Textile fabric cutting process data monitoring method and system based on Internet of Things technology

Abstract

The invention discloses a textile fabric cutting process data monitoring method and system based on the Internet of Things technology, and relates to the technical field of textile manufacturing. According to the textile fabric cutting process data monitoring method based on the Internet of Things technology, a textile fabric cutting device is monitored through an Internet of Things sensor to obtain a cutting device performance data set before cutting, the obtained cutting device performance data set before cutting is comprehensively analyzed, and a cutting device performance evaluation value before cutting is obtained; determining qualified cutting equipment based on the performance evaluation value of the cutting equipment before cutting; the material of the to-be-cut fabric is monitored through an Internet of Things sensor to obtain a material data set of the to-be-cut fabric, and the obtained material data set of the to-be-cut fabric is comprehensively analyzed; the problems that due to the fact that traditional cutting equipment is single in monitoring means and not comprehensive enough, the cutting quality is possibly affected, the reject ratio of products is increased, it is difficult to ensure that cutting meets the quality standard, and a large number of unqualified products are possibly generated are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of textile manufacturing, and in particular to a method and system for monitoring data of a textile fabric cutting process based on the Internet of Things technology. Background Art

[0002] In the textile manufacturing industry, cutting is one of the key steps in converting fabrics into finished garments. In the process of cutting clothing fabrics, the first thing to do is to calculate the cutting bed plan for the material layout. Secondly, the layout personnel will manually or intelligently layout the material according to the cutting bed plan according to the situation of each bed, and then pull and spread the cloth according to the cutting bed plan and the material layout of each bed. After the cloth is spread, the cutting personnel use manual or computer-automatic cutting beds to cut along the cutting line. With the advancement of technology, the Internet of Things technology has been introduced into various manufacturing fields, including the textile industry. By embedding sensors in the equipment and connecting the network, the Internet of Things technology enables the equipment to collect and exchange data, thereby realizing the automation and intelligent management of the process.

[0003] For example, the patent with announcement number: CN115204426B announces a textile assembly line operation supervision system based on the Internet of Things. Through the fault analysis module, the operation supervision and analysis of the equipment of the textile assembly line can be carried out, the operating parameters of each equipment of the assembly line can be monitored, and an early warning can be issued when the equipment has an abnormal operation. At the same time, the overall operation status of the assembly line can be fed back based on the equipment failure frequency of the assembly line during the monitoring period. When the overall operation status is abnormal, the cause of the abnormality is analyzed, thereby improving the efficiency of subsequent abnormality handling.

[0004] Traditional cutting equipment monitoring methods are relatively simple and not comprehensive enough, which may affect the cutting quality, increase the defective rate of products, make it difficult to ensure that the cutting meets the quality standards, and may result in a large number of defective products. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a method for monitoring data of the textile fabric cutting process based on the Internet of Things technology, which solves the problem that the traditional cutting equipment monitoring means are relatively single and not comprehensive, which may affect the cutting quality, increase the defective rate of products, make it difficult to ensure that the cutting meets the quality standards, and may result in a large number of defective products.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a textile fabric cutting process data monitoring method based on the Internet of Things technology, comprising the following steps: monitoring the textile fabric cutting equipment through the Internet of Things sensor to obtain a cutting equipment performance data set before cutting, comprehensively analyzing the obtained cutting equipment performance data set before cutting, obtaining a cutting equipment performance evaluation value before cutting, and determining a qualified cutting equipment based on the cutting equipment pre-cutting performance evaluation value; monitoring the material of the fabric to be cut through the Internet of Things sensor to obtain a fabric material data set to be cut, comprehensively analyzing the obtained fabric material data set to be cut, obtaining a fabric material evaluation value to be cut, and determining a qualified fabric to be cut based on the fabric material evaluation value to be cut; monitoring the cutting process of qualified fabric to be cut by qualified cutting equipment through the Internet of Things sensor to obtain a textile fabric cutting process accuracy data set, comprehensively analyzing the obtained textile fabric cutting process accuracy data set to obtain a textile fabric cutting process accuracy evaluation value, and determining a qualified cutting accuracy based on the textile fabric cutting process accuracy evaluation value.

[0007] Furthermore, the cutting device performance data set before cutting includes: cutting speed of the cutting blade, pressure applied by the cutting device, and degree of wear of the cutting blade.

[0008] Furthermore, the obtained pre-cutting cutting equipment performance data set is comprehensively analyzed to obtain the pre-cutting cutting equipment performance evaluation value. The specific analysis process is: based on the obtained pre-cutting cutting equipment performance data set and the pre-cutting cutting equipment performance parameter data set stored in the database, a comprehensive analysis is performed to obtain the textile fabric pre-cutting cutting equipment performance evaluation value, the textile fabric pre-cutting cutting equipment performance evaluation value is used as the analysis basis for judging whether the cutting equipment performance in the textile fabric cutting process is qualified, and the pre-cutting cutting equipment performance parameter data set stored in the database includes: a parameterized cutting speed of the cutting blade, a parameterized applied pressure, and a parameterized degree of shear blade wear; the textile fabric pre-cutting cutting equipment performance evaluation value is compared with the textile fabric pre-cutting parameter threshold stored in the database; if the textile fabric pre-cutting cutting equipment performance evaluation value is less than or equal to the textile fabric pre-cutting parameter threshold stored in the database, the textile fabric pre-cutting cutting equipment performance is qualified; if the textile fabric pre-cutting cutting equipment performance evaluation value is greater than the textile fabric cutting process parameter threshold stored in the database, the textile fabric pre-cutting cutting equipment performance is unqualified.

[0009] Furthermore, the performance evaluation value of the cutting equipment before cutting the textile fabric is calculated by the formula: ; In the formula, It is the performance evaluation value of cutting equipment before cutting textile fabrics. is the cutting speed of the cutting blade, The cutting speed of the cutting blade is stored in the database. The pressure applied to the cutting equipment, The pressure applied to the parameters stored in the database, The degree of shear blade wear. The wear degree of the shear blade is stored in the database. is the weight factor of the cutting speed of the cutting blade stored in the database, is the weighting factor for the pressure applied by the cutting device stored in the database, is the weight factor of the shear blade wear degree stored in the database, is a natural constant.

