Galvanized steel strand waste heat recovery temperature control intelligent method and system

Abstract

The invention discloses a galvanized steel strand waste heat recovery temperature control intelligent method and a galvanized steel strand waste heat recovery temperature control intelligent system, and particularly relates to the field of industrial adaptive control systems, temperature fluctuation trend is analyzed and smoothed by collecting temperature data such as cooling speed, heat exchange efficiency and heating power in real time, and temperature change is analyzed and predicted in combination with a trend line. And on the basis of prediction data, parameters of the temperature control system are adjusted, whether the temperature fluctuation exceeds a set range or not is judged by comparing the temperature fluctuation in real time, and temperature control strategy optimization is automatically carried out, so that accurate temperature adjustment and energy efficiency maximization are realized. Accurate prediction and real-time adjustment of temperature changes are achieved, and by dynamically optimizing parameters such as the cooling speed, the heat exchange efficiency and the heating power, the temperature control precision is effectively improved, and energy waste is reduced. The system can adapt to temperature fluctuation, manual intervention is reduced, production efficiency and stability are improved, efficient operation of the temperature control system in a complex production environment is ensured, and the overall energy utilization rate and production benefits are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of industrial adaptive control systems, particularly to a galvanized steel strand waste heat recovery temperature control intelligent method and system. BACKGROUND

[0002] The technical field of industrial adaptive control systems includes automation control, process control and regulation systems, and is widely used in various fields of industrial production, especially in situations that require the adjustment of physical quantities such as temperature, pressure, flow, etc. The core content of this technical field is to use adaptive control algorithms to enable the system to automatically adjust its parameters according to different working environments and external disturbances, in order to achieve optimal control. Adaptive control systems usually rely on feedback information from the system to automatically adjust under changing operating conditions, to ensure that the system can still operate stably under unstable or changing conditions. This technical field has a wide range of applications, including chemical, mechanical, electrical and other industries, and is an important part of modern industrial automation and intelligent manufacturing.

[0003] Among them, the galvanized steel strand waste heat recovery temperature control intelligent method refers to the method of recycling the waste heat generated during the production process of galvanized steel strand and adjusting the temperature through an intelligent control system, which covers real-time monitoring and control of temperature during waste heat recovery. Using temperature sensors and adaptive control algorithms, the production environment temperature of steel strand is accurately adjusted. This method uses adaptive control technology to automatically adjust the parameters of the control system in combination with the recycling benefits of waste heat, to ensure that the temperature control of waste heat recovery is within the appropriate range. This method can dynamically adjust the control strategy according to real-time data during the production process of steel strand, improve the accuracy of temperature control, and maximize energy utilization during the production process.

[0004] In the application process of the prior art, temperature adjustment is often dependent on a simple feedback mechanism, lacking accurate prediction and real-time adjustment of temperature trends. Typically, the system adjusts the control strategy based on real-time temperature data, but this method often ignores long-term trends in temperature fluctuations and differences in fluctuations during different production stages, resulting in inaccurate temperature control and an inability to respond to various changes in complex production processes. For example, in the case of rapid temperature changes, simply relying on current temperature values for adjustment can result in overcooling or overheating, causing energy waste or affecting product quality. In addition, existing technologies often lack automated adjustment mechanisms, and when the temperature exceeds the set range, manual intervention is often required to adjust the parameters again, which not only increases labor costs, but also may result in delayed adjustment and affect production efficiency. Because multiple parameters such as cooling speed, heat exchange efficiency, and heating power are not dynamically optimized and adjusted in real time, the temperature control system of the prior art has the problems of insufficient flexibility and slow response, and is unable to adapt to rapidly changing production demands, thereby affecting overall production efficiency. SUMMARY

[0005] The main purpose of the present application is to provide a zinc-plated steel strand waste heat recovery temperature control intelligent method and system, which solves the problems of insufficient precision, slow response and energy waste of traditional temperature control systems through real-time temperature data acquisition, trend prediction and dynamic adjustment mechanism, and realizes more accurate and efficient temperature control.

[0006] To achieve the above purpose, the technical scheme adopted by the present application is: A zinc-plated steel strand waste heat recovery temperature control intelligent method and system, the method comprising: Through the sensor installed on the zinc-plated steel strand production line, real-time production temperature data is obtained, the temperature fluctuation range of each production stage is obtained, and temperature change trend data is generated; the real-time production temperature data includes cooling speed, heat exchange efficiency, heating power data; Based on the temperature change trend data, the temperature data fluctuation is smoothed, combined with the real-time production temperature data, the temperature change trend in the future period of time is predicted through trend line analysis, and the temperature prediction change trend value is generated; Based on the temperature prediction change trend value, the cooling speed, heat exchange efficiency and heating power parameters of the temperature control system that need to be adjusted are calculated, and the adjusted temperature control strategy result is generated; According to the temperature control strategy result, through real-time temperature data analysis, whether the temperature fluctuation trend exceeds the set range is compared, if it exceeds the set range, the cooling speed, heat exchange efficiency and heating power data are adjusted according to the comparison result, and the temperature adjustment trend result is obtained.

[0007] Preferably, the temperature change trend data includes temperature fluctuation amplitude, production stage difference, time interval, the temperature prediction change trend value is specifically temperature growth trend value, temperature weakening trend value and temperature stable trend value, and the temperature adjustment trend result is specifically temperature offset amplitude, temperature correction interval and temperature stability level.

