CN113342875A - Correction factor obtaining method and device for strip steel coiling temperature - Google Patents
Correction factor obtaining method and device for strip steel coiling temperature Download PDFInfo
- Publication number
- CN113342875A CN113342875A CN202110625075.9A CN202110625075A CN113342875A CN 113342875 A CN113342875 A CN 113342875A CN 202110625075 A CN202110625075 A CN 202110625075A CN 113342875 A CN113342875 A CN 113342875A
- Authority
- CN
- China
- Prior art keywords
- similar
- current
- blank
- strip steel
- stored
- 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 107
- 239000010959 steel Substances 0.000 title claims abstract description 107
- 238000012937 correction Methods 0.000 title claims abstract description 73
- 238000000034 method Methods 0.000 title claims abstract description 55
- 238000005096 rolling process Methods 0.000 claims abstract description 51
- 238000003860 storage Methods 0.000 claims abstract description 33
- 238000004590 computer program Methods 0.000 claims description 13
- 238000012216 screening Methods 0.000 claims description 10
- 238000001816 cooling Methods 0.000 abstract description 15
- 238000010586 diagram Methods 0.000 description 10
- 230000008569 process Effects 0.000 description 8
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000012217 deletion Methods 0.000 description 1
- 230000037430 deletion Effects 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 239000002436 steel type Substances 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2474—Sequence data queries, e.g. querying versioned data
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21B—ROLLING OF METAL
- B21B15/00—Arrangements for performing additional metal-working operations specially combined with or arranged in, or specially adapted for use in connection with, metal-rolling mills
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21B—ROLLING OF METAL
- B21B15/00—Arrangements for performing additional metal-working operations specially combined with or arranged in, or specially adapted for use in connection with, metal-rolling mills
- B21B2015/0057—Coiling the rolled product
Abstract
The invention relates to the technical field of hot-rolled strip steel laminar cooling, in particular to a method and a device for acquiring a correction factor of strip steel coiling temperature, wherein the method comprises the following steps: acquiring current rolling data of the current strip steel; searching a current similar blank of the current strip steel from a shared memory according to the current rolling data; when the similar billets stored in the shared memory reach the upper limit, similar storage data similar to the previous similar billet of the strip steel are searched from the similar billet in the previous set range in the shared memory, and the similar storage data are deleted and then the previous similar billet of the strip steel is written in; and obtaining the current correction factor of the current strip steel according to the current similar blank. The method stores more similar blank data for the strip steel, is convenient for continuously storing the data of the non-rolled steel grade in the shared memory for a long time, and corrects the subsequent rolling and coiling of the strip steel. The similar billets stored in the shared memory are modified and stored according to similar storage data similar to the previous similar billets with strip steel, so that the number of the current similar billets is increased, and the current similar billets are better in quality.
Description
Technical Field
The invention relates to the technical field of hot-rolled strip steel laminar cooling, in particular to a method and a device for acquiring a correction factor of strip steel coiling temperature.
Background
In the production process of hot rolled strip steel, a laminar cooling device is used for cooling the strip steel before coiling after rolling, and ideal metallographic structure and mechanical properties are obtained by controlling the coiling temperature and the cooling rate of the strip steel. However, in the existing scheme, the laminar cooling model has the problems that related data of some accommodating belts are insufficient, the principle of data deletion is unreasonable, reading of similar blanks of strip steel is slow and the like, so that the accuracy of correction factors influencing the coiling temperature of the strip steel is not high, and the accuracy of the coiling temperature of the strip steel is low. For example, the migrating steel 2160 hot-rolled laminar cooling adopts a Siemens control model. Due to the fact that the migrated steel 2160 hot-rolled steel has various specifications and scattered orders, and the data stored in the layer-cooling self-learning file is limited, the data of the strip steel is deleted by adopting a first-in first-out principle, similar blanks of the strip steel cannot be found, and the problem that the accuracy of a correction factor influencing the strip steel coiling temperature is not high is caused, and further the deviation of the actual temperature of the coiling temperature and a set value is overlarge.
Disclosure of Invention
The embodiment of the application provides the method and the device for acquiring the correction factor of the strip steel coiling temperature, solves the technical problem that the accuracy of the correction factor influencing the strip steel coiling temperature is not high in the prior art, realizes the accurate control of the strip steel coiling temperature, and improves the strip steel coiling efficiency.
