Steel pipe intermediate frequency heating temperature control method and system based on multiple parameters

Abstract

The invention relates to the technical field of steel pipe medium-frequency heating, and discloses a steel pipe medium-frequency heating temperature control method and system based on multiple parameters, and the method comprises the steps: obtaining real-time monitoring data of a steel pipe in a medium-frequency heating process; according to the real-time monitoring data, the target power distribution proportion of the preheating stage, the main heating stage and the heat preservation stage of medium-frequency heating of the steel pipe is obtained; according to the target power distribution proportion, an optimized power curve of the preheating stage, a stable power mode of the main heating stage and a heat preservation power mode of the heat preservation stage are obtained; according to the optimized power curve of the preheating stage, the stable power mode of the main heating stage, the heat preservation power mode of the heat preservation stage and the temperature distribution data of the steel pipe heating process, a power adjustment response model is obtained; the method has the following effects that the heat efficiency and the temperature uniformity in the intermediate-frequency heating process of the steel pipe can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intermediate frequency heating of steel pipes, and particularly to a method and system for controlling the intermediate frequency heating temperature of steel pipes based on multiple parameters. Background Art

[0002] As an indispensable key link in modern industrial manufacturing, the intermediate frequency heating technology of steel pipes is widely used in fields such as metal processing and heat treatment. Its core lies in achieving efficient and uniform heating effects through electromagnetic induction, which is of decisive significance for improving product quality and production efficiency. With the continuous upgrading of industrial demands, the accuracy of temperature control and energy efficiency optimization have become important indicators to measure the advancement of this technology. However, traditional methods for intermediate frequency heating of steel pipes have shown significant limitations in practical applications. Existing solutions often rely on fixed power output modes and are difficult to adapt to the dynamic requirements brought about by changes in the material, size, and heating stage of steel pipes, resulting in low energy utilization efficiency or uneven temperature distribution. In particular, it is difficult to achieve a balance between surface overheating and insufficient internal heat penetration.

[0003] These limitations stem from the core challenges that have not been solved in the field. First, the problem of matching between energy input and actual temperature rise is prominent, and there is a lag in the response between real-time monitoring data and power adjustment, making it difficult to achieve an ideal dynamic balance in the heating process. Second, in the implementation of the segmented heating strategy, the accuracy of power switching in each stage is insufficient. Especially in the preheating stage, it is easy to cause too high surface temperature, while in the main heating stage, improper power increase may lead to poor heat penetration. In addition, the maintenance of the target temperature in the heat preservation stage relies on static control logic and lacks full utilization of real-time feedback, thus affecting the optimization of the overall thermal efficiency. These three technical factors - the dynamic matching of the ratio of energy input to temperature rise, the power switching accuracy of segmented control, and the temperature maintenance stability in the heat preservation stage - together constitute the root cause of the current technical problems, directly restricting the efficiency and quality of the intermediate frequency heating process of steel pipes.

[0004] Therefore, how to design a control method for dynamically adjusting the power output of the intermediate frequency power supply based on the ratio of real-time monitored energy input to actual temperature rise, and achieve precise power control under the segmented strategy in the preheating, main heating, and heat preservation stages, has become a key issue in improving the thermal efficiency and temperature uniformity of the intermediate frequency heating process of steel pipes. Summary of the Invention

[0005] The present invention provides a method and system for controlling the intermediate frequency heating temperature of steel pipes based on multiple parameters to improve the thermal efficiency and temperature uniformity of the intermediate frequency heating process of steel pipes.

[0006] In a first aspect, to solve the above technical problems, the present invention provides a method for controlling the intermediate frequency heating temperature of steel pipes based on multiple parameters, including:

[0007] Obtain real-time monitoring data of the steel pipe during medium-frequency heating;

[0008] According to the real-time monitoring data, obtain the target power distribution ratios for the preheating stage, main heating stage, and heat preservation stage of the medium-frequency heating of the steel pipe;

[0009] According to the target power distribution ratios, obtain the optimized power curve for the preheating stage;

[0010] According to the target power distribution ratios, obtain the stable power mode for the main heating stage;

[0011] According to the target power distribution ratios, obtain the heat preservation power mode for the heat preservation stage;

[0012] According to the optimized power curve for the preheating stage, the stable power mode for the main heating stage, the heat preservation power mode for the heat preservation stage, and the temperature distribution data during the heating process of the steel pipe, obtain a power adjustment response model; wherein, the power adjustment response model is used to adjust the power during the medium-frequency heating process of the steel pipe.

[0013] In an alternative embodiment, obtaining the target power distribution ratios for the preheating stage, main heating stage, and heat preservation stage of the medium-frequency heating of the steel pipe according to the real-time monitoring data includes:

[0014] Obtain the deviation value between the real-time monitoring data of the preheating stage and the corresponding target temperature;

[0015] If the deviation value is greater than the preset deviation value threshold, adjust the power distribution value of the preheating stage according to the deviation value to obtain the first power distribution ratio;

[0016] According to the first power distribution ratio, the power adjustment signal of the main heating stage, and the real-time monitoring data of the main heating stage, adjust the power distribution value corresponding to the main heating stage to obtain the second power distribution ratio;

[0017] According to the second power distribution value and the real-time monitoring data of the heat preservation stage, adjust the power distribution value corresponding to the heat preservation stage to obtain the third power distribution ratio;

[0018] Optimize the third power distribution ratio according to the adjusted power distribution value of the heat preservation stage and the corresponding historical power distribution value of the heat preservation stage to obtain the target power distribution ratio.

[0019] In an alternative embodiment, the power adjustment signal of the main heating stage is determined according to the following steps:

[0020] According to the real-time monitoring data of the main heating stage, obtain the temperature change curve of the main heating stage;

[0021] According to the temperature change curve in the main heating stage, the ratio of the current energy input to the actual temperature rise corresponding to the main heating stage is obtained, and a set of ratios is obtained.

[0022] If the ratio of any current energy input to the actual temperature rise in the set of ratios is not within the preset ratio threshold range, it is determined as a key ratio.

[0023] According to the key ratio, a power adjustment signal is obtained; wherein, the power adjustment signal includes a power adjustment direction and a power adjustment amplitude.

[0024] In an alternative embodiment, according to the multi-parameter-based intermediate frequency heating temperature control method for steel pipes described in claim 1, it is characterized in that the obtaining of the optimized power curve in the preheating stage according to the target power distribution ratio includes:

[0025] According to the target power distribution ratio, the target power distribution value in the preheating stage is obtained.

[0026] According to the target power distribution value in the preheating stage, the target output frequency in the preheating stage is obtained.

[0027] According to the target output frequency in the preheating stage, the target current value is obtained.

[0028] According to the target current value, the curve correction data in the preheating stage is obtained.

[0029] According to the curve correction data in the preheating stage, the optimized power curve in the preheating stage is obtained.

