Liquid level regulation method, device, equipment, storage medium and program product

Abstract

The application discloses a liquid level regulation method, device, equipment, storage medium and program product, and relates to the technical field of nuclear reactor control. The method comprises the following steps: acquiring a liquid level measurement value of a liquid storage container at a first time; inputting a first liquid level regulation signal of the liquid storage container at the first time and the liquid level measurement value into a target extended state observer to obtain a liquid level observation value and a disturbance observation value; the liquid level observation value is used to represent a liquid level estimation result obtained by correcting the liquid level measurement value, and the disturbance observation value is used to represent a comprehensive action amount of environmental disturbance affecting the liquid level of the liquid storage container; generating a second liquid level regulation signal of the liquid storage container at a second time based on a liquid level standard value, the liquid level observation value and the disturbance observation value; the second time is a liquid level regulation time after the first time; and regulating the liquid level of the liquid storage container based on the second liquid level regulation signal. The application can improve the accuracy of liquid level regulation.

Description

Technical Field

[0001] This application belongs to the field of nuclear reactor control technology, and particularly relates to a liquid level control method, device, equipment, storage medium and program product. Background Technology

[0002] In industrial production and energy sectors, precise control of liquid levels in storage containers is crucial for their safe and stable operation. Taking the steam generator in a pressurized water reactor nuclear power plant as an example, as a key heat exchanger in both the primary and secondary loops, maintaining the liquid level within a reasonable range is a core element in ensuring the safe and economical operation of the nuclear power plant.

[0003] Currently, the control of liquid level in traditional liquid storage containers is achieved by generating a corresponding control signal based on the deviation between the standard liquid level value and the measured liquid level value.

[0004] However, liquid storage containers are often subject to interference from multiple physical quantities, such as pressure fluctuations, temperature changes, and water flow disturbances. Traditional control methods have limited anti-interference capabilities, resulting in poor accuracy of liquid level control. Summary of the Invention

[0005] This application provides a liquid level control method, apparatus, device, storage medium, and program product that can improve the accuracy of liquid level control.

[0006] A first aspect of this application provides a liquid level control method, comprising: Obtain the liquid level measurement value of the storage container at the first moment; The first liquid level control signal and liquid level measurement value of the storage container at the first moment are input into the target expansion state observer to obtain the liquid level observation value and the disturbance observation value. The liquid level observation value is used to characterize the liquid level estimation result obtained after correcting the liquid level measurement value, and the disturbance observation value is used to characterize the comprehensive effect of environmental disturbances that affect the liquid level of the storage container. Based on the standard liquid level value, observed liquid level value, and disturbance observed value, a second liquid level control signal for the storage container is generated at the second time point; the second time point is the liquid level control time after the first time point. The liquid level in the storage container is regulated based on the second liquid level control signal.

[0007] A second aspect of this application provides a liquid level control device, comprising: The liquid level measurement module is used to obtain the liquid level measurement value of the liquid storage container at the first moment; The disturbance observation module is used to input the first liquid level control signal and liquid level measurement value of the liquid storage container at the first moment into the target expansion state observer to obtain the liquid level observation value and the disturbance observation value. The liquid level observation value is used to characterize the liquid level estimation result obtained after correcting the liquid level measurement value, and the disturbance observation value is used to characterize the comprehensive effect of environmental disturbances that affect the liquid level of the liquid storage container. The signal generation module is used to generate a second liquid level control signal for the storage container at a second time point based on the standard liquid level value, observed liquid level value, and disturbance observed value; the second time point is the liquid level control time after the first time point. The liquid level control module is used to control the liquid level of the storage container based on the second liquid level control signal.

[0008] A third aspect of the embodiments of this application provides an electronic device, which includes: a memory and a program or instructions stored in the memory and executable on a processor, wherein when the program or instructions are executed by the processor, they implement the liquid level control method provided in any aspect of the embodiments of this application described above.

[0009] A fourth aspect of the embodiments of this application provides a readable storage medium on which a program or instructions are stored, and when the program or instructions are executed by a processor, they implement the liquid level control method provided by any aspect of the embodiments of this application described above.

[0010] A fifth aspect of the embodiments of this application provides a computer program product in which instructions, when executed by a processor of an electronic device, cause the electronic device to perform a liquid level control method as provided in any aspect of the embodiments of this application described above.

[0011] In the liquid level control method provided in this application embodiment, the liquid level measurement value of the storage container at a first moment is first obtained, and then input into a target expansion state observer along with a first liquid level control signal at the first moment to obtain a liquid level observation value and a disturbance observation value. The liquid level observation value corrects the liquid level measurement value, enabling a more accurate estimation of the true liquid level at the first moment; in addition, the disturbance observation value can accurately capture the combined effect of environmental disturbances. Next, a second-moment control signal is generated based on the standard liquid level value, the liquid level observation value, and the disturbance observation value, fully considering the current liquid level state and environmental disturbance conditions; finally, the liquid level of the storage container is controlled according to the second-moment control signal. Thus, because the true liquid level at the first moment is accurately estimated based on the liquid level observation value during the control process, and disturbance compensation is performed based on the disturbance observation value, it can better cope with various disturbances, thereby improving the accuracy of liquid level control. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic flowchart of a liquid level control method provided in one embodiment of this application; Figure 2 This is a schematic diagram of liquid level control based on an expansion state observer provided in one embodiment of this application; Figure 3 This is a schematic diagram of the structure of a steam generator feedwater flow control system based on a linearly extended state observer provided in one embodiment of this application; Figure 4 This is a flowchart illustrating a particle swarm optimization algorithm provided in one embodiment of this application; Figure 5 This is a schematic diagram of the structure of a liquid level control device provided in one embodiment of this application; Figure 6 This is a schematic diagram of the structure of a liquid level control device provided in one embodiment of this application. Detailed Implementation

[0014] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0015] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0016] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations.

[0017] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0018] Traditional liquid level control methods for storage containers, which generate control signals based on the deviation between the standard liquid level value and the measured liquid level value, struggle to effectively handle the combined disturbances caused by the dynamic coupling of multiple variables. Because environmental disturbances are not quantified in real time, the measured liquid level value contains unmodeled dynamic errors. The controller output signal cannot accurately compensate for the actual disturbance components, resulting in decreased liquid level tracking accuracy and lag in dynamic response. This problem directly affects the operational stability of the storage container and may lead to liquid level overshoot or undershoot under transient conditions.

[0019] For example, in the scenario of liquid level control in a nuclear power plant steam generator, pressure fluctuations caused by the operation of the feedwater flow regulating valve and changes in the secondary loop steam load create a strong coupled disturbance. When steam demand suddenly increases, the temperature of the primary loop coolant rises, causing vibration of the heat transfer tube bundle, resulting in mechanical vibration noise in the liquid level sensor measurement. Traditional proportional-integral-derivative (PID) controllers generate control signals based on noisy measurements, failing to consider the time-varying impact of pressure-temperature coupled disturbances on liquid level dynamics, causing feedwater flow regulation to lag behind actual demand changes. This phenomenon is particularly pronounced during rapid load increases and decreases, causing the liquid level to deviate from the setpoint range beyond the safety threshold.