In a first aspect, an embodiment of the present invention provides a method for obtaining a correction factor of a strip steel coiling temperature, including:
acquiring current rolling data of the current strip steel;
searching the current similar blank of the current strip steel from a shared memory according to the current rolling data; when the similar blanks stored in the shared memory reach the upper limit, similar storage data similar to the previous similar blank of the strip steel is searched from the similar blank in the previous set range in the shared memory, and the similar storage data is deleted and written into the previous similar blank of the strip steel;
and obtaining the current correction factor of the current band steel according to the current similar blank.
Preferably, after obtaining the current correction factor of the current strip steel, the method further includes:
and generating a new similar billet by using the current rolling data and the current correction factor, and synchronously storing the new similar billet into a self-learning file and the shared memory.
Preferably, after storing the new similar blank into the shared memory, the method further includes:
when the similar blanks stored in the shared memory reach the upper limit, calling a historical similar blank set in the previous set range;
searching in the historical similar blank set according to the current similar blank;
if so, taking the current similar blank with the earliest searched storage time as a target similar blank, deleting the target similar blank from the historical similar blank set, and sequentially storing the historical similar blanks before the target similar blank behind the last historical similar blank in the previous set range;
and taking the new similar blank as the last similar blank in the shared memory, and updating the stored similar blanks.
Preferably, the searching in the historical similar blank set according to the current similar blank further includes:
and if not, deleting the first historical similar blank in the historical similar blank set, taking the new similar blank as the last similar blank in the shared memory, and updating the stored similar blanks.
Preferably, the storing the new similar blank into the self-learning file includes:
and when the similar blanks stored in the self-learning file reach the upper limit, deleting the first similar blank in the self-learning file, and taking the new similar blank as the last similar blank in the self-learning file.
Preferably, the obtaining of the current correction factor of the current strip steel according to the current similar blank includes:
according to the sequence from bottom to top and according to the current rolling data, obtaining the similarity of each stored similar blank; wherein the bottom-up order is an order of visits from a last one to a first one of the stored similar blanks;
and screening the current similar blanks from the stored similar blanks according to the similarity of each stored similar blank, wherein the current similar blank is the stored similar blank with the similarity within a threshold range.
Preferably, the obtaining of the current correction factor of the current strip steel according to the current similar blank includes:
and obtaining the current correction factor according to the similarity of the current similar billet, the rolling time and the correction factor of the current similar billet.
Based on the same inventive concept, in a second aspect, the present invention further provides a correction factor obtaining device for a strip steel coiling temperature, including:
the first acquisition module is used for acquiring the current rolling data of the current strip steel;
the screening module is used for searching the current similar blank of the current band steel from the shared memory according to the current rolling data; when the similar blanks stored in the shared memory reach the upper limit, similar storage data similar to the previous similar blank of the strip steel is searched from the similar blank in the previous set range in the shared memory, and the similar storage data is deleted and written into the previous similar blank of the strip steel;
and the second acquisition module is used for acquiring the current correction factor of the current strip steel according to the current similar blank.
Based on the same inventive concept, in a third aspect, the invention provides a computer device, comprising a memory, a processor and a computer program stored on the memory and operable on the processor, wherein the processor implements the steps of the method for obtaining the correction factor of the strip coiling temperature when executing the program.
Based on the same inventive concept, in a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method for obtaining the correction factor for the strip coiling temperature.
One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages:
in the embodiment of the application, more data are provided for searching the current similar blank by opening up the shared memory to store more similar blank data, so that the searched current similar blank is more accurate. The self-learning file adopts a first-in first-out deleting principle, and the shared memory adopts an optimized deleting rule, so that the data of the non-rolled steel grade can be continuously stored in the shared memory for a long time, and the subsequent strip steel rolling can be corrected. The method solves the problems that the precision of the obtained correction factor is low because the model cannot find the similar billets and the like can be caused by the problems that the similar billets are deleted, the similar billets stored in the self-learning file are deleted, the rolling data are modified, or the self-learning file is emptied due to abnormal restarting of the model, and the like, because the quantity of the similar billets stored in the shared memory is more, and the similar stored data are deleted and the similar stored data are written into the deleting rule of the similar billets of the previous strip steel after the similar stored data are deleted.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a schematic flow chart showing steps of a correction factor obtaining method for strip coiling temperature according to an embodiment of the present invention;
FIG. 2 is a block diagram showing a correction factor acquiring apparatus for a strip coiling temperature in an embodiment of the present invention;
fig. 3 shows a schematic structural diagram of a computer device in an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Example one
The first embodiment of the invention provides a correction factor obtaining method for the coiling temperature of a strip steel, which is applied to pre-calculation before a heating furnace process or a rough rolling process or a finish rolling process. For example, the pre-calculation of the finish rolling process on the layer cooling is required to be calculated before finish rolling, so as to provide a basis for calculation or judgment for subsequent coiling of the strip steel temperature.
