A method, device, electronic equipment and storage medium for monitoring temperature in continuous casting and rolling.

Abstract

This invention provides a method, device, electronic device, and storage medium for continuous casting and rolling temperature monitoring. The method includes determining the current values ​​of multiple first monitoring parameters and multiple second monitoring parameters based on the current temperature distribution and multiple preset mapping relationships. Each first monitoring parameter includes a two-phase composition ratio, and each second monitoring parameter includes a weighted specific heat capacity determined based on the two-phase composition ratio. The two-phase composition ratio is determined based on the current temperature distribution, the length of the temperature range for the austenite-ferrite phase transformation, and the average temperature of the temperature range. Based on the current temperature distribution, the current value of the weighted enthalpy, the current values ​​of each second monitoring parameter, and a preset finite difference heat conduction equation, the estimated enthalpy for the next moment is determined. The current value of the two-phase composition ratio is used as the estimated ratio value for the next moment, and the estimated temperature value for the next moment is determined based on the estimated enthalpy for the next moment. This invention introduces the thermodynamic effects of the austenite-ferrite phase transformation to improve the accuracy of temperature monitoring.

Description

Technical Field

[0001] This invention relates to the field of steel rolling technology, and in particular to a method, device, electronic equipment and storage medium for continuous casting and rolling temperature monitoring. Background Technology

[0002] Temperature is a crucial control parameter in strip rolling. Temperature and its rate of change significantly impact the microstructure and properties of strip. Therefore, a more accurate temperature calculation model can improve the control precision of the rolling process, facilitating stable rolling production. The phase transformation temperature of low-carbon steel, from austenite to ferrite during cooling, is approximately 911°C, which may be higher than the final rolling temperature. During the finishing rolling process, the austenite-to-ferrite transformation may occur. In continuous casting and rolling production, the yield strength ratio of strip is relatively high, which is detrimental to subsequent stamping processes. To improve the yield strength ratio of strip, an effective method is ferritic rolling. The development of ferritic rolling modes requires controlling the location of the phase transformation process between the finishing mills, which places higher demands on the study of strip temperature coupled with phase transformation.

[0003] Most of the strip temperature calculation models in related technologies are based on the temperature calculation of thickness or rolling direction, and take into account the thermal radiation, thermal conduction and thermal convection of the strip surface. There is little research on the impact of the austenite-ferrite phase transformation process that occurs in the finishing rolling stage on the strip temperature calculation. Summary of the Invention

[0004] This invention provides a method, device, electronic equipment, and storage medium for continuous casting and rolling temperature monitoring, in order to solve the technical problem of low accuracy in strip temperature monitoring caused by not considering the austenite-ferrite phase transformation process.

[0005] This invention provides a continuous casting and rolling temperature monitoring method, the method comprising: acquiring the current temperature distribution of the strip steel, the current temperature distribution being estimated based on the surface measurement temperature of the continuously cast billet and along the thickness direction of the strip steel; determining the current values ​​of a plurality of first monitoring parameters and a plurality of second monitoring parameters according to the current temperature distribution and a plurality of preset mapping relationships, each of the first monitoring parameters including a two-phase composition ratio and a weighted enthalpy determined based on the two-phase composition ratio, the second monitoring parameters including a weighted specific heat capacity determined based on the two-phase composition ratio, the two-phase composition ratio being determined based on the current temperature distribution, the length of the temperature range for the austenite-ferrite phase transformation, and the average temperature of the temperature range; and determining the current temperature distribution, the current value of the weighted enthalpy, the current values ​​of each of the second monitoring parameters, and a preset finite difference heat conduction equation. The system calculates the estimated enthalpy for the next time step and determines the current value of the two-phase component ratio as the estimated ratio value for the next time step. Based on the estimated enthalpy for the next time step, it determines the estimated temperature value for the next time step. If the current monitoring parameter meets a preset convergence condition, the estimated ratio value is determined as the new current value of the two-phase component ratio, and the estimated temperature value is determined as the new current temperature distribution. This process continues to determine the new current value of the second monitoring parameter, and a new estimated enthalpy, a new estimated ratio value, and a new estimated temperature value, until temperature monitoring is completed. The current monitoring parameter includes the estimated temperature value and the estimated ratio value. If the current monitoring parameter does not meet the preset convergence condition, the estimated ratio value is updated based on the estimated temperature value, and the estimated temperature value is updated based on the estimated enthalpy and the updated estimated ratio value, until the preset convergence condition is met.

[0006] In one embodiment of the present invention, the two-phase composition ratio includes a ferrite composition ratio and an austenite composition ratio. The determination of the ferrite composition ratio includes: determining an offset temperature based on the difference between the temperature distribution value in the current temperature distribution and the average temperature; determining the rate of change based on a preset empirical parameter and the length of the temperature interval, wherein the empirical parameter is extracted based on the curve of the ferrite composition ratio changing with temperature; determining an exponential independent variable based on the rate of change and the offset temperature, and converting the exponential independent variable into the format of a logistic function to obtain the ferrite composition ratio.

[0007] In one embodiment of the present invention, the determination of the weighted specific heat capacity includes: determining the specific heat capacity of ferrite and the specific heat capacity of austenite according to the corresponding preset mapping relationship; weighting the specific heat capacity of ferrite and the specific heat capacity of austenite based on the ratio of the two phase components to obtain an initial specific heat capacity; determining the latent heat of phase transformation between austenite and ferrite based on the difference between the enthalpy of ferrite and the enthalpy of austenite, and the rate of change of the ratio of the two phase components with temperature, and correcting the initial specific heat capacity based on the latent heat of phase transformation to obtain the weighted specific heat capacity.

[0008] In one embodiment of the present invention, the preset mapping relationship corresponding to the specific heat capacity of austenite is obtained by fitting a linear polynomial with respect to the temperature distribution value; the specific heat capacity of ferrite is obtained by fitting a two-sided crystal ball function and a first polynomial with respect to the temperature distribution value, wherein the two-sided crystal ball function is a probability density function with respect to the temperature distribution value.

