CN111460376B - Multi-mode identification method, equipment and storage equipment for annealing heating process - Google Patents
Multi-mode identification method, equipment and storage equipment for annealing heating process Download PDFInfo
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Abstract
The invention provides a multi-mode identification method, equipment and storage equipment for an annealing heating process, wherein the method comprises the following steps: determining the production operation mode and key variables of the strip steel when the strip steel is heated in a continuous annealing furnace according to the continuous annealing production process of the cold-rolled strip steel; determining the data distribution characteristics of the key variables according to the historical operating data of the key variables, and establishing a state detection model of the key variables according to the data distribution characteristics; carrying out state detection on the key variable by adopting the state detection model to obtain the state of the key variable; and identifying the mode of the continuous annealing heating process of the cold-rolled strip steel according to the state and the multi-mode identification strategy. The invention has the beneficial effects that: the computer replaces central control personnel to identify the production state of the strip steel during annealing and heating, so that the manpower is reduced; the production states are effectively judged by combining a multi-parameter detection common decision, so that errors caused by the subjectivity of manual identification are reduced.
Description
Technical Field
The invention relates to the technical field of process control, in particular to a multi-mode identification method, equipment and storage equipment for an annealing heating process.
Background
Along with the increasingly intense homogenization competition of steel enterprises in China in recent years, the production of high-quality steel is more and more emphasized. Among them, cold-rolled steel strip is one of representatives of high-quality steel. The Continuous Annealing Process (CAP), i.e. the process of heating various combinations of strip steels by a strip steel continuous annealing furnace, is the most critical process on a cold-rolled strip steel production line and is directly related to the quality of the final strip steel products.
In order to improve the quality of the strip steel, the stability, uniformity and consistency of CAP need to be ensured, so that the modeling, control and optimization methods under different production states need to be researched. Although a great deal of research has been conducted in the modeling, control and optimization aspects of the process in recent years, most of the research assumes that the problem of distinguishing and identifying different production states in the process is not solved on the premise of a certain type of production state. The actual production state is the premise of the evaluation of the operation performance of the system model and the simulation and optimization of the system. Different production states correspond to different operation schemes so as to ensure the normal operation of the continuous annealing production line and the quality of finished products of the strip steel.
Therefore, in the face of CAP, it is important to provide a targeted detection method to identify the production state of the process. Due to the complexity of industrial processes, there is currently no uniform definition for the differentiation of production states, and different industrial objects need to be analyzed and identified according to their actual process characteristics. One method for state discrimination and identification of an industrial process is to utilize a mechanistic model of the process. However, as the specification of the strip steel on the continuous annealing production line is continuously increased, the field production mode is more complex, so that a mechanism model cannot be obtained, or the simplified model is difficult to describe the actual continuous annealing production process; on the other hand, with the development and maturity of big data research technologies in recent years, more and more research is beginning to utilize the massive data resources in industrial processes to distinguish and identify the different production states of the processes. But the data-based driving method is not highly physically interpretable, so that the control of the production process has no practical reference value.
Therefore, for the current situation that the differentiation and identification of the production state in the CAP are lack of research, different production states existing in the process are analyzed in detail according to the process characteristics of the process and by combining field experience and a large amount of historical data, and a production state identification method based on multi-feature parameter detection is provided according to the features of key parameters, so that reference is provided for online identification of the different production states in the process, and support is provided for subsequent research work of the CAP.
Disclosure of Invention
In order to solve the above problems, the present invention provides a multi-modal identification method, device and storage device for annealing heating process, and the multi-modal identification method for annealing heating process mainly comprises the following steps:
s101: determining the production operation mode and key variables of the strip steel when the strip steel is heated in a continuous annealing furnace according to the continuous annealing production process of the cold-rolled strip steel;
s102: determining the data distribution characteristics of the key variables according to the historical operating data of the key variables, and establishing a state detection model of the key variables according to the data distribution characteristics;
s103: carrying out state detection on the key variable by adopting the state detection model to obtain the state of the key variable;
s104: and identifying the mode of the continuous annealing heating process of the cold-rolled strip steel according to the state and the multi-mode identification strategy.
