Digital twin-based equipment monitoring system for modular cleanroom

Abstract

This invention provides a digital twin equipment monitoring system for modular cleanrooms, comprising a 3D model building module, a sensing data processing module, a digital twin module, and a visual display module. The 3D model building module is used to build 3D models of the modular cleanroom and its equipment. The sensing data processing module is used to sense the operating parameters of the equipment and consumable usage parameters of the modular cleanroom, generating corresponding equipment status data and consumable replacement data. The digital twin module is used to perform simulation calculations on the equipment status data and consumable replacement data, and predict equipment maintenance time and consumable replacement timing based on a machine learning model, generating maintenance suggestion data. The visual display module is used to visually present the above data, providing users with real-time alerts and maintenance suggestions.

Description

[Technical Field]

[0001] This invention relates to a digital twin system, and more particularly to a digital twin equipment monitoring system applied to modular cleanrooms. [Previous Technology]

[0002] In the modern biopharmaceutical and cell therapy industries, cell culture processes have extremely high requirements for aseptic environment and standardized operation. Cell factories typically use cleanrooms to control contamination risks and ensure that the culture process meets strict production standards. However, existing cleanroom systems still face many challenges in cell culture and equipment monitoring, mainly including the following:

[0002] 1. Difficulties in equipment maintenance and consumable replacement management: Traditional cell culture equipment in cleanrooms (such as cell culture incubators, biosafety workbenches, centrifuges, etc.) relies on manual inspections to monitor operating status and consumable usage. However, due to the long cell culture cycle, changes in equipment operating status may occur within the inspection interval, making it impossible to detect equipment failures or consumable shortages in a timely manner, affecting production progress and cell culture quality.

[0002] 2. Aseptic environment monitoring relies on manual operation: Existing technologies typically test the air quality in clean rooms (such as the number of suspended particles and microbial contamination) through periodic sampling. However, these tests often cannot reflect environmental changes in real time, and the sampling process may increase the risk of contamination. In addition, personnel entering and leaving the clean room are themselves potential sources of contamination, and the lack of a real-time monitoring mechanism makes it difficult to quickly control the risk of contamination.

[0002] 3. Lack of digital twin monitoring of equipment and consumable usage status: The usage status of consumables for cell culture equipment (such as cell culture dishes, pipettes, culture media, filter membranes, etc.) currently still relies on manual recording or periodic replacement, resulting in inaccurate replacement timing, which may lead to waste of consumables or affect the cell culture process due to consumable depletion. In addition, existing systems cannot track and optimize the internal environment of the equipment (such as CO2 concentration, temperature, humidity) and consumable consumption in real time.

[0002] 4. Poor Equipment Malfunctions and Maintenance Scheduling: When equipment malfunctions occur during cell culture (such as abnormal incubator temperature, CO2 concentration imbalance, or pH changes in the culture medium), manual inspection and repair are usually required, leading to production stoppages or batch product scrapping. Furthermore, existing equipment maintenance models often rely on scheduled maintenance, failing to adjust in real time according to the actual usage status of the equipment, which can easily result in over-maintenance or insufficient response to equipment failures.

[0002] 5. Insufficient standardization and data integration in cell culture processes: Although equipment in traditional cell factories has monitoring functions, most devices lack data integration capabilities, resulting in the inability to perform real-time analysis and decision optimization of key cell culture parameters (such as cell growth curves, changes in culture medium composition, equipment operation data, etc.). In addition, differences in the technical level and experience of different operators may affect the consistency of culture results, increasing production variability and quality risks.

[0003] To address the aforementioned problems, existing technologies have begun to apply Digital Twin technology to cleanroom monitoring and equipment management to achieve real-time data tracking and intelligent decision-making. However, existing technologies still have the following drawbacks:

[0003] 1. Limited application scope of digital twin technology: Existing digital twin systems are mainly used for monitoring the operating status of production equipment, and are rarely integrated for cell culture equipment and consumable management. As a result, equipment monitoring and consumable replacement management still rely on manual intervention and have failed to achieve truly unmanned operation.

