Intelligent temperature field regulation method and system for aluminum plate strip hot rolling process

Abstract

This application relates to the field of intelligent control technology in hot rolling processing, and in particular to an intelligent temperature field control method and system for the hot rolling process of aluminum sheet and strip. The method includes: acquiring temperature distribution data and performing interpolation processing to generate a continuous temperature field distribution; identifying high-temperature and low-temperature regions and extracting temperature deviation values; querying a historical heating database to obtain temperature response relationships; calculating the initial adjustment amount of each heating zone within the heating equipment based on the temperature response relationships and temperature deviation values; establishing a numerical model of heat conduction and solving the heat transfer process between adjacent heating zones; and correcting the initial adjustment amount of each heating zone based on the solution results. This application has the effect of reconstructing the temperature field distribution of aluminum sheet and strip, quantifying the adjustment requirements of each heating zone, and effectively compensating for the heat conduction coupling effect, thereby reducing temperature non-uniformity and improving rolling quality and product consistency.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for hot rolling processes, and in particular to an intelligent temperature field control method and system for the hot rolling process of aluminum sheet and strip. Background Technology

[0002] In the hot rolling process of aluminum sheet and strip, heating equipment is used to heat the aluminum sheet and strip to the target temperature to meet the requirements of subsequent rolling processes. Heating equipment is typically divided into multiple heating zones, each with independently controlled heating power. However, in actual production, due to differences in the material, thickness, width, and other specifications of the aluminum sheet and strip, as well as the heat transfer and mutual influence between the different zones of the heating equipment, uneven temperature distribution easily occurs during the heating process. This manifests as the coexistence of localized high-temperature and low-temperature areas, affecting rolling quality and product performance.

[0003] Traditional temperature control methods mainly rely on operators manually adjusting the power of each heating zone based on temperature measurement data and experience. This method has the following shortcomings: First, the number of temperature measurement points is limited, which cannot fully reflect the overall temperature distribution of the aluminum strip and is prone to missing local temperature anomalies. Second, operators rely on personal experience for adjustment, lacking quantitative temperature response patterns, resulting in insufficient adjustment accuracy. Third, there is a thermal conduction coupling effect between heating zones. Adjusting the power of one zone will affect the temperature field of adjacent zones. Existing methods usually do not consider this linkage effect, which may result in an uneven temperature field after adjustment, or even cause new temperature deviations.

[0004] In addition, some existing technologies employ feedback control strategies based on temperature deviations, performing independent PID regulation on each heating zone. However, these methods still treat each zone as an independent control object, ignoring the heat transfer process between zones. In practical applications, the regulation effect is limited, and oscillations and overshoot are prone to occur. Summary of the Invention

[0005] In view of the aforementioned problems, this application is proposed to provide an intelligent temperature field control method and system for hot rolling processes of aluminum sheet and strip, which overcomes or at least partially solves the aforementioned problems, comprising:

[0006] The above-mentioned objective of this application is achieved through the following technical solution:

[0007] A method for intelligent temperature field control in the hot rolling process of aluminum sheet and strip includes the following steps:

[0008] The temperature distribution data of the aluminum strip to be rolled in the heating equipment is obtained, and the temperature distribution data is interpolated to generate a continuous temperature field distribution.

[0009] Based on the continuous temperature field distribution, high-temperature and low-temperature regions are identified, and temperature deviation values ​​are extracted.

[0010] Based on the specifications of the aluminum sheet and strip to be rolled, the historical heating database is queried to obtain the corresponding matching temperature response relationship;

[0011] Based on the temperature response relationship and temperature deviation, the initial adjustment amount of each heating zone in the heating equipment is calculated;

[0012] Establish a numerical model of heat conduction in the heating equipment, and solve the heat transfer process between adjacent heating zones using the numerical model of heat conduction.

[0013] The initial adjustment values ​​of each heating zone are corrected based on the solution results, and the heating equipment is adjusted based on the corrected adjustment values.

[0014] The second objective of this invention is achieved through the following technical solution:

[0015] An intelligent temperature field control system for the hot rolling process of aluminum sheet and strip includes:

[0016] The interpolation processing module is used to acquire the temperature distribution data of the aluminum strip to be rolled in the heating equipment, and to perform interpolation processing on the temperature distribution data to generate a continuous temperature field distribution.

[0017] The deviation extraction module is used to identify high-temperature and low-temperature regions based on the continuous temperature field distribution and extract temperature deviation values.

[0018] The response query module is used to query the historical heating database based on the specifications of the aluminum sheet and strip to be rolled, and obtain the corresponding matching temperature response relationship;

[0019] The adjustment calculation module is used to calculate the initial adjustment of each heating zone in the heating equipment based on the temperature response relationship and temperature deviation value.

[0020] The heat transfer solution module is used to establish a numerical model of heat conduction in the heating equipment and solve the heat transfer process between adjacent heating zones through the numerical model of heat conduction.

[0021] The adjustment correction module is used to correct the initial adjustment of each heating zone based on the solution results, and to adjust the heating equipment based on the corrected adjustment.

[0022] This application also relates to a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described intelligent temperature field control method for hot rolling of aluminum sheet and strip.

[0023] This application has the following advantages:

[0024] In the embodiments of this application, an intelligent temperature field control method and system for hot rolling of aluminum sheet and strip is provided. By interpolating the temperature distribution to generate a continuous temperature field, combining historical data to quantify the temperature response relationship, and correcting the adjustment amount based on the heat conduction numerical model to compensate for the inter-zone thermal coupling effect, it can effectively solve the problem of uneven temperature distribution. It has the effect of reconstructing the temperature field distribution of aluminum sheet and strip, quantifying the adjustment requirements of each heating zone, and effectively compensating for the heat conduction coupling effect, thereby reducing temperature non-uniformity and improving rolling quality and product consistency. Attached Figure Description

[0025] Figure 1 This is a flowchart of an embodiment of an intelligent temperature field control method for hot rolling of aluminum sheet and strip according to this application;

[0026] Figure 2 This is a schematic block diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] All terms used in this application, including technical or scientific terms, have the same meaning as understood by one of ordinary skill in the art to which this application pertains, unless otherwise specifically defined. It should also be understood that terms defined in general dictionaries, such as those in common dictionaries, should be interpreted as having a meaning consistent with their meaning in the context of the relevant art, and not as having an idealized or highly formalized meaning, unless expressly defined herein.

[0030] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment shall be considered part of the specification.

[0031] First, some nouns or terms that appear in the description of the embodiments of this application shall be interpreted as follows:

[0032] The hot rolling process of aluminum sheet and strip refers to the production process in which aluminum ingots or aluminum alloy flat ingots are heated and then rolled in multiple passes on a rolling mill to reduce their thickness and increase their width, thereby obtaining sheet and strip materials with the required mechanical properties and surface quality. In this process, the heating stage has an impact on the quality of the final product.

[0033] Heating equipment typically refers to industrial furnaces used for heating aluminum sheets and strips, such as walking beam furnaces or roller hearth furnaces. These furnaces are usually divided into multiple independent heating zones, each with its own independently controllable heating power.

[0034] Heating zones refer to areas within the heating equipment that are divided into zones with independent heating control capabilities. By adjusting the power of each heating zone, the local temperature of the aluminum strip can be controlled.

[0035] Temperature distribution data refers to the set of discrete temperature measurements obtained on the surface or inside of the aluminum strip to be rolled through various temperature measurement methods, such as infrared thermometers or thermocouples. It can reflect the temperature state of the aluminum strip at a specific moment.

[0036] Interpolation is a mathematical method used to infer the values ​​of unknown data points, either between or outside of known discrete data points. In embodiments of this application, interpolation is used to convert discrete temperature measurement data into continuous temperature distribution information.

[0037] Continuous temperature field distribution refers to a description of the continuous temperature value of the aluminum strip to be rolled at any position in the heating equipment, obtained through interpolation or other modeling methods. It can provide more comprehensive temperature information than discrete temperature measurement points.

[0038] High-temperature and low-temperature regions refer to local areas in a continuous temperature field where the temperature value is higher or lower than the target temperature range.

[0039] Temperature deviation refers to the difference between the actual temperature and the target temperature in the identified high-temperature or low-temperature region, in order to quantify the degree of temperature non-uniformity.

[0040] A historical heating database refers to a collection of data related to the historical heating process of aluminum strips, including heating parameters, temperature changes, power adjustments, and other information for aluminum strips of different specifications.

[0041] Temperature response relationship refers to the correspondence between the power adjustment of each heating zone in the heating equipment and the temperature change of the aluminum strip in the corresponding area, describing the degree of influence of heating power change on the temperature of the aluminum strip.