Further, in step S101, the production operation mode includes: the method comprises the following steps of stabilizing a heating production mode, switching modes of steel coils with different specifications, a steady center offset mode and an abnormal state mode of the steel coils with the same specification; the key variables include: plate temperature, strip steel specification and production running speed.
Further, in step S102, the specific step of establishing a state detection model for a certain key variable includes:
s201: acquiring historical operating data of the key variable, counting a plurality of transition times between every two continuous stable states in the historical operating data, and averaging to obtain an average transition time t between the two continuous stable states of the key variable;
s202: judging whether a condition t < delta is satisfied or not; if yes, it indicates that the key variable has transient characteristics, go to step S203; otherwise, the variable is described to have a slowly varying characteristic, and the step S204 is executed; wherein, delta is a preset value and is determined according to the field working condition and the historical experience;
s203: establishing a state detection model of the key variable by adopting a nonparametric CUSUM algorithm, and going to step S205;
s204: establishing a state detection model of the key variable by adopting a confidence interval algorithm;
s205: and (6) ending.
Further, in step S103, performing state detection on the key variable by using the state detection model; the method comprises the following steps: performing online state detection on the key variable with the slowly varying characteristic by adopting a state detection model established by a confidence interval algorithm, and performing online state detection on the key variable with the transient characteristic by adopting a state detection model established by a nonparametric CUSUM algorithm;
performing online state detection on a key variable with slowly varying characteristics by adopting a state detection model established by a confidence interval algorithm to obtain a state corresponding to the key variable; the method specifically comprises the following steps:
s401: aiming at key variables with slowly-varying characteristics, screening out a first historical operating data sequence y in a normal production state1,y2,...,yk；
Calculating to obtain the first historical operating data sequence y1,y2,...,ykHas an average value of
A deviation value of
S402: calculating the standard deviation of the first historical operation data sequence by using a formula (1), and obtaining an interval boundary +/-n sigma corresponding to P according to the occurrence probability P of the data in a stable state in all data counted in advance and a standard normal distribution table; determining that the steady-state data is distributed within a range of ± n σ centered on the reference value;
in the above formula, viFor the ith data y in the first historical operating data sequenceiA corresponding deviation value;
s402: acquiring an operation data sequence to be detected, and determining the state of the operation data sequence to be detected by using continuous s data as a group of detection data for the data in the operation data sequence to be detected;
for each group of detection data, the specific judgment method is as follows: respectively calculating the set of s data y1,y2,...,ysObtaining s deviations according to the corresponding deviations; averaging the s deviations to obtain a mean value v;
if the | v | > n σ indicates that the group of detection data belongs to the variable point region, judging that the state of the key variable in the group of detection data is a fluctuation state; otherwise, it is in a steady state.
Further, in step S103, performing state detection on the key variable with transient characteristics by using a state detection model established by a nonparametric CUSUM algorithm to obtain a state corresponding to the key variable; the method specifically comprises the following steps:
s301: aiming at key variables with transient characteristics, acquiring operation data sequence y to be detected of key variables1’,y2’,...,yk’；
Suppose a running data sequence y1’,y2’,...,yk' data sequence therein
Is a sequence of operational data in a steady state,
is a running data sequence in a fluctuating state;
for data sequence y1’,...,yi' (i ═ 2.. said., k) whose probability density functions belong to the steady state and the wave state, respectively, are P0(yi') and P1(yi') to a host; if i<t0Then P is1(yi’)<P0(yi') to a host; otherwise, P1(yi’)>P0(yi’)；
Defining a log-likelihood ratio as shown in equation (2):
the upper typeIn, siFor a data sequence y1’,...,yi' corresponding log-likelihood ratios;
s302: calculating cumulative sums of log-likelihood ratios
S303: training a CUSUM (compute unified device architecture) method by using a labeled speed test data set to obtain a decision threshold h;
s304: according to a decision function gjJudging the state of the key variable with transient characteristics; decision function gjThe calculation formula is shown in formula (3):
if decision function gj>h, indicating that a change point, i.e., a surge state, has occurred, and the change point is
Namely, it is
Followed by a surge condition; otherwise, it belongs to the stable state.
