A method and system for regulating the temperature of a steel rolling furnace

Abstract

The present application relates to the technical field of furnace temperature control, and particularly relates to a rolling steel heating furnace temperature adjusting method and system, the method first responds to the input of a target billet, calculates the remaining time of the target billet in the furnace, then matches the remaining time in the furnace with a preset processing section processing time table to obtain the current processing section of the target billet, then uses a table lookup method to retrieve the correction coefficient of the current processing section, then uses the correction coefficient to correct the theoretical furnace temperature set value of the current processing section to obtain the actual furnace temperature set value, finally, judges whether the monitoring temperature of the current processing section is lower than the actual furnace temperature set value, and if so, warms up the current processing section in the heating furnace. Compared with the prior art, the method of the present application can reliably adjust the furnace temperature when the infrared temperature feedback is inaccurate, and maintain safe and reliable production.

Description

Technical Field

[0001] This invention relates to the field of furnace temperature control technology. More specifically, this invention relates to a method and system for regulating the furnace temperature of a steel rolling heating furnace. Background Technology

[0002] Steel rolling heating furnaces are used for hot rolling of steel. After being heated and put into operation, each process section within the furnace needs to maintain a high temperature to ensure the structural integrity of the furnace body. Temperature control of the steel rolling heating furnace is a crucial aspect of the hot rolling process. Compared to traditional heating processes, the furnace temperature of steel rolling furnaces typically reaches thousands of degrees Celsius. The temperature monitoring device usually employs an infrared pyrometer. While infrared pyrometers offer advantages such as non-contact measurement and fast response, when monitoring the temperature of different hot rolling sections (heat recovery section, preheating section, waiting-to-roll section, homogenization rolling section, and cooling section), the infrared pyrometer's reflectivity can change significantly due to factors such as variations in steel roughness, the presence of steam, smoke, or impurities in the furnace. This can lead to inaccurate temperature feedback and consequently, significant deviations in the furnace's temperature regulation.

[0003] In practical applications, after a steel rolling heating furnace is put into operation, in order to ensure the continuity of monitoring data, the safety of heating operations, and the stability of the steel supply chain, it is difficult to clean the furnace body, optimize the ventilation structure, or optimize the process section by shutting down the furnace, especially for some heavy industrial areas with large steel shipment volumes and high requirements for timely delivery.

[0004] Therefore, how to reliably regulate furnace temperature while maintaining production has become an urgent technical challenge. Summary of the Invention

[0005] To solve the aforementioned technical problem of reliably regulating furnace temperature, this invention discloses a furnace temperature regulation method and system for a steel rolling heating furnace.

[0006] In a first aspect, the present invention discloses a method for regulating the furnace temperature of a steel rolling heating furnace, comprising:

[0007] In response to the input of the target billet, calculate the remaining in-furnace time of the target billet;

[0008] The remaining time in the furnace is matched with the preset processing time of the processing section to obtain the current processing section of the target steel billet;

[0009] The correction coefficient for the current processing section is retrieved using a table lookup method;

[0010] The theoretical furnace temperature setpoint for the current processing section is corrected using a correction factor to obtain the actual furnace temperature setpoint.

[0011] Determine if the monitored temperature of the current processing section is lower than the actual furnace temperature setting value. If so, increase the temperature of the current processing section in the heating furnace.

[0012] Beneficial Effects: When the target steel billet is fed into the furnace, the method of this invention first matches the remaining time of the target steel billet in the furnace with the processing schedule of the processing section to obtain the current processing section of the target steel billet. Then, it retrieves the correction coefficient for the current processing section using a lookup table method, and then uses the correction coefficient to correct the actual furnace temperature setpoint. This allows for adaptive adjustment of the actual furnace temperature setpoint based on the situation of each processing section in the heating furnace. Finally, it combines the monitored temperature to determine whether the heating furnace needs to be heated. Compared with the prior art, the method of this invention uses a lookup table compensation temperature regulation strategy to achieve reasonable control of the heating furnace temperature. It can reliably regulate the furnace temperature even when the infrared temperature feedback is inaccurate, maintaining safe and reliable production.