[0010] Furthermore, the obtained material data set of the fabric to be cut is comprehensively analyzed to obtain the material evaluation value of the fabric to be cut. The specific analysis process is: the cutting accuracy is monitored in real time by the Internet of Things sensor to obtain the textile fabric cutting process accuracy data set, and the material data set of the fabric to be cut specifically includes: fabric tension and fabric thickness; based on the obtained material data set of the fabric to be cut and the parameter data set of the fabric to be cut stored in the database, a comprehensive analysis is performed to obtain the material evaluation value of the textile fabric before cutting, and the material evaluation value of the textile fabric before cutting is used as an analysis basis for judging whether the fabric processing in the textile fabric cutting process is qualified, and the parameter data set of the fabric to be cut includes the parameter fabric tension and the parameter fabric thickness.

[0011] Furthermore, the fabric material evaluation value of the textile fabric before cutting is used as an analysis basis for judging whether the fabric processing in the textile fabric cutting process is qualified. The specific analysis process is: comparing the fabric material evaluation value of the textile fabric before cutting with the threshold interval of the fabric processing parameters before cutting of the textile fabric stored in the database; if the fabric material evaluation value of the textile fabric before cutting falls within the threshold interval of the fabric processing parameters before cutting of the textile fabric stored in the database, the fabric material of the textile fabric before cutting is qualified; if the fabric material evaluation value of the textile fabric before cutting does not fall within the threshold interval of the fabric processing parameters before cutting of the textile fabric stored in the database, the fabric material of the textile fabric before cutting is unqualified.

[0012] Furthermore, the obtained textile fabric cutting process accuracy data set is comprehensively analyzed to obtain a textile fabric cutting process accuracy evaluation value. The specific analysis process is: the cutting accuracy is monitored in real time by an Internet of Things sensor to obtain a textile fabric cutting process accuracy data set, and the textile fabric cutting process accuracy data set specifically includes: cutting error, repeatability accuracy and alignment accuracy; based on the obtained textile fabric cutting process accuracy data set and the textile fabric cutting process accuracy parameter data set stored in the database, a comprehensive analysis is performed to obtain a textile fabric cutting process accuracy processing evaluation value, and the textile fabric cutting process accuracy evaluation value is used as an analysis basis for judging whether the textile fabric cutting process accuracy is qualified. The textile fabric cutting process accuracy parameter data set stored in the database includes a parameter cutting error, a parameter repeatability accuracy and a parameter alignment accuracy.

[0013] Furthermore, the textile fabric cutting process accuracy evaluation value is used as an analysis basis for judging whether the textile fabric cutting process accuracy is qualified. The specific analysis process is: comparing the textile fabric cutting process accuracy performance evaluation value with the textile fabric cutting process accuracy reference threshold stored in the database; if the textile fabric cutting process accuracy performance evaluation value is less than or equal to the textile fabric cutting process accuracy reference threshold stored in the database, then the textile fabric cutting process accuracy performance is qualified; if the textile fabric cutting process accuracy performance evaluation value is greater than the textile fabric cutting process accuracy reference threshold stored in the database, then the textile fabric cutting process accuracy performance is unqualified.

[0014] Furthermore, the calculation formula of the textile fabric cutting process accuracy evaluation value is: ; In the formula, is the precision evaluation value of textile fabric cutting process, is the clipping error, For repeatability, is the alignment accuracy, is the parameterized clipping error stored in the database, The parameter repeatability stored in the data set is is the parameter alignment accuracy stored in the database, is the weight factor of the clipping error stored in the database, is the weight factor of the repeatability stored in the database, is the weight factor for the alignment accuracy stored in the database.

[0015] A textile fabric cutting process data monitoring system based on the Internet of Things technology comprises a cutting equipment monitoring module, a material monitoring module of fabrics to be cut and a cutting process precision monitoring module, wherein: the cutting equipment monitoring module is used to monitor the textile fabric cutting equipment through the Internet of Things sensor to obtain a cutting equipment performance data set before cutting, conduct a comprehensive analysis on the obtained cutting equipment performance data set before cutting, obtain a cutting equipment performance evaluation value before cutting, and determine a qualified cutting equipment based on the cutting equipment pre-cutting performance evaluation value; the material monitoring module of fabrics to be cut is used to monitor the material of fabrics to be cut through the Internet of Things sensor to obtain a material data set of fabrics to be cut, conduct a comprehensive analysis on the obtained material data set of fabrics to be cut, obtain a material evaluation value of fabrics to be cut, and determine qualified fabrics to be cut based on the material evaluation value of fabrics to be cut; the cutting process precision monitoring module is used to monitor the cutting process of qualified fabrics to be cut by qualified cutting equipment through the Internet of Things sensor to obtain a textile fabric cutting process precision data set, conduct a comprehensive analysis on the obtained textile fabric cutting process precision data set to obtain a textile fabric cutting process precision evaluation value, and determine qualified cutting precision based on the textile fabric cutting process precision evaluation value.

[0016] The present invention has the following beneficial effects: The textile fabric cutting process data monitoring method and system based on the Internet of Things technology can timely adjust the blade setting to ensure the cutting quality by real-time monitoring of the blade status. The fabric material monitoring reflects whether the fabric has reached the quality standard before cutting, ensuring that the fabric is cut in the best state, improving the cutting accuracy and reducing fabric loss. The cutting accuracy monitoring ensures that each cutting reaches a high standard of accuracy, which can significantly improve the overall quality of the finished product and reduce the defect rate. It solves the problem that the traditional cutting equipment monitoring means are relatively single and not comprehensive, which may affect the cutting quality, increase the product defect rate, make it difficult to ensure that the cutting meets the quality standards, and may cause a large number of defective products.

[0017] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of the data monitoring method for the textile fabric cutting process based on the Internet of Things technology of the present invention.

