CN114934249A

CN114934249A - Method and device for controlling hot-dip galvanized strip steel C warping defect and electronic equipment

Info

Publication number: CN114934249A
Application number: CN202210676707.9A
Authority: CN
Inventors: 夏江涛; 罗军; 彭文杰; 杜蓉
Original assignee: Wuhan Iron and Steel Co Ltd
Current assignee: Wuhan Iron and Steel Co Ltd
Priority date: 2022-06-15
Filing date: 2022-06-15
Publication date: 2022-08-23

Abstract

The invention provides a method, a device and electronic equipment for controlling the C-warp defect of hot-dip galvanized strip steel, which are applied to a hot-dip galvanized production line, wherein the hot-dip galvanized production line comprises a zinc pot, an air knife, a cooling fan and an electromagnetic device arranged between the air knife and the cooling fan, and the method comprises the following steps: before a target strip steel section enters the air knife, acquiring characteristic data of the target strip steel section, and determining the C warping type of the target strip steel section based on the characteristic data and a C warping prediction model corresponding to the grade to which the target strip steel section belongs, wherein the C warping prediction model is obtained based on historical production data training of the corresponding grade strip steel in a hot galvanizing production line; and controlling the magnetic force applied to the strip steel by the electromagnetic device based on the C warping type of the target strip steel section so as to improve the C warping defect of the target strip steel section before the target strip steel section enters the air knife through the zinc pot, reduce the plate shape error of the strip steel at the air knife and improve the uniformity of a zinc coating.

Description

Method and device for controlling hot-dip galvanized strip steel C warping defect and electronic equipment

Technical Field

The invention relates to the field of cold-rolled strip steel production, in particular to a method and a device for controlling the C warping defect of hot-dip galvanized strip steel and electronic equipment.

Background

In the production process of a hot galvanizing unit, strip steel is influenced by various factors, and when the strip steel passes through a zinc pot and enters an air knife, the strip steel has a transverse buckling deformation phenomenon, namely C-warp, as shown in figure 1. Due to the existence of the C warping, the distance between the middle part of the strip steel and the air knife nozzle is larger than the distance between the edge part of the strip steel and the air knife nozzle. The farther the nozzle is away from the strip steel, the lower the air flow pressure is, the smaller the impulse is, and the larger the thickness of the zinc layer is, finally, the thickness of the zinc layer on the upper surface of the strip steel is distributed in a mode of thick middle part and thin edge part, and the lower surface is opposite to the middle surface. The thickness and uniformity of the galvanized layer greatly affect the quality of the galvanized sheet product. Unstable thickness and uniformity can influence the performance of zinc sheet, reduce the yield of product, bring great economic loss for the unit.

Disclosure of Invention

The embodiment of the invention provides a method and a device for controlling the C warping defect of hot-dip galvanized strip steel and electronic equipment, which can effectively improve the C warping defect of the hot-dip galvanized strip steel, reduce the plate shape error of the strip steel at an air knife and further improve the uniformity of a zinc coating of the strip steel.

In a first aspect, an embodiment of the present invention provides a method for controlling a C warp defect of a hot-dip galvanized steel strip, which is applied to a hot-dip galvanized production line, where the hot-dip galvanized production line includes a zinc pot, an air knife, a cooling fan, and an electromagnetic device disposed between the air knife and the cooling fan, and the method includes:

before a target strip segment enters the air knife, obtaining characteristic data of the target strip segment, the characteristic data comprising: process data in a hot galvanizing production line and plate shape data of the target strip steel section;

determining the C warping type of the target strip steel section based on the characteristic data and a C warping prediction model corresponding to the grade to which the target strip steel section belongs, wherein the C warping prediction model is obtained based on historical production data training of the corresponding grade strip steel in a hot galvanizing production line;

and controlling the magnetic force applied to the strip steel by the electromagnetic device based on the C warping type of the target strip steel section so as to compensate the C warping defect of the target strip steel section before the target strip steel section passes through the zinc pot and enters the air knife.

Further, the process data comprises: heating temperature of each process area of the hot galvanizing heating section, strip steel tension value of the zinc pot section and the push-up amount of a correcting roller in the zinc pot; the strip shape data includes: and the plate stress value of each sampling point along the width direction.

