Crystallizer periodic liquid level control method, storage medium and computer equipment

Abstract

The invention discloses a crystallizer periodic liquid level control method, a storage medium and computer equipment, and the method comprises the steps: converting a frequency feature vector of liquid level fluctuation data into a time domain signal, carrying out the feed-forward compensation of the time domain signal through a feed-forward controller, and inputting the time domain signal into a PID controller; and when liquid level control is carried out on the molten steel in the target crystallizer based on the PID controller and the time domain signal after feedforward compensation, parameters of the PID controller are adjusted in real time based on the fuzzy parameter adjuster until the liquid level adjustment execution structure is adjusted and controlled based on the PID controller, and the liquid level of the molten steel in the crystallizer is controlled to meet the control requirement. In a PID control architecture, a fuzzy parameter regulator is added, a fuzzy rule is made based on field big data analysis, control parameters are changed in real time, different control effects can be achieved in different control stages, meanwhile, a fuzzy feed-forward parameter regulator is added, output of a feed-forward controller is regulated according to the change speed of signals, and the control efficiency is improved. And the control accuracy of the fluctuation of the liquid level is improved.

Description

Technical Field

[0001] This application relates to the technical field of metallurgical continuous casting, and particularly to a method for controlling the periodic liquid level of a mold, a storage medium, and a computer device. Background Art

[0002] In the modern steel production process, the continuous casting link occupies a crucial position and plays an irreplaceable role. As one of the core processes in steel manufacturing, the application of continuous casting technology has greatly improved production efficiency and product quality. However, the complexity of the continuous casting process also brings many challenges, among which the surface defect problem of continuous casting billets is particularly prominent.

[0003] Numerous studies and practical experiences have shown that the fluctuation of the liquid level in the mold has a crucial impact on the surface quality of continuous casting billets. When abnormal fluctuations occur in the liquid level of the mold, it often leads to a series of defects such as slag inclusion and surface cracks, seriously damaging the product quality. The periodic abnormal fluctuation of the liquid level in the mold is one of the most common abnormal phenomena in the continuous casting process, which has attracted extensive attention and in-depth discussion among researchers in the metallurgical field.

[0004] Faced with this problem, traditional control systems are unable to cope. Due to technical limitations, traditional systems often cannot respond quickly and accurately to the periodic abnormal fluctuations of the liquid level in the mold, thus unable to effectively solve this problem, which has a great impact on production quality and efficiency. Summary of the Invention

[0005] In view of this, this application provides a method for controlling the periodic liquid level of a mold, a storage medium, and a computer device, which converts the frequency feature vector of liquid level fluctuation data into a time-domain signal, inputs the time-domain signal into a PID controller after feed-forward compensation by a feed-forward controller; when controlling the liquid level of the molten steel in the target mold based on the PID controller and the time-domain signal after feed-forward compensation, the parameters of the PID controller are adjusted in real time based on a fuzzy parameter regulator until the liquid level adjustment execution structure is controlled by the PID controller to control the liquid level of the molten steel in the mold to meet the control requirements. By adding a fuzzy parameter regulator to the PID control architecture and formulating fuzzy rules based on on-site big data analysis to change the control parameters in real time, different control effects can be achieved in different control stages. At the same time, a fuzzy feed-forward parameter regulator is added to adjust the output of the feed-forward controller according to the change speed of the signal, improving the control accuracy of liquid level fluctuations.

[0006] According to one aspect of this application, a method for controlling the periodic liquid level of a mold is provided, and the method includes:

[0007] Collect the liquid level fluctuation data of the molten steel in the target mold. Among them, the liquid level fluctuation of the molten steel changes periodically, and the liquid level fluctuation data includes the liquid levels of the molten steel at each time node collected based on a preset collection frequency within a preset control period;

[0008] Perform a fast Fourier transform on the liquid level fluctuation data to obtain an initial frequency feature vector reflecting the change frequency of the liquid level fluctuation, and perform a filtering process on the initial frequency feature vector to obtain a frequency feature vector;

[0009] Convert the frequency feature vector into a time-domain signal. After performing feedforward compensation on the time-domain signal through a feedforward controller, input it into a PID controller. Among them, the PID control system includes a feedforward controller, a PID controller, and a fuzzy parameter regulator;

[0010] When performing liquid level control on the molten steel in the target mold based on the PID controller and the time-domain signal after feedforward compensation, adjust the parameters of the PID controller in real time based on the fuzzy parameter regulator until the liquid level adjustment execution structure is regulated based on the PID controller, and control the liquid level of the molten steel in the mold to meet the control requirements.

[0011] Optionally, the performing feedforward compensation on the time-domain signal through the feedforward controller includes:

[0012] Perform a first-order link process on the time-domain signal through the feedforward controller, and perform a differential operation on the processed time-domain signal to obtain the change speed of the time-domain signal;

[0013] Adjust the output of the feedforward controller according to the change speed.

