Liquid level nonlinear dynamic feature construction method and device and storage medium

Abstract

The invention discloses a liquid level nonlinear dynamic feature construction method and device and a storage medium, and relates to the technical field of system control, and the method comprises the steps: carrying out the preprocessing of collected input data and output data of a steady state working condition at any moment of a system, and constructing an input data state vector and an output data state vector; constructing a system time sequence basic structure function according to the input data state vector and the output data state vector; constructing a state time sequence vector and a time factor attenuation to rewrite the system time sequence basic structure function into a system state space equation; and solving a nonlinear dynamic characteristic matrix of the system based on the system state space equation. According to the method, a state vector and a time sequence basic structure function are constructed, the dependence on a mechanism model is reduced, a state-space equation is constructed by using data, a tedious model simplification process is avoided, dynamic characteristics of the system are directly extracted from input and output data, and a large number of structure parameters do not need to be counted.

Description

Technical Field

[0001] The present application relates to the field of system control technology, and more specifically, to a construction method, device and storage medium for nonlinear dynamic characteristics of liquid level. Background Art

[0002] The performance of the water supply system of a steam power system directly affects the operating efficiency and safety of the entire steam power system. The water supply system has highly nonlinear dynamic characteristics, including the interaction and changes of multiple variables such as flow, pressure, and temperature. These characteristics make it difficult for traditional linear models to accurately describe the system behavior.

[0003] The nonlinear characteristics of the power plant are obvious during operation, and it is often difficult to obtain the state space model of its dynamic characteristics for controller design. The system has characteristics such as multiple inputs and multiple outputs and overdrive, and the coupling relationship between various control and state variables is obvious. The existing methods generally achieve decoupling control of nonlinear systems through local linearization and relative order calculation.

[0004] However, the methods in the prior art rely on the accuracy of the mechanism model of the water supply system of the steam power system. The model simplification steps are complicated, and the model requires statistics of a large number of structural parameters. The internal parameters of some systems are difficult to obtain, making it difficult to apply the designed controller in engineering. Summary of the invention

[0005] In response to at least one defect or improvement need in the prior art, the present invention provides a method, device and storage medium for constructing nonlinear dynamic characteristics of liquid level, which are used to solve the problems in the prior art that the accuracy of the mechanism model is dependent, the model simplification steps are complicated, the model needs to count a large number of structural parameters, the internal parameters of some systems are difficult to obtain, and it is difficult to apply the designed controller in an engineering manner.

[0006] To achieve the above object, according to a first aspect of the present invention, a method for constructing nonlinear dynamic characteristics of liquid level is provided, comprising:

[0007] Preprocess the input data and output data of the system's steady-state working condition at any time to construct an input data state vector and an output data state vector;

[0008] Construct the basic structure function of system timing according to the input data state vector and the output data state vector;

[0009] Construct the state time series vector and time factor attenuation to rewrite the system time series basic structure function into the system state space equation;

[0010] The nonlinear dynamic characteristic matrix of the system is solved based on the system state space equation.

[0011] In a possible implementation, the collected input data and output data of the steady-state working condition of the system at any time are preprocessed to construct an input data state vector and an output data state vector, respectively, including:

[0012] Synchronize the timestamps of the input and output data collected by the sensor and acquisition board;

[0013] Filtering the synchronized input data and output data;

[0014] The input data and output data after filtering are averaged;

[0015] Resample the averaged input data and output data according to a preset acquisition period;

[0016] An input data state vector and an output data state vector are constructed based on the resampled input data and output data.

[0017] In a possible implementation, the input data state vector is u=[np l , Qp l , Op, Qs, Qw, Qc]; the output data state vector is x = [△P, L];

[0018] Among them, l is the number of water supply pumps, np l is the speed of the feed water pump, Qp l is the feedwater pump flow, Op is the feedwater valve opening, Qs is the steam flow, Qw is the feedwater valve flow, Qc is the primary side coolant flow; △P is the pressure difference before and after the feedwater valve, and L is the liquid level of the steam generator.

