Dynamic Parameter Integrated Monitoring Digital Twin System Applied to Modular Cleanroom

Abstract

A dynamic parameter integrated monitoring digital twin system is provided for modular cleanrooms. The system includes a 3D model construction module, a sensing data processing module, a digital twin module, and a visual display module. The 3D model construction module constructs a 3D model of the modular cleanroom and its internal facilities and equipment. The sensing data processing module receives multiple dynamic parameters and generates a sensing parameter dataset. The digital twin module instantly simulates the modular cleanroom based on the sensing parameter dataset and uses a machine learning model to predict contamination risks and propose schedule plans. The visual display module instantly visualizes and presents the 3D model, the sensing parameter dataset, contamination risk prediction results, and proposed schedule plans.

Description

[Technical Field]

[0001] The present invention relates to a digital twin system, and more particularly to a dynamic parameter integrated monitoring digital twin system applied to a modular clean room. [Background technology]

[0002] In the modern biologics and cell therapy industries, cell culture manufacturing processes require strict control of the sterile environment and standardization of operational processes. To ensure consistent product quality and avoid contamination risks, cell factories generally employ cleanroom environments and closed-loop operations. Particularly in the manufacturing of cell therapies or high-end pharmaceuticals, even the slightest contamination can lead to the disposal of an entire product lot, resulting in costly losses. Therefore, real-time cleanroom monitoring, early risk warning, and process optimization management have become key technological challenges in the fields of smart manufacturing and smart medicine. However, existing cleanroom monitoring technologies still face numerous limitations and challenges, including:

[0003] 1. Equipment monitoring and management relies too much on manual inspections. In many clean rooms, the operating status of equipment (incubators, work tables, ventilation systems, etc.) is still inspected by manual inspections on a regular basis, which makes it difficult to immediately identify abnormal conditions and can easily miss important early warnings.

[0004] 2. The information presentation method is static and distributed. Existing systems only provide data tables or single parameter graphs, and lack the ability to visualize the overall space, making it difficult for operators to grasp the system situation from a macroscopic perspective.

[0005] 3. Many contamination incidents are discovered after the fact. When a pressure imbalance, a sudden rise in particle concentration, or cross-contamination occurs in the clean room, the system cannot immediately integrate relevant dynamic parameters to provide early warnings. In many cases, abnormalities are only discovered through quality inspection after the production process is completed, resulting in serious losses.

[0006] 4. Dynamic changes in modular clean rooms lack a mechanism for updating relevant data. Modular clean rooms have the ability to be quickly set up and reconfigured, but existing monitoring systems cannot immediately reflect changes in the modular clean room's structure and updates to environmental parameters, resulting in a lack of synchronization between the management system and the physical space.

[0007] 5. Lack of simulation of flow lines and schedules: In application scenarios where multiple production lines intersect and work lots are diversified, existing systems are unable to simulate or predict contamination risks based on the routes of personnel, equipment, and materials, and lack the basis for supporting overall work schedules.

[0008] In recent years, attempts have begun to introduce digital twin technology into manufacturing systems to solve the above problems, with the hope that this will enable the synchronization of equipment operation simulations with environmental data and the merging of online and offline (online merges with offline, OMO) to achieve predictive maintenance and process streamlining.However, when applied to the cleanroom field, existing digital twin systems still have the following technical shortcomings:

[0009] 1. The scope of application is limited to single-point monitoring of equipment. Most systems only return data and provide basic visualizations for a single piece of equipment, and are unable to fully cover the entire spatial structure and work processes of the cleanroom.

[0010] 2. Early warning logic lacks intelligent processing capabilities. Existing systems generally use fixed threshold settings to issue warnings, and are unable to use machine learning models to combine historical data, environmental change trends, and human-machine interaction behaviors to make proactive predictions and dynamic adjustments.

[0011] 3. Unable to integrate personnel, equipment, and material flow information. Cleanroom contamination and production capacity bottlenecks are often related to flow planning, but the current system lacks a cross-module data integration mechanism, making it difficult to perform cross-analysis of work flow and rationalize spatial layout.

[0012] 4. System platforms are distributed and lack a consistent, integrated architecture. Monitoring, recording, analysis, and visualization are often presented through different systems or interfaces, resulting in isolated information and failing to provide managers or operators with a unified, immediate basis for decision-making.

