Automatic control method for wide flow range regulating valves for all steel grades in continuous casting secondary cooling water.

Abstract

The present application relates to the field of automatic control technology, and especially relates to an automatic control method for a continuous casting secondary cooling water full-steel wide flow interval regulating valve. The method comprises the following steps: obtaining flow demand data; determining whether the flow demand data is greater than a preset first flow interval threshold value data, and if so, performing a large-bore regulating valve control operation according to preset large-bore flow data; determining whether the flow demand data is greater than or equal to a preset second flow interval threshold value data, and if so, performing a double-bore regulating valve control operation according to the preset large-bore flow data and preset small-bore flow data; determining whether the flow demand data is less than the preset second flow interval threshold value data, and if so, performing a small-bore regulating valve control operation according to the preset small-bore flow data. The present application solves the problem of different steel types requiring different secondary cooling water flows, and avoids flow fluctuations caused by exceeding the characteristics of the valve at low flow, which affects the quality of the cast slab.

Description

Technical Field

[0001] This invention relates to the field of automatic control technology, and in particular to an automatic control method for a wide flow range regulating valve for all steel grades in continuous casting secondary cooling water. Background Technology

[0002] Continuous casting is a key technology in modern steel production, producing billets of various specifications by continuously casting molten steel at high temperatures. The quality and efficiency of continuous casting directly affect the quality of steel products and production costs. Therefore, the optimization and control of continuous casting has always been an important research direction in the steel production field.

[0003] In continuous casting, the secondary cooling water system is a crucial component for controlling the cooling rate of the slab. The flow rate and temperature of the secondary cooling water directly affect the quality and surface properties of the slab. Due to the different process requirements of various steel grades, the set flow rate for water distribution in each loop of the continuous casting machine varies depending on the steel grade. While the casting machine is initially designed with high-flow-rate regulating valves to meet large water volumes, some steel grades require only very low flow rates. In these cases, the valve core setting is often not within the optimal 30%-70% operating range of the regulating valve, easily leading to flow fluctuations and thus affecting the slab quality. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes an automatic control method for a wide flow range regulating valve for all steel grades in continuous casting secondary cooling water, thereby solving at least one of the aforementioned technical problems.

[0005] This application provides an automatic control method for a wide flow range regulating valve for all steel grades in continuous casting secondary cooling water, the method comprising:

[0006] S1. Obtain traffic demand data;

[0007] S2. Determine whether the flow demand data is greater than or equal to the preset first flow interval threshold data, and if the flow demand data is greater than the preset first flow interval threshold data, then perform large-diameter regulating valve control operation according to the preset large-diameter flow data.

[0008] S3. If the traffic demand data is determined to be less than the preset first traffic interval threshold data, then determine whether the traffic demand data is greater than or equal to the preset second traffic interval threshold data.

[0009] S4. When the flow demand data is determined to be greater than or equal to the preset second flow range threshold data, the dual-bore regulating valve control operation is performed based on the preset large-bore flow data and the preset small-bore flow data.

[0010] S5. When the flow demand data is determined to be less than the preset second flow range threshold data, the small-diameter regulating valve control operation is performed according to the preset small-diameter flow data.

[0011] This invention achieves precise control of the secondary cooling water in continuous casting by judging different flow rate demand data and selecting different valve control strategies, ensuring that the flow rate of cooling water remains within the optimal range throughout the steel production process. By avoiding flow fluctuations caused by the regulating valve exceeding its optimal operating range at low flow rates, this method improves the quality of the cast billet and reduces defects and scrap rates. By dynamically selecting appropriate valve control strategies based on flow rate demand, this method optimizes the use of regulating valves, extends equipment life, and improves production efficiency.

[0012] Optionally, S1 includes:

[0013] Obtain steel grade parameter data and valve characteristic parameter data;

[0014] The flow demand is calculated based on the steel grade parameter data and valve characteristic parameter data to obtain the flow demand data.

[0015] This invention acquires steel grade parameter data and valve characteristic parameter data, and performs comprehensive calculations to accurately predict flow demand, ensuring the precision of flow control. Real-time acquisition and calculation of flow demand data allows for dynamic adjustment of flow control strategies based on production changes, ensuring the flexibility and adaptability of the production process.

[0016] Optionally, S2 includes:

[0017] Determine whether the traffic demand data is greater than or equal to the preset first traffic interval threshold data;

[0018] When the flow demand data is determined to be greater than the preset first flow range threshold data, a large-diameter regulating valve signal is generated based on the preset large-diameter flow data to perform large-diameter regulating valve control operation.