The method of the present embodiment, as shown in fig. 1, includes:
s101, acquiring current rolling data of current strip steel;
s102, searching a current similar blank of the current strip steel from a shared memory according to the current rolling data; when the similar billets stored in the shared memory reach the upper limit, similar storage data similar to the previous similar billet of the strip steel are searched from the similar billet in the previous set range in the shared memory, and the similar storage data are deleted and then the previous similar billet of the strip steel is written in;
s103, obtaining the current correction factor of the current strip steel according to the current similar blank.
It should be noted that the stored data relating to the similar slabs are rolled steel strips or rolled steel strips.
The method of the embodiment develops a shared memory, stores more similar blank data for the strip steel, is convenient for continuously storing the related data of the non-rolled steel grade in the shared memory for a long time, and corrects the subsequent rolling and coiling of the strip steel. The similar billets stored in the shared memory are modified and stored according to similar storage data similar to the previous similar billets with the strip steel, so that the number of the searched current similar billets is increased, the time distance is closer, and the effect is better.
The following describes in detail the specific implementation steps of the method provided in this embodiment with reference to fig. 1:
first, step S101 is executed to obtain current rolling data of the current strip steel. Wherein, the current rolling data comprises the thickness, the width, the speed of the speed area, the setting of the curling temperature, the finishing temperature and the like of the current strip steel.
It should be noted that, in the method of this embodiment, not only the shared memory but also the local memory, i.e., the self-learning file rolldstrip. The self-learning files are local files, and the storage space of the self-learning files is limited. In order to supplement the defects of the self-learning file, the embodiment also adopts the shared memory to store more similar blank data, so that the shared memory stores more similar blanks, and more similar blanks are provided for searching the current similar blank of the current strip steel. The storage capacity of the shared memory is usually twice that of the self-learning file, and can also be set according to actual requirements.
Therefore, the method of the embodiment not only searches the current similar blank in the self-learning file, but also searches the current similar blank in the shared memory. Specifically, if the current similar blank of the strip steel is not found in the self-learning file, the current similar blank is found in the shared memory. The process of searching the current similar blank in the self-learning file is the same as the process of searching the current similar blank in the shared memory. Step S102 is a process of searching for the current similar blank in the shared memory, which is described in detail below.
After the current rolling data of the current band steel is obtained, executing step S102, and searching the current similar blank of the current band steel from the shared memory according to the current rolling data; when the similar billets stored in the shared memory reach the upper limit, similar storage data similar to the previous similar billet of the strip steel are searched from the similar billets in the previous set range in the shared memory, and the similar storage data are deleted and written into the previous similar billet of the strip steel.
Specifically, according to the current rolling data, the similarity of each stored similar billet is obtained from bottom to top; wherein the bottom-up order is an order of access from the last to the first of the stored similar blanks; and screening the current similar blanks from the stored similar blanks according to the similarity of each stored similar blank, wherein the current similar blanks are the stored similar blanks of which the similarity is within a threshold range.
It is to be explained that the bottom-up order is the order of access from the last to the first of the stored similar blanks. For example, a total of 150 stored similar blanks are sequentially ordered from the number 001 of the 1 st similar blank to the number 150 of the 150 th similar blank, and the sequence from bottom to top is the sequence from 150 to 001.
In the actual operation process, firstly, in a self-learning file, according to the sequence from bottom to top, the similarity of each stored similar billet is obtained according to the current rolling data and the similar billets stored in the self-learning file. And if the similarity of each stored similar blank is within the threshold range, screening the stored similar blanks with the similarities within the threshold range to serve as the current similar blanks. Generally, the threshold range is 0 to 1, and may also be set according to actual requirements, for example, two levels of threshold ranges are set, where the first level threshold range is 0 to 1, and the second level threshold range is 1 to 3, and when a stored similar blank with a satisfactory similarity is not found in the first level threshold range, a stored similar blank with a satisfactory similarity may be found in the second level threshold range. Here, the smaller the similarity, the higher the similarity of the current rolling data of the current strip steel with the stored similar billets, and the smaller the difference.
If the current similar billets are not found in the similar billets stored in the self-learning file, namely the similar billets stored in the self-learning file with the similarity within the threshold range are not found, the similarity of each similar billet stored in the shared memory is obtained according to the rolling data and the similar billets stored in the shared memory in the sequence from bottom to top in the shared memory. And if the similarity of each stored similar blank in the shared memory is within the threshold range, screening out each stored similar blank in the shared memory with the similarity within the threshold range to serve as the current similar blank.