[0013] 5. The degree of automation is insufficient, and a high degree of manual intervention is still required. Currently, most digital twins are used as auxiliary tools for information reference, and do not form a closed loop of interactive control with automated equipment (such as automated transport equipment and automated culture medium exchange equipment), making it impossible to achieve the goals of high automation and reducing human error.

[0014] Therefore, it is necessary to design a digital twin system applicable to modular cleanrooms that can monitor dynamic parameters in the modular cleanroom to realize real-time simulation of the cleanroom's dynamic state and early warning judgment, thereby improving the management control of contamination risks, equipment maintenance efficiency, and the quality of schedule decision-making. Summary of the Invention [Means for solving the problem]

[0015] In view of this, the present invention solves the above-mentioned known problems by providing a dynamic parameter integrated monitoring digital twin system applied to a modular cleanroom.

[0016] The present invention provides a dynamic parameter integrated monitoring digital twin system for a modular cleanroom. The system includes a 3D model construction module, a sensing data processing module, a digital twin module, and a visual display module. The 3D model construction module is for constructing a 3D model of the modular cleanroom and the facilities and equipment installed therein. The modular cleanroom includes a plurality of cleanrooms. The sensing data processing module is connected to the 3D model construction module and is configured to receive a plurality of dynamic parameters and generate a sensing parameter dataset. The digital twin module is connected to the 3D model construction module and the sensing data processing module and is configured to instantly simulate the modular cleanroom based on the sensing parameter dataset, predict contamination risks and propose schedule plans based on a machine learning model, and correspondingly generate contamination risk prediction results and a proposed schedule plan. The visual display module is connected to the digital twin module and is configured to instantly visualize and present the 3D model, the sensing parameter dataset, the contamination risk prediction results, and the proposed schedule plan of the modular cleanroom, thereby allowing users to monitor and operate the system in real time.

[0017] Here, the dynamic parameters further include personnel-related parameters, equipment-related parameters, material-related parameters, process-related parameters, and environment-related parameters.

[0018] Here, the sensing data processing module further comprises at least one sensing device, which is installed in at least one of the clean room, the facility and the equipment, for detecting and receiving the dynamic parameters in real time.

[0019] Here, the digital twin module further includes a data analysis unit, which generates a contamination risk grade prediction result, an equipment maintenance prediction result, and a cleanroom environment quality fluctuation assessment result based on the sensing parameter dataset and historical data.

[0020] Here, the dynamic parameter integrated monitoring digital twin system applied to the modularized clean room further includes an early warning module connected to the digital twin module and the visual display module, for activating visual and audio alarms and sending an immediate warning to the user via the visual display module and providing maintenance suggestions when any of the contamination risk grade prediction result, the equipment maintenance prediction result, and the clean room environmental quality fluctuation assessment result generated by the data analysis unit is higher than a preset threshold.

[0021] Here, the dynamic parameter integrated monitoring digital twin system applied to the modular clean room further includes a schedule module, which is connected to the digital twin module, for simulating and streamlining task schedules of multiple production lines according to the dynamic parameters of equipment availability, manufacturing process sequence, personnel operation actions and clean room reconfiguration status.

[0022] Here, the dynamic parameter integrated monitoring digital twin system applied to the modularized clean room further includes an unmanned transport equipment control module connected to the digital twin module, which automatically adjusts the driving routes of multiple unmanned transport equipment based on the route rationalization command generated by the digital twin module, to avoid contamination hotspots or traffic line overlap areas.

[0023] Here, the 3D model construction module is further used to support the reconfiguration of the modularized clean room space and to simulate the layout results and logistics route schedule results of the equipment and facilities within the modularized clean room under different combinations.

[0024] The dynamic parameter integrated monitoring digital twin system for the modular clean room is applied to a cell factory. Each clean room in the modular clean room includes a plurality of partitions that can be quickly assembled to form an interior space and an exterior area. Each partition is a movable partition, and the interior space of each clean room is an airtight space. Positive pressure and laminar flow are maintained in the airtight space, so that the interior space of the clean room reaches a predetermined cleanliness class.