[0008] Preferably, the sensor installed on the zinc-plated steel strand production line is used to obtain real-time production temperature data, obtain the temperature fluctuation range of each production stage, and generate temperature change trend data, which comprises: Through the zinc-plated steel strand production line sensor, the cooling speed, heat exchange efficiency and heating power in the production process are collected, the temperature fluctuation range of each production stage is obtained, combined with the real-time production temperature data, the temperature fluctuation amplitude is calculated, the time interval is divided, and the temperature fluctuation amplitude and time interval data are obtained; Based on the temperature fluctuation amplitude and time interval data, the temperature difference of each production stage is analyzed, the temperature fluctuation difference value of different stages is statistically compared, and the production stage difference analysis result is obtained; Based on the production stage difference analysis result, the temperature change trend data is generated by combining the real-time production temperature data, and the temperature change trend data is obtained.

[0009] Preferably, the method for calculating the temperature fluctuation amplitude is to statistically collect the cooling speed, heat exchange efficiency and heating power data, and calculate the difference between the maximum and minimum values of the cooling speed, heat exchange efficiency and heating power data, respectively. The method for dividing the time period interval is to divide the time period according to the fluctuation trend of the temperature fluctuation, and the fluctuation trend includes the temperature rising trend and the temperature falling trend.

[0010] Preferably, the temperature data fluctuation is smoothed based on the temperature change trend data, the real-time production temperature data is combined, the temperature change trend in the future period of time is predicted through trend line analysis, and the temperature prediction change trend value is generated, including: The temperature data fluctuation is smoothed by collecting the temperature change trend data, short-term fluctuations and noise are removed, and then the smoothed temperature fluctuation data is obtained. The trend line analysis is combined with the real-time production temperature data, the temperature fluctuation trend in the historical data is analyzed, the temperature change trend in the future period of time is inferred, and the temperature trend prediction value is obtained. Based on the temperature trend prediction value, the temperature prediction value is compared with the real-time cooling speed, heat exchange efficiency and heating power data, the future temperature change trend is verified, and the temperature prediction change trend value is generated.

[0011] Preferably, the temperature prediction value is compared with the real-time cooling speed, heat exchange efficiency and heating power data based on the temperature trend prediction value, and the future temperature change trend is verified, specifically: The real-time cooling speed, heat exchange efficiency and heating power data are obtained, the relationship of the temperature prediction change trend is analyzed, the temperature change caused by the cooling speed, heat exchange efficiency and heating power data in the temperature change trend period is calculated, and the absolute value of the temperature value difference in the temperature prediction change trend is obtained, and the temperature prediction change trend value is obtained.

[0012] Preferably, the cooling speed, heat exchange efficiency and heating power parameters of the temperature control system that need to be adjusted are calculated based on the temperature prediction change trend value, the adjusted temperature control strategy result is generated, and specifically: Based on the temperature prediction change trend value, the temperature change direction is judged: if the temperature change direction is rising, any one or several of the methods of increasing the cooling speed, increasing the heat exchange efficiency and reducing the heating power is used to adjust the temperature control parameters of the temperature control equipment. If the temperature change direction is cooling, the temperature control parameters of the temperature control device are adjusted by any one or several of the following ways: reducing the cooling speed, reducing the heat exchange efficiency, and increasing the heating power; The adjusted temperature change value is equal to the absolute value of the difference in the temperature trend change period, and the adjustment parameters of the cooling speed, the heat exchange efficiency, and the heating power are the adjusted temperature control strategy results.

[0013] Preferably, the temperature control strategy result is obtained by analyzing whether the temperature fluctuation trend exceeds the set range based on real-time temperature data, and if the temperature fluctuation exceeds the set range, the heat exchange efficiency or the cooling time is fine-tuned to obtain a temperature adjustment trend result, including: According to the temperature control strategy result, real-time temperature data is obtained and monitored, the temperature fluctuation trend is analyzed, and it is judged whether the adjusted temperature exceeds the set temperature control range, if not, the adjustment is performed according to the temperature control strategy result; if it exceeds the range, the temperature change amplitude is recorded and temperature fluctuation out-of-range data is generated; Based on the temperature fluctuation out-of-range data, it is further judged whether the temperature change direction is changed, and the heat exchange efficiency or the cooling time is adjusted again according to the change direction and the adjusted temperature fluctuation is monitored to confirm whether the temperature change is restored to the set range, and finally the temperature adjustment trend result is obtained.

[0014] A galvanized steel strand waste heat recovery temperature control intelligent system for executing the above method, the system comprising: A data acquisition module and an analysis module obtain real-time production temperature data including cooling speed, heat exchange efficiency, and heating power data through sensors installed on the galvanized steel strand production line, obtain the temperature fluctuation range of each production stage, and generate temperature change trend data; A temperature prediction module performs smoothing processing on the temperature fluctuation based on the temperature change trend data, predicts the temperature change in a future period of time through trend line analysis, and generates a temperature prediction change trend value in combination with real-time production temperature data; A temperature control strategy adjustment module calculates the cooling speed, heat exchange efficiency, and heating power parameters of the temperature control system that need to be adjusted according to the temperature prediction change trend value, and generates a temperature control strategy result; A temperature adjustment monitoring module analyzes whether the temperature fluctuation trend exceeds the set range based on the temperature control strategy result in combination with real-time temperature data, and fine-tunes the heat exchange efficiency or the cooling time if the temperature fluctuation exceeds the set range, and obtains a temperature adjustment trend result.

