A method and system for controlling the thickness of a zinc layer in a continuous hot-dip galvanizing process based on self-learning

Abstract

The application provides a zinc layer thickness control method and system based on self-learning continuous hot galvanizing, and relates to the technical field of hot galvanizing strip steel production. The method comprises the following steps: obtaining production parameters; calculating layer numbers according to the production parameters; reading result data of short-term self-learning, long-term self-learning and inherited self-learning corresponding to the layer numbers of the current strip steel; when the interval distance between the head of the strip steel to be galvanized and the air knife is a first preset distance, and the layer numbers are different, performing long-term self-learning and inherited self-learning calculation, and clearing the result data of short-term self-learning corresponding to the layer numbers of the current strip steel; when the head of the strip steel to be galvanized passes through a zinc layer thickness gauge and is at a second preset distance from the zinc layer thickness gauge, performing short-term self-learning calculation of the strip steel to be galvanized; summing the calculated learning results to calculate a self-learning sum value representing a predicted zinc layer thickness; and controlling the zinc layer thickness in the hot galvanizing process according to the predicted zinc layer thickness.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hot-dip galvanized strip production, in particular to a zinc layer thickness control method and system for continuous hot-dip galvanizing based on self-learning. BACKGROUND

[0002] With the wide application of galvanized sheets in various industries, especially in the automobile manufacturing industry, its corrosion resistance has become the key to ensuring long-term use and enhancing structural durability. The quality of galvanized sheets is mainly evaluated by the thickness and uniformity of the zinc layer, and the thickness of the zinc layer directly affects the corrosion resistance and durability of the steel sheet. Therefore, it is crucial to strictly control the thickness of the zinc layer.

[0003] However, the existing zinc layer thickness control method mainly relies on traditional preset models and fixed algorithms, which cannot adapt to changes in production environment and working conditions in real time, resulting in insufficient control accuracy of zinc layer thickness.

[0004] In addition, the existing zinc layer thickness prediction model fails to comprehensively consider all factors that may affect the thickness of the zinc layer during the production process, and the precision of the model prediction value is limited due to changes in system characteristics and environment, resulting in a certain deviation from the actual value, making it impossible to timely and effectively control the thickness of the zinc layer, thereby affecting product quality. SUMMARY

[0005] To solve the technical problems in the prior art that mainly rely on traditional preset models and fixed algorithms, which cannot adapt to changes in production environment and working conditions in real time, resulting in insufficient control accuracy of zinc layer thickness, and fail to comprehensively consider all factors that may affect the thickness of the zinc layer during the production process, and the precision of the model prediction value is limited due to changes in system characteristics and environment, resulting in a certain deviation from the actual value, making it impossible to timely and effectively control the thickness of the zinc layer, thereby affecting product quality, the present application provides a zinc layer thickness control method and system for continuous hot-dip galvanizing based on self-learning.

[0006] The technical scheme provided by the embodiments of the present application is as follows:

[0007] First aspect:

[0008] The zinc layer thickness control method for continuous hot-dip galvanizing based on self-learning provided by the embodiments of the present application comprises:

[0009] S1: acquiring a plurality of production parameters in the hot-dip galvanizing process;

[0010] S2: calculating the layer number of the zinc-plated strip steel according to each of the production parameters;

[0011] S3: reading the result data of the three learning processes of short-term self-learning, long-term self-learning and inherited self-learning corresponding to the layer number of the current strip steel from the database;

[0012] S4: When the head of the to-be-galvanized strip adjacent to the current strip is spaced from the air knife by a first preset distance, and the layer number of the current strip is different from that of the to-be-galvanized strip, long-term self-learning of the current strip and inheritance self-learning of the to-be-galvanized strip are respectively calculated according to the result data of the long-term self-learning and the inheritance self-learning corresponding to the layer number of the current strip, and the result data of short-term self-learning corresponding to the layer number of the current strip is cleared;

[0013] S5: When the head of the to-be-galvanized strip passes through the zinc layer thickness gauge and is spaced from the zinc layer thickness gauge by a second preset distance, short-term self-learning of the to-be-galvanized strip is calculated;

[0014] S6: The learning results of the long-term self-learning, the inheritance self-learning and the short-term self-learning are all stored in the database;

[0015] S7: The learning results are summed up to calculate a self-learning sum value representing the predicted zinc layer thickness of the to-be-galvanized strip;

[0016] S8: The zinc layer thickness control in the hot galvanizing process is performed according to the predicted zinc layer thickness.

[0017] The second aspect:

[0018] The zinc layer thickness control system for continuous hot galvanizing based on self-learning provided by the embodiment of the present application comprises:

[0019] a processor;

[0020] a memory, wherein the memory stores computer readable instructions, and the computer readable instructions are executed by the processor to implement the zinc layer thickness control method for continuous hot galvanizing based on self-learning as described in the first aspect.