[0019] Figure 2 This is a flow chart of the textile fabric cutting process data monitoring system based on the Internet of Things technology of the present invention. DETAILED DESCRIPTION

[0020] The embodiments of the present application use a textile fabric cutting process data monitoring method and system based on Internet of Things technology to solve the problem that traditional cutting equipment monitoring methods are relatively simple and not comprehensive, which may affect the cutting quality, increase the defective rate of products, make it difficult to ensure that the cutting meets quality standards, and may result in a large number of defective products.

[0021] The overall idea of ​​the problem in the embodiment of this application is as follows: First, by monitoring the textile fabric cutting equipment, determine whether the cutting equipment is qualified. After determining that the cutting equipment is qualified, monitor the material of the fabric to be cut to determine whether the material of the fabric to be cut is qualified. After determining that the fabric to be cut is qualified, cut the qualified fabric to be cut through the monitoring qualified cutting equipment. At the same time, monitor the collection accuracy of the cutting process to determine whether the cutting process accuracy is qualified.

[0022] See also Figure 1 The embodiment of the present invention provides a technical solution: a textile fabric cutting process data monitoring method based on the Internet of Things technology, comprising the following steps: monitoring the textile fabric cutting equipment through the Internet of Things sensor to obtain a pre-cutting cutting equipment performance data set, comprehensively analyzing the obtained pre-cutting cutting equipment performance data set to obtain a pre-cutting cutting equipment performance evaluation value, and determining a qualified cutting equipment based on the pre-cutting performance evaluation value of the cutting equipment; monitoring the material of the fabric to be cut through the Internet of Things sensor to obtain a material data set of the fabric to be cut, comprehensively analyzing the obtained material data set of the fabric to be cut to obtain a material evaluation value of the fabric to be cut, and determining a qualified fabric to be cut based on the material evaluation value of the fabric to be cut; monitoring the cutting process of the qualified fabric to be cut by the qualified cutting equipment through the Internet of Things sensor to obtain a textile fabric cutting process accuracy data set, comprehensively analyzing the obtained textile fabric cutting process accuracy data set to obtain a textile fabric cutting process accuracy evaluation value, and determining a qualified cutting accuracy based on the textile fabric cutting process accuracy evaluation value.

[0023] Specifically, the cutting device performance data set before cutting includes: cutting speed of the cutting blade, pressure applied by the cutting device, and degree of wear of the cutting blade.

[0024] In this embodiment, the cutting speed of the cutting blade refers to the moving speed of the blade when cutting the textile fabric. By installing a speed sensor on the blade part of the cutting device, the movement speed of the cutting blade during the cutting process can be measured in real time. The cutting speed of the cutting blade has a direct impact on the neatness and accuracy of the cutting edge. The appropriate speed can reduce the tearing and wear of the textile fabric and improve the overall quality of the finished product. The appropriate cutting speed can reduce the wear of the cutting blade and extend the service life of the cutting blade, thereby reducing maintenance costs and equipment update frequency. The pressure applied by the cutting device can be obtained by a pressure sensor, such as a piezoelectric sensor or a piezoresistive sensor, which is installed on the cutting blade or the corresponding force-applying component of the cutting device to measure the pressure applied to the fabric. The appropriate pressure can ensure the effective contact between the cutting blade and the textile fabric, thereby reducing the sliding or dislocation of the material and ensuring the accuracy and neatness of the cutting. Too high or too low pressure may cause damage to the textile fabric. Too high pressure may cut too deep and damage the underlying layer or workbench; too low pressure may result in incomplete cutting. Appropriate pressure settings can avoid these problems. By collecting data on the pressure applied by the cutting equipment, it can be used to analyze the efficiency of the entire cutting process and provide a basis for improving cutting parameters and equipment upgrades. The degree of wear of the shear blade can be obtained through vibration analysis sensors or image recognition technology. The wear sensor installed on the equipment can monitor the vibration generated when the blade is in operation. Since the degree of wear affects the mode and amplitude of vibration, the wear state of the blade can be indirectly judged by analyzing the vibration data; image recognition technology uses a camera installed on the equipment to automatically detect signs of wear through image recognition software; by monitoring the degree of wear of the shear blade, unexpected shutdowns caused by sudden breakage or damage of the cutting blade can be prevented, and worn cutting blades can be replaced or sharpened in time to ensure that the edges are flat and burr-free when cutting fabrics, thereby maintaining consistency in product quality.

[0025] Specifically, the obtained pre-cutting cutting equipment performance data set is comprehensively analyzed to obtain the pre-cutting cutting equipment performance evaluation value. The specific analysis process is: based on the obtained pre-cutting cutting equipment performance data set and the pre-cutting cutting equipment performance parameter data set stored in the database, a comprehensive analysis is performed to obtain the textile fabric pre-cutting cutting equipment performance evaluation value, the textile fabric pre-cutting cutting equipment performance evaluation value is used as the analysis basis for judging whether the cutting equipment performance in the textile fabric cutting process is qualified, and the pre-cutting cutting equipment performance parameter data set stored in the database includes: a parameter cutting speed of the cutting blade, a parameter applied pressure and a parameter shear blade wear degree; the textile fabric pre-cutting cutting equipment performance evaluation value is compared with the textile fabric pre-cutting parameter threshold stored in the database; if the textile fabric pre-cutting cutting equipment performance evaluation value is less than or equal to the textile fabric pre-cutting parameter threshold stored in the database, the textile fabric pre-cutting cutting equipment performance is qualified; if the textile fabric pre-cutting cutting equipment performance evaluation value is greater than the textile fabric cutting process parameter threshold stored in the database, the textile fabric pre-cutting cutting equipment performance is unqualified.