Further, the determining the type of the C warp of the target strip steel section based on the feature data and the C warp prediction model corresponding to the grade to which the target strip steel section belongs includes:

performing polynomial fitting on the plate shape stress values of the sampling points;

and inputting the polynomial coefficient obtained by fitting and the process data into the C warp prediction model to obtain the C warp type of the target strip steel section.

Further, the type of the C warp is one of the following types:

c, warping upwards, wherein the position of the C warping is located in the central area;

c, upward warping is carried out, and the position of the C warping is close to the transmission side;

c, upward warping is carried out, and the position of the C warping is close to the operation side;

c, lower warping, wherein the position of the C warping is located in the central area;

c, bending the lower part of the lower;

the lower C warp is close to the transmission side; and

has no C warp.

Further, electromagnetic means includes the multiunit electromagnetic induction module that distributes along belted steel width direction, based on the C of target strip steel section sticks up the type, controls electromagnetic means is to belted steel applied magnetic force, includes:

searching an additional value sequence corresponding to the C warp type in a preset additional value information base based on the C warp type of the target strip steel section, wherein the additional value information base stores multiple additional value sequences corresponding to different C warp types, and the additional value sequence comprises additional values corresponding to control parameters of each group of electromagnetic induction modules;

and based on the searched additional value sequence, increasing the corresponding additional value of the control parameter of each group of electromagnetic induction modules, and adjusting the magnetic force applied to the strip steel.

Further, the C warp prediction model is obtained by training based on the following steps:

obtaining a training data set from historical production data, the training data set comprising: each subdata set corresponds to a band steel mark and comprises a plurality of groups of characteristic data of band steel of corresponding marks and a C warp type corresponding to each group of characteristic data;

and training a preset machine learning model by respectively utilizing each subdata set to obtain a C warp prediction model corresponding to each band steel mark.

Further, after determining the type of C warp of the target strip steel segment, the method further includes:

and displaying the C warp type of the target strip steel section to a user.

Further, after the C warp defect of the target strip steel section is compensated, the method further comprises the following steps:

and displaying the surface state of the target strip steel section at the air knife to a user.

In a second aspect, an embodiment of the present invention provides a device for controlling a C warp defect of a hot-dip galvanized steel strip, which is applied to a hot-dip galvanized production line, where the hot-dip galvanized production line includes a zinc pot, an air knife, a cooling fan, and an electromagnetic device disposed between the air knife and the cooling fan, and the device includes:

a characteristic acquisition module for acquiring characteristic data of a target strip steel segment before the target strip steel segment enters the air knife, the characteristic data comprising: process data in a hot galvanizing production line and plate shape data of the target strip steel section;

the prediction module is used for determining the C warping type of the target strip steel section based on the characteristic data and a C warping prediction model corresponding to the grade of the target strip steel section, and the C warping prediction model is obtained based on historical production data training of the corresponding grade of strip steel in a hot galvanizing production line;

and the magnetic control module is used for controlling the magnetic force applied to the strip steel by the electromagnetic device based on the C warping type of the target strip steel section so as to compensate the C warping defect of the target strip steel section when the target strip steel section passes through the zinc pot and enters the air knife.

In a third aspect, an embodiment of the present invention provides an electronic device, including: the control method comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the steps of the method for controlling the hot-dip galvanized steel strip C warping defect provided by the first aspect.

According to the method, the device and the electronic equipment for controlling the hot-dip galvanized steel strip C-warp defect, the C-warp prediction model is constructed by learning the process data and the strip steel plate shape data of the hot-dip galvanized production line, so that the C-warp type of the steel strip is predicted by using the C-warp prediction model, and the magnetic force applied to the steel strip by the electromagnetic device is controlled based on the C-warp type of the steel strip before the steel strip enters the air knife through the zinc pot, so that the C-warp defect of the steel strip is improved, the plate shape error of the steel strip at the air knife is reduced, the uniformity of a zinc coating is improved, and the yield of products is improved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.

