Control method, device and equipment for roller way acceleration

Abstract

The invention discloses a control method, device and equipment for roller way acceleration, and the method comprises the steps: obtaining the actual basic parameters of a target roller way for each target roller way of a roller way production system; according to the actual basic parameters and the initial prediction temperature change model, a time period before prediction acceleration is determined; under the condition that the roller bed production system operates within the time period before the predicted acceleration, according to the actual roller bed surface temperature and the initial roller bed surface temperature, the initial predicted temperature change model is corrected, and a target predicted temperature change model is obtained; determining a target predicted temperature change curve according to the actual basic parameters and the target predicted temperature change model; and under the condition that the highest single-point target predicted temperature on the target predicted temperature change curve does not exceed a preset safety threshold value, a roller way acceleration scheme is determined, and the roller way production system is controlled to accelerate according to the roller way acceleration scheme. Based on the temperature change model capable of accurately predicting future temperature change, the adaptive capacity of the acceleration process is improved.

Description

Technical Field

[0001] The present invention relates to the field of automation control technology, and in particular, to a control method, device and equipment for roller table speed increase. Background Art

[0002] The temperature change of the roller table has a significant impact on the production efficiency of the roller table speed increase production process.

[0003] In the traditional roller table speed increase control scheme, it usually relies on real-time monitoring of the roller table temperature and lacks the ability to respond to future temperature changes. Therefore, how to predict the temperature change before the roller table speed increase and determine a suitable speed increase scheme is a technical problem that needs to be solved urgently at present. Summary of the Invention

[0004] Embodiments of the present application provide a control method, device and equipment for roller table speed increase, solve the technical problem that the roller table speed increase in the prior art lacks the ability to respond to future temperature changes, and achieve the technical effect of predicting the temperature change before the roller table speed increase.

[0005] In a first aspect, the present application provides a control method for roller table speed increase, including:

[0006] For each target roller in each roller to be speeded up in the roller table production system, obtain the actual basic parameters of the target roller; the actual basic parameters include the initial temperature of the roller surface.

[0007] According to the actual basic parameters and the preset initial predicted temperature change model, determine the predicted time period before speed increase.

[0008] When the roller table production system is operating within the predicted time period before speed increase, according to the actual temperature of the roller surface and the initial temperature of the roller surface of the roller table production system, correct the initial predicted temperature change model to obtain the target predicted temperature change model.

[0009] According to the actual basic parameters and the target predicted temperature change model, determine the target predicted temperature change curve corresponding to the predicted time period before speed increase.

[0010] When the highest single-point target predicted temperature on the target predicted temperature change curve does not exceed the preset safety threshold, determine the roller table speed increase scheme matching the target predicted temperature change curve, and control the roller table production system to speed up according to the roller table speed increase scheme.

[0011] In some embodiments of the present application, based on the foregoing solution, obtaining the actual basic parameters of the target roller includes:

[0012] Obtain the actual monitoring parameters corresponding to multiple monitoring positions on the target roller;

[0013] Preprocess multiple actual monitoring parameters to obtain the actual basic parameters of the target roller path.

[0014] In some embodiments of the present application, based on the foregoing solution, the method for determining the initial predicted temperature change model includes:

[0015] Select pre-trained historical production data that matches the actual production data of the target roller path from the historical production data of multiple roller paths in the roller path production system;

[0016] Perform statistical analysis on the pre-trained historical production data to obtain pre-trained features related to the surface temperature change of the roller path, and determine the initial predicted temperature change model by means of time series analysis.

[0017] In some embodiments of the present application, based on the foregoing solution, according to the actual basic parameters and the preset initial predicted temperature change model, determine the time period before predicted speed increase, including:

[0018] Preprocess the actual basic parameters to obtain actual standard parameters;

[0019] Input the actual standard parameters into the initial predicted temperature change model through multi-dimensional mapping to obtain an initial predicted temperature change curve; the single-point initial predicted temperature on the initial predicted temperature change curve corresponds one-to-one with the single-point predicted time;

[0020] Determine the time period before predicted speed increase according to the initial predicted temperature change curve.

[0021] In some embodiments of the present application, based on the foregoing solution, according to the initial predicted temperature change curve, determine the time period before predicted speed increase, including:

[0022] Determine the single-point predicted time corresponding to the start of the decrease in the rising rate of the single-point initial predicted temperature on the initial predicted temperature change curve as the initial predicted temperature change time node;

[0023] Use the predicted temperature change time node as the time end point of the time period before predicted speed increase, and trace back a preset time length as the time start point of the time period before predicted speed increase to determine the time period before predicted speed increase.

[0024] In some embodiments of the present application, based on the foregoing solution, determine the time period before predicted speed increase, including:

[0025] When the actual running time of the roller path production system reaches the time start point, adjust the time length of the time period before predicted speed increase according to the actual change rate of the actual surface temperature of the roller path.

[0026] In some embodiments of the present application, based on the foregoing solution, according to the actual temperature of the roller surface of the roller path production system and the initial temperature of the roller surface, the initial predicted temperature change model is corrected to obtain a target predicted temperature change model, including:

[0027] When the operating environment data of the target roller path meets the standard operating conditions, according to the actual temperature of the roller surface and the initial predicted temperature change model, the model predicted temperature is obtained;

[0028] The error index is determined according to the initial temperature of the roller surface and the model predicted temperature, and the initial predicted temperature change model is corrected according to the error index to obtain the target predicted temperature change model.

[0029] In some embodiments of the present application, based on the foregoing solution, when the highest single-point target predicted temperature exceeds the preset safety threshold, the method further includes:

[0030] According to the highest single-point target predicted temperature and the preset safety threshold, a roller path speed-up plan to be verified is determined;

[0031] When the roller path speed-up plan to be verified meets the preset safety requirements, the roller path production system is controlled to speed up according to the roller path speed-up plan to be verified.

[0032] In a second aspect, the present application provides a control device for roller path speed-up, including:

[0033] An actual basic parameter acquisition module, configured to acquire the actual basic parameters of each target roller in each roller to be speeded up of the roller path production system; the actual basic parameters include the initial temperature of the roller surface;

[0034] A predicted speed-up pre-period determination module, configured to determine the predicted speed-up pre-period according to the actual basic parameters and the preset initial predicted temperature change model;

[0035] A target predicted temperature change model determination module, configured to correct the initial predicted temperature change model according to the actual temperature of the roller surface and the initial temperature of the roller surface of the roller path production system to obtain a target predicted temperature change model when the roller path production system is operating within the predicted speed-up pre-period;

[0036] A target predicted temperature change curve determination module, configured to determine a target predicted temperature change curve corresponding to the predicted speed-up pre-period according to the actual basic parameters and the target predicted temperature change model;

[0037] A speed-up control module, configured to determine a roller path speed-up plan matching the target predicted temperature change curve and control the roller path production system to speed up according to the roller path speed-up plan when the highest single-point target predicted temperature on the target predicted temperature change curve does not exceed the preset safety threshold.