[0019] The specific method for generating the preset first flow interval threshold data is as follows:

[0020] Historical steel grade flow data are obtained based on steel grade parameter data;

[0021] Characteristic data of historical steel grade flow rate are obtained by extracting characteristics from historical steel grade flow rate data;

[0022] Based on historical steel grade flow rate characteristic data and steel grade parameter data, parameter mapping is performed to obtain steel grade flow rate parameter mapping data;

[0023] A regression model is constructed based on the steel grade flow parameter mapping data to obtain a steel grade flow parameter identification model;

[0024] The steel grade flow parameter identification model is used to identify the steel grade parameter data and valve characteristic parameter data to obtain the threshold data of the first flow range.

[0025] This invention ensures precise flow control and improves production process stability through accurate threshold judgment and control signal generation. Automated data analysis and model building reduce manual intervention and enhance system automation. Rapid response to changes in flow demand ensures continuous and efficient production. Precise flow control reduces resource waste and improves production efficiency and economic benefits. Stable flow control improves billet quality and reduces defects and scrap rates. Continuous optimization of the model and thresholds using historical and real-time data ensures sustained improvement in system performance.

[0026] Optionally, S4 includes:

[0027] A flow model for large-diameter valves is constructed based on preset large-diameter flow data and valve characteristic parameter data.

[0028] A flow model for small-diameter valves is constructed based on preset small-diameter flow data and valve characteristic parameter data.

[0029] Based on the flow demand data, the flow model of large-diameter valves, and the flow model of small-diameter valves, flow distribution calculations are performed to obtain valve flow distribution data for dual-diameter control valve operation.

[0030] This invention, through the construction and application of flow models for large-diameter and small-diameter valves, enables precise control of flow demand, improving the stability of the production process. The flow distribution and valve control processes are highly automated, reducing manual intervention and enhancing the system's automation level. Real-time data and model calculations allow for rapid response to changes in flow demand, ensuring the continuity and efficiency of the production process. Precise flow control reduces resource waste and improves production efficiency and economic benefits. Stable flow control improves the quality of cast billets, reducing defects and scrap rates. Continuous optimization of the model and control strategy using historical and real-time data ensures sustained improvement in system performance.

[0031] Optionally, the flow model construction for large-diameter valves includes:

[0032] Historical large-diameter flow rate data is obtained based on preset large-diameter flow rate data and valve characteristic parameter data;

[0033] Based on historical large-diameter flow data, flow characteristics and opening characteristics are extracted to obtain large-diameter flow characteristic data and large-diameter opening characteristic data.

[0034] A flow regression model for large-diameter valves is constructed based on the flow characteristic data and opening characteristic data of large-diameter valves, resulting in a flow model for large-diameter valves.

[0035] This invention utilizes historical data and preset parameters for model construction, ensuring the model has a strong data foundation and predictive capabilities. The constructed flow model can accurately predict flow demand, improving the precision and stability of flow control. Automated data extraction and model construction enhance the efficiency and response speed of flow control decisions. Precise flow prediction and control reduce resource waste and improve production efficiency and economic benefits. Stable flow control improves billet quality and reduces defects and scrap rates. Continuously updated data and models allow for continuous optimization of flow control strategies, ensuring sustained improvement in system performance.

[0036] Optionally, the flow model construction for small-bore valves includes:

[0037] Historical small-diameter flow rate data is obtained based on preset small-diameter flow rate data and valve characteristic parameter data;

[0038] Based on historical small-diameter flow data, flow characteristics and opening characteristics are extracted to obtain small-diameter flow characteristic data and small-diameter opening characteristic data.

[0039] A flow regression model for small-diameter valves is constructed based on the flow characteristic data and opening characteristic data of small-diameter valves, thus obtaining the flow model for small-diameter valves.

[0040] This invention utilizes historical data and preset parameters for model construction, ensuring the model has a strong data foundation and predictive capabilities. The constructed small-diameter flow model can accurately predict flow demand, improving the precision and stability of flow control. Automated data extraction and model construction enhance the efficiency and response speed of flow control decisions. Precise flow prediction and control reduce resource waste and improve production efficiency and economic benefits. Stable flow control improves billet quality and reduces defects and scrap rates.

[0041] Optionally, the step of generating the preset second flow interval threshold data includes:

[0042] The second flow range threshold data is obtained by weighting the valve characteristic parameter data and the first flow range threshold data; or,

[0043] Based on the characteristic parameter data of small-diameter valves in the valve characteristic parameter data, characteristic curve fitting is performed to obtain the characteristic curve data of small-diameter valves;

[0044] Curve features are extracted from the characteristic curve data of small-diameter valves to obtain characteristic feature data of small-diameter valves;

[0045] The second flow range threshold data is obtained by weighting the characteristic data of small-diameter valves and the threshold data of the first flow range.