[0013] Preferably, a correction factor is used to correct the theoretical furnace temperature setpoint for the current processing section to obtain the actual furnace temperature setpoint, including:

[0014] The correction coefficient and the theoretical furnace temperature setpoint are input into the weighted average model to calculate the actual furnace temperature setpoint.

[0015] The preferred weighted average model is as follows:

[0016]

[0017] In the formula, Indicates the first The actual furnace temperature setpoint for the current processing section. This indicates the total number of segments in the steel billet. Indicates the first The steel billet in the first The correction factor for the current processing segment. Indicates the first The steel billet in the first The theoretical furnace temperature setting value for the current processing section.

[0018] Beneficial effects: The method of the present invention can dynamically assign a correction coefficient to the target steel billet according to the processing section in which the target steel billet is located, so that the target steel billet is processed at a more reasonable temperature, thereby improving the output quality of the target steel billet.

[0019] Preferably, if the pyrometer is temporarily removed from the current processing section, a correction factor is used to correct the theoretical furnace temperature setpoint for the current processing section to obtain the actual furnace temperature setpoint, including:

[0020] Obtain expert compensation parameters;

[0021] The correction coefficient, expert compensation parameters, and theoretical furnace temperature setpoint are input into the correction equation to calculate the actual furnace temperature setpoint.

[0022] Preferably, the modified equation is as follows:

[0023]

[0024] In the formula, Indicates the first The actual furnace temperature setpoint for the current processing section. Indicates the first The theoretical furnace temperature setpoint for the current processing section. This indicates that experts have corrected the temperature. Indicates the first The actual furnace temperature value from the last monitoring and metering of the current processing section. Indicates the first Expert compensation parameters for the current processing segment. This indicates the waiting time for the steel billet to be rolled in the heating furnace. This represents the correction factor.

[0025] Preferred, the first The current processing section is the preheating section in the heating furnace.

[0026] Preferably, a deep learning model is used to obtain the correction coefficients. The method of the present invention further includes:

[0027] Acquire three-dimensional point cloud data and thermal imaging data of the target steel billet surface;

[0028] Extracting laser features from 3D point cloud data and thermal imaging features from thermal imaging data;

[0029] The laser features and thermal imaging features are stitched together to obtain a feature matrix;

[0030] The feature matrix is ​​input into a pre-trained spatiotemporal attention neural network, which outputs correction coefficients.

[0031] Preferably, calculating the remaining furnace time of the target billet includes:

[0032] Obtain the processed information of the target steel billet and the production cycle time of each processing segment;

[0033] Based on the preset total processing information and processed information, determine the remaining processing sections of the target steel billet;

[0034] The remaining furnace time is calculated by summing up the production cycles of all remaining processing sections.

[0035] Preferably, the current processing section includes at least a preheating section, a rolling treatment section, a uniform temperature rolling section, and a heat recovery section.

[0036] In a second aspect, the present invention discloses a furnace temperature regulation system for a steel rolling heating furnace, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the furnace temperature regulation method for the steel rolling heating furnace described in the first aspect is implemented.

[0037] The beneficial effects of this invention are as follows:

[0038] (1) Compared with the prior art, the method of the present invention adopts a lookup table compensation temperature regulation strategy to achieve reasonable control of the heating furnace temperature. It can reliably regulate the furnace temperature when the infrared temperature feedback is inaccurate, and maintain safe and reliable production.

[0039] (2) Compared with the prior art, the method of the present invention can dynamically adjust the correction coefficient according to the actual situation of the target steel ingot in the processing section by using artificial intelligence technology, which makes the method of the present invention more reliable and universal.

