Monitoring method, device, medium and electronic equipment for hot rolling thickness control model

Abstract

The present application relates to the technical field of hot-rolled strip thickness, and discloses a monitoring method and device for a hot-rolled thickness control model, the method comprising: obtaining a self-learning coefficient calculated and generated by a self-learning module in the hot-rolled thickness control model during multiple hot-rolling processes to obtain a first monitoring parameter array; judging whether there are J1 consecutive values ​​in the first monitoring parameter array whose absolute values ​​all exceed a set limit value, wherein J1 is a natural number greater than 2; if there are J1 consecutive values ​​in the first monitoring parameter array whose absolute values ​​all exceed a set limit value, then outputting alarm information about the hot-rolled thickness control model. The technical solution provided by the present application monitors whether there is a problem with the hot-rolled thickness control model through the self-learning coefficient generated by the self-learning module in the hot-rolled thickness control model, and can timely adjust the hot-rolled thickness control model, thereby improving the thickness control accuracy of the hot-rolled thickness control model in the hot-rolling process of the rolled piece.

Description

Technical Field

[0001] The present application relates to the technical field of hot-rolled strip thickness, and in particular to a monitoring method, device, medium and electronic equipment for a hot-rolled thickness control model. Background Art

[0002] At present, in the hot-rolled strip production line, product thickness control accuracy is one of the most core indicators. In the thickness control process, the thickness calculation results in the hot-rolled thickness control model and the model self-learning coefficient are superimposed together to control the thickness. Although the model learning coefficient makes up for the deviation of the model calculation, it also conceals the problems in the model calculation itself. The hot-rolled thickness control model needs to be strictly monitored in order to find problems in time and deal with them in time.

[0003] Based on this, how to monitor whether there are problems with the hot rolling thickness control model and improve the thickness control accuracy of the hot rolling thickness control model during the hot rolling process of the rolled piece are technical problems that need to be solved urgently. Summary of the invention

[0004] The embodiments of the present application provide a monitoring method, device, computer medium and electronic device for a hot rolling thickness control model. The self-learning coefficient generated by the self-learning module in the hot rolling thickness control model is used to monitor whether there is a problem with the hot rolling thickness control model. The hot rolling thickness control model can be adjusted in time, thereby improving the thickness control accuracy of the hot rolling thickness control model during the hot rolling process of the rolled piece.

[0005] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0006] According to one aspect of the present application, a monitoring method for a hot rolling thickness control model is provided, wherein the hot rolling thickness control model comprises a control module and a self-learning module, wherein the control module is used to control the thickness of a workpiece when hot rolling is performed by hot rolling equipment, and the self-learning module is used to correct the thickness of a workpiece when hot rolling is performed by hot rolling equipment, and the method comprises: obtaining a self-learning coefficient calculated and generated by the self-learning module in the hot rolling thickness control model during multiple hot rolling processes to obtain a first monitoring parameter array; determining whether there are J1 consecutive values ​​in the first monitoring parameter array whose absolute values ​​all exceed a set limit value, wherein J1 is a natural number greater than 2; if there are J1 consecutive values ​​in the first monitoring parameter array whose absolute values ​​all exceed a set limit value, outputting alarm information about the hot rolling thickness control model.

[0007] In some embodiments of the present application, based on the aforementioned scheme, the method further includes: if there are not J1 consecutive values ​​in the first monitoring parameter array whose absolute values ​​all exceed the set limit value, then according to the set envelope number, the first monitoring parameter array is subjected to envelope median processing to obtain a reference monitoring parameter array; according to the reference monitoring parameter array, a reference monitoring parameter function is constructed; the reference monitoring parameter function is subjected to discrete differential processing three times to obtain a second monitoring parameter array, a third monitoring parameter array, and a fourth monitoring parameter array respectively; it is determined whether there are J2 consecutive values ​​in the second monitoring parameter array that are all negative or all positive, wherein J2 is a natural number greater than 10; if there are J2 consecutive values ​​in the second monitoring parameter array that are all negative or all positive, an alarm message about the hot rolling thickness control model is output.

[0008] In some embodiments of the present application, based on the aforementioned scheme, the method further includes: if there are not J2 consecutive values ​​in the second monitoring parameter array that are all negative or all positive, then determining whether there are J3 consecutive values ​​in the second monitoring parameter array that are all positive, and determining whether the values ​​in the third monitoring parameter array that have the same array positions as the J3 values ​​are all positive, wherein J3 is a natural number greater than 3 and less than J2; if there are J3 consecutive values ​​in the second monitoring parameter array that are all positive, and the values ​​in the third monitoring parameter array that have the same array positions as the J3 values ​​are all positive, then outputting alarm information about the hot rolling thickness control model.