[0025] Here, when the cell factory allocates annual maintenance, equipment maintenance, or production schedules, the clean rooms match a specific one of the clean rooms to the annual maintenance, equipment maintenance, or production schedule by quickly removing the movable partition based on the annual maintenance or production schedule. [Effects of the Invention]

[0026] Compared with known technologies, the dynamic parameter integrated monitoring digital twin system applied to the modular cleanroom of the present invention integrates multiple sensing parameters such as the modular cleanroom's structure, equipment, personnel, and environment, and uses 3D models and digital twin technology to instantly map the physical cleanroom operating status while establishing a virtual model to simulate and predict contamination risks. The dynamic parameter integrated monitoring digital twin system applied to the modular cleanroom of the present invention uses a sensing data processing module to receive and integrate multiple environmental and equipment dynamic parameters to construct a sensing parameter data set, and uses a data analysis module and machine learning model to predict equipment abnormalities, evaluate cleanroom quality fluctuations, and propose work schedules, effectively solving the shortcomings of traditional systems that only provide single-point monitoring and post-event warnings.

[0027] Furthermore, the dynamic parameter integrated monitoring digital twin system applied to the modular cleanroom of the present invention can further construct a 3D scene model of the spatial structure and reconfiguration characteristics of the modular cleanroom, thereby simulating the cleanroom assembly method and equipment layout according to production needs, calculating the movement routes of automated transport devices (such as automated guided vehicles) and personnel, predicting potential contamination risks in intersecting areas, and providing route optimization and movement plan suggestions. Users can simultaneously view the 3D model, contamination prediction results, and schedule suggestions on the interface and make remote adjustments and control, reducing the need for human operation and reaction time.

[0028] Furthermore, the dynamic parameter integrated monitoring digital twin system applied to the modular cleanroom of the present invention can also establish a contamination risk assessment mechanism between multiple cleanrooms, simulate the cross-contamination situations of different production lines, predict the probability of cross-contamination, and adjust the cleanroom layout and production node distribution based on the simulation results, thereby improving the overall space and equipment utilization efficiency.Through continuous data accumulation and streamlined learning model, the accuracy of contamination early warning and the effectiveness of schedule proposals can be continuously improved, realizing smart, modular, and data-driven integrated management of cleanrooms.

[0029] Furthermore, the dynamic parameter integrated monitoring digital twin system of the present invention can effectively integrate existing manufacturing execution systems (MES), supervisory control and data acquisition systems (SCADA), and enterprise resource planning systems (ERP), bridging and converting data via data exchange interfaces (OPC UA, RESTful API, etc.) or intermediary software to establish a unified data architecture. The dynamic parameter integrated monitoring digital twin system of the present invention can use sensing devices to instantly collect information on the cleanroom's environmental control and equipment operating status, link operation information from the MES and ERP systems, and vertically integrate it into the decision-making layer to provide contamination predictions and dynamic schedule suggestions, effectively overcoming the problems of insufficient information integration and slow responsiveness of conventional systems.

[0030] Overall, this invention realizes simulation and interaction analysis between multiple cleanroom areas through the digital twin module, significantly improving module usage flexibility, operational efficiency, and quality stability. It also provides an integrated platform with real-time monitoring, decision-making suggestions, and streamlined operations, which can be applied to the development of high-end manufacturing and cell factory automation, providing an innovative and forward-thinking solution to current cleanroom management systems. [Brief explanation of the drawings]

[0031] [Figure 1] FIG. 1 is a functional block diagram of a dynamic parameter integrated monitoring digital twin system applied to a modular clean room according to a specific embodiment of the present invention. [Figure 2] FIG. 10 is a functional block schematic diagram of a dynamic parameter integrated monitoring digital twin system applied to a modular clean room according to another specific embodiment of the present invention. [Figure 3] FIG. 10 is a functional block schematic diagram of a dynamic parameter integrated monitoring digital twin system applied to a modular clean room according to another specific embodiment of the present invention. [Figure 4] FIG. 10 is a functional block schematic diagram of a dynamic parameter integrated monitoring digital twin system applied to a modular clean room according to another specific embodiment of the present invention. [Figure 5] FIG. 10 is a functional block schematic diagram of a dynamic parameter integrated monitoring digital twin system applied to a modular clean room according to another specific embodiment of the present invention. [Figure 6] FIG. 10 is a functional block schematic diagram of a dynamic parameter integrated monitoring digital twin system applied to a modular clean room according to another specific embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0032] To make the advantages, spirit, and labeling of the present invention more easily and clearly understood, specific examples will be described and examined in detail below with reference to the accompanying drawings. It should be noted that these specific examples are merely representative examples of the present invention, and the specific methods, devices, conditions, materials, etc. exemplified do not limit the present invention or the corresponding specific examples. Furthermore, the components in the drawings are used only to indicate their relative positions and are not drawn to scale. Furthermore, the step numbers of the present invention are used only to distinguish different steps and do not represent the order of the steps. The above will be explained in advance.

