A composite fabric production management optimization method and system based on data module

Abstract

The present invention relates to the technical field of composite cloth production management, and specifically to a composite cloth production management optimization method and system based on a data module, comprising: collecting quality parameters such as air permeability, tensile strength, water absorption and color fastness of the composite cloth, constructing a quality assessment model, and generating a comprehensive quality score; predicting the demand for composite cloth by combining external market factors such as seasonal factors, price fluctuations, random events and historical demand; evaluating internal production factors such as process difficulty, production efficiency and working environment, generating production constraints, and constructing a production model by combining workload parameters and process parameters of production equipment to calculate production volume; and optimizing the workload and process parameters by using a particle swarm optimization algorithm based on the production balance rate and the comprehensive quality score, thereby achieving efficient optimization of the production process and improvement of product quality, thereby increasing the flexibility of production management.

Description

Technical Field

[0001] The present invention relates to the technical field of composite cloth production management, and in particular to a composite cloth production management optimization method and system based on a data module. Background Art

[0002] Composite fabrics, combining the excellent properties of multiple materials, are widely used in clothing, medical, and industrial applications. Therefore, with their unique functionality, composite fabrics offer significant advantages in meeting the needs of diverse scenarios. However, with increasingly stringent market requirements for composite fabric product quality and the diversification of user needs, production management has become significantly more complex, posing greater challenges to traditional production management methods. Therefore, efficient production management optimization solutions are urgently needed to comprehensively improve production efficiency, product quality, and resource utilization.

[0003] Currently, some existing solutions are able to determine the quality parameters of composite fabrics, which can help improve the production process to a certain extent and make product quality more standardized. However, the production process of composite fabrics is complex, involving multiple key quality parameters such as breathability, waterproofness, and comfort. These parameters are not only affected by the production process, but are also closely related to equipment settings and environmental conditions. Static management methods that rely solely on quality parameters are difficult to fully optimize the production process and are prone to problems such as quality fluctuations and waste of resources. On the other hand, market demand fluctuates frequently due to seasonal changes, price fluctuations, and random events, which places higher demands on the flexibility of production management and resource scheduling capabilities. However, existing production management methods generally lack the in-depth application of big data technology, resulting in insufficient responsiveness of the production process to market changes, and the level of flexibility still needs to be improved.

[0004] Therefore, a composite fabric production management optimization method and system based on data module is proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide a composite cloth production management optimization method and system based on a data module, which constructs a quality assessment model and generates a comprehensive quality score by collecting quality parameters such as air permeability, tensile strength, water absorption and color fastness of the composite cloth; predicts the demand for composite cloth by combining external market factors such as seasonal factors, price fluctuations, random events and historical demand; evaluates internal production factors such as process difficulty, production efficiency and working environment, generates production constraints, and constructs a production model based on the workload parameters and process parameters of the production equipment to calculate the production volume; based on the production balance rate and the comprehensive quality score, uses the particle swarm optimization algorithm to optimize the workload and process parameters, realizes efficient optimization of the production process and improves product quality, thereby improving the flexibility of composite cloth production management.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The first part is a composite fabric production management optimization method based on data module, including:

[0008] Collecting composite fabric quality parameters of the sample to be tested, wherein the composite fabric quality parameters include air permeability parameters, tensile strength parameters, water absorption parameters and color fastness parameters;

[0009] Establishing a quality assessment model, the quality assessment model is used to output an air permeability assessment value, a tensile strength assessment value, a water absorption assessment value, and a color fastness assessment value according to the quality parameters of the composite fabric; normalizing the air permeability assessment value, the tensile strength assessment value, the water absorption assessment value, and the color fastness assessment value and performing weighted summation to obtain a comprehensive quality score;

[0010] Obtaining external market factors, including seasonal factors, price fluctuations, random events, and historical demand, and building a forecasting model based on the external market factors, wherein the forecasting model is used to forecast the demand for composite fabrics;

[0011] Evaluate internal production factors to obtain production constraints, wherein the internal production factors include process difficulty, production efficiency, and working environment for producing the composite fabric; obtain workload parameters and process parameters of the production equipment, and construct a production model based on the production constraints, the workload parameters, and the process parameters, wherein the production model is used to output the composite fabric production volume;

[0012] Calculating a production balance rate based on the demand for the composite fabric and the production volume of the composite fabric;

[0013] The production balance rate and the comprehensive quality score are optimized using a particle swarm optimization algorithm, and the optimized workload parameters and process parameters are output.

[0014] Furthermore, the calculation process of the air permeability evaluation value includes:

[0015] Obtaining the air permeability parameters, wherein the air permeability parameters include the fabric area, test time, air volume, and fabric components of the sample to be tested;

[0016] Dividing the air volume by the fabric area and the test time to obtain the air permeability per unit area;

[0017] The air permeability per unit area is corrected according to the fabric components to obtain the air permeability evaluation value.

[0018] Furthermore, the calculation process of the tensile strength evaluation value includes:

[0019] Obtaining the tensile strength parameters, wherein the tensile strength parameters include the transverse cross-sectional area, the longitudinal cross-sectional area, the transverse maximum tensile force, and the longitudinal maximum tensile force of the sample to be tested;

[0020] Dividing the maximum transverse tensile force by the transverse cross-sectional area to obtain the transverse tensile strength;

[0021] Dividing the longitudinal maximum tensile force by the longitudinal cross-sectional area to obtain the longitudinal tensile strength;

[0022] The tensile strength evaluation value is obtained by weighted summing the transverse tensile strength and the longitudinal tensile strength.

