Digital twin equipment monitoring system applied to modularized clean room

Abstract

To provide a digital twin facility monitoring system applied to a modularized clean room.SOLUTION: To provide a digital twin facility monitoring system applied to a modularized clean room. The system includes a three dimensional model construction module, a sensed data processing module, a digital twin module, and a visual display module. The three dimensional model construction module is used to construct a three dimensional model of the modularized clean room and equipment. The sensing data processing module is used for sensing the operating parameters of the equipments in the modularized clean room and the usage parameters of the consumables, and generating corresponding equipment state data and consumable replacement data. The digital twin module is configured to perform a simulation operation on the equipment state data and the consumable replacement data, predict a maintenance time and a consumable replacement time of the equipment based on a machine learning model, and generate maintenance proposal data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] In the modern biopharmaceutical and cell therapy industry, cell culture manufacturing processes require extremely strict standards for aseptic environments and operations. Cell factories typically employ cleanrooms to control contamination risks and ensure that the culture process meets strict production specifications. However, existing cleanroom systems still face many challenges in cell culture and equipment monitoring, including the following: 1. Difficulty in equipment maintenance and consumable replacement management: Cell culture equipment in conventional clean rooms (cell incubators, biosafety cabinets, centrifuges, etc.) relies on manual inspections to monitor their operating status and consumable usage. However, due to the long cell culture period, changes in the equipment's operating status may occur within the inspection intervals, and equipment failures or consumable shortages may not be detected in a timely manner, affecting production progress and cell culture quality. 2. Monitoring the sterile environment relies on human operation: Existing technologies typically detect air quality in cleanrooms (such as suspended particle counts and microbial contamination) through periodic sampling, but these detection methods often cannot immediately reflect changes in the environment, and the sampling process can increase the risk of contamination. Furthermore, personnel entering cleanrooms is itself a potential source of contamination, and the lack of an immediate monitoring mechanism makes it difficult to quickly control contamination risks. 3. Lack of digital twin monitoring of equipment consumable usage status: Currently, the usage status of cell culture equipment consumables (cell culture dishes, pipettes, medium, filtration membranes, etc.) is still recorded manually or replaced periodically, which can lead to inaccurate replacement timing, resulting in consumable waste or depletion, which can affect the cell culture process. In addition, existing systems are unable to instantly track and optimize the internal environment of the equipment (CO2 concentration, temperature, humidity, etc.) and the consumption status of consumables. 4. Insufficient management of equipment abnormalities and maintenance schedules: When an equipment abnormality occurs in the cell culture process (such as an abnormal temperature in the incubator, an imbalance in CO2 concentration, or a change in pH in the culture medium), manual detection and repair is usually required, resulting in production delays and the disposal of a lot of product. In addition, most existing equipment maintenance methods use scheduled maintenance, which does not allow for immediate adjustments based on the actual usage status of the equipment, leading to excessive maintenance and insufficient response when equipment malfunctions. 5. Lack of standardization and data integration in the cell culture process: Although the equipment in a traditional cell factory has monitoring capabilities, there is a lack of data integration capabilities between multiple pieces of equipment, making it impossible to instantly analyze key cell culture parameters (cell growth curves, changes in culture medium components, equipment operation data, etc.) and optimize decision-making. In addition, differences in the skill levels and experience of different operators can affect the consistency of culture results, increasing production variability and quality risks.