[0019] In a possible implementation, a system timing basic structure function is constructed according to an input data state vector and an output data state vector, including:

[0020] Determine an input data timing vector matrix and an output data timing vector matrix according to the input data state vector and the output data state vector respectively;

[0021] The basic structure function of the system timing is constructed according to the input data timing vector matrix, the output data timing vector matrix and the system parameters.

[0022] In a possible implementation, the basic structure function of the system timing is:

[0023]

[0024] in, Represents the output data time series vector matrix, Represents the input data time series vector matrix, a i，1 、a i，2、a i，3 、a i，4 and b j，l，1 、b j，l，2 、b j，l，3 、b j，l，4 、b j，l，5 、b j，l，6 、b j，l，7 、b j，l，8 、b j，l，9 、b j，l，10 、b j，l，11 、b j，l，12 is the system parameter.

[0025] In a possible implementation, the construction state time series vector is α k =[X|b j u k +b j-1 u k-1 |b j-1 u k ] Τ ;

[0026] Among them, the time factor attenuation is b j ;u k is the input data state vector at time k.

[0027] In one possible implementation, the system state space equation is:

[0028] α k+1 =Aα k +Bu k+1 ;

[0029] in, A and B are the nonlinear dynamic characteristic matrices of the system.

[0030] In one possible implementation, solving the nonlinear dynamic characteristic matrix of the system based on the state space equation includes:

[0031] The nonlinear dynamic characteristic matrix of the system is solved from the state space equations by the gradient descent method and the fitting accuracy is calculated;

[0032] If the fitting progress is not greater than the preset accuracy threshold, the system parameters of the state space equation are adjusted.

[0033] According to the second aspect of the present invention, there is also provided a device for constructing nonlinear dynamic characteristics of liquid level, which includes at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program, and when the computer program is executed by the processing unit, the processing unit executes the steps of any one of the above-mentioned methods for constructing nonlinear dynamic characteristics of liquid level.

[0034] According to the third aspect of the present invention, there is also provided a storage medium storing a computer program executable by an access authentication device. When the computer program runs on the access authentication device, the access authentication device executes any step of the above-mentioned method for constructing nonlinear dynamic characteristics of liquid level.

[0035] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0036] The present invention provides a method for constructing the nonlinear dynamic characteristics of liquid level. By preprocessing the collected system input and output data, noise can be removed, missing values ​​can be filled, etc., so as to improve the accuracy and reliability of the data. The input and output data are respectively constructed as state vectors, which is helpful to extract the key features of the system, provide a basis for subsequent analysis, and reduce the dependence on the mechanism model. The basic structure function of the time series constructed based on the input data state vector and the output data state vector directly extracts the dynamic characteristics of the system from the input and output data without the need to count a large number of structural parameters, and can reflect the dynamic change characteristics of the system. By constructing the state time series vector and the time factor attenuation, the basic structure function of the system time series is rewritten into the system state space equation, which avoids the cumbersome model simplification process and can more accurately describe the dynamic characteristics of the system changing over time. The nonlinear dynamic characteristic matrix obtained by solving the system state space equation can reveal the nonlinear relationship existing in the system, can more deeply understand the nonlinear dynamic characteristics of the system, and provide strong support for optimizing the control strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 A schematic flow chart of an embodiment of a method for constructing nonlinear dynamic characteristics of liquid level provided by the present invention;

[0039] Figure 2 A system structure diagram of an embodiment of a steam power system provided by the present invention;

[0040] Figure 3 A schematic diagram of the structure of a device for constructing nonlinear dynamic characteristics of liquid level provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention 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 invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0042] The terms "first", "second", "third", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0043] The present invention provides a method, device and storage medium for constructing nonlinear dynamic characteristics of liquid level, which are respectively described below.

