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

Abstract

The embodiment of the invention 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 steps that operation data of a reactor control system under preset conditions within a first preset time period are sampled, multiple sets of measurement data are obtained, and each set of measurement data comprises at least one control parameter of the reactor control system for controlling a target assembly; determining performance indexes of the reactor control system under the condition that the control parameters in each group of measurement data are adopted; selecting a target performance index of which the absolute value of the difference value between the target performance index and the index corresponding to the preset condition is minimum from the performance indexes corresponding to each group of control parameters; and taking a group of control parameters corresponding to the target performance index as control parameters when the reactor control system controls the target component under a preset condition. According to the embodiment of the invention, control parameter debugging and optimization of the reactor under different application scenes and different constraint conditions can be realized.

Description

Technical Field

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

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

[0003] At present, the parameter adjustment of the reactor control system is mainly aimed at the stable operation of the unit and grid-connected power generation, and insufficient consideration is given to the special needs in other specific application scenarios. When new application scenarios are introduced, the original control system parameter debugging method is difficult to simultaneously meet the constraint requirements of different application scenarios, and the control effect is poor. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose a method, device, 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-mentioned purpose, a first aspect of an embodiment of the present application provides a method for determining parameters of a reactor control system, wherein the reactor control system is used to control a target component of the reactor, and the method comprises:

[0006] Sampling the operating data of the reactor control system within a first preset time period under preset conditions to obtain multiple groups of measurement data, each group of measurement data including at least one control parameter of the reactor control system for controlling the target component;

[0007] Determining performance indicators of the reactor control system when using the control parameters in each set of measurement data;

[0008] Selecting a target performance indicator having the smallest absolute difference between the indicators corresponding to the preset conditions from the performance indicators corresponding to each group of control parameters;

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

[0010] In some embodiments, determining the performance index of the reactor control system when each set of control parameters is used includes:

[0011] The multiple groups of control parameters are processed in sequence according to the sampling order. For the Mth group of control parameters, the performance indicators of the reactor control system when the Mth group of control parameters is adopted are obtained according to the Mth group of control parameters and the performance indicator target function of the reactor control system obtained in advance. The performance indicator target function is used to calculate the performance of the reactor control system, and M is a positive integer.

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

[0013] Determining the performance index of the reactor control system when 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 operation model, a plurality of sets of simulation control parameters are obtained, wherein the target component operation model is used to simulate the operation state of the target component;

[0015] Determining a second value range of each of the indicator parameters according to the multiple groups of simulation control parameters;

[0016] For each of the indicator parameters in each set of measurement data, executing: when taking the minimum value within the target range of the indicator parameter, calculating the weight coefficient of each of the indicator parameters in each set of measurement data and the parameter value of the indicator parameter, wherein the target range of the indicator parameter is determined according to the first value range of the indicator parameter in the measurement data and the second value range of the indicator parameter;

[0017] According to the weight coefficient of each indicator parameter in each set of measurement data and the parameter value of the indicator parameter, the performance indicator corresponding to each set of measurement data is obtained.

[0018] In some embodiments, the multiple sets of simulation control parameters are obtained by randomly perturbing the coefficients of the transfer function of the target component operation model, including:

[0019] Performing model identification on the controlled object model and the internal and external disturbance models through the historical measurement data of the target component to obtain the target component operation model;

[0020] A set of initial simulation control parameters is obtained according to a preset amplitude margin and a preset phase margin;

[0021] According to the initial simulation control parameters, the transfer function coefficients of the target component operation model are randomly perturbed a preset number of times within a target preset range to obtain multiple groups of simulation control parameters.

[0022] In some embodiments, the transfer function coefficients of the target component operation model are randomly perturbed 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, including:

[0023] According to the initial simulation control parameters, the transfer function coefficient of the target component operation model is randomly perturbed N times within the target preset range, where N is a positive integer. For each random perturbation, the following is executed:

[0024] Control the target component operation model to randomly perturb within the target preset range;

[0025] If the target component operation model satisfies the preset conditions after the target component operation model is randomly perturbed within the target preset range, a set of simulation control parameters corresponding to this random perturbation is output;

[0026] If the target component operation 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, and the preset range corresponding to the next random perturbation is used as the target preset range, and the step of controlling the target component operation model to randomly perturb within the target preset range is executed.

[0027] In some embodiments, determining the second value range of each of the indicator parameters according to the multiple sets of simulation control parameters includes:

[0028] For each group of simulation control parameters in the plurality of groups of simulation control parameters, a parameter value of each indicator parameter corresponding to each group of simulation control parameters is obtained by performing a step signal simulation on the target component operation model;

[0029] According to the maximum value and the minimum value of the parameter value of each indicator parameter corresponding to each group of analog control parameters in the multiple groups of analog control parameters, the second value range of each indicator parameter is determined.

[0030] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides a parameter determination device for a reactor control system, the device comprising:

[0031] a sampling module, used for sampling the operation data of the reactor control system within a first preset time period under preset conditions to obtain multiple groups of measurement data, each group of measurement data including at least one control parameter for the reactor control system to control the target component; the reactor control system is used to control the target component of the reactor;

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

[0033] A selection module is used to select, from the performance indicators corresponding to each group of control parameters, a target performance indicator having the smallest absolute difference with the indicator corresponding to the preset condition;

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

[0035] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in the first aspect is implemented.

[0036] To achieve the above objectives, the fourth aspect of the embodiments of the present application proposes a computer program product to implement the method. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the method described in the first aspect.