[0009] In one embodiment of the present invention, the second monitoring parameter further includes thermal conductivity; if the temperature distribution value corresponding to a location is less than a preset temperature threshold, the preset mapping relationship corresponding to the thermal conductivity is obtained by fitting a second polynomial about the temperature distribution value; if the temperature distribution value corresponding to a location is greater than or equal to the preset temperature threshold, the preset mapping relationship corresponding to the thermal conductivity is obtained by fitting a third polynomial about the temperature distribution value and a fourth polynomial about the preset temperature threshold, wherein the coefficients of the fourth polynomial are based on the coefficients of the second polynomial and the coefficients of the third polynomial, and the continuity of the thermal conductivity at the preset temperature threshold is maintained.

[0010] In one embodiment of the present invention, the finite difference heat conduction equation includes multiple discrete differential heat conduction equations about enthalpy corresponding to the interior of the strip, and two boundary heat conduction equations about enthalpy corresponding to the surface of the strip; each of the second monitoring parameters further includes heat source power density and boundary heat flux density, wherein the heat source power density is used to solve each of the discrete differential heat conduction equations and each of the boundary heat conduction equations, and the boundary heat flux density is used to solve each of the boundary heat conduction equations.

[0011] In one embodiment of the present invention, the boundary heat flux density is used to solve each of the boundary heat conduction equations, which includes: correcting the boundary heat flux density according to the rate of change of the boundary heat flux density with enthalpy; and solving each of the boundary heat conduction equations based on the corrected boundary heat flux density.

[0012] This invention provides a continuous casting and rolling temperature monitoring device, comprising: a temperature acquisition module for acquiring the current temperature distribution of the strip steel, the current temperature distribution being estimated based on the surface measurement temperature of the continuously cast billet and along the thickness direction of the strip steel; a parameter determination module for determining the current values ​​of multiple first monitoring parameters and multiple second monitoring parameters according to the current temperature distribution and multiple preset mapping relationships, each of the first monitoring parameters including a two-phase composition ratio and a weighted enthalpy determined based on the two-phase composition ratio, and the second monitoring parameters including a weighted specific heat capacity determined based on the two-phase composition ratio, the two-phase composition ratio being determined based on the current temperature distribution, the length of the temperature range for the austenite-ferrite phase transformation, and the average temperature of the temperature range; and a first temperature estimation module for estimating the current temperature distribution, the current value of the weighted enthalpy, the current values ​​of each of the second monitoring parameters, and a preset finite difference heat conduction method. The process involves determining the estimated enthalpy for the next moment and setting the current value of the two-phase component ratio as the estimated ratio for the next moment. Based on the estimated enthalpy for the next moment, the temperature estimate for the next moment is determined. A second temperature estimation module is used to, if the current monitoring parameter meets a preset convergence condition, set the estimated ratio as the new current value of the two-phase component ratio and the estimated temperature as the new current temperature distribution. This continues to determine the new current value of the second monitoring parameter, and a new estimated enthalpy, a new estimated ratio, and a new estimated temperature, until temperature monitoring is completed. The current monitoring parameter includes the estimated temperature and the estimated ratio. A temperature convergence estimation module is used to, if the current monitoring parameter does not meet the preset convergence condition, update the estimated ratio based on the estimated temperature, and update the estimated temperature based on the estimated enthalpy and the updated estimated ratio, until the preset convergence condition is met.

[0013] The present invention provides an electronic device comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the electronic device enables the continuous casting and rolling temperature monitoring method as described in any of the above embodiments.

[0014] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a computer processor, causes the computer to perform the continuous casting and rolling temperature monitoring method described in any of the above embodiments.

[0015] The beneficial effects of the present invention are as follows: The continuous casting and rolling temperature monitoring method, device, electronic equipment and storage medium proposed in the present invention can effectively cover the phase transformation temperature region of austenite-ferrite by introducing the thermodynamic influence brought about by the austenite-ferrite phase transformation during the continuous casting and rolling temperature monitoring process, such as the weighted specific heat capacity and weighted enthalpy determined by the ratio of the two phase components, thereby achieving high-precision temperature monitoring. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0017] In the attached diagram:

[0018] Figure 1 A schematic diagram of an exemplary system architecture provided in an embodiment of the present invention; Figure 2 This is a schematic flowchart of a continuous casting and rolling temperature monitoring method provided in one embodiment of the present invention; Figure 3 This is a schematic diagram of the implementation process of the continuous casting and rolling temperature monitoring method provided in one embodiment of the present invention; Figure 4 This is a block diagram of a continuous casting and rolling temperature monitoring device provided in one embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of a computer system for an electronic device provided in one embodiment of the present invention. Detailed Implementation

[0019] 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. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0020] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. The drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0021] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0022] Please see Figure 1 , Figure 1 This is a schematic diagram of an exemplary system architecture provided in an embodiment of the present invention. Figure 1 As shown, the system architecture may include a data acquisition device 110 and a computer device 120. The computer device may be at least one of an embedded computer, an industrial computer, a general-purpose computer, or a neural network computer. The data acquisition device 110 includes a temperature measuring device for acquiring the surface temperature of the continuously cast billet and transmitting the data to the computer device 120 for temperature monitoring.

[0023] For example, computer device 120 acquires the current temperature distribution of the strip steel, which is estimated based on the surface measurement temperature of the continuously cast billet and along the thickness direction of the strip steel; it determines the current values ​​of multiple first monitoring parameters and multiple second monitoring parameters based on the current temperature distribution and multiple preset mapping relationships. Each first monitoring parameter includes a two-phase composition ratio and a weighted enthalpy determined based on the two-phase composition ratio, and each second monitoring parameter includes a weighted specific heat capacity determined based on the two-phase composition ratio. The two-phase composition ratio is determined based on the current temperature distribution, the length of the temperature range for the austenite-ferrite phase transformation, and the average temperature of the temperature range; and it determines the next moment's temperature based on the current temperature distribution, the current value of the weighted enthalpy, the current values ​​of each second monitoring parameter, and a preset finite difference heat conduction equation. The enthalpy is estimated, and the current value of the two-phase component ratio is determined as the ratio estimate for the next time step. The temperature estimate for the next time step is determined based on the estimated enthalpy. If the current monitoring parameter meets the preset convergence condition, the ratio estimate is determined as the new current value of the two-phase component ratio, and the temperature estimate is determined as the new current temperature distribution. This process continues to determine the new current value of the second monitoring parameter, and new estimated enthalpy, new ratio estimate, and new temperature estimate are determined until temperature monitoring is completed. The current monitoring parameter includes the temperature estimate and the ratio estimate. If the current monitoring parameter does not meet the preset convergence condition, the ratio estimate is updated based on the temperature estimate, and the temperature estimate is updated based on the estimated enthalpy and the updated ratio estimate, until the preset convergence condition is met.