[0033] Please refer to FIG. 1. FIG. 1 is a functional block diagram of a dynamic parameter integrated monitoring digital twin system 1 applied to a modularized cleanroom according to a specific embodiment of the present invention. This specific embodiment provides a dynamic parameter integrated monitoring digital twin system 1 applied to a modularized cleanroom, which system 1 includes a 3D model construction module 11, a sensing data processing module 12, a digital twin module 13, and a visual display module 14. In this specific embodiment, the 3D model construction module 11 is for constructing a 3D model (shown) of a modularized cleanroom (shown) and facilities (shown) and equipment (shown) installed in the modularized cleanroom. The modularized cleanroom may include multiple cleanrooms (shown) and may have assembleable and disassembleable structural characteristics to accommodate different production layout requirements. The sensing data processing module 12 is connected to the 3D model construction module 11. The sensing data processing module 12 is for receiving multiple dynamic parameters and generating a sensing parameter dataset. The digital twin module 13 is connected to the 3D model construction module 11 and the sensing data processing module 12 to instantly simulate the modular cleanroom based on the sensing parameter data set, predict contamination risks and propose schedule plans based on the machine learning model, and correspondingly generate contamination risk prediction results and schedule proposals. The visual display module 14 is connected to the digital twin module 13 to instantly visualize and present the 3D model of the modular cleanroom, the sensing parameter data set, the contamination risk prediction results, and the schedule proposals for users to monitor and operate in real time. In practice, the 3D model construction module 11, the sensing data processing module 12, the digital twin module 13, and the visual display module 14 of the digital twin equipment monitoring system 1 applied to the modular cleanroom may be integrated into a central processing unit of a computer system or a cloud system, or may be integrated into an integrated chip.

[0034] In this specific embodiment, the equipment may further include cell incubators, biosafety cabinets, centrifuges, shakers, gas supply systems, automated guided vehicles (AGVs), robots, sensor modules, conveyor belts, and automated control devices, which are used to perform functions such as cell culture, production and manufacturing processes, logistics transportation, and environmental sensing. The facility may also include cleanroom walls, partitions, air purifiers, airtight doors, lighting systems, air conditioning and filtration systems, piping systems, and floor drainage systems, which form the basic structure of the modular cleanroom and are used to maintain the cleanliness class of the space and the stability of the working environment. To improve the smart management of overall operation, the digital twin equipment monitoring system 1 applied to the modular cleanroom in this specific embodiment may simultaneously integrate the sensing parameters and control commands of the AGVs or robots, thereby enabling both the static modular cleanroom and the dynamic AGVs or robots to achieve smart and modular integrated management under the same data-driven architecture. In practical application, the types and layout of equipment and facilities are not limited to the above, but can be selected and adjusted according to the user's actual needs, production type and manufacturing process conditions.

[0035] In this specific embodiment, the dynamic parameters received by the detection data processing module 12 may further include personnel-related parameters (entering and exiting records, staying time, movement trajectory, etc.), equipment-related parameters (operating state, start-up and shutdown time, internal environment, etc.), material-related parameters (used lot number, expiration date, storage state, etc.), method-related parameters (operation steps, program time, operation method of operating personnel), and environment-related parameters (temperature, humidity, pressure difference, particle concentration, gas composition, etc.). However, in practical application, the types of dynamic parameters are not limited to those mentioned above and can be expanded and adjusted according to the control goals of the manufacturing process and the needs of user monitoring.

[0036] In this specific embodiment, the machine learning model used may be a decision tree model (Decision Tree), a support vector machine (SVM), a random forest (Random Forest), a deep neural network (DNN), or a convolutional neural network (CNN), and may be continuously trained and refined based on collected parameter data. However, in practical applications, the type and algorithm of the machine learning model are not limited to the above and may be adjusted according to the characteristics of the system's training data and the requirements of the prediction task.

