Rotating speed calibration method and system based on temperature and humidity data analysis
By conducting comparative experiments on the film roller, collecting speed and temperature and humidity data, dividing the data intervals and planning the number of experiments, and constructing a speed calibration model, the problem of film roller speed measurement being interfered with by environmental parameters was solved, and more accurate speed calibration was achieved.
Patent Information
- Application Number
- CN202510810928.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-17
- Publication Date
- 2025-09-12
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
In industrial fields such as film material manufacturing and printing and packaging, the rotational speed measurement of film rollers is affected by changes in environmental parameters such as temperature and humidity, resulting in a significant deviation between the original measured rotational speed and the actual rotational speed. Existing calibration methods cannot effectively solve this problem.
Through comparison experiments, the original measured speed, actual speed and synchronized temperature and humidity parameters of the film roller are collected to establish a speed comparison data set; the temperature and humidity data intervals are divided, and the data demand is determined based on sensitivity analysis; the number of experiments is planned, and a speed calibration model is constructed to achieve real-time calibration.
Improves the accuracy of film roller speed calibration, making it suitable for industrial scenarios with significant temperature and humidity changes, and optimizes data collection and model accuracy.
Smart Images
Figure CN120629642A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of film roller speed calibration, and in particular to a speed calibration method and system based on temperature and humidity data analysis. Background Art
[0002] In industrial fields such as film material manufacturing, printing and packaging, the rotational speed accuracy of film rollers directly affects product quality and production efficiency. However, in actual production environments, the rotational speed measurement of film rollers is often interfered by changes in environmental parameters such as temperature and humidity, resulting in a significant deviation between the original measured rotational speed and the actual rotational speed. Traditional calibration methods mostly use direct sensor correction or empirical formula adjustment, but such methods have obvious defects: First, the influence of temperature and humidity parameters is regionally sensitive, and a single global correction cannot cover the nonlinear error characteristics under different temperature and humidity combinations; second, static experimental designs are difficult to adapt to dynamic environmental changes, and the uneven distribution of experimental data leads to weak model generalization capabilities; third, existing models lack in-depth analysis of the correlation mechanism between temperature, humidity and rotational speed errors, especially in scenarios where parameters interact, the error amplification problem is prominent. Summary of the Invention
[0003] The object of the present invention is to provide a rotational speed calibration method and system with more accurate rotational speed calibration.
[0004] The present invention discloses a speed calibration method based on temperature and humidity data analysis, comprising: Step S100, performing a comparison data experiment, collecting the original measured speed of the film roller, synchronously collecting temperature parameters and humidity parameters, and collecting the actual speed of the film roller, establishing a correspondence between the original measured speed and the temperature parameter, and the humidity parameter and the actual speed, to obtain a speed comparison data set; Step S200: Delimiting the data intervals of the temperature parameter and the humidity parameter, and determining the required amount of comparison data corresponding to the data intervals based on the sensitivity of the delimited data intervals; Step S300: determining the number of experiments for the comparison data experiment based on the comparison data demand corresponding to the data interval, and classifying the speed comparison data groups obtained from the comparison data experiment into the data interval to obtain a speed comparison data set; Step S400, performing linear change analysis on the speed comparison data groups in the speed comparison data set, and determining whether to expand the number of experiments corresponding to the speed comparison data set based on the analysis result; Step S500: Using the rotational speed comparison data set as basic data, a rotational speed calibration model is constructed.
[0005] In some embodiments disclosed herein, a method for defining sensitive data intervals for temperature and humidity parameters includes: Step S201: Establish a three-dimensional sensitivity analysis coordinate system. The x-axis is identified as the temperature parameter interval variation axis, including a plurality of temperature parameter intervals, and the temperature parameter intervals are connected end to end according to the extension of the x-axis. The y-axis is identified as the humidity parameter interval variation axis, including a plurality of humidity parameter intervals, and the humidity parameter intervals are connected end to end according to the extension of the y-axis. The vertical z-axis is identified as the speed difference variation axis, where the speed difference is the difference between the original measured speed and the actual speed. Step S202: For each temperature parameter interval and each humidity parameter interval, a combination is performed, and a speed difference variation interval corresponding to the parameter interval combination is determined. Based on the upper and lower limits of the speed difference variation interval, a speed difference expression vertical line is constructed at the corresponding position in the sensitivity analysis three-dimensional coordinate system. The lower endpoint of the speed difference expression vertical line corresponds to the lower limit of the speed difference variation interval, and the upper endpoint of the speed difference expression vertical line corresponds to the upper limit of the speed difference variation interval. Step S203: Analyze the lengths of adjacent vertical lines representing the difference in the three-dimensional sensitivity analysis coordinate system, and based on the analysis results, correlate the adjacent vertical lines representing the difference, combine the temperature parameter intervals corresponding to the correlated vertical lines representing the difference, and combine the corresponding humidity parameter intervals to obtain new temperature parameter intervals and new humidity parameter intervals. Step S204 , based on the length of the vertical line representing the difference, determine the data requirements corresponding to the new temperature parameter interval and the new humidity parameter interval. The longer the vertical line representing the difference, the larger the number of experiments.
