A Study on Service-Oriented Digital Twin Modeling Methods for Weaving Workshops

Abstract

With the rapid development of intelligent manufacturing, Digital Twin technology, as an advanced tool for the intelligentization of weaving workshops, has endowed weaving services with real-time simulation and dynamic optimization capabilities while also placing higher demands on the digital capabilities of workshops. The diverse and multi-manufacturer equipment in weaving workshops exacerbates the complexity of multi-source heterogeneous data. Moreover, traditional data collection methods, which are mostly based on fixed frequencies, increase the network load during real-time high-frequency data reception, making stable, long-term operation difficult. Conversely, low-frequency collection might miss important state changes, thus affecting the quality of weaving big data. To address these issues, this paper proposes a service-oriented Digital Twin modeling method for weaving workshops. This method combines OPC Unified Architecture (OPC UA) with a state change-based data collection approach, utilizing a sliding time window (STW) to identify anomalous data and employing median interpolation to correct these anomalies. The goal is to enhance the representation capability of the Digital Twin in the weaving workshop by improving the data quality. For a specific service of predicting the warp-out time of 288 air-jet looms in a workshop, the average error of the predicted warp-out time using the dynamic data set proposed in this study was reduced from 0.85 h to 0.78 h compared to the static data set based on fixed frequency, an improvement of 8.2%, thereby validating the effectiveness of the proposed method.

1. Introduction

With the rapid advancement of new-generation information technologies, including the Internet of Things (IoT), big data, cloud computing, and artificial intelligence (AI), their integrated application in manufacturing is leading the industry towards a new era of intelligentization. Among these, Digital Twin technology serves as a crucial driver, playing a central role in the intelligent manufacturing system. This concept was first introduced in 2003 by Professor Grieves of the University of Michigan during his “Product Lifecycle Management” (PLM) course, where he envisioned that “a virtual representation of that exact physical system is created in digital space”. In 2011, Professor Grieves further clarified the concept of “Digital Twin (DT)”, defining it as a virtual mirror image that corresponds to a physical entity [1]. Digital Twin technology quickly attracted the attention of the U.S. Air Force Research Laboratory and NASA, who applied it in the aerospace field, significantly enhancing the design accuracy and maintenance efficiency of spacecraft [2]. In 2012, Glaessgen and others [3] provided a broader definition of Digital Twin, describing it as an advanced simulation technology that integrates multi-physics domains and multi-scale analyses. By combining physical models with real-time sensor data, it can reflect the state of physical systems in real time. The characteristics of a Digital Twin, such as virtual–real fusion, real-time data exchange, continuous optimization capability, coverage of all elements and processes, and comprehensive data-driven approach [4], make it an ideal tool for managing the entire lifecycle of industrial products from design and production to maintenance. This concept has rapidly expanded from initial theoretical exploration to widespread application in modern industrial practice, greatly promoting the intelligent transformation of manufacturing [5].

In the manufacturing industry, the application of Digital Twin technology is extensive. Based on research by scholars such as Tao [5], the Digital Twin shop floor (DTS) is envisioned as a five-dimensional model integrating physical entities, virtual entities, twin data, services, and connections. This model aims to achieve real-time interaction and deep integration between the physical and information spaces within the workshop. In the DTS, twin data act as a bridge, enabling the various components of the workshop to enter a continuous optimization loop, thereby achieving optimal management, planning, and control of the workshop. The application scope of the DTS is broad, covering 14 key areas including product design, virtual prototyping, rapid workshop design, process planning, production scheduling optimization, precise logistics distribution, intelligent equipment control, human–machine interaction, assembly and testing, manufacturing energy management, product quality analysis, fault prediction and health management (PHM), and product service systems. Through the DTS, workshops can achieve more intelligent and efficient operations, paving the way for the future of manufacturing.

Based on the above theories and concepts, applying Digital Twin technology in workshops can effectively transform it into practical industrial practice. It not only the improves production efficiency and product quality but also plays a crucial role in workshop management, planning and control, process optimization, resource allocation, and product design, providing a solid technical foundation for the intelligent transformation of manufacturing. Through these applications, Digital Twin technology is gradually changing the traditional landscape of manufacturing, moving towards a more intelligent, efficient, and sustainable direction. For example, Schleich et al. [6] proposed a comprehensive reference model for the Digital Twin of physical products in design and production engineering. This model emphasizes important attributes such as scalability, interoperability, expandability, and fidelity, involving various operations at different stages of the product lifecycle, such as combination, decomposition, transformation, and evaluation. Ricondo et al. [7] proposed a Digital Twin framework for the design and operation of production systems, aiming to achieve comprehensive simulation and management of production processes through the integration of discrete event simulation, monitoring, and optimization technologies. Zhuang et al. [8] proposed a Digital Twin-based intelligent production management and control framework, which involves the real-time collection, organization, and management of physical assembly workshop data; the construction of an assembly workshop Digital Twin; predictive analysis based on Digital Twin technology and big data; and the provision of production management and control services based on Digital Twin technology. Kumbhar et al. [9] proposed a Digital Twin-based framework for detecting, diagnosing, and improving bottleneck resources in manufacturing systems. This framework utilizes utilization-based bottleneck analysis, process mining, and diagnostic analysis techniques to identify key resources affecting system productivity directly from multi-level enterprise data such as production planning and process execution. Zhang [10] proposed a microservice-oriented system architecture that integrates various parts through an end-to-end industrial integration bus, supporting the on-demand development and expansion of software functions, reducing costs, enhancing flexibility and scalability, and adopting the management shell technology to package production units into independent modules that are integrated through standard interfaces, making the production line more flexible and easier to upgrade and retrofit. RabbitMQ was chosen as the service bus to solve problems such as system decoupling and asynchronous message processing, improving the communication performance and reliability. Liu et al. [11] discussed an intelligent process planning method based on Digital Twin technology, focusing on the reuse and evaluation of process knowledge. Kunath et al. [12] explored how to use the Digital Twin of manufacturing systems and proposed a conceptual framework for a decision support system based on the Digital Twin of manufacturing systems to address the increasing complexity of customer demands, rising resource costs, and growing uncertainties in modern manufacturing. This system aims to improve the order management process. Urbina et al. [13] discussed the integration of part data in the Digital Twin of the workshop, supporting the Manufacturing Execution System through mobile and cloud computing technologies. Schmidt et al. [14] described a new simulation experiment approach (SEA) that combines experimental testing and simulation model calibration, aiming to replace extensive physical testing with “virtual” measurements through a Digital Twin. Zheng et al. [15] explored the application of Digital Twin technology in textile intelligent manufacturing. The article first analyzed the differences between Digital Twin technology, Cyber–Physical Systems (CPSs), and digital thread technology, pointing out that Digital Twin technology can bring new development momentum to the textile industry. Shen [16] explored how to use Digital Twin technology to enhance the evenness control and anomaly detection efficiency of drawing frames under the background of intelligent manufacturing. Ye et al. [17] discussed the operational architecture of the pre-spinning workshop based on Digital Twin technology.