[0026] In this implementation scheme, firstly, the performance parameter data set of the cutting equipment before cutting stored in the database is obtained through historical data, and real-time data on the cutting speed of the cutting blade, the pressure applied by the cutting equipment, and the degree of wear of the shear blade are collected. By selecting different batches of historical data on the performance of the cutting equipment before cutting, the average cutting speed of the cutting blade, the average pressure applied by the cutting equipment, and the average degree of wear of the shear blade of all the cutting equipment before cutting are calculated to obtain the performance parameter data set of the cutting equipment before cutting stored in the database. The performance data set of the cutting equipment before cutting is compared with the performance parameter data set of the cutting equipment before cutting stored in the database, so as to obtain the performance evaluation value of the cutting equipment before cutting of the textile fabric. The performance evaluation value of the cutting equipment before cutting of the textile fabric reflects the performance of the current cutting equipment state relative to the ideal state. The performance evaluation value of the cutting equipment before cutting of the textile fabric is compared with the pre-cutting parameter threshold of the textile fabric stored in the database. The parameter threshold is a set standard, which indicates the level of equipment performance to be regarded as meeting the cutting quality and safety standards. The pre-cutting parameter threshold of the textile fabric stored in the database can be obtained by collecting a large amount of historical data on the performance of the pre-cutting cutting equipment and analyzing its distribution. Characteristics, by calculating the statistical parameters of historical data, such as mean, standard deviation, etc., the threshold value can be set as the mean plus a certain multiple of the standard deviation. For example, by collecting past cutting pressure data, the average pressure value is calculated to be 200 Newtons and the standard deviation is 20 Newtons. The threshold can be set to the mean plus two times the standard deviation, that is, 240 Newtons, as the optimal cutting pressure threshold for safety and efficiency; if the performance evaluation value of the cutting equipment before cutting the textile fabric is less than or equal to the threshold value before cutting the textile fabric stored in the database, it indicates that the cutting equipment is in good condition and the cutting operation can be started; if the evaluation value is higher than the threshold, it indicates that there may be problems with the equipment and further adjustment or maintenance is required. Correct cutting speed and pressure are the key to ensuring cutting quality. By ensuring that these parameters meet the standards, defective products can be avoided and the yield rate can be improved. The cutting equipment operates in a good performance state, which can reduce unexpected risks during operation and protect the safety of operators and equipment.

[0027] Specifically, the performance evaluation value of the cutting equipment before cutting the textile fabric is calculated as follows: ; In the formula, It is the performance evaluation value of cutting equipment before cutting textile fabrics. is the cutting speed of the cutting blade, The cutting speed of the cutting blade is stored in the database. The pressure applied to the cutting equipment, The pressure applied to the parameters stored in the database, The degree of shear blade wear. The wear degree of the shear blade is stored in the database. is the weight factor of the cutting speed of the cutting blade stored in the database, is the weighting factor for the pressure applied by the cutting device stored in the database, is the weight factor of the shear blade wear degree stored in the database, is a natural constant.

[0028] In this embodiment, the formula uses an exponential function to amplify the effect of the cutting speed and pressure of the cutting blades deviating from the parameter values. The use of the exponential function can make the performance evaluation value of the cutting equipment before cutting the textile fabric more sensitive when the deviation is large, emphasizing any significant deviation. When the deviation of the cutting speed of the cutting blade is large, the performance evaluation value of the cutting equipment before cutting the textile fabric will be significantly increased by amplifying the weight factor of the cutting speed of the cutting blade, emphasizing the importance of speed control and the impact on the overall equipment performance. The deviation of the pressure applied by the cutting equipment is also processed by an exponential function, and combined with the deviation of the cutting speed of the cutting blade, they jointly determine the sensitivity and response of the performance evaluation value of the cutting equipment before cutting the textile fabric. The logarithmic function ensures that the wear effect grows relatively smoothly in the evaluation, and the logarithmic form can handle the relative changes in the degree of wear of the shear blade, which is suitable for evaluating the nonlinear characteristics of wear growth. The relative changes in the degree of wear are adjusted by the weight factor of the degree of wear of the shear blade, indicating the importance of wear in equipment performance evaluation. Each weight factor , , , determines the relative importance of each parameter in performance evaluation. For example, if If the value is larger, it means that the precise control of the cutting speed of the cutting blade is more critical to the overall equipment performance. The larger the value, the more significant the impact of wear status on equipment performance. The use of exponential and logarithmic functions emphasizes the importance of significant deviations, making the evaluation more sensitive and discriminatory, which helps to promptly detect and correct equipment status that may cause production quality problems. Based on the performance evaluation results, the maintenance plan and operating parameters of the equipment can be optimized, thereby improving efficiency, reducing costs and extending the service life of the equipment.

[0029] Through the relationship between the historically monitored cutting speed of the cutting blade, the pressure applied by the cutting equipment, the degree of wear of the shear blade and their corresponding performance evaluation values ​​of the cutting equipment before cutting the textile fabric, a mapping set of the cutting speed of the cutting blade, the pressure applied by the cutting equipment, the degree of wear of the shear blade and their corresponding weight factors is established, and the current cutting speed of the cutting blade, the pressure applied by the cutting equipment and the degree of wear of the shear blade are input into the mapping set to obtain the weight factors corresponding to the current cutting speed of the cutting blade, the pressure applied by the cutting equipment and the degree of wear of the shear blade.

[0030] Specifically, the obtained material data set of the fabric to be cut is comprehensively analyzed to obtain the material evaluation value of the fabric to be cut. The specific analysis process is: the cutting accuracy is monitored in real time by the Internet of Things sensor to obtain the textile fabric cutting process accuracy data set, and the material data set of the fabric to be cut specifically includes: fabric tension and fabric thickness; based on the obtained material data set of the fabric to be cut and the parameter data set of the fabric to be cut stored in the database, a comprehensive analysis is performed to obtain the material evaluation value of the textile fabric before cutting, and the material evaluation value of the textile fabric before cutting is used as an analysis basis for judging whether the fabric processing in the textile fabric cutting process is qualified, and the parameter data set of the fabric to be cut includes the parameter fabric tension and the parameter fabric thickness.