[0003] To solve the above problems, existing technologies have been applying digital twin technology to cleanroom monitoring and facility management to enable real-time data tracking and smart decision-making. However, existing technologies still have the following drawbacks: 1. The scope of application of digital twin technology is limited: Existing digital twin systems are mainly applied to monitoring the operating status of production equipment, and there are few cases of integrating the management of cell culture equipment and consumables. As a result, equipment monitoring and consumable replacement management still rely on human intervention, and true unmanned operation has not been achieved. 2. Insufficient prediction of equipment abnormalities and optimization of maintenance: Many existing systems adopt simple threshold alarm mechanisms, such as an alarm being issued only when the temperature of an incubator exceeds a certain range. However, they are unable to predict equipment abnormalities based on historical data and machine learning models. This means that many equipment problems are not detected early, affecting production stability. 3. The consumable replacement mechanism is not integrated with the equipment monitoring system: In conventional technologies, consumable replacement is usually performed based on a fixed schedule or human judgment rather than dynamically adjusting according to actual usage conditions. This can lead to increased costs due to premature replacement of consumables or impact on cell culture results due to insufficient replacement. In addition, existing technologies lack a mechanism for linking with supply chain management systems, so when consumable inventory runs low, a request for inventory replenishment is not automatically triggered, affecting production planning. 4. Limited data integration capabilities for cleanroom environmental monitoring: Although existing systems can monitor air quality and environmental parameters in cleanrooms, these data are often stored on different equipment platforms, and there is a lack of a unified data analysis and decision-making mechanism, making it difficult for production managers to grasp the overall situation in real time, which affects cleanroom pollution control and equipment operation management. 5. Human operation remains a key factor affecting production efficiency and quality: Existing digital twin technologies are still primarily used for passive monitoring and are not highly integrated with manipulators or automated equipment to replace manual cell culture operations (seeding, changing culture medium, passaging, harvesting, etc.). Therefore, cell factories still need to rely on human operation, which not only increases the risk of human error but also affects production efficiency and the degree of standardization.

[0004] Therefore, it is necessary to design a digital twin system applied to modular cleanrooms to monitor the equipment in the modular cleanrooms and thereby solve the above problems. Summary of the Invention

[0005] In view of this, the present invention provides a digital twin facility monitoring system applied to a modular cleanroom, thereby solving the above-mentioned known problems.

[0006] The present invention provides a digital twin equipment monitoring system for a modularized clean room, comprising: a 3D model construction module for constructing a 3D model of a modularized clean room and equipment, wherein the modularized clean room includes a clean room having a plurality of partitions; a sensory data processing module connected to the 3D model construction module for detecting operating parameters and consumable usage parameters of equipment in the modularized clean room to generate corresponding equipment status data and consumable replacement data; a digital twin module connected to the 3D model construction module and the sensory data processing module for performing simulation calculations on the equipment status data and the consumable replacement data and predicting the equipment maintenance time and consumable replacement timing based on a machine learning model to generate maintenance suggestion data; and a visual display module connected to the digital twin module for visualizing and presenting the equipment status data, the consumable replacement data, and the maintenance suggestion data to provide a user with immediate warnings and maintenance suggestions.

[0007] Among these, the sensing data processing module further includes at least one sensing device installed on the partition plate and on the equipment, for instantly detecting the operating parameters and the consumable usage parameters of the equipment, and further generating sensing data based on the operating parameters and the consumable usage parameters, and transmitting the sensing data to the digital twin module.

[0008] Among these, the digital twin module further includes a data calculation and analysis unit that predicts the optimal maintenance cycle and optimal consumable replacement timing of the equipment based on the equipment status data and the consumable replacement data using the machine learning model, and provides equipment operation optimization suggestions.

[0009] The visual display module further comprises a mobile device, which may further include one of a mobile phone, a computer, and a tablet, for receiving the maintenance suggestion data via wireless communication and providing the user with instant adjustment control and remote monitoring.

[0010] Here, the digital twin module further performs historical data analysis on the equipment status data to build an equipment operation failure model, and provides equipment maintenance and replacement strategy suggestions based on the equipment operation failure model.

[0011] Here, the equipment further comprises one or more of a cell incubator, a cell sterile work bench, a conveyor belt, a turntable, and a manipulator.

[0012] Here, the digital twin facility monitoring system applied to a modular clean room is also applied to a cell factory. The modular clean room further includes a plurality of clean rooms, each of which includes a plurality of partitions for rapid assembly to form an interior space and an exterior area. Furthermore, the cell factory is a contract development and manufacturing organization (CDMO) cell factory, each of which includes movable partitions, and each of which includes an airtight space. Positive pressure and laminar flow are maintained in the airtight space, ensuring that the interior space of the clean room achieves a predetermined cleanliness class.

[0013] Here, when the cell factory assigns a shutdown or production schedule, the clean rooms, based on the shutdown or production schedule, quickly remove the movable partition to match a specific one of the clean rooms to the shutdown or production schedule.

