Garment production simulation and optimization system based on digital twinning

Abstract

The invention discloses a garment production simulation and optimization system based on digital twinning, and the system comprises a data collection module which is used for collecting various data in a garment production process; the digital twinning model building module is used for building a digital twinning model of garment production according to the data, the digital twinning model comprises a physical entity layer and a virtual model layer, and a data mapping relation between a virtual model and a physical entity is built; the production simulation module is used for carrying out production simulation by utilizing the constructed digital twinborn model, simulating different production scenes and predicting possible problems in the production process; the optimization decision-making module is used for generating an optimization decision-making scheme according to a production simulation result and optimizing a production plan, equipment scheduling and personnel arrangement; the scheduling execution module issues the optimized decision scheme to a production workshop for execution, and remote control and scheduling are realized; the monitoring and evaluation module monitors and evaluates the garment production process in real time. According to the invention, efficient management of garment production and scheduling management of workshops are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of garment production management, and particularly relates to a garment production simulation and optimization system based on digital twinning, which is used for realizing efficient management of garment production and scheduling management of workshops. BACKGROUND

[0002] In the garment production industry, there are many problems in traditional production management and workshop scheduling methods. Traditional methods often rely on experience and manual planning, which makes it difficult to accurately predict problems that may occur during production, such as equipment failure and process delay, resulting in low production efficiency and increased production costs. At the same time, due to the lack of real-time monitoring and precise analysis of the production process, it is difficult to adjust the production plan in a timely manner, and it is difficult to meet the rapidly changing market demand.

[0003] Digital twinning technology, as an emerging technology, can create a virtual model corresponding to a physical entity, and realize comprehensive perception, diagnosis and prediction of the physical entity through real-time data interaction. However, at present, there is no mature system based on digital twinning in the garment production field that can comprehensively realize efficient management of garment production and scheduling management of workshops. SUMMARY

[0004] The purpose of the present application is to provide a garment production simulation and optimization system based on digital twinning to solve the problems raised in the background art.

[0005] To achieve the above purpose, the present application provides the following technical solution: a garment production simulation and optimization system based on digital twinning, comprising the following modules:

[0006] Data acquisition module

[0007] This module is used to collect various data in the garment production process, including but not limited to equipment operating state data (such as equipment speed, temperature, vibration, etc.), process processing time data, raw material usage data, personnel operation data, etc. Data acquisition methods include sensors, RFID tags, industrial cameras, etc. Sensors are installed on production equipment to collect real-time equipment operating parameters; RFID tags are used to track the location and status of raw materials and work-in-process; industrial cameras are used to monitor personnel operations and process completion.

[0008] Digital twinning model construction module

[0009] Based on the collected data, a digital twin model of garment production is constructed. This model includes a physical entity layer and a virtual model layer. The physical entity layer represents the actual garment production workshop, equipment, personnel, etc.; the virtual model layer is a digital mapping of the physical entity layer, created using 3D modeling software to create virtual models of the production workshop, equipment, production lines, etc., and assigning them the same attributes and behaviors as the physical entities. Simultaneously, a data mapping relationship is established between the virtual model and the physical entity to ensure that the virtual model can reflect the state of the physical entity in real time.

[0010] Production simulation module

[0011] Production simulation is performed using a pre-constructed digital twin model. This module can simulate different production scenarios, such as different order combinations, equipment failures, and personnel scheduling schemes. Through simulation, potential problems in the production process are predicted, such as process bottlenecks and equipment conflicts, and the impact of different solutions on indicators such as production efficiency and production costs is analyzed. During the simulation, the state of the virtual model is continuously updated based on real-time collected data to ensure the accuracy of the simulation results.

[0012] Optimize decision-making module

[0013] Based on the results of production simulation, optimized decision-making schemes are generated. This module employs intelligent algorithms, such as genetic algorithms and ant colony algorithms, to optimize production planning, equipment scheduling, and personnel allocation. Optimization objectives include improving production efficiency, reducing production costs, and shortening production cycles. The optimized decision-making schemes consider production constraints, such as equipment capacity, personnel skills, and order delivery dates, to ensure the feasibility of the solutions.