FIG. 1 shows a schematic view of the C warp deformation of a steel strip at an air knife;

FIG. 2 is a flowchart illustrating a method for controlling a C warp defect of a hot-dip galvanized steel strip according to a first aspect of an embodiment of the present invention;

FIG. 3 is a schematic diagram showing a part of a hot galvanizing production line in the embodiment of the invention;

FIG. 4 is a schematic diagram illustrating a type of C warp in an embodiment of the invention;

FIG. 5 illustrates a human-machine interface diagram in an embodiment of the invention;

FIG. 6 shows a block diagram of an apparatus for controlling C warp defect of a hot-dip galvanized steel strip according to a second aspect of the embodiment of the present invention;

fig. 7 is a schematic structural diagram of an electronic device according to a third aspect of the embodiment of the present invention.

Detailed Description

The inventor finds that the forming influence factors of the strip steel C warping of the hot galvanizing unit are many and complex through long-term research, and generally the forming influence factors are directly related to the shape of the strip steel incoming material. Secondly, preheating, heating, soaking and cooling processes in an annealing furnace of a hot galvanizing unit can affect the temperature difference between the upper surface and the lower surface of the strip steel, so that uneven plastic deformation is caused, and the formation of C warping is aggravated. Moreover, before the strip steel enters the air knife and passes through the zinc pot, the pushing-up amount of the correcting roller in the zinc pot also has different degrees of influence on the stress of the strip steel along the width direction, or causes the C warping to increase, or causes the stress to decrease and tend to be uniformly distributed, so that the strip steel is flat.

Therefore, according to the embodiment of the invention, the C warping prediction model of the strip steel at the air knife is established by utilizing the incoming material plate shape data of the hot galvanizing unit and combining with the process parameters in the unit process control, the C warping defect degree is quantified, and the magnetic force applied to the strip steel by the electromagnetic device at the upper part of the air knife is adjusted according to the C warping defect degree, so that the C warping is improved, the strip steel at the air knife tends to be straight, and the good zinc layer thickness and uniformity are obtained.

The technical solutions of the present invention are described in detail below with reference to the accompanying drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples are not limitations of the technical solutions of the present invention, but may be combined with each other without conflict.

In a first aspect, the embodiment of the invention provides a method for controlling the C warping defect of a hot-dip galvanized strip steel, which is applied to a hot-dip galvanized production line. As shown in fig. 2, the method may include the steps of:

step S101, before the target strip steel section enters the air knife, acquiring characteristic data of the target strip steel section, wherein the characteristic data comprises: process data in a hot galvanizing production line and plate shape data of a target strip steel section;

s102, determining the C warping type of the target strip steel section based on the characteristic data and a C warping prediction model corresponding to the grade of the target strip steel section, wherein the C warping prediction model is obtained based on historical production data training of the corresponding grade of the strip steel in the hot galvanizing production line;

and S103, controlling the magnetic force applied to the strip steel by the electromagnetic device based on the C warping type of the target strip steel section so as to compensate the C warping defect of the target strip steel section before the target strip steel section enters the air knife through the zinc pot.

As shown in fig. 3, the hot galvanizing line comprises a zinc pot 21, an air knife 23, a cooling fan 25, and an electromagnetic device 24 arranged between the air knife 23 and the cooling fan 25. The hot galvanizing air knife 23 is a device for controlling the thickness of a galvanized layer by blowing air flow, and a nozzle in the air knife 23 blows air flow with certain pressure to the upper surface and the lower surface of the strip steel 200 to wipe off redundant zinc liquid on the surface of the galvanized strip steel 200. The zinc pot 21 is internally provided with stabilizing rollers 22, and after being conveyed out of the zinc pot 21, the strip steel 200 firstly enters an air knife 23, then passes through an electromagnetic device 24 and then enters a cooling fan 25. The electromagnetic device 24 is an electromagnetic vibration damping device. For example, the electromagnetic device 24 may include a plurality of sets of electromagnetic induction modules 240 distributed along the width direction of the steel strip 200, each set of electromagnetic induction modules 240 includes a first electromagnetic induction unit 241 disposed near the upper surface of the steel strip 200 and a second electromagnetic induction unit 242 disposed near the lower surface of the steel strip 200, and a magnetic field perpendicular to the surface of the steel strip 200 is generated between the first electromagnetic induction unit 241 and the second electromagnetic induction unit 242, so that the steel strip 200 is acted on by a magnetic force, thereby suppressing the vibration of the steel strip 200.