[0037] To achieve the above objectives, the fifth aspect of an embodiment of the present application proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0038] The parameter determination method, device, electronic device and storage medium of the reactor control system proposed in the present application, which obtains multiple groups of measurement data by sampling the operating data of the reactor control system in the first preset time period under preset conditions, each group of measurement data includes at least one control parameter for the reactor control system to control the target component; based on the control parameters in each group of measurement data, the corresponding performance index is calculated or simulated, and then the performance index with the smallest absolute value of the difference between the index corresponding to the preset condition is selected from the performance index corresponding to each group of control parameters as the target performance index; a group 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 embodiment of the present application analyzes the system performance under different control parameters, selects the control parameters that can 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 then realizing the control parameter debugging and optimization of the reactor under different application scenarios and different constraints. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1It is a flow chart of a method for determining parameters of a reactor control system provided in an embodiment of the present application;

[0040] Figure 2 It is a schematic diagram of a flow chart of obtaining simulation control parameters of a method for determining parameters of a reactor control system provided in an embodiment of the present application;

[0041] Figure 3 It is a schematic diagram of the OTSG steam pressure cascade control structure of the parameter determination method of the reactor control system provided in an embodiment of the present application;

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

[0043] Figure 5 It is a schematic diagram of the performance constraint interval under the effect of disturbance applied to the parameter determination method of the reactor control system provided in the embodiment of the present application;

[0044] Figure 6 It is a structural schematic diagram of a parameter determination device of a reactor control system provided in an embodiment of the present application;

[0045] Figure 7 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] It should be noted that, although the functional modules are divided in the device schematic diagram and the 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 above 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 those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0049] First, some nouns involved in this application are analyzed:

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

[0051] Transfer function: refers to the ratio of the Laplace transform of the linear system response (i.e. output) to the Laplace transform of the excitation (i.e. input) under zero initial conditions. It is a mathematical model that describes the input-output relationship of a linear time-invariant system. The transfer function can be obtained through Laplace transform, which 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 the system. In the control system, the transfer function can be used to describe the relationship between the controller, the controlled object and the sensor.

[0052] Monte Carlo simulation: is a numerical simulation method based on statistical sampling theory, which relies on repeated random sampling to obtain numerical results. This method is named after the famous casino Monte Carlo in Monaco, symbolizing its random sampling nature. The basic concept is to use randomness to solve problems that may be deterministic in theory.

[0053] Time domain analysis: refers to the analysis of the stability, transient and steady-state performance of the control system based on the time domain expression of the output under certain input. Since time domain analysis is a method of analyzing the system directly in the time domain, it has the advantages of being intuitive and accurate. The time domain expression of the system output can be obtained from differential equations or transfer functions. Time domain analysis directly analyzes the system in the time domain, so it has the advantages of being intuitive and accurate, and can provide all the information of the system's time response.

[0054] Frequency domain analysis: It is a method of converting a signal in the time domain to the frequency domain through Fourier transform for analysis. It is mainly used to describe the frequency structure of the signal and the amplitude of each frequency component. Frequency domain analysis has a wide range of applications in control systems. It evaluates system performance by studying the frequency characteristics of the system without directly solving the differential equations of the system. This method can indirectly reveal the time domain performance of the system, conveniently show the impact of system parameters on system performance, and guide how to design corrections.

[0055] Amplitude margin: refers to the amplitude of the open-loop transfer function when the closed-loop control system is in a critical stable state. The amplitude margin can be obtained by calculating the amplitude of the open-loop transfer function at the crossover frequency and taking its inverse. The crossover frequency refers to the frequency at which the amplitude of the open-loop transfer function is equal to 1. The larger the amplitude margin, the more robust the system is to parameter changes and external disturbances. A larger amplitude margin means that the system can tolerate larger parameter errors or disturbances without losing stability. The amplitude margin is generally set to be greater than 1 (for example, 6dB) to ensure that the system is stable and has sufficient robustness.

[0056] Phase margin: 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°. The larger the phase margin, the better the stability of the system. A larger phase margin means that the system can tolerate a larger phase lag before approaching an unstable state. The phase margin is generally set to 30° to 60° to ensure that the system is stable and has good dynamic response.

[0057] In different application scenarios and operating conditions of the reactor, a good control system needs to ensure the stability and safety of the system. However, for the control system, it is often difficult to obtain better control parameters, especially since the reactor often has the characteristics of uncertainty, nonlinearity, correlation between variables, incomplete information and large hysteresis. It is very difficult to obtain control parameters that meet different application scenarios and different operating requirements.

[0058] At present, the design of reactor control systems is generally based on time domain methods and frequency domain methods. The time domain method mainly considers indicators such as system overshoot and adjustment time, while the frequency domain method mainly considers indicators such as amplitude margin, phase margin, and frequency domain crossing. The reactor control system design generally sets the amplitude margin to no less than 6dB and the phase margin to 30° to 60°. These design requirements are used to ensure that the system can basically remain stable when facing input signals of different frequencies in this application scenario. However, under normal circumstances, there are many groups of control parameters that can meet the requirements of amplitude margin and phase margin of a single application scenario. In actual operation, it is necessary to further determine the optimal control parameters to meet different performance indicators such as steady-state error, overshoot, and adjustment time. This process generally requires the operator to manually fine-tune the given control parameters multiple times based on experience. The process is relatively cumbersome, and the control effect mostly depends on the operator's experience and judgment. New application scenarios will lead to the increase of new operating constraints. There is no quantitative method for the performance indicators that need to be focused on in different application scenarios, and it is difficult to identify them. However, the existing control system parameter debugging methods cannot simultaneously meet the constraint requirements of different application scenarios, and have low flexibility, making it difficult to further optimize control parameters. As a result, it is difficult to formulate reasonable and feasible parameter debugging and optimization plans for reactors in different application scenarios.

[0059] Therefore, improving the existing time domain design method, optimizing the control system parameters, and enabling the reactor to meet the control performance index requirements under different application scenarios and different operating conditions are the key points and difficulties in the current reactor control system design. The control system frequency domain analysis method can directly obtain the frequency characteristics through experiments to analyze the quality of the control system, analyze the system stability, steady-state characteristics, and dynamic characteristics based on the frequency characteristics or frequency response, and draw qualitative and quantitative conclusions. It has obvious physical meanings and can make up for the shortcomings of the time domain analysis method for complex object or system analysis.