[0014] Optionally, the adjusting the parameters of the PID controller in real time based on the fuzzy parameter regulator includes:

[0015] Configure the initial parameters of the PID controller, and set the proportional coefficient output range, integral coefficient output range, and differential coefficient output range of the fuzzy parameter regulator. Among them, the initial parameters include the proportional coefficient kp, integral coefficient ki, and differential coefficient kd. The proportional coefficient output range is [-0.7, 0.7], the integral coefficient output range is [-1.1, 1.1], and the differential coefficient output range is [-0.21, 0.21];

[0016] Determine the fuzzy level to which the input time-domain signal belongs according to the amplitude and direction of the input time-domain signal. Among them, the fuzzy levels include negative huge, negative medium, negative small, zero, positive small, positive medium, and positive huge;

[0017] Calculate the fuzzy output of the PID controller parameters based on the preset fuzzy rules and the fuzzy level, where the preset fuzzy rules include the adjustment methods of the PID controller parameters corresponding to different fuzzy levels;

[0018] After defuzzifying the fuzzy output, add the obtained proportional coefficient error value Δkp, integral coefficient error value Δki, and derivative coefficient error value Δkd to the initial parameters of the PID controller, so that the PID controller adjusts the liquid level adjustment execution structure based on the latest parameters.

[0019] Optionally, the liquid level control of the molten steel in the target mold based on the PID controller and the time-domain signal after feedforward compensation includes:

[0020] Take the output of the PID controller as the control signal and send it to the liquid level adjustment execution structure, so that the liquid level adjustment execution structure adjusts the inflow of the molten steel in the mold according to the control signal;

[0021] Real-time detect the liquid level of the molten steel through a sensor, and return the detected liquid level value of the molten steel as a feedback signal to the PID control system, so that the PID control system continues to adjust the control signal according to the difference between the feedback signal and the set value of the liquid level of the molten steel.

[0022] Optionally, the fast Fourier transform of the liquid level fluctuation data to obtain the initial frequency feature vector reflecting the change frequency of the liquid level fluctuation includes:

[0023] According to the fast Fourier transform formula, perform a fast Fourier transform on the liquid level fluctuation data to obtain the initial frequency feature vector reflecting the change frequency of the liquid level fluctuation, where the fast Fourier transform formula is:

[0024]

[0025] Y(i) represents the initial frequency feature vector obtained after performing the fast Fourier transform, i represents the frequency index, K represents the length of the liquid level fluctuation data, n represents the acquisition time index of the liquid level fluctuation data, y even (n) represents the even sequence in the liquid level fluctuation data, y odd (n) represents the odd sequence in the liquid level fluctuation data, j represents the imaginary unit, and k represents the ordinal number of the acquisition time node.

[0026] Optionally, the filtering process of the initial frequency feature vector to obtain the frequency feature vector includes:

[0027] In the initial frequency feature vector, remove the frequencies greater than the preset frequency threshold to obtain the frequency feature vector.

[0028] Optionally, after filtering the initial frequency feature vector to obtain a frequency feature vector, the method further includes:

[0029] Based on the frequency feature vector, determine the main frequency affecting the molten steel liquid level fluctuation, and determine the molten steel liquid level fluctuation amplitude based on the determined main frequency;

[0030] Determine the liquid level fluctuation state based on the molten steel liquid level fluctuation amplitude, and control the molten steel liquid level in the mold to meet the control requirements based on the liquid level fluctuation state.

[0031] Optionally, the liquid level fluctuation state includes an abnormal state and a normal state. The determining the liquid level fluctuation state based on the molten steel liquid level fluctuation amplitude and controlling the molten steel liquid level in the mold to meet the control requirements based on the liquid level fluctuation state includes:

[0032] When the molten steel liquid level fluctuation amplitude is outside the preset normal fluctuation range, the molten steel liquid level fluctuation is in an abnormal state, and when the molten steel liquid level fluctuation amplitude is within the preset normal fluctuation range, the molten steel liquid level fluctuation is in a normal state;

[0033] If the molten steel liquid level fluctuation is in an abnormal state, adjust the liquid level adjustment execution structure until the molten steel liquid level fluctuation amplitude in the mold is within the preset normal fluctuation range.

[0034] According to another aspect of the present application, there is provided a storage medium on which a computer program is stored, and when the program is executed by a processor, the above-mentioned mold periodic liquid level control method is implemented.

[0035] According to still another aspect of the present application, there is provided a computer device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the program, the above-mentioned mold periodic liquid level control method is implemented.

[0036] By means of the above technical solution, a mold periodic liquid level control method, a storage medium, and a computer device provided by the present application convert the frequency feature vector of the liquid level fluctuation data into a time domain signal, and input the time domain signal into a PID controller after feedforward compensation by a feedforward controller; when controlling the molten steel in the target mold based on the PID controller and the time domain signal after feedforward compensation, the parameters of the PID controller are adjusted in real time based on a fuzzy parameter regulator until the liquid level adjustment execution structure is controlled by the PID controller to control the molten steel liquid level in the mold to meet the control requirements. By adding a fuzzy parameter regulator to the PID control architecture and formulating fuzzy rules based on on-site big data analysis to change the control parameters in real time, different control effects can be achieved in different control stages. At the same time, a fuzzy feedforward parameter regulator is added to adjust the output of the feedforward controller according to the change speed of the signal, improving the control accuracy of the liquid level fluctuation.

[0037] The above description is only an overview of the technical solution of the present application. In order to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the specific embodiments of the present application are specifically given below. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0039] Figure 1 shows a schematic flowchart of a method for controlling the periodic liquid level of a mold;

[0040] Figure 2 shows a schematic diagram of a frequency characteristic:

[0041] Figure 3 shows a schematic flowchart of another method for controlling the periodic liquid level of a mold provided by an embodiment of the present application;

[0042] Figure 4 shows a schematic diagram of the input, inference, and output relationship after defuzzification of a feedforward controller provided by an embodiment of the present application;

[0043] Figure 5 shows a schematic flowchart of yet another method for controlling the periodic liquid level of a mold provided by an embodiment of the present application;

[0044] Figure 6 shows a schematic flowchart of yet another method for controlling the periodic liquid level of a mold provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The present application will be described in detail below with reference to the drawings and in conjunction with the embodiments. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other.

