Water cooling control method and device for hot rolling mill

Abstract

The invention provides a hot mill water cooling control method and device, and relates to the field of water cooling control. The method comprises the steps that in response to water cooling control operation for a target hot rolling mill, cooling water flow adjusting parameters are obtained; feature extraction is conducted on the cooling water flow adjusting parameters, and target feature parameters are obtained; the target characteristic parameter characteristics serve as input, and target cooling water flow corresponding to the target hot rolling mill is output through a cooling water flow control model; and according to the target cooling water flow, water cooling control operation is conducted on the target hot rolling mill through an electric proportional valve. The problem that when a hydraulic station cooling water pipeline is controlled through a hand valve, the cooling amount cannot be accurately controlled, and energy is wasted is solved.

Description

Technical Field

[0001] The present application relates to the field of water cooling control, and in particular to a water cooling control method and device for a hot rolling mill. Background Art

[0002] Large motors and most hydraulic stations in copper and copper alloy processing enterprises are usually cooled by water, and the circulating water takes away the heat to achieve the purpose of cooling. The circulating water is provided by the water pump station of the processing enterprise. After the heat exchange, the circulating water is cooled by the cooling tower and then returns to the water pump station. The circulating water of the water pump station is supplied by multiple water pumps. If the circulating water consumption increases, the speed of the water pump of the water pump station is increased or the standby pump is turned on. Otherwise, the speed is reduced or the water pump is turned off.

[0003] Many large motors and most hydraulic station cooling water pipelines are controlled by hand valves. Especially when hot rolling mills are performing hot rolling production, cooling is controlled by hand valves. During production, the cooling water hand valve is fully open, and the flow rate is too large, exceeding the cooling needs of the hydraulic station and the main motor. At the same time, the production of hot rolling equipment is discontinuous, and there are auxiliary times such as insulation, discharging, and curling. Therefore, the cooling water cannot be accurately controlled by hand valve control, resulting in energy waste.

[0004] Therefore, there is an urgent need for a hot rolling mill water cooling control method and device. Summary of the invention

[0005] The present application provides a water cooling control method and device for a hot rolling mill, which solves the problem that when the cooling water pipeline of a hydraulic station is controlled by a manual valve, the cooling amount cannot be accurately controlled, resulting in energy waste.

[0006] In a first aspect of the present application, a hot rolling mill water cooling control method is provided, the method comprising: in response to a water cooling control operation for a target hot rolling mill, obtaining cooling water flow regulation parameters, the cooling water flow regulation parameters including main motor load parameters, main motor temperature parameters and main motor working cycle parameters; performing feature extraction on the cooling water flow regulation parameters, and obtaining target feature parameters; taking the target feature parameter features as input, and outputting a target cooling water flow corresponding to the target hot rolling mill through a cooling water flow control model; and performing water cooling control operations on the target hot rolling mill through an electric proportional valve according to the target cooling water flow.

[0007] Optionally, the target feature parameters include fused derivative features, fused timing features and fused cross features, and feature extraction is performed on the cooling water flow regulation parameters to obtain the target feature parameters, specifically including: obtaining the motor load parameters, temperature change rate parameters and vibration intensity parameters in the cooling water flow regulation parameters; constructing the fused derivative features in the target feature parameters according to the motor load parameters, temperature change rate parameters and vibration intensity parameters; obtaining the historical cooling water flow parameters, historical temperature parameters and historical power parameters in the cooling water flow regulation parameters; constructing the fused timing features in the target feature parameters according to the historical cooling water flow parameters, historical temperature parameters and historical power parameters; obtaining the cross-term parameters of temperature and load, and the cross-term parameters of speed and ambient temperature in the cooling water flow regulation parameters; constructing the fused cross features in the target feature parameters according to the cross-term parameters of temperature and load, and the cross-term parameters of speed and ambient temperature; performing feature parameter fusion on the fused derivative features, fused timing features and fused cross features, and using the feature parameters after the feature parameter fusion as the target feature parameters.

[0008] Optionally, according to the motor load parameter, the temperature change rate parameter and the vibration intensity parameter, a fusion derivative feature in the target feature parameter is constructed, specifically including: using a nonlinear mapping function, performing a first fusion operation on the cooling water flow regulation parameter through the following formula, and obtaining the fusion derivative feature:

[0009] f d =α1log(P motor +∈)+α2*exp(ΔT motor )+α3*A vibration 2 ;

[0010] Among them, f d To fuse the derived features, P motor is the motor load parameter, α1 is the weight coefficient corresponding to the motor load parameter, ΔT motor is the temperature change rate parameter, α2 is the weight coefficient corresponding to the temperature change rate parameter, A vibration is the vibration intensity parameter, α3 is the weight coefficient corresponding to the vibration intensity parameter, and ∈ is the adjustment factor, which is used to avoid zero when taking the logarithm.