[0060] Based on this, the embodiment of the present application provides a parameter determination method, device, electronic device and storage medium for a reactor control system, aiming to solve the problem that the reactor is difficult to optimize the control parameters under different application scenarios and different constraints. The embodiment of the present application adopts a time domain and frequency domain comprehensive analysis method combining multiple performance indicators and variable weight coefficients to realize the control parameter debugging and optimization of different application scenarios and different constraints of the reactor. By using the process data of different scenarios and different working conditions of the reactor as modeling preparation data, the corresponding relationship model of the input variables and output variables of the system is established using the actual operation data on site; the Monte Carlo method is combined with the frequency domain analysis to obtain the constraint boundary of the control system performance indicator, and the initial optimal control parameter set is obtained under the requirements of the amplitude margin and phase margin of the control system being met first; the performance indicator range corresponding to different control parameters in each set is calculated, and the indicators that need to be focused on in the current operation stage are ranked and prioritized, while limiting other performance indicators within the constraint boundary; the variable weight method is combined to determine the most suitable control parameters for the current state from the initial optimal control parameter set, thereby realizing the optimization of control parameters under different scenarios and different working conditions of the reactor.

[0061] The parameter determination method, device, electronic device and storage medium of the reactor control system provided in the embodiments of the present application are specifically explained through the following embodiments. First, the parameter determination method of the reactor control system in the embodiments of the present application is described.

[0062] The parameter determination method of the reactor control system provided in the embodiment of the present application relates to the field of nuclear power technology. The parameter determination method of the reactor control system provided in the embodiment of the present application can be applied to the terminal, can also be applied to the server side, and can also be software running in the terminal or the server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the parameter determination method of the reactor control system, etc., but is not limited to the above forms.

[0063] The present application can be used in many general or special computer system environments or configurations. For example: 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, distributed computing environments including any of the above systems or devices, etc. The present 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. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0064] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

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

[0066] Step S100, sampling the operating data of the reactor control system under preset conditions within a first preset time period to obtain multiple groups of measurement data, each group of measurement data including 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 within a first preset time period is sampled. The preset conditions may be the operating state of the reactor in different application scenarios, or a specific operating state of the reactor that is preset, such as full power operation, etc., which is not limited here. The first preset time period is a preset time range, which depends on the required statistical data and system characteristics, which is not limited here. By setting different preset conditions, the control parameter debugging and optimization of the reactor in different application scenarios can be achieved.

[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 adjust, which may include but are not limited to: temperature and power control system, primary flow control system, direct current steam generator pressure control system and steam emission control system, which are not limited here.

[0069] By sampling the operation data of the reactor control system, a series of data points are obtained, and these data points form multiple groups of measurement data in chronological order. Each group of measurement data includes at least one control parameter of the reactor control system for controlling the target component. The control parameter is determined according to the characteristics of the target component and the required control parameter type, which is not limited here.

[0070] For multiple groups of measurement data obtained by sampling, since the measurement data are sometimes missing or abnormal values ​​for a period of time due to factors such as sudden equipment failure during the process of collection and recording, it is necessary to fill and correct the measurement data. The way to deal with missing values ​​is: fill them by using the average value of the adjacent data before and after the missing data; the way to deal with abnormal values ​​is: make a horizontal comparison through the continuity characteristics of the process, use the adjacent time data as a reference, set a maximum range of variation, and when the collected data is outside the set range, use the average value of the previous and next moments to stabilize the data. By preprocessing the measurement data, the quality of the data set can be effectively improved, providing a reliable foundation for subsequent data analysis and control system optimization.

[0071] Step S200, determining the performance index of the reactor control system when using the control parameters in each set of measurement data.

[0072] Based on the control parameters in each set of measurement data, the corresponding performance indicators are calculated or simulated. The multiple groups of control parameters are processed in sequence according to the sampling order. For the Mth group of control parameters, the performance indicators of the reactor control system under the condition of using the Mth group of control parameters are obtained according to the Mth group of control parameters and the performance indicator objective function of the reactor control system obtained in advance. The performance indicator objective function is used to calculate the performance of the reactor control system, and M is a positive integer. Among them, the performance indicator objective function includes multiple indicator parameters and a weight coefficient of each of the indicator parameters. The performance indicator objective function is a comprehensive evaluation function, which can combine multiple indicator parameters and indicate the importance of each indicator parameter through a weight coefficient. By analyzing all the calculated performance indicators, one or more groups of control parameters whose performance indicators are most in line with expectations can be selected. The performance of the reactor control system can also be systematically evaluated and optimized by adjusting the weight coefficient or changing the value range of certain control parameters to ensure that it can work safely and effectively under various operating conditions.

[0073] Step S300 , selecting a target performance indicator having the smallest absolute difference between the indicators corresponding to the preset conditions from the performance indicators corresponding to each group of control parameters.

[0074] Compare 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, and select the smallest one from all the calculated absolute values ​​of the difference. The performance index corresponding to this minimum difference is the target performance index. This set of control parameters can make the performance of the reactor control system closest to the index corresponding to the ideal preset conditions under the preset conditions. Among them, the index corresponding to the preset conditions refers to the ideal value of the performance index corresponding to the preset conditions obtained by simulation or calculation based on various parameters in the preset conditions, which is used to judge the control effect of the reactor control system under the preset conditions.

[0075] By selecting the target performance indicator with the smallest absolute value of the indicator difference corresponding to the preset conditions, it can be ensured that the control system can achieve the best control effect under different application scenarios and operating conditions. This method can effectively solve the problem of parameter debugging and optimization of the reactor control system and improve the stability and safety of the control system.

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

[0077] A set of control parameters corresponding to the target performance index is used as the control parameters of the reactor control system when controlling the target component under preset conditions. Under specific application scenarios and operating conditions, this set of control parameters can enable the control system to achieve the best control effect and meet the preset performance index requirements. The optimized control parameters can improve the performance of the control system, enhance the anti-interference ability of the control system, enable it to better cope with external disturbances and internal parameter changes, and improve the stability of the system.