Moreover, the Digital Twin shop floor represents an advanced manufacturing model driven by new-generation information technology, where real-time data accuracy and completeness are key to constructing effective Digital Twins. Especially in the weaving industry, where complex, multi-source heterogeneous data and real-time high-frequency transmission requirements are prevalent, data quality becomes a critical factor in determining whether a Digital Twin shop floor can successfully reflect the state of the physical workshop. Therefore, how to efficiently deconstruct and accurately transmit big data, ensuring accurate mapping between virtual models and physical entities, has become a primary issue to be addressed. In the context of the Internet of Things, data collection in workshops was initially realized and has been continuously developing. The IoT involves multiple technologies, including device sensing technology, data integration via general protocols, network transmission, industrial Internet buses, and Ethernet. Researchers have explored these areas, and some have further discussed the application of IoT technologies for research purposes under the concept of Digital Twin. This study explores how to enhance the accuracy and intelligence of Digital Twin modeling for weaving workshops by utilizing data collection solutions that align with the characteristics of the equipment data, based on existing Internet of Things technologies. Roberto et al. [18] describe the technical features and application scenarios of Digital Twin technology in the IoT context, demonstrating its application through four specific cases. The paper also explores future development paths for Digital Twin technology. Lelli [19] examines the methods and applications of achieving the intelligent interoperability of devices in Industry 4.0 through semantic descriptions in the context of the Internet of Things. Atmoko et al.’s [20] research confirmed the excellent performance of the Message Queuing Telemetry Transport (MQTT) protocol in real-time temperature and humidity sensor data collection, providing a feasible solution to this problem. The lightweight nature of the MQTT protocol makes it an ideal choice for efficient data transmission in resource-constrained environments, and it is especially suitable for the real-time collection of massive sensor data in weaving workshops. Li et al. [21] explored the application of wireless sensor networks and IoT technology in manufacturing environment monitoring, emphasizing their potential to improve resource and energy efficiency, providing new perspectives for the intelligent transformation of weaving workshops. Zhang et al. [22] designed an IoT-based online monitoring system that uses magnetostrictive displacement sensors to monitor the equipment status in real time and converts raw data into standard XML format using DOM and Simple API for XML (SAX) technologies, ensuring data accuracy and consistency, which is crucial for constructing accurate Digital Twins. Ma et al. [23] proposed an optimized system based on Niagara Files (NiFi), Mini Niagara Files (MiNiFi), Spark Streaming, HBase, and Hive technologies, specifically addressing the inefficiencies in traditional data architecture for collection, parsing, and storage, improving the real-time data transmission performance and architecture scalability, and providing efficient solutions for data management in Digital Twin workshops. Zhao et al. [24] focused on the application of industrial big data in intelligent manufacturing, demonstrating the importance of industrial big data in production control through case studies while also pointing out challenges in data preprocessing and quality assurance, which are obstacles that must be overcome to construct effective Digital Twins. Glatt et al.’s [25] material flow Digital Twin modeling method based on physical simulation provides technical support for the adaptability and flexibility of small-batch customized products, further enriching the application scenarios of Digital Twins in manufacturing. Li et al. [26] proposed an OPC UA information model construction plan, providing a standardized data interaction framework for the textile dyeing and finishing industry, helping to achieve the interconnection of workshop equipment. Liu et al.’s [27,28,29] research on Cyber–Physical Machine Tools (CPMTs) promotes the intelligent progress of a new generation of machine tools through the combination of OPC UA and MTConnect protocols, optimizing production processes and enhancing workshop automation. Wang [30] adopted the MTConnect protocol, which serves as the foundation for equipment interconnectivity, not only standardizing data communication but also designing task interfaces for interactions between equipment and clients, which are significant for constructing efficient data transmission mechanisms. Similarly, Wang [31] addressed the integration and monitoring issues of heterogeneous systems in manufacturing workshops by applying the MTConnect standard, achieving the unified management of CNC systems from different manufacturers and models, improving the workshop flexibility and response speed. Kong et al. [32] proposed a data construction method based on Digital Twins by designing a framework that includes data representation, organization, and management modules, providing a structured solution to the complexity of manufacturing data and further promoting the mature application of Digital Twin workshops.

In summary, Digital Twin technology, as an integrated application of new-generation information technologies, is profoundly transforming the intelligence level of manufacturing. Since the concept of Digital Twin was first proposed, it has undergone a development process from theoretical exploration to widespread application. With its powerful data-driven capabilities and cross-disciplinary integration advantages, Digital Twin technology is leading manufacturing towards a more intelligent, efficient, and sustainable direction, becoming a key force in promoting Industry 4.0 and the intelligent manufacturing revolution. The core of Digital Twin technology lies in establishing high-fidelity virtual models in the virtual space to simulate workshop production activities, thereby providing intelligent services for specific production activities. Ensuring the quality of the data in Digital Twin workshops is crucial, characterized by the diversity of equipment types and the complex and variable manufacturers and models involved. During the digitalization process, different semantic standards for the same equipment type and similar semantics for different equipment pose significant challenges. Additionally, traditional data transmission processes with too high a frequency can lead to a large volume of data, increasing the probability of network errors, while too low a frequency can affect the high fidelity of data transmission characteristics.

2. Materials and Methods

2.1. Architecture of Digital Twin System in Weaving Workshop

As shown in Figure 1, the weaving Digital Twin workshop system architecture is based on a universal five-dimensional model. This model is composed of physical entities, virtual entities, the twin data layer, the application service layer, and the connections between them. By establishing connections between these layers, the system achieves a virtual–real integration, enabling high-fidelity mapping of the workshop and intelligent workshop management.

1.