Further, in step S105, identifying the mode of the continuous annealing heating process of the cold-rolled steel strip in real time according to the real-time state and the multi-mode identification strategy; the method specifically comprises the following steps:
the multi-modal recognition strategy is:
when the plate temperature is in a stable state, the mode is an S1 mode;
when the plate temperature is in a fluctuation state and the specification of the strip steel is in a fluctuation state, the strip steel is in an S2 mode;
when the plate temperature is in a fluctuation state, the specification of the strip steel is in a stable state, and the production running speed is in a fluctuation state, the mode is an S3 mode;
when the plate temperature is in a fluctuation state, the specification of the strip steel is in a stable state, and the production running speed is in a stable state, the mode is an S4 mode;
wherein, S1 is a stable heating production mode, S2 is a switching mode of steel coils with different specifications, S3 is a steady center offset mode of steel coils with the same specification, and S4 is an abnormal state mode.
A computer-readable storage medium storing instructions and data for implementing a multi-modal identification method for an annealing heating process.
A multi-modal identification device for an annealing heating process, comprising: a processor and the storage device; the processor loads and executes the instructions and data in the storage device to realize a multi-mode recognition method for the annealing heating process.
The technical scheme provided by the invention has the beneficial effects that: the technical scheme provided by the application aims at the complex industrial process of continuous annealing and heating of the strip steel, and provides the multi-mode identification method aiming at some practical industrial problems existing when the strip steel is annealed in a continuous annealing furnace.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a multi-modal identification method for an annealing heating process according to an embodiment of the present invention;
FIG. 2 is a graphical illustration of the cumulative sum of production run speeds for an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating the CUSUM method training according to an embodiment of the present invention;
fig. 4 is a schematic diagram of the operation of the hardware device in the embodiment of the present invention.
Detailed Description
For a more clear understanding of the technical features, objects and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
The embodiment of the invention provides a multi-mode identification method, equipment and storage equipment for an annealing heating process.
Referring to fig. 1, fig. 1 is a flowchart of a multi-modal identification method for an annealing heating process in an embodiment of the present invention, which specifically includes the following steps:
s101: determining the production operation mode and key variables of the strip steel when the strip steel is heated in a continuous annealing furnace according to the continuous annealing production process of the cold-rolled strip steel;
s102: determining the data distribution characteristics of the key variables according to the historical operating data of the key variables, and establishing a state detection model of the key variables according to the data distribution characteristics;
s103: carrying out state detection on the key variable by adopting the state detection model to obtain the state of the key variable;
s104: and identifying the mode of the continuous annealing heating process of the cold-rolled strip steel according to the state and the multi-mode identification strategy.
In step S101, the production operation mode includes: the method comprises the following steps of stabilizing a heating production mode, switching modes of steel coils with different specifications, a steady center offset mode and an abnormal state mode of the steel coils with the same specification; the key variables include: plate temperature, strip steel specification and production running speed.
In step S102, the specific step of establishing a state detection model for a certain key variable includes:
s201: acquiring historical operating data of the key variable, counting a plurality of transition times between every two continuous stable states in the historical operating data, and averaging to obtain an average transition time t between the two continuous stable states of the key variable;
s202: judging whether a condition t < delta is satisfied or not; if yes, it indicates that the key variable has transient characteristics, go to step S203; otherwise, the variable is described to have a slowly varying characteristic, and the step S204 is executed; wherein, delta is a preset value and is determined according to the field working condition and the historical experience;
s203: establishing a state detection model of the key variable by adopting a nonparametric CUSUM algorithm, and going to step S205;
s204: establishing a state detection model of the key variable by adopting a confidence interval algorithm;
s205: and (6) ending.
