A method, device and storage medium for constructing liquid level nonlinear dynamic characteristics

Abstract

The application discloses a liquid level nonlinear dynamic characteristic construction method, equipment and a storage medium, and relates to the technical field of system control. The method comprises the following steps: preprocessing input data and output data of a system at any moment under a steady state working condition to construct 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; rewriting the system time sequence basic structure function into a system state space equation by constructing a state time sequence vector and a time factor decay amount; and solving a nonlinear dynamic characteristic matrix of the system based on the system state space equation. The application constructs a state vector and a time sequence basic structure function, reduces the dependence on a mechanism model, constructs a state space equation by using data, avoids a complicated model simplification process, directly extracts dynamic characteristics of the system from input and output data, and does not need to statistically obtain a large number of structural parameters.

Description

Technical Field

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

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

[0003] The power plant exhibits significant nonlinear characteristics during operation, making it difficult to obtain a state-space model of its dynamic features for controller design. The system also exhibits characteristics such as multiple inputs and multiple outputs (MIMO) and overdrive, with clear coupling relationships between various control and state variables. Existing methods generally achieve decoupling control of nonlinear systems through local linearization and relative order calculation.

[0004] However, existing methods rely on the accuracy of the mechanism model of the steam power system's feedwater system. The model simplification process is complicated, the model requires the statistical analysis of a large number of structural parameters, and some internal parameters of the system are difficult to obtain, making it difficult to apply the designed controller in engineering. Summary of the Invention

[0005] To address at least one deficiency 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 solves the problems in the prior art that rely on the accuracy of mechanistic models, have complicated model simplification steps, require the statistical analysis of a large number of structural parameters, have difficulty obtaining the internal parameters of some systems, and make it difficult to apply the designed controller in engineering.

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

[0007] The input and output data of the system at any time steady state are preprocessed to construct the input data state vector and the output data state vector, respectively.

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

[0009] By constructing state-time vectors and time factor decay, the basic time structure function of the system is rewritten into the system state-space equation.

[0010] Solving the nonlinear dynamic characteristic matrix of a system based on its state-space equations.

[0011] In one possible implementation, the input and output data of the system at any given time steady-state condition are preprocessed to construct input and output state vectors, including:

[0012] Synchronize the input and output data acquired by the sensors and acquisition board using timestamps;

[0013] The synchronized input and output data are then filtered.

[0014] The filtered input and output data are then averaged.

[0015] The mean-valued input and output data are resampled according to a preset collection period.

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

[0017] In one possible implementation, the input data state vector is u = [np] l Qp l The output data state vector is x = [△P, L].

[0018] Where l represents the number of water pumps, and np l For water pump speed, Qp l ΔP is the feedwater pump flow rate, Op is the feedwater valve opening, Qs is the steam flow rate, Qw is the feedwater valve flow rate, Qc is the primary side coolant flow rate; ΔP is the pressure difference across the feedwater valve, and L is the steam generator liquid level.

[0019] In one possible implementation, the system's basic timing structure function is constructed based on the input data state vector and the output data state vector, including:

[0020] The input data time series vector matrix and the output data time series vector matrix are determined based on the input data state vector and the output data state vector, respectively.

[0021] Construct the basic timing structure function of the system based on the input data timing vector matrix, the output data timing vector matrix, and the system parameters.

[0022] In one possible implementation, the system's basic timing structure function is:

[0023]

[0024] in, This represents the output data time-series vector matrix. Let a represent the input data time series vector matrix. i，1 a i，2a 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 These are system parameters.

[0025] In one possible implementation, the state-time vector is constructed as α. k =[X|b j u k +b j-1 u k-1 |b j-1 u k ] Τ ;

[0026] Wherein, the time factor decay is b j ;u k Let k be the input data state vector at time k.

[0027] In one possible implementation, the system state-space equations are:

[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, the nonlinear dynamic characteristic matrix of the system is solved based on the state-space equations, including:

[0031] The nonlinear dynamic characteristic matrix of the system is solved from the state-space equations using 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 equations will be adjusted.