[0014] Here, the digital twin equipment monitoring 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 sending an immediate warning to the user via the visual display module and providing the maintenance suggestion when the early warning module receives equipment status data indicating that the equipment may be malfunctioning or when the consumable replacement data reaches a predetermined threshold.

[0015] Here, the equipment's consumable replacement data further includes information on the useful life, immediate consumption rate, and replacement cycle of the consumables, and an optimal replacement schedule is constructed through the digital twin module.

[0016] Compared with conventional technologies, the digital twin equipment monitoring system applied to the modular cleanroom of the present invention uses digital twin technology to create a virtual cell culture environment, allowing for real-time simulation and monitoring of the operating status of equipment in multiple cleanrooms. Furthermore, the system incorporates machine learning models to predict equipment abnormalities and optimize maintenance. It adjusts maintenance cycles based on actual equipment operating data, thereby improving equipment lifespan and reducing the risk of production interruptions. The system also integrates the simulation and learning functions of an AI-driven manipulator to analyze the impact of manipulator operation on cell culture results, ensuring the standardization and stability of the culture process. It also optimizes the machine's clamping force, movement trajectory, and culture medium replacement method through data analysis, thereby reducing the risk of cell damage. The system can further integrate data on equipment status, culture parameters, and consumable replacement, ensuring real-time monitoring of all key equipment and production processes within the cell factory. It also optimizes equipment operation strategies through data analysis, reducing contamination and resource waste. In addition, sensing devices are used to instantly monitor cleanroom environmental parameters (such as temperature and humidity, airflow, vibration, and gas concentration), enabling dynamic environmental control and ensuring stable production conditions. The system of the present invention also establishes a contamination risk assessment and control mechanism between multiple cleanrooms. Digital twins are used to simulate the impact of manipulator and equipment operation in different cleanrooms to prevent cross-contamination. Data accumulation and computational analysis provide optimal cleanroom configurations and contamination control strategies, thereby reducing production risks and improving production efficiency. The system of the present invention also combines AR / VR remote monitoring technology with real-time adjustment and control functions to further improve the degree of automation in cell factories. This allows operators to remotely monitor equipment status and instantly adjust operating parameters, reducing the need for personnel to enter the cleanroom and effectively reducing contamination risks while improving production efficiency and operational stability.As described above, the digital twin equipment monitoring system applied to the modular clean room provided by the present invention can comprehensively optimize the operation mode of the cell factory, improve the equipment management efficiency, ensure that the production environment complies with high cleanliness standards, and promote the development of smart and automated cell culture technology. [Brief explanation of the drawings]

[0017] [Figure 1] FIG. 1 is a functional block diagram of a digital twin equipment monitoring system applied to a modularized clean room based on a specific embodiment of the present invention. [Figure 2] FIG. 10 is a functional block diagram of a digital twin facility monitoring system applied to a modular clean room according to another specific embodiment of the present invention. [Figure 3] FIG. 10 is a functional block diagram of a digital twin facility monitoring system applied to a modular clean room according to another specific embodiment of the present invention. [Figure 4] FIG. 10 is a functional block diagram of a digital twin facility monitoring system applied to a modular clean room according to another specific embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0018] 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.

[0019] Please refer to Figure 1, which is a functional block diagram of a digital twin equipment monitoring system 1 applied to a modularized cleanroom according to a specific embodiment of the present invention. This specific embodiment provides a digital twin equipment monitoring system 1 applied to a modularized cleanroom, which includes a 3D model construction module 11, a sensing data processing module 12, a digital twin module 13, and a visual display module 14.

[0020] In this specific embodiment, the 3D model construction module 11 is used to construct a 3D model (not shown) of a modularized clean room (not shown) and equipment (not shown). The modularized clean room in this specific embodiment may include a clean room (not shown), which includes multiple partitions that can be quickly assembled to form an interior space and an exterior area. The equipment in this specific embodiment may be installed in the interior space. The equipment may further include a cell incubator, a sterile cell work table, a conveyor belt, a turntable, a manipulator, etc. Among these, the manipulator can simulate manual cell culture procedures (e.g., cell seeding, culture medium replacement, passaging, sampling, etc.) and can accurately monitor and optimize their operation using digital twin technology. However, in practice, the types of equipment are not limited to those described above. Equipment may be installed and selected according to user needs and the equipment required for the manufacturing process.