[0023] Furthermore, the calculation process of the water absorption evaluation value includes:

[0024] Obtaining the water absorption parameters, wherein the water absorption parameters include a first cloth weight, a second cloth weight, a cloth area, a cloth thickness, and a cloth component;

[0025] Wherein, the first cloth weight is the cloth weight of the sample to be tested in a dry state, and the second cloth weight is the cloth weight of the sample to be tested after absorbing water;

[0026] Subtract the weight of the first cloth from the weight of the second cloth, and divide the result by the area of ​​the cloth to obtain the water absorption rate per unit area;

[0027] Dividing the water absorption per unit area by the thickness of the cloth to obtain the water absorption per unit thickness;

[0028] The water absorption rate per unit thickness is corrected according to the fabric components to obtain the water absorption evaluation value.

[0029] Furthermore, the calculation process of the color fastness evaluation value includes:

[0030] Acquiring the color fastness parameters, wherein the color fastness parameters include a first fabric area, a second fabric area, a first brightness value, a first chromaticity value, a second brightness value, and a second chromaticity value;

[0031] Wherein, the first cloth area is the cloth area of ​​the sample to be tested before friction, and the second cloth area is the cloth area of ​​the sample to be tested whose color falls off after friction; the first brightness value and the first chromaticity value are the brightness and chromaticity of the sample to be tested before washing, and the second brightness value and the second chromaticity value are the brightness and chromaticity of the sample to be tested after washing;

[0032] Calculating the ratio of the second fabric area to the first fabric area to obtain the rubbing color fastness;

[0033] Calculating the color change degree before and after washing based on the first brightness value, the first chromaticity value, the second brightness value, and the second chromaticity value to obtain the washing color fastness;

[0034] The color fastness evaluation value is obtained by weighted summing the rubbing color fastness and the washing color fastness.

[0035] Furthermore, the production constraints include:

[0036] A process difficulty constraint, used to limit the upper limit of the temperature and pressure of the production equipment according to the process difficulty;

[0037] Production efficiency constraints, used to limit upper limits of the frequency and load rate of the production equipment according to the production efficiency;

[0038] The working environment constraint is used to limit the upper limit of the operating time of the production equipment according to the working environment.

[0039] Furthermore, the calculation process of the production balance rate includes:

[0040] The inventory of the previous cycle and the target inventory are obtained, and the production balance rate is calculated according to the inventory of the previous cycle, the target inventory, the demand for the composite fabric, and the production of the composite fabric, which is expressed as:

[0041] ;

[0042] in, is the production balance rate, is the production volume of the composite fabric, is the inventory volume of the previous cycle, is the demand for the composite fabric, is the target inventory level.

[0043] Furthermore, the decision space of the particle swarm optimization algorithm includes:

[0044] A process parameter space, used to adjust the process parameters that affect the comprehensive quality score; the process parameters include the temperature and pressure of the production equipment;

[0045] The workload parameter space is used to adjust the workload parameters that affect the production balance rate; the workload parameters include the frequency, load rate and operating time of the production equipment.

[0046] Furthermore, the objective function of the particle swarm optimization algorithm is expressed as:

[0047] ;

[0048] in, is the target value after maximization, Score the overall quality. is the normalized production balance rate, is the normalized production cost, 、 and Weight coefficient, is the decision space of the particle swarm optimization algorithm.

[0049] The second part is a composite fabric production management optimization system based on data modules, including:

[0050] A data acquisition module is used to collect the composite fabric quality parameters of the sample to be tested;

[0051] a quality assessment module, configured to establish a quality assessment model, wherein the quality assessment model is configured to output an air permeability assessment value, a tensile strength assessment value, a water absorption assessment value, and a color fastness assessment value based on the quality parameters of the composite fabric; and normalize and weightedly sum the air permeability assessment value, the tensile strength assessment value, the water absorption assessment value, and the color fastness assessment value to obtain a comprehensive quality score;

[0052] A demand calculation module is used to obtain external market factors, including seasonal factors, price fluctuations, random events and historical demand, and to build a forecasting model based on the external market factors. The forecasting model is used to predict the demand for composite fabrics.

[0053] a production calculation module for evaluating internal production factors to obtain production constraints, wherein the internal production factors include the difficulty of the composite fabric production process, production efficiency, and working environment; obtaining workload parameters and process parameters of the production equipment; and constructing a production model based on the production constraints, the workload parameters, and the process parameters. The production model is used to output the composite fabric production volume;

[0054] The parameter optimization module calculates a production balance rate according to the composite cloth demand and the composite cloth production; and optimizes the production balance rate and the comprehensive quality score using a particle swarm optimization algorithm to obtain the optimized workload parameters and process parameters.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] 1. This invention collects key quality parameters of composite fabrics and establishes a quality assessment model to evaluate multiple indicators, including air permeability, tensile strength, water absorption, and color fastness, comprehensively reflecting the various properties of the composite fabric. By normalizing and weighting these various quality parameters to generate a comprehensive quality score, the comprehensive quality level of the composite fabric is accurately quantified, intuitively reflecting the multi-dimensional performance of the composite fabric. This provides a scientific basis for subsequent production decisions, ensuring that product quality meets market demand, thereby increasing the flexibility of composite fabric production management.