[0021] The third aspect:

[0022] The computer readable storage medium provided by the embodiment of the present application stores a computer program, and the program is executed by the processor to implement the zinc layer thickness control method for continuous hot galvanizing based on self-learning as described in the first aspect.

[0023] The technical scheme provided by the embodiment of the present application has at least the following beneficial effects:

[0024] In the embodiment of the present application, by acquiring a plurality of production parameters in the hot galvanizing process, and calculating the layer number of the to-be-galvanized strip steel according to each production parameter, the traditional preset model and fixed algorithm are no longer relied on, the changes of the production environment and working conditions can be adapted in real time, the zinc layer thickness control precision is improved, the long-term self-learning of the current strip steel, the inherited self-learning calculation of the to-be-galvanized strip steel, and the short-term self-learning calculation of the to-be-galvanized strip steel are performed, the learning results of the long-term self-learning, the inherited self-learning and the short-term self-learning are summed up, the self-learning sum value representing the predicted zinc layer thickness of the to-be-galvanized strip steel is calculated, the zinc layer thickness control in the hot galvanizing process is performed according to the predicted zinc layer thickness, all factors that can affect the zinc layer thickness in the production process can be comprehensively considered, the precision of the model prediction value is not limited due to the changes of the system characteristics and the environment, the deviation from the actual value is reduced, the zinc layer thickness can be timely and effectively controlled, and the product quality is improved. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0026] Figure 1 A flowchart of a zinc layer thickness control method for continuous hot galvanizing based on self-learning provided by the embodiment of the present application;

[0027] Figure 2 A structure diagram of a zinc layer thickness control system for continuous hot galvanizing based on self-learning provided by the embodiment of the present application. DETAILED DESCRIPTION

[0028] The technical solutions in the present application will be described below with reference to the drawings.

[0029] In the embodiments of the present application, the words such as "example", "for example" and the like are used to represent as an example, illustration or description. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word "example" is intended to present the concept in a specific manner. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0030] In the embodiments of the present application, the terms "image" and "picture" can be used interchangeably, and it should be noted that the meanings expressed are consistent when the distinction is not emphasized.

[0031] In the embodiments of the present application, the subscript such as W1 may be written in the form of non-subscript such as W1, and the meanings expressed are consistent when the distinction is not emphasized.

[0032] To make the technical problems, technical solutions and advantages of the present application clearer, specific embodiments will be described in detail below with reference to the accompanying drawings.

[0033] Reference is made to the accompanying drawings and specific embodiments described in the specification Figure 1 , a flowchart of a self-learning-based continuous hot-dip galvanizing zinc layer thickness control method provided by an embodiment of the present application is shown.

[0034] The embodiment of the present application provides a self-learning-based continuous hot-dip galvanizing zinc layer thickness control method, which can be implemented by a self-learning-based continuous hot-dip galvanizing zinc layer thickness control device, which can be a terminal or a server. The processing flow of the self-learning-based continuous hot-dip galvanizing zinc layer thickness control method can include the following steps:

[0035] S1: Obtain a plurality of production parameters in the hot-dip galvanizing process.

[0036] Optionally, the production parameters include air knife gas source, coating specification, strip width, strip thickness, and strip steel grade.

[0037] In the present application, by obtaining a plurality of production parameters in the hot-dip galvanizing process, the influence of various factors on the zinc layer thickness can be considered comprehensively, thereby improving the precision and adaptability of the self-learning system. Calculating the layer number according to these parameters helps to achieve accurate zinc layer control in complex and variable production environments and ensures the quality and stability of the final product.

[0038] S2: Calculate the layer number of the zinc-plated strip steel according to each production parameter.

[0039] It should be noted that the air knife gas source, the coating specification, the strip width, the strip thickness, and the strip steel grade each include a plurality of gears.

[0040] Optionally, the air knife gas source includes 2 gears: air gear (0) and nitrogen gear (1). G G Optionally, the air knife gas source includes 2 gears: air gear (0) and nitrogen gear (1). ​

[0041] Optionally, the coating specifications include 6 levels: coating specifications ≤80 ( W Values ​​are 0), 80 < coating specification ≤ 100 ( W Values ​​are 1), 100 < coating specification ≤ 120 ( W Values ​​are 2), 120 < coating specification ≤ 140 ( W Values ​​are 3), 140 < coating specification ≤ 160 ( W The value is 4) and 160 < coating specification level ( W The value is 5. (Unit: g / m³) 2 ).