[0024] The related technologies do not consider the austenite-ferrite phase transformation process, resulting in low accuracy in strip temperature monitoring.

[0025] To address the aforementioned technical problems, this invention provides a method, device, electronic equipment, and storage medium for continuous casting and rolling temperature monitoring. The implementation details of the technical solutions of this invention are described in detail below.

[0026] Please see Figure 2 , Figure 2 This is a schematic flowchart of a continuous casting and rolling temperature monitoring method provided in one embodiment of the present invention. Figure 2 As shown, in an exemplary embodiment, the continuous casting and rolling temperature monitoring method includes at least steps S210 to S250, which are described in detail below: Step S210: Obtain the current temperature distribution of the strip steel.

[0027] The current temperature distribution is based on the surface temperature of the continuously cast billet and estimated along the thickness direction of the strip.

[0028] In one embodiment of the present invention, if a calculated value of temperature distribution exists for the continuously cast billet, the calculated value of temperature distribution is determined as the current temperature distribution; if a calculated value of temperature distribution does not exist for the continuously cast billet, the temperature difference between the surface measurement temperature and the core temperature is matched from a preset parameter table based on the continuous casting speed and the billet thickness, and the temperature difference and the surface measurement temperature are interpolated to obtain the temperature distribution in the thickness direction. The preset parameter table stores the mapping relationship between the continuous casting speed, the billet thickness, and the temperature difference between the surface measurement temperature and the core temperature.

[0029] In one embodiment of the present invention, the initial conditions required for solving the strip temperature are estimated based on the surface temperature measured on the continuously cast billet, and the temperature distribution inside the continuously cast billet is estimated.

[0030] Step S220: Determine the current values ​​of multiple first monitoring parameters and multiple second monitoring parameters based on the current temperature distribution and multiple preset mapping relationships. Each first monitoring parameter includes the two-phase composition ratio and the weighted enthalpy determined based on the two-phase composition ratio. The second monitoring parameter includes the weighted specific heat capacity determined based on the two-phase composition ratio. The two-phase composition ratio is determined based on the current temperature distribution, the length of the temperature range for the austenite-ferrite phase transformation, and the average temperature of the temperature range.

[0031] In one embodiment of the present invention, the two-phase composition ratio includes a ferrite composition ratio and an austenite composition ratio. The determination of the ferrite composition ratio includes: determining an offset temperature based on the difference between the temperature distribution value and the average temperature in the current temperature distribution; determining the rate of change based on preset empirical parameters and the length of the temperature range, wherein the empirical parameters are extracted based on the curve of the ferrite composition ratio changing with temperature; determining an exponential independent variable based on the rate of change and the offset temperature, and converting the exponential independent variable into the format of a logistic function to obtain the ferrite composition ratio.

[0032] In one embodiment of the present invention, an austenite-ferrite phase transformation model is established, including the determination of parameters such as phase transformation temperature, austenite enthalpy, ferrite enthalpy, austenite composition ratio, ferrite composition ratio, austenite specific heat capacity, and ferrite specific heat capacity.

[0033] In one embodiment of the present invention, the temperature distribution value is used to characterize the temperature value at any location in the current temperature distribution; in the following embodiments, the temperature distribution value is simply referred to as temperature. .

[0034] In one embodiment of the present invention, the ferrite composition ratio is as follows:

[0035] in, For temperature The proportion of ferrite composition, These are preset empirical parameters, extracted based on the curve of ferrite composition ratio changing with temperature. This represents the length of the temperature range for the austenite-ferrite phase transformation. The average temperature of the phase transition range.

[0036] In one embodiment of the present invention, the ferrite composition ratio ranges from [0,1].

[0037] In one embodiment of the present invention, the main difference between austenite and ferrite in the heat conduction equation lies in their specific heat capacity. Therefore, a specific heat capacity model for the two-phase composition needs to be described. When the strip temperature is above the phase transformation (A3) temperature, the strip is considered to be entirely austenite. When the strip temperature is below the A3 temperature but above the eutectoid transformation (A1) temperature, the strip is a mixture of austenite and ferrite. When the strip temperature is below the A1 temperature, the strip can be considered to be entirely ferrite.

[0038] In one embodiment of the present invention, the actual phase transition temperature is determined as follows:

[0039] in, This is the actual phase transition temperature. This is the standard phase transformation temperature of iron-carbon alloys. This represents the phase transformation temperature offset caused by the chemical composition of the strip steel.

[0040] In one embodiment of the present invention, the standard phase transition temperature of iron-carbon alloys is... It is 911 degrees Celsius.

[0041] In one embodiment of the present invention, the phase transition temperature offset Determined based on the GS line (ferrite initiation line) in the iron-carbon alloy phase diagram:

[0042] in, This refers to the shift in phase transformation temperature caused by the chemical composition of the strip steel. This refers to the carbon mass fraction in the chemical composition of strip steel, expressed as a percentage.

[0043] In one embodiment of the present invention, the actual eutectoid transformation temperature is determined as follows:

[0044] in, This is the actual eutectoid transformation temperature. This is the standard phase transformation temperature of iron-carbon alloys. This represents the shift in eutectoid transformation temperature caused by the chemical composition of the strip steel.