[0009] In some embodiments of the present application, based on the aforementioned scheme, the method further includes: if there are not J3 consecutive values ​​in the second monitoring parameter array that are all positive, or the values ​​in the third monitoring parameter array that have the same array positions as the J3 values ​​are not all positive, then determining whether there are J3 consecutive values ​​in the second monitoring parameter array that are all negative, and determining whether the values ​​in the third monitoring parameter array that have the same array positions as the J3 values ​​are all negative; if there are J3 consecutive values ​​in the second monitoring parameter array that are all negative, and the values ​​in the third monitoring parameter array that have the same array positions as the J3 values ​​are all negative, then outputting alarm information about the hot rolling thickness control model.

[0010] In some embodiments of the present application, based on the aforementioned scheme, the method further includes: if there are not J3 consecutive values ​​in the second monitoring parameter array that are all positive or negative, or the values ​​in the third monitoring parameter array that have the same array position as the J3 values ​​are not all positive or negative, then judging whether there are J4 consecutive values ​​in the fourth monitoring parameter array that are all negative or all positive, wherein J4 is a natural number greater than J3; if there are J4 consecutive values ​​in the fourth monitoring parameter array that are all negative or all positive, then outputting alarm information about the hot rolling thickness control model.

[0011] According to one aspect of the present application, a monitoring device for a hot rolling thickness control model is provided, wherein the hot rolling thickness control model comprises a control module and a self-learning module, wherein the control module is used to control the thickness of a workpiece when hot rolling is performed by the hot rolling equipment, and the self-learning module is used to correct the thickness of a workpiece when hot rolling is performed by the hot rolling equipment, and the device comprises: an acquisition unit, which is used to acquire a self-learning coefficient calculated and generated by the self-learning module in the hot rolling thickness control model during multiple hot rolling processes to obtain a first monitoring parameter array; a first judgment unit, which is used to judge whether there are J1 consecutive values ​​in the first monitoring parameter array whose absolute values ​​all exceed a set limit value, wherein J1 is a natural number greater than 2; and a first output unit, which is used to output alarm information about the hot rolling thickness control model if there are J1 consecutive values ​​in the first monitoring parameter array whose absolute values ​​all exceed a set limit value.

[0012] In some embodiments of the present application, based on the aforementioned scheme, the device further includes: an envelope median processing unit, which is used to perform envelope median processing on the first monitoring parameter array according to the set number of envelope times to obtain a reference monitoring parameter array if there are no consecutive J1 values ​​in the first monitoring parameter array whose absolute values ​​all exceed the set limit value; a construction unit, which is used to construct a reference monitoring parameter function based on the reference monitoring parameter array; a discrete differential processing unit, which is used to perform discrete differential processing on the reference monitoring parameter function three times to obtain a second monitoring parameter array, a third monitoring parameter array, and a fourth monitoring parameter array respectively; a second judgment unit, which is used to judge whether there are consecutive J2 values ​​in the second monitoring parameter array that are all negative or all positive, wherein J2 is a natural number greater than 10; a second output unit, which is used to output alarm information about the hot rolling thickness control model if there are consecutive J2 values ​​in the second monitoring parameter array that are all negative or all positive.

[0013] In some embodiments of the present application, based on the aforementioned scheme, the device also includes: a third judgment unit, which is used to judge whether there are J3 consecutive values ​​that are all positive in the second monitoring parameter array if there are not J2 consecutive values ​​that are all negative or all positive in the second monitoring parameter array, and to judge whether the values ​​in the third monitoring parameter array that have the same array positions as the J3 values ​​are all positive, wherein J3 is a natural number greater than 3 and less than J2; a third output unit, which is used to output alarm information about the hot rolling thickness control model if there are J3 consecutive values ​​that are all positive in the second monitoring parameter array, and the values ​​in the third monitoring parameter array that have the same array positions as the J3 values ​​are all positive.

[0014] In some embodiments of the present application, based on the aforementioned scheme, the device also includes: a fourth judgment unit, which is used to judge whether there are J3 consecutive values ​​that are all negative values ​​in the second monitoring parameter array, and to judge whether the values ​​in the third monitoring parameter array that have the same array position as the J3 values ​​are all negative values, if there are not J3 consecutive values ​​that are all positive values ​​in the second monitoring parameter array, or the values ​​in the third monitoring parameter array that have the same array position as the J3 values ​​are not all positive values; a fourth output unit, which is used to output alarm information about the hot rolling thickness control model, if there are J3 consecutive values ​​that are all negative values ​​in the second monitoring parameter array, and the values ​​in the third monitoring parameter array that have the same array position as the J3 values ​​are all negative values.