[0031] In this embodiment, the fabric tension is obtained using a tension sensor. The tension sensor is usually installed at the feed end or clamping device of the textile equipment, and can measure the slight deformation of the textile fabric when it is subjected to tension. The strain gauge is a resistance change sensor whose resistance value changes with the deformation of the textile fabric. This change can be converted into an electrical signal to measure the magnitude of the tension. Monitoring the fabric tension is the key to ensuring that the cutting lines are straight and the edges are neat. The correct tension setting can prevent the textile fabric from sliding or stretching during the cutting process, thereby improving the accuracy and consistency of cutting. Too high or too low tension may cause fabric damage, such as breakage or deformation. Through real-time monitoring, it can be ensured that the tension is maintained at a safe and effective level, thereby protecting the fabric from damage. Fabric thickness uses ultrasonic pulses to measure fabric thickness. Ultrasonic pulses are emitted from the sensor, penetrate the fabric and are reflected back to the sensor. The thickness of the fabric is calculated by measuring the time it takes for the ultrasonic wave to go back and forth. Accurate fabric thickness measurement can ensure that the cutting machine sets the correct cutting parameters, such as blade depth and pressure, thereby improving cutting accuracy and avoiding excessive or insufficient blade penetration. Accurate control of fabric thickness helps ensure the consistency of size and shape of each product. The real-time data of fabric tension and thickness are compared with the reference fabric tension and reference fabric thickness stored in the database. The reference data of the fabric material to be cut includes the ideal fabric tension and fabric thickness set in advance for various fabric types and cutting requirements. According to the difference between the real-time data and the reference data, the fabric material evaluation value is calculated using statistical and analytical methods. This evaluation value is a comprehensive indicator that reflects whether the fabric meets the quality standards before cutting.

[0032] Specifically, the fabric material evaluation value of the textile fabric before cutting is used as an analysis basis for judging whether the fabric processing in the textile fabric cutting process is qualified. The specific analysis process is: comparing the fabric material evaluation value of the textile fabric before cutting with the threshold interval of the fabric processing parameters before cutting of the textile fabric stored in the database; if the fabric material evaluation value of the textile fabric before cutting falls within the threshold interval of the fabric processing parameters before cutting of the textile fabric stored in the database, the fabric material of the textile fabric before cutting is qualified; if the fabric material evaluation value of the textile fabric before cutting does not fall within the threshold interval of the fabric processing parameters before cutting of the textile fabric stored in the database, the fabric material of the textile fabric before cutting is unqualified.

[0033] In this embodiment, the Internet of Things technology is used to monitor the fabric tension and fabric thickness in real time, and the fabric material evaluation value of the textile fabric before cutting is calculated based on the fabric tension and fabric thickness. The fabric material evaluation value of the textile fabric before cutting reflects whether the fabric is in an ideal state for subsequent cutting processes, and the calculated fabric material evaluation value of the textile fabric before cutting is compared with the threshold interval of the fabric processing parameter of the textile fabric before cutting stored in the database. If the fabric material evaluation value of the textile fabric before cutting is within the threshold interval of the fabric processing parameter of the textile fabric before cutting, it is judged as qualified and the subsequent cutting operation is performed. If the fabric material evaluation value of the textile fabric before cutting is not within the threshold interval of the fabric processing parameter of the textile fabric before cutting, it is judged as unqualified and excluded from the cutting process. This process ensures that each batch of fabrics meets the best physical state before entering the cutting, reduces the finished product defects caused by fabric quality problems, and avoids unqualified fabrics from entering the cutting stage, thereby reducing material waste and cost and improving material utilization.

[0034] For the threshold values ​​of fabric processing parameters before cutting of textile fabrics stored in the database, a large amount of fabric material monitoring data can be collected, and then descriptive statistical analysis (such as calculating the mean, median, standard deviation) and inferential statistical techniques (such as confidence intervals and hypothesis testing) are used to analyze the distribution and variability of the data, and the threshold values ​​are determined based on these analysis results.

[0035] The material evaluation value of textile fabrics before cutting is calculated as follows: ; In the formula, It is the material evaluation value of textile fabric before cutting. For fabric tension, is the fabric thickness, The fabric tension stored in the database. The thickness of the specified fabric stored in the database. is the weight factor of fabric tension stored in the database, is the weight factor of the fabric thickness stored in the database.

[0036] The fabric material evaluation value before cutting of textile fabrics is a quantitative indicator that comprehensively considers the deviation of fabric tension and fabric thickness, and is used to reflect the difference between the current state of the fabric and the ideal state. Fabric tension and reference fabric tension parameters measure the deviation between actual fabric tension and standard tension. The deviation is used to calculate the relative deviation ratio. This proportional treatment helps standardize the evaluation under different fabrics and conditions; fabric thickness and reference fabric thickness are similar to the treatment of fabric tension. By calculating the deviation ratio of fabric thickness, it is possible to evaluate whether the fabric thickness meets the expected standard; the weight factor adjusts the influence of fabric tension and fabric thickness deviation on the fabric material evaluation value before cutting of textile fabrics. If a certain characteristic (such as tension) is considered to be more critical to the success of the cutting process, the corresponding weight factor is used. will be set higher, and the weight factor reflects the importance of different fabric properties in cutting quality control. Through the square process, the formula adds processing for large deviations. This calculation method amplifies the impact of deviations from the parameter value, ensuring that the evaluation value is extremely sensitive to fabric quality. The formula allows for comprehensive consideration of multiple fabric properties to provide a comprehensive mathematical assessment of the fabric's suitability for cutting. This calculation method provides a way to accurately measure the degree of deviation of the fabric material state from the standard, which helps to ensure that each batch of fabric enters the cutting process in the best condition. By accurately controlling the fabric tension and fabric thickness, the settings of the cutting machine can be optimized, the cutting accuracy can be improved, and material waste can be reduced.

[0037] Through the relationship between the historically monitored fabric tension and fabric thickness and their corresponding fabric material evaluation values ​​before textile fabric cutting, a mapping set of fabric tension and fabric thickness and their corresponding weight factors is established, and the current fabric tension and fabric thickness are input into the mapping set to obtain the weight factors corresponding to the current fabric tension and fabric thickness.