[0006] In some embodiments disclosed herein, a method for analyzing the length representation of adjacent vertical lines representing difference quantities in a sensitivity analysis three-dimensional coordinate system includes: In step S2031, several preset length intervals are set for the lengths of the vertical lines expressing the difference, and the preset length intervals to which the lengths of adjacent vertical lines expressing the difference belong are determined. If the lengths of adjacent vertical lines expressing the difference all belong to a certain preset length interval, or there is a vertical line expressing the difference whose length is less than or equal to the end value of the preset length interval, then the adjacent vertical lines expressing the difference are correlated with each other.
[0007] In some embodiments disclosed herein, a method for performing linear change analysis on rotational speed comparison data groups in a rotational speed comparison data set includes: Step S401, creating a linear change analysis graph, the linear change analysis graph including a temperature parameter vertical axis, a humidity parameter vertical axis, an original measured speed vertical axis, and an actual speed vertical axis; Step S402: Based on the speed comparison data set, mark the mapping points on the corresponding vertical axis on the linear change analysis graph, connect the temperature parameter mapping points and the humidity parameter mapping points with a vector line, record it as the temperature and humidity vector line, and connect the original measured speed mapping points and the actual speed mapping points with a vector line, the speed vector line, and connect and associate the corresponding temperature and humidity vector line with the speed vector line; Step S403: randomly selecting temperature and humidity vector lines that are equivalent to each other, and correlating the speed vector lines corresponding to the selected temperature and humidity vector lines to form an analysis speed vector line group; Step S404 : performing a performance equivalence analysis on the analysis speed vector lines of the analysis speed vector line group, and determining whether to expand the number of experiments corresponding to the speed comparison data set based on the analysis result.
[0008] In some embodiments disclosed herein, a method for performing performance equivalence analysis on vector lines includes: Step S4031: randomly select a number of vector lines and randomly combine them in pairs to form a number of vector line groups. Determine the inter-line relative distance sequence between each vector line group. The inter-line relative distance sequence includes a number of inter-line relative distances. Calculate the sum of all inter-line relative distances and record it as the total inter-line relative distance. Step S4032: determining the sub-equivalent parameters of a single vector line group based on the relative distances between all bus lines, and determining the comprehensive equivalent parameters of all vector lines based on the sub-equivalent parameters of all vector line groups.
[0009] In some embodiments disclosed in the present invention, the expression for determining the sub-equivalent parameters of the vector line group is: ; Among them, d is the sub-identity parameter, is the preset maximum relative distance between buses, is the relative distance between the main lines of the vector line group; The expression for determining the comprehensive equivalent parameters of all vector lines is: ; Among them, D is the comprehensive equivalent parameter, is the sub-identical parameter of the i-th vector line group, R is the sub-identical parameter influence adjustment coefficient, b is the influence adjustment constant of the sub-identical parameter, and n is the number of all vector line groups.
[0010] In some embodiments disclosed herein, a method for constructing a speed calibration model using a speed comparison dataset as basic data includes: In step S501, a retrieval condition directory is constructed using the temperature parameter and the humidity parameter as the first retrieval condition, an out-of-set index relationship is constructed using the first retrieval condition directory as the speed comparison data set, a second retrieval condition directory is constructed using the original measured speed as the second retrieval condition, an in-set index relationship is constructed using the second retrieval condition directory as the speed comparison data set, and based on the common index framework of the first retrieval condition directory and the second retrieval condition directory, the speed comparison data set is integrated into a speed calibration model.
[0011] In some embodiments disclosed herein, a method for finding a corresponding actual speed in a speed comparison data set includes: Step S5011, obtaining a real-time temperature parameter and a real-time humidity parameter, and determining a rotation speed comparison data set to be called based on a matching relationship between the real-time temperature parameter and the real-time humidity parameter in the first search condition directory; Step S5012: Acquire the real-time original measured rotation speed, and find the best matching reference actual rotation speed based on the matching relationship of the real-time original measured rotation speed in the second search condition directory.
[0012] In some embodiments disclosed herein, a method for finding the best-matching reference actual speed includes: Step S50121, respectively calculating the temperature parameter difference, the humidity parameter difference, and the original measured speed difference, determining the preset temperature parameter interval to which the real-time temperature parameter belongs, and determining the preset humidity parameter interval to which the real-time humidity parameter belongs, and determining the temperature matching weight and the humidity matching weight based on the determined preset temperature parameter interval and humidity parameter interval; Step S50122: Determine the reference actual speed with the highest matching parameter value based on the temperature matching weight, the humidity matching weight, the temperature parameter difference, the humidity parameter difference, and the original measured speed difference; The expression for calculating the matching parameter is: ; Among them, P is the matching parameter, is the temperature matching weight, is the humidity matching weight, Match the weights to the preset raw measured speed. is the preset maximum temperature parameter difference, is the temperature parameter difference, is the preset maximum humidity parameter difference, is the humidity parameter difference, is the preset maximum original measured speed difference, is the original measured speed difference.
