CN114854978A - Method and device for predicting strip steel deviation value - Google Patents
Method and device for predicting strip steel deviation value Download PDFInfo
- Publication number
- CN114854978A CN114854978A CN202210360159.9A CN202210360159A CN114854978A CN 114854978 A CN114854978 A CN 114854978A CN 202210360159 A CN202210360159 A CN 202210360159A CN 114854978 A CN114854978 A CN 114854978A
- Authority
- CN
- China
- Prior art keywords
- deviation
- training data
- continuous annealing
- value
- strip steel
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 229910000831 Steel Inorganic materials 0.000 title claims abstract description 62
- 239000010959 steel Substances 0.000 title claims abstract description 62
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000012549 training Methods 0.000 claims abstract description 63
- 238000000137 annealing Methods 0.000 claims abstract description 46
- 230000002159 abnormal effect Effects 0.000 claims abstract description 23
- 230000007246 mechanism Effects 0.000 claims abstract description 7
- 238000012937 correction Methods 0.000 claims abstract description 5
- 230000009467 reduction Effects 0.000 claims description 12
- 238000004422 calculation algorithm Methods 0.000 claims description 8
- 238000010801 machine learning Methods 0.000 claims description 7
- 238000004590 computer program Methods 0.000 claims description 6
- 238000003860 storage Methods 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 2
- 238000004519 manufacturing process Methods 0.000 abstract description 19
- 238000012545 processing Methods 0.000 abstract description 10
- 230000008569 process Effects 0.000 abstract description 6
- 230000008901 benefit Effects 0.000 abstract description 4
- 238000004364 calculation method Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000012372 quality testing Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000005097 cold rolling Methods 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 239000012467 final product Substances 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000005554 pickling Methods 0.000 description 1
- 239000000047 product Substances 0.000 description 1
Images
Classifications
-
- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21D—MODIFYING THE PHYSICAL STRUCTURE OF FERROUS METALS; GENERAL DEVICES FOR HEAT TREATMENT OF FERROUS OR NON-FERROUS METALS OR ALLOYS; MAKING METAL MALLEABLE, e.g. BY DECARBURISATION OR TEMPERING
- C21D11/00—Process control or regulation for heat treatments
-
- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21D—MODIFYING THE PHYSICAL STRUCTURE OF FERROUS METALS; GENERAL DEVICES FOR HEAT TREATMENT OF FERROUS OR NON-FERROUS METALS OR ALLOYS; MAKING METAL MALLEABLE, e.g. BY DECARBURISATION OR TEMPERING
- C21D1/00—General methods or devices for heat treatment, e.g. annealing, hardening, quenching or tempering
- C21D1/26—Methods of annealing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The application relates to the technical field of image data processing, and discloses a method for predicting a strip steel deviation value, which comprises the steps of obtaining training data, and inputting the training data into a continuous annealing deviation value prediction model; the continuous deviation prediction model generates a deviation prediction curve according to the training data and outputs abnormal data information exceeding a preset upper limit value; and outputting the deviation prediction curve and the abnormal data information as prediction results to optimize a deviation correction mechanism of the continuous annealing line. According to the method, a deviation prediction result of the strip steel in the continuous annealing process is obtained through a continuous annealing deviation value prediction model; deviation correcting measures are taken in advance through the prediction results, the deviation correcting control parameters of the strip steel in the production process are adjusted, the deviation value of the strip steel in the continuous annealing process is controlled within the industry error allowable range, the strip steel processing quality and production efficiency are improved, the production cost is reduced, and the economic benefit is improved.
Description
Technical Field
The application relates to the technical field of image data processing, in particular to a method and a device for predicting a strip steel deviation value.
Background
After the strip steel is subjected to the cold rolling process, the strip steel shape has a great influence on the processing quality of a subsequent unit. In the production of a continuous annealing unit, the stress distribution of the strip shape of the strip steel is an important factor causing the strip steel to deviate; in a continuous annealing unit for rapid production, if the strip shape deviation of strip steel is serious, the strip steel can be seriously deviated in the continuous annealing unit, so that severe production accidents such as production speed reduction and even strip breakage are caused.