[0057] 2. This invention accurately predicts market demand for composite fabrics by collecting external market factors and establishing a forecasting model. Combined with established production constraints, a production model is further constructed to obtain actual output values ​​within these constraints, ensuring that the production process matches the actual environment. By combining the demand forecasting model with the production model, the production balance rate is calculated, effectively reducing inventory backlogs and resource waste caused by overproduction while avoiding market gaps caused by insufficient supply, thereby improving the flexibility of composite fabric production management and resource utilization efficiency.

[0058] 3. This invention incorporates a particle swarm optimization algorithm, comprehensively considering the production balance rate, comprehensive quality score, and production cost, to construct a multi-objective optimization function that meets the balancing requirements of different production objectives. Furthermore, by setting a corresponding particle swarm decision space, it is possible to seek the optimal solution within this multidimensional decision space, achieving optimal adjustment of the workload parameters and process parameters of composite fabric production equipment. This invention not only meets market demand but also improves product quality, thereby achieving efficient resource utilization and maximizing production efficiency, thereby enhancing the flexibility and overall benefits of composite fabric production management. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A schematic flow chart of a composite fabric production management optimization method based on a data module provided in an embodiment of the present invention;

[0060] Figure 2 A schematic diagram of the quality structure of the composite fabric provided by an embodiment of the present invention;

[0061] Figure 3 A schematic structural diagram of a composite fabric production management optimization system based on a data module is provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0063] See also Figures 1 to 3 The present invention provides a composite fabric production management optimization method and system based on a data module, and the technical solution is as follows:

[0064] With the growing market demand for high-performance composite fabrics, composite fabric companies are facing increasingly complex production management challenges. On the one hand, it is necessary to ensure the high quality of products to meet market demand; on the other hand, it is necessary to balance production efficiency and resource utilization to avoid inventory backlogs caused by overproduction or market shortages caused by insufficient supply. In addition, under the current "dual carbon" goals, production material control has also become a key issue for companies to optimize production management. Existing methods may lack comprehensive considerations when coordinating production and quality. To solve this problem, the present invention proposes a composite fabric production management optimization method based on a data module to enhance the flexibility of composite fabric production management.

[0065] Example 1 is as follows:

[0066] Figure 1 A schematic flow chart of a composite fabric production management optimization method based on a data module is provided in an embodiment of the present invention.

[0067] like Figure 1 As shown, a composite fabric production management optimization method based on a data module includes:

[0068] Step S1: collecting composite fabric quality parameters of a sample to be tested, wherein the composite fabric quality parameters include air permeability parameters, tensile strength parameters, water absorption parameters and color fastness parameters.

[0069] The sample to be tested is a composite fabric sample of standard size, typically 10 cm x 10 cm. After cutting multiple samples from the same batch, the sample is mounted on the test bench of an air permeability tester. The flow rate parameters are set, the tester is started, and the parameters required for airflow through the sample are recorded to obtain the air permeability parameters. Next, the sample is clamped between the upper and lower clamps of a tensile testing machine. The tensile speed is set and the test is started until the sample breaks. The parameters required for breaking the sample are recorded to obtain the tensile strength parameters. The sample is then completely immersed in clean water for a specified period of time. The sample is removed and gently wiped with absorbent paper to remove any surface moisture. The sample is quickly weighed, and the sample parameters before and after water absorption are measured and recorded multiple times to obtain the water absorption parameters. Finally, the sample is mounted on a color fastness tester and dry-rubbed at a fixed pressure and speed. The sample is then washed in a detergent according to standard washing conditions. A colorimeter is used to record parameters such as the area of ​​color loss to obtain the color fastness parameters. These parameters are stored in the database as the basis for subsequent analysis and model training.

[0070] Figure 2 This is a schematic diagram of the composite cloth mass structure provided by an embodiment of the present invention.

[0071] Step S2: Figure 2 As shown, a quality assessment model is established, which is used to output an air permeability assessment value, a tensile strength assessment value, a water absorption assessment value, and a color fastness assessment value according to the quality parameters of the composite fabric; the air permeability assessment value, the tensile strength assessment value, the water absorption assessment value, and the color fastness assessment value are normalized and weightedly summed to obtain a comprehensive quality score.

[0072] Among them, the comprehensive quality score is expressed as:

[0073] ;

[0074] in, For the overall quality rating, is the weight coefficient of each evaluation value, are the normalized evaluation values, is the serial number of each evaluation value;

[0075] In this embodiment, the weight coefficient of each evaluation value is set to 0.25.

[0076] Furthermore, the calculation process of the air permeability evaluation value includes:

[0077] Obtaining the air permeability parameters, wherein the air permeability parameters include the fabric area, test time, air volume, and fabric components of the sample to be tested;

[0078] Wherein, the air volume is the volume of air that the gas passes through the sample to be tested;

[0079] Dividing the air volume by the fabric area and the test time to obtain the air permeability per unit area;

[0080] The air permeability per unit area is corrected according to the fabric components to obtain the air permeability evaluation value, which is expressed as:

[0081] ;

[0082] in, is the air permeability evaluation value, is the volume of air, is the cloth area, For test time, It is the air permeability correction coefficient, which is used to correct the impact of process differences and environmental conditions on air permeability. It is the proportion of breathable materials, such as 30% cotton fiber is breathable material.