[0013] In some embodiments disclosed in the present invention, a speed calibration system based on temperature and humidity data analysis is also disclosed, including: The first module is used to conduct a comparison data experiment, collect the original measured speed of the film roller, and simultaneously collect temperature and humidity parameters, and collect the actual speed of the film roller. The correspondence between the original measured speed and temperature parameters and the humidity parameters and actual speed is established to obtain a speed comparison data set; The second module is used to delineate the data intervals of the temperature parameters and the humidity parameters, and determine the required amount of comparison data corresponding to the data intervals based on the sensitivity of the delineated data intervals; The third module is used to determine the number of experiments for the comparison data experiment based on the comparison data demand corresponding to the data interval, and classify the speed comparison data groups obtained from the comparison data experiment into the data interval to obtain the speed comparison data set; The fourth module is used to perform linear change analysis on the speed comparison data groups in the speed comparison data set, and determine whether to expand the number of experiments corresponding to the speed comparison data set based on the analysis results; The fifth module is used to construct a speed calibration model using the speed comparison dataset as basic data.
[0014] The present invention proposes a speed calibration method and system based on temperature and humidity data analysis, relating to the technical field of film roller speed calibration. The method includes: collecting the film roller's original measured speed, actual speed, and synchronized temperature and humidity parameters through comparison experiments, establishing corresponding relationships, and forming a speed comparison data set; dividing the temperature and humidity data into intervals and determining the data requirements for each interval based on sensitivity analysis; planning the number of experiments based on the data requirements, and classifying the data sets into speed comparison data sets; performing linear change analysis on the data sets to determine whether the number of experiments needs to be increased; and constructing a speed calibration model based on the data sets, achieving real-time calibration through dual indexing of temperature, humidity, and speed. The present invention uses three-dimensional sensitivity analysis and vector linear analysis to optimize data acquisition and model accuracy, making it suitable for industrial scenarios with significant temperature and humidity variations.
[0015] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Figure 1 This is a method step diagram of a speed calibration method based on temperature and humidity data analysis disclosed in an embodiment of the present invention. DETAILED DESCRIPTION
[0017] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.
[0018] The following will be combined with the accompanying drawings and specific embodiments to clearly and completely describe the technical solutions of the present invention. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and cannot be understood as limiting the scope of protection of the present invention. Those skilled in the art in this field can make some non-essential improvements and adjustments based on the content of the present invention described below. In the present invention, unless otherwise clearly specified and limited, the technical terms used in the present invention should have the common meanings understood by those skilled in the art of the present invention.
[0019] Example:
[0020] The present invention discloses a speed calibration method based on temperature and humidity data analysis, see Figure 1 ,include: In step S100, a comparison data experiment is performed to collect the original measured rotational speed of the film roller, simultaneously collect the temperature parameters and humidity parameters, and collect the actual rotational speed of the film roller, establish the corresponding relationship between the original measured rotational speed-temperature parameters and the humidity parameters-actual rotational speed, and obtain a rotational speed comparison data group.
[0021] Step S100 forms the foundation of the entire speed calibration method. It aims to experimentally collect the film roller's original measured speed, actual speed, and synchronized temperature and humidity parameters, establish a corresponding relationship between them, and generate a speed comparison data set. The core principle is to synchronously collect multidimensional data (speed, temperature, and humidity) to provide a raw data set for subsequent analysis, ensuring that the calibration model can reflect the impact of environmental factors on speed measurement. In specific implementation, sensors are used to record the film roller's original measured speed (e.g., speed measured by an encoder) and actual speed (measured by a high-precision reference device) in real time. The ambient temperature (e.g., 25°C) and humidity (e.g., 60% RH) are also recorded. Through multiple experiments, a speed comparison data set containing multiple data sets is generated, for example: {(original speed 1000 rpm, temperature 25°C, humidity 60%, actual speed 990 rpm), (original speed 1000 rpm, temperature 30°C, humidity 70%, actual speed 995 rpm)}. The purpose of this step is to provide a reliable data basis for subsequent sensitivity analysis and model building, ensuring that the calibration model can capture the impact of temperature and humidity on speed deviation.
[0022] In step S200 , the data intervals of the temperature parameter and the humidity parameter are delineated, and based on the sensitivity of the delineated data intervals, the comparison data requirement corresponding to the data intervals is determined.
[0023] Step S200 divides the temperature and humidity parameters into intervals and determines the required amount of comparison data based on the sensitivity of each interval. Its core goal is to quantify the impact of environmental parameters on speed deviation. In principle, the impact of temperature and humidity on speed measurement is not uniformly distributed, and different intervals may result in different speed deviations. Therefore, sensitivity analysis is necessary to optimize the focus of data collection.
[0024] Step S300 : determining the number of experiments for the comparison data experiment based on the comparison data demand corresponding to the data interval, and classifying the speed comparison data groups obtained from the comparison data experiment into the data interval to obtain a speed comparison data set.
