Textile printing and dyeing process collaborative optimization method based on 5G communication

Abstract

The invention provides a 5G communication-based textile printing and dyeing process collaborative optimization method, and relates to the technical field of textile printing and dyeing, and the method comprises the steps: transmitting all equipment and technological parameter data in real time through a 5G communication network, and achieving the efficient collaboration and precise scheduling between equipment; dye use and equipment load are optimized according to dye concentration, temperature, pressure and other data fed back in real time, and production parameters are dynamically adjusted; and energy consumption in the production process is subjected to optimization management by using an intelligent algorithm, so that energy efficiency optimization is ensured. According to the technical scheme, the production efficiency can be remarkably improved, energy consumption and resource waste are reduced, the consistency of product quality is improved, the production cost is reduced, and green and sustainable development of the textile printing and dyeing industry is promoted.

Description

Technical Field

[0001] The present invention relates to the field of textile printing and dyeing technology, and in particular to a textile printing and dyeing process collaborative optimization method based on 5G communication. Background Art

[0002] With the rapid development of the global textile industry, textile printing and dyeing, as an important process in textile production, has a direct impact on the economic benefits and environmental impact of the entire industry through its production efficiency, resource utilization and product quality. At present, the textile printing and dyeing process faces many challenges, including waste of resources in the production process, excessive energy consumption, uneven equipment load, unreasonable use of dyes and fluctuations in product quality. These problems not only lead to high production costs, but also impose a heavy burden on the environment, especially in terms of dye emissions and energy consumption. The industry urgently needs to seek more efficient, green and sustainable production solutions.

[0003] In the traditional textile printing and dyeing production process, the operation of each device is usually relatively independent, lacking real-time data feedback and systematic process coordination, resulting in asynchrony in multiple links of the production process. For example, the regulation of process parameters such as dye concentration, temperature, and pressure often relies on manual experience and non-real-time data, resulting in uneven equipment load and fluctuations in dyeing quality. Traditional equipment scheduling and energy consumption management methods also fail to fully consider the synergy between equipment, and fail to achieve precise scheduling and control based on real-time data, which in turn affects production efficiency and product quality.

[0004] In the textile printing and dyeing process, process optimization and resource allocation are difficult, especially in large-scale production, where the flexibility and response speed of the production line are particularly important. However, most existing technologies focus on local equipment optimization and lack global coordinated scheduling and real-time optimization of the entire printing and dyeing process. In particular, in the use of dyes and chemicals, it is impossible to accurately calculate and adjust their usage at different production stages, resulting in problems such as waste of dye resources, extended production cycles, and environmental pollution.

[0005] In addition, most traditional production equipment uses independent control systems, and their respective data collection, analysis and feedback are often closed, which leads to serious data islands. Even in some advanced textile printing and dyeing production lines, although some automation and intelligent equipment are used, due to the lack of unified real-time data monitoring and cross-equipment collaborative scheduling, its operating efficiency and collaborative optimization effect are still limited, making it difficult to achieve the best process and economic benefits.

[0006] In recent years, with the development of 5G communication technology, its high speed, low latency and large capacity make it possible to realize real-time data transmission and coordinated scheduling between equipment in the manufacturing industry. Through the 5G network, the textile printing and dyeing production line can achieve seamless connection between equipment, sensors and control systems, thereby obtaining real-time production data and equipment status information. Based on this data, the production process can be intelligently scheduled and optimized to achieve improved production efficiency, reduced energy consumption, quality consistency and precise control of equipment coordination.

[0007] Therefore, how to use 5G communication technology in the textile printing and dyeing process, combined with advanced sensors and intelligent scheduling algorithms, to achieve real-time monitoring and adjustment of process parameters, optimize the collaborative operation of equipment, improve production efficiency, and reduce production costs, is a technical problem that needs to be solved urgently. Solving this problem can not only greatly improve the automation and intelligence level of textile printing and dyeing production lines, but also effectively reduce environmental pollution, save resources and reduce energy consumption, and promote the sustainable development of the textile printing and dyeing industry. Summary of the invention

[0008] In order to solve the technical problems of insufficient equipment coordination, excessive energy consumption, serious waste of resources, unreasonable use of dyes and fluctuating product quality in the textile printing and dyeing production process in the prior art, the present invention provides a textile printing and dyeing process collaborative optimization method based on 5G communication.