[0015] Compared with the prior art, the present application has the following beneficial effects: The present application can accurately reflect the actual situation of temperature change in the production process by collecting production data such as temperature, cooling speed, heat exchange efficiency, heating power, etc. in real time. After smoothing processing, these data can effectively eliminate short-term fluctuations and noise, thus revealing the real temperature change trend and making temperature prediction through trend line analysis. This processing logic not only can predict the future temperature change trend based on the current temperature fluctuation, but also can accurately calculate the required adjustment parameters to dynamically optimize the temperature control strategy. Based on the comparison and trend analysis of real-time temperature data, the system can determine whether the temperature is out of the set range and automatically adjust when the temperature fluctuation exceeds, ensuring the adaptability of the temperature control system. Through this dynamic adjustment mechanism, the temperature fluctuation in the production process is effectively controlled, avoiding the production quality problems caused by inaccurate temperature control, reducing energy waste and improving energy utilization efficiency. In addition, the system can monitor and adjust temperature fluctuations in real time, making temperature control more accurate in a larger range, reducing the need for human intervention, improving production efficiency and saving costs. Through this highly automated and intelligent temperature control, the stability of the production process is ensured, and the overall performance and energy efficiency of the production line are greatly improved. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 Flow chart of the steps of the galvanized steel strand waste heat recovery temperature control intelligent method in some embodiments of the present application; Figure 2 Process diagram for obtaining temperature change trend data in some embodiments of the present application; Figure 3 Process diagram for obtaining temperature prediction change trend value in some embodiments of the present application; Figure 4 Process diagram for obtaining temperature control strategy adjustment in some embodiments of the present application; Figure 5 Process diagram for obtaining temperature adjustment trend result in some embodiments of the present application. DETAILED DESCRIPTION

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some examples or embodiments of the present application, and for those skilled in the art, the present application can also be applied to other similar scenarios without creative labor. Unless it is obvious from the language environment or otherwise stated, the same reference numbers in the figures represent the same structures or operations.

[0018] It should be understood that the terms "system," "device," "unit," and / or "module" as used in this specification are a method of distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0019] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0020] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0021] It should be noted that the following equipment is required for this solution: Sensor devices: Temperature sensor, used to acquire temperature data at each stage of the production process in real time; Cooling rate sensor, used to acquire cooling rate data in real time; Heat exchange efficiency sensor, used to acquire heat exchange efficiency data; Heating power sensor, used to acquire heating power data.

[0022] Data acquisition equipment: used to collect real-time production temperature data, cooling rate, heat exchange efficiency, heating power and other information from sensors.

[0023] The temperature control devices in this solution include the following: Cooling system: This equipment is used to control the cooling rate during the production process. By adjusting the cooling rate, the temperature is reduced to ensure that the temperature does not become too high during the production process and to achieve the required temperature control effect.

[0024] Heat exchange system: Used to adjust heat exchange efficiency to ensure effective heat recovery and utilization during production. By adjusting heat exchange efficiency, energy use is optimized and the required temperature is maintained.

[0025] Heating system: Used to provide the necessary heating power to ensure that the temperature does not fall below the required level. The temperature is increased by adding heating power when needed to maintain the required temperature level during production.

[0026] It should be noted that the above-mentioned equipment in this solution can all be equipment that has been disclosed in the prior art. This solution mainly provides an intelligent method for temperature control of the above-mentioned equipment by providing a waste heat recovery method for galvanized steel strand.

[0027] This invention discloses an intelligent system for waste heat recovery and temperature control of galvanized steel strand, the system comprising: The data acquisition and analysis modules acquire real-time production temperature data, including cooling rate, heat exchange efficiency, and heating power data, through sensors installed on the galvanized steel strand production line. They also acquire the temperature fluctuation range at each production stage and generate temperature change trend data. The temperature prediction module smooths temperature fluctuations based on temperature change trend data, predicts temperature changes over a future period through trend line analysis, and generates predicted temperature trend values ​​by combining real-time production temperature data. The temperature control strategy adjustment module calculates the cooling rate, heat exchange efficiency, and heating power parameters of the temperature control system that need to be adjusted based on the predicted temperature change trend, and generates the temperature control strategy results. The temperature adjustment monitoring module analyzes whether the temperature fluctuation trend exceeds the set range based on the temperature control strategy results and real-time temperature data. If the temperature fluctuation exceeds the set range, it fine-tunes the heat exchange efficiency or cooling time to obtain the temperature adjustment trend results.

[0028] The intelligent method for waste heat recovery and temperature control of galvanized steel strand provided in the embodiments of this specification will be described in detail below with reference to the accompanying drawings.

[0029] Figure 1 This is an exemplary flowchart of an intelligent method for waste heat recovery and temperature control of galvanized steel strand according to some embodiments of this specification. In some embodiments, the intelligent method for waste heat recovery and temperature control of galvanized steel strand can be executed by processing logic, which may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (instructions running on a processing device to execute hardware simulations), and any combination thereof. In some embodiments, Figure 1 One or more operations in the flowchart of the intelligent method for waste heat recovery and temperature control of galvanized steel strand shown can be implemented by processing equipment and / or terminal equipment. For example, the intelligent method for waste heat recovery and temperature control of galvanized steel strand can be stored in a storage device in the form of computer programs and / or instructions, and can be invoked and / or executed by processing equipment and / or terminal equipment.

