Full-process spinning digital twin system and method

Abstract

The invention discloses a full-process spinning digital twin system and method, and relates to the technical field of full-process spinning, and the full-process spinning digital twin system comprises a data acquisition and transmission module, a data analysis module and a monitoring and feedback module. Data acquisition is carried out in each link of the whole spinning process through the digital twin system, the equipment quality operation index of each link of the whole spinning process is analyzed, the occurrence probability of faults is reduced, the potential fault hidden danger of the equipment is found in time, the equipment operation data is monitored and analyzed in real time in time, the quality of whole-process spinning is improved, and the working efficiency is improved. The equipment state is adjusted in time, the stability of the production process is improved, the deviation value of the equipment quality operation index of each abnormal link of the whole spinning process is obtained through matching, the deviation qualified grade of each abnormal link is obtained through matching, adjustment and optimization are carried out, the production efficiency is improved, and the production cost is reduced. The problems of end breakage rate and uneven yarn evenness of the yarns are reduced, and the qualified rate of spinning is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of full-process spinning, and in particular to a full-process spinning digital twin system and method. Background Art

[0002] With the rapid development of new-generation information technologies such as the Internet of Things, big data, and artificial intelligence, new ideas and methods have been provided to solve the problems existing in the traditional spinning production model. Although some textile companies have begun to try to apply digital twin technology to spinning production, there are still some problems in the actual application process. For example, the accuracy and completeness of data collection need to be improved, the data processing and analysis capabilities are insufficient, the construction and verification methods of virtual models are not mature enough, and the stability and reliability of the system need to be further improved. Therefore, the development of an efficient, stable, and reliable full-process spinning digital twin system is of great practical significance for promoting the intelligent and digital transformation of the textile industry. Therefore, it is necessary to analyze the full-process spinning digital twin system.

[0003] Prior art, such as the invention application patent with announcement number: CN118778533A, discloses an intelligent monitoring system for textile workshop management, which includes a spinning process monitoring module, a weaving process monitoring module, a printing and dyeing process monitoring module, a process operation feedback module, an abnormal process feedback module, a normal operation feedback module, a process production control module and a data storage library. When analyzing the production quality of a textile workshop, the invention conducts production quality analysis on each production equipment of different processes such as spinning, weaving and printing and dyeing. This analysis method can accurately locate the bottleneck links and potential problems in the production process. When analyzing whether the production of a textile workshop meets the production requirements, the invention conducts quality fluctuation and quality abnormality analysis based on the production quality of the equipment corresponding to each process, obtains the equipment production operation quality index of the textile workshop and analyzes whether it meets the production requirements, ensuring the comprehensiveness of the production quality analysis.

[0004] Based on the above solutions, it can be found that a full-process spinning digital twin system and method in the prior art can meet the basic requirements, but there are also some potential defects and challenges, which are specifically reflected in the following aspects:

[0005] 1. Most of the existing technologies have low analysis of the equipment quality operation index of each link in the entire spinning process. Traditional detection methods often focus on the overall evaluation of petrochemical enterprises. In the long-term operation of spinning equipment, due to the influence of equipment status compliance index, production process compliance index and quality compliance index factors of each link in the entire spinning process, it is easy to malfunction, resulting in production interruption, increasing the difficulty of process management, and failing to timely discover potential equipment failure hazards. There is a lack of real-time monitoring and analysis of equipment operation data, which reduces the company's accurate probability of evaluating the performance of the equipment, reduces the quality of the entire spinning process, increases the waste of raw materials caused by equipment problems, and increases production costs.

[0006] 2. In the prior art, the attention paid to the equipment quality operation index of each abnormal link in the whole spinning process is not deep enough. Since the deviation value of the equipment quality operation index of each abnormal link in the whole spinning process and the analysis of the deviation qualification level of each abnormal link are not accurate, the potential abnormality of the equipment cannot be discovered in time, which increases the occurrence of sudden shutdown of the equipment and the time of production interruption, thereby reducing production efficiency, increasing the breakage rate of yarn and the occurrence of uneven yarn, and reducing the qualified rate of spinning. Due to the untimely adjustment of the equipment status, the stability of the production process cannot be guaranteed. Summary of the invention

[0007] The purpose of the present invention is to provide a full-process spinning digital twin system and method to solve the problems existing in the background technology.