[0025] Based on the data requirements determined in step S200, step S300 plans the scale of the comparison data experiment and categorizes the experimental data into corresponding temperature-humidity intervals to form a speed comparison dataset. The principle behind this is to rationally plan the number of experiments to ensure data coverage across all critical intervals while avoiding redundant experiments to improve efficiency. Specifically, the number of experiments is allocated based on the sensitivity of the speed differences in each interval in step S200. For example, an interval with a large speed difference (e.g., a speed difference of 20 rpm) may require 50 experiments, while an interval with a smaller speed difference (e.g., 5 rpm) may only require 10 experiments. After the experiment is completed, the collected data sets (e.g., {(1000 rpm, 25°C, 60% RH, 990 rpm), …}) are categorized by temperature-humidity interval to form a structured speed comparison dataset. For example, the intervals [25°C, 30°C] and [60% RH, 70% RH] may contain multiple data sets for subsequent analysis.
[0026] Step S400 : performing linear change analysis on the speed comparison data groups in the speed comparison data set, and determining whether to expand the number of experiments corresponding to the speed comparison data set based on the analysis result.
[0027] Step S400 performs linear variation analysis on the data sets in the speed comparison dataset to determine regularities between the data and determine whether additional experiments are needed to improve the dataset. The core principle is to use linear relationships to analyze the mutual influence of temperature, humidity, and speed, determining whether the data is sufficient to support the construction of a calibration model. In specific implementation (steps S401-S404), a four-axis linear variation analysis chart (temperature, humidity, original speed, and actual speed) is constructed, mapping the data sets into points and connecting them into vectors. For example, the points corresponding to a temperature of 25°C and a humidity of 60% RH are connected to form a temperature-humidity vector line, and the points corresponding to an original speed of 1000 rpm and an actual speed of 990 rpm are connected to form a speed vector line. By analyzing the correlation between the temperature-humidity vector line and the speed vector line, vector groups with equivalent behavior are identified (steps S4031-S4032), and sub-equivalent and comprehensive equivalent parameters are calculated. For example, if the distance between the two sets of temperature-humidity vector lines is small (e.g., less than a preset value of 5), they are considered to have similar influences and can be associated with the corresponding speed vector lines. If the analysis reveals insufficient data regularity (the comprehensive equivalent parameter is below the threshold), the number of experiments needs to be increased. This step serves as a data quality verification and optimization step throughout the paper, ensuring the reliability and representativeness of the dataset and providing high-quality input for model construction in step S500.
[0028] Step S500: Using the rotational speed comparison data set as basic data, a rotational speed calibration model is constructed.
[0029] Step S500 builds a speed calibration model based on the speed comparison dataset. This model is used to predict the actual speed based on real-time temperature, humidity, and the original measured speed. The core principle is to integrate the datasets through a dual indexing mechanism (temperature-humidity and original speed), establishing an efficient search and matching framework for accurate speed calibration.
[0030] In some embodiments disclosed herein, a method for defining sensitive data intervals for temperature and humidity parameters includes: Step S201, establish a three-dimensional coordinate system for sensitivity analysis, identify the plane x-axis as the temperature parameter interval change axis, including several temperature parameter intervals, and the temperature parameter intervals are connected end to end according to the extension of the x-axis, identify the plane y-axis as the humidity parameter interval change axis, including several humidity parameter intervals, and the humidity parameter intervals are connected end to end according to the extension of the y-axis, and identify the vertical z-axis as the speed difference change axis, where the speed difference is the difference between the original measured speed and the actual speed.
[0031] Step S201 constructs a three-dimensional coordinate system, providing a visual and quantitative framework for analyzing the impact of temperature and humidity on speed variation. The core principle is to map temperature, humidity, and speed variation (the difference between the original measured speed and the actual speed) into three-dimensional space, enabling a systematic analysis of the impact of environmental parameters. Specifically, the x-axis is defined as the temperature parameter interval variation axis, for example, divided into continuous intervals such as [20°C, 25°C] and [25°C, 30°C], which are connected end to end to cover the entire temperature range; the y-axis is defined as the humidity parameter interval variation axis, for example, [50%RH, 60%RH] and [60%RH, 70%RH], which are also connected end to end; and the z-axis is defined as the speed variation axis, representing the difference between the original measured speed and the actual speed (e.g., 10 rpm). For example, if the original speed is 1000 rpm and the actual speed is 990 rpm at 25°C and 60% RH, the speed difference is 10 rpm, which is mapped to the point (25, 60, 10) in the coordinate system. This step provides a structured analytical framework for subsequent interval combination and difference analysis.
[0032] In step S202, each temperature parameter interval and each humidity parameter interval are combined, and the speed difference change interval corresponding to the parameter interval combination is determined. Based on the upper limit value and the lower limit value of the speed difference change interval, a vertical line representing the speed difference is constructed at the corresponding position in the sensitive analysis three-dimensional coordinate system. The lower end point of the vertical line representing the speed difference corresponds to the lower limit value of the speed difference change interval, and the upper end point of the vertical line representing the speed difference corresponds to the upper limit value of the speed difference change interval.