[0030] Specifically, such as Figure 1 As shown, in some embodiments of the present invention, the intelligent method for waste heat recovery and temperature control of galvanized steel strand includes steps 1-4: Step 1: Acquire real-time production temperature data by using sensors installed on the galvanized steel strand production line, obtain the temperature fluctuation range of each production stage, and generate temperature change trend data; real-time production temperature data includes cooling rate, heat exchange efficiency, and heating power data. The temperature change trend data includes the temperature fluctuation range, differences in production stages, and time intervals.

[0031] Step 2: Based on temperature change trend data, smooth the temperature data fluctuations. Combined with real-time production temperature data, predict the temperature change trend in the future period through trend line analysis, and generate temperature prediction trend values. The temperature prediction trend values ​​are specifically temperature increase trend values, temperature decrease trend values, and temperature stabilization trend values.

[0032] Step 3: Based on the predicted temperature change trend, calculate the cooling rate, heat exchange efficiency, and heating power parameters of the temperature control system that need to be adjusted, and generate the adjusted temperature control strategy results; the adjusted temperature control strategy results include the cooling rate setpoint, heat exchange efficiency coefficient, and heating power parameters; Step 4: Based on the temperature control strategy results, analyze and compare the real-time temperature data to see if the temperature fluctuation trend exceeds the set range. If it does, adjust the heat cooling rate, heat exchange efficiency, and heating power data according to the comparison results to obtain the temperature adjustment trend results. The temperature adjustment trend results specifically refer to the temperature deviation amplitude, temperature correction range, and temperature stability level.

[0033] like Figure 2 As shown, in some embodiments of the present invention, the generation of temperature change trend data is achieved through the following sub-steps, the process of which is executed by the data acquisition module and analysis module described above: Sub-step 11: Using sensors on the galvanized steel strand production line, collect data on cooling rate, heat exchange efficiency, and heating power during the production process to obtain the temperature fluctuation range at each production stage. Combine this with real-time production temperature data to calculate the temperature fluctuation amplitude, divide the time intervals, and obtain the temperature fluctuation amplitude and time interval data. In actual production, sensors installed at key locations on the galvanized steel strand production line continuously monitor various temperature-related data, such as cooling rate, heat exchange efficiency, and heating power. For example, sensors might record real-time data such as a cooling rate of 10°C per minute, a heat exchange efficiency of 85%, and a heating power of 500W. Based on this real-time data, the temperature fluctuation range at each production stage can be calculated.

[0034] In this embodiment of the invention, the temperature fluctuation amplitude is calculated as follows: Real-time collected data on cooling rate, heat exchange efficiency, and heating power are statistically analyzed, and the differences between the maximum and minimum values ​​of these data are calculated respectively. For example, the cooling rate in one production stage might be 5°C / min, while in another it might be 8°C / min; the heat exchange efficiency might fluctuate between 80% and 90%; and the heating power might be between 400W and 500W. By acquiring this data, we can calculate the difference between the maximum and minimum values ​​for each parameter. For instance, the maximum cooling rate is 8°C / min, and the minimum is 5°C / min, so the fluctuation range is 3°C / min. The maximum heat exchange efficiency is 90%, and the minimum is 80%, so the fluctuation range is 10%. The maximum heating power is 500W, and the minimum is 400W, so the fluctuation range is 100W. Finally, the difference between the maximum and minimum values ​​of these three parameters is obtained to further analyze and optimize temperature fluctuation control in the production process.

[0035] Furthermore, in this embodiment of the invention, the time period is divided as follows: the time period is divided according to the magnitude of the temperature fluctuation and the fluctuation trend, wherein the fluctuation trend includes a warming trend and a cooling trend. For example, sensors monitor changes in cooling rate, heating power, and heat exchange efficiency to obtain the actual temperature fluctuation range. In this process, the direction of the temperature fluctuation is first determined by comparing the current temperature value with the temperature value of the previous sampling period to determine whether it is an upward or downward trend. If the current temperature value is higher than the previous value, it indicates an upward trend; conversely, it indicates a downward trend. For example, during a certain cooling phase, if the sensor records the temperature rising from 500℃ to 520℃, this indicates an upward trend; if the temperature subsequently drops from 520℃ to 510℃, it indicates a downward trend. For heating trends, time periods can be divided by analyzing the rate of temperature change. Heating phases with large temperature fluctuations may require shorter time periods to capture detailed changes. For example, if the heating rate is 5°C per minute and the fluctuation is large (e.g., 20°C), the time period might be divided into 5-minute intervals to ensure real-time adjustment of heating power. Conversely, for heating phases with smaller temperature changes and smaller fluctuations (e.g., only 5°C), the time period can be extended, for example, into 10-minute intervals. Cooling trends can be similarly divided into time periods. Assuming a cooling rate of 3°C per minute, when the temperature drop is large, such as 30°C, the time period can be divided into 2-minute intervals to precisely control the cooling rate and prevent the temperature from dropping too quickly. When the temperature drop is small, longer time periods, such as 8-minute intervals, might be used.

[0036] In this way, the production line can flexibly respond to temperature changes and avoid production problems caused by temperature control failure, such as unstable surface quality of steel strands due to rapid temperature changes. Ultimately, based on real-time data collection and temperature fluctuation trends, the entire production process can be rationally segmented, providing data support for subsequent temperature control optimization.