[0046] This invention utilizes weighted calculations and characteristic curve fitting to accurately generate threshold data for the second flow range, ensuring the accuracy of flow control. Both Method 1 and Method 2 comprehensively consider the influence of valve characteristic parameters and the threshold data for the first flow range, improving the scientific validity and rationality of the threshold data. Utilizing historical data and characteristic parameter data for calculation and fitting ensures that the generated threshold data has a strong data foundation and predictive capabilities. Rapidly generating and validating threshold data improves the efficiency and response speed of flow control decisions. Precise flow control reduces resource waste and improves production efficiency and economic benefits. Stable flow control improves billet quality and reduces defects and scrap rates.

[0047] Optionally, this application also provides an automatic control system for a wide flow range regulating valve for all steel grades in continuous casting secondary cooling water, used to execute the automatic control method for the wide flow range regulating valve for all steel grades in continuous casting secondary cooling water as described above. The automatic control system for the wide flow range regulating valve for all steel grades in continuous casting secondary cooling water includes:

[0048] Traffic demand data acquisition module, used to acquire traffic demand data;

[0049] The large-diameter regulating valve control module is used to determine whether the flow demand data is greater than or equal to the preset first flow interval threshold data, and when the flow demand data is greater than the preset first flow interval threshold data, the large-diameter regulating valve control operation is performed according to the preset large-diameter flow data.

[0050] The traffic interval threshold judgment module is used to determine whether the traffic demand data is greater than or equal to the preset second traffic interval threshold data if the traffic demand data is less than the preset first traffic interval threshold data.

[0051] The dual-bore regulating valve control module is used to determine that when the flow demand data is greater than or equal to the preset second flow range threshold data, the dual-bore regulating valve control operation is performed based on the preset large-bore flow data and the preset small-bore flow data.

[0052] The small-diameter regulating valve control module is used to determine when the flow demand data is less than the preset second flow range threshold data, and then to perform small-diameter regulating valve control operation according to the preset small-diameter flow data.

[0053] Optionally, this application also provides an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the method described in any of the preceding claims.

[0054] Optionally, this application also provides a computer-readable storage medium storing a computer program, wherein the computer program is configured to perform the method described in any of the preceding claims when executed.

[0055] The purpose of this invention is to improve the accuracy of flow control through hierarchical judgment, ensuring that the most suitable control strategy can be adopted under different flow demands. Based on different flow ranges, large-diameter control valves, small-diameter control valves, and dual-diameter control valves are used for flow control to ensure accuracy. Real-time data processing can respond promptly to changes in flow demand, avoiding the impact of flow fluctuations on system operation. By constructing flow models for large-diameter and small-diameter valves, valve control is performed based on these models, ensuring the scientific nature and stability of the control strategy. The model-driven control strategy can accurately predict flow demand, reduce valve control errors, and improve system stability. Preset threshold data for the first and second flow ranges are generated through historical data analysis, feature extraction, and model construction, ensuring the scientific nature and reliability of the threshold data. The multi-level threshold generation method improves the accuracy of the threshold data, ensuring the stability and reliability of the flow control strategy. Flow allocation calculations are performed based on the large-diameter and small-diameter valve flow models to obtain optimal valve flow allocation data, ensuring efficient resource utilization. Attached Figure Description

[0056] Other features, objects, and advantages of this application will become more apparent from the following detailed description of the non-limiting embodiments, taken with reference to the accompanying drawings:

[0057] Figure 1 A flowchart illustrating the steps of an automatic control method for a wide flow range regulating valve for all steel grades in continuous casting secondary cooling water is shown in one embodiment.

[0058] Figure 2 A flowchart illustrating the steps of a method for generating threshold data for a first flow range according to an embodiment is shown.

[0059] Figure 3 A flowchart illustrating the steps of a dual-bore control valve control method according to one embodiment is shown.

[0060] Figure 4 A flowchart illustrating the steps of a method for constructing a flow model for a large-diameter valve according to an embodiment is shown.

[0061] Figure 5A flowchart illustrating the steps of a method for constructing a flow model for a small-diameter valve according to an embodiment is shown.

[0062] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0063] The technical method of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0064] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0065] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0066] Please see Figures 1 to 5 This application provides an automatic control method for a wide flow range regulating valve for all steel grades in continuous casting secondary cooling water, the method comprising:

[0067] S1. Obtain traffic demand data;

[0068] Specifically, flow sensors and data acquisition modules are used to monitor current flow demand data in real time. The collected data includes instantaneous flow rate, cumulative flow rate, valve opening degree, steel grade parameters, etc.

[0069] S2. Determine whether the flow demand data is greater than or equal to the preset first flow interval threshold data, and if the flow demand data is greater than the preset first flow interval threshold data, then perform large-diameter regulating valve control operation according to the preset large-diameter flow data.

[0070] Specifically, the system loads preset threshold data for the first flow interval from the database. These thresholds are pre-set based on historical data and steel grade parameters. It then determines whether the current flow demand is greater than or equal to the first flow interval threshold using a simple conditional algorithm: `if current_flow >= first_threshold: control_valve(large_orifice_valve, current_flow)`.