[0045] In one embodiment of the present invention, the eutectoid transformation temperature offset caused by chemical composition is determined based on the GPS lines (a collective term for the ferrite initiation line, eutectoid point P, and ferrite dissolution line) in the iron-carbon alloy phase diagram:

[0046] in, This refers to the shift in eutectoid transformation temperature caused by the chemical composition of the strip. This refers to the carbon mass fraction in the chemical composition of strip steel, expressed as a percentage.

[0047] In one embodiment of the present invention, in actual production, the measured temperature value and the estimated temperature value of the strip steel can be used to... and Statistical analysis was conducted to extract the appropriate parameters for different steel grades and chemical compositions. and A temperature offset description model is used to improve the accuracy of temperature calculation.

[0048] In one embodiment of the present invention, the length of the temperature range for the austenite-ferrite phase transformation is as follows:

[0049] in, This represents the length of the temperature range between the austenite and ferrite phase transformation. This is the actual eutectoid transformation temperature. This represents the actual phase transition temperature.

[0050] In one embodiment of the present invention, the average temperature of the phase transition region is as follows:

[0051] in, The average temperature of the phase transition region. This is the actual eutectoid transformation temperature. This represents the actual phase transition temperature.

[0052] In one embodiment of the present invention, in order to reduce the complexity of the phase transformation model, the changes in enthalpy and specific heat capacity of ferrite and austenite with temperature are described by polynomial fitting using measurement data of pure ferrite and pure austenite.

[0053] In one embodiment of the present invention, the weighted enthalpy can be obtained by weighting the enthalpy of ferrite and the enthalpy of austenite by the ratio of the two phase compositions, as follows:

[0054] in, The weighted enthalpy of the strip steel, The proportion of ferrite composition. This refers to the austenitic composition ratio. For temperature Ferrite enthalpy at time For temperature austenitic enthalpy at time.

[0055] In one embodiment of the present invention, both the austenite enthalpy and the ferrite enthalpy are obtained by polynomial fitting of the temperature distribution values.

[0056] In one embodiment of the present invention, the change of austenite enthalpy with temperature is represented by a quadratic function, as follows:

[0057] in, For temperature austenitic enthalpy at time The first constant coefficient, The coefficient of the first-order term, is the coefficient of the first and second quadratic terms.

[0058] In one embodiment of the present invention, , , The value was obtained by fitting the measurement data of pure austenite.

[0059] In one embodiment of the present invention, the relationship between ferrite enthalpy and temperature is described by a cubic function, as follows:

[0060] in, For temperature Ferrite enthalpy at time The first constant coefficient, for, The coefficient of the second quadratic term, This is the coefficient of the second and third terms.

[0061] In one embodiment of the present invention, , , , The value was obtained by fitting the measurement data of pure ferrite.

[0062] In one embodiment of the present invention, the determination of the weighted specific heat capacity includes: determining the specific heat capacity of ferrite and the specific heat capacity of austenite according to the corresponding preset mapping relationship; weighting the specific heat capacity of ferrite and the specific heat capacity of austenite based on the composition ratio of the two phases to obtain the initial specific heat capacity; determining the latent heat of phase transformation between austenite and ferrite based on the difference between the enthalpy of ferrite and the enthalpy of austenite, and the rate of change of the composition ratio of the two phases with temperature, and correcting the initial specific heat capacity based on the latent heat of phase transformation to obtain the weighted specific heat capacity.

[0063] In one embodiment of the present invention, the weighted specific heat capacity of the two-phase components changes with temperature as follows:

[0064] in, For temperature Weighted specific heat capacity at time The proportion of ferrite composition. For temperature ferrite specific heat capacity at that time This refers to the austenitic composition ratio. For temperature The specific heat capacity of austenite at that time For temperature Ferrite enthalpy at time For temperature austenitic enthalpy at time Ferrite varies with temperature rate of change, It is the latent heat of phase transformation between austenite and ferrite.

[0065] In one embodiment of the present invention, the preset mapping relationship corresponding to the specific heat capacity of austenite is obtained by fitting a linear polynomial with respect to the temperature distribution value; the specific heat capacity of ferrite is obtained by fitting a bilateral crystal ball function and a first polynomial with respect to the temperature distribution value, wherein the bilateral crystal ball function is a probability density function with respect to the temperature distribution value.

[0066] In one embodiment of the present invention, the curve of the specific heat capacity of austenite changing with temperature exhibits a clear linearity. The change of the specific heat capacity of austenite with temperature can be described by a linear function, as follows:

[0067] in, For temperature The specific heat capacity of austenite at that time.

[0068] In one embodiment of the present invention, the curve of ferrite specific heat capacity changing with temperature is more complex, and its changing trend is difficult to describe using only a polynomial function. Based on the distribution characteristics of the measured data, the change of ferrite specific heat capacity with temperature can be described by combining a two-sided crystal sphere function (a common non-standard distribution pattern: the main body resembles a Gaussian distribution with a long tail on both sides) and a polynomial, as follows:

[0069] in, For temperature ferrite specific heat capacity at that time The fitting coefficient for the total ferrite specific heat capacity is . For a two-sided crystal ball function, This is a temperature measurement value. This represents the center temperature value of the Gaussian distribution. Let be the standard deviation of the Gaussian distribution. For the critical point parameters of the left tail, The power exponent of the left tail is... These are the critical point parameters for the right tail. is the power exponent of the right tail. The coefficient of the third-order term, This is the coefficient of the third quadratic term.

[0070] In one embodiment of the present invention, the parameters in the bilateral crystal ball function, such as and the coefficient of the third term coefficient of the third quadratic term It was obtained by fitting the measurement data of ferrite.

[0071] In one embodiment of the present invention, the second monitoring parameter further includes thermal conductivity; if the temperature distribution value corresponding to a location is less than a preset temperature threshold, the preset mapping relationship corresponding to thermal conductivity is obtained by fitting a second polynomial about the temperature distribution value; if the temperature distribution value corresponding to a location is greater than or equal to the preset temperature threshold, the preset mapping relationship corresponding to thermal conductivity is obtained by fitting a third polynomial about the temperature distribution value and a fourth polynomial about the preset temperature threshold, wherein the coefficients of the fourth polynomial are based on the coefficients of the second polynomial and the coefficients of the third polynomial, and the continuity of thermal conductivity at the preset temperature threshold is maintained.