[0033] Step S202 analyzes each temperature and humidity range combination to determine the corresponding speed difference range and represents it as a vertical line in a three-dimensional coordinate system. The principle behind this approach is to use the range of speed difference under different temperature and humidity combinations to quantify the specific impact of environmental parameters on speed measurement. Specifically, experiments are conducted for each temperature and humidity range combination (e.g., [25°C, 30°C] and [60%RH, 70%RH]), collecting multiple sets of speed difference data and calculating their upper and lower limits (for example, a difference range of [5rpm, 15rpm]). In a three-dimensional coordinate system, this range is represented as a vertical line located at the coordinate point (27.5, 65) (the midpoint of the temperature and humidity ranges), with the lower endpoint corresponding to the lower limit of 5rpm and the upper endpoint corresponding to the upper limit of 15rpm. For example, if the difference between the other combination [20°C, 25°C] and [50%RH, 60%RH] is [2 rpm, 8 rpm], draw another vertical line at (22.5, 55). This step visualizes the range of speed differences and reveals the sensitivity differences between different interval combinations, providing a basis for subsequent interval merging and determining data requirements.
[0034] In step S203, the length expression of adjacent vertical lines expressing the difference amount in the three-dimensional coordinate system of the sensitivity analysis is analyzed, and based on the analysis results, the adjacent vertical lines expressing the difference amount are correlated with each other, and the temperature parameter intervals corresponding to the correlated vertical lines expressing the difference amount are combined with each other, and the corresponding humidity parameter intervals are combined with each other to obtain new temperature parameter intervals and new humidity parameter intervals.
[0035] Step S203 analyzes the lengths of adjacent vertical lines representing speed differences in the three-dimensional coordinate system and merges temperature and humidity intervals with similar effects to form new, larger parameter intervals. This process is based on the similarity of speed difference ranges, merging intervals with similar speed impacts to reduce the complexity of subsequent experiments and optimize data acquisition efficiency. In specific implementation (see step S2031), several preset length intervals (e.g., [0, 5 rpm], [5 rpm, 10 rpm], etc.) are set for the vertical line lengths, and the lengths of adjacent vertical lines are compared. If the lengths of adjacent vertical lines fall within the same preset interval, or if the difference between the length of a vertical line and the endpoint of a preset interval is less than or equal to a preset value (e.g., 1 rpm), they are linked. For example, if the vertical line length between the intervals [25°C, 30°C] and [60%RH, 70%RH] is 10 rpm, and the length of the adjacent intervals [20°C, 25°C] and [60%RH, 70%RH] is 9 rpm, and the difference is less than 1 rpm, then these two temperature intervals are merged into [20°C, 30°C], and the humidity interval remains unchanged. After the merger, new temperature and humidity intervals are formed, reducing the number of intervals. This step plays a role in optimizing interval division in the entire text. By merging intervals with similar influences, the complexity of data analysis is reduced, providing a basis for determining the data demand in step S204.
[0036] Step S204 , based on the length of the vertical line representing the difference, determine the data requirements corresponding to the new temperature parameter interval and the new humidity parameter interval. The longer the vertical line representing the difference, the larger the number of experiments.
[0037] In step S204, the amount of data required for the new temperature and humidity intervals is determined based on the length of the vertical line representing the speed difference. The longer the length, the greater the number of experiments required. This is because intervals with larger speed differences indicate a more significant impact of environmental parameters on speed measurement, requiring more experimental data to ensure calibration accuracy. In practice, the length of the vertical line corresponding to the new intervals merged in step S203 is analyzed. For example, if the vertical line length for the new intervals [20°C, 30°C] and [60%RH, 70%RH] is 10 rpm, indicating a significant speed difference, 50 experiments may be required. On the other hand, an interval with a length of 3 rpm (such as [30°C, 35°C] and [50%RH, 60%RH]) may only require 10 experiments. The amount of data required is proportional to the length of the vertical line and can be determined using a pre-set mapping rule (e.g., 5 experiments per 1 rpm). This step plays a role in resource allocation throughout the paper. By quantifying sensitivity, it ensures that the number of experiments is concentrated in the high-impact interval, improves data collection efficiency, and provides guidance for the subsequent step S300 to determine the experimental scale and construct the data set.
[0038] In some embodiments disclosed herein, a method for analyzing the length representation of adjacent vertical lines representing difference quantities in a sensitivity analysis three-dimensional coordinate system includes: In step S2031, several preset length intervals are set for the lengths of the vertical lines expressing the difference, and the preset length intervals to which the lengths of adjacent vertical lines expressing the difference belong are determined. If the lengths of adjacent vertical lines expressing the difference all belong to a certain preset length interval, or there is a vertical line expressing the difference whose length is less than or equal to the end value of the preset length interval, then the adjacent vertical lines expressing the difference are correlated with each other.
[0039] In some embodiments disclosed herein, a method for performing linear change analysis on rotational speed comparison data groups in a rotational speed comparison data set includes: Step S401, establish a linear change analysis diagram, the linear change analysis diagram includes a temperature parameter vertical axis, a humidity parameter vertical axis, an original measured speed vertical axis and an actual speed vertical axis. Step S402, based on the speed comparison data group, mark the mapping points on the corresponding vertical axis on the linear change analysis diagram, connect the temperature parameter mapping points and the humidity parameter mapping points with a vector line, recorded as the temperature and humidity vector line, and connect the original measured speed mapping points and the actual speed mapping points with a vector line, the speed vector line, and connect and associate the corresponding temperature and humidity vector lines with the speed vector line.