[0020] Faced with the aforementioned problems, this application first analyzes that the root cause of the failure of traditional liquid level control methods lies in the lack of real-time observation and compensation for multi-physical quantity coupled disturbances. Conventional solutions employ two technical approaches: one is to directly measure each disturbance variable by increasing the number of sensors, but this leads to a surge in system complexity and makes dynamic decoupling difficult; the other is to use fixed-parameter filters to suppress measurement noise, but this cannot adapt to changes in disturbance intensity under transient conditions. Further research reveals that the effect of environmental disturbances on liquid level is essentially a comprehensive effect on the control system through energy transfer paths. If the total disturbance amount can be estimated in real time and fed forward to the control loop, the hysteresis defect of traditional feedback control can be overcome. Based on this, this application creatively combines an extended state observer with closed-loop control, constructing a disturbance observation channel to achieve online estimation of unmodeled dynamics and external disturbances, and designing a feedforward compensation mechanism to correct the controller output signal.

[0021] In this regard, such as Figure 1 As shown in the figure, this application provides a schematic flowchart of a liquid level control method. This liquid level control method can be applied to electronic devices and may include the following steps S100 to S400: S100: Obtain the liquid level measurement value of the storage container at the first moment.

[0022] In this step, the liquid storage container is a device used to store liquid. For example, the liquid storage container can be a steam generator, or it can be a water tank, oil tank, or other container.

[0023] Liquid level measurements are used to characterize the actual liquid level height or volume measured by liquid level sensors (such as ultrasonic sensors, pressure sensors, etc.), and to reflect the actual state of the current liquid level.

[0024] Specifically, liquid level data is collected in real time by a liquid level sensor installed on the liquid storage container. The liquid level sensor converts physical quantities such as liquid level height or volume into electrical signals, which are then processed by the signal conditioning circuit and transmitted to the controller to finally obtain the liquid level measurement value. Its implementation principle is based on the interaction mechanism between the sensor and the liquid. For example, an ultrasonic sensor calculates the liquid level by measuring the time difference between the emitted and reflected waves, and a pressure sensor uses the linear relationship between the hydrostatic pressure of the liquid and its height to estimate the liquid level. The measured value may contain noise or errors, which need to be corrected later to improve accuracy.

[0025] S200, the first liquid level control signal and liquid level measurement value of the liquid storage container at the first moment are input into the target expansion state observer to obtain the liquid level observation value and the disturbance observation value; the liquid level observation value is used to characterize the liquid level estimation result obtained after correcting the liquid level measurement value, and the disturbance observation value is used to characterize the comprehensive effect of the environmental disturbance that affects the liquid level of the liquid storage container.

[0026] In this step, the first liquid level control signal is used to characterize the initial signal generated at the first moment for controlling the liquid level, which may be the liquid level value or a control command.

[0027] The Extended State Observer (ESO) is a dynamic observer that can simultaneously estimate the system state and external disturbances. Specifically, it can be implemented using a linear extended state observer or a nonlinear extended state observer. By receiving the liquid level control signal and the liquid level measurement value, it outputs the corrected liquid level estimation result and the combined effect of environmental disturbances in real time.

[0028] The observed liquid level is an ESO-corrected estimate, which is closer to the actual liquid level. This can be achieved through filtering algorithms or state estimation models to eliminate measurement noise and systematic errors, thereby improving the accuracy of the liquid level data.

[0029] The disturbance observation value is the combined impact of external disturbances on the liquid level estimated by ESO, such as temperature changes, pipeline leaks, and fluctuations in influent flow. Specifically, it can be achieved through extended state variable modeling to quantify the combined effects of pressure fluctuations, temperature changes, and flow disturbances, providing a basis for compensation in liquid level regulation.

[0030] Specifically, the first liquid level control signal generated at the first moment and the liquid level measurement value are input together into the target expansion state observer. The target expansion state observer estimates the system state and external disturbances in real time through a dynamic model. Its core principle is to use mathematical modeling of the relationship between the input signal and the system dynamics, and to treat the noise and unknown disturbances in the liquid level measurement value as expansion state variables for observation. Finally, it outputs the corrected liquid level observation value and disturbance observation value, and eliminates the measurement error through filtering algorithm or state estimation model, so as to provide an accurate basis for subsequent control.

[0031] S300 generates a second liquid level control signal for the storage container at the second moment, based on the standard liquid level value, observed liquid level value, and disturbance observed value; the second moment is the liquid level control moment after the first moment.

[0032] In this step, the standard liquid level value is used to characterize the target liquid level value to be achieved, which is usually a preset fixed value or a dynamically adjusted value.

[0033] The second level control signal is used to characterize the new control command generated based on the estimated results of the ESO output (including level observations and disturbance observations) and the level standard value.

[0034] Specifically, the controller calculates the deviation between the current liquid level and the target liquid level based on the preset standard liquid level value, the liquid level observation value output by the ESO, and the disturbance observation value, using a control algorithm (such as PID control, fuzzy control, or model predictive control). It then generates a new second liquid level control signal by combining the disturbance compensation amount. The principle behind this is to use the liquid level observation value to eliminate measurement noise, quantify the impact of external disturbances on the liquid level through the disturbance observation value, and then adjust the control command to counteract the disturbance, ensuring that the system quickly and stably reaches the target liquid level.

[0035] S400 regulates the liquid level in the storage container based on the second liquid level control signal.

[0036] In this step, the control action refers to the actual operation performed according to the second liquid level control signal, such as adjusting the opening of the inlet valve or starting the pump.

[0037] Specifically, based on the second liquid level control signal generated at the second moment, the actuator (such as an inlet valve, a drain pump, or a heater) adjusts the liquid level in the storage container, for example, by increasing the opening of the inlet valve to increase the liquid level, or by starting the drain pump to decrease the liquid level. The principle behind this is that the second liquid level control signal directly controls the action range of the actuator (such as the valve opening percentage or the pump speed), so that the actual liquid level change cancels out the disturbance estimated by the ESO, while approaching the standard liquid level value, forming a closed-loop control to cope with the uncertainty in the dynamic environment, and ultimately achieving precise and stable control of the liquid level.

[0038] This application acquires liquid level and disturbance observation values ​​in real time through a target expansion state observer, combines liquid level measurement error correction with environmental disturbance compensation, and simultaneously eliminates internal system errors and external interference effects when generating the second liquid level control signal, thereby significantly improving the anti-interference capability and dynamic adjustment accuracy of liquid level control.

[0039] As an example, this level control method is implemented in the level control system of a steam generator in a nuclear power plant. First, the level measurement value h1 of the steam generator at the current time t1 is obtained through a level sensor. Simultaneously, the first level control signal u1 executed at time t1 is obtained, which represents the opening degree of the feedwater flow regulating valve.