[0044] See also Figure 1 , Figure 1 The present invention provides a flow chart of an embodiment of a method for constructing a nonlinear dynamic characteristic of a liquid level. In a specific embodiment of the present invention, a method for constructing a nonlinear dynamic characteristic of a liquid level is disclosed, comprising:

[0045] S101, preprocessing the input data and output data of the system steady-state working condition collected at any time to construct an input data state vector and an output data state vector;

[0046] S102, constructing a system timing basic structure function according to the input data state vector and the output data state vector;

[0047] S103, constructing a state time series vector and a time factor attenuation to rewrite the system time series basic structure function into a system state space equation;

[0048] S104. Solve the nonlinear dynamic characteristic matrix of the system based on the system state space equation.

[0049] In the above embodiment, it is first necessary to pre-process the input data and output data of the steady-state condition collected from the system at any time. The input data includes but is not limited to the number of water supply pumps m, the speed of the water supply pumps np, l , water supply pump flow Qp l, feedwater valve opening Op, steam flow Qs, feedwater valve flow Qw, primary side coolant flow Qc, and the output data include but are not limited to the pressure difference △P before and after the feedwater valve, and the steam generator liquid level L. The preprocessing steps include but are not limited to data cleaning (noise and outlier removal), data normalization or standardization processing to ensure data consistency and comparability. Based on the preprocessed data, the input data state vector and the output data state vector are constructed. The constructed state vector is formed by selecting continuous data points within a certain time window, which can reflect the dynamic changes of the system within the time period.

[0050] Based on the obtained input data state vector and output data state vector, the basic structure function of the system timing is further constructed to capture the nonlinear relationship between input and output and reflect the dynamic characteristics of the system over time. In the construction process, machine learning or deep learning techniques, such as neural networks, support vector machines, etc., can be used to achieve the fitting of complex nonlinear mapping relationships. It is understandable that this solution can also consider introducing a time delay term to more accurately reflect the time dependence of the system.

[0051] In order to transform the basic structure function of the system timing into a form that is easier to analyze and solve, it is necessary to construct the state timing vector and the time factor attenuation. The state timing vector is to expand the input and output state vectors on the time series to form a higher-dimensional vector space to fully describe the state changes of the system. The time factor attenuation is used to describe the law of the system state decaying over time, which is usually related to the stability and response time of the system. By constructing the state timing vector and the time factor attenuation, the basic structure function of the system timing can be rewritten into the system state space equation, which can more intuitively show the dynamic evolution of the system state.

[0052] Finally, based on the obtained system state space equation, numerical calculation or optimization algorithm is used to solve the nonlinear dynamic characteristic matrix of the system. The nonlinear dynamic characteristic matrix contains the dynamic characteristic parameters of the system under different steady-state conditions, such as gain, damping ratio, natural frequency, etc. These parameters are crucial for understanding and controlling the system behavior. The solution process may involve mathematical tools such as matrix operations, linear algebra, and differential equation solving to ensure the accuracy and reliability of the results. By solving the nonlinear dynamic characteristic matrix, the dynamic characteristics of the system can be deeply analyzed to provide support for subsequent control system design, fault diagnosis, and performance optimization.

[0053] See also Figure 2 , Figure 2This is a system structure diagram of an embodiment of the steam power system provided by the present invention. The present invention is explained by the system of the steam power system. The system mainly includes the joint control of the number of pressure heads of the feedwater pump group, the opening of the feedwater regulating valve, the primary side cooling water flow, the steam flow and other factors. The steam flow is mainly affected by the turbine load, which introduces uncertainty disturbances to the liquid level control. The feedwater pump group is generally equipped with multiple feedwater pumps to ensure the safety of the system. This characteristic is a typical over-driven system. The number of working pumps is related to the working conditions, which introduces switching characteristics to the system, resulting in the system having strong nonlinear operating characteristics.

[0054] Compared with the prior art, the method for constructing the nonlinear dynamic characteristics of the liquid level provided in this embodiment can remove noise, fill in missing values, etc. by preprocessing the collected system input and output data, thereby improving the accuracy and reliability of the data, and constructing the input and output data as state vectors respectively, which is helpful to extract the key features of the system, provide a basis for subsequent analysis, and reduce the dependence on the mechanism model. The basic structure function of the time series constructed based on the input data state vector and the output data state vector directly extracts the dynamic characteristics of the system from the input and output data without the need to count a large number of structural parameters, and can reflect the dynamic change characteristics of the system. By constructing the state time series vector and the time factor attenuation, the basic structure function of the system time series is rewritten into the system state space equation, avoiding the cumbersome model simplification process, and can more accurately describe the dynamic characteristics of the system changing over time. The nonlinear dynamic characteristic matrix obtained by solving the system state space equation can reveal the nonlinear relationship in the system, can more deeply understand the nonlinear dynamic characteristics of the system, and provide strong support for optimizing the control strategy.