In step S103, the state detection model is adopted to carry out state detection on the key variables; the method comprises the following steps: performing online state detection on the key variable with the slowly varying characteristic by adopting a state detection model established by a confidence interval algorithm, and performing online state detection on the key variable with the transient characteristic by adopting a state detection model established by a nonparametric CUSUM algorithm;
performing online state detection on a key variable with slowly varying characteristics by adopting a state detection model established by a confidence interval algorithm to obtain a state corresponding to the key variable; the method specifically comprises the following steps:
s401: aiming at key variables with slowly-varying characteristics, screening out a first historical operating data sequence y under a normal production state (a state when a continuous annealing furnace normally operates and is not shut down or under other abnormal conditions)1,2，...，yk；
Calculating to obtain the first historical operating data sequence y1，y2，...，ykReference value of
A deviation value of
Wherein the reference value
Generally, a mean number, a median number or a mode number is selected, and the mean number is used as a reference value in the embodiment of the invention;
s402: calculating the standard deviation of the first historical operation data sequence by using a formula (1), and obtaining an interval boundary +/-n sigma corresponding to P according to the occurrence probability P of the data in a stable state in all data and a standard normal distribution table; determining that the steady-state data is distributed within a range of ± n σ centered on the reference value;
in the above formula, viFor the ith data y in the first historical operating data sequenceiA corresponding deviation value; the probability P is obtained in advance through statistical experiments; in the embodiment of the present invention, n is 3, that is, the interval boundary corresponding to P is ± 3 σ;
s402: acquiring an operation data sequence to be detected, and determining the state of the operation data sequence to be detected by using continuous s data as a group of detection data for the data in the operation data sequence to be detected;
for each group of detection data, the specific judgment method is as follows: respectively calculating the set of s data y1，y2,...，ysObtaining s deviations according to the corresponding deviations; averaging the s deviations to obtain a mean value v;
if the | v | > n σ indicates that the group of detection data belongs to the variable point region, judging that the state of the key variable in the group of detection data is a fluctuation state; otherwise, it is in a steady state.
In step S103, a state detection model established by a nonparametric CUSUM algorithm is adopted to carry out state detection on the key variable with transient characteristics, and the state of the corresponding key variable is obtained; the method specifically comprises the following steps:
s301: aiming at key variables with transient characteristics, acquiring operation data sequence y to be detected of key variables1’，y2’，...，yk’；
Suppose a running data sequence y1’,y2’,...,yk' data sequence therein
Is a sequence of operational data in a steady state,
is a running data sequence in a fluctuating state;
for data sequence y1’,...,yi' (i ═ 2.. k), which belongs to the probability density function of steady state and fluctuating stateEach number is P0(yi') and P1(yi') to a host; if i<t0Then P is1(yi’)<P0(yi') to a host; otherwise, P1(yi’)>P0(yi’)；
Defining a log-likelihood ratio as shown in equation (2):
in the above formula, siFor a data sequence y1’,...,yi' corresponding log-likelihood ratios;
s302: calculating cumulative sums of log-likelihood ratios
When S isjCorresponding data sequence y1’,...,yj' when all belong to the steady state, the sum S is accumulatedjMonotonically decreasing; when S isjCorresponding data sequence y1’,...,yj' when data in the state of fluctuation, the sum S is accumulatedjMonotonically increasing; FIG. 2 is a graphical illustration of the cumulative sum of production run speeds for an embodiment of the present invention, as shown in FIG. 2;
s303: training a CUSUM (compute unified device architecture) method by using a labeled speed test data set to obtain a decision threshold h;
s304: according to a decision function gj(namely the difference between the current moment and the minimum value of the accumulated sum of the log-likelihood ratios in the steady state), judging the state of the key variable with transient characteristics; decision function gjThe calculation formula is shown in formula (3):
if decision function gj>h, indicating that a change point, i.e., a surge state, has occurred, and the change point is
Namely, it is
Followed by a surge condition; otherwise, it belongs to the stable state.