[0072] In one embodiment of the present invention, based on the distribution characteristics of the measurement data, the change of thermal conductivity with temperature is constructed using a piecewise function, as follows:

[0073] in, For temperature Thermal conductivity at that time The coefficient of the fourth constant term. The coefficient of the fourth-order term, The coefficient of the quadratic term. The preset temperature threshold; This is the coefficient of the fifth-order term.

[0074] Step S230: Based on the current temperature distribution, the current value of the weighted enthalpy, the current values ​​of each second monitoring parameter and the preset finite difference heat conduction equation, determine the estimated enthalpy for the next moment, and determine the current value of the ratio of the two phase components as the ratio estimate for the next moment. Then, determine the estimated temperature value for the next moment based on the estimated enthalpy for the next moment.

[0075] In one embodiment of the present invention, the finite difference heat conduction equation includes multiple discrete differential heat conduction equations about enthalpy corresponding to the interior of the strip, and two boundary heat conduction equations about enthalpy corresponding to the surface of the strip; each second monitoring parameter also includes heat source power density and boundary heat flux density, the heat source power density is used to solve each discrete differential heat conduction equation and each boundary heat conduction equation, and the boundary heat flux density is used to solve each boundary heat conduction equation.

[0076] In one embodiment of the present invention, based on the characteristics of endless rolling in continuous casting and rolling, and the fact that the thickness of the strip is much smaller than its width, the heat conduction process of the strip in three dimensions can be simplified to a one-dimensional heat conduction equation containing a heat source. A temperature-based heat conduction equation is established along the thickness direction of the strip, as follows:

[0077] in, These are the position coordinates of the strip along its thickness direction. The thermal conductivity of the strip steel, For strip temperature, For heat source power density, For the density of the strip steel, The isobaric specific heat capacity of the strip steel. For time coordinates.

[0078] In one embodiment of the present invention, a heat conduction equation based on enthalpy is established according to the relationship between isobaric specific heat capacity and enthalpy and temperature. The relationship between isobaric specific heat capacity and enthalpy and temperature is as follows:

[0079] in, The isobaric specific heat capacity of the strip steel. For the enthalpy of the strip steel, For strip temperature, This is an isobaric condition.

[0080] In one embodiment of the present invention, the temperature-based heat conduction equation is converted into an enthalpy-based heat conduction equation, as follows:

[0081] in, The thermal conductivity of the strip steel, The isobaric specific heat capacity of the strip steel. For the enthalpy of the strip steel, These are the position coordinates of the strip along its thickness direction. For heat source power density, For the density of the strip steel, For time coordinates.

[0082] In one embodiment of the present invention, the time coordinates can be determined using the finite difference method. and position coordinates Discretization is performed to achieve the finite difference of the heat conduction equation. The strip steel is located at... ,time enthalpy express.

[0083] In one embodiment of the present invention, Enthalpy at time (known, i.e., the current time) and The implicit relationship of enthalpy at time step (unknown, i.e., the next time step), that is, the multiple discrete difference heat conduction equations about enthalpy inside the strip, are as follows:

[0084] in, The thermal conductivity of the strip steel, For time step, The isobaric specific heat capacity of the strip steel. For position step size, For the density of the strip steel, For the strip steel in position ,time enthalpy, For the strip steel in position ,time enthalpy, For the strip steel in position ,time enthalpy, For the strip steel in position ,time enthalpy, For heat source power density, , This represents the total number of discrete locations.

[0085] In one embodiment of the present invention, the effective discrete positions where the above equation holds are traversed. able to obtain A about The equation.

[0086] In one embodiment of the present invention, regarding Unknown enthalpy of time , There are a total Therefore, boundary conditions for the upper and lower surfaces are also needed to solve for the problem. .

[0087] In one embodiment of the present invention, the boundary heat flux density is used to solve the boundary heat conduction equations, which includes: correcting the boundary heat flux density according to the rate of change of the boundary heat flux density with enthalpy; and solving the boundary heat conduction equations based on the corrected boundary heat flux density.

[0088] In one embodiment of the present invention, for the upper surface of the strip steel Based on thermal equilibrium, the following equation can be obtained:

[0089] in, The thermal conductivity of the strip steel, The isobaric specific heat capacity of the strip steel. For the strip steel in position ,time enthalpy, For the strip steel in position ,time enthalpy, For position step size, This is the corrected boundary heat flux density on the upper surface of the strip. For heat source power density, For the density of the strip steel, For the strip steel in position ,time enthalpy, For time step.

[0090] In one embodiment of the present invention, the modified boundary heat flux density of the upper surface of the strip is as follows:

[0091] in, This is the corrected boundary heat flux density on the upper surface of the strip. The boundary heat flux density of the strip. For the enthalpy of the strip steel, For the strip steel in position ,time enthalpy, For the strip steel in position ,time Enthalpy.

[0092] In one embodiment of the invention, similar to the upper surface of the strip, the lower surface of the strip... The heat balance equation is as follows:

[0093] in, The thermal conductivity of the strip steel, The isobaric specific heat capacity of the strip steel. For the strip steel in position ,time enthalpy, For the strip steel in position ,time enthalpy, For position step size, This is the corrected boundary heat flux density of the lower surface of the strip. For heat source power density, For position step size, For the density of the strip steel, For the strip steel in position ,time enthalpy, For time step.

[0094] In one embodiment of the present invention, the modified boundary heat flux density of the lower surface of the strip is as follows:

[0095] in, This is the corrected boundary heat flux density on the upper surface of the strip. The boundary heat flux density of the strip. For the enthalpy of the strip steel, For the strip steel in position ,time enthalpy, For the strip steel in position ,time Enthalpy.

[0096] In one embodiment of the present invention, by solving this A about The system of linear equations can then be used to obtain... The enthalpy distribution along the thickness direction of the strip at all times. Combined with enthalpy... With temperature The functional relationship between the two can be used to further determine the temperature distribution, i.e., the temperature estimate. By decomposing the strip rolling process into a series of time steps, and repeating the above solution process at the corresponding time points when the strip passes through each region of the rolling line, the temperature distribution of the strip along the thickness direction at each moment during the rolling process can be obtained.