Physical Entities

Physical entities include the essential elements required for an intelligent workshop, primarily encompassing people, machines, materials, methods, and the environment. These elements are digitally represented, including data from sensors on equipment, PLC computations, and data transmitted through network protocols. Data that cannot be directly sensed but are necessary for intelligent operations are uploaded through human–machine interaction modules.

2.

Virtual Entities

Virtual entities are models constructed on the foundation of physical entities, including geometric models, physical models, action models, and rule models. The geometric model contains the three-dimensional shapes and spatial positions of equipment and the environment. The physical model includes the physical properties of workshop elements, such as operational status, output, and speed. The action model defines the mechanisms behind physical quantities, such as the relationship between motor speed and mechanical transmission ratios for operational speed. The rule model defines the operational logic within and between different processes in the workshop, such as scheduling rules. Virtual entities update geometric and physical models when physical entities change and continuously update action and rule models based on twin data and service requirements.

3.

Twin Data Layer

The twin data layer integrates all data from physical and virtual entities, including real-time data, historical data, model data, rule data, and service process data. It facilitates data exchange with the real world through network protocols and provides data storage and management functions, supporting subsequent application services.

4.

Application Service Layer

The application service layer offers intelligent services based on workshop management needs, leveraging twin data and virtual entities to achieve goals such as visualization, fault prediction, and warp-out time prediction.

5.

Connections

The connections within the five-dimensional Digital Twin model represent the interactions and collaborations between different modules. The following explanations describe the roles of these connections from the perspective of the actions initiated by each module:

Physical entity: Within the physical entity, data is sensed through sensors, computed using PLCs, and output based on standard protocols. In the industrial field, this data is transmitted to the Digital Twin data layer via control buses, achieving the initial digitization of the physical entity. Elements such as workshops and internal equipment utilize digital representation methods to output simple mathematical models, such as geometric models, to the virtual entity.

Twin database: The twin database receives and stores the data transmitted from both physical and virtual entities, providing the necessary data support for modeling within the virtual entity. During the operation of specific services, the twin database not only supplies data for computation but also receives production instructions issued based on these services, transmitting them to the physical entity’s workshop via serial connections.

Virtual entity: The virtual entity is a comprehensive model constructed based on the twin database using 3D modeling, digital modeling, and other techniques. The models within the virtual entity can continuously update and optimize based on the ongoing data flow, resulting in increasingly accurate representations. Additionally, the new data generated during the modeling and computation processes within the virtual entity are fed back into the twin database, supporting further model improvements and service provision.

Intelligent services: The intelligent services module leverages real-time and historical data from the twin database to provide intelligent functions supported by the virtual entity models. These services can issue production instructions to the physical entity through interfaces with the twin database. Throughout the service process, human involvement may also impose additional requirements on the models within the virtual entity, leading to the continuous refinement and optimization of the models to better support specific intelligent services.

Through the active actions and collaboration of these modules, the Digital Twin system achieves a close interconnection between physical and virtual entities, offering comprehensive support from data collection to intelligent decision making.

Overall, the operational logic of the Digital Twin system is to construct virtual entities that mirror physical entities based on physical entity data and various mechanisms. By combining these with static geometric–physical models and running rules stored in the twin data, the system provides intelligent services on the application platform. In summary, data are the core of the Digital Twin system’s operation, and the authenticity and accuracy of the data are crucial for ensuring high-fidelity mapping of virtual entities and highly effective intelligent services.

2.2. Heterogeneous Data Sources from Multiple Sources

The weaving workshop is generally divided into several main processes: warping, sizing, drawing in, weaving, and fabric inspection. The workshop involves a wide variety of equipment, and the production process generates a vast amount of complex and disorganized data. Additionally, multiple job roles are involved, and the data generated from workers’ activities serve as a crucial supplement to the equipment data, contributing significantly to the overall data of the weaving workshop.

1.

Digitization Process

The digitization process is primarily driven by equipment manufacturers who produce devices with sensing, computing, and communication capabilities. These devices use sensors and PLC to collect production data in real time and transmit them to upper-level systems through network protocols. The core of the digitization process is to transform the state and operations of physical equipment into digital information that can be recorded and analyzed.

2.

Informatization Process

The informatization process is managed by IT personnel who handle the informatization of workshop equipment, ensuring data collection and management through protocols and networks. Additionally, to meet production management needs, extra data fields (such as management data) are manually uploaded via human–machine interaction modules. These data include, but are not limited to, production plans, process parameters, and equipment maintenance records.

3.

Intelligentization Process

The intelligentization process builds on the foundation of real-world knowledge bases, expert mechanism libraries, and real-time and historical data from informatization. By using modeling techniques and machine learning methods, it explores the mechanisms and rules within the workshop. Ultimately, it provides services for intelligent production and management through platforms and terminals. The goal of the intelligentization process is to enhance the production efficiency, optimize resource allocation, improve the product quality, and achieve intelligent control of the production process through data analysis and intelligent decision making.

By utilizing the above framework, the weaving workshop can achieve comprehensive coverage from physical equipment to data collection and then to intelligent analysis. This ensures the real-time nature, accuracy, and completeness of the data, thereby providing a solid foundation for the intelligent management of the workshop.

Table 1 presents the information model for some warping machines and air-jet looms in a weaving workshop. The information model is defined by the equipment type, equipment model, manufacturer, protocol field, and physical meaning. An analysis of the table reveals the following characteristics of the weaving workshop equipment data:

• Same equipment type—same physical meaning—different protocol fields;
• Different equipment—different physical meaning—same protocol fields;
• Different units of measurement and precision.

Table 1. Equipment model, protocol, and physical meaning.

Equipment Type	Equipment Model/Manufacturer	Protocol Fields	Physical Meaning (Unit)
Warping machine	Model/manufacturer 1	speed	Running speed of the warping machine (m/min)
	Model/manufacturer 2	yield	Yield of the warping machine (km)
	Model/manufacturer 1	run_speed	Running speed of the warping machine (m/min)
	Model/manufacturer 2	length	Yield of the warping machine(m)
Air-jet loom	Model/manufacturer 1	speed	Running speed of the air-jet loom (r/min)
	Model/manufacturer 2	yield	Yield of the air-jet loom (m)
	Model/manufacturer 1	rpm	Running speed of the air-jet loom (r/min)
	Model/manufacturer 2	product_meter	Yield of the air-jet loom (m)

Table 1. Equipment model, protocol, and physical meaning.