[0040] h1 and u1 are input into a pre-designed linear expansion state observer. This linear expansion state observer is constructed based on a mathematical model of the steam generator and includes two parts: liquid level state estimation and disturbance state estimation. The linear expansion state observer outputs the liquid level observation h_est and the disturbance observation d_est through iterative calculation. h_est reflects the correction result to h1, and d_est quantifies the combined impact of environmental disturbances such as pressure fluctuations and temperature changes.

[0041] Next, the preset standard liquid level value h_ref is compared with h_est, and the liquid level deviation e = h_ref - h_est is calculated. e is input into the PID controller to obtain the initial liquid level control value u_pid. Then, the second liquid level control signal u2, considering disturbance compensation, is calculated by u_pid - d_est. u2 will be executed at time t2 in the next control cycle.

[0042] Finally, at time t2, the control system converts u2 into an opening command for the feedwater flow regulating valve, thereby controlling the liquid level in the steam generator. The entire process repeats periodically, achieving continuous control of the liquid level.

[0043] In this embodiment, the liquid level measurement value of the storage container at a first moment is first obtained, and this value, along with the first liquid level control signal at the first moment, is input into the target expansion state observer to obtain the liquid level observation value and the disturbance observation value. The liquid level observation value corrects the liquid level measurement value, enabling a more accurate estimation of the true liquid level at the first moment; in addition, the disturbance observation value accurately captures the combined effect of environmental disturbances. Next, a second-moment control signal is generated based on the standard liquid level value, the liquid level observation value, and the disturbance observation value, fully considering the current liquid level state and environmental disturbance conditions; finally, the liquid level of the storage container is controlled according to this second-moment control signal. Thus, because the true liquid level at the first moment is accurately estimated based on the liquid level observation value during the control process, and disturbance compensation is performed based on the disturbance observation value, it can better cope with various disturbances, thereby improving the accuracy of liquid level control.

[0044] In some of the solutions described above in this application, the combined effect of environmental disturbances is not fully considered during the generation of the liquid level control signal, resulting in the control signal being unable to effectively compensate for the impact of actual disturbances on the liquid level, thereby affecting the accuracy of liquid level control.

[0045] In this regard, this application further proposes that S300 includes: The liquid level deviation between the standard liquid level value and the observed liquid level value is input into the feedback controller to obtain the first liquid level control value of the liquid storage container. Based on the disturbance observations, the first liquid level control value is corrected to generate the second liquid level control signal for the storage container at the second time.

[0046] In this embodiment, the feedback controller can be a proportional-plus-derivative (PD) controller, which adjusts the control response speed through a combination of proportional coefficient and derivative time constant. The disturbance observation value is acquired in real time by the target expansion state observer, and its value reflects the superposition effect of pressure fluctuations, temperature changes, and feedwater flow disturbances. The correction process includes performing algebraic operations on the first liquid level control value and the disturbance observation value, for example, using subtraction to eliminate the disturbance component, and adjusting the amplitude of the calculation result based on the target gain parameter of the target expansion state observer.

[0047] Specifically, in the process of generating the liquid level control signal, the feedback controller first generates a basic control quantity based on the liquid level deviation between the standard liquid level value and the observed liquid level value. This control quantity includes a combination of proportional and derivative terms. Subsequently, the disturbance observation value is introduced into the control loop as a feedforward compensation term, and the influence of environmental disturbances on the control quantity is eliminated through subtraction. For example, when a pressure fluctuation is detected that causes the disturbance observation value to rise, the control signal output is automatically reduced to offset the liquid level fluctuation caused by the disturbance. The corrected control quantity is then processed by gain adjustment to ensure that the control signal matches the dynamic response characteristics of the actuator. This process, through a real-time correction mechanism, enables the control signal to simultaneously consider deviation adjustment and disturbance suppression, effectively improving the steady-state accuracy and anti-interference capability of the liquid level control.

[0048] As an example, the level deviation between the standard level and the observed level is first input into the feedback controller to obtain the first level control value for the storage container. The feedback controller can be a PD controller. Specifically, the proportional gain of the PD controller can be set to 0.5, and the derivative time constant can be set to 2 seconds.

[0049] Furthermore, based on the perturbation observation value, the first liquid level control value is corrected to generate the second liquid level control signal for the storage container at the second time. Specifically, the perturbation observation value is subtracted from the first liquid level control value to obtain the second liquid level control value. Then, based on the target gain parameter of the target expansion state observer, the second liquid level control value is corrected to generate the second liquid level control signal for the storage container at the second time.

[0050] This embodiment effectively suppresses the impact of external disturbances on liquid level control, improving the accuracy and stability of liquid level control. Specifically, by introducing an extended state observer to observe and compensate for system disturbances in real time, the shortcomings of traditional PID control in suppressing external disturbances are overcome. Simultaneously, by correcting the first liquid level control value based on the disturbance observation value, the system's anti-interference capability is further enhanced. Therefore, the liquid level control method of this application can maintain good control performance under complex operating conditions, effectively improving the operational safety and reliability of the liquid storage container.

[0051] In some of the above-mentioned schemes in this application, when the first liquid level control value is corrected based on the disturbance observation value, if the original dimensions or amplitude of the disturbance observation value are directly used for cancellation, the corrected control signal may not match the actual needs due to the change in the dynamic gain of the system, thereby affecting the liquid level control accuracy.

[0052] In response, this application further proposes a method to correct the first liquid level control value based on the perturbation observation value, and generate a second liquid level control signal for the storage container at the second time, including: Subtract the disturbance observation value from the first liquid level control value to obtain the second liquid level control value; Based on the target gain parameter of the target expansion state observer, the second liquid level control value is corrected to generate the second liquid level control signal of the storage container at the second time.

[0053] In this embodiment, the specific calculation process of the second liquid level control signal is as follows: the liquid level deviation between the standard liquid level value and the observed liquid level value is first input into the proportional-derivative controller to generate a preliminary control quantity containing a proportional term and a derivative term; then, the output of the proportional-derivative controller is subtracted from the disturbance observation value output in real time by the target expansion state observer to obtain the second liquid level control value after disturbance compensation; finally, the second liquid level control value is multiplied by the reciprocal 1 / b0 of the target gain parameter to generate the second liquid level control value.

[0054] The core principle of preliminary correction, achieved through algebraic operations between the first level control value and the disturbance observation value, lies in: quantifying the comprehensive impact of environmental disturbances on the level using the disturbance observation value, and eliminating the disturbance component through subtraction, so that the control quantity only reflects the level deviation itself. For example, if the ESO detects a negative disturbance observation value (a downward trend in level) due to pipeline leakage, the corrected second level control value will increase the compensation amount to offset the level deviation caused by the leakage; conversely, if the disturbance observation value is positive (such as a rise in level caused by fluctuations in influent flow), the corrected value will decrease the control quantity accordingly. This process, through a feedforward compensation mechanism, directly weakens the interference of disturbances on level control, improving the system's anti-interference capability.