[0038] Specifically, the obtained textile fabric cutting process accuracy data set is comprehensively analyzed to obtain a textile fabric cutting process accuracy evaluation value. The specific analysis process is: the cutting accuracy is monitored in real time by an Internet of Things sensor to obtain a textile fabric cutting process accuracy data set, and the textile fabric cutting process accuracy data set specifically includes: cutting error, repeatability accuracy and alignment accuracy; based on the obtained textile fabric cutting process accuracy data set and the textile fabric cutting process accuracy parameter data set stored in the database, a comprehensive analysis is performed to obtain a textile fabric cutting process accuracy processing evaluation value, and the textile fabric cutting process accuracy evaluation value is used as an analysis basis for judging whether the textile fabric cutting process accuracy is qualified. The textile fabric cutting process accuracy parameter data set stored in the database includes a parameter cutting error, a parameter repeatability accuracy and a parameter alignment accuracy.

[0039] In this embodiment, the cutting error is monitored and measured by using a high-resolution camera and image processing software (such as Photoshop and GIMP) to monitor and measure the position of the cutting line. The software automatically calculates the exact number of deviations by analyzing the cut image and comparing it with the design pattern. Repeatability is achieved by cutting the same material multiple times and using a visual system or other measurement tools (such as a digital measurement system) after each cutting to evaluate the position and quality of each cutting. The consistency and variability of the cutting results are calculated through statistical analysis. Alignment accuracy is achieved by using a high-resolution camera system to capture the placement of the fabric and using image analysis software (such as MATLAB and HALCON) to detect the alignment of the pattern. Accurate cutting ensures that the size and appearance of the product meet the design specifications, reduces rework and scrap rates, and reduces manual adjustment and measurement time through automated and accurate cutting processes, thereby improving the efficiency of the production line. The ideal cutting accuracy standard is pre-set in the database, including the parameter cutting error, parameter repeatability and parameter alignment accuracy. The parameter threshold of the textile fabric cutting process accuracy stored in the database can be obtained by collecting a large amount of historical data of textile fabric cutting process accuracy, analyzing its distribution characteristics, and calculating the statistical parameters of the historical data, such as the mean, standard deviation, etc., and setting the value to be the mean plus a certain multiple of the standard deviation to determine the threshold; for example, when setting the threshold of cutting accuracy, if the historical data shows that the mean value of the cutting error is 0.5 mm and the standard deviation is 0.1 mm, then the threshold can be set to the mean plus two times the standard deviation, that is, 0.7 mm, to ensure that all cutting operations are maintained within this acceptable error range. The monitored cutting error, repeatability and alignment accuracy data are uploaded to the database, and the textile fabric cutting process accuracy data set is comprehensively analyzed with the textile fabric cutting process accuracy parameter data set stored in the database to obtain the textile fabric cutting process accuracy processing evaluation value, which is used as the analysis basis for judging whether the textile fabric cutting process accuracy is qualified. By ensuring that each cut is made to a high standard of accuracy, the overall quality of the finished product can be significantly improved and the defect rate can be reduced. Accurate cutting reduces material misuse and rework, optimizes material utilization efficiency and reduces costs.

[0040] Specifically, the textile fabric cutting process accuracy evaluation value is used as an analysis basis for judging whether the textile fabric cutting process accuracy is qualified. The specific analysis process is: comparing the textile fabric cutting process accuracy performance evaluation value with the textile fabric cutting process accuracy reference threshold stored in the database; if the textile fabric cutting process accuracy performance evaluation value is less than or equal to the textile fabric cutting process accuracy reference threshold stored in the database, then the textile fabric cutting process accuracy performance is qualified; if the textile fabric cutting process accuracy performance evaluation value is greater than the textile fabric cutting process accuracy reference threshold stored in the database, then the textile fabric cutting process accuracy performance is unqualified.

[0041] In this embodiment, the precision performance evaluation value of the textile fabric cutting process is compared with the precision parameter threshold of the textile fabric cutting process stored in the database; the precision parameter threshold of the textile fabric cutting process stored in the database can be determined by collecting a large amount of historical data of the fabric cutting process precision, analyzing its distribution characteristics, and calculating the statistical parameters of the historical data, such as the mean, standard deviation, etc., and setting the value to the mean plus a certain multiple of the standard deviation to determine the threshold. For example, if the mean of the cutting error is 0.5 mm and the standard deviation is 0.05 mm after analyzing the historical data of the cutting accuracy of the textile fabric, the parameter threshold of the cutting accuracy can be set to the mean plus three times the standard deviation, that is, 0.65 mm, as the qualified standard of the cutting accuracy. The precision performance evaluation value of the textile fabric cutting process is compared with the precision parameter threshold of the textile fabric cutting process stored in the database to determine whether the precision performance evaluation value of the textile fabric cutting process is qualified. Through precise cutting technology, the size and appearance of each product can meet the design requirements, greatly reducing the product failure rate caused by inaccurate size or appearance defects. By monitoring the cutting accuracy and comparing it with the preset parameter threshold, deviations in the production process can be discovered immediately and adjustments or corrections can be made quickly. This rapid response reduces duplication of work and material waste, thereby optimizing the production process and improving the operating efficiency and output speed of the production line. Accurate cutting reduces the waste of raw materials and also reduces the cost of rework and waste disposal caused by unqualified products.

[0042] Specifically, the calculation formula for the textile fabric cutting process accuracy evaluation value is:

[0043] In the formula, is the precision evaluation value of textile fabric cutting process, is the clipping error, For repeatability, is the alignment accuracy, is the parameterized clipping error stored in the database, The parameter repeatability stored in the data set is is the parameter alignment accuracy stored in the database, is the weight factor of the clipping error stored in the database, is the weight factor of the repeatability stored in the database, is the weight factor for the alignment accuracy stored in the database.