[0033] According to a second aspect of the present invention, an apparatus for constructing a nonlinear dynamic characteristic of liquid level is also provided, comprising at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program that, when executed by the processing unit, causes the processing unit to perform the steps of any of the above-described methods for constructing a nonlinear dynamic characteristic of liquid level.

[0034] According to a third aspect of the invention, a storage medium is also provided, which stores a computer program executable by an access authentication device, which, when run on the access authentication device, causes the access authentication device to perform the steps of the method for constructing any of the above-described nonlinear dynamic characteristics of liquid level.

[0035] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0036] This invention provides a method for constructing nonlinear dynamic characteristics of liquid level. By preprocessing the collected system input and output data, noise can be removed and missing values ​​filled, thereby improving the accuracy and reliability of the data. Constructing state vectors from the input and output data respectively helps to extract key system features, providing a foundation for subsequent analysis and reducing reliance on mechanistic models. The time-series basic structure function constructed based on the input and output data state vectors directly extracts the system's dynamic characteristics from the input and output data, without the need to statistically analyze a large number of structural parameters, and can reflect the dynamic changes of the system. By constructing state-series vectors and time factor decay, the system's time-series basic structure function is rewritten into a system state-space equation, avoiding cumbersome model simplification processes and more accurately describing the system's dynamic characteristics over time. The nonlinear dynamic characteristic matrix obtained from solving the system state-space equation can reveal the nonlinear relationships existing in the system, enabling a deeper understanding of the system's nonlinear dynamic characteristics and providing strong support for optimizing control strategies. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 A flowchart illustrating an embodiment of the 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 the steam power system provided by the present invention;

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

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this 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, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0043] This invention provides a method, device, and storage medium for constructing nonlinear dynamic characteristics of liquid level, which will be described below.

[0044] Please see Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the method for constructing nonlinear dynamic characteristics of liquid level provided by the present invention. In a specific embodiment of the present invention, a method for constructing nonlinear dynamic characteristics of liquid level is disclosed, including:

[0045] S101. Preprocess the input and output data of the system at any time steady state to construct the input data state vector and the output data state vector respectively.

[0046] S102. Construct the basic timing structure function of the system based on the input data state vector and the output data state vector;

[0047] S103. Constructing the state-time vector and time factor decay amount rewrites the basic time structure function of the system into the 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 embodiments, it is first necessary to preprocess the input and output data of steady-state operating conditions collected from the system at any time. The input data includes, but is not limited to, the number of water pumps m and the water pump speed np. l Water pump flow rate Qp lThe data includes, but is not limited to, feedwater valve opening Op, steam flow rate Qs, feedwater valve flow rate Qw, and primary side coolant flow rate Qc. Output data includes, but is not limited to, the pressure difference across the feedwater valve ΔP and the steam generator level L. Preprocessing steps include, but are not limited to, data cleaning (noise and outlier removal) and data normalization or standardization to ensure data consistency and comparability. Based on the preprocessed data, input and output state vectors are constructed. These state vectors are formed by selecting continuous data points within a certain time window, reflecting the dynamic changes of the system during that time period.

[0050] Based on the obtained input and output state vectors, a fundamental temporal structure function is further constructed to capture the nonlinear relationship between input and output and to reflect the dynamic characteristics of the system over time. Machine learning or deep learning techniques, such as neural networks and support vector machines, can be employed during the construction process to fit complex nonlinear mapping relationships. Understandably, this scheme could also consider introducing a time delay term to more accurately reflect the system's time dependence.

[0051] To transform the system's fundamental temporal structure function into a more easily analyzed and solved form, it is necessary to construct state-time vectors and time-factor decay. The state-time vector expands the input and output state vectors over time, forming a higher-dimensional vector space to comprehensively describe the system's state changes. The time-factor decay describes the decay of the system state over time, typically related to the system's stability and response time. By constructing the state-time vector and time-factor decay, the system's fundamental temporal structure function can be rewritten as a system state-space equation, which more intuitively demonstrates the dynamic evolution of the system state.

[0052] Finally, based on the obtained system state-space equations, the nonlinear dynamic characteristic matrix of the system is solved using numerical computation or optimization algorithms. This nonlinear dynamic characteristic matrix contains the dynamic characteristic parameters of the system under different steady-state conditions, such as gain, damping ratio, and natural frequency. These parameters are crucial for understanding and controlling 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 analyzed in depth, providing support for subsequent control system design, fault diagnosis, and performance optimization.