When the strip steel deviates in the continuous annealing unit, an operator usually adopts a speed reduction mode to gradually correct the position of the strip steel, but on one hand, the speed reduction of the strip steel can cause the retention time of the strip steel in a furnace to be increased, and the final product performance of the strip steel is also influenced due to the overlong heating time of the strip steel; on the other hand, the speed reduction mode enables a passive control strategy to have low control efficiency and lag.
Disclosure of Invention
In order to predict the deviation condition of the strip steel in advance, a strip steel deviation rectifying mechanism in a continuous annealing unit is arranged in advance, the deviation value of the strip steel is controlled within the allowable range of product error,
in a first aspect, the present application provides a method for predicting a strip steel deviation value, including:
acquiring training data, and inputting the training data to a continuous annealing deviation value prediction model;
the continuous annealing deviation prediction model generates a deviation prediction curve and abnormal data information exceeding a preset upper limit value according to the training data;
and outputting the deviation prediction curve and the abnormal data information as prediction results to optimize a deviation correction mechanism of the continuous annealing line.
Further, the training data comprises plate shape data and deviation data of the strip steel; the strip shape data comprises strip shape stress values of each strip shape along the width direction or the length direction; the deviation data comprises deviation values corresponding to each frame of plate shape data on a continuous annealing line.
Further, the plate shape data is acquired by a field plate shape meter; and the deviation data is acquired by a continuous annealing line deviation correction roller encoder.
Further, the preset abnormal data information of the upper limit value includes the number of the plate-shaped frames of the early warning number value or the early warning percentage upper limit value, and the position and the length of the area where the deviation value is greater than the early warning number value or the early warning percentage upper limit value.
Further, the method for predicting the strip steel deviation value provided by the application further comprises the following steps:
generating a training data format list through the training data;
and inputting the training data format list into a machine learning algorithm to generate the continuous deviation value prediction model.
Further, the training data format list comprises an initial training data format list and a secondary training data format list generated by dimensionality reduction of the initial training data format list; the initial training format list is obtained by sequencing the training data; and the dimensionality reduction mode comprises the step of fitting the initial training data format list by adopting a quintic Legendre orthogonal polynomial to obtain the fitting polynomial coefficient of each frame.
Further, substituting the training data format list into a machine learning algorithm comprises judging whether to update the secondary training data format list; if so, reconstructing the continuous annealing deviation value prediction model by adopting the updated secondary training data format list; and if not, adopting the continuous annealing deviation value prediction model constructed by the current secondary training data format list.
In a second aspect, the present application provides an apparatus for predicting a strip steel deviation value, comprising:
the input unit is used for inputting the training data to a continuous deviation value prediction model;
the calculating unit is used for generating a deviation prediction curve according to the training data and outputting abnormal data information exceeding a preset upper limit value;
and the output unit is used for outputting the deviation prediction curve and the abnormal data information as prediction results and optimizing a deviation rectifying mechanism of the continuous annealing line.
In a third aspect, the present application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method steps according to any of the first aspect when executing the program.
In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the method steps of any of the first aspects.