[0042] The initial adjustment amount refers to the value initially calculated based on the temperature deviation and temperature response relationship to adjust the heating power of each heating zone, serving as the basis for subsequent corrections.

[0043] A heat conduction numerical model is a mathematical model based on physical laws that simulates the heat transfer process inside heating equipment and in aluminum plates and strips through numerical calculation methods. In the embodiments of this application, the heat conduction numerical model can predict the thermal coupling effect between heating zones.

[0044] Heat transfer refers to the phenomenon of heat energy being transferred from a high-temperature region to a low-temperature region, including forms such as conduction, convection, and radiation. In heating equipment, heat transfer occurs between heating zones and between the heating zones and the aluminum plate / strip.

[0045] The corrected adjustment amount refers to the amount used for adjustment obtained by taking into account the influence of heat transfer between adjacent heating zones and adjusting the initial adjustment amount. It is used to guide the actual power adjustment of the heating equipment.

[0046] In one embodiment, such as Figure 1 As shown, this application discloses an intelligent temperature field control method for the hot rolling process of aluminum sheet and strip, which specifically includes the following steps:

[0047] S10: Obtain the temperature distribution data of the aluminum strip to be rolled in the heating equipment, and perform interpolation processing on the temperature distribution data to generate a continuous temperature field distribution;

[0048] In the embodiments of this application, temperature distribution data can be obtained through various methods. For example, multiple thermocouples or infrared temperature measuring points at specific locations can be used to collect discrete temperature data as the aluminum strip to be rolled passes through. These temperature measuring points can be distributed along the width of the aluminum strip or concentrated at several specific locations. After obtaining the discrete temperature data, interpolation processing is required to obtain a continuous temperature field distribution. One implementation method is to use global linear interpolation, linearly fitting the data from all temperature measuring points to generate a continuous temperature distribution map across the entire surface of the aluminum strip. Another method is to use nearest neighbor interpolation, assigning the temperature value of its nearest measuring point to unmeasured points, thereby forming a piecewise continuous temperature field.

[0049] S20: Identify high-temperature and low-temperature regions based on the continuous temperature field distribution, and extract temperature deviation values;

[0050] In the embodiments of this application, after obtaining the continuous temperature field distribution, it is necessary to analyze it to identify temperature anomaly regions. One identification method is to set a target temperature range, then traverse the entire continuous temperature field, marking regions with temperature values ​​exceeding the target temperature range as high-temperature regions or low-temperature regions. For example, two temperature thresholds can be set, identifying regions above one threshold as high-temperature regions and regions below the other as low-temperature regions. After identifying the high-temperature and low-temperature regions, it is necessary to further extract temperature deviation values. This can be achieved by calculating the difference between the average temperature of each anomaly region and the target temperature; alternatively, the difference between the maximum or minimum temperature within the anomaly region and the target temperature can be directly taken as the temperature deviation value.

[0051] S30: Based on the specifications of the aluminum sheet and strip to be rolled, query the historical heating database to obtain the corresponding matching temperature response relationship;

[0052] In the embodiments of this application, to achieve temperature control, it is necessary to understand the impact of heating power changes on the temperature of the aluminum strip. One method to obtain the temperature response relationship is to search for all historical heating records of the same material grade in the historical heating database according to the material grade of the aluminum strip to be rolled, and select a record that is close to the thickness, width, and other specifications of the current aluminum strip to be rolled. Then, the pre-stored correspondence between the heating zone power adjustment and the aluminum strip temperature change is extracted from the selected historical record. Another approach is to perform statistical analysis on all relevant records in the historical database to calculate the average temperature response curve of aluminum strips of different specifications under the power changes of each heating zone.

[0053] Furthermore, in the temperature response relationship A time lag factor can be further introduced. Its value can usually be extracted from a historical database based on the strip specifications. Specifically, for strips with thickness h and transmission speed v, the time lag factor... ,in This is an empirical coefficient related to the thermal diffusivity of the material; for example, the value for aluminum alloys ranges from 0.8 to 1.2. In actual adjustment, the power adjustment amount... In time The resulting temperature change is: .when At this point, the temperature can be considered to have reached the steady-state response value. Under rapid adjustment requirements, the initial adjustment can be amplified by 1.2-1.5 times through pre-compensation, until the temperature response reaches 70%-80% of the target value (approximately...). Then, the value is adjusted back to the theoretical calculation value to avoid overshoot.

[0054] S40: Calculate the initial adjustment amount of each heating zone in the heating equipment based on the temperature response relationship and temperature deviation value;

[0055] In the embodiments of this application, after obtaining the temperature deviation value and the temperature response relationship, the power adjustment amount of each heating zone can be initially determined. For example, a proportional control strategy can be adopted, multiplying the temperature deviation value of each heating zone by a proportional coefficient to calculate the initial power adjustment amount of that zone, wherein the proportional coefficient can be obtained from the temperature response relationship. Another approach is to determine the corresponding initial adjustment amount by consulting a preset lookup table based on the magnitude of the temperature deviation value, wherein the lookup table can be established based on historical experience or a simplified model.

[0056] S50: Establish a numerical model of heat conduction in the heating equipment, and solve the heat transfer process between adjacent heating zones using the numerical model of heat conduction.

[0057] In the embodiments of this application, the thermal coupling effect between heating zones needs to be considered for temperature control. One method for establishing a numerical model of heat conduction is to simplify the heating device into a one-dimensional or two-dimensional heat transfer system, considering only the direct heat conduction between adjacent zones. In this case, the numerical model of heat conduction can be discretized based on simplified geometry and material parameters using the finite difference method or the finite element method. Using the numerical model of heat conduction, the direct heat transfer effect on the temperature field of adjacent zones when the power of one heating zone changes can be calculated.

[0058] Furthermore, for aluminum strips with a thickness of less than 10 mm, when the width-to-thickness ratio is greater than 100, the heating equipment can be simplified into a one-dimensional heat transfer system, considering only heat conduction along the transmission direction; when the width-to-thickness ratio is between 20 and 100, the heating equipment can be simplified into a two-dimensional heat transfer system, considering heat conduction in both the transmission and thickness directions; and when the width-to-thickness ratio is less than 20 or the strip thickness is greater than 20 mm, a three-dimensional heat transfer model needs to be established, considering heat transfer in the transmission, thickness, and width directions simultaneously.

[0059] The simplified judgment criteria include: when the temperature gradient in a certain direction is less than 5% of the temperature gradient in other dominant directions, the heat conduction in that direction can be ignored.

[0060] S60: Based on the solution results, correct the initial adjustment amount of each heating zone, and adjust the heating equipment based on the corrected adjustment amount.

[0061] In the embodiments of this application, after solving the heat transfer process between adjacent heating zones, the initial adjustment amount needs to be adjusted. One correction method is to apply a compensation value to the initial adjustment amount of each heating zone based on the heat transfer amount calculated by the numerical model. This compensation value aims to offset the thermal influence of adjacent zones. For example, if the prediction result for a heating zone is that it will be affected by the increased heating of adjacent zones, then its own initial adjustment amount may be reduced accordingly. Finally, the corrected adjustment amount is then sent to the controller of each heating zone in the heating device to achieve automatic adjustment of the heating power.

[0062] For example, as a specific implementation, suppose that in a hot rolling process of aluminum sheet and strip, the aluminum sheet and strip to be rolled is a 5000 series alloy, with a thickness of 3 mm and a width of 1500 mm, and the target furnace exit temperature is set at 580 degrees Celsius. At this time, the aluminum sheet and strip to be rolled is passing through a heating device with five heating zones.

[0063] First, to obtain the temperature distribution of the aluminum strip, multiple infrared thermometers can be placed at the outlet of the heating equipment to collect discrete temperature data at different locations on the surface of the aluminum strip. For example, a temperature measuring point can be set at regular intervals along the width to obtain a series of discrete temperature values. Subsequently, the discrete temperature data can be input into a preset data processing module, and an interpolation algorithm, such as bilinear interpolation, can be used to generate a continuous temperature field distribution map covering the entire width and part of the length of the aluminum strip. This continuous temperature field distribution map can provide the temperature status of the aluminum strip at the outlet of the heating equipment, thereby avoiding the omission of local temperature anomalies that may be caused by relying on only a limited number of temperature measuring points.

[0064] Based on this, the generated continuous temperature field distribution map is analyzed. Specifically, by comparing it with the target temperature of 580 degrees Celsius, the system can identify a low-temperature region of 570 degrees Celsius in the middle of the aluminum strip and a high-temperature region of 585 degrees Celsius at the edge. Based on this, the temperature deviation value of -10 degrees Celsius for the low-temperature region in the middle and +5 degrees Celsius for the high-temperature region at the edge are extracted to quantify the degree of non-uniformity of the current temperature field.