[0038] In a third aspect, the present application provides an electronic device, including:

[0039] A processor;

[0040] A memory for storing instructions executable by the processor;

[0041] Wherein, the processor is configured to execute to implement a control method for roller table speed increase provided in the first aspect.

[0042] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0043] The embodiments of the present application provide a control method for roller table speed increase, including: for each target roller among the rollers to be speeded up in the roller table production system, obtaining the actual basic parameters of the target roller; the actual basic parameters include the initial temperature of the roller table surface; according to the actual basic parameters and a preset initial predicted temperature change model, determining the period before predicted speed increase; when the roller table production system is operating within the period before predicted speed increase, correcting the initial predicted temperature change model according to the actual temperature of the roller table surface and the initial temperature of the roller table surface in the roller table production system to obtain a target predicted temperature change model; according to the actual basic parameters and the target predicted temperature change model, determining a target predicted temperature change curve corresponding to the period before predicted speed increase; when the highest single-point target predicted temperature on the target predicted temperature change curve does not exceed a preset safety threshold, determining a roller table speed increase plan matching the target predicted temperature change curve, and controlling the roller table production system to speed up according to the roller table speed increase plan. It can be seen that by inputting the actual basic parameters of each target roller into the preset initial predicted temperature change model, the period before predicted speed increase is obtained, the actual temperature of the roller table surface is obtained within the period before predicted speed increase, and the model is corrected in combination with the actual basic parameters to obtain a target predicted temperature change model that adapts to the actual production state and can accurately predict future temperature changes. The roller table speed increase plan determined accordingly not only enhances the dynamic adjustment ability, but also reduces the probability of problems such as equipment damage or production impact caused by excessive temperature, improving production safety and production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0045] Figure 1 It is a schematic flowchart of a control method for roller table speed increase provided in the embodiments of the present application;

[0046] Figure 2 Schematic structural diagram of a control device for roller table speed increase provided by an embodiment of the present application;

[0047] Figure 3 Schematic structural diagram of an electronic device provided by an embodiment of the present application. Specific implementation manners

[0048] By providing a control method for roller table speed increase in an embodiment of the present application, the technical problem in the prior art that the roller table speed increase lacks the ability to cope with future temperature changes is solved.

[0049] The technical solution of the embodiment of the present application for solving the above technical problem has the following general idea:

[0050] An embodiment of the present application provides a control method for roller table speed increase, including: for each target roller in each roller to be speeded up in the roller table production system, obtaining the actual basic parameters of the target roller; the actual basic parameters include the initial temperature on the surface of the roller; according to the actual basic parameters and a preset initial predicted temperature change model, determining the time period before predicted speed increase; when the roller table production system operates within the time period before predicted speed increase, correcting the initial predicted temperature change model according to the actual temperature on the surface of the roller and the initial temperature on the surface of the roller in the roller table production system to obtain a target predicted temperature change model; according to the actual basic parameters and the target predicted temperature change model, determining a target predicted temperature change curve corresponding to the time period before predicted speed increase; when the highest single-point target predicted temperature on the target predicted temperature change curve does not exceed a preset safety threshold, determining a roller table speed increase plan matching the target predicted temperature change curve, and controlling the roller table production system to speed up according to the roller table speed increase plan.

[0051] It can be seen that by inputting the actual basic parameters of each target roller into a preset initial predicted temperature change model to obtain the time period before predicted speed increase, obtaining the actual temperature on the surface of the roller within the time period before predicted speed increase, and combining the actual basic parameters to correct the model to obtain a target predicted temperature change model that adapts to the actual production state and can accurately predict future temperature changes, the roller table speed increase plan determined accordingly not only enhances the dynamic adjustment ability, but also reduces the probability of problems such as equipment damage or production impact caused by too high temperature, and improves production safety and production efficiency.

[0052] To better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0053] First, it should be noted that the term "and / or" appearing in this article is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. Additionally, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0054] In industrial production, with the continuous improvement of the requirements for production efficiency, the demand for roller table speed increase is increasing day by day. However, in the scenario of roller table speed increase, the problem of temperature change is particularly prominent, and too high temperature may damage equipment and affect production safety. Accurately predicting the temperature change before the roller table speed increase to formulate a scientific and reasonable speed increase plan has become a crucial link in ensuring the safe and efficient operation of industrial production. Traditional roller table temperature management is often relatively simple and localized, relying only on real-time temperature monitoring data, lacking in-depth analysis and utilization of historical temperature data, and it is difficult to comprehensively and accurately predict the temperature change trend during the roller table speed increase process. When dealing with different operating conditions and environmental changes, it lacks sufficient adaptability and dynamic adjustment capabilities. The formulation of the temperature control plan is relatively fixed and cannot well cope with the actual operating conditions of the continuously changing roller table, thus resulting in frequent problems of equipment damage or production impact due to too high temperature.

[0055] To solve the above problems, the embodiments of the present application provide a control method for roller table speed increase. As Figure 1 shown, it is a schematic flowchart of a control method for roller table speed increase provided by the embodiments of the present application, including steps S1 - S5.

[0056] Step S1, for each target roller among the rollers to be speeded up in the roller table production system, obtain the actual basic parameters of the target roller; the actual basic parameters include the initial temperature of the roller table surface.

[0057] Step S2, according to the actual basic parameters and the preset initial predicted temperature change model, determine the predicted time period before speed increase.

[0058] Step S3, when the roller table production system is operating within the predicted time period before speed increase, according to the actual temperature of the roller table surface and the initial temperature of the roller table surface in the roller table production system, correct the initial predicted temperature change model to obtain the target predicted temperature change model.

[0059] Step S4, according to the actual basic parameters and the target predicted temperature change model, determine the target predicted temperature change curve corresponding to the predicted time period before speed increase.

[0060] Step S5, when the highest single point target predicted temperature on the target predicted temperature change curve does not exceed the preset safety threshold, determine a roller speed-up plan that matches the target predicted temperature change curve, and control the roller production system to speed up according to the roller speed-up plan.

[0061] Regarding step S1, for each target roller in each roller to be accelerated in the roller production system, the actual basic parameters of the target roller are obtained; the actual basic parameters include the initial temperature of the roller surface.

[0062] The actual basic parameters of the target roller table are obtained, including steps S11 and S12.

[0063] Step S11, obtaining actual monitoring parameters corresponding to multiple monitoring positions on the target roller table;

[0064] Step S12, pre-processing a plurality of actual monitoring parameters to obtain actual basic parameters of the target roller table.

[0065] Regarding step S11, actual monitoring parameters corresponding to multiple monitoring positions on the target roller table are obtained.

[0066] By setting multiple temperature measuring devices arranged at intervals on the target roller, such as high-precision contact or non-contact temperature sensors, the actual monitoring parameters of the corresponding monitoring positions can be collected according to the preset collection frequency. For example, a temperature sensor is set every 5 meters on a target roller with a length of 50 meters, and the preset collection frequency is to collect the actual monitoring parameters every 2 minutes.

