0058] Currently, reactor control system design is generally based on time-domain and frequency-domain methods. Time-domain methods primarily consider metrics such as system overshoot and settling time, while frequency-domain methods mainly consider metrics such as gain margin, phase margin, and crossover frequency. Reactor control system design typically sets the gain margin to be no less than 6 dB and the phase margin to be between 30° and 60°. These design requirements ensure that the system remains relatively stable under different frequency input signals in the application scenario. However, in practice, there are often many sets of control parameters that can meet the gain and phase margin requirements of a single application scenario. In actual operation, it is necessary to further determine the optimal control parameters to meet different performance requirements such as steady-state error, overshoot, and settling time. This process usually requires operators to manually fine-tune the given control parameters multiple times based on experience, which is tedious, and the control effect largely depends on the operator's experience and judgment. New application scenarios lead to new operational constraints, and there are currently no quantitative methods for identifying the key performance indicators that need to be focused on in different application scenarios. Existing control system parameter tuning methods cannot simultaneously meet the constraints of different application scenarios and have limited flexibility, making it difficult to further optimize control parameters. This makes it difficult to formulate reasonable and feasible parameter tuning and optimization schemes for reactors in different application scenarios.

[0059] Therefore, improving existing time-domain design methods and optimizing control system parameters to enable reactors to meet the control performance requirements of different application scenarios and operating conditions is currently the key and challenging aspect of reactor control system design. Frequency-domain analysis methods for control systems can directly determine frequency characteristics through experiments to analyze the quality of the control system. Based on frequency characteristics or frequency response, the stability, steady-state characteristics, and dynamic characteristics of the system can be analyzed, yielding qualitative and quantitative conclusions with clear physical meaning. This can compensate for the shortcomings of time-domain analysis methods in analyzing complex objects or systems.

[0060] Based on this, embodiments of this application provide a method, apparatus, electronic device, and storage medium for determining parameters of a reactor control system, aiming to solve the problem of difficulty in optimizing control parameters of a reactor under different application scenarios and constraints. Embodiments of this application employ a time-domain and frequency-domain integrated analysis method combining multiple performance indicators and variable weight coefficients to achieve control parameter debugging and optimization for reactors under different application scenarios and constraints. By utilizing process data from different reactor scenarios and operating conditions as modeling preparation data, a correspondence model between the system's input and output variables is established using actual field operating data. The Monte Carlo method combined with frequency domain analysis is used to obtain the constraint boundaries of the control system's performance indicators. While prioritizing the satisfaction of the control system's gain margin and phase margin requirements, an initial set of relatively optimal control parameters is obtained. The performance indicator ranges corresponding to different control parameters in each set are calculated. Indicators requiring priority attention during the current operating phase are prioritized and satisfied, while other performance indicators are restricted within the constraint boundaries. The variable weight method is used to determine the most suitable control parameters for the current state from the initial set of relatively optimal control parameters, thereby achieving control parameter optimization for reactors under different scenarios and operating conditions.

[0061] The method, apparatus, electronic device and storage medium for determining parameters of the reactor control system provided in this application are specifically described through the following embodiments. First, the method for determining parameters of the reactor control system in this application is described.

[0062] The method for determining parameters of a reactor control system provided in this application relates to the field of nuclear power technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the method for determining parameters of a reactor control system, but is not limited to the above forms.

[0063] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0064] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0065] Figure 1 This is a flowchart of a method for determining parameters of a reactor control system provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S100 to S400.

[0066] Step S100: Sample the operating data of the reactor control system under preset conditions within a first preset time period to obtain multiple sets of measurement data. Each set of measurement data includes at least one control parameter of the reactor control system for controlling the target component.

[0067] First, under preset conditions, the operating data of the reactor control system is sampled within a first preset time period. The preset conditions can be the reactor's operating state in different application scenarios, or a pre-set specific operating state of the reactor, such as full-power operation, etc., and are not limited here. The first preset time period is a pre-defined time range, specifically depending on the required amount of statistical data and system characteristics, and is not limited here. By setting different preset conditions, the control parameters of the reactor can be debugged and optimized under different application scenarios.

[0068] The reactor control system is used to control the target components of the reactor. Each set of data represents the state of the reactor control system at a certain moment. The target components of the reactor are specific parts or equipment that the reactor control system needs to monitor and regulate, and may include, but are not limited to: temperature and power control systems, primary loop flow control systems, direct current steam generator pressure control systems, and steam discharge control systems.

[0069] By sampling the operational data of the reactor control system, a series of data points are obtained. These data points are arranged in chronological order to form multiple sets of measurement data. Each set of measurement data includes at least one control parameter used by the reactor control system to control the target component. The control parameter is determined based on the characteristics of the target component and the type of control parameter required, and is not limited here.

[0070] For multiple sets of measurement data obtained through sampling, missing or outlier values ​​may sometimes occur during the collection and recording process due to factors such as sudden equipment failures. Therefore, it is necessary to impute and correct the measurement data. Missing values ​​are handled by averaging the data before and after the missing data. Outliers are handled by making horizontal comparisons based on the continuity of the process, using adjacent time points as a reference, and setting a maximum range of variation. When the collected data falls outside the set range, the average value of the data before and after the missing data is used for stabilization. Preprocessing the measurement data effectively improves the quality of the dataset, providing a reliable foundation for subsequent data analysis and control system optimization.