[0053] Please see 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 described using a steam power system as an example. The system mainly involves the joint control of factors such as the number of feedwater pump heads, the opening degree of the feedwater regulating valve, the primary side cooling water flow rate, and the steam flow rate. The steam flow rate is mainly affected by the turbine load, introducing uncertainty disturbances to the liquid level control. The feedwater pump set is generally equipped with multiple feedwater pumps to ensure system safety. This characteristic is typical of an overdrive system. The number of operating pumps is also related to the operating conditions, introducing switching characteristics into the system, resulting in a system with strong nonlinear operating characteristics.

[0054] Compared with existing technologies, this embodiment provides a method for constructing nonlinear dynamic characteristics of liquid level. By preprocessing the collected system input and output data, noise can be removed and missing values ​​filled, thereby improving the accuracy and reliability of the data. Constructing state vectors from the input and output data respectively helps to extract key system features, providing a foundation for subsequent analysis and reducing reliance on mechanistic models. The time-series basic structure function constructed based on the input and output data state vectors directly extracts the system's dynamic characteristics from the input and output data, without the need to statistically analyze a large number of structural parameters, and can reflect the dynamic change characteristics of the system. By constructing state time-series vectors and time factor decay, the system's time-series basic structure function is rewritten into a system state-space equation, avoiding cumbersome model simplification processes and more accurately describing 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 relationships existing in the system, enabling a deeper understanding of the system's nonlinear dynamic characteristics and providing strong support for optimizing control strategies.

[0055] Please see Figure 3 , Figure 3 Provided by the present invention Figure 1 A flowchart illustrating an embodiment of step S101. In some embodiments of the present invention, the input data and output data of the system at any time steady-state condition are preprocessed to construct input data state vectors and output data state vectors, including:

[0056] S301. Synchronize the input and output data acquired by the sensor and the acquisition board using timestamps;

[0057] S302. Filter the synchronized input and output data;

[0058] S303. Average the filtered input and output data.

[0059] S304. Resample the mean-valued input and output data according to a preset acquisition cycle;

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

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

[0062] The purpose of filtering is to remove high-frequency noise and outliers from the data. This noise may be caused by factors such as sensor error and environmental interference. By selecting a suitable filter (such as a low-pass filter, a median filter, etc., this invention does not impose further restrictions on 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 and improve data stability and reliability. Averaging can be achieved by calculating the average value of the data within a certain time window; the window size should be determined based on the system's dynamic characteristics and sampling frequency.

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

[0065] Based on the resampled input and output data, input data state vectors and output data state vectors are constructed. A state vector is a set of variables describing the system state, comprehensively reflecting the dynamic characteristics of the system at a given moment. In this invention, the input data state vector may include the current and historical values ​​of parameters such as flow rate and pressure, while the output data state vector mainly includes the current and historical values ​​of liquid level. By constructing the state vectors of the input and output data, basic data can be provided for system modeling, analysis, and control.

[0066] In some embodiments of the present invention, the input data state vector is u = [np l Qp l The output data state vector is x = [△P, L].

[0067] Where l represents the number of water pumps, and np lFor water pump speed, Qp l ΔP is the feedwater pump flow rate, Op is the feedwater valve opening, Qs is the steam flow rate, Qw is the feedwater valve flow rate, Qc is the primary side coolant flow rate; ΔP is the pressure difference across the feedwater valve, and L is the steam generator liquid level.

[0068] In the above embodiment, np in the input data state vector l Qp reflects the operating status of the feedwater pump. l This 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 actual amount of water 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. In the output data state vector, ΔP 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 dynamic characteristics of the system are transformed into specific mathematical expressions. Based on these state vectors, advanced technologies such as machine learning and deep learning are used to construct accurate system models, thereby achieving precise control and optimization of liquid levels.

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

[0071] The input data time series vector matrix and the output data time series vector matrix are determined based on the input data state vector and the output data state vector, respectively.

