Slab continuous casting secondary cooling spray coefficient sequential correction method and system based on intelligent roller online temperature measurement

Abstract

The present application relates to a kind of based on intelligent stick online temperature measurement slab continuous casting secondary cooling spray coefficient sequence correction method and system, belong to metallurgical continuous casting process control technical field.It includes: constructing slab continuous casting secondary heat transfer simulation model, determines the initial spray coefficient of each secondary cooling partition;Make target surface temperature curve, obtains basic secondary cooling water table based on secondary heat transfer simulation model reverse iteration calculation;Based on basic secondary cooling water table, off-line fitting each secondary cooling partition spray coefficient variation and the functional relationship of this partition and subsequent partition outlet temperature variation;In stable state of continuous casting production, through the intelligent roller arranged in the outlet of each secondary cooling partition, real-time measurement slab surface actual temperature, and according to from front to back in turn to each secondary cooling partition spray coefficient is corrected;The corrected spray coefficient is substituted into secondary heat transfer simulation model, and updates basic secondary cooling water table.The present application improves secondary heat transfer model precision, can provide reliable support for continuous casting process dynamic control.

Description

Technical Field

[0001] This invention belongs to the field of metallurgical continuous casting process control technology, and relates to a method and system for correcting the sequence of secondary cooling spray coefficients in slab continuous casting based on intelligent roller online temperature measurement. Background Technology

[0002] Secondary cooling is a crucial step in slab continuous casting production, directly affecting the internal quality, surface cracks, and solidification structure of the slab. Accurately obtaining the heat transfer coefficient between the slab and cooling water in the secondary cooling zone is essential for optimizing water distribution and ensuring slab quality. Currently, determining the secondary cooling heat transfer coefficient mainly relies on two methods: theoretical calculations based on empirical formulas and back-calculation corrections based on measured temperatures.

[0003] In existing technologies, the surface temperature of the cast billet is typically measured using an infrared thermal imager. This is combined with a solidification heat transfer model and a bisection method iterative approach to calculate the actual heat transfer coefficient of each secondary cooling zone, and then the correction factor for the Nozaki formula is calculated. While this method can correct the heat transfer coefficient to some extent, it still has the following shortcomings: 1) Limitations of temperature measurement methods: The surface temperature of the billet is measured by an infrared thermal imager. However, the accuracy and stability of the measurement are difficult to guarantee due to factors such as water mist and iron oxide scale on site. Furthermore, it can only measure the temperature at the outlet of the casting machine or at a local location, and cannot obtain continuous, real-time temperature data at the outlet of each secondary cooling zone.

[0004] 2) The correction process does not consider the influence of partition coupling: The method solves the heat transfer coefficient of each secondary cooling zone independently and iteratively, ignoring the coupling influence of the heat transfer state change of the front partition on the temperature field of the rear partition, resulting in cumulative error in the correction results. Especially for long-process multi-partition continuous casting machines, the correction accuracy is difficult to meet the dynamic control requirements.

[0005] 3) Single correction parameter: Only the correction coefficient in the Nozaki formula is adjusted, without involving the fine correction of the spray coefficient (a key parameter characterizing the nozzle cooling efficiency). The spray coefficient directly reflects the actual working condition of the nozzle (such as blockage, aging, etc.) and has a significant impact on the heat transfer effect of the secondary cooling system.

[0006] Therefore, there is an urgent need for a method that can obtain the surface temperature of the billet in each secondary cooling zone in real time and accurately, and consider the zone coupling effect to achieve fine correction of the spray coefficient, so as to improve the accuracy of the secondary cooling heat transfer model and provide reliable support for the dynamic control of continuous casting process. Summary of the Invention

[0007] In view of this, the purpose of this invention is to provide a method and system for sequential correction of the secondary cooling spray coefficient in slab continuous casting based on online temperature measurement of intelligent rollers. By using intelligent rollers arranged along the casting flow direction to measure the surface temperature of the slab at the outlet of each secondary cooling zone in real time, and based on the spray coefficient-temperature change function relationship obtained from offline simulation, the spray coefficient of each secondary cooling zone is corrected sequentially from front to back under stable casting conditions. The influence factor of the correction of the front zone on the temperature of the rear zone is introduced to achieve accurate correction of the key parameters of the secondary cooling heat transfer model, so as to solve the problems of limited temperature measurement methods, failure to consider the influence of zone coupling, and single correction parameters in the existing technology.