[0071] Step S200: Determine the performance indicators of the reactor control system under the condition of using the control parameters in each set of measurement data.

[0072] Based on the control parameters in each set of measurement data, corresponding performance indicators are calculated or simulated. The multiple sets of control parameters are processed sequentially according to the sampling order. For the Mth set of control parameters, the performance indicators of the reactor control system under the Mth set of control parameters are obtained based on the Mth set of control parameters and the pre-acquired performance indicator objective function of the reactor control system. The performance indicator objective function is used to calculate the performance of the reactor control system, where M is a positive integer. The performance indicator objective function includes multiple indicator parameters and a weighting coefficient for each indicator parameter. The performance indicator objective function is a comprehensive evaluation function that combines multiple indicator parameters and indicates the importance of each indicator parameter through the weighting coefficients. By analyzing all calculated performance indicators, one or more sets of control parameters whose performance indicators best meet expectations can be selected. Furthermore, by adjusting the weighting coefficients or changing the value range of certain control parameters, the performance of the reactor control system can be systematically evaluated and optimized to ensure its safe and effective operation under various operating conditions.

[0073] Step S300: Select the target performance index with the smallest absolute value of the difference between the performance index corresponding to each set of control parameters and the index corresponding to the preset conditions.

[0074] The absolute value of the difference between the performance index corresponding to each set of control parameters and the index corresponding to the preset conditions is compared, and the smallest absolute value of the difference is selected from all calculated differences. The performance index corresponding to this smallest difference is the target performance index. This set of control parameters enables the reactor control system to perform as close as possible to the ideal index corresponding to the preset conditions under the preset conditions. The index corresponding to the preset conditions refers to the ideal value of the performance index corresponding to the preset conditions, obtained through simulation or calculation based on various parameters in the preset conditions, and is used to judge the control effect of the reactor control system under the preset conditions.

[0075] By selecting the target performance index with the smallest absolute value of the difference between the index and the preset conditions, the control system can achieve optimal control performance under different application scenarios and operating conditions. This method can effectively solve the challenges of parameter tuning and optimization in reactor control systems, and improve the stability and safety of the control system.

[0076] Step S400: Use a set of control parameters corresponding to the target performance index as control parameters when the reactor control system controls the target component under preset conditions.

[0077] A set of control parameters corresponding to the target performance indicators is used as the control parameters for the reactor control system when controlling the target components under preset conditions. Under specific application scenarios and operating conditions, this set of control parameters enables the control system to achieve optimal control performance and meet the preset performance requirements. Optimized control parameters can improve the performance of the control system, enhance its anti-interference capability, enable it to better cope with external disturbances and internal parameter changes, and improve system stability.

[0078] In one implementation of this embodiment, the weighting coefficients of the index parameters corresponding to the obtained target performance index can be determined, and the obtained weighting coefficients can be output as the optimal weighting coefficients of the performance index objective function. By optimizing the weighting coefficients, the conflicts between different performance indices can be balanced, thereby obtaining better control performance; helping the control system to cope with model errors and external disturbances, improving the robustness of the system; and allowing for more precise parameter adjustments for control requirements under different application scenarios and operating conditions, thereby obtaining better control effects.

[0079] This embodiment analyzes the system performance under different control parameters and selects the control parameters that best approximate the performance of the system to the index corresponding to the preset conditions, thereby achieving parameter debugging and optimization of the reactor control system. By analyzing and comparing performance indicators, the optimal control parameters can be automatically selected, reducing manual intervention and debugging workload. By comparing the index corresponding to the preset conditions with the actual performance indicators, the performance of the control system can be intuitively evaluated and optimized. Parameter debugging and optimization are performed for different application scenarios and operating conditions to ensure that the control system maintains good performance under various conditions, improving the performance and reliability of the control system.

[0080] In some embodiments, step S200 may include, but is not limited to, steps S210 to S240:

[0081] Step S210: By randomly perturbing the coefficients of the transfer function of the target component running model, multiple sets of simulation control parameters are obtained. The target component running model is used to simulate the running state of the target component.

[0082] Step S220: Determine the second value range of each of the index parameters based on multiple sets of simulation control parameters;

[0083] Step S230: For each indicator parameter in each set of measurement data, perform the following: Calculate the weight coefficient of each indicator parameter in each set of measurement data and the parameter value of the indicator parameter, taking the minimum value within the target range of the indicator parameter. The target range of the indicator parameter is determined based on the first value range and the second value range of the indicator parameter in the measurement data.

[0084] Step S240: Based on the weight coefficient of each indicator parameter in each set of measurement data and the parameter value of the indicator parameter, obtain the performance index corresponding to each set of measurement data.

[0085] First, a model capable of simulating the operational state of the target component is constructed; this is the target component operational model. The target component operational model is used to simulate the operational state of the target component in order to evaluate the effect of the control parameters. The target component operational model includes all key physical processes and can describe the relationship between inputs and outputs using a transfer function. The coefficients of the transfer function represent the degree of influence of each part of the system on the overall dynamic characteristics of the system.

[0086] Based on the coefficients of the original transfer function, the coefficients of the transfer function of the target component's operating model are randomly perturbed. Multiple sets of simulation control parameters are generated through random perturbation, and these parameters will be used to simulate different operating states of the target component.