[0008] In order to solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a full-process spinning digital twin system and method, including a data acquisition and transmission module, a data analysis module and a monitoring and feedback module.

[0009] Data collection and transmission module: Through the digital twin system, data is collected in each link of the entire spinning process to obtain equipment operation parameters, production process parameters and quality inspection data. The collected data is transmitted to the data platform in real time through the Internet of Things technology, and then the equipment quality operation index of each link in the entire spinning process is analyzed.

[0010] Data analysis module: Based on the obtained equipment quality operation index of each link in the whole spinning process, the equipment quality operation index of each abnormal link in the whole spinning process is analyzed.

[0011] Monitoring and feedback module: Based on the equipment quality operation index of each abnormal link in the whole spinning process, the deviation value of the equipment quality operation index of each abnormal link in the whole spinning process is matched, and the deviation qualification level of each abnormal link is matched to make adjustments and optimizations.

[0012] Furthermore, the equipment quality operation index of each link in the whole spinning process is specifically analyzed by the following method: based on the obtained equipment operation parameters, production process parameters and quality inspection data, the equipment operation parameters include: the number of equipment shutdowns and textile speeds in each link of the whole spinning process, the production process parameters include: the elongated length and diameter of the yarn in each link of the whole spinning process, and the quality inspection data include: the number of yarn surface hairiness and cotton knots in each link of the whole spinning process, and then analyze the equipment status of each link in the whole spinning process. Compliance index B l , the production process of each link in the whole spinning process meets the index D l The quality of each link in the whole spinning process meets the index K l , analyze the equipment quality operation index of each link in the whole spinning process, and the specific calculation formula is: α l =B l +D l +K l , where l represents the link number, l=1,2,...,p, and p represents the number of links.

[0013] Furthermore, the equipment status conformity index of each link in the whole spinning process is specifically analyzed by the following method: based on the obtained number of shutdowns and textile speeds of the equipment in each link in the whole spinning process, the reference number of shutdowns and textile reference speeds of the equipment in each link in the whole spinning process are extracted from the database, and the equipment status conformity index of each link in the whole spinning process is analyzed. The specific calculation formula is: Among them, w l It is expressed as the reference shutdown times of the equipment in the lth link of the whole spinning process, w l ' represents the number of equipment shutdowns in the first link of the entire spinning process, e l It is the reference speed of the spinning equipment in the first link of the whole spinning process, e l 'It represents the spinning speed of the equipment in the first link of the whole spinning process.

[0014] Furthermore, the production process compliance index of each link in the whole spinning process is specifically analyzed by: based on the obtained yarn elongation length and diameter of each link in the whole spinning process, the yarn elongation reference length and reference diameter of each link in the whole spinning process are extracted from the database, and the production process compliance index of each link in the whole spinning process is analyzed. The specific calculation formula is: f l It is expressed as the reference length of the yarn stretched in the first link of the whole spinning process, f l ' represents the length of the yarn stretched in the first link of the whole spinning process, g l It is expressed as the reference diameter of the first link in the whole spinning process, g l' represents the diameter of the lth link in the entire spinning process.

[0015] Furthermore, the yarn quality compliance index of each link in the whole spinning process is specifically calculated as follows: based on the obtained yarn surface hairiness number and cotton nep number of each link in the whole spinning process, the yarn surface hairiness reference number and cotton nep reference number of each link in the whole spinning process are extracted from the database, and the yarn quality compliance index of each link in the whole spinning process is analyzed. The specific calculation formula is: s l It is expressed as the reference number of yarn surface hairiness in the lth link of the whole spinning process, s l ' represents the number of yarn surface hairiness in the lth link of the whole spinning process, h l It is expressed as the reference number of cotton knots in the lth link of the whole spinning process, h l ' represents the number of cotton knots in the lth link of the whole spinning process.