[0030] In an alternative embodiment, the obtaining of the stable power mode in the main heating stage according to the target power distribution ratio includes:

[0031] According to the target power distribution ratio, the total initial power distribution value in the main heating stage is determined.

[0032] In response to the start of the main heating stage, the initial temperature distribution characteristics in the main heating stage are obtained.

[0033] According to the initial temperature distribution characteristics, a speed adjustment parameter is obtained.

[0034] If the speed adjustment parameter is greater than the preset speed adjustment threshold, the power increase amplitude is controlled to obtain a target increase value.

[0035] According to the target increase value and the operation status data in the main heating stage, the power switching timing in the main heating stage is determined.

[0036] Power switching is performed according to the power switching timing in the main heating stage and real-time feedback data is obtained.

[0037] According to the real-time feedback data, a quantization value of the temperature distribution characteristics is obtained.

[0038] If the quantization value of the temperature distribution feature is greater than the preset quantization value threshold, adjust the power of the main heating stage and obtain the updated operation status data;

[0039] Based on the updated operation status data, obtain the stable power mode of the main heating stage.

[0040] In an alternative embodiment, the obtaining the heat preservation power mode of the heat preservation stage according to the target power distribution ratio includes:

[0041] Determine the start time of the heat preservation stage according to the target power distribution ratio;

[0042] In response to the start of the heat preservation stage, obtain the matching degree between the real-time monitoring data of the heat preservation stage and the target temperature corresponding to the heat preservation stage;

[0043] Obtain the power adjustment value of the heat preservation stage according to the matching degree;

[0044] Adjust the power output of the heat preservation stage according to the power adjustment value of the heat preservation stage to obtain the adjusted operation status data of the heat preservation stage;

[0045] Determine the trend stable point according to the adjusted operation status data of the heat preservation stage;

[0046] According to the trend stable point, apply the time series analysis algorithm to obtain the initial power sequence interval;

[0047] Adjust the sampling frequency of the real-time monitoring data according to the initial power sequence interval;

[0048] Obtain the heat preservation power mode according to the adjusted sampling frequency of the real-time monitoring data.

[0049] In an alternative embodiment, the obtaining the power adjustment response model according to the optimized power curve of the preheating stage, the stable power mode of the main heating stage, the heat preservation power mode of the heat preservation stage, and the temperature distribution data of the steel pipe heating process includes:

[0050] Collect the temperature distribution data of the steel pipe heating process and determine the achievement degree of the dynamic balance ability;

[0051] According to the achievement degree of the dynamic balance ability, the optimized power curve of the preheating stage, the stable power mode of the main heating stage, and the heat preservation power mode of the heat preservation stage, use the iterative algorithm to update the segmented control precision parameters to obtain the power adjustment response model.

[0052] In a second aspect, the present invention provides a medium-frequency heating temperature control system for steel pipes based on multiple parameters, including:

[0053] An acquisition module, configured to acquire real-time monitoring data of a steel pipe during intermediate frequency heating;

[0054] A proportional acquisition module, configured to obtain target power distribution ratios for the preheating stage, main heating stage, and heat preservation stage of the intermediate frequency heating of the steel pipe according to the real-time monitoring data;

[0055] A curve acquisition module, configured to obtain an optimized power curve for the preheating stage according to the target power distribution ratio;

[0056] A mode acquisition module, configured to obtain a stable power mode for the main heating stage according to the target power distribution ratio;

[0057] A sequence acquisition module, configured to obtain a heat preservation power mode for the heat preservation stage according to the target power distribution ratio;

[0058] A model acquisition module, configured to obtain a power adjustment response model according to the optimized power curve for the preheating stage, the stable power mode for the main heating stage, the heat preservation power mode for the heat preservation stage, and the temperature distribution data of the steel pipe heating process; wherein, the power adjustment response model is used to adjust the power during the intermediate frequency heating process of the steel pipe.

[0059] In a third aspect, the present invention further provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the multi-parameter-based intermediate frequency heating temperature control method for steel pipes described in any one of the above.

[0060] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the multi-parameter-based intermediate frequency heating temperature control method for steel pipes described in any one of the above.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] The temperature control method for medium-frequency heating of steel pipes based on multiple parameters disclosed in the present invention first obtains the real-time monitoring data during the medium-frequency heating of the steel pipes; secondly, according to the real-time monitoring data, obtains the target power distribution ratios for the preheating stage, the main heating stage, and the heat preservation stage of the medium-frequency heating of the steel pipes; then respectively according to the target power distribution ratios, obtains the optimized power curve for the preheating stage; according to the target power distribution ratio, obtains the stable power mode for the main heating stage; according to the target power distribution ratio, obtains the heat preservation power mode for the heat preservation stage; finally, according to the optimized power curve for the preheating stage, the stable power mode for the main heating stage, the heat preservation power mode for the heat preservation stage, and the temperature distribution data during the heating process of the steel pipes, obtains a power adjustment response model; wherein, the power adjustment response model is used to adjust the power during the medium-frequency heating process of the steel pipes. The present invention continuously optimizes the power output through a real-time feedback mechanism to ensure the temperature distribution uniformity and thermal efficiency, and the finally obtained power adjustment response model realizes the dynamic balance control of the steel pipe heating process, improving the heating quality and energy utilization efficiency. Description of the Drawings

[0063] Figure 1 is a schematic flow chart of a temperature control method for medium-frequency heating of steel pipes based on multiple parameters provided in the first embodiment of the present invention;

[0064] Figure 2 is a schematic structural diagram of a temperature control system for medium-frequency heating of steel pipes based on multiple parameters provided in the second embodiment of the present invention. Detailed Embodiments

[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0066] Refer to Figure 1 , the first embodiment of the present invention provides a temperature control method for medium-frequency heating of steel pipes based on multiple parameters, including the following steps:

[0067] S100, obtain the real-time monitoring data during the medium-frequency heating of the steel pipes.

[0068] Specifically, medium-frequency heating of steel pipes is a heating technology that uses the magnetic field generated by medium-frequency alternating current to generate induced current in steel pipes, thereby heating the steel pipes. By collecting the surface temperature data and internal temperature data of the medium-frequency heating equipment through sensors, and combining time series to calculate the temperature rise rate, an original data set is obtained. Apply the mean filtering algorithm to the original data set to filter out noise interference and obtain a smooth data set. According to the smooth data set, calculate the changing trends of the surface temperature and internal temperature of the steel pipe over time to obtain a temperature change sequence. Finally, use the polynomial fitting algorithm to perform curve fitting on the temperature change sequence to obtain a smooth temperature change curve.

[0069] In a possible implementation, when calculating the temperature rise rate in combination with time series, the change in temperature per unit time can be calculated based on the collected data. As an example, the surface of the steel pipe is preferentially heated. The surface temperature rises from 30°C to 50°C in 50 seconds, and the temperature rise rate is 0.4°C / s; the internal temperature rises from 25°C to 35°C in 60 seconds, and the temperature rise rate is 0.17°C / s. This calculation can quickly reveal the speed of temperature change and intuitively reflect the heat dissipation performance of the equipment or the potential overheating risk.