Has the advantages that:
in the application, a continuous annealing deviation value prediction model generates a deviation prediction curve and abnormal data information exceeding a preset upper limit value according to strip shape data and deviation data of strip steel, and the deviation prediction curve can visually display the predicted deviation condition of the strip steel during continuous annealing unit processing; the deviation correcting mechanism is set in advance by analyzing the abnormal data information of the preset upper limit value, the deviation correcting control parameter of the strip steel in the production process is adjusted, the deviation value of the strip steel in the continuous annealing process is controlled within the industry error allowable range, the processing quality and the production efficiency of the strip steel are improved, the production cost is reduced, and the economic benefit is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flowchart of a method for predicting strip steel deviation values according to embodiment 1 of the present application;
FIG. 2 is a schematic structural diagram of an apparatus for predicting strip steel deviation values provided in embodiment 2 of the present application;
fig. 3 is a schematic structural diagram of an electronic device provided in embodiment 3 of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
Example 1
Referring to fig. 1, embodiment 1 provides a method for predicting a strip steel deviation value, including the following steps,
s101, inputting training data, wherein the training data comprises plate shape data and deviation data of the strip steel;
collecting strip shape data of different brands and different models of strip steel through an on-site strip shape instrument; acquiring deviation data of different brands and different types of strip steel in the continuous annealing line through a continuous annealing line deviation rectification roller encoder; taking the plate shape data and the deviation data as training data;
s102, generating a training data format list through training data;
the initial training data is formatted, as shown in table 1,
table 1: initial training data Format List
| Zone1 | Zone2 | Zone3 | …… | Zone(n-2) | Zone(n-1) | Zonen | Running deviation value |
Zonen is the stress value of each strip shape of each frame of strip steel along the width direction, and is measured by a strip shape instrument sensor; the deviation value is a deviation value corresponding to the continuous annealing line of the corresponding frame plate shape;
taking (Zone1, Zone2, Zone3, … … Zonen) as a characteristic value and taking a deviation value as a target value;
because the number of the used plate shape meter sensors is large, generally dozens to hundreds, the corresponding dimension taking (Zone1, Zone2, Zone3, … … Zonen) as the feature vector is also high, and the defect of dimension explosion caused by directly substituting the high-dimensional feature vector into the machine learning algorithm is overcome, so that the dimension reduction should be performed firstly;
selecting quintic Legendre orthogonal polynomials for each frame of plate-shaped data Zone 1-Zonen for fitting to obtain fitting polynomial coefficients of each frame: (c0, c1, c2, c3, c4),
to update the training data, the format is set up, as shown in table 2,
table 2: second level training data Format List
| C0 | C1 | C2 | C3 | C4 | Running deviation value |
S103, substituting updated training data subjected to dimensionality reduction into a machine learning algorithm to perform KNN training to obtain a current continuous annealing deviation value prediction model;
s104.1, judging whether to update the current continuous annealing deviation value prediction model or not;
s104.2, when the judgment result is yes, reconstructing a continuous annealing deviation value prediction model;
s104.3, adopting a current continuous annealing deviation value prediction model when the judgment result is negative;
s105, calculating a deviation prediction curve through a deviation prediction model; setting an early warning upper limit value, counting abnormal data information exceeding the early warning upper limit value, and taking a deviation prediction curve and the abnormal data information as prediction results;
and S106, outputting a prediction result of the continuous annealing deviation value prediction model.
Example 2
The difference from the embodiment 1 is that the embodiment 2 provides an apparatus for predicting a strip deviation value, and in combination with fig. 2, the apparatus for predicting a strip deviation value comprises:
the input unit 201 comprises a database 2011 and a data selection column 2012, wherein the database 2011 is used for collecting and inputting training data; the data selection field 2012 is used to select training data; the training data comprises plate shape data and deviation data of the strip steel;
a calculating unit 202, configured to generate a training data format list through training data; substituting the training data format list into a machine learning algorithm to obtain a continuous annealing deviation value prediction model; calculating a deviation prediction curve through a deviation prediction model; setting an early warning upper limit value, counting abnormal data information exceeding the early warning upper limit value, and taking a deviation prediction curve and the abnormal data information as prediction results;
the output unit 203 is configured to output a prediction result of the continuous deviation value prediction model, and includes a continuous deviation curve display frame 2031 and a deviation value statistical result output column 2032; the continuous annealing deviation curve display frame 2031 is used for displaying a continuous annealing deviation curve, and the deviation value statistical result output column 2032 is used for displaying abnormal data information;
the device for predicting the strip steel deviation value provided by the embodiment 2 adopts the graphical human-computer interaction interface 200, and the graphical interface is beneficial to improving the analysis processing efficiency;
a user inputs the brand and the model of a steel coil to be predicted to an off-tracking value statistical result output column, an input unit 201 acquires corresponding training data, a calculation module 202 generates a secondary training data format list through the training data, the calculation module 202 calculates the off-tracking value of the strip steel, and the off-tracking curve of the whole roll of the strip steel is calculated from a quantitative angle; according to the set early warning upper limit value, the calculation module 202 counts abnormal data information exceeding the early warning upper limit value, and takes a deviation prediction curve and the abnormal data information as prediction results;
the output unit 203 displays the continuous annealing deviation curve on the continuous annealing deviation curve display frame 2031; the abnormal data information of the running deviation value statistics of the continuous running deviation is displayed on the running deviation value statistics result output column 2032, and the abnormal data information includes the number of the plate frames exceeding the early warning number value or the upper limit value of the early warning percentage, and the position and the length of the area where the deviation value is greater than the early warning number value or the upper limit value of the early warning percentage.
