An artificial intelligence-based yarn-dyed fabric production scheduling and optimization method and platform

Abstract

The application provides a yarn-dyed fabric production scheduling and optimization method and platform based on artificial intelligence, relates to the technical field of production scheduling, acquires a yarn-dyed fabric dyeing task according to yarn-dyed fabric dyeing demand information, analyzes the task resource proportion of the yarn-dyed fabric dyeing task according to task resource demand information of the yarn-dyed fabric dyeing task, and obtains yarn-dyed fabric resource allocation data; acquires task weight information and weight adjustment information of the yarn-dyed fabric dyeing task, and then acquires a task yarn-dyed fabric dyeing sequence; performs the task according to the task yarn-dyed fabric dyeing sequence combined with the yarn-dyed fabric resource allocation data, acquires task execution consumption data; acquires a sequence breaking point, performs sequence segmentation and sorting update, and acquires an updated dyeing sequence, and then acquires updated dyeing data; and the application realizes the optimized scheduling and adjustment of the yarn-dyed fabric dyeing production, and improves the yarn-dyed fabric dyeing efficiency and precision.

Description

Technical Field

[0001] This invention proposes an artificial intelligence-based method and platform for scheduling and optimizing yarn-dyed fabric production, which relates to the field of production scheduling technology, specifically to the field of artificial intelligence-based yarn-dyed fabric production scheduling technology. Background Technology

[0002] The production of yarn-dyed fabrics relies on a core process of dyeing yarn before weaving, and its traditional dyeing production scheduling faces numerous bottlenecks. Dyeing tasks must consider multiple dimensions such as color, material, and delivery time. Color changes must be matched with equipment cleaning processes, and material differences require adjustments to parameters such as dyeing temperature and dye liquor formula. Traditional experience-driven static scheduling is prone to resource misallocation and cost losses. After scheduling, it is difficult to respond promptly to abnormal resource consumption, resulting in low overall production efficiency and poor on-time order delivery. Existing scheduling solutions lack data closure and cannot optimize strategies based on historical consumption data, making it difficult to adapt to the flexible production needs of yarn-dyed fabrics. Summary of the Invention

[0003] This invention provides an artificial intelligence-based method and platform for scheduling and optimizing yarn-dyed fabric production to solve the above-mentioned problems:

[0004] This invention proposes an artificial intelligence-based method and platform for scheduling and optimizing yarn-dyed fabric production. The method includes:

[0005] S1. Obtain yarn-dyed fabric dyeing tasks based on yarn-dyed fabric dyeing demand information, and perform task resource ratio analysis on yarn-dyed fabric dyeing tasks based on task resource demand information to obtain yarn-dyed fabric resource allocation data.

[0006] S2. Obtain the task weight information and weight adjustment information of the yarn-dyed fabric dyeing task based on the yarn-dyed fabric dyeing demand information, and then obtain the yarn-dyed fabric dyeing sequence of the task. Perform the task execution based on the yarn-dyed fabric dyeing sequence and the yarn-dyed fabric resource allocation data, and obtain the task execution consumption data.

[0007] S3. Based on the task resource consumption data and the task yarn-dyed fabric dyeing sequence, obtain the sequence breakpoint, perform sequence segmentation and sorting updates to obtain the updated dyeing sequence, and then obtain the updated dyeing data.

[0008] Further, S1 includes:

[0009] Obtain dyeing demand information for yarn-dyed fabrics, and then obtain dyeing tasks for yarn-dyed fabrics based on the dyeing demand information.

[0010] Retrieve dyeing resource information for yarn-dyed fabrics based on the yarn-dyed fabric dyeing task;

[0011] Based on the dyeing resource information of yarn-dyed fabrics, dyeing resources are allocated for the dyeing task to obtain task resource allocation data.

[0012] Furthermore, the step of allocating dyeing resources for the yarn-dyed fabric dyeing task based on the yarn-dyed fabric dyeing resource information to obtain task resource allocation data includes:

[0013] Obtain the task resource requirements information for each task through the yarn-dyed fabric dyeing task;

[0014] Obtain the task weight information for each task, and obtain the task resource coefficient based on the task weight information and the corresponding task resource requirement information.

[0015] Calculate the sum of the task resource coefficients of all tasks in the yarn-dyed fabric dyeing task to obtain the total task resource coefficient of the yarn-dyed fabric dyeing task;

[0016] Obtain the proportion of the total task resource coefficient of a single yarn-dyed fabric dyeing task in the sum of the total task resource coefficients of all yarn-dyed fabric dyeing tasks, and obtain the task proportion coefficient.

[0017] Task resource allocation data for yarn-dyed fabric dyeing tasks is obtained by combining task resource proportion coefficients with yarn-dyed fabric dyeing resource information.

[0018] Further, S2 includes:

[0019] The dyeing tasks for yarn-dyed fabrics are sorted according to the dyeing demand information to obtain the dyeing sequence for yarn-dyed fabrics.

[0020] Resource supply is provided for yarn-dyed fabric dyeing tasks based on yarn-dyed fabric resource allocation data;

[0021] The dyeing task of yarn-dyed fabric is simulated based on resource supply (which can be estimated based on historical trends or existing data) to obtain task resource consumption data.