[0042] Optionally, the strip width includes 6 increments: strip width ≤ 800 ( B The value is 0), and the range is 800 < strip width ≤ 1000. B Values ​​are 1), 1000 < strip width ≤ 1200 ( B The value is 2), and the range is 1200 < strip width ≤ 1400. B Values ​​are 3), 1400 < strip width ≤ 1600 ( B The value is 4) and the 1600 < strip width range ( B The value is 5. (Unit: mm).

[0043] Optionally, the strip thickness includes 7 levels: strip thickness ≤ 0.4 ( H Values ​​are 0), 0.4 < strip thickness ≤ 0.8 ( H Values ​​are 1), 0.8 < strip thickness ≤ 1.2 ( ) H Values ​​are 2), 1.2 < strip thickness ≤ 1.6 ( ) H The value is 3), and the range is 1.6 < strip thickness ≤ 2.0. H Values ​​are 4), 2.0 < strip thickness ≤ 2.4 ( ) H The value is 5) and 2.4 < strip thickness ( H The value is 6. (Unit: mm).

[0044] Optionally, the strip steel grades include 15 levels: DC04-1 level ( S Value is 0), DC04H-1 gear ( S Value is 1), DC05G-1 gear ( S Value is 2), DC51D-1 gear ( S Value is 3), DC51DQ-1 gear ( S Value is 4), DC51DZ1-1 gear ( SValue 5), DC51DZ3-1 gear (Value 6), DC52D-1 gear (Value 7), DC52DL-1 gear (Value 8), F590QX-1 gear (Value 9), H180BH1-1 gear (Value 10), HC180BH-1 gear (Value 11), HC180YD-1 gear (Value 12) and standby gear (Value 13 or 14). S Value 5), DC51DZ3-1 gear (Value 6), DC52D-1 gear (Value 7), DC52DL-1 gear (Value 8), F590QX-1 gear (Value 9), H180BH1-1 gear (Value 10), HC180BH-1 gear (Value 11), HC180YD-1 gear (Value 12) and standby gear (Value 13 or 14). S Value 5), DC51DZ3-1 gear (Value 6), DC52D-1 gear (Value 7), DC52DL-1 gear (Value 8), F590QX-1 gear (Value 9), H180BH1-1 gear (Value 10), HC180BH-1 gear (Value 11), HC180YD-1 gear (Value 12) and standby gear (Value 13 or 14). S Value 5), DC51DZ3-1 gear (Value 6), DC52D-1 gear (Value 7), DC52DL-1 gear (Value 8), F590QX-1 gear (Value 9), H180BH1-1 gear (Value 10), HC180BH-1 gear (Value 11), HC180YD-1 gear (Value 12) and standby gear (Value 13 or 14). S Value 5), DC51DZ3-1 gear (Value 6), DC52D-1 gear (Value 7), DC52DL-1 gear (Value 8), F590QX-1 gear (Value 9), H180BH1-1 gear (Value 10), HC180BH-1 gear (Value 11), HC180YD-1 gear (Value 12) and standby gear (Value 13 or 14). S Value 5), DC51DZ3-1 gear (Value 6), DC52D-1 gear (Value 7), DC52DL-1 gear (Value 8), F590QX-1 gear (Value 9), H180BH1-1 gear (Value 10), HC180BH-1 gear (Value 11), HC180YD-1 gear (Value 12) and standby gear (Value 13 or 14). S Value 5), DC51DZ3-1 gear (Value 6), DC52D-1 gear (Value 7), DC52DL-1 gear (Value 8), F590QX-1 gear (Value 9), H180BH1-1 gear (Value 10), HC180BH-1 gear (Value 11), HC180YD-1 gear (Value 12) and standby gear (Value 13 or 14). S Value 5), DC51DZ3-1 gear (Value 6), DC52D-1 gear (Value 7), DC52DL-1 gear (Value 8), F590QX-1 gear (Value 9), H180BH1-1 gear (Value 10), HC180BH-1 gear (Value 11), HC180YD-1 gear (Value 12) and standby gear (Value 13 or 14). S Value 5), DC51DZ3-1 gear (Value 6), DC52D-1 gear (Value 7), DC52DL-1 gear (Value 8), F590QX-1 gear (Value 9), H180BH1-1 gear (Value 10), HC180BH-1 gear (Value 11), HC180YD-1 gear (Value 12) and standby gear (Value 13 or 14).

[0045] Specifically, according to the air knife gas source gear corresponding to the to-be-zinc-plated strip steel, the plating layer specification gear, the strip steel width gear, the strip steel thickness gear and the strip steel grade gear, the layer number of the to-be-zinc-plated strip steel is calculated.