[0040] In step S403 , temperature and humidity vector lines that are equivalent to each other are randomly selected, and the speed vector lines corresponding to the selected temperature and humidity vector lines are correlated with each other to form an analysis speed vector line group.
[0041] Step S404 : performing a performance equivalence analysis on the analysis speed vector lines of the analysis speed vector line group, and determining whether to expand the number of experiments corresponding to the speed comparison data set based on the analysis result.
[0042] In some embodiments disclosed herein, a method for performing performance equivalence analysis on vector lines includes: In step S4031, a number of vector lines are randomly selected and randomly combined in pairs to form a number of vector line groups. The relative distance sequence between lines in each vector line group is determined. The relative distance sequence between lines includes a number of relative distances between lines. The sum of all relative distances between lines is calculated and recorded as the total relative distance between lines.
[0043] Step S4032: determining the sub-equivalent parameters of a single vector line group based on the relative distances between all bus lines, and determining the comprehensive equivalent parameters of all vector lines based on the sub-equivalent parameters of all vector line groups.
[0044] In some embodiments disclosed in the present invention, the expression for determining the sub-equivalent parameters of the vector line group is: .
[0045] Among them, d is the sub-identity parameter, is the preset maximum relative distance between buses, is the relative distance between the main lines of the vector line group; The expression for determining the comprehensive equivalent parameters of all vector lines is: .
[0046] Among them, D is the comprehensive equivalent parameter, is the sub-identical parameter of the i-th vector line group, R is the sub-identical parameter influence adjustment coefficient, b is the influence adjustment constant of the sub-identical parameter, and n is the number of all vector line groups.
[0047] In some embodiments disclosed herein, a method for constructing a speed calibration model using a speed comparison dataset as basic data includes: In step S501, a retrieval condition directory is constructed using the temperature parameter and the humidity parameter as the first retrieval condition, an out-of-set index relationship is constructed using the first retrieval condition directory as the speed comparison data set, a second retrieval condition directory is constructed using the original measured speed as the second retrieval condition, an in-set index relationship is constructed using the second retrieval condition directory as the speed comparison data set, and based on the common index framework of the first retrieval condition directory and the second retrieval condition directory, the speed comparison data set is integrated into a speed calibration model.
[0048] In some embodiments disclosed herein, a method for finding a corresponding actual speed in a speed comparison data set includes: Step S5011, obtaining a real-time temperature parameter and a real-time humidity parameter, and determining a rotation speed comparison data set to be called based on a matching relationship between the real-time temperature parameter and the real-time humidity parameter in the first search condition directory; Step S5012: Acquire the real-time original measured rotation speed, and find the best matching reference actual rotation speed based on the matching relationship of the real-time original measured rotation speed in the second search condition directory.
[0049] In some embodiments disclosed herein, a method for finding the best-matching reference actual speed includes: Step S50121, respectively calculate the temperature parameter difference, humidity parameter difference and original measured speed difference, determine the preset temperature parameter interval to which the real-time temperature parameter belongs, and determine the preset humidity parameter interval to which the real-time humidity parameter belongs, and determine the temperature matching weight and humidity matching weight based on the determined preset temperature parameter interval and humidity parameter interval.
[0050] Step S50122: Determine the reference actual speed with the highest matching parameter value based on the temperature matching weight, the humidity matching weight, the temperature parameter difference, the humidity parameter difference, and the original measured speed difference; The expression for calculating the matching parameter is: .
[0051] Among them, P is the matching parameter, is the temperature matching weight, is the humidity matching weight, Match the weights to the preset raw measured speed. is the preset maximum temperature parameter difference, is the temperature parameter difference, is the preset maximum humidity parameter difference, is the humidity parameter difference, is the preset maximum original measured speed difference, is the original measured speed difference.
[0052] In some embodiments disclosed in the present invention, a speed calibration system based on temperature and humidity data analysis is also disclosed, including: The first module is used to conduct a comparison data experiment, collect the original measured speed of the film roller, synchronously collect temperature parameters and humidity parameters, and collect the actual speed of the film roller, establish the corresponding relationship between the original measured speed-temperature parameters and humidity parameters-actual speed, and obtain a speed comparison data group.
[0053] The second module is used to define the data intervals of the temperature parameter and the humidity parameter, and determine the comparison data requirement corresponding to the data interval based on the sensitivity of the defined data interval.
[0054] The third module is used to determine the number of experiments for the comparison data experiment based on the comparison data demand corresponding to the data interval, and classify the speed comparison data group obtained from the comparison data experiment into the data interval to obtain a speed comparison data set.
[0055] The fourth module is used to perform linear change analysis on the speed comparison data groups in the speed comparison data set, and based on the analysis results, determine whether to expand the number of experiments corresponding to the speed comparison data set.
[0056] The fifth module is used to construct a speed calibration model using the speed comparison dataset as basic data.