[0065] Based on this, the system obtains the specifications of the aluminum strip to be rolled, namely, 5000 series alloy, 3 mm thickness, 1500 mm width, and a target furnace exit temperature of 580 degrees Celsius. Subsequently, the system queries the historical heating database based on these specifications. Specifically, the system searches the historical heating database for historical heating records with the same 5000 series alloy material grade and whose thickness, width, and target furnace exit temperature deviate from the current aluminum strip's parameters within a preset tolerance range. For example, the system might find a historical heating record with specifications close to the current aluminum strip. This record records the correspondence between the power adjustment of each heating zone and the temperature change of the aluminum strip, i.e., the temperature response relationship. This provides a quantitative basis for adjustment, thus avoiding the limitations of empirical adjustments.

[0066] Furthermore, based on the acquired temperature response relationship and the identified temperature deviation, the initial adjustment amount for each heating zone can be calculated. For example, according to the temperature response relationship, if the central low-temperature region needs to be raised by 10 degrees Celsius, the system can calculate the initial power adjustment amount that the heating zone responsible for the central region needs to increase. Similarly, if the peripheral high-temperature region needs to be lowered by 5 degrees Celsius, the system can calculate the initial power adjustment amount that the heating zone responsible for the peripheral region needs to decrease. At this point, these calculated initial adjustment amounts are based on a preliminary determination of local temperature deviations.

[0067] Then, to address the thermal coupling issue between heating zones, a numerical model of heat conduction for the heating equipment was established. This model considered structural parameters such as the geometric dimensions of each heating zone, their spacing, and the thermal conductivity of the furnace material. Through this numerical model, the system can simulate and solve for the heat transfer process between adjacent zones, such as between the second and third zones, and between the third and fourth zones, including axial and radial heat flow, when the power of the third zone increases while the power of the first and fifth zones decreases. The solution then provides the impact of the initial adjustment of each heating zone on the temperature field of its adjacent zones; for example, an increase in the power of the third zone may lead to a slight increase in the temperature of the second and fourth zones.

[0068] Finally, the system corrects the initial adjustment values ​​of each heating zone based on the solution results of the heat conduction numerical model. For example, if the solution results of the heat conduction numerical model predict that an increase in the power of the third zone will lead to a 2-degree Celsius increase in the temperature of the fourth zone, and the fourth zone itself does not have an obvious low-temperature problem, then the initial adjustment value of the fourth zone may need to be compensated accordingly to offset the additional heat from the third zone. In this way, the system iteratively or all at once corrects the initial adjustment values ​​of all heating zones to obtain the adjustment values, and sends them to the controllers of each heating zone in the heating equipment to adjust the heating power of each heating zone. Thus, the solution of this embodiment can achieve precise linkage control of the temperature field of aluminum strip, thereby effectively solving the problem of local temperature non-uniformity and ensuring that the aluminum strip enters the subsequent rolling process at a uniform target temperature.

[0069] Based on the above examples, firstly, the solution in this embodiment, by acquiring the temperature distribution data of the aluminum strip to be rolled and performing interpolation processing to generate a continuous temperature field distribution, can overcome the limitations of traditional methods, which have a limited number of temperature measurement points and cannot comprehensively reflect the overall temperature distribution of the aluminum strip. In the example above, by using an infrared thermometer combined with interpolation processing, the system can identify localized temperature anomaly areas in the middle and edges of the aluminum strip, while traditional methods may only provide temperature values ​​at a few discrete points, making it difficult to detect areas of uneven temperature. Therefore, comprehensive and accurate temperature status information can be provided for subsequent control.

[0070] Building upon this, the solution in this embodiment queries a historical heating database based on the specifications of the aluminum strip to be rolled, obtaining corresponding temperature response relationships. This addresses the problems of traditional methods that rely on operator experience and lack quantitative adjustment basis. In the example above, the system can extract a quantitative relationship between heating zone power and temperature changes from historical data based on parameters such as the material, thickness, and width of the aluminum strip. Compared to operator judgment based on experience or simple PID control, this method provides data-driven adjustment basis, thereby improving the accuracy and efficiency of adjustment.

[0071] Furthermore, the solution in this embodiment establishes a numerical model of the heat conduction of the heating equipment and solves for the heat transfer process between adjacent heating zones. Based on the solution results, the initial adjustment amount is corrected, thus addressing the deficiency in existing technologies that neglect the heat conduction coupling effect between heating zones. In the example above, when it is calculated that the third zone requires increased power, the system can predict its temperature impact on adjacent zones and compensate for the initial adjustment amount of the adjacent fourth zone. Through this linkage correction mechanism, the problem of uneven temperature field after adjustment or even new temperature deviations that may result from traditional independent adjustment can be effectively reduced, thereby ensuring the uniformity of the overall temperature field.

[0072] In summary, the solution in this embodiment forms a complete temperature control system by introducing continuous temperature field generation, obtaining quantitative response relationships based on historical data, and adjusting the amount of adjustment considering the thermal coupling effect of the heating zones. This system can not only accurately identify the temperature distribution state and quantitatively calculate the adjustment amount, but also fully consider the thermal conduction coupling effect of the heating zones, thereby achieving precise temperature control of the aluminum plate and strip heating process and effectively improving product quality and production efficiency.

[0073] The following will further explain a method for intelligent temperature field control in the hot rolling process of aluminum sheet and strip in this exemplary embodiment.

[0074] In one embodiment, step S10, "acquiring temperature distribution data of the aluminum strip to be rolled within the heating equipment and interpolating the temperature distribution data to generate a continuous temperature field distribution," specifically includes:

[0075] Multiple temperature measurement points are determined by a preset temperature measurement strategy, and discrete temperature data of the aluminum strip to be rolled are collected through the determined multiple temperature measurement points.

[0076] In the embodiments of this application, the temperature measurement strategy can be designed according to actual process requirements and equipment conditions. For example, an infrared thermometer array or a thermocouple array can be used to uniformly or non-uniformly arrange temperature measurement points in the width and length directions of the aluminum strip to be rolled, so as to cover the entire surface of the strip. Simultaneously, the number and density of temperature measurement points can be adjusted according to the requirements for temperature field accuracy. For example, the number of temperature measurement points can be appropriately increased at the edges of the strip or in areas where temperature anomalies may exist. Furthermore, the collected discrete temperature data can be digitally processed and stored through a data acquisition system.

[0077] Each temperature measurement point is triangulated, the temperature gradient value within each triangular unit is calculated, and the first gradient region and the second gradient region are divided according to the preset gradient threshold.

[0078] In the embodiments of this application, the purpose of this step is to transform discrete temperature measurement point data into a structured mesh and identify regions with drastic temperature changes and regions with gradual temperature changes. The triangulation can employ the Delaunay triangulation algorithm, which generates a series of non-overlapping triangles, with each triangle's circumcircle not containing other temperature measurement points, thus ensuring the quality of the triangulation. For each triangular element, the average temperature gradient within that element can be calculated using the finite difference method or the least squares method based on the temperature values ​​of its three vertices. A preset gradient threshold can be determined based on the material properties of the aluminum strip, heating process requirements, or historical data analysis results, used to distinguish regions with different degrees of temperature change.

[0079] Interpolation is performed on the first gradient region and the second gradient region using a preset interpolation strategy, and the results of the interpolation are then processed to achieve boundary coordination, thereby generating a continuous temperature field distribution.

[0080] In the embodiments of this application, the preset interpolation strategy can be selected according to the characteristics of the region. For example, for the first gradient region with drastic temperature changes, a higher-order interpolation method, such as radial basis function interpolation or kriging interpolation, can be used to better capture temperature details and local changes. For the second gradient region with gentle temperature changes, an interpolation method with lower computational cost and better smoothness, such as bilinear interpolation or inverse distance weighted interpolation, can be used. Furthermore, at the boundary between the first and second gradient regions, boundary coordination processing is required to ensure the continuity and smoothness of the interpolation results. Specifically, this can be achieved by selecting several coordination points on the boundary line and adjusting the parameters of the interpolation function so that the interpolation function values ​​from different regions and their first derivatives along the boundary normal remain consistent at these coordination points.

[0081] As an example, in one specific implementation, an infrared thermometer array can be used to arrange a temperature measuring point every 100mm along the width of the aluminum strip to be rolled, with additional temperature measuring points added along the center line and edge areas of the strip, forming a 20*50 temperature measuring point array for collecting discrete temperature data.