[0015] In some embodiments of the present application, based on the aforementioned scheme, the device also includes: a fifth judgment unit, which is used to judge whether there are J4 consecutive values ​​that are all negative or all positive in the fourth monitoring parameter array if there are not J3 consecutive values ​​that are all positive or negative in the second monitoring parameter array, or the values ​​in the third monitoring parameter array that have the same array position as the J3 values ​​are not all positive or negative, wherein J4 is a natural number greater than J3; a fifth output unit, which is used to output alarm information about the hot rolling thickness control model if there are J4 consecutive values ​​that are all negative or all positive in the fourth monitoring parameter array.

[0016] According to one aspect of the present application, a computer-readable storage medium is provided, in which at least one program code is stored. The at least one program code is loaded and executed by a processor to implement the operations performed by the monitoring method of the hot rolling thickness control model as described.

[0017] According to one aspect of the present application, an electronic device is provided, characterized in that the electronic device includes one or more processors and one or more memories, at least one program code is stored in the one or more memories, and the at least one program code is loaded and executed by the one or more processors to implement the operations performed by the monitoring method of the hot rolling thickness control model as described.

[0018] In the technical solutions provided in some embodiments of the present application, the self-learning coefficient generated by the self-learning module in the hot rolling thickness control model is used to monitor whether there is a problem with the hot rolling thickness control model, and the hot rolling thickness control model can be adjusted in time to improve the thickness control accuracy of the hot rolling thickness control model during the hot rolling process of the rolled piece.

[0019] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0021] Figure 1 A simplified flow chart of a monitoring method for a hot rolling thickness control model in one embodiment of the present application is shown;

[0022] Figure 2 A curve diagram showing the discrete points of a self-learning coefficient function in one embodiment of the present application is shown;

[0023] Figure 3 A schematic diagram of a monitoring device for a hot rolling thickness control model in one embodiment of the present application is shown;

[0024] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0025] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of example embodiments to those skilled in the art.

[0026] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0027] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontrollers.

[0028] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the objects used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those shown or described.

[0030] It should be noted that the monitoring scheme of the hot rolling thickness control model proposed in this application is mainly used in the field of hot rolling strip thickness technology. In the hot rolling strip production line, the product thickness control accuracy is one of the most core indicators. In the thickness control process, the thickness of the rolled piece is controlled based on the pre-designed hot rolling thickness control model. In actual production, the actual thickness of the rolled piece produced may be inconsistent with the thickness of the rolled piece in the ideal control. There are two main reasons for this. One is that there are problems with the hot rolling thickness control model itself, and the other is that there are errors in the production environment parameters of the rolled piece (for example, the actual production environment temperature, humidity and other parameters are inconsistent with the theoretical environment temperature and humidity corresponding to the hot rolling thickness control model). In the existing scheme, the self-learning function of the model can make up for the thickness error of the rolled piece caused by the model's own problems or the error of the production environment parameters of the rolled piece. Although the model self-learning coefficient makes up for the deviation of the model calculation, it also conceals the problems of the model calculation itself. It is necessary to strictly monitor the hot rolling thickness control model in order to find problems in time and deal with them in time.

[0031] Next, the present application will describe in detail the technical solution provided by the present application in conjunction with the accompanying drawings.

[0032] Figure 1 A flow chart of a monitoring method of a hot rolling thickness control model in an embodiment of the present application is shown. The method can be executed in a control device in a hot rolling equipment. The hot rolling thickness control model includes a control module and a self-learning module. The control module is used to control the thickness of the hot rolling equipment when hot rolling the rolled piece. The self-learning module is used to correct the thickness of the hot rolling equipment when hot rolling the rolled piece. The method includes steps 101 to 103:

[0033] In step 101, a self-learning coefficient calculated and generated by a self-learning module in the hot rolling thickness control model during multiple hot rolling processes is obtained to obtain a first monitoring parameter array.

[0034] In one embodiment of the present application, after the i-th calculation of the hot rolling thickness control model, the self-learning coefficient C generated by the self-learning module in the model can be obtained accordingly. i and the corresponding time point t i .

[0035] Furthermore, an "X coordinate-time point t" mapping array can be created: the "X coordinate" array is a natural number array {n}, and the "time point t" array is an array of time points when the hot rolling thickness control model is calculated {t n}, where any number i in the "X coordinate" array corresponds to time point t in the "time point t" array i .

[0036] Furthermore, we can create a "Y coordinate" array, which is the model self-learning coefficient array {C n}, and then the model self-learning coefficient array {C n} as the first monitoring parameter array.