And the staff controls the subsequent production process according to the prediction result:
the early warning value is set, the observation object is the A-section strip steel in the embodiment 2, the observation length is 50m, the on-line proportion of the early warning value is set to be 30 percent,
when the number of the deviation values of the A-section strip steel within 50m length is more than 5, the deviation problem of the A-section strip steel occurs with high probability. And during subsequent continuous annealing production, parameter revision is carried out on the set value of the strip steel deviation correcting device of the continuous annealing line, or the speed reduction treatment is carried out on the strip steel section needing speed reduction.
During production, after a coil of strip steel is rolled off the production line from the pickling line, deviation prediction data of the coil can be automatically calculated according to the plate shape data of the coil. If the section with reduced speed and the steel coil needing locking are available, the system sends the calculation result to a production control management window and gives an alarm prompt so that operators and managers can conveniently process the steel coil in the next step.
And if the percentage of the accumulated deviation value of all the steel coils exceeds the alarm value by 30 percent or the deviation value of a certain local strip steel exceeds the alarm value by 30 percent, judging that the steel coils are the steel coils with the plate shape problems, and locking the steel coils in a production system. The roll is not allowed to proceed to the next manufacturing process until the inspector performs further quality testing.
In this embodiment 2, a continuous annealing deviation value prediction model device is adopted, analysis processing efficiency is improved through a graphical human-computer interaction interface, quality testing personnel are assisted to know the strip shape condition, deviation rectification measures are taken in advance, the deviation value of the strip steel in a continuous annealing process is controlled within an industry error allowable range by adjusting control parameters of strip steel deviation rectification, strip steel processing quality and production efficiency are improved, production cost is reduced, and economic benefits are improved.
Example 3
Based on the same inventive concept, embodiment 3 of the present application provides an electronic device, as shown in fig. 3, including a memory 304, a processor 302, and a computer program stored on the memory 304 and executable on the processor 302, where the processor 302 implements the steps of the above-mentioned directed graph drawing method when executing the program.
Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 306 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other apparatus over a transmission medium. The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
Example 4
Based on the same inventive concept, embodiment 4 of the present application provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the above method for predicting a strip deviation value.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. In addition, this application is not directed to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and any descriptions of specific languages are provided above to disclose the best modes of the present application.
The foregoing are merely exemplary embodiments of the present application and no attempt is made to show structural details of the invention in more detail than is necessary for the fundamental understanding of the art, the description taken with the drawings making apparent to those skilled in the art how the several forms of the invention may be embodied in practice with the teachings of the invention. It should be noted that, for those skilled in the art, without departing from the structure of the present application, several changes and modifications can be made, which should also be regarded as the protection scope of the present application, and these will not affect the effect of the implementation of the present application and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (10)
1. A method for predicting a strip steel deviation value is characterized by comprising the following steps:
acquiring training data, and inputting the training data to a continuous annealing deviation value prediction model;
the continuous annealing deviation prediction model generates a deviation prediction curve and abnormal data information exceeding a preset upper limit value according to the training data;
and outputting the deviation prediction curve and the abnormal data information as prediction results to optimize a deviation correction mechanism of the continuous annealing line.
2. The method of claim 1, wherein the step of predicting the strip deviation value comprises: the training data comprises plate shape data and deviation data of the strip steel; the strip shape data comprises strip shape stress values of each strip shape along the width direction or the length direction; the deviation data comprises deviation values corresponding to each frame of plate shape data on a continuous annealing line.
3. The method of claim 2, wherein the step of predicting the strip deviation value comprises: the plate shape data is acquired by a field plate shape meter; and the deviation data is acquired by a continuous annealing line deviation correction roller encoder.