[0021] In this specific embodiment, the sensing data processing module 12 is connected to the 3D model construction module 11 and is further configured to sense the operation parameters and consumable use parameters of the equipment in the modular clean room and generate corresponding equipment status data and consumable replacement data. In this specific embodiment, the operation parameters may further include environmental temperature, equipment temperature, environmental humidity, airflow changes, vibration frequency, etc., and the consumable use parameters may include medium consumption, filter membrane replacement frequency, etc. In practice, the sensing data processing module 12 can also monitor the manipulator and instantly record the manipulator's operation trajectory, clamping pressure, movement speed, and operation accuracy, ensuring that the manipulator's operation complies with the standardized process and reducing the risk of errors.

[0022] In addition, in this specific embodiment, the digital twin module 13 is connected to the 3D model construction module 11 and the detection data processing module 12 to perform simulation calculations on equipment status data and consumable replacement data, and predict equipment maintenance times and consumable replacement timings based on machine learning models to generate maintenance suggestion data. In this specific embodiment, the digital twin module 13 can also simulate the manipulator's operating performance under different environmental conditions. For example, it can evaluate the effects of different airflow conditions or temperature and humidity conditions and optimize settings to ensure operational stability and reproducibility of the cell culture process. In this specific embodiment, the visual display module 14 is connected to the digital twin module 13 to visualize and present the equipment status data, consumable replacement data, and maintenance suggestion data, thereby providing users with immediate warnings and maintenance suggestions. In practice, the 3D model construction module 11, the detection 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 a modular cleanroom may be integrated into the central processing unit of a computer system or a cloud system, or may be integrated into an integrated chip.

[0023] In a specific embodiment, when the digital twin equipment monitoring system 1 for a modular cleanroom is used to monitor a manipulator during cell culture, it can monitor the equipment status using digital twin technology and machine learning based on artificial intelligence (AI), thereby reducing the risk of cleaning and operational errors caused by human manipulation of the manipulator. Furthermore, by using equipment operation data and airflow analysis, it can quickly predict potentially affected sensitive areas, fundamentally avoiding airflow interference or damage to the cell culture process. This is particularly important. Because cell culture requires precise and stable operation, any environmental impact from vibration or changes in airflow can significantly reduce the cell division rate and increase data fluctuations, potentially affecting cell culture results. In this specific embodiment, the digital twin module 13 can also simulate the movement of airflow within the cleanroom, thereby assessing the potential contamination risk during manipulator operation and providing optimal operational recommendations. Furthermore, through data accumulation and algorithm optimization, the digital twin module 13 can gradually improve the degree of standardization of the culture process, ensuring the operational stability of the manipulator and improving the consistency and reproducibility of the production process.

[0024] In practical applications, the digital twin equipment monitoring system 1 applied to the modular clean room of this specific embodiment can be applied to high-purity environments, including biological product factories, cleanroom production lines, and cell factories, thereby monitoring the operating parameters of the equipment to ensure the stable operation of the equipment. When the digital twin equipment monitoring system 1 applied to the modular clean room of this specific embodiment is applied to a cell factory, the modular clean room may further include multiple clean rooms. Each clean room may include multiple partitions that can be quickly assembled to form an interior space and an exterior area. In addition, the cell factory may also be a contract development and manufacturing organization (CDMO) cell factory. Each partition may be a movable partition, and may be made of stainless steel, aluminum alloy, high-pressure laminate (HPL), or other composite materials. Each partition may be quickly attached or detached via connecting members such as sealing strips, tenons, or connecting clips. The interior space of each clean room is an airtight space, and positive pressure and laminar flow are maintained in the airtight space to ensure that the interior space of the clean room reaches a predetermined cleanliness class. The predetermined cleanliness class may be Class 4 (compliant with ISO8 standard). In practice, the materials of the partitions and connecting members are not limited to these and may be adjusted and designed according to the needs and production requirements or specifications of the user.