[0046] In this embodiment, a method for controlling the periodic liquid level of a mold is provided. As Figure 1 shown, it is applied to a mold liquid level control system. The mold liquid level control system includes a mold, a PID control system, and a liquid level adjustment execution structure. When the PID control system adjusts the liquid level adjustment execution structure, the liquid level of the molten steel in the mold changes. The method includes:

[0047] Step 101: Collect the liquid level fluctuation data of the molten steel in the target mold. The liquid level fluctuation of the molten steel changes periodically. The liquid level fluctuation data includes the liquid levels of the molten steel at each time node collected based on a preset collection frequency within a preset control period.

[0048] In the above embodiments of the present application, the liquid level fluctuation data can be extracted from the continuous casting mold in the steel plant. Specifically, collect the liquid level fluctuation data of the molten steel in the target mold. The liquid level fluctuation of the molten steel changes periodically. Therefore, the liquid level fluctuation frequency characteristics can be extracted from the collected liquid level fluctuation data, so that the liquid level adjustment strategy can be determined according to the liquid level fluctuation frequency characteristics.

[0049] The preset collection frequency is, for example, 10 Hz, and the preset control period is, for example, 2 s. Subsequently, the liquid level of the molten steel can be controlled according to the liquid level fluctuation data collected at a frequency of 10 Hz within 2 s.

[0050] In particular, relevant parameters of the continuous casting machine, the position data of the stopper rod in the mold, the argon blowing amount at different positions, and the steel grade composition of the continuous casting billet can also be obtained for assisting in controlling the liquid level of the molten steel.

[0051] The collection results of the steel grade composition are shown in Table 1, and the relevant parameters of the continuous casting machine are shown in Table 2:

[0052] Table 1

[0053]

[0054] Table 2

[0055] Type Parameter Continuous casting machine model Vertical bending type Mold length (mm) 900 Mold oscillation frequency (times / min) 25-400 Mold amplitude (mm) 2-10 Slab width (mm) 1065-1522 Slab thickness (mm) 230 Casting speed (m / min) 0.9-1.3

[0056] Step 102: Perform a fast Fourier transform on the liquid level fluctuation data to obtain an initial frequency feature vector reflecting the liquid level fluctuation change frequency, and perform a filtering process on the initial frequency feature vector to obtain a frequency feature vector.

[0057] Next, perform a fast Fourier transform on the periodic liquid level fluctuation data to analyze its frequency domain characteristics, obtain an initial frequency feature vector, and perform a filtering process on the collected liquid level fluctuation data to eliminate the interference caused by noise in the data. Specifically, perform a filtering process on the initial frequency feature vector to obtain a frequency feature vector. The final conversion result is shown, for example Figure 2 as Figure 2 shown. The left side in the figure is the initial frequency feature result, and the right side is the result after the filtering process. It can be seen that there are very obvious peaks in the periodic liquid level fluctuation, indicating that the frequency corresponding to the peak is the main frequency of the liquid level fluctuation.

[0058] Step 103: Convert the frequency feature vector into a time-domain signal. After performing feed-forward compensation on the time-domain signal through a feed-forward controller, input it into the PID controller. Herein, the PID control system includes a feed-forward controller, a PID controller, and a fuzzy parameter regulator.

[0059] Step 104: When performing liquid level control on the molten steel in the target mold based on the PID controller and the time-domain signal after feed-forward compensation, adjust the parameters of the PID controller in real time based on the fuzzy parameter regulator until the liquid level adjustment execution structure is regulated based on the PID controller to control the molten steel liquid level in the mold to meet the control requirements.

[0060] Next, after converting the frequency feature vector into a time-domain signal, add a feed-forward controller to the control logic of the PID control system, and adjust the output of the feed-forward controller according to the change speed of the signal. That is, after performing feed-forward compensation on the time-domain signal through the feed-forward controller and inputting it into the PID controller, the accuracy of the input signal of the PID controller can be improved. Then, introduce the fuzzy control method and use the fuzzy parameter regulator to realize the real-time adjustment of the parameters of the PID controller. By adding a feed-forward controller on the basis of adding fuzzy rules and adjusting the output of the feed-forward controller according to the change speed of the signal, the overshoot phenomenon can be optimized. Finally, until the liquid level adjustment execution structure is regulated based on the PID controller to control the molten steel liquid level in the mold to meet the control requirements. In particular, the liquid level adjustment execution structure is, for example, a hydraulic actuator, an electric AC brushless servo motor actuator, and a high-precision pneumatic digital cylinder. The hydraulic actuator has a fast speed and good linearity, but it requires devices such as an oil pump and an oil tank, and these devices need to be fixed on the ground, so it may be a bit inconvenient when connected with a hose. The electric AC brushless servo motor actuator is a type developed in recent years and has many advantages. However, due to the presence of a speed reducer, it is not easy to control in terms of volume and weight. The high-precision pneumatic digital cylinder applies the pneumatic digital cylinder technology of machine tools to continuous casting production, has high control precision, a simple structure, and is also convenient for maintenance.