[0008] To achieve the above objectives, the first aspect of the present invention provides a method for correcting the sequence of secondary cooling spray coefficients in slab continuous casting based on online temperature measurement using an intelligent roller, the method comprising: A simulation model of heat transfer in the secondary cooling zone of slab continuous casting was constructed, and the initial spray coefficient of each secondary cooling zone was determined by nozzle performance testing. The target surface temperature curve is determined, and the basic secondary cooling water meter is obtained by reverse iterative calculation based on the secondary cooling heat transfer simulation model. Based on the basic secondary cooling water meter, the functional relationship between the change of the spray coefficient of each secondary cooling zone and the change of the outlet temperature of this zone and subsequent zones is fitted offline. Under stable continuous casting production conditions, the actual surface temperature of the billet is measured in real time by intelligent rollers arranged at the outlet of each secondary cooling zone along the casting flow direction, and the spray coefficient of each secondary cooling zone is corrected in sequence from front to back. Substitute the corrected spray coefficient into the secondary cooling heat transfer simulation model and update the basic secondary cooling water meter.

[0009] Furthermore, the construction of the slab continuous casting secondary cooling heat transfer simulation model includes: conducting cold and hot performance tests on the secondary cooling zone nozzles to obtain the water flow density distribution and water impact heat transfer coefficient; combining the casting machine roll parameters, the water impact heat transfer, pinch roll heat transfer, water accumulation evaporation heat transfer, and radiation heat transfer are uniformly converted into a comprehensive heat transfer coefficient, which is expressed as an empirical model related to water flow density, as the slab continuous casting secondary cooling heat transfer simulation model.

[0010] In the formula, For the first The overall heat transfer coefficient of the two cooling zones, The spray coefficient to be corrected. For the first Water flow density in the secondary cooling zone For cooling water temperature, , , It is a constant.

[0011] Furthermore, the development of target surface temperature profiles includes, based on the high-temperature mechanical properties of the steel grade and metallurgical limiting criteria, determining the target temperature value at the center point of the slab width at the outlet of each secondary cooling zone. This forms the target surface temperature profile. Obtaining the basic secondary cooling water meter involves using the target surface temperature curve as the optimization objective and performing reverse iterative calculations based on the secondary cooling heat transfer simulation model to obtain the water flow density value of each secondary cooling zone at each pulling speed, which is the basic secondary cooling water meter.

[0012] Furthermore, the offline fitting of the functional relationship between the change in the spray coefficient of each secondary cooling zone and the change in the outlet temperature of this zone and subsequent zones includes: Keeping the overall heat transfer coefficient of the other two cooling zones constant, for the first For the secondary cooling zone, multiple spray coefficients are taken near the water flow density corresponding to the basic secondary cooling water meter. The value was calculated using a secondary cooling heat transfer simulation model to determine the outlet temperature of the zone. ; Calculate the change in spray coefficient Temperature change , For the first Initial spray coefficient for the secondary cooling zone For the first Target temperature value at the center point of the slab width at the outlet of the secondary cooling zone; For multiple groups By fitting the data, we obtain the first... Functional relationship between the change in zoned spray coefficient and the change in outlet temperature ; Calculate the first The effect of changes in the spray coefficient in the secondary cooling zone on subsequent... The influence of the outlet temperature of the second cooling zone was fitted to obtain the first... Variation of zoned spray coefficient and the first Functional relationship of the temperature change at the outlet of the secondary cooling zone , For the first Temperature change at the outlet of the second cooling zone .

[0013] Furthermore, under stable continuous casting production conditions (casting speed fluctuation ≤ ±0.02 m / min), the outlet temperature of the secondary cooling zone is measured in real time using intelligent rollers arranged along the casting flow direction at the outlet of each secondary cooling zone. ; The spray coefficients of each secondary cooling zone are corrected sequentially from front to back, including: 1) Correct the first secondary cooling partition ( ) Obtain the measured temperature at the outlet of the first secondary cooling zone. Calculate temperature deviation ; Based on functional relationships Inversely calculate the change in the spray coefficient. (Usually, Newton's iteration or direct solution is used); Update the spray coefficient for the first secondary cooling zone: .