[0025] The modular clean room can be assembled using movable partitions, which improves the space utilization efficiency within the cell factory and allows for quick and flexible planning of the space required for each schedule according to production demand. The rapid assembly of multiple internal areas using multiple partitions improves the overall flexibility and production efficiency of the cell factory. Furthermore, digital twin simulation technology can be used to instantly monitor the manipulator's operating environment under different environmental parameters, ensuring the accuracy and stability of the manipulator's operation under different clean room conditions. Furthermore, in this specific embodiment, the partitions, which can be quickly assembled and disassembled, allow each clean room to be an independent and isolated space. Therefore, when the digital twin equipment monitoring system for the modular clean room provided by the present invention is applied to a CDMO cell factory, even if equipment in the cell factory requires downtime and maintenance, the number of clean rooms can be adjusted or clean rooms can be moved to different locations depending on the length and status of the equipment downtime and production schedule. In addition, according to the need for independence or rotational shutdown of equipment within each clean room, i.e., when the cell factory assigns a shutdown or production schedule, the clean room can also match a specific clean room within the clean room to the shutdown or production schedule by quickly removing the movable partition based on the shutdown or production schedule.

[0026] In this specific embodiment, the digital twin equipment monitoring system 1 applied to the modular cleanroom can be combined with various machine learning models to improve the accuracy and automation of equipment monitoring, anomaly prediction, maintenance optimization, and contamination risk control within the modular cleanroom. Among these, equipment anomaly detection and prediction models (e.g., LSTM, random forest) can identify potential faults early based on equipment operation data to prevent unplanned shutdowns. Furthermore, optimal maintenance scheduling models (e.g., reinforcement learning, Bayesian networks) dynamically adjust maintenance cycles based on real-time data to extend the equipment's service life. For manipulator motion control, the system employs deep reinforcement learning and CNN models to optimize clamping force and motion trajectory to reduce the effects of shear force in the cell culture process. Furthermore, environmental control and contamination risk assessment models (e.g., decision trees, graph neural networks) enable the system to instantly monitor airflow, temperature, humidity, and particle concentration within the cleanroom, predict contamination risks, and adjust operational strategies to avoid cross-contamination. In terms of consumables management and supply chain optimization, the system can predict when to replace consumables such as culture media and filtration membranes using LSTM and XGBoost models, ensuring supply chain stability. Finally, the system of this specific embodiment can be combined with digital twin simulation and operation optimization models (such as GAN and simulation reinforcement learning) to predict optimal production processes, further improving the operational efficiency and production stability of cleanroom equipment and ensuring high-level performance of cell culture and biopharmaceutical processes. However, in practice, the types and formats of machine learning models are not limited to these, and different algorithms can be selected or combined according to specific application scenarios and data characteristics to achieve more accurate equipment monitoring, maintenance management, and production optimization.

[0027] Please refer to FIG. 2. FIG. 2 is a functional block diagram of a digital twin equipment monitoring system 2 applied to a modularized clean room according to a specific embodiment of the present invention. The difference between this specific embodiment and the previous specific embodiments is that the detection data processing module 12 in this specific embodiment further includes at least one sensing device 121. The sensing device 121 is installed on the partition board, the equipment body, the airflow piping, and key areas of the clean room to instantly detect the equipment's operating parameters and consumable usage parameters, generate sensing data based on the operating parameters and consumable usage parameters, and transmit the sensing data to the digital twin module 13. In practice, the installation location of the sensing device 121 is not limited to the above and can be flexibly adjusted and optimized based on the parameters to be monitored, the equipment's operating characteristics, environmental conditions, and measurement requirements. The sensing device 121 in this specific embodiment may include, but is not limited to, the following types: 1. Temperature and humidity sensor: Monitors changes in temperature and humidity inside and outside the facility to ensure a stable cell culture environment. 2. Airflow sensor: Detects the speed and direction of airflow within the clean room to ensure optimal sterility conditions are maintained. 3. Vibration and pressure sensors: Real-time monitoring of vibration and stress changes during manipulator operation to ensure the stability of the cell manipulation process. 4. Video and optical sensors: High-resolution cameras and optical sensing technology are used to monitor the state of cell culture, the operation status of equipment, and any abnormal changes. 5. Gas and contaminant sensors: Monitoring CO2, O2 concentrations and airborne particle counts within cleanrooms ensures environmental quality meets biomanufacturing process standards. 6. RFID and barcode scanning sensors: Track the operating history of consumables and equipment, ensure accurate replacement cycles, and optimize logistics management.