[0046] In the present application, by dividing the production parameters into multiple gears for calculation, the influence of different production conditions on the zinc layer thickness can be accurately reflected. This helps to achieve higher control accuracy and adaptability, can handle complex production environments and variable strip steel specifications, and improves the stability and product quality of the zinc plating process.

[0047] In one possible implementation, the calculation formula of the layer number is specifically:

[0048]

[0049] Wherein, N i represents the layer number of the first i coiled strip steel, G i represents the gear of the air knife gas source corresponding to the first i coiled strip steel, W num represents the total number of gears of the plating layer specification, B num represents the total number of gears of the strip steel width, H num represents the total number of gears of the strip steel thickness, S num represents the total number of gears of the strip steel grade, W i represents the gear of the plating layer specification corresponding to the first i coiled strip steel, B i represents the gear of the strip steel width corresponding to the first i coiled strip steel, S i represents the gear of the strip steel thickness corresponding to the first iThe grade of the steel strip corresponds to the grade of the steel strip.

[0050] In the present application, by converting each production parameter into a grade and using a layer number calculation formula, the production conditions of each steel strip can be accurately mapped to a unique layer number. This method can systematically classify different specifications of steel strips, help optimize the self-learning process, improve the accuracy of zinc layer thickness control and the adaptability of the production line, and ensure stable product quality.

[0051] S3: Read the result data of the short-term self-learning, long-term self-learning and inherited self-learning of the current steel strip from the database.

[0052] In the present application, by reading the result data of different self-learning processes from the database, the historical learning achievements can be obtained in real time, ensuring that the current production process can be adjusted and optimized based on previous experience, thereby improving the accuracy of zinc layer thickness prediction and control accuracy.

[0053] S4: When the interval distance between the head of the steel strip to be galvanized adjacent to the current steel strip and the air knife is the first preset distance, and the layer numbers of the current steel strip and the steel strip to be galvanized are different, the long-term self-learning of the current steel strip and the inherited self-learning of the steel strip to be galvanized are calculated according to the result data of the long-term self-learning and the inherited self-learning corresponding to the layer number of the current steel strip, respectively, and the result data of the short-term self-learning corresponding to the layer number of the current steel strip is cleared.

[0054] It should be noted that the result data of the short-term self-learning corresponding to the layer number of the current steel strip is cleared when the calculation of the long-term self-learning of the current steel strip is completed.

[0055] It should be noted that the size of the first preset distance can be set by the person skilled in the art according to actual needs, which is not limited in the present application.

[0056] Optionally, the first preset distance is in the range of 50-150 meters.

[0057] In the present application, by calculating the long-term self-learning and inherited self-learning according to the layer number of the steel strip, and clearing the short-term self-learning data after completing the long-term self-learning, the self-learning results of different layer number steel strips can be avoided from interfering with each other, ensuring that the learning process of each coil of steel strip is independent and accurate. At the same time, setting a flexible first preset distance helps to adapt to the actual situation of different production lines, further improving the accuracy of zinc layer thickness control and the adaptability of the system.

[0058] In one possible implementation, the calculation of the long-term self-learning of the current steel strip and the inherited self-learning of the steel strip to be galvanized according to the result data of the long-term self-learning and the inherited self-learning corresponding to the layer number of the current steel strip in S4 specifically includes sub-steps S401 and S402:

[0059] S401: Perform long-term self-learning calculation of the current strip steel according to the result data of long-term self-learning corresponding to the layer number of the current strip steel.

[0060] Optionally, S401 is specifically:

[0061] Perform long-term self-learning calculation of the current strip steel according to the following formula:

[0062]

[0063] Wherein, β l ( j +1) represents the long-term self-learning amount of the zinc layer thickness of the galvanizing unit No. j +1) with the same layer number as the current strip steel, β l ( j ) represents the long-term self-learning amount of the zinc layer thickness of the galvanizing unit No. j +1) with the same layer number as the current strip steel, α 1 represents a long-term self-learning smoothing coefficient, β s ( j +1) represents the short-term self-learning amount of the zinc layer thickness of the galvanizing unit No. j +1) with the same layer number as the current strip steel.

[0064] It should be noted that the size of the long-term self-learning smoothing coefficient can be set according to actual needs by those skilled in the art, which is not limited in the present application.

[0065] It should be noted that the galvanizing unit refers to a group of continuously produced strip steel batches with the same layer number.