[0011] Optionally, according to the historical cooling water flow parameters, the temperature parameters at historical moments, and the power parameters at historical moments, a fusion time series feature in the target feature parameter is constructed, specifically including: using weighted sliding window processing, performing a second fusion operation on the cooling water flow adjustment parameter through the following formula, and obtaining the fusion time series feature:

[0012]

[0013] Among them, f t is the fusion time series feature, ti is the historical moment, Q history (ti) is the historical cooling water flow parameter, T motor (ti) is the temperature parameter at the historical moment, P motor (ti) n The power parameter at the historical moment, β1 is the weight corresponding to the historical cooling water flow parameter, β2 is the weight corresponding to the temperature parameter at the historical moment, and β3 is the weight corresponding to the power parameter at the historical moment.

[0014] Optionally, according to the cross-term parameters of temperature and load, and the cross-term parameters of speed and ambient temperature, a fused cross-feature in the target feature parameter is constructed, specifically including: sampling a high-order cross-term polynomial combination, performing a third fusion operation on the cooling water flow regulation parameter through the following formula, and obtaining the fused cross-feature:

[0015] f mix =γ1*(T motor ·P motor )+γ2*(N motor ·T ambient )+γ3*(T motor 2 ·

[0016] P motor );

[0017] Among them, f mix To fuse cross features, N motor is the main motor speed parameter, · represents the element-by-element operation between each cooling water flow adjustment parameter, γ1 is the weight corresponding to the cross-term parameter of temperature and load, γ2 is the weight corresponding to the cross-term parameter of speed and ambient temperature, and γ3 is the weight corresponding to the quadratic cross-term of temperature and load.

[0018] Optionally, before taking the target characteristic parameter characteristics as input and outputting the target cooling water flow corresponding to the target hot rolling mill through the cooling water flow control model, it is necessary to construct a cooling water flow control model, specifically including: obtaining historical cooling water flow adjustment parameters, the historical cooling water flow adjustment parameters include historical main motor load parameters, historical main motor temperature parameters and historical main motor working cycle parameters; according to the historical cooling water flow adjustment parameters, construct a cooling water flow control model.

[0019] Optionally, outputting a target cooling water flow rate corresponding to the target hot rolling mill through a cooling water flow rate control model specifically includes: calculating the target cooling water flow rate according to the following formula:

[0020]

[0021] Among them, Q target is the target cooling water flow rate, d0 is the bias term, X is the set of target characteristic parameter features, f i (X) is the output corresponding to the i-th target feature parameter feature, F i is the i-th post-term, which includes high-order cross-terms and time series information processing terms. is the weight parameter corresponding to the i-th post-item.

[0022] In a second aspect of the present application, a water cooling control device for a hot rolling mill is provided, the device comprising an acquisition module and a processing module, wherein:

[0023] The acquisition module is used to obtain cooling water flow adjustment parameters in response to the water cooling control operation for the target hot rolling mill, and the cooling water flow adjustment parameters include main motor load parameters, main motor temperature parameters and main motor working cycle parameters.

[0024] The processing module is used to extract the characteristics of the cooling water flow regulation parameters and obtain the target characteristic parameters; take the characteristics of the target characteristic parameters as input, and output the target cooling water flow corresponding to the target hot rolling mill through the cooling water flow control model; according to the target cooling water flow, the target hot rolling mill is water-cooled through the electric proportional valve.

[0025] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0026] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to perform any of the above methods.

[0027] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0028] 1. Acquire cooling water flow regulation parameters, perform feature extraction on the cooling water flow regulation parameters, and obtain target feature parameters, so as to use the target feature parameter features as input, output the target cooling water flow corresponding to the target hot rolling mill through the cooling water flow control model, and perform the water cooling control operation on the target hot rolling mill through the electric proportional valve according to the target cooling water flow, thereby saving the use of circulating water, reducing energy consumption, achieving green development, and effectively reducing the production energy consumption of copper processing enterprises.

[0029] 2. By labeling and classifying the target feature parameters into fused derivative features, fused time series features, and fused cross features, the cooling water flow regulation parameters are extracted and the extracted feature parameters are refined according to the classification results. This enables us to more accurately capture the relationships and action mechanisms between various features, reveal the interactions between multiple parameters, and provide strong feature data support for subsequent model processing.