Before executing the steps S101 to S103, the C warp prediction model needs to be obtained by learning the historical production data on the hot galvanizing production line. In an optional implementation mode, considering that different grades of strip steels with different width-thickness ratios have different plate-shaped characteristics, the C warping expressions under various influence factors are different, and corresponding C warping prediction models can be trained respectively for the different grades of strip steels. Specifically, a training data set may be obtained from historical production data, the training data set comprising: each subdata set corresponds to a band steel mark and comprises a plurality of groups of characteristic data of band steel of the corresponding mark and a C warp type corresponding to each group of characteristic data. And then, training a preset machine learning model by using each subdata set to obtain a C warp prediction model corresponding to each band steel mark.

In an alternative embodiment, in the training data of the C warp prediction model, the feature data may include: the process data and the shape data of the incoming strip steel plate in the hot galvanizing production line.

The process data of the hot galvanizing production line comprise heating temperature of each process area of a hot galvanizing heating section, a strip steel tension value and the pushing-up amount of a correcting roller in a zinc pot 21. For example, the hot galvanizing heating section comprises the following process stages:

a preheating section (PHS) for preheating the strip steel at the temperature of 200 ℃;

in a fast heating section (DFS), an open flame burner burns the surface of the strip steel to rapidly heat the strip steel, wherein the temperature is 580-670 ℃;

a radiation section (RHS), wherein the radiation pipe heats the strip steel to a recrystallization temperature of 770-790 ℃;

the rapid Cooling Section (CS) is symmetrically provided with slit-shaped nozzles on the upper surface and the lower surface of the strip steel, and sprays protective gas to the surface of the strip steel at a high speed, wherein the temperature is 470-520 ℃;

and the compensation section (TDRS) compensates the temperature of the strip steel by utilizing furnace wall electric heating elements which are symmetrically arranged, so that the galvanizing temperature condition is met, and the temperature is 470 ℃ (460 ℃ in the zinc pot 21).

Wherein the temperature (T) of each process zone of the heating section _PHS 、T _DFS 、T _RHS 、T _CS 、T _DRS ) The steel strip tension value T is provided for a tension meter at 21 sections of the zinc pot, and the pushing-up amount L of the correcting roller is provided by a coding sensor at a bearing.

The strip steel shape data can comprise strip steel shape stress values collected by a shape meter of hot galvanizing incoming materials in a five-cold-rolling stand. And the shape meter records the stress value of each sampling point of each frame of plate shape along the width direction. As an embodiment, the sheet shape stress value of each sampling point can be used as an input feature for training a machine learning model together with the process data.

Considering that the quantity of sensors of the shape gauge is large, generally dozens to hundreds, the dimension of the characteristic value is high when the shape stress value of each sampling point is directly used as the input characteristic, and the defect of dimension explosion is caused when the characteristic value is substituted into a machine learning algorithm.

For example, for each frame of tile data zone 1-zone n, a quintic legendre orthogonal polynomial may be selected for fitting to obtain a fitting polynomial coefficient for each frame: (C0, C1, C2, C3, C4) as characteristic values corresponding to the plate shape data. Therefore, on the premise of keeping a large amount of original information, the data dimensionality is greatly reduced, and the training efficiency is favorably improved. Of course, in other embodiments of the present invention, other dimension reduction methods may also be adopted, for example, other polynomial fitting methods may also be adopted, which is not limited in this embodiment.

Thus, for each frame of the plate shape, a set of characteristic values can be obtained according to the characteristic data, including: polynomial coefficients obtained by performing polynomial fitting on one frame of plate-shaped data, the temperature of each process zone of the heating section, the tension value of the strip steel and the push-up amount of the correcting roller in the zinc pot 21 are shown in table 1 below.

TABLE 1 characteristic value Table

C0

C1

C2

C3

C4

T _PHS

T _DFS

T _RHS

T _CS

T _DRS

T

L

Of course, in addition to the characteristic data, the type of C warp at the air knife 23 of each grade of strip steel on the hot galvanizing production line needs to be determined in advance. For example, positional data of the strip off the center line 201 at the air knives 23 may be collected. The position data is acquired by a position sensor 243 provided in the electromagnetic device 24 in the upper part of the air knife 23, as shown in fig. 3. The number of the position sensors 243 is determined according to the width of the strip steel, and for example, 3 to 7 position sensors may be activated. The type of C warp of the strip at the air knife 23 is divided according to the position data provided by the position sensor 243 at the electromagnetic device 24, and different values are used to represent different types of C warp. For example, the value may be used as a target value of training data when training the C warp prediction model, as shown in table 2.