[0083] In this embodiment, the air flow rate of the air permeability tester is 5 L / min. Taking Samples 1, 2, and 3 as examples, each measuring 10 cm x 10 cm, the collected data is shown in Table 1. Sample 1 has the highest air permeability assessment value, while Sample 3 has the lowest. The air permeability assessment value comprehensively considers the air permeability per unit area and fabric composition, and can guide process parameter adjustments to maintain product air permeability within the target range, meeting diverse market demands. This improves the scientific nature and flexibility of composite fabric production management.

[0084] Table 1 Examples of air permeability parameters

[0085]

[0086] Furthermore, the calculation process of the tensile strength evaluation value includes:

[0087] Obtaining the tensile strength parameters, wherein the tensile strength parameters include the transverse cross-sectional area, the longitudinal cross-sectional area, the transverse maximum tensile force, and the longitudinal maximum tensile force of the sample to be tested;

[0088] The maximum transverse tensile force is the maximum tensile force during transverse stretching, and the maximum longitudinal tensile force is the maximum tensile force during longitudinal stretching.

[0089] Dividing the maximum transverse tensile force by the transverse cross-sectional area to obtain the transverse tensile strength;

[0090] Dividing the longitudinal maximum tensile force by the longitudinal cross-sectional area to obtain the longitudinal tensile strength;

[0091] The tensile strength evaluation value is obtained by weighted summing the transverse tensile strength and the longitudinal tensile strength, which is expressed as:

[0092] ;

[0093] in, is the tensile strength evaluation value, is the maximum lateral tension, is the transverse cross-sectional area, is the maximum longitudinal tension, is the longitudinal tensile strength, is the weight of transverse and longitudinal tensile strength.

[0094] The weights of transverse and longitudinal tensile strength are set according to the purpose of the fabric. Assuming that the composite fabric sample 4 used to make protective clothing needs to provide stronger tensile strength at the shoulder when stretched, the weights can be set. If the width of the sample 4 to be tested is 10 cm, the length is 20 cm, and the thickness is 0.1 cm, then the cross-sectional area is 1 , the longitudinal cross-sectional area is 2 In the tensile test, the maximum transverse tensile force was 500N, the maximum longitudinal tensile force was 600N, and the tensile strength evaluation value was calculated to be 420 .

[0095] The calculated tensile strength assessment value comprehensively considers the difference in tensile strength between the transverse and longitudinal directions, making the assessment results more comprehensive and applicable to different types of composite fabrics. Furthermore, the weighting can be adjusted based on specific applications (such as clothing, protective equipment, and industrial applications), improving the applicability of the tensile strength assessment and thus enhancing the flexibility of composite fabric production management.

[0096] Furthermore, the calculation process of the water absorption evaluation value includes:

[0097] Obtaining the water absorption parameters, wherein the water absorption parameters include a first cloth weight, a second cloth weight, a cloth area, a cloth thickness, and a cloth component;

[0098] Wherein, the first cloth weight is the cloth weight of the sample to be tested in a dry state, and the second cloth weight is the cloth weight of the sample to be tested after absorbing water;

[0099] Subtract the weight of the first cloth from the weight of the second cloth, and divide the result by the area of ​​the cloth to obtain the water absorption rate per unit area;

[0100] Dividing the water absorption per unit area by the thickness of the cloth to obtain the water absorption per unit thickness;

[0101] The water absorption rate per unit thickness is corrected according to the fabric components to obtain the water absorption evaluation value, which is expressed as:

[0102] ;

[0103] in, is the water absorption evaluation value, is the second cloth weight, is the first cloth weight, is the cloth area, is the thickness of the cloth, is the fiber density per unit area, is the proportion of non-absorbent material.

[0104] Specifically, suppose there is a sample to be tested 5, and the cloth area is 0.01 The thickness of the cloth is 2mm, it is composed of 70% cotton and 30% polyester, and the fiber density per unit area is 1.2 The first cloth weighs 20g and the second cloth weighs 23g. The calculated water absorption per unit area is 300 , the water absorption rate per unit thickness is 150 , the water absorption evaluation value is about 178.57 It can be observed that the revised water absorption evaluation value combines the thickness and material composition of the fabric, and can fully reflect the water absorption performance of the fabric. It is applicable to a variety of composite fabrics, such as towels with high water absorption requirements or waterproof fabrics with low water absorption. Based on this quantitative indicator, it provides a reference for the quality evaluation of composite fabrics, thereby improving the flexibility of composite fabric production management.

[0105] Furthermore, the calculation process of the color fastness evaluation value includes:

[0106] Acquiring the color fastness parameters, where the color fastness parameters include a first fabric area, a second fabric area, a first brightness value, a first chromaticity value, a second brightness value, and a second chromaticity value;

[0107] Wherein, the first cloth area is the cloth area of ​​the sample to be tested before friction, and the second cloth area is the cloth area of ​​the sample to be tested whose color falls off after friction; the first brightness value and the first chromaticity value are the brightness and chromaticity of the sample to be tested before washing, and the second brightness value and the second chromaticity value are the brightness and chromaticity of the sample to be tested after washing;

[0108] Calculating the ratio of the second fabric area to the first fabric area to obtain the rubbing color fastness;

[0109] Calculating the color change degree before and after washing based on the first brightness value, the first chromaticity value, the second brightness value, and the second chromaticity value to obtain the washing color fastness;

[0110] The color fastness evaluation value is obtained by weighted summing the rubbing color fastness and the washing color fastness, which is expressed as:

[0111] ;

[0112] in, is the color fastness evaluation value, is the second cloth area, is the first cloth area, is the color difference value, is the maximum color difference value, and are the weighting factors for color fastness to rubbing and color fastness to washing, respectively.

