A digital textile equipment management method based on big data

Abstract

The present application relates to the technical field of textile, particularly relates to a digital textile equipment management method based on big data, the method comprises the following steps: step 1, equipment connection and data initialization stage, check the data interface type of each link equipment of textile process and connect to workshop data acquisition network, establish electronic archives for each equipment and set initial data acquisition parameters, pre-run test and calibration are carried out on the equipment;Step 2, production process monitoring and data recording stage;In the scheme, the equipment performance evaluation model established by using big data analysis technology, comprehensively considers energy efficiency, production stability index, equipment failure rate trend and other factors, and carries out comprehensive and accurate evaluation on equipment performance, which effectively avoids equipment sudden failure, reduces the risk of production interruption, improves the overall management efficiency and operation stability of equipment, guarantees the continuity of production process, and reduces the cost increase caused by equipment failure.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of textiles, in particular to a digital textile equipment management method based on big data. BACKGROUND

[0002] The textile industry, as an important part of traditional manufacturing, plays a vital role in the modern industrial system. With the continuous progress of technology and the increasing competition in the market, textile enterprises are facing challenges in improving production efficiency, ensuring product quality, reducing costs, and enhancing equipment management.

[0003] Traditional equipment management methods mainly rely on manual experience and regular inspections, but manual monitoring of equipment operation status is difficult to achieve real-time and comprehensive, often only after the equipment has obvious failures, leading to production interruption, increased maintenance costs and time loss. In addition, the evaluation of equipment performance lacks scientific and accurate quantitative methods, which cannot timely detect the performance decline trend and intervene in advance. Furthermore, in the production process, data transmission between devices in different stages is not smooth, and the connection is not tight, which easily causes information islands, affecting production efficiency and product quality. For the large amount of data accumulated in the production process, there is a lack of effective integration and in-depth analysis, which cannot fully tap the value of data and optimize the production process from a global perspective, reduce energy consumption, and improve product quality.

[0004] Therefore, the present application proposes a digital textile equipment management method based on big data, which uses a device performance evaluation model established by big data analysis technology to comprehensively and accurately evaluate the performance of the device by considering factors such as energy efficiency, production stability indicators, and device failure rate trends. For example, in the spinning stage, the carding machine not only focuses on the current carding state, but also evaluates the performance in combination with historical data to timely detect potential problems, which effectively avoids equipment sudden failure, reduces the risk of production interruption, improves the overall management efficiency and operation stability of the equipment, ensures the continuity of the production process, and reduces the increase in costs caused by equipment failure. SUMMARY

[0005] The technical problem solved by the present application is that manual monitoring of equipment operation status is difficult to achieve real-time and comprehensive, and in the production process, data transmission between devices in different stages is not smooth, and the connection is not tight, which easily causes information islands.

[0006] To address the technical problems mentioned in the background, the present application provides a digital textile equipment management method based on big data.

[0007] To achieve the above purpose, the present application is implemented by the following technical solutions:

[0008] A digital textile equipment management method based on big data, the method comprising the following steps:

[0009] Step 1, equipment connection and data initialization stage, checking the data interface type of the equipment in each link of the textile process and connecting to the workshop data acquisition network, establishing an electronic file for each equipment and setting initial data acquisition parameters, pre-running test and calibration of the equipment;

[0010] Step 2, production process monitoring and data recording stage, real-time acquisition of operation data of equipment in each production link, calculation and display of production progress, real-time analysis of equipment operation data, and warning information and processing when key process parameters exceed the preset range;

[0011] Step 3, equipment performance optimization and intelligent adjustment stage, using big data analysis technology to establish an equipment performance evaluation model and evaluate equipment performance in real time, generating intelligent optimization strategies when the performance score is below the preset threshold, automatically adjusting equipment parameters and remotely monitoring execution;

[0012] Step 4, equipment connection and data transition stage, when the textile production link is converted, the data of each link is associated and transmitted, the data acquisition system of the receiving data equipment is calibrated, and the equipment is prepared;

[0013] Step 5, data summary and analysis stage, integrating textile production process data, using data analysis tools to analyze data and generate production reports and analysis reports.