[0066] For example, assuming that the production sequence of the strip steel is A—A—A—B—B—A—A—C—C (A, B, and C represent different layer numbers). Among them, the first continuously produced A—A—A forms the first galvanizing unit, and the subsequent B—B, A—A, and C—C form the second, third, and fourth galvanizing units, respectively. Although the first unit (A—A—A) and the third unit (A—A) are both A-layer strip steel, they are considered as independent galvanizing units due to the interruption of the B-layer strip steel. If the first galvanizing unit (A—A—A) is identified as j , then the subsequent continuously produced A-layer strip steel (A—A) will be updated to j +1, and only when the layer number of the next coil is switched (such as A→B or A→C), the long-term self-learning calculation of the strip steel will be triggered.

[0067] Optionally, the long-term self-learning smoothing coefficient ranges from 0.3 to 0.7.

[0068] In the present application, the long-term self-learning smoothing coefficient is used to calculate the long-term self-learning amount of the strip steel, which can dynamically adjust the prediction of the zinc layer thickness according to historical data, and ensure that the control system more smoothly adapts to production changes. By setting the value range of the smoothing coefficient, it can be flexibly adjusted according to the actual production demand, thereby improving the robustness and self-adaptive ability of the system, reducing fluctuations, and improving the control precision of the zinc layer thickness.

[0069] S402: According to the result data of the inherited self-learning corresponding to the layer number of the current strip steel, the calculation of the inherited self-learning of the to-be-zinc-plated strip steel is performed.

[0070] Optionally, S402 specifically comprises:

[0071] According to the following formula, the calculation of the inherited self-learning of the to-be-zinc-plated strip steel is performed:

[0072]

[0073] wherein, β r ( i +1) represents the zinc layer thickness of the first i +1 coil of strip steel in the same zinc plating unit as the to-be-zinc-plated strip steel, α 2 represents the inherited self-learning smoothing coefficient, and Δ T h ( i ) represents the deviation between the predicted value and the measured value of the zinc layer thickness of the first i coil of strip steel in the same zinc plating unit as the to-be-zinc-plated strip steel.

[0074] It should be noted that the size of the inherited self-learning smoothing coefficient can be set according to actual needs by those skilled in the art, which is not limited in the present application.

[0075] Optionally, the long-term self-learning smoothing coefficient ranges from 0.1 to 0.3.

[0076] In the present application, the inherited self-learning smoothing coefficient is used to calculate the inherited self-learning amount of the strip steel, which can effectively adjust the deviation between the predicted value and the actual measured value of the zinc layer thickness. This method can smooth the thickness difference between different batches of strip steel, improve the adaptability of the control system to new strip steel, and reduce the instability in the production process. By setting the range of the smoothing coefficient, it can be flexibly adjusted according to the actual production conditions, thereby optimizing the control precision and stability.

[0077] In a possible implementation, when the head of the to-be-zinc-plated strip steel adjacent to the current strip steel is spaced apart from the air knife by a first preset distance, and the layer number of the current strip steel is the same as that of the to-be-zinc-plated strip steel, the calculation of long-term self-learning of the current strip steel (the long-term self-learning value is 0) is not required, and only the calculation of inherited self-learning and short-term self-learning of the to-be-zinc-plated strip steel is required.

[0078] S5: When the head of the to-be-zinc-plated strip steel passes through the zinc layer thickness gauge and is spaced apart from the zinc layer thickness gauge by a second preset distance, the calculation of short-term self-learning of the to-be-zinc-plated strip steel is performed.

[0079] It should be noted that a person skilled in the art can set the size of the second preset distance according to actual needs, which is not limited in the present application.

[0080] Optionally, the second preset distance is 30-100 meters.

[0081] In the present application, by setting the second preset distance, the calculation time of short-term self-learning can be flexibly adjusted according to the actual needs of the production line. This helps to perform short-term self-learning in time after the strip steel passes through the zinc layer thickness gauge, and improves the real-time performance and accuracy of zinc layer thickness control.

[0082] Optionally, S5 specifically includes:

[0083] According to the following formula, the calculation of short-term self-learning of the to-be-zinc-plated strip steel is performed:

[0084]

[0085] wherein, β s ( i +1) represents the short-term self-learning amount of the zinc layer thickness of the first i +1th strip steel in the same zinc plating unit as the to-be-zinc-plated strip steel, β s ( i ) represents the short-term self-learning amount of the zinc layer thickness of the first i th strip steel in the same zinc plating unit as the to-be-zinc-plated strip steel, α 3 represents a short-term self-learning smoothing coefficient, and Δ T h ( i +1) represents the deviation between the predicted value and the measured value of the zinc layer thickness of the first i +1th strip steel in the same zinc plating unit as the to-be-zinc-plated strip steel.

[0086] It should be noted that a person skilled in the art can set the size of the short-term self-learning smoothing coefficient according to actual needs, which is not limited in the present application.

[0087] Optionally, the long-term self-learning smoothing coefficient can range from 0.3 to 0.7.