[0040] (3) Compared with the prior art, the method of the present invention can support the replacement of the high temperature meter in the preheating section during continuous production. During this replacement period, the target steel billet is always preheated within a reasonable temperature range and will not damage its product quality. Attached Figure Description

[0041] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein:

[0042] Figure 1 This is a flowchart of the furnace temperature adjustment method for the steel rolling heating furnace in Embodiment 1 of the present invention;

[0043] Figure 2 This is a schematic diagram of temperature adjustment in the rolling section of Embodiment 1 of the present invention;

[0044] Figure 3 This is a schematic diagram of the furnace temperature regulation system of the steel rolling heating furnace in Embodiment 2 of the present invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] This embodiment discloses a furnace temperature regulation method and system for a steel rolling heating furnace, which is used to overcome the technical problem of reliably regulating the furnace temperature while maintaining production.

[0047] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0048] Example 1

[0049] like Figure 1 As shown, this embodiment discloses a method for regulating the furnace temperature of a steel rolling heating furnace, including:

[0050] S10: In response to the input of the target billet, calculate the remaining furnace time of the target billet.

[0051] In this embodiment, after the target steel billet is put in, it will first enter the preheating section of the heating furnace. The preheating section is equipped with a position sensor (which can be a laser sensor) to determine whether the target steel billet has been put in. If it has been put in, the position sensor will send a position signal to the main control device. At this time, the main control device calculates the remaining furnace time of the target steel billet based on the built-in program logic.

[0052] Step S10 includes:

[0053] S11: Obtain the processed information of the target steel billet and the production cycle of each processing segment.

[0054] It should be explained that the target steel billet has its own production number, and corresponding processing information will be uploaded after each processing segment is completed. The production cycle (rhythm) refers to the theoretical time required to complete each processing segment.

[0055] S12: Based on the preset total processing information and processed information, determine the remaining processing section of the target steel billet.

[0056] In this embodiment, the total processing information includes the order in which the target steel billet flows through the processing sections and the total number of processing sections. The already processed information refers to the information on the processing sections that the target steel billet has already passed through.

[0057] Specifically, the unfinished processing segments in the total processing information can be determined by using the processed information.

[0058] S13: Add up the production cycle times of all remaining processing sections to calculate the remaining furnace time.

[0059] By using the steps S11-S13 above, the remaining furnace time for each target billet can be quickly calculated. This method is more suitable for step-by-step rolling mill processes.

[0060] S20: Match the remaining time in the furnace with the preset processing time schedule to obtain the current processing segment of the target billet.

[0061] In this embodiment, the current processing section includes at least a preheating section, a rolling section, a uniform rolling section, and a heat recovery section. The target steel billet flows through the preheating section, the rolling section, the uniform rolling section, and the heat recovery section sequentially from front to back.

[0062] It should be explained that the preheating section mainly utilizes the waste heat of the furnace gas to initially heat the target steel billet, causing the billet temperature to rise evenly and slowly, reducing thermal stress when entering the high-temperature section. For example... Figure 2 As shown, the rolling section in this embodiment mainly involves a heat treatment process of holding, cooling, holding, and heating up on the target steel billet after the preheating section. This process has a decisive impact on the mechanical properties of the target steel billet. Specifically, different temperature ranges and cooling rates will cause the target steel billet to form different microstructures, such as ferrite, pearlite, bainite, and hipposite-martensite. By precisely controlling the cooling rate and holding time in the rolling section, the microstructure transformation process of the steel can be controlled, thereby obtaining the desired mechanical properties, such as strength, toughness, and hardness. More specifically, in Figure 2 middle, ~ The time period refers to the first heat preservation time period in the rolling section. ~ The time period refers to the cooling time in the rolling section. ~ The time period refers to the second heat preservation time period in the rolling process section. ~ The time period refers to the heating period in the pre-rolling treatment section. By adding the pre-rolling treatment section and adjusting the temperature, the method in this embodiment can reduce the probability of rolling defects in the target steel billet entering the homogenized rolling section. This improves production quality and reduces the possibility of inaccurate temperature monitoring by the pyrometer due to defects (cracks or increased roughness). The homogenized rolling section mainly ensures uniform temperature inside and outside the target steel billet, eliminates temperature gradients, and guarantees a consistent overall temperature before rolling the target steel billet. The heat recovery section is mainly used to recover waste heat from the flue gas discharged from the heating furnace.