[0014] Scheduling Execution Module

[0015] The optimized decision-making plan is then distributed to the production workshop for execution. This module integrates with production equipment and control systems to enable remote control and scheduling of production equipment. Scheduling commands are sent to equipment controllers via the industrial network to control equipment startup, shutdown, speed adjustment, and other operations. Simultaneously, production execution is monitored in real time, and results are promptly fed back to ensure the smooth implementation of the production plan.

[0016] Monitoring and Evaluation Module

[0017] This module enables real-time monitoring and evaluation of the garment production process. It displays key performance indicators (KPIs) such as production progress, equipment utilization, and product quality through a visual interface. Simultaneously, it performs data analysis to assess production efficiency and quality, identify potential problems and improvement opportunities. Based on the evaluation results, it promptly adjusts and optimizes decision-making processes to achieve continuous improvement in the production process.

[0018] In summary, the technical effects and advantages of this invention are as follows:

[0019] Improve production efficiency: Through production simulation and optimization decision-making, problems in the production process can be identified in advance, production plans and scheduling schemes can be optimized, process bottlenecks and equipment conflicts can be reduced, and production efficiency can be improved.

[0020] Reduce production costs: Optimize equipment and personnel allocation to minimize equipment downtime and personnel waste, thereby lowering production costs. Simultaneously, anticipate equipment failures and perform maintenance proactively to reduce repair costs.

[0021] Enhanced production flexibility: Enables rapid response to changes in market demand, timely adjustment of production plans and scheduling schemes based on different order requirements, thereby improving the company's market competitiveness.

[0022] Improve product quality: Monitor the production process in real time, promptly identify quality problems and take corrective measures to ensure product quality stability. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating a digital twin-based clothing production simulation and optimization system in an embodiment of this application. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] In the description of this disclosure, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," "outer," "top," and "bottom," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this disclosure and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this disclosure. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0027] It should also be noted that all standard parts used in this application are commercially available, and can be custom-made according to the description and drawings. Unless otherwise expressly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this disclosure based on the specific circumstances, and unless explicitly limited, machinery, parts, and equipment can all adopt conventional models in the prior art.

[0028] In this document, the term "comprising" is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0029] Example: Reference Figure 1 The system shown is a digital twin-based garment production simulation and optimization system, which includes a data acquisition module for collecting various data during the garment production process, including equipment operating status data, process processing time data, raw material usage data, and personnel operation data.

[0030] The digital twin model building module constructs a digital twin model of garment production based on the collected data, including a physical entity layer and a virtual model layer, and establishes a data mapping relationship between the virtual model and the physical entity.

[0031] The production simulation module uses a pre-built digital twin model to simulate different production scenarios and predict potential problems during the production process.

[0032] The optimization decision-making module generates optimized decision-making schemes based on the results of production simulation, and optimizes production plans, equipment scheduling, and personnel arrangements.

[0033] The scheduling and execution module distributes optimized decision-making plans to the production workshop for execution, enabling remote control and scheduling of production equipment;

[0034] The monitoring and evaluation module performs real-time monitoring and evaluation of the garment production process, and adjusts and optimizes decision-making schemes based on the evaluation results.

[0035] The specific implementation is as follows:

[0036] Implementation of the data acquisition module: Sensors, such as temperature sensors, pressure sensors, and vibration sensors, are installed on key equipment in the garment production workshop to collect equipment operating parameters in real time. RFID tags are affixed to raw materials and work-in-process, and RFID readers are installed at workshop entrances, processing stations, and other locations to track the location and status of raw materials and work-in-process. Industrial cameras are installed on the production line to monitor personnel operations and process completion. The collected data is transmitted to a data storage server via an industrial network.

[0037] Implementation of the digital twin model building module: Using 3D modeling software such as SolidWorks and 3ds Max, virtual models of garment production workshops, equipment, and production lines are created. During the modeling process, detailed settings are made for the model's geometry, dimensions, materials, and other attributes. Simultaneously, a programming interface connects the virtual model to a data acquisition module, enabling data interaction between the virtual model and the physical entity.