[0037] Sub-step 12: Based on the temperature fluctuation amplitude and time interval data, analyze the temperature differences in each production stage, statistically compare the temperature fluctuation differences in different stages, and obtain the production stage difference analysis results. This step requires summarizing and analyzing the temperature fluctuation range of each production stage. For example, the temperature fluctuation range in the cooling stage might be 40℃, while the fluctuation range in the heating stage might be 30℃. By comparing the temperature fluctuation range of each stage, the temperature difference between different stages can be calculated; for example, the temperature fluctuation difference between the cooling and heating stages might be 10℃. This difference is crucial for optimizing the temperature control system because it reveals deviations in temperature management at each production stage. In practice, these differences can be used to assess the temperature control requirements of different stages and whether it is necessary to increase or decrease the cooling or heating intensity at a certain stage. For example, if the fluctuation range is large in the cooling stage, it may be necessary to improve the efficiency of the cooling system or adjust the cooling rate to reduce the impact of temperature fluctuations.

[0038] Sub-step 13: Based on the results of the production stage difference analysis and combined with real-time production temperature data, generate temperature change trend data to obtain temperature change trend data.

[0039] This step begins by combining the production stage difference analysis results obtained in step 12 with real-time production temperature data. For example, in a certain heating stage, the analysis shows a temperature fluctuation difference of 15℃, while in the cooling stage it is 10℃, indicating that the temperature control requirements for the heating stage are relatively higher. Based on this, and combined with real-time temperature data, such as a current temperature of 550℃, and the current cooling efficiency of the production line being 90% and the heating power being 600W, predicted temperature change trend data is generated.

[0040] Temperature trend data typically includes parameters such as temperature fluctuation amplitude, differences in production stages, and time intervals. This data provides a basis for subsequent temperature control adjustments. Based on real-time temperature data and the results of difference analysis, if it is predicted that the future temperature will exceed the set range, adjustment strategies may include increasing the cooling rate or increasing the heating power to ensure that the temperature control system throughout the production process can be maintained within a reasonable operating range.

[0041] like Figure 3As shown, in some embodiments of the present invention, step 2 is performed by the temperature prediction module mentioned above, specifically including sub-steps 21 to 23: Sub-step 21: By collecting temperature change trend data, smooth the temperature data fluctuations, remove short-term fluctuations and reduce noise, and thus obtain smoothed temperature fluctuation data. For example, in practice, each collected temperature data point first needs to be analyzed in detail. For instance, the temperature might fluctuate from 520℃ to 525℃ over a certain period, then drop back to 518℃. Such fluctuations may contain short-term random fluctuations or noise. Therefore, to obtain a more accurate temperature change trend, smoothing processing is required.

[0042] Common smoothing methods include moving averages and weighted averages. For example, a weighted average can be calculated for temperature values ​​at every 5 sampling points, with the most recent sampling point having a higher weight and earlier sampling points having a lower weight. Suppose the temperature data for a certain period is: 520℃, 522℃, 518℃, 523℃, 521℃. Applying a weighted average, 520℃ can be given a lower weight, such as 0.1, while 522℃ can have a higher weight, such as 0.3, and so on for other data. This method can eliminate short-term fluctuations, resulting in smoother temperature data. This smoothed data better reflects the long-term temperature trend during production, avoiding erroneous adjustments to the temperature control system due to short-term fluctuations.

[0043] Sub-step 22: Combine real-time production temperature data to perform trend line analysis, analyze the temperature fluctuation trend in historical data, infer the temperature change trend in the future period, and obtain the temperature trend prediction value. In practice, it's necessary to first obtain historical temperature fluctuation trends and perform trend line analysis on these data. For example, suppose the temperature data has shown a gradually increasing trend over a period of time. By performing linear regression analysis on the historical temperature data, a best-fit line can be obtained, thus inferring future temperature change trends. Assuming the historical temperature data are: 530°C, 540°C, 550°C, 560°C, and 550°C, the slope of the temperature change (e.g., 0.5°C / minute) is calculated using linear regression to predict the temperature value at the next moment. If the current temperature is 550°C and the slope is 0.5°C / minute, then the predicted temperature at the next moment is 550.5°C. Through trend line analysis, temperature changes over a future period can be predicted, obtaining a temperature trend forecast. If the analyzed trend is an upward trend, a target temperature can be further set to facilitate subsequent adjustments to the temperature control system.

[0044] Sub-step 23: Based on the temperature trend prediction value, compare the temperature prediction value with the real-time cooling rate, heat exchange efficiency, and heating power data to verify the future temperature change trend and generate the temperature prediction change trend value.

[0045] In detail, this step involves acquiring real-time cooling rate, heat exchange efficiency, and heating power data, analyzing their relationship with the predicted temperature change trend, calculating the absolute value of the difference between the temperature change caused by the cooling rate, heat exchange efficiency, and heating power data within the temperature change trend period and the temperature value in the predicted temperature change trend, and obtaining the predicted temperature change trend value.