[0087] Then, the target ranges for each indicator parameter are determined, including a first value range and a second value range. Specifically, each set of measurement data includes the first value range for each indicator parameter, which can be obtained directly from the measurement data or calculated from it. For each set of simulated control parameters, the target component running model is executed, and the actual output values ​​of each performance indicator are recorded. Based on these output values, the second value range for each indicator parameter is determined. This range is derived from the simulation results and reflects the possible range of variation of the indicator parameter under simulated conditions.

[0088] For each indicator parameter in each set of measurement data, taking the minimum value within its target range, perform the following operations: Calculate the weight coefficient of each indicator parameter in each set of measurement data based on the minimum value of each indicator parameter, and determine the parameter value of the indicator parameter. The weight coefficient reflects the importance of the indicator parameter to the overall performance indicator.

[0089] Finally, based on the weighting coefficient and parameter value of each indicator parameter in each set of measurement data, the performance index corresponding to each set of measurement data is calculated based on the performance index objective function.

[0090] Wherein, the objective function for the performance index is ,in, To control the system performance at time t, The system adjusts the time at time t; Let t be the recovery time of the control system after the disturbance. The overshoot of the control system at time t; The maximum disturbance offset of the control system at time t; , , , These are the weighting coefficients.

[0091] ;

[0092] ;

[0093] ;

[0094] ;

[0095] in, To control the overshoot, This represents the maximum output value under a step jump. This represents the steady-state value of the output under a step action. This represents the maximum offset of the control system from disturbances. The maximum value of the output under the influence of the disturbance. The steady-state value of the output under disturbance; To adjust the time for the control system, The steady-state time under a step action. This is the initial time of the step action; For control system disturbance recovery time, The steady-state time under the influence of the disturbance. The initial time of the disturbance.

[0096] The settling time and overshoot of a control system can be constrained by the oscillation frequency and damping ratio (0.4-0.8 underdamped). The oscillation frequency refers to the frequency at which the system output signal oscillates during the dynamic response, and the damping ratio refers to the degree of attenuation of the oscillation amplitude during the dynamic response. A higher oscillation frequency results in a faster system response and shorter settling time, but also a larger overshoot; a higher damping ratio results in a smoother system response and smaller overshoot, but a longer settling time. 0.4-0.8 underdamped means the system's damping ratio is between 0.4 and 0.8. Within this range, the system has sufficient damping to suppress overshoot and sufficient response speed to ensure settling time. By using the oscillation frequency and damping ratio to constrain the settling time and overshoot, the dynamic response characteristics of the control system can be effectively controlled, thereby achieving the desired control effect. The corresponding constraint relationships are shown below:

[0097] ;

[0098] ;

[0099] ;

[0100] ;

[0101] ;

[0102] ;

[0103] ;

[0104] in, The damping ratio of the controlled object, The oscillation frequency of the controlled object. For steady-state error accuracy, The open-loop cutoff frequency, For phase margin, This represents the gain margin.

[0105] In other words, performance indicators are not only constrained by the target range of the indicator parameters, but also by other related indicators such as damping ratio, oscillation frequency, steady-state error accuracy, open-loop cutoff frequency, phase margin, and gain margin. These constraint indicators can be set according to actual conditions and are not limited here. Through the combined effect of these constraints, it can be ensured that the optimization results not only meet the performance indicators but also comply with the requirements of system stability and safety. By reasonably setting constraints, multi-objective optimization of the control system can be achieved, ensuring the safe and stable operation of the system.

[0106] In this embodiment, a comprehensive performance objective function is set for both a fast response mode and a stable operation mode. In fast response mode, the objective function obtained by weighting the control system settling time and the control system disturbance recovery time is prioritized; that is, the objective function that prioritizes the control system's settling time and the control system disturbance recovery time is given priority. , Under the above constraints, the minimum value is obtained; and at this time... , The constraints that also need to be satisfied are the upper and lower bounds of the overshoot and maximum disturbance offset performance indicators obtained from the Monte Carlo experiment. Under stable operating mode, priority should be given to satisfying the objective function obtained by weighting the control system overshoot and the control system maximum disturbance offset; that is, priority should be given to ensuring that... , Under the above constraints, the minimum value is obtained; and at this time... , The constraints that also need to be satisfied are the upper and lower bounds of the settling time and disturbance recovery time performance indices obtained from the Monte Carlo experiment.

[0107] By combining the performance objective function of the fast response mode and the stable operation mode, we can obtain an optimal solution under the combined conditions by integrating the values ​​of the fast response mode and the stable operation mode. The optimal solution is found. The combined performance objective function of the fast response mode and the smooth operation mode is shown below:

[0108] .

[0109] This embodiment, through random perturbation of the transfer function coefficients, determination of the target range of the index parameters, and calculation of weighting coefficients and parameter values, can determine the performance index of the reactor control system under different control parameters, and find the optimal control parameters and their weighting coefficients, thereby improving the performance of the control system. By setting different constraints, the control parameters of the reactor can be arbitrarily adjusted and optimized under different constraint conditions.

[0110] In some embodiments, step S210 may include, but is not limited to, steps S211 to S213:

[0111] Step S211: The target component's operating model is obtained by performing model identification on the controlled object model and the internal and external disturbance model using the historical measurement data of the target component.

[0112] Step S212: Obtain a set of initial simulation control parameters based on the preset gain margin and preset phase margin;

[0113] Step S213: Based on the initial simulation control parameters, the transfer function coefficients of the target component running model are randomly perturbed a preset number of times within the target preset range to obtain multiple sets of simulation control parameters.