[0088] In this invention, by using a short-term self-learning smoothing coefficient to adjust the self-learning amount of the zinc layer thickness, the smoothness of the learning process can be flexibly controlled according to actual production conditions. Setting a range for the smoothing coefficient can help optimize the system's response speed to thickness prediction deviations, avoid over-adjustment, thereby improving prediction accuracy and reducing fluctuations in the production process.

[0089] S6: Store the calculated learning results of long-term self-learning, inherited self-learning, and short-term self-learning in the database.

[0090] In this invention, the results of long-term self-learning, inherited self-learning, and short-term self-learning are stored in a database, ensuring the persistence of historical learning data and facilitating subsequent querying and use. This helps to update the system in real time, improve prediction accuracy, and optimize future zinc layer thickness control.

[0091] S7: Sum the learning results to calculate the self-learning sum representing the predicted zinc layer thickness of the strip steel to be galvanized.

[0092] In this invention, by summing the results of long-term self-learning, inherited self-learning, and short-term self-learning, the influence of different learning processes can be comprehensively considered, thereby more accurately predicting the zinc layer thickness of the strip steel to be galvanized. This improves the precision and stability of control, ensuring the quality of the galvanizing process.

[0093] Alternatively, S7 specifically refers to:

[0094] Calculate the self-learning sum value representing the predicted zinc layer thickness of the strip steel to be galvanized using the following formula:

[0095]

[0096] in, β T This represents the self-learning sum value indicating the predicted zinc layer thickness of the steel strip to be galvanized. β l This indicates the amount of time spent on self-learning. β s Indicates short-term self-learning volume. β r This indicates that it is inherited from the learning quantity.

[0097] In this invention, by adding the results of long-term self-learning, short-term self-learning, and inherited self-learning, the influence of different self-learning processes can be comprehensively considered to form a more comprehensive zinc layer thickness prediction. This method not only improves the accuracy of the prediction but also effectively adapts to changes under different production conditions, thereby optimizing the control of the galvanizing process and ensuring the quality stability of the final product.

[0098] S8: Controlling the zinc layer thickness during the hot galvanizing process according to the predicted zinc layer thickness.

[0099] In the present application, the control is performed according to the predicted zinc layer thickness, which can adjust the air knife parameters in real time during the hot galvanizing process, and ensure that the zinc layer thickness always remains within the target range. This improves the precision and stability of the production process, effectively ensuring product quality.

[0100] Referring to the accompanying drawings Figure 2 , a structure diagram of a zinc layer thickness control system for continuous hot galvanizing based on self-learning provided by the present application is shown.

[0101] The present application also provides a zinc layer thickness control system 20 for continuous hot galvanizing based on self-learning, which is applied to the above-mentioned zinc layer thickness control method for continuous hot galvanizing based on self-learning, and comprises:

[0102] a processor 201;

[0103] a memory 202, wherein the memory 202 stores computer readable instructions, and the computer readable instructions are executed by the processor 201 to realize the zinc layer thickness control method for continuous hot galvanizing based on self-learning as described in the method embodiment.

[0104] The zinc layer thickness control system 20 for continuous hot galvanizing based on self-learning provided by the present application can execute the above-mentioned zinc layer thickness control method for continuous hot galvanizing based on self-learning, and achieve the same or similar technical effects. To avoid repetition, the present application will not be described again.

[0105] The technical solutions provided by the embodiments of the present application have at least the following beneficial effects:

[0106] In the embodiments of the present application, by obtaining a plurality of production parameters in the hot galvanizing process, and calculating the layer number of the zinc-plated strip steel according to each production parameter, the present application no longer relies on traditional preset models and fixed algorithms, and can adapt to changes in the production environment and working conditions in real time, thereby improving the zinc layer thickness control precision. Through long-term self-learning of the current strip steel, inherited self-learning of the zinc-plated strip steel, and short-term self-learning of the zinc-plated strip steel, and summing the learning results of the long-term self-learning, inherited self-learning, and short-term self-learning, the self-learning sum value representing the predicted zinc layer thickness of the zinc-plated strip steel is calculated. According to the predicted zinc layer thickness, the zinc layer thickness control during the hot galvanizing process is performed, which can comprehensively consider all factors that may affect the zinc layer thickness in the production process, and will not be limited by the precision of the model predicted value due to changes in system characteristics and environment, thereby reducing the deviation from the actual value, so that the zinc layer thickness can be timely and effectively controlled, and the product quality is improved.

[0107] It should be appreciated that a processor in the embodiments of the present application can be a central processing unit (CPU). The processor can also be other general purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic, discrete hardware components, etc. The general purpose processor can be a microprocessor or the processor can be any conventional processor.