[0014] 2) Correction of the first Two cooling zones ( ) Get the Measured temperature in each zone ; Calculate the preceding partitions ( Spray coefficient correction for the first The cumulative effect of zone outlet temperature:

[0015] In the formula, For the first The change in the spray coefficient of the zone has been determined; Taking into account the effects of measured deviations and previous zone corrections on the temperature changes in this zone, the calculation of the first... The equivalent temperature change that needs to be adjusted for the secondary cooling zone:

[0016] In the formula, For the first Target temperature value at the center point of the slab width at the outlet of the secondary cooling zone; Based on functional relationships ,make Inversely calculate the change in the spray coefficient. ; Update the spray coefficient: .

[0017] Furthermore, updating the basic secondary cooling water meter involves substituting the corrected spray coefficient into the secondary cooling heat transfer simulation model and recalculating the billet temperature field; using the target surface temperature curve as the optimization target, performing reverse iteration calculations again to obtain the water flow density values ​​of each secondary cooling zone at each casting speed, which are then used as the updated basic secondary cooling water meter.

[0018] A second aspect of the present invention provides a system for implementing the method of the first aspect, the system comprising: Intelligent rollers are arranged along the casting flow direction at the outlet of each secondary cooling zone to measure the surface temperature of the billet in real time. The data acquisition module communicates with the intelligent roller to collect temperature signals; The simulation calculation module stores the secondary cooling heat transfer simulation model and performs offline fitting and reverse iterative calculation. The sequence correction module performs sequence correction of the spray coefficient based on the measured temperature and the offline fitting function; The secondary cooling water meter update module updates the basic secondary cooling water meter according to the corrected spray coefficient.

[0019] The intelligent roller includes a roller sleeve, a measuring disk coaxially installed inside the roller sleeve, and a MEMS temperature sensor array embedded in the outer edge of the measuring disk. The outer edge of the measuring disk is tightly fitted to the inner wall of the roller sleeve, and the sensor signal is output through a slip ring or wireless transmission module via a lead wire.

[0020] A third aspect of the present invention provides an electronic device including a memory and one or more processors, the memory storing a computer program that, when executed by the one or more processors, causes the electronic device to perform the method described in the first aspect.

[0021] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0022] The beneficial effects of this invention are as follows: (1) Real-time and accurate measurement of the outlet temperature of each secondary cooling zone. The present invention uses an intelligent roller based on a MEMS temperature sensor to directly contact and measure the surface temperature of the billet, avoiding the defects of infrared temperature measurement due to environmental interference. It has high measurement accuracy, fast response, and can continuously acquire the temperature distribution along the casting flow direction, providing a reliable data source for parameter correction.

[0023] (2) Establish an offline functional relationship between the spray coefficient and temperature change. This invention pre-fits the influence function of the spray coefficient change of each zone on the temperature of the zone and subsequent zones through simulation, transforming the complex nonlinear heat transfer relationship into a simple function that can be calculated in real time, providing a fast back-calculation basis for sequential correction.

[0024] (3) Sequential correction and consideration of partition coupling effects. A sequential correction strategy from front to back is adopted. Each correction step deducts the temperature change caused by the previous partition correction, which effectively decouples the mutual influence between partitions, avoids cumulative error, and makes the correction results more consistent with the actual heat transfer physical process.

[0025] (4) Refined correction of spray coefficient. The spray coefficient, which represents the working state of the nozzle, is directly corrected, which can reflect changes in equipment status such as nozzle blockage and aging, so that the secondary cooling heat transfer model always matches the real-time status of the casting machine, significantly improving the simulation accuracy of the model.

[0026] (5) Closed-loop optimization of secondary cooling water meter. The corrected spray coefficient is used to update the basic secondary cooling water meter, forming a closed-loop control of "measurement-correction-optimization", which provides a precise water distribution benchmark for the dynamic control of secondary cooling in continuous casting, and helps to improve the quality of billet and reduce defects.

[0027] Tests show that, when the method of this invention is applied to a 250 mm × 2000 mm slab continuous casting machine in a certain factory, after one sequential correction, the average deviation between the calculated and measured values ​​of the outlet temperature of each secondary cooling zone is reduced from ±25℃ to within ±5℃. After the secondary cooling water distribution is optimized, the incidence of central segregation and surface cracks in the slab is reduced by more than 30%.

[0028] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0029] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is a schematic diagram of a method for correcting the sequence of secondary cooling spray coefficients in slab continuous casting based on online temperature measurement of intelligent rollers, provided in an embodiment of the present invention.