Equipment Type	Equipment Model/Manufacturer	Protocol Fields	Physical Meaning (Unit)
Warping machine	Model/manufacturer 1	speed	Running speed of the warping machine (m/min)
	Model/manufacturer 2	yield	Yield of the warping machine (km)
	Model/manufacturer 1	run_speed	Running speed of the warping machine (m/min)
	Model/manufacturer 2	length	Yield of the warping machine(m)
Air-jet loom	Model/manufacturer 1	speed	Running speed of the air-jet loom (r/min)
	Model/manufacturer 2	yield	Yield of the air-jet loom (m)
	Model/manufacturer 1	rpm	Running speed of the air-jet loom (r/min)
	Model/manufacturer 2	product_meter	Yield of the air-jet loom (m)

These phenomena of multi-source heterogeneous data are due to the different protocol standards established during the equipment development stage, leading to semantic confusion for subsequent data collection personnel.

Table 2 shows the naming conventions for real-world meaning fields in different systems during the informatization process of a weaving workshop. An analysis of the table reveals the following characteristics of the weaving workshop system:

• Same real-world meaning—different systems—different field definitions;
• Different real-world meanings—different systems—same field definition;
• Different real-world meanings—same system—same field definition.

Table 2. Definition of different system fields.

Physical Meaning	System	Fields
Process sheet	ERP	Process_sheet
	MES	ProcessForm
	SCADA	WorkList
Speed of the jet loom	MES	Jet_rpm
	SCADA	LoomSpeed
Speed of the warping machine	MES	WarpSetSpeed
	SCADA	Warping_speed

Table 2. Definition of different system fields.

Physical Meaning	System	Fields
Process sheet	ERP	Process_sheet
	MES	ProcessForm
	SCADA	WorkList
Speed of the jet loom	MES	Jet_rpm
	SCADA	LoomSpeed
Speed of the warping machine	MES	WarpSetSpeed
	SCADA	Warping_speed

These phenomena not only exacerbate the problem of multi-source heterogeneity but also make it difficult for intelligent researchers to understand the data during their studies.

To address these issues, data collection and management personnel need to standardize different protocol fields to ensure consistency and accuracy during the data collection, transmission, and storage processes. Additionally, establishing a unified semantic model is necessary to help parse and convert data from different devices, ensuring their usability and interoperability on the data platform. Moreover, strengthening data governance through data cleaning, transformation, and integration will ensure data consistency and accuracy. These measures can effectively reduce the confusion caused by multi-source heterogeneous data and improve the efficiency and accuracy of intelligent research.

2.3. Geometric and Physical Modeling Method Based on OPC UA

The Digital Twin geometric–physical model describes the geometric dimensions, spatial positions, assembly relationships, and physical properties of equipment or research objects. This model is a digital representation of the physical entities defined and established in a virtual space. Using 3D modeling software and other tools, it can be rendered to create a three-dimensional scene that spatially aligns with all entities in the real environment. During the modeling preparation stage, the following steps are undertaken based on the actual information of the workshop’s physical space:

1.

Collect workshop layout information:

• Gather layout plans of the workshop.
• Obtain design drawings/models of parts and equipment.
• For components without existing design information, conduct on-site measurements and record the details.

2.

Optimize model structure:

• Based on the collected information and specific manufacturing resource modeling requirements, optimize the model structure.
• For example, equipment like looms and AGVs (Automated Guided Vehicles) do not require detailed internal structures; instead, record their external shapes, dimensions, materials, and any movable parts.

By integrating the above information, a comprehensive and accurate digital model can be created. Figure 2 and Figure 3 illustrate a real scene from a workshop in a weaving enterprise and its corresponding Digital Twin 3D model.

OPC UA is a platform-independent, cross-vendor communication protocol with advantages in semantic interoperability, security, and scalability. It supports data exchange and semantic understanding between devices from different manufacturers, provides data encryption and authentication mechanisms to ensure data transmission security, and supports the dynamic addition of new devices and data types to meet the ever-changing production needs. This study uses OPC UA to achieve the unified management and efficient transmission of multi-source heterogeneous data in the weaving workshop, thereby improving the data quality and reliability of intelligent applications.

Faced with multi-source heterogeneous data in the weaving workshop, semantic standardization ensures that the information output by different equipment manufacturers and system providers is semantically consistent, facilitating a smooth intelligent transformation process. During the intelligentization process, new fields with smaller dimensions or other spaces may be introduced, such as the average shift speed. These fields are not directly sensed by the workshop but are derived from further statistics or research. OPC UA provides the capability for semantic unification and the extension of physical models, ensuring the efficient transmission and management of multi-source data under a unified semantic framework.

As shown in Figure 4, the comprehensive air-jet loom model includes all fields of the looms, effectively avoiding data collection difficulties and the complexity of constructing intelligent services due to semantic understanding issues. By employing these methods, the semantic understanding problems posed by multi-source heterogeneous data in the weaving workshop can be effectively resolved, enhancing the efficiency and accuracy of data collection and intelligent applications. This provides a solid technical foundation for the intelligent transformation of the weaving workshop.

In conclusion, OPC UA has the ability to unify and extend semantics, which, combined with its robust security and scalability features, makes it an ideal solution for managing the complexities of multi-source heterogeneous data in weaving workshops. This approach ensures reliable data transmission and management, facilitating the workshop’s transition towards intelligent manufacturing.

2.4. Physical Data Characteristics of Weaving Workshop

As previously analyzed, weaving workshop data sources are extensive. This study uses data from a specific model of air-jet loom to analyze the characteristics of weaving workshop data. As a manufacturing unit in the weaving process, the loom weaves warp beams prepared in preceding processes and weft yarns provided by weft yarn bobbins into fabrics, serving as the final step in fabric production. Over time, modern looms have developed comprehensive parameter input, production data calculation, statistical, and upload functions. Weavers input the prepared process order data into the equipment via the loom’s human–machine interface module, and after adjustments by maintenance workers, normal production can begin.

During normal production, loom data can be divided into two categories: static setup data and dynamic production process data. Static setup data refer to data that do not change throughout a shift or the complete production of a single product type. This includes production processes, equipment setup attributes, and weaver personnel information. Dynamic production process data refer to data that continuously change during the production process. This includes the loom speed, loom status, current output, current energy consumption, and operating efficiency.

Loom processing can be divided into three stages, from smallest to largest: the process of weaving a specific length of fabric and dropping the fabric beam (single warp beam producing a certain length of fabric), the entire process of producing fabric from a single warp beam (complete production of a single warp beam), and continuous production of the same product type, which involves switching to a different product type after completing the production of multiple warp beams of the same type (continuous production of the same product type).