[0070] It should be noted that applying the mean filtering algorithm to the original data is to eliminate noise interference. Suppose the surface temperature data of a certain medium-frequency heating equipment fluctuates around 30°C, including values such as 29.8°C, 30.2°C, 31°C, 29.5°C, etc. Take the average value of 5 consecutive data (such as 30.1°C) as the smoothing result. This method can effectively filter out abnormal points introduced by environmental jitter or sensor errors, making the data more representative. The smoothed data set lays a stable foundation for subsequent trend analysis.

[0071] Specifically, when calculating the temperature change trend based on the smooth data set, it can be observed that the surface temperature may show the characteristic of rising slowly first and then tending to be stable, while the internal temperature may continue to rise due to heat accumulation. As an example, after the medium-frequency heating equipment operates for 5 minutes, the surface temperature rises from 25°C to 40°C and then stabilizes, while the internal temperature rises continuously from 30°C to 60°C. This trend reveals the difference in internal and external heat transfer and helps to judge whether the heat dissipation design is reasonable.

[0072] In one embodiment, when using the polynomial fitting algorithm to perform curve fitting on the temperature change sequence, a quadratic or cubic polynomial can be selected to describe the change of temperature over time. As an example: if the internal temperature shows an upward trend of first slow and then fast, the cubic polynomial can better capture the characteristics of accelerating change. This smooth curve not only intuitively shows the temperature evolution but also facilitates predicting future changes. The smooth temperature change curve generated by the above method can identify abnormal points during equipment operation.

[0073] Among them, predicting the overheating time point of the device based on the smooth curve can take cooling measures in advance to avoid failures.

[0074] In one embodiment, the temperature rise rate and the smooth curve can also be combined to evaluate the thermal performance of the intermediate frequency heating device under different loads. As an example: the temperature rise rate is only 0.1 °C / s at low load and rises to 0.5 °C / s at high load.

[0075] It should be noted that the real-time monitoring data of the intermediate frequency heating device during operation can be collected at preset time intervals to generate a corresponding smooth temperature change curve.

[0076] S200. According to the real-time monitoring data, obtain the target power distribution ratios for the preheating stage, the main heating stage, and the heat preservation stage of the intermediate frequency heating of the steel pipe.

[0077] Specifically, the goal of the preheating stage is to rapidly increase the temperature, the main heating stage maintains a stable temperature rise, and the heat preservation stage maintains a constant temperature. For the preheating stage, whether the finally reached temperature meets the target temperature is the most important. For the main heating stage, being able to stably increase the temperature, that is, maintaining a stable temperature rise rate is the most important, and the stable temperature rise rate depends on the stable output of the intermediate frequency heating device; for the heat preservation stage, being able to maintain a stable temperature is crucial.

[0078] In this embodiment, first, obtain the deviation value between the real-time monitoring data of the preheating stage and the corresponding target temperature; secondly, if the deviation value is greater than the preset deviation value threshold, adjust the power distribution value of the preheating stage according to the deviation value to obtain the first power distribution ratio. As an example: assume that the target temperature in the preheating stage is 100 °C, the real-time monitoring data shows that the current temperature is 90 °C, and the deviation value is 10 °C. If the preset deviation value threshold is 5 °C, then this deviation is greater than the preset deviation value threshold, and the adjustment logic is triggered. The specific adjustment method can be as follows: As an example: assume that the initial preheating power ratio is 40%, the main heating is 50%, and the heat preservation is 10%. A deviation of 10 °C indicates insufficient preheating. The preheating ratio can be increased to 45%, correspondingly compress the main heating ratio to 45%, and the heat preservation stage remains unchanged for the time being. That is, obtain the first power distribution ratio as the first adjustment. It should be noted that the ratio adjustment needs to consider the device load. For example, the preheating ratio should not be too high at low load to prevent overheating. According to the adjusted ratio data, the power adjustment value in the main heating stage needs to be recalculated. This reallocation ensures the reasonable flow of energy between stages and avoids insufficient or excessive power in a certain stage.

[0079] After that, according to the first power distribution ratio, the power adjustment signal in the main heating stage, and the real-time monitoring data in the main heating stage, the power distribution value corresponding to the main heating stage is adjusted to obtain the second power distribution ratio. Then, according to the second power distribution value and the real-time monitoring data in the heat preservation stage, the power distribution value corresponding to the heat preservation stage is adjusted to obtain the third power distribution ratio; finally, the third power distribution ratio is optimized according to the adjusted power distribution value in the heat preservation stage and the historical power distribution value corresponding to the heat preservation stage to obtain the target power distribution ratio. Here, the power distribution value in the main heating stage is adjusted according to the power adjustment signal, while the heat preservation stage is adjusted according to the target temperature data and the real-time monitoring data. As an example: if the monitored temperature in the heat preservation stage is 98°C, slightly lower than the target of 100°C, the deviation is 2°C, which does not exceed the threshold, but the trend shows that the temperature is slowly decreasing. The heat preservation ratio can be fine-tuned from 10% to 12%. Such heat preservation adjustment data can prevent temperature fluctuations.

[0080] Therefore, the power distribution ratio in the main heating stage is affected by the power adjustment signal in the main heating stage, and the power adjustment signal in the main heating stage is determined according to the following steps: First step, according to the real-time monitoring data in the main heating stage, obtain the temperature change curve in the main heating stage; Second step, according to the temperature change curve in the main heating stage, obtain the ratio of the current energy input and the actual temperature rise corresponding to the main heating stage to obtain a ratio set; Third step, if any ratio of the current energy input and the actual temperature rise in the ratio set is not within the preset ratio threshold range, it is determined as the key ratio; Fourth step, according to the key ratio, obtain the power adjustment signal; where the power adjustment signal includes the power adjustment direction and the power adjustment amplitude. It can be understood that: the main heating stage is the most important heating stage, and it is crucial for controlling the temperature rise rate. Obtaining the ratio data of energy input and actual temperature rise through the temperature change curve is an important means to analyze the thermal efficiency of the equipment. As an example: the ratio in the first 5 minutes is 40°C / kW, and it drops to 25°C / kW in the next 5 minutes due to heat loss, revealing the change of efficiency with the running time. Comparing the ratio set with the preset ratio threshold range can effectively judge the running state. As an example: set the normal ratio range to 20 - 50°C / kW. If the ratio at a certain moment is 55°C / kW, exceeding the upper limit, it indicates that the energy input may be too high or the heat dissipation is insufficient. On the contrary, if the ratio at a certain moment is less than 20°C / kW, lower than the lower limit, it indicates that the energy input is insufficient.