[0030] Figure 2 This is a schematic diagram of the intelligent roller structure.

[0031] Figure 3 This is a schematic diagram showing the arrangement of the measuring plate and MEMS temperature sensor.

[0032] Figure 4 This is a schematic diagram showing the fitting of the functional relationship between the change in spray coefficient and the change in temperature.

[0033] Figure 5 This is a logical diagram of the sequential correction process.

[0034] Figure 6 This is a system composition block diagram provided for an embodiment of the present invention. Detailed Implementation

[0035] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0036] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0037] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0038] Example 1 Taking a 250 mm × 2000 mm slab continuous casting machine in a certain factory as an example, the secondary cooling zone is divided into 8 cooling sections. Each section outlet is equipped with a smart roller, which includes a roller sleeve, a measuring disk coaxially mounted within the roller sleeve, and a MEMS temperature sensor array. The process of correcting the sequence of the secondary cooling spray coefficients for slab continuous casting based on the method described in this invention is as follows: 1. Establish a simulation model for heat transfer in the secondary cooling system and obtain the initial spray coefficient. (1) Based on the actual dimensions of the casting machine and the arrangement of the rollers, a two-dimensional unsteady heat transfer finite difference model was established with a mesh size of 5 mm × 5 mm and a time step of 0.1 s.

[0039] (2) Perform cold and hot performance tests on the secondary cooling nozzles to obtain the water flow density distribution and water impact heat transfer coefficient. Combined with roller heat transfer, radiation heat transfer, etc., fit the numerical calculation model of the comprehensive heat transfer coefficient of each zone shown in Equation (1) and determine the initial spray coefficient. ( ).

[0040] (1) in, For the first The overall heat transfer coefficient of the two cooling zones, The spray coefficient to be corrected. For the first Water flow density in the secondary cooling zone This refers to the cooling water temperature. , , It is a constant. Take 1570, Take 0.55, Take 0.0075.

[0041] 2. Develop the target surface temperature profile and generate the basic secondary cooling water meter. (1) Based on the high-temperature mechanical properties of the steel, the target temperatures of the center point of the slab width at the outlet of each secondary cooling zone are set as follows: Zone 1: 1100℃, Zone 2: 1050℃, Zone 3: 1000℃, Zone 4: 950℃, Zone 5: 900℃, Zone 6: 870℃, Zone 7: 850℃, Zone 8: 830℃.

[0042] Using the target temperature curve as the optimization objective, a reverse iterative calculation (pull speed 1.2 m / min) was performed based on the secondary cooling heat transfer simulation model to obtain the water flow density values ​​of each secondary cooling zone, forming the basic secondary cooling water table shown in Table 1: Table 1

[0043] 3. Offline fitting of the functional relationship between the change in spray coefficient and the change in temperature (1) Keeping the overall heat transfer coefficient of the other two cooling zones constant, for the first In the secondary cooling zone, near the water flow density corresponding to the basic secondary cooling water meter, the spray coefficient is adjusted. The outlet temperature of the partition was simulated by taking initial values ​​of 0.8, 0.9, 1.0, 1.1, and 1.2 times respectively.

[0044] Taking partition 3 as an example, the data points are obtained as follows: Table 2

[0045] in, This represents the change in the spray coefficient for zone 3. This represents the temperature change at the outlet of zone 3.

[0046] (2) For multiple groups By fitting the data, the functional relationship between the change in the spray coefficient and the change in the outlet temperature in zone 3 was obtained: .

[0047] (3) Simultaneously calculate the impact of the change in the spray coefficient of the third zone on subsequent zones. For example, the impact on the fourth zone can be fitted as follows: The effect on partition 5 can be fitted as follows: And so on.

[0048] 4. Sequence correction based on real-time temperature measurement of the intelligent roller. Under stable continuous casting production conditions, with a casting speed of 1.2 m / min, the measured surface temperatures of the billet at the exit of each zone of the intelligent roller are shown in Table 3. Table 3

[0049] The spray coefficients for each secondary cooling zone are adjusted sequentially from front to back: Correction to Part 1: The equivalent temperature change is the temperature deviation, i.e. , by function ,have to Update the sprinkler coefficient for Zone 1. .

[0050] Correcting Partition 2: First calculate the impact of correcting Partition 1 on Partition 2, i.e. Measured deviation The equivalent temperature change By the second partitioning function ,have to Update the sprinkler coefficient for Zone 2. .