During normal production, looms frequently stop due to reasons related to warp and weft yarns or mechanical issues. Between continuous productions of the same product type, warp beams are joined and loaded for production. When multiple warp beams of the same product type are completed or when switching product types, the harness frame needs to be changed for production.

The Figure 5 shows the complete shift data of the output, loom speed, and air consumption rate for a specific loom at a collection frequency of one minute (the blue circles represent the production recording points, with lines connecting the points in the production time series; the red crosses indicate the speed recording points, and since the speed data are discrete, they are set as discrete points without connecting lines in the time series). In this shift, the loom went through four major stages: stable production of Product 1, loading of the warp beam for Product 2, loom adjustment, and stable production of Product 2. From the overall view of the graph, we can observe that during the stable operation phase, the production output shows a good linear growth trend when the speed is not zero, while the speed fluctuates within a small range around the set speed. However, on a more localized level, we notice anomalies in both the production output and speed data points that deviate from the expected trend. These anomalies may arise from sensor detection errors, microcomputer calculation errors, network transmission issues, or protocol parsing errors. Therefore, an appropriate data cleaning method is necessary to identify and correct these anomalous data points.

This analysis provides insights into how looms operate and how data are categorized and used, helping to improve the understanding and management of the weaving workshop’s intelligent processes.

In the figure, the overall trend of the output data shows continuous growth during the stable production of both product types. During the beam loading stage, the loom is not operating, and output growth pauses. In the loom adjustment stage, due to insufficient warp tension, the loom speed is relatively slow, and the output growth is also slow. The involvement of manual intervention makes the growth linearity more complex compared to the stable running stages. The loom speed shows fluctuations during non-stop periods due to mechanical reasons, which prevent the maintenance of a completely consistent production pace. Additionally, during the adjustment stage, the loom speed, influenced by manual intervention, does not exhibit normal mechanical performance, with higher variability and stop frequencies than during normal operation.

From the perspective of shift time progression, loom data are arranged in a time series. Among the loom data, the output continuously increases over time, reflecting a strong temporal correlation, while the loom speed exhibits short-term discrete jumps over time. As shown in Figure 6, due to issues with equipment sensors, PLC calculation module errors, and communication anomalies, the data show outliers that deviate from the overall data trends.

2.5. Method for Establishing Confidence Interval of Data Cleaning

The correctness of data is an important factor for the Digital Twin model to correctly describe the production process. Data cleaning becomes an important process for the Digital Twin modeling process. Data cleaning steps mainly include missing value handling, abnormal value detection, and repair. In the process of data cleaning, confidence intervals can be used as a means to evaluate and improve the data quality and reliability. This is because confidence intervals can help identify and handle outliers, evaluate data consistency, and ensure the reliability of statistical inference.

Assuming we have a sample data set, the sample mean $\overline{\mathcal{x}}$ is a point estimate of the population sample mean. Assuming an overall standard deviation $\mathsf{\sigma }$, the sample size is $\mathcal{n}$. The confidence interval (CI) of the data can be calculated using Formula (1). The underlying principle of confidence intervals is based on the sampling distribution and the Central Limit Theorem in statistics, providing a range within which the true population parameter lies with a specified probability, known as the confidence level. By using the standard error of the sample statistic and the critical values from the standard normal distribution or other relevant distributions, confidence intervals quantify the uncertainty of point estimates, reflecting the probability that the population parameter will fall within this range in repeated sampling, thereby making the estimation of the population parameter more reliable and comprehensive.

$$\mathrm{C}\mathrm{I}=\overline{\mathcal{x}}\pm \mathrm{Z}\cdot \left({\displaystyle \frac{\mathsf{\sigma }}{\sqrt{\mathcal{n}}}}\right)$$

(1)

Here, $\overline{\mathcal{x}}$ is the sample mean, $\mathrm{Z}$ is the Z value corresponding to the confidence level (for example, the Z value corresponding to the 95% confidence level is 1.96), $\mathsf{\sigma }$ is the sample standard deviation, and $\mathfrak{n}$ is the sample size.

Based on the timestamps in the data collected from equipment, the credibility intervals of the data can be determined through trend analysis and time series modeling. A combined approach using sliding time windows and median filtering can be employed:

1.

Time window processing:

Define the event window size to $\mathrm{W}$ (for example, $\mathrm{W}=5$).

For each data point ${\mathrm{x}}_{\mathrm{i}}$, calculate the sliding median ${\mathrm{M}}_{\mathrm{i}}$.

2.

Calculate the sliding median and standard deviation:

For each data point ${\mathrm{x}}_{\mathrm{i}}$, calculate the median of the front and rear $\mathrm{W}$ data points, ${\mathrm{M}}_{\mathrm{i}}$:

$${\mathrm{M}}_{\mathrm{i}}=\mathrm{m}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{a}\mathrm{n}({\mathrm{x}}_{\mathrm{i}}-\mathrm{W},{\mathrm{x}}_{\mathrm{i}}-\mathrm{W}+1,\cdots ,{\mathrm{x}}_{\mathrm{i}}+\mathrm{W},)$$

Calculate the standard deviation of the data in the window ${\mathsf{\sigma }}_{\mathrm{i}}$:

$${\mathsf{\sigma }}_{\mathrm{i}}=\sqrt{{\displaystyle \frac{1}{2\mathrm{W}+1}}\sum _{\mathrm{j}=\mathrm{i}-\mathrm{W}}^{\mathrm{i}+\mathrm{W}}{({\mathrm{x}}_{\mathrm{j}}-{\mathrm{M}}_{\mathrm{i}})}^2}$$

(2)

3.

Define the confidence interval (for example, 95% CI):

$$\mathrm{U}\mathrm{p}\mathrm{p}\mathrm{e}\mathrm{r} \mathrm{B}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{d}={\mathrm{M}}_{\mathrm{i}}+1.96·{\displaystyle \frac{{\mathsf{\sigma }}_{\mathrm{i}}}{\sqrt{2\mathrm{W}+1}}}$$

(3)

$$\mathrm{L}\mathrm{o}\mathrm{w}\mathrm{e}\mathrm{r} \mathrm{B}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{d}={\mathrm{M}}_{\mathrm{i}}-1.96·{\displaystyle \frac{{\mathsf{\sigma }}_{\mathrm{i}}}{\sqrt{2\mathrm{W}+1}}}$$

(4)

4.