[0078] In one implementation of this embodiment, the weight coefficient of the index parameter corresponding to the target performance index can also be determined according to the obtained target performance index, and the obtained weight coefficient is output as the optimal weight coefficient of the performance index objective function. By optimizing the weight coefficient, the conflict between different performance indicators is balanced, thereby obtaining better control performance; helping the control system to deal with model errors and external disturbances and improving the robustness of the system; and more precise parameter adjustments can be made according to the 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 can make the system performance closest to the indicators corresponding to the preset conditions, thereby realizing parameter debugging and optimization of the reactor control system. By analyzing and comparing the performance indicators, the optimal control parameters can be automatically selected to reduce manual intervention and debugging workload; by comparing the indicators 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 can maintain good performance under various conditions and improve 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, obtaining multiple sets of simulation control parameters by randomly perturbing the coefficients of the transfer function of the target component operation model, wherein the target component operation model is used to simulate the operation state of the target component;

[0082] Step S220, determining a second value range of each of the indicator parameters according to the multiple groups of simulation control parameters;

[0083] Step S230, for each of the indicator parameters in each set of measurement data, executing: when taking a minimum value within a target range of the indicator parameter, calculating a weight coefficient of each of the indicator parameters in each set of measurement data and a parameter value of the indicator parameter, wherein the target range of the indicator parameter is determined according to a first value range of the indicator parameter in the measurement data and a second value range of the indicator parameter;

[0084] Step S240: obtaining the performance indicator corresponding to each set of measurement data according to the weight coefficient of each indicator parameter in each set of measurement data and the parameter value of the indicator parameter.

[0085] First, a model that can simulate the operating state of the target component is constructed, namely the target component operation model. The target component operation model is used to simulate the operating state of the target component in order to evaluate the effect of the control parameters. The target component operation model includes all key physical processes and can describe the relationship between input and output through transfer functions. 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 transfer function coefficients of the target component operation model are randomly perturbed, and multiple groups of simulation control parameters are generated through random perturbations. These parameters will be used to simulate different operating states of the target component.

[0087] Then, determine the target range of each indicator parameter, including the first value range and the second value range. Specifically, each set of measurement data includes the first value range of each indicator parameter, and the first value range of the indicator parameter can be directly obtained through the measurement data or calculated through the measurement data. For each set of simulation control parameters, run the target component operation model and record the actual output values ​​of each performance indicator. Based on these output values, determine the second value range of each indicator parameter. This range is based on the simulation results and reflects the possible range of variation of the indicator parameter under the simulation conditions.

[0088] For each indicator parameter in each set of measurement data, when the minimum value is taken within its target range, the following operations are performed: according to the minimum value of each indicator parameter, the weight coefficient of each indicator parameter in each set of measurement data is calculated, and the parameter value of the indicator parameter is determined. Among them, the weight coefficient reflects the importance of the indicator parameter to the overall performance indicator.

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

[0090] Among them, the performance indicator objective function is Among them, F(t) is the control system performance at time t, T t (t) is the control system adjustment time at time t; T r (t) is the recovery time of the control system disturbance at time t; t (t) is the overshoot of the control system at time t; r (t) is the maximum disturbance deviation of the control system at time t; α, β, λ are weight coefficients.

[0091]

[0092] T t =T t∞ -T t0 ;

[0093] T r =T r∞ -T r0 ;

[0094] Among them, σ t is the control system overshoot, C t (t p ) is the maximum output value under step action, C t (∞) is the steady-state value of the output under step action; σ r is the maximum deviation of the control system disturbance, C r (t p ) is the maximum value of the output under the disturbance, C r (∞) is the steady-state value of the output under disturbance; T t is the control system adjustment time, T t∞ is the steady-state time under step action, T t0 is the initial time of step action; T r is the control system disturbance recovery time, T r∞ is the steady-state time under disturbance, T r0 is the initial time of the disturbance.

[0095] The adjustment time and overshoot of the control system can be constrained by the oscillation frequency and damping ratio (0.4-0.8 underdamping). 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 attenuation degree 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 adjustment time, but the greater the overshoot; the higher the damping ratio, the smoother the system response, the smaller the overshoot, but the longer the adjustment time. 0.4-0.8 underdamping means that the system's damping ratio is between 0.4 and 0.8. Within this range, the system has a certain damping that can suppress overshoot, and a certain response speed that can ensure the adjustment time. By using the oscillation frequency and damping ratio to constrain the adjustment time and overshoot, the dynamic response characteristics of the control system can be effectively controlled, thereby achieving the expected control effect. The corresponding constraint relationship is as follows:

[0096]

[0097] 300≤γ=1800+∠G(jwc)≤600|Gk(jwc)|=1;

[0098] k g =-20lgG k (jw g )≥0φ(jw g ) = -1800;

[0099] Among them, ξ is the damping ratio of the controlled object (disturbance), w n is the oscillation frequency of the controlled object (disturbance), Δ is the steady-state error accuracy, w c is the open-loop cut-off frequency, γ is the phase margin, k g is the amplitude margin.

[0100] In other words, the performance index is not only constrained by the target range of the index parameter, but also by other related indicators such as damping ratio, oscillation frequency, steady-state error accuracy, open-loop cutoff frequency, phase margin, amplitude margin, etc. The related constraint indicators can be set according to the actual situation and are not limited here. Through the joint action of the above constraints, it can be ensured that the optimization results not only meet the performance indicators, but also meet the requirements of system stability and safety. By setting the constraints reasonably, the multi-objective optimization of the control system can be achieved and the safe and stable operation of the system can be ensured.

[0101] In this embodiment, the comprehensive performance objective function of the fast response mode and the smooth operation mode is set. In the fast response mode, priority is given to satisfying the objective function obtained by weighting the control system adjustment time and the control system disturbance recovery time, that is, priority is given to making T t (t), T r (t) Under the above constraints, the minimum value is obtained; and at this time Ot (t), O r (t) The constraints that need to be met are the upper and lower bounds of the overshoot and maximum disturbance offset performance indicators obtained by the Monte Carlo experiment. In the stable operation mode, priority is given to satisfying the objective function obtained by weighting the control system overshoot and the control system maximum disturbance offset, that is, priority is given to making O t (t), O r (t) Under the above constraints, the minimum value is obtained; and at this time T t (t), T r (t) The constraints that need to be met are the upper and lower bounds of the adjustment time and disturbance recovery time performance indicators obtained by the Monte Carlo experiment.

[0102] Through the comprehensive performance objective function of the fast response mode and the smooth operation mode, the values ​​of the fast response mode and the smooth operation mode can be combined to obtain an optimal solution under comprehensive conditions, that is, the optimal solution of F(t). The comprehensive performance objective function of the fast response mode and the smooth operation mode is as follows:

[0103]

[0104] This embodiment can determine the performance index of the reactor control system under different control parameters through random perturbation of the transfer function coefficient, determination of the target range of the index parameter, and calculation of the weight coefficient and parameter value, and find the optimal control parameter and its weight coefficient, thereby improving the performance of the control system. By setting different constraints, the control parameter debugging and optimization of the reactor under different constraints can be arbitrarily realized.