[0016] Furthermore, the specific analysis method of analyzing the equipment quality operation index of each abnormal link in the whole spinning process is as follows: based on the obtained equipment quality operation index of each link in the whole spinning process, the equipment quality operation index of each link in the whole spinning process is compared with the equipment quality operation index threshold of each link in the whole spinning process stored in the database; if the equipment quality operation index of a link in the whole spinning process is less than the equipment quality operation index threshold of each link in the whole spinning process stored in the database, the equipment quality operation index of this link in the whole spinning process is recorded as an abnormal link, thereby obtaining the equipment quality operation index of each abnormal link in the whole spinning process.

[0017] Furthermore, the deviation qualified level of the matching actual production abnormal equipment operation performance and the predicted production abnormal equipment operation performance requirements is specifically analyzed by the following method: based on the obtained equipment quality operation index of each abnormal link in the whole spinning process, the equipment quality operation index of each abnormal link predicted in the whole spinning process is extracted from the database, and the equipment quality operation index of each abnormal link in the whole spinning process is subjected to difference processing with the equipment quality operation index of each abnormal link in the whole spinning process predicted in the whole spinning process to obtain the deviation value of the equipment quality operation index of each abnormal link in the whole spinning process, and the obtained deviation value of the equipment quality operation index of each abnormal link in the whole spinning process is compared with the grade standard to match the deviation qualified level of each abnormal link, wherein the deviation qualified levels are good, qualified and excellent respectively; when the deviation qualified level is good, the daily maintenance and maintenance of the equipment is strengthened, the components with performance degradation are repaired or replaced in time, and the number of inspections of key equipment operation parameters and yarn quality indicators is increased; when the deviation qualified level is qualified or excellent, the practicality of the standard is improved, and the evaluation results are fed back to production managers and operators.

[0018] The second aspect of the present invention provides a method for a full-process spinning digital twin system, characterized in that it includes: step one, data collection and transmission: data collection is performed at each link of the entire spinning process through the digital twin system to obtain equipment operating parameters, production process parameters and quality inspection data, and the collected data is transmitted to the data platform in real time through the Internet of Things technology, thereby analyzing the equipment quality operation index of each link of the entire spinning process.

[0019] Step 2: Data analysis: Based on the obtained equipment quality operation index of each link in the whole spinning process, analyze the equipment quality operation index of each abnormal link in the whole spinning process.

[0020] Step 3: Monitoring and feedback: Based on the equipment quality operation index of each abnormal link in the whole spinning process, the deviation value of the equipment quality operation index of each abnormal link in the whole spinning process is matched, and the deviation qualification level of each abnormal link is matched to make adjustments and optimizations.

[0021] The beneficial effects of the present invention are: 1. Data acquisition and transmission module: Through the digital twin system, data is collected in each link of the entire spinning process to obtain equipment operating parameters, production process parameters and quality inspection data, and the collected data is transmitted to the data platform in real time through the Internet of Things technology, and then the equipment quality operation index of each link in the entire spinning process is analyzed, which reduces the probability of failure, avoids production interruptions, reduces the difficulty of process management, and promptly discovers potential hidden dangers of equipment failures. Real-time monitoring and analysis of equipment operation data are timely performed, which increases the company's accurate probability of evaluating the performance of the equipment, improves the quality of the entire spinning process, and avoids waste of raw materials due to equipment problems, thereby reducing production costs.

[0022] 2. Monitoring and feedback module: Based on the equipment quality operation index of each abnormal link in the whole spinning process, the deviation value of the equipment quality operation index of each abnormal link in the whole spinning process is matched, and the deviation qualification level of each abnormal link is matched, so as to make adjustments and optimizations, timely discover the potential abnormalities of the equipment, avoid the sudden shutdown of the equipment, reduce the time of production interruption, thereby improving production efficiency, reducing the breakage rate of yarn and uneven yarn, improving the qualified rate of spinning, timely adjusting the equipment status, making decisions in time, improving the stability of the production process, and thus improving management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 It is a schematic diagram of the system structure connection of the present invention.

[0025] Figure 2 The present invention is a schematic flow chart of the steps for implementing the method. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Reference Figure 1 As shown, the present invention provides a full-process spinning digital twin system, including: a data acquisition and transmission module, a data analysis module and a monitoring and feedback module.