[0081] It should be noted that the preset ratio threshold range can be adjusted according to the device material or load requirements. For example, the upper limit of the threshold range decreases under low load, and the preset ratio threshold range is appropriately widened under high load. Such comparison results provide a basis for subsequent decisions. If the comparison result shows that it is not within the preset threshold range, it is particularly crucial to determine the power adjustment signal through the signal generation algorithm. In one embodiment, if it is higher than the upper limit of the preset threshold range, the direction category is "power reduction"; if it is lower than the lower limit of the preset threshold range, it is "power increase".

[0082] In one embodiment, the power adjustment amplitude can be generated according to the exceeding amplitude. As an example, when the ratio exceeds the standard by 10 °C / kW, a signal to reduce the power by 10% is generated; when it exceeds the standard by 20 °C / kW, the power is reduced by 20%.

[0083] After separate adjustments are made in each stage according to the real-time monitoring data, in order to ensure that the total power remains unchanged, the finally determined target power distribution ratio needs to be further optimized. Here, the third power distribution ratio is mainly optimized through the historical power distribution value and the power distribution value in the adjusted heat preservation stage, because the final heat preservation stage plays a crucial role in the final effect of the intermediate frequency heating of the steel pipe. Mainly on the premise of meeting the power in the heat preservation stage, the power ratio in the heat preservation stage is finely adjusted, and the other two stages are adjusted slightly in coordination, so that the finally obtained target power distribution ratio meets the requirement that the total power remains unchanged and can ensure temperature stability.

[0084] As an example: If the initial preheating power accounts for 40%, the main heating accounts for 50%, and the heat preservation accounts for 10%. In the third power distribution ratio, the preheating power accounts for 45%, the main heating accounts for 43%, and the heat preservation accounts for 12%. According to the feedback of the real-time monitoring data, the heat preservation stage can be maintained at 11%, then it is adjusted to: the preheating power accounts for 45%, the main heating accounts for 44%, and the heat preservation accounts for 11%. If the temperature is stable at this ratio, then the preheating power accounting for 45%, the main heating accounting for 44%, and the heat preservation accounting for 11% are determined as the target power distribution ratio. The target power distribution ratio can be used for the corresponding power distribution ratio of the subsequent workpiece (steel pipe) during intermediate frequency heating.

[0085] S300, obtain the optimized power curve in the preheating stage according to the target power distribution ratio.

[0086] Specifically, first, obtain the target power distribution value in the preheating stage according to the target power distribution ratio; second, obtain the target output frequency in the preheating stage according to the target power distribution value in the preheating stage; then, obtain the target current value according to the target output frequency in the preheating stage; then, obtain the curve correction data in the preheating stage according to the target current value; finally, obtain the optimized power curve in the preheating stage according to the curve correction data in the preheating stage.

[0087] Here, the optimized power curve of the preheating stage is used to describe the power regulation during the entire preheating stage.

[0088] Based on the target power allocation ratio, a preliminary total power allocation value for the entire preheating phase is determined. However, due to the varying conditions of different workpieces (steel pipes), this preliminary total power allocation value may not be suitable for every workpiece and therefore needs to be adjusted based on actual conditions. Specifically, the target output frequency for the preheating phase is preliminarily determined based on the corresponding total power allocation value. During the preheating phase, real-time monitoring data is continuously used to determine whether control requirements (i.e., the target temperature) are consistently met. If not, the initial power adjustment value needs to be adjusted. If the heating rate does not meet expectations, the preliminary total power allocation value for the preheating phase can be increased. After power adjustment, the output frequency needs to be dynamically adjusted based on the current state of the medium-frequency power supply to generate frequency adjustment data. For example, if the power supply is currently operating at 50Hz (the target output frequency) and the load is light, the frequency can be dynamically increased to 55Hz if the temperature rise is insufficient. The device supports 50-60Hz. This frequency adjustment data is intended to improve energy transfer efficiency. After the frequency is increased, it needs to be optimized to match the current intensity. Assuming the original current is 200A (AC RMS), after frequency adjustment, the control logic can increase the current to 220A, creating the optimized current value. The optimized current value directly affects power output stability. It is particularly important to determine the power curve's changing trend based on the optimized current value and the target power allocation ratio during the preheat phase. For example, if the target power allocation ratio is increased to 40% and the current is increased to 220A, the power curve may exhibit a steep upward trend. To avoid overshoot, curve correction data can be obtained from historical operating data. For example, if the temperature exceeded the target by 5°C during the previous run, the curve can be appropriately smoothed, reducing the power increase by 2% (i.e., 38%). If the target is not achieved after adjustment, such as the temperature stagnating at 75°C, the frequency can be fine-tuned to 58Hz and the power allocation increased to 39%, forming the final power curve. This approach allows for rapid response to temperature changes and ensures effective preheating. Specifically, from multiple perspectives, the initial power adjustment value can be flexibly set based on load fluctuations, for example, not exceeding 40% at low loads and up to 45% at high loads. The frequency adjustment data needs to match the response speed of the equipment. Too high a frequency may cause unstable current. In this embodiment, the current optimization value is adjusted through real-time monitoring feedback to avoid energy waste. The curve correction data relies on historical experience to ensure that the trend is controllable. It is understandable that this multi-link linkage adjustment method can effectively improve the accuracy and stability of the preheating stage and lay the foundation for subsequent main heating. As an example, under high load conditions, the initial ratio can be set to 45%, the frequency is 60Hz, the current is 230A, and the final temperature reaches 80°C accurately, with a smooth curve and no fluctuations.

[0089] In this embodiment, after initially determining the total power distribution value corresponding to the entire preheating stage, based on the feedback of real-time data, the total power distribution value of the preheating stage is dynamically adjusted according to data such as current, and finally an optimized power curve representing the power adjustment situation of the entire preheating stage is obtained.

[0090] S400. Obtain the stable power mode of the main heating stage according to the target power distribution ratio.

[0091] Specifically, first, determine the total initial power distribution value of the main heating stage according to the target power distribution ratio; second, in response to the start of the main heating stage, obtain the initial temperature distribution characteristics of the main heating stage; then, according to the initial temperature distribution characteristics, obtain the speed adjustment parameter; if the speed adjustment parameter is greater than the preset speed adjustment threshold, control the power increase amplitude to obtain the target increase value; then, according to the target increase value and the operation state data of the main heating stage, determine the power switching timing of the main heating stage; then perform power switching according to the power switching timing of the main heating stage and obtain the real-time feedback data; then, according to the real-time feedback data, obtain the quantization value of the temperature distribution characteristics; if the quantization value of the temperature distribution characteristics is greater than the preset quantization value threshold, adjust the power of the main heating stage and obtain the updated operation state data; finally, according to the updated operation state data, obtain the stable power mode of the main heating stage.

[0092] Here, the power of the main heating stage is variable, and power switching needs to be performed at an appropriate time. Specifically, the initial temperature distribution of the main heating stage is collected by a sensor array. The sensor array covers multiple areas on the surface of the workpiece (steel pipe). As an example: the monitoring point data are 80°C, 78°C, and 82°C respectively, indicating that there are slight differences in the temperature distribution. It should be noted that when collecting distribution data, the layout of the sensor array needs to cover the key areas of the workpiece. For example, on a cylindrical workpiece, one point is set at the top, middle, and bottom respectively to ensure comprehensive data.