[0022] Further, the step of sorting and segmenting the yarn-dyed fabric dyeing tasks according to the yarn-dyed fabric dyeing demand information to obtain the yarn-dyed fabric dyeing sequence includes:

[0023] Based on the dyeing requirements of yarn-dyed fabrics, obtain the dyeing color, material, and time information for each dyeing task;

[0024] Obtain color preset weight information, set the weight of the dyeing color of the yarn-dyed fabric according to the color preset weight information, and obtain the first weight information of the task;

[0025] Obtain the preset weight information of the material, set the weight of the dyeing material of the yarn-dyed fabric according to the preset weight information of the material, and obtain the second weight information of the task;

[0026] Obtain time preset weight information; set weights for the dyeing time of yarn-dyed fabric according to the time preset weight information to obtain the third weight information of the task;

[0027] Obtain preset category weight information, and adjust the weights of the first task weight information, the second task weight information, and the third task weight information according to the preset category weight information to obtain weight adjustment information;

[0028] The yarn-dyed fabric dyeing tasks are sorted according to the weight adjustment information to obtain the yarn-dyed fabric dyeing sequence.

[0029] Further, the step of obtaining preset category weight information and adjusting the weights of the first task weight information, the second task weight information, and the third task weight information according to the preset category weight information to obtain weight adjustment information includes:

[0030] The preset category weight information is such that the preset weight information for color is greater than the preset weight information for material, which is greater than the preset weight information for time.

[0031] Set the basic coefficients for each category based on the preset category weight information;

[0032] The category-based coefficients include color-based coefficients, material-based coefficients, and time-based coefficients;

[0033] The color base coefficient is greater than the material base coefficient, which is greater than the time base coefficient.

[0034] The weight adjustment information is obtained by multiplying the first weight information of the task by the color base coefficient, the second weight information of the task by the material base coefficient, and the third weight information of the task by the time base coefficient.

[0035] Further, the yarn-dyed fabric dyeing tasks are sorted according to the weight adjustment information to obtain the task yarn-dyed fabric dyeing sequence, including:

[0036] The yarn-dyed fabric dyeing tasks are sorted according to the product of the first weight information of the task and the basic color coefficient, to obtain the first yarn-dyed fabric dyeing sequence.

[0037] The first dyeing sequence of the fabric is sorted and updated based on the product of the second weight information of the task and the basic material coefficient, so as to obtain the second dyeing sequence of the fabric.

[0038] The second color fabric dyeing sequence is sorted and updated based on the product of the task third weight information and the time base coefficient, to obtain the third color fabric dyeing sequence.

[0039] The third yarn-dyed fabric dyeing sequence is the task yarn-dyed fabric dyeing sequence.

[0040] Further, S3 includes:

[0041] Based on the dyeing sequence of the yarn-dyed fabric task, the execution operation of the yarn-dyed fabric dyeing task is simulated to obtain the task resource consumption data during the execution simulation process.

[0042] Based on the task resource consumption data, the task yarn-dyed fabric dyeing sequence is broken to obtain the sequence break point;

[0043] The dyeing sequence of the task yarn-dyed fabric is segmented according to the sequence breakpoint to obtain multiple dyeing sequences;

[0044] The multiple staining sequences are rewritten and rearranged according to the weight adjustment information to obtain the updated staining sequence, and then the updated staining is performed to obtain the updated staining data.

[0045] Furthermore, the step of breaking the dyeing sequence of the task-specific yarn-dyed fabric based on task resource consumption data to obtain the sequence breakpoint includes:

[0046] Obtain the consumption change data of each two adjacent yarn-dyed fabric dyeing tasks in the task sequence;

[0047] Compare the consumption change data with a preset consumption change threshold;

[0048] When the consumption change data is greater than the preset consumption change threshold, the adjacent yarn-dyed fabric dyeing tasks are determined to be a sequence breakpoint.

[0049] Furthermore, the platform includes:

[0050] The resource analysis module is used to obtain yarn-dyed fabric dyeing tasks based on yarn-dyed fabric dyeing demand information, and to perform task resource ratio analysis on yarn-dyed fabric dyeing tasks based on task resource demand information to obtain yarn-dyed fabric resource allocation data.

[0051] The weight sorting module is used to obtain the task weight information and weight adjustment information of the yarn-dyed fabric dyeing task based on the yarn-dyed fabric dyeing demand information, and then obtain the task yarn-dyed fabric dyeing sequence. Based on the task yarn-dyed fabric dyeing sequence and the yarn-dyed fabric resource allocation data, the task is executed and the task execution consumption data is obtained.

[0052] The sequence update module is used to obtain the sequence breakpoint based on the task resource consumption data and the task yarn-dyed fabric dyeing sequence, perform sequence segmentation and sorting updates, obtain the updated dyeing sequence, and then obtain the updated dyeing data.

[0053] The beneficial effects of this invention are as follows: This invention overcomes the limitations of traditional static scheduling and passive response in yarn-dyed fabric dyeing. By analyzing resource proportions, it avoids resource allocation imbalances and improves resource utilization. Weighted sequence generation takes into account multiple dimensions of requirements, including color, material, and time, reducing equipment cleaning costs and parameter adjustment losses, and lowering production energy consumption. The dynamic sequence update mechanism can respond to abnormal resource consumption in real time, avoiding the impact of abnormal task sequence consumption on the overall progress and improving the on-time delivery rate of orders. At the same time, through data closed-loop continuous optimization of scheduling strategies, it adapts to the production needs of small batches and multiple varieties of yarn-dyed fabrics, enhancing production flexibility and stability. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of an artificial intelligence-based method for scheduling and optimizing the production of yarn-dyed fabrics. Detailed Implementation

[0055] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0056] In one embodiment of the present invention, an artificial intelligence-based method and platform for scheduling and optimizing yarn-dyed fabric production is proposed, the method comprising:

[0057] S1. Obtain yarn-dyed fabric dyeing tasks based on yarn-dyed fabric dyeing demand information, and perform task resource ratio analysis on yarn-dyed fabric dyeing tasks based on task resource demand information to obtain yarn-dyed fabric resource allocation data.