[0108] It should also be appreciated that the memory in the embodiments of the present application can be a volatile memory or a nonvolatile memory, or can include both volatile and nonvolatile memory. The nonvolatile memory can be a read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), electrically EPROM (EEPROM), or flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example, and not limitation, many forms of random access memory (RAM) are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0109] The above-described embodiments can be implemented in whole or in part by software, hardware (such as a circuit), firmware, or any combination thereof. When implemented in software, the above-described embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center through a wired (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0110] It should be understood that the term "and / or" herein merely describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent the following three cases: A exists alone, A and B exist together, and B exists alone, where A and B can be singular or plural. In addition, the character " / " herein generally represents that the associated objects before and after it are in an "or" relationship, but it can also represent an "and / or" relationship, which can be understood according to the context before and after it.

[0111] In the present application, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or the like means any combination of the items, including any combination of single or multiple items. For example, at least one of a, b, or c can represent a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0112] It should be understood that in various embodiments of the present application, the size of the sequence number of the above-described processes does not mean the order of execution, and the execution order of the processes should be determined according to their functions and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0113] Those skilled in the art can clearly understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0114] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the devices, apparatuses and units described above can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0115] In several embodiments provided by the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0116] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0117] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit.

[0118] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or parts of the present application that essentially contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0119] The embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to realize the self-learning based continuous hot galvanizing zinc layer thickness control method as described in the method embodiment.

[0120] The computer readable storage medium provided by the present application can realize the steps and effects of the self-learning based continuous hot galvanizing zinc layer thickness control method of the above-mentioned method embodiment. To avoid repetition, the present application will not be described again.

[0121] The technical solutions provided by the embodiments of the present application have at least the following beneficial effects:

[0122] In the embodiment of the present application, by acquiring a plurality of production parameters in the hot galvanizing process, and calculating the layer number of the to-be-galvanized strip steel according to each production parameter, the traditional preset model and fixed algorithm are no longer relied on, the changes of the production environment and working conditions can be adapted in real time, the zinc layer thickness control precision is improved, the long-term self-learning of the current strip steel, the inherited self-learning calculation of the to-be-galvanized strip steel, and the short-term self-learning calculation of the to-be-galvanized strip steel are performed, the learning results of the long-term self-learning, the inherited self-learning and the short-term self-learning are summed up, the self-learning sum value representing the predicted zinc layer thickness of the to-be-galvanized strip steel is calculated, the zinc layer thickness control in the hot galvanizing process is performed according to the predicted zinc layer thickness, all factors that can affect the zinc layer thickness in the production process can be considered comprehensively, the precision of the model prediction value is not limited due to the changes of the system characteristics and the environment, the deviation from the actual value is reduced, the zinc layer thickness can be controlled in time and effectively, and the product quality is improved.

[0123] The above merely describes the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0124] The following points need to be explained:

[0125] (1) The drawings of the embodiments of the present application only involve the structures involved in the embodiments of the present application, and other structures can be referred to the general design.

[0126] (2) In order to be clear, the thickness of the layer or region is enlarged or reduced in the drawings used to describe the embodiments of the present application, that is, the drawings are not drawn according to the actual proportion. It can be understood that when an element such as a layer, a film, a region or a substrate is referred to as being located "on" or "under" another element, the element can be "directly" located on or under another element or there can be an intermediate element.

[0127] (3) In the case of no conflict, the embodiments of the present application and the features in the embodiments can be combined with each other to obtain new embodiments.

[0128] The above merely describes the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for controlling zinc layer thickness in continuous hot-dip galvanizing based on self-learning, characterized in that, include: S1: Obtain multiple production parameters during the hot-dip galvanizing process; S2: Calculate the layer number of the strip steel to be galvanized based on the production parameters described above; S3: Read the result data of the three learning processes corresponding to the current strip steel layer number from the database: short-term self-learning, long-term self-learning, and inherited self-learning. S4: When the distance between the head of the strip to be galvanized adjacent to the current strip and the air knife is a first preset distance, and the layer number of the current strip and the strip to be galvanized are different, the long-term self-learning of the current strip and the inherited self-learning of the strip to be galvanized are calculated according to the result data of the long-term self-learning and inherited self-learning of the current strip and the strip to be galvanized, respectively, and the result data of the short-term self-learning of the current strip is cleared to zero. S5: When the head of the strip steel to be galvanized passes the zinc layer thickness gauge and is at a second preset distance from the zinc layer thickness gauge, perform short-term self-learning calculations for the strip steel to be galvanized. S6: Store the calculated learning results of long-term self-learning, inherited self-learning, and short-term self-learning into the database; S7: Sum the learning results to calculate the self-learning sum value representing the predicted zinc layer thickness of the strip steel to be galvanized; S8: Control the zinc layer thickness during the hot-dip galvanizing process based on the predicted zinc layer thickness.