[0063] For example, the processing schedule for the above processing section can be:

[0064]

[0065] It should be noted that the required production cycle time for each processing stage will vary depending on the carbon content, specifications, size, and thickness of the target steel billet, and the remaining furnace time will be adjusted accordingly based on the actual situation.

[0066] S30: Use a lookup table to retrieve the correction coefficient for the current processing section.

[0067] In this embodiment, laser detection or thermal imaging technology can be used to inspect the steel billets in each processing section, obtaining sampling inspection results. These results are then input into a pre-trained deep learning model to dynamically update the correction coefficients in each processing section. In other embodiments, experts can periodically sample and inspect the intermediate products in each processing section, assigning values ​​to the correction coefficients based on the sampling inspection results, thus achieving dynamic updates to the correction coefficients. This method of dynamically updating the correction coefficients can better adapt to the complex environment of actual steel rolling.

[0068] S40: The theoretical furnace temperature setpoint for the current processing section is corrected using a correction factor to obtain the actual furnace temperature setpoint.

[0069] Specifically, step S40 includes:

[0070] The correction coefficient and the theoretical furnace temperature setpoint are input into the weighted average model to calculate the actual furnace temperature setpoint.

[0071] More specifically, the above weighted average model is as follows:

[0072]

[0073] In the formula, Indicates the first The actual furnace temperature setpoint for the current processing section. This indicates the total number of segments in the steel billet. Indicates the first The steel billet in the first The correction factor for the current processing segment. Indicates the first The steel billet in the first The theoretical furnace temperature setting value for the current processing section.

[0074] It should be explained that, in most cases, for processing sections involving critical processes and high temperature requirements, it is generally difficult to remove the high-temperature gauge with a large measurement deviation midway (removal would severely disrupt the continuity of monitoring data). However, for the preheating section, the temperature process requirements are lower, and when the measurement deviation of the high-temperature gauge is large, it can be removed and replaced midway. Specifically, if the high-temperature gauge is removed in the preheating section, step S40 above includes:

[0075] Obtain expert compensation parameters.

[0076] The correction coefficient, expert compensation parameters, and theoretical furnace temperature setpoint are input into the correction equation to calculate the actual furnace temperature setpoint.

[0077] The aforementioned corrected equation is specifically as follows:

[0078]

[0079] In the formula, Indicates the first The actual furnace temperature setpoint for the current processing section. Indicates the first The theoretical furnace temperature setpoint for the current processing section. This indicates that experts have corrected the temperature. Indicates the first The actual furnace temperature value from the last monitoring and metering of the current processing section. Indicates the first Expert compensation parameters for the current processing segment. This indicates the waiting time for the steel billet to be rolled in the heating furnace. This represents the correction factor.

[0080] In this embodiment, the above Take 600℃~700℃, Take 2~5, Take values ​​from -1 to -0.5.

[0081] Preferably, the above Set the temperature to 650℃.

[0082] It should be explained that when a malfunction in steel rolling production causes a prolonged heating time of the billet in the furnace, the furnace temperature setpoint should be corrected. This should be determined by referring to the furnace temperature drop table for high-speed wire rod heating furnaces in wire rod mills during the waiting-to-roll phase, and combining this with practical operational experience. .

[0083] Through the above technical solution, the method of this embodiment supports the temporary replacement of the pyrometer in actual production while maintaining the data continuity and integrity of the monitoring system. In addition to maintaining the data continuity and integrity of the monitoring system, the method of this embodiment can also ensure that the target steel billet is preheated within a reasonable temperature range in the preheating section.

[0084] S50: Determine whether the monitored temperature of the current processing section is lower than the actual furnace temperature setting value. If so, increase the temperature of the current processing section in the heating furnace.

[0085] It should be noted that, generally, each processing section in a high-temperature furnace is equipped with one or more pyrometers, and the above-mentioned monitored temperature refers to the real-time monitored temperature of the pyrometer.