[0030] 3. By obtaining various types of historical cooling water flow adjustment parameters such as historical main motor load parameters, historical main motor temperature parameters, and historical main motor working cycle parameters, we can fully understand the changes in cooling requirements of the motor under different working conditions, and combine these multi-dimensional historical data to build a cooling water flow control model, making the model's prediction results more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a flow chart of a hot rolling mill water cooling control method provided in an embodiment of the present application;

[0032] Figure 2 It is a module schematic diagram of a water cooling control device for a hot rolling mill provided in an embodiment of the present application;

[0033] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0034] Explanation of the reference numerals: 21, acquisition module; 22, processing module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0035] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0036] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items.

[0037] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0038] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0039] Please refer to Figure 1 , which shows a flow chart of a hot rolling mill water cooling control method provided in an embodiment of the present application, and the flow chart mainly includes the following steps: S101 to S104.

[0040] Step S101, in response to the water cooling control operation for the target hot rolling mill, obtain cooling water flow adjustment parameters, the cooling water flow adjustment parameters including main motor load parameters, main motor temperature parameters and main motor working cycle parameters.

[0041] Specifically, by installing multiple types of sensors, the water cooling control of the target hot rolling mill is monitored in real time, and the cooling water flow adjustment parameters are obtained through different types of sensors. The cooling water flow adjustment parameters include but are not limited to: main motor load parameters, main motor temperature parameters, main motor working cycle parameters, etc. The obtained cooling water flow adjustment parameters need to undergo certain preprocessing, such as filtering, denoising, correction, etc., to ensure their accuracy and effectiveness. For example, the main motor load parameters can be calculated by current or power measurement equipment, and the main motor temperature parameters can be directly measured by temperature sensors.

[0042] Step S102, extracting the characteristics of the cooling water flow rate adjustment parameters and obtaining target characteristic parameters.

[0043] Specifically, the target characteristic parameters are obtained by adjusting the cooling water flow rate parameters obtained. The core of this process is to convert the original cooling water flow rate adjustment parameters into meaningful characteristics so as to be used as the input of the cooling water flow rate control model later.

[0044] In a possible implementation, step S102 further includes: obtaining a motor load parameter, a temperature change rate parameter, and a vibration intensity parameter in the cooling water flow rate adjustment parameter;

[0045] According to the motor load parameters, temperature change rate parameters and vibration intensity parameters, the fused derivative features in the target feature parameters are constructed; the historical cooling water flow parameters, the historical temperature parameters and the historical power parameters in the cooling water flow regulation parameters are obtained; according to the historical cooling water flow parameters, the historical temperature parameters and the historical power parameters, the fused time series features in the target feature parameters are constructed; the cross-term parameters of temperature and load, and the cross-term parameters of speed and ambient temperature in the cooling water flow regulation parameters are obtained; according to the cross-term parameters of temperature and load, and the cross-term parameters of speed and ambient temperature, the fused cross features in the target feature parameters are constructed; the feature parameters of the fused derivative features, the fused time series features and the fused cross features are fused, and the feature parameters after the feature parameter fusion are used as the target feature parameters.

[0046] Specifically, first, the system obtains key indicators in the cooling water flow regulation parameters, such as motor load parameters, temperature change rate parameters, and vibration intensity parameters. These parameters reflect the basic state and abnormal characteristics of the motor during operation, and provide basic information for the regulation of cooling water flow. Then, by processing these parameters, the fusion derivative features in the target feature parameters are constructed, which includes synthesizing the motor load parameters, temperature change rate parameters, and vibration intensity parameters. The steps are as follows: Using a nonlinear mapping function, the cooling water flow regulation parameters are subjected to the first fusion operation through the following formula, and the fusion derivative features are obtained:

[0047] f d =α1log(P motor +∈)+α2*exp(ΔT motor )+α3*A vibration 2 ;

[0048] Among them, f d To fuse the derived features, P motor is the motor load parameter, α1 is the weight coefficient corresponding to the motor load parameter, ΔT motor is the temperature change rate parameter, α2 is the weight coefficient corresponding to the temperature change rate parameter, A vibration is the vibration intensity parameter, α3 is the weight coefficient corresponding to the vibration intensity parameter, ∈ is the adjustment factor used to avoid zero when taking the logarithm, f dIt is used to reveal the complex relationship between the motor's operating status and cooling requirements. At the same time, historical data of cooling water flow regulation parameters are obtained, including historical cooling water flow parameters, historical temperature parameters, and historical power parameters. These historical data can reflect the performance changes of the motor under different working cycles. Through these historical data, the system can construct fused time series features. The process steps are as follows: weighted sliding window processing is used to perform the second fusion operation on the cooling water flow regulation parameters through the following formula, and the fused time series features are obtained:

[0049]

[0050] Among them, f t is the fusion time series feature, ti is the historical moment, Q history (ti) is the historical cooling water flow parameter, T motor (ti) is the temperature parameter at the historical moment, P motor (ti) n The historical moment power parameter, β1 is the weight corresponding to the historical cooling water flow parameter, β2 is the weight corresponding to the historical moment temperature parameter, and β3 is the weight corresponding to the historical moment power parameter. t It is used to capture the trend of cooling water flow rate changing over time. In addition, the cross-term features in the cooling water flow regulation parameters are extracted, including the cross-term parameters of temperature and load and the cross-term parameters of speed and ambient temperature. These cross-term features reveal the interaction between multiple key parameters. The process steps are as follows: sample high-order cross-term polynomial combinations, perform the third fusion operation on the cooling water flow regulation parameters through the following formula, and obtain the fused cross-term features:

[0051] f mix =γ1*(T motor ·P motor )+γ2*(N motor ·T ambient )+γ3*(T motor 2 ·

[0052] P motor );

[0053] Among them, f mix To fuse cross features, N motor is the main motor speed parameter, · represents the element-by-element operation between the various cooling water flow adjustment parameters, γ1 is the weight corresponding to the cross term parameter of temperature and load, γ2 is the weight corresponding to the cross term parameter of speed and ambient temperature, γ3 is the weight corresponding to the quadratic cross term of temperature and load, f mixIt provides more detailed input information for the regulation of cooling water flow. Finally, the fusion derivative features, fusion timing features and fusion cross features are further fused into feature parameters. Through weighted average, nonlinear mapping and other methods, these feature parameters are integrated into a comprehensive target feature parameter as the input of the subsequent cooling water flow control model. This fused target feature parameter comprehensively reflects the load state, temperature change, timing change and interaction between parameters of the motor, thus providing an accurate basis for the optimization and regulation of cooling water flow.

[0054] Step S103, taking the target characteristic parameter characteristics as input, and outputting the target cooling water flow corresponding to the target hot rolling mill through the cooling water flow control model.

[0055] Specifically, the target characteristic parameters extracted and fused in step S102 are used as input and passed into the cooling water flow control model to output the target cooling water flow corresponding to the target hot rolling mill. Specifically, the cooling water flow control model will accurately predict and adjust the cooling demand of the hot rolling mill based on these target characteristic parameters. These characteristic parameters include motor load, temperature change, historical time series data, and the cross-influence between parameters, which fully reflect the current operating status of the equipment and its cooling needs. At the input end of the model, the target characteristic parameters will undergo a series of calculations and processing. The model uses a trained algorithm to optimize the output cooling water flow based on the different input characteristic parameters to ensure that it adapts to the current load, temperature and other operating conditions of the target hot rolling mill.

[0056] In a possible implementation, step S103 also includes: before taking the target characteristic parameter characteristics as input and outputting the target cooling water flow corresponding to the target hot rolling mill through the cooling water flow control model, it is necessary to construct a cooling water flow control model, specifically including: obtaining historical cooling water flow adjustment parameters, the historical cooling water flow adjustment parameters include historical main motor load parameters, historical main motor temperature parameters and historical main motor working cycle parameters; according to the historical cooling water flow adjustment parameters, constructing a cooling water flow control model.

[0057] Specifically, the construction process starts with obtaining historical cooling water flow regulation parameters, which include historical main motor load parameters, historical main motor temperature parameters, and historical main motor duty cycle parameters. By collecting these historical data, the load changes, temperature fluctuations, and duty cycle patterns of the motor in different time periods can be reflected, thus providing a basis for subsequent modeling. Then, based on these historical cooling water flow regulation parameters, a cooling water flow control model is constructed using data analysis and modeling techniques. During the construction process, the structure and parameters of the model are optimized by learning the patterns and relationships in the historical data so that it can accurately predict and regulate the cooling water flow. This process may involve a training phase, in which the model self-adjusts according to the association between the changes in historical parameters and the actual cooling demand to improve its prediction accuracy and response efficiency. Finally, the constructed and trained cooling water flow control model can calculate and output the target cooling water flow required by the target hot rolling mill in real time based on the new input features.