2. The zinc layer thickness control method for continuous hot-dip galvanizing based on self-learning according to claim 1, characterized in that, The production parameters include: air knife air source, coating specifications, strip width, strip thickness, and strip steel type.

3. The zinc layer thickness control method for continuous hot-dip galvanizing based on self-learning according to claim 2, characterized in that, The formula for calculating the floor number is as follows: ； in, N i Indicates the first i The layer number of coiled steel. G i Indicates the first i The corresponding air source setting for the coiled steel strip. W num This indicates the total number of coating specifications. B num This indicates the total number of strip width increments. H num This indicates the total number of strip thickness increments. S num This indicates the total number of strip steel grades. W i Indicates the first i The grade of coating specifications corresponding to coiled steel. B i Indicates the first i The grade corresponding to the width of the coiled strip steel. S i Indicates the first i The grade of the strip steel corresponds to the grade of the strip steel.

4. The zinc layer thickness control method for continuous hot-dip galvanizing based on self-learning according to claim 1, characterized in that, The calculation of long-term self-learning for the current strip and the inherited self-learning for the strip to be galvanized, based on the results of long-term self-learning and inherited self-learning corresponding to the current strip layer number in step S4, specifically includes: S401: Calculate the long-term self-learning results of the current strip based on the long-term self-learning results of the current strip layer number; S402: Based on the inheritance self-learning result data corresponding to the current strip layer number, perform inheritance self-learning calculation for the strip to be galvanized.

5. The zinc layer thickness control method for continuous hot-dip galvanizing based on self-learning according to claim 4, characterized in that, Specifically, S401 is: The long-term self-learning calculation for the current strip steel is performed according to the following formula: ； in, β l ( j +1) indicates the first strip layer with the same alias as the current strip layer. j Long-term self-learning of zinc layer thickness for +1 galvanized unit. β l ( j ) indicates the first strip layer with the same alias as the current strip layer. j The long-term self-learning value of the zinc layer thickness of each galvanized unit. α 1 represents the long-term self-learning smoothing coefficient. β s ( j +1) indicates the first strip layer with the same alias as the current strip layer. j Short-term self-learning of zinc layer thickness for +1 galvanized unit.

6. The zinc layer thickness control method for continuous hot-dip galvanizing based on self-learning according to claim 4, characterized in that, Specifically, S402 is: The following formula is used to calculate the inheritance of the self-learning process for the steel strip to be galvanized: ； in, β r ( i +1) indicates the first (in the same galvanizing unit as the strip to be galvanized) i The zinc coating thickness of +1 coil of steel is inherited from the learning amount. α 2 indicates that the smoothing coefficient is inherited from the learning curve, Δ T h ( i ) indicates the first galvanizing unit in the same galvanizing unit as the strip to be galvanized. i The deviation between the predicted and measured values ​​of zinc coating thickness of coiled steel.

7. The zinc layer thickness control method for continuous hot-dip galvanizing based on self-learning according to claim 1, characterized in that, Specifically, S5 is: The short-term self-learning calculation for the strip steel to be galvanized is performed according to the following formula: ； in, β s ( i +1) indicates the first (in the same galvanizing unit as the strip to be galvanized) i Short-term self-learning amount of zinc coating thickness for +1 coil of strip steel. β s ( i ) indicates the first galvanizing unit in the same galvanizing unit as the strip to be galvanized. i Short-term self-learning value for zinc coating thickness of coiled steel. α 3 represents the short-term self-learning smoothing coefficient, Δ T h ( i +1) indicates the first (in the same galvanizing unit as the strip to be galvanized) i The deviation between the predicted and measured values ​​of the zinc coating thickness of +1 coiled steel.

8. The zinc layer thickness control method for continuous hot-dip galvanizing based on self-learning according to claim 1, characterized in that, Specifically, S7 is: The self-learning sum representing the predicted zinc layer thickness of the strip to be galvanized is calculated using the following formula: ； in, β T This represents the self-learning sum value indicating the predicted zinc layer thickness of the steel strip to be galvanized. β l This indicates the amount of time spent on self-learning. β s Indicates short-term self-learning volume. β r This indicates that it is inherited from the learning quantity.

9. A zinc layer thickness control system for continuous hot-dip galvanizing based on self-learning, characterized in that, include: processor; A memory storing computer-readable instructions, which, when executed by the processor, implement the self-learning-based continuous hot-dip galvanizing zinc layer thickness control method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the self-learning-based continuous hot-dip galvanizing zinc layer thickness control method as described in any one of claims 1 to 8.

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