According to the embodiment, the current similar blanks are searched in the self-learning file, the current similar blanks are also searched in the shared memory, so that the number of the searched current similar blanks is larger, the time of the similar blanks stored in the shared memory is closer to the time of the current similar blanks, and the quality of the found current similar blanks is better.
And after the current similar blank is found, executing the step S103, and obtaining the current correction factor of the current strip steel according to the current similar blank.
Specifically, the current correction factor is obtained according to the similarity of the current similar billet, the rolling time and the correction factor of the current similar billet.
It should be noted that, firstly, the rolling time is the time interval between a current similar blank and a strip, for example, after the current similar blank of the currently coiled F steel type is found, the current similar blank has 2 pieces of A, B respectively, where the rolling time of a is 5 if the current similar blank a is 5 years apart from the F strip, and the rolling time of B is 2 if the current similar blank B is 2 years apart from the F strip. Secondly, the correction factor of the current similar blank is the correction factor adopted when the current similar blank is coiled, for example, the correction factor adopted when the current similar blank A is coiled is 1, and 1 is the correction factor of the current similar blank A.
In the specific implementation process, there are many specific methods for obtaining the current correction factor according to the similarity of the current similar billet, the rolling time, and the correction factor of the current similar billet, which are not limited herein. For example, the following methods are used:
(1) the current correction factor is obtained based on the equation cofa ═ dist × a + T × b + kcofa × c. The method comprises the steps of obtaining correction factors of similar blanks, determining the similarity of the similar blanks, and determining the similarity of the similar blanks according to the comparison result.
(2) The current correction factor is obtained based on the equation cofa ═ dist × a + kcofax c-T × b. The method comprises the steps of obtaining correction factors of similar blanks, determining the similarity of the similar blanks, and determining the similarity of the similar blanks according to the comparison result.
From the actual data results, it is found that the closer the rolling time is, the greater the similarity is, the greater the weight coefficient is, and the greater the influence on the current correction factor is.
And after the current correction factor is obtained, obtaining the coiling temperature according to the current correction factor and the cooling efficiency of the laminar cooling.
In the implementation process, there are many specific methods for obtaining the coiling temperature according to the current correction factor and the cooling efficiency of the laminar cooling, and the method is not limited herein. For example, the following methods are used:
(1) the coiling temperature of the strip steel is obtained based on the equation temp ═ cofa × eff, where temp is the coiling temperature, cofa is the current correction factor, and eff is the cooling efficiency.
(2) And obtaining the coiling temperature of the strip steel based on the equation temp ═ (cofa × d) × eff, wherein temp is the coiling temperature, cofa is the current correction factor, eff is the cooling efficiency, d is the weight factor of the current correction factor, and the value range of d is 0 to 1.
According to the actual data result, after the current correction factor is obtained, the current correction factor needs to be continuously corrected until the strip steel is successfully coiled.
After the current correction factor of the strip steel is obtained, the current rolling data and the current correction factor are required to generate a new similar billet, and the new similar billet is synchronously stored in the self-learning file and the shared memory. It should be noted that the new similar blanks are synchronously stored in the self-learning file and the shared memory, and specifically, the new similar blanks are stored as the last similar blank in the self-learning file and the last similar blank in the shared memory.
When the similar blanks stored in the shared memory reach the upper limit, a new similar blank needs to be stored in the shared memory, and then one similar blank in the shared memory needs to be deleted. Specifically, when the similar blanks stored in the shared memory reach the upper limit, a historical similar blank set is called in the previous set range; searching in a historical similar blank set according to the current similar blank; if the current similar blank is searched, the searched current similar blank with the earliest storage time is taken as a target similar blank, the target similar blank is deleted from the historical similar blank set, and the historical similar blanks before the target similar blank are sequentially stored behind the last historical similar blank in the previous set range; and taking the new similar blank as the last similar blank in the shared memory, and updating the stored similar blanks.
And if not, deleting the first historical similar blank in the historical similar blank set, taking the new similar blank as the last similar blank in the shared memory, and updating the stored similar blanks.
It should be noted that the former setting range is usually the first 1000 pieces of the shared memory, and may also be set according to actual requirements.
For example, 6 ten thousand similar blanks can be stored in the shared memory, and the blanks are sorted in the order of 00001, 00002. When the similar blanks stored in the shared memory reach the upper limit, extracting previous thousand historical similar blanks in the shared memory, namely the historical similar blank with the number of 00001 to the historical similar blank with the number of 01000. The method comprises the steps that 2 current similar blanks are respectively A and B, the storage time of A is 2 months and 10 days in 2012, the storage time of B is 9 months and 22 days in 2015, and the current similar blank with the earliest storage time is deleted, namely A is deleted. Then, whether A exists in the previous thousand historical similar blanks is judged.