TABLE 2C tilted Table

Type of C warp	Categorizing numerical values
		Upper C seesaw (center)	1
Upper C seesaw (partial transmission side)	2
		Upper C tilted (inclined operation side)	3
Lower C Qiao (center)	4
		Lower C warped (inclined operation side)	5
Lower C warped (offset transmission side)	6
		No C warp	7

It should be noted that, in table 2, "upper" and "lower" are relative terms, two opposite surfaces of the strip steel 200 may be respectively referred to as an upper surface and a lower surface, a C warp type with a convex upper surface is divided into an upper C warp, and a C warp type with a convex lower surface is divided into a lower C warp. Further, according to the position of the C warp in the width direction of the steel strip 200, the upper C warp is divided into a central upper C warp (as shown in (a) of fig. 4, the position of the C warp is located in the central area of the steel strip 200), an upper C warp on the transmission side (as shown in (b) of fig. 4, the position of the C warp is close to the transmission side) and an upper C warp on the operation side (as shown in (C) of fig. 4, the position of the C warp is close to the operation side), a lower C warp on the operation side (as shown in (d) of fig. 4, the position of the C warp is located in the central area of the steel strip 200), and a lower C warp on the operation side (as shown in (e) of fig. 4, the position of the C warp is close to the operation side) and a lower C warp on the transmission side (as shown in (f) of fig. 4, the position of the C warp is close to the transmission side). In addition, the case where no C warp occurs or the degree of C warp is small is classified as a type without C warp (as shown in (g) of fig. 4).

Further, the above feature values and the corresponding target values, i.e. the classification values corresponding to the type of C warp, can be combined into a set of training data, as shown in table 3.

TABLE 3 training data Table

C ₀

C ₁

C ₂

C ₃

C ₄

T _PHS

T _DFS

T _RHS

T _CS

T _DRS

T

L

C fructus forsythiae numerical value

Therefore, process data and incoming strip steel plate shape data in hot galvanizing production lines of different grades can be collected from historical production data such as a factory database, position data of strip steel at the air knife 23 is collected, and multiple groups of training data of the strip steel of different grades are obtained according to the data, so that a sub data set corresponding to each grade is formed. It should be noted that, in order to ensure the accuracy of model training, for the same brand, the sub data set needs to cover each of the divided types, so that the C warp prediction model obtained by training can sufficiently learn the association relationship between each C warp type and the corresponding feature data.

After the training data sets are collected, each subdata set can be used for training a preset machine learning model to obtain a C warp prediction model corresponding to each band steel mark. For example, the machine learning model may use a KNN algorithm, or may also use another supervised classification algorithm, for example, a neural network algorithm may be used, which is not limited in this embodiment. It should be noted that, considering that the process environments of the hot galvanizing production lines in different periods are different, the C warp prediction model training can be performed by using new production data at preset time intervals to achieve a better prediction effect.

After the C warp prediction model is obtained through training, the steps S101 and S102 can be executed, and the C warp type of the subsequent steel coil on the hot galvanizing production line is predicted by using the C warp prediction model. Herein, the strip section to be predicted in the steel coil is referred to as a target strip section. For example, each frame of plate shape of the steel coil can be used as a target strip steel section, and the length of each frame of plate shape can be determined according to an actual scene.

The prediction process of the C warp prediction model is similar to the training process. For example, when the sheet shape data obtained in step S101 includes sheet shape stress values of each sampling point in the width direction, polynomial fitting may be performed on the sheet shape stress values of each sampling point, for example, the quintic legendre orthogonal polynomial may be used for fitting, and then the polynomial coefficients obtained through fitting and the process data (such as the characteristic values shown in table 1) are input into the C warp prediction model, so as to obtain the C warp type of the target strip steel segment.

Further, after the type of the C warp is predicted, the step S103 may be executed, and before the target strip enters the air knife 23, the electromagnetic device 24 is controlled according to the type of the C warp to compensate for the C warp of the strip. That is, in the embodiment, the magnetic force applied to the strip steel by the electromagnetic device 24 can effectively improve the C warp problem of the strip steel before the strip steel enters the air knife 23, in addition to suppressing the strip steel vibration.