[0046] For example, the first step is to collect real-time data on cooling rate, heat exchange efficiency, and heating power using sensors. This data reflects the actual operating status of the temperature control system during production. For instance, the cooling rate might be 3°C / min, the heat exchange efficiency 80%, and the heating power 350W over a certain period. Combining this with predicted temperature trends, future temperature changes are predicted based on historical data; for example, trend line analysis might predict a 5°C increase in temperature over a given period. Next, the temperature changes caused by the cooling rate, heat exchange efficiency, and heating power need to be calculated and compared with the predicted values. Suppose that in the future, due to a change in the cooling rate, the temperature will decrease by 1°C, while the increased heating power might cause a 2°C increase. Based on this data, the difference between the actual temperature change and the predicted value can be calculated. For example, if the predicted temperature increase is 5°C, but the actual calculation shows a 3°C increase, the difference is 2°C. The absolute value of this difference serves as a key reference point for the predicted temperature trend. Based on this, the trend of temperature increase, decrease, or stabilization can be further evaluated. If the calculated difference in temperature growth trends is large, it can be considered a temperature growth trend; if the difference is small and the temperature tends to stabilize, it is judged as a stable temperature trend; if the temperature difference indicates a temperature decrease, it is judged as a temperature weakening trend. Through this analysis, the temperature control strategy in the production process can be precisely controlled to ensure that temperature changes are within the set range, thereby avoiding production problems caused by excessive or insufficient temperature fluctuations.

[0047] like Figure 4 As shown, in some embodiments of the present invention, based on the predicted temperature change trend value, the cooling rate, heat exchange efficiency, and heating power parameters of the temperature control system that need to be adjusted are calculated, and the adjusted temperature control strategy result is generated, specifically as follows: Based on the predicted temperature trend, determine the direction of temperature change: If the temperature change is upward, adjust the temperature control parameters of the temperature control equipment by increasing the cooling rate, increasing the heat exchange efficiency, or reducing the heating power. If the temperature change is downward, adjust the temperature control parameters of the temperature control equipment by reducing the cooling rate, reducing the heat exchange efficiency, or increasing the heating power. This step is performed by the temperature control strategy adjustment module mentioned above. Its purpose is to make the adjusted temperature change value equal to the absolute value of the difference within the time period of temperature trend change. The adjustment parameters of cooling rate, heat exchange efficiency, and heating power are the results of the adjusted temperature control strategy.

[0048] For example, in this embodiment, the direction of the current temperature change is first identified by judging the temperature change trend value.

[0049] For example, assuming a predicted temperature rise of 5°C, appropriate measures are needed to control the rapid temperature increase. This can be achieved by adjusting one or more parameters, such as cooling rate, heat exchange efficiency, or heating power. For instance, if the cooling rate is 3°C / min, one could increase it to 5°C / min, improve the heat exchange efficiency from 80% to 90%, or reduce the heating power from 500W to 450W. These adjustments slow the temperature rise, ensuring the actual temperature change closely matches the predicted value, thus achieving the set temperature control target. On the other hand, if the temperature trend is downward, it is necessary to prevent the temperature from dropping too quickly by reducing the cooling rate, reducing heat exchange efficiency, or increasing heating power. For example, if the real-time cooling rate is 6°C / min and the predicted temperature drop is too rapid, potentially leading to unstable product quality, the cooling rate could be reduced to 4°C / min, or the heat exchange efficiency could be reduced from 85% to 75% to decrease the cooling rate. Additionally, the heating power can be moderately increased (e.g., from 400W to 450W) to provide heat and slow down the cooling process. Assuming that in this case, the original predicted temperature drop of 3°C is reduced, and the adjusted temperature change is controlled within the expected range of 1°C, then the adjustment strategy can be considered successful. Therefore, by dynamically adjusting temperature control parameters such as cooling rate, heat exchange efficiency, and heating power, it can be ensured that the temperature change trend conforms to the predicted direction and magnitude. The final adjusted temperature control strategy will ensure that the absolute value of the temperature change difference from the prediction is equal, thus maintaining the temperature control system within a reasonable operating range throughout the entire production process.

[0050] like Figure 5As shown, after obtaining the adjusted temperature control strategy results, the temperature is further fine-tuned based on the real-time status through the temperature adjustment monitoring module mentioned above. Specifically, based on the temperature control strategy results, the temperature fluctuation trend is analyzed through real-time temperature data to see if it exceeds the set range. If it does, the heat exchange efficiency or cooling time is fine-tuned to obtain the temperature adjustment trend result. This part is implemented through the following two sub-steps: Sub-step 41: Based on the temperature control strategy results, acquire and monitor real-time temperature data, analyze temperature fluctuation trends, and determine whether the adjusted temperature exceeds the set temperature control range. If it does not exceed the range, perform the adjustment according to the temperature control strategy results; if it exceeds the range, record the temperature change amplitude and generate temperature fluctuation exceeding the range data. First, the current temperature data needs to be collected in real time using a temperature sensor and compared with the set temperature control range. The set temperature control range is, for example, between 500℃ and 520℃. Suppose the collected real-time temperature data is 522℃, which exceeds the set temperature control range. In this case, the degree of temperature deviation needs to be recorded; in this case, it exceeds the range by 2℃, and a data point indicating temperature fluctuation exceeding the range is generated for subsequent adjustments to the temperature control system.

[0051] If the temperature is within the set range, the temperature control system will continue to perform adjustments according to the original strategy. If the temperature is within the set range, it may continue monitoring until the next data acquisition, and then reassess whether the temperature control strategy needs to continue.

[0052] Sub-step 43: Based on the temperature fluctuation data that exceeds the range, determine the direction of temperature change, adjust the heat exchange efficiency or cooling time according to the direction of change, monitor the temperature fluctuation after adjustment, confirm whether the temperature change has returned to the set range, and finally obtain the temperature adjustment trend result.