[0014] In one possible implementation, the equipment performance evaluation model considers energy efficiency, production stability index, and equipment failure rate trend, and the formula is:

[0015] P = 50 + 30 x (1-E) + 20 x (1-S) - 10 x F;

[0016] Where: P is the equipment performance score, E is the energy efficiency of the equipment (unit time energy consumption and standard energy consumption ratio, value range 0-1, closer to 0 indicates higher energy efficiency), S is the production stability index (measured by the sum of standard deviations of key process parameters within a certain period of time, for example, for a spinning machine, the sum of standard deviations of parameters such as roller draft ratio and yarn tension, value range 0-1, closer to 0 indicates more stable production), F is the equipment failure rate trend (obtained by fitting the number of faults in the past period of time, value range -1 to 1, -1 indicates rapid decline in failure rate, 0 indicates stable failure rate, 1 indicates rapid increase in failure rate);

[0017] In the formula, 50 is a basic score representing the basic performance level of the equipment under normal conditions, 30x(1-E) represents the impact of energy efficiency on the performance score, the lower the energy efficiency (the smaller the value of E), the higher the score, and the greater the contribution to the total score, because low energy consumption is an important manifestation of good equipment performance. 20x(1-S) is the same, the higher the production stability (the smaller the value of S), the higher the score, and the greater the contribution to the total score, -10xF represents the impact of the failure rate trend on the score, the better the failure rate trend (the smaller the value of F), the higher the score, otherwise it will reduce the total score, because the increase of the failure rate means that there may be potential problems in the performance of the equipment.

[0018] In a possible implementation, the data interface type includes RS485, Ethernet, the equipment profile content includes basic information such as model, production date, and maintenance period, and the pre-operation test checks the accuracy and stability of data acquisition and transmission.

[0019] In a possible implementation, the operation data collected at each production link includes process parameters and equipment operation state information, the production progress calculation is based on the production capacity and yield data of the equipment at each link, and the abnormality early warning and processing includes providing possible cause analysis and processing process records.

[0020] In a possible implementation, the information transmitted by the data management system at the link conversion includes raw material, production process, and quality detection data, and the equipment calibration and preparation includes checking the calibration data acquisition system and recording equipment preparation state information; in the data summarization and analysis stage, the data analysis content includes production cycle difference, process parameter and quality relationship, equipment failure, and energy consumption.

[0021] Compared with the prior art, the beneficial effects are as follows:

[0022] 1. In the present scheme, the equipment performance evaluation model established by using big data analysis technology comprehensively considers factors such as energy efficiency, production stability index, and equipment failure rate trend to comprehensively and accurately evaluate the performance of the equipment. For example, for a spinning link carding machine, not only the current carding state is concerned, but also the performance is evaluated in combination with historical data, potential problems can be found in time, and when the equipment performance score is lower than the preset threshold, an optimization strategy is intelligently generated to automatically adjust the equipment parameters and remotely monitor the execution, which effectively avoids equipment sudden failure, reduces the risk of production interruption, improves the overall management efficiency and operation stability of the equipment, guarantees the continuity of the production process, and reduces the increase of cost caused by equipment failure;

[0023] 2. In this scheme, through the device connection and data transition stage, the data management system can accurately associate and transfer data when each production link is converted, such as from spinning to weaving link, the bobbin winder and the warper can automatically adjust the parameters according to the yarn data, ensure the rationality of the process parameter setting of the subsequent production link, at the same time, through the integration and analysis of the data of the whole production process in the data collection and analysis stage, the best process parameter combination can be determined, such as finding the key factors affecting the quality by comparing the production data of different batches of cloth, and then optimizing the process, when the fabric dyeing color difference is too large, the data of each link can be traced back to find the root cause and adjust the process parameters, effectively reducing product quality problems, improving product qualification rate, improving overall product quality, and enhancing market competitiveness;

[0024] 3. In this scheme, through the production process monitoring and data recording stage, real-time acquisition of equipment running data of each link is realized, when an abnormality such as high yarn breakage rate occurs, the system can record the equipment adjustment and related parameter changes in detail, providing accurate basis for problem tracing, and the production report and analysis report generated in the data collection and analysis stage cover production overview, equipment running status, quality analysis and other aspects, enterprise management can understand the production process according to these reports, such as determining the energy consumption of each link to develop energy saving strategies, arranging maintenance plans according to equipment failure frequency, etc., providing strong support for enterprise decision-making, which is helpful to optimize production management strategy and improve enterprise operation efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0025] The above description is only a summary of the technical scheme of the present application. In order to more clearly understand the technical means of the present application and can be implemented according to the content of the description, the following will be described in detail with reference to the preferred embodiments of the present application and the accompanying drawings.