[0114] A controlled object model refers to a mathematical model of the physical process or equipment that needs to be controlled in a reactor control system. It describes the relationship between the input variables (such as control signals) and output variables (such as temperature and pressure) of the controlled object. An internal and external disturbance model refers to a mathematical model of external disturbances and internal disturbances that affect the performance of a reactor control system. It describes the impact of these disturbances on the output variables of the controlled object.

[0115] The target component's operating model can be obtained through model identification using historical measurement data or through step response experiments under small-scale operating conditions. Historical data of the target component, including inputs (such as control signals) and outputs (such as performance indicators like temperature and pressure), is obtained from the reactor control system. This historical measurement data or step response experimental data is used to perform model identification of the controlled object model and the internal and external disturbance models. Model identification of the controlled object model and the internal and external disturbance models is performed based on historical measurement data. The main model identification methods that can be used include, but are not limited to, least squares identification, asymptotic identification, subspace system identification, and finite impulse response identification, etc., and are not limited here. Various identification methods can also be combined to form a combined variable-weight identification method. The selection of the weights for each identification method is related to the error of the identification method; the larger the error of the identification method, the smaller its weight. Variable-weight methods include, but are not limited to, the equal-weight method, the inverse square error method, and the dominance matrix method, and are not limited here.

[0116] Specifically, controlled object model identification involves establishing a dynamic mathematical model of the controlled object (e.g., temperature, power, flow rate), describing the relationship between its inputs (e.g., control signals) and outputs (e.g., actual measured values). Internal and external disturbance model identification involves establishing a model of the impact of internal and external disturbances (e.g., temperature disturbances, flow rate disturbances) on the controlled object, describing the relationship between the disturbances and the controlled object's output. Through model identification, the behavior of the controlled object can be predicted and controlled more accurately, improving the accuracy and stability of the control system. Based on the model identification results, more effective control strategies can be designed, such as optimizing PID controller parameters to improve the performance of the control system. Furthermore, model identification can also predict the behavior of the controlled object under different operating conditions, providing a reference for system design and operation.

[0117] like Figure 2 As shown, the process for obtaining multiple sets of simulation control parameters is as follows:

[0118] First, obtain the target component's operating model, and design a control scheme based on the target component's operating model, including single-level, cascade, and composite designs, which are not limited here.

[0119] Next, define the gain margin and phase margin requirements. For example, set the gain margin to be no less than 6 dB and the phase margin to be between 30° and 60°, without specific limitations. Gain margin and phase margin are important indicators of control system stability, ensuring the system remains stable within a certain disturbance range. Gain margin refers to the amount of increase in input amplitude the system can tolerate before oscillation begins, while phase margin refers to the amount of phase lag the system can tolerate before oscillation begins. Based on the simulation results of the target component's operating model, determine whether the control scheme meets the gain margin and phase margin requirements. If it does, calculate a set of initial simulation control parameters based on the set gain margin and phase margin. If it does not meet the requirements, modify the control structure or control scheme and re-simulate until a set of initial control parameters is obtained.

[0120] Finally, based on the obtained initial control parameters, Monte Carlo simulation is used to randomly perturb the transfer function coefficients of the target component's operating model a predetermined number of times within the target preset range, resulting in multiple sets of simulation control parameters. The target preset range can be set empirically and is not limited here; the number of preset perturbations is determined by the amount of data required for subsequent simulations and is also not limited here.

[0121] In one implementation of this embodiment, the steps for obtaining multiple sets of analog control parameters are illustrated using the parameter optimization of an OTSG steam pressure cascade control system as an example.

[0122] The transfer function models of each component of the control system under full load conditions, obtained through model identification, are shown in Table 1:

[0123]

[0124] Table 1

[0125] The control system is designed using a PID cascade control scheme, and its control system structure is as follows: Figure 3 As shown in the figure. In the figure, r is the OTSG pressure setpoint; y is the actual output value of the control system. δT i This refers to the temperature disturbance at the primary inlet. δW S This represents the relative steam flow rate disturbance; PM and FM represent the pressure measurement feedback and flow measurement feedback, respectively. f q G1(S) is the correction coefficient for steam flow rate when the valve is fully open and steam flow rate at full load; G2(S) is the transfer function of valve opening and relative feedwater flow rate at the secondary inlet; G3(S) is the transfer function of relative feedwater flow rate at the secondary inlet and secondary steam pressure; G4(S) is the transfer function of primary inlet temperature and secondary steam pressure; G5(S) is the transfer function of relative steam flow rate and secondary steam pressure.

[0126] A set of initial control parameters was obtained by setting the gain margin to no less than 6 dB and the phase margin to between 30° and 60°. Then, the transfer function coefficients of both the controlled object model and the disturbance model were perturbed by ±40%, and 50 random simulations were performed using the Monte Carlo method. The control parameters for each of the 50 simulations were recorded. These control parameters meet the control system design requirements in both the time and frequency domains; therefore, the corresponding combinations of controller parameters can be considered optimal.

[0127] This embodiment obtains multiple sets of simulated control parameters by randomly perturbing the coefficients of the transfer function of the target component's operating model. This allows for the evaluation of the adaptability of the control parameters to different operating conditions and model errors, thereby selecting more robust control parameters to ensure stable system operation under various circumstances. Based on these multiple sets of simulated control parameters, combined with a variable weighting method, the weights of performance indicators can be adjusted according to the current operating state to further optimize the control parameters and improve the control effect.