Judge the abnormal value:

If data point ${\mathrm{x}}_{\mathrm{i}}$ exceeds the confidence interval, mark it as an outlier:

$${\mathrm{x}}_{\mathrm{i}}>\mathrm{U}\mathrm{p}\mathrm{p}\mathrm{e}\mathrm{r} \mathrm{B}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{d} \mathrm{o}\mathrm{r} {\mathrm{x}}_{\mathrm{i}}<\mathrm{L}\mathrm{o}\mathrm{w}\mathrm{e}\mathrm{r} \mathrm{B}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{d}$$

5.

Use the median to fill outlier data:

For data point ${\mathrm{x}}_{\mathrm{i}}$, marked as an outlier, fill the outlier with the sliding median ${\mathrm{M}}_{\mathrm{i}}$:

$${\mathrm{x}}_{\mathrm{i}}{=\mathrm{M}}_{\mathrm{i}}$$

2.6. Data Processing Evaluation Indicators

1.

Mean Absolute Error (MAE)

The mean absolute error is a measure of errors between paired observations expressing the same phenomenon. It is the average of the absolute differences between the predicted and actual values. The calculation formula is as follows:

$$\mathrm{M}\mathrm{A}\mathrm{E}={\displaystyle \frac{1}{\mathcal{n}}}{\sum }_{\mathcal{i}=1}^{\mathcal{n}}|{\mathrm{y}}_{\mathcal{i}}-{\mathrm{ŷ}}_{\mathcal{i}}|$$

(5)

where

$\mathcal{n}$ is the number of observations;

${\mathrm{y}}_{\mathcal{i}}$ is the actual value;

${\mathrm{ŷ}}_{\mathcal{i}}$ is the predicted value.

2.

Mean Squared Error (MSE)

The mean squared error measures the average of the squares of the errors, which is the average squared difference between the estimated values and the actual values. The calculation formula is as follows:

$$\mathrm{M}\mathrm{S}\mathrm{E}={\displaystyle \frac{1}{\mathcal{n}}}{\sum }_{\mathcal{i}=1}^{\mathcal{n}}{({\mathrm{y}}_{\mathcal{i}}-{\mathrm{ŷ}}_{\mathcal{i}})}^2$$

(6)

3.

Root Mean Squared Error (RMSE)

The root mean squared error is the square root of the mean squared error. It represents the square root of the second sample moment of the differences between predicted values and observed values, and thus, it is a measure of the differences between the values predicted by a model and the values observed. The calculation formula is as follows:

$$\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}=\sqrt{{\displaystyle \frac{1}{\mathcal{n}}}{\sum }_{\mathcal{i}=1}^{\mathcal{n}}{({\mathrm{y}}_{\mathcal{i}}-{\mathrm{ŷ}}_{\mathcal{i}})}^2}$$

(7)

2.7. Action Modeling Based on Device State Transition

The Digital Twin action model describes the operational changes and mechanisms of a physical model over time. Using an air-jet loom as an example, during normal operation, the output is a linear function of the loom’s running speed and the weft density, and it drops to zero when the loom is not running. The running speed is the product of the motor speed and the mechanical transmission ratio. The air consumption rate is a complex relationship influenced by several factors, including warp and weft yarns, weft density, and running speed.

Building an action model that closely mirrors physical reality is crucial for the high fidelity of the virtual entity. With the aid of regression and other analytical algorithms that combine real-time and historical data, a highly accurate action model can be developed. This model, combined with static geometric–physical models, enables the simulation of dynamic operations in a real-world workshop driven by data.

As analyzed in Section 2.4, the running speed of an air-jet loom is relatively constant during stable operation, and the output growth is fairly linear. However, during the initial stages of product type change or warp beam replacement, the warp tension is unstable, requiring a slow startup until normal production resumes. Additionally, frequent stoppages occur even during stable operation due to issues with warp and weft yarns or mechanical problems. The transitions from running to stoppage and vice versa disrupt the constant speed and linear output growth. The Figure 7 analyzes the disruption of the linear relationship of the output around stoppages during normal production.

Most workshop data are collected in real time at fixed frequencies. High-frequency data collection poses significant challenges to the workshop’s network, collection servers, and storage servers, making stable, long-term operation difficult. On the other hand, low-frequency data collection cannot capture the characteristics of the workshop and equipment production processes. As shown in the figure, the output (number of weft insertions) and speed are sampled at second-level intervals before and after a loom stoppage (the blue discrete circles in the graph represent the actual speed values, the green line represents the actual production values, and the red line connects the production data points collected at 10-s intervals). The points marked with a cross indicate transitions between running and stoppage states. During these transitions, the speed does not instantly return to normal running or stoppage states but exhibits fluctuations due to mechanical adjustments. Therefore, the output stagnates during stoppage because the weft insertion mechanism is inactive. Upon startup, the output shows nonlinear growth due to the loom’s drive program and, within a short time, exceeds the normal running speed, resulting in complex linear growth. As illustrated by the black vertical line in the figure, if the loom data are collected at a relatively low frequency greater than a second, and if this happens during a phase of nonlinear output growth, the linear relationship of the output will be disrupted. Due to frequent stoppages caused by warp and weft yarn issues or mechanical failures, the number of stoppages in a 12-h shift can range from dozens to hundreds.

Low-frequency data collection can result in the loss of loom-state transition characteristics. For instance, an analysis of the data from a single loom during a specific shift shows the loss rate of state transitions at different collection frequencies. The loom starts in an operational state and ends in an operational state, with a total of 61 stoppages recorded. A state transition is defined as a change from running to stoppage (1 to 0) or stoppage to running (0 to 1), making a total of 122 state transitions during the shift. Using low-frequency data collection at 1-min and 5-min intervals, the number of detected state transitions was calculated. The results, shown in

Table 3. The loss rate of state by low-frequency data acquisition.

Interval	1 to 0 Changes	0 to 1 Changes	Rate of Loss
1 min	41	41	32.8%
5 min	18	18	70.5%

, indicate that 1-min data collection missed 32.8% of the loom’s state transition characteristics, while 5-min data collection missed 70.5% of these characteristics. Such cumulative errors significantly impact the fidelity of the Digital Twin virtual model. To maintain a fixed frequency and ensure high-fidelity simulation performance of the Digital Twin virtual model, a second-level data collection frequency is necessary.