[0105] In some embodiments, step S210 may include, but is not limited to, steps S211 to S213:

[0106] Step S211, performing model identification on the controlled object model and the internal and external disturbance models through the historical measurement data of the target component to obtain the target component operation model;

[0107] Step S212, obtaining a set of initial simulation control parameters according to a preset amplitude margin and a preset phase margin;

[0108] Step S213, performing random perturbations of a preset number of times on the transfer function coefficients of the target component operation model within a target preset range according to the initial simulation control parameters, to obtain multiple sets of simulation control parameters.

[0109] The controlled object model refers to the mathematical model of the physical process or equipment that needs to be controlled in the reactor control system. It describes the relationship between the input variables (such as control signals) and the output variables (such as temperature, pressure, etc.) of the controlled object. The internal and external disturbance model refers to the mathematical model of the external interference and internal disturbance that affect the performance of the reactor control system. It describes the impact of these disturbances on the output variables of the controlled object.

[0110] The target component operation model can be obtained by model identification of historical measurement data, or by applying a small range of operating condition step response experiments. The historical data of the target component is obtained from the reactor control system, including input (such as control signal) and output (such as temperature, pressure and other performance indicators), and the model of the controlled object model and the internal and external disturbance model are model identified using the historical measurement data of the target component or the step response experiment data. The model identification of the controlled object model and the internal and external disturbance model is performed based on the historical measurement data. The main model identification methods that can be used include but are not limited to the least squares identification method, the asymptotic identification method, the subspace system identification method, the finite impulse response identification method, etc., which are not limited here. Various identification methods can also be combined with each other to form a combined variable weight identification method. The selection of weights of various identification methods is related to the error of the identification method. The larger the error of the identification method, the smaller the weight. The variable weight method includes but is not limited to the equal weight method, the inverse square error method, and the advantage matrix method, which are not limited here.

[0111] Specifically, the model identification of the controlled object is to establish a dynamic mathematical model of the controlled object (such as temperature, power, flow, etc.) to describe the relationship between its input (such as control signal) and output (such as actual measurement value). The internal and external disturbance model identification is to establish a model of the impact of internal and external disturbances (such as temperature disturbance, flow disturbance, etc.) on the controlled object, and describe the relationship between the disturbance and the output of the controlled object. Through model identification, the behavior of the controlled object can be more accurately predicted and controlled, and the accuracy and stability of the control system can be improved; based on the model identification results, more effective control strategies can be designed, such as the optimization of PID controller parameters, to improve the performance of the control system; through model identification, the behavior of the controlled object under different working conditions can also be predicted, providing a reference for system design and operation.

[0112] like Figure 2 As shown, the process of obtaining multiple sets of simulation control parameters is as follows:

[0113] First, the target component operation model is obtained, and a control scheme is designed according to the target component operation model, including single-stage, cascade, composite and other designs, which are not limited here.

[0114] Then, formulate the amplitude margin and phase margin requirements, for example: set the amplitude margin to no less than 6dB, and the phase margin to 30° to 60°, which are not limited here. Amplitude margin and phase margin are important indicators of control system stability, which ensure that the system remains stable within a certain disturbance range. Amplitude margin refers to the amount of input amplitude increase that the system can withstand before it starts to oscillate, while phase margin refers to the amount of phase lag that the system can withstand before it starts to oscillate. According to the simulation results of the target component operation model, it is judged whether the control scheme meets the amplitude margin and phase margin requirements. If it meets the requirements, a set of initial simulation control parameters are calculated based on the set amplitude margin and phase margin; if it does not meet the requirements, the control structure or control scheme is changed again, and the simulation is re-performed until a set of initial control parameters is obtained.

[0115] Finally, based on the initial control parameters obtained, the transfer function coefficients of the target component operation model are randomly perturbed for a preset number of times within the target preset range through Monte Carlo simulation to obtain multiple sets of simulation control parameters. Among them, the target preset range can be set manually based on experience and is not limited here; the preset number is determined based on the amount of data required for subsequent simulations and is not limited here.

[0116] In one implementation of this embodiment, taking the parameter optimization of the OTSG steam pressure cascade control system as an example, the steps of obtaining multiple groups of simulation control parameters are exemplarily demonstrated.

[0117] Through model identification, the transfer function model of each link of the control system under full load conditions is obtained as follows:

[0118] As shown in Table 1:

[0119]

[0120] Table 1

[0121] The PID cascade control scheme is used to design the control system. The control system structure is as follows: Figure 3 As shown in the figure, r is the OTSG pressure setting value; y is the actual value of the control system output; δT i is the primary inlet temperature disturbance; δW S is the relative steam flow disturbance; PM and FM represent pressure measurement feedback and flow measurement feedback respectively; f q is the steam flow correction coefficient when the valve is fully open and at full load; G1(S) is the transfer function between the valve opening and the relative feedwater flow at the secondary inlet; G2(S) is the transfer function between the relative feedwater flow at the secondary inlet and the secondary steam pressure; G3(S) is the transfer function between the primary inlet temperature and the secondary steam pressure; G4(S) is the transfer function between the relative steam flow and the secondary steam pressure.

[0122] The amplitude margin is set to no less than 6dB, and the phase margin is set to 30° to 60° to obtain a set of initial control parameters. Then the transfer function coefficients of the controlled object model and the disturbance model are perturbed by ±40%, and the Monte Carlo method is used to perform random simulations 50 times, and each set of control parameters of the 50 simulations is recorded. These control parameters meet the control system design requirements in both the time domain and the frequency domain. Therefore, the corresponding controller parameter combinations can be regarded as better control parameters.

[0123] This embodiment obtains multiple sets of simulated control parameters by randomly perturbing the coefficients of the transfer function of the target component operation model, and can evaluate the adaptability of the control parameters to different operating conditions and model errors, so as to select more robust control parameters to ensure that the system can maintain stable operation under various conditions; on the basis of multiple sets of simulated control parameters, combined with the variable weight method, the weights of performance indicators can be adjusted according to the current operating status, so as to further optimize the control parameters and improve the control effect.