[0057] The present invention proposes a speed calibration method and system based on temperature and humidity data analysis, relating to the technical field of film roller speed calibration. The method includes: collecting the film roller's original measured speed, actual speed, and synchronized temperature and humidity parameters through comparison experiments, establishing corresponding relationships, and forming a speed comparison data set; dividing the temperature and humidity data into intervals and determining the data requirements for each interval based on sensitivity analysis; planning the number of experiments based on the data requirements, and classifying the data sets into speed comparison data sets; performing linear change analysis on the data sets to determine whether the number of experiments needs to be increased; and constructing a speed calibration model based on the data sets, achieving real-time calibration through dual indexing of temperature, humidity, and speed. The present invention uses three-dimensional sensitivity analysis and vector linear analysis to optimize data acquisition and model accuracy, making it suitable for industrial scenarios with significant temperature and humidity variations.
[0058] Through the above description of the embodiments, those skilled in the art will clearly understand that the present invention can be implemented via hardware or via software combined with a necessary general-purpose hardware platform. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product. This software product can be stored on a non-volatile storage medium (such as a CD-ROM, USB flash drive, or external hard drive) and includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute the methods described in various implementation scenarios of the present invention.
[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.
Claims
1. A speed calibration method based on temperature and humidity data analysis, characterized in that: include: Step S100, performing a comparison data experiment, collecting the original measured speed of the film roller, synchronously collecting temperature parameters and humidity parameters, and collecting the actual speed of the film roller, establishing a correspondence between the original measured speed and the temperature parameter, and the humidity parameter and the actual speed, to obtain a speed comparison data set; Step S200: Delimiting the data intervals of the temperature parameter and the humidity parameter, and determining the required amount of comparison data corresponding to the data intervals based on the sensitivity of the delimited data intervals; Step S300: determining the number of experiments for the comparison data experiment based on the comparison data demand corresponding to the data interval, and classifying the speed comparison data groups obtained from the comparison data experiment into the data interval to obtain a speed comparison data set; Step S400, performing linear change analysis on the speed comparison data groups in the speed comparison data set, and determining whether to expand the number of experiments corresponding to the speed comparison data set based on the analysis result; Step S500: Using the rotational speed comparison data set as basic data, a rotational speed calibration model is constructed.
2. The speed calibration method based on temperature and humidity data analysis according to claim 1 is characterized in that: Methods for defining sensitive data intervals for temperature and humidity parameters include: Step S201: Establish a three-dimensional sensitivity analysis coordinate system. The x-axis is identified as the temperature parameter interval variation axis, including a plurality of temperature parameter intervals, and the temperature parameter intervals are connected end to end according to the extension of the x-axis. The y-axis is identified as the humidity parameter interval variation axis, including a plurality of humidity parameter intervals, and the humidity parameter intervals are connected end to end according to the extension of the y-axis. The vertical z-axis is identified as the speed difference variation axis, where the speed difference is the difference between the original measured speed and the actual speed. Step S202: For each temperature parameter interval and each humidity parameter interval, a combination is performed, and a speed difference variation interval corresponding to the parameter interval combination is determined. Based on the upper and lower limits of the speed difference variation interval, a speed difference expression vertical line is constructed at the corresponding position in the sensitivity analysis three-dimensional coordinate system. The lower endpoint of the speed difference expression vertical line corresponds to the lower limit of the speed difference variation interval, and the upper endpoint of the speed difference expression vertical line corresponds to the upper limit of the speed difference variation interval. Step S203: Analyze the lengths of adjacent vertical lines representing the difference in the three-dimensional sensitivity analysis coordinate system, and based on the analysis results, correlate the adjacent vertical lines representing the difference, combine the temperature parameter intervals corresponding to the correlated vertical lines representing the difference, and combine the corresponding humidity parameter intervals to obtain new temperature parameter intervals and new humidity parameter intervals. Step S204 , based on the length of the vertical line representing the difference, determine the data requirements corresponding to the new temperature parameter interval and the new humidity parameter interval. The longer the vertical line representing the difference, the larger the number of experiments.
3. The speed calibration method based on temperature and humidity data analysis according to claim 2, characterized in that: Methods for analyzing the lengths of adjacent vertical lines representing difference quantities in a sensitivity analysis three-dimensional coordinate system include: In step S2031, several preset length intervals are set for the lengths of the vertical lines expressing the difference, and the preset length intervals to which the lengths of adjacent vertical lines expressing the difference belong are determined. If the lengths of adjacent vertical lines expressing the difference all belong to a certain preset length interval, or there is a vertical line expressing the difference whose length is less than or equal to the end value of the preset length interval, then the adjacent vertical lines expressing the difference are correlated with each other.
4. The speed calibration method based on temperature and humidity data analysis according to claim 1, characterized in that: The method of performing linear change analysis on the rotation speed comparison data groups in the rotation speed comparison data set includes: Step S401, creating a linear change analysis graph, the linear change analysis graph including a temperature parameter vertical axis, a humidity parameter vertical axis, an original measured speed vertical axis, and an actual speed vertical axis; Step S402: Based on the speed comparison data set, mark the mapping points on the corresponding vertical axis on the linear change analysis graph, connect the temperature parameter mapping points and the humidity parameter mapping points with a vector line, record it as the temperature and humidity vector line, and connect the original measured speed mapping points and the actual speed mapping points with a vector line, the speed vector line, and connect and associate the corresponding temperature and humidity vector line with the speed vector line; Step S403: randomly selecting temperature and humidity vector lines that are equivalent to each other, and correlating the speed vector lines corresponding to the selected temperature and humidity vector lines to form an analysis speed vector line group; Step S404 : performing a performance equivalence analysis on the analysis speed vector lines of the analysis speed vector line group, and determining whether to expand the number of experiments corresponding to the speed comparison data set based on the analysis result.