[0037] Please refer to FIG. 2. FIG. 2 is a functional block diagram of a dynamic parameter integrated monitoring digital twin system 2 applied to a modularized cleanroom according to a specific embodiment of the present invention. The difference between this specific embodiment and the previous examples is that the sensing data processing module 12 in this specific embodiment further includes at least one sensing device 121. The sensing device 121 can be installed on the internal structure, facility, or equipment of the cleanroom to instantly detect and receive the above-mentioned dynamic parameters. The dynamic parameters include real-time information related to personnel, equipment, materials, methods, or the environment. The data input from the sensing device 121 can enhance the completeness and timeliness of the sensing parameter data set, further improving the accuracy of contamination risk assessment and schedule analysis. In practical application, the installation location of the sensing device 121 is not limited to a specific location and can be flexibly adjusted and optimized based on the type of parameters to be monitored, the operating characteristics of the equipment, spatial conditions, and measurement requirements, thereby meeting the requirements of different regions and manufacturing processes. The sensing device 121 in this embodiment may further include a temperature and humidity sensor, a pressure difference sensor, a particle counter, a CO2 concentration sensor, a volatile organic compound (VOC) sensor, an infrared personnel movement detector, an RFID reader, a weight sensor, a vibration sensor, a power monitoring module, etc. It should be noted that the other modules, models and corresponding functions in the dynamic parameter integrated monitoring digital twin system 2 applied to the modularized clean room of this specific embodiment are generally the same as the corresponding modules in the aforementioned specific embodiment, and therefore will not be described again.

[0038] Please refer to FIG. 3. FIG. 3 is a functional block diagram of a dynamic parameter integrated monitoring digital twin system 3 applied to a modularized cleanroom according to a specific embodiment of the present invention. This specific embodiment differs from the previous embodiments in that the digital twin module 13 in this specific embodiment further includes a data analysis unit 131. The data analysis unit generates a contamination risk prediction result, an equipment maintenance prediction result, and a cleanroom environmental quality fluctuation assessment result based on the sensing parameter data set and historical data. The contamination risk prediction result can be used to determine the contamination risk level in a cleanroom or a specific work area. The equipment maintenance prediction result evaluates the potential equipment failure risk and the optimal maintenance timing based on the equipment operating status and abnormal parameter trends. The cleanroom environmental quality fluctuation assessment result can evaluate the fluctuation trend and provide early quality warnings for key indicators such as the internal pressure difference, temperature and humidity, and airflow stability in the space.

[0039] In practical application, the data analysis unit 131 can further combine machine learning models to perform automated training and accurate prediction. By continuously collecting and analyzing sensing parameter datasets and operating history data, the accuracy and adaptability of the prediction model can be improved, resulting in optimized contamination risk level prediction and schedule proposals. The contamination risk level prediction results, equipment maintenance prediction results, and cleanroom environmental quality fluctuation assessment results generated by the data analysis unit 131 can be immediately fed back to the visual display module 14, allowing users to instantly understand the system operating status, contamination risk, and optimal schedule proposals, thereby enhancing the overall operational stability and management efficiency of the cleanroom. The visual display module 14 may also include at least one mobile device. The mobile device may be a mobile phone, computer, or tablet, and can receive real-time information output from the data analysis unit 131 via a wireless communication module. The user can instantly view the contamination risk prediction results and schedule proposals through the mobile device's human-machine interface and perform remote operation and adjustment control, thereby achieving the goals of improving response efficiency and reducing the number of personnel entering and exiting the cleanroom and the contamination risk.

[0040] In this specific embodiment, the machine learning model can be trained based on historical operating data and a sensing parameter dataset, and then converted into a model-readable input vector through data cleanup, normalization, and feature engineering. In establishing the model, the data analysis unit 131 can apply algorithms such as random forests, gradient boosting trees, and long short-term memory (LSTM) networks to build a classification model capable of predicting contamination risk grades for different cleanroom operating conditions, or a contamination index regression model. Furthermore, the predictive model can combine equipment usage records and maintenance schedules and employ streamlined strategies such as reinforcement learning and evolutionary algorithms to generate production node schedule recommendations that minimize contamination risk. To improve prediction accuracy and model flexibility, the data analysis unit 131 can implement error feedback learning and incremental learning mechanisms, triggering a model retraining program under conditions of environmental parameter drift, thereby achieving self-learning and continuous streamlining of the predictive model. In addition, the other modules, models and corresponding functions in the dynamic parameter integrated monitoring digital twin system 3 applied to the modularized clean room of this specific embodiment are generally the same as the corresponding modules in the above-mentioned specific embodiment, so the description will be omitted.