[0113] Among them, the color difference value Expressed as:

[0114] ;

[0115] in, is the first brightness value before washing, and is the first chromaticity value before washing, is the second brightness value after washing, and It is the second chromaticity value after washing.

[0116] Specifically, assuming that there is a sample to be tested 6, the first cloth area is 10 , the second cloth area is 1 , then the rubbing color fastness is 0.9. The first brightness value and the first chromaticity value before washing are , , The second brightness value and the second chromaticity value after washing are , , ,set up If is 10, the color fastness evaluation value is 0.654. and Setting the values ​​to 0.6 and 0.4, respectively, yields a colorfastness evaluation value of 0.8016. By comprehensively considering multiple factors, including both dry and wet friction, the colorfastness evaluation value is more comprehensive, meeting the needs of different composite fabric applications, such as highly washable workwear or highly abrasion-resistant sportswear. This provides a reference for composite fabric quality assessment and enhances the flexibility of composite fabric production management.

[0117] Step S3: Obtain external market factors, including seasonal factors, price fluctuations, random events and historical demand, and build a forecasting model based on the external market factors. The forecasting model is used to predict the demand for composite fabrics.

[0118] The prediction model may be a Random Forest model, an XGBoost model, or an LSTM model.

[0119] Specifically, demand for composite fabrics is often influenced by a variety of external factors. For example, demand for thermal insulation composite fabrics increases in winter, while demand for sun protection or breathability composite fabrics is higher in summer. Changes in composite fabric prices also directly impact market demand; a price drop can stimulate demand growth. Furthermore, random events (such as natural disasters or fashion trends) often lead to short-term fluctuations in market demand; historical demand data is a crucial basis for forecasting models. Therefore, external market factors include extracting timestamps (such as month and quarter) as seasonal factors, collecting historical composite fabric prices and their fluctuations as price fluctuations, marking key time points as random events to quantify the impact of unexpected factors, and compiling monthly, quarterly, or annual sales data as historical demand data to provide a reference for demand trends and cyclical trends. Using these factors as inputs, the forecasting model outputs the demand for composite fabrics at the next moment, enabling foreseeable demand fluctuations and reducing inventory pressure.

[0120] Step S4: Evaluate internal production factors to obtain production constraints, wherein the internal production factors include the difficulty of the composite fabric production process, production efficiency, and working environment.

[0121] Furthermore, the production constraints include:

[0122] The process difficulty constraint is used to limit the upper limit of the temperature and pressure of the production equipment according to the process difficulty, and is expressed as:

[0123] ;

[0124] in, is temperature; The difficulty value of the process evaluated by experts ranges from 1 to 5, with higher difficulty values ​​receiving higher scores; is the maximum temperature; It is the initial upper limit of temperature, which can be set to 300℃; is the influence coefficient, which can be set to 20℃ and 0.1MPa; For pressure; is the maximum pressure; It is the initial upper limit of pressure, which can be set to 2MPa;

[0125] The production efficiency constraint is used to limit the upper limit of the frequency and load rate of the production equipment according to the production efficiency, and is expressed as:

[0126] ;

[0127] in, The frequency of the production equipment; The automation level of the production line and the operator proficiency of the workers evaluated by experts are in the range of 1 to 5, with higher efficiency being the higher the score; is the highest frequency; It is the initial upper limit of frequency, which can be set to 50HZ; is the gain coefficient, which can be set to 10HZ and 10%; is the load factor; is the maximum load rate; It is the initial upper limit of the load rate, which can be set to 50%;

[0128] The working environment constraint is used to limit the upper limit of the operating time of the production equipment according to the working environment, and is expressed as:

[0129] ;

[0130] in, The operating time of the production equipment; It is a comprehensive evaluation value of temperature, humidity range and noise level evaluated by experts, ranging from 1 to 5, with higher scores indicating a more suitable environment; is the maximum operating time; The initial upper limit of the running time can be set to 2 hours; is the gain coefficient, which can be set to 1 hour;

[0131] Specifically, processes with higher difficulty may have stricter requirements on parameters such as temperature and pressure, requiring more precise control. Through process difficulty constraints, the upper limits of temperature and pressure of production equipment are dynamically adjusted to match the specific process requirements. For processes with higher difficulty, the control range can be automatically tightened to ensure the accuracy of key parameters. The higher the production efficiency, the higher the load and frequency the equipment can bear. Through production efficiency constraints, production equipment can dynamically adjust the upper limits of frequency and load rate according to the degree of automation of the production line and the proficiency of workers, avoiding overload operation of equipment due to excessive frequency and load on low-efficiency production lines. In addition, temperature, humidity, and noise have a great impact on the continuous operation of equipment. By setting working environment constraints and dynamically adjusting the upper limit of equipment operation time, the equipment operation time is limited, equipment loss is reduced, and maintenance costs are reduced, thereby improving equipment utilization and production flexibility.