[0061] By applying the technical solution of this embodiment, it can be integrated into the liquid level control device to achieve fast and precise control of liquid level fluctuations during the control process. By adding a fuzzy parameter regulator to the PID control architecture and formulating fuzzy rules based on on-site big data analysis to change the control parameters in real time, different control effects can be achieved at different control stages. And add a fuzzy feed-forward parameter regulator to adjust the output of the feed-forward controller according to the change speed of the signal, which further improves the control precision of liquid level fluctuations.

[0062] Further, as a refinement and extension of the specific implementation manner of the above embodiment, in order to completely illustrate the specific implementation process of this embodiment, another method for controlling the periodic liquid level of a mold is provided, which is applied to a mold liquid level control system. The mold liquid level control system includes a mold, a PID control system, and a liquid level adjustment execution structure. When the PID control system regulates the liquid level adjustment execution structure, the liquid level of the molten steel in the mold changes; as Figure 3 shown, this method includes:

[0063] Step 201, collect the liquid level fluctuation data of the molten steel in the target mold, and perform a fast Fourier transform on the liquid level fluctuation data according to the fast Fourier transform formula to obtain an initial frequency feature vector reflecting the change frequency of the liquid level fluctuation. Among them, the liquid level fluctuation of the molten steel is periodically changed, and the liquid level fluctuation data includes the liquid levels of the molten steel at each time node collected based on a preset collection frequency within a preset control period. The fast Fourier transform formula is:

[0064]

[0065] Y(i) represents the initial frequency feature vector obtained after performing the fast Fourier transform, i represents the frequency index, K represents the length of the liquid level fluctuation data, n represents the collection time index of the liquid level fluctuation data, and y even (n) represents the even sequence in the liquid level fluctuation data, and y odd (n) represents the odd sequence in the liquid level fluctuation data, j represents the imaginary unit, and k represents the ordinal number of the collection time node.

[0066] In the above embodiment of the present application, the fast Fourier transform method is used to perform frequency domain feature analysis on the periodic liquid level fluctuation data, deeply excavate the fluctuation features, and obtain an initial frequency feature vector reflecting the change frequency of the liquid level fluctuation.

[0067] Step 202, remove the frequencies greater than the preset frequency threshold in the initial frequency feature vector to obtain a frequency feature vector, and convert the frequency feature vector into a time domain signal.

[0068] Next, in the initial frequency feature vector, remove the frequencies greater than the preset frequency threshold, such as 3HZ, to obtain a frequency feature vector, and then convert the frequency feature vector into a time domain signal. In particular, when filtering the collected liquid level fluctuation data, that is, eliminating the interference caused by noise in the data, the FIR filter design method can also be used. Specifically, the basic idea of the FIR filter design method is to approximate the required frequency response curve. At present, the commonly used methods are the window function method and the optimal approximation method. The window function method can be expressed by the following formula:

[0069]

[0070] where Fd(e jω) is the required frequency response, f d [n] is derived from the unit impulse response through the inverse Fourier transform, ω is the frequency, and j is a random integer.

[0071] The selection principle for the window function is as follows: The main lobe width should be as narrow as possible to improve the frequency domain resolution and reduce data loss, thereby achieving greater stopband attenuation. It has a lower side lobe amplitude, especially the first side lobe amplitude. The amplitude decays with frequency as quickly as possible. In addition, considering the periodic characteristics of the liquid level fluctuation, the Blackman window function can be combined with the FIR, and its definition is shown in the following formula

[0072] w(n) = 0.42 - 0.5cos(2πn / N) + 0.08cos(4πn / N),

[0073] where N is the window length, and n = 0, 1, …, N - 1.

[0074] Step 203, perform a first-order link processing on the time-domain signal through a feedforward controller, and perform a differential operation on the processed time-domain signal to obtain the change speed of the time-domain signal. After adjusting the output of the feedforward controller according to the change speed, input it into the PID controller, where the PID control system includes a feedforward controller, a PID controller, and a fuzzy parameter regulator.

[0075] Next, add a feedforward controller to the control logic of the PID control system. When the time-domain signal enters the feedforward controller, the feedforward controller will first perform a differential operation on the time-domain signal after passing through a first-order link to obtain the change speed of the time-domain signal, and adjust the output of the feedforward controller according to the change speed of the time-domain signal. The specific adjustment value can be calculated by the following formula:

[0076] K = -0.0034e 2 + 0.0369e - 1.3322,

[0077] where K is the adjustment value and e is the input time-domain signal.

[0078] The relationship between the input and inference of the feedforward controller and the output after defuzzification is as Figure 4 shown. As shown in Figure 4 , when the input signal (the input time-domain signal) changes greatly, the feedforward controller will output a smaller adjustment amount to assist the system to quickly reach the desired position. When the input signal (the input time-domain signal) tends to be stable, the feedforward controller will output a larger adjustment amount to offset the overshoot in the feedback controller, thereby reducing the adjustment time of the PID control system and enabling the PID control system to reach stability more quickly.

[0079] Step 204: Use the output of the PID controller as a control signal and send it to the liquid level adjustment actuator structure, so that the liquid level adjustment actuator structure adjusts the inflow of molten steel in the mold according to the control signal.

[0080] Step 205: Real-time detect the molten steel liquid level through a sensor, and use the detected molten steel liquid level value as a feedback signal to return to the PID control system, so that the PID control system continues to adjust the control signal according to the difference between the feedback signal and the set value of the molten steel liquid level.

[0081] Step 206: Based on the fuzzy parameter regulator, adjust the PID controller parameters in real time until the molten steel liquid level in the mold is controlled to meet the control requirements by regulating the liquid level adjustment actuator structure based on the PID controller.