[0037] Furthermore, the model self-learning coefficient array {C n}, "n" is the horizontal axis, {C n} is the corresponding function value, establish the "XY coordinate system", and get the function C i = f(i) corresponding to the discrete points of the curve diagram, as shown in the attached figure Figure 2 shown.

[0038] In step 102, it is determined whether there are J1 consecutive values ​​in the first monitoring parameter array whose absolute values ​​all exceed a set limit value, wherein J1 is a natural number greater than 2.

[0039] In the present application, it should be noted that the value of J1 may be preset.

[0040] In step 103, if there are J1 consecutive absolute values ​​of the first monitoring parameter array that all exceed the set limit value, an alarm message about the hot rolling thickness control model is output.

[0041] In the present application, if there are J1 consecutive absolute values ​​of the first monitoring parameter array that exceed the set limit values, it means that the hot rolling thickness control model may have its own problems. At this time, an alarm information about the hot rolling thickness control model can be output to make timely adjustments to the hot rolling thickness control model.

[0042] In the present application, for the hot rolling thickness control model, the absolute value range of the given limit value may be [0.01, 0.1] mm.

[0043] In the present application, the monitoring scheme of the hot rolling thickness control model may further perform the following steps 104 to 108:

[0044] In step 104, if there are no J1 consecutive values ​​in the first monitoring parameter array whose absolute values ​​all exceed the set limit value, the first monitoring parameter array is subjected to envelope median processing according to the set envelope times to obtain a reference monitoring parameter array.

[0045] In this application, the number of envelopes can be set to N times, and the original data can be processed by envelope median line: Where M=N-1, N is a non-zero natural number.

[0046] Furthermore, we can get a new array {YN n}, and can use the new array {YN n} Refer to the monitoring parameter array.

[0047] In step 105, a reference monitoring parameter function is constructed according to the reference monitoring parameter array.

[0048] In this application, when referring to the monitoring parameter array {YN n} and the above {n}, we can construct a new function YN i =f(i), as the reference monitoring parameter function.

[0049] In step 106, the reference monitoring parameter function is subjected to discrete differentiation processing three times to obtain a second monitoring parameter array, a third monitoring parameter array, and a fourth monitoring parameter array respectively.

[0050] In this application, the reference monitoring parameter function YN i = f(i) is discretely differentiated three times:

[0051] Further, according to The second monitoring parameter array f can be obtained respectively (1) (n), the third monitoring parameter array f (2) (n), and the fourth monitoring parameter array f (3) (n).

[0052] In step 107 , it is determined whether there are J2 consecutive values ​​in the second monitoring parameter array that are all negative or all positive, where J2 is a natural number greater than 10.

[0053] In the present application, it should be noted that the value of J2 may be preset.

[0054] In step 108, if there are J2 consecutive values ​​in the second monitoring parameter array that are all negative or all positive, an alarm message about the hot rolling thickness control model is output.

[0055] In the present application, if there are J2 consecutive values ​​in the second monitoring parameter array that are all negative or all positive, it means that the hot rolling thickness control model may have its own problems. At this time, alarm information about the hot rolling thickness control model can be output to make timely adjustments to the hot rolling thickness control model.

[0056] In the present application, the monitoring scheme of the hot rolling thickness control model may further perform the following steps 109 to 110:

[0057] In step 109, if there are not J2 consecutive values ​​in the second monitoring parameter array that are all negative or all positive, then determine whether there are J3 consecutive values ​​in the second monitoring parameter array that are all positive, and determine whether the values ​​in the third monitoring parameter array that have the same array positions as the J3 values ​​are all positive, where J3 is a natural number greater than 3 and less than J2.

[0058] In the present application, it should be noted that the value of J3 may be preset.

[0059] In step 110, if there are J3 consecutive positive values ​​in the second monitoring parameter array, and the values ​​in the third monitoring parameter array that have the same array positions as the J3 values ​​are all positive, an alarm message about the hot rolling thickness control model is output.

[0060] In the present application, if there are J3 consecutive positive values ​​in the second monitoring parameter array, and the values ​​in the third monitoring parameter array that have the same array positions as the J3 values ​​are all positive, it means that the hot rolling thickness control model may have its own problems. At this time, alarm information about the hot rolling thickness control model can be output to make timely adjustments to the hot rolling thickness control model.

