Digital textile equipment management method based on big data

Abstract

The invention relates to the technical field of textile, in particular to a digital textile equipment management method based on big data, and the method comprises the following steps: 1, an equipment connection and data initialization stage: checking the data interface type of equipment in each link of a textile process, and connecting the data interface type to a workshop data collection network, establishing an electronic file for each device, setting initial data acquisition parameters, and carrying out pre-operation test and calibration on the devices; 2, a production process monitoring and data recording stage; according to the scheme, the equipment performance evaluation model is established by using a big data analysis technology, multiple factors such as energy consumption efficiency, a production stability index and an equipment failure rate trend are comprehensively considered, and the equipment performance is comprehensively and accurately evaluated, so that sudden equipment failures are effectively avoided, the risk of production interruption is reduced, and the production efficiency is improved. The overall management efficiency and the operation stability of the equipment are improved, the continuity of the production process is guaranteed, and the cost increase caused by equipment faults is reduced.

Description

Technical Field

[0001] The present invention relates to the field of textile technology, and in particular to a digital textile equipment management method based on big data. Background Art

[0002] As an important part of traditional manufacturing, the textile industry occupies an important position in the modern industrial system. With the continuous advancement of science and technology and increasingly fierce market competition, textile enterprises are faced with many challenges such as improving production efficiency, ensuring product quality, reducing costs and improving equipment management.

[0003] Traditional equipment management methods mainly rely on manual experience and regular inspections, but it is difficult to monitor the operating status of equipment in real time and comprehensively. Often, the equipment is not discovered until an obvious fault occurs, resulting in production interruptions, increased maintenance costs and time losses. Moreover, there is a lack of scientific and accurate quantitative means for evaluating equipment performance, making it impossible to promptly discover the trend of equipment performance decline and intervene in advance. In addition, during the production process, data transmission between equipment in various links is not smooth and the connection is not tight, which can easily lead to information islands and affect production efficiency and product quality. There is a lack of effective integration and in-depth analysis of the large amount of data accumulated during the production process, making it impossible to fully tap the value of the data and to optimize the production process, reduce energy consumption and improve product quality from a global perspective.

[0004] Therefore, the present invention proposes a digital textile equipment management method based on big data, which comprehensively and accurately evaluates equipment performance by using an equipment performance evaluation model established by big data analysis technology, taking into account multiple factors such as energy efficiency, production stability indicators, and equipment failure rate trends. For example, the carding machine in the spinning process not only pays attention to the current carding status, but also evaluates the performance in combination with historical data, and can discover potential problems in a timely manner, which effectively avoids sudden equipment failures, reduces the risk of production interruptions, improves the overall management efficiency and operational stability of the equipment, ensures the continuity of the production process, and reduces the cost increase caused by equipment failures. Summary of the invention

[0005] Technical problems to be solved: It is difficult to monitor the operating status of equipment manually in real time and comprehensively. In the production process, data transmission between equipment in various links is not smooth and the connection is not tight, which easily leads to the problem of information islands.

[0006] In view of the deficiencies in the prior art, the present invention provides a digital textile equipment management method based on big data, thereby solving the technical problems mentioned in the background technology.

[0007] To achieve the above objectives, the present invention is implemented through 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: Check the data interface type of equipment in each link of the textile process and connect it to the workshop data acquisition network, create an electronic file for each device and set the initial data acquisition parameters, and perform pre-operation testing and calibration on the equipment;

[0010] Step 2: During the production process monitoring and data recording phase, the operation data of equipment in each production link is collected in real time, the production progress is calculated and displayed, and the equipment operation data is analyzed in real time. When the key process parameters exceed the preset range, early warning information is issued and processed;

[0011] Step 3: Equipment performance optimization and intelligent adjustment phase: Use big data analysis technology to establish an equipment performance evaluation model and evaluate equipment performance in real time. When the performance score is lower than the preset threshold, an intelligent optimization strategy is generated to automatically adjust equipment parameters and remotely monitor execution.

[0012] Step 4: Equipment connection and data transition stage: when the textile production links are switched, 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 aggregation and analysis phase: aggregate and integrate the data of the entire textile production process, use data analysis tools to analyze the data and generate production statements and analysis reports.

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

[0015] P=50+30×(1-E)+20×(1-S)-10×F;

[0016] Among them: the equipment performance score is P, the energy efficiency of the equipment is E (the ratio of energy consumption per unit time to standard energy consumption, the value range is 0-1, the closer to 0, the higher the energy efficiency), the production stability index is S (measured by the sum of standard deviations of key process parameters within a certain period of time, for example, for spinning machines, the sum of standard deviations of parameters such as roller drafting multiples and yarn tension can be considered, the value range is 0-1, the closer to 0, the more stable the production), the equipment failure rate trend is F (the failure rate change 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 a rapid decrease in the failure rate, 0 indicates a stable failure rate, and 1 indicates a rapid increase in the failure rate);

[0017] In the formula, 50 is the basic score, representing the basic performance level of the equipment under normal conditions. 30×(1-E) represents the impact of energy efficiency on the 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 excellent equipment performance. 20×(1-S) Similarly, the higher the production stability (the smaller the S value), the higher the score, and the greater the contribution to the total score. -10×F represents the impact of the 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 an increase in the failure rate means that there may be potential problems with equipment performance.

[0018] In a possible implementation, the data interface types include RS485 and Ethernet, the equipment file content includes basic information such as model, production date, and maintenance cycle, and the pre-operation test checks the accuracy and stability of data collection and transmission.

[0019] In one possible implementation, the operating data collected at each production link includes process parameters and equipment operating status information, the production progress calculation is based on the production capacity and output data of the equipment in each link, and the abnormal warning and processing includes providing possible cause analysis and processing process records.

[0020] In one possible implementation, the information transmitted by the data management system during link conversion includes raw materials, production processes and quality inspection data, and the equipment calibration and preparation includes checking the calibration data acquisition system and recording equipment preparation status information; in the data aggregation and analysis stage, the data analysis content includes production cycle differences, the relationship between process parameters and quality, equipment failures and energy consumption.

[0021] Beneficial effects compared with the prior art:

[0022] 1. In this solution, by using the equipment performance evaluation model established by big data analysis technology, comprehensive consideration is given to multiple factors such as energy efficiency, production stability indicators, and equipment failure rate trends to conduct a comprehensive and accurate evaluation of equipment performance. For example, the carding machine in the spinning process not only pays attention to the current carding status, but also combines historical data to evaluate performance, and can promptly discover potential problems. When the equipment performance score is lower than the preset threshold, it intelligently generates optimization strategies, automatically adjusts equipment parameters, and remotely monitors execution. This effectively avoids sudden equipment failures, reduces the risk of production interruptions, improves the overall management efficiency and operational stability of the equipment, ensures the continuity of the production process, and reduces the cost increase caused by equipment failures;

[0023] 2. In this solution, during the equipment connection and data transition stage, when each production link is converted, the data management system can accurately associate and transmit data. For example, from spinning to weaving, the winding machine and the warping machine can automatically adjust the parameters according to the yarn data to ensure the rationality of the process parameter settings in the subsequent production links. At the same time, during the data summary and analysis stage, the best process parameter combination can be determined by integrating and analyzing the data of the entire production process. For example, by comparing the production data of different batches of fabrics, the key factors affecting the quality can be found, and then the process can be optimized. When dealing with the problem of excessive color difference in fabric dyeing, 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 the overall product quality, and enhancing market competitiveness;

[0024] 3. In this solution, by collecting real-time equipment operation data in each link during the production process monitoring and data recording stage, when an abnormality occurs, such as a high yarn breakage rate, the system can record the equipment adjustment situation and related parameter changes in detail, providing an accurate basis for problem tracing. The production reports and analysis reports generated in the data aggregation and analysis stage cover production overview, equipment operation status, quality analysis and other aspects. The company's management can use these reports to fully understand the production process, such as clarifying the energy consumption of each link to formulate energy-saving strategies, arranging maintenance plans according to the frequency of equipment failures, etc., to provide strong support for corporate decision-making, help optimize production management strategies, and improve corporate operating efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings.

[0026] Figure 1 The figure is a flowchart of the steps of a digital textile equipment management method based on big data. DETAILED DESCRIPTION

[0027] Preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the present invention can also be implemented in various forms, so the present invention is not limited to the embodiments described below.

[0028] The technical solution in the embodiment of the present application is to solve the problems of the above-mentioned background technology, and the overall idea is as follows:

[0029] Example:

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

[0031] Step 1: Device connection and data initialization phase

[0032] Step 1.1, interface investigation and network connection, targeting equipment in each link of the textile process;

[0033] Check the data interface types of the opening and cleaning machine, carding machine, etc. in the spinning process, the winding machine, warping machine, etc. in the weaving process, the dyeing machine, dryer, etc. in the dyeing process, and the stenter, calender, etc. in the finishing process. Common ones include RS485, Ethernet, etc.

[0034] Connect each device to the workshop data collection network accurately according to its interface type to ensure the establishment of data transmission channels;

[0035] Step 1.2: Equipment file creation and parameter setting: Create an independent electronic file for each device in the data management system. The file content includes basic information such as the equipment model, production date, maintenance cycle, etc. This information helps to manage the equipment throughout its life cycle.

[0036] For example, reasonably arrange equipment maintenance plans according to production dates and maintenance cycles to prevent equipment failures;

[0037] According to the functions of the equipment and the production process requirements, the initial data collection parameters are set for it. For example, the collection frequency of the fiber raw material processing volume of the opening and cleaning machine in the spinning process is set to once every 5 minutes, and the single fiber combing status monitoring data collection interval of the carding machine is set to 3 minutes, etc., to ensure that the key data during the operation of the equipment can be accurately obtained;

[0038] Step 1.3: Pre-operation test and calibration: Start the equipment data acquisition software and perform a pre-operation test on the equipment. This test is designed to check whether the data can be accurately and stably collected from the equipment and transmitted to the central database;

[0039] Carefully check the integrity and accuracy of the data. If you find any abnormal situations such as missing data, errors or transmission interruptions, thoroughly investigate the root cause of the problem. It may be caused by factors such as failure of the data transmission module of the equipment, improper parameter settings of the acquisition software, or unstable network connection. For different problems, take corresponding adjustment measures in time, such as repairing equipment hardware failures, recalibrating data acquisition software parameters or optimizing network configuration, until data acquisition and transmission reach a normal and stable state.

[0040] Step 2: Production process monitoring and data recording stage

[0041] Step 2.1, real-time data collection, continuous operation in each production link (spinning, weaving, dyeing, finishing), real-time acquisition of equipment operation data, in the spinning link, collect the motor speed of each single equipment, roller draft multiple, yarn tension, number of broken ends and other process parameters and equipment operation status (start, stop, fault alarm, etc.) information;

[0042] In the weaving process, the winding speed, winding tension, and number of yarn defects removed of the winding machine are monitored; the warp arrangement density, warp beam winding length, tension uniformity and other parameters of the warping machine are monitored; and relevant key data of the sizing machine and loom are monitored;

[0043] In the dyeing process, pay attention to the dyeing machine's dye liquid temperature, dye liquid circulation flow, fabric immersion time in the dye liquid and other process parameters and equipment operation status, and collect data such as the drying temperature, drying time, and fabric moisture content of the dryer;

[0044] In the finishing process, obtain the parameters such as fabric width change, warp tension adjustment value and drying temperature of the tenter, roller pressure and fabric gloss change of the calender, roller speed, sanding depth and fabric surface fluff state of the sanding machine, fabric shrinkage rate, shrinking temperature and humidity of the shrinking machine and the operating status of each equipment;

[0045] Step 2.2: Calculate and display the production progress. Calculate the production progress based on the production capacity of the equipment in each link and the completed output data;

[0046] In the spinning stage, it is known that the design capacity of the spinning machine is to produce X kilograms of yarn with a specific count per hour. By comparing the real-time collected yarn output data with the total amount of yarn required for the order, the spinning completion ratio is obtained and displayed intuitively in the form of a progress bar on the monitoring interface. At the same time, the progress data of each time node is recorded in detail;

[0047] In the weaving process, based on the data collected by the loom that can weave Y meters of fabric of a specific specification per minute and the fabric length sensor, the weaving progress is calculated and the chart in the monitoring system is updated to record the progress data of each section of the fabric weaving process and the corresponding equipment operating parameters;

[0048] In the dyeing process, the total dyeing time of a certain fabric under a specific dyeing process is T minutes. The dyeing completion ratio is calculated by monitoring the cumulative value of the dyeing time, and the relevant data is visually displayed and recorded on the monitoring interface;

[0049] In the post-finishing stage, according to the estimated processing time of the post-finishing process including stentering, calendering, sanding and pre-shrinking, the overall progress is calculated by monitoring the actual execution time and completion status of each step, and the progress data of each step and the changes of equipment process parameters are displayed and recorded in the form of a flow chart on the monitoring interface;

[0050] Step 2.3: abnormal warning and processing: real-time analysis of equipment operation data. 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 cylinder speed of the carding machine fluctuates abnormally or the unevenness of the slivers 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, when the color deviation exceeds the allowable range; in the finishing process, when the fabric dimensional stability, feel, glossiness and other indicators do not meet the standards, the system will not only highlight the abnormal equipment and parameter information on the monitoring interface, but also provide possible cause analysis, such as equipment component failure, improper process parameter settings, raw material quality problems, etc., to help operators quickly locate and solve problems;

[0053] After receiving the early warning notification, the relevant technicians will quickly check the equipment status based on the information and data records provided by the system. If the equipment component fails, the damaged component will be replaced in time;

[0054] 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 status (including adjusted parameters, replaced parts and corresponding time points) for subsequent traceability and analysis.

[0055] Step 3: Equipment performance optimization and intelligent adjustment stage

[0056] Step 3.1: Real-time evaluation of equipment performance: Use big data analysis technology to establish an equipment performance evaluation model based on real-time collected equipment operation data and historical data;

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

[0058] Assume that the equipment performance score is P, the energy efficiency of the equipment is E (the ratio of energy consumption per unit time to standard energy consumption, the value range is 0-1, the closer to 0, the higher the energy efficiency), the production stability index is S (measured by the sum of the standard deviations of key process parameters within a certain period of time, for example, for spinning machines, the sum of the standard deviations of parameters such as roller drafting multiples and yarn tension can be considered, the value range is 0-1, the closer to 0, the more stable the production), the equipment failure rate trend is F (the failure rate change 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 a rapid decrease in the failure rate, 0 indicates a stable failure rate, and 1 indicates a rapid increase in the failure rate), then the equipment performance evaluation model formula can be expressed as:

[0059] P=50+30×(1-E)+20×(1-S)-10×F;

[0060] In the above formula, 50 is the basic score, which represents the basic performance level of the equipment under normal conditions. 30×(1-E) represents the impact of energy efficiency on the 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 excellent equipment performance. 20×(1-S) Similarly, the higher the production stability (the smaller the S value), the higher the score, and the greater the contribution to the total score. -10×F represents the impact of the 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 an increase in the failure rate means that there may be potential problems with equipment performance;

[0061] The model comprehensively considers multiple key performance indicators of the equipment, such as the energy efficiency of the equipment, production stability (measured by the fluctuation range of each process parameter), equipment failure rate trend, etc. For the carding machine in the spinning process, it not only pays attention to its current single fiber combing status, but also comprehensively evaluates its performance by combining the combing quality data in the past period of time and the energy consumption changes of the equipment. According to the evaluation model, the performance of each device is scored in real time at a certain time interval (such as every hour), and the score range is set to 0-100. The higher the score, the better the equipment performance.

[0062] Step 3.2: Intelligent performance optimization strategy generation: When the device performance score is lower than the preset threshold (e.g., 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 bases. For example, for the dyeing machine in the dyeing process, if the evaluation finds that the dye circulation flow is unstable, resulting in a decrease in dyeing uniformity, the system analyzes possible reasons, including dye pump failure, pipeline blockage, or control system parameter imbalance. By comparing with historical data and combining solutions to similar failure cases, the system may suggest checking the working status of the dye pump first. If the pump is normal, further check whether the pipeline is blocked. At the same time, according to the current fabric type and dyeing process requirements, the system intelligently adjusts the relevant parameters of the control system, such as dye circulation frequency and pressure, 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 equipment control system to achieve automatic optimization and adjustment of equipment parameters;

[0065] Operators can observe the equipment adjustment process and performance changes in real time through the remote monitoring platform. During the adjustment process, the system continuously collects equipment data, compares the performance indicators before and after the adjustment, and evaluates the effectiveness of the optimization strategy. If the equipment performance is significantly improved after the adjustment (the performance score reaches 85 points or above), the optimization strategy and adjustment process will be recorded as a reference case for subsequent similar situations. 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 equipment performance reaches the optimal state.

[0066] Step 4: Equipment connection and data transition phase

[0067] Step 4.1: Data association and transfer

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

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

[0070] After dyeing, the fabric enters the finishing stage. The data management system transmits the data of the dyeing stage (dyeing process, color detection results, energy consumption, etc.) to the finishing equipment management module. The finishing equipment formulates a finishing process plan according to the final use of the fabric and customer requirements, such as the setting of parameters such as the stretching width, calendering pressure, sanding degree, and pre-shrinkage rate.

[0071] Step 4.2, equipment calibration and preparation. At each stage of the transition, the data collection system of the equipment receiving the data is rechecked and calibrated to ensure that the equipment can accurately identify and receive data from the previous stage and prepare for the production process data collection of its own stage;

[0072] Before weaving begins, calibrate the data acquisition systems of winders, warping machines and other equipment to check whether they can correctly receive 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 post-finishing process, ensure that the data acquisition system of the post-finishing equipment can successfully receive the data from the dyeing process and complete the equipment initialization settings, record the equipment's preparation status information (such as equipment cleaning status, roller surface status, etc.), and ensure that the equipment operates normally according to the predetermined process plan and accurately collects data.

[0075] Step 5: Data aggregation and analysis phase

[0076] Step 5.1, data integration. After the entire textile production process is completed, the data of each link of spinning, weaving, dyeing and finishing are summarized and integrated to establish a complete fabric production data file. The file covers the entire process data from raw material input to final finished product output, including raw material information, such as raw material type, origin, quality indicators, etc.;

[0077] Equipment operating parameters for each process, such as the process parameter settings of equipment at each link, equipment operating status data, etc.; production progress data, which records the production progress of each link and the whole; quality inspection results, which include quality inspection data of each link and quality assessment data of the final product; energy consumption data, which counts the energy consumption of equipment at each link; and equipment maintenance records, which record the maintenance time, content, and maintenance personnel of the equipment during the production process;

[0078] Step 5.2: Data analysis and report generation: Use professional data analysis tools to conduct in-depth analysis of the aggregated production data;

[0079] Analyze the differences in production cycles of different batches of fabrics and their causes. By comparing the equipment operation time, process parameter adjustment and other data of each batch in each link, find out the key factors affecting production efficiency, such as frequent equipment failures and unreasonable process parameters. Compare product quality data under different process parameter settings, determine the best process parameter combination, provide process optimization reference for subsequent production, count the failure frequency and maintenance cost of each equipment, evaluate the reliability of equipment and the effectiveness of maintenance strategies, so as to reasonably arrange equipment maintenance plans and update equipment budgets.

[0080] Generate detailed production statements and analysis reports based on data analysis results. The report content includes production overview (output, quality, progress, etc.), intuitive display of production results, equipment operation status analysis, summary of the operation stability and fault conditions of each equipment; quality analysis and improvement suggestions, analyze the causes of quality problems and propose targeted improvement measures, energy consumption analysis, clarify the energy consumption of each link and the space for optimization, and cost-benefit analysis, evaluate the cost input and output benefits in the production process, provide decision-making support for enterprise management, and help enterprises optimize production processes, improve product quality, reduce production costs, and enhance equipment management and market competitiveness.

[0081] The specific implementation is as follows:

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

[0083] Analysis: Thread breakage may be caused by various factors such as unstable equipment process parameters in the spinning process (such as abnormal roller drafting multiples, excessive yarn tension), wear of equipment components (such as spindles and wire rings of spinning machines) or fluctuations in raw material quality. This will not only interrupt the production process and increase the workers' joint workload, but also reduce the overall quality and output of the yarn, seriously affecting production efficiency;

[0084] Processing: In the production process monitoring of the spinning link, the data acquisition system monitors the yarn tension, roller drafting multiples, 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 issues an early warning message and highlights the abnormal equipment and related parameter information on the monitoring interface. For example, if the broken end rate of a spinning frame suddenly increases and the yarn tension data shows large fluctuations, the system will immediately notify the relevant technicians;

[0086] 1.2 Based on the equipment adjustment information and corresponding time points recorded in the system, the technical staff will check whether there have been any recent process parameter modifications or equipment maintenance operations. If so, they will analyze whether these operations may lead to an increase in the breakage rate. At the same time, combined with the equipment operation data, the components that may cause breakage (such as wire rings, rollers, etc.) will be inspected in detail to check whether there is any wear or damage.

[0087] 1.3 If it is a process parameter problem, the technicians will adjust the roller drafting multiple or yarn tension and other parameters according to the data analysis results to restore them to the normal range. If the equipment parts are worn, the damaged parts will be replaced in time. After that, the data acquisition system will be used to observe whether the breakage rate returns to normal to verify the effectiveness of the adjustment measures.

[0088] 1.4 The system will also record the problem solving process, including the adjusted parameters, replaced parts, and the time when the problem was solved, to provide a reference for the subsequent handling of similar problems;

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

[0090] Analysis: Dyeing color difference may be caused by inaccurate dye formula in the dyeing process, improper control of dyeing process parameters (such as dyeing temperature, time, dye concentration fluctuations), unstable operation of dyeing equipment (such as uneven dye circulation) or the impact of batch differences in raw material yarns on dyeing effects. Color difference problems will cause products to fail to meet quality standards and require rework or downgrade, increasing production costs and production cycles.

[0091] Processing: In the process monitoring of dyeing, the color change data of fabrics is monitored in real time through color sensors or sampling detection combined with spectral analysis. When the color deviation exceeds the allowable range, the system automatically starts the adjustment mechanism;

[0092] 2.1 First, the system will check the set values ​​of dyeing process parameters and the actual operation 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 lead to insufficient dye uptake, resulting in lighter color;

[0093] 2.2 At the same time, the data management system traces back the imported weaving data, checks the batch information of the yarn and the weaving process parameters, and analyzes whether there are dyeing differences caused by differences in yarn batches. If it is found to be a problem with the yarn batch, the dye formula or dyeing process parameters are adjusted based on the dyeing experience data of similar yarn batches in the past;

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

[0095] 2.4 In addition, the time when the color difference problem occurred, the fabric batches involved and the relevant equipment parameters should be fully recorded to facilitate subsequent quality traceability and process improvement to avoid similar problems from happening again;

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

[0097] Analysis: The poor dimensional stability of fabrics after finishing may be caused by unreasonable pre-shrinkage rate setting of the pre-shrinking machine in the finishing process, improper tension control of the tentering machine, unstable drying temperature or humidity, etc., which will 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 post-finishing process, the pre-shrinking machine data acquisition system monitors the fabric shrinkage rate, pre-shrinking temperature and humidity in real time, and the tentering machine data acquisition system monitors the fabric width change, warp tension adjustment value and drying temperature and other parameters;

[0099] 3.1 When it is found that the dimensional stability of the fabric does not meet the standard, the system automatically adjusts the process parameters of the finishing equipment or issues an alarm to notify the operator to intervene manually. For example, if the shrinkage machine detects that the shrinkage rate of the fabric does not meet the expected standard, the system first checks whether the shrinkage rate setting parameters are correct and whether the shrinkage temperature and humidity are stable;

[0100] 3.2 The operator shall check whether the tension setting of the tenter is too large or too small according to the system prompts, combined with the equipment operation data and process plan, because excessive tension may cause excessive shrinkage of the fabric during the subsequent pre-shrinkage process, while too small tension may cause the fabric to become dimensionally unstable during the finishing process. 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, thereby affecting the dimensional stability;

[0101] 3.3 According to the inspection results, the operator adjusts the shrinkage rate, temperature and humidity parameters of the shrinking machine, or adjusts the tension setting of the tenter frame. After the adjustment, the operator continues to monitor the dimensional changes of the fabric to ensure that the dimensional stability meets the requirements. The system records in detail the time when the quality problem occurred, the fabric batches involved, and the adjustment process of related equipment parameters, providing a basis for subsequent traceability and improvement of the finishing process. By continuously optimizing the process parameter settings, the dimensional stability of the fabric after finishing is improved.

[0102] Finally, it should be noted that: Obviously, the above embodiments are only examples for clearly explaining the present invention, and are not intended to limit the implementation methods. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from this are still within the scope of protection of the present invention.

Claims

1. A digital textile equipment management method based on big data, characterized in that: The method comprises the following steps: Step 1: Equipment connection and data initialization stage: Check the data interface type of equipment in each link of the textile process and connect it to the workshop data acquisition network, create an electronic file for each device and set the initial data acquisition parameters, and perform pre-operation testing and calibration on the equipment; Step 2: During the production process monitoring and data recording phase, the operation data of equipment in each production link is collected in real time, the production progress is calculated and displayed, and the equipment operation data is analyzed in real time. When the key process parameters exceed the preset range, early warning information is issued and processed; Step 3: Equipment performance optimization and intelligent adjustment phase: Use big data analysis technology to establish an equipment performance evaluation model and evaluate equipment performance in real time. When the performance score is lower than the preset threshold, an intelligent optimization strategy is generated to automatically adjust equipment parameters and remotely monitor execution. Step 4: Equipment connection and data transition stage: when the textile production links are switched, 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; Step 5: Data aggregation and analysis phase: aggregate and integrate the data of the entire textile production process, use data analysis tools to analyze the data and generate production statements and analysis reports.

2. A digital textile equipment management method based on big data as claimed in claim 1, characterized in that: The equipment performance evaluation model comprehensively considers energy efficiency, production stability indicators, and equipment failure rate trends. The formula is: P=50+30×(1-E)+20×(1-S)-10×F; Among them: the equipment performance score is P, the energy efficiency of the equipment is E (the ratio of energy consumption per unit time to standard energy consumption, the value range is 0-1, the closer to 0, the higher the energy efficiency), the production stability index is S (measured by the sum of standard deviations of key process parameters within a certain period of time, for example, for spinning machines, the sum of standard deviations of parameters such as roller drafting multiples and yarn tension can be considered, the value range is 0-1, the closer to 0, the more stable the production), the equipment failure rate trend is F (the failure rate change 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 a rapid decrease in the failure rate, 0 indicates a stable failure rate, and 1 indicates a rapid increase in the failure rate); In the formula, 50 is the basic score, representing the basic performance level of the equipment under normal conditions. 30×(1-E) represents the impact of energy efficiency on the 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 excellent equipment performance. 20×(1-S) Similarly, the higher the production stability (the smaller the S value), the higher the score, and the greater the contribution to the total score. -10×F represents the impact of the 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 an increase in the failure rate means that there may be potential problems with equipment performance.

3. A digital textile equipment management method based on big data as claimed in claim 1, characterized in that: The data interface types include RS485 and Ethernet. The equipment file content includes basic information such as model, production date, and maintenance cycle. The pre-operation test checks the accuracy and stability of data collection and transmission.

4. A digital textile equipment management method based on big data as claimed in claim 1, characterized in that: The operating data collected in each production link includes process parameters and equipment operating status information. The production progress calculation is based on the production capacity and output data of the equipment in each link. The abnormal warning and processing includes providing possible cause analysis and processing process records.

5. A digital textile equipment management method based on big data as claimed in claim 1, characterized in that: The information transmitted by the data management system during link conversion includes raw materials, production processes and quality inspection data. The equipment calibration and preparation includes checking the calibration data acquisition system and recording equipment preparation status information. In the data aggregation and analysis stage, the data analysis content includes production cycle differences, the relationship between process parameters and quality, equipment failures and energy consumption.

Images