[0058] In a possible implementation, step S103 further includes: calculating the target cooling water flow rate according to the following formula:

[0059]

[0060] Among them, Q target is the target cooling water flow rate, d0 is the bias term, X is the set of target characteristic parameter features, f i (X) is the output corresponding to the i-th target feature parameter feature, a i is the weight parameter corresponding to the output corresponding to the i-th target feature parameter feature, F i is the i-th post-term, which includes high-order cross-terms and time series information processing terms. is the weight parameter corresponding to the i-th post-item.

[0061] Specifically, when calculating the target cooling water flow, in addition to using basic historical cooling water flow, temperature, load, speed and other parameters, more levels of feedback factors, nonlinear relationships, dynamic effects, etc. can also be considered to improve accuracy and adaptability. Multiple original features are integrated to construct a multi-dimensional feature vector X, where X is also a set of target feature parameter features, X = [T motor ,P motor ,N motor ,T ambient ,Q history ,…,f d ,f t ,f mix], in order to further enhance the complexity of the model, a more complex nonlinear mapping function is used to map the multi-dimensional feature vector X to the target cooling water flow. Assuming that a structure containing a multi-layer neural network is adopted, the input of the network is X, and the output is. The specific calculation process can be expressed as Q target , that is, the target cooling water flow rate, the conventional calculation process can be expressed as:

[0062]

[0063] Among them, σ is the activation function, W1, W2, W3 are all weight matrices of the neural network, d1, d2, d3 are bias terms, and h1 and h2 are the intermediate layer feature representations. Based on the above calculation process, a more accurate calculation method can be proposed to improve Q target The calculation accuracy can be calculated by the formula: Calculate Q target In the embodiment of the present application, the complex calculation process is recorded as a post-term Post-items can be increased or decreased. The number of post-items set is related to the actual scenario application. The more complex the calculation of the actual scenario, the more post-items there are. For example, considering that the cooling water flow may be affected by historical operating conditions, environmental changes, and real-time feedback, time-varying items and dynamic update mechanisms can be introduced into the model, that is, LSTM is used to process the time series relationship between the historical cooling water flow and the main motor load. Assuming that the use of LSTM to process the time series relationship between the historical cooling water flow and the main motor load is the first item of the post-item, we can get:

[0064]

[0065] Among them, LSTM is used to capture the temporal relationship; or, further increase the combination of high-order cross terms, these cross terms can reveal more complex physical relationships and mutual influences. Assuming that the combination of high-order cross terms is further increased as the second term of the post-term, we can get:

[0066]

[0067] Among them, X ij is the jth item in the input feature, is the weight parameter corresponding to the i-th input feature in the second post-term, θ ij The power exponent corresponding to the j-th input feature is used to enhance or suppress the influence of the cross term.

[0068] Step S104, performing water cooling control operation on the target hot rolling mill through the electric proportional valve according to the target cooling water flow rate.

[0069] Specifically, according to the target cooling water flow output by the cooling water flow control model, the system uses the target cooling water flow as a reference value and performs precise water cooling control operations on the target hot rolling mill through the electric proportional valve. Specifically, the electric proportional valve adjusts the flow and pressure of the cooling water according to the set value of the target cooling water flow to ensure that the cooling system of the hot rolling mill can respond to the requirements of working conditions such as motor load and temperature changes in real time. The electric proportional valve finely adjusts the water flow so that the cooling system can be dynamically adjusted under different production conditions to ensure that the equipment can obtain sufficient cooling under various working conditions without causing overcooling or insufficient cooling, thereby keeping the operating temperature of the hot rolling mill within a safe range, improving production efficiency and extending the service life of the equipment. During this process, the electric proportional valve continuously monitors the target cooling water flow and makes real-time adjustments based on the feedback signal to ensure the stability and reliability of the operation of the target hot rolling mill.

[0070] According to actual application data, the present invention can save 230m3 of water per hour during the hot rolling production process. 3 , can save about 1181280m3 of water every year 3 , greatly reducing the water pressure of enterprises. By saving water resources and electricity consumption, the present invention can save about 190,000 yuan in electricity consumption for enterprises each year, significantly reducing the operating cost of the hot rolling production line. While improving the efficiency of water resource utilization, the present invention reduces wastewater discharge, is beneficial to environmental protection, and complies with my country's policy orientation of energy conservation and emission reduction. Therefore, the present invention has significant technical effects and economic benefits, and provides an efficient, energy-saving, and environmentally friendly solution for the water cooling control system of the main motor and hydraulic station of the hot rolling mill.