[0028] It should be noted that the data acquisition and transmission module is connected to the data analysis module, the data analysis module is connected to the monitoring and feedback module, the data acquisition and transmission module is connected to the database, the data analysis module is connected to the database, and the monitoring and feedback module is connected to the database.

[0029] It should be noted that the database is used to store the reference shutdown times and textile reference speeds of equipment in each link of the entire spinning process, the reference length and reference diameter of yarn elongation in each link of the entire spinning process, the reference number of yarn surface hairiness and the reference number of cotton knots in each link of the entire spinning process, the equipment quality operation index threshold of each link in the entire spinning process, and the predicted equipment quality operation index of each abnormal link in the entire spinning process.

[0030] Data collection and transmission module: Through the digital twin system, data is collected in each link of the entire spinning process to obtain equipment operation parameters, production process parameters and quality inspection data. The collected data is transmitted to the data platform in real time through the Internet of Things technology, and then the equipment quality operation index of each link in the entire spinning process is analyzed.

[0031] In the above embodiment, the equipment quality operation index of each link in the whole spinning process is specifically analyzed by the following method: based on the obtained equipment operation parameters, production process parameters and quality inspection data, the equipment operation parameters include: the number of equipment shutdowns and textile speeds in each link of the whole spinning process, the production process parameters include: the elongated length and diameter of the yarn in each link of the whole spinning process, and the quality inspection data include: the number of yarn surface hairiness and cotton knots in each link of the whole spinning process, and then analyze the equipment status of each link in the whole spinning process. l , the production process of each link in the whole spinning process meets the index D l The quality of each link in the whole spinning process meets the index K l , analyze the equipment quality operation index of each link in the whole spinning process, and the specific calculation formula is: α l =B l +D l +K l , where l represents the link number, l=1,2,...,p, and p represents the number of links.

[0032] In the above embodiment, the equipment status conformity index of each link in the whole spinning process is specifically analyzed by: based on the obtained shutdown times and textile speed of the equipment in each link of the whole spinning process, the reference shutdown times and textile reference speed of the equipment in each link of the whole spinning process are extracted from the database, and the equipment status conformity index of each link in the whole spinning process is analyzed, and the specific calculation formula is: Among them, w l It is expressed as the reference shutdown times of the equipment in the lth link of the whole spinning process, w l ' represents the number of equipment shutdowns in the first link of the entire spinning process, e l It is the reference speed of the spinning equipment in the first link of the whole spinning process, e l 'It represents the spinning speed of the equipment in the first link of the whole spinning process.

[0033] It should be noted that the reference downtime of equipment in each link of the entire spinning process is obtained through a large number of tests and experiments conducted by equipment manufacturers during the development and production of equipment, and the expected downtime of equipment under normal operating conditions is obtained as an initial reference. For example, after testing, the manufacturer of a certain model of carding machine has given that under standard production conditions, there may be 1-2 downtimes due to routine maintenance or minor faults for every 100 hours of operation.

[0034] It should be noted that the reference speed of the equipment in each link of the spinning process is marked in the equipment's technical manual and instruction manual. For example, the technical parameters of a new spinning machine show that the average speed of its spindle is 15,000 rpm.

[0035] In the above embodiment, the production process compliance index of each link in the whole spinning process is specifically analyzed by: based on the obtained yarn elongation length and diameter of each link in the whole spinning process, and extracting the yarn elongation reference length and reference diameter of each link in the whole spinning process from the database, analyzing the production process compliance index of each link in the whole spinning process, and the specific calculation formula is: f l It is expressed as the reference length of the yarn stretched in the first link of the whole spinning process, f l ' represents the length of the yarn stretched in the first link of the whole spinning process, g l It is expressed as the reference diameter of the first link in the whole spinning process, g l ' represents the diameter of the lth link in the entire spinning process.

[0036] It should be noted that the reference length of yarn elongation in each link of the spinning process is calculated by analyzing the company's historical production data and counting the actual length of yarn elongation in each link. For example, in the company's historical roving production, the yarn elongation length fluctuated between 4-7 times, with an average of 5.5 times, which was used as the reference length of yarn elongation in this link.