[0093] Regarding the temperature distribution characteristics, it is necessary to analyze in combination with the internal heat penetration requirements. The area with the highest surface temperature of 82°C may have faster heat penetration, while the 78°C area may be slower. Based on this, by calculating the power increase speed, the speed adjustment parameter can be obtained. As an example: the ideal increase speed is 10°C per minute, while the actual monitoring shows that it is only 8°C in a certain area, and the speed adjustment parameter can be set to increase the power increase speed by 20%. If the speed adjustment parameter exceeds the preset threshold, for example, the upper limit is 25%, then the power increase amplitude needs to be adjusted through the medium-frequency power supply control logic. Thus, the output corresponding to the initial power increase speed is 500W per minute. After exceeding the threshold, the adjustment amplitude is reduced by 15% to obtain the optimized target increase value. This adjustment ensures the controllability of the power output. Without adjustment, too fast a power increase may lead to too high a surface temperature and insufficient internal temperature rise. According to the optimized target increase value, it is particularly important to match the operating state of the main heating stage. As an example: after entering the main heating, the load gradually increases, and the combination of the initial frequency of 60Hz and the current of 230A needs to be re-evaluated. Suppose the surface temperature rises to 120°C at this time, and the internal temperature is only 50°C, the power switching timing needs to be advanced. It can be understood that the judgment of the power switching timing depends on real-time feedback. For example, when the internal temperature sensor shows 60°C, it indicates that the heat penetration has reached a certain depth and it is suitable to increase the power to 600W per minute.

[0094] In a possible implementation, the calculation of the speed adjustment parameter can also refer to historical data. As an example, in the previous run, when the power increase speed was 12°C per minute, the internal temperature distribution was uniform. This time, it can be used as a benchmark and fine-tuned to 13°C per minute. This method improves the heat penetration efficiency.

[0095] It should be noted that the accuracy of the switching time point depends on the cooperation of sensor data and control logic. For example, when the surface temperature suddenly increases to 150°C, timely switching can avoid energy waste. This multi-link linkage strategy can provide a stable temperature basis for subsequent processes.

[0096] Furthermore, according to the power switching timing in the main heating stage, power switching is performed. Data collection is carried out through a sensor array to obtain real-time feedback data, and the dynamic change characteristics of the temperature distribution are determined. The uniformity index is used to calculate the dynamic change characteristics to obtain the quantization value of the distribution characteristics. The quantization value is compared with the preset quantization value threshold. If it exceeds the preset quantization value threshold, an adjustment value of the power output is generated through the control logic. The power output is updated according to the adjustment value to obtain the updated operating state data. The formation trend of the stable mode is analyzed through the operating state data, and the formation time point is judged. The time series analysis algorithm is applied to the formation time point to determine the duration of the stable mode. The sampling frequency of the real-time feedback is adjusted using the duration data to obtain the optimized temperature distribution data to obtain the stable power mode in the main heating stage.

[0097] Specifically, in an intermediate frequency heating device, the sensor array is arranged on the surface of the workpiece, covering the top, middle and bottom, and temperature data is collected once per second to obtain real-time feedback. The top temperature is 85 °C, the middle is 80 °C, and the bottom is 78 °C, indicating that there are dynamic changes in the distribution. After obtaining these data, analysis can be carried out through the uniformity index. The uniformity index can be defined as the percentage deviation of the temperature in each area from the average value. If the average temperature is 81 °C, the deviation at the top is 4.9%, and the deviation at the bottom is 3.7%. The quantified value is about 4% of the average deviation. Comparing the quantified value with the preset quantified value threshold, assuming the preset quantified value threshold is 5%. The current quantified value of 4% does not exceed, but is close to the upper limit, and subsequent changes need to be monitored. If it exceeds, for example, the quantified value reaches 6%, the control logic will generate an adjustment value.

[0098] It can be understood that the analysis of the operating state data can reveal the formation trend of the stable mode.

[0099] In one embodiment, if the temperature fluctuation range remains within 2 °C for 3 minutes, it indicates that the stable mode is initially formed. For the formation time point, for example, at the 6th minute, applying the time series analysis algorithm and observing the smoothness of the temperature change curve, it is determined that the stable mode duration is from the 6th to the 10th minute.

[0100] It should be noted that the data in the duration can be used to adjust the sampling frequency. As an example: if the initial frequency is once per second, it can be reduced to once every 2 seconds after stabilization. The optimized temperature distribution data is more focused on key changes. After optimizing the sampling frequency, data collection is more efficient. As an example: the temperature at the top of the steel pipe slowly rises from 85 °C to 90 °C, and the bottom rises from 78 °C to 82 °C, the distribution uniformity improves, and the average deviation drops to 2.5%. This method reduces the collection of invalid data while ensuring the accuracy of real-time feedback.

[0101] In this embodiment, if the sampling frequency is not adjusted, too high a collection rate may lead to data redundancy, while too low a rate may miss dynamic changes. Through multi-faceted analysis, the power adjustment and sampling optimization work together to ensure the controllability of the temperature distribution.

[0102] In one embodiment, if the workpiece load increases, the stable mode duration may be shortened to 2 minutes. At this time, the sampling frequency can be dynamically increased to 2 times per second to capture the change characteristics in a timely manner. This flexible adjustment provides a reliable basis for the subsequent heating process.

[0103] S500, according to the target power distribution ratio, obtain the heat preservation power mode in the heat preservation stage.

[0104] Specifically, first, determine the start time of the heat preservation stage according to the target power distribution ratio; second, in response to the start of the heat preservation stage, obtain the matching degree between the real-time monitoring data of the heat preservation stage and the target temperature corresponding to the heat preservation stage; then, obtain the power adjustment value of the heat preservation stage according to the matching degree; then, adjust the power output of the heat preservation stage according to the power adjustment value of the heat preservation stage to obtain the adjusted operation state data of the heat preservation stage; then, determine the trend stable point according to the adjusted operation state data of the heat preservation stage; according to the trend stable point, apply the time series analysis algorithm to obtain the initial power sequence interval; adjust the sampling frequency of the real-time monitoring data according to the initial power sequence interval; finally, obtain the heat preservation power mode according to the adjusted sampling frequency of the real-time monitoring data.

[0105] Here, the real-time monitoring data is collected by the sensor to determine the matching degree between the temperature and the target temperature. Calculate the adjustment requirement for the power fine-tuning according to the matching degree to obtain the initial value of the fine-tuning frequency. Perform an adjustment operation on the power output using the initial value to obtain the adjusted operation data. Judge the dynamic change trend of the power sequence through the operation data to determine the trend stable point. Apply the time series analysis algorithm to the trend stable point to obtain a stable power sequence interval. Adjust the sampling frequency of the real-time monitoring according to the interval data to obtain the optimized fine-tuning frequency. Update the power sequence with the optimized fine-tuning frequency to determine the final heat preservation power mode.