[0071] If the condition is true, a control signal for the large-diameter regulating valve is generated. The control signal is calculated based on the current flow demand data and the preset large-diameter flow data: def control_valve(valve,flow):valve_opening=calculate_opening(flow,valve_characteristics); send_control_signal(valve,valve_opening).

[0072] S3. If the traffic demand data is determined to be less than the preset first traffic interval threshold data, then determine whether the traffic demand data is greater than or equal to the preset second traffic interval threshold data.

[0073] Specifically, it loads preset second traffic interval threshold data from the database. It then determines whether the current traffic demand data is less than the first threshold and greater than or equal to the second threshold. This is done using a conditional judgment algorithm: if current_flow<first_threshold and current_flow> =second_threshold:control_valve(both_valves,current_flow).

[0074] S4. When the flow demand data is determined to be greater than or equal to the preset second flow range threshold data, the dual-bore regulating valve control operation is performed based on the preset large-bore flow data and the preset small-bore flow data.

[0075] Specifically, load the flow models for large-diameter and small-diameter valves, including their respective flow characteristic data. Based on the current flow demand data, use a flow distribution algorithm to calculate the opening degree of the large-diameter and small-diameter valves: `def calculate_distribution(flow,large_valve_model,small_valve_model):large_flow=large_valve_model.predict(flow);small_flow=small_valve_model.predict(flow);returnlarge_flow,small_flow`. Generate control signals for the large-diameter and small-diameter valves to perform dual-diameter control valve operation.

[0076] S5. When the flow demand data is determined to be less than the preset second flow range threshold data, the small-diameter regulating valve control operation is performed according to the preset small-diameter flow data.

[0077] Specifically, a flow model for small-diameter valves is loaded, including flow characteristic data. This model is then used to calculate the valve opening under the current flow demand, generating a control signal for the small-diameter valve to control its operation.

[0078] This invention achieves precise control of the secondary cooling water in continuous casting by judging different flow rate demand data and selecting different valve control strategies, ensuring that the flow rate of cooling water remains within the optimal range throughout the steel production process. By avoiding flow fluctuations caused by the regulating valve exceeding its optimal operating range at low flow rates, this method improves the quality of the cast billet and reduces defects and scrap rates. By dynamically selecting appropriate valve control strategies based on flow rate demand, this method optimizes the use of regulating valves, extends equipment life, and improves production efficiency.

[0079] Optionally, S1 includes:

[0080] Obtain steel grade parameter data and valve characteristic parameter data;

[0081] Specifically, basic parameter data for steel grades, including steel type, chemical composition, and physical properties, is loaded from the database. Characteristic parameter data for valves, including valve type, pipe diameter, and flow characteristic curves, is also loaded from the database.

[0082] The flow demand is calculated based on the steel grade parameter data and valve characteristic parameter data to obtain the flow demand data.

[0083] Specifically, key features, such as chemical composition and physical properties, are extracted from steel grade parameter data. Key features, such as valve opening degree and flow coefficient, are extracted from valve characteristic parameter data. The extracted steel grade parameter feature data and valve characteristic parameter feature data are integrated to form a comprehensive feature dataset. Based on this comprehensive feature dataset, a regression model or machine learning algorithm is used to calculate the flow demand data. G = αA + βB, where G is the flow demand data, α is the regression parameter data corresponding to the steel grade parameter data, A is the steel grade parameter data, β is the regression parameter data corresponding to the valve characteristic parameter data, and B is the valve characteristic parameter data.

[0084] This invention acquires steel grade parameter data and valve characteristic parameter data, and performs comprehensive calculations to accurately predict flow demand, ensuring the precision of flow control. Real-time acquisition and calculation of flow demand data allows for dynamic adjustment of flow control strategies based on production changes, ensuring the flexibility and adaptability of the production process.

[0085] Optionally, S2 includes:

[0086] Determine whether the traffic demand data is greater than or equal to the preset first traffic interval threshold data;

[0087] Specifically, the system loads a preset first traffic interval threshold from the database. It then compares the traffic demand data to see if it is greater than or equal to the first traffic interval threshold.

[0088] When the flow demand data is determined to be greater than the preset first flow range threshold data, a large-diameter regulating valve signal is generated based on the preset large-diameter flow data to perform large-diameter regulating valve control operation.

[0089] Specifically, based on the judgment result, a decision is made on whether to initiate large-diameter control valve operation. If it is determined to initiate large-diameter control valve operation, a control signal for the control valve is generated based on the flow demand data and the large-diameter flow data.

[0090] The specific method for generating the preset first flow interval threshold data is as follows:

[0091] S21. Obtain historical steel grade flow data based on steel grade parameter data;

[0092] Specifically, steel grade parameter data is loaded from the database. Based on the steel grade parameter data, relevant historical steel grade flow data is retrieved from the historical database.