[0058] S2. Obtain the task weight information and weight adjustment information of the yarn-dyed fabric dyeing task based on the yarn-dyed fabric dyeing demand information, and then obtain the yarn-dyed fabric dyeing sequence of the task. Perform the task execution based on the yarn-dyed fabric dyeing sequence and the yarn-dyed fabric resource allocation data, and obtain the task execution consumption data.

[0059] S3. Based on the task resource consumption data and the task yarn-dyed fabric dyeing sequence, obtain the sequence breakpoint, perform sequence segmentation and sorting updates to obtain the updated dyeing sequence, and then obtain the updated dyeing data, such as... Figure 1 As shown.

[0060] The working principle and technical effects of the above technical solution are as follows: Starting from the staining requirements, this invention clarifies the specific staining tasks. Based on the task's requirements for resources such as the staining host, energy, and time, it analyzes the resource proportion of each task and outputs resource allocation data. A staining sequence is generated by combining task weights and adjustment information. The task is advanced according to the sequence and resource allocation data, and execution consumption data is collected synchronously to achieve seamless integration of sorting, execution, and data feedback. The consumption data is used to identify resource consumption mutation points in the staining sequence. The sequence is then segmented, reordered, and updated to form a dynamically optimized staining scheme, outputting updated staining data. The entire process is data-centric, spanning the three major stages of resource allocation, sequence generation, and dynamic adjustment, constructing a closed loop of analysis, execution, and optimization.

[0061] This invention addresses the limitations of traditional static scheduling and passive response in yarn-dyed fabric dyeing. It avoids resource allocation imbalances and improves resource utilization through resource proportion analysis. Weighted sequence generation takes into account multiple dimensions of requirements, including color, material, and time, reducing equipment cleaning costs and parameter adjustment losses, and lowering production energy consumption. The dynamic sequence update mechanism can respond to resource consumption anomalies in real time, preventing the overall progress from being affected by abnormal task sequence consumption and improving order delivery timeliness. At the same time, it continuously optimizes the scheduling strategy through data closed-loop to adapt to the production needs of small batches and multiple varieties of yarn-dyed fabrics, enhancing production flexibility and stability.

[0062] In one embodiment of the present invention, S1 includes:

[0063] Obtain dyeing demand information for yarn-dyed fabrics, and obtain dyeing tasks for yarn-dyed fabrics based on the dyeing demand information; the dyeing tasks for yarn-dyed fabrics include dyeing yarns with different colors, dyeing yarns in different quantities, and dyeing yarns for different durations, etc.

[0064] The dyeing resource information for yarn-dyed fabrics is retrieved according to the dyeing task; the dyeing resources for yarn-dyed fabrics include the dyeing host, dyeing energy, operating time, and clean energy, etc.

[0065] Based on the dyeing resource information of yarn-dyed fabrics, dyeing resources are allocated for the dyeing task to obtain task resource allocation data.

[0066] The working principle and technical effect of the above technical solution are as follows: This method focuses on the resource allocation of dyeing tasks. It breaks down specific dyeing tasks by using dyeing demand information for yarn-dyed fabrics (such as order color, quantity, and material requirements), clarifying the dyeing requirements for different colors, quantities, and times included in the tasks; then it retrieves dyeing resource information such as dyeing host (skein dyeing vat, yarn dyeing vat), dyeing energy (steam, electricity), operating time, and clean energy (soft water) to establish the correspondence between tasks and resources; based on the specific resource requirements of each task, combined with the total amount of resources and task priority, it completes resource matching and allocation, and outputs allocation data such as the quantity and type of resources corresponding to each type of task, ensuring accurate matching between tasks and resources.

[0067] This method addresses the problems of traditional experience-driven and inefficient resource allocation. By breaking down requirements, it clarifies task resource needs, avoids resource mismatch, and improves resource matching accuracy. Based on scientific allocation of resource information, it balances resource usage across tasks and reduces equipment idle time. By outputting resource allocation data in advance, it allows time for material preparation and equipment debugging, reducing pre-production preparation time and shortening the production cycle. At the same time, it provides clear resource supply information for task execution, ensuring the orderly start of the production process and reducing the risk of downtime due to resource shortages.

[0068] In one embodiment of the present invention, the step of allocating dyeing resources for yarn-dyed fabric dyeing tasks based on yarn-dyed fabric dyeing resource information to obtain task resource allocation data includes:

[0069] Obtain the task resource requirements information for each task through the yarn-dyed fabric dyeing task;

[0070] Obtain the task weight information for each task, and obtain the task resource coefficient based on the task weight information and the corresponding task resource requirement information.

[0071] The total task resource coefficient of the yarn-dyed fabric dyeing task is obtained by summing the task resource coefficients of all tasks in the yarn-dyed fabric dyeing task; the data dimensions can be adjusted by methods such as normalization.