Referring to fig. 3, fig. 3 is a schematic diagram illustrating the principle of training the CUSUM method according to the embodiment of the present invention; in step S303, training a CUSUM method by using a labeled speed test data set to obtain a decision threshold h; the method specifically comprises the following steps:
s501: aiming at key variables with transient characteristics, screening n labeled historical operating data sequences under the normal production state (the state of normal operation of the continuous annealing furnace) to form a labeled speed test data set, and respectively training a CUSUM (compute unified modeling) method by using the n labeled historical operating data sequences to obtain corresponding decision threshold h1,h2,...,hn(ii) a Get h1,h2,...，hnThe average value of (a) is used as a decision threshold h;
historical operating data sequence y for the Tth labeled1”y2”，...，yk", the specific training process includes:
suppose a data sequence y1”，y2”，...，yt0"is a running data sequence in steady state, yto”,y2”，...，yk"is a running data sequence in a fluctuating state;
for data sequence y1”，y2”，...，yi"(i ═ 2.., k), whose probability density functions belong to the steady state and the wave state, respectively, are P0(yi") and P1(yi"); if i<t0Then P is1(yi”)<P0(yi"); otherwise, P1(yi”)>P0(yi”)；
Defining a log-likelihood ratio as shown in equation (4):
in the above formula, siFor a data sequence y1”,y2”,...,yi"corresponding log-likelihood ratios;
s502: calculating cumulative sums of log-likelihood ratios
When S isjCorresponding data sequence y1’,...,yj' when all belong to the steady state, the sum S is accumulatedjMonotonically decreasing; when S isjCorresponding data sequence y1’,...,yj' when data in the state of fluctuation, the sum S is accumulatedjMonotonically increasing;
s503: calculating a decision threshold h according to equation (5)T
In the above formula, StoFor a data sequence y1”,y2”,...,yt0"corresponding log-likelihood ratio cumulative sums; h isTTraining a CUSUM method for the T-th labeled historical operation data sequence to obtain a decision threshold; t1, 2.
The multi-modal recognition strategy is specifically shown in the following table:
step S105, identifying the mode of the continuous annealing heating process of the cold-rolled strip steel in real time according to the real-time state and the multi-mode identification strategy; the method specifically comprises the following steps:
the multi-modal recognition strategy is:
the priority of each key variable is: the plate temperature is higher than the specification of the strip steel and the production running speed is higher than the production running speed;
when the plate temperature is in a stable state, the mode is an S1 mode;
when the plate temperature is in a fluctuation state and the specification of the strip steel is in a fluctuation state, the strip steel is in an S2 mode;
when the plate temperature is in a fluctuation state, the specification of the strip steel is in a stable state, and the production running speed is in a fluctuation state, the mode is an S3 mode;
when the plate temperature is in a fluctuation state, the specification of the strip steel is in a stable state, and the production running speed is in a stable state, the mode is an S4 mode;
wherein, S1 is a stable heating production mode, S2 is a switching mode of steel coils with different specifications, S3 is a steady center offset mode of steel coils with the same specification, and S4 is an abnormal state mode.
Referring to fig. 4, fig. 4 is a schematic diagram of a hardware device according to an embodiment of the present invention, where the hardware device specifically includes: a multi-modal identification device 401, a processor 402 and a storage device 303 for an annealing heating process.
A multi-modal recognition device 401 for annealing heating processes: the multi-modal identification device 401 for the annealing heating process realizes the multi-modal identification method for the annealing heating process.
The processor 402: the processor 402 loads and executes the instructions and data in the storage device 403 to realize the multi-modal identification method for the annealing heating process.
Computer-readable storage medium 403: the computer-readable storage medium 403 stores instructions and data; the storage device 403 is used to implement the multi-modal identification method for the annealing heating process.