[0072] Construct the basic timing structure function of the system based on the input data timing vector matrix, the output data timing vector matrix, and the system parameters.

[0073] In the above embodiment, based on the input data state vector u = [np] l Qp l Let [Op, Qs, Qw, Qc] and the output data state vector x = [ΔP, L] be used to construct the input data time series vector matrix U and the output data time series vector matrix X, respectively. 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 input data state vectors from a certain starting time to the current time, 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] Based on the input data time-series vector matrix, the output data time-series vector matrix, and other system parameters (such as physical constants, system structural parameters, etc.), the basic time-series structure function of the system is constructed. The basic time-series structure function can capture the nonlinear relationship between the system input and output and reflect the dynamic characteristics of the system as time changes.

[0075] When constructing the basic time structure function of a system, it is necessary to train and verify the function using historical or experimental data 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 time structure function of the system.

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

[0077]

[0078] in, This represents the output data time-series vector matrix. Let a represent the input data time series vector matrix. 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 These are system parameters.

[0079] In some embodiments of the present invention, the constructed 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] Wherein, the time factor decay is b j ;u k Let k be the input data state vector at time k.

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

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

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

[0086] The nonlinear dynamic characteristic matrix of the system is solved from the state-space equations using 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 equations will be adjusted.

[0088] In the above embodiment, the gradient descent method is used to solve for the nonlinear dynamic characteristic matrix of the liquid level system from the state-space equations, reflecting the nonlinear relationship between the system's state variables. In the gradient descent method, a loss function (or error function) is defined, which measures the difference between the solution to the state-space equations and the actual observed data. The value of the nonlinear dynamic characteristic matrix is ​​iteratively adjusted 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 matrix value 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 observation data. Usually, a quantitative index (such as mean square error, relative error, etc.) is 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. 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 the actual situation, and this invention does not impose further limitations on it.

[0092] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of the device for constructing nonlinear dynamic characteristics of liquid level provided in an embodiment of the present invention. Based on the above-described method for constructing nonlinear dynamic characteristics of liquid level, the present invention also provides a device for constructing nonlinear dynamic characteristics of liquid level. The device for constructing nonlinear dynamic characteristics of liquid level can be a computing device such as a mobile terminal, desktop computer, laptop, handheld computer, or server. The device 300 for constructing nonlinear dynamic characteristics of liquid level includes a processor 310, a memory 320, and a display 330. Figure 3 Only a portion of the components of the synchronous tracking flight welding equipment for real-time battery altitude measurement are shown; however, it should be understood that implementation of all shown components is not required, 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 characteristic construction device 300, such as a hard disk or memory of the liquid level nonlinear dynamic characteristic construction device 300. In other embodiments, the memory 320 may be an external storage device of the liquid level nonlinear dynamic characteristic construction device 300, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the liquid level nonlinear dynamic characteristic construction device 300. Furthermore, the memory 320 may include both internal storage units and external storage devices of the liquid level nonlinear dynamic characteristic construction device 300. The memory 320 is used to store application software and various types of data installed on the liquid level nonlinear dynamic characteristic construction device 300, such as program code installed on the liquid level nonlinear dynamic characteristic construction device 300. The memory 320 may also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 320 stores a construction program 340 for nonlinear dynamic characteristics of liquid level, which can be executed by the processor 310 to implement the construction method of nonlinear dynamic characteristics of liquid level in various embodiments of this application.

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

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

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

[0097] In summary, a method for constructing nonlinear dynamic characteristics of liquid level improves data accuracy and reliability by preprocessing the collected system input and output data to remove noise and fill in missing values. Constructing state vectors from the input and output data respectively helps extract key system features, providing a foundation for subsequent analysis and reducing reliance on mechanistic models. The time-series fundamental structure function constructed based on the input and output state vectors directly extracts the system's dynamic characteristics from the input and output data, eliminating the need to statistically analyze numerous structural parameters and reflecting the system's dynamic changes. By constructing state-series vectors and time factor decay, the system's time-series fundamental structure function is rewritten into a system state-space equation, avoiding cumbersome model simplification processes and more accurately describing the system's dynamic characteristics over time. The nonlinear dynamic characteristic matrix obtained from solving the system state-space equation reveals the nonlinear relationships within the system, providing a deeper understanding of its nonlinear dynamic characteristics and strong support for optimizing control strategies.