[0093] S22. Extract characteristics based on historical steel grade flow data to obtain historical steel grade flow characteristic data;

[0094] Specifically, key characteristics such as average, maximum, minimum, and standard deviation are extracted from historical steel flow data.

[0095] S23. Based on historical steel grade flow rate characteristic data and steel grade parameter data, perform parameter mapping to obtain steel grade flow rate parameter mapping data;

[0096] Specifically, parameter mapping is performed based on historical steel grade flow characteristic data and steel grade parameter data to obtain steel grade flow parameter mapping data.

[0097] S24. Based on the steel grade flow parameter mapping data, a regression model is constructed to obtain the steel grade flow parameter identification model;

[0098] Specifically, based on the mapping data, a regression model is used to model the steel grade flow parameter identification model.

[0099] S25. Using the steel grade flow parameter identification model, identify the steel grade parameter data and valve characteristic parameter data to obtain the threshold data of the first flow range.

[0100] Specifically, the constructed regression model is used to identify steel grade parameter data and valve characteristic parameter data to obtain the threshold data for the first flow range.

[0101] This invention ensures precise flow control and improves production process stability through accurate threshold judgment and control signal generation. Automated data analysis and model building reduce manual intervention and enhance system automation. Rapid response to changes in flow demand ensures continuous and efficient production. Precise flow control reduces resource waste and improves production efficiency and economic benefits. Stable flow control improves billet quality and reduces defects and scrap rates. Continuous optimization of the model and thresholds using historical and real-time data ensures sustained improvement in system performance.

[0102] Optionally, S4 includes:

[0103] S41. Construct a flow model for a large-diameter valve based on the preset large-diameter flow data and valve characteristic parameter data.

[0104] Specifically, pre-set large-diameter flow rate data and valve characteristic parameter data are loaded from the database. Key characteristics, such as flow coefficient and valve opening degree, are extracted from the large-diameter flow rate data. The extracted characteristic data is then used to construct a flow rate model for the large-diameter valve.

[0105] S42. Construct a small-diameter valve flow model based on the preset small-diameter flow data and valve characteristic parameter data.

[0106] Specifically, pre-set small-diameter flow rate data and valve characteristic parameter data are loaded from the database. Key characteristics, such as flow coefficient and valve opening degree, are extracted from the small-diameter flow rate data. The extracted characteristic data is then used to construct a flow rate model for the small-diameter valve.

[0107] S43. Calculate the flow distribution based on the flow demand data, the flow model of the large-diameter valve, and the flow model of the small-diameter valve to obtain the valve flow distribution data for the dual-diameter control valve operation.

[0108] Specifically, the process involves loading flow demand data and flow models for large-diameter and small-diameter valves. Using these flow models, flow allocation is calculated based on the flow demand data. Finally, control signals for the large-diameter and small-diameter control valves are generated based on the flow allocation data.

[0109] Flow models for large-diameter and small-diameter valves were constructed using historical data and valve characteristic parameters. These models describe the flow rate that the valves can provide under different opening degrees. Real-time flow demand data was acquired, calculated by sensors and a computer system. Based on the flow demand data, the optimal combination of valve opening degrees was determined using the large-diameter and small-diameter valve flow models to meet the total flow demand.

[0110] Through historical data analysis and model building, the following two flow characteristic curves for valves were obtained: Flow model for large-diameter valves: Q L =α L O L +b L Q L For flow data of large-diameter valves, α L For large-diameter valve weighting data, O L For the opening characteristic data of large-diameter valves, b L This is a flow correction term for large-diameter valves.

[0111] Large-diameter valve flow model: Q S =α S O S +b S Q S For flow data of small-diameter valves, α S For small-diameter valve weighting data, Q S For the opening characteristic data of small-diameter valves, b S This is a flow correction term for small-diameter valves.

[0112] Based on flow demand data and valve flow models, determine the optimal opening combinations for large-diameter and small-diameter valves to meet the total flow demand. The goal is to find the real-time flow demand data Q. D =Q L +QS Initial conditions: Set Q L and Q S The initial value of Q can usually start from 0. Optimization process: Adjust Q using an optimization algorithm (such as gradient descent). L and Q S , making Q L +Q S As close to Q as possible D .

[0113] Large-diameter valve model parameters: α L =1.5, b L =0. Small diameter valve model parameters: α S =0.5, b S =0. Current traffic demand: Q D =100L / min, Q L =0, Q S =0. Assume optimization is achieved by gradually increasing the opening degree: increase Q... L Up to 50: Q L =1.5×50+0=75L / min. At this point, Q is still needed. S =100-75=25L / min. Increase Q S At 50%: QS = 0.5 × 50 + 0 = 25 L / min. At this point, Q is satisfied. L +Q S =75 + 25 = 100 L / min. Therefore, the optimal opening combination is: large diameter valve opening Q L =50%. Small-diameter valve opening Q S =50%.