[0072] Obtain the proportion of the total task resource coefficient of a single yarn-dyed fabric dyeing task in the sum of the total task resource coefficients of all yarn-dyed fabric dyeing tasks, and obtain the task proportion coefficient.

[0073] Task resource allocation data for yarn-dyed fabric dyeing tasks is obtained by combining task resource proportion coefficients with yarn-dyed fabric dyeing resource information.

[0074] The working principle and technical effect of the above technical solution are as follows: Extract resource requirement information from each yarn-dyed fabric dyeing task; then obtain the weight information of each task (e.g., urgent orders have high weight, light-colored tasks have high weight), multiply the task weight by the corresponding resource requirement to obtain the task resource coefficient reflecting the matching degree between the importance of the task and the resource requirement; calculate the sum of the resource coefficients of all tasks to clarify the overall base of resource allocation; obtain the task proportion coefficient by the proportion of the resource coefficient of a single task to the total coefficient, and quantify the priority of each task in resource allocation; combine the total dyeing resources, allocate resources according to the proportion coefficient, and output the specific resource allocation data of each task.

[0075] By using the logic of coefficient calculation, proportion quantification, and proportional allocation, resource allocation is made quantifiable and transparent, avoiding the unfairness and inefficiency caused by subjective assumptions in traditional allocation. The task resource coefficient, combined with weight and demand, ensures that important tasks receive sufficient resources, improving order delivery priority and corporate profits. The introduction of the proportion coefficient balances the total amount of resources and task requirements, avoiding excessive concentration of resources and reducing resource waste. Precise resource allocation data provides quantitative information for material preparation and equipment scheduling, reducing redundant resource reserves, lowering inventory costs, and ensuring stable resource supply for each task, thus reducing the probability of production interruptions.

[0076] In one embodiment of the present invention, S2 includes:

[0077] The dyeing tasks for yarn-dyed fabrics are sorted according to the dyeing demand information to obtain the dyeing sequence for yarn-dyed fabrics.

[0078] Resource supply is provided for yarn-dyed fabric dyeing tasks based on yarn-dyed fabric resource allocation data;

[0079] The dyeing task of yarn-dyed fabric is simulated based on resource supply (which can be estimated based on historical trends or existing data) to obtain task resource consumption data.

[0080] The working principle and technical effect of the above technical solution are as follows: Based on the dyeing demand information of yarn-dyed fabric (color, material, time), the dyeing tasks are sorted according to the preset weight rules to generate an initial dyeing sequence; according to the resource allocation data, the corresponding dyeing host, dye, energy and other resources are supplied to the sorted tasks to ensure that the resources match the task order; through task execution simulation (using historical dyeing data trends or existing process parameters to calculate), the task execution process is simulated, and data such as dye consumption, energy consumption, and time consumption in the simulation are collected to obtain task resource consumption data, and the association mapping between sequence, resources and consumption is completed.

[0081] By generating ordered sequences, frequent equipment cleaning and parameter adjustments caused by disordered task execution are avoided, reducing equipment debugging time. Resource supply and sequence matching prevent resource supply from being out of sync with task order, improving the timeliness of resource supply and reducing task waiting time. Task simulation obtains consumption data in advance, avoiding resource shortages caused by inaccurate consumption estimates during actual execution and reducing production interruption rate. At the same time, simulation data provides a basis for sequence optimization, enabling early intervention through pre-simulation and feedback, reducing trial and error costs in actual production, and improving production efficiency and stability.

[0082] In one embodiment of the present invention, the step of sorting and segmenting the yarn-dyed fabric dyeing tasks according to the yarn-dyed fabric dyeing demand information to obtain a yarn-dyed fabric dyeing sequence includes:

[0083] Based on the dyeing requirements of yarn-dyed fabrics, obtain the dyeing color, material, and time information for each dyeing task;

[0084] Obtain color preset weight information, and set the weight of the dyeing color of the yarn-dyed fabric according to the color preset weight information to obtain the first weight information of the task; the lighter the color, the greater the weight. Dyeing the lighter color first can reduce the equipment cleaning time and cost, and reduce the risk of cross-dyeing of the fabric.

[0085] Obtain the preset weight information of the material, and set the weight of the dyeing material of the yarn-dyed fabric according to the preset weight information of the material to obtain the second weight information of the task; the setting of the material weight depends on the adjustment difference of the temperature and other data. The closer it is to the preset value of the parameter, the greater the weight, which reduces the cost of repeated parameter adjustment, shortens the parameter adjustment range, and reduces the abnormal impact of parameter adjustment.

[0086] Obtain time preset weight information; set weights for the dyeing time of yarn-dyed fabric according to the time preset weight information to obtain the third weight information of the task; the setting of time weight depends on the remaining production time, the shorter the remaining production time, the greater the weight, reducing the time pressure and increasing the delivery probability;

[0087] Obtain preset category weight information, and adjust the weights of the first task weight information, the second task weight information, and the third task weight information according to the preset category weight information to obtain weight adjustment information;

[0088] The yarn-dyed fabric dyeing tasks are sorted according to the weight adjustment information to obtain the yarn-dyed fabric dyeing sequence.

[0089] The working principle and technical effects of the above solution are as follows: Extract the dyeing color, material, and time information for each task from the dyeing requirements of yarn-dyed fabrics; set three weights according to preset rules: color weight (higher weight for lighter colors) to reduce equipment cleaning costs and cross-contamination; material weight (higher weight for material parameters with smaller differences from preset values) to reduce parameter adjustment costs and anomaly risks; and time weight (higher weight for shorter remaining time) to ensure order delivery; adjust the three weights by combining the preset category weights (color > material > time) to obtain comprehensive weight adjustment information; sort the tasks according to the adjustment information to generate a dyeing sequence that takes into account multiple requirements. The preset category weights can be adjusted according to actual needs.