[0028] The sensing data generated by the sensing device 121 is immediately transmitted to the digital twin module 13 for subsequent analysis and optimization of the equipment operation strategy. This method not only improves the stability of the cell culture process, but also reduces the need for manual monitoring through a data-driven approach, thereby mitigating production risks. Note that the other modules, models, and corresponding functions in the digital twin equipment monitoring system 2 applied to the modular cleanroom of this specific embodiment are generally the same as those in the previous specific embodiment, and therefore will not be described here. Please refer to FIG. 3. FIG. 3 is a functional block diagram of the digital twin equipment monitoring system 3 applied to the modular cleanroom of this specific embodiment. The digital twin module 13 in the digital twin equipment monitoring system 3 applied to the modular cleanroom of this specific embodiment further includes a data computing and analysis unit 131. The data computing and analysis unit 131 is used to predict the optimal equipment maintenance cycle and optimal consumable replacement timing based on equipment status data and consumable replacement data using a machine learning model, thereby providing equipment operation optimization suggestions. In practice, the digital twin equipment monitoring system 3 applied to the modular cleanroom of this specific embodiment can dynamically analyze and optimize the manipulator's operating status and adjust the manipulator's operating strategy using AI technology. For example, by optimizing the machine's clamping force, motion trajectory, and culture medium replacement method through AI analysis, it is possible to ensure low shear force effects during cell culture operations and reduce the possibility of cell damage. The system of this specific embodiment can also instantly detect the manipulator's temperature changes, vibration frequency, and operating accuracy, ensuring long-term operational stability of the equipment and early prediction of possible abnormal conditions, thereby preventing equipment deviations from affecting the culture results. Note that the other modules, models, and corresponding functions of the digital twin equipment monitoring system 3 applied to the modular cleanroom of this specific embodiment are generally the same as those of the corresponding modules in the previous specific embodiment, and therefore will not be described here.

[0029] In another specific embodiment, the digital twin module 13 further performs historical data analysis on the equipment status data to build an equipment operation failure model and provide equipment maintenance and replacement strategy suggestions based on the equipment operation failure model. The equipment consumable replacement data further includes information on the consumables' useful life, current consumption rate, and replacement cycle, and an optimal replacement schedule is built through the digital twin module, thereby reducing unplanned equipment downtime. In addition, the system of this specific embodiment can also build a predictive model using accumulated data to further optimize the equipment's useful life and maintenance strategy, ensure long-term stable operation of the equipment, and reduce the risk of sudden failure.

[0030] In another specific embodiment, the visual display module 14 further includes a mobile device. The mobile device may include a mobile phone, computer, or tablet, which can receive maintenance suggestion data via wireless communication and provide users with real-time adjustment control and remote monitoring. In practical applications, the visual display module 14 may also include multiple equipment monitoring interfaces, equipped with AR / VR remote monitoring functions, to display the real-time status of all equipment in the cleanroom, including the operating status of incubators, work tables, manipulators, and other equipment. Furthermore, AR / VR technology enables the system to provide remote monitoring and operation simulation, allowing operators to instantly adjust equipment parameters, thereby reducing the need for personnel to enter the cleanroom and further reducing the risk of cross-contamination. In this specific embodiment, data synchronization between the 3D model construction module 11, the sensing data processing module 12, the digital twin module 13, and the visual display module 14 can be achieved via wireless communication (Wi-Fi, Zigbee, 5G) or wired communication (Ethernet, RS-485), thereby providing real-time operation monitoring and ensuring the accuracy and timeliness of equipment data.