[0106] Specifically, similar to the above preheating stage and main heating stage, on the basis of the initially determined target power distribution ratio, the actual power output of the heat preservation stage is adjusted in real time through the actual monitoring data. As an example: the deviation at the top is 2°C from the target, and the deviation at the bottom reaches 4°C, indicating that the power needs to be fine-tuned to narrow the gap. The matching degree can be initially quantified by the average deviation. For example, the current average deviation is 3°C. Assuming that the preset fine-tuning threshold of the system is 2°C, the adjustment requirement needs to be calculated. The adjustment requirement can be determined according to the deviation ratio. For example, since the deviation at the bottom is larger, the power needs to be increased by about 5%. The initial fine-tuning frequency can be set to adjust once per minute, and the power is increased from 500W to 525W. The adjusted operation data shows that the bottom temperature rises to 88°C and the rising speed increases somewhat. The collection of the operation data provides a basis for the subsequent trend judgment. Judging the dynamic change trend of the power sequence through the operation data is the key. If the temperature gradually approaches the target after the power adjustment and the fluctuation drops from 3°C to 1.5°C, it indicates that the trend is approaching stability.

[0107] In one embodiment, the trend stable point can be defined as the temperature fluctuation remaining within 1°C for 3 consecutive minutes, assuming it appears at the 5th minute. For the trend stable point, applying the time series analysis algorithm can further confirm the stable interval of the power sequence. This interval provides data support for subsequent optimization. Adjusting the sampling frequency of the real-time monitoring according to the interval data can improve the efficiency.

[0108] Exemplarily, the initial frequency is 1 time per second, and after stabilization, it can be reduced to 1 time per 2 seconds, reducing redundant data. The optimized fine-tuning frequency updates the power sequence. For example, the adjustment frequency changes from once per minute to fine-tuning once every 2 minutes, and the final heat preservation power mode is determined to be 515W. Preferably, this adjustment ensures that the temperature is maintained at 90°C ± 0.5°C for a long time, and the workpiece is heated more evenly. In a possible implementation, if the change in the workpiece material causes a difference in heat conduction, the sampling frequency can be dynamically increased to 2 times per second to capture fluctuations in a timely manner. This flexibility provides a reliable guarantee for process optimization. In one embodiment, historical data shows that when the fine-tuning frequency is once every 90 seconds, the temperature matching degree is the highest and can be used as a reference further. It can be understood that the stable power mode not only improves the temperature control accuracy but also saves debugging time for subsequent production links. The synergistic effect of each link, from data acquisition to frequency optimization, forms a closed-loop control system to ensure the efficient and controllable heating process.

[0109] S600. According to the optimized power curve in the preheating stage, the stable power mode in the main heating stage, the heat preservation power mode in the heat preservation stage, and the temperature distribution data of the steel pipe heating process, a power adjustment response model is obtained; wherein, the power adjustment response model is used to adjust the power in the intermediate frequency heating process of the steel pipe.

[0110] Specifically, first, collect the temperature distribution data of the steel pipe heating process to determine the degree of realization of the dynamic balance ability; second, according to the degree of realization of the dynamic balance ability, the optimized power curve in the preheating stage, the stable power mode in the main heating stage, and the heat preservation power mode in the heat preservation stage, use an iterative algorithm to update the segmented control accuracy parameters to obtain a power adjustment response model.

[0111] In this embodiment, according to the power mode data and other data determined in each stage above, and the continuous feedback of real-time data, a power adjustment response model is constructed, which can realize the adjustment of the power in the intermediate frequency heating process of the steel pipe.

[0112] First, collect the temperature distribution data of the entire heating process to obtain a complete sequence of distribution data. Calculate the real-time change trend of energy input according to the distribution data, and determine the stable interval of the change trend. Use the data within the stable interval to judge the fluctuation range of input matching, and obtain the distribution characteristics of the matching degree. Through the distribution characteristics of the matching degree, obtain the dynamic adjustment demand of the thermal efficiency, and determine the target interval of efficiency optimization. For the efficiency optimization demand within the target interval, judge the degree of realization of dynamic balance.

[0113] Furthermore, a quantization sequence of the balance ability is obtained. Adjust the sampling frequency of the collected data according to the quantization sequence to obtain an optimized sequence of distribution data. Update the control strategy of energy input through the optimized sequence of distribution data to determine the final dynamic balance mode.

[0114] Here, the change trend can be observed through a time window. For example, the average change rate is calculated every minute to determine whether the trend tends to be stable. The stable interval may occur between the 4th and 6th minutes, at which time the temperature change range shrinks to ±1°C. When using the data within the stable interval to judge the fluctuation range of the input match, the proximity of the temperature to the target value needs to be analyzed. The matching degree can be quantified by the deviation range. For example, the average deviation at the top is 0.5°C and at the bottom is 1.5°C. To obtain the dynamic adjustment requirement of the thermal efficiency through the distribution characteristics of the matching degree, the effectiveness of energy utilization needs to be considered. The target interval can be set so that the temperature fluctuation is controlled within ±0.8°C. At this time, the efficiency optimization requirement is to reduce the loss of ineffective energy. When judging the degree of realization of dynamic balance for the efficiency optimization requirement within the target interval, it can be evaluated through the uniformity of the temperature distribution. If the top and bottom temperatures are both stable at 88°C ± 0.5°C after adjustment, the balancing ability can be quantified as a sequence, such as the uniformity index rising from 0.7 to 0.9.

[0115] In one embodiment, the initial frequency is 1 time per second, and it drops to 1 time per 3 seconds after stabilization. The optimized distribution data sequence shows smoother temperature fluctuations. As an example: After adjustment, the bottom temperature is maintained at 88°C, and the data redundancy is reduced by about 30%. When updating the control strategy of the energy input through the optimized distribution data sequence and determining the final dynamic balance mode, it can be flexibly adjusted according to real-time requirements.

[0116] It can be understood that this mode ensures the uniformity of workpiece heating, reduces energy consumption at the same time, and provides guarantee for the improvement of production efficiency.

[0117] Furthermore, by processing the comparison results of real-time data and historical data through an iterative algorithm, the initial precision parameters of segmented control are obtained. According to the initial precision parameters, the response model of power adjustment is adjusted to determine the dynamic change range of control precision. If the control precision exceeds the preset threshold, the adjustment parameters are updated through the iterative algorithm to obtain an optimized segmented control sequence. The optimized segmented control sequence is used to process real-time data to obtain the correction value of power adjustment. The response model is updated through the correction value to determine the real-time state of dynamic balance. According to the real-time state, the sampling frequency of historical data is adjusted to obtain an optimized comparison result. The input parameters of the iterative algorithm are updated through the optimized comparison result to determine the final control precision sequence. In order to obtain the final power adjustment response model.