When the historical similar blank with the number of 00001 is A, the historical similar blank with the number of 00001 is deleted, the positions of the remaining 59999 similar blanks are moved upwards as a whole, and the new similar blank is stored in the position of the similar blank with the number of 60000.
When the history similar blank of 00001 is not a and the history similar blank of 00002 is a, the history similar blank of 00002 is deleted, the history similar blank of 00001 is stored in the position behind the history similar blank of 01000, the positions of the remaining 59999 similar blanks are wholly moved upwards, and then the new similar blank is stored in the position of the similar blank with the number of 60000.
When the history similar blanks 00001 and 00002 are not A and the history similar blank 00003 is A, deleting the history similar blank 00003, sequentially storing the history similar blanks 00001 and 00002 in the positions behind the history similar blank 01000, moving the positions of the remaining 59999 similar blanks upwards as a whole, and storing the new similar blank in the position of the similar blank with the number 60000. And so on.
When none of the previous one thousand historical similar blanks finds A, 00001 historical similar blanks are directly deleted, the positions of the remaining 59999 similar blanks are integrally moved upwards, and new similar blanks are stored in the position of the similar blank with the number of 60000.
When the similar blanks stored in the self-learning file reach the upper limit, a new similar blank needs to be stored in the self-learning file, and one similar blank in the self-learning file needs to be deleted. Specifically, when the similar blanks stored in the self-learning file reach the upper limit, the first similar blank in the self-learning file is deleted, and then the new similar blank is used as the last similar blank in the self-learning file.
For example, 200 similar billets can be stored in the self-learning file, ordered in the order of 001, 002. When the similar blanks stored in the self-learning file reach the upper limit, the No. 001 historical similar blank is directly deleted, the No. 002 to No. 200 historical similar blanks are integrally moved upwards, and then the new similar blank is stored in the No. 200 historical similar blank position.
When new similar billets are stored in the self-learning file and the shared memory at the same time, the self-learning file and the shared memory are updated once, and a foundation is provided for the follow-up rolling of the strip steel and the strip steel coiling control. The self-learning file still adopts a first-in first-out deleting rule, the shared memory adopts an optimized deleting rule, and the two deleting rules can efficiently and reliably screen and delete the historical similar blanks, so that the reliable historical similar blanks are provided for the next time of rolling strip steel and coiling the strip steel.
One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages:
in the embodiment, more data are provided for searching the current similar blank by opening up the shared memory to store more data of the similar blank, so that the searched current similar blank is more accurate. The self-learning file adopts a first-in first-out deleting principle, and the shared memory adopts an optimized deleting rule, so that the data of the non-rolled steel grade can be continuously stored in the shared memory for a long time, and the subsequent strip steel rolling can be corrected. The method solves the problems that the precision of the obtained correction factor is low because the model cannot find the similar billets and the like can be caused by the problems that the similar billets are deleted, the similar billets stored in the self-learning file are deleted, the rolling data are modified, or the self-learning file is emptied due to abnormal restarting of the model, and the like, because the quantity of the similar billets stored in the shared memory is more, and the similar stored data are deleted and the similar stored data are written into the deleting rule of the similar billets of the previous strip steel after the similar stored data are deleted.
Example two
Based on the same inventive concept, a second embodiment of the present invention further provides a correction factor obtaining device for a coiling temperature of a strip steel, as shown in fig. 2, including:
a first obtaining module 201, configured to obtain current rolling data of a current strip steel;
the screening module 202 is configured to search the current similar blank of the current strip steel from the shared memory according to the current rolling data; when the similar blanks stored in the shared memory reach the upper limit, similar storage data similar to the previous similar blank of the strip steel is searched from the similar blank in the previous set range in the shared memory, and the similar storage data is deleted and written into the previous similar blank of the strip steel;
and the second obtaining module 203 is configured to obtain a current correction factor of the current strip steel according to the current similar blank.
As an alternative embodiment, the apparatus further comprises an update module 204;
the updating module 204 is configured to generate a new similar billet from the current rolling data and the current correction factor, and synchronously store the new similar billet into a self-learning file and the shared memory.
As an alternative embodiment, the update module 204 is further configured to:
when the similar blanks stored in the shared memory reach the upper limit, calling a historical similar blank set in the previous set range;
searching in the historical similar blank set according to the current similar blank;
if so, taking the current similar blank with the earliest searched storage time as a target similar blank, deleting the target similar blank from the historical similar blank set, and sequentially storing the historical similar blanks before the target similar blank behind the last historical similar blank in the previous set range;
and taking the new similar blank as the last similar blank in the shared memory, and updating the stored similar blanks.