[0055] Building upon the initial revisions, this application further introduces a target gain parameter b0 from the target expansion state observer to dynamically adjust the amplitude of the second liquid level control value, ultimately generating the second liquid level control signal at the second moment. The scaling effect of 1 / b0 essentially optimizes the control signal strength based on the dynamic response characteristics of the actuator (such as a valve or pump). This process, through a gain adaptation mechanism, ensures that the second liquid level control signal simultaneously considers both rapid response and stability, ultimately achieving synergistic optimization of the steady-state accuracy and dynamic performance of liquid level control.

[0056] As an example, such as Figure 2As shown, during the liquid level control process, the liquid level control signal and the liquid level measurement value at the first moment are first input into the expanded state observer to obtain the liquid level observation value and the disturbance observation value. Then, the liquid level deviation between the standard liquid level value and the liquid level observation value corrected by the expanded state observer is input into the PD controller to generate a first liquid level control value containing proportional and derivative terms. Next, the disturbance observation value is subtracted from the first liquid level control value to obtain a second liquid level control value. For example, when the first liquid level control value is 10 and the disturbance observation value is 2, the second liquid level control value is 8. Then, based on the target gain parameter of 1.6 of the target expanded state observer, its reciprocal 1 / 1.6 is multiplied by the second liquid level control value to generate a second liquid level control signal of 5. Finally, this second liquid level control signal is used to control the opening degree of the inlet valve or outlet valve of the liquid storage container, so as to achieve precise control of the liquid level of the liquid storage container, effectively suppress the influence of external disturbances, and improve the liquid level control accuracy and stability.

[0057] This embodiment effectively eliminates the impact of environmental disturbances on liquid level control, improving the accuracy and stability of liquid level control. As a result, the liquid level in the storage container can be maintained more accurately within the target range, avoiding safety hazards or efficiency losses caused by liquid level fluctuations.

[0058] In some of the above-mentioned schemes in this application, the liquid level control method achieves liquid level control by combining an extended state observer and a feedback controller. However, traditional extended state observers and feedback controllers may have complex parameter tuning due to nonlinear characteristics, affecting the dynamic response speed of the system. Furthermore, they are prone to the accumulation of observation errors under transient conditions, resulting in lag or overshoot in the generation of liquid level control signals.

[0059] In this regard, this application further proposes that the target extended state observer is a linear extended state observer and the feedback controller is a proportional-derivative controller.

[0060] In this embodiment, the Linear Extended State Observer (LESO) employs a linear feedback mechanism to perform state estimation on the measured liquid level. The target gain parameter in its observation equation is configured according to a preset linear relationship, effectively avoiding the high-order computational complexity problem caused by nonlinear observers. LESO can compensate for disturbances inside and outside the system and achieve real-time estimation, providing accurate disturbance information to the system. The output of the PD controller consists of a proportional term and a derivative term. The proportional term eliminates liquid level deviations, ensuring the liquid level approaches the standard value; the derivative term predicts the trend of liquid level changes, adjusting the system in advance to suppress system oscillations and make the system more stable. A feedforward compensation channel is established between LESO and the PD controller through the transmission of disturbance observations, forming a linear composite control structure. This structure combines the disturbance compensation capability of LESO with the fast response and prediction capability of the PD controller, improving the overall control performance of the system.

[0061] Specifically, the linear extended state observer takes the measured liquid level and the liquid level control signal as inputs, builds an observation model using first- or second-order linear differential equations, and then outputs the observed liquid level and disturbance values ​​in real time. The PD controller receives the liquid level deviation between the standard liquid level value and the observed liquid level. During the generation of the liquid level control signal, the disturbance value is directly superimposed on the output of the PD controller to offset the influence of environmental disturbances on the liquid level. This combined structure ensures that the system's open-loop transfer function maintains second-order linearity, avoiding the phase lag problem caused by nonlinear elements.

[0062] As an example, the target extended state observer uses a linear extended state observer, and the feedback controller uses a proportional-derivative (PD) controller. The linear extended state observer consists of a state observer and a disturbance observer, and its main function is to estimate the system state and unknown disturbances. The PDR controller generates a control signal based on the liquid level deviation, which includes a term proportional to the deviation and a term proportional to the rate of change of the deviation.

[0063] As another example, such as Figure 3The diagram illustrates a structural schematic of a steam generator feedwater flow control system based on a linearly extended state observer (LESO). In this system, the measured feedwater flow rate is first compared with the measured steam flow rate, and the difference is adjusted using a proportional coefficient K1. Then, the feedwater flow rate demand is generated by combining the feedback signal (adjusted by a proportional coefficient K2) of the steam generator level setpoint and the measured steam generator level after processing by LESO. LESO, as the core component, estimates the system state and unknown disturbances in real time, effectively compensating for internal and external disturbances and achieving accurate state estimation. The opening calculation unit calculates the opening command of the feedwater valve based on the feedwater flow rate demand, and then controls the opening of the feedwater valve to regulate the feedwater flow rate entering the steam generator. This ensures that the steam generator maintains a stable operating state even under various disturbances, achieving precise control of the feedwater flow rate and guaranteeing the efficient and stable operation of the steam generator.

[0064] This embodiment enables precise control of the liquid level in a storage container. The linearly extended state observer effectively estimates the system state and unknown disturbances, significantly improving the system's anti-interference capability. The proportional-derivative controller rapidly generates control signals based on the liquid level deviation, enhancing the system's dynamic response performance. Used in conjunction with the linearly extended state observer, they ensure the accuracy and stability of the liquid level control in the presence of external disturbances, effectively solving the problem of limited anti-interference capability in traditional control methods.

[0065] In some of the solutions described above in this application, the dynamic characteristics of the liquid storage container vary significantly when it operates under different energy source powers. If a fixed-parameter expansion state observer is used, it is difficult to accurately estimate the liquid level and disturbances, resulting in inaccurate generation of liquid level control signals.

[0066] In this regard, prior to S200, the method further includes: Obtain the target energy source power of the liquid storage container; the target energy source power is used to characterize the operating power of the energy source supplying energy to the liquid storage container at the first moment. Based on the target energy source power and the mapping relationship between the energy source power and the observer parameters, the target observer parameters corresponding to the target energy source power are determined. Based on the target observer parameters, the parameters of the preset extended state observer are configured to obtain the target extended state observer.

[0067] In this embodiment, the target energy source power is obtained by real-time monitoring of the energy source's operating status, such as through direct measurement using a power sensor or indirect calculation based on the energy source's control signals. The mapping relationship between the energy source power and the observer parameters is determined through pre-established experimental data or mathematical models, for example, by optimizing the corresponding observer parameters under different preset energy source powers using a particle swarm optimization algorithm. The parameter configuration of the preset extended state observer includes adjusting the observer gain, bandwidth, or state feedback coefficient to match the system's dynamic characteristics under the current energy source power.