[0082] Based on this, these temperature measurement points are triangulated using Delaunay triangulation to form a triangular mesh. For each triangular cell, the average temperature gradient can be calculated based on the temperature values ​​of its three vertices. For example, if the gradient value exceeds 5℃ / cm, the triangular cell and its adjacent areas are classified as the first gradient region; otherwise, they are classified as the second gradient region.

[0083] Furthermore, in the first gradient region, radial basis function interpolation can be used to capture drastic changes due to its good local adaptability. In the second gradient region, bilinear interpolation can be used, which is computationally efficient and smooth. Then, on the boundary line between the first and second gradient regions, several coordination points are selected. By adjusting the weighting coefficients of the radial basis interpolation function, the radial basis interpolation results and the bilinear interpolation results are kept consistent in both numerical value and normal derivative at these coordination points, thereby achieving a smooth transition and ultimately generating a continuous temperature field distribution.

[0084] Through the above technical solution, this application can address the complexity of the surface temperature distribution of aluminum strips to be rolled, particularly in areas with drastic local temperature changes, by employing differentiated interpolation strategies. This reduces the accuracy issues that may arise from using a single interpolation method, especially in high-gradient regions where it can capture temperature details more precisely, while maintaining computational efficiency in low-gradient regions. Simultaneously, by performing boundary coordination processing on the interpolation results of different regions, the physical continuity and smoothness of the entire continuous temperature field can be ensured, eliminating abrupt changes at regional boundaries. Therefore, the solution in this embodiment can generate a more accurate continuous temperature field distribution, thereby improving the precision and effectiveness of temperature control in heating equipment.

[0085] In one embodiment, the step of "performing interpolation processing on the first gradient region and the second gradient region respectively using a preset interpolation strategy, and performing boundary coordination processing on the interpolation results to generate a continuous temperature field distribution" specifically includes:

[0086] In the first gradient region, the location coordinates and temperature values ​​of each temperature measurement point are used as control points, and cubic spline interpolation is used to generate interpolation curves.

[0087] In the embodiments of this application, the cubic spline interpolation used in the first gradient region is a piecewise low-order polynomial interpolation method. By using a cubic polynomial in each sub-interval and ensuring the continuity of function values, first derivatives, and second derivatives at the nodes, the interpolation curve has high overall smoothness and can more accurately capture the details of local temperature field changes. Control points refer to key data points used to define the shape of the interpolation curve. In this embodiment, they are specifically the location coordinates and corresponding temperature values ​​of the temperature measurement points. Cubic spline interpolation can determine the coefficients of each cubic polynomial by solving a series of linear equations based on the continuity condition of function values ​​and derivatives at the control points; alternatively, it can utilize the cubic spline interpolation functions provided in existing numerical computing libraries, and the interpolation curve can be generated by inputting the control point data.

[0088] In the second gradient region, linear interpolation is used to calculate the temperature value at any location within each triangular unit.

[0089] In the embodiments of this application, linear interpolation used in the second gradient region is a simple and computationally efficient interpolation method, which assumes that the function value changes linearly between two known data points. Within a triangular cell, the temperature of internal points is typically calculated by weighted averaging of the temperature values ​​at the cell vertices. Linear interpolation can be performed by using the centroid coordinate system to calculate the temperature value based on the relative position of any point within the cell to the three vertices; alternatively, it can be performed by constructing a plane equation, substituting the coordinates and temperature values ​​of the three vertices of the triangle into the equation, solving for the plane equation, and then substituting the coordinates of any point into the plane equation to obtain its temperature value.

[0090] Furthermore, at the segmentation point x k At this point, let the cubic spline interpolation function be... The linear interpolation function is To achieve a smooth transition at the boundary, a weighted average method can be used to handle continuity mismatches, that is: in the interval [x] to the left of the segmentation point... k-1 ,x k Temperature interpolation results within the last 10%-20% of the length of the image. The weighting coefficient It varies linearly with position, from x k-1 place =1 gradually transitions to x k place =0.5. Simultaneously, by adjusting the boundary conditions of the cubic spline interpolation, such as natural boundary conditions or given first derivative boundary conditions, so that... First derivative numerical approximation The slope of the two should be controlled within 10%.

[0091] Extract the boundary line between the first gradient region and the second gradient region, and select several coordination points on the boundary line;

[0092] In the embodiments of this application, the boundary line refers to the geometric boundary line naturally formed when dividing regions according to a preset gradient threshold. The coordination point refers to a discrete point selected on the boundary line to ensure a smooth transition between the interpolation results of the two regions at that boundary. Specifically, the boundary line can be identified by a geometric algorithm to determine the shared edges or line segments between the two regions, and then coordination points can be selected uniformly at certain intervals along the identified edges or line segments; alternatively, coordination points can be adaptively selected based on the curvature or length of the boundary line, for example, by increasing the density of coordination points in areas with significant curvature changes.

[0093] Boundary coordination processing is performed on several selected coordination points to generate a continuous temperature field distribution.

[0094] In the embodiments of this application, boundary coordination processing of selected coordination points refers to adjusting the interpolation results of two regions to meet certain continuity conditions on the boundary line, thereby eliminating or reducing jumps or unevenness in the interpolation results at the boundary. Boundary coordination processing can be achieved by adjusting the parameters of the interpolation function so that the interpolation function values ​​of the two regions are equal at the coordination point, and their normal derivatives are also equal, thus achieving C1 continuity; alternatively, methods such as weighted averaging or smoothing filtering can be used to fuse the interpolation results of the two regions near the boundary to achieve a visually smooth transition.

[0095] For example, as a specific implementation, after measuring the temperature of the aluminum strip, a series of discrete temperature measurement point data can be obtained. For instance, areas where temperature changes may be large, such as the edge region of the aluminum strip or below the heater, are designated as the first gradient region. Within the first gradient region, the X and Y coordinates of each temperature measurement point and their corresponding temperature values ​​can be used as control points. By using the cubic spline interpolation function in numerical calculation software, a series of piecewise cubic polynomials can be constructed to generate a smooth temperature interpolation curve.

[0096] Meanwhile, in the central region of the aluminum strip or in areas far from the heating source, where the temperature change is relatively uniform, these regions are designated as the second gradient region. Within the second gradient region, for each triangular element, a linear interpolation method can be used. For example, by calculating the centroid coordinates of any point within the element and its three vertices, and then weighting the temperature values ​​of the three vertices according to the centroid coordinates, the temperature value of that point can be obtained.

[0097] After initial interpolation of the two regions, the system identifies the boundary between the first and second gradient regions. Along this boundary, a coordination point is selected at regular physical intervals. At each coordination point, the coefficients of the cubic spline interpolation function at the boundary are adjusted to ensure that the function value at that point is equal to the linear interpolation function value, and that the rate of temperature change along the boundary normal is also equal. This ensures a smooth transition of the temperature fields between the two regions at the boundary, ultimately resulting in a continuous and physically reasonable temperature field distribution on the aluminum strip surface.

[0098] Through the above technical solution, this application can employ the most suitable interpolation method for regions with different temperature gradients in the temperature field of aluminum sheet and strip. Specifically, it uses high-precision cubic spline interpolation in regions with drastic temperature changes, while employing efficient linear interpolation in regions with gradual temperature changes. This differentiated interpolation strategy for different regions can improve computational efficiency while ensuring interpolation accuracy. More importantly, by coordinating the boundaries of different interpolation regions, it can effectively solve the problem of temperature discontinuity or unevenness that may occur at the boundaries of different interpolation methods, thereby ensuring the global smoothness and physical rationality of the entire continuous temperature field distribution. Based on this, it can provide more accurate and reliable temperature field data for subsequent identification of high-temperature and low-temperature regions, extraction of temperature deviation values, and calculation of heating zone adjustment amounts, thereby improving the accuracy and stability of temperature control in the hot rolling process of aluminum sheet and strip.

[0099] In one embodiment, the step of "performing boundary coordination processing on selected coordination points to generate a continuous temperature field distribution" specifically includes:

[0100] At each coordination point, calculate the cubic spline interpolation function value from the first gradient region and its first derivative along the boundary normal, as well as the linear interpolation function value from the second gradient region and its first derivative along the boundary normal.

[0101] In the embodiments of this application, this step aims to obtain the specific numerical performance and changing trend of the interpolation results on both sides of the boundary at the coordination point. Specifically, the cubic spline interpolation function value represents the predicted temperature value of the first gradient region at the coordination point, and its first derivative along the boundary normal reflects the rate of temperature change in this direction. Similarly, the linear interpolation function value and its first derivative along the boundary normal represent the corresponding information of the second gradient region at the coordination point. These values ​​form the basis for boundary coordination processing and are used to evaluate the degree of matching on both sides of the boundary.