[0124] In some embodiments, step S213 may include but is not limited to steps S2131 to S2134:

[0125] Step S2131, performing N random perturbations on the transfer function coefficients of the target component operation model within a target preset range according to the initial simulation control parameters, where N is a positive integer, and for each random perturbation, executing:

[0126] Step S2132, if the target component operation model is controlled to be randomly perturbed within the target preset range;

[0127] Step S2133, if the target component operation model satisfies the preset condition after the target component operation model is randomly perturbed within the target preset range, a set of simulation control parameters corresponding to this random perturbation is output;

[0128] Step S2134, if the target component operation model does not meet the preset conditions after the random perturbation, the value range of the target preset range is reduced to obtain the preset range corresponding to the next random perturbation, and the preset range corresponding to the next random perturbation is used as the target preset range, and the step of controlling the target component operation model to randomly perturb within the target preset range is executed.

[0129] First, it is necessary to determine the target preset range of the random perturbation of the transfer function coefficient and the preset number N (a positive integer). Then, the transfer function coefficient of the target component running model is randomly perturbed N times within the target preset range.

[0130] For each random perturbation, the following steps are performed:

[0131] After each random perturbation, check whether the target component operation model meets the preset conditions, that is, whether the target component operation model is stable. Stability can be determined by eigenvalue analysis, frequency response analysis or other stability criteria of the closed-loop system, which is not limited here.

[0132] If the model remains stable after the perturbation, a set of simulated control parameters corresponding to the perturbation is output. If the model is unstable, the range of the target preset range is reduced to reduce the perturbation amplitude, which helps to narrow the search space and avoid unstable control parameters. The random perturbations are re-performed within the reduced target preset range, and the model stability is evaluated again until a set of simulated control parameters that stabilize the model is found, which is then saved and output.

[0133] This embodiment ensures that all output analog control parameters can keep the system running stably through stability checks; dynamically adjusts the perturbation range to ultimately obtain multiple sets of stable and effective analog control parameters, reducing unnecessary calculations and experiments; and improves the reliability and practicality of the analog control parameter set through multiple iterations and stability verifications.

[0134] In some embodiments, step S220 may include, but is not limited to, steps S221 to S222:

[0135] Step S221, for each group of simulation control parameters in the multiple groups of simulation control parameters, by performing step signal simulation on the target component operation model, obtaining a parameter value of each indicator parameter corresponding to each group of simulation control parameters;

[0136] Step S222: determining a second value range of each indicator parameter according to the maximum value and the minimum value of the parameter value of each indicator parameter corresponding to each group of analog control parameters in the multiple groups of analog control parameters.

[0137] For each set of parameters in the multiple sets of simulated control parameters, apply it to the target component operation model. Apply a step signal to the target component operation model to simulate the response of the system. The step signal is a commonly used test signal used to evaluate the dynamic performance of the system. Specifically, the set value tracking simulation and external disturbance simulation can be performed through the step signal. The set value tracking simulation is to simulate the operation of the model under the input signal by setting the input signal of the model to obtain the operation result of the model; the external disturbance simulation is to simulate the influence of external disturbance factors on the model to obtain the operation result of the model. Performing set value tracking simulation and external disturbance simulation on the target component operation model is helpful to evaluate the performance of the control system when facing various disturbances that may be encountered in the actual working environment. Record the parameter value of each indicator parameter corresponding to each set of simulated control parameters during the simulation process. These indicator parameters may include but are not limited to adjustment time, overshoot, disturbance recovery time, and maximum disturbance offset, which are not limited here.

[0138] Analyze all simulation results to find the maximum and minimum values ​​of each indicator parameter. According to these maximum and minimum values, determine the second value range of each indicator parameter. The second value range of each indicator parameter defines the value interval of each indicator parameter. The indicator parameter combinations within the value interval can meet the requirements of the performance indicator.

[0139] In one implementation of this embodiment, two index parameter constraints are specified, including the constraint under the step action of the controlled object and the constraint under the disturbance action. The constraint under the step action of the controlled object takes the adjustment time as the independent variable (horizontal axis) and the overshoot as the dependent variable (vertical axis). Figure 4 As shown, at the same time scale and the same spatial scale, the Monte Carlo method is used to make the controlled object model and the disturbance model undergo large-scale random perturbations, establish a one-to-one correspondence between the adjustment time and the overshoot, and at the same time form quantifiable boundaries of the control system index parameters. The lower boundary is set to the minimum adjustment time and the minimum overshoot, and the upper boundary is set to the maximum adjustment time and the maximum overshoot, so as to determine the feasible constraint interval (i.e., the second value range) of the control system index parameters as the input condition of different objective functions.

[0140] The constraints under the applied disturbance have the disturbance recovery time as the independent variable (horizontal axis) and the maximum disturbance offset as the dependent variable (vertical axis). Figure 5As shown, at the same time scale and the same spatial scale, the Monte Carlo method is used to make the controlled object model and the disturbance model undergo large-scale random perturbations, establish a one-to-one correspondence between the disturbance recovery time and the maximum disturbance offset, and at the same time form a quantifiable boundary of the control system index parameters. The lower boundary is set to the minimum value of the minimum disturbance recovery time and the maximum disturbance offset, and the upper boundary is set to the maximum value of the maximum disturbance recovery time and the maximum disturbance offset, so as to determine the feasible constraint interval (i.e., the second value range) of the control system index parameters as the input condition of different objective functions.

[0141] This embodiment can comprehensively evaluate the impact of control parameters on system performance through simulation of step signals and external disturbances; the second value range can be used as a reference for subsequent optimization and adjustment of control parameters to help determine which parameter values ​​are feasible and reasonable; by determining the value range of the indicator parameters, different indicator parameters in the objective function can be weighted according to different application scenarios and operating conditions, and the optimal control parameter combination can be found.