[0097] In one embodiment of the present invention, the heat source power density in the difference equation set, i.e., the preset finite difference heat conduction equation, is... It mainly includes two categories: electromagnetic induction heating and rolling process heating.

[0098] In continuous casting and rolling production lines, thinner slabs typically require induction heating furnaces to reheat them between roughing and finishing rolling. The power density distribution of electromagnetic induction heating is calculated using the exponential decay of induced eddy currents in the metal, combined with parameters such as the electrical conductivity of the strip.

[0099] When strip steel is rolled through a rolling mill, deformation work and friction work are generated due to the deformation of the metal under stress and the friction between the surface and the rolls. These deformation and friction work are calculated by a rolling force calculation model using the relevant specifications and mechanical parameters of the rolling mill and the strip steel.

[0100] In one embodiment of the present invention, the boundary conditions of the difference equation system are determined by the boundary heat flux density. The boundary conditions of the strip, namely the heat flux density of the upper and lower surfaces, are given. These mainly include the thermal radiation from the strip surface and the thermal convection between the strip and external media (rollers, mill rolls, air, cooling water, etc.).

[0101] The heat flux density calculation of thermal radiation utilizes the Stefan-Boltzmann formula and the current strip and medium temperatures.

[0102] The heat flux density of thermal convection is calculated separately based on the heat transfer law and heat transfer coefficient of the medium in contact with the strip.

[0103] Step S240: If the current monitoring parameters meet the preset convergence conditions, the ratio estimate is determined as the new current value of the two-phase component ratio, and the temperature estimate is determined as the new current temperature distribution, so as to continue to determine the new current value of the second monitoring parameter, and determine the new estimated enthalpy, the new ratio estimate and the new temperature estimate, until the temperature monitoring is completed. The current monitoring parameters include the temperature estimate and the ratio estimate.

[0104] In one embodiment of the present invention, the preset convergence conditions include the rate of change between the current temperature distribution and the temperature estimate, and the rate of change between the proportion of the two phase components and the proportion estimate, satisfying the corresponding preset change range.

[0105] In one embodiment of the present invention, to improve the calculation accuracy and applicability of temperature monitoring, the surface temperature of the continuously cast billet can be continuously monitored, and a self-learning process is incorporated. The calculation process of temperature monitoring is corrected by a self-learning coefficient. For example, based on the actual installation location of the temperature measuring equipment, the estimated temperature value corresponding to the strip is determined, and the actual measured value of the temperature measuring equipment is used for feedback learning, thereby updating the self-learning coefficient in temperature monitoring. The self-learning coefficient includes various coefficients corresponding to the determination of the two-phase component ratio and weighted enthalpy in the first monitoring parameter, and various coefficients corresponding to the determination of the weighted specific heat capacity and thermal conductivity in the second monitoring parameter.

[0106] Step S250: If the current monitoring parameters do not meet the preset convergence conditions, the proportional estimate is updated based on the temperature estimate, and the temperature estimate is updated based on the estimated enthalpy and the updated proportional estimate, until the preset convergence conditions are met.

[0107] In one embodiment of the present invention, please refer to Figure 3 , Figure 3 This is a schematic diagram illustrating the implementation process of a continuous casting and rolling temperature monitoring method provided in one embodiment of the present invention. Figure 3 As shown, the continuous casting billet temperature data includes: surface temperature measurements of the continuously casting billet collected at various times. The current time is the initial time 0; estimate the temperature distribution. Estimate the current temperature distribution based on surface temperature measurements; calculate the ferrite proportion. enthalpy : Calculate the current values ​​of ferrite composition ratio and weighted enthalpy; calculate Specific heat capacity, thermal conductivity, heat source power density, and boundary heat flux density at time t: calculate the current value of the second monitoring parameter; calculate... : Calculate the estimated enthalpy at the next moment; calculate Estimated value: The current value of the ferrite composition ratio is determined as the estimated ratio value for the next time step; calculation Estimate: Calculate the estimated temperature for the next moment; determine... and Convergence Check: If the proportional estimate and the temperature estimate converge, proceed to the step of determining whether the rolling mill temperature calculation is complete. If not, then based on... Estimated values The estimated values ​​are updated, and new values ​​are recalculated. Estimated value; determine whether the rolling mill temperature calculation is complete: if not, set the next moment as the current moment. Continue temperature monitoring; if complete, then end the process.

[0108] In one embodiment of the present invention, during the actual solution process of the strip temperature model, i.e., the preset finite difference heat conduction equation, the current temperature distribution of the strip along the thickness direction is first estimated based on the surface measurement temperature of the continuously cast billet. Deviations in the temperature distribution estimation can be quickly corrected through a self-learning process based on the actual measured temperature.

[0109] In one embodiment of the present invention, the weighted enthalpy and ferrite composition ratio at the current moment can be calculated based on the current temperature distribution.

[0110] In one embodiment of the invention, in subsequent iterations, according to the given... Time and The iterative calculation method for enthalpy at time step is also known as equations (19) to (23), and utilizes... The weighted enthalpy, temperature distribution, weighted specific heat capacity, thermal conductivity, heat source power density, and boundary heat flux density at time t are used to calculate the results. Weighted enthalpy at time.

[0111] In one embodiment of the present invention, a numerical calculation method is used... The ferrite composition ratio at time t is used as The estimated proportion of ferrite at time t, combined with known values. The weighted enthalpy at time t can be solved. Temperature estimate at any given time.

[0112] In one embodiment of the present invention, it is possible to utilize The estimated temperature and ferrite composition ratio at any given time can be iteratively calculated to obtain the corresponding ferrite composition ratio and temperature estimates until the values ​​converge. Experiments have verified that after several iterations, the estimated ferrite composition ratio and temperature can converge to stable values ​​with high accuracy, ensuring the real-time performance of the temperature model calculation.