[0086] Through the above steps S10-S50, when there is a measurement deviation in the pyrometer, the method of this embodiment can adjust the temperature weighting according to the actual situation of different processing sections of the target steel billet, so that the actual temperature of the processing section is corrected, thereby maintaining safe and reliable production.

[0087] Furthermore, if a deep learning model is used to obtain the correction coefficients, the method in this embodiment further includes:

[0088] S100: Acquire three-dimensional point cloud data and thermal imaging data of the target steel billet surface.

[0089] In this embodiment, the aforementioned three-dimensional point cloud data can be acquired using a Keyence LK-H052 laser displacement sensor at a sampling frequency of 50kHz, achieving an accuracy of ±2μm. The aforementioned thermal imaging data can be obtained using a FLIR A655sc thermal imager equipped with a 160×120 pixel uncooled microbolometer, capable of achieving a thermal sensitivity of 50mK in the 8-14μm band. The laser displacement sensor and thermal imager are synchronized through control by an industrial control computer (primarily a PLC), ensuring that the acquisition deviation between the two is less than 1ms, thus achieving synchronous data acquisition.

[0090] S200: Extracts laser features from 3D point cloud data and thermal imaging features from thermal imaging data.

[0091] Furthermore, the algorithm for extracting the aforementioned laser features is as follows:

[0092]

[0093] In the formula, Indicates the effective detection area. Indicates time Characteristics of the output laser Indicates the target steel billet at time 3D point cloud data, Represents the three-dimensional coordinates of three-dimensional point cloud data.

[0094] Furthermore, the algorithm for extracting the aforementioned thermal imaging features is as follows:

[0095]

[0096] In the formula, Indicates time Output thermal imaging features Indicates the partial derivative sign. Indicates time Thermal imaging data, The horizontal axis data represents the thermal imaging data. The vertical axis data represents the thermal imaging data.

[0097] S300: The laser features and thermal imaging features are stitched together to obtain a feature matrix.

[0098] Furthermore, the algorithm for generating the aforementioned feature matrix is ​​as follows:

[0099]

[0100] In the formula, Indicates time The concatenated feature matrix Symbol indicating channel splicing This represents the mean of the training set. This represents the standard deviation of the training set.

[0101] S400: Input the feature matrix into a pre-trained spatiotemporal attention neural network (STANN) and output correction coefficients.

[0102] The output expression for the correction coefficient is as follows:

[0103]

[0104] In the formula, Indicates time Correction factor, This indicates that the spatiotemporal attention neural network is focused on time. The output result, if time confidence level Greater than the threshold The correction factor is then adopted. Otherwise, the retrieval time Correction coefficient .

[0105] Through steps S100-S400 above, the method of this embodiment first acquires three-dimensional point cloud data and thermal imaging data of the target steel billet surface through a laser displacement sensor and a thermal imager, so as to realize the real-time acquisition of the actual processing status of the target steel billet; then, the above algorithm is used to extract laser features and thermal imaging features to reduce the amount of data computation; then, the extracted laser features and thermal imaging features are spliced ​​to obtain a feature matrix, which is used as the data input of the spatiotemporal attention neural network; finally, the dynamically updated correction coefficient is obtained through the spatiotemporal attention neural network and the output expression.

[0106] Compared to existing technologies, the method in this embodiment can utilize a spatiotemporal attention neural network to generate dynamic correction coefficients to adapt to changes in real-world scenarios. Therefore, the method in this embodiment has greater universality and flexibility.

[0107] Preferably, in order to improve the computational efficiency (iteration speed) and accuracy of the spatiotemporal attention neural network, the method in this embodiment adopts the Huber loss function for the robust loss function of the spatiotemporal attention neural network, and the threshold parameter of the loss function can be set to 0.5.

[0108] Preferably, in order to further improve the computational accuracy of the above-mentioned spatiotemporal attention neural network, an incremental learning optimization mechanism can be introduced, and a momentum optimizer can be used to update the hyperparameters of the spatiotemporal attention neural network.