[0067] Furthermore, the preset acquisition frequency is determined according to the operating characteristics of the target roller. If the roller runs at a fast speed and the temperature change may be more drastic, a higher preset acquisition frequency can be set to adapt to the rapid temperature change. For example, if the roller runs at a speed of 30 meters per minute and the temperature change is about 2°C per minute, the preset acquisition frequency is set to collect data every 30 seconds. If the roller runs at a relatively stable and slow speed and the temperature changes relatively slowly, the preset acquisition frequency can be appropriately lowered to save resources and improve data processing efficiency. For example, if the roller runs at a speed of 10 meters per minute and the temperature change does not exceed 0.5°C per minute, the preset acquisition frequency can be set to collect data every 2 minutes. By reasonably setting the acquisition frequency, comprehensive and accurate temperature change data can be obtained, providing a basis for subsequent analysis and modeling.

[0068] Regarding step S12, a plurality of actual monitoring parameters are preprocessed to obtain actual basic parameters of the target roller table.

[0069] Preprocessing is to remove interfering factors such as outliers and noise to ensure the accuracy and reliability of the data. For example, the actual monitoring parameters that deviate significantly from the normal range can be removed by setting thresholds. Or filtering algorithms can be used to remove high-frequency noise in the data. For instance, the mean filtering algorithm can be adopted, and the average value of adjacent 5 data points is taken as the filtered value.

[0070] Furthermore, based on the actual basic parameters obtained after preprocessing, a real-time temperature change curve is constructed. Taking the roller path running time as the abscissa and the actual temperature of the roller path as the ordinate, the processed temperature data points are plotted in the coordinate system, and these data points are connected into a continuous curve by methods such as interpolation or curve fitting. For example, after preprocessing, 100 data points are obtained. After plotting these points in the coordinate system, the cubic spline interpolation method is used to connect the data points into a curve.

[0071] The real-time temperature change curve can intuitively represent the trend of temperature change over time, quickly display the temperature state of the roller path at different time points and the speed and direction of temperature change. Exemplarily, after a period of collection, the obtained data shows that within the first 10 minutes, the temperature gradually rises from 25°C to 28°C, and then within the next 20 minutes, the temperature fluctuates around 28°C, with the fluctuation range between ±1°C.

[0072] Furthermore, the actual basic parameters also include at least one of the following: ambient temperature, ambient humidity, roller path running time, roller path length, and roller path width.

[0073] Regarding step S2, based on the actual basic parameters and the preset initial prediction temperature change model, the time period before speed increase prediction is determined.

[0074] The determination method of the initial prediction temperature change model includes steps S211 - S212.

[0075] In step S211, from the historical production data of multiple roller paths in the roller path production system, pre-training historical production data that matches the actual production data of the target roller path is selected;

[0076] In step S212, statistical analysis is performed on the pre-training historical production data to obtain pre-training features related to the surface temperature change of the roller path, and the initial prediction change model is determined through time series analysis.

[0077] Regarding step S211, from the historical production data of multiple roller paths in the roller path production system, pre-training historical production data that matches the actual production data of the target roller path is selected.

[0078] Historical production data includes the roller conveyor operation data within multiple past time periods (e.g., 100 time periods) and the historical production data of other roller conveyors that can be obtained in the same industry. Further, the historical production data includes historical temperature change data, and based on the historical temperature change data, the general regularity of temperature change can be obtained. For example, when the ambient temperature is between 20°C and 25°C and the roller conveyor running speed is 5 meters per minute, the temperature usually reaches a stable state after running for 30 minutes, and the temperature is usually stable between 27°C and 30°C.

[0079] To match the actual production data of the target roller conveyor, at least one of the factors such as the type of the roller conveyor, the operating environment, and the load condition needs to be considered. For example, if the target roller conveyor is used for material transportation in a specific industry (such as the automotive manufacturing industry), then historical production data that is similar or relevant to the actual production data of the target roller conveyor, such as the type (both are roller conveyors for transporting automotive parts), the operating environment (temperature between 20°C and 30°C, humidity between 40% and 60%), and the load condition (average load is 500 kilograms), can be selected as the pre-training historical production data.

[0080] Step S212: Conduct statistical analysis on the pre-training historical production data to obtain pre-training features related to the surface temperature change of the roller conveyor, and determine the initial prediction change model through time series analysis.

[0081] Conduct statistical analysis on the pre-training historical production data. Identify the laws and features of temperature change, calculate statistical quantities such as the mean, variance, maximum value, and minimum value of the pre-training historical production data to understand the distribution range and fluctuation of temperature. At the same time, the changing trend of temperature over time can also be analyzed, such as whether there are periodic changes, trend changes, etc. Through statistical analysis, an in-depth understanding of the laws and features of temperature change can be obtained, providing a basis for establishing the initial prediction temperature change model.

[0082] Based on the statistical analysis results, determine the initial prediction temperature change model through time series analysis. The time series analysis algorithm can predict the future temperature change trend based on the time series features in the historical data. During the process of determining the model, the laws and pre-training features obtained from the statistical analysis need to be used as input parameters to adjust the parameters and structure of the model to improve the accuracy and reliability of the model.

[0083] For example, according to the periodic characteristics of temperature changes, appropriate cycle parameters can be set. For instance, based on the results of statistical analysis, the cycle parameter is set to 4 hours. Then, according to the trend characteristics, an appropriate trend model can be selected. For example, if it is found that the temperature has a slow upward trend, a linear trend model can be chosen. Finally, by continuously optimizing the model, a preliminary model that can relatively accurately predict the temperature change trend of the roller path can be determined, that is, the initial predicted temperature change model. For example, after preliminary modeling, the average error between the temperature predicted by the model and the actual temperature is ±0.5°C. By continuously adjusting the model parameters, such as adjusting the weight coefficient and increasing data smoothing processing, the average error is gradually reduced to within ±0.3°C.

[0084] Regarding step S2, according to the actual basic parameters and the preset initial predicted temperature change model, the time period before speed increase prediction is determined, including steps S221 - S223.

[0085] In step S221, the actual basic parameters are preprocessed to obtain actual standard parameters;

[0086] In step S222, the actual standard parameters are input into the initial predicted temperature change model through the method of multi-dimensional mapping to obtain the initial predicted temperature change curve; the single-point initial predicted temperature on the initial predicted temperature change curve corresponds one-to-one with the single-point prediction time;

[0087] In step S223, according to the initial predicted temperature change curve, the time period before speed increase prediction is determined.

[0088] Regarding step S221, the actual basic parameters are preprocessed to obtain actual standard parameters.

[0089] The preprocessing is to improve the reliability and accuracy of the data, including anomaly identification and normalization processing. The actual basic parameters will be affected by various factors and abnormal values may appear. These abnormal values may have an adverse impact on subsequent analysis and prediction. Therefore, these abnormal values need to be preprocessed to achieve the purpose of improving the reliability and accuracy of the actual basic parameters.