[0113] In one embodiment of the present invention, a method for monitoring the temperature of strip steel in continuous casting and rolling based on weighted phase transformation coupling is provided. This method incorporates the thermodynamic effects of the austenite-ferrite phase transformation into a highly efficient strip steel temperature calculation model, thereby enabling high-precision calculation of strip steel temperature and effectively covering the austenite-ferrite phase transformation temperature range. Furthermore, it supports further parameter optimization using statistical analysis of actual data to improve the accuracy of strip steel temperature calculation and monitoring. For example, the temperature accuracy of the strip steel temperature model within a ±15℃ deviation is 93.2%. The average absolute deviation of the temperature estimate is 6.4℃, and the root mean square deviation is 8.1℃. By comparing with measured temperatures, the model can accurately calculate strip steel production with a final rolling temperature between 700℃ and 950℃, effectively covering the austenite-ferrite phase transformation temperature range.

[0114] Please see Figure 4 , Figure 4 This is a block diagram of a continuous casting and rolling temperature monitoring device provided in one embodiment of the present invention. This device can be applied to... Figure 1 The implementation environment shown is specifically configured in computer device 120. This device can also be applied to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment to which the device is applicable.

[0115] like Figure 4 As shown, a continuous casting and rolling temperature monitoring device 400 according to an embodiment of the present invention includes: a temperature acquisition module 410, a parameter determination module 420, a first temperature estimation module 430, a second temperature estimation module 440, and a temperature convergence estimation module 450.

[0116] The temperature acquisition module 410 is used to acquire the current temperature distribution of the strip steel. The current temperature distribution is based on the surface temperature of the continuously cast billet and estimated along the thickness direction of the strip steel. The parameter determination module 420 is used to determine the current values ​​of multiple first monitoring parameters and multiple second monitoring parameters based on the current temperature distribution and multiple preset mapping relationships. Each first monitoring parameter includes the two-phase composition ratio and the weighted enthalpy determined based on the two-phase composition ratio. The second monitoring parameter includes the weighted specific heat capacity determined based on the two-phase composition ratio. The two-phase composition ratio is determined based on the current temperature distribution, the length of the temperature range for the austenite-ferrite phase transformation, and the average temperature of the temperature range. The first temperature estimation module 430 is used to determine the estimated enthalpy at the next moment based on the current temperature distribution, the current value of the weighted enthalpy, the current values ​​of each second monitoring parameter and the preset finite difference heat conduction equation, and to determine the current value of the ratio of the two phase components as the ratio estimate at the next moment, and to determine the temperature estimate at the next moment based on the estimated enthalpy at the next moment. The second temperature estimation module 440 is used to determine the proportional estimate as the new current value of the two-phase component ratio and the temperature estimate as the new current temperature distribution if the current monitoring parameter meets the preset convergence condition, so as to continue to determine the new current value of the second monitoring parameter, and determine the new estimated enthalpy, the new proportional estimate and the new temperature estimate, until the temperature monitoring is completed. The current monitoring parameter includes the temperature estimate and the proportional estimate. The temperature convergence estimation module 450 is used to update the proportional estimate based on the temperature estimate if the current monitored parameter does not meet the preset convergence condition, and to update the temperature estimate based on the estimated enthalpy and the updated proportional estimate, until the preset convergence condition is met.

[0117] It should be noted that the continuous casting and rolling temperature monitoring device and the continuous casting and rolling temperature monitoring method provided in the above embodiments belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the continuous casting and rolling temperature monitoring device provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0118] Embodiments of the present invention also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the electronic device enables the continuous casting and rolling temperature monitoring method provided in the above embodiments.

[0119] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of a computer system for an electronic device provided in one embodiment of the present invention. Figure 5 The computer system 500 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0120] like Figure 5 As shown, the computer system 500 includes a central processing unit 501, which can perform various appropriate actions and processes based on a program stored in the read-only memory 502 or a program loaded from the storage section 508 into the random access memory 503, such as performing the methods described in the above embodiments. The random access memory 503 also stores various programs and data required for system operation. The central processing unit 501, the read-only memory 502, and the random access memory 503 are interconnected via a bus 504. An input / output interface 505 is also connected to the bus 504.

[0121] The following components are connected to the input / output interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the input / output interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 510 as needed so that computer programs read from it can be installed into the storage section 508 as needed.

[0122] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit 501, it performs various functions defined in the system of the present invention.

[0123] The computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium, a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory (EPROM), flash memory, optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. Computer programs contained on computer-readable media can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0124] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0125] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself. Therefore, the technical solutions according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, portable hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of the present invention.

[0126] Another aspect of the present invention provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer processor, causes the computer to perform the continuous casting and rolling temperature monitoring method provided in the above embodiments. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not incorporated into the electronic device.

[0127] In the above embodiments, unless otherwise specified, the use of ordinal numbers such as "first" and "second" to describe common objects only indicates that they refer to different instances of the same object, rather than indicating that the objects being described must be in a given order, whether temporally, spatially, sequentially, or in any other way.

[0128] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for monitoring temperature in continuous casting and rolling, characterized in that, The method includes: The current temperature distribution of the strip is obtained, which is based on the surface measurement temperature of the continuously cast billet and estimated along the thickness direction of the strip; The current values ​​of multiple first monitoring parameters and multiple second monitoring parameters are determined based on the current temperature distribution and multiple preset mapping relationships. Each first monitoring parameter includes a two-phase composition ratio and a weighted enthalpy determined based on the two-phase composition ratio. The second monitoring parameter includes a weighted specific heat capacity determined based on the two-phase composition ratio. The two-phase composition ratio is determined based on the current temperature distribution, the length of the temperature range for the austenite-ferrite phase transformation, and the average temperature of the temperature range. Based on the current temperature distribution, the current value of the weighted enthalpy, the current values ​​of each of the second monitoring parameters, and the preset finite difference heat conduction equation, the estimated enthalpy for the next moment is determined, and the current value of the ratio of the two-phase components is determined as the ratio estimate for the next moment. The estimated temperature value for the next moment is determined based on the estimated enthalpy for the next moment. If the current monitoring parameter meets the preset convergence condition, the ratio estimate is determined as the new current value of the ratio of the two phase components, and the temperature estimate is determined as the new current temperature distribution, so as to continue to determine the new current value of the second monitoring parameter, and determine the new estimated enthalpy, the new ratio estimate and the new temperature estimate, until the temperature monitoring is completed. The current monitoring parameter includes the temperature estimate and the ratio estimate. If the current monitoring parameters do not meet the preset convergence conditions, the proportional estimate is updated based on the temperature estimate, and the temperature estimate is updated based on the estimated enthalpy and the updated proportional estimate, until the preset convergence conditions are met.