[0055] See also Figure 3 , Figure 3 The present invention provides Figure 1 In some embodiments of the present invention, the input data and output data of the steady-state working condition of the system at any time collected are preprocessed to construct the input data state vector and the output data state vector, including:

[0056] S301, performing time stamp synchronization on input data and output data collected by the sensor and the acquisition board;

[0057] S302, filtering the synchronized input data and output data;

[0058] S303, performing averaging processing on the input data and output data after the filtering processing;

[0059] S304, resampling the averaged input data and output data according to a preset collection period;

[0060] S305 . Construct an input data state vector and an output data state vector based on the resampled input data and output data.

[0061] In the above embodiments, since the input (such as flow rate, pressure, etc.) and output (such as liquid level height) of the system may be provided by different sensors and acquisition devices, there may be slight differences in their timestamps. In order to ensure the consistency and accuracy of the data, it is necessary to align these data according to the timestamps to ensure that each pair of input and output data corresponds to the system status at the same time, thereby facilitating the analysis and control of the system operation from a timing perspective.

[0062] The purpose of filtering is to remove high-frequency noise and outliers in the data. These noises may be caused by factors such as sensor errors and environmental interference. By selecting a suitable filter (such as a low-pass filter, a median filter, etc., the present invention does not further limit the filtering method), the data curve can be smoothed and the signal-to-noise ratio of the data can be improved.

[0063] After filtering, the input and output data are averaged to further reduce random fluctuations in the data and improve data stability and reliability. Averaging can be achieved by calculating the average value of the data within a certain time window. The selection of the window size should be determined according to the dynamic characteristics of the system and the sampling frequency.

[0064] Since the sampling frequency of the original data may be high or irregular, which may lead to data redundancy or difficulty in processing, resampling can convert the data into a discrete time series with a fixed frequency to facilitate subsequent analysis and processing. In the process of resampling, interpolation algorithms (such as linear interpolation, spline interpolation, etc.) may be needed to estimate missing data points.

[0065] Based on the resampled input data and output data, an input data state vector and an output data state vector are constructed. The state vector is a set of variables that describe the state of the system, and they can fully reflect the dynamic characteristics of the system at a certain moment. In the present invention, the input data state vector may include the current value and historical value of parameters such as flow rate and pressure, while the output data state vector mainly includes the current value and historical value of the liquid level. By constructing the state vectors of the input data and the output data, basic data can be provided for the modeling, analysis and control of the system.

[0066] In some embodiments of the present invention, the input data state vector is u=[np l , Qp l , Op, Qs, Qw, Qc]; the output data state vector is x = [△P, L];

[0067] Among them, l is the number of water supply pumps, np lis the speed of the feed water pump, Qp l is the feedwater pump flow, Op is the feedwater valve opening, Qs is the steam flow, Qw is the feedwater valve flow, Qc is the primary side coolant flow; △P is the pressure difference before and after the feedwater valve, and L is the liquid level of the steam generator.

[0068] In the above embodiment, np in the input data state vector l Reflects the operating status of the water supply pump, Qp l It reflects the output capacity of the feedwater pump, Op determines the flow rate of feedwater entering the steam generator, Qs represents the amount of steam generated by the steam generator, Qw represents the amount of water actually flowing into the steam generator through the feedwater valve, and Qc does not directly affect the liquid level, but may affect the overall thermal balance of the system. The △P in the output data state vector reflects the resistance of the feedwater system, and L directly reflects the liquid level state of the system.

[0069] By defining these state vectors, the complex system dynamic characteristics are converted into specific mathematical expressions. Based on these state vectors, advanced technologies such as machine learning and deep learning are used to build accurate system models to achieve precise control and optimization of liquid levels.