[0003] 2. Insufficient equipment anomaly prediction and maintenance optimization: Existing systems mostly adopt simple threshold alarm mechanisms, such as issuing an alarm only when the incubator temperature exceeds a certain range, but cannot predict equipment anomalies based on historical data and machine learning models, resulting in many equipment problems not being detected in advance, affecting production stability.

[0003] 3. The consumable replacement mechanism is not integrated with the equipment monitoring system: In the prior art, consumable replacement is usually based on a fixed schedule or manual judgment, rather than dynamically adjusted according to the actual usage status. This may lead to premature replacement of consumables, increasing costs, or affect cell culture results due to failure to replace consumables in a timely manner. In addition, the existing technology lacks a linkage mechanism with the supply chain management system. When consumable inventory is insufficient, it cannot automatically trigger replenishment demand, which affects the production plan.

[0003] 4. Limited data integration capability 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, lacking a unified data analysis and decision-making mechanism, making it difficult for production managers to grasp the overall situation in a timely manner, affecting the pollution control and equipment operation management of cleanrooms.

[0003] 5. Manual operation remains a key factor affecting production efficiency and quality: Existing digital twin technology is still mainly used for passive monitoring and has not been deeply integrated with robotic arms or automated equipment to replace manual cell culture operations (such as inoculation, medium change, subculture, collection, etc.). This means that cell factories still need to rely on manual operation, which not only increases the risk of human error but also affects production efficiency and standardization.

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

[0005] In view of the above, the present invention provides a digital twin equipment monitoring system for modular cleanrooms, thereby solving the above-mentioned conventional problems.

[0006] This invention provides a digital twin device monitoring system for modular cleanrooms. The system includes: a three-dimensional model building module for building a three-dimensional model of a modular cleanroom and a device, wherein the modular cleanroom includes a cleanroom containing a plurality of partitions; a sensing data processing module coupled to the three-dimensional model building module for sensing an operating parameter and a consumable usage parameter of the device in the modular cleanroom to generate corresponding device status data and consumable replacement data; a digital twin module coupled to the three-dimensional model building module and the sensing data processing module for performing a simulation calculation on the device status data and the consumable replacement data, and predicting the maintenance time and consumable replacement timing of the device based on a machine learning model to generate maintenance suggestion data; and a visual display module coupled to the digital twin module for visually presenting the device status data, the consumable replacement data, and the maintenance suggestion data to provide a user with an immediate warning and maintenance suggestion.

[0007] The sensing data processing module further includes: at least one sensing device disposed on the partition and the device, for real-time detection of the device's operating parameters and consumable usage parameters, and for generating sensing data based on the operating parameters and consumable usage parameters, and transmitting the sensing data to the digital twin module.

[0008] The digital twin module further includes: a data processing and analysis unit, which predicts an optimal maintenance cycle and an optimal consumable replacement time for the device based on the device status data and the consumable replacement data through the machine learning model, and provides a device operation optimization suggestion.

[0009] The visual display module further includes: a mobile device, which further includes one of a mobile phone, a computer and a tablet, and receives the maintenance suggestion data in a wireless communication manner, and provides the user with real-time control and remote monitoring.

[0010] The digital twin module is further used to perform historical data analysis on the device status data to establish a device operation failure model, and to provide a device maintenance and replacement strategy suggestion based on the device operation failure model.

[0011] The equipment further includes one or more of the following: a cell culture incubator, a sterile cell operating table, a conveyor belt, a turntable, and a robotic arm.

[0012] The digital twin equipment monitoring system applied to the modular cleanroom is applied to a cell factory. The modular cleanroom further includes a plurality of cleanrooms, each cleanroom including a plurality of partitions. The partitions are used for rapid assembly to form an internal space and an external area. The cell factory is further a commissioned development and manufacturing service (CDMO) cell factory. Each of the partitions is a movable partition. The internal space of each of the cleanrooms is an airtight space. The airtight space is maintained at positive pressure and laminar flow to achieve a predetermined cleanliness level.

[0013] Wherein, when the cell factory arranges a year-long break or a production schedule, the cleanrooms shall, in accordance with the year-long break or the production schedule, quickly disassemble the movable partitions of a specific cleanroom within the cleanrooms to comply with the year-long break or the production schedule.