[0051] The 3rd to 8th partitions were corrected sequentially. The calculation process is the same as that for the 2nd partition, and will not be repeated here.

[0052] The final corrected spray coefficients are shown in Table 4: Table 4

[0053] 5. Update the heat transfer model and secondary cooling water meter. Substituting the corrected spray coefficient into the secondary cooling heat transfer model, the temperature field was recalculated, and the deviation between the calculated and measured temperatures at the outlet of each zone was within ±5℃.

[0054] Using the target temperature curve as the optimization objective, a reverse iteration was performed again to obtain the updated basic secondary cooling water meter shown in Table 5, which serves as the basis for water distribution in subsequent production.

[0055] Table 5

[0056] Example 2 The method is basically the same as in Example 1, except that the MEMS temperature sensor of the smart stick is a thin-film thermocouple type, and eight sensors are embedded on the outer edge of the measuring disk, with signals extracted through slip rings. A quadratic polynomial is used for the offline fitting function to improve fitting accuracy.

[0057] Example 3 The method is basically the same as in Example 1, except that: during the sequential correction process, if the correction amount of a certain zone exceeds the preset threshold (e.g., ±0.3), an alarm is issued to prompt the nozzle status to be checked, so as to prevent abnormal correction due to severe nozzle blockage.

[0058] Example 4 This embodiment provides a slab continuous casting secondary cooling spray coefficient sequence correction system based on intelligent roller online temperature measurement, including: Intelligent roller sets are arranged along the casting flow direction at the outlets of each secondary cooling zone to measure the surface temperature of the billet in real time. The data acquisition module communicates with the intelligent roller to collect temperature signals and perform preprocessing. The simulation calculation module stores the secondary cooling heat transfer simulation model and performs offline fitting and reverse iterative calculation. The sequential correction module executes the spray coefficient correction algorithm sequentially based on the measured temperature and the offline fitting function. The secondary cooling water meter update module updates the basic secondary cooling water meter according to the corrected spray coefficient. The human-computer interaction module is used to display the correction process and results for process personnel to monitor.

[0059] The intelligent roll includes a roll sleeve, a measuring disk coaxially mounted inside the roll sleeve, and a MEMS temperature sensor array. The outer edge of the measuring disk is tightly fitted to the inner wall of the roll sleeve, and the MEMS temperature sensor array is embedded in the outer edge of the measuring disk and evenly distributed along the circumference. The sensor signals are output to the data acquisition module through the inner lead wire of the roll via a slip ring or a wireless transmission module. The intelligent roll is installed at the outlet of each secondary cooling zone, and the MEMS temperature sensors distributed on the outer edge of the measuring disk sense the billet temperature in real time through infrared radiation.

[0060] Example 5 This embodiment provides an electronic device including one or more processors and a memory. The memory stores a program, which, when executed by the processor, causes the electronic device to perform the method described in Embodiment 1.

[0061] Example 6 This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in Embodiment 1.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for correcting the sequence of secondary cooling spray coefficients in slab continuous casting based on online temperature measurement using an intelligent roller, characterized in that, A simulation model of heat transfer in the secondary cooling zone of slab continuous casting was constructed, and the initial spray coefficient of each secondary cooling zone was determined by nozzle performance testing. The target surface temperature curve is determined, and the basic secondary cooling water meter is obtained by reverse iterative calculation based on the secondary cooling heat transfer simulation model. Based on the basic secondary cooling water meter, the functional relationship between the change of the spray coefficient of each secondary cooling zone and the change of the outlet temperature of this zone and subsequent zones is fitted offline. Under stable continuous casting production conditions, the actual surface temperature of the billet is measured in real time by intelligent rollers arranged at the outlet of each secondary cooling zone along the casting flow direction, and the spray coefficient of each secondary cooling zone is corrected in sequence from front to back. Substitute the corrected spray coefficient into the secondary cooling heat transfer simulation model and update the basic secondary cooling water meter.