The invention has the beneficial effects that: the technical scheme provided by the application aims at the complex industrial process of continuous annealing and heating of the strip steel, and provides the multi-mode identification method aiming at some practical industrial problems existing when the strip steel is annealed in a continuous annealing furnace.
The generation reasons of 4 production states in the process and the identification strategy of the joint decision combining the key parameters are explained, meanwhile, the specific production state in the actual continuous annealing and heating process of the strip steel cannot be identified by explaining the result of single parameter detection by combining historical data of an industrial field, and the joint decision combining multi-parameter detection can effectively judge the production states.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (8)
1. A multi-mode identification method for an annealing heating process is characterized in that: the method comprises the following steps:
s101: determining the production operation mode and key variables of the strip steel when the strip steel is heated in a continuous annealing furnace according to the continuous annealing production process of the cold-rolled strip steel;
s102: determining the data distribution characteristics of the key variables according to the historical operating data of the key variables, and establishing a state detection model of the key variables according to the data distribution characteristics;
s103: carrying out online state detection on the key variable with the slowly changing characteristic by adopting a state detection model established by a confidence interval algorithm, and carrying out online state detection on the key variable with the transient characteristic by adopting a state detection model established by a nonparametric CUSUM algorithm to obtain the state of the key variable;
s104: and identifying the mode of the continuous annealing heating process of the cold-rolled strip steel according to the state and the multi-mode identification strategy.
2. The multi-modal identification method for the annealing heating process as claimed in claim 1, wherein: in step S101, the production operation mode includes: the method comprises the following steps of stabilizing a heating production mode, switching modes of steel coils with different specifications, a steady center offset mode and an abnormal state mode of the steel coils with the same specification; the key variables include: plate temperature, strip steel specification and production running speed.
3. The multi-modal identification method for the annealing heating process as claimed in claim 2, wherein: in step S102, the specific step of establishing a state detection model for a certain key variable includes:
s201: acquiring historical operating data of the key variable, counting a plurality of transition times between every two continuous stable states in the historical operating data, and averaging to obtain an average transition time t between the two continuous stable states of the key variable;
s202: judging whether the condition t is more than delta or not; if yes, it indicates that the key variable has transient characteristics, go to step S203; otherwise, the variable is described to have a slowly varying characteristic, and the step S204 is executed; wherein, delta is a preset value and is determined according to the field working condition and the historical experience;
s203: establishing a state detection model of the key variable by adopting a nonparametric CUSUM algorithm, and going to step S205;
s204: establishing a state detection model of the key variable by adopting a confidence interval algorithm;
s205: and (6) ending.
4. The multi-modal identification method for the annealing heating process as claimed in claim 3, wherein: the state detection model established by adopting the confidence interval algorithm is used for carrying out online state detection on the key variable with the slowly varying characteristic to obtain the state of the corresponding key variable; the method specifically comprises the following steps:
s401: aiming at key variables with slowly-varying characteristics, screening out a first historical operating data sequence y in a normal production state1，y2，...，yk；
Calculating to obtain the first historical operating data sequence y1，y2，...，ykHas a reference value of
The reference value is y1，y2，...，ykHas an average value and a deviation value of
S402: calculating the standard deviation of the first historical operation data sequence by using a formula (1), and obtaining an interval boundary +/-n sigma corresponding to P according to the occurrence probability P of the data in a stable state in all data counted in advance and a standard normal distribution table; determining that the steady-state data is distributed within a range of ± n σ centered on the reference value;
in the above formula, viFor the ith data y in the first historical operating data sequenceiA corresponding deviation value;
s403: acquiring an operation data sequence to be detected, and determining the state of the operation data sequence to be detected by using continuous s data as a group of detection data in the operation data sequence to be detected;
for each group of detection data, the specific judgment method is as follows: respectively calculating the set of s data y1，y2，...，ysObtaining s deviations according to the corresponding deviations; averaging the s deviations to obtain a mean value v;
if the | v | > n σ indicates that the group of detection data belongs to the variable point region, judging that the state of the key variable in the group of detection data is a fluctuation state; otherwise, it is in a steady state.