[0009] The technical solution provided by the present invention is as follows:

[0010] The present invention provides a collaborative optimization method for textile printing and dyeing processes based on 5G communication, comprising:

[0011] S1. By deploying 5G communication network, various process equipment in the textile printing and dyeing workshop are connected to the central collaborative management platform to form a communication link for real-time data collection and high-speed transmission;

[0012] S2. Collect real-time operating data of each device, including temperature, pressure, humidity, dye concentration, processing time and energy consumption data, and upload the data to the central collaborative management platform via the 5G network;

[0013] S3. On the central collaborative management platform, multi-dimensional data analysis is performed on the collected real-time data, including the process deviation PDD based on the dynamic adjustment standard of historical process parameter setting and the process response synchronization PRS based on real-time data differences;

[0014] S4. Based on PDD and PRS parameters, dynamically monitor the process coordination status between various equipment in the textile printing and dyeing process, and identify the process links with low coordination efficiency;

[0015] S5. Based on the identification results, optimization adjustments are performed, including dynamic adjustment of equipment operating parameters and optimization of process sequence, so as to achieve coordinated optimization of the entire textile printing and dyeing process;

[0016] S6. Feedback the optimized and adjusted process parameters and coordination status to each device in real time, and control the equipment through the 5G network to achieve closed-loop control optimization;

[0017] S7. After the process is completed, the optimization results are stored and the historical process parameter database in the central collaborative management platform is updated for reference in subsequent process optimization.

[0018] Furthermore, the calculation of the process deviation PDD is specifically as follows:

[0019]

[0020] Among them, T i represents the temperature of the ith device, T ref Indicates the set standard temperature, P i represents the pressure of the ith device, P ref represents the set standard pressure, n represents the number of devices, and α is the balance coefficient of temperature and pressure parameters, which is calculated as follows:

[0021]

[0022] By calculating PDD, the overall deviation degree of process parameters is obtained for the evaluation of process status.

[0023] Furthermore, the calculation of the process response synchronization PRS is as follows:

[0024]

[0025] in, represents the actual response time of the jth process step, It represents the ideal response time of the jth process step, and m represents the number of process steps. Through the calculation of PRS, the synchronization of equipment response is evaluated and the collaborative efficiency of the process flow is determined.

[0026] Furthermore, the temperature and pressure balance coefficient α is further adaptively adjusted based on the data collected in the historical database, including the following steps:

[0027] S401, calculating the standard deviation of historical temperature and pressure data;

[0028] S402. Dynamically adjust the value of parameter α according to the calculation result so that it reflects the influence of historical process data on the balance of current process parameters.

[0029] Furthermore, the response time difference The dynamic time warping algorithm is introduced for calculation to ensure the accuracy of data alignment.

[0030] Furthermore, the optimization adjustment includes prioritizing the equipment operating parameters in the process flow, which is implemented based on a weighted priority queue algorithm.

[0031] Furthermore, the weight calculation method of the weighted priority queue algorithm is:

[0032]

[0033] Among them, W k represents the weight of the kth device, represents the energy consumption of device k at time t, represents the operating cost of device k at time t, and T represents the sampling period.

[0034] Furthermore, the 5G communication link ensures the stability and low latency of real-time data transmission by setting a dynamic bandwidth allocation strategy.

[0035] Furthermore, the dynamic bandwidth allocation strategy is based on the device data transmission priority sorting and real-time monitoring of the data transmission status between devices.

[0036] Furthermore, the textile printing and dyeing process collaborative optimization method based on 5G communication is applied to a multi-workshop collaborative production mode, and the collaborative optimization of process flows across workshops is achieved by unifying the data of multiple workshops into a central collaborative management platform.

[0037] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0038] (1) In the present invention, through real-time data transmission and equipment coordinated scheduling based on 5G communication network, efficient collaboration and real-time optimization of equipment in the textile printing and dyeing production process can be achieved, which effectively improves production efficiency, reduces equipment idle time and waiting time, and thus greatly improves the overall operation efficiency of the production line;

[0039] (2) In the present invention, by introducing an intelligent dye concentration adjustment mechanism and a load balancing strategy, the amount of dye used can be accurately controlled, resource waste is reduced, and the problem of excessive use or uneven distribution of dye is avoided, thereby reducing production costs and ensuring product quality consistency;

[0040] (3) In the present invention, an energy consumption optimization management strategy based on real-time data feedback is adopted, which can adjust the equipment operation status in real time, reduce energy consumption, and achieve the optimization of energy efficiency and cost through dynamic scheduling, thereby reducing the dependence on energy in the production process and promoting green and sustainable production methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 A schematic diagram of a process flow of a collaborative optimization method for a textile printing and dyeing process based on 5G communication provided in an embodiment of the present invention;

[0043] Figure 2 A schematic flow chart of a collaborative optimization method for a textile printing and dyeing process based on 5G communication provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0045] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0046] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0047] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are consistent.