[0031] Please refer to Figure 4. Figure 4 is a functional block diagram of a digital twin equipment monitoring system 4 applied to a modularized cleanroom according to a specific embodiment of the present invention. The digital twin equipment monitoring system applied to the modularized cleanroom according to this specific embodiment further includes an early warning module 15, which is connected to the digital twin module 13 and the visual display module 14, for real-time monitoring of the equipment's operating status and providing abnormality warnings and maintenance suggestions. When the early warning module 15 receives equipment status data indicating a possible equipment malfunction (such as excessive temperature, abnormal airflow, abnormal vibration, or abnormal consumable use), or when the consumable replacement data reaches a preset threshold, it can send an immediate warning and maintenance suggestions to the user via the visual display module 14. The early warning module 15 can also send notifications to mobile devices (such as mobile phones, tablets, and computers) to enable the operator to remotely adjust and control the equipment and ensure its stability and continuity.

[0032] Furthermore, the system of the present invention can correspond to the actual factory line process of a cell factory. It continuously monitors data to proactively search for, discover, or detect abnormalities or discrepancies, and then classifies and evaluates the abnormalities using built-in data analysis and machine learning algorithms. When the system detects an abnormality, the early warning module 15 immediately issues an alert and provides diagnostic information, such as the possible causes of the abnormality, the extent of the impact, and recommended solutions, to assist the operator in subsequent management control and decision-making. For example, in a cell culture process, when a manipulator performs a culture medium change, the manipulator's operating parameters (e.g., clamping force, movement trajectory, shear force effect, etc.) are monitored. If an abnormality in the culture medium change or unevenness in the medium change is detected, the system immediately sends a notification and suggests adjusting the arm parameters or calibrating the equipment to ensure the stability of the culture process. The application of this technology not only improves the stability of the operating environment inside the cleanroom, but also enables the construction of accurate predictive models through data analysis, optimizing equipment maintenance cycles, reducing unplanned shutdowns, and mitigating production risks. In addition, the other modules, models and corresponding functions in the digital twin equipment monitoring 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.

[0033] Compared with conventional technologies, the digital twin equipment monitoring system applied to the modular cleanroom of the present invention uses digital twin technology to create a virtual cell culture environment, allowing for real-time simulation and monitoring of the operating status of equipment in multiple cleanrooms. Furthermore, the system incorporates machine learning models to predict equipment abnormalities and optimize maintenance. It adjusts maintenance cycles based on actual equipment operating data, thereby improving equipment lifespan and reducing the risk of production interruptions. The system also integrates the simulation and learning functions of an AI-driven manipulator to analyze the impact of manipulator operation on cell culture results, ensuring the standardization and stability of the culture process. It also optimizes the machine's clamping force, movement trajectory, and culture medium replacement method through data analysis, thereby reducing the risk of cell damage. The system can further integrate data on equipment status, culture parameters, and consumable replacement, ensuring real-time monitoring of all key equipment and production processes within the cell factory. It also optimizes equipment operation strategies through data analysis, reducing contamination and resource waste. In addition, sensing devices are used to instantly monitor cleanroom environmental parameters (such as temperature and humidity, airflow, vibration, and gas concentration), enabling dynamic environmental control and ensuring stable production conditions. The system of the present invention also establishes a contamination risk assessment and control mechanism between multiple cleanrooms. Digital twins are used to simulate the impact of manipulator and equipment operation in different cleanrooms to prevent cross-contamination. Data accumulation and computational analysis provide optimal cleanroom configurations and contamination control strategies, thereby reducing production risks and improving production efficiency. The system of the present invention also combines AR / VR remote monitoring technology with real-time adjustment and control functions to further improve the degree of automation in cell factories. This allows operators to remotely monitor equipment status and instantly adjust operating parameters, reducing the need for personnel to enter the cleanroom and effectively reducing contamination risks while improving production efficiency and operational stability.As described above, the digital twin equipment monitoring system applied to the modular clean room provided by the present invention can comprehensively optimize the operation mode of the cell factory, improve the equipment management efficiency, ensure that the production environment complies with high cleanliness standards, and promote the development of smart and automated cell culture technology.