[0068] Specifically, during the operation of the liquid storage container, changes in the power of the energy source directly affect the response speed and disturbance sensitivity of the liquid level system. By acquiring the target energy source power in real time and dynamically adjusting the observer parameters according to a preset mapping relationship, the extended state observer can adapt to the system characteristics under different power levels. For example, when the energy source power is high, the dynamic response of the liquid level system is faster, and a higher gain observer parameter is used to improve the real-time performance of disturbance estimation; when the energy source power is low, the system dynamics tend to be smoother, and a lower gain parameter is used to suppress noise interference. Through the target extended state observer with configured parameters, the estimation accuracy of liquid level observations and disturbance observations is significantly improved, thus providing a reliable basis for the generation of subsequent liquid level control signals, ultimately achieving robustness and accuracy in liquid level control.

[0069] As an example, we first obtain the target energy source power for the liquid storage container. The target energy source power characterizes the operating power of the energy source supplying energy to the liquid storage container at a given moment. For example, for a nuclear power plant steam generator, the target energy source power could be the thermal power of the reactor.

[0070] Then, based on the target energy source power and the mapping relationship between energy source power and observer parameters, the target observer parameters corresponding to the target energy source power are determined. Specifically, this can be done by looking up a pre-established mapping table between energy source power and observer parameters, or by using a pre-trained neural network model to obtain the corresponding target observer parameters based on the input target energy source power.

[0071] Based on the target observer parameters, the parameters of the preset extended state observer are configured to obtain the target extended state observer. Furthermore, the target observer parameters can be substituted into the mathematical model of the preset extended state observer to update various parameters of the observer, thereby obtaining a target extended state observer suitable for the current energy source power conditions.

[0072] Therefore, by dynamically adjusting the parameters of the extended state observer to adapt to the system characteristics under different energy source powers, the accuracy of liquid level observation and disturbance estimation is improved.

[0073] This embodiment achieves adaptive adjustment of the extended state observer parameters, improving the observer's performance under different energy source power conditions. Furthermore, by establishing a mapping relationship between energy source power and observer parameters, the parameter configuration process is simplified, improving the system's real-time performance and robustness. Therefore, the solution presented in this application can better adapt to the dynamic characteristics of the liquid storage container under different operating conditions, improving the accuracy and stability of liquid level control.

[0074] In some of the above-mentioned schemes in this application, when the observer parameters are determined by the target energy source power, the dynamic change of the energy source power may lead to low parameter tuning efficiency. Manual adjustment is difficult to adapt to the real-time requirements under transient operating conditions, affecting the tracking accuracy of the expansion state observer for liquid level disturbances.

[0075] In response, this application further proposes that, before determining the target observer parameters corresponding to the target energy source power based on the target energy source power and the mapping relationship between the energy source power and the observer parameters, the method further includes: Obtain the power of multiple preset energy sources; For the liquid storage container under each preset energy source power, the active disturbance rejection parameter tuning experiment of the preset extended state observer is performed by the particle swarm algorithm to obtain the observer parameters corresponding to the preset extended state observer under the preset energy source power. Based on the observer parameters corresponding to each preset energy source power, a mapping relationship between energy source power and observer parameters is generated.

[0076] In this embodiment, the preset power of the energy source covers the typical power range during the operation of the liquid storage container, ensuring the completeness of the mapping relationship. The particle swarm optimization algorithm iteratively optimizes the particle swarm composed of candidate parameter sets, including the bandwidth and gain parameters of the extended state observer.

[0077] Specifically, during particle swarm initialization, each particle carries a candidate parameter set, including bandwidth and gain parameters of the extended state observer. Then, during the operation of the storage container, the deviation between the observed and actual liquid level values ​​is collected in real time, and the objective function value for each particle's corresponding candidate parameter set is calculated. Through multiple iterations to update particle positions, the candidate parameter set with the highest objective function value is retained as the observer parameters for that energy source power. All observer parameters for preset energy source powers are integrated into a power-parameter mapping table, enabling rapid matching of observer parameters under different power conditions. This process uses automatic algorithm tuning to replace manual trial and error, ensuring real-time matching of parameter configuration with dynamic operating conditions and improving the anti-interference performance of the liquid level control.

[0078] As an example, first obtain the power of multiple preset energy sources. For example, you can obtain five preset energy source powers: 100kW, 200kW, 300kW, 400kW, and 500kW.

[0079] Then, for the liquid storage containers under each preset energy source power, an active disturbance rejection parameter tuning experiment is performed on the preset extended state observer using the particle swarm optimization algorithm to obtain the observer parameters corresponding to the preset extended state observer under the preset energy source power. Specifically, active disturbance rejection parameter tuning experiments can be performed for five preset energy source powers: 100kW, 200kW, 300kW, 400kW, and 500kW.

[0080] like Figure 4 The diagram illustrates a flowchart of a particle swarm optimization algorithm, with the following implementation process: In each experiment, the particle swarm and parameter settings are first initialized. The number of particles can be set to 50, with each particle representing a candidate parameter group. These candidate parameter groups will be used for configuring the subsequent pre-defined extended state observer. The iteration count T is initialized to 1. During the operation of the liquid storage container, the objective function value is calculated. This objective function value can be understood as an evaluation of the performance of the candidate parameter group corresponding to each particle. Next, based on the calculated objective function value, the individual optimal value Pbest and the population optimal value Gbest are updated, i.e., a comparison and update of the particle's own optimal position pbest and the population optimal position gbest. Then, the flow direction is determined by whether the convergence criterion is met. If it is met, the optimal result and iteration count are output, and the process ends. If not, the position vector and velocity vector of each particle are updated, the iteration count T is incremented by 1, and the process re-enters the step of calculating the objective function value. This process is repeated until the maximum number of iterations is reached or the convergence condition is met, ultimately obtaining the observer parameters corresponding to the pre-defined extended state observer under each pre-defined energy source power.

[0081] Finally, based on the observer parameters corresponding to the preset extended state observers at each preset energy source power, a mapping relationship between energy source power and observer parameters is generated. This mapping relationship can be established using methods such as polynomial fitting or interpolation, so that the corresponding observer parameters can be quickly determined for any given energy source power.

[0082] This embodiment enables automatic optimization of the expansion state observer's parameter configuration based on the characteristics of the liquid storage container under different energy source powers. This improves the observer's adaptability and observation accuracy under various operating conditions, thereby enhancing the anti-interference capability and control precision of the liquid level control system. Simultaneously, establishing a mapping relationship between energy source power and observer parameters allows for rapid parameter configuration, improving the system's response speed and operating efficiency.

[0083] In some of the solutions described above in this application, the parameter tuning process of the pre-set expansion state observer suffers from insufficient adaptability to dynamic operating conditions. Traditional parameter tuning methods struggle to quickly adjust observer parameters to adapt to transient operating conditions when the standard value of the liquid level in the storage container undergoes a step change, resulting in deviations between the observed liquid level and the actual operating conditions.