[0070] In some embodiments of the present invention, a system timing basic structure function is constructed according to an input data state vector and an output data state vector, including:

[0071] Determine an input data timing vector matrix and an output data timing vector matrix according to the input data state vector and the output data state vector respectively;

[0072] The basic structure function of the system timing is constructed according to the input data timing vector matrix, the output data timing vector matrix and the system parameters.

[0073] In the above embodiment, according to the input data state vector u=[np l , Qp l , Op, Qs, Qw, Qc] and output data state vector x = [△P, L], respectively construct the input data time series vector matrix U and the output data time series vector matrix X. These two matrices record the input and output states of the system at different time points. The input data time series vector matrix U contains all the input data state vectors from a certain starting moment to the current moment, forming a time series. Similarly, the output data time series vector matrix X contains the output data state vectors corresponding to U, also forming a time series.

[0074] According to the input data timing vector matrix, the output data timing vector matrix and other parameters of the system (such as physical constants, system structure parameters, etc.), the system timing basic structure function is constructed. The system timing basic structure function can capture the nonlinear relationship between the system input and output and reflect the dynamic characteristics of the system changing over time.

[0075] When constructing the basic timing structure function of the system, it is necessary to use historical data or experimental data to train and verify the function to ensure that it can accurately reflect the dynamic behavior of the system. In addition, strategies such as regularization and sparse representation can be introduced to optimize the structure and performance of the basic timing structure function of the system.

[0076] In some embodiments of the present invention, the system timing basic structure function is:

[0077]

[0078] in, Represents the output data time series vector matrix, Represents the input data time series vector matrix, a i，1 、a i，2 、a i，3 、a i，4 and b j，l，1 、b j，l，2 、b j，l，3 、b j，l，4 、b j，l，5 、b j，l，6 、b j，l，7 、b j，l，8 、b j，l，9 、b j，l，10 、b j，l，11 、b j，l，12 is the system parameter.

[0079] In some embodiments of the present invention, the construction state time series vector is α k =[X|b j u k +b j-1 u k-1 |b j-1 u k ] Τ ;

[0080] Among them, the time factor attenuation is b j ;u k is the input data state vector at time k.

[0081] In some embodiments of the present invention, the system state space equation is:

[0082] α k+1 =Aα k +Buk+1 ;

[0083] in, A and B are the nonlinear dynamic characteristic matrices of the system.

[0084] In the above embodiment, it is also necessary to construct a nonlinear mapping function set. As a preferred embodiment, d = 3. The input data state vector and the output data state vector are projected along the constructed nonlinear function set basis space to obtain the mapping of the system in different function spaces, thereby realizing the mapping in space.

[0085] In some embodiments of the present invention, solving the nonlinear dynamic characteristic matrix of the system based on the state space equation includes:

[0086] The nonlinear dynamic characteristic matrix of the system is solved from the state space equations by the gradient descent method and the fitting accuracy is calculated;

[0087] If the fitting progress is not greater than the preset accuracy threshold, the system parameters of the state space equation are adjusted.

[0088] In the above embodiment, the nonlinear dynamic characteristic matrix of the liquid level system is solved from the state space equation using the gradient descent method, which reflects the nonlinear relationship between the system state variables. In the gradient descent method, a loss function (or error function) is defined, which measures the difference between the solution of the state space equation and the actual observation data, and iteratively adjusts the value of the nonlinear dynamic characteristic matrix to minimize the loss function. In each iteration, the gradient of the loss function with respect to the nonlinear dynamic characteristic matrix is ​​calculated, and the value of the matrix is ​​updated according to the gradient direction.

[0089] In the process of solving the nonlinear dynamic characteristic matrix of the system, it is necessary to calculate the fitting accuracy to evaluate whether the solution of the current nonlinear dynamic characteristic matrix is ​​accurate enough. The fitting accuracy can be obtained by comparing the difference between the solution of the state space equation and the actual observed data. A quantitative indicator (such as mean square error, relative error, etc.) is usually used to measure this difference.