[0048] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0049] Reference Manual Attached Figure 1 , showing a flow chart of a collaborative optimization method for textile printing and dyeing process based on 5G communication provided in an embodiment of the present invention.

[0050] The embodiment of the present invention provides a collaborative optimization method for a textile printing and dyeing process based on 5G communication, and the processing flow may include the following steps:

[0051] S1. By deploying 5G communication network, various process equipment in the textile printing and dyeing workshop are connected to the central collaborative management platform to form a communication link for real-time data collection and high-speed transmission;

[0052] S2. Collect real-time operating data of each device, including temperature, pressure, humidity, dye concentration, processing time and energy consumption data, and upload the data to the central collaborative management platform via the 5G network;

[0053] S3. Perform multi-dimensional data analysis on the collected real-time data on the central collaborative management platform, including the process deviation degree (PDD) based on the dynamic adjustment standard of historical process parameter settings and the process response synchronization (PRS) based on the real-time data difference;

[0054] S4. Based on PDD and PRS parameters, dynamically monitor the process coordination status between various equipment in the textile printing and dyeing process, and identify the process links with low coordination efficiency;

[0055] S5. Based on the identification results, optimization adjustments are performed, including dynamic adjustment of equipment operating parameters and optimization of process sequence, so as to achieve coordinated optimization of the entire textile printing and dyeing process;

[0056] S6. Feedback the optimized and adjusted process parameters and coordination status to each device in real time, and control the equipment through the 5G network to achieve closed-loop control optimization;

[0057] S7. After the process is completed, the optimization results are stored and the historical process parameter database in the central collaborative management platform is updated for reference in subsequent process optimization.

[0058] Reference Manual Attached Figure 2 , showing a schematic flow chart of an implementation method of a collaborative optimization method for textile printing and dyeing process based on 5G communication provided in an embodiment of the present invention.

[0059] Specifically, the following specific implementations are provided:

[0060] Step 1: 5G communication network deployment and device connection

[0061] Deploy 5G communication network in textile printing and dyeing workshop, including:

[0062] 5G base stations are set up to cover the entire textile printing and dyeing production area to ensure that the network signal is stable and has no dead spots.

[0063] All kinds of process equipment in the textile printing and dyeing workshop, such as dyeing vats, dryers, steamers, setting machines, circulating pumps, etc., are connected to the network through 5G communication modules. Each device is equipped with an independent 5G communication terminal to achieve high-speed data transmission.

[0064] Build a central collaborative management platform, which includes data acquisition module, data analysis module, equipment control module and process optimization module, etc. Each device forms a stable communication link with the platform through the 5G network to ensure real-time data collection and high-speed upload.

[0065] Step 2: Real-time data collection and upload

[0066] With the support of 5G communication network, real-time data collection is carried out on various process equipment in the workshop. The collected data includes:

[0067] Temperature data (T): records the temperature conditions inside each device.

[0068] Pressure data (P): including the pressure of the dyeing vat, the steam pressure of the steaming equipment, etc.

[0069] Humidity data: for example, the humidity of the working environment of the setting machine.

[0070] Dye Concentration: The concentration level of the dye solution is monitored by an online concentration sensor.

[0071] Processing time: record the execution time of each process.

[0072] Energy consumption data: collect energy consumption of each device, such as power consumption, heat consumption, etc.

[0073] The collected data is uploaded to the central collaborative management platform through the 5G communication module on the device. The data transmission adopts a dynamic bandwidth allocation strategy to ensure data transmission stability and low latency.

[0074] Step 3: Multi-dimensional analysis of process data

[0075] After receiving the data uploaded by each device, the central collaborative management platform starts the data analysis module to conduct multi-dimensional data analysis:

[0076] (1) Process Deviation Degree (PDD) calculation

[0077] Based on the temperature and pressure data collected by the equipment, PDD is calculated based on the following formula:

[0078]

[0079] Among them, T irepresents the temperature of the ith device, T ref Indicates the set standard temperature, P i represents the pressure of the ith device, P ref represents the set standard pressure, n represents the number of devices, and α is the balance coefficient of temperature and pressure parameters, which is dynamically calculated from the historical standard deviation.