[0132] Step S5: obtaining workload parameters and process parameters of the production equipment, and constructing a production model based on the production constraints, the workload parameters and the process parameters, wherein the production model is used to output the production volume of the composite fabric;

[0133] The production model is represented as:

[0134] ;

[0135] in, For production volume, is the workload parameter, is the process parameter, is the production function. The production value can be obtained based on the results of the actual factory or by fitting it using nonlinear regression method.

[0136] Step S6: Calculate the production balance rate according to the composite cloth demand and the composite cloth production; use the particle swarm optimization algorithm to optimize the production balance rate and the comprehensive quality score to obtain the optimized workload parameters and process parameters.

[0137] Furthermore, the calculation process of the production balance rate includes:

[0138] The inventory of the previous cycle and the target inventory are obtained, and the production balance rate is calculated according to the inventory of the previous cycle, the target inventory, the demand for the composite fabric, and the production of the composite fabric, which is expressed as:

[0139] ;

[0140] in, is the production balance rate, is the production volume of the composite fabric, is the inventory volume of the previous cycle, is the demand for the composite fabric, is the target inventory level.

[0141] Specifically, is 0, indicating that the total supply fully matches the demand. If it is greater than 0, it means that the total supply exceeds the demand, there is a risk of oversupply, and the production strategy should be adjusted. A value less than 0 indicates that total supply is lower than demand, creating a supply-demand gap and requiring improved capacity management. Assuming the inventory level in the previous cycle was 500 kg, the target inventory level is 400 kg, the demand for composite fabric in the current cycle is 800 kg, and the production volume is 600 kg, the calculated production balance rate is -8.33%, indicating that total supply is insufficient to meet both demand and the target inventory level. Production needs to be increased to balance supply and demand. The production balance rate allows for real-time assessment of the rationality of production plans and dynamic adjustments to production cadence, enabling intelligent scheduling and optimization throughout the entire process and increasing the flexibility of composite fabric production management.

[0142] Furthermore, the decision space of the particle swarm optimization algorithm includes:

[0143] A process parameter space, used to adjust the process parameters that affect the comprehensive quality score; the process parameters include the temperature and pressure of the production equipment;

[0144] The workload parameter space is used to adjust the workload parameters that affect the production balance rate; the workload parameters include the frequency, load rate and operating time of the production equipment.

[0145] Table 2 Example of decision space range

[0146]

[0147] Specifically, each particle is represented as a vector. In this embodiment, the decision space range of the particles is shown in Table 2. The decision space range can be modified according to actual conditions, and particles within the parameter space can also be added or deleted according to actual conditions. For example, the coating thickness and curing time for composite fabric quality can be added to the particle swarm according to production requirements and optimized accordingly. During the production optimization process, the particle swarm optimization algorithm can effectively search for the optimal production parameter combination by considering process parameters and workload parameters, thereby enabling rapid response to changes in market demand and balancing production quality and efficiency. For example, high temperature or high pressure may help improve the strength or comfort of composite fabrics. Adjusting these parameters can improve production quality, thereby increasing the flexibility of composite fabric production management.

[0148] Furthermore, the objective function of the particle swarm optimization algorithm is expressed as:

[0149] ;

[0150] in, is the target value after maximization, Score the overall quality. is the normalized production balance rate, is the normalized production cost, 、 and is the weight coefficient, is the decision space of the particle swarm optimization algorithm.

[0151] Specifically, the objective function is used to evaluate the fitness of each particle, that is, the performance of each particle's solution in optimizing composite fabric production. 、 and The values ​​were set to 0.4, 0.4, and 0.2, respectively, and the number of iterations was set to 400. The target value before optimization was 0.65, and the target value after optimization was 0.72, indicating that the comprehensive optimization target was effectively improved. According to the set objective function, the comprehensive quality score, production balance rate, and production cost can be comprehensively considered, avoiding the problem of excessive pursuit of quality while ignoring cost, thereby improving the flexibility of composite fabric production management.

[0152] The present invention collects quality parameters and establishes a quality assessment model to ensure that key indicators such as the product's air permeability, tensile strength, water absorption and color fastness can be accurately quantified, thereby improving the comprehensiveness of product quality assessment. The demand forecasting model is constructed in combination with external market factors, as well as the analysis of internal production factors and the setting of constraints, to ensure the scientific nature and market adaptability of production planning. At the same time, the production balance rate is calculated using the demand forecasting model and the production model, which optimizes the matching degree between production volume and demand volume, and reduces inventory and resource waste. Finally, the particle swarm optimization algorithm is introduced to jointly optimize the production balance rate and the comprehensive quality score, dynamically adjust the workload parameters and process parameters of the production equipment, and achieve efficient resource allocation and refined production management, thereby improving production efficiency and the flexibility of composite fabric production management, and enhancing the market competitiveness and comprehensive benefits of the enterprise.

[0153] The second embodiment is as follows:

[0154] Company A is an enterprise specializing in the production and processing of composite fabrics, mainly producing composite fabric materials for sportswear and functional textiles. However, with the diversification of market demand and the intensification of competition, Company A faces many production management challenges. For example, due to the influence of random events, the company's production plan is difficult to adapt to market changes in a timely manner, and supply and demand imbalances or inventory backlogs often occur. In order to cope with the above problems, Company A urgently needs a new solution to improve the flexibility of composite fabric production management. Based on the same inventive concept as the composite fabric production management optimization method based on a data module in the aforementioned embodiment, the present invention also provides a composite fabric production management optimization system based on a data module, which is applied to Company A, including:

[0155] Figure 3 A schematic structural diagram of a composite fabric production management optimization system based on a data module is provided in an embodiment of the present invention.