[0041] Please refer to FIG. 4. FIG. 4 is a functional block diagram of a dynamic parameter integrated monitoring digital twin system 4 applied to a modularized cleanroom according to a specific embodiment of the present invention. A further difference between this specific embodiment and the previous embodiment is that the system 4 further includes an early warning module 15, which is connected to the digital twin module 13 and the visual display module 14. The early warning module 15 automatically activates visual and audio alarms when any of the contamination risk level prediction results, equipment maintenance prediction results, and cleanroom environmental quality fluctuation assessment results generated by the data analysis unit 131 is higher than a preset threshold, thereby immediately notifying the user of any abnormal situation that may occur in the system. The early warning module 15 can also transmit the content of the early warning to the user's operation interface via the visual display module 14 and provide a set of corresponding maintenance suggestion data, helping the user to make immediate adjustments and control of the equipment or cleanroom environment and further reducing the potential contamination risks and the probability of production interruptions. In addition, the other modules, models and corresponding functions in the dynamic parameter integrated monitoring digital twin system 4 applied to the modularized clean room of this specific embodiment are generally the same as the corresponding modules in the above-mentioned specific embodiment, so the description will be omitted.

[0042] Please refer to FIG. 5. FIG. 5 is a functional block diagram of a dynamic parameter integrated monitoring digital twin system 5 applied to a modular cleanroom according to a specific embodiment of the present invention. A further difference between this specific embodiment and the previous embodiments is that the dynamic parameter integrated monitoring digital twin system 5 applied to a modular cleanroom according to this specific embodiment further includes a schedule module 16. The schedule module 16 is connected to the digital twin module 13 and is used to simulate and streamline task schedules for multiple production lines based on multiple dynamic parameters, such as equipment availability, the sequence of manufacturing processes, personnel operations, and the cleanroom reconfiguration status. The schedule module 16 of this specific embodiment can integrate sensing parameter data sets and production history data to evaluate the dependency relationships, cross-contamination risks, and resource conflict situations between each production unit, and generate an optimized production schedule plan based on the evaluation results. Furthermore, the schedule module 16 can also cross-reference with the prediction results of the data analysis unit 131 and automatically adjust the schedule order and production node allocation based on the predicted equipment maintenance time or contamination risk grade, thereby realizing intelligent management and flexible scheduling of multiple production lines in the clean room and effectively improving production efficiency and system operation stability. Note that the other modules, models and corresponding functions in the dynamic parameter integrated monitoring digital twin system 5 applied to the modularized clean room of this specific embodiment are generally the same as the corresponding modules in the previous specific embodiment, and therefore will not be described here.

[0043] Please refer to FIG. 6. FIG. 6 is a functional block diagram of a dynamic parameter integrated monitoring digital twin system 6 applied to a modularized cleanroom according to a specific embodiment of the present invention. The dynamic parameter integrated monitoring digital twin system 6 applied to a modularized cleanroom according to this specific embodiment further includes an automated transport equipment control module 17. The automated transport equipment control module 17 is connected to the digital twin module 13 and automatically adjusts the driving routes of multiple automated transport equipment (e.g., automated guided vehicles (AGVs)) based on the route rationalization command generated by the digital twin module 13 to avoid contamination hotspots or areas with overlapping traffic lines, thereby reducing the risk of cross-contamination and logistics interference. In this embodiment, the automated transport equipment control module 17 can perform dynamic route planning and real-time rearrangement based on the 3D model and real-time sensing parameters. If the system detects an increase in the contamination level or a concentration of personnel movement in a specific area, it can immediately reassign the logistics movement order, improving the operational safety and route smoothness of the cleanroom. In addition, the other modules, models and corresponding functions in the dynamic parameter integrated monitoring digital twin system 6 applied to the modularized clean room of this specific embodiment are generally the same as the corresponding modules in the above-mentioned specific embodiment, so the description will be omitted.

[0044] In another specific embodiment, the 3D model construction module 11 further supports a simulation of the spatial structure of a modularized clean room, and is used to simulate the layout results of equipment and facilities for different clean room combinations and the corresponding logistics route schedule results based on the production conditions, area restrictions, and work requirements input by the user. The 3D model construction module 11 can integrate information such as modularized structure parameters (such as the number of clean room walls, air purification units, and partitions), equipment dimensions and installation restrictions, personnel flow lines, and logistics flow lines, and present the simulation results in a 3D visualization model, helping users to quickly make advance decisions and streamline spatial layouts, equipment placement, and logistics strategies during the system planning phase, thereby significantly improving the efficiency of clean room construction, installation, and operation.