The general data cominllection strategy for weaving production enterprises involves connecting equipment physically located in the same workshop through a local area network (LAN). A data collection server set up within the LAN traverses the network to collect data from each piece of equipment. In small- and medium-sized weaving production enterprises, a workshop typically has 4–6 warping machines, 2–3 sizing machines, 200–300 looms, as well as transport auxiliary equipment and human–machine interaction terminals, all connected within the same network. Due to the ongoing digital transformation and the initial stages of intelligent development in manufacturing enterprises, the cost of data collection cannot support second-level frequency data collection for such a number of devices.

To reduce the network load and more accurately capture state transitions, this study proposes a data collection strategy based on loom-state transitions. High-frequency data collectors are deployed at state change nodes (with the collection frequency and duration determined by the loom’s capabilities). During normal operation or extended stoppages, low-frequency data collectors are used.

Under this method, data collection can identify and distinguish between non-steady-state data collected immediately after a state change and steady-state operation data. This differentiation is crucial for creating more refined behavior models by distinguishing between stable operation data and state transition data during the modeling process.

An experiment was designed to compare the proposed method with the common 1-min, 5-min, and second-level data collection frequencies in weaving workshops. In this study, the high-frequency collector is set to collect data every second for 10 s, while the low-frequency collector is set to collect data every 2 min. The comparison dimensions include the data volume and state capture rate.

2.8. Warp-Out Time Prediction

The loom warp-out time refers to the remaining time for the current warp beam to complete production. In the weaving industry, the complete production of a warp beam typically takes 9–20 days, while the time from order placement to warp beam preparation usually takes 2–3 days. Traditionally, predicting the warp-out time requires scheduling personnel to manually inspect the workshop and make estimations. Since a warp beam consists of thousands of yarns, each several kilometers long, it has significant volume and weight, necessitating ample time for transportation to ensure uninterrupted loom operation. Therefore, accurate warp-out time prediction is crucial for production enterprises.

The research team previously developed a model to predict the warp-out time for air-jet looms using data collected from the weaving workshop [33]. The basic warp-out time prediction model is as follows:

• Running time (T_r):

$${\mathrm{T}}_{\mathrm{r}}={\mathrm{T}}_{\mathrm{u}}-{\mathrm{T}}_{\mathrm{s}}$$

(8)

• Average production capacity (P):

$$\mathrm{P}=\mathrm{V}\times \left(1-{\displaystyle \frac{{\mathrm{T}}_{\mathrm{s}}}{{\mathrm{T}}_{\mathrm{u}}}}\right)$$

(9)

• Warp-out time (T_w):

$${\mathrm{T}}_{\mathrm{w}}={\displaystyle \frac{\mathrm{L}}{\mathrm{V}}}\times {\displaystyle \frac{1}{1-\frac{{\mathrm{T}}_{\mathrm{s}}}{{\mathrm{T}}_{\mathrm{u}}}}}$$

(10)

where

${\mathrm{T}}_{\mathrm{u}}$ is the unit time;

${\mathrm{T}}_{\mathrm{s}}$ is the stoppage time within the unit period;

$\mathrm{V}$ is the running speed;

$\mathrm{L}$ is the remaining length of the warp beam.

This model allows for a more accurate and automated prediction of the loom warp-out time, significantly enhancing the efficiency of production scheduling and resource allocation and ensuring continuous and smooth loom operation.

3. Results

3.1. The Results of the Data by Sliding Time Window and Median Data

The processing result of the yield data is shown in Figure 8, and the evaluation indicators for the data are shown in

Table 4. Comparison of yield data before and after processing.

Data	MAE	MSE	RMSE
Original data	20.61	6860.52	82.82
Rolling median filled	0.51	1.28	1.13

. As shown in the figure (the blue line connecting the circular dots in the graph represents the collected production data values, the green line connecting the triangular points represents the actual production values, the purple line connecting the diamond-shaped points represents the smoothed production values filled in using the sliding time window combined with median filtering, and the red crosses indicate the anomalous data points identified using the current method), the confidence interval calculated using the method of setting a sliding time window identified the vast majority of deviated erroneous yield values. Through analysis in the table, it can be seen that the MAE decreased from 20.61 to 0.51, indicating that the processed data are closer to the true value. The MSE decreased from 6860.52 to 1.28, and the RMSE decreased from 82.82 to 1.13, indicating that the processed data are not only closer to the true value but also significantly reduce the error. This indicates that the method can identify and insert the vast majority of values with larger errors in the processing of yield values.

The processing result of the rpm data is shown in Figure 9, and the evaluation indicators for the data are shown in

Table 5. Comparison of production data before and after processing.

Data	MAE	MSE	RMSE
Original data	18.44	8773.85	93.66
Rolling median filled	16.76	12,521.01	111.89

. As shown in the figure, the method of calculating the confidence intervals using a sliding time window and filling it with median values identified most of the rpm deviations from the global trend (the blue circles in the graph represent the collected speed data values, the green triangles represent the actual speed values, the yellow squares represent the smoothed speed values obtained through sliding window processing, and the red crosses indicate the anomalous data points identified using the current method). However, the filled median values resulted in many points deviating from the actual values. The analysis shows that the MAE decreased from 18.44 to 16.76, indicating that the processed values are closer to the true values. However, both the MSE and RMSE increased, suggesting that the combined sliding time window and median filling method resulted in greater errors compared to the initial collected data.

As discussed in Section 2.7, the rpm of the loom is relatively stable during normal operation, with only minor fluctuations. However, during state transitions, the rpm values may deviate from the global trend but still be correctly collected. Therefore, this method corrected the erroneous global deviations but also treated the transitional data during state changes as erroneous, filling them with median values, which led to an increase in the MSE and RMSE.

3.2. Comparison of State Transition-Based Collection Strategy with High- and Low-Frequency Collection

Data collection was conducted on 288 air-jet looms at a branch factory of a weaving enterprise in Jinhua, China. The data collection lasted for one week, with a 12-h shift cycle for the statistics on data volume and loom-state recognition rate. The statistical results are shown in

Table 6. Comparison of data volume and recognition rate under different collection strategies.

The Frequency of Data Acquis	Number of Records	The Recognition Rate of Loom’s State
1 Min	720	68.4%
5 Min	144	33.7%
1 S	43,200	100%
State transition-based collection strategy	1176	100%

. This result only includes the equipment that remained powered on for the entire shift. On average, each loom experienced 43.7 stoppages per shift.