[0061] In the present application, the monitoring scheme of the hot rolling thickness control model may further perform the following steps 111 to 112:

[0062] In step 111, if there are not J3 consecutive values ​​in the second monitoring parameter array that are all positive values, or the values ​​in the third monitoring parameter array that have the same array positions as the J3 values ​​are not all positive values, then it is determined whether there are J3 consecutive values ​​in the second monitoring parameter array that are all negative values, and it is determined whether the values ​​in the third monitoring parameter array that have the same array positions as the J3 values ​​are all negative values.

[0063] In step 112, if there are J3 consecutive values ​​in the second monitoring parameter array that are all negative, and the values ​​in the third monitoring parameter array that have the same array positions as the J3 values ​​are all negative, an alarm message about the hot rolling thickness control model is output.

[0064] In the present application, if there are J3 consecutive values ​​in the second monitoring parameter array that are all negative, and the values ​​in the third monitoring parameter array that have the same array position as the J3 values ​​are all negative, it means that the hot rolling thickness control model may have its own problems. At this time, an alarm information about the hot rolling thickness control model can be output to make timely adjustments to the hot rolling thickness control model.

[0065] In the present application, the monitoring scheme of the hot rolling thickness control model may further perform the following steps 113 to 114:

[0066] In step 113, if there are not J3 consecutive values ​​in the second monitoring parameter array that are all positive or negative, or if the values ​​in the third monitoring parameter array that have the same array positions as the J3 values ​​are not all positive or negative, then it is determined whether there are J4 consecutive values ​​in the fourth monitoring parameter array that are all negative or all positive, where J4 is a natural number greater than J3.

[0067] In the present application, it should be noted that the value of J4 may be preset.

[0068] In step 114, if there are J4 consecutive values ​​in the fourth monitoring parameter array that are all negative or all positive, an alarm message about the hot rolling thickness control model is output.

[0069] In the present application, if there are J4 consecutive values ​​in the fourth monitoring parameter array that are all negative or all positive, it means that the hot rolling thickness control model may have its own problems. At this time, alarm information about the hot rolling thickness control model can be output to make timely adjustments to the hot rolling thickness control model.

[0070] In order to enable those skilled in the art to better understand the present application, the monitoring scheme of the above-mentioned hot rolling thickness control model will be described below in conjunction with a specific embodiment.

[0071] In a specific embodiment, 500 consecutive pieces of strip steel (rolled product) data are randomly selected to obtain the hot rolling thickness control model after i=500 calculations. The corresponding model self-learning coefficient C i , the corresponding time point t i .

[0072] Create an "X coordinate-time point t" mapping array: the "X coordinate" array is a natural number array n = {1,2,...,500}, and the "time point t" array is the time point array {t n}, where any number i in the "X coordinate" array corresponds to time point t in the "time point t" array i .

[0073] Create the "Y coordinate" array, which is the model self-learning coefficient array C n ={0.034,0.154,0.162,…,0.395,0.425}.

[0074] With the above array {n} as the horizontal coordinate and {Cn} as the corresponding function value, an "XY coordinate system" is established to obtain a curve graph consisting of discrete points corresponding to the function Ci=f(i).

[0075] Perform envelope median processing on the original data: Where M=N-1, N is a non-zero natural number (to ensure the processing effect, select N≥5, and this embodiment selects N=9), and then obtain the new array Y9 i ={0.272, 0.321, 0.359, 0.381, …, 0.037, 0.008}, combined with the above horizontal coordinate {n}, we get the new function Y9 i =f(i).

[0076] For function Y9 i = f(i) is discretely differentiated three times:

[0077] Based on the above scheme, we get:

[0078] The second monitoring parameter array: Cn={0.034, 0.154, 0.162, ..., 0.395, 0.425};

[0079] Reference monitoring parameter array: Y9i = {0.272, 0.321, 0.359, 0.381, ..., 0.037, 0.008};

[0080] Second monitoring parameter array: f(1)(n)={0.048, 0.038, 0.022, 0.009, ..., -0.029, -0.007};

[0081] The second monitoring parameter array: f(2)(n)={-0.009,-0.016,-0.012,-0.011,…,0.021,0.006};

[0082] The second monitoring parameter array: f(3)(n={-0.007, 0.004, 0.0002, -0.006, ..., -0.015, -0.005}.

[0083] The core judgment and alarm process is as follows. The counting variables J1, J2, J3, and J4 are given in advance:

[0084] (1) If the values ​​of J1 consecutive values ​​in {Cn} are all greater than the given limit value, an alarm message is issued and the next step of processing is stopped; otherwise, the next step of processing is transferred; wherein J1 is a natural number greater than 2, and the preset value here is 7. The given limit is given according to the calculation result of the actual control model. For the hot rolling thickness control model, the absolute value range of the given limit value is [0.01, 0.75] mm; in the example, {C n}No alarm is issued, proceed to the next step.