5. The multi-modal identification method for the annealing heating process as claimed in claim 4, wherein: in step S103, a state detection model established by a nonparametric CUSUM algorithm is adopted to carry out state detection on the key variable with transient characteristics, and the state of the corresponding key variable is obtained; the method specifically comprises the following steps:
s301: aiming at key variables with transient characteristics, acquiring operation data sequence y to be detected of key variables1’，y2’，...，yk’；
Suppose a running data sequence y1’，y2’，...，yk' data sequence therein
Is a sequence of operational data in a steady state,
is a running data sequence in a fluctuating state;
for data sequence y1’，...，yi', the probability density functions of which belong to the steady state and the wave state, respectively, are P0(yi') and P1(yi') to a host; i 2, …, k, if i<t0Then P is1(yi’)＜P0(yi') to a host; otherwise, P1(yt’)＞P0(yt’)；
Defining a log-likelihood ratio as shown in equation (2):
in the above formula, siFor a data sequence y1’，...，yi' corresponding log-likelihood ratios;
s302: calculating cumulative sums of log-likelihood ratios
S303: training a CUSUM (compute unified device architecture) method by using a labeled speed test data set to obtain a decision threshold h;
s304: according to a decision function gjJudging the state of the key variable with transient characteristics; decision function gjThe calculation formula is shown in formula (3):
if decision function gj>h, indicating that a change point, i.e., a surge state, has occurred, and the change point is
Namely, it is
Followed by a surge condition; otherwise, it belongs to the stable state.
6. The multi-modal identification method for the annealing heating process as claimed in claim 5, wherein: step S104, identifying the mode of the continuous annealing heating process of the cold-rolled strip steel according to the state and the multi-mode identification strategy; the method specifically comprises the following steps:
the multi-modal recognition strategy is:
when the plate temperature is in a stable state, the mode is an S1 mode;
when the plate temperature is in a fluctuation state and the specification of the strip steel is in a fluctuation state, the strip steel is in an S2 mode;
when the plate temperature is in a fluctuation state, the specification of the strip steel is in a stable state, and the production running speed is in a fluctuation state, the mode is an S3 mode;
when the plate temperature is in a fluctuation state, the specification of the strip steel is in a stable state, and the production running speed is in a stable state, the mode is an S4 mode;
wherein, S1 is a stable heating production mode, S2 is a switching mode of steel coils with different specifications, S3 is a steady center offset mode of steel coils with the same specification, and S4 is an abnormal state mode.
7. A computer-readable storage medium characterized by: the computer readable storage medium stores instructions and data for implementing any one of the annealing heating process-oriented multimodal identification methods of claims 1-6.
8. A multi-mode recognition device for an annealing heating process is characterized in that: the method comprises the following steps: a processor and a storage device; the processor loads and executes instructions and data in the storage device to realize the multi-mode identification method for the annealing heating process as claimed in any one of claims 1 to 6.
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| CN102002582A (en) * | 2009-09-03 | 2011-04-06 | 复旦大学 | Method and device for synchronously analyzing and monitoring band steel by using image identification |
| CN109570665A (en) * | 2018-12-28 | 2019-04-05 | 太仓新思成模具钢有限公司 | A kind of high-precision detection device and its detection method for mould steel |
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| CN102486811B (en) * | 2010-12-06 | 2016-10-05 | 上海宝钢工业检测公司 | Continuous annealing machine set detection signals and the correlating method of strip steel position |
| CN102653817B (en) * | 2011-03-04 | 2013-09-04 | 上海梅山钢铁股份有限公司 | Continuous annealing and stable running control method for band steel |
| CN104714519B (en) * | 2014-12-30 | 2017-07-28 | 东北大学 | A kind of continuous annealing unit production process technology parameter setting and method for on-line optimization |
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| CN109570665A (en) * | 2018-12-28 | 2019-04-05 | 太仓新思成模具钢有限公司 | A kind of high-precision detection device and its detection method for mould steel |
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