[0038] Implementation of the production simulation module: Different production scenario parameters are set in the virtual model, such as order quantity, order type, and equipment failure probability. The production simulation program is run to simulate the production process. During the simulation, the virtual model's state is updated in real time, and simulation parameters are adjusted based on collected actual data. Through simulation, production efficiency, production costs, and other indicators under different scenarios are analyzed.

[0039] Implementation of the optimization decision-making module: A genetic algorithm is used to optimize production plans and scheduling schemes. First, optimization objectives and constraints are defined, such as maximizing production efficiency, minimizing production costs, and meeting order delivery deadlines. Then, an initial population of production plans and scheduling schemes is generated, and each scheme is evaluated. Through genetic operations such as selection, crossover, and mutation, the population is continuously evolved until the optimal scheme is found.

[0040] Implementation of the scheduling and execution module: Optimized decision-making schemes are converted into equipment control commands and sent to the equipment controller via the industrial network. The equipment controller controls the operation of the equipment according to the commands, such as starting, stopping, and speed adjustment. Simultaneously, the operating status of the equipment is monitored in real time during production to ensure the correct execution of the commands.

[0041] Implementation of the monitoring and evaluation module: Develop a visual monitoring interface to display key indicators in the production process through charts, reports, and other formats. Utilize data analysis tools to mine and analyze production data to evaluate production efficiency and quality. Based on the evaluation results, adjust and optimize decision-making plans in a timely manner to achieve continuous improvement in the production process.

[0042] In summary, this invention improves production efficiency by: identifying problems in the production process in advance through production simulation and optimized decision-making; optimizing production plans and scheduling schemes; reducing process bottlenecks and equipment conflicts; and reducing production costs by: rationally allocating equipment and personnel, reducing equipment idle time and personnel waste; and reducing equipment maintenance costs by predicting equipment failures and performing maintenance in advance. It also enhances production flexibility by: enabling rapid response to changes in market demand; and adjusting production plans and scheduling schemes in a timely manner according to different order requirements, thereby improving the company's market competitiveness. Finally, it improves product quality by: real-time monitoring of the production process; timely detection of quality problems; and corrective measures to ensure product quality stability.

[0043] Finally, it should be noted that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A clothing production simulation and optimization system based on digital twins, characterized in that: include: The data acquisition module is used to collect various data during the garment production process, including equipment operating status data, process time data, raw material usage data, and personnel operation data. The digital twin model building module constructs a digital twin model of garment production based on the collected data, including a physical entity layer and a virtual model layer, and establishes a data mapping relationship between the virtual model and the physical entity. The production simulation module uses a pre-built digital twin model to simulate different production scenarios and predict potential problems during the production process. The optimization decision-making module generates optimized decision-making schemes based on the results of production simulation, and optimizes production plans, equipment scheduling, and personnel arrangements. The scheduling and execution module distributes optimized decision-making plans to the production workshop for execution, enabling remote control and scheduling of production equipment; The monitoring and evaluation module performs real-time monitoring and evaluation of the garment production process, and adjusts and optimizes decision-making schemes based on the evaluation results.

2. The clothing production simulation and optimization system based on digital twins according to claim 1, characterized in that, The data acquisition module acquires data through sensors, RFID tags, and industrial cameras.

3. The garment production simulation and optimization system based on digital twins according to claim 1, characterized in that, The digital twin model building module creates virtual models of production workshops, equipment, and production lines using 3D modeling software, and assigns them the same attributes and behaviors as the physical entities.

4. The garment production simulation and optimization system based on digital twins according to claim 1, characterized in that, During the simulation process, the production simulation module continuously updates the state of the virtual model based on the data collected in real time.

5. The garment production simulation and optimization system based on digital twins according to claim 1, characterized in that, The optimization decision-making module employs intelligent algorithms, such as genetic algorithms or ant colony algorithms, to optimize production plans, equipment scheduling, and personnel arrangements.

6. The garment production simulation and optimization system based on digital twins according to claim 1, characterized in that, The scheduling execution module is integrated with the production equipment and control system, and sends scheduling instructions to the equipment controller through the industrial network.

7. The garment production simulation and optimization system based on digital twins according to claim 1, characterized in that, The monitoring and evaluation module displays key indicators in the production process through a visual interface and analyzes and evaluates production data.

Images