[0071] The present application adopts the above method to obtain cooling water flow regulation parameters, extract features of the cooling water flow regulation parameters, and obtain target feature parameters, so as to use the target feature parameter features as input, output the target cooling water flow corresponding to the target hot rolling mill through a cooling water flow control model, and perform the water cooling control operation on the target hot rolling mill through an electric proportional valve according to the target cooling water flow, thereby saving the use of circulating water, reducing energy consumption, achieving green development, and effectively reducing the production energy consumption of copper processing enterprises.

[0072] Please refer to Figure 2 , which shows a module schematic diagram of a hot rolling mill water cooling control device provided in an embodiment of the present application, the device includes an acquisition module 21 and a processing module 22, wherein:

[0073] The acquisition module 21 is used to obtain cooling water flow adjustment parameters in response to the water cooling control operation for the target hot rolling mill, and the cooling water flow adjustment parameters include main motor load parameters, main motor temperature parameters and main motor working cycle parameters.

[0074] The processing module 22 is used to extract the characteristics of the cooling water flow adjustment parameters and obtain the target characteristic parameters; take the characteristics of the target characteristic parameters as input, and output the target cooling water flow corresponding to the target hot rolling mill through the cooling water flow control model; according to the target cooling water flow, the target hot rolling mill is water-cooled through the electric proportional valve.

[0075] In a possible implementation, the target feature parameters include fused derivative features, fused timing features, and fused cross features. The processing module 22 is used to extract features from the cooling water flow regulation parameters and obtain the target feature parameters, specifically including: obtaining the motor load parameters, temperature change rate parameters, and vibration intensity parameters in the cooling water flow regulation parameters; constructing fused derivative features in the target feature parameters based on the motor load parameters, temperature change rate parameters, and vibration intensity parameters; obtaining the historical cooling water flow parameters, historical temperature parameters, and historical power parameters in the cooling water flow regulation parameters; constructing fused timing features in the target feature parameters based on the historical cooling water flow parameters, historical temperature parameters, and historical power parameters; obtaining the cross-term parameters of temperature and load, and the cross-term parameters of speed and ambient temperature in the cooling water flow regulation parameters; constructing fused cross features in the target feature parameters based on the cross-term parameters of temperature and load, and the cross-term parameters of speed and ambient temperature; performing feature parameter fusion on the fused derivative features, fused timing features, and fused cross features, and using the feature parameters after the feature parameter fusion as the target feature parameters.

[0076] In a possible implementation, the processing module 22 is used to construct a fusion derivative feature in the target feature parameter according to the motor load parameter, the temperature change rate parameter, and the vibration intensity parameter, specifically including: using a nonlinear mapping function, performing a first fusion operation on the cooling water flow adjustment parameter through the following formula, and obtaining the fusion derivative feature:

[0077] f d =α1log(P motor +∈)+α2*exp(ΔT motor )+α3*A vibration 2 ;

[0078] Among them, f d To fuse the derived features, P motor is the motor load parameter, α1 is the weight coefficient corresponding to the motor load parameter, ΔT motor is the temperature change rate parameter, α2 is the weight coefficient corresponding to the temperature change rate parameter, A vibration is the vibration intensity parameter, α3 is the weight coefficient corresponding to the vibration intensity parameter, and ∈ is the adjustment factor, which is used to avoid zero when taking the logarithm.

[0079] In a possible implementation, the processing module 22 is used to construct a fusion time series feature in the target feature parameter according to the historical cooling water flow parameter, the temperature parameter at the historical moment, and the power parameter at the historical moment, specifically including: using weighted sliding window processing, performing a second fusion operation on the cooling water flow adjustment parameter through the following formula, and obtaining the fusion time series feature:

[0080]

[0081] Among them, f t is the fusion time series feature, ti is the historical moment, Q history (ti) is the historical cooling water flow parameter, T motor (ti) is the temperature parameter at the historical moment, P motor (ti) n The power parameter at the historical moment, β1 is the weight corresponding to the historical cooling water flow parameter, β2 is the weight corresponding to the temperature parameter at the historical moment, and β3 is the weight corresponding to the power parameter at the historical moment.