[0080] PDD is used to evaluate the degree of deviation between the current process parameters and the standard set values.

[0081] (2) Process Response Synchronization (PRS) Calculation

[0082] During the equipment execution, the actual response time of each process step is recorded and compared with the ideal response time. The PRS is calculated by the following formula:

[0083]

[0084] in, represents the actual response time of the jth process step, It represents the ideal response time of the jth process step, and m represents the number of process steps. Through the calculation of PRS, the synchronization of equipment response is evaluated, the collaborative efficiency of the process flow is determined, and the links with large response delays are identified.

[0085] Step 4: Process collaborative status monitoring and optimization adjustment

[0086] Based on the calculation results of PDD and PRS, the central collaborative management platform dynamically monitors the collaborative status of each process equipment, including:

[0087] Identify equipment with higher PDD, analyze the cause of the deviation, and determine whether the process instability is caused by excessive temperature and pressure parameters.

[0088] Identify devices with low PRS and analyze the reasons for poor response synchronization, such as slow execution speed or large delay.

[0089] It should be noted that for equipment with process coordination problems, optimization adjustments are performed:

[0090] Dynamic parameter adjustment: According to the deviation information fed back by the central platform, the equipment operating parameters, including temperature, pressure, processing time, etc., are automatically adjusted to reduce the PDD and PRS deviations.

[0091] Process sequence optimization: Use the weighted priority queue algorithm (WPQ) to reorder the operating order of process equipment, giving priority to high-energy consumption or slow-response equipment to improve overall collaborative efficiency.

[0092] The weight calculation formula is:

[0093]

[0094] Among them, W k represents the weight of the kth device, represents the energy consumption of device k at time t, represents the operating cost of device k at time t, and T represents the sampling period.

[0095] Step 5: Real-time closed-loop feedback and control

[0096] The optimized and adjusted process parameters are fed back to each device in real time through the 5G network, and the device automatically adjusts its operating status to achieve closed-loop control. Closed-loop control includes:

[0097] Dynamically adjust key equipment parameters such as temperature setpoints, pressure control ranges, and processing time.

[0098] Monitor the adjustment effects in real time to ensure that the equipment responds quickly and meets optimization requirements.

[0099] Step 6: Process data storage and historical parameter update

[0100] After the process is completed, the central collaborative management platform stores the optimized process data in the historical process parameter database. The stored data includes:

[0101] PDD and PRS parameter values ​​before and after optimization;

[0102] Dynamically adjusted equipment operating parameters;

[0103] Process collaborative status improvement results.

[0104] By updating the historical parameter database, data support is provided for the optimization of subsequent process flows.

[0105] Step 7: Cross-workshop collaborative optimization

[0106] In the multi-workshop collaborative production mode, the equipment data of each workshop is uniformly connected to the central collaborative management platform, which monitors and optimizes the process of multiple workshops as a whole. Through cross-workshop data analysis and parameter adjustment, the collaborative optimization of textile printing and dyeing processes on a global scale is achieved.

[0107] In a possible implementation, to address the problem of uneven load on textile printing and dyeing process equipment, an equipment load balance degree (LBD) parameter is introduced to monitor the load status of the equipment in real time, and optimize equipment scheduling through a formula.

[0108] (1) Calculation of device load balance degree (LBD)

[0109] Equipment load balance is used to evaluate the load distribution of each process equipment. The specific formula is as follows:

[0110]

[0111] Among them, L i represents the actual load value of the i-th device, Indicates the average load value of all devices, calculated as:

[0112]

[0113] n represents the total number of devices. When the LBD value is close to 1, it means that the device load distribution is relatively balanced. When the LBD value is low, it means that the load on some devices is too high or too low.

[0114] (2) Load Optimization Scheduling

[0115] Based on the LBD calculation results, the central collaborative management platform dynamically adjusts the task allocation of process equipment:

[0116] For devices with a load higher than the threshold, some tasks are diverted to devices with a lower load.

[0117] Optimize the start and stop sequence of equipment, and give priority to shutting down equipment with lower load to save energy.