[0084] In response, this application further proposes to perform an active disturbance rejection parameter tuning experiment on a preset extended state observer using a particle swarm optimization algorithm, to obtain the observer parameters corresponding to the preset extended state observer under a preset energy source power, including: The transient operating conditions of the target particle swarm and the liquid storage container under the preset energy source power are obtained; each target particle in the target particle swarm is used to characterize a set of candidate parameters of the preset extended state observer, and the transient operating conditions are used to characterize the operating conditions in which the standard value of the liquid level in the liquid storage container changes abruptly. When the liquid storage container is under transient operating conditions, the objective function value of the preset expansion state observer is obtained under each target particle configuration; the objective function value is used to characterize the liquid level observation performance of the preset expansion state observer. Based on the objective function value of the preset extended state observer under each target particle configuration, the target particle swarm is iteratively updated to determine the observer parameters corresponding to the preset extended state observer under the preset energy source power.

[0085] In this embodiment, the target particle swarm includes multiple sets of candidate parameters, each corresponding to different response characteristics of the extended state observer. The transient operating condition simulates a sudden change in the liquid level setpoint, constructing a dynamic test environment for parameter tuning. The objective function value is calculated using the liquid level tracking error integral index to quantify the observer performance under different parameter combinations. The iterative update process follows the position-velocity update formula of the particle swarm algorithm, gradually approximating the optimal parameter solution set.

[0086] Specifically, when the standard liquid level undergoes a step change, a transient operating condition simulation program is initiated. During the initialization of the target particle swarm, multiple candidate parameter sets are randomly generated. During the calculation of the objective function value, the deviation data between the observed and actual liquid level values ​​are collected in real time, and an evaluation index for the objective function value is generated through integration. The particle swarm optimization engine dynamically adjusts the search direction and step size of each particle based on the current ranking of the objective function value. After a preset number of iterations, the candidate parameter set of particles with the optimal objective function value is selected as the observer parameters. This process, through online optimization under dynamic operating conditions, enables the extended state observer to quickly track liquid level changes and accurately estimate disturbances.

[0087] As an example, we first acquire the transient operating conditions of the target particle swarm and the storage container under a preset energy source power. Each target particle in the target particle swarm is used to characterize a set of candidate parameters for a preset extended state observer, and the transient operating condition is used to characterize the condition where the standard value of the liquid level in the storage container undergoes a step change. Specifically, the target particle swarm may contain 100 target particles, and each target particle contains 4 parameters, corresponding to the 4 adjustable parameters of the preset extended state observer. The transient operating condition can be set as the standard value of the liquid level abruptly changes from 50% to 60% within 10 seconds.

[0088] When the liquid storage container is under transient operating conditions, the objective function value of the preset expansion state observer is obtained under each target particle configuration. The objective function value is used to characterize the liquid level observation performance of the preset expansion state observer. Furthermore, the liquid level observation value of the preset expansion state observer under each target particle configuration can be obtained through simulation calculation. The liquid level observation value is compared with the actual liquid level value, and the root mean square error is calculated as the objective function value index.

[0089] Then, based on the objective function value of the preset extended state observer under each target particle configuration, the target particle swarm is iteratively updated to determine the observer parameters corresponding to the preset extended state observer under the preset energy source power.

[0090] Therefore, the iteration termination condition can be set as follows: the optimal objective function value shows no significant improvement for 10 consecutive iterations or the maximum number of iterations (100) is reached. In each iteration, the particles are sorted according to the objective function value, and the top 20% of particles with the best objective function value are retained. The remaining particles are updated through operations such as crossover and mutation. Finally, the particles with the best objective function value are selected as the determined observer parameters.

[0091] This embodiment enables the automated determination of optimal expansion state observer parameters for liquid storage containers under different energy source power levels. By introducing a particle swarm optimization algorithm, the optimal solution can be quickly searched within a large parameter space, avoiding the blindness and inefficiency of manual parameter tuning. Simultaneously, by setting transient operating conditions for evaluation, the observation performance of the obtained parameters during dynamic processes can be guaranteed, improving the accuracy and stability of level control. This adaptive parameter tuning method can adapt to changes in the characteristics of liquid storage containers under different operating conditions, enhancing the robustness and versatility of the level control system.

[0092] In some of the schemes described above in this application, when performing an active disturbance rejection parameter tuning experiment on a preset extended state observer using the particle swarm optimization algorithm, if the actual number of iterations exceeds the preset number of iterations, it may lead to a decrease in the efficiency of the parameter tuning process, affecting the speed of determining the observer parameters, and thus affecting the real-time performance of the liquid level control.

[0093] In response, this application further proposes a method that, after performing an active disturbance rejection parameter tuning experiment on a preset extended state observer using a particle swarm optimization algorithm to obtain the observer parameters corresponding to the preset extended state observer under a preset energy source power, also includes: Obtain the actual number of iterations of the particle swarm optimization algorithm in the experiment of tuning the active disturbance rejection parameters in the preset extended state observer; The actual number of iterations is compared with the preset number of iterations to obtain the comparison result; In response to the comparison result indicating that the actual number of iterations is greater than the preset number of iterations, the algorithm parameters of the particle swarm optimization algorithm are dynamically adjusted.

[0094] In this embodiment, dynamically adjusting the algorithm parameters includes modifying at least one of the particle swarm optimization algorithm's inertia weight, individual learning factor, or social learning factor. For example, when the actual number of iterations exceeds a preset number of iterations, the inertia weight can be reduced to accelerate convergence, or the learning factor can be adjusted to balance global and local search capabilities. The adjustment range of the algorithm parameters can be set based on historical experimental data, with the adjustment range of the inertia weight controlled between 0.1 and 0.3, and the adjustment range of the learning factor controlled between 0.5 and 2.0. The adjusted parameters are used in the subsequent iteration process of the particle swarm optimization algorithm to ensure a reduction in the number of iterations required in subsequent experiments.

[0095] Specifically, after completing the active disturbance rejection parameter tuning experiment, the actual number of iterations when the particle swarm optimization algorithm reaches convergence is automatically recorded and compared with the preset number of iterations. If the actual number of iterations exceeds the preset number, it indicates that the current algorithm parameter settings are causing insufficient search efficiency, triggering the parameter adjustment mechanism. By dynamically reducing the inertia weight, the proportion of particle velocity inheritance is reduced, prompting the particle swarm to gather in the optimal solution region more quickly; at the same time, the individual learning factor and social learning factor are adjusted to enhance the information sharing ability among particles. For example, the inertia weight is gradually reduced from the initial value of 0.9 to 0.6, the individual learning factor is increased from 1.5 to 2.0, and the social learning factor is increased from 1.5 to 2.0. After adjustment, the particle swarm optimization algorithm can complete parameter tuning with fewer iterations in subsequent experiments, avoiding the efficiency bottleneck caused by fixed algorithm parameters, thereby improving the overall speed of the observer parameter tuning process.