As an alternative embodiment, the update module 204 is further configured to: and if not, deleting the first historical similar blank in the historical similar blank set, taking the new similar blank as the last similar blank in the shared memory, and updating the stored similar blanks.
As an alternative embodiment, the update module 204 is further configured to: and when the similar blanks stored in the self-learning file reach the upper limit, deleting the first similar blank in the self-learning file, and taking the new similar blank as the last similar blank in the self-learning file.
As an alternative embodiment, the screening module 202 is further configured to:
according to the sequence from bottom to top and according to the current rolling data, obtaining the similarity of each stored similar blank; wherein the bottom-up order is an order of visits from a last one to a first one of the stored similar blanks;
and screening the current similar blanks from the stored similar blanks according to the similarity of each stored similar blank, wherein the current similar blank is the stored similar blank with the similarity within a threshold range.
As an alternative embodiment, the second obtaining module 203 is further configured to:
and obtaining the current correction factor according to the similarity of the current similar blank, the rolling time and the correction factor of the current similar blank.
Since the correction factor obtaining device for strip steel coiling temperature described in this embodiment is a device used for implementing the correction factor obtaining method for strip steel coiling temperature described in the first embodiment of this application, based on the correction factor obtaining method for strip steel coiling temperature described in the first embodiment of this application, a person skilled in the art can understand a specific implementation manner of the correction factor obtaining device for strip steel coiling temperature described in this embodiment of this application and various variations thereof, and therefore, how to implement the method described in the first embodiment of this application by the correction factor obtaining device for strip steel coiling temperature is not described in detail here. As long as the device adopted by the person skilled in the art to implement the method for obtaining the correction factor of the coiling temperature of the strip steel in the embodiment of the present application is within the scope of the protection of the present application.
EXAMPLE III
Based on the same inventive concept, the third embodiment of the present invention further provides a computer device, as shown in fig. 3, including a memory 304, a processor 302, and a computer program stored in the memory 304 and executable on the processor 302, wherein the processor 302 implements the steps of any one of the above methods for obtaining the correction factor of the strip coiling temperature 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 four
Based on the same inventive concept, a fourth embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of any one of the methods of the correction factor acquisition method for strip coiling temperature described in the first embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. The method for acquiring the correction factor of the strip steel coiling temperature is characterized by comprising the following steps of:
acquiring current rolling data of the current strip steel;
searching the current similar blank of the current strip steel from a shared memory according to the current rolling data; when the similar blanks stored in the shared memory reach the upper limit, similar storage data similar to the previous similar blank of the strip steel is searched from the similar blank in the previous set range in the shared memory, and the similar storage data is deleted and written into the previous similar blank of the strip steel;
and obtaining the current correction factor of the current band steel according to the current similar blank.
2. The method of claim 1, wherein after obtaining the current correction factor for the current strip, further comprising:
and generating a new similar billet by using the current rolling data and the current correction factor, and synchronously storing the new similar billet into a self-learning file and the shared memory.
3. The method of claim 2, wherein after storing the new similar blank in the shared memory, further comprising:
when the similar blanks stored in the shared memory reach the upper limit, calling a historical similar blank set in the previous set range;
searching in the historical similar blank set according to the current similar blank;
if so, taking the current similar blank with the earliest searched storage time as a target similar blank, deleting the target similar blank from the historical similar blank set, and sequentially storing the historical similar blanks before the target similar blank behind the last historical similar blank in the previous set range;
and taking the new similar blank as the last similar blank in the shared memory, and updating the stored similar blanks.
4. The method of claim 3, wherein said searching in said historical set of similar blanks based on said current similar blank further comprises:
and if not, deleting the first historical similar blank in the historical similar blank set, taking the new similar blank as the last similar blank in the shared memory, and updating the stored similar blanks.
5. The method of claim 2, wherein said storing said new similar billet into said self-learning file comprises:
and when the similar blanks stored in the self-learning file reach the upper limit, deleting the first similar blank in the self-learning file, and taking the new similar blank as the last similar blank in the self-learning file.
6. The method of claim 1, wherein said searching for the current similar slab of the current strip from the shared memory comprises:
according to the sequence from bottom to top and according to the current rolling data, obtaining the similarity of each stored similar blank; wherein the bottom-up order is an order of visits from a last one to a first one of the stored similar blanks;
and screening the current similar blanks from the stored similar blanks according to the similarity of each stored similar blank, wherein the current similar blank is the stored similar blank with the similarity within a threshold range.