[0118] Through real-time calculation and scheduling strategies of LBD parameters, the load distribution of process equipment is more balanced, improving overall production efficiency.

[0119] In a possible implementation, in order to solve the problems of high energy consumption and high operating costs in textile printing and dyeing production, an energy-cost factor (ECF) is introduced to optimize equipment operation.

[0120] (1) Calculation of Energy Cost Factor (ECF)

[0121] The energy cost factor combines the energy consumption level and operating cost of the equipment. The specific formula is as follows:

[0122]

[0123] Among them, E j represents the energy consumption of the jth device, Cj represents the operating cost of the jth device, and β represents the weight ratio of energy consumption and cost, which is calculated by the following formula:

[0124]

[0125] in, and They represent the average energy consumption and average cost of all equipment, respectively, and T represents the process execution time. ECF is used to quantify the energy consumption and cost level of each equipment. The lower the ECF value, the lower the comprehensive energy consumption cost of the equipment.

[0126] (2) Based on the ECF calculation results, optimize the operating parameters of the equipment:

[0127] Prioritize scheduling of low ECF devices: Prioritize tasks to devices with lower ECF to reduce overall energy consumption costs.

[0128] Dynamic energy consumption adjustment: For equipment with high energy consumption, reduce equipment energy consumption by adjusting process parameters such as temperature and pressure.

[0129] Through ECF parameters and dynamic adjustment strategies, energy consumption and costs can be jointly minimized to improve production economy.

[0130] In a possible implementation, in order to solve the problem of product color difference caused by uneven dye concentration, a dye concentration deviation factor (DCDF) is introduced to optimize the dye concentration distribution.

[0131] (1) Calculation of dye concentration deviation factor (DCDF)

[0132] DCDF is used to quantify the degree of deviation in dye concentration. The specific formula is as follows:

[0133]

[0134] Among them, C k represents the actual dye concentration at the kth monitoring point, C ref It represents the set standard fuel concentration, p represents the total number of monitoring points, and the smaller the DCDF value is, the more uniform the dye concentration distribution is and the smaller the product color difference is.

[0135] (2) Dynamic adjustment of dye concentration

[0136] Based on the DCDF calculation results, the concentration of the dye solution is adjusted in real time:

[0137] Add dye or dilute the solution to make the dye concentration gradually approach the standard value C ref .

[0138] Through the 5G network, dye concentration data is fed back to the dye vat control system in real time to automatically control the dye addition rate and dilution ratio.

[0139] Through DCDF parameters and dynamic control strategies, the problem of uneven dye concentration distribution can be effectively solved to ensure the color consistency of textile printing and dyeing products.

[0140] In a possible implementation, a response time balance (RTB) is introduced to focus on solving the problem of asynchronous response time of process equipment.

[0141] (1) Response Time Balance (RTB) Calculation

[0142] RTB is used to evaluate the response time distribution of process equipment. The specific formula is as follows:

[0143]

[0144] Among them, Δ i represents the response time of the i-th device, Represents the average response time of all devices, calculated as:

[0145]

[0146] When the RTB value is close to 1, it means that the device response time is relatively balanced; when the RTB value is low, it means poor synchronization.

[0147] (2) Synchronicity optimization

[0148] Based on the RTB calculation results, adjust the device startup and execution time:

[0149] For devices with long response time, start the execution program in advance to reduce the time difference with other devices. Monitor the execution status of the device in real time through the 5G network and dynamically adjust the operation sequence of each device. Improve the synchronization of process equipment and reduce waiting time in the production process through RTB parameters and dynamic time control strategies.

[0150] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0151] (1) In the present invention, through real-time data transmission and equipment coordinated scheduling based on 5G communication network, efficient collaboration and real-time optimization of equipment in the textile printing and dyeing production process can be achieved, which effectively improves production efficiency, reduces equipment idle time and waiting time, and thus greatly improves the overall operation efficiency of the production line;

[0152] (2) In the present invention, by introducing an intelligent dye concentration adjustment mechanism and a load balancing strategy, the amount of dye used can be accurately controlled, resource waste is reduced, and the problem of excessive use or uneven distribution of dye is avoided, thereby reducing production costs and ensuring product quality consistency;

[0153] (3) In the present invention, an energy consumption optimization management strategy based on real-time data feedback is adopted, which can adjust the equipment operation status in real time, reduce energy consumption, and achieve the optimization of energy efficiency and cost through dynamic scheduling, thereby reducing the dependence on energy in the production process and promoting green and sustainable production methods.