[0044] In this embodiment, the comparison between the cutting error and the reference cutting error directly affects the evaluation of the cutting accuracy. By squaring and cubing the ratio of the actual cutting error to the standard error, this mathematical operation amplifies the impact of the deviation of the cutting error from the reference value, so that even a small deviation can reflect its importance in the evaluation value. The repeatability and reference repeatability are performed through logarithmic functions, taking into account the difference between the actual repeatability and the standard repeatability. The use of logarithmic functions provides a nonlinear difference amplification method, which makes the evaluation value very sensitive to the deviation of repeatability, especially when the actual value is close to or deviates from the predetermined value. The alignment accuracy and reference alignment accuracy evaluation method process the deviation through an exponential function, which rapidly increases the contribution of the alignment deviation to the total evaluation value, especially when the deviation is large, the evaluation value will increase significantly, thereby highlighting the importance of alignment accuracy in the cutting process. The weight factor determines the relative importance of each accuracy parameter in the total evaluation value, which means that according to the specific requirements of the cutting process and the characteristics of the fabric, these weights can be adjusted to reflect the priority of different accuracy indicators. For example, for products with high precision requirements, alignment accuracy may have a higher weight.

[0045] Through the evaluation values ​​of the cropping error, repeatability and alignment accuracy monitored historically, a mapping set of cropping error, repeatability and alignment accuracy and their corresponding weight factors is established, and the current cropping error, repeatability and alignment accuracy are input into the mapping set to obtain the weight factors corresponding to the current cropping error, repeatability and alignment accuracy.

[0046] See also Figure 2 , a textile fabric cutting process data monitoring system based on Internet of Things technology, including: a cutting equipment monitoring module, a material monitoring module of fabrics to be cut and a cutting process precision monitoring module, wherein: the cutting equipment monitoring module is used to monitor the textile fabric cutting equipment through the Internet of Things sensor to obtain a cutting equipment performance data set before cutting, conduct a comprehensive analysis on the obtained cutting equipment performance data set before cutting, obtain a cutting equipment performance evaluation value before cutting, and determine a qualified cutting equipment based on the cutting equipment pre-cutting performance evaluation value; the material monitoring module of fabrics to be cut is used to monitor the material of fabrics to be cut through the Internet of Things sensor to obtain a material data set of fabrics to be cut, conduct a comprehensive analysis on the obtained material data set of fabrics to be cut, obtain a material evaluation value of fabrics to be cut, and determine qualified fabrics to be cut based on the material evaluation value of fabrics to be cut; the cutting process precision monitoring module is used to monitor the cutting process of qualified fabrics to be cut by qualified cutting equipment through the Internet of Things sensor to obtain a textile fabric cutting process precision data set, conduct a comprehensive analysis on the obtained textile fabric cutting process precision data set to obtain a textile fabric cutting process precision evaluation value, and determine qualified cutting precision based on the textile fabric cutting process precision evaluation value.

[0047] In summary, this application has at least the following effects: By real-time monitoring of the blade status, the blade settings can be adjusted in time to ensure the cutting quality. Fabric material monitoring can reflect whether the fabric has reached the quality standards before cutting, ensuring that the fabric is cut in the best condition, improving cutting accuracy and reducing fabric loss. Cutting accuracy monitoring ensures that each cutting reaches a high standard of accuracy, which can significantly improve the overall quality of the finished product and reduce the defect rate. It solves the problem that the traditional cutting equipment monitoring methods are relatively single and not comprehensive, which may affect the cutting quality, increase the product defect rate, make it difficult to ensure that the cutting meets the quality standards, and may result in a large number of defective products.

[0048] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may 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.

[0049] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes 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 a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0050] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0051] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0052] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0053] 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 equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A textile fabric cutting process data monitoring method based on Internet of Things technology, characterized in that: The following steps are involved: The textile fabric cutting equipment is monitored by IoT sensors to obtain a pre-cutting cutting equipment performance data set, a pre-cutting cutting equipment performance evaluation value is obtained by comprehensive analysis, and qualified cutting equipment is determined based on the pre-cutting performance evaluation value of the cutting equipment; The material of the fabric to be cut is monitored by an Internet of Things sensor to obtain a material data set of the fabric to be cut, a comprehensive analysis is performed on the obtained material data set of the fabric to be cut to obtain a material evaluation value of the fabric to be cut, and qualified fabric to be cut is determined based on the material evaluation value of the fabric to be cut; The cutting process of qualified fabrics to be cut by qualified cutting equipment is monitored by IoT sensors to obtain a data set of textile fabric cutting process accuracy. A comprehensive analysis is performed on the obtained data set of textile fabric cutting process accuracy to obtain a textile fabric cutting process accuracy evaluation value. Based on the textile fabric cutting process accuracy evaluation value, the qualified cutting accuracy is determined.

2. According to claim 1, a textile fabric cutting process data monitoring method based on Internet of Things technology is characterized by: The cutting device performance data set before cutting includes: cutting speed of the cutting blade, pressure applied by the cutting device, and degree of wear of the cutting blade.

3. According to claim 2, a textile fabric cutting process data monitoring method based on Internet of Things technology is characterized by: The obtained pre-cutting cutting equipment performance data set is comprehensively analyzed to obtain the pre-cutting cutting equipment performance evaluation value, and the specific analysis process is as follows; Based on the obtained pre-cutting cutting equipment performance data set and the pre-cutting cutting equipment performance parameter data set stored in the database, a comprehensive analysis is performed to obtain a performance evaluation value of the cutting equipment before cutting of the textile fabric. The performance evaluation value of the cutting equipment before cutting of the textile fabric is used as an analysis basis for judging whether the performance of the cutting equipment in the textile fabric cutting process is qualified. The pre-cutting cutting equipment performance parameter data set stored in the database includes: a parameterized cutting speed of the cutting blade, a parameterized applied pressure, and a parameterized degree of shear blade wear; Comparing the performance evaluation value of the cutting equipment before cutting the textile fabric with the parameter threshold value before cutting the textile fabric stored in the database; If the performance evaluation value of the cutting equipment before cutting the textile fabric is less than or equal to the threshold value before cutting the textile fabric stored in the database, the performance of the cutting equipment before cutting the textile fabric is qualified; If the performance evaluation value of the cutting equipment before the textile fabric cutting is greater than the textile fabric cutting process parameter threshold stored in the database, the performance of the cutting equipment before the textile fabric cutting is unqualified.