The state transition-based collection strategy strikes a balance between data resolution and network load. By capturing high-resolution data only at critical points (state changes) and using low-frequency collection during stable states, it ensures that important dynamic behaviors are recorded without overwhelming the network and data storage systems. This approach enhances the accuracy of behavior models and improves the overall efficiency and reliability of the data collection process in weaving production enterprises.

3.3. Comparison of Warp-Out Time Prediction Models under Different Data Sets and Service Implementation

In previous research, the machine-off time prediction was conducted using a static data set collected at fixed intervals. This method showed poor performance in capturing state changes in the operation of air-jet looms and lacked scientific analysis of stoppage duration distribution. In the current study, using a behavior model-based state transition data collection scheme, data from 288 air-jet looms were collected. Applying the same machine-off time prediction model to this dynamic data set improved the average prediction error from 0.85 h to 0.78 h, an improvement of 8.2%.

As shown in Figure 10, to facilitate management, the predicted machine-off times are displayed in a table format within the management system. This allows for easy filtering of looms expected to run out of warp beams in less than 2–3 days, enabling the scheduling of preparatory processes in advance.

As shown in Figure 11, the machine-off time predictions are also integrated into a 3D Digital Twin model, providing a visual representation of the workshop. This visualization service allows management personnel to monitor loom operations and warp beam transportation across different times and spaces, enhancing the decision-making process. In the practical case, we first used SolidWorks v2022 software to create 3D models of the auxiliary production equipment. These models were then imported into 3ds MAX for the overall modeling and rendering of the weaving workshop, ensuring that the representation of the equipment closely approximates the real physical space. Finally, we used Visual Studio v2022 software with C-sharp to access the workshop data stored in the MySQL database. We incorporated the pre-established models, adding interactive buttons and data visualization features to the model.

4. Discussion and Conclusions

All experimental results are shown in Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, Figure 10 and Figure 11 and Table 1, Table 2, Table 3, Table 4, Table 5 and Table 6. This study first defined the basic elements of the Digital Twin system for the weaving workshop. Subsequently, using the OPC UA method, semantic unification modeling of the geometric and physical data of the workshop equipment was conducted, clarifying the geometric–physical model in the virtual entity. Next, based on data collection, a sliding time window was used to identify anomalous data in the time series, and median interpolation was employed for missing and anomalous values. To address the issue of stable-state fluctuation values of the equipment status and running speed being identified as anomalous data during state transitions, a state transition-based data collection method was designed. After state transitions, high-frequency data collection was performed for a certain number of times, while low-frequency data collection was used during steady operation or stoppage. This formed the basis for more accurate behavior model modeling.

Compared to traditional fixed- and low-frequency data collection, the proposed new collection strategy increased the state recognition rate to 100%, with only a slight increase in the data volume. Compared to high-frequency collection, it achieved a 100% state recognition rate using only 2.8% of the data volume, retaining data during transitional states and supplementing attributes such as the output, speed, and energy consumption during steady operation using regression, classification, and clustering algorithms. Finally, experiments were conducted on the loom machine-off time prediction model based on K-means, previously studied by the team. Using the new Digital Twin modeling method, the dynamic data set improved the prediction error rate by 8.2% compared to the static data set, verifying the effectiveness and feasibility of the method.

Digital Twin technology is an integrated application of various technologies in intelligent manufacturing, such as CPS, big data technology, artificial intelligence, and modeling and simulation technologies. The accuracy of data is fundamental in the modeling process of Digital Twin models. As a semi-discrete, semi-process industry, the weaving industry relies less on geometric modeling outside logistics transportation and warehouse space calculations, thus depending more on accurate data for knowledge-based, mechanistic, or data-driven modeling.

In existing research, data accuracy is often assumed because data collection schemes and data cleaning methods are relatively mature. Therefore, most research focuses on modeling and simulation based on these accurate data. After an in-depth study of production behaviors in weaving workshops, we concluded that most weaving equipment operates stably, producing under preset process parameters. In such cases, data such as output exhibit a good linear relationship, and speed data maintain very small fluctuations under the set process conditions (with errors kept below 1% under motor drive systems). Thus, high-frequency data sampling is unnecessary to model the production process.

However, issues like yarn tension failures or yarn quality problems can lead to equipment downtime. Although yarn splicing or end-picking processes are relatively simple and short with manual intervention, the involvement of thousands of yarns in a single piece of equipment in weaving leads to frequent downtime. Especially in workshops producing lower-quality fabrics, a single machine may experience over a hundred stops per day, significantly impacting production modeling. To mitigate the effect of frequent but short-duration stops on modeling, this situation demands a high data collection frequency and robust data cleaning capabilities. Furthermore, for most weaving companies, high-cost investments in information technology and intelligence are not the primary development choice. Thus, this study aims to stabilize data collection and improve data accuracy. It proposes a data collection scheme based on state changes and different data cleaning methods to achieve high data accuracy in weaving workshops with a relatively low cost. The approach was practically applied in the context of loom fault prediction, validating the accuracy of the data set and the effectiveness of the data collection and cleaning scheme.

The modeling methods used in Digital Twin models are not fundamentally different from traditional geometric and mathematical modeling methods. The analytical processes and software utilized have not undergone significant innovation. Specifically, like CPS, Digital Twins also involve data interaction between the physical and virtual spaces. However, Digital Twins place greater emphasis on constructing virtual models with identical physical actions and processes in the virtual space using modeling and simulation methods after acquiring data. These virtual models are then used for specific analyses to monitor, predict, and optimize the physical entities. Even if the virtual models do not have direct control over the physical space, they still play a crucial role. While digital workshops also use data for the digital modeling of equipment and production processes, they predominantly rely on historical data and static models for the simulation and optimization of the production process. In contrast, Digital Twins emphasize dynamically adjusting virtual models driven by real-time data.

In summary, this study employs industrial fieldbus and OPC UA protocols for data collection and integration, with data storage and modeling methods similar to traditional mathematical modeling. The proposed method of high-frequency data collection during state changes and low-frequency collection during other states is not an innovation in modeling or data collection technology. Instead, it represents a new concept within the existing frameworks of data transmission, integration, and mathematical modeling. This new concept enables the precise digital modeling of the production processes in weaving workshops under conditions of a lower network load, less powerful servers, and smaller database capacities, providing support for production management and decision making in manufacturing enterprises.