[0128] In some embodiments, step S213 may include, but is not limited to, steps S2131 to S2134:

[0129] Step S2131: Based on the initial simulation control parameters, the transfer function coefficients of the target component running model are subjected to N random perturbations within a target preset range, where N is a positive integer. For each random perturbation, the following is executed:

[0130] Step S2132: If the target component operation model is controlled to randomly perturb within the target preset range;

[0131] Step S2133: If the target component operation model satisfies the preset conditions after random perturbation within the target preset range, then output a set of simulation control parameters corresponding to this random perturbation.

[0132] Step S2134: If the target component running model does not meet the preset conditions after this random perturbation, then reduce the value range of the target preset range to obtain the preset range corresponding to the next random perturbation, and use the preset range corresponding to the next random perturbation as the target preset range, and execute the step of controlling the target component running model to randomly perturb within the target preset range.

[0133] First, it is necessary to determine the target preset range for the random perturbation of the transfer function coefficients, as well as the preset number of times N (a positive integer). Then, the transfer function coefficients of the target component model are subjected to N random perturbations within the target preset range.

[0134] For each random perturbation, perform the following steps:

[0135] After each random perturbation, it is checked whether the target component's operating model meets the preset conditions, i.e., whether the target component's operating model is operating stably. Stability can be determined through eigenvalue analysis, frequency response analysis, or other stability criteria of the closed-loop system, and is not limited here.

[0136] If the model remains stable after the perturbation, a set of simulated control parameters corresponding to that perturbation is output. If the model is unstable, the range of values ​​in the target preset range is reduced to decrease the perturbation amplitude. This helps to narrow the search space and avoid generating unstable control parameters. The random perturbation is re-executed within the reduced target preset range, and the model stability is evaluated again until a set of simulated control parameters that stabilizes the model is found. This set is then saved and output.

[0137] This embodiment ensures that all output analog control parameters enable the system to operate stably through stability checks; by dynamically adjusting the perturbation range, multiple sets of stable and effective analog control parameters are obtained, reducing unnecessary calculations and experiments; and through multiple iterations and stability verifications, the reliability and practicality of the analog control parameter set are improved.

[0138] In some embodiments, step S220 may include, but is not limited to, steps S221 to S222:

[0139] Step S221: For each set of simulation control parameters in the plurality of sets of simulation control parameters, the parameter value of each index parameter corresponding to each set of simulation control parameters is obtained by performing step signal simulation on the target component running model.

[0140] Step S222: Determine the second value range of each index parameter based on the maximum and minimum values ​​of the parameter values ​​of each index parameter corresponding to each set of simulation control parameters in the multiple sets of simulation control parameters.

[0141] For each set of simulated control parameters, apply it to the target component's operating model. Apply a step signal to the target component's operating model to simulate the system's response. The step signal is a commonly used test signal for evaluating the system's dynamic performance. Specifically, setpoint tracking simulation and external disturbance simulation can be performed using the step signal. Setpoint tracking simulation simulates the model's operation under a set input signal to obtain the model's operating results; external disturbance simulation simulates the impact of external disturbances on the model to obtain the model's operating results. Performing setpoint tracking and external disturbance simulations on the target component's operating model helps evaluate the control system's performance when facing various disturbances that may be encountered in the actual working environment. Record the parameter values ​​of each index parameter corresponding to each set of simulated control parameters during the simulation process. These index parameters may include, but are not limited to, settling time, overshoot, disturbance recovery time, and maximum disturbance offset, and are not limited here.

[0142] Analyze all simulation results to identify the maximum and minimum values ​​of each indicator parameter. Based on these maximum and minimum values, determine the second range of values ​​for each indicator parameter. The second range of values ​​for each indicator parameter defines the value interval for each parameter; combinations of indicator parameters within this interval will meet the performance requirements.

[0143] In one implementation of this embodiment, two types of index parameter constraints are specified: constraints under a step action of the controlled object and constraints under an applied disturbance. The constraint under a step action of the controlled object uses settling time as the independent variable (x-axis) and overshoot as the dependent variable (y-axis). For example... Figure 4 As shown, under the same time and space scale, the Monte Carlo method is used to subject the controlled object model and the disturbance model to large-scale random perturbations, establishing a one-to-one correspondence between the settling time and the overshoot, and forming quantifiable boundaries for the control system index parameters. The lower boundary is set as the minimum settling time and the minimum overshoot, and the upper boundary is set as the maximum settling time and the maximum overshoot. This determines the feasible constraint interval (i.e., the second value range) of the control system index parameters, which serves as the input conditions for different objective functions.

[0144] The constraints under the applied disturbance have the disturbance recovery time as the independent variable (x-axis) and the maximum disturbance offset as the dependent variable (y-axis). For example... Figure 5As shown, under the same time and space scale, the Monte Carlo method is used to subject the controlled object model and the disturbance model to large-scale random perturbations, establishing a one-to-one correspondence between the disturbance recovery time and the maximum disturbance offset. At the same time, quantifiable boundaries of the control system index parameters are formed. The lower boundary is set as the minimum value of the minimum disturbance recovery time and the maximum disturbance offset, and the upper boundary is set as the maximum value of the maximum disturbance recovery time and the maximum disturbance offset. This determines the feasible constraint interval (i.e., the second value range) of the control system index parameters, which serves as the input condition for different objective functions.