[0154] The above contents are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0155] There are a few points to note:

[0156] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0157] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0158] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0159] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A collaborative optimization method for textile printing and dyeing process based on 5G communication, characterized in that: include: S1. By deploying 5G communication network, various process equipment in the textile printing and dyeing workshop are connected to the central collaborative management platform to form a communication link for real-time data collection and high-speed transmission; S2. Collect real-time operating data of each device, including temperature, pressure, humidity, dye concentration, processing time and energy consumption data, and upload the data to the central collaborative management platform via the 5G network; S3. On the central collaborative management platform, multi-dimensional data analysis is performed on the collected real-time data, including the process deviation PDD based on the dynamic adjustment standard of historical process parameter setting and the process response synchronization PRS based on real-time data differences; S4. Based on PDD and PRS parameters, dynamically monitor the process coordination status between various equipment in the textile printing and dyeing process, and identify the process links with low coordination efficiency; S5. Based on the identification results, optimization adjustments are performed, including dynamic adjustment of equipment operating parameters and optimization of process sequence, so as to achieve coordinated optimization of the entire textile printing and dyeing process; S6. Feedback the optimized and adjusted process parameters and coordination status to each device in real time, and control the equipment through the 5G network to achieve closed-loop control optimization; S7. After the process is completed, the optimization results are stored and the historical process parameter database in the central collaborative management platform is updated for reference in subsequent process optimization.

2. According to a collaborative optimization method for textile printing and dyeing process based on 5G communication according to claim 1, it is characterized in that: include: The calculation of the process deviation PDD is as follows: Among them, T i represents the temperature of the ith device, T ref Indicates the set standard temperature, P i represents the pressure of the ith device, P ref represents the set standard pressure, n represents the number of devices, and α is the balance coefficient of temperature and pressure parameters, which is calculated as follows: By calculating PDD, the overall deviation degree of process parameters is obtained for the evaluation of process status.

3. According to a collaborative optimization method for textile printing and dyeing process based on 5G communication according to claim 1, it is characterized in that: include: The calculation of the process response synchronization PRS is as follows: in, represents the actual response time of the jth process step, It represents the ideal response time of the jth process step, and m represents the number of process steps. Through the calculation of PRS, the synchronization of equipment response is evaluated and the collaborative efficiency of the process flow is determined.

4. According to a collaborative optimization method for textile printing and dyeing process based on 5G communication according to claim 2, it is characterized in that: include: The temperature and pressure balance coefficient α is further adaptively adjusted based on the data collected in the historical database, including the following steps: S401, calculating the standard deviation of historical temperature and pressure data; S402. Dynamically adjust the value of parameter α according to the calculation result so that it reflects the influence of historical process data on the balance of current process parameters.

5. According to a collaborative optimization method for textile printing and dyeing process based on 5G communication according to claim 3, it is characterized in that: include: The response time difference The dynamic time warping algorithm is introduced for calculation to ensure the accuracy of data alignment.

6. The collaborative optimization method for textile printing and dyeing process based on 5G communication according to claim 1 is characterized in that: include: The optimization adjustment includes prioritizing the equipment operating parameters in the process flow, which is implemented based on a weighted priority queue algorithm.

7. The collaborative optimization method for textile printing and dyeing process based on 5G communication according to claim 6 is characterized in that: include: The weight calculation method of the weighted priority queue algorithm is: Among them, W k represents the weight of the kth device, represents the energy consumption of device k at time t, represents the operating cost of device k at time t, and T represents the sampling period.

8. The collaborative optimization method for textile printing and dyeing process based on 5G communication according to claim 1 is characterized in that: include: The 5G communication link ensures the stability and low latency of real-time data transmission by setting a dynamic bandwidth allocation strategy.

9. The collaborative optimization method for textile printing and dyeing process based on 5G communication according to claim 8 is characterized in that: include: The dynamic bandwidth allocation strategy is based on the device data transmission priority sorting and real-time monitoring of the data transmission status between devices.

10. The collaborative optimization method for textile printing and dyeing process based on 5G communication according to claim 1, characterized in that: include: The textile printing and dyeing process collaborative optimization method based on 5G communication is applied to a multi-workshop collaborative production mode. By unifying the data of multiple workshops into a central collaborative management platform, collaborative optimization of process flows across workshops is achieved.

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