[0026] Figure 1 A step flow chart of a digital textile equipment management method based on big data. DETAILED DESCRIPTION

[0027] The preferred embodiments of the present application will be described in detail with reference to the accompanying drawings, but the present application can be realized in various different forms, and therefore the present application is not limited to the embodiments described below;

[0028] The technical scheme in the embodiments of the present application is to solve the problems in the above background art, and the general idea is as follows:

[0029] Embodiment:

[0030] Please refer to Figure 1 The present embodiment introduces a digital textile equipment management method based on big data, the steps are as follows:

[0031] Step 1, device connection and data initialization stage

[0032] Step 1.1, interface troubleshooting and network connection, for each link of the textile process equipment;

[0033] The opening and cleaning combined machine, the carding machine in the spinning link, the bobbin winder, the warping machine in the weaving link, the dyeing machine, the drying machine in the dyeing link, the stenter, the calender in the finishing link, etc. Check the data interface types of each device. Common types include RS485, Ethernet, etc.

[0034] Connect each device to the workshop data acquisition network according to its interface type accurately, and ensure the establishment of data transmission channel;

[0035] Step 1.2, device file creation and parameter setting, create an independent electronic file for each device in the data management system. The file contains the device model, production date, maintenance period and other basic information. These information helps to manage the device throughout its life cycle;

[0036] For example, according to the production date and maintenance period, arrange the device maintenance plan reasonably to prevent device failure;

[0037] According to the function and production process requirements of the device, set the initial data acquisition parameters, such as the fiber raw material processing capacity acquisition frequency of the opening and cleaning combined machine in the spinning link is set to once every 5 minutes, the single fiber carding state monitoring data acquisition interval of the carding machine is 3 minutes, etc. To ensure that the key data in the device running process can be accurately obtained;

[0038] Step 1.3, pre-run test and calibration, start the device data acquisition software and test the device in pre-run. This test aims to check whether the data can be accurately and stably collected from the device end and transmitted to the central database;

[0039] Check the integrity and accuracy of the data carefully. If you find that the data is missing, incorrect or transmission is interrupted, etc. Further investigate the root cause of the problem. It may be caused by device data transmission module failure, improper parameter setting of acquisition software, or unstable network connection, etc. Take appropriate adjustment measures in time according to different problems, such as repairing device hardware failure, recalibrating data acquisition software parameters or optimizing network configuration, until the data acquisition and transmission reach normal and stable state.

[0040] Step 2, production process monitoring and data recording phase

[0041] Step 2.1, real-time data acquisition, continuously run in each production link (spinning, weaving, dyeing, finishing), and real-time acquire the running data of the device. In the spinning link, collect the process parameters such as motor speed, roller draft ratio, yarn tension, number of broken ends, etc. and the running state information (start, stop, fault alarm, etc.) of the device.

[0042] In the weaving link, the bobbin winding speed, winding tension, yarn defect removal quantity of the winding machine, the warp arrangement density, warp beam winding length, tension uniformity of the warping machine, and the related key data of the sizing machine and the loom are monitored;

[0043] In the dyeing link, the dyeing machine's dyeing liquid temperature, dyeing liquid circulation flow, fabric's immersion time in the dyeing liquid, and other process parameters and equipment operation states are focused on, and the drying temperature, drying time, fabric moisture content, and other data of the drying machine are collected;

[0044] In the finishing link, the fabric width change, warp tension adjustment value, and drying temperature of the tentering machine, the roller pressure, fabric glossiness change of the calender, the grinding roller speed, grinding depth, and fabric surface pile state of the sanding machine, the fabric shrinkage, pre-shrinking temperature and humidity of the pre-shrinking machine, and the operation states of each equipment are obtained;

[0045] Step 2.2, production progress calculation and display, according to the production capacity of each link equipment and the completed production data, the production progress is calculated;

[0046] In the spinning link, the designed capacity of the spinning machine is known to be X kilograms of yarn of a certain count per hour, by comparing the real-time collected yarn production data with the total amount of yarn required by the order, the spinning completion ratio is obtained, and the progress bar is intuitively displayed on the monitoring interface, and the progress data at each time node is recorded in detail;