[0085] (2) If {f (1) (n)}, if all the values ​​of J2 consecutive values ​​are positive or negative, an alarm message is issued and the next step of processing is stopped; otherwise, the next step of processing is transferred; where J2 is a natural number greater than 5, and the preset value here is 7. In the example, {f (1) (n)} array has negative values ​​{-0.005, -0.018, -0.013, -0.008, -0.008, -0.006, -0.001} continuously in the interval 44≦n≦50. The system issues an alarm and stops the next step of processing.

[0086] The inventors of this application have shown in actual experiments that at the time point corresponding to the above algorithm alarm, there are indeed signs that the model input parameter configuration deviation is too large, causing the model self-learning coefficient to begin to become disordered. After the model input parameter configuration was corrected in a timely manner, the subsequent operation of the model was stable, and no malicious faults caused by this occurred.

[0087] The hot-rolled product thickness control model monitoring method proposed in this application is to solve the problem in the prior art that the final strip thickness calculation result has too large deviation due to the instability of the thickness control model self-learning coefficient. By obtaining the thickness control model learning coefficient and its corresponding time point, the obtained data is used to create an "X coordinate-time point t" mapping array, and the model learning coefficient is used to create a corresponding "Y coordinate" array; based on these data, an "XY coordinate system" is established to obtain an implicit function with the learning coefficient as the dependent variable and the number of time points as the independent variable, and the function curve is processed by the envelope median line to eliminate the influence of the discontinuity points of the discrete points on the subsequent calculation and processing, and then a new function is obtained, and a series of judgment processes are performed on this new function after discrete differentiation. In this way, model learning problems can be discovered in a timely manner, thereby greatly improving the control accuracy of strip thickness.

[0088] The following introduces an apparatus embodiment of the present application, which can be used to execute the monitoring method of the hot rolling thickness control model in the above-mentioned embodiment of the present application.

[0089] Figure 3 A schematic diagram of a monitoring device for a hot rolling thickness control model in an embodiment of the present application is shown. The hot rolling thickness control model includes a control module and a self-learning module. The control module is used to control the thickness of the workpiece when the hot rolling equipment is hot rolling, and the self-learning module is used to correct the thickness of the workpiece when the hot rolling equipment is hot rolling. The control device 300 includes: an acquisition unit 301, a first judgment unit 302 and a first output unit 303.

[0090] Among them, the acquisition unit 301 is used to obtain the self-learning coefficient calculated and generated by the self-learning module in the hot rolling thickness control model during multiple hot rolling processes to obtain the first monitoring parameter array; the first judgment unit 302 is used to judge whether there are J1 consecutive values ​​in the first monitoring parameter array whose absolute values ​​all exceed the set limit value, wherein J1 is a natural number greater than 2; the first output unit 303 is used to output alarm information about the hot rolling thickness control model if there are J1 consecutive values ​​in the first monitoring parameter array whose absolute values ​​all exceed the set limit value.

[0091] In some embodiments of the present application, based on the aforementioned scheme, the device further includes: an envelope median processing unit, which is used to perform envelope median processing on the first monitoring parameter array according to the set number of envelope times to obtain a reference monitoring parameter array if there are no consecutive J1 values ​​in the first monitoring parameter array whose absolute values ​​all exceed the set limit value; a construction unit, which is used to construct a reference monitoring parameter function based on the reference monitoring parameter array; a discrete differential processing unit, which is used to perform discrete differential processing on the reference monitoring parameter function three times to obtain a second monitoring parameter array, a third monitoring parameter array, and a fourth monitoring parameter array respectively; a second judgment unit, which is used to judge whether there are consecutive J2 values ​​in the second monitoring parameter array that are all negative or all positive, wherein J2 is a natural number greater than 10; a second output unit, which is used to output alarm information about the hot rolling thickness control model if there are consecutive J2 values ​​in the second monitoring parameter array that are all negative or all positive.

[0092] In some embodiments of the present application, based on the aforementioned scheme, the device also includes: a third judgment unit, which is used to judge whether there are J3 consecutive values ​​that are all positive in the second monitoring parameter array if there are not J2 consecutive values ​​that are all negative or all positive in the second monitoring parameter array, and to judge whether the values ​​in the third monitoring parameter array that have the same array positions as the J3 values ​​are all positive, wherein J3 is a natural number greater than 3 and less than J2; a third output unit, which is used to output alarm information about the hot rolling thickness control model if there are J3 consecutive values ​​that are all positive in the second monitoring parameter array, and the values ​​in the third monitoring parameter array that have the same array positions as the J3 values ​​are all positive.