[0090] By using a preset algorithm or statistical method, the abnormal values in the basic parameters can be detected. For example, the mean and standard deviation of the actual basic parameters can be calculated. Suppose the mean of the initial temperature on the surface of the roller path in the actual basic parameters is 25°C and the standard deviation is 2°C. The data points outside the range of the mean ± 3 times the standard deviation are regarded as abnormal values. Once the abnormal values are identified, appropriate processing methods can be taken, such as deleting, replacing, or correcting the abnormal values.

[0091] Normalization is carried out to make different actual basic parameters comparable. Different actual basic parameters may have different dimensions and value ranges. If normalization is not carried out, it will lead to some actual basic parameters dominating in analysis and prediction, while the influence of other actual basic parameters is ignored. Through normalization, actual standard parameters are obtained, providing more reliable and effective data for subsequent analysis and model determination.

[0092] Normalization can map the values of basic parameters into a specific interval, such as [0, 1] or [-1, 1]. Common normalization methods include min-max normalization, Z-score normalization, etc. Assuming the min-max normalization method is adopted, specifically: map the initial temperature on the roller table surface in the basic parameters [20℃, 30℃] to the interval [0, 1]. If the initial temperature of a certain roller table surface is 24℃, the value after normalization is (24 - 20) / (30 - 20) = 0.4.

[0093] Regarding step S222, the actual standard parameters are input into the initial predicted temperature change model through multi-dimensional mapping to obtain the initial predicted temperature change curve; the single-point initial predicted temperature on the initial predicted temperature change curve corresponds one-to-one with the single-point prediction time.

[0094] Specifically, the actual standard parameters are combined with multiple data sources to obtain multi-dimensional data information; deep learning is used to map the multi-dimensional data information to obtain multi-dimensional mapping input information; the multi-dimensional mapping input information is input into the initial predicted temperature change model, and through trend analysis, the initial predicted temperature change curve is obtained.

[0095] Combining the actual standard parameters with multiple data sources aims to enrich the dimension and information content of the data. In addition to the actual standard parameters, other data sources related to the temperature change of the roller table, such as ambient temperature, roller table load, running speed, etc., can also be considered. By integrating different data sources, more comprehensive and accurate multi-dimensional data information can be obtained.

[0096] For example, the actual standard parameters can be combined with ambient temperature data. Assuming the ambient temperature fluctuates between 18℃ and 25℃, analyze the influence of ambient temperature on the temperature change of the roller table. Or associate the actual standard parameters with roller table load data. Assuming the roller table load changes between 500 kg and 1000 kg, study the effect of load change on temperature.

[0097] Using deep learning to map multi-dimensional data information to obtain multi-dimensional mapped input information. Deep learning has powerful feature extraction and data mapping capabilities and can automatically learn complex patterns and relationships in the data. By constructing an appropriate deep learning model, such as a deep neural network, multi-dimensional data information can be used as input. After being processed through a series of hidden layers and activation functions, more abstract and advanced multi-dimensional mapped input information can be obtained to capture the non-linear relationships and potential features in the data, providing more valuable input for subsequent temperature prediction.

[0098] Input the multi-dimensional mapped input information into the initial predicted temperature change model, and through trend analysis, obtain the initial predicted temperature change curve. The initial predicted temperature change curve can reflect the initial predicted temperature change trend. In some embodiments, the initial predicted temperature change trend can also be presented in forms such as charts or text. In the embodiments of this application, the subsequent description will continue with the form of a data curve as an example.

[0099] For example, in the initial predicted temperature change curve, it is characterized that within the next 30 minutes, the temperature will rise at a rate of 0.5 °C per minute and tend to be stable after reaching 26.5 °C. If presented in the form of a text description, it may be expressed as "Before the roller table speed is increased, it is expected that the temperature will rise slowly, may increase by about 1.5 °C within the next half hour, and then remain relatively stable."

[0100] It can be understood that the multi-dimensional mapped input information includes all data related to the target roller table. For example, the initial value of the roller table surface temperature is 25 °C, the ambient humidity is 45%, the surrounding air temperature is 20 °C, the length of the roller table is 100 meters, and the width is 2 meters.

[0101] The initial predicted temperature change model is established based on historical data and has a certain prediction ability. When the multi-dimensional mapped input information is input into the model, the model will analyze and process the input information according to its internal algorithms and logic. Trend analysis is a commonly used data analysis method and can be used to predict the future trend of time series data. The model will consider various factors in the multi-dimensional mapped input information, such as time, temperature, environment, etc., and their mutual relationships, and through calculation and analysis, obtain the initial temperature prediction trend before the roller table speed is increased. The initial temperature prediction trend can provide an important reference basis for subsequent decision-making and control. For example, after model prediction, within the next 30 minutes, the roller table temperature will rise at a rate of 0.5 °C per minute, and the temperature fluctuation range will be within ±1 °C after reaching the stable state.

[0102] Regarding step S223, according to the initial predicted temperature change curve, determine the time period before the predicted speed increase, including steps S2231 - S2232.

[0103] Step S2231: Determine the initial predicted temperature change time node as the single-point predicted time corresponding to the start of the decline in the rising rate of the single-point initial predicted temperature in the initial predicted temperature change curve.

[0104] Step S2232: Use the predicted temperature change time node as the time end point of the time period before predicted speed increase, and trace back a preset time length as the time start point of the time period before predicted speed increase to determine the time period before predicted speed increase.

[0105] Regarding Step S2231, determine the initial predicted temperature change time node as the single-point predicted time corresponding to the start of the decline in the rising rate of the single-point initial predicted temperature in the initial predicted temperature change curve.

[0106] Specifically, analyze the initial predicted temperature change curve to determine the curve form, change rate, and possible fluctuations of the initial predicted temperature change curve over time. For example, the initial predicted temperature change curve shows that the temperature rises slowly at a rate of 0.2 °C per minute in the first 20 minutes, then the temperature rising speed accelerates to 0.5 °C per minute between 20 minutes and 40 minutes, and the temperature rising speed gradually slows down after 40 minutes. By carefully analyzing the trend, some key features of the temperature change can be identified, laying a foundation for the subsequent steps.

[0107] Based on the analysis of the initial predicted temperature change curve, determine the initial predicted temperature change time node. The initial predicted temperature change time node refers to the single-point predicted time corresponding to the start of the decline in the rising rate of the single-point initial predicted temperature. For example, through analysis and prediction, the temperature rising speed may start to decline at about 45 minutes, and this time point is determined as the initial predicted temperature change time node. By identifying this key element of the initial predicted temperature change time node, the dynamics of the temperature change can be grasped more accurately, providing a clear target for subsequent measurement and adjustment.

[0108] Furthermore, determine the sensitive area based on the analysis of the initial predicted temperature change curve. The sensitive area refers to the area where the temperature change is relatively significant or has a greater impact on the running state of the roller table. For example, analysis finds that when the temperature is between 35 °C and 40 °C, the running efficiency of the roller table is significantly affected, so this temperature range can be determined as the sensitive area.

[0109] Regarding Step S2232, use the predicted temperature change time node as the time end point of the time period before predicted speed increase, and trace back a preset time length as the time start point of the time period before predicted speed increase to determine the time period before predicted speed increase.