[0156] like Figure 3 As shown, a composite fabric production management optimization system based on a data module includes:

[0157] A data acquisition module is used to collect the composite fabric quality parameters of the sample to be tested;

[0158] a quality assessment module, configured to establish a quality assessment model, wherein the quality assessment model is configured to output an air permeability assessment value, a tensile strength assessment value, a water absorption assessment value, and a color fastness assessment value based on the quality parameters of the composite fabric; and normalize and weightedly sum the air permeability assessment value, the tensile strength assessment value, the water absorption assessment value, and the color fastness assessment value to obtain a comprehensive quality score;

[0159] Table 3 Evaluation results example

[0160]

[0161] In this embodiment, the quality assessment module performs a complete quality assessment on Sample A. Sample A is a composite fabric used in high-end sportswear, requiring excellent air permeability and tensile strength to meet the high demands of comfort and durability for sportswear. The evaluation results for Sample A are shown in Table 3. If the overall quality score exceeds 0.8, it is considered excellent. Table 3 yields an overall quality score of 0.855, thus categorizing Sample A as a composite fabric of excellent quality. In particular, its air permeability and tensile strength performance are outstanding, meeting the requirements of sportswear.

[0162] A demand calculation module is used to obtain external market factors, including seasonal factors, price fluctuations, random events and historical demand, and to build a forecasting model based on the external market factors. The forecasting model is used to predict the demand for composite fabrics.

[0163] Among them, in this embodiment, the LSTM model is selected as the prediction model, and the LSTM model is compared with other models. As shown in Table 4, it can be seen that the LSTM model shows the lowest prediction error in both MAE and RMSE, indicating that its prediction accuracy is higher and it can well capture the characteristics of demand changes, thereby providing a reliable basis for production planning.

[0164] Table 4 Comparison of model results

[0165]

[0166] a production calculation module for evaluating internal production factors to obtain production constraints, wherein the internal production factors include the difficulty of the composite fabric production process, production efficiency, and working environment; obtaining workload parameters and process parameters of the production equipment; and constructing a production model based on the production constraints, the workload parameters, and the process parameters. The production model is used to output the composite fabric production volume;

[0167] The parameter optimization module calculates a production balance rate according to the composite cloth demand and the composite cloth production; and optimizes the production balance rate and the comprehensive quality score using a particle swarm optimization algorithm to obtain the optimized workload parameters and process parameters.

[0168] Table 5 Comparison of key indicators before and after optimization

[0169]

[0170] Specifically, since fabric quality data must be obtained through real experiments in each iteration, in order to reduce the workload, sufficient fabric test data was collected through experiments in the early iterations. The LSTM model was also used to predict the fabric quality score based on process parameters and workload parameters. The error of the trained LSTM model was 10.5%, which can well predict the fabric quality score.

[0171] Comparing the system of the present invention with the original non-optimized solution of Company A, as shown in Table 5, the production balance rate, comprehensive quality score and production cost have all been improved to a certain extent, indicating that the optimized system can respond to the market more accurately, can flexibly adapt to environmental changes, effectively reduce production costs, and improve the production quality of composite fabrics, thereby enhancing the company's market competitiveness and improving the flexibility of composite fabric production management.

[0172] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A composite fabric production management optimization method based on data module, characterized in that: include: Collecting the composite fabric quality parameters of the sample to be tested, the composite fabric quality parameters include air permeability parameters, tensile strength parameters, water absorption parameters and color fastness parameters; A quality assessment model is established, which is used to output an air permeability assessment value, a tensile strength assessment value, a water absorption assessment value, and a color fastness assessment value based on the quality parameters of the composite fabric; the air permeability assessment value, the tensile strength assessment value, the water absorption assessment value, and the color fastness assessment value are normalized and weighted summed to obtain a comprehensive quality score; the color fastness assessment value is obtained by weighted summing the rubbing color fastness and the washing color fastness; and the air permeability per unit area is corrected according to the fabric components to obtain the air permeability assessment value; Obtain external market factors, including seasonal factors, price fluctuations, random events and historical demand, and build a forecasting model based on these external market factors. The forecasting model is used to predict the demand for composite fabrics. Evaluate internal production factors to derive production constraints, including: Process difficulty constraint, used to limit the upper limit of temperature and pressure of production equipment according to process difficulty; Production efficiency constraints, used to limit the upper limits of the frequency and load rate of production equipment based on production efficiency; Working environment constraints, which are used to limit the upper limit of the operating time of production equipment according to the working environment; Internal production factors include the difficulty of the composite fabric production process, production efficiency, and working environment. The workload parameters and process parameters of the production equipment are obtained, and a production model is constructed based on the production constraints, workload parameters, and process parameters. The production model is used to output the composite fabric production volume. The production balance rate is calculated based on the inventory level of the previous cycle, the target inventory level, the demand for composite fabrics, and the production volume of composite fabrics. The specific formula is: in, is the production balance rate, is the production volume of the composite fabric, is the inventory volume of the previous cycle, is the demand for the composite fabric, is the target inventory level; The particle swarm optimization algorithm is used to optimize the production balance rate and comprehensive quality score, and the optimized workload parameters and process parameters are output.