[0053] For example, assuming the current temperature is 522℃ and the temperature fluctuation trend is upward, the system will determine that the temperature change trend is rising, and therefore choose to reduce the heating power or increase the cooling efficiency. If the temperature fluctuation trend is downward, the system will choose to increase the heating power or reduce the cooling rate to adapt to the temperature change trend.

[0054] By adjusting parameters such as heat exchange efficiency or cooling time, the rapid rise or fall of temperature can be slowed down. Suppose that after exceeding the set range, the system decides to increase the cooling rate from 5°C per minute to 7°C per minute, and monitors the adjusted temperature fluctuations. The ultimate goal is to restore the temperature change to the set range.

[0055] Finally, through multiple adjustments, the system confirmed that the temperature had returned to the set range, thus completing the temperature adjustment and obtaining the temperature adjustment trend results, including data such as temperature deviation amplitude, temperature correction range, and temperature stability level, to ensure that the temperature in the production process can meet the requirements.

[0056] The intelligent method and system for waste heat recovery and temperature control of galvanized steel strand disclosed in this invention have at least the following beneficial effects: ① By monitoring temperature data in real time and adjusting according to temperature fluctuation trends, combined with precise control of parameters such as cooling rate, heat exchange efficiency, and heating power, temperature fluctuations can be effectively reduced, ensuring that the temperature is always maintained within the set range, thereby improving the temperature control accuracy of the production process and avoiding product quality instability caused by excessive temperature fluctuations; ② The system can automatically adjust the temperature control strategy according to real-time temperature changes and trend predictions, such as flexibly adjusting the cooling rate, heat exchange efficiency, or heating power under heating or cooling trends. This adaptive adjustment mechanism ensures that the system can cope with temperature changes in various complex production environments, enhancing the adaptability and stability of the temperature control system; ③ By precisely adjusting various temperature control parameters (such as cooling rate, heating power, etc.), the energy consumption of excessive cooling or heating is reduced, thereby reducing energy consumption and optimizing production efficiency. In addition, by quickly restoring the temperature to the set range, production interruptions or excessive equipment operation caused by temperature runaway are avoided, further improving overall production efficiency.

[0057] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0058] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0059] Furthermore, those skilled in the art will understand that various aspects of this specification can be described and illustrated in several patentable ways, including any new and useful combinations of processes, machines, products, or substances, or any new and useful improvements thereof. Accordingly, various aspects of this specification can be implemented entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. All of the above hardware or software may be referred to as a “data block,” “module,” “engine,” “unit,” “component,” or “system.” Furthermore, various aspects of this specification may be represented as a computer product located on one or more computer-readable media, including computer-readable program code.

[0060] Computer storage media may contain a propagated data signal containing computer program encoding, for example, on baseband or as part of a carrier wave. This propagated signal may take various forms, including electromagnetic, optical, and suitable combinations thereof. Computer storage media can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program encoding located on the computer storage medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0061] The computer program code required for the operation of each part of this manual can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc.; conventional procedural programming languages ​​such as C, Visual Basic, Fortran2003, Perl, COBOL2002, PHP, ABAP; dynamic programming languages ​​such as Python, Ruby, and Groovy; or other programming languages. This program code can run entirely on the user's computer, or as a standalone software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or processing device. In the latter case, the remote computer can be connected to the user's computer through any network, such as a local area network (LAN) or wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as Software as a Service (SaaS).

[0062] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented by hardware devices, they can also be implemented solely by software solutions, such as installing the described system on existing processing devices or mobile devices.

[0063] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0064] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A galvanized steel strand waste heat recovery temperature control intelligent method, characterized in that, The method comprises: Obtaining real-time production temperature data through sensors installed on the galvanized steel strand production line, obtaining temperature fluctuation ranges of each production stage, and generating temperature change trend data; the real-time production temperature data includes cooling speed, heat exchange efficiency, and heating power data; Based on the temperature change trend data, the temperature data fluctuation is smoothed, combined with the real-time production temperature data, the temperature change trend in the future period of time is predicted through trend line analysis, and a temperature prediction change trend value is generated; Based on the temperature prediction change trend value, the cooling speed, heat exchange efficiency and heating power parameters of the temperature control system that need to be adjusted are calculated, and an adjusted temperature control strategy result is generated; According to the temperature control strategy result, whether the temperature fluctuation trend exceeds the set range is analyzed and compared through real-time temperature data, if it exceeds the set range, the cooling speed, heat exchange efficiency and heating power data are adjusted according to the comparison result, and a temperature adjustment trend result is obtained.

2. The method of claim 1, wherein the zinc-coated steel strand waste heat recovery temperature control intelligent method is characterized by, The temperature change trend data includes temperature fluctuation amplitude, production stage difference, and time interval, the temperature prediction change trend value is specifically temperature growth trend value, temperature weakening trend value, and temperature stable trend value, and the temperature adjustment trend result specifically refers to temperature offset amplitude, temperature correction interval, and temperature stability level.

3. The method of claim 1, wherein the zinc-coated steel strand waste heat recovery temperature control intelligent method is characterized by, The temperature change trend data is generated by obtaining real-time production temperature data through sensors installed on the galvanized steel strand production line, obtaining temperature fluctuation ranges of each production stage, and generating temperature change trend data, comprising: Through the galvanized steel strand production line sensor, the cooling speed, heat exchange efficiency and heating power in the production process are collected, the temperature fluctuation range of each production stage is obtained, combined with the real-time production temperature data, the temperature fluctuation amplitude is calculated, the time interval is divided, and the temperature fluctuation amplitude and time interval data are obtained; Based on the temperature fluctuation amplitude and time interval data, the temperature difference of each production stage is analyzed, the temperature fluctuation difference values of different stages are compared, and a production stage difference analysis result is obtained; Based on the production stage difference analysis result, combined with the real-time production temperature data, the temperature change trend data is generated, and the temperature change trend data is obtained.