[0037] It should be noted that the reference diameter of the yarn in each link of the whole spinning process is calculated by analyzing the historical production data of the enterprise and the actual value of the yarn diameter of different products in each link. For example, in the linen yarn products produced by a certain enterprise in the past, after the combing process, the yarn diameter is 4 mm, which is used as the reference diameter of the yarn.

[0038] In the above embodiment, the yarn quality compliance index of each link in the whole spinning process is specifically calculated as follows: based on the obtained yarn surface hairiness number and cotton nep number of each link in the whole spinning process, the yarn surface hairiness reference number and cotton nep reference number of each link in the whole spinning process are extracted from the database, and the yarn quality compliance index of each link in the whole spinning process is analyzed. The specific calculation formula is: s l It is expressed as the reference number of yarn surface hairiness in the lth link of the whole spinning process, s l ' represents the number of yarn surface hairiness in the lth link of the whole spinning process, h l It is expressed as the reference number of cotton knots in the lth link of the whole spinning process, h l ' represents the number of cotton knots in the lth link of the whole spinning process.

[0039] It should be noted that the reference number of yarn surface hairiness at each link of the entire spinning process is statistically analyzed by the hairiness of various yarn products at each link in history. The average value of the hairiness number is found by sorting out the hairiness data of products from different batches and different orders.

[0040] It should be noted that the reference number of cotton neps in each link of the entire spinning process is obtained by the company's detailed statistics and analysis of the number of cotton neps in each link of various yarn products produced in the past. The average number of cotton neps is obtained by sorting out the cotton neps data of historical batches of products. For example, after the cotton cleaning process, the number of cotton neps per gram of polyester-cotton blended yarn produced by a certain company is 15, which is used as the reference number of cotton neps.

[0041] Data collection and transmission module: Through the digital twin system, data is collected in each link of the entire spinning process to obtain equipment operating parameters, production process parameters and quality inspection data. Through the Internet of Things technology, the collected data is transmitted to the data platform in real time, and then the equipment quality operation index of each link in the entire spinning process is analyzed, which reduces the probability of failure, avoids production interruptions, reduces the difficulty of process management, and promptly discovers potential equipment failure hazards. Real-time monitoring and analysis of equipment operation data in a timely manner increases the company's accurate probability of evaluating the performance of the equipment, improves the quality of the entire spinning process, and avoids the waste of raw materials caused by equipment problems, thereby reducing production costs.

[0042] Data analysis module: Based on the obtained equipment quality operation index of each link in the whole spinning process, the equipment quality operation index of each abnormal link in the whole spinning process is analyzed.

[0043] In the above embodiment, the specific analysis method of analyzing the equipment quality operation index of each abnormal link in the whole spinning process is as follows: based on the obtained equipment quality operation index of each link in the whole spinning process, the equipment quality operation index of each link in the whole spinning process is compared with the equipment quality operation index threshold of each link in the whole spinning process stored in the database. If the equipment quality operation index of a link in the whole spinning process is less than the equipment quality operation index threshold of each link in the whole spinning process stored in the database, the equipment quality operation index of this link in the whole spinning process is recorded as an abnormal link, and then the equipment quality operation index of each abnormal link in the whole spinning process is obtained.

[0044] Monitoring and feedback module: Based on the equipment quality operation index of each abnormal link in the whole spinning process, the deviation value of the equipment quality operation index of each abnormal link in the whole spinning process is matched, and the deviation qualification level of each abnormal link is matched to make adjustments and optimizations.

[0045] In the above embodiment, the deviation qualified level of the abnormal equipment operating performance of the actual production is matched with the abnormal equipment operating performance of the predicted production, and the specific analysis method is: based on the obtained equipment quality operation index of each abnormal link in the whole spinning process, the equipment quality operation index of each abnormal link predicted in the whole spinning process is extracted from the database, and the equipment quality operation index of each abnormal link in the whole spinning process is subjected to difference processing with the equipment quality operation index of each abnormal link in the whole spinning process predicted in the whole spinning process to obtain the deviation value of the equipment quality operation index of each abnormal link in the whole spinning process, and the obtained deviation value of the equipment quality operation index of each abnormal link in the whole spinning process is compared with the grade standard to match the deviation qualified level of each abnormal link, wherein the deviation qualified levels are good, qualified and excellent respectively. When the deviation qualified level is good, the daily maintenance and maintenance of the equipment is strengthened, the components with performance degradation are repaired or replaced in time, and the number of detections of key equipment operating parameters and yarn quality indicators is increased. When the deviation qualified level is qualified or excellent, the practicality of the standard is improved, and the evaluation results are fed back to production managers and operators.