[0090] Compared to traditional single-dimensional sorting, multi-dimensional weighted sorting achieves synergistic optimization of quality, cost, and delivery: color-priority sorting reduces the number of equipment cleanings and solvent consumption, lowers cleaning costs, and avoids rework caused by cross-contamination, thereby improving dyeing pass rates; material-priority sorting reduces repeated parameter adjustments, shortens equipment debugging time, and reduces quality problems caused by abnormal parameter adjustments; time-priority sorting ensures the delivery of urgent orders and improves the on-time rate of urgent orders; the sequence generated by comprehensive sorting is adapted to the production characteristics of multi-variety, small-batch yarn-dyed fabrics, enhancing scheduling flexibility, and providing a clear task sequence basis for resource allocation and execution simulation.

[0091] In one embodiment of the present invention, the step of obtaining preset category weight information and adjusting the weights of the first task weight information, the second task weight information, and the third task weight information according to the preset category weight information to obtain weight adjustment information includes:

[0092] The preset category weight information is such that the preset weight information for color is greater than the preset weight information for material, which is greater than the preset weight information for time.

[0093] Set the basic coefficients for each category based on the preset category weight information;

[0094] The category-based coefficients include color-based coefficients, material-based coefficients, and time-based coefficients;

[0095] The color base coefficient is greater than the material base coefficient, which is greater than the time base coefficient.

[0096] The weight adjustment information is obtained by multiplying the first weight information of the task by the color base coefficient, the second weight information of the task by the material base coefficient, and the third weight information of the task by the time base coefficient.

[0097] The working principle and technical effect of the above technical solution are as follows: The calculation logic of weight adjustment is refined, and the preset category weight rules are clarified (color preset weight > material preset weight > time preset weight). The rules are set based on the production needs of prioritizing dyeing and cleaning costs of yarn-dyed fabrics, followed by parameter loss, and supplementing delivery guarantees. Category base coefficients are set according to the rules to ensure that the coefficient size is consistent with the category weight, quantifying the influence ratio of each category weight. The first task weight (color) multiplied by the color base coefficient, the second task weight (material) multiplied by the material base coefficient, and the third task weight (time) multiplied by the time base coefficient are calculated separately. The three product results are integrated to obtain weight adjustment information reflecting the comprehensive influence of multi-dimensional weights, achieving a scientific integration of color, material, and time weights.

[0098] By quantifying category weight priority using basic coefficients, the problem of priority ambiguity during multi-dimensional weight fusion is avoided, thus improving the accuracy of weight adjustment. The setting of basic coefficients makes the weight adjustment results interpretable and traceable, avoiding the black-box operation of traditional weight fusion. The adjusted weight information takes into account multi-dimensional needs, ensuring the reduction of cleaning costs brought about by color priority, and supplementing and optimizing through material and time coefficients to steadily improve the role of sequence optimization in promoting production efficiency. At the same time, the coefficients can be flexibly adjusted according to production needs, enhancing scheduling adaptability.

[0099] In one embodiment of the present invention, the yarn-dyed fabric dyeing tasks are sorted according to the weight adjustment information to obtain a yarn-dyed fabric dyeing sequence, including:

[0100] The yarn-dyed fabric dyeing tasks are sorted according to the product of the first weight information of the task and the basic color coefficient, to obtain the first yarn-dyed fabric dyeing sequence.

[0101] The first dyeing sequence of the fabric is sorted and updated based on the product of the second weight information of the task and the basic material coefficient, so as to obtain the second dyeing sequence of the fabric.

[0102] The second color fabric dyeing sequence is sorted and updated based on the product of the task third weight information and the time base coefficient, to obtain the third color fabric dyeing sequence.

[0103] The third yarn-dyed fabric dyeing sequence is the task yarn-dyed fabric dyeing sequence.

[0104] The working principle and technical effect of the above technical solution are as follows: This method uses a hierarchical progressive sorting logic to generate a coloring sequence. The first step is to sort all coloring tasks based on the first weight of the task multiplied by the basic color coefficient, and prioritize the lighter-colored tasks with higher coefficient products to generate a first coloring sequence that only considers color priority. The second step is to readjust the tasks of the same color based on the second weight of the task multiplied by the basic material coefficient, based on the second weight of the task, to generate a second coloring sequence that takes into account both color and material. The third step is to fine-tune the tasks of the same color and material based on the third weight of the task multiplied by the basic time coefficient, based on the second sequence, to finally generate a third coloring sequence, which is the final task coloring sequence.

[0105] Layered, progressive sorting ensures that category weights are prioritized (color > material > time), avoiding priority confusion during multi-dimensional sorting and ensuring that the sorting logic aligns with production needs. Each layer of sorting focuses on only a single dimension, simplifying sorting complexity, improving sorting efficiency, and making sequence adjustments traceable. Through three layers of sorting, the sequence satisfies the cost reduction benefits of color priority, reduces parameter adjustment losses through material sorting, and ensures the delivery of urgent orders through time sorting, thereby improving overall production efficiency. The sequence generation process is transparent, making it easy for workshop personnel to understand and execute, and reducing operational errors caused by ambiguity in the sorting logic.