[0102] By setting boundary conditions for cubic spline interpolation at each coordination point, the cubic spline interpolation function value of the first gradient region at each coordination point is equal to the linear interpolation function value of the second gradient region, and the first derivative value of the first gradient region along the boundary normal is equal to the first derivative value of the second gradient region along the boundary normal.

[0103] In embodiments of this application, setting boundary conditions may include adjusting the coefficients of the cubic spline interpolation function to satisfy specific numerical constraints at the coordination points. For example, the unknown coefficients of the spline function can be determined by solving a system of linear equations to force the satisfaction of the continuity conditions of the function value and the first derivative. Another approach is to use an iterative optimization algorithm to gradually adjust the cubic spline interpolation function until it achieves a preset matching accuracy with the linear interpolation result at the coordination points.

[0104] The adjusted interpolation results of the first gradient region are merged with those of the second gradient region to generate a continuous temperature field distribution.

[0105] In embodiments of this application, the merging operation may involve splicing discrete data points or interpolation functions from two regions onto a data structure to form a complete temperature field model. For example, grid data from two regions may be merged, or the domains of interpolation functions from two regions may be expanded and unified to obtain a continuous temperature field distribution covering the entire aluminum strip to be rolled.

[0106] As an example, in one specific implementation, several coordination points can be uniformly selected along the boundary line between the first gradient region and the second gradient region. For example, a point can be selected at certain physical distances, or the coordination points can be densified in regions with large temperature gradient changes.

[0107] At this point, for each coordination point, the function value and the first derivative along the boundary normal can be calculated using the mathematical expressions of the cubic spline interpolation function and the linear interpolation function, respectively. For example, if the cubic spline interpolation function is S(x,y) and the linear interpolation function is L(x,y), then at the coordination point (x... c ,y c At point ), S(x) needs to be calculated. c ,y c ) and L(x c ,y c ), and the derivative of S on the boundary normal n and the derivative of L on the boundary normal n.

[0108] Subsequently, the coefficients of the cubic spline interpolation function can be adjusted to satisfy S(x) at all coordination points. c ,y c )=L(x c ,y c The condition that the two derivatives are equal can be obtained. Specifically, this can be accomplished by constructing a system of linear equations, where the unknowns are the undetermined coefficients of the cubic spline interpolation function, and the constraints of the system of equations are the above equations. After solving the system of equations, the cubic spline interpolation function that satisfies the boundary conditions can be obtained.

[0109] Finally, the adjusted interpolated data of the first gradient region is integrated with the linear interpolated data of the second gradient region. For example, the grid data of the two regions are merged into a unified two-dimensional array or matrix, thereby forming a continuous and smooth temperature field distribution map covering the entire aluminum strip to be rolled.

[0110] Through the above technical solution, this application can effectively solve the problem of discontinuities in temperature values ​​and temperature gradients that may occur at the boundaries of regions when different interpolation methods are used to process different temperature gradient regions. Specifically, by forcing the cubic spline interpolation function value of the first gradient region to be equal to the linear interpolation function value of the second gradient region at the coordination point, and simultaneously making their first derivative values ​​along the boundary normal equal, a smooth transition of the temperature field at the boundary can be ensured. Through this boundary coordination processing method, the generated continuous temperature field distribution can be not only numerically continuous, but also maintain a consistent temperature change trend in a physical sense, avoiding local temperature abrupt changes or distortions caused by discontinuities. This improves the accuracy and reliability of the entire temperature field distribution, thereby enhancing the overall precision of the intelligent temperature field control process in the hot rolling process of aluminum sheet and strip.

[0111] In one embodiment, step S30, "querying the historical heating database based on the specifications of the aluminum strip to be rolled and obtaining the corresponding matching temperature response relationship," specifically includes:

[0112] Obtain the specifications of the aluminum sheet and strip to be rolled, including thickness, width, material grade and target furnace exit temperature;

[0113] In the embodiments of this application, the specification parameters are key information describing the physical and chemical properties of the aluminum strip to be rolled, and directly affect its thermal response behavior during the heating process. Thickness and width determine the geometric dimensions and heated area of ​​the strip; the material grade determines the thermophysical properties of the material, such as specific heat capacity, thermal conductivity, and emissivity; the target furnace exit temperature is the ultimate control target of the heating process. In one implementation, the above specification parameters can be obtained through a production planning system, an order management system, or manual input. For example, the specification information of the aluminum strip for the current production batch can be read from the manufacturing execution system; or the operator can manually input or select the corresponding specification parameters before starting heating.

[0114] Retrieve historical heating records with the same material grade as the aluminum strip to be rolled from the historical heating database, and calculate the parameter deviations between the historical heating records and the aluminum strip to be rolled, including thickness deviation, width deviation, and target furnace exit temperature deviation.

[0115] In the embodiments of this application, this step aims to initially screen historical data with similar thermal response characteristics to the currently rolled aluminum sheet and strip, and to evaluate the similarity by quantifying parameter deviations. Material grade is the primary screening criterion because the thermophysical properties of aluminum alloys from different materials differ significantly, while the calculation of parameter deviations further refines the matching degree. Database queries can use structured query language statements or other data retrieval techniques to match based on material grade. Parameter deviations can be calculated using absolute differences, relative differences, or weighted differences. For example, thickness deviation can be defined as the absolute difference between historical thickness and current thickness; width deviation can be defined as the absolute difference between historical width and current width; and target furnace exit temperature deviation can be defined as the absolute difference between historical target furnace exit temperature and current target furnace exit temperature.

[0116] Based on the preset parameter tolerance range, target historical records are filtered, and the correspondence between the power adjustment amount of each heating zone and the temperature change amount of the aluminum plate strip in the target historical records is extracted to obtain the temperature response relationship.

[0117] In the embodiments of this application, this step aims to set an acceptable tolerance range based on the calculated parameter deviation, thereby selecting historical records that most closely resemble the thermal response behavior of the aluminum strip to be rolled. The correspondence between the power adjustment and temperature change extracted from the selected target historical records is the desired temperature response relationship, which reflects the degree of influence of heating power changes on the temperature of the aluminum strip under specific heating conditions. Specifically, a multi-dimensional tolerance range can be set, for example, the thickness deviation is less than a certain preset value, the width deviation is less than a certain preset value, and the target furnace exit temperature deviation is less than a certain preset value. Only historical records that simultaneously meet all tolerance conditions are considered target historical records. Based on this, the actual adjustment of heating power and the resulting actual temperature change of the aluminum strip under different heating zones and different heating stages, as well as their corresponding relationships, can be extracted from the selected target historical records. This correspondence can be represented as a function, a lookup table, or a model based on regression analysis. For example, it is possible to statistically analyze how much the average temperature of a certain area of ​​the strip increases when the power in a certain heating zone increases by a certain amount, thereby establishing a response relationship between power and temperature change rate.

[0118] For example, as a specific implementation, assume that the specifications of the aluminum strip to be rolled are: thickness 3mm, width 1500mm, material grade AA3003, and target furnace exit temperature 580℃.

[0119] First, the system can obtain the aforementioned specifications from the production management system. Then, in the historical heating database, the system can retrieve all historical heating records for material grade AA3003.

[0120] Based on this, for each retrieved historical record, the system calculates its parameter deviation from the current aluminum strip to be rolled. For example, if a historical record has a thickness of 3.1 mm, a width of 1490 mm, and a target furnace exit temperature of 575℃, then the thickness deviation is 0.1 mm, the width deviation is 10 mm, and the target furnace exit temperature deviation is 5℃.

[0121] Based on this, the system's preset parameter tolerance ranges are: thickness deviation less than 0.2mm, width deviation less than 20mm, and target furnace exit temperature deviation less than 10℃. Therefore, only historical records that simultaneously meet these conditions will be selected as target historical records.

[0122] Finally, from the selected target historical records, the system can further analyze and extract, for example, the correspondence between the power adjustment amount and the temperature change of the aluminum strip when the power is increased by 10kW in the first heating zone. This allows the system to construct a temperature response relationship for the aluminum strip to be rolled.

[0123] Through the above technical solution, the solution of this embodiment can ensure that the obtained temperature response relationship is highly targeted and accurate, thereby providing a solid foundation for the initial adjustment calculation of each heating zone of the subsequent heating equipment. It has the effect of improving the accuracy and reliability of temperature field control in the hot rolling process of aluminum strip, which helps to achieve a more uniform strip temperature distribution, reduce product defects, and improve production efficiency and product quality.