[0142] The embodiment of the present application obtains multiple groups of measurement data by sampling the reactor control system within a first preset time period, each group containing at least one control parameter. Then, based on the pre-acquired performance index objective function, each group of control parameters is processed in turn, the corresponding performance index is calculated, and the target performance index with the smallest absolute value of the index difference corresponding to the preset condition is selected. In order to evaluate the performance more accurately, multiple groups of simulated control parameters are generated using random perturbations of the transfer function coefficients. These parameters are the operating models obtained by model identification of the historical measurement data of the target component, and the initial simulated control parameters are set according to the amplitude and phase margin. For each random perturbation, if the model is stable, the group of parameters is output; if it is unstable, the perturbation range is reduced until multiple groups of stable simulated control parameters are obtained. Subsequently, by performing step signal simulation on the target component operation model, the maximum and minimum values ​​of each index parameter are determined, thereby defining the second value range. Finally, a group of control parameters corresponding to the target performance index and its weight coefficient are applied to the reactor control system as the optimal control setting. The embodiment of the present application provides a time domain and frequency domain comprehensive analysis method combining multiple performance indicators and variable weight coefficients, which can obtain the importance ranking of various performance indicators in the current state according to the constraints of different application scenarios and different operating conditions of the reactor, and assign reasonable weight coefficients to various indicators, thereby realizing multi-scenario and multi-objective control parameter optimization. The embodiment of the present application has a relatively wide adaptability to the reactor application scenarios and operating conditions. While ensuring a good control effect, it can reduce frequent control parameter setting operations, which is conducive to the safe and stable operation of the reactor.

[0143] See also Figure 6The embodiment of the present application further provides a parameter determination device 500 for a reactor control system, which can implement the parameter determination method of the reactor control system, and the device includes:

[0144] The sampling module 10 is used to sample the operation data of the reactor control system within a first preset time period under preset conditions to obtain multiple groups of measurement data, each group of measurement data including at least one control parameter of the reactor control system for controlling the target component; the reactor control system is used to control the target component of the reactor;

[0145] A calculation module 20, for determining the performance index of the reactor control system when the control parameters in each set of measurement data are used;

[0146] A selection module 30 is used to select, from the performance indicators corresponding to each group of control parameters, a target performance indicator having the smallest absolute difference between the indicators corresponding to the preset conditions;

[0147] 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.

[0148] In some embodiments, the calculation module 20 may include:

[0149] A processing submodule is used to process the multiple groups of control parameters in sequence according to the sampling order. For the Mth group of control parameters, according to the Mth group of control parameters and the pre-acquired performance indicator target function of the reactor control system, the performance indicator of the reactor control system when the Mth group of control parameters is adopted is obtained, and the performance indicator target function is used to calculate the performance of the reactor control system, and M is a positive integer.

[0150] In some implementations, the calculation module 20 may further include:

[0151] a perturbation submodule, used to obtain multiple sets of simulation control parameters by randomly perturbing the coefficients of the transfer function of the target component operation model, wherein the target component operation model is used to simulate the operation state of the target component;

[0152] A range acquisition submodule, used to determine a second value range of each of the indicator parameters according to multiple groups of simulation control parameters;

[0153] A calculation submodule, for each of the indicator parameters in each set of measurement data, executing: when taking the minimum value within the target range of the indicator parameter, calculating the weight coefficient of each of the indicator parameters in each set of measurement data, and the parameter value of the indicator parameter, wherein the target range of the indicator parameter is determined according to the first value range of the indicator parameter in the measurement data and the second value range of the indicator parameter; the performance indicator objective function includes multiple indicator parameters and the weight coefficient of each of the indicator parameters, and each set of measurement data includes the first value range of each of the indicator parameters;

[0154] The performance indicator acquisition submodule is used to obtain the performance indicator corresponding to each set of measurement data according to the weight coefficient of each indicator parameter in each set of measurement data and the parameter value of the indicator parameter.

[0155] In some embodiments, the perturbation submodule may include:

[0156] A model identification unit, used to perform model identification on a controlled object model and internal and external disturbance models through historical measurement data of the target component to obtain an operation model of the target component;

[0157] An analog control parameter acquisition unit, used to obtain a set of initial analog control parameters according to a preset amplitude margin and a preset phase margin;

[0158] The perturbation unit is used to randomly perturb the transfer function coefficients of the target component operation model within a target preset range for a preset number of times according to the initial simulation control parameters to obtain multiple groups of simulation control parameters.

[0159] In some embodiments, the perturbation unit may include:

[0160] The perturbation subunit is used to perform N random perturbations on the transfer function coefficients of the target component operation model within a target preset range according to the initial simulation control parameters, where N is a positive integer, and for each random perturbation, execute:

[0161] A control subunit, used for controlling the target component operation model to be randomly perturbed within the target preset range;

[0162] An output subunit, configured to output a set of simulation control parameters corresponding to the random perturbation if the target component operation model satisfies a preset condition after the target component operation model is randomly perturbed within the target preset range;

[0163] 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, 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 operation model to randomly perturb within the target preset range.

[0164] In some implementations, the range acquisition submodule may include:

[0165] A simulation unit, configured to obtain a parameter value of each indicator parameter corresponding to each group of simulation control parameters by performing a step signal simulation on the target component operation model for each group of simulation control parameters in the plurality of groups of simulation control parameters;

[0166] The range acquisition unit is used to determine the second value range of each indicator parameter according to the maximum value and the minimum value of the parameter value of each indicator parameter corresponding to each group of analog control parameters in the multiple groups of analog control parameters.

[0167] The specific implementation of the parameter determination device of the reactor control system is basically the same as the specific implementation of the parameter determination method of the above-mentioned reactor control system, and will not be repeated here.

[0168] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned parameter determination method of the reactor control system when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0169] An embodiment of the present application also provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device implements the parameter determination method of the reactor control system.

[0170] See also Figure 7 , Figure 7 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0171] The processor 701 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (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 the present application;

[0172] The memory 702 may be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 702 may store an operating system and other application programs. When the technical solution provided in the embodiments of this specification is implemented by software or firmware, the relevant program code is stored in the memory 702, and the processor 701 calls and executes the parameter determination method of the reactor control system of the embodiment of the present application;

[0173] Input / output interface 703, used to implement information input and output;

[0174] Communication interface 704, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0175] A bus 705 that transmits information between the various components of the device (e.g., the processor 701, the memory 702, the input / output interface 703, and the communication interface 704);

[0176] The processor 701 , the memory 702 , the input / output interface 703 and the communication interface 704 are connected to each other in communication within the device via a bus 705 .