4. According to claim 3, a textile fabric cutting process data monitoring method based on Internet of Things technology is characterized by: The performance evaluation value of the cutting equipment before cutting the textile fabric is calculated as follows: ； In the formula, It is the performance evaluation value of cutting equipment before cutting textile fabrics. is the cutting speed of the cutting blade, The cutting speed of the cutting blade is stored in the database. The pressure applied to the cutting equipment, The pressure applied to the parameters stored in the database, The degree of shear blade wear. The wear degree of the shear blade is stored in the database. is the weight factor of the cutting speed of the cutting blade stored in the database, is the weighting factor for the pressure applied by the cutting device stored in the database, is the weight factor of the shear blade wear degree stored in the database, is a natural constant.

5. According to claim 1, a textile fabric cutting process data monitoring method based on Internet of Things technology is characterized by: The obtained material data set of the fabric to be cut is comprehensively analyzed to obtain the material evaluation value of the fabric to be cut. The specific analysis process is as follows: The cutting accuracy is monitored in real time by IoT sensors to obtain a data set of textile fabric cutting process accuracy. The data set of fabric materials to be cut specifically includes: fabric tension and fabric thickness. Based on a comprehensive analysis of the acquired material data set of the fabric to be cut and the parameter data set of the fabric to be cut material stored in the database, the fabric material evaluation value before the textile fabric is cut is obtained. The fabric material evaluation value before the textile fabric is cut is used as an analysis basis for judging whether the fabric processing in the textile fabric cutting process is qualified. The parameter data set of the fabric material to be cut includes the parameter fabric tension and the parameter fabric thickness.

6. According to claim 5, a textile fabric cutting process data monitoring method based on Internet of Things technology is characterized by: The material evaluation value of the textile fabric before cutting is used as an analysis basis for judging whether the fabric treatment during the textile fabric cutting process is qualified. The specific analysis process is: Comparing the material evaluation value of the textile fabric before cutting with the threshold interval of the fabric processing parameter of the textile fabric before cutting stored in the database; If the material evaluation value of the textile fabric before cutting falls within the threshold range of the textile fabric processing parameter before cutting stored in the database, the material of the textile fabric before cutting is qualified; If the fabric material evaluation value of the textile fabric before cutting does not fall within the threshold range of the fabric processing parameters of the textile fabric before cutting stored in the database, the fabric material of the textile fabric before cutting is unqualified.

7. According to claim 1, a textile fabric cutting process data monitoring method based on Internet of Things technology is characterized by: The textile fabric cutting process accuracy data set is comprehensively analyzed to obtain the textile fabric cutting process accuracy evaluation value. The specific analysis process is as follows: The cutting accuracy data set of textile fabric cutting process is obtained by real-time monitoring of cutting accuracy through IoT sensors. The textile fabric cutting process accuracy data set specifically includes: cutting error, repeatability accuracy and alignment accuracy. Based on a comprehensive analysis of the acquired textile fabric cutting process accuracy data set and the textile fabric cutting process accuracy parameter data set stored in the database, the textile fabric cutting process accuracy processing evaluation value is obtained. The textile fabric cutting process accuracy evaluation value serves as an analysis basis for judging whether the textile fabric cutting process accuracy is qualified. The textile fabric cutting process accuracy parameter data set stored in the database includes parameter cutting error, parameter repeatability accuracy and parameter alignment accuracy.

8. According to claim 7, a textile fabric cutting process data monitoring method based on Internet of Things technology is characterized by: The textile fabric cutting process accuracy evaluation value is used as an analysis basis for judging whether the textile fabric cutting process accuracy is qualified. The specific analysis process is: Comparing the textile fabric cutting process precision performance evaluation value with the textile fabric cutting process precision parameter threshold stored in the database; If the textile fabric cutting process precision performance evaluation value is less than or equal to the textile fabric cutting process precision parameter threshold stored in the database, the textile fabric cutting process precision performance is qualified; If the precision performance evaluation value of the textile fabric cutting process is greater than the precision parameter threshold of the textile fabric cutting process stored in the database, the precision performance of the textile fabric cutting process is unqualified.

9. The method for monitoring data of textile fabric cutting process based on Internet of Things technology according to claim 8 is characterized in that: The calculation formula of the textile fabric cutting process accuracy evaluation value is: ； In the formula, is the precision evaluation value of textile fabric cutting process, is the clipping error, For repeatability, is the alignment accuracy, is the parameterized clipping error stored in the database, The parameter repeatability stored in the data set is is the parameter alignment accuracy stored in the database, is the weight factor of the clipping error stored in the database, is the weight factor of the repeatability stored in the database, is the weight factor for the alignment accuracy stored in the database.

10. A textile fabric cutting process data monitoring system based on the Internet of Things technology, using a textile fabric cutting process data monitoring method based on the Internet of Things technology as claimed in any one of claims 1 to 9, characterized in that: include: Cutting equipment monitoring module, material monitoring module of fabric to be cut and cutting process accuracy monitoring module, including: The cutting equipment monitoring module is used to monitor the textile fabric cutting equipment through the Internet of Things sensor to obtain a cutting equipment performance data set before cutting, conduct a comprehensive analysis on the obtained cutting equipment performance data set before cutting, obtain a cutting equipment performance evaluation value before cutting, and determine a qualified cutting equipment based on the cutting equipment pre-cutting performance evaluation value; The material monitoring module for the fabric to be cut is used to monitor the material of the fabric to be cut through an Internet of Things sensor to obtain a material data set of the fabric to be cut, perform a comprehensive analysis on the obtained material data set of the fabric to be cut, obtain a material evaluation value of the fabric to be cut, and determine qualified fabric to be cut based on the material evaluation value of the fabric to be cut; The cutting process accuracy monitoring module is used to monitor the cutting process of qualified cutting equipment on qualified fabrics to be cut through Internet of Things sensors, obtain a textile fabric cutting process accuracy data set, perform a comprehensive analysis on the obtained textile fabric cutting process accuracy data set, obtain a textile fabric cutting process accuracy evaluation value, and determine the qualified cutting accuracy based on the textile fabric cutting process accuracy evaluation value.

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