[0093] In some embodiments of the present application, based on the aforementioned scheme, the device also includes: a fourth judgment unit, which is used to judge whether there are J3 consecutive values ​​that are all negative values ​​in the second monitoring parameter array, and to judge whether the values ​​in the third monitoring parameter array that have the same array position as the J3 values ​​are all negative values, if there are not J3 consecutive values ​​that are all positive values ​​in the second monitoring parameter array, or the values ​​in the third monitoring parameter array that have the same array position as the J3 values ​​are not all positive values; a fourth output unit, which is used to output alarm information about the hot rolling thickness control model, if there are J3 consecutive values ​​that are all negative values ​​in the second monitoring parameter array, and the values ​​in the third monitoring parameter array that have the same array position as the J3 values ​​are all negative values.

[0094] In some embodiments of the present application, based on the aforementioned scheme, the device also includes: a fifth judgment unit, which is used to judge whether there are J4 consecutive values ​​that are all negative or all positive in the fourth monitoring parameter array if there are not J3 consecutive values ​​that are all positive or negative in the second monitoring parameter array, or the values ​​in the third monitoring parameter array that have the same array position as the J3 values ​​are not all positive or negative, wherein J4 is a natural number greater than J3; a fifth output unit, which is used to output alarm information about the hot rolling thickness control model if there are J4 consecutive values ​​that are all negative or all positive in the fourth monitoring parameter array.

[0095] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.

[0096] It should be noted that Figure 4 The computer system 400 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0097] like Figure 4 As shown, the computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 to the random access memory (RAM) 403, such as the monitoring method of the hot rolling thickness control model described in the above embodiment. In RAM403, various programs and data required for system operation are also stored. CPU 401, ROM 402 and RAM 403 are connected to each other through bus 404. Input / output (I / O) interface 405 is also connected to bus 404.

[0098] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0099] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 409, and / or installed from a removable medium 411. When the computer program is executed by a central processing unit (CPU) 401, various functions defined in the system of the present application are executed.

[0100] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by an instruction execution system, device or device or used in combination with it. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0101] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0102] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0103] As another aspect, the present application also provides a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of an electronic device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the electronic device executes the monitoring method of the hot rolling thickness control model described in the above embodiment.

[0104] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the monitoring method of the hot rolling thickness control model described in the above embodiment.

[0105] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0106] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solution according to the embodiment of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the monitoring method of the hot rolling thickness control model described in the above embodiment.

[0107] Those skilled in the art will readily come up with other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

Claims

1. A monitoring method for a hot rolling thickness control model, characterized in that: The hot rolling thickness control model includes a control module and a self-learning module, wherein the control module is used to control the thickness of the hot rolling equipment when hot rolling the rolled piece, and the self-learning module is used to correct the thickness of the hot rolling equipment when hot rolling the rolled piece. The method includes: Obtaining a self-learning coefficient calculated and generated by a self-learning module in the hot rolling thickness control model during multiple hot rolling processes to obtain a first monitoring parameter array; Determine whether there are J1 consecutive values ​​in the first monitoring parameter array whose absolute values ​​all exceed the set limit value, where J1 is a natural number greater than 2; If the absolute values ​​of J1 consecutive values ​​in the first monitoring parameter array all exceed the set limit value, then output alarm information about the hot rolling thickness control model; If there are no J1 consecutive values ​​in the first monitoring parameter array whose absolute values ​​all exceed the set limit value, then according to the set envelope times, the first monitoring parameter array is subjected to envelope median processing to obtain a reference monitoring parameter array; a reference monitoring parameter function is constructed according to the reference monitoring parameter array; the reference monitoring parameter function is subjected to discrete differential processing three times to obtain a second monitoring parameter array, a third monitoring parameter array, and a fourth monitoring parameter array respectively; it is determined whether there are J2 consecutive values ​​in the second monitoring parameter array that are all negative or all positive, wherein J2 is a natural number greater than 10; if there are J2 consecutive values ​​in the second monitoring parameter array that are all negative or all positive, an alarm message about the hot rolling thickness control model is output; If there are not J2 consecutive values ​​in the second monitoring parameter array that are all negative or all positive, then determine whether there are J3 consecutive values ​​in the second monitoring parameter array that are all positive, and determine whether the values ​​in the third monitoring parameter array that have the same array position as the J3 values ​​are all positive, wherein J3 is a natural number greater than 3 and less than J2; if there are J3 consecutive values ​​in the second monitoring parameter array that are all positive, and the values ​​in the third monitoring parameter array that have the same array position as the J3 values ​​are all positive, then output alarm information about the hot rolling thickness control model; If there are not J3 consecutive values ​​in the second monitoring parameter array that are all positive values, or the values ​​in the third monitoring parameter array that have the same array position as the J3 values ​​are not all positive values, then determine whether there are J3 consecutive values ​​in the second monitoring parameter array that are all negative values, and determine whether the values ​​in the third monitoring parameter array that have the same array position as the J3 values ​​are all negative values; if there are J3 consecutive values ​​in the second monitoring parameter array that are all negative values, and the values ​​in the third monitoring parameter array that have the same array position as the J3 values ​​are all negative values, then output alarm information about the hot rolling thickness control model; If there are not J3 consecutive values ​​in the second monitoring parameter array that are all positive or negative, or the values ​​in the third monitoring parameter array that have the same array position as the J3 values ​​are not all positive or negative, then determine whether there are J4 consecutive values ​​in the fourth monitoring parameter array that are all negative or all positive, wherein J4 is a natural number greater than J3; if there are J4 consecutive values ​​in the fourth monitoring parameter array that are all negative or all positive, then output alarm information about the hot rolling thickness control model.