[0082] Next, use the output of the PID controller as a control signal and send it to the liquid level adjustment actuator structure, so that the liquid level adjustment actuator structure adjusts the inflow of molten steel in the mold according to the control signal. Real-time detect the molten steel liquid level through a sensor, and use the detected molten steel liquid level value as a feedback signal to return to the PID control system, so that the PID control system continues to adjust the control signal according to the difference between the feedback signal and the set value of the molten steel liquid level. Based on the fuzzy parameter regulator, adjust the PID controller parameters in real time until the molten steel liquid level in the mold is controlled to meet the control requirements by regulating the liquid level adjustment actuator structure based on the PID controller.

[0083] By applying the technical solution of this embodiment, through on-site sampling, for the liquid level fluctuation data of molten steel in the continuous casting mold of the steel plant collected, the frequency domain characteristics of the periodic liquid level fluctuation data are analyzed by using the fast Fourier transform method to deeply explore the fluctuation characteristics. And filter the collected liquid level fluctuation data to eliminate the interference caused by noise in the data. Add a feedforward controller to the control logic of the PID control system. After the input signal enters the feedforward controller, the feedforward controller will first differentiate the input signal after passing through a first-order link to obtain the change speed of the input signal, and adjust the output of the feedforward controller according to the change speed of the signal, which can improve the accuracy of liquid level control.

[0084] Further, as a refinement and extension of the specific implementation manner of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, another method for controlling the periodic liquid level of the mold is provided, which is applied to the mold liquid level control system. The mold liquid level control system includes a mold, a PID control system and a liquid level adjustment actuator structure. When the PID control system regulates the liquid level adjustment actuator structure, the molten steel liquid level in the mold changes; as Figure 5 shown, this method includes:

[0085] Step 301: Collect the liquid level fluctuation data of the molten steel in the target mold, perform a fast Fourier transform on the liquid level fluctuation data to obtain an initial frequency feature vector reflecting the frequency of the liquid level fluctuation change, and perform a filtering process on the initial frequency feature vector to obtain a frequency feature vector. Herein, the liquid level fluctuation of the molten steel changes periodically, and the liquid level fluctuation data includes the molten steel liquid levels at each time node collected based on a preset collection frequency within a preset control period.

[0086] In the above embodiment of the present application, the liquid level fluctuation data of the molten steel in the target mold is collected, a fast Fourier transform is performed on the liquid level fluctuation data to obtain an initial frequency feature vector reflecting the frequency of the liquid level fluctuation change, and a filtering process is performed on the initial frequency feature vector to obtain a frequency feature vector, preparing for subsequent control of the molten steel liquid level.

[0087] Step 302: Convert the frequency feature vector into a time-domain signal, perform a feedforward compensation on the time-domain signal through a feedforward controller, and then input it into a PID controller. Herein, the PID control system includes a feedforward controller, a PID controller, and a fuzzy parameter regulator.

[0088] Next, convert the frequency feature vector into a time-domain signal, perform a feedforward compensation on the time-domain signal through a feedforward controller, and then input it into a PID controller. The PID controller implements "control" on the molten steel liquid level in the mold based on the time-domain signal.

[0089] Step 303: When performing liquid level control on the molten steel in the target mold based on the PID controller and the time-domain signal after feedforward compensation, configure the initial parameters of the PID controller, and set the proportional coefficient output range, integral coefficient output range, and derivative coefficient output range of the fuzzy parameter regulator. Herein, the initial parameters include a proportional coefficient kp, an integral coefficient ki, and a derivative coefficient kd. The proportional coefficient output range is [-0.7, 0.7], the integral coefficient output range is [-1.1, 1.1], and the derivative coefficient output range is

[0090] [-0.21, 0.21].

[0091] Step 304: Determine the fuzzy level to which the input time-domain signal belongs according to the amplitude and direction of the input time-domain signal. Herein, the fuzzy levels include negative huge, negative medium, negative small, zero, positive small, positive medium, and positive huge.

[0092] Step 305: Calculate the fuzzy output of the PID controller parameters based on the preset fuzzy rules and the fuzzy level. Herein, the preset fuzzy rules include the PID controller parameter adjustment methods corresponding to different fuzzy levels.

[0093] In step 306, after defuzzifying the fuzzy output, the obtained proportional coefficient error value Δkp, integral coefficient error value Δki, and derivative coefficient error value Δkd are added to the initial parameters of the PID controller, so that the PID controller adjusts the liquid level adjustment execution structure based on the latest parameters until the liquid level of the molten steel in the mold is controlled to meet the control requirements based on the regulation of the PID controller on the liquid level adjustment execution structure.

[0094] By adding fuzzy rules to dynamically adjust the PID parameters in the traditional PID control system, the control accuracy can be improved, that is, introducing the fuzzy control method and using the fuzzy parameter regulator to realize the real-time adjustment of the PID controller parameters. The output ranges of the fuzzy parameter regulator are Δkp = [-0.7, 0.7], Δki = [-1.1, 1.1], and Δkd = [-0.21, 0.21] respectively. The fuzzy subsets of the input and output are defined as: negative huge (NH), negative medium (NM), negative small (NS), zero (O), positive small (PS), positive medium (PM), and positive huge (PH). Then, fuzzy inference and defuzzification are performed to obtain the parameters. After obtaining the fuzzified error information, the fuzzy regulator will imitate the human parameter tuning experience, perform fuzzy inference on the controller parameters according to the fuzzy information, and defuzzify the inference result to finally obtain the actual adjustment amount.