2. The composite fabric production management optimization method based on data module according to claim 1, characterized in that: The calculation process of the air permeability evaluation value includes: Obtaining the air permeability parameters, wherein the air permeability parameters include the fabric area, test time, air volume, and fabric components of the sample to be tested; Dividing the air volume by the fabric area and the test time to obtain the air permeability per unit area; The air permeability per unit area is corrected according to the fabric components to obtain the air permeability evaluation value.

3. The composite fabric production management optimization method based on data module according to claim 1, characterized in that: The calculation process of the tensile strength evaluation value includes: Obtaining the tensile strength parameters, wherein the tensile strength parameters include the transverse cross-sectional area, the longitudinal cross-sectional area, the transverse maximum tensile force, and the longitudinal maximum tensile force of the sample to be tested; Dividing the maximum transverse tensile force by the transverse cross-sectional area to obtain the transverse tensile strength; Dividing the longitudinal maximum tensile force by the longitudinal cross-sectional area to obtain the longitudinal tensile strength; The tensile strength evaluation value is obtained by weighted summing the transverse tensile strength and the longitudinal tensile strength.

4. The composite fabric production management optimization method based on data module according to claim 1, characterized in that: The calculation process of the water absorption evaluation value includes: Obtaining the water absorption parameters, wherein the water absorption parameters include a first cloth weight, a second cloth weight, a cloth area, a cloth thickness, and a cloth component; Wherein, the first cloth weight is the cloth weight of the sample to be tested in a dry state, and the second cloth weight is the cloth weight of the sample to be tested after absorbing water; Subtract the weight of the first cloth from the weight of the second cloth, and divide the result by the area of ​​the cloth to obtain the water absorption rate per unit area; Dividing the water absorption per unit area by the thickness of the cloth to obtain the water absorption per unit thickness; The water absorption rate per unit thickness is corrected according to the fabric components to obtain the water absorption evaluation value.

5. The composite fabric production management optimization method based on data module according to claim 1, characterized in that: The calculation process of the color fastness evaluation value includes: Acquiring the color fastness parameters, wherein the color fastness parameters include a first fabric area, a second fabric area, a first brightness value, a first chromaticity value, a second brightness value, and a second chromaticity value; Wherein, the first cloth area is the cloth area of ​​the sample to be tested before friction, and the second cloth area is the cloth area of ​​the sample to be tested whose color falls off after friction; the first brightness value and the first chromaticity value are the brightness and chromaticity of the sample to be tested before washing, and the second brightness value and the second chromaticity value are the brightness and chromaticity of the sample to be tested after washing; Calculating the ratio of the second fabric area to the first fabric area to obtain the rubbing color fastness; Calculating the color change degree before and after washing based on the first brightness value, the first chromaticity value, the second brightness value, and the second chromaticity value to obtain the washing color fastness; The color fastness evaluation value is obtained by weighted summing the rubbing color fastness and the washing color fastness.

6. The composite fabric production management optimization method based on data module according to claim 1, characterized in that: The calculation process of the production balance rate includes: The inventory volume of the previous cycle and the target inventory volume are obtained, and the production balance rate is calculated according to the inventory volume of the previous cycle, the target inventory volume, the demand for the composite fabric, and the production volume of the composite fabric.

7. The composite fabric production management optimization method based on data module according to claim 1, characterized in that: The decision space of the particle swarm optimization algorithm includes: A process parameter space, used to adjust the process parameters that affect the comprehensive quality score; the process parameters include the temperature and pressure of the production equipment; The workload parameter space is used to adjust the workload parameters that affect the production balance rate; the workload parameters include the frequency, load rate and operating time of the production equipment.

8. The composite fabric production management optimization method based on data module according to claim 1, characterized in that: The objective function of the particle swarm optimization algorithm is expressed as: in, is the target value after maximization, Score the overall quality. is the normalized production balance rate, is the normalized production cost, 、 and is the weight coefficient, is the decision space of the particle swarm optimization algorithm.

9. A composite fabric production management optimization system based on a data module, the system being used to execute a composite fabric production management optimization method based on a data module according to any one of claims 1 to 8, characterized in that: include: A data acquisition module is used to collect the composite fabric quality parameters of the sample to be tested; a quality assessment module, configured to establish a quality assessment model, wherein the quality assessment model is configured to output an air permeability assessment value, a tensile strength assessment value, a water absorption assessment value, and a color fastness assessment value based on the quality parameters of the composite fabric; and normalize and weightedly sum the air permeability assessment value, the tensile strength assessment value, the water absorption assessment value, and the color fastness assessment value to obtain a comprehensive quality score; A demand calculation module is used to obtain external market factors, including seasonal factors, price fluctuations, random events and historical demand, and to build a forecasting model based on the external market factors. The forecasting model is used to predict the demand for composite fabrics. a production calculation module for evaluating internal production factors to obtain production constraints, wherein the internal production factors include the difficulty of the composite fabric production process, production efficiency, and working environment; obtaining workload parameters and process parameters of the production equipment; and constructing a production model based on the production constraints, the workload parameters, and the process parameters. The production model is used to output the composite fabric production volume; The parameter optimization module calculates a production balance rate according to the composite cloth demand and the composite cloth production; and optimizes the production balance rate and the comprehensive quality score using a particle swarm optimization algorithm to obtain the optimized workload parameters and process parameters.

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