[0034] 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]

[0035] 1, 2, 3, 4: Digital twin equipment monitoring system applied to modular cleanrooms 11: 3D model construction module 12: Detection data processing module 121: Sensing device 13: Digital Twin Module 131: Data Computing and Analysis Unit 14: Visual display module 15: Early warning module

Claims

1. A digital twin facility monitoring 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 equipment, wherein the modularized clean room comprises a clean room, and the clean room comprises a three-dimensional model construction module having a plurality of partitions; a sensing data processing module connected to the three-dimensional model construction module, for sensing operation parameters and consumables usage parameters of the equipment in the modularized clean room to generate corresponding equipment status data and consumables replacement data; a digital twin module connected to the three-dimensional model construction module and the detection data processing module, the digital twin module performing simulation calculations on the equipment status data and the consumable replacement data, and predicting the maintenance time and consumable replacement timing of the equipment based on a machine learning model, thereby generating maintenance proposal data; and A digital twin equipment monitoring system applicable to a modularized clean room, comprising a visual display module connected to the digital twin module, for visualizing and presenting the equipment status data, the consumable replacement data, and the maintenance suggestion data, thereby providing users with immediate warnings and maintenance suggestions.

2. The sensor data processing module includes: The digital twin equipment monitoring system applied to the modularized clean room described in claim 1, further comprising at least one sensing device installed on the partition plate and on the equipment, for instantly detecting the operating parameters and the consumable usage parameters of the equipment, generating sensing data based on the operating parameters and the consumable usage parameters, and delivering the sensing data to the digital twin module.

3. The digital twin module comprises:

2. The digital twin equipment monitoring system applicable to the modular clean room according to claim 1, further comprising a data calculation and analysis unit for predicting an optimal maintenance cycle and an optimal timing for replacing consumables for the equipment using the machine learning model based on the equipment status data and the consumable replacement data, and providing suggestions for optimizing the operation of the equipment.

4. The visual display module comprises:

2. The digital twin equipment monitoring system applicable to a modular clean room according to claim 1, further comprising a mobile device, the mobile device including one of a mobile phone, a computer, and a tablet, which receives the maintenance suggestion data via a wireless communication method and provides the user with immediate adjustment control and remote monitoring.

5. The digital twin equipment monitoring system applied to the modular clean room described in claim 1, wherein the digital twin module is further used to perform historical data analysis on the equipment status data, thereby constructing an equipment operation failure model, and providing equipment maintenance and replacement strategy suggestions based on the equipment operation failure model.

6. The digital twin equipment monitoring system applied to a modular clean room according to claim 1 , wherein the equipment further comprises one or more of a cell incubator, a cell sterile workbench, a conveyor belt, a turntable, and a manipulator.

7. 2. The digital twin equipment consumable replacement monitoring system applicable to a modular clean room as described in claim 1, wherein the digital twin equipment monitoring system applicable to a modular clean room is applicable to a cell factory, the modular clean room further comprising a plurality of clean rooms, each of which comprises a plurality of partitions for rapid assembly to form an interior space and an exterior area, the cell factory further comprising a contract development and manufacturing organization (CDMO) cell factory, each of the partitions being movable, the interior space of each of the clean rooms being an airtight space, and positive pressure and laminar flow being maintained in the airtight space so that the interior space of the clean room reaches a predetermined cleanliness class.

8. A digital twin equipment monitoring system applied to a modular clean room as described in claim 7, wherein when the cell factory assigns a shutdown or production schedule, the clean room matches a specific one of the clean rooms to the shutdown or production schedule by quickly removing the movable partition based on the shutdown or production schedule.

9. 2. The digital twin equipment monitoring system applied to the modularized clean room of claim 1, further comprising an early warning module connected to the digital twin module and the visual display module, the early warning module sending the immediate warning to the user via the visual display module and providing the maintenance suggestion when the equipment status data indicating that a failure may occur in the equipment is received or when the consumable replacement data reaches a predetermined threshold.

10. The digital twin equipment monitoring system applied to a modular clean room as described in claim 1, wherein the equipment consumable replacement data further includes information on the useful life, immediate consumption rate, and replacement cycle of the consumables, and an optimal replacement schedule is constructed through the digital twin module.

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