4. The method of claim 1, wherein the step of predicting the strip deviation value comprises: the abnormal data information of the preset upper limit value comprises the number of plate-shaped frames of an early warning number value or an early warning percentage upper limit value, and the position and the length of an area of which the deviation value is greater than the early warning number value or the early warning percentage upper limit value.
5. The method of claim 1, further comprising:
generating a training data format list through the training data;
and inputting the training data format list into a machine learning algorithm to generate the continuous deviation value prediction model.
6. The method of claim 5, wherein the step of predicting the strip deviation value comprises: the training data format list comprises an initial training data format list and a secondary training data format list generated by dimension reduction of the initial training data format list; the initial training format list is obtained by sequencing the training data; and the dimensionality reduction mode comprises the step of fitting the initial training data format list by adopting a quintic Legendre orthogonal polynomial to obtain the fitting polynomial coefficient of each frame.
7. The method of claim 6, wherein the step of predicting the strip deviation value comprises: substituting the training data format list into a machine learning algorithm, wherein the step of judging whether to update the secondary training data format list or not is included; if so, reconstructing the continuous annealing deviation value prediction model by adopting the updated secondary training data format list; and if not, adopting the continuous annealing deviation value prediction model constructed by the current secondary training data format list.
8. An apparatus for predicting a strip runout value, comprising:
the input unit is used for inputting the training data to a continuous deviation value prediction model;
the calculating unit is used for generating a deviation prediction curve according to the training data and outputting abnormal data information exceeding a preset upper limit value;
and the output unit is used for outputting the deviation prediction curve and the abnormal data information as prediction results and optimizing a deviation rectifying mechanism of the continuous annealing line.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method steps of any of claims 1-7 when executing the program.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method steps of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210360159.9A CN114854978A (en) | 2022-04-06 | 2022-04-06 | Method and device for predicting strip steel deviation value |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210360159.9A CN114854978A (en) | 2022-04-06 | 2022-04-06 | Method and device for predicting strip steel deviation value |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114854978A true CN114854978A (en) | 2022-08-05 |
Family
ID=82630022
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210360159.9A Pending CN114854978A (en) | 2022-04-06 | 2022-04-06 | Method and device for predicting strip steel deviation value |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114854978A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116603869A (en) * | 2023-07-20 | 2023-08-18 | 江苏甬金金属科技有限公司 | Deviation correction control method and system based on feedback optimization |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107541597A (en) * | 2016-06-29 | 2018-01-05 | 宝山钢铁股份有限公司 | Strip running deviation monitoring and diagnostic method and the system of continuous annealing unit soaking pit |
| CN112029990A (en) * | 2020-07-16 | 2020-12-04 | 宝钢湛江钢铁有限公司 | Automatic deviation rectifying control method for cold-rolled strip steel continuous annealing heating furnace |
| WO2020248365A1 (en) * | 2019-06-14 | 2020-12-17 | 平安科技（深圳）有限公司 | Intelligent model training memory allocation method and apparatus, and computer-readable storage medium |
| CN112446130A (en) * | 2020-10-15 | 2021-03-05 | 宝钢日铁汽车板有限公司 | Strip steel deviation simulation system of continuous hot galvanizing unit annealing furnace and control method |
| CN112598026A (en) * | 2020-12-07 | 2021-04-02 | 东北大学 | Continuous annealing production process fault diagnosis method based on unbalanced data deep learning |
| HUE052799T2 (en) * | 2017-05-29 | 2021-05-28 | Andritz Ag Maschf | Method for controlling the coiling temperature of a metal strip |
| CN113850020A (en) * | 2021-09-16 | 2021-12-28 | 北京科技大学 | Continuous annealing tension setting method and device based on neural network |