[0124] In one embodiment, step S50, "establishing a numerical model of heat conduction for the heating device and solving the heat transfer process between adjacent heating zones using the numerical model of heat conduction," specifically includes:

[0125] Obtain the structural parameters of the heating equipment, including the position and size of each heating zone, the gap distance between the heating zones, and the thermal conductivity of the heating equipment material;

[0126] In the embodiments of this application, the structural parameters of the heating equipment are key information describing the physical characteristics of the heating furnace, directly affecting the heat transfer path and efficiency within the equipment. For example, the location and size of each heating zone determine the distribution range and effective area of ​​the heat source; the gap distance between heating zones affects the intensity of thermal radiation and convection between zones; and the thermal conductivity coefficient of the heating equipment material directly determines the ability of heat to be conducted through the furnace body material. These parameters, as prerequisites for constructing an accurate heat conduction model, ensure that the heat conduction model truly reflects the physical characteristics of the heating equipment. These parameters can be obtained by consulting equipment design drawings, supplier-provided technical manuals, or through on-site measurements.

[0127] A heat transfer equation between adjacent heating zones is established based on Fourier's law of heat conduction. The heat transfer equation includes axial heat conduction terms and radial heat conduction terms.

[0128] In the embodiments of this application, Fourier's law of heat conduction is the fundamental law describing heat conduction phenomena, stating that heat flux density is proportional to the temperature gradient. In heating equipment, heat transfer occurs not only along the direction of movement of the aluminum strip (axial direction), but also perpendicular to the strip surface (radial direction), and between heating zones. Therefore, establishing a heat transfer equation that includes axial and radial heat conduction terms can comprehensively describe the complex heat transfer process inside the heating equipment. The axial heat conduction term mainly considers heat transfer along the length of the strip, while the radial heat conduction term mainly considers heat transfer perpendicular to the width and thickness of the strip, as well as heat exchange between the furnace wall and the strip. Furthermore, the heat transfer equation can be discretized using numerical methods such as the finite element method, finite difference method, or finite volume method.

[0129] The heat transfer equation is solved numerically to obtain the heat transfer amount and temperature influence coefficient between adjacent heating zones.

[0130] In the embodiments of this application, since the heat conduction equation is usually a complex partial differential equation, it is difficult to obtain an analytical solution. Therefore, a numerical method is required to solve it. Through numerical solutions, the amount of heat transferred from one heating zone to an adjacent heating zone under specific operating conditions can be quantified, i.e., the heat transfer amount, and the degree to which this heat transfer causes changes in the temperature field of the adjacent zones, i.e., the temperature influence coefficient. The heat transfer amount can be expressed as the energy transferred from one zone to another per unit time; the temperature influence coefficient can be defined as the change in the temperature field of the adjacent zones when the power or temperature of one heating zone changes by a unit. These solution results are crucial for subsequent adjustments to the initial adjustment amount, providing data support for temperature field control. Furthermore, the numerical solution process can be performed using simulation software platforms such as MATLAB, ANSYS Fluent, and COMSOL Multiphysics.

[0131] For example, as a specific implementation method, when establishing a numerical model of heat conduction of the heating equipment, the precise location, length, width, and other geometric dimensions of each heating zone can be obtained by consulting the design drawings and equipment manual of the heating furnace, such as the preheating section, heating section, and soaking section, as well as the gap distance of the refractory material between each zone.

[0132] Simultaneously, the thermal conductivity coefficients of the furnace body refractory bricks, furnace lining materials, and the aluminum strip itself at different temperatures can be obtained from a materials database. When establishing the heat transfer equation, a three-dimensional finite difference method or finite element method can be used to mesh the internal space of the heating furnace. For the axial heat transfer term, heat transfer along the direction of aluminum strip movement can be considered, for example, through the heat conduction of the strip itself and the axial convective heat transfer of the furnace gas. For the radial heat transfer term, radiative heat transfer from the furnace wall to the strip, convective heat transfer from the furnace gas to the strip, and heat conduction along the thickness direction within the strip can be considered. These terms can be integrated into a single transient or steady-state heat transfer differential equation.

[0133] Based on this, when numerically solving the heat transfer equation, commercial computational fluid dynamics software, such as ANSYS Fluent or COMSOL Multiphysics, can be used. The aforementioned structural parameters and boundary conditions, such as the set temperature of each heating zone and the furnace gas velocity, can be input for iterative calculation. Through simulation, the heat transfer distribution between zones and between zones and the plate / strip under different heating zone power combinations can be obtained. Furthermore, the change in temperature field of adjacent zones when the power of a certain heating zone changes by one unit (i.e., the temperature influence coefficient) and the actual heat transferred can be extracted.

[0134] Through the above technical solution, this application overcomes the problem of insufficient control precision caused by neglecting the complex heat transfer between heating zones within the heating equipment in traditional temperature field control methods. Specifically, by establishing a numerical model of heat conduction and solving for the heat transfer amount and temperature influence coefficient between zones, the system can comprehensively perceive the thermal coupling effect between each heating zone. This allows for targeted correction after calculating the initial adjustment amount, effectively avoiding the negative impact of adjusting a single zone on adjacent zones, thus ensuring coordinated and precise control of the aluminum strip temperature field. Ultimately, the solution in this embodiment helps achieve a more uniform temperature distribution in the aluminum strip before hot rolling, reducing sheet shape defects and product quality problems caused by uneven temperature, and improving the production efficiency and product qualification rate of the hot rolling process.

[0135] In one embodiment, the step of "establishing a heat transfer equation between adjacent heating zones based on Fourier's law of heat conduction, wherein the heat transfer equation includes axial heat conduction terms and radial heat conduction terms" specifically includes:

[0136] Based on the temperature difference and gap distance between the heating zone and adjacent heating zones, as well as the thermal conductivity of the heating equipment material, an axial heat conduction term is established, and the first heat flux density along the transmission direction of the aluminum strip to be rolled is calculated.

[0137] In the embodiments of this application, the axial heat conduction term is established based on Fourier's law of heat conduction, combined with consideration of the temperature gradient between the heating zone and adjacent zones, physical distance, and the thermal conductivity of the medium. By calculating the first heat flux density along the transport direction of the aluminum strip to be rolled, the heat exchange caused by temperature unevenness between different regions inside the heating equipment can be evaluated, which is crucial for understanding and predicting the temperature change of the aluminum strip during the heating process. One implementation is to discretize the heating equipment into multiple units along the transport direction of the aluminum strip, calculate the temperature difference between adjacent units, and determine the axial heat flux term by combining the effective heat transfer area between units and the thermal conductivity of the material. Another implementation is to mesh the temperature field inside the heating equipment using the finite difference method or the finite element method, and then calculate the axial heat transfer rate based on the temperature value and material properties at the mesh nodes, thereby obtaining the first heat flux density.

[0138] Obtain the specification parameters and temperature parameters of the aluminum strip to be rolled, establish the radial heat conduction term based on the specification parameters and temperature parameters, and calculate the second heat flux density perpendicular to the surface of the aluminum strip to be rolled.

[0139] In the embodiments of this application, the establishment of the radial heat conduction term needs to consider the geometry, material properties, and current temperature state of the aluminum strip. By calculating the second heat flux density perpendicular to the surface of the aluminum strip to be rolled, the heating efficiency of the heating medium on the aluminum strip and the temperature uniformity along the thickness direction inside the aluminum strip can be evaluated. One implementation is to acquire the surface and internal temperatures of the aluminum strip in real time using sensors, and combine its thickness, width, and other specifications with the thermal conductivity of the aluminum material to calculate the radial heat flux using a one-dimensional or two-dimensional Fourier heat conduction equation. Another implementation is to divide the aluminum strip into layers along the thickness direction in the numerical model, and calculate the interlayer heat flux based on the temperature and thermal properties of each layer to obtain the second heat flux density.

[0140] Substituting the axial and radial heat conduction terms into the pre-defined heat conduction differential equation yields the heat transfer equation.

[0141] In the embodiments of this application, the pre-defined heat conduction differential equation is typically established based on the principle of energy conservation, reflecting the relationship between temperature changes and heat transfer in time and space. By substituting axial and radial contribution terms into the heat conduction differential equation, a more complete and accurate heat transfer equation can be constructed, providing a foundation for numerical solutions. One implementation is to use a three-dimensional transient heat conduction equation, which includes convection, conduction, and radiation terms, and substitute the axial and radial conduction terms as spatial derivative terms of this three-dimensional transient heat conduction equation. Another implementation is to select appropriate boundary and initial conditions based on the specific structure and heating method of the heating device, and integrate the axial and radial heat flux densities as source terms or boundary condition terms into the heat conduction equation of the finite element or finite difference scheme.