[0045] In a specific embodiment, the dynamic parameter integrated monitoring digital twin system applied to the modular clean room of the present invention can be applied within a cell factory. The cell factory may further include multiple modular clean rooms, each composed of multiple partitions. The partitions are designed for quick assembly and disassembly, thereby forming an interior space and an exterior area. In this specific embodiment, each partition may be movable, allowing the clean room to be flexibly arranged according to spatial requirements. The interior space of each clean room is airtight, achieving a predetermined cleanliness class (e.g., ISO Class 5-7) through an air control mechanism that maintains positive pressure and unidirectional laminar flow. When the cell factory needs annual maintenance work, repair of specific equipment, or production schedule reassignment, the system can dynamically adjust the available and disassembled status of each clean room based on established scheduling rules. Users can determine specific clean rooms that need to be suspended or relocated through the system's visualization interface or simulation results provided by the digital twin module. In addition, the movable partition can be quickly removed to complete space release or modification work, thereby meeting the requirements of annual repairs, equipment maintenance or flexible production capacity scheduling, and further improving the flexibility and operational efficiency of the entire cell factory.

[0046] Compared with known technologies, the dynamic parameter integrated monitoring digital twin system applied to the modular cleanroom of the present invention integrates multiple sensing parameters such as the modular cleanroom's structure, equipment, personnel, and environment, and uses 3D models and digital twin technology to instantly map the physical cleanroom operating status while establishing a virtual model to simulate and predict contamination risks. The dynamic parameter integrated monitoring digital twin system applied to the modular cleanroom of the present invention uses a sensing data processing module to receive and integrate multiple environmental and equipment dynamic parameters to construct a sensing parameter data set, and uses a data analysis module and machine learning model to predict equipment abnormalities, evaluate cleanroom quality fluctuations, and propose work schedules, effectively solving the shortcomings of traditional systems that only provide single-point monitoring and post-event warnings.

[0047] Furthermore, the dynamic parameter integrated monitoring digital twin system applied to the modular cleanroom of the present invention can further construct a 3D scene model of the spatial structure and reconfiguration characteristics of the modular cleanroom, thereby simulating the cleanroom assembly method and equipment layout according to production needs, calculating the movement routes of automated transport devices (such as automated guided vehicles) and personnel, predicting potential contamination risks in intersecting areas, and providing route optimization and movement plan suggestions. Users can simultaneously view the 3D model, contamination prediction results, and schedule suggestions on the interface and make remote adjustments and control, reducing the need for human operation and reaction time.

[0048] Furthermore, the dynamic parameter integrated monitoring digital twin system applied to the modular cleanroom of the present invention can also establish a contamination risk assessment mechanism between multiple cleanrooms, simulate the cross-contamination situations of different production lines, predict the probability of cross-contamination, and adjust the cleanroom layout and production node distribution based on the simulation results, thereby improving the overall space and equipment utilization efficiency.Through continuous data accumulation and streamlined learning model, the accuracy of contamination early warning and the effectiveness of schedule proposals can be continuously improved, realizing smart, modular, and data-driven integrated management of cleanrooms.

[0049] Furthermore, the dynamic parameter integrated monitoring digital twin system of the present invention can effectively integrate existing manufacturing execution systems (MES), supervisory control and data acquisition systems (SCADA), and enterprise resource planning systems (ERP), bridging and converting data via data exchange interfaces (OPC UA, RESTful API, etc.) or intermediary software to establish a unified data architecture. The dynamic parameter integrated monitoring digital twin system of the present invention can use sensing devices to instantly collect information on the cleanroom's environmental control and equipment operating status, link operation information from the MES and ERP systems, and vertically integrate it into the decision-making layer to provide contamination predictions and dynamic schedule suggestions, effectively overcoming the problems of insufficient information integration and slow responsiveness of conventional systems.

[0050] Overall, this invention can realize simulation and interaction analysis between multiple cleanroom areas through the digital twin module, significantly improving module usage flexibility, operational efficiency, and quality stability. It also provides an integrated platform with real-time monitoring, decision-making suggestions, and streamlined operations, which is applicable to the development of high-end manufacturing and cell factory automation, providing an innovative and forward-thinking solution to current cleanroom management systems.