[0114] This invention, through the construction and application of flow models for large-diameter and small-diameter valves, enables precise control of flow demand, improving the stability of the production process. The flow distribution and valve control processes are highly automated, reducing manual intervention and enhancing the system's automation level. Real-time data and model calculations allow for rapid response to changes in flow demand, ensuring the continuity and efficiency of the production process. Precise flow control reduces resource waste and improves production efficiency and economic benefits. Stable flow control improves the quality of cast billets, reducing defects and scrap rates. Continuous optimization of the model and control strategy using historical and real-time data ensures sustained improvement in system performance.

[0115] Optionally, the flow model construction for large-diameter valves includes:

[0116] S411. Obtain historical large-diameter flow data based on preset large-diameter flow data and valve characteristic parameter data;

[0117] Specifically, preset large-diameter flow rate data and valve characteristic parameter data are loaded from the database. Based on the preset large-diameter flow rate data and valve characteristic parameters, relevant historical flow rate data is retrieved from the historical database.

[0118] S412. Extract flow characteristics and opening characteristics based on historical large-diameter flow data to obtain large-diameter flow characteristic data and large-diameter opening characteristic data.

[0119] Specifically, key flow characteristics, such as average, maximum, minimum, and standard deviation, are extracted from historical large-diameter flow data. Feature data related to valve opening, such as opening value and opening change rate, are also extracted from historical data.

[0120] S413. Based on the large-diameter flow characteristic data and the large-diameter opening characteristic data, construct a large-diameter valve flow regression model to obtain the large-diameter valve flow model.

[0121] Specifically, flow characteristic data and opening characteristic data are integrated into a comprehensive feature dataset. A flow regression model for large-diameter valves is constructed using the extracted feature data. The model is trained using historical data, and the model parameters are adjusted to minimize prediction error. The trained regression model is validated using a test dataset to evaluate its accuracy and robustness.

[0122] This invention utilizes historical data and preset parameters for model construction, ensuring the model has a strong data foundation and predictive capabilities. The constructed flow model can accurately predict flow demand, improving the precision and stability of flow control. Automated data extraction and model construction enhance the efficiency and response speed of flow control decisions. Precise flow prediction and control reduce resource waste and improve production efficiency and economic benefits. Stable flow control improves billet quality and reduces defects and scrap rates. Continuously updated data and models allow for continuous optimization of flow control strategies, ensuring sustained improvement in system performance.

[0123] Optionally, the flow model construction for small-bore valves includes:

[0124] S421. Obtain historical small-diameter flow data based on preset small-diameter flow data and valve characteristic parameter data;

[0125] Specifically, preset small-diameter flow rate data and valve characteristic parameter data are loaded from the database. Based on the preset small-diameter flow rate data and valve characteristic parameters, relevant historical flow rate data is retrieved from the historical database.

[0126] S422. Extract flow characteristics and opening characteristics based on historical small-diameter flow data to obtain small-diameter flow characteristic data and small-diameter opening characteristic data.

[0127] Specifically, key flow characteristics, such as average, maximum, minimum, and standard deviation, are extracted from historical small-diameter flow data. Feature data related to valve opening, such as opening value and rate of change of opening, are extracted from historical data. The flow characteristic data and opening characteristic data are then integrated into a comprehensive feature dataset.

[0128] S423. Based on the small-diameter flow characteristic data and the small-diameter opening characteristic data, construct a small-diameter valve flow regression model to obtain the small-diameter valve flow model.

[0129] Specifically, a flow regression model for small-diameter valves is constructed using extracted feature data. The model is trained using historical data, and its parameters are adjusted to minimize prediction error. The trained regression model is then validated using a test dataset to evaluate its accuracy and robustness.

[0130] This invention utilizes historical data and preset parameters for model construction, ensuring the model has a strong data foundation and predictive capabilities. The constructed small-diameter flow model can accurately predict flow demand, improving the precision and stability of flow control. Automated data extraction and model construction enhance the efficiency and response speed of flow control decisions. Precise flow prediction and control reduce resource waste and improve production efficiency and economic benefits. Stable flow control improves billet quality and reduces defects and scrap rates.

[0131] Optionally, the step of generating the preset second flow interval threshold data includes:

[0132] The second flow range threshold data is obtained by weighting the valve characteristic parameter data and the first flow range threshold data; or,

[0133] Specifically, the valve characteristic parameter data is loaded from the database. The first flow interval threshold data is loaded from the database. A weighted calculation is performed based on the valve characteristic parameter data and the first flow interval threshold data. The function `def calculate_second_threshold(valve_characteristics, first_threshold): weights = valve_characteristics['weights']; second_threshold = sum(w * ft for w, ft in zip(weights, first_threshold)); return second_threshold` is defined.