[0095] Specifically, configure the initial parameters of the PID controller, that is, a reasonable set of initial parameters need to be set for the PID controller, namely the proportional coefficient kp, integral coefficient ki, and derivative coefficient kd. The selection of these initial parameters is usually based on experience, system characteristics, and control objectives.

[0096] Regarding the proportional coefficient kp: It determines the response speed and overshoot of the system. The initial value can be selected according to the dynamic characteristics of the system, and generally a moderate value is selected to ensure that the system is neither too sluggish nor too oscillatory.

[0097] Regarding the integral coefficient ki: It is used to eliminate the static error. The selection of the initial value should consider the static characteristics of the system and the influence of the integral action on the system stability.

[0098] Regarding the derivative coefficient kd: It improves the stability and response speed of the system and is used to reduce the overshoot. The selection of the initial value should balance the dynamic performance and stability of the system.

[0099] Then, set the output ranges of the fuzzy parameter regulator, that is, Δkp = [-0.7, 0.7], Δki = [-1.1, 1.1], and Δkd = [-0.21, 0.21]. These output ranges limit the range of adjustment of the PID controller parameters by the fuzzy parameter regulator to ensure that the adjusted parameters are still within a reasonable range.

[0100] Next, determine the fuzzy level of the target time-domain signal. According to the amplitude and direction of the target time-domain signal, classify it into preset fuzzy levels, including negative large, negative medium, negative small, zero, positive small, positive medium, and positive large. This is usually achieved by comparing the target signal with preset thresholds.

[0101] Next, formulate preset fuzzy rules. The preset fuzzy rules define the adjustment methods of PID controller parameters under different fuzzy levels. These rules can be formulated based on experience, system characteristics, and control objectives. For example: when the target signal is in the negative large level, it may be necessary to significantly increase the proportional coefficient kp to quickly respond to signal changes, and at the same time appropriately reduce the integral coefficient ki to prevent integral saturation; when the target signal is in the zero level, it may not be necessary to adjust the PID controller parameters; when the target signal is in the positive small level, it may be necessary to appropriately reduce the proportional coefficient kp to reduce the overshoot, and appropriately increase the derivative coefficient kd to improve the stability of the system.

[0102] Next, calculate the fuzzy output of the PID controller parameters. Based on the fuzzy level of the target time-domain signal and the preset fuzzy rules, use a fuzzy parameter regulator to calculate the fuzzy output of the PID controller parameters, namely Δkp, Δki, and Δkd.

[0103] Next, perform defuzzification processing on the fuzzy output to obtain specific values of Δkp, Δki, and Δkd. Defuzzification methods can include the centroid method, the maximum membership degree method, etc.

[0104] Finally, update the parameters of the PID controller. Add the obtained values of Δkp, Δki, and Δkd to the initial parameters of the PID controller to obtain new PID controller parameters. Then, use these new parameters to control the liquid level of the molten steel in the target mold.

[0105] Next, monitoring and adjustment can also be carried out, that is, during the actual operation process, it is necessary to continuously monitor the system response and control effect. If the control effect is not ideal, the initial parameters, fuzzy levels, fuzzy rules, etc. can be adjusted and optimized according to the actual situation.

[0106] Through the above steps, real-time adjustment and optimization of the PID controller parameters can be achieved, thereby improving the accuracy and stability of the liquid level control of the molten steel in the target mold.

[0107] By applying the technical solution of this embodiment, by adding a fuzzy parameter regulator to the PID control architecture and formulating fuzzy rules based on on-site big data analysis to change the control parameters in real time, different control effects can be achieved at different control stages. At the same time, a fuzzy feedforward parameter regulator is added to adjust the output of the feedforward controller according to the change speed of the signal, improving the control accuracy of the liquid level fluctuation.

[0108] Further, as a refinement and extension of the specific implementation manner of the above embodiment, in order to completely illustrate the specific implementation process of this embodiment, another mold periodic liquid level control method is provided. As shown in Figure 6 the figure, this method includes:

[0109] Step 401: Collect the liquid level fluctuation data of the molten steel in the target mold. Among them, the liquid level fluctuation of the molten steel changes periodically, and the liquid level fluctuation data includes the molten steel liquid levels at each time node collected based on a preset collection frequency within a preset control period.

[0110] Step 402: Perform a fast Fourier transform on the liquid level fluctuation data to obtain an initial frequency feature vector reflecting the change frequency of the liquid level fluctuation, and perform a filtering process on the initial frequency feature vector to obtain a frequency feature vector.

[0111] In the embodiment of the present application, the liquid level fluctuation data of the molten steel in the target mold is collected. Among them, the liquid level fluctuation of the molten steel changes periodically, and the liquid level fluctuation data includes the molten steel liquid levels at each time node collected based on a preset collection frequency within a preset control period. A fast Fourier transform is performed on the liquid level fluctuation data to obtain an initial frequency feature vector reflecting the change frequency of the liquid level fluctuation, and a filtering process is performed on the initial frequency feature vector to obtain a frequency feature vector. This prepares for subsequently determining the liquid level fluctuation state based on the molten steel liquid level fluctuation amplitude and controlling the molten steel liquid level in the mold to meet the control requirements based on the liquid level fluctuation state.

[0112] Step 403: Based on the frequency feature vector, determine the main frequency affecting the liquid level fluctuation of the molten steel, and determine the molten steel liquid level fluctuation amplitude based on the determined main frequency. Among them, the fluctuation state includes stable fluctuation and unstable fluctuation.