[0106] In one embodiment of the present invention, S3 includes:

[0107] Based on the dyeing sequence of the yarn-dyed fabric, the execution operation of the dyeing task is simulated to obtain the task resource consumption data during the execution simulation (or the execution operation can be performed directly).

[0108] Based on the task resource consumption data, the task yarn-dyed fabric dyeing sequence is broken to obtain the sequence break point;

[0109] The dyeing sequence of the task yarn-dyed fabric is segmented according to the sequence breakpoint to obtain multiple dyeing sequences;

[0110] The multiple staining sequences are rewritten and rearranged according to the weight adjustment information to obtain the updated staining sequence, and then the updated staining is performed to obtain the updated staining data.

[0111] The working principle and technical effect of the above technical solution are as follows: monitor the dynamic optimization of the staining sequence, obtain the resource consumption data of each task in the staining sequence through task execution simulation (or actual execution; if actual execution, the sequence can be updated for subsequent batch production), and establish the correspondence between consumption data and task order; then analyze the differences in consumption data of adjacent tasks, identify consumption mutation points, and determine the mutation points as sequence breakpoints; divide the original staining sequence into multiple segments according to the breakpoints; rearrange the multiple segments based on weight adjustment information to generate an updated staining sequence, execute the staining task according to the updated sequence, output the updated staining data, and complete the dynamic optimization of the sequence.

[0112] Dynamic sequence optimization breaks through the limitations of traditional one-time scheduling and unchanging throughout the process. It identifies anomalies by consuming data, avoids abnormal tasks from affecting subsequent sequences, and reduces the risk of production interruption. Sequence segmentation and rearrangement make resource consumption more balanced and reduce energy waste. Updating the dyeing sequence can adapt to the real-time resource status and improve the production anti-interference capability. At the same time, the sequence is continuously optimized through data feedback, so that the scheduling strategy is upgraded with the production data iteration, which enhances the dynamic adaptability of yarn-dyed fabric production and shortens the order delivery cycle.

[0113] In one embodiment of the present invention, the step of breaking the dyeing sequence of the task yarn-dyed fabric based on task resource consumption data to obtain the sequence breakpoint includes:

[0114] Obtain the consumption change data of each two adjacent yarn-dyed fabric dyeing tasks in the task sequence;

[0115] Compare the consumption change data with a preset consumption change threshold;

[0116] When the consumption change data is greater than the preset consumption change threshold, the adjacent yarn-dyed fabric dyeing tasks are determined to be a sequence breakpoint.

[0117] The working principle and technical effect of the above technical solution are as follows: quantitative identification of sequence breakpoints is performed. Task consumption data of every two adjacent dyeing tasks are extracted from the task yarn-dyed fabric sequence. The difference between adjacent tasks on the same consumption dimension is calculated to obtain consumption change data. Then, based on the historical consumption fluctuation range and resource supply capacity of yarn-dyed fabric dyeing, a consumption change threshold is preset as a standard for judging whether the consumption is abnormal. The consumption change data of adjacent tasks is compared with the preset threshold. If the change data exceeds the threshold, it is determined that there is a sudden change in resource consumption between the two adjacent tasks, and the position is determined as the sequence breakpoint.

[0118] By comparing quantitative consumption change data with thresholds, the error of subjective judgment in traditional breakpoint identification is avoided, thus improving the accuracy of breakpoint identification. Clear threshold standards make breakpoint identification replicable and scalable, adaptable to coloring tasks of different colors and materials. Accurate breakpoint identification can promptly detect high-consumption mutation tasks in the sequence, preventing the task from excessively consuming resources and causing subsequent tasks to run out of materials, reducing the chain reaction of resource waste. Breakpoints provide clear boundaries for sequence segmentation, making the consumption of the segmented subsequences more stable, and making it easier to achieve balanced resource allocation during subsequent rearrangement, thus improving overall production stability.

[0119] In one embodiment of the present invention, the platform includes:

[0120] The resource analysis module is used to obtain yarn-dyed fabric dyeing tasks based on yarn-dyed fabric dyeing demand information, and to perform task resource ratio analysis on yarn-dyed fabric dyeing tasks based on task resource demand information to obtain yarn-dyed fabric resource allocation data.

[0121] The weight sorting module is used to obtain the task weight information and weight adjustment information of the yarn-dyed fabric dyeing task based on the yarn-dyed fabric dyeing demand information, and then obtain the task yarn-dyed fabric dyeing sequence. Based on the task yarn-dyed fabric dyeing sequence and the yarn-dyed fabric resource allocation data, the task is executed and the task execution consumption data is obtained.

[0122] The sequence update module is used to obtain the sequence breakpoint based on the task resource consumption data and the task yarn-dyed fabric dyeing sequence, perform sequence segmentation and sorting updates, obtain the updated dyeing sequence, and then obtain the updated dyeing data.

[0123] The working principle and technical effects of the above technical solution are as follows: Starting from the staining requirements, this invention clarifies the specific staining tasks. Based on the task's requirements for resources such as the staining host, energy, and time, it analyzes the resource proportion of each task and outputs resource allocation data. A staining sequence is generated by combining task weights and adjustment information. The task is advanced according to the sequence and resource allocation data, and execution consumption data is collected synchronously to achieve seamless integration of sorting, execution, and data feedback. The consumption data is used to identify resource consumption mutation points in the staining sequence. The sequence is then segmented, reordered, and updated to form a dynamically optimized staining scheme, outputting updated staining data. The entire process is data-centric, spanning the three major stages of resource allocation, sequence generation, and dynamic adjustment, constructing a closed loop of analysis, execution, and optimization.