[0110] Specifically, according to the predicted temperature change time node determined in step S2231, trace back a preset time length forward to determine the time starting point of the time period before the preset speed increase. The preset time length can capture the precursor signals of temperature changes. If the preset time length is too short, important temperature change precursors may be missed, affecting subsequent measurements and analyses. If the preset time length is too long, unnecessary measurement costs and time will be increased. For example, after analysis, if the initial predicted temperature change time node is 45 minutes, and the preset time length is set to 30 minutes, there will be enough time to observe the precursor signals of temperature changes, thus better grasping the trend of temperature changes.

[0111] Furthermore, determining the time period before the predicted speed increase includes: when the actual running time of the roller path production system reaches the time starting point, adjusting the time length of the time period before the predicted speed increase according to the actual change rate of the actual temperature on the roller path surface.

[0112] Specifically, when the actual running time of the roller path production system reaches the time starting point, perform multiple measurement tasks to obtain multiple sets of measurement data reflecting the actual temperature on the roller path surface. The measurement data includes: time stamp, measurement position, and the corresponding temperature value.

[0113] During the process of performing the measurement tasks, adjust the time length of the time period before the predicted speed increase according to the actual change rate of the actual temperature on the roller path surface. If the temperature change rate is fast, it may be necessary to shorten the time period before the speed increase to perform temperature measurement and analysis more timely. If the temperature change rate is slow, the time period before the speed increase can be appropriately extended to ensure that the process of temperature change can be fully captured. For example, at the time starting point, the temperature change rate is 0.1 °C per minute. As time goes by, the temperature change rate gradually increases to 0.4 °C per minute. According to this change, the predicted time period before the speed increase is adjusted from the initially set 60 minutes to 50 minutes.

[0114] After adjusting the time period before the predicted speed increase, perform multiple temperature measurements within this time period. Each measurement records the time stamp, measurement position, and the corresponding temperature value, forming multiple sets of measurement data. For example, at the time stamp of [specific time 1], the measurement position is at 20 meters on the roller path, and the temperature value is 32 °C; at the time stamp of [specific time 2], the measurement position is at 50 meters on the roller path, and the temperature value is 35 °C, etc. The multiple sets of measurement data will provide an actual observational basis for subsequent correction of the initial predicted temperature change model and obtaining a more accurate temperature prediction result.

[0115] Regarding step S3, when the roller path production system is operating within the time period before the predicted speed increase, correct the initial predicted temperature change model according to the actual temperature on the roller path surface and the initial temperature on the roller path surface of the roller path production system to obtain the target predicted temperature change model.

[0116] Step S3 may specifically include Step S31 - Step S32.

[0117] In Step S31, when the operating environment data of the target roller path meets the standard operating conditions, based on the actual temperature of the roller path surface and the initial prediction temperature change model, the model prediction temperature is obtained.

[0118] In Step S32, the error index is determined based on the initial temperature of the roller path surface and the model prediction temperature, and the initial prediction temperature change model is corrected according to the error index to obtain the target prediction temperature change model.

[0119] Regarding Step S31, when the operating environment data of the target roller path meets the standard operating conditions, based on the actual temperature of the roller path surface and the initial prediction temperature change model, the model prediction temperature is obtained.

[0120] Specifically, the operating environment data of the target roller path is obtained and compared with the standard operating conditions. The operating environment data includes factors such as environmental temperature, humidity, and air circulation. Based on the comparison result of the operating environment data and the standard operating conditions, it is judged whether there is an abnormality in the initial temperature of the roller path surface.

[0121] For example, if the operating environment temperature is low, but the initial temperature of the roller path surface is abnormally high, then there may be a problem with the roller path. Assume that under normal circumstances, when the environmental temperature is 22°C, the initial temperature of the roller path surface should be around 25°C. If the initial temperature of the roller path surface is 35°C at this time, which is significantly higher than the expected range, it can be judged that the roller path is abnormal. Through the above method, the accuracy of the initial value of the roller path surface temperature is ensured, and it is used as the standard initial value to provide a reliable basis for subsequent analysis and correction.

[0122] When the roller path production system is operating during the time period before predicted speed increase, multiple measurement tasks are executed to obtain multiple sets of measurement data reflecting the actual temperature of the roller path surface. The multiple sets of measurement data are preprocessed to obtain the actual temperature of the roller path surface.

[0123] The preprocessing process includes operations such as removing outliers, smoothing data, and data normalization to improve the quality and usability of the data. For example, by setting a threshold to remove temperature values that deviate significantly from the normal range. Assume that the normal temperature range is between 20°C and 40°C, and data points outside this range are regarded as outliers and removed. The moving average method can also be used to smooth the data, and the average value of adjacent 5 data points is taken as the smoothed value. Data normalization can also be performed to map the temperature value to the [0,1] interval. Assume that a certain temperature value is 30°C, and after normalization, it becomes 0.6.

[0124] Use the actual temperature of the pre-processed roller table surface as the training or validation data set, and input it into the initial predicted temperature change model. The initial predicted temperature change model will calculate based on the input data to obtain the model predicted temperature, which represents the prediction result of the model for the temperature change of the roller table.

[0125] Regarding step S32, determine the error index based on the initial temperature of the roller table surface and the model predicted temperature, and correct the initial predicted temperature change model according to the error index to obtain the target predicted temperature change model.

[0126] First, evaluate the difference between the initial temperature of the roller table surface and the model predicted temperature based on the machine learning algorithm, and quantify this difference as the error index.

[0127] The machine learning algorithm can compare the differences in aspects such as the magnitude and trend between two numerical values, and quantify this difference as the error index. For example, indicators such as the mean square error and mean absolute error between the initial temperature of the roller table surface and the model predicted temperature can be calculated. The error index reflects the accuracy and reliability of the model prediction. For example, after the machine learning algorithm, the mean square error is 2.5 and the mean absolute error is 1.5. The error index reflects the accuracy and reliability of the model prediction.

[0128] Furthermore, correct the initial predicted temperature change model according to the error index to obtain the target predicted temperature change model.

[0129] If the error index is large, it indicates that there is a large deviation between the prediction result of the initial predicted temperature change model and the actual situation, and the model needs to be adjusted. By adjusting the parameters, structure, or algorithm of the model, etc., to reduce the error index. For example, the specific adjustment methods for the model can be to adjust the temperature change coefficient, time delay parameter, increase the complexity of the model, adjust the learning rate of the model, adopt different optimization algorithms, etc. Suppose the original temperature change coefficient is an increase of 0.1 °C per minute, and it is adjusted to an increase of 0.15 °C per minute according to the actual measurement data. At the same time, if it is found that there is a deviation between the temperature change time predicted by the model and the actual situation, the time delay parameter can be adjusted. For example, if the temperature change predicted by the model is 2 minutes later than the actual, the time delay parameter can be adjusted accordingly.