| US20220100932A1 (en) * | 2019-01-21 | 2022-03-31 | Jfe Steel Corporation | Design support method for metal material, prediction model generation method, metal material production method, and design support apparatus |
-
2022
- 2022-04-06 CN CN202210360159.9A patent/CN114854978A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107541597A (en) * | 2016-06-29 | 2018-01-05 | 宝山钢铁股份有限公司 | Strip running deviation monitoring and diagnostic method and the system of continuous annealing unit soaking pit |
| HUE052799T2 (en) * | 2017-05-29 | 2021-05-28 | Andritz Ag Maschf | Method for controlling the coiling temperature of a metal strip |
| US20220100932A1 (en) * | 2019-01-21 | 2022-03-31 | Jfe Steel Corporation | Design support method for metal material, prediction model generation method, metal material production method, and design support apparatus |
| WO2020248365A1 (en) * | 2019-06-14 | 2020-12-17 | 平安科技（深圳）有限公司 | Intelligent model training memory allocation method and apparatus, and computer-readable storage medium |
| CN112029990A (en) * | 2020-07-16 | 2020-12-04 | 宝钢湛江钢铁有限公司 | Automatic deviation rectifying control method for cold-rolled strip steel continuous annealing heating furnace |
| CN112446130A (en) * | 2020-10-15 | 2021-03-05 | 宝钢日铁汽车板有限公司 | Strip steel deviation simulation system of continuous hot galvanizing unit annealing furnace and control method |
| CN112598026A (en) * | 2020-12-07 | 2021-04-02 | 东北大学 | Continuous annealing production process fault diagnosis method based on unbalanced data deep learning |
| CN113850020A (en) * | 2021-09-16 | 2021-12-28 | 北京科技大学 | Continuous annealing tension setting method and device based on neural network |
Non-Patent Citations (1)
| Title |
|---|
| 西德钢铁工程师协会，武汉钢铁设计研究院技术情报科: "《Python 项目实战从入门到精通》", 北京机械工业出版社, pages: 129 - 130 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116603869A (en) * | 2023-07-20 | 2023-08-18 | 江苏甬金金属科技有限公司 | Deviation correction control method and system based on feedback optimization |
| CN116603869B (en) * | 2023-07-20 | 2023-11-17 | 江苏甬金金属科技有限公司 | Deviation correction control method and system based on feedback optimization |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113468479B (en) | Cold continuous rolling industrial process monitoring and abnormality detection method based on data driving | |
| JPH11338848A (en) | Data abnormality detector | |
| KR20120095273A (en) | Energy expend volume forecast device | |
| US20090259331A1 (en) | Automated system for checking proposed human adjustments to operational or planning parameters at a plant | |
| CN106845826B (en) | PCA-Cpk-based cold continuous rolling production line service quality state evaluation method | |
| CN114854978A (en) | Method and device for predicting strip steel deviation value | |
| KR102426172B1 (en) | Tightening Occurrence Prediction System | |
| CN115993856A (en) | Multi-region environmental condition management and control method and system for factory building | |
| KR20000048928A (en) | A process and device for identification or pre-caculation of parameters of a time-variant industrial process | |
| JP2020157327A (en) | Control method for outlet side temperature of finished steel sheet, control device therefor and production method for steel sheet | |
| CN113834828A (en) | Product quality analysis support system | |
| KR102338546B1 (en) | Steel plant maintenance support device | |
| KR20200000783A (en) | Monitoring assistance system for steel plant | |
| US20160195857A1 (en) | Apparatus and method for model adaptation | |
| KR101840419B1 (en) | System and method for predicting fault of elements in skin pass mill and leveler | |
| WO2022239157A1 (en) | Learning control device for rolling process | |
| CN117831659B (en) | Method and device for online detection of quality of wide and thick plates, electronic equipment and storage medium | |
| JP2934178B2 (en) | Prediction method of thickness accuracy in hot rolling | |
| CN114611376B (en) | Continuous annealing deviation prediction method and device based on neural network | |
| EP3923094A1 (en) | Inspection rate adaptation | |
| CN116765283B (en) | Correction system and method based on cutting, stripping, riveting and screwing integrated machine | |
| JP7211386B2 (en) | Model learning method, running strip thickness changing method, steel plate manufacturing method, model learning device, running strip thickness changing device, and steel plate manufacturing device | |
| CN111266405B (en) | Plate and strip hot rolling process control method and control device | |
| CN114367545B (en) | Rolling force correction method and device | |
| CN114101337B (en) | Thickness control method and device of single-frame reversible rolling mill |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |