Digital combing machine equipment management method and system based on data analysis

Abstract

The invention relates to the technical field of equipment management, in particular to a digital combing machine equipment management method and system based on data analysis, and the method comprises the steps: building a quality-stability combined prediction model based on historical production data, evaluating the combination of fiber attributes and technological parameters, and predicting the expected processing quality and the expected equipment stability; in combination with production plan information, aiming at switching of different fiber batches, by taking optimization of processing quality, equipment stability and equipment utilization rate as a management target, calculating a recommended process parameter combination and a recommended parameter switching path; actual processing quality and actual equipment stability are collected, management evaluation is carried out by combining the quality-stability combined prediction model, and the model is iteratively updated; and analyzing a residual sequence of the actual equipment stability and the expected equipment stability, and generating a predictive maintenance suggestion in combination with historical equipment maintenance data. The equipment management method disclosed by the invention ensures that the quality, the efficiency and the stability are considered during alternate treatment of the differentiated fibers.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment management, and specifically to a digital comber equipment management method and system based on data analysis. Background Art

[0002] With the rapid growth of the demand for differentiated fiber products in the textile market, the processing scenarios of combers show significant differentiation characteristics: high-value fibers, due to their high raw material costs and low error tolerance, rely on high-precision process parameter configurations to ensure the quality of finished products, but their production batches are usually small; while ordinary fibers, although the processing accuracy requirements are relatively loose, require the equipment to be in a high-production state for a long time to match the demand for large-scale orders. In this context, when alternating the processing of the two types of fibers on the same production line, how to ensure the process accuracy of high-value fibers, maintain the high-efficiency output of ordinary fibers, and avoid the decline in stability caused by frequent switching of equipment operating conditions has become the key contradiction restricting the improvement of the comprehensive efficiency of combers.

[0003] Therefore, a digital comber equipment management method and system based on data analysis are proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide a digital comber equipment management method and system based on data analysis to ensure the balance of quality, efficiency and stability during the alternating processing of differentiated fibers. It includes: constructing a quality-stability joint prediction model based on historical production data to evaluate fiber attributes and process parameter combinations, and predicting the expected processing quality and expected equipment stability; combining production plan information, for the switching of different fiber batches, taking the optimization of processing quality, equipment stability and equipment utilization rate as the management goal, calculating the recommended process parameter combinations and recommended parameter switching paths; collecting the actual processing quality and actual equipment stability, combining with the quality-stability joint prediction model for management evaluation, and iteratively updating the model; analyzing the residual sequence of the actual equipment stability and the expected equipment stability, and generating predictive maintenance suggestions in combination with historical equipment maintenance data.

[0005] To achieve the above purpose, the present invention provides the following technical solutions: A digital comber equipment management method based on data analysis, including.

[0006] Obtain production plan information, fiber attributes, historical production data and historical equipment maintenance data; Based on historical production data, use machine learning algorithms to construct a quality-stability joint prediction model, receive fiber attributes and corresponding process parameter combinations, and predict the corresponding expected processing quality and expected equipment stability; Combined with the production plan information, use the quality-stability joint prediction model to predict and evaluate the candidate process parameter combinations. For the switching of different fiber batches, with the management objectives of optimizing processing quality, equipment stability, and equipment utilization rate, calculate the recommended process parameter combinations and the recommended parameter switching paths; Collect the actual processing quality and actual equipment stability during equipment operation, conduct management evaluation in combination with the quality-stability joint prediction model, and iteratively update the quality-stability joint prediction model; Analyze the residual sequence of the actual equipment stability and the expected equipment stability, and generate predictive maintenance suggestions in combination with historical equipment maintenance data.

[0007] Preferably, use LightGBM to construct the quality-stability joint prediction model, adopt a multi-objective regression strategy, and train a quality-stability joint prediction sub-model for each index of the expected processing quality and the expected equipment stability respectively; form an input feature vector with the fiber attributes to be processed and the corresponding process parameter combinations, input the input feature vector into the trained quality-stability joint prediction sub-model, and obtain the corresponding index prediction values; integrate the index prediction values output by all quality-stability joint prediction sub-models to obtain the expected processing quality and the expected equipment stability.

[0008] Preferably, the prediction process of the quality-stability joint prediction sub-model includes: S1. Process the input feature vector through the first decision tree to obtain the preliminary index prediction value; S2. Process the input feature vector through the second decision tree to obtain the residual prediction value, and add the preliminary index prediction value and the residual prediction value to obtain the updated preliminary index prediction value; S3. Repeat step S2 to traverse the remaining decision trees in turn, and obtain the index prediction value after the traversal ends.

[0009] Preferably, the steps of calculating the recommended process parameter combinations and the recommended parameter switching paths specifically include: Define the decision variables as the process parameter combinations and parameter adjustment paths of the new batch of fibers, and construct an objective function with the goals of maximizing the expected processing quality, minimizing the impact of the switching process on equipment stability, and minimizing the switching time; Set the parameter physical constraints, stability constraints, and path smoothness constraints; select the Bayesian optimization algorithm, guide the search process by constructing a surrogate model of the objective function, and iteratively generate candidate process parameter combinations and candidate parameter switching paths; Evaluate the objective function value corresponding to the candidate process parameter combination in combination with the quality-stability joint prediction model. When the stopping condition is met, output the recommended process parameter combination that optimizes the objective function under the constraint conditions and the corresponding recommended parameter switching path.

[0010] Preferably, the steps of iteratively updating the quality-stability joint prediction model specifically include: calculating management evaluation indicators according to the expected processing quality and the expected equipment stability, including prediction error, target quality achievement rate, stability compliance rate, and improvement of overall equipment efficiency; setting model update trigger conditions according to the management evaluation indicators, and when the conditions are met, adopting a periodic batch retraining strategy, merging the newly obtained actual production data with part of the historical production data, and retraining the quality-stability joint prediction sub-model that needs to be updated.

[0011] Preferably, the steps of generating predictive maintenance suggestions specifically include: Calculating the residual sequence between the actual equipment stability and the expected equipment stability in real time; applying time series analysis techniques to the residual sequence to extract trend feature vectors; Training a fault mode classifier using historical equipment maintenance data and the corresponding historical residual sequences, for outputting the predicted fault type according to the current trend feature vectors; Setting predictive maintenance trigger rules based on the confidence level of the predicted fault type and the residual amplitude, and automatically generating predictive maintenance suggestions when the predictive maintenance trigger rules are met.

[0012] A digital comber equipment management system based on data analysis, comprising: A data acquisition unit for acquiring production plan information, fiber properties, historical production data, and historical equipment maintenance data; A joint prediction unit for constructing a quality-stability joint prediction model based on historical production data using machine learning algorithms, receiving fiber properties and corresponding process parameter combinations, and predicting the corresponding expected processing quality and expected equipment stability; A parameter switching unit for predicting and evaluating candidate process parameter combinations using the quality-stability joint prediction model in combination with production plan information, and calculating recommended process parameter combinations and recommended parameter switching paths for different fiber batch switches with the management objectives of optimizing processing quality, equipment stability, and equipment utilization rate; An evaluation and update unit for collecting the actual processing quality and actual equipment stability during equipment operation, conducting management evaluation in combination with the quality-stability joint prediction model, and iteratively updating the quality-stability joint prediction model; A predictive maintenance unit for analyzing the residual sequence of the actual equipment stability and the expected equipment stability, and generating predictive maintenance suggestions in combination with historical equipment maintenance data.

[0013] Compared with the prior art, the beneficial effects of the present invention are: 1. A quality-stability joint prediction model is constructed using machine learning algorithms, which can simultaneously predict processing quality and equipment stability based on fiber properties and process parameter combinations. The model is implemented using the LightGBM framework and adopts a multi-objective regression strategy. Sub-models are trained separately for each prediction index to form an integrated model system. The joint evaluation mechanism of the model enables the production line to consider both product quality and equipment operating status during parameter adjustment, and can pre-insight the comprehensive impact of different parameter settings on quality and stability. In addition, the model supports regular updates and iterative learning, can continuously absorb new production experience, and improve prediction accuracy. The model provides a decision-making basis for achieving the balance between quality and stability during the processing switch of different fiber batches in combers, and solves the problem of conflicts between quality assurance and equipment stability during the alternating processing of high-value fibers and ordinary fibers.

[0014] 2. An objective function is constructed by comprehensively considering three objectives: maximizing processing quality, minimizing the impact on equipment stability, and minimizing switching time. Under the constraint conditions, the optimal process parameter combination and parameter switching path are searched. The Bayesian optimization algorithm is used to effectively handle the search problem in the high-dimensional parameter space. By iteratively generating candidate process parameter combinations and candidate parameter switching paths, and combining with the quality-stability joint prediction model for evaluation, the optimal recommended solution is finally output. It realizes the intelligent parameter configuration and smooth transition of the comber during the fiber switching of different batches, and solves the problems of equipment stability decline and efficiency loss caused by the alternating processing of high-value fibers and ordinary fibers.

[0015] 3. A predictive maintenance method based on residual analysis, by analyzing the residual sequence between the actual equipment stability and the expected equipment stability, combines time series analysis techniques to extract trend feature vectors, and then predicts the possible failure types. This method uses historical equipment maintenance data to train a fault mode classifier, can predict potential faults according to the current trend feature vector, and sets trigger rules based on the confidence level of the predicted fault type and the residual amplitude. When the conditions are met, predictive maintenance suggestions are automatically generated. By discovering abnormal trends in advance, maintenance can be arranged at an appropriate time in the production plan, avoiding production losses caused by sudden shutdowns and also avoiding resource waste caused by over-maintenance. It provides a guarantee for the long-term stable operation of the comber and ensures the continuous and efficient operation of the production line during the alternating processing of different fibers. Brief Description of the Drawings

[0016] Figure 1 It is a schematic flow chart of a digital comber equipment management method based on data analysis according to the present invention; Figure 2 It is a schematic flow chart of generating predictive maintenance suggestions according to the present invention; Figure 3Schematic structural diagram of a digital comber equipment management system based on data analysis according to the present invention. Specific embodiments

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] Please refer to Figures 1 to 3 , the present invention provides a digital comber equipment management method and system based on data analysis, and the technical solutions are as follows:

[0019] Embodiment 1: This embodiment is applied to the actual production requirements of the comber workshop of a textile enterprise. The workshop is equipped with 6 combers, which are responsible for processing high-value Egyptian long-staple cotton and ordinary Xinjiang cotton. Due to market demand fluctuations, the two types of fibers need to be alternately processed on the same production line, resulting in frequent switching of process parameters. To improve the comprehensive efficiency of comber equipment management, this embodiment applies a digital comber equipment management method based on data analysis, as Figure 1 shown, including: Obtain production plan information, fiber attributes, historical production data, and historical equipment maintenance data; Based on historical production data, use machine learning algorithms to construct a quality-stability joint prediction model, receive fiber attributes and corresponding process parameter combinations, and predict the corresponding expected processing quality and expected equipment stability; Combined with production plan information, use the quality-stability joint prediction model to predict and evaluate candidate process parameter combinations. For the switching of different fiber batches, with the management objectives of optimizing processing quality, equipment stability, and equipment utilization rate, calculate the recommended process parameter combinations and recommended parameter switching paths; Collect the actual processing quality and actual equipment stability during equipment operation, combine with the quality-stability joint prediction model for management evaluation, and iteratively update the quality-stability joint prediction model; Analyze the residual sequence of the actual equipment stability and the expected equipment stability, and generate predictive maintenance suggestions in combination with historical equipment maintenance data.

[0020] In this embodiment, the production plan information is sourced from the enterprise resource planning system and includes the fiber batch number to be processed, fiber type, planned output, planned start / end time, and target quality range; the fiber attributes are sourced from the quality management system and include the detailed physical test indicators for each fiber batch to be processed, such as average length, length dispersion, fineness, impurity content, oil content, moisture regain, and short fiber rate; the historical production data includes the fiber attributes of historical processing batches, the historical process parameter combinations adopted, the historical actual processing quality of the corresponding final products, and the historical actual equipment stability. The historical equipment maintenance data is sourced from the equipment maintenance log and includes the failure time, failure phenomenon, failed components, maintenance content, maintenance duration, and spare part replacement information; the process parameter combinations include the cylinder speed, feed roller speed, separating roller speed, key part gauge, and total draft ratio; the processing quality includes multiple indicators: evenness, number of neps, number of impurities, and short fiber content; the equipment stability includes multiple indicators: main shaft vibration amplitude, motor load rate, and energy consumption index.

[0021] Further, the quality-stability joint prediction model is constructed using LightGBM (Light Gradient Boosting Machine), and a multi-objective regression strategy is adopted to train a quality-stability joint prediction sub-model for each indicator of the expected processing quality and the expected equipment stability respectively; the fiber attributes to be processed and the corresponding process parameter combinations are used to form an input feature vector, and the input feature vector is input into the trained quality-stability joint prediction sub-model to obtain the corresponding indicator prediction value; the indicator prediction values output by all the quality-stability joint prediction sub-models are integrated to obtain the expected processing quality and the expected equipment stability.

[0022] Using LightGBM (Light Gradient Boosting Machine) to construct the quality-stability joint prediction model and combining with the multi-objective regression strategy ensures that the prediction model can efficiently process high-dimensional and non-linear data, accurately capture the complex relationships between fiber attributes, process parameters, and multiple quality and stability indicators, improve the accuracy and reliability of the prediction, and provide reliable decision-making support for parameter optimization of different fiber batches.

[0023] Further, the prediction process of the quality-stability joint prediction sub-model includes: S1. Processing the input feature vector through the first decision tree to obtain a preliminary indicator prediction value; S2. Processing the input feature vector through the second decision tree to obtain a residual prediction value, and adding the preliminary indicator prediction value and the residual prediction value to obtain the updated preliminary indicator prediction value; S3. Repeating step S2 to sequentially traverse the remaining decision trees, and after the traversal is completed, obtaining the indicator prediction value.

[0024] Through the residual learning mechanism of gradient boosting decision trees, the prediction model can gradually learn and correct prediction errors, continuously improve prediction accuracy, effectively capture the complex interaction relationship between fiber properties and process parameters, and enhance the model's ability to identify marginal operating conditions.

[0025] Table 1 shows the performance evaluation results of the quality-stability joint prediction sub-models corresponding to each indicator on the training set and the test set. Based on the collected historical production data, more than 5,000 valid samples were extracted and divided into a training set and a test set. Each sample includes fiber properties, process parameter combinations, actual processing quality, and actual equipment stability. As can be seen from Table 1, the sub-models of all indicators show good prediction ability, indicating that the model can accurately capture the influence relationship between fiber properties and process parameters on processing quality and equipment stability.

[0026] Table 1 Evaluation Results of Quality-Stability Joint Prediction Sub-Models

[0027] Furthermore, the steps for calculating the recommended process parameter combinations and the recommended parameter switching paths specifically include: Define the decision variables as the process parameter combinations and parameter adjustment paths for the new batch of fibers, and construct an objective function with the goal of maximizing the expected processing quality, minimizing the impact of the switching process on equipment stability, and minimizing the switching time; Set physical constraints, stability constraints, and path smoothness constraints for the parameters; select the Bayesian optimization algorithm, and guide the search process by constructing a surrogate model of the objective function to iteratively generate candidate process parameter combinations and candidate parameter switching paths; Combine the quality-stability joint prediction model to evaluate the objective function values corresponding to the candidate process parameter combinations, and when the stopping condition is met, output the recommended process parameter combinations that optimize the objective function under the constraint conditions and the corresponding recommended parameter switching paths.

[0028] The process parameter recommendation mechanism based on Bayesian optimization comprehensively considers processing quality, equipment stability, and switching efficiency, and guides the search process through a surrogate model, greatly improving the efficiency of parameter optimization. The setting of multiple constraint conditions ensures the practicality and safety of the recommended parameter scheme, while the smooth path planning reduces the equipment impact during the parameter switching process, effectively solves the problem of process parameter switching when processing high-value fibers and ordinary fibers alternately, and improves the adaptability and flexibility of the production line.

[0029] Table 2 gives an example of the process parameter combinations and the optimization results of the parameter switching paths for the batch switching from Egyptian long-staple cotton to Xinjiang cotton: Table 2 Example of Process Parameter Combinations and Optimization Results of Parameter Switching Paths

[0030] The parameter switching path divides the switching process into a starting state, a first stage, a second stage, and a final state, and gradually reaches each parameter value in the recommended process parameter combination through different time steps. The time steps of the recommended parameter switching path exemplified in Table 2 are [0, 10 min, 20 min, 30 min, 35 min]. The parameter changes in each stage are within the acceptable range of the equipment, avoiding equipment shock caused by mutations and achieving a smooth transition.

[0031] Furthermore, maximizing the expected processing quality means predicting the values of various indicators of the processing quality when processing a new batch of fibers using the candidate process parameter combination by invoking the quality-stability joint prediction model. The goal is to make these indicators as close to or better than the target quality range in the production plan information. Deviating from the target quality range will result in a negative evaluation. Minimizing the impact of the switching process on equipment stability means using the quality-stability joint prediction model to evaluate the fluctuation degree of the values of various indicators of equipment stability during the process of adjusting from the current process parameter combination to the candidate process parameter combination according to the candidate parameter switching path. The goal is to make these fluctuation degrees as small as possible and avoid exceeding the safe fluctuation threshold. Exceeding the safe fluctuation threshold will result in a significant negative evaluation. Minimizing the switching time means that the total time consumed by the parameter switching process should be as short as possible to improve equipment utilization. The longer the time, the lower the evaluation. Set different weights according to the focus of production management, and weighted sum the evaluation results of these three aspects to form the objective function value of the process parameter combination and the corresponding parameter adjustment path. The goal of the optimization algorithm is to find the solution that makes the objective function value optimal.

[0032] Parameter physical constraints mean that each parameter value in the recommended process parameter combination must be within the physical adjustment range allowed by the equipment. Stability constraints mean that at any moment during the entire parameter switching path, the expected equipment stability must not exceed the preset safe operating threshold. Path smoothness constraints mean that the speed of parameter adjustment cannot exceed the maximum change rate allowed by the equipment execution to ensure the smoothness of the adjustment process.

[0033] The Bayesian optimization iteration process includes: The Bayesian optimization algorithm first calls the prediction model based on a small number of candidate solutions (candidate process parameter combinations and candidate parameter switching paths) to obtain the true objective function scores, and then uses the Gaussian process model to construct a surrogate model of the objective function. In each iteration, the surrogate model is used to quickly predict the potential objective function values of the unevaluated candidate solutions, and a acquisition function is used to select the currently most worthy candidate solution for true evaluation from the unevaluated candidate solutions. The quality-stability joint prediction model is called to calculate the true objective function value of the selected candidate solution, and the new true objective function value is added to the known data, and the surrogate model is updated to make it more accurately approximate the true objective function; this process is repeated until the preset stop condition is reached. The final output is the recommended process parameter combination and the corresponding recommended parameter switching path that optimize the objective function score under all constraint conditions. The stop condition can be set as the maximum number of iterations, the calculation time limit, or the objective function value no longer improves significantly.

[0034] Further, the steps of iteratively updating the quality-stability joint prediction model specifically include: calculating management evaluation indicators according to the expected processing quality and the expected equipment stability, including prediction error, target quality achievement rate, stability compliance rate, and improvement of overall equipment efficiency; setting model update trigger conditions according to the management evaluation indicators, and when the conditions are met, adopting a periodic batch retraining strategy, and merging the newly obtained actual production data with part of the historical production data to retrain the quality-stability joint prediction sub-model that needs to be updated.

[0035] By setting the model update trigger conditions and adopting a periodic batch retraining strategy, continuous optimization of the joint prediction model is achieved. This iterative update mechanism can adapt to long-term influencing factors such as equipment aging and seasonal changes, and maintain the timeliness and accuracy of the model. The design of the management evaluation indicators directly reflects the application effect of the method, provides a quantitative basis for continuous improvement, and ensures that the comber management system can continuously self-improve with production practice and improve the overall management level.

[0036] Further, the calculation method of the prediction error is: by comparing the index values of the expected processing quality and the expected equipment stability predicted by the model in real time during the actual production process with the corresponding index values actually collected, calculating the mean absolute error between the two, and the smaller the error, the more accurate the prediction; The calculation method of the target quality achievement rate is: counting the quantity ratio of the actual processing quality within the target quality range after producing with the recommended process parameters, and the higher the ratio, the better the effectiveness of the method in ensuring the target quality; The calculation method of the stability compliance rate is as follows: Statistically, during actual production operation, calculate the proportion of the time when the actual equipment stability does not exceed the preset safe operation threshold. The higher the proportion, the better the method is in maintaining the equipment stability. The improvement of the overall equipment efficiency is achieved by comparing the changes in the overall equipment efficiency of the comber before and after applying this method. The overall equipment efficiency takes into account the time utilization rate, performance utilization rate, and quality qualification rate of the equipment.

[0037] To ensure the timeliness and accuracy of the model, reasonable trigger conditions need to be set to initiate the iterative update of the model. The model update trigger conditions can be set as a combination of one or more of the following: Based on time period: Set a fixed update period, such as performing model updates once a week, once every two weeks, or once a month. Based on data volume accumulation: Trigger an update when the amount of new valid production data samples collected reaches a certain threshold. Based on model performance decline: Continuously monitor the prediction error. When the prediction error corresponding to the processing quality index exceeds the preset threshold for a continuous period of time, or the accuracy rate is significantly lower than the historical level, it indicates that the model performance may have declined and an update needs to be triggered. Based on significant changes in working conditions or environment: When it is detected that there are major changes in the production environment, such as equipment modification, long-term deviation of environmental temperature and humidity from the normal range, etc., which may cause the original model to become invalid, actively trigger model updates.

[0038] Furthermore, to calculate the residual sequence in the management evaluation index and predictive maintenance, during the normal production operation of the comber equipment, continuously use the quality-stability joint prediction model for real-time prediction, specifically including: obtaining the combination of process parameters of the equipment actually running at the current moment and the attribute data of the fiber being processed currently, to obtain real-time fiber attributes and real-time process parameter combinations; using the real-time fiber attributes and real-time process parameter combinations as inputs, calling the quality-stability joint prediction model, and outputting the expected processing quality and expected equipment stability at the current moment; subtracting the index value predicted by the model from the actual index value at the same moment to obtain the residual value at the corresponding moment.

[0039] Furthermore, the flowchart for generating predictive maintenance suggestions is shown in Figure 2 , and the steps specifically include: Real-time calculate the residual sequence between the actual equipment stability and the expected equipment stability; apply time series analysis techniques to the residual sequence to extract trend feature vectors. Time series analysis techniques can be moving average, exponential smoothing, or Fourier transform, etc. Using historical equipment maintenance data and the corresponding historical residual sequences, a fault mode classifier is trained to output a predicted fault type based on the current trend feature vector. Optional models for the fault mode classifier include support vector machines, random forests, or recurrent neural networks. Based on the confidence level of the predicted fault type and the residual amplitude, a predictive maintenance trigger rule is set. When the predictive maintenance trigger rule is satisfied, predictive maintenance suggestions are automatically generated.

[0040] The predictive maintenance method based on residual analysis realizes the early detection of potential problems in equipment through time series analysis and fault mode classification, reducing the risk of sudden failures and unplanned downtime. The automatically generated maintenance suggestions are based on data analysis rather than empirical judgment, improving the scientificity and pertinence of maintenance decisions, and providing a strong guarantee for the stable operation of the comber under frequent working condition switches.

[0041] The rules for triggering predictive maintenance need to comprehensively consider the possibility and severity of fault occurrence. The specific setting methods for setting the predictive maintenance trigger rule based on the confidence level and residual amplitude include: Based on the confidence level: When the predicted confidence level of the fault type output by the fault mode classifier exceeds the confidence level threshold set for this fault type, predictive maintenance is directly triggered. Different confidence level thresholds can be set for different fault types. Based on the residual amplitude: When the residual value corresponding to the index in equipment stability exceeds the set warning threshold for a continuous period of time, predictive maintenance is triggered. The warning threshold can be set according to historical production data analysis and expert experience. When any of the above predictive maintenance trigger rules is satisfied, predictive maintenance suggestions are automatically generated through the following process: Obtain the predictive maintenance trigger time as the early warning timestamp; determine the most likely faulty component according to the predicted fault type with the highest output confidence level of the fault mode classifier; retrieve a matching, standardized list of inspection steps, diagnostic methods, and recommended repair operations from the predefined maintenance knowledge base according to the predicted fault type; determine the urgency of the fault based on the predicted fault type, confidence level, and residual amplitude, and combine the production plan information to determine the available downtime information, and recommend specific maintenance execution time suggestions.

[0042] Table 3 shows the effect comparison before and after implementing the method of this embodiment. It can be seen that all key indicators have been significantly improved. In particular, the first-class product rate of high-value fibers has increased by 17.7%, the batch switching time has decreased by 57.8%, and the unplanned downtime has decreased by 72.2%.

[0043] Table 3 Effect Comparison of Implementation

[0044] The present invention forms a closed-loop digital management solution by constructing a quality-stability joint prediction model, realizing intelligent recommendation of process parameters and optimization of parameter switching paths, and predictive maintenance based on residual analysis. Taking fiber processing quality and equipment operation stability as the joint optimization objectives, when alternately processing different fiber types on the same production line, it can automatically calculate the optimal combination of process parameters and its smooth switching path according to the differences in fiber physical properties, ensuring both the fine processing quality of high-value fibers and the efficient output of ordinary fibers. At the same time, by real-time monitoring the deviation between the equipment state and the theoretical predicted value, potential faults can be identified early, effectively avoiding equipment abnormalities caused by frequent switching of working conditions. It solves the key contradictions among quality, efficiency, and stability in the scenario of alternate processing of different fibers by combers.

[0045] Embodiment 2: This embodiment provides a digital management system for comber equipment based on data analysis, as Figure 3 shown, including: A data acquisition unit that acquires production plan information, fiber attributes, historical production data, and historical equipment maintenance data; A joint prediction unit that constructs a quality-stability joint prediction model using machine learning algorithms based on historical production data, receives fiber attributes and corresponding process parameter combinations, and predicts the corresponding expected processing quality and expected equipment stability; A parameter switching unit that combines production plan information, uses the quality-stability joint prediction model to predict and evaluate candidate process parameter combinations, and calculates recommended process parameter combinations and recommended parameter switching paths with the management objectives of optimizing processing quality, equipment stability, and equipment utilization rate for the switching of different fiber batches; An evaluation and update unit that collects the actual processing quality and actual equipment stability during equipment operation, conducts management evaluation in combination with the quality-stability joint prediction model, and iteratively updates the quality-stability joint prediction model; A predictive maintenance unit that analyzes the residual sequence of actual equipment stability and expected equipment stability, and generates predictive maintenance suggestions in combination with historical equipment maintenance data.

[0046] Furthermore, the LightGBM is used to construct the quality-stability joint prediction model, and a multi-objective regression strategy is adopted to train a quality-stability joint prediction sub-model for each index of expected processing quality and expected equipment stability respectively; the fiber attributes to be processed and the corresponding process parameter combinations are used to form an input feature vector, and the input feature vector is input into the trained quality-stability joint prediction sub-model to obtain the corresponding index prediction value; the index prediction values output by all quality-stability joint prediction sub-models are integrated to obtain the expected processing quality and expected equipment stability.

[0047] Furthermore, the prediction process of the quality-stability joint prediction sub-model includes: S1. Processing the input feature vector through a first decision tree to obtain a preliminary index prediction value; S2. Processing the input feature vector through a second decision tree to obtain a residual prediction value, and adding the preliminary index prediction value and the residual prediction value to obtain the updated preliminary index prediction value; S3. Repeating step S2 to sequentially traverse the remaining decision trees, and obtaining the index prediction value after the traversal ends.

[0048] Furthermore, the steps of calculating the recommended process parameter combination and the recommended parameter switching path specifically include: Defining the decision variables as the process parameter combination and the parameter adjustment path of the new batch of fibers, and constructing an objective function with the goal of maximizing the expected processing quality, minimizing the impact of the switching process on equipment stability, and minimizing the switching time; Setting parameter physical constraints, stability constraints, and path smoothness constraints; selecting the Bayesian optimization algorithm, guiding the search process by constructing a surrogate model of the objective function, and iteratively generating candidate process parameter combinations and candidate parameter switching paths; Combining the quality-stability joint prediction model to evaluate the objective function value corresponding to the candidate process parameter combination, and when the stop condition is met, outputting the recommended process parameter combination that optimizes the objective function under the constraint conditions and the corresponding recommended parameter switching path.

[0049] Furthermore, the steps of iteratively updating the quality-stability joint prediction model specifically include: calculating management evaluation indicators according to the expected processing quality and the expected equipment stability, including prediction error, target quality achievement rate, stability compliance rate, and improvement of overall equipment efficiency; setting model update trigger conditions according to the management evaluation indicators, and when the conditions are met, adopting a periodic batch re-training strategy, merging the newly obtained actual production data with part of the historical production data, and re-training the quality-stability joint prediction sub-model that needs to be updated.

[0050] Furthermore, the steps of generating predictive maintenance suggestions specifically include: Calculating the residual sequence between the actual equipment stability and the expected equipment stability in real time; applying time series analysis techniques to the residual sequence to extract trend feature vectors; Using historical equipment maintenance data and the corresponding historical residual sequences to train a fault mode classifier for outputting predicted fault types according to the current trend feature vectors; Setting predictive maintenance trigger rules based on the confidence level of the predicted fault type and the residual amplitude, and automatically generating predictive maintenance suggestions when the predictive maintenance trigger rules are met.

[0051] Although embodiments of the present invention have been shown and described, those of ordinary skill in the art will appreciate that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A digital comber equipment management method based on data analysis, characterized in that, Including: Obtain production plan information, fiber properties, historical production data, and historical equipment maintenance data; Based on historical production data, use machine learning algorithms to construct a quality-stability joint prediction model, receive fiber properties and corresponding process parameter combinations, and predict the corresponding expected processing quality and expected equipment stability; Combined with production plan information, use the quality-stability joint prediction model to predict and evaluate candidate process parameter combinations. For the switching of different fiber batches, with the management goals of optimizing processing quality, equipment stability, and equipment utilization rate, calculate the recommended process parameter combinations and recommended parameter switching paths; Collect the actual processing quality and actual equipment stability during equipment operation, combine with the quality-stability joint prediction model for management evaluation, and iteratively update the quality-stability joint prediction model; Analyze the residual sequence of actual equipment stability and expected equipment stability, and combine with historical equipment maintenance data to generate predictive maintenance recommendations.

2. The digital comber equipment management method based on data analysis according to claim 1, characterized in that: Use LightGBM to construct the quality-stability joint prediction model, adopt a multi-objective regression strategy, and train a quality-stability joint prediction sub-model for each index of expected processing quality and expected equipment stability respectively; construct an input feature vector from the fiber properties to be processed and the corresponding process parameter combinations, input the input feature vector into the trained quality-stability joint prediction sub-model, and obtain the corresponding index prediction values; integrate the index prediction values output by all quality-stability joint prediction sub-models to obtain the expected processing quality and expected equipment stability.

3. The digital comber equipment management method based on data analysis according to claim 2, characterized in that, The prediction process of the quality-stability joint prediction sub-model includes: S1. Process the input feature vector through the first decision tree to obtain a preliminary index prediction value; S2. Process the input feature vector through the second decision tree to obtain a residual prediction value, and add the preliminary index prediction value and the residual prediction value to obtain the updated preliminary index prediction value; S3. Repeat step S2 to sequentially traverse the remaining decision trees, and after the traversal is completed, obtain the index prediction value.

4. The digital comber equipment management method based on data analysis according to claim 1, characterized in that, The steps of calculating the recommended process parameter combinations and recommended parameter switching paths specifically include: Define the decision variables as the process parameter combinations and parameter adjustment paths of the new batch of fibers, and construct an objective function with the goals of maximizing the expected processing quality, minimizing the impact of the switching process on equipment stability, and minimizing the switching time; Set parameter physical constraints, stability constraints, and path smoothness constraints; select the Bayesian optimization algorithm, guide the search process by constructing a surrogate model of the objective function, and iteratively generate candidate process parameter combinations and candidate parameter switching paths; Combine the quality-stability joint prediction model to evaluate the objective function value corresponding to the candidate process parameter combinations, and until the stop condition is met, output the recommended process parameter combinations that optimize the objective function under the constraint conditions and the corresponding recommended parameter switching paths.

5. The digital comber equipment management method based on data analysis according to claim 1, characterized in that, The steps of iteratively updating the quality-stability joint prediction model specifically include: calculating management evaluation indicators according to the expected processing quality and expected equipment stability, including prediction error, target quality achievement rate, stability compliance rate, and improvement of overall equipment efficiency; setting model update trigger conditions based on the management evaluation indicators, and when the conditions are met, adopting a periodic batch retraining strategy, merging newly obtained actual production data with some historical production data, and retraining the quality-stability joint prediction sub-model that needs to be updated.

6. The digital comber equipment management method based on data analysis according to claim 1, characterized in that, The steps of generating predictive maintenance suggestions specifically include: Calculating the residual sequence between the actual equipment stability and the expected equipment stability in real time; applying time series analysis techniques to the residual sequence to extract trend feature vectors; Training a fault mode classifier using historical equipment maintenance data and the corresponding historical residual sequences, which is used to output the predicted fault type according to the current trend feature vectors; Setting a predictive maintenance trigger rule based on the confidence level of the predicted fault type and the residual amplitude, and automatically generating predictive maintenance suggestions when the predictive maintenance trigger rule is met.

7. A digital comber equipment management system based on data analysis, characterized in that, Including: A data acquisition unit that acquires production plan information, fiber properties, historical production data, and historical equipment maintenance data; A joint prediction unit that constructs a quality-stability joint prediction model based on historical production data using machine learning algorithms, receives fiber properties and corresponding process parameter combinations, and predicts the corresponding expected processing quality and expected equipment stability; A parameter switching unit that combines production plan information and uses the quality-stability joint prediction model to predict and evaluate candidate process parameter combinations, and calculates recommended process parameter combinations and recommended parameter switching paths for different fiber batch switches with the management goals of optimizing processing quality, equipment stability, and equipment utilization rate; An evaluation and update unit that collects the actual processing quality and actual equipment stability during equipment operation, conducts management evaluation in combination with the quality-stability joint prediction model, and iteratively updates the quality-stability joint prediction model; A predictive maintenance unit that analyzes the residual sequence between the actual equipment stability and the expected equipment stability, and generates predictive maintenance suggestions in combination with historical equipment maintenance data.

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