[0130] Next, the revised model is verified with the temperature measurement data that is not involved in the correction process. If the degree of fit is high, the revised model is determined as the final target prediction temperature change model, providing a reliable basis for the subsequent calculation of temperature change trends and the generation of speed-up plans. For example, another set of temperature data measured in different time periods is selected, with a total of 10 time points. The data is input into the revised model for prediction, and then compared with the actual measured values. If the average error between the predicted value and the actual value is within ±1°C, and the error at most time points is small, it can be considered that the degree of fit is high, and the revised model is determined to be the final target prediction temperature change model. After repeated corrections and optimizations, the target prediction temperature change model is finally obtained, which can more accurately predict the temperature changes of the roller.

[0131] Regarding step S4, a target predicted temperature change curve corresponding to the predicted time period before speed increase is determined according to the actual basic parameters and the target predicted temperature change model.

[0132] Set the input parameters of the target prediction temperature change model to values that match the actual basic parameters. For example, the actual basic parameters include the initial roller surface temperature of 25°C, the ambient temperature of 20°C, the humidity of 40%, the roller length of 100 meters, and the width of 2 meters. It is crucial to ensure the consistency and matching of the input parameters with the actual basic parameters, which will directly affect the accuracy and reliability of the results. If the actual basic parameters indicate that the roller is operating under specific environmental conditions, then the input parameters should also reflect the same environmental conditions. By carefully setting the input parameters, the model can be better adapted to specific application scenarios, laying the foundation for accurately predicting temperature changes.

[0133] According to the input parameters, the target prediction temperature change model is used to simulate the temperature change of the roller at various time points before speeding up. The target prediction temperature change model will predict the temperature of the roller at different time points based on the input parameters and its internal algorithms and logic, which is similar to simulating the operation of the roller in a virtual environment to understand the temperature change trend over time. Through simulation, the temperature prediction value of the roller at various time points before speeding up can be obtained, providing data support for subsequent analysis. For example, at the 10th minute after the simulation starts, the temperature prediction value is 26°C; at the 20th minute, the temperature prediction value is 27°C.

[0134] Furthermore, the difference equation solution is used to perform iterative calculations on the target predicted temperature change model, gradually updating the temperature state of the roller table until the end point of the time period before the predicted speed increase is reached. The difference equation is a method for numerically solving dynamic systems and can gradually update the temperature state of the roller table. Suppose a first-order difference equation is used with a time step of 1 minute. In each iteration, the model calculates the temperature value at the next time point based on the current temperature state and input parameters. Through continuous iterative calculations, the temperature state of the roller table is gradually updated until the end point of the time period before the predicted speed increase is reached. For example, if the time period before the predicted speed increase is 60 minutes, through multiple iterative calculations, the temperature state is gradually updated to more accurately capture the dynamic process of temperature change and improve the prediction accuracy.

[0135] Finally, the temperature prediction values at each time point within the time period before the predicted speed increase are extracted to form the target predicted temperature change curve, and then the final temperature prediction result is determined. The temperature prediction values at each time point within the time period before the predicted speed increase are obtained through the simulation and iterative calculations of the target predicted temperature change model, representing the temperature prediction situation of the roller table at different time points. Plotting the temperature prediction values into a curve, the target predicted temperature change curve, visually shows the temperature change trend of the roller table before the speed increase and provides clear temperature change information for users. For example, the target predicted temperature change curve shows that the temperature rises slowly within the first 20 minutes, then the rising speed accelerates, reaches a peak at about 45 minutes, and then tends to be stable. By analyzing this prediction curve, the final temperature prediction result can be determined, providing a strong basis for decision-making.

[0136] Regarding step S5, when the highest single-point target predicted temperature on the target predicted temperature change curve does not exceed the preset safety threshold, a roller table speed increase plan matching the target predicted temperature change curve is determined, and the roller table production system is controlled to increase speed according to the roller table speed increase plan.

[0137] Specifically, analyze the temperature change, fluctuation situation, and possible peak and valley values presented in the target predicted temperature change curve to determine the safe temperature range of the roller table during the speed increase process. Historical production data, industry standards, and expert opinions can be referred to in this process. Suppose historical production data shows that when the roller table temperature is below 40°C, the equipment operates stably and the failure rate is low; the industry standard stipulates that the safe operating temperature of a similar roller table should not exceed 42°C; experts suggest that it is best to control the safe temperature range below 38°C.

[0138] Based on the analysis of the target predicted temperature change curve results and the determination of the safe temperature range, a speed increase plan is formulated by comprehensively considering the current state of the roller path, production requirements, temperature change trends, etc. If the temperature rises slowly and is within the safe range, the speed increase amplitude can be appropriately increased; otherwise, the speed increase amplitude is reduced or measures such as adding heat dissipation equipment are taken. For example, the current running speed of the roller path is 20 meters per minute, and the production requirement is to improve production efficiency as soon as possible while ensuring safety. If the temperature rises slowly, such as rising 0.2 °C per minute, and the current temperature is 30 °C, which is far below the safe temperature range, the speed increase amplitude can be considered to be increased to 5 meters per minute. However, if the temperature rises rapidly, such as rising 0.5 °C per minute, and the current temperature is already close to the upper limit of the safe temperature, such as 37 °C, the speed increase amplitude can be reduced to 2 meters per minute, or measures such as adding heat dissipation equipment can be taken, such as installing a fan for forced heat dissipation. Assume that the fan can increase the temperature drop speed to 0.3 °C per minute.

[0139] At the same time, ensure that the plan is feasible and operable and formulate a detailed plan. After formulating the plan, verify and optimize it through simulation experiments, actual tests, etc. Closely monitor the temperature and related parameter changes. If there are problems, adjust them in a timely manner to improve the quality and reliability of the plan, ensure that the roller path speeds up within the safe temperature range, and improve production efficiency. For example, formulate a detailed speed increase plan, including the adjustment of the speed increase amplitude at different time periods, the opening time of the heat dissipation equipment, etc. Conduct a simulation experiment. Assume that in the simulation experiment, when the plan is implemented, the error between the temperature change and the predicted result is within ±1 °C, proving that the plan is feasible. In actual tests, continuously monitor parameters such as temperature and running speed. If it is found that the temperature exceeds the safe range, adjust the plan in a timely manner to ensure that the roller path speeds up stably within the safe temperature range and improve production efficiency. In some embodiments, the optimized plan increases the production efficiency by 20%.

[0140] Furthermore, determine the highest single-point target predicted temperature and its time node during the speed increase process on the target predicted temperature change curve. For example, the target predicted temperature change curve shows the temperature change situation within the next 60 minutes, with each minute as a time point, and there are 60 data points in total. Determine the temperature values at each time point. For example, the temperature is 30 °C at the 10th minute, 32 °C at the 20th minute, 35 °C at the 30th minute, etc. Through the analysis of the target predicted temperature change curve, determine the highest single-point target predicted temperature and its corresponding time node during the speed increase process of the roller path. Assume that through analysis, it is found that the temperature reaches the highest value of 38 °C at the 45th minute, and this time node is the time node of the highest single-point target predicted temperature. The highest single-point target predicted temperature and the time node directly reflect the maximum temperature problem that the roller path may face during the speed increase process.