[0046] Reference Figure 2 As shown, the present invention provides a method for a full-process spinning digital twin system, characterized in that it includes: step one, data collection and transmission: data collection is performed in each link of the whole spinning process through the digital twin system to obtain equipment operating parameters, production process parameters and quality inspection data, and the collected data is transmitted to the data platform in real time through the Internet of Things technology, and then the equipment quality operation index of each link of the whole spinning process is analyzed.

[0047] Monitoring and feedback module: Based on the equipment quality operation index of each abnormal link in the whole spinning process, the deviation value of the equipment quality operation index of each abnormal link in the whole spinning process is matched, and the deviation qualification level of each abnormal link is matched, so as to make adjustments and optimizations, timely discover potential abnormalities of equipment, avoid sudden shutdown of equipment, reduce the time of production interruption, thereby improving production efficiency, reducing the breakage rate of yarn and uneven yarn, improving the qualified rate of spinning, timely adjusting the equipment status, making decisions in time, improving the stability of the production process, and thus improving management efficiency.

[0048] Step 2: Data analysis: Based on the obtained equipment quality operation index of each link in the whole spinning process, analyze the equipment quality operation index of each abnormal link in the whole spinning process.

[0049] Step 3: Monitoring and feedback: Based on the equipment quality operation index of each abnormal link in the whole spinning process, the deviation value of the equipment quality operation index of each abnormal link in the whole spinning process is matched, and the deviation qualification level of each abnormal link is matched to make adjustments and optimizations.

[0050] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A full-process spinning digital twin system, characterized in that: include: Data collection and transmission module: Through the digital twin system, data is collected in each link of the entire spinning process to obtain equipment operation parameters, production process parameters and quality inspection data. Through the Internet of Things technology, the collected data is transmitted to the data platform in real time, and then the equipment quality operation index of each link of the entire spinning process is analyzed; Data analysis module: based on the obtained equipment quality operation index of each link in the whole spinning process, analyze the equipment quality operation index of each abnormal link in the whole spinning process; Monitoring and feedback module: Based on the equipment quality operation index of each abnormal link in the whole spinning process, the deviation value of the equipment quality operation index of each abnormal link in the whole spinning process is matched, and the deviation qualification level of each abnormal link is matched to make adjustments and optimizations.

2. A full-process spinning digital twin system according to claim 1, characterized in that: The specific analysis method of the equipment quality operation index of each link in the entire spinning process is as follows: Based on the obtained equipment operation parameters, production process parameters and quality inspection data, the equipment operation parameters include: the number of equipment shutdowns and textile speeds in each link of the entire spinning process, the production process parameters include: the length and diameter of the yarn elongation in each link of the entire spinning process, and the quality inspection data include: the number of yarn surface hairiness and cotton knots in each link of the entire spinning process, and then analyze the equipment status of each link in the entire spinning process. Compliance index B l , the production process of each link in the whole spinning process meets the index D l The quality of each link in the whole spinning process meets the index K l , analyze the equipment quality operation index of each link in the whole spinning process, and the specific calculation formula is: α l =B l +D l +K l , where l represents the link number, l=1,2,...,p, and p represents the number of links.

3. A full-process spinning digital twin system according to claim 2, characterized in that: The equipment status compliance index of each link in the entire spinning process is analyzed in the following specific method: Based on the obtained shutdown times and textile speed of each link of the whole spinning process, the reference shutdown times and textile reference speed of each link of the whole spinning process are extracted from the database, and the equipment status compliance index of each link of the whole spinning process is analyzed. The specific calculation formula is: Among them, w l It is expressed as the reference shutdown times of the equipment in the lth link of the whole spinning process, w l ' represents the number of equipment shutdowns in the first link of the entire spinning process, e l It is the reference speed of the spinning equipment in the first link of the whole spinning process, e l 'It represents the spinning speed of the equipment in the first link of the whole spinning process.