7. The method of claim 1, wherein said obtaining a current correction factor for said current strip based on said current similar blank comprises:
and obtaining the current correction factor according to the similarity of the current similar billet, the rolling time and the correction factor of the current similar billet.
8. The utility model provides a correction factor acquisition device of strip steel coiling temperature which characterized in that includes:
the first acquisition module is used for acquiring the current rolling data of the current strip steel;
the screening module is used for searching the current similar blank of the current band steel from the shared memory according to the current rolling data; when the similar blanks stored in the shared memory reach the upper limit, similar storage data similar to the previous similar blank of the strip steel is searched from the similar blank in the previous set range in the shared memory, and the similar storage data is deleted and written into the previous similar blank of the strip steel;
and the second acquisition module is used for acquiring the current correction factor of the current strip steel according to the current similar blank.
9. A computer 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 |
|---|---|---|---|
| CN202110625075.9A CN113342875A (en) | 2021-06-04 | 2021-06-04 | Correction factor obtaining method and device for strip steel coiling temperature |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110625075.9A CN113342875A (en) | 2021-06-04 | 2021-06-04 | Correction factor obtaining method and device for strip steel coiling temperature |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113342875A true CN113342875A (en) | 2021-09-03 |
Family
ID=77473997
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110625075.9A Pending CN113342875A (en) | 2021-06-04 | 2021-06-04 | Correction factor obtaining method and device for strip steel coiling temperature |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113342875A (en) |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| BE822244A (en) * | 1973-11-15 | 1975-05-14 | PROCESS FOR MAINTAINING STEEL AT A PREFIXED TEMPERATURE DURING ITS ROLLING ON A CONTINUOUS HOT ROLLING MACHINE | |
| JP2000263110A (en) * | 1999-03-10 | 2000-09-26 | Toshiba Corp | Learning apparatus for rolling control model |
| KR20030053621A (en) * | 2001-12-22 | 2003-07-02 | 주식회사 포스코 | Hot strip cooling control mothode for chage target temperature |
| KR20080060686A (en) * | 2006-12-27 | 2008-07-02 | 주식회사 포스코 | A calibration method of coiling temperature of hot rolled steel sheet and the prediction of mechanical properties |
| CN104841701A (en) * | 2014-02-14 | 2015-08-19 | 宝山钢铁股份有限公司 | Sheet coiling temperature control method in big-speed-reduction rolling of hot-rolled strip steel |
| CN105537281A (en) * | 2015-12-07 | 2016-05-04 | 北京首钢股份有限公司 | Method for improving coiling temperature control accuracy under laminar cooling model |
| US20160171009A1 (en) * | 2014-12-10 | 2016-06-16 | International Business Machines Corporation | Method and apparatus for data deduplication |
| EP3076283A1 (en) * | 2015-03-31 | 2016-10-05 | Advanced Digital Broadcast S.A. | System and method for managing content deletion |
| JP2017202514A (en) * | 2016-05-13 | 2017-11-16 | 新日鐵住金株式会社 | Cooling control method and production method for thick steel plate |
| CN108921232A (en) * | 2018-07-31 | 2018-11-30 | 东北大学 | A kind of hot-strip Cooling History data clusters and method for measuring similarity |
| CN109772900A (en) * | 2017-11-14 | 2019-05-21 | 宝山钢铁股份有限公司 | A method of improving hot rolling new steel grade new spec oiler temperature control |
| CN110340156A (en) * | 2019-07-31 | 2019-10-18 | 首钢京唐钢铁联合有限责任公司 | Strip Steel Coiling Temperature control method, device and strip system of processing |
-
2021
- 2021-06-04 CN CN202110625075.9A patent/CN113342875A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| BE822244A (en) * | 1973-11-15 | 1975-05-14 | PROCESS FOR MAINTAINING STEEL AT A PREFIXED TEMPERATURE DURING ITS ROLLING ON A CONTINUOUS HOT ROLLING MACHINE | |
| JP2000263110A (en) * | 1999-03-10 | 2000-09-26 | Toshiba Corp | Learning apparatus for rolling control model |
| KR20030053621A (en) * | 2001-12-22 | 2003-07-02 | 주식회사 포스코 | Hot strip cooling control mothode for chage target temperature |
| KR20080060686A (en) * | 2006-12-27 | 2008-07-02 | 주식회사 포스코 | A calibration method of coiling temperature of hot rolled steel sheet and the prediction of mechanical properties |