[0014] The digital twin equipment monitoring system applied to modular cleanrooms further includes: an early warning module coupled to the digital twin module and the visual display module, which sends an immediate warning to the user through the visual display module and provides maintenance suggestions when the early warning module receives equipment status data showing that the equipment may malfunction or the consumable replacement data reaches a preset threshold.

[0015] The consumable replacement data of the device further includes a consumable lifespan, a real-time consumption rate and a replacement cycle information, and establishes an optimal replacement schedule through the digital twin module.

[0016] Compared to conventional technologies, the digital twin equipment monitoring system of this invention, applied to modular cleanrooms, can establish a virtual cell culture environment through digital twin technology to simulate and monitor the operating status of equipment in multiple cleanrooms in real time. Furthermore, by incorporating machine learning models, this system predicts equipment anomalies and optimizes maintenance, adjusting maintenance cycles based on actual equipment operating data to extend equipment lifespan and reduce the risk of production interruptions. In addition, by integrating AI-driven robotic arm simulation and learning functions, this system analyzes the impact of robotic arm operations on cell culture results, ensuring the standardization and stability of the culture process. Data analysis is used to optimize mechanical clamping force, motion trajectory, and fluid replacement patterns, reducing the risk of cell damage. This system can further integrate data on equipment status, culture parameters, and consumable replacement, ensuring that all key equipment and production processes within the cell factory can be monitored in real time. Data analysis is used to optimize equipment operation strategies to reduce contamination and resource waste. Furthermore, by using sensing devices to monitor cleanroom environmental parameters (such as temperature, humidity, airflow, vibration, and gas concentration) in real time, dynamic environmental control is achieved, ensuring stable production conditions. This invention further establishes a multi-cleanroom contamination risk assessment and control mechanism. By simulating the operation of robotic arms and equipment in different cleanrooms using digital twins, it prevents cross-contamination. Through data accumulation and analysis, it provides optimal cleanroom configuration and contamination control strategies to reduce production risks and improve efficiency. Furthermore, by combining AR / VR remote monitoring technology with real-time control functions, this system further enhances the automation level of cell factories, allowing operators to remotely monitor equipment status and adjust operating parameters in real time. This reduces the need for personnel to enter and exit cleanrooms, effectively lowering contamination risks while improving production efficiency and operational stability. In summary, the digital twin equipment monitoring system for modular cleanrooms provided by this invention can comprehensively optimize the operation mode of cell factories, improve equipment management efficiency, ensure that the production environment meets high cleanliness standards, and promote the development of intelligent and automated cell culture technology. [Simplified Explanation of the Diagram]

[0017] Figure 1 is a functional block diagram of a digital twin device monitoring system applied to a modular cleanroom according to a specific embodiment of the present invention.

[0017] Figure 2 is a functional block diagram of a digital twin device monitoring system applied to a modular cleanroom according to another specific embodiment of the present invention.

[0017] Figure 3 is a functional block diagram of a digital twin device monitoring system applied to a modular cleanroom according to another specific embodiment of the present invention.

[0017] Figure 4 is a functional block diagram of a digital twin device monitoring system applied to a modular cleanroom according to another specific embodiment of the present invention.

Implementation Method

[0018] To make the advantages, spirit, and labels of the present invention easier and clearer to understand, detailed descriptions and discussions will follow with reference to the accompanying drawings and specific embodiments. It should be noted that these specific embodiments are merely representative examples of the present invention, and the specific methods, apparatus, conditions, materials, etc., exemplified are not intended to limit the present invention or the corresponding specific embodiments. Furthermore, the elements in the figures are only used to express their relative positions and are not drawn to scale; the step numbers in the present invention are only for distinguishing different steps and do not represent the order of the steps, as will be stated previously.