[0134] Based on the characteristic parameter data of small-diameter valves in the valve characteristic parameter data, characteristic curve fitting is performed to obtain the characteristic curve data of small-diameter valves;

[0135] Specifically, the characteristic parameter data of small-diameter valves is loaded from the database. Characteristic curves are then fitted based on this data.

[0136] Curve features are extracted from the characteristic curve data of small-diameter valves to obtain characteristic feature data of small-diameter valves;

[0137] Specifically, the fitted characteristic curve model is used for prediction to obtain characteristic curve data for small-diameter valves.

[0138] The second flow range threshold data is obtained by weighting the characteristic data of small-diameter valves and the threshold data of the first flow range.

[0139] Specifically, load the first flow range threshold data from the database (skip this step if it has already been loaded). Perform a weighted calculation based on the small-diameter valve characteristic data and the first flow range threshold data to obtain the second flow range threshold data.

[0140] This invention utilizes weighted calculations and characteristic curve fitting to accurately generate threshold data for the second flow range, ensuring the accuracy of flow control. Both Method 1 and Method 2 comprehensively consider the influence of valve characteristic parameters and the threshold data for the first flow range, improving the scientific validity and rationality of the threshold data. Utilizing historical data and characteristic parameter data for calculation and fitting ensures that the generated threshold data has a strong data foundation and predictive capabilities. Rapidly generating and validating threshold data improves the efficiency and response speed of flow control decisions. Precise flow control reduces resource waste and improves production efficiency and economic benefits. Stable flow control improves billet quality and reduces defects and scrap rates.

[0141] Optionally, this application also provides an automatic control system for a wide flow range regulating valve for all steel grades in continuous casting secondary cooling water, used to execute the automatic control method for the wide flow range regulating valve for all steel grades in continuous casting secondary cooling water as described above. The automatic control system for the wide flow range regulating valve for all steel grades in continuous casting secondary cooling water includes:

[0142] Traffic demand data acquisition module, used to acquire traffic demand data;

[0143] The large-diameter regulating valve control module is used to determine whether the flow demand data is greater than or equal to the preset first flow interval threshold data, and when the flow demand data is greater than the preset first flow interval threshold data, the large-diameter regulating valve control operation is performed according to the preset large-diameter flow data.

[0144] The traffic interval threshold judgment module is used to determine whether the traffic demand data is greater than or equal to the preset second traffic interval threshold data if the traffic demand data is less than the preset first traffic interval threshold data.

[0145] The dual-bore regulating valve control module is used to determine that when the flow demand data is greater than or equal to the preset second flow range threshold data, the dual-bore regulating valve control operation is performed based on the preset large-bore flow data and the preset small-bore flow data.

[0146] The small-diameter regulating valve control module is used to determine when the flow demand data is less than the preset second flow range threshold data, and then to perform small-diameter regulating valve control operation according to the preset small-diameter flow data.

[0147] Optionally, this application also provides an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the method described in any of the preceding claims.

[0148] Optionally, this application also provides a computer-readable storage medium storing a computer program, wherein the computer program is configured to perform the method described in any of the preceding claims when executed.

[0149] The purpose of this invention is to improve the accuracy of flow control through hierarchical judgment, ensuring that the most suitable control strategy can be adopted under different flow demands. Based on different flow ranges, large-diameter control valves, small-diameter control valves, and dual-diameter control valves are used for flow control to ensure accuracy. Real-time data processing can respond promptly to changes in flow demand, avoiding the impact of flow fluctuations on system operation. By constructing flow models for large-diameter and small-diameter valves, valve control is performed based on these models, ensuring the scientific nature and stability of the control strategy. The model-driven control strategy can accurately predict flow demand, reduce valve control errors, and improve system stability. Preset threshold data for the first and second flow ranges are generated through historical data analysis, feature extraction, and model construction, ensuring the scientific nature and reliability of the threshold data. The multi-level threshold generation method improves the accuracy of the threshold data, ensuring the stability and reliability of the flow control strategy. Flow allocation calculations are performed based on the large-diameter and small-diameter valve flow models to obtain optimal valve flow allocation data, ensuring efficient resource utilization.

[0150] Therefore, the embodiments should be regarded as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended application documents rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of the equivalents of the application documents be incorporated into the invention.