[0096] As an example, during the Particle Swarm Optimization (PSO) algorithm's active disturbance rejection (ADOSE) parameter tuning experiment, after the observer parameters of the preset extended state observer under the target energy source power are determined, the actual number of iterations in this parameter tuning experiment is recorded. The actual number of iterations is compared with the preset number of iterations. When the actual number of iterations exceeds the preset number, the PSO algorithm's dynamic parameter adjustment mechanism is triggered. Specifically, based on the proportion by which the actual number of iterations exceeds the preset number, the inertia weight of the PSO algorithm is linearly decreased from the initial value of 0.9 to 0.4, while the individual learning factor and social learning factor are increased from the initial values ​​of 2.0 to 2.5, respectively. In the next parameter tuning experiment, the adjusted PSO algorithm uses the dynamically changing inertia weight and learning factor to update the particle velocity and position.

[0097] This embodiment effectively solves the problems of insufficient convergence speed or premature convergence caused by fixed algorithm parameters in traditional parameter tuning processes. By monitoring the number of iterations in real time and dynamically adjusting the algorithm parameters, it is possible to optimize the allocation of computational resources while ensuring parameter tuning accuracy, avoiding the waste of computational resources caused by excessive iteration, thereby improving the online tuning efficiency of the active disturbance rejection control system. This mechanism further enhances the adaptability of the extended state observer to transient operating conditions, providing algorithmic-level guarantees for improving the anti-interference performance of the liquid level control system.

[0098] Based on the liquid level control method provided in this application, correspondingly, this application also provides specific embodiments of the liquid level control device.

[0099] Figure 5 A schematic diagram of the liquid level control device provided in the embodiment of this application is shown. The liquid level control device 500 includes a liquid level measurement module 510, a disturbance observation module 520, a signal generation module 530, and a liquid level control module 540.

[0100] The liquid level measurement module 510 is used to acquire the liquid level measurement value of the liquid storage container at the first moment; The disturbance observation module 520 is used to input the first liquid level control signal and the liquid level measurement value of the liquid storage container at the first moment into the target expansion state observer to obtain the liquid level observation value and the disturbance observation value; the liquid level observation value is used to characterize the liquid level estimation result obtained after correcting the liquid level measurement value, and the disturbance observation value is used to characterize the comprehensive effect of the environmental disturbance that affects the liquid level of the liquid storage container; The signal generation module 530 is used to generate a second liquid level control signal for the storage container at a second time based on the standard liquid level value, the observed liquid level value, and the observed disturbance value; the second time is the liquid level control time after the first time. The liquid level control module 540 is used to control the liquid level of the storage container based on the second liquid level control signal.

[0101] In the liquid level control device provided in this application embodiment, the liquid level measurement value of the storage container at a first moment is first acquired, and then input into the target expansion state observer along with the first liquid level control signal at the first moment to obtain the liquid level observation value and the disturbance observation value. The liquid level observation value corrects the liquid level measurement value, enabling a more accurate estimation of the true liquid level at the first moment; in addition, the disturbance observation value can accurately capture the combined effect of environmental disturbances. Then, a second-moment control signal is generated based on the standard liquid level value, the liquid level observation value, and the disturbance observation value, fully considering the current liquid level state and environmental disturbance conditions; finally, the liquid level of the storage container is controlled according to the second-moment control signal. Thus, because the true liquid level at the first moment is accurately estimated based on the liquid level observation value during the control process, and disturbance compensation is performed based on the disturbance observation value, it can better cope with various disturbances, thereby improving the accuracy of liquid level control.

[0102] Furthermore, this application also proposes a signal generation module 530, comprising the following units: The control value generation unit is used to input the liquid level deviation between the standard liquid level value and the observed liquid level value into the feedback controller to obtain the first liquid level control value of the liquid storage container. The control value correction unit is used to correct the first liquid level control value based on the disturbance observation value and generate the second liquid level control signal of the liquid storage container at the second time.

[0103] Furthermore, this application also proposes a control value correction unit, used for: Subtract the disturbance observation value from the first liquid level control value to obtain the second liquid level control value; Based on the target gain parameter of the target expansion state observer, the second liquid level control value is corrected to generate the second liquid level control signal of the storage container at the second time.

[0104] Furthermore, this application proposes that the target extended state observer is a linear extended state observer and the feedback controller is a proportional-derivative controller.

[0105] Furthermore, this application also proposes that, before inputting the first liquid level control signal and the liquid level measurement value of the storage container into the target expansion state observer at the first moment, the liquid level control device 500 further includes: The power acquisition module is used to acquire the target energy source power of the liquid storage container; the target energy source power is used to characterize the operating power of the energy source supplying energy to the liquid storage container at the first moment. The parameter determination module is used to determine the target observer parameters corresponding to the target energy source power based on the target energy source power and the mapping relationship between the energy source power and the observer parameters. The observer configuration module is used to configure the parameters of the preset extended state observer based on the target observer parameters to obtain the target extended state observer.

[0106] Furthermore, this application also proposes that, before determining the target observer parameters corresponding to the target energy source power based on the target energy source power and the mapping relationship between the energy source power and the observer parameters, the liquid level control device 500 further includes: The power acquisition module is also used to acquire the power of multiple preset energy sources; The parameter tuning module is used to perform active disturbance rejection parameter tuning experiments on the preset expansion state observers for liquid storage containers under various preset energy source powers, respectively, using the particle swarm algorithm, to obtain the observer parameters corresponding to the preset expansion state observers under the preset energy source powers. The relationship generation module is used to generate a mapping relationship between energy source power and observer parameters based on the observer parameters corresponding to each preset energy source power of the preset extended state observer.

[0107] Furthermore, this application also proposes a parameter tuning module for: The transient operating conditions of the target particle swarm and the liquid storage container under the preset energy source power are obtained; each target particle in the target particle swarm is used to characterize a set of candidate parameters of the preset extended state observer, and the transient operating conditions are used to characterize the operating conditions in which the standard value of the liquid level in the liquid storage container changes abruptly. When the liquid storage container is under transient operating conditions, the objective function value of the preset expansion state observer is obtained under each target particle configuration; the objective function value is used to characterize the liquid level observation performance of the preset expansion state observer. Based on the objective function value of the preset extended state observer under each target particle configuration, the target particle swarm is iteratively updated to determine the observer parameters corresponding to the preset extended state observer under the preset energy source power.

[0108] Furthermore, this application also proposes that, after performing an active disturbance rejection parameter tuning experiment on the preset extended state observer using a particle swarm optimization algorithm to obtain the observer parameters corresponding to the preset extended state observer under a preset energy source power, the liquid level control device 500 further includes: The iteration acquisition module is used to obtain the actual number of iterations of the particle swarm algorithm in the experiment of tuning the active disturbance rejection parameters in the preset extended state observer; The iteration comparison module is used to compare the actual number of iterations with the preset number of iterations to obtain the iteration comparison result. The parameter adjustment module is used to dynamically adjust the algorithm parameters of the particle swarm optimization algorithm in response to the comparison result indicating that the actual number of iterations is greater than the preset number of iterations.