[0145] This embodiment can comprehensively evaluate the impact of control parameters on system performance by simulating step signals and external disturbances; the second value range can serve as a reference for subsequent optimization and adjustment of control parameters, helping to determine which parameter values ​​are feasible and reasonable; by determining the value range of the index parameters, different index parameters in the objective function can be weighted according to different application scenarios and operating conditions, and the optimal combination of control parameters can be found.

[0146] This embodiment of the application samples the reactor control system within a first preset time period to obtain multiple sets of measurement data, each set containing at least one control parameter. Then, based on a pre-acquired performance index objective function, each set of control parameters is processed sequentially to calculate its corresponding performance index, and the target performance index with the smallest absolute value of the index difference corresponding to preset conditions is selected. To more accurately evaluate performance, multiple sets of simulated control parameters are generated using random perturbations of the transfer function coefficients. These parameters are obtained by model identification of the target component's historical measurement data, and initial simulated control parameters are set according to amplitude and phase margin. For each random perturbation, if the model is stable, the set of parameters is output; if unstable, the perturbation range is reduced until multiple sets of stable simulated control parameters are obtained. Subsequently, by performing step signal simulation on the target component's operating model, the maximum and minimum values ​​of each index parameter are determined, thereby defining a second value range. Finally, a set of control parameters corresponding to the target performance index and their weighting coefficients are applied to the reactor control system as the optimal control setting. This application provides a time-domain and frequency-domain integrated analysis method combining multiple performance indicators and variable weighting coefficients. Based on the constraints of different reactor application scenarios and operating conditions, it can obtain the importance ranking of various performance indicators under the current state and assign reasonable weighting coefficients to each indicator, thereby achieving multi-scenario, multi-objective control parameter optimization. This application has broad adaptability to reactor application scenarios and operating conditions, ensuring good control performance while reducing frequent control parameter tuning operations, which is beneficial to the safe and stable operation of the reactor.

[0147] Please see Figure 6This application also provides a parameter determination device 500 for a reactor control system, which can implement the above-described parameter determination method for a reactor control system. The device includes:

[0148] The sampling module 10 is used to sample the operating data of the reactor control system under preset conditions within a first preset time period to obtain multiple sets of measurement data. Each set of measurement data includes at least one control parameter used by the reactor control system to control the target component. The reactor control system is used to control the target component of the reactor.

[0149] Calculation module 20 is used to determine the performance indicators of the reactor control system under the condition of using the control parameters in each set of measurement data;

[0150] Selection module 30 is used to select the target performance index with the smallest absolute value of the difference between the performance index corresponding to the preset conditions and the performance index corresponding to each group of control parameters.

[0151] The parameter determination module 40 is used to use a set of control parameters corresponding to the target performance index as control parameters when the reactor control system controls the target component under preset conditions.

[0152] In some implementations, the computing module 20 may include:

[0153] The processing submodule is used to process the multiple sets of control parameters sequentially according to the sampling order. For the Mth set of control parameters, the performance index of the reactor control system under the condition of using the Mth set of control parameters is obtained according to the Mth set of control parameters and the pre-acquired performance index objective function of the reactor control system. The performance index objective function is used to calculate the performance of the reactor control system, where M is a positive integer.

[0154] In some implementations, the computing module 20 may further include:

[0155] The perturbation submodule is used to obtain multiple sets of simulation control parameters by randomly perturbing the coefficients of the transfer function of the target component's running model, wherein the target component's running model is used to simulate the running state of the target component.

[0156] The range acquisition submodule is used to determine the second value range of each of the index parameters based on multiple sets of simulation control parameters;

[0157] The calculation submodule is used to perform the following for each indicator parameter in each set of measurement data: calculating the weight coefficient and parameter value of each indicator parameter in each set of measurement data, taking the minimum value within the target range of the indicator parameter; the target range of the indicator parameter is determined based on a first value range and a second value range of the indicator parameter in the measurement data; the performance indicator objective function includes multiple indicator parameters and a weight coefficient for each indicator parameter, and each set of measurement data includes a first value range for each indicator parameter;

[0158] The performance index acquisition submodule is used to obtain the performance index corresponding to each set of measurement data based on the weight coefficient of each index parameter in each set of measurement data and the parameter value of the index parameter.

[0159] In some implementations, the perturbation submodule may include:

[0160] The model identification unit is used to perform model identification on the controlled object model and the internal and external disturbance model using the historical measurement data of the target component to obtain the target component's operating model;

[0161] The analog control parameter acquisition unit is used to obtain a set of initial analog control parameters based on the preset gain margin and the preset phase margin.

[0162] The perturbation unit is used to randomly perturb the transfer function coefficients of the target component running model a preset number of times within a target preset range according to the initial simulation control parameters, so as to obtain multiple sets of simulation control parameters.

[0163] In some implementations, the perturbation unit may include:

[0164] The perturbation subunit is used to perform N random perturbations on the transfer function coefficients of the target component's running model within a preset target range based on the initial simulation control parameters, where N is a positive integer. For each random perturbation, the following is executed:

[0165] The control subunit is used to control the target component's operating model to randomly perturb within the target preset range;

[0166] The output subunit is used to output a set of analog control parameters corresponding to the random perturbation if the target component operation model meets the preset conditions after random perturbation within the target preset range.