[0151] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. An automatic control method for a continuous casting secondary cooling water full steel type wide flow range adjusting valve, characterized by, The method comprises: S1, obtaining steel grade parameter data and valve characteristic parameter data; performing flow demand calculation according to the steel grade parameter data and the valve characteristic parameter data to obtain flow demand data; S2, judging whether the flow demand data is greater than or equal to preset first flow interval threshold data, and determining that, when the flow demand data is greater than the preset first flow interval threshold data, a large-bore regulating valve control operation is performed according to preset large-bore flow data; S3, determining that the flow demand data is less than the preset first flow interval threshold data, and judging whether the flow demand data is greater than or equal to preset second flow interval threshold data; S4, determining that, when the flow demand data is greater than or equal to the preset second flow interval threshold data, a double-bore regulating valve control operation is performed according to the preset large-bore flow data and preset small-bore flow data; S5, determining that, when the flow demand data is less than the preset second flow interval threshold data, a small-bore regulating valve control operation is performed according to preset small-bore flow data; The preset first flow interval threshold data is generated in the following manner: obtaining historical steel grade flow data according to the steel grade parameter data; performing characteristic extraction according to the historical steel grade flow data to obtain historical steel grade flow characteristic data; performing parameter mapping according to the historical steel grade flow characteristic data and the steel grade parameter data to obtain steel grade flow parameter mapping data; performing regression model construction according to the steel grade flow parameter mapping data to obtain a steel grade flow parameter identification model; and identifying the steel grade parameter data and the valve characteristic parameter data by using the steel grade flow parameter identification model to obtain the first flow interval threshold data; The preset second flow interval threshold data is generated in the following manner: performing weighted calculation according to the valve characteristic parameter data and the first flow interval threshold data to obtain the second flow interval threshold data; or performing characteristic curve fitting according to small-bore valve characteristic parameter data in the valve characteristic parameter data to obtain small-bore valve characteristic curve data; performing curve characteristic extraction according to the small-bore valve characteristic curve data to obtain small-bore valve characteristic feature data; and performing weighted calculation according to the small-bore valve characteristic feature data and the first flow interval threshold data to obtain the second flow interval threshold data.

2. The method of claim 1, wherein, S4 comprises: performing large-bore valve flow model construction according to the preset large-bore flow data and the valve characteristic parameter data to obtain a large-bore valve flow model; performing small-bore valve flow model construction according to the preset small-bore flow data and the valve characteristic parameter data to obtain a small-bore valve flow model; performing flow distribution calculation according to the flow demand data, the large-bore valve flow model and the small-bore valve flow model to obtain valve flow distribution data for double-bore regulating valve control operation.

3. The method of claim 2, wherein, The large-bore valve flow model construction comprises: obtaining historical large-bore flow data according to the preset large-bore flow data and the valve characteristic parameter data; performing flow characteristic extraction and opening characteristic extraction according to the historical large-bore flow data to obtain large-bore flow characteristic data and large-bore opening characteristic data; According to the large-diameter flow characteristic data and the large-diameter opening characteristic data, a large-diameter valve flow regression model is constructed, and a large-diameter valve flow model is obtained.

4. The method of claim 2, wherein, The small-diameter valve flow model construction includes: According to the preset small-diameter flow data and the valve characteristic parameter data, historical small-diameter flow data is obtained; According to the historical small-diameter flow data, flow characteristic extraction and opening characteristic extraction are performed, and small-diameter flow characteristic data and small-diameter opening characteristic data are obtained; According to the small-diameter flow characteristic data and the small-diameter opening characteristic data, a small-diameter valve flow regression model is constructed, and a small-diameter valve flow model is obtained.

5. An automatic control system for a continuous casting secondary cooling water full steel grade wide flow range regulating valve, characterized in that, The automatic control method for the continuous casting secondary cooling water full-steel wide flow interval regulating valve as claimed in claim 1, the automatic control system for the continuous casting secondary cooling water full-steel wide flow interval regulating valve comprises: a flow demand data acquisition module for acquiring flow demand data; a large-diameter regulating valve control operation module for determining whether the flow demand data is greater than or equal to a preset first flow interval threshold data, and determining that when the flow demand data is greater than the preset first flow interval threshold data, a large-diameter regulating valve control operation is performed according to preset large-diameter flow data; a flow interval threshold judgment module for determining that the flow demand data is less than the preset first flow interval threshold data, and determining whether the flow demand data is greater than or equal to a preset second flow interval threshold data; a double-diameter regulating valve control operation module for determining that when the flow demand data is greater than or equal to the preset second flow interval threshold data, a double-diameter regulating valve control operation is performed according to the preset large-diameter flow data and the preset small-diameter flow data; a small-diameter regulating valve control operation module for determining that when the flow demand data is less than the preset second flow interval threshold data, a small-diameter regulating valve control operation is performed according to the preset small-diameter flow data. 6.An electronic device comprising a memory and a processor, the electronic device comprising: The memory stores a computer program, and the processor is configured to run the computer program to execute the method in any one of claims 1 to 4.

7. A computer readable storage medium characterized by The computer readable storage medium stores a computer program, wherein the computer program is configured to run to execute the method in any one of claims 1 to 4.

Images