2. The method according to claim 1, characterized in that, The construction of a simulation model for heat transfer in the secondary cooling stage of slab continuous casting includes: conducting cold and hot performance tests on the nozzles in the secondary cooling zone to obtain the water flow density distribution and water impact heat transfer coefficient; combining the casting machine roll parameters, the water impact heat transfer, roll clamping heat transfer, water accumulation evaporation heat transfer, and radiation heat transfer are uniformly converted into a comprehensive heat transfer coefficient, which is expressed as an empirical model related to water flow density, serving as the simulation model for heat transfer in the secondary cooling stage of slab continuous casting. In the formula, For the first The overall heat transfer coefficient of the two cooling zones, The spray coefficient to be corrected. For the first Water flow density in the secondary cooling zone For cooling water temperature, , , It is a constant.

3. The method according to claim 1, characterized in that, Developing the target surface temperature profile includes determining the target temperature value at the center point of the slab width at the outlet of each secondary cooling zone, based on the high-temperature mechanical properties of the steel and metallurgical limiting criteria. This forms the target surface temperature profile. Obtaining the basic secondary cooling water meter involves using the target surface temperature curve as the optimization objective and performing reverse iterative calculations based on the secondary cooling heat transfer simulation model to obtain the water flow density value of each secondary cooling zone at each pulling speed, which is the basic secondary cooling water meter.

4. The method according to claim 1, characterized in that, The offline fitting of the functional relationship between the change in the spray coefficient of each secondary cooling zone and the change in the outlet temperature of this zone and subsequent zones includes: Keeping the overall heat transfer coefficient of the other two cooling zones constant, for the first For the secondary cooling zone, multiple spray coefficients are taken near the water flow density corresponding to the basic secondary cooling water meter. The value was calculated using a secondary cooling heat transfer simulation model to determine the outlet temperature of the zone. ; Calculate the change in spray coefficient Temperature change , For the first Initial spray coefficient for the secondary cooling zone For the first Target temperature value at the center point of the slab width at the outlet of the secondary cooling zone; For multiple groups By fitting, the first... Functional relationship between the change in zoned spray coefficient and the change in outlet temperature ; Calculate the first The effect of changes in the spray coefficient in the secondary cooling zone on subsequent... The influence of the outlet temperature of the second cooling zone was fitted to obtain the first... Variation of zoned spray coefficient and the first Functional relationship of the temperature change at the outlet of the secondary cooling zone , For the first Temperature change at the outlet of the second cooling zone .

5. The method according to claim 4, characterized in that, The spray coefficients of each secondary cooling zone are corrected sequentially from front to back, including: Get the Measured temperature in each zone ; Calculate the correction of each preceding partition to the first The cumulative effect of zone outlet temperature: In the formula, For the first The change in the spray coefficient of the zone has been determined; Calculate the first The equivalent temperature change that needs to be adjusted for each zone: In the formula, For the first Target temperature value at the center point of the slab width at the outlet of the secondary cooling zone; Based on functional relationships ,make Inversely calculate the change in the spray coefficient. ; Update the spray coefficient: .

6. The method according to claim 1, characterized in that, The process of updating the basic secondary cooling water meter includes substituting the corrected spray coefficient into the secondary cooling heat transfer simulation model and recalculating the billet temperature field; taking the target surface temperature curve as the optimization target, performing reverse iteration calculation again to obtain the water flow density value of each secondary cooling zone at each casting speed, which is used as the updated basic secondary cooling water meter.

7. A system for implementing the method according to any one of claims 1 to 6, characterized in that, The system includes: Intelligent rollers are arranged along the casting flow direction at the outlet of each secondary cooling zone to measure the surface temperature of the billet in real time. The data acquisition module communicates with the intelligent roller to collect temperature signals; The simulation calculation module stores the secondary cooling heat transfer simulation model and performs offline fitting and reverse iterative calculation. The sequence correction module performs sequence correction of the spray coefficient based on the measured temperature and the offline fitting function; The secondary cooling water meter update module updates the basic secondary cooling water meter according to the corrected spray coefficient.

8. The system according to claim 7, characterized in that, The intelligent roller includes a roller sleeve, a measuring disk coaxially mounted inside the roller sleeve, and a MEMS temperature sensor array embedded in the outer edge of the measuring disk. The outer edge of the measuring disk is tightly fitted to the inner wall of the roller sleeve, and the sensor signal is output through a slip ring or wireless transmission module via a lead wire.

9. An electronic device comprising a memory and one or more processors, characterized in that, The memory stores a computer program that, when executed by one or more processors, causes the electronic device to perform the method of any one of claims 1 to 6.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method as described in any one of claims 1 to 6.

Images