[0142] For example, as a specific implementation, when establishing the axial heat conduction term, multiple heating zones in the heating equipment can be considered, for example, dividing the heating furnace along its length into several independent heating sections. For any two adjacent heating sections, the average temperature difference between these two heating sections can be measured or estimated, and the heat flow along the aluminum strip transport direction can be calculated using Fourier's law, taking into account the thermal conductivity of the furnace wall material and the effective heat transfer area and gap distance between the heating sections. For example, if the average temperatures of adjacent heating sections are respectively... and The gap distance is The effective heat transfer area is The material's thermal conductivity is Then the axial heat flux density can be approximately expressed as: .

[0143] When establishing the radial heat conduction term, the thickness, width, and material grade of the aluminum strip to be rolled, as well as the surface temperature of the aluminum strip obtained through an infrared thermometer, can be obtained. Assuming the internal temperature distribution of the aluminum strip is one-dimensional, either steady-state or transient, a heat conduction equation perpendicular to the surface of the aluminum strip can be established based on the thermal conductivity and specific heat capacity of the aluminum, as well as the temperature and convective heat transfer coefficient of the heating medium. For example, if the surface temperature of the aluminum strip is... The furnace gas temperature is The convective heat transfer coefficient is Then the surface convection heat transfer heat flux density can be expressed as: At the same time, radiative heat transfer also needs to be considered.

[0144] Subsequently, the aforementioned axial and radial heat conduction terms, such as the calculated heat flux density expression, are substituted into a three-dimensional transient heat conduction differential equation, which typically takes the form: ,in For density, For specific heat capacity, Thermal conductivity, This is an internal heat source term. The axial and radial conduction effects are incorporated into... In this term, and by combining appropriate boundary conditions, such as surface convection and radiation heat transfer, we can obtain the heat transfer equation that describes the temperature field changes of the aluminum strip within the heating device.

[0145] By considering the axial heat conduction along the transmission direction of the aluminum strip inside the heating equipment and the radial heat conduction perpendicular to the transmission direction between the aluminum strip itself and the heating medium, and integrating them into a unified heat conduction differential equation, the established numerical model can more comprehensively and realistically reflect the temperature field changes of the aluminum strip during the heating process. This can effectively solve the problem of insufficient accuracy caused by the simplification of the heat conduction direction in traditional models, and provide a more accurate physical basis for the calculation and correction of the initial adjustment amount of the heating zone. It has the effect of improving the precision level and control effect of temperature field regulation in the hot rolling process of aluminum strip.

[0146] In one embodiment, the solution results include heat transfer and temperature influence coefficient. Step S60, "correcting the initial adjustment amount of each heating zone based on the solution results, and adjusting the heating equipment based on the corrected adjustment amount," specifically includes:

[0147] Based on the heat transfer rate and temperature influence coefficient, the influence of the initial adjustment of each heating zone on the temperature field of its adjacent heating zones is calculated.

[0148] In the embodiments of this application, this step aims to quantify the potential impact of a heating zone's power adjustment on the temperature distribution of adjacent areas, in order to avoid local overheating or undercooling and ensure the uniformity of the overall temperature field. The impact can be calculated by substituting the initial adjustment amount into a pre-established numerical model of heat conduction to simulate the diffusion of heat in the heating zone and its adjacent areas under the influence of the initial adjustment amount, thereby calculating the impact on the temperature field of adjacent zones. Alternatively, a mapping relationship between the initial adjustment amount and the impact on the temperature field of adjacent zones can be established based on historical data or empirical formulas, and can be quickly obtained through table lookup or function calculation.

[0149] Determine whether the impact exceeds the preset impact threshold. If it does, compensate and correct the initial adjustment amount of the corresponding heating zone to obtain the corrected adjustment amount.

[0150] In the embodiments of this application, this step aims to assess whether the calculated impact is significant enough to require further intervention. If the impact is too large, the initial adjustment amount needs to be corrected a second time to eliminate or reduce this adverse effect and ensure the accuracy and stability of the adjustment. The preset impact threshold can be a temperature change; for example, if the adjustment of a heating zone causes a temperature change of more than 1°C in adjacent zones, it is considered necessary to correct. Alternatively, compensation correction can employ an iterative method, gradually adjusting the initial adjustment amount until the impact is below the threshold, or using feedback correction based on PID control principles. The impact threshold can also be an empirically set percentage; for example, if the impact accounts for a certain percentage of the target temperature deviation, correction is performed. Compensation correction can be achieved by increasing or decreasing the initial adjustment amount according to the direction and magnitude of the impact through a preset correction coefficient or correction function.

[0151] The corrected adjustment amount is sent to the controllers of each heating zone in the heating equipment to adjust the heating equipment.

[0152] In embodiments of this application, the modified adjustment amount can be transmitted as a digital signal to the programmable logic controller or distributed control system of each heating zone via Industrial Ethernet, Modbus protocol, or other fieldbus protocols. Alternatively, the adjustment amount can be transmitted as an analog signal to the actuators of the heating zone, such as solid-state relays or SCR power regulators, thereby changing the heating power.

[0153] Furthermore, an iterative correction can be performed using a successive approximation method, with a maximum of 5 iterations. In each iteration, the deviation between the actual temperature and the target temperature for all partitions is calculated. Convergence is considered achieved when the maximum deviation is less than ±3℃ and the deviation for more than 80% of the partitions is less than ±2℃. If convergence is not achieved after the nth iteration, a damping coefficient can be applied to the correction amount in the (n+1)th iteration. Its value is typically between 0.6 and 0.8, meaning the actual power regulation applied in this instance is: ,in To avoid oscillations caused by over-adjustment, the theoretical correction value is calculated based on the current temperature deviation and heat conduction model. Furthermore, a limit protection mechanism can be set for the single correction amount, including: the power adjustment of a single heating zone should not exceed ±15% of its rated power, and the difference in power adjustment between adjacent zones should not exceed 10% of their rated power, preventing local overheating or excessive thermal stress. If convergence is not achieved after 5 iterations, the current adjustment result is maintained, and a manual review flag is triggered.

[0154] For example, as a specific implementation method, when calculating the influence of the initial adjustment amount of each heating zone on the temperature field of its adjacent heating zones, a pre-trained neural network model can be used. This neural network model takes the initial adjustment amount of each heating zone, the structural parameters of the heating equipment, and the specification parameters of the aluminum strip to be rolled as inputs, and outputs the influence of each heating zone on the temperature field of its adjacent zones.

[0155] When determining whether the impact exceeds a preset impact threshold, this threshold can be set according to the specific scenario, for example, to 0.5℃. If the calculated impact, such as a heating zone causing a temperature change of 0.8℃ to an adjacent zone, exceeds the 0.5℃ impact threshold, the system will initiate compensation correction.

[0156] At this point, compensation and correction can be achieved using a fuzzy logic controller, which dynamically adjusts the initial adjustment amount based on the magnitude and direction of the influencing factor and the current temperature deviation. For example, if the influencing factor is positive, causing the temperature of adjacent zones to rise, the initial adjustment amount can be appropriately reduced; if the influencing factor is negative, causing the temperature of adjacent zones to fall, the initial adjustment amount can be appropriately increased.

[0157] Ultimately, the corrected adjustment can be sent to the programmable logic controllers (PLCs) of each heating zone in the heating equipment via an industrial fieldbus. The PLCs then control the conduction angle of the solid-state relays, thereby precisely adjusting the output power of the heating rods and achieving real-time control of the heating equipment.

[0158] Through the above technical solution, this application can effectively solve the problem that when regulating the temperature of a heating zone in heating equipment, the adjustment of one heating zone may have an undesirable temperature effect on adjacent zones, resulting in a decrease in the overall temperature field uniformity. Specifically, by quantifying and evaluating this mutual influence and making targeted compensation corrections to the initial adjustment amount, local over-adjustment or under-adjustment can be avoided, ensuring that the heat transfer between each heating zone is under control. This enables the heating equipment to achieve more precise and stable temperature field regulation, thereby improving the temperature uniformity of aluminum strip in the hot rolling process, and ultimately improving product quality and production efficiency.

[0159] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0160] In one embodiment, an intelligent temperature field control system for the hot rolling process of aluminum sheet and strip is provided. This intelligent temperature field control system for the hot rolling process of aluminum sheet and strip corresponds one-to-one with the intelligent temperature field control method for the hot rolling process of aluminum sheet and strip described in the above embodiment. The intelligent temperature field control system for the hot rolling process of aluminum sheet and strip includes:

[0161] The interpolation processing module is used to acquire the temperature distribution data of the aluminum strip to be rolled in the heating equipment, and to perform interpolation processing on the temperature distribution data to generate a continuous temperature field distribution.

[0162] The deviation extraction module is used to identify high-temperature and low-temperature regions based on the continuous temperature field distribution and extract temperature deviation values.