[0047] In the weaving link, according to the fabric length sensor collected data and the fabric Y meters that can be woven per minute by the loom, the weaving progress is calculated and the chart in the monitoring system is updated, and the progress data and corresponding equipment operation parameters in the weaving process of each section of fabric are recorded;

[0048] In the dyeing link, according to the total dyeing time T minutes of a certain fabric under a certain dyeing process, the dyeing completion ratio is calculated by monitoring the cumulative value of the dyeing time, and the related data is visualized and recorded on the monitoring interface;

[0049] In the finishing link, according to the estimated processing time of the steps such as tentering, calendering, sanding, and pre-shrinking included in the finishing process, the overall progress is calculated by monitoring the actual execution time and completion state of each step, and the progress data of each step and the change of equipment process parameters are displayed in the form of flowchart on the monitoring interface and recorded;

[0050] Step 2.3, abnormality warning and treatment, real-time analysis is performed on the equipment operation data, and when the key process parameters of a certain equipment exceed the preset normal range, the system automatically issues a warning message;

[0051] In the spinning process, if the speed of the cylinder of the carding machine fluctuates abnormally or the unevenness of the sliver of the drawing frame is too high; in the weaving process, the weft yarn breakage rate is too high or the defect density is too large;

[0052] In the dyeing process, the color deviation exceeds the allowed range; in the finishing process, the fabric size stability, hand feeling, glossiness and other indicators do not meet the standards, etc. When these situations occur, the system not only highlights the abnormal equipment and parameter information on the monitoring interface, but also provides possible cause analysis, such as equipment component failure, improper process parameter setting, raw material quality problem, etc., to assist the operator to quickly locate and solve the problem;

[0053] The relevant technical personnel receive the early warning notice and quickly check the equipment condition according to the information and data records provided by the system. If it is equipment component failure, replace the damaged component in time;

[0054] If it is a process parameter problem, adjust the process parameters to a reasonable range according to the data analysis results. During the processing, the system continuously records the equipment adjustment situation (including the adjusted parameters, replaced components and corresponding time points) for subsequent tracing and analysis.

[0055] Step 3, equipment performance optimization and intelligent adjustment phase

[0056] Step 3.1, real-time evaluation of equipment performance: using big data analysis technology, based on real-time collected equipment running data and historical data, establish equipment performance evaluation model;

[0057] The equipment performance evaluation model is as follows:

[0058] Let the equipment performance score be P, the energy efficiency of the equipment be E (the ratio of energy consumption per unit time to standard energy consumption, the value range is 0-1, the closer to 0 indicates the higher the energy efficiency), the production stability index be S (measured by the sum of standard deviations of key process parameters within a certain period of time, for example, for a spinning machine, the sum of standard deviations of parameters such as roller draft ratio and yarn tension, the value range is 0-1, the closer to 0 indicates the more stable the production), the equipment failure rate trend be F (the failure rate trend value obtained by fitting the number of failures of the equipment in the past period of time, the value range is -1 to 1, -1 indicates that the failure rate is rapidly decreasing, 0 indicates that the failure rate is stable, and 1 indicates that the failure rate is rapidly increasing), then the equipment performance evaluation model formula can be expressed as:

[0059] P = 50 + 30 × (1-E) + 20 × (1-S) - 10 × F;

[0060] The above formula, where 50 is the base score representing the basic performance level of the equipment under normal conditions, 30x(1-E) represents the impact of energy efficiency on performance score, the lower the energy efficiency (the smaller the E value), the higher the score, and the greater the contribution to the total score, because low energy consumption is an important manifestation of good equipment performance. 20x(1-S) is the same, the higher the production stability (the smaller the S value), the higher the score, and the greater the contribution to the total score, -10xF represents the impact of failure rate trend on the score, the better the failure rate trend (the smaller the F value), the higher the score, otherwise it will reduce the total score, because the increase in failure rate means that there may be potential problems with the performance of the equipment;

[0061] The model takes into account multiple key performance indicators of the equipment, such as energy efficiency, production stability (measured by the fluctuation range of each process parameter), equipment failure rate trend, etc. For a carding machine in the spinning process, not only the current single fiber carding state is considered, but also the carding quality data in the past period of time and the energy consumption change of the equipment are combined to comprehensively evaluate its performance. According to the evaluation model, the performance of each device is scored in real time every certain time interval (such as every hour), and the score range is set to 0-100, the higher the score, the better the performance of the equipment;

[0062] Step 3.2, intelligent performance optimization strategy generation, when the device performance score is lower than the pre-set threshold (such as 70 points), the system automatically starts the intelligent optimization strategy generation mechanism;

[0063] This mechanism is based on the massive data stored in the big data analysis platform, including optimization cases of the same type of equipment under different working conditions, industry best practice data, and expert experience rule base, etc. For example, for a dyeing machine in the dyeing process, if the evaluation finds that the dyeing uniformity is decreased due to unstable dye circulation flow, the system analyzes the possible reasons including dye pump failure, pipe blockage or control system parameter adjustment, etc. By comparing with historical data and combining the solutions of similar fault cases, the system may suggest to check the working state of the dye pump first, if the pump is normal, then check whether the pipe is blocked, and at the same time, according to the current fabric type and dyeing process requirements, intelligently adjust the related parameters of the control system, such as dye circulation frequency, pressure, etc., to improve the performance of the dyeing machine;

[0064] Step 3.3, automatic adjustment and remote monitoring execution, according to the generated optimization strategy, the system automatically sends adjustment instructions to the device control system to realize automatic optimization adjustment of device parameters;

[0065] The operator can observe the adjustment process and performance changes of the equipment in real time through the remote monitoring platform. During the adjustment process, the system continuously collects equipment data, compares performance indicators before and after adjustment, and evaluates the effectiveness of the optimization strategy. If the performance of the equipment is significantly improved after adjustment (performance score reaches 85 points or above), the optimization strategy and adjustment process are recorded as a reference case for similar situations in the future. If the performance improvement is not obvious or new problems arise, the system will re-analyze the data and further optimize the adjustment strategy until the performance of the equipment reaches the best state.

[0066] Step 4, equipment connection and data transition phase

[0067] Step 4.1, data association and transmission

[0068] When the production process of textiles is converted, such as from spinning to weaving, the data management system plays a key role. It records information such as the batch, specification, quantity, and final quality inspection data of the yarn in the spinning process, and associates these data with the weaving equipment, enabling the weaving equipment to obtain detailed information about the yarn, thereby providing a basis for setting subsequent weaving process parameters. For example, the winding machine can automatically adjust the winding parameters based on the received yarn information, and the warping machine can set the beam winding specifications according to the fabric width and yarn density required by the order;

[0069] Similarly, after weaving is completed and enters the dyeing process, the data management system imports the weaving process data of the fabric (yarn source, weaving process parameters, quality inspection results, etc.) and interfaces with the dyeing equipment. The dyeing equipment sets the corresponding dyeing process parameters (such as dye type, dyeing temperature, dyeing time, dye concentration, etc.) based on these data;

[0070] After dyeing, the data management system transmits the data of the dyeing process (dyeing process, color detection results, energy consumption, etc.) to the finishing equipment management module. The finishing equipment develops a finishing process plan based on the final use and customer requirements of the fabric, such as tension width, calendering pressure, sanding degree, and pre-shrinking rate;

[0071] Step 4.2, equipment calibration and preparation. At each process conversion, the data acquisition system of the equipment receiving the data is checked and calibrated again to ensure that the equipment can accurately identify and receive data from the previous process and prepare for data acquisition in its own process.

[0072] Before the weaving process begins, the data acquisition systems of the winding machine, warping machine, and other equipment are calibrated to check whether they can correctly receive the yarn data from the spinning process and operate normally.

[0073] Before the dyeing process begins, confirm that the data acquisition system of the dyeing equipment can accurately identify the data of the weaving process and start data acquisition normally.

[0074] Before the start of the finishing process, ensure that the data acquisition system of the finishing equipment can successfully receive the data from the dyeing process and complete the initialization settings of the equipment, record the preparation state information of the equipment (such as the cleaning condition of the equipment, the surface state of the roller, etc.), and ensure the normal operation of the equipment according to the predetermined process plan and accurate data collection.

[0075] Step 5, data collection and analysis stage

[0076] Step 5.1, data integration, after the completion of the entire textile production process, the data of spinning, weaving, dyeing and finishing processes are integrated to establish a complete fabric production data file, which covers the whole process data from raw material input to final product output, including raw material information such as raw material type, origin, quality index, etc.

[0077] Each process equipment operating parameter, such as process parameter setting of each process equipment, equipment running state data, production progress data, which records the production progress of each process and the whole process, quality test result, which contains quality test data of each process and quality evaluation data of final product, energy consumption data, which statistics energy consumption of each process equipment, and equipment maintenance record, which records the maintenance time, content and maintenance personnel of equipment in production process, etc.

[0078] Step 5.2, data analysis and report generation, using professional data analysis tools to analyze the collected production data in depth;

[0079] Analyze the production cycle difference of different batches of fabric and its reasons, find out the key factors affecting production efficiency such as frequent equipment failure and unreasonable process parameters by comparing the equipment running time, process parameter adjustment and other data of each batch in each process, compare the product quality data under different process parameter settings to determine the best process parameter combination, provide process optimization reference for subsequent production, statistics the fault frequency and maintenance cost of each equipment, evaluate the reliability of equipment and the effectiveness of maintenance strategy, in order to reasonably arrange the equipment maintenance plan and update the equipment budget;

[0080] According to the data analysis results, detailed production reports and analysis reports are generated, including production overview (yield, quality, progress, etc.), intuitive display of production results, equipment operation status analysis, summary of the running stability and fault conditions of each device, quality analysis and improvement suggestions, analysis of the causes of quality problems and the provision of targeted improvement measures, energy consumption analysis, clear energy consumption at each link and optimization space, and cost-benefit analysis to evaluate the cost input and output benefits in the production process, providing decision support for enterprise management and helping enterprises optimize production processes, improve product quality, reduce production costs, improve equipment management level and market competitiveness.

[0081] The specific implementation is as follows:

[0082] 1. Assume that the yarn breakage rate is too high

[0083] Analysis: Yarn breakage may be caused by unstable equipment and process parameters in the spinning process (such as abnormal roller draft ratio, excessive yarn tension), equipment component wear (such as spindles and rings of the spinning machine), or fluctuations in raw material quality, etc. This not only disrupts the production process and increases the workload of workers, but also reduces the overall quality and yield of the yarn, severely affecting production efficiency.

[0084] Treatment: In the production process monitoring of the spinning process, the data acquisition system monitors yarn tension, roller draft ratio, and the number of broken ends in real time.

[0085] 1.1 When the number of broken ends exceeds the preset threshold, the system automatically sends a warning message and highlights the abnormal equipment and related parameter information on the monitoring interface. For example, if the yarn breakage rate of a spinning machine suddenly rises and the yarn tension data shows large fluctuations, the system will immediately notify the relevant technical personnel.

[0086] 1.2 The technical personnel check whether there have been any process parameter modifications or equipment maintenance operations in the recent period based on the system-recorded equipment adjustment conditions and corresponding time points. If so, they analyze whether these operations may have caused the increase in yarn breakage rate. At the same time, they conduct a detailed inspection of the components that may cause yarn breakage (such as rings and rollers) to check for wear or damage.

[0087] 1.3 If the problem is with the process parameters, the technical personnel adjust the roller draft ratio or yarn tension and other parameters based on the data analysis results to restore them to the normal range. If the problem is with the equipment component wear, the damaged components are replaced in a timely manner. After that, the data acquisition system is used to observe whether the yarn breakage rate has returned to normal to verify the effectiveness of the adjustment measures.

[0088] 1.4 The system also records the problem-solving process, including the adjusted parameters, replaced parts, and the time taken to solve the problem, providing a reference for subsequent similar problems;

[0089] 2. Assume that the fabric dyeing color difference is too large

[0090] Analysis: The dyeing color difference may be caused by inaccurate dye formula, improper dyeing process parameters (such as dyeing temperature, time, dye concentration fluctuation), unstable dyeing equipment operation (such as uneven dye circulation) or batch difference of raw material yarn affecting dyeing effect, etc. Color difference problem will cause product not to meet quality standards, need to rework or reduce processing, increase production cost and production cycle

[0091] Treatment: In the process monitoring of dyeing link, real-time monitoring of fabric color change data is realized by combining color sensor or sampling detection with spectral analysis, when the color deviation exceeds the allowed range, the system automatically starts the adjustment mechanism;

[0092] 2.1 First, the system checks the dyeing process parameter setting value and actual running data, such as whether the dyeing temperature is stable within the set value range, whether the dye concentration meets the requirements, whether the dyeing time is accurate, etc. For example, if the dyeing temperature is found to be lower than the set value, it may cause insufficient dye uptake, resulting in color lightness;