[0141] Further, compare the highest single-point target predicted temperature with a preset safety threshold. The preset safety threshold is set according to the material properties of the roller table, operating requirements, and relevant safety standards. By comparing the highest single-point target predicted temperature with the safety temperature threshold, it can be evaluated whether the speed increase plan meets the safety requirements. If the highest single-point target predicted temperature is lower than the safety temperature threshold, it indicates that the speed increase plan is safe in terms of temperature and can be continued. If the highest single-point target predicted temperature exceeds the safety temperature threshold, then the speed increase plan needs to be adjusted to ensure the safety of the roller table during operation.

[0142] Further, when the highest single-point target predicted temperature exceeds the preset safety threshold, the method further includes step S51-step S52.

[0143] Step S51, determine the roller table speed increase plan to be verified according to the highest single-point target predicted temperature and the preset safety threshold;

[0144] Step S52, when the roller table speed increase plan to be verified meets the preset safety requirements, control the roller table production system to increase speed according to the roller table speed increase plan to be verified.

[0145] In step S51-step S52, determine the roller table speed increase plan to be verified according to the degree to which the highest single-point target predicted temperature exceeds the preset safety threshold. If the exceeded degree is small, it can be considered to fine-tune the parameters of the speed increase plan, such as reducing the speed increase amplitude, increasing heat dissipation measures, etc. Assume the highest temperature is 40°C, exceeding the safety temperature threshold of 39°C by only 1°C. The speed increase amplitude can be adjusted from the original increase of 5 meters per minute to an increase of 3 meters per minute, and at the same time, add a cooling fan. Assume the cooling fan can reduce the temperature by 0.5°C per minute.

[0146] If the exceeded degree is large, it may be necessary to make a large adjustment to the speed increase plan, or even redesign the speed increase plan. For example, if the highest temperature is 43°C, exceeding the safety temperature threshold by a large amount, it can be considered to suspend the speed increase, first take stronger heat dissipation measures, such as installing a water cooling system, and then re-evaluate the speed increase plan after the temperature drops to the safe range. During the process of adjusting the parameters, multiple factors need to be comprehensively considered, including the current state of the roller table, production requirements, temperature change trend, etc. By continuously adjusting and optimizing the parameters, a roller table speed increase plan that meets the preset safety requirements is generated.

[0147] In summary, the embodiment of the present application provides a control method for roller table speed increase, including: for each target roller in each roller to be speeded up in the roller table production system, obtaining the actual basic parameters of the target roller; the actual basic parameters include the initial temperature of the roller table surface; according to the actual basic parameters and the preset initial predicted temperature change model, determining the period before predicted speed increase; when the roller table production system operates within the period before predicted speed increase, correcting the initial predicted temperature change model according to the actual temperature of the roller table surface and the initial temperature of the roller table surface in the roller table production system to obtain a target predicted temperature change model; according to the actual basic parameters and the target predicted temperature change model, determining a target predicted temperature change curve corresponding to the period before predicted speed increase; when the highest single-point target predicted temperature on the target predicted temperature change curve does not exceed the preset safety threshold, determining a roller table speed increase plan matching the target predicted temperature change curve, and controlling the roller table production system to speed up according to the roller table speed increase plan.

[0148] It can be seen that by inputting the actual basic parameters of each target roller into the preset initial predicted temperature change model, the period before predicted speed increase is obtained. The actual temperature of the roller table surface is obtained within the period before predicted speed increase, and the model is corrected in combination with the actual basic parameters to obtain a target predicted temperature change model that adapts to the actual production state and can accurately predict future temperature changes. The roller table speed increase plan determined accordingly not only enhances the dynamic adjustment ability, but also reduces the occurrence probability of problems such as equipment damage or production impact caused by too high temperature, improving production safety and production efficiency.

[0149] Based on the same inventive concept, the embodiment of the present application provides a control device for roller table speed increase as shown in Figure 2 and includes:

[0150] An actual basic parameter acquisition module 21, configured to obtain the actual basic parameters of each target roller for each roller to be speeded up in the roller table production system; the actual basic parameters include the initial temperature of the roller table surface;

[0151] A period before predicted speed increase determination module 22, configured to determine the period before predicted speed increase according to the actual basic parameters and the preset initial predicted temperature change model;

[0152] A target predicted temperature change model determination module 23, configured to correct the initial predicted temperature change model according to the actual temperature of the roller table surface and the initial temperature of the roller table surface in the roller table production system to obtain a target predicted temperature change model when the roller table production system operates within the period before predicted speed increase;

[0153] A target predicted temperature change curve determination module 24, configured to determine a target predicted temperature change curve corresponding to the period before predicted speed increase according to the actual basic parameters and the target predicted temperature change model;

[0154] The speed-up control module 25 is configured to determine a roller table speed-up plan that matches the target predicted temperature change curve and control the speed-up of the roller table production system according to the roller table speed-up plan when the highest single-point target predicted temperature on the target predicted temperature change curve does not exceed a preset safety threshold.

[0155] Furthermore, the device includes an actual basic parameter preprocessing module, which is used for:

[0156] Obtain the actual monitoring parameters corresponding to multiple monitoring positions on the target roller table;

[0157] Preprocess the multiple actual monitoring parameters to obtain the actual basic parameters of the target roller table.

[0158] Furthermore, the device includes an initial predicted temperature change model determination module, which is used for:

[0159] Select pre-trained historical production data that matches the actual production data of the target roller table from the historical production data of multiple groups of roller tables in the roller table production system;

[0160] Perform statistical analysis on the pre-trained historical production data to obtain pre-trained features related to the change of the roller table surface temperature, and determine the initial predicted temperature change model by means of time series analysis.

[0161] Furthermore, the device includes an initial predicted temperature change curve determination module, which is used for:

[0162] Preprocess the actual basic parameters to obtain actual standard parameters;

[0163] Input the actual standard parameters into the initial predicted temperature change model by means of multi-dimensional mapping to obtain an initial predicted temperature change curve; the single-point initial predicted temperature on the initial predicted temperature change curve corresponds to the single-point prediction time one by one;

[0164] Determine the period before predicted speed-up according to the initial predicted temperature change curve.

[0165] Furthermore, the device includes an initial predicted temperature change time node determination module, which is used for:

[0166] Determine the single-point prediction time corresponding to when the rising rate of the single-point initial predicted temperature on the initial predicted temperature change curve starts to decline as the initial predicted temperature change time node;

[0167] Take the predicted temperature change time node as the time end point of the period before predicted speed-up, and trace back a preset time length as the time start point of the period before predicted speed-up to determine the period before predicted speed-up.

[0168] Further, the device includes a prediction pre - speed - up time - period adjustment module for:

[0169] When the actual operation time of the roller - path production system reaches the time starting point, adjust the time length of the prediction pre - speed - up time - period according to the actual change rate of the actual temperature on the roller path surface.