[0113] Step 404: When the molten steel liquid level fluctuation amplitude is outside the preset normal fluctuation range, the molten steel liquid level fluctuation is in an abnormal state, and when the molten steel liquid level fluctuation amplitude is within the preset normal fluctuation range, the molten steel liquid level fluctuation is in a normal state.

[0114] Step 405: If the molten steel liquid level fluctuation is in an abnormal state, then adjust the liquid level adjustment execution structure until the molten steel liquid level fluctuation amplitude in the mold is within the preset normal fluctuation range.

[0115] Next, the preset normal fluctuation range is, for example, ±5 mm. When the molten steel liquid level fluctuation amplitude is outside the preset normal fluctuation range, the molten steel liquid level fluctuation is in an abnormal state at this time, while when the molten steel liquid level fluctuation amplitude is within the preset normal fluctuation range, the molten steel liquid level fluctuation is in a normal state. When in an abnormal state, it is necessary to adjust the liquid level adjustment execution structure until the molten steel liquid level fluctuation amplitude in the mold is within the preset normal fluctuation range.

[0116] By applying the technical solution of this embodiment, the main frequency affecting the molten steel liquid level fluctuation is determined based on the frequency feature vector, and the fluctuation characteristics of the molten steel are judged accordingly, so as to realize the precise regulation of the mold operation parameters. That is, when the molten steel fluctuates violently and the molten steel liquid level fluctuation is in an abnormal state, the liquid level fluctuation can be effectively stabilized and the quality of the molten steel can be improved.

[0117] It should be noted that for other corresponding descriptions of each functional unit involved in the mold periodic liquid level control device provided in the embodiments of the present application, reference can be made to Figure 1 、 Figure 3 、 Figure 5 and Figure 6 the corresponding descriptions in the method, which will not be elaborated here.

[0118] Based on the above Figure 1 、 Figure 3 、 Figure 5 and Figure 6 shown methods, correspondingly, the embodiments of the present application also provide a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the mold periodic liquid level control method as shown in the above Figure 1 、 Figure 3 、 Figure 5 and Figure 6 is implemented.

[0119] Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, and the software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various implementation scenarios of the present application.

[0120] Based on the above Figure 1 、 Figure 3 、 Figure 5 and Figure 6 shown methods, in order to achieve the above object, the embodiments of the present application also provide a computer device, which can specifically be a personal computer, a server, a network device, etc. The computer device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to implement the mold periodic liquid level control method as shown in the above Figure 1 、 Figure 3 、 Figure 5 and Figure 6 is implemented.

[0121] Optionally, the computer device may further include a user interface, a network interface, a camera, a Radio Frequency (RF) circuit, sensors, an audio circuit, a WI-FI module, and so on. The user interface may include a display screen, an input unit such as a keyboard, etc. Optionally, the user interface may further include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a Bluetooth interface, a WI-FI interface), etc.

[0122] Those skilled in the art can understand that the structure of a computer device provided in this embodiment does not constitute a limitation on the computer device, and it may include more or fewer components, or combine certain components, or have different component arrangements.

[0123] The storage medium may further include an operating system and a network communication module. The operating system is a program for managing and storing the hardware and software resources of the computer device, and supports the operation of information processing programs and other software and / or programs. The network communication module is used to implement communication between the components inside the storage medium, as well as communication between the storage medium and other hardware and software in the entity device.

[0124] Through the description of the above embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus a necessary general hardware platform, or can also be implemented by hardware to convert the frequency feature vector of the liquid level fluctuation data into a time-domain signal, and input the time-domain signal into a PID controller after feedforward compensation by a feedforward controller; when implementing liquid level control on the molten steel in the target mold based on the PID controller and the time-domain signal after feedforward compensation, the parameters of the PID controller are adjusted in real time based on a fuzzy parameter regulator until the liquid level adjustment execution structure is regulated based on the PID controller, and the molten steel liquid level in the mold is controlled to meet the control requirements. By adding a fuzzy parameter regulator to the PID control architecture, formulating fuzzy rules based on on-site big data analysis, and changing the control parameters in real time, different control effects can be achieved in different control stages. At the same time, a fuzzy feedforward parameter regulator is added to adjust the output of the feedforward controller according to the change speed of the signal, improving the control accuracy of the liquid level fluctuation.

[0125] Those skilled in the art can understand that the drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the drawings are not necessarily essential for implementing this application. Those skilled in the art can understand that the modules in the device in the implementation scenario can be distributed in the device in the implementation scenario according to the description of the implementation scenario, or can be correspondingly changed and located in one or more devices different from this implementation scenario. The modules in the above implementation scenario can be combined into one module, or further split into multiple sub-modules.