[0090] If the calculated fitting accuracy is not greater than the preset accuracy threshold, it means that the solution of the current nonlinear dynamic characteristic matrix is ​​not accurate enough, and the system parameters of the state-space equation need to be adjusted. The system parameters may include the physical constants and structural parameters of the system. After adjusting the system parameters, the nonlinear dynamic characteristic matrix of the system is solved again and the fitting accuracy is calculated until the obtained fitting accuracy reaches or exceeds the preset accuracy threshold.

[0091] It should be noted that the fitting accuracy can be set according to actual conditions, and the present invention does not make any further limitations on this.

[0092] See also Figure 3 , Figure 3 A schematic diagram of the structure of a liquid level nonlinear dynamic characteristic construction device provided by an embodiment of the present invention. Based on the above-mentioned liquid level nonlinear dynamic characteristic construction method, the present invention also provides a liquid level nonlinear dynamic characteristic construction device, and the liquid level nonlinear dynamic characteristic construction device can be a computing device such as a mobile terminal, a desktop computer, a notebook, a palm computer, and a server. The liquid level nonlinear dynamic characteristic construction device 300 includes a processor 310, a memory 320, and a display 330. Figure 3 Only some components of the synchronous tracking flying welding equipment for real-time measurement of battery height are shown, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.

[0093] In some embodiments, the memory 320 may be an internal storage unit of the liquid level nonlinear dynamic characteristics construction device 300, such as a hard disk or memory of the liquid level nonlinear dynamic characteristics construction device 300. In other embodiments, the memory 320 may also be an external storage device of the liquid level nonlinear dynamic characteristics construction device 300, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the liquid level nonlinear dynamic characteristics construction device 300. Further, the memory 320 may also include both the internal storage unit of the liquid level nonlinear dynamic characteristics construction device 300 and an external storage device. The memory 320 is used to store application software and various data installed in the liquid level nonlinear dynamic characteristics construction device 300, such as the program code of the liquid level nonlinear dynamic characteristics construction device 300. The memory 320 may also be used to temporarily store data that has been output or is to be output. In one embodiment, the memory 320 stores a liquid level nonlinear dynamic characteristic construction program 340, which can be executed by the processor 310 to implement the liquid level nonlinear dynamic characteristic construction method of each embodiment of the present application.

[0094] In some embodiments, the processor 310 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 320, such as executing a method for constructing nonlinear dynamic characteristics of liquid level.

[0095] In some embodiments, the display 330 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 330 is used to display information on the liquid level nonlinear dynamic characteristics construction device 300 and to display a visual user interface. The components 310-330 of the liquid level nonlinear dynamic characteristics construction device 300 communicate with each other via a system bus.

[0096] In one embodiment, when the processor 310 executes the liquid level nonlinear dynamic characteristic construction program 340 in the memory 320 , the steps in the above liquid level nonlinear dynamic characteristic construction method are implemented.

[0097] In summary, a method for constructing nonlinear dynamic characteristics of liquid level can remove noise, fill missing values, etc. by preprocessing the collected system input and output data, thereby improving the accuracy and reliability of the data. Constructing the input and output data into state vectors respectively is helpful to extract the key characteristics of the system, provide a basis for subsequent analysis, and reduce the dependence on the mechanism model. The basic time structure function constructed based on the input data state vector and the output data state vector directly extracts the dynamic characteristics of the system from the input and output data without counting a large number of structural parameters, and can reflect the dynamic change characteristics of the system. By constructing the state time series vector and the time factor attenuation, the basic time structure function of the system is rewritten into the system state space equation, avoiding the tedious model simplification process, and can more accurately describe the dynamic characteristics of the system over time. The nonlinear dynamic characteristic matrix obtained by solving the system state space equation can reveal the nonlinear relationship in the system, can more deeply understand the nonlinear dynamic characteristics of the system, and provide strong support for optimizing the control strategy.

[0098] The present application also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0099] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0100] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0101] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which 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 service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0102] The units described 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.

[0103] 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.