Specifically, when each set of electromagnetic induction modules 240 in the electromagnetic device 24 operates according to a preset initial control parameter, such as an initial exciting current, the effect of suppressing the strip steel vibration can be achieved. In order to further improve the C warping problem of the strip shape of the electromagnetic device 24, on the basis of the initial control parameter, a certain additional value is added for each group of electromagnetic induction modules 240 according to the C warping type to adjust the magnetic force applied to each position on the strip surface in the width direction, so as to improve the C warping defect. It should be noted that the magnetic force adjustment may include adjustment of the magnetic force direction and adjustment of the magnetic force magnitude. For example, when the predicted C warp type is an upward C warp (center), the center position of the strip steel may be subjected to a magnetic force toward the lower surface along the width direction, and the magnetic force is relatively large, so that the two end positions are subjected to a magnetic force toward the upper surface, and the magnetic force is relatively small, and under the action of these magnetic forces, the position of the surface of the strip steel is restored to the position of the center line 201 (i.e., the position when there is no C warp), so that the strip steel at the air knife 23 tends to be flat and straight, thereby reducing the strip steel shape error at the air knife 23.

For example, for convenience of control, the additional value information base may be constructed in advance from a plurality of tests. The added value information base stores added value sequences corresponding to a plurality of different C fructus forsythiae types. For example, the type 1 to type 6C warps types in the table 2 can be respectively subjected to a strip shape correction test in advance, that is, the control parameters of each group of the electromagnetic induction modules 240 are adjusted on the basis of the initial control parameters until the correction of the corresponding type C warps is realized, the added value added to the control parameters of each group of the electromagnetic induction modules 240 at this time is recorded, and an added value sequence corresponding to the type C warps is obtained and correspondingly stored in an added value information base. In addition, for the 7 th class of C warp type, that is, in the case that the prediction result is no C warp, the control parameter additional value may be configured to be 0, that is, the original initial control parameter is kept unchanged.

At this time, in the step S103, an additional value sequence corresponding to the C warp type may be searched in a preset additional value information base based on the C warp type of the target strip steel segment; and then based on the found additional value sequence, the corresponding additional value is added to the control parameters of each group of electromagnetic induction modules 240, the magnetic force applied to the strip steel is adjusted, the position of the surface of the strip steel at the air knife 23 is corrected in advance, the strip steel plate shape at the air knife 23 tends to be straight, and the quality problem of the galvanized plate caused by the strip steel plate shape defect is favorably reduced.

Further, in order to facilitate the user to know the problem of the C warp defect of the strip steel in time, the C warp type of the target strip steel section may be displayed to the user after the C warp type of the target strip steel section is determined in step S102. The specific display form may be set according to the needs of an actual scene, and this embodiment does not limit this. For example, when each frame of plate shape of the steel coil is taken as the target strip steel section in sequence, the C warp type of each target strip steel section can be displayed in real time in a form of a table or a curve and the like by using a sliding window manner in a designated display area on a pre-configured human-computer interface.

In addition, in order to facilitate the user to know the surface position state of the strip steel at the air knife 23 in time, the surface state of the target strip steel segment at the air knife 23 may be displayed to the user after the C warp defect of the target strip steel segment is compensated through the step S103. For example, the surface state may be displayed in another designated display area on a pre-configured human-machine interface, and a concrete representation form of the surface state may be set according to a requirement of an actual scene, for example, the surface state may be represented by surface position data (including coordinates of sampling points at various positions in a width direction) of the strip steel at the air knife 23, or may be position data of the strip steel at the air knife 23 deviating from the center line 201, or may be a plate shape curve of the strip steel at the air knife 23, which is not limited in this embodiment.

In order to understand the present solution more clearly, a C warp prediction and control system constructed by the above method is described as an example.

For the convenience of user control, the C warp prediction and control system is equipped with a human-machine interface 50. For example, as shown in fig. 5, the human-machine interface 50 is provided with a steel coil selection bar 501, an "automatic" button 502, and a "retrain" button 503, and is provided with a strip C warp prediction curve display area 504 and a position state display area 505 at the current strip air knife 23.