[0124] This invention addresses the limitations of traditional static scheduling and passive response in yarn-dyed fabric dyeing. It avoids resource allocation imbalances and improves resource utilization through resource proportion analysis. Weighted sequence generation takes into account multiple dimensions of requirements, including color, material, and time, reducing equipment cleaning costs and parameter adjustment losses, and lowering production energy consumption. The dynamic sequence update mechanism can respond to resource consumption anomalies in real time, preventing the overall progress from being affected by abnormal task sequence consumption and improving order delivery timeliness. At the same time, it continuously optimizes the scheduling strategy through data closed-loop to adapt to the production needs of small batches and multiple varieties of yarn-dyed fabrics, enhancing production flexibility and stability.

[0125] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for scheduling and optimizing yarn-dyed fabric production based on artificial intelligence, characterized in that, The method includes: S1. Obtain yarn-dyed fabric dyeing tasks based on yarn-dyed fabric dyeing demand information, and perform task resource ratio analysis on yarn-dyed fabric dyeing tasks based on task resource demand information to obtain yarn-dyed fabric resource allocation data. S2. Obtain the task weight information and weight adjustment information of the yarn-dyed fabric dyeing task based on the yarn-dyed fabric dyeing demand information, and then obtain the yarn-dyed fabric dyeing sequence of the task. Perform the task execution based on the yarn-dyed fabric dyeing sequence and the yarn-dyed fabric resource allocation data, and obtain the task execution consumption data. Wherein, S2 includes: The dyeing tasks for yarn-dyed fabrics are sorted according to the dyeing demand information to obtain the dyeing sequence for yarn-dyed fabrics. Resource supply is provided for yarn-dyed fabric dyeing tasks based on yarn-dyed fabric resource allocation data; The dyeing task of yarn-dyed fabric is simulated based on resource supply to obtain task resource consumption data. The step of sorting the yarn-dyed fabric dyeing tasks according to the yarn-dyed fabric dyeing demand information to obtain the task yarn-dyed fabric dyeing sequence includes: Based on the dyeing requirements of yarn-dyed fabrics, obtain the dyeing color, material, and time information for each dyeing task; Obtain color preset weight information, set the weight of the dyeing color of the yarn-dyed fabric according to the color preset weight information, and obtain the first weight information of the task; Obtain the preset weight information of the material, set the weight of the dyeing material of the yarn-dyed fabric according to the preset weight information of the material, and obtain the second weight information of the task; Obtain time preset weight information; set weights for the dyeing time of yarn-dyed fabric according to the time preset weight information to obtain the third weight information of the task; Obtain preset category weight information, and adjust the weights of the first task weight information, the second task weight information, and the third task weight information according to the preset category weight information to obtain weight adjustment information; The yarn-dyed fabric dyeing tasks are sorted according to the weight adjustment information to obtain the yarn-dyed fabric dyeing sequence. The step of obtaining preset category weight information and adjusting the weights of the first task weight information, the second task weight information, and the third task weight information according to the preset category weight information to obtain weight adjustment information includes: The preset category weight information is such that the preset weight information for color is greater than the preset weight information for material, which is greater than the preset weight information for time. Set the basic coefficients for each category based on the preset category weight information; The category-based coefficients include color-based coefficients, material-based coefficients, and time-based coefficients; The color base coefficient is greater than the material base coefficient, which is greater than the time base coefficient. Obtain the product of the first weight information of the task and the basic color coefficient, the product of the second weight information of the task and the basic material coefficient, and the product of the third weight information of the task and the basic time coefficient, and then obtain the weight adjustment information. S3. Based on the task resource consumption data and the task yarn-dyed fabric dyeing sequence, obtain the sequence breakpoint, perform sequence segmentation and sorting update, obtain the updated dyeing sequence, and then obtain the updated dyeing data.

2. The method for scheduling and optimizing yarn-dyed fabric production based on artificial intelligence according to claim 1, characterized in that, S1 includes: Obtain dyeing demand information for yarn-dyed fabrics, and then obtain dyeing tasks for yarn-dyed fabrics based on the dyeing demand information. Retrieve dyeing resource information for yarn-dyed fabrics based on the yarn-dyed fabric dyeing task; Based on the dyeing resource information of yarn-dyed fabrics, dyeing resources are allocated for the dyeing task to obtain task resource allocation data.

3. The method for scheduling and optimizing yarn-dyed fabric production based on artificial intelligence according to claim 2, characterized in that, The step of allocating dyeing resources for yarn-dyed fabric dyeing tasks based on yarn-dyed fabric dyeing resource information to obtain task resource allocation data includes: Obtain the task resource requirements information for each task through the yarn-dyed fabric dyeing task; Obtain the task weight information for each task, and obtain the task resource coefficient based on the task weight information and the corresponding task resource requirement information. Calculate the sum of the task resource coefficients of all tasks in the yarn-dyed fabric dyeing task to obtain the total task resource coefficient of the yarn-dyed fabric dyeing task; Obtain the percentage of the task resource coefficient of a single yarn-dyed fabric dyeing task in the sum of the total task resource coefficients of all yarn-dyed fabric dyeing tasks, and obtain the task percentage coefficient. The task resource allocation data for yarn-dyed fabric dyeing tasks is obtained by combining the task proportion coefficient with the yarn-dyed fabric dyeing resource information.