[0082] In a possible implementation, the processing module 22 is used to construct a fused cross feature in the target feature parameter according to the cross-term parameter of temperature and load, and the cross-term parameter of speed and ambient temperature, specifically including: sampling a high-order cross-term polynomial combination, performing a third fusion operation on the cooling water flow regulation parameter through the following formula, and obtaining the fused cross feature:

[0083] f mix =γ1*(T motor ·P motor )+γ2*(N motor ·T ambient )+γ3*(T motor 2 ·

[0084] P motor );

[0085] Among them, f mix To fuse cross features, N motor is the main motor speed parameter, · represents the element-by-element operation between each cooling water flow adjustment parameter, γ1 is the weight corresponding to the cross-term parameter of temperature and load, γ2 is the weight corresponding to the cross-term parameter of speed and ambient temperature, and γ3 is the weight corresponding to the quadratic cross-term of temperature and load.

[0086] In one possible implementation, the processing module 22 is used to construct a cooling water flow control model before taking the target characteristic parameter characteristics as input and outputting the target cooling water flow corresponding to the target hot rolling mill through the cooling water flow control model, specifically including: obtaining historical cooling water flow adjustment parameters, the historical cooling water flow adjustment parameters include historical main motor load parameters, historical main motor temperature parameters and historical main motor working cycle parameters; and constructing a cooling water flow control model based on the historical cooling water flow adjustment parameters.

[0087] In a possible implementation, the processing module 22 is used to output the target cooling water flow corresponding to the target hot rolling mill through the cooling water flow control model, specifically including: calculating the target cooling water flow according to the following formula:

[0088]

[0089] Among them, Q target is the target cooling water flow rate, d0 is the bias term, X is the set of target characteristic parameter features, f i (X) is the output corresponding to the i-th target feature parameter feature, F i is the i-th post-term, which includes high-order cross-terms and time series information processing terms. is the weight parameter corresponding to the i-th post-item.

[0090] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0091] The present application also provides an electronic device. Figure 3 , Figure 3 30 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include: at least one processor 301, at least one communication bus 302, a user interface 303, at least one network interface 304, and a memory 305.

[0092] The communication bus 302 is used to realize the connection and communication between these components.

[0093] The user interface 303 may include a display screen (Display) and a camera (Camera). The optional user interface 303 may also include a standard wired interface and a wireless interface.

[0094] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0095] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0096] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and a hot rolling mill water cooling control application.

[0097] exist Figure 3 In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the hot rolling mill water cooling control application stored in the memory 305. When executed by one or more processors 301, the electronic device executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0098] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors, the electronic device executes one or more of the methods described in the above embodiments.

[0099] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0100] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0101] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0102] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0103] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0104] The above are only exemplary embodiments disclosed in this application, and the scope of the disclosure of this application cannot be limited by this. That is, any equivalent changes and modifications made according to the teachings disclosed in this application are still within the scope of the disclosure of this application. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other implementation schemes disclosed in this application.

[0105] This application is intended to cover any modifications, uses or adaptive changes disclosed in this application, which follow the general principles disclosed in this application and include common knowledge or customary technical means in the technical field not recorded in this application.

Claims

1. A hot rolling mill water cooling control method, characterized in that: The method comprises: In response to the water cooling control operation for the target hot rolling mill, a cooling water flow adjustment parameter is obtained, wherein the cooling water flow adjustment parameter includes a main motor load parameter, a main motor temperature parameter, and a main motor duty cycle parameter; Extracting the characteristics of the cooling water flow rate adjustment parameters and obtaining target characteristic parameters; Taking the target characteristic parameter characteristics as input, outputting the target cooling water flow corresponding to the target hot rolling mill through a cooling water flow control model; The water cooling control operation is performed on the target hot rolling mill through an electric proportional valve according to the target cooling water flow rate.

2. The method according to claim 1, characterized in that The target feature parameters include fusion derivative features, fusion time series features, and fusion cross features. The feature extraction of the cooling water flow adjustment parameters and the acquisition of the target feature parameters specifically include: Obtaining a motor load parameter, a temperature change rate parameter, and a vibration intensity parameter from among the cooling water flow rate adjustment parameters; Constructing the fused derivative feature in the target feature parameter according to the motor load parameter, the temperature change rate parameter and the vibration intensity parameter; Obtaining historical cooling water flow parameters, historical temperature parameters, and historical power parameters from the cooling water flow adjustment parameters; Constructing the fused time series feature in the target feature parameter according to the historical cooling water flow parameter, the historical temperature parameter and the historical power parameter; Obtaining the cross-term parameters of temperature and load, and the cross-term parameters of rotation speed and ambient temperature in the cooling water flow rate adjustment parameters; Constructing the fused cross feature in the target feature parameter according to the cross-term parameter of the temperature and the load, and the cross-term parameter of the rotation speed and the ambient temperature; Feature parameter fusion is performed on the fused derived feature, the fused temporal feature and the fused cross feature, and the feature parameter after the feature parameter fusion is performed is used as the target feature parameter.