4. The galvanized steel strand waste heat recovery temperature control intelligent method according to claim 3, characterized in that: The way of calculating the temperature fluctuation amplitude is to statistically collect the cooling speed, heat exchange efficiency and heating power data, and calculate the difference between the maximum and minimum values of the cooling speed, heat exchange efficiency and heating power data, respectively; The way of dividing the time interval is to divide the time interval according to the fluctuation trend according to the size of the temperature fluctuation, the fluctuation trend includes the temperature rising trend and the temperature falling trend.

5. The method of claim 1, wherein the zinc-coated steel strand waste heat recovery temperature control intelligent method is characterized by, The temperature data fluctuation is smoothed based on the temperature change trend data, combined with the real-time production temperature data, the temperature change trend in the future period of time is predicted through trend line analysis, and a temperature prediction change trend value is generated, comprising: Through the collected temperature change trend data, the temperature data fluctuation is smoothed, short-term fluctuation is removed and noise is reduced, and then the smoothed temperature fluctuation data is obtained; In combination with real-time production temperature data, trend line analysis is performed to analyze temperature fluctuation trends in historical data, infer temperature change trends in a future period of time, and obtain temperature trend prediction values; Based on the temperature trend prediction values, the temperature prediction values are compared with real-time cooling speed, heat exchange efficiency, and heating power data to verify future temperature change trends and generate temperature prediction change trend values.

6. The method of claim 5, wherein the zinc-coated steel strand waste heat recovery temperature control intelligent method is characterized by, Specifically, based on the temperature trend prediction values, the temperature prediction values are compared with real-time cooling speed, heat exchange efficiency, and heating power data to verify future temperature change trends. Real-time cooling speed, heat exchange efficiency, and heating power data are obtained, the relationship of temperature prediction change trends is analyzed, the temperature value difference absolute value between temperature changes caused by cooling speed, heat exchange efficiency, and heating power data in the temperature change trend period and the temperature prediction change trend is calculated, and the temperature prediction change trend value is obtained.

7. The method of claim 1, wherein the method is a zinc-coated steel strand waste heat recovery temperature control intelligent method, characterized by, Based on the temperature prediction change trend value, the cooling speed, heat exchange efficiency, and heating power parameters of the temperature control system that need to be adjusted are calculated, and an adjusted temperature control strategy result is generated. Based on the temperature prediction change trend value, the temperature change direction is determined: if the temperature change direction is warming, any one or several of increasing cooling speed, increasing heat exchange efficiency, and reducing heating power are used to adjust the temperature control parameters of the temperature control equipment; If the temperature change direction is cooling, any one or several of reducing cooling speed, reducing heat exchange efficiency, and increasing heating power are used to adjust the temperature control parameters of the temperature control equipment; In the temperature trend change period, the adjusted temperature change value is equal to the difference absolute value, and the adjustment parameters of the cooling speed, heat exchange efficiency, and heating power are the adjusted temperature control strategy result.

8. The method of claim 1, wherein the method is a zinc-coated steel strand waste heat recovery temperature control intelligent method, characterized by, According to the temperature control strategy result, whether the temperature fluctuation trend exceeds the set range is analyzed through real-time temperature data analysis. If it exceeds the set range, the heat exchange efficiency or cooling time is fine-tuned to obtain a temperature adjustment trend result, including: According to the temperature control strategy result, real-time temperature data is obtained and monitored, the temperature fluctuation trend is analyzed, and whether the adjusted temperature exceeds the set temperature control range is determined. If it does not exceed the range, the adjustment is performed according to the temperature control strategy result. If it exceeds the range, the temperature change amplitude is recorded and temperature fluctuation out-of-range data is generated; Based on the temperature fluctuation out-of-range data, the temperature change direction is determined again, the heat exchange efficiency or cooling time is adjusted again according to the change direction, and the adjusted temperature fluctuation is monitored to confirm whether the temperature change has returned to the set range, and finally the temperature adjustment trend result is obtained.

9. A galvanized steel stranded wire waste heat recovery temperature control intelligent system for performing the method of any one of claims 1-8, characterized by, The system comprises: A data acquisition module and an analysis module obtain real-time production temperature data including cooling speed, heat exchange efficiency, and heating power data through sensors installed on a galvanized steel strand production line, obtain temperature fluctuation ranges in each production stage, and generate temperature change trend data; A temperature prediction module performs smoothing processing on temperature fluctuations based on the temperature change trend data, predicts temperature changes in a future period of time through trend line analysis, and generates temperature prediction change trend values in combination with real-time production temperature data; The temperature control strategy adjustment module calculates the cooling speed, heat exchange efficiency and heating power parameters of the temperature control system that need to be adjusted according to the temperature prediction change trend value, and generates a temperature control strategy result. The temperature adjustment monitoring module analyzes whether the temperature fluctuation trend exceeds the set range according to the temperature control strategy result and the real-time temperature data, and if the temperature fluctuation exceeds the set range, the heat exchange efficiency or the cooling time is fine-tuned to obtain a temperature adjustment trend result.

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