In specific implementation, an operator may select a steel coil number for predicting the C warp in the steel coil selection column 501. If the steel coil needs to be included in the model training data, a retraining button 503 is clicked, the production data of the steel coil, including the feature data of each strip steel section and the corresponding C warp type, is added to the model training data, and the C warp prediction model of the corresponding brand is retrained and updated.

And clicking an 'automatic' button 502 to start a prediction and control mode, namely triggering the system to execute the steps S101 to S103 to predict and control the C warp defect of the strip steel. Specifically, when a factory material tracking system tracks a new steel coil to enter a hot galvanizing production line, information such as a mark of the steel coil is judged, then characteristic data of each target strip steel section are collected in sequence, a C warp type of each target strip steel section is obtained by combining a C warp prediction model corresponding to the mark, and then the electromagnetic device 24 is controlled to compensate the C warp type of each target strip steel section. In the process, a C warp type prediction curve is displayed in the strip steel C warp prediction curve display area 504, and the curve is updated in real time according to the currently predicted C warp type. The position status display area 505 at the current strip air knife 23 displays the real-time position status of the strip at the air knife 23.

And clicking the 'automatic' button 502 again to exit the prediction and control mode and stop predicting and controlling the C warp defect of the strip steel.

In summary, according to the method for controlling the C-warp defect of the hot-dip galvanized steel strip provided by the embodiment of the invention, the C-warp prediction model is constructed by learning the process data and the strip steel plate shape data of the hot-dip galvanizing production line, so that the C-warp type of the steel strip is predicted by using the C-warp prediction model, and before the strip steel enters the air knife 23 through the zinc pot 21, the magnetic force applied to the strip steel by the electromagnetic device 24 is controlled based on the C-warp type of the steel strip, so that the C-warp defect of the steel strip is improved, the strip steel shape at the air knife 23 tends to be straight, the strip steel shape error at the air knife 23 is reduced, and the good thickness and uniformity of the zinc layer are obtained, so that the yield of the product is improved.

In addition, the human-computer interface 40 is arranged to provide convenience for manual operation, and online data is visualized, so that the data analysis and processing efficiency during hot galvanizing production is improved.

In a second aspect, an embodiment of the present invention further provides a device for controlling a C-warp defect of a hot-dip galvanized steel strip, which is applied to a hot-dip galvanized production line, where the hot-dip galvanized production line includes a zinc pot 21, an air knife 23, a cooling fan 25, and an electromagnetic device 24 disposed between the air knife 23 and the cooling fan 25. As shown in fig. 6, the apparatus 60 for controlling the C warp defect of the hot-dip galvanized steel strip includes:

a characteristic obtaining module 601, configured to obtain characteristic data of a target strip steel segment before the target strip steel segment enters the air knife, where the characteristic data includes: process data in a hot galvanizing production line and plate shape data of the target strip steel section;

the prediction module 602 is configured to determine a C warp type of the target strip steel section based on the feature data and a C warp prediction model corresponding to the grade to which the target strip steel section belongs, where the C warp prediction model is obtained based on historical production data training of a corresponding grade strip steel in a hot galvanizing production line;

and the magnetic control module 603 is used for controlling the magnetic force applied to the strip steel by the electromagnetic device based on the C warping type of the target strip steel section so as to compensate the C warping defect of the target strip steel section when the target strip steel section passes through the zinc pot and enters the air knife.

In an alternative embodiment, the process data comprises: heating temperature of each process area of the hot galvanizing heating section, strip steel tension value of the zinc pot section and the push-up amount of a correcting roller in the zinc pot; the strip shape data includes: and the plate stress value of each sampling point along the width direction.

In an alternative embodiment, the prediction module 602 is configured to:

In an alternative embodiment, the C warp type is one of the following types:

c, upward warping is carried out, and the position of the C warping is located in the central area;

the lower C warp is positioned in the central area;

c, bending the lower part of the lower;

the lower C warp is close to the transmission side; and

has no C warp.