[0051] The detailed description of the preferred specific embodiments above is intended to more clearly explain the features and spirit of the present invention, and the preferred specific embodiments disclosed above are not intended to limit the scope of the present invention. On the contrary, the intention is to include all modifications and equivalent structures within the scope of the claims to be filed by the present invention. Therefore, the scope of the claims to be filed by the present invention should be interpreted in the broadest possible manner based on the above description so as to include all possible modifications and equivalent structures. [Explanation of symbols]

[0052] 1, 2, 3, 4, 5, 6: Dynamic parameter integrated monitoring digital twin system applied to modular cleanroom 11: 3D model construction module 12: Detection data processing module 121: Sensing device 13: Digital Twin Module 131: Data Analysis Unit 14: Visual display module 15: Early warning module 16: Schedule module 17: Unmanned transport device control module

Claims

1. A dynamic parameter integrated monitoring digital twin system applied to a modular clean room, the system comprising: A three-dimensional model construction module for constructing a three-dimensional model of a modularized clean room and facilities and equipment installed in the modularized clean room, wherein the modularized clean room comprises a plurality of clean rooms; a sensing data processing module coupled to the three-dimensional model construction module for receiving a plurality of dynamic parameters and generating a sensing parameter data set; a digital twin module connected to the three-dimensional model construction module and the sensing data processing module, for instantly simulating the modularized clean room based on the sensing parameter data set, performing contamination risk prediction and scheduling plan proposal based on a machine learning model, and correspondingly generating a contamination risk prediction result and a scheduling proposal plan; and A dynamic parameter integrated monitoring digital twin system applied to a modular clean room, comprising: a visual display module connected to the digital twin module, for visualizing and instantly presenting the three-dimensional model of the modular clean room, the sensing parameter data set, the contamination risk prediction result, and the schedule proposal plan, thereby providing users with instant monitoring and operation.

2. The dynamic parameter integrated monitoring digital twin system applied to a modular clean room according to claim 1 , wherein the dynamic parameters further include personnel-related parameters, equipment-related parameters, material-related parameters, method-related parameters, and environment-related parameters.

3. The sensor data processing module includes: The dynamic parameter integrated monitoring digital twin system applied to the modular clean room according to claim 1, further comprising at least one sensing device installed in at least one of the clean room, the facility, and the equipment for detecting and receiving the dynamic parameters in real time.

4. The digital twin module comprises: The dynamic parameter integrated monitoring digital twin system applied to the modular cleanroom according to claim 1, further comprising a data analysis unit that generates a contamination risk grade prediction result, an equipment maintenance prediction result, and a cleanroom environmental quality fluctuation evaluation result based on the sensing parameter data set and historical data.

5. 5. The dynamic parameter integrated monitoring digital twin system applied to a modularized clean room according to claim 4, further comprising an early warning module connected to the digital twin module and the visual display module, the early warning module activating visual and audio alarms and sending an immediate warning to the user via the visual display module and providing maintenance suggestions when any of the contamination risk grade prediction result, the equipment maintenance prediction result, and the clean room environmental quality fluctuation assessment result generated by the data analysis unit is higher than a preset threshold.

6. The dynamic parameter integrated monitoring digital twin system applied to the modular clean room as described in claim 1, further comprising a schedule module connected to the digital twin module for simulating and streamlining task schedules for multiple production lines based on equipment availability, manufacturing process sequence, personnel operation actions, and clean room reconfiguration status.

7. The dynamic parameter integrated monitoring digital twin system applied to the modular clean room described in claim 1, further comprising an unmanned transport equipment control module connected to the digital twin module, the unmanned transport equipment control module automatically adjusting the driving routes of multiple unmanned transport equipment based on the route rationalization command generated by the digital twin module to avoid contamination hotspots or areas of overlapping traffic lines.

8. The dynamic parameter integrated monitoring digital twin system applied to a modular clean room according to claim 1, wherein the three-dimensional model construction module is further used to assist in the reconfiguration of the modular clean room space and to simulate the layout results and logistics route schedule results of the equipment and facilities within the modular clean room with different combinations.

9. The dynamic parameter integrated monitoring digital twin system applied to the modular clean room is applied to a cell factory, and each clean room in the modular clean room is provided with a plurality of partition plates that can be quickly assembled to form an internal space and an external area, and each of the partition plates is a movable partition plate, and the internal space of each of the clean rooms is an airtight space, and positive pressure and laminar flow are maintained in the airtight space so that the internal space of the clean room reaches a predetermined cleanliness class. The consumable replacement monitoring system for digital twin equipment applied to a modular clean room as described in claim 1.

10. 10. The dynamic parameter integrated monitoring digital twin system applied to a modular clean room as described in claim 9, wherein when the cell factory allocates annual maintenance, equipment maintenance, or production schedules, the clean room quickly removes the movable partitions to match a specific one of the clean rooms to the annual maintenance, equipment maintenance, or production schedule based on the annual maintenance or production schedule.

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