[0170] Further, the device includes a correction module for:

[0171] When the operation environment data of the target roller path meets the standard operation conditions, obtain the model - predicted temperature according to the actual temperature on the roller path surface and the initial prediction temperature change model;

[0172] Determine an error index according to the initial temperature on the roller path surface and the model - predicted temperature, and correct the initial prediction temperature change model according to the error index to obtain the target prediction temperature change model.

[0173] Further, the device includes a speed - up verification control module for:

[0174] Determine the roller - path speed - up plan to be verified according to the highest single - point target prediction temperature and the preset safety threshold;

[0175] When the roller - path speed - up plan to be verified meets the preset safety requirements, control the roller - path production system to speed up according to the roller - path speed - up plan to be verified.

[0176] Based on the same inventive concept, an embodiment of the present application also provides an electronic device as shown in Figure 3 including:

[0177] A processor 31;

[0178] A memory 32 for storing executable instructions of the processor 31;

[0179] Wherein, the processor 31 is configured to execute to implement a control method for roller - path speed - up as provided above.

[0180] Based on the same inventive concept, an embodiment of the present application also provides a non - transitory computer - readable storage medium. When the instructions in the storage medium are executed by the processor 31 of the electronic device, the electronic device can execute to implement a control method for roller - path speed - up as provided above.

[0181] Since the electronic device introduced in this embodiment is the one used to implement the information processing method in the embodiments of this application, based on the information processing method introduced in the embodiments of this application, those skilled in the art can understand the specific implementation manners and various variations of the electronic device in this embodiment. Therefore, the specific implementation of how this electronic device implements the method in the embodiments of this application will not be described in detail here. As long as the electronic device used by those skilled in the art to implement the information processing method in the embodiments of this application falls within the scope protected by this application.

[0182] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0183] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0184] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0185] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide means for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1Steps of the functions specified in one or more boxes.

[0186] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0187] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A control method for increasing the speed of a roller table, characterized in that, Including: For each target roller path among the roller paths to be speeded up in the roller path production system, obtain the actual basic parameters of the target roller path; the actual basic parameters include the initial temperature of the roller path surface. Determine the period before predicted speed increase according to the actual basic parameters and the preset initial predicted temperature change model. When the roller path production system is operating within the period before predicted speed increase, correct the initial predicted temperature change model according to the actual temperature of the roller path surface and the initial temperature of the roller path surface in the roller path production system to obtain the target predicted temperature change model. Determine the target predicted temperature change curve corresponding to the period before predicted speed increase according to the actual basic parameters and the target predicted temperature change model. When the highest single-point target predicted temperature on the target predicted temperature change curve does not exceed the preset safety threshold, determine the roller path speed increase plan matching the target predicted temperature change curve, and control the speed increase of the roller path production system according to the roller path speed increase plan.

2. The control method for accelerating the roller table according to claim 1, wherein The obtaining of the actual basic parameters of the target roller path includes: Obtain the actual monitoring parameters corresponding to multiple monitoring positions on the target roller path. Perform preprocessing on the multiple actual monitoring parameters to obtain the actual basic parameters of the target roller path.

3. The control method for roller table speed increase according to claim 1, characterized in that, The determination method of the initial predicted temperature change model includes: Select the pre-trained historical production data matching the actual production data of the target roller path from the historical production data of multiple roller paths in the roller path production system. Perform statistical analysis on the pre-trained historical production data to obtain pre-trained features related to the change of the roller path surface temperature, and determine the initial predicted temperature change model by means of time series analysis.

4. The control method for roller table speed increase according to claim 1, characterized in that, The determining of the period before predicted speed increase according to the actual basic parameters and the preset initial predicted temperature change model includes: Perform preprocessing on the actual basic parameters to obtain actual standard parameters. Input the actual standard parameters into the initial predicted temperature change model by means of multi-dimensional mapping to obtain the initial predicted temperature change curve; the single-point initial predicted temperature on the initial predicted temperature change curve corresponds one-to-one with the single-point prediction time. Determine the period before predicted speed increase according to the initial predicted temperature change curve.

5. The control method for roller table speed increase according to claim 4, wherein The determining of the period before predicted speed increase according to the initial predicted temperature change curve includes: Determine the single-point prediction time corresponding to the start of the decrease in the rising rate of the single-point initial predicted temperature in the initial predicted temperature change curve as the initial predicted temperature change time node. Take the predicted temperature change time node as the time end point of the period before predicted speed increase, and trace back a preset time length as the time start point of the period before predicted speed increase to determine the period before predicted speed increase.

6. The control method for roller table speed increase according to claim 5, characterized in that, The determining of the period before predicted speed increase includes: When the actual operating time of the roller path production system reaches the time start point, adjust the time length of the period before predicted speed increase according to the actual change rate of the actual temperature of the roller path surface.

7. The control method for roller table speed increase according to claim 1, characterized in that, Modifying the initial predicted temperature change model according to the actual temperature of the roller surface of the roller path production system and the initial temperature of the roller surface to obtain a target predicted temperature change model, including: When the operating environment data of the target roller path meets the standard operating conditions, obtaining a model predicted temperature according to the actual temperature of the roller surface and the initial predicted temperature change model; Determining an error index according to the initial temperature of the roller surface and the model predicted temperature, and modifying the initial predicted temperature change model according to the error index to obtain the target predicted temperature change model.

8. The control method for accelerating the roller table according to claim 1, characterized in that, When the highest single-point target predicted temperature exceeds the preset safety threshold, the method further includes: Determining a roller path speed-up plan to be verified according to the highest single-point target predicted temperature and the preset safety threshold; When the roller path speed-up plan to be verified meets the preset safety requirements, controlling the roller path production system to speed up according to the roller path speed-up plan to be verified.

9. A control device for accelerating the roller table, characterized in that, Including: An actual basic parameter acquisition module, configured to acquire the actual basic parameters of each target roller in each roller to be speeded up in the roller path production system; the actual basic parameters include the initial temperature of the roller surface; A predicted pre-speed-up time period determination module, configured to determine a predicted pre-speed-up time period according to the actual basic parameters and a preset initial predicted temperature change model; A target predicted temperature change model determination module, configured to modify the initial predicted temperature change model according to the actual temperature of the roller surface of the roller path production system and the initial temperature of the roller surface to obtain a target predicted temperature change model when the roller path production system is operating within the predicted pre-speed-up time period; A target predicted temperature change curve determination module, configured to determine a target predicted temperature change curve corresponding to the predicted pre-speed-up time period according to the actual basic parameters and the target predicted temperature change model; A speed-up control module, configured to determine a roller path speed-up plan matching the target predicted temperature change curve and control the roller path production system to speed up according to the roller path speed-up plan when the highest single-point target predicted temperature on the target predicted temperature change curve does not exceed the preset safety threshold.

10. An electronic device, characterized in that, Including: A processor; A memory for storing executable instructions of the processor; Wherein, the processor is configured to execute to implement a control method for roller path speed-up as described in any one of claims 1 to 8.

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