In an alternative embodiment, the electromagnetic device includes a plurality of groups of electromagnetic induction modules distributed along the width direction of the strip, and the magnetic force control module 603 is configured to:

searching an additional value sequence corresponding to the C warp type in a preset additional value information base based on the C warp type of the target strip steel section, wherein multiple additional value sequences corresponding to different C warp types are stored in the additional value information base, and the additional value sequence comprises additional values of control parameters corresponding to each group of electromagnetic induction modules;

In an alternative embodiment, the C warp prediction model is trained based on the following steps:

obtaining a training data set from historical production data, the training data set comprising: each subdata set corresponds to a band steel mark and comprises a plurality of groups of characteristic data of band steel of the corresponding mark and a C warp type corresponding to each group of characteristic data;

and respectively training a preset machine learning model by utilizing each subdata set to obtain a C warp prediction model corresponding to each band steel mark.

In an optional embodiment, the apparatus 60 for controlling C warp defect of the hot-dip galvanized steel strip further includes: and the display module is used for displaying the C warp type of the target strip steel section to a user.

In an alternative embodiment, the display module is further configured to display the surface condition of the target strip segment at the air knife to a user.

It should be noted that, in the apparatus 60 for controlling C warp defect of hot-dip galvanized steel strip according to the embodiment of the present invention, the specific manner in which each module performs the operation has been described in detail in the embodiment of the method provided in the first aspect, and the specific implementation process may refer to the embodiment of the method provided in the first aspect, which will not be described in detail herein.

In a third aspect, an embodiment of the present invention further provides an electronic device, as shown in fig. 7, where the electronic device 70 includes: the memory 701, the processor 702 and a computer program stored on the memory 701 and executable on the processor 702 are configured to, when the processor 702 executes the program, implement the steps of any embodiment of the method for controlling C warp defects of hot dip galvanized steel strip according to the first aspect. The specific implementation process may refer to the method embodiment provided in the first aspect, and will not be described in detail here. Of course, the electronic device 70 may include more components than those described above, for example, a display screen, so as to further realize the visualization of the online data. For example, the electronic device may be a server, or may be a terminal device having a data processing function, such as a computer (PC), a notebook computer, and a PDA (Personal Digital Assistant).

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. The term "plurality" includes both and more than two. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims

1. The method for controlling the C warping defect of the hot-dip galvanized strip steel is characterized by being applied to a hot-dip galvanized production line, wherein the hot-dip galvanized production line comprises a zinc pot, an air knife, a cooling fan and an electromagnetic device arranged between the air knife and the cooling fan, and the method comprises the following steps:

determining the C warp type of the target strip steel section based on the characteristic data and a C warp prediction model corresponding to the grade of the target strip steel section, wherein the C warp prediction model is obtained based on historical production data training of the corresponding grade of strip steel in a hot galvanizing production line;

2. The method of claim 1, wherein the process data comprises: heating temperature of each process area of the hot galvanizing heating section, strip steel tension value of the zinc pot section and the push-up amount of a correcting roller in the zinc pot; the strip shape data includes: and the plate stress value of each sampling point along the width direction.

3. The method of claim 2, wherein the determining the type of the C warp of the target strip steel section based on the characteristic data and a C warp prediction model corresponding to the grade to which the target strip steel section belongs comprises:

4. The method of claim 1, wherein the C warp type is one of the following types:

c, upwarping, wherein the position of the C warping is close to the operation side;

the lower C warp is positioned in the central area;

c, bending the lower part of the lower;

the lower C warp is close to the transmission side; and

has no C warp.

5. The method of claim 1, wherein the electromagnetic device comprises a plurality of groups of electromagnetic induction modules distributed along the width direction of the strip steel, and the controlling the magnetic force applied to the strip steel by the electromagnetic device based on the C warp type of the target strip steel section comprises:

6. The method of claim 1, wherein the C warp prediction model is trained based on the following steps:

7. The method of claim 1, further comprising, after determining the type of C pass for the target strip segment:

and displaying the C warp type of the target strip steel section to a user.

8. The method of claim 1, further comprising, after compensating for the C warp defect of the target strip steel segment:

9. The utility model provides a device of defect is stuck up to control hot-galvanize belted steel C, its characterized in that is applied to hot-galvanize production line, hot-galvanize production line includes zinc pot, air knife, cooling blower and sets up electromagnetic means between air knife and the cooling blower, the device includes:

10. An electronic device, comprising: memory, processor and computer program stored on said memory and executable on said processor, said processor implementing the steps of the method of any of claims 1-8 when said program is executed.