[0104] 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 memory. Based on this understanding, the technical solution of the present application is essentially 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 memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program code.

[0105] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which can include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0106] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

[0107] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0108] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for constructing nonlinear dynamic characteristics of liquid level, characterized in that: include: Preprocess the input data and output data of the system's steady-state working condition at any time to construct an input data state vector and an output data state vector; Constructing a system timing basic structure function according to the input data state vector and the output data state vector; Constructing the state time series vector and the time factor attenuation to rewrite the system time series basic structure function into the system state space equation; The nonlinear dynamic characteristic matrix of the system is solved based on the state space equation of the system.

2. The method for constructing the nonlinear dynamic characteristics of liquid level according to claim 1, characterized in that: The step of preprocessing the collected input data and output data of the steady-state working condition of the system at any time to construct an input data state vector and an output data state vector comprises: Synchronize the timestamps of the input and output data collected by the sensor and acquisition board; Filtering the synchronized input data and output data; The input data and output data after filtering are averaged; Resample the averaged input data and output data according to a preset acquisition period; An input data state vector and an output data state vector are constructed based on the resampled input data and output data.

3. The method for constructing the nonlinear dynamic characteristics of liquid level according to claim 1, characterized in that: The input data state vector is u=[np l , Qp l , Op, Qs, Qw, Qc]; the output data state vector is x=[△P, L]; Among them, l is the number of water supply pumps, np l is the speed of the feed water pump, Qp l is the feedwater pump flow, Op is the feedwater valve opening, Qs is the steam flow, Qw is the feedwater valve flow, Qc is the primary side coolant flow; △P is the pressure difference before and after the feedwater valve, and L is the liquid level of the steam generator.

4. The method for constructing the nonlinear dynamic characteristics of liquid level according to claim 1, characterized in that: The constructing of a system timing basic structure function according to the input data state vector and the output data state vector comprises: Determine an input data timing vector matrix and an output data timing vector matrix according to the input data state vector and the output data state vector respectively; A system timing basic structure function is constructed according to the input data timing vector matrix, the output data timing vector matrix and system parameters.

5. The method for constructing the nonlinear dynamic characteristics of liquid level according to claim 4, characterized in that: The basic structure function of the system timing is: in, Represents the output data time series vector matrix, Represents the input data time series vector matrix, a i，1 、a i，2 、a i，3 、a i，4 and b j，l，1 、b j，l，2 、b j，l，3 、b j，l，4 、b j，l，5 、b j，l，6 、b j，l，7 、b j，l，8 、b j，l，9 、b j，l，10 、b j，l，11 、b j，l，12 is the system parameter.

6. The method for constructing the nonlinear dynamic characteristics of liquid level according to claim 1, characterized in that: The construction state time series vector is α k =[X|b j u k +b j-1 u k-1 |b j-1 u k ] Τ ; Among them, the time factor attenuation is b j ;u k is the input data state vector at time k.

7. The method for constructing the nonlinear dynamic characteristics of liquid level according to claim 1, characterized in that: The system state space equation is: a k+1 =Aa k +Bu k+1 ; in, A and B are the nonlinear dynamic characteristic matrices of the system.

8. The method for constructing the nonlinear dynamic characteristics of liquid level according to claim 1, characterized in that: The nonlinear dynamic characteristic matrix of the system is solved based on the state space equation, including: Solving the nonlinear dynamic characteristic matrix of the system from the state space equations by gradient descent method and calculating the fitting accuracy; If the fitting progress is not greater than a preset accuracy threshold, the system parameters of the state-space equation are adjusted.

9. A device for constructing nonlinear dynamic characteristics of liquid level, characterized in that: It comprises at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program, and when the computer program is executed by the processing unit, the processing unit executes the steps of the method for constructing the nonlinear dynamic characteristics of the liquid level according to any one of claims 1 to 8.

10. A storage medium, characterized in that: It stores a computer program executable by an access authentication device. When the computer program runs on the access authentication device, the access authentication device executes the steps of the method for constructing the nonlinear dynamic characteristics of the liquid level according to any one of claims 1 to 8.

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