[0167] The loop subunit is used to reduce the value range of the target preset range if the target component operation model does not meet the preset conditions after the current random perturbation, to obtain the preset range corresponding to the next random perturbation, and to use the preset range corresponding to the next random perturbation as the target preset range, and to execute the step of controlling the target component operation model to randomly perturb within the target preset range.

[0168] In some implementations, the range acquisition submodule may include:

[0169] The simulation unit is used to obtain the parameter value of each index parameter corresponding to each set of simulation control parameters by performing step signal simulation on the target component running model for each set of simulation control parameters.

[0170] The range acquisition unit is used to determine the second value range of each index parameter based on the maximum and minimum values ​​of the parameter values ​​of each index parameter corresponding to each set of simulation control parameters in the plurality of sets of simulation control parameters.

[0171] The specific implementation of the parameter determination device for the reactor control system is basically the same as the specific implementation of the parameter determination method for the reactor control system described above, and will not be repeated here.

[0172] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the parameter determination method for the reactor control system described above. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0173] This application also provides a computer program product in which the instructions are executed by the processor of an electronic device, causing the electronic device to implement the above-described method for determining the parameters of the reactor control system.

[0174] Please see Figure 7 , Figure 7 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0175] The processor 701 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0176] The memory 702 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 702 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 702 and is called and executed by the processor 701 to execute the parameter determination method of the reactor control system of the embodiments of this application.

[0177] The input / output interface 703 is used to implement information input and output;

[0178] The communication interface 704 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0179] Bus 705 transmits information between various components of the device (e.g., processor 701, memory 702, input / output interface 703, and communication interface 704);

[0180] The processor 701, memory 702, input / output interface 703, and communication interface 704 are connected to each other within the device via bus 705.

[0181] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for determining parameters of a reactor control system.

[0182] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0183] The reactor control system parameter determination method, device, electronic equipment, and storage medium provided in this application embodiment sample the operating data of the reactor control system under preset conditions within a first preset time period to obtain multiple sets of measurement data. Each set of measurement data includes at least one control parameter for the reactor control system to control a target component. Based on the control parameters in each set of measurement data, corresponding performance indicators are calculated or simulated. Then, the performance indicator with the smallest absolute value of the difference between the performance indicators corresponding to each set of control parameters and the indicator corresponding to the preset conditions is selected as the target performance indicator. The set of control parameters corresponding to the target performance indicator is used as optimized control parameters for the reactor control system to control the target component under preset conditions. This application embodiment analyzes the system performance under different control parameters and selects the control parameters that make the system performance closest to the indicator corresponding to the preset conditions, thereby realizing the parameter debugging and optimization of the reactor control system, and further realizing the debugging and optimization of control parameters of the reactor under different application scenarios and different constraints.

[0184] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0185] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0186] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0187] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0188] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0189] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0190] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0191] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0192] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0193] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0194] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for determining parameters of a reactor control system, wherein the reactor control system is used to control target components of the reactor, characterized in that, The method includes: The operating data of the reactor control system under preset conditions within a first preset time period are sampled to obtain multiple sets of measurement data. Each set of measurement data includes at least one control parameter of the reactor control system for controlling the target component. Determine the performance indicators of the reactor control system under the condition of using the control parameters in each set of measurement data; Select the target performance index with the smallest absolute value of the difference between the performance index corresponding to each set of control parameters and the index corresponding to the preset conditions. The set of control parameters corresponding to the target performance index shall be used as the control parameters when the reactor control system controls the target component under preset conditions. Determining the performance indicators of the reactor control system under the condition of using the control parameters in each set of measurement data includes: The multiple sets of control parameters are processed sequentially according to the sampling order. For the Mth set of control parameters, the performance index of the reactor control system under the condition of using the Mth set of control parameters is obtained based on the Mth set of control parameters and the pre-acquired performance index objective function of the reactor control system. The performance index objective function is used to calculate the performance of the reactor control system, where M is a positive integer. The performance objective function is ,in, To control the system performance at time t, The system adjusts the time at time t. Let t be the recovery time of the control system due to disturbance. Let t be the overshoot of the control system. Let t be the maximum disturbance offset of the control system at time t. , , , These are the weighting coefficients; ； ； ； ； in, This represents the maximum output value under a step jump. This represents the steady-state value of the output under a step action. The maximum value of the output under the influence of the disturbance. The steady-state value of the output under disturbance. To adjust the time for the control system, The steady-state time under a step action. The initial time of the step action. For control system disturbance recovery time, The steady-state time under the influence of the disturbance. The initial time of the disturbance; The settling time and overshoot of the control system are constrained by the oscillation frequency and damping ratio; Among them, the oscillation frequency refers to the oscillation frequency of the system output signal during the dynamic response process, and the damping ratio refers to the degree of attenuation of the oscillation amplitude of the system output signal during the dynamic response process. The higher the oscillation frequency, the faster the system response, the shorter the settling time, and the larger the overshoot; the higher the damping ratio, the more stable the system response, the smaller the overshoot, and the longer the settling time. The corresponding constraints are shown below: ； ； ； ； ； ； ； in, The damping ratio of the controlled object, The oscillation frequency of the controlled object. For steady-state error accuracy, The open-loop cutoff frequency, For phase margin, This represents the gain margin.