4. The method for scheduling and optimizing yarn-dyed fabric production based on artificial intelligence according to claim 1, characterized in that, The yarn-dyed fabric dyeing tasks are sorted according to the weight adjustment information to obtain the yarn-dyed fabric dyeing sequence, including: The yarn-dyed fabric dyeing tasks are sorted according to the product of the first weight information of the task and the basic color coefficient, to obtain the first yarn-dyed fabric dyeing sequence. The first dyeing sequence of the fabric is sorted and updated based on the product of the second weight information of the task and the basic material coefficient, so as to obtain the second dyeing sequence of the fabric. The second color fabric dyeing sequence is sorted and updated based on the product of the task third weight information and the time base coefficient, to obtain the third color fabric dyeing sequence. The third yarn-dyed fabric dyeing sequence is the task yarn-dyed fabric dyeing sequence.

5. The method for scheduling and optimizing yarn-dyed fabric production based on artificial intelligence according to claim 1, characterized in that, S3 includes: Based on the dyeing sequence of the yarn-dyed fabric task, the execution operation of the yarn-dyed fabric dyeing task is simulated to obtain the task resource consumption data during the execution simulation process. Based on the task resource consumption data, the task yarn-dyed fabric dyeing sequence is broken to obtain the sequence break point; The dyeing sequence of the task yarn-dyed fabric is segmented according to the sequence breakpoint to obtain multiple dyeing sequences; The multiple staining sequences are rewritten and rearranged according to the weight adjustment information to obtain the updated staining sequence, and then the updated staining is performed to obtain the updated staining data.

6. The method for scheduling and optimizing yarn-dyed fabric production based on artificial intelligence according to claim 5, characterized in that, The step of breaking down the dyeing sequence of the task-specific yarn-dyed fabric based on task resource consumption data to obtain the sequence breakpoint includes: Obtain the consumption change data of each two adjacent yarn-dyed fabric dyeing tasks in the task sequence; Compare the consumption change data with a preset consumption change threshold; When the consumption change data is greater than the preset consumption change threshold, the adjacent yarn-dyed fabric dyeing tasks are determined to be a sequence breakpoint.

7. An artificial intelligence-based platform for scheduling and optimizing yarn-dyed fabric production, characterized in that, The platform includes: The resource analysis module is used to obtain yarn-dyed fabric dyeing tasks based on yarn-dyed fabric dyeing demand information, and to perform task resource ratio analysis on yarn-dyed fabric dyeing tasks based on task resource demand information to obtain yarn-dyed fabric resource allocation data. The weight sorting module is used to obtain the task weight information and weight adjustment information of the yarn-dyed fabric dyeing task based on the yarn-dyed fabric dyeing demand information, and then obtain the task yarn-dyed fabric dyeing sequence. Based on the task yarn-dyed fabric dyeing sequence and the yarn-dyed fabric resource allocation data, the task is executed and the task execution consumption data is obtained. The weighted sorting module includes: The dyeing tasks for yarn-dyed fabrics are sorted according to the dyeing demand information to obtain the dyeing sequence for yarn-dyed fabrics. Resource supply is provided for yarn-dyed fabric dyeing tasks based on yarn-dyed fabric resource allocation data; The dyeing task of yarn-dyed fabric is simulated based on resource supply to obtain task resource consumption data. The step of sorting the yarn-dyed fabric dyeing tasks according to the yarn-dyed fabric dyeing demand information to obtain the task yarn-dyed fabric dyeing sequence includes: Based on the dyeing requirements of yarn-dyed fabrics, obtain the dyeing color, material, and time information for each dyeing task; Obtain color preset weight information, set the weight of the dyeing color of the yarn-dyed fabric according to the color preset weight information, and obtain the first weight information of the task; Obtain the preset weight information of the material, set the weight of the dyeing material of the yarn-dyed fabric according to the preset weight information of the material, and obtain the second weight information of the task; Obtain time preset weight information; set weights for the dyeing time of yarn-dyed fabric according to the time preset weight information to obtain the third weight information of the task; Obtain preset category weight information, and adjust the weights of the first task weight information, the second task weight information, and the third task weight information according to the preset category weight information to obtain weight adjustment information; The yarn-dyed fabric dyeing tasks are sorted according to the weight adjustment information to obtain the yarn-dyed fabric dyeing sequence. The step of obtaining preset category weight information and adjusting the weights of the first task weight information, the second task weight information, and the third task weight information according to the preset category weight information to obtain weight adjustment information includes: The preset category weight information is such that the preset weight information for color is greater than the preset weight information for material, which is greater than the preset weight information for time. Set the basic coefficients for each category based on the preset category weight information; The category-based coefficients include color-based coefficients, material-based coefficients, and time-based coefficients; The color base coefficient is greater than the material base coefficient, which is greater than the time base coefficient. Obtain the product of the first weight information of the task and the basic color coefficient, the product of the second weight information of the task and the basic material coefficient, and the product of the third weight information of the task and the basic time coefficient, and then obtain the weight adjustment information. The sequence update module is used to obtain the sequence breakpoint based on the task resource consumption data and the task yarn-dyed fabric dyeing sequence, perform sequence segmentation and sorting updates, obtain the updated dyeing sequence, and then obtain the updated dyeing data.

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