5. The speed calibration method based on temperature and humidity data analysis according to claim 4, characterized in that: Methods for performing performance equivalence analysis on vector lines include: Step S4031: randomly select a number of vector lines and randomly combine them in pairs to form a number of vector line groups. Determine the inter-line relative distance sequence between each vector line group. The inter-line relative distance sequence includes a number of inter-line relative distances. Calculate the sum of all inter-line relative distances and record it as the total inter-line relative distance. Step S4032: determining the sub-equivalent parameters of a single vector line group based on the relative distances between all bus lines, and determining the comprehensive equivalent parameters of all vector lines based on the sub-equivalent parameters of all vector line groups.
6. The rotation speed calibration method based on temperature and humidity data analysis according to claim 5, characterized in that: The expression for determining the sub-equivalent parameters of the vector line group is: ； Among them, d is the sub-identity parameter, is the preset maximum relative distance between buses, is the relative distance between the main lines of the vector line group; The expression for determining the comprehensive equivalent parameters of all vector lines is: ； Among them, D is the comprehensive equivalent parameter, is the sub-identical parameter of the i-th vector line group, R is the sub-identical parameter influence adjustment coefficient, b is the influence adjustment constant of the sub-identical parameter, and n is the number of all vector line groups.
7. The speed calibration method based on temperature and humidity data analysis according to claim 1, characterized in that: The speed comparison dataset is used as the basic data to construct a speed calibration model. The methods include: In step S501, a retrieval condition directory is constructed using the temperature parameter and the humidity parameter as the first retrieval condition, an out-of-set index relationship is constructed using the first retrieval condition directory as the speed comparison data set, a second retrieval condition directory is constructed using the original measured speed as the second retrieval condition, an in-set index relationship is constructed using the second retrieval condition directory as the speed comparison data set, and based on the common index framework of the first retrieval condition directory and the second retrieval condition directory, the speed comparison data set is integrated into a speed calibration model.
8. The rotation speed calibration method based on temperature and humidity data analysis according to claim 7, characterized in that: Methods for finding the corresponding actual speed in the speed comparison data set include: Step S5011, obtaining a real-time temperature parameter and a real-time humidity parameter, and determining a rotation speed comparison data set to be called based on a matching relationship between the real-time temperature parameter and the real-time humidity parameter in the first search condition directory; Step S5012: Acquire the real-time original measured rotation speed, and find the best matching reference actual rotation speed based on the matching relationship of the real-time original measured rotation speed in the second search condition directory.
9. The rotation speed calibration method based on temperature and humidity data analysis according to claim 8, characterized in that: Methods for finding the best matching reference actual speed include: Step S50121, respectively calculating the temperature parameter difference, the humidity parameter difference, and the original measured speed difference, determining the preset temperature parameter interval to which the real-time temperature parameter belongs, and determining the preset humidity parameter interval to which the real-time humidity parameter belongs, and determining the temperature matching weight and the humidity matching weight based on the determined preset temperature parameter interval and humidity parameter interval; Step S50122: Determine the reference actual speed with the highest matching parameter value based on the temperature matching weight, the humidity matching weight, the temperature parameter difference, the humidity parameter difference, and the original measured speed difference; The expression for calculating the matching parameter is: ； Among them, P is the matching parameter, is the temperature matching weight, is the humidity matching weight, Match the weights to the preset raw measured speed. is the preset maximum temperature parameter difference, is the temperature parameter difference, is the preset maximum humidity parameter difference, is the humidity parameter difference, is the preset maximum original measured speed difference, is the original measured speed difference.
10. The speed calibration system based on temperature and humidity data analysis is characterized by: The rotation speed calibration method for executing any one of claims 1 to 9 comprises: The first module is used to conduct a comparison data experiment, collect the original measured speed of the film roller, and simultaneously collect temperature and humidity parameters, and collect the actual speed of the film roller. The correspondence between the original measured speed and temperature parameters and the humidity parameters and actual speed is established to obtain a speed comparison data set; The second module is used to delineate the data intervals of the temperature parameters and the humidity parameters, and determine the required amount of comparison data corresponding to the data intervals based on the sensitivity of the delineated data intervals; The third module is used to determine the number of experiments for the comparison data experiment based on the comparison data demand corresponding to the data interval, and classify the speed comparison data groups obtained from the comparison data experiment into the data interval to obtain the speed comparison data set; The fourth module is used to perform linear change analysis on the speed comparison data groups in the speed comparison data set, and determine whether to expand the number of experiments corresponding to the speed comparison data set based on the analysis results; The fifth module is used to construct a speed calibration model using the speed comparison dataset as basic data.