[0098] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0099] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0100] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0101] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0102] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0103] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0104] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0105] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0106] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of embodiments of this disclosure upon considering the specification and practicing the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

[0107] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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] Those skilled in the art will readily understand that the above description is merely 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 within the scope of protection of the present invention.

Claims

1. A method for constructing nonlinear dynamic characteristics of liquid level, characterized in that, include: The input and output data of the system at any time steady state are preprocessed to construct the input data state vector and the output data state vector, respectively. Construct the system timing basic structure function based on the input data state vector and the output data state vector; The system's basic time structure function is rewritten into a system state-space equation by constructing a state-time vector and a time factor decay. Solve the nonlinear dynamic characteristic matrix of the system based on the system state-space equations; The step of constructing the system timing basic structure function based on the input data state vector and the output data state vector includes: The input data time-series vector matrix and the output data time-series vector matrix are determined based on the input data state vector and the output data state vector, respectively. Construct the basic timing structure function of the system based on the input data timing vector matrix, the output data timing vector matrix, and the system parameters; The basic timing structure function of the system is as follows: ； in, This represents the output data time-series vector matrix. This represents the time-series vector matrix of the input data. 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 For system parameters; The input data state vector is u=[npl, Qpl, Op, Qs, Qw, Qc]; the output data state vector is x=[△P, L]; Where l is the number of feedwater pumps, npl is the feedwater pump speed, Qpl is the feedwater pump flow rate, Op is the feedwater valve opening, Qs is the steam flow rate, Qw is the feedwater valve flow rate, Qc is the primary side coolant flow rate; △P is the pressure difference across the feedwater valve, and L is the steam generator liquid level.

2. The method for constructing the nonlinear dynamic characteristics of liquid level as described in claim 1, characterized in that, The preprocessing of the input and output data of the system at any time steady-state condition to construct the input data state vector and output data state vector includes: Synchronize the input and output data acquired by the sensors and acquisition board using timestamps; The synchronized input and output data are then filtered. The filtered input and output data are then averaged. The mean-valued input and output data are resampled according to a preset collection period. The input data state vector and the 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 as described in claim 1, characterized in that, The input data state vector is u=[npl, Qpl, Op, Qs, Qw, Qc]; the output data state vector is x=[△P, L]; Where l is the number of feedwater pumps, npl is the feedwater pump speed, Qpl is the feedwater pump flow rate, Op is the feedwater valve opening, Qs is the steam flow rate, Qw is the feedwater valve flow rate, Qc is the primary side coolant flow rate; △P is the pressure difference across the feedwater valve, and L is the steam generator liquid level.

4. The method for constructing the nonlinear dynamic characteristics of liquid level as described in claim 1, characterized in that, The constructed state time vector is ; Wherein, the time factor decay amount is b j ;u k Let k be the input data state vector at time k.

5. The method for constructing the nonlinear dynamic characteristics of liquid level as described in claim 4, characterized in that, The system state-space equation is: ； in, ； A and B are the nonlinear dynamic characteristic matrices of the system.

6. The method for constructing the nonlinear dynamic characteristics of liquid level as described in claim 1, characterized in that, Solving the nonlinear dynamic characteristic matrix of the system based on the state-space equations includes: The nonlinear dynamic characteristic matrix of the system is solved from the state-space equations using the gradient descent method, and the fitting accuracy is calculated. If the fitting progress is not greater than a preset accuracy threshold, the system parameters of the state space equation are adjusted.

7. A device for constructing nonlinear dynamic characteristics of liquid level, characterized in that, It includes at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program that, when executed by the processing unit, causes the processing unit to perform the steps of the method for constructing the nonlinear dynamic characteristics of liquid level according to any one of claims 1 to 6.

8. A storage medium, characterized in that, It stores a computer program executable by an access authentication device, which, when run on the access authentication device, causes the access authentication device to perform the steps of the method for constructing the nonlinear dynamic characteristics of the liquid level as described in any one of claims 1 to 6.

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