2. The method according to claim 1, characterized in that, The performance indicator objective function includes multiple indicator parameters and a weight coefficient for each indicator parameter, and each set of measurement data includes a first value range for each indicator parameter; Determining the performance indicators of the reactor control system under the condition of using the control parameters in each set of measurement data includes: By randomly perturbing the coefficients of the transfer function of the target component's operating model, multiple sets of simulation control parameters are obtained. The target component's operating model is used to simulate the operating state of the target component. Based on multiple sets of simulation control parameters, determine the second value range for each of the aforementioned index parameters; For each indicator parameter in each set of measurement data, the following is performed: assuming the minimum value is taken within the target range of the indicator parameter, the weight coefficient of each indicator parameter in each set of measurement data and the parameter value of the indicator parameter are calculated, wherein the target range of the indicator parameter is determined based on the first value range and the second value range of the indicator parameter in the measurement data. Based on the weighting coefficient of each indicator parameter in each set of measurement data and the parameter value of the indicator parameter, the performance index corresponding to each set of measurement data is obtained.

3. The method according to claim 2, characterized in that, The process involves randomly perturbing the coefficients of the transfer function of the target component's operating model to obtain multiple sets of simulation control parameters, including: The target component's operational model is obtained by performing model identification on the controlled object model and the internal and external disturbance model using historical measurement data of the target component. A set of initial simulation control parameters is obtained based on the preset gain margin and preset phase margin; Based on the initial simulation control parameters, the transfer function coefficients of the target component's operating model are randomly perturbed a preset number of times within the target preset range to obtain multiple sets of simulation control parameters.

4. The method according to claim 3, characterized in that, The transfer function coefficients of the target component's operating model are randomly perturbed a preset number of times within a target preset range based on the initial simulation control parameters to obtain multiple sets of simulation control parameters, including: Based on the initial simulation control parameters, the transfer function coefficients of the target component's operating model are subjected to N random perturbations within a preset target range. For each random perturbation, the following is executed: The target component's operating model is controlled to randomly perturb within the target's preset range; If the target component operation model satisfies the preset conditions after random perturbation within the target preset range, then a set of simulation control parameters corresponding to this random perturbation will be output. If the target component operating model does not meet the preset conditions after this random perturbation, the value range of the target preset range is reduced to obtain the preset range corresponding to the next random perturbation. The preset range corresponding to the next random perturbation is then used as the target preset range, and the step of controlling the target component operating model to randomly perturb within the target preset range is executed.

5. The method according to claim 2, characterized in that, The step of determining the second value range of each index parameter based on multiple sets of simulation control parameters includes: For each set of simulated control parameters, the parameter value of each index parameter corresponding to each set of simulated control parameters is obtained by performing step signal simulation on the target component running model. Based on the maximum and minimum values ​​of each index parameter corresponding to each set of simulation control parameters in the multiple sets of simulation control parameters, a second value range for each index parameter is determined.

6. A parameter determination apparatus for a reactor control system, the reactor control system being used to control target components of the reactor, characterized in that, The device includes: The sampling module is used to sample the operating data of the reactor control system under preset conditions within a first preset time period to obtain multiple sets of measurement data. Each set of measurement data includes at least one control parameter used by the reactor control system to control the target component. The reactor control system is used to control the target component of the reactor. The calculation module is used to determine the performance index of the reactor control system under the control parameters in each set of measurement data; the multiple sets of control parameters are processed sequentially according to the sampling order; for the Mth set of control parameters, the performance index of the reactor control system under the Mth set of control parameters is obtained according to the Mth set of control parameters and the pre-acquired performance index objective function of the reactor control system, where M is a positive integer; The performance objective function is ,in, To control the system performance at time t, The system adjusts the time at time t. Let t be the recovery time of the control system due to disturbance. Let t be the overshoot of the control system. Let t be the maximum disturbance offset of the control system at time t. , , , These are the weighting coefficients; ； ； ； ； in, This represents the maximum output value under a step jump. This represents the steady-state value of the output under a step action. The maximum value of the output under the influence of the disturbance. The steady-state value of the output under disturbance. To adjust the time for the control system, The steady-state time under a step action. The initial time of the step action. For control system disturbance recovery time, The steady-state time under the influence of the disturbance. The initial time of the disturbance; The settling time and overshoot of the control system are constrained by the oscillation frequency and damping ratio; Among them, the oscillation frequency refers to the oscillation frequency of the system output signal during the dynamic response process, and the damping ratio refers to the degree of attenuation of the oscillation amplitude of the system output signal during the dynamic response process. The higher the oscillation frequency, the faster the system response, the shorter the settling time, and the larger the overshoot; the higher the damping ratio, the more stable the system response, the smaller the overshoot, and the longer the settling time. The corresponding constraints are shown below: ； ； ； ； ； ； ； in, The damping ratio of the controlled object, The oscillation frequency of the controlled object. For steady-state error accuracy, The open-loop cutoff frequency, For phase margin, This is the gain margin; The selection module is used to select the target performance index with the smallest absolute value of the difference between the performance index corresponding to the preset conditions and the performance index corresponding to each group of control parameters. The parameter determination module is used to use a set of control parameters corresponding to the target performance index as the control parameters when the reactor control system controls the target component under preset conditions.

7. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the parameter determination method of the reactor control system according to any one of claims 1 to 5.

8. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device causes the electronic device to perform the parameter determination method of the reactor control system as described in any one of claims 1 to 5.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the parameter determination method for the reactor control system according to any one of claims 1 to 5.

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