[0177] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the parameter determination method of the reactor control system.

[0178] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0179] The parameter determination method of the reactor control system, the parameter determination device of the reactor control system, the electronic device and the storage medium provided in the embodiment of the present application obtain multiple groups of measurement data by sampling the operating data of the reactor control system in the first preset time period under preset conditions, each group of measurement data includes at least one control parameter for the reactor control system to control the target component; based on the control parameters in each group of measurement data, the corresponding performance index is calculated or simulated, and then the performance index with the smallest absolute value of the difference between the index corresponding to the preset condition is selected from the performance index corresponding to each group of control parameters as the target performance index; a group of control parameters corresponding to the target performance index is used as the optimized control parameters for the control of the target component by the reactor control system under preset conditions. The embodiment of the present application analyzes the system performance under different control parameters, selects the control parameters that can 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 then realizing the control parameter debugging and optimization of the reactor under different application scenarios and different constraints.

[0180] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0181] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0182] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0183] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0184] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0185] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0186] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0187] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0188] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0189] If the integrated unit is implemented in the form of 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 the present application, 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, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0190] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for determining parameters of a reactor control system, wherein the reactor control system is used to control a target component of the reactor, characterized in that: The method comprises: Sampling the operating data of the reactor control system within a first preset time period under preset conditions to obtain multiple groups of measurement data, each group of measurement data including at least one control parameter of the reactor control system for controlling the target component; Determining performance indicators of the reactor control system when using the control parameters in each set of measurement data; Selecting a target performance indicator having the smallest absolute difference between the indicators corresponding to the preset conditions from the performance indicators corresponding to each group of control parameters; A set of control parameters corresponding to the target performance index is used as control parameters when the reactor control system controls the target component under preset conditions.

2. The method according to claim 1, characterized in that The step of determining the performance index of the reactor control system when each group of control parameters is used includes: The multiple groups of control parameters are processed in sequence according to the sampling order. For the Mth group of control parameters, the performance indicators of the reactor control system when the Mth group of control parameters is adopted are obtained according to the Mth group of control parameters and the performance indicator target function of the reactor control system obtained in advance. The performance indicator target function is used to calculate the performance of the reactor control system, and M is a positive integer.

3. The method according to claim 2, characterized in that The performance indicator objective function includes a plurality of indicator parameters and a weight coefficient of each of the indicator parameters, and each set of measurement data includes a first value range of each of the indicator parameters; Determining the performance index of the reactor control system when using the control parameters in each set of measurement data includes: By randomly perturbing the coefficients of the transfer function of the target component operation model, a plurality of sets of simulation control parameters are obtained, wherein the target component operation model is used to simulate the operation state of the target component; Determining a second value range of each of the indicator parameters according to the multiple groups of simulation control parameters; For each of the indicator parameters in each set of measurement data, executing: when taking the minimum value within the target range of the indicator parameter, calculating the weight coefficient of each of the indicator parameters in each set of measurement data and the parameter value of the indicator parameter, wherein the target range of the indicator parameter is determined according to the first value range of the indicator parameter in the measurement data and the second value range of the indicator parameter; According to the weight coefficient of each indicator parameter in each set of measurement data and the parameter value of the indicator parameter, the performance indicator corresponding to each set of measurement data is obtained.

4. The method according to claim 3, characterized in that By randomly perturbing the coefficients of the transfer function of the target component operation model, multiple sets of simulation control parameters are obtained, including: Performing model identification on the controlled object model and the internal and external disturbance models through the historical measurement data of the target component to obtain the target component operation model; A set of initial simulation control parameters is obtained according to a preset amplitude margin and a preset phase margin; According to the initial simulation control parameters, the transfer function coefficients of the target component operation model are randomly perturbed a preset number of times within a target preset range to obtain multiple groups of simulation control parameters.

5. The method according to claim 4, characterized in that The transfer function coefficients of the target component operation model are randomly perturbed 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, including: According to the initial simulation control parameters, the transfer function coefficient of the target component operation model is randomly perturbed N times within the target preset range. For each random perturbation, the following is executed: Control the target component operation model to randomly perturb within the target preset range; If the target component operation model satisfies the preset conditions after the target component operation model is randomly perturbed within the target preset range, a set of simulation control parameters corresponding to this random perturbation is output; If the target component operation 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, and the preset range corresponding to the next random perturbation is used as the target preset range, and the step of controlling the target component operation model to randomly perturb within the target preset range is executed.

6. The method according to claim 3, characterized in that: Determining the second value range of each of the indicator parameters according to the multiple groups of simulation control parameters includes: For each group of simulation control parameters in the plurality of groups of simulation control parameters, a parameter value of each indicator parameter corresponding to each group of simulation control parameters is obtained by performing a step signal simulation on the target component operation model; According to the maximum value and the minimum value of the parameter value of each indicator parameter corresponding to each group of analog control parameters in the multiple groups of analog control parameters, the second value range of each indicator parameter is determined.

7. A parameter determination device for a reactor control system, characterized in that: The device comprises: a sampling module, used for sampling the operation data of the reactor control system within a first preset time period under preset conditions to obtain multiple groups of measurement data, each group of measurement data including at least one control parameter for the reactor control system to control the target component; the reactor control system is used to control the target component of the reactor; A calculation module, used to determine the performance index of the reactor control system under the condition of using the control parameters in each set of measurement data; A selection module is used to select, from the performance indicators corresponding to each group of control parameters, a target performance indicator having the smallest absolute difference with the indicator corresponding to the preset condition; The parameter determination module is used to use a group of control parameters corresponding to the target performance index as control parameters when the reactor control system controls the target component under preset conditions.

8. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the parameter determination method of the reactor control system according to any one of claims 1 to 6 when executing the computer program.

9. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the parameter determination method for a reactor control system according to any one of claims 1 to 6.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the parameter determination method of the reactor control system according to any one of claims 1 to 6 is implemented.

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