[0118] The core of this method lies in using the difference of data to calibrate the control system.

[0119] As an example: In a power adjustment scenario of a heating device, the real-time data may show that the current temperature is 85 degrees, while the historical data indicates that the temperature should be stable at 90 degrees under the same power. This 5-degree deviation can be used as the starting point of the initial precision parameters.

[0120] The average deviation of 5 degrees can be calculated as the initial value by comparing the data collected multiple times. For example, the deviations of 5 consecutive measurements are 5 degrees, 4 degrees, and 6 degrees respectively. The advantage of this method is that it can quickly locate the starting error of the control system and provide a basis for subsequent adjustments. Adjust the response model of power adjustment according to the initial accuracy parameters to determine the dynamic change range of control accuracy.

[0121] The threshold is usually set based on equipment requirements. For example, the temperature fluctuation shall not exceed ±2 degrees. When the adjusted temperature still fluctuates to 93 degrees, exceeding the threshold, the iterative algorithm will re-analyze the comparison between real-time data and historical data. It may be found that the power increase is too fast, resulting in overshoot. So the increase amplitude is adjusted from 50W to 30W to form a new control sequence. The advantage of this iteration is to gradually approach the ideal state and improve the stability of the system. Process the real-time data using the optimized segmented control sequence to obtain the corrected value of power adjustment. At this time, the adjusted power of 30W can be applied to the equipment to observe whether the temperature is stable at about 90 degrees. If the temperature rises from 85 degrees to 89 degrees within 3 minutes, the corrected value may be further fine-tuned to 35W. The role of this corrected value is to achieve refined control and ensure that the real-time data gradually approaches the target. Update the response model through the corrected value to determine the real-time state of dynamic balance. The corrected power of 35W stabilizes the temperature at 90 degrees ±1 degree, and the response model will record this state, indicating that the system has entered dynamic balance. The determination of this real-time state provides a reliable basis for subsequent optimization and avoids blind adjustment. Adjust the sampling frequency of historical data according to the real-time state to obtain an optimized comparison result. If the temperature fluctuation is extremely small after the system stabilizes, the sampling frequency can be reduced from 1 time per second to 1 time per 5 seconds to reduce data redundancy. The optimized comparison result may show that the deviation between the newly sampled historical data and the real-time data is reduced to less than 1 degree. This adjustment can improve the efficiency of data processing. Update the input parameters of the iterative algorithm through the optimized comparison result to determine the final control accuracy sequence.

[0122] In one embodiment, after the deviation is reduced to 1 degree, the iterative algorithm will use this result as a new parameter to generate an accuracy sequence. For example, the temperature fluctuates within 90 degrees ±1 degree for 10 consecutive measurements. The generation of such a sequence helps to form a long-term stable control strategy and improve the reliability of the overall system.

[0123] Using the data obtained from a large number of different workpieces (the target power distribution ratios in the preheating stage, main heating stage, and heat preservation stage, the optimized power curve in the preheating stage; the stable power mode in the main heating stage; the heat preservation power mode in the heat preservation stage) and the corresponding real-time feedback data, a power adjustment response model is constructed, so that the obtained power adjustment response model is used to adjust the power in the intermediate frequency heating process of steel pipes.

[0124] Refer toFigure 2 , the second embodiment of the present invention provides a multi-parameter-based intermediate frequency heating temperature control system for steel pipes, including:

[0125] An acquisition module for acquiring real-time monitoring data during the intermediate frequency heating of the steel pipe;

[0126] A proportional acquisition module for obtaining the target power distribution ratios for the preheating stage, main heating stage, and heat preservation stage of the intermediate frequency heating of the steel pipe according to the real-time monitoring data;

[0127] A curve acquisition module for obtaining an optimized power curve for the preheating stage according to the target power distribution ratio;

[0128] A mode acquisition module for obtaining a stable power mode for the main heating stage according to the target power distribution ratio;

[0129] A sequence acquisition module for obtaining a heat preservation power mode for the heat preservation stage according to the target power distribution ratio;

[0130] A model acquisition module for obtaining a power adjustment response model according to the optimized power curve for the preheating stage, the stable power mode for the main heating stage, the heat preservation power mode for the heat preservation stage, and the temperature distribution data during the steel pipe heating process; wherein, the power adjustment response model is used to adjust the power during the intermediate frequency heating of the steel pipe.

[0131] It should be noted that a multi-parameter-based intermediate frequency heating temperature control system for steel pipes provided in the embodiments of the present invention is used to execute all the process steps of a multi-parameter-based intermediate frequency heating temperature control method in the above embodiments. Their working principles and beneficial effects correspond one by one, so they will not be elaborated here.

[0132] The embodiments of the present invention also provide an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a data acquisition program. When the processor executes the computer program, it implements the steps in the above embodiments of various multi-parameter-based intermediate frequency heating temperature control methods, such as Figure 1 the step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above device embodiments, such as the data acquisition module.

[0133] Exemplarily, the computer program can be divided into one or more modules / units. The one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the electronic device.

[0134] The electronic device may be a computing device such as a desktop computer, a notebook, a handheld computer, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above components are only examples of the electronic device and do not constitute a limitation on the electronic device. It may include more or fewer components than the above, or combine some components, or different components. For example, the electronic device may further include input / output devices, network access devices, a bus, etc.

[0135] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the electronic device and connects various parts of the entire electronic device through various interfaces and lines.

[0136] The memory may be used to store the computer program and / or module. The processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and by calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created according to the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0137] Among them, if the modules / units integrated in the electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0138] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0139] The above-described specific embodiments have further elaborated on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. It is particularly pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for controlling the intermediate frequency heating temperature of steel pipes based on multiple parameters, characterized in that, The method includes: Obtaining real-time monitoring data during the intermediate-frequency heating of the steel pipe; Performing threshold comparison and data adjustment based on the real-time monitoring data to obtain the target power distribution ratios for the preheating stage, main heating stage, and heat preservation stage of the intermediate-frequency heating of the steel pipe; Performing data conversion processing based on the target power distribution ratios to obtain the optimized power curve for the preheating stage; Performing data analysis and threshold comparison based on the target power distribution ratios to obtain the stable power mode for the main heating stage; Performing data matching and power adjustment based on the target power distribution ratios to obtain the heat preservation power mode for the heat preservation stage; Iteratively updating the segmented control precision parameters based on the optimized power curve for the preheating stage, the stable power mode for the main heating stage, the heat preservation power mode for the heat preservation stage, and the temperature distribution data during the steel pipe heating process to obtain a power adjustment response model; wherein, the power adjustment response model is used to adjust the power during the intermediate-frequency heating process of the steel pipe to control the temperature change during the intermediate-frequency heating process of the steel pipe.