[0163] The response query module is used to query the historical heating database based on the specifications of the aluminum sheet and strip to be rolled, and obtain the corresponding matching temperature response relationship;

[0164] The adjustment calculation module is used to calculate the initial adjustment of each heating zone in the heating equipment based on the temperature response relationship and temperature deviation value.

[0165] The heat transfer solution module is used to establish a numerical model of heat conduction in the heating equipment and solve the heat transfer process between adjacent heating zones through the numerical model of heat conduction.

[0166] The adjustment correction module is used to correct the initial adjustment of each heating zone based on the solution results, and to adjust the heating equipment based on the corrected adjustment.

[0167] Specific limitations regarding the intelligent temperature field control system for the hot rolling process of aluminum sheet and strip can be found in the above-described limitations regarding the intelligent temperature field control method for the hot rolling process of aluminum sheet and strip, and will not be repeated here. Each module in the aforementioned intelligent temperature field control system for the hot rolling process of aluminum sheet and strip can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0168] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows. Figure 2 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database is used for data storage, data processing, and data analysis. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an intelligent temperature field control method for the hot rolling process of aluminum sheet and strip.

[0169] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements an intelligent temperature field control method for a hot rolling process of aluminum sheet and strip.

[0170] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0171] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0172] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An intelligent temperature field regulation method for an aluminum sheet and strip hot rolling process, characterized in that, Including the following steps: The temperature distribution data of the aluminum strip to be rolled in the heating equipment is obtained, and the temperature distribution data is interpolated to generate a continuous temperature field distribution. Based on the continuous temperature field distribution, high-temperature and low-temperature regions are identified, and temperature deviation values ​​are extracted. Based on the specifications of the aluminum sheet and strip to be rolled, the historical heating database is queried to obtain the corresponding matching temperature response relationship; Based on the temperature response relationship and temperature deviation, the initial adjustment amount of each heating zone in the heating equipment is calculated; Establish a numerical model of heat conduction in the heating equipment, and solve the heat transfer process between adjacent heating zones using the numerical model of heat conduction. The initial adjustment values ​​of each heating zone are corrected based on the solution results, and the heating equipment is adjusted based on the corrected adjustment values.

2. The intelligent temperature field regulation method of the hot rolling process of aluminum sheet and strip according to claim 1, characterized in that, The steps of acquiring temperature distribution data of the aluminum strip to be rolled within the heating equipment and interpolating the temperature distribution data to generate a continuous temperature field distribution specifically include: Multiple temperature measurement points are determined by a preset temperature measurement strategy, and discrete temperature data of the aluminum strip to be rolled are collected through the determined multiple temperature measurement points. Each temperature measurement point is triangulated, the temperature gradient value within each triangular unit is calculated, and the first gradient region and the second gradient region are divided according to the preset gradient threshold. Interpolation is performed on the first gradient region and the second gradient region using a preset interpolation strategy, and the results of the interpolation are then processed to achieve boundary coordination, thereby generating a continuous temperature field distribution.

3. The intelligent temperature field regulation method of the hot rolling process of aluminum sheet and strip according to claim 2, characterized in that, The steps of interpolating the first gradient region and the second gradient region using a preset interpolation strategy, and then performing boundary reconciliation processing on the interpolation results to generate a continuous temperature field distribution, specifically include: In the first gradient region, the location coordinates and temperature values ​​of each temperature measurement point are used as control points, and cubic spline interpolation is used to generate interpolation curves. In the second gradient region, linear interpolation is used to calculate the temperature value at any location within each triangular unit. Extract the boundary line between the first gradient region and the second gradient region, and select several coordination points on the boundary line; Boundary coordination processing is performed on several selected coordination points to generate a continuous temperature field distribution.

4. The intelligent temperature field regulation method of the hot rolling process of aluminum sheet and strip according to claim 3, characterized in that, The step of performing boundary coordination processing on several selected coordination points to generate a continuous temperature field distribution specifically includes: At each coordination point, calculate the cubic spline interpolation function value from the first gradient region and its first derivative along the boundary normal, as well as the linear interpolation function value from the second gradient region and its first derivative along the boundary normal. By setting boundary conditions for cubic spline interpolation at each coordination point, the cubic spline interpolation function value of the first gradient region at each coordination point is equal to the linear interpolation function value of the second gradient region, and the first derivative value of the first gradient region along the boundary normal is equal to the first derivative value of the second gradient region along the boundary normal. The adjusted interpolation results of the first gradient region are merged with those of the second gradient region to generate a continuous temperature field distribution.

5. The intelligent temperature field regulation method for the hot rolling process of aluminum sheet and strip according to claim 1, characterized in that, The step of querying the historical heating database based on the specifications of the aluminum sheet and strip to be rolled to obtain the corresponding matching temperature response relationship specifically includes: Obtain the specifications of the aluminum sheet and strip to be rolled, including thickness, width, material grade and target furnace exit temperature; Retrieve historical heating records with the same material grade as the aluminum strip to be rolled from the historical heating database, and calculate the parameter deviations between the historical heating records and the aluminum strip to be rolled, including thickness deviation, width deviation, and target furnace exit temperature deviation. Based on the preset parameter tolerance range, target historical records are filtered, and the correspondence between the power adjustment amount of each heating zone and the temperature change amount of the aluminum plate strip in the target historical records is extracted to obtain the temperature response relationship.

6. The intelligent temperature field regulation method for the hot rolling process of aluminum sheet and strip according to claim 1, characterized in that, The steps of establishing a numerical model of heat conduction for the heating equipment and solving the heat transfer process between adjacent heating zones using the numerical model specifically include: Obtain the structural parameters of the heating equipment, including the position and size of each heating zone, the gap distance between the heating zones, and the thermal conductivity of the heating equipment material; A heat transfer equation between adjacent heating zones is established based on Fourier's law of heat conduction. The heat transfer equation includes axial heat conduction terms and radial heat conduction terms. The heat transfer equation is solved numerically to obtain the heat transfer amount and temperature influence coefficient between adjacent heating zones.

7. The intelligent temperature field regulation method of the hot rolling process of aluminum sheet and strip according to claim 6, characterized in that, The step of establishing a heat transfer equation between adjacent heating zones based on Fourier's law of heat conduction, wherein the heat transfer equation includes axial heat conduction terms and radial heat conduction terms, specifically includes: Based on the temperature difference and gap distance between the heating zone and adjacent heating zones, as well as the thermal conductivity of the heating equipment material, an axial heat conduction term is established, and the first heat flux density along the transmission direction of the aluminum strip to be rolled is calculated. Obtain the specification parameters and temperature parameters of the aluminum strip to be rolled, establish the radial heat conduction term based on the specification parameters and temperature parameters, and calculate the second heat flux density perpendicular to the surface of the aluminum strip to be rolled. Substituting the axial and radial heat conduction terms into the pre-defined heat conduction differential equation yields the heat transfer equation.

8. The intelligent temperature field regulation method for the hot rolling process of aluminum sheet and strip according to claim 1, characterized in that, The solution results include heat transfer and temperature influence coefficients. The step of correcting the initial adjustment amount of each heating zone based on the solution results, and adjusting the heating equipment based on the corrected adjustment amount, specifically includes: Based on the heat transfer rate and temperature influence coefficient, the influence of the initial adjustment of each heating zone on the temperature field of its adjacent heating zones is calculated. Determine whether the impact exceeds the preset impact threshold. If it does, compensate and correct the initial adjustment amount of the corresponding heating zone to obtain the corrected adjustment amount. The corrected adjustment amount is sent to the controllers of each heating zone in the heating equipment to adjust the heating equipment.

9. An intelligent temperature field control system for the hot rolling process of aluminum sheet and strip, characterized in that, include: The interpolation processing module is used to acquire the temperature distribution data of the aluminum strip to be rolled in the heating equipment, and to perform interpolation processing on the temperature distribution data to generate a continuous temperature field distribution. The deviation extraction module is used to identify high-temperature and low-temperature regions based on the continuous temperature field distribution and extract temperature deviation values. The response query module is used to query the historical heating database based on the specifications of the aluminum sheet and strip to be rolled, and obtain the corresponding matching temperature response relationship; The adjustment calculation module is used to calculate the initial adjustment of each heating zone in the heating equipment based on the temperature response relationship and temperature deviation value. The heat transfer solution module is used to establish a numerical model of heat conduction in the heating equipment and solve the heat transfer process between adjacent heating zones through the numerical model of heat conduction. The adjustment correction module is used to correct the initial adjustment of each heating zone based on the solution results, and to adjust the heating equipment based on the corrected adjustment.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the method as described in any one of claims 1 to 8.

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