[0093] 2.2 At the same time, the data management system traces back the imported weaving link data, checks the yarn batch information and weaving process parameters, analyzes whether there is dyeing difference caused by yarn batch difference, if it is found that it is yarn batch problem, according to the dyeing experience data of similar yarn batch in the past, adjust the dye formula or dyeing process parameters;

[0094] 2.3 The system automatically adjusts the dye addition amount or dyeing process parameters (such as increasing the dyeing temperature, prolonging the dyeing time or adjusting the dye concentration) according to the analysis results, and records the adjustment process and reason in detail. After adjustment, the color change data is continuously monitored until the color meets the order requirements;

[0095] 2.4 In addition, the time of this color difference problem, the fabric batches involved and the related equipment parameters are recorded in detail to facilitate subsequent quality traceability and process improvement, so as to avoid similar problems from happening again;

[0096] 3. Poor dimensional stability of fabric after finishing

[0097] Analysis: The poor dimensional stability of fabric after finishing may be caused by improper pre-shrinking rate setting in the pre-shrinking machine, improper tension control in the tentering machine, unstable drying temperature or humidity, etc. This can cause the fabric to shrink or deform during subsequent use, affecting the appearance and performance of the product, and reducing the market competitiveness of the product.

[0098] Processing: In the effect monitoring of the finishing process, the pre-shrinking machine data acquisition system monitors the fabric shrinkage rate, pre-shrinking temperature and humidity, etc. in real time, and the tentering machine data acquisition system monitors the fabric width change, warp tension adjustment value and drying temperature, etc.

[0099] 3.1 When the dimensional stability of the fabric is found to be substandard, the system automatically adjusts the process parameters of the finishing equipment or sends an alarm to the operator for manual intervention. For example, if the pre-shrinking machine monitors that the fabric shrinkage rate does not meet the expected standard, the system first checks whether the pre-shrinking rate setting parameters are correct, and whether the pre-shrinking temperature and humidity are stable.

[0100] 3.2 The operator checks whether the tension setting of the tentering machine is too large or too small according to the system prompt, combined with the equipment operation data and process plan, because too large tension may cause the fabric to shrink excessively in the subsequent pre-shrinking process, while too small tension may cause the fabric to be unstable in size during finishing. At the same time, check whether the drying temperature and humidity are within the appropriate range, inappropriate drying conditions may affect the fiber structure of the fabric, and thus affect the dimensional stability.

[0101] 3.3 According to the inspection results, the operator adjusts the pre-shrinking rate, temperature and humidity parameters of the pre-shrinking machine, or adjusts the tension setting of the tentering machine, and continues to monitor the dimensional change of the fabric after adjustment to ensure that the dimensional stability meets the requirements. The system records in detail the time when the quality problem occurs, the fabric batch involved and the adjustment process of the related equipment parameters, provides basis for subsequent traceability and improvement of the finishing process, and improves the dimensional stability of the fabric after finishing by continuously optimizing the process parameter settings.

[0102] Finally, it should be noted that the above embodiments are merely examples for clearly illustrating the present application, and are not limiting of the embodiments. For those skilled in the art, other different forms of changes or variations can be made on the basis of the above description. Here, it is not necessary or possible to exhaust all the embodiments. The obvious changes or variations derived therefrom are still within the protection scope of the present application.