4. A full-process spinning digital twin system according to claim 2, characterized in that: The production process compliance index of each link in the entire spinning process is analyzed in the following specific methods: Based on the obtained yarn elongation length and diameter of each link in the whole spinning process, the yarn elongation reference length and reference diameter of each link in the whole spinning process are extracted from the database, and the production process compliance index of each link in the whole spinning process is analyzed. The specific calculation formula is: f l It is expressed as the reference length of the yarn stretched in the first link of the whole spinning process, f l ' represents the length of the yarn stretched in the first link of the whole spinning process, g l It is expressed as the reference diameter of the first link in the whole spinning process, g l ' represents the diameter of the lth link in the entire spinning process.

5. A full-process spinning digital twin system according to claim 2, characterized in that: The yarn quality compliance index of each link in the entire spinning process is calculated as follows: Based on the obtained yarn surface hairiness and neps number of each link in the whole spinning process, the yarn surface hairiness and neps reference number of each link in the whole spinning process are extracted from the database, and the yarn quality compliance index of each link in the whole spinning process is analyzed. The specific calculation formula is: s l It is expressed as the reference number of yarn surface hairiness in the lth link of the whole spinning process, s l ' represents the number of yarn surface hairiness in the lth link of the whole spinning process, h l It is expressed as the reference number of cotton knots in the lth link of the whole spinning process, h l ' represents the number of cotton knots in the lth link of the whole spinning process.

6. A full-process spinning digital twin system according to claim 1, characterized in that: The specific analysis method of analyzing the equipment quality operation index of each abnormal link in the entire spinning process is as follows: Based on the obtained equipment quality operation index of each link in the whole spinning process, the equipment quality operation index of each link in the whole spinning process is compared with the equipment quality operation index threshold of each link in the whole spinning process stored in the database. If the equipment quality operation index of a link in the whole spinning process is less than the equipment quality operation index threshold of each link in the whole spinning process stored in the database, the equipment quality operation index of this link in the whole spinning process is recorded as an abnormal link, and then the equipment quality operation index of each abnormal link in the whole spinning process is obtained.

7. A full-process spinning digital twin system according to claim 1, characterized in that: The specific analysis method of the deviation qualification level between the abnormal equipment operating performance of the actual production and the abnormal equipment operating performance requirements of the predicted production is as follows: Based on the obtained equipment quality operation index of each abnormal link in the whole spinning process, the predicted equipment quality operation index of each abnormal link in the whole spinning process is extracted from the database, and the equipment quality operation index of each abnormal link in the whole spinning process is differenced with the equipment quality operation index of each abnormal link in the whole spinning process, so as to obtain the deviation value of the equipment quality operation index of each abnormal link in the whole spinning process, and compare the obtained deviation value of the equipment quality operation index of each abnormal link in the whole spinning process with the grade standard, and match the deviation qualified grade of each abnormal link, wherein the deviation qualified grades are good, qualified and excellent, respectively. When the deviation qualified grade is good, the daily maintenance and maintenance of the equipment is strengthened, the components with performance degradation are repaired or replaced in time, and the number of inspections of key equipment operating parameters and yarn quality indicators is increased. When the deviation qualified grade is qualified or excellent, the practicability of the standard is improved, and the evaluation results are fed back to production managers and operators.

8. A method for executing a full-process spinning digital twin system as described in any one of claims 1 to 7, characterized in that: include: Step 1: Data collection and transmission: The digital twin system is used to collect data in each link of the entire spinning process to obtain equipment operation parameters, production process parameters and quality inspection data. The collected data is then transmitted to the data platform in real time through the Internet of Things technology, and then the equipment quality operation index of each link of the entire spinning process is analyzed; Step 2, data analysis: based on the obtained equipment quality operation index of each link in the whole spinning process, analyze the equipment quality operation index of each abnormal link in the whole spinning process; Step 3: Monitoring and feedback: Based on the equipment quality operation index of each abnormal link in the whole spinning process, the deviation value of the equipment quality operation index of each abnormal link in the whole spinning process is matched, and the deviation qualification level of each abnormal link is matched to make adjustments and optimizations.

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