[0109] Based on the liquid level control method provided in this application, correspondingly, this application also provides specific embodiments of the liquid level control device.

[0110] Figure 6 A schematic diagram of the liquid level control device provided in an embodiment of this application is shown.

[0111] The liquid level control device may include a processor 601 and a memory 602 storing computer program instructions.

[0112] Specifically, the processor 601 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0113] Memory 602 may include mass storage for data or instructions. For example, and not limitingly, memory 602 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 602 may include removable or non-removable (or fixed) media. Where appropriate, memory 602 may be internal to an integrated gateway disaster recovery device. In a particular embodiment, memory 602 is non-volatile solid-state memory.

[0114] The processor 601 reads and executes computer program instructions stored in the memory 602 to implement any of the liquid level control methods in the above embodiments.

[0115] In one example, the level control device may further include a communication interface 603 and a bus 610. For example, Figure 6 As shown, the processor 601, memory 602, and communication interface 603 are connected through bus 610 and complete communication with each other.

[0116] The communication interface 603 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0117] Bus 610 includes hardware, software, or both, that couples components of the level control device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 610 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0118] Furthermore, in conjunction with the liquid level control methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the liquid level control methods in the above embodiments.

[0119] In addition, in conjunction with the liquid level control method in the above embodiments, this application embodiment can provide a computer program product to implement it. When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device performs the liquid level control method provided by any aspect of the above embodiments of this application.

[0120] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0121] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0122] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0123] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0124] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A liquid level control method, characterized in that, The method includes: Obtain the liquid level measurement value of the storage container at the first moment; The first liquid level control signal of the liquid storage container at the first moment and the liquid level measurement value are input into the target expansion state observer to obtain the liquid level observation value and the disturbance observation value; the liquid level observation value is used to characterize the liquid level estimation result obtained after correcting the liquid level measurement value, and the disturbance observation value is used to characterize the comprehensive effect of environmental disturbances that affect the liquid level of the liquid storage container; Based on the standard liquid level value, the observed liquid level value, and the observed disturbance value, a second liquid level control signal for the storage container is generated at a second time point; the second time point is the liquid level control time after the first time point. The liquid level of the storage container is regulated based on the second liquid level control signal.

2. The method according to claim 1, characterized in that, The generation of the second liquid level control signal for the storage container at the second moment, based on the standard liquid level value, the observed liquid level value, and the observed disturbance value, includes: The liquid level deviation between the standard liquid level value and the observed liquid level value is input into the feedback controller to obtain the first liquid level control value of the liquid storage container. Based on the disturbance observation, the first liquid level control value is corrected to generate a second liquid level control signal for the storage container at the second time.

3. The method according to claim 2, characterized in that, The step of correcting the first liquid level control value based on the perturbation observation value to generate a second liquid level control signal for the storage container at a second time includes: Subtract the disturbance observation value from the first liquid level control value to obtain the second liquid level control value; Based on the target gain parameter of the target expansion state observer, the second liquid level control value is corrected to generate the second liquid level control signal of the storage container at the second time.

4. The method according to claim 2, characterized in that, The target extended state observer is a linear extended state observer, and the feedback controller is a proportional-derivative controller.

5. The method according to claim 1, characterized in that, Before inputting the first liquid level control signal of the storage container at the first moment and the liquid level measurement value into the target expansion state observer, the method further includes: Obtain the target energy source power of the liquid storage container; the target energy source power is used to characterize the operating power of the energy source supplying energy to the liquid storage container at the first moment; Based on the target energy source power and the mapping relationship between the energy source power and the observer parameters, the target observer parameters corresponding to the target energy source power are determined. Based on the target observer parameters, the preset extended state observer is configured to obtain the target extended state observer.

6. The method according to claim 5, characterized in that, Before determining the target observer parameters corresponding to the target energy source power based on the target energy source power and the mapping relationship between the energy source power and the observer parameters, the method further includes: Obtain the power of multiple preset energy sources; For each of the preset energy source powers, the active disturbance rejection parameter tuning experiment is performed on the preset expansion state observer using the particle swarm optimization algorithm to obtain the observer parameters corresponding to the preset expansion state observer under the preset energy source power. Based on the observer parameters corresponding to each preset energy source power of the preset extended state observer, a mapping relationship between the energy source power and the observer parameters is generated.

7. The method according to claim 6, characterized in that, The step of performing an active disturbance rejection parameter tuning experiment on the preset extended state observer using a particle swarm optimization algorithm to obtain the observer parameters corresponding to the preset extended state observer under the preset energy source power includes: The transient operating conditions of the target particle swarm and the liquid storage container under the preset energy source power are obtained; each target particle in the target particle swarm is used to characterize a set of candidate parameters of the preset expansion state observer, and the transient operating conditions are used to characterize the condition in which the standard value of the liquid level of the liquid storage container changes abruptly. When the liquid storage container is under the transient operating condition, the objective function value of the preset expansion state observer is obtained under each of the target particle configurations; the objective function value is used to characterize the liquid level observation performance of the preset expansion state observer. Based on the objective function value of the preset extended state observer under each of the target particle configurations, the target particle swarm is iteratively updated to determine the observer parameters corresponding to the preset extended state observer under the preset energy source power.

8. The method according to claim 6, characterized in that, After obtaining the observer parameters of the preset extended state observer under the preset energy source power by performing an active disturbance rejection parameter tuning experiment on the preset extended state observer using the particle swarm optimization algorithm, the method further includes: Obtain the actual number of iterations of the particle swarm optimization algorithm in the active disturbance rejection parameter tuning experiment performed by the preset extended state observer; The actual number of iterations is compared with the preset number of iterations to obtain the comparison result. In response to the comparison result indicating that the actual number of iterations is greater than the preset number of iterations, the algorithm parameters of the particle swarm optimization algorithm are dynamically adjusted.

9. A liquid level control device, characterized in that, The device includes: The liquid level measurement module is used to obtain the liquid level measurement value of the liquid storage container at the first moment; The disturbance observation module is used to input the first liquid level control signal of the liquid storage container at the first moment and the liquid level measurement value into the target expansion state observer to obtain the liquid level observation value and the disturbance observation value; the liquid level observation value is used to characterize the liquid level estimation result obtained after correcting the liquid level measurement value, and the disturbance observation value is used to characterize the comprehensive effect of environmental disturbances that affect the liquid level of the liquid storage container; The signal generation module is used to generate a second liquid level control signal for the liquid storage container at a second time point based on the standard liquid level value, the observed liquid level value, and the observed disturbance value; the second time point is the liquid level control time after the first time point. The liquid level control module is used to control the liquid level of the storage container based on the second liquid level control signal.

10. An electronic device, characterized in that, The electronic device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the liquid level control method as described in any one of claims 1-8.

11. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the liquid level control method according to any one of claims 1 to 8.

12. A computer program product comprising a computer program that is read and executed by a processor of a computer device, causing the computer device to perform the liquid level control method according to any one of claims 1 to 8.

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