2. The method for controlling the intermediate frequency heating temperature of steel pipes based on multiple parameters according to claim 1, wherein, Performing threshold comparison and data adjustment based on the real-time monitoring data to obtain the target power distribution ratios for the preheating stage, main heating stage, and heat preservation stage of the intermediate-frequency heating of the steel pipe, including: Obtaining the deviation value between the real-time monitoring data for the preheating stage and the corresponding target temperature; If the deviation value is greater than the preset deviation value threshold, adjusting the power distribution value for the preheating stage according to the deviation value to obtain the first power distribution ratio; Adjusting the power distribution value corresponding to the main heating stage according to the first power distribution ratio, the power adjustment signal for the main heating stage, and the real-time monitoring data for the main heating stage to obtain the second power distribution ratio; Adjusting the power distribution value corresponding to the heat preservation stage according to the second power distribution value and the real-time monitoring data for the heat preservation stage to obtain the third power distribution ratio; Optimizing the third power distribution ratio according to the adjusted power distribution value for the heat preservation stage and the historical power distribution value corresponding to the heat preservation stage to obtain the target power distribution ratio.

3. The method for controlling the intermediate frequency heating temperature of a steel pipe based on multiple parameters according to claim 2, wherein The power adjustment signal for the main heating stage is determined according to the following steps: Performing smoothing processing on the real-time monitoring data for the main heating stage to obtain the temperature change curve for the main heating stage; Obtaining a ratio set according to the ratio of the current energy input and the actual temperature rise corresponding to the temperature change curve for the main heating stage; If any ratio of the current energy input and the actual temperature rise in the ratio set is not within the preset ratio threshold range, it is determined as a key ratio; Substituting the key ratio into the preset power adjustment signal adjustment rule to generate a power adjustment signal; wherein, the power adjustment signal includes a power adjustment direction and a power adjustment amplitude.

4. The method for controlling the intermediate frequency heating temperature of steel pipes based on multiple parameters according to claim 1, wherein, The performing data conversion processing based on the target power distribution ratios to obtain the optimized power curve for the preheating stage includes: Performing calculation according to the target power distribution ratio to obtain the target power distribution value for the preheating stage; Performing calculation according to the target power distribution value for the preheating stage to obtain the target output frequency for the preheating stage; Matching the current according to the target output frequency for the preheating stage to obtain the target current value; Adjusting the initial power curve for the preheating stage according to the target current value and the target power distribution value for the preheating stage to obtain the optimized power curve for the preheating stage.

5. The method for controlling the intermediate frequency heating temperature of steel pipes based on multiple parameters according to claim 1, characterized in that, Performing data analysis and threshold comparison according to the target power distribution ratio to obtain the stable power mode in the main heating stage, including: Calculating according to the target power distribution ratio to determine the total initial power distribution in the main heating stage; In response to the start of the main heating stage, obtaining the initial temperature distribution characteristics of the main heating stage; Performing data analysis according to the initial temperature distribution characteristics to obtain the speed adjustment parameter; If the speed adjustment parameter is greater than the preset speed adjustment threshold, controlling the power increase amplitude to obtain the target increase value; Determining the power switching timing in the main heating stage according to the target increase value and the operation status data of the main heating stage; Performing power switching according to the power switching timing in the main heating stage and obtaining real-time feedback data; Calculating the uniformity index according to the real-time feedback data to obtain the quantization value of the temperature distribution characteristics; If the quantization value of the temperature distribution characteristics is greater than the preset quantization value threshold, adjusting the power in the main heating stage and obtaining the updated operation status data; Performing trend analysis of the stable mode according to the updated operation status data to obtain the stable power mode in the main heating stage.

6. The method for controlling the intermediate frequency heating temperature of steel pipes based on multiple parameters according to claim 1, characterized in that Performing data matching and power adjustment according to the target power distribution ratio to obtain the heat preservation power mode in the heat preservation stage, including: Calculating according to the target power distribution ratio to determine the start time of the heat preservation stage; In response to the start of the heat preservation stage, obtaining the matching degree between the real-time monitoring data in the heat preservation stage and the target temperature corresponding to the heat preservation stage; Calculating according to the matching degree to obtain the power adjustment value in the heat preservation stage; Adjusting the power output in the heat preservation stage according to the power adjustment value in the heat preservation stage to obtain the adjusted operation status data of the heat preservation stage; Determining the trend stable point according to the adjusted operation status data of the heat preservation stage; According to the trend stable point, applying the time series analysis algorithm to obtain the initial power sequence interval; Adjusting the sampling frequency of the real-time monitoring data according to the initial power sequence interval to obtain the heat preservation power mode.

7. The method for controlling the intermediate frequency heating temperature of a steel pipe based on multiple parameters according to claim 1, characterized in that, Iteratively updating the segmented control accuracy parameters according to the optimized power curve in the preheating stage, the stable power mode in the main heating stage, the heat preservation power mode in the heat preservation stage, and the temperature distribution data in the steel pipe heating process to obtain the power adjustment response model, including: Collecting the temperature distribution data in the steel pipe heating process; Calculating according to the temperature distribution data in the steel pipe heating process and judging the fluctuation range to determine the realization degree of the dynamic balance ability; According to the realization degree of the dynamic balance ability, the optimized power curve in the preheating stage, the stable power mode in the main heating stage, and the heat preservation power mode in the heat preservation stage, using the iterative algorithm to update the segmented control accuracy parameters to obtain the power adjustment response model; the power adjustment response model is used to adjust the power in the intermediate frequency heating process of the steel pipe to control the temperature change in the intermediate frequency heating process of the steel pipe.

8. A medium-frequency heating temperature control system for steel pipes based on multiple parameters, characterized in that, The system includes: An acquisition module, configured to acquire real-time monitoring data during the intermediate frequency heating of the steel pipe; A ratio acquisition module, configured to perform threshold comparison and data adjustment according to the real-time monitoring data to obtain the target power distribution ratios of the preheating stage, the main heating stage, and the heat preservation stage of the intermediate frequency heating of the steel pipe; A curve acquisition module, configured to perform data conversion processing according to a target power distribution ratio to obtain an optimized power curve in the preheating stage; A mode acquisition module, configured to perform data analysis and threshold comparison according to a target power distribution ratio to obtain a stable power mode in the main heating stage; A sequence acquisition module, configured to perform data matching and power adjustment according to a target power distribution ratio to obtain a heat preservation power mode in the heat preservation stage; A model acquisition module, configured to update the segmented control precision parameters by using an iterative algorithm according to the optimized power curve in the preheating stage, the stable power mode in the main heating stage, the heat preservation power mode in the heat preservation stage, and the temperature distribution data in the steel pipe heating process, so as to obtain a power adjustment response model; wherein, the power adjustment response model is used to adjust the power in the intermediate frequency heating process of the steel pipe to control the temperature change in the intermediate frequency heating process of the steel pipe.

9. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the multi-parameter-based intermediate frequency heating temperature control method for steel pipes according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the multi-parameter-based intermediate frequency heating temperature control method for steel pipes according to any one of claims 1 to 7.

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