3. The method according to claim 2, characterized in that The constructing the fused derivative feature in the target feature parameter according to the motor load parameter, the temperature change rate parameter and the vibration intensity parameter specifically includes: A nonlinear mapping function is used to perform a first fusion operation on the cooling water flow rate adjustment parameter through the following formula, and the fusion derivative feature is obtained: f d =α1log(P motor +∈)+α2*exp(ΔT motor )+α3*A vibration 2 ; Among them, f d is the fusion-derived feature, P motor is the motor load parameter, α1 is the weight coefficient corresponding to the motor load parameter, ΔT motor is the temperature change rate parameter, α2 is the weight coefficient corresponding to the temperature change rate parameter, A vibration is the vibration intensity parameter, α3 is the weight coefficient corresponding to the vibration intensity parameter, and ∈ is an adjustment factor used to avoid zero when taking the logarithm.

4. The method according to claim 2, characterized in that: The constructing the fused time series feature in the target feature parameter according to the historical cooling water flow parameter, the historical temperature parameter and the historical power parameter specifically includes: The weighted sliding window processing is adopted to perform the second fusion operation on the cooling water flow regulation parameter through the following formula, and obtain the fusion time series feature: Among them, f t is the fusion time series feature, ti is the historical moment, Q history (ti) is the historical cooling water flow parameter, T motor (ti) is the temperature parameter at the historical moment, P motor (ti) n The power parameter at the historical moment, β1 is the weight corresponding to the historical cooling water flow parameter, β2 is the weight corresponding to the temperature parameter at the historical moment, and β3 is the weight corresponding to the power parameter at the historical moment.

5. The method according to claim 2, characterized in that: The step of constructing the fused cross feature in the target feature parameter according to the cross-term parameter of the temperature and the load, and the cross-term parameter of the rotation speed and the ambient temperature, specifically includes: The high-order cross-term polynomial combination is sampled, and the third fusion operation is performed on the cooling water flow adjustment parameter through the following formula to obtain the fusion cross feature: f mix =γ1*(T motor ·P motor )+γ2*(N motor ·T ambient )+γ3*(T motor 2 · P motor )； Among them, f mix is the fusion cross feature, N motor is the main motor speed parameter, · represents the element-by-element operation between the cooling water flow adjustment parameters, γ1 is the weight corresponding to the cross-term parameter of the temperature and load, γ2 is the weight corresponding to the cross-term parameter of the speed and the ambient temperature, and γ3 is the weight corresponding to the quadratic cross-term of the temperature and load.

6. The method according to claim 1, characterized in that Before taking the target characteristic parameter feature as input and outputting the target cooling water flow corresponding to the target hot rolling mill through the cooling water flow control model, it is necessary to construct the cooling water flow control model, which specifically includes: Acquire historical cooling water flow regulation parameters, wherein the historical cooling water flow regulation parameters include historical main motor load parameters, historical main motor temperature parameters, and historical main motor duty cycle parameters; The cooling water flow control model is constructed according to the historical cooling water flow adjustment parameters.

7. The method according to claim 1, characterized in that Outputting the target cooling water flow rate corresponding to the target hot rolling mill through the cooling water flow rate control model specifically includes: The target cooling water flow rate is calculated according to the following formula: Among them, Q target is the target cooling water flow rate, d0 is the bias term, X is the set of target characteristic parameter features, f i (X) is the output corresponding to the i-th target feature parameter feature, a i is the weight parameter corresponding to the output corresponding to the i-th target feature parameter feature, F i is the i-th post-term, which includes a high-order cross term and a timing information processing term, is the weight parameter corresponding to the i-th post-item.

8. A water cooling control device for a hot rolling mill, characterized in that: The device comprises an acquisition module and a processing module, wherein: The acquisition module is used to acquire cooling water flow adjustment parameters in response to the water cooling control operation for the target hot rolling mill, wherein the cooling water flow adjustment parameters include main motor load parameters, main motor temperature parameters and main motor working cycle parameters; The processing module is used to extract features of the cooling water flow adjustment parameters and obtain target feature parameters; take the target feature parameter features as input, and output the target cooling water flow corresponding to the target hot rolling mill through a cooling water flow control model; and perform the water cooling control operation on the target hot rolling mill through an electric proportional valve according to the target cooling water flow.

9. An electronic device, characterized in that: It includes a processor, a communication bus, a user interface, a network interface and a memory, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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