[0126] The above serial numbers of this application are only for description and do not represent the advantages or disadvantages of the implementation scenarios. The above disclosure is only several specific implementation scenarios of this application. However, this application is not limited thereto, and any changes that can be conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. A method for controlling the periodic liquid level of a crystallizer, characterized in that: Applied to a crystallizer liquid level control system, the crystallizer liquid level control system includes a crystallizer, a PID control system and a liquid level adjustment execution structure. When the PID control system adjusts the liquid level adjustment execution structure, the liquid level of the molten steel in the crystallizer changes; the method includes: Collecting liquid level fluctuation data of the molten steel in the target crystallizer, wherein the liquid level fluctuation of the molten steel changes periodically, and the liquid level fluctuation data includes the liquid level of the molten steel at each time node collected based on a preset collection frequency within a preset control period; Performing a fast Fourier transform on the liquid level fluctuation data to obtain an initial frequency characteristic vector reflecting the frequency of the liquid level fluctuation change, and performing filtering processing on the initial frequency characteristic vector to obtain a frequency characteristic vector; The frequency characteristic vector is converted into a time domain signal, and the time domain signal is feedforward compensated by a feedforward controller and then input into a PID controller, wherein the PID control system includes a feedforward controller, a PID controller and a fuzzy parameter regulator; When the liquid level control is performed on the molten steel in the target crystallizer based on the PID controller and the time domain signal after feedforward compensation, the PID controller parameters are adjusted in real time based on the fuzzy parameter regulator until the liquid level adjustment execution structure is adjusted based on the PID controller to control the liquid steel level in the crystallizer to meet the control requirements.

2. The method according to claim 1, characterized in that The feedforward compensation of the time domain signal by a feedforward controller includes: The time domain signal is processed by a first-order link through a feedforward controller, and a differential operation is performed on the processed time domain signal to obtain a change speed of the time domain signal; The output of the feedforward controller is adjusted according to the rate of change.

3. The method according to claim 1, characterized in that The real-time adjustment of the PID controller parameters based on the fuzzy parameter regulator includes: Configure the initial parameters of the PID controller, and set the proportional coefficient output domain, integral coefficient output domain and differential coefficient output domain of the fuzzy parameter regulator, wherein the initial parameters include the proportional coefficient kp, the integral coefficient ki and the differential coefficient kd, the proportional coefficient output domain is [-0.7, 0.7], the integral coefficient output domain is [-1.1, 1.1], and the differential coefficient output domain is [-0.21, 0.21]; Determine the fuzzy level to which the input time domain signal belongs according to the amplitude and direction of the input time domain signal, wherein the fuzzy level includes negative huge, negative medium, negative small, zero, positive small, positive medium and positive huge; Based on the preset fuzzy rules and the fuzzy levels, calculating the fuzzy output of the PID controller parameters, wherein the preset fuzzy rules include PID controller parameter adjustment methods corresponding to different fuzzy levels; After defuzzifying the fuzzy output, the obtained proportional coefficient error value Δkp, integral coefficient error value Δki and differential coefficient error value Δkd are added to the initial parameters of the PID controller so that the PID controller can adjust the liquid level based on the latest parameters.

4. The method according to claim 1, characterized in that The liquid level control of the molten steel in the target crystallizer based on the PID controller and the time domain signal after feedforward compensation includes: The output of the PID controller is sent as a control signal to the liquid level adjustment execution structure, so that the liquid level adjustment execution structure adjusts the inflow of molten steel in the crystallizer according to the control signal; The molten steel level is detected in real time by the sensor, and the detected molten steel level value is returned to the PID control system as a feedback signal, so that the PID control system continues to adjust the control signal according to the difference between the feedback signal and the molten steel level set value.

5. The method according to claim 1, characterized in that The fast Fourier transform of the liquid level fluctuation data to obtain an initial frequency feature vector reflecting the frequency of the liquid level fluctuation change includes: According to the fast Fourier transform formula, the liquid level fluctuation data is subjected to fast Fourier transform to obtain an initial frequency feature vector reflecting the frequency of the liquid level fluctuation change, wherein the fast Fourier transform formula is: Y(i) represents the initial frequency feature vector obtained after fast Fourier transform, i represents the frequency index, K represents the length of the liquid level fluctuation data, n represents the acquisition time index of the liquid level fluctuation data, and y even (n) represents the even number sequence in the liquid level fluctuation data, y odd (n) represents the odd number sequence in the liquid level fluctuation data, j represents the imaginary unit, and k represents the ordinal number of the acquisition time node.

6. The method according to claim 1, characterized in that The filtering process is performed on the initial frequency feature vector to obtain the frequency feature vector, including: In the initial frequency feature vector, frequencies greater than a preset frequency threshold are removed to obtain a frequency feature vector.

7. The method according to claim 1, characterized in that After filtering the initial frequency feature vector to obtain the frequency feature vector, the method further includes: Determining a main frequency that affects the fluctuation of the molten steel level based on the frequency characteristic vector, and determining an amplitude of the fluctuation of the molten steel level based on the determined main frequency; The liquid level fluctuation state is determined based on the liquid level fluctuation amplitude of the molten steel, and the liquid level of the molten steel in the crystallizer is controlled based on the liquid level fluctuation state to meet the control requirements.

8. The method according to claim 7, characterized in that The liquid level fluctuation state includes an abnormal state and a normal state, and the liquid level fluctuation state is determined based on the liquid steel level fluctuation amplitude, and the liquid steel level in the crystallizer is controlled based on the liquid level fluctuation state to meet the control requirements, including: When the fluctuation amplitude of the molten steel level is outside the preset normal fluctuation range, the fluctuation of the molten steel level is in an abnormal state, and when the fluctuation amplitude of the molten steel level is within the preset normal fluctuation range, the fluctuation of the molten steel level is in a normal state; If the fluctuation of the molten steel level is abnormal, the liquid level adjustment execution structure is regulated until the fluctuation amplitude of the molten steel level in the crystallizer is within the preset normal fluctuation range.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for controlling the periodic liquid level of the crystallizer according to any one of claims 1 to 8 is implemented.

10. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method for periodic liquid level control of the crystallizer according to any one of claims 1 to 8 is implemented.

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