[0109] Example 2

[0110] like Figure 3 As shown, this embodiment discloses a furnace temperature regulation system for a steel rolling heating furnace, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the furnace temperature regulation method for the steel rolling heating furnace described in Embodiment 1 is implemented.

[0111] The system in this embodiment also includes other components well known to those skilled in the art, such as communication interfaces. Their settings and functions are known in the art, and therefore will not be described in detail here.

[0112] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device. Any application or module described in this invention can be implemented using computer-readable / executable instructions that can be stored or otherwise maintained by such a computer-readable medium.

[0113] In the description of this specification, "multiple" means at least two, such as two, three or more, etc., unless otherwise expressly and specifically defined.

[0114] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A method of regulating the temperature of a steel rolling furnace, characterized in that, The method comprises the following steps: calculating the remaining in-furnace time of the target billet in response to the input of the target billet; matching the remaining in-furnace time with a preset processing section processing time table to obtain a current processing section of the target billet; obtaining the correction coefficient of the current processing section by using a lookup table method; obtaining the correction coefficient by using a deep learning model, comprising: obtaining three-dimensional point cloud data and thermal imaging data on the surface of the target billet; extracting laser features of the three-dimensional point cloud data and thermal imaging features of the thermal imaging data; splicing the laser features and the thermal imaging features to obtain a feature matrix; inputting the feature matrix into a pre-trained spatio-temporal attention neural network to output the correction coefficient; correcting the theoretical furnace temperature set value of the current processing section by using the correction coefficient to obtain an actual furnace temperature set value; judging whether the monitoring temperature of the current processing section is lower than the actual furnace temperature set value, and if so, heating the current processing section in the heating furnace; if the current processing section temporarily removes a pyrometer, correcting the theoretical furnace temperature set value of the current processing section by using the correction coefficient to obtain an actual furnace temperature set value, comprising: obtaining an expert compensation parameter; inputting the correction coefficient, the expert compensation parameter and the theoretical furnace temperature set value into a correction equation to calculate the actual furnace temperature set value; the correction equation is specifically: actual furnace temperature set value of the first current processing section, actual furnace temperature set value of the first current processing section, theoretical furnace temperature set value of the first current processing section, theoretical furnace temperature set value of the first current processing section, expert correction temperature, furnace temperature actual value in the last monitoring measurement of the first current processing section, furnace temperature actual value in the last monitoring measurement of the first current processing section, expert compensation parameter of the first current processing section, expert compensation parameter of the first current processing section, billet heating furnace waiting time, correction coefficient; No. The current processing section is the preheating section in the heating furnace.

2. The method of adjusting the temperature of a steel rolling furnace according to claim 1, wherein, correcting the theoretical furnace temperature set value of the current processing section by using the correction coefficient to obtain an actual furnace temperature set value, comprising: inputting the correction coefficient and the theoretical furnace temperature set value into a weighted average model to calculate the actual furnace temperature set value.

3. The method of adjusting the temperature of a steel rolling furnace according to claim 2, wherein, The weighted average model is specifically: In the formula, Indicates the first The actual furnace temperature setpoint for the current processing section. This indicates the total number of segments in the steel billet. Indicates the first The steel billet in the first The correction factor for the current processing segment. Indicates the first The steel billet in the first The theoretical furnace temperature setting value for the current processing section.

4. The method of adjusting the temperature of a steel rolling furnace according to claim 1, wherein, calculating the remaining in-furnace time of the target billet comprises: obtaining processed information of the target billet and production cycle of each processing section; determining the remaining processing section of the target billet according to preset total processing information and the processed information; adding up the production cycles of all the remaining processing sections to calculate the remaining in-furnace time.

5. The method of adjusting the temperature of a steel rolling furnace according to claim 1, wherein, The current processing section at least comprises a preheating section, a waiting-to-roll processing section, a temperature equalization rolling section and a heat recovery section.

6. A furnace temperature regulating system for a steel rolling furnace, characterized by, The method comprises a processor and a memory, and the memory stores computer program instructions which, when executed by the processor, implement the furnace temperature regulation method of the rolling heating furnace according to any one of claims 1-5.

Images