2. A monitoring device for a hot rolling thickness control model, characterized in that: The hot rolling thickness control model includes a control module and a self-learning module, wherein the control module is used to control the thickness of the hot rolling equipment when hot rolling the rolled piece, and the self-learning module is used to correct the thickness of the hot rolling equipment when hot rolling the rolled piece. The device includes: An acquisition unit is used to acquire a self-learning coefficient calculated and generated by a self-learning module in the hot rolling thickness control model during multiple hot rolling processes to obtain a first monitoring parameter array; A first judgment unit is used to judge whether there are J1 consecutive values ​​in the first monitoring parameter array whose absolute values ​​all exceed a set limit value, wherein J1 is a natural number greater than 2; A first output unit is used to output alarm information about the hot rolling thickness control model if the absolute values ​​of J1 consecutive values ​​in the first monitoring parameter array all exceed the set limit value; The envelope median processing unit is used to perform envelope median processing on the first monitoring parameter array according to the set envelope times to obtain a reference monitoring parameter array if there are no consecutive J1 values ​​in the first monitoring parameter array whose absolute values ​​all exceed the set limit value; the construction unit is used to construct a reference monitoring parameter function according to the reference monitoring parameter array; the discrete differential processing unit is used to perform discrete differential processing on the reference monitoring parameter function three times to obtain a second monitoring parameter array, a third monitoring parameter array, and a fourth monitoring parameter array respectively; the second judgment unit is used to judge whether there are consecutive J2 values ​​in the second monitoring parameter array that are all negative or all positive, wherein J2 is a natural number greater than 10; the second output unit is used to output alarm information about the hot rolling thickness control model if there are consecutive J2 values ​​in the second monitoring parameter array that are all negative or all positive; a third judgment unit, used to judge whether there are J3 consecutive values ​​in the second monitoring parameter array that are all positive values ​​if there are not J2 consecutive values ​​in the second monitoring parameter array that are all negative values ​​or all positive values, and to judge whether the values ​​in the third monitoring parameter array that have the same array position as the J3 values ​​are all positive values, wherein J3 is a natural number greater than 3 and less than J2; a third output unit, used to output alarm information about the hot rolling thickness control model if there are J3 consecutive values ​​in the second monitoring parameter array that are all positive values, and the values ​​in the third monitoring parameter array that have the same array position as the J3 values ​​are all positive values; a fourth judgment unit, used to judge whether there are J3 consecutive values ​​in the second monitoring parameter array that are all negative values, and to judge whether the values ​​in the third monitoring parameter array that have the same array position as the J3 values ​​are all negative values, if there are not J3 consecutive values ​​in the second monitoring parameter array that are all positive values, or the values ​​in the third monitoring parameter array that have the same array position as the J3 values ​​are not all positive values; a fourth output unit, used to output alarm information about the hot rolling thickness control model, if there are J3 consecutive values ​​in the second monitoring parameter array that are all negative values, and the values ​​in the third monitoring parameter array that have the same array position as the J3 values ​​are all negative values; The fifth judgment unit is used to judge whether there are J4 consecutive values ​​that are all negative or all positive in the fourth monitoring parameter array if there are not J3 consecutive values ​​that are all positive or negative in the second monitoring parameter array, or the values ​​in the third monitoring parameter array that have the same array position as the J3 values ​​are not all positive or negative, wherein J4 is a natural number greater than J3; the fifth output unit is used to output alarm information about the hot rolling thickness control model if there are J4 consecutive values ​​that are all negative or all positive in the fourth monitoring parameter array.

Images