| CN104841701A (en) * | 2014-02-14 | 2015-08-19 | 宝山钢铁股份有限公司 | Sheet coiling temperature control method in big-speed-reduction rolling of hot-rolled strip steel |
| US20160171009A1 (en) * | 2014-12-10 | 2016-06-16 | International Business Machines Corporation | Method and apparatus for data deduplication |
| EP3076283A1 (en) * | 2015-03-31 | 2016-10-05 | Advanced Digital Broadcast S.A. | System and method for managing content deletion |
| CN105537281A (en) * | 2015-12-07 | 2016-05-04 | 北京首钢股份有限公司 | Method for improving coiling temperature control accuracy under laminar cooling model |
| JP2017202514A (en) * | 2016-05-13 | 2017-11-16 | 新日鐵住金株式会社 | Cooling control method and production method for thick steel plate |
| CN109772900A (en) * | 2017-11-14 | 2019-05-21 | 宝山钢铁股份有限公司 | A method of improving hot rolling new steel grade new spec oiler temperature control |
| CN108921232A (en) * | 2018-07-31 | 2018-11-30 | 东北大学 | A kind of hot-strip Cooling History data clusters and method for measuring similarity |
| CN110340156A (en) * | 2019-07-31 | 2019-10-18 | 首钢京唐钢铁联合有限责任公司 | Strip Steel Coiling Temperature control method, device and strip system of processing |
| US20210031252A1 (en) * | 2019-07-31 | 2021-02-04 | Shougang Jingtang Iron & Steel Co., Ltd. | Steel strip coiling temperature control method, device for the same and steel strip processing system |
Non-Patent Citations (4)
| Title |
|---|
| 彭良贵;刘恩洋;张殿华;杨贵玲;郭宏伟;王丰祥;: "基于案例推理的层流冷却自学习方法研究", 钢铁, no. 12, 15 December 2011 (2011-12-15) * |
| 李振垒;陈冬;袁国;尹锋;曹利芹;王国栋;: "热轧带钢超快速冷却系统智能化控制策略研究", 轧钢, no. 06, 15 December 2019 (2019-12-15) * |
| 赵强;唐婧;王海玉;王淑志;: "基于数据挖掘的热轧带钢卷取温度控制方法", 冶金自动化, no. 01, 6 January 2017 (2017-01-06), pages 2 - 3 * |
| 邢俊芳;彭良贵;陆凤慧;田鹏;: "1780线自适应模型在控制卷取温度精度上的研究", 冶金管理, no. 05, 15 March 2019 (2019-03-15) * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2019001120A1 (en) | Method and system for processing dynamic pricing data of commodity | |
| JP2022500769A (en) | Power system heat load prediction method and prediction device | |
| KR101832653B1 (en) | Rolling process learning control device | |
| JP2019087152A (en) | Manufacturing process state prediction device, method, and program, and manufacturing process control system | |
| CN108921232B (en) | Hot-rolled strip steel cooling historical data clustering and similarity measuring method | |
| CN112419729B (en) | Method and system for determining decision strategy according to predicted traffic flow | |
| CN112201075B (en) | Parking lot parking space availability prediction method and system | |
| CN112950262A (en) | Sales prediction method, sales prediction apparatus, computer device, and storage medium | |
| JP2007061870A (en) | Device and method for preparing rolling schedule, computer program and computer readable storage medium | |
| CN114474072A (en) | Track fusion method, device, equipment and storage medium | |
| CN117236529B (en) | Large-scale movable people flow prediction method based on big data | |
| CN104765782A (en) | Index sequencing updating method and device | |
| CN113342875A (en) | Correction factor obtaining method and device for strip steel coiling temperature | |
| CN105512313B (en) | A kind of method and apparatus of incremented data processing | |
| JP2003015734A (en) | Time series data compressing method and time series data storing device and its program | |
| JP6593194B2 (en) | PBS shipping permutation determination device for automobile production line | |
| EP3745344A1 (en) | Prediction system, prediction method, and program | |
| CN106537442A (en) | Collection amount regulation assist apparatus, collection amount regulation assist method, and computer-readable recording medium | |
| JP4298581B2 (en) | Power demand forecasting device, power demand forecasting system, power demand forecasting program, recording medium, and power demand forecasting method | |
| JP7135435B2 (en) | Information processing device and program | |
| CN114152263B (en) | Path planning method, system, electronic equipment and storage medium | |
| CN110794778B (en) | Method for processing water cooling efficiency value | |
| CN112686416A (en) | Wind power probability prediction method and system | |
| JP2766032B2 (en) | Travel time calculation method | |
| CN110858355A (en) | Project budget balance prediction method and device |
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 |