Claims

1. A big data based digitized textile equipment management method, characterized by, The method comprises the following steps: Step 1, equipment connection and data initialization stage, checking the data interface type of the equipment in each link of the textile process and connecting to the workshop data acquisition network, establishing an electronic file for each equipment and setting initial data acquisition parameters, pre-running test and calibration of the equipment; Step 2, production process monitoring and data recording stage, real-time acquisition of the running data of the equipment in each production link, calculation and display of the production progress, real-time analysis of the equipment running data, and sending of early warning information and processing when the key process parameters exceed the preset range; Step 3, device performance optimization and intelligent adjustment stage, using big data analysis technology to establish a device performance evaluation model and real-time evaluation of device performance, the device performance evaluation model takes into account the energy efficiency, production stability index, device failure rate trend, the formula is: ; Wherein: The equipment performance score is P, and the score range is 0-100; The energy consumption efficiency E of the equipment is the ratio of the actual energy consumption per unit time to the standard rated energy consumption of the equipment out of the factory, with a value range of 0-1, and the closer to 0, the higher the energy consumption efficiency; The production stability index S is the sum of the standard deviations of the key process parameters within 1 hour, including the roller draft ratio, yarn tension for spinning equipment, and the warp density, weft density for weaving equipment, with a value range of 0-1, and the closer to 0, the more stable the production; The equipment failure rate trend F is the failure rate trend value obtained by linear fitting of the number of failures of the equipment in the past 30 days through the least squares method, with a value range of -1 to 1, -1 indicating a rapid decline in failure rate, 0 indicating a stable failure rate, and 1 indicating a rapid increase in failure rate; According to the evaluation model, the performance of each equipment is scored in real time at certain time intervals, with a score range of 0-100, and the higher the score, the better the equipment performance; When the performance score is lower than the preset threshold, the system automatically starts the intelligent optimization strategy generation mechanism, which is based on the massive data stored in the big data analysis platform, compares with historical data, and combines with the solutions to similar fault cases; According to the generated optimization strategy, the system automatically sends adjustment instructions to the equipment control system to realize automatic optimization and adjustment of the equipment parameters; Step 4, equipment connection and data transition stage, when the textile production links are converted, the data of each link is associated and transmitted, the data acquisition system of the receiving data equipment is calibrated, and the equipment is prepared, wherein: when converting from the spinning link to the weaving link, the winding tension of the winder is automatically adjusted according to the linear density and twist data of the yarn, and the warp beam speed of the warping machine is automatically adjusted according to the breaking strength of the yarn; Step 5, data summary and analysis stage, the whole process data of textile production is summarized and integrated, the data analysis tool is used to analyze the data and generate production reports and analysis reports, which include production cycle difference analysis, process parameter and product quality correlation analysis, equipment failure frequency statistics, and energy consumption statistics, wherein the process parameter and product quality correlation analysis includes the corresponding relationship analysis of fabric color difference and yarn color absorption rate under different dyeing temperatures; production reports and analysis reports are generated according to the analysis results.

2. The big data based digitized textile equipment management method as claimed in claim 1 wherein, The running data collected in each production link includes process parameters and equipment running state information, and the production progress calculation is based on the production capacity and yield data of each link equipment, which includes: In the spinning link, the process parameters of each single machine equipment such as motor speed, roller draft ratio, yarn tension, and broken end number, and the running state information of the equipment are collected; In the weaving process, the yarn winding speed, winding tension, and the number of yarn defects removed by the bobbin winder, the warp arrangement density, warp beam winding length, and tension uniformity parameters of the warping machine, and the relevant key data of the sizing machine and loom are monitored; In the dyeing process, the dyeing machine's dyeing temperature, dyeing circulation flow, and fabric immersion time in the dyeing process parameters and equipment operating status are monitored, and the drying temperature, drying time, and fabric moisture content data of the drying machine are collected; In the finishing process, the fabric width change, warp tension adjustment value, and drying temperature related parameters of the tentering machine, the roller pressure and fabric gloss change of the calender, the grinding roller speed, grinding depth, and fabric surface pile state of the sanding machine, the fabric shrinkage, pre-shrunk temperature and humidity related data of the pre-shrunk machine, and the operating status of each device are obtained; Abnormal early warning and processing include providing possible cause analysis and processing records, which include: In the spinning process, if the cylinder speed of the carding machine fluctuates abnormally or the sliver unevenness of the drawing frame is too high; in the weaving process, the weft end breakage rate is too high or the defect density is too large; In the dyeing process, the color deviation exceeds the allowed range; in the finishing process, when the fabric dimensional stability, hand feel, and gloss related indicators do not meet the standard, the system not only highlights the abnormal device and parameter information on the monitoring interface, but also provides possible cause analysis, such as equipment component failure, improper process parameter setting, and raw material quality problem, to assist the operator to quickly locate and solve the problem; Related technical personnel receive early warning notifications and quickly check the equipment status based on the information and data records provided by the system; if it is a device component failure, replace the damaged component in time; If it is a process parameter problem, adjust the process parameters to a reasonable range based on the data analysis results; during the processing, the system continuously records the equipment adjustment conditions, including the adjusted parameters, replaced components, and corresponding time points, for subsequent tracing and analysis.

Images