2. The continuous casting and rolling temperature monitoring method according to claim 1, characterized in that, The two-phase composition ratio includes the ferrite composition ratio and the austenite composition ratio, and the determination of the ferrite composition ratio includes: The offset temperature is determined based on the difference between the temperature distribution value in the current temperature distribution and the average temperature; The rate of change is determined based on preset empirical parameters and the length of the temperature range. The empirical parameters are extracted based on the curve of the ferrite composition ratio changing with temperature. The exponential independent variable is determined based on the rate of change and the offset temperature, and the exponential independent variable is converted into the format of a logistic function to obtain the ferrite composition ratio.

3. The continuous casting and rolling temperature monitoring method according to claim 1, characterized in that, The determination of the weighted specific heat capacity includes: The specific heat capacity of ferrite and the specific heat capacity of austenite are determined according to the corresponding preset mapping relationship; The initial specific heat capacity is obtained by weighting the specific heat capacity of ferrite and the specific heat capacity of austenite based on the composition ratio of the two phases. The latent heat of phase transformation between austenite and ferrite is determined based on the difference between the enthalpy of ferrite and the rate of change of the composition ratio of the two phases with temperature. The initial specific heat capacity is then corrected based on the latent heat of phase transformation to obtain the weighted specific heat capacity.

4. The continuous casting and rolling temperature monitoring method according to claim 3, characterized in that, The preset mapping relationship corresponding to the specific heat capacity of austenite is obtained by fitting a linear polynomial with respect to the temperature distribution value; The specific heat capacity of ferrite is obtained based on a two-sided crystal ball function and a first polynomial fit with respect to the temperature distribution value, wherein the two-sided crystal ball function is a probability density function with respect to the temperature distribution value.

5. The continuous casting and rolling temperature monitoring method according to claim 1, characterized in that, The second monitoring parameter also includes thermal conductivity; If the temperature distribution value corresponding to a location is less than a preset temperature threshold, then the preset mapping relationship corresponding to the thermal conductivity is obtained by fitting a second polynomial about the temperature distribution value. If the temperature distribution value corresponding to a position is greater than or equal to a preset temperature threshold, the preset mapping relationship corresponding to the thermal conductivity is obtained by fitting a third polynomial about the temperature distribution value and a fourth polynomial about the preset temperature threshold. The coefficients of the fourth polynomial are based on the coefficients of the second polynomial and the coefficients of the third polynomial, and the continuity of the thermal conductivity at the preset temperature threshold is maintained.

6. The continuous casting and rolling temperature monitoring method according to any one of claims 1-5, characterized in that, The finite difference heat conduction equations include multiple discrete difference heat conduction equations about enthalpy corresponding to the interior of the strip, and two boundary heat conduction equations about enthalpy corresponding to the surface of the strip. Each of the second monitoring parameters further includes heat source power density and boundary heat flux density, wherein the heat source power density is used to solve each of the discrete differential heat conduction equations and each of the boundary heat conduction equations, and the boundary heat flux density is used to solve each of the boundary heat conduction equations.

7. The continuous casting and rolling temperature monitoring method according to claim 6, characterized in that, The boundary heat flux density is used to solve the respective boundary heat conduction equations, including: The boundary heat flux density is corrected based on the rate of change of the boundary heat flux density with enthalpy; Solve the boundary heat conduction equations based on the modified boundary heat flux density.

8. A continuous casting and rolling temperature monitoring device, characterized in that, The device includes: The temperature acquisition module is used to acquire the current temperature distribution of the strip steel, which is estimated based on the surface measurement temperature of the continuously cast billet and along the thickness direction of the strip steel. The parameter determination module is used to determine the current values ​​of multiple first monitoring parameters and multiple second monitoring parameters based on the current temperature distribution and multiple preset mapping relationships. Each first monitoring parameter includes a two-phase composition ratio and a weighted enthalpy determined based on the two-phase composition ratio. The second monitoring parameter includes a weighted specific heat capacity determined based on the two-phase composition ratio. The two-phase composition ratio is determined based on the current temperature distribution, the length of the temperature range for the austenite-ferrite phase transformation, and the average temperature of the temperature range. The first temperature estimation module is used to determine the estimated enthalpy at the next moment based on the current temperature distribution, the current value of the weighted enthalpy, the current values ​​of each of the second monitoring parameters and the preset finite difference heat conduction equation, and to determine the current value of the ratio of the two-phase components as the ratio estimate at the next moment, and to determine the temperature estimate at the next moment based on the estimated enthalpy at the next moment. The second temperature estimation module is used to determine the ratio estimate as the new current value of the two-phase component ratio and the temperature estimate as the new current temperature distribution if the current monitoring parameter meets the preset convergence condition, so as to continue to determine the new current value of the second monitoring parameter, and determine the new estimated enthalpy, the new ratio estimate and the new temperature estimate, until the temperature monitoring is completed. The current monitoring parameter includes the temperature estimate and the ratio estimate. The temperature convergence estimation module is used to update the proportional estimate based on the temperature estimate if the current monitored parameter does not meet the preset convergence condition, and to update the temperature estimate based on the estimated enthalpy and the updated proportional estimate, until the preset convergence condition is met.

9. An electronic device, characterized in that, The electronic device includes: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the continuous casting and rolling temperature monitoring method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by the computer's processor, causes the computer to perform the continuous casting and rolling temperature monitoring method according to any one of claims 1 to 7.

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