[0019] Please refer to Figure 1, which is a functional block diagram of a digital twin device monitoring system 1 applied to a modular cleanroom according to a specific embodiment of the present invention. This specific embodiment provides a digital twin device monitoring system 1 applied to a modular cleanroom, including a three-dimensional model building 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 building module 11 is used to build 3D models of the modular cleanroom (shown in the figure) and the equipment (shown in the figure). The modular cleanroom in this specific embodiment may include a cleanroom (shown in the figure), which includes a plurality of partitions. The partitions are used for rapid assembly to form an internal space and an external area. The equipment in this specific embodiment may be installed in the internal space. The equipment may further include a cell culture incubator, a cell sterile operating table, a conveyor belt, a turntable, a robotic arm, etc. The robotic arm can simulate human operations of cell culture steps (e.g., cell seeding, medium change, subculturing, sampling, etc.) and can be precisely monitored and optimized through digital twin technology. However, in practice, the types of equipment are not limited to those mentioned above. The equipment can be set up and selected according to user needs and the instruments required by the process.

[0021] In this specific embodiment, the sensing data processing module 12 is coupled to the three-dimensional model building module 11 and is used to sense the operating parameters and consumable usage parameters of the modular cleanroom equipment to generate corresponding equipment status data and consumable replacement data. In this specific embodiment, the operating parameters may further include ambient temperature, equipment temperature, ambient humidity, airflow changes, vibration frequency, etc., and the consumable usage parameters may include culture medium consumption, filter membrane replacement frequency, etc. In addition, in practice, for monitoring the robot arm, the sensing data processing module 12 can also record the robot arm's running trajectory, gripping pressure, moving speed, and operational accuracy in real time, ensuring that its actions conform to standardized procedures and reducing error risks.

[0022] In addition, the digital twin module 13 in this embodiment is coupled to the 3D model building module 11 and the perception data processing module 12 to simulate and calculate equipment status data and consumable replacement data, and predict equipment maintenance time and consumable replacement timing based on machine learning models to generate maintenance suggestion data. In this embodiment, for the robotic arm, the digital twin module 13 can also simulate the robotic arm's operating performance under different environmental conditions, such as different airflow conditions or temperature and humidity conditions, evaluate their impact and perform optimization settings to ensure motion stability and the repeatability of the cell culture process. The visual display module 14 in this embodiment is coupled to the digital twin module 13 and is used to visually present equipment status data, consumable replacement data, and maintenance suggestion data to provide a user with an immediate warning and maintenance suggestion. In practice, the 3D model building 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 used in modular cleanrooms can be integrated into the central processing unit of a computer system or cloud system, or integrated into an integrated chip.

[0023] In one specific embodiment, when the digital twin equipment monitoring system 1 applied to a modular cleanroom is used to monitor a robotic arm undergoing cell culture, the system can monitor the equipment status through digital twin technology and artificial intelligence (AI) machine learning, thereby reducing the cleanliness risks and operational errors caused by human operation of the robotic arm. Furthermore, through equipment operation data and airflow analysis, it can predict potentially affected sensitive areas in advance, preventing airflow interference or damage to the cell culture process from the source. This is particularly critical because cell culture requires precise and stable operation. Once the environment is affected by vibration or airflow changes, the cell division rate may decrease significantly, leading to greater data fluctuations and affecting the cell culture results. In this specific embodiment, the digital twin module 13 can also simulate the airflow dynamics within the cleanroom to assess the potential contamination risks caused by the robotic arm during operation and provide optimal operational recommendations. In addition, through data accumulation and algorithm optimization, the digital twin module 13 can gradually improve the standardization of the culture process, ensuring the operational stability of the robotic arm and improving the consistency and repeatability of the production process.

[0024] In practical applications, the digital twin device monitoring system 1 for modular cleanrooms in this specific embodiment can be applied to a high-cleanliness environment, which further includes a biopharmaceutical factory, a cleanroom production line, and a cell factory, to monitor the operating parameters of the equipment and ensure the operational stability of the equipment. When the digital twin device monitoring system 1 for modular cleanrooms in this specific embodiment is applied to a cell factory, the modular cleanroom can further include a plurality of cleanrooms. Each cleanroom includes a plurality of partitions, which are used for rapid assembly to form an internal space and an external area. In addition, the cell factory can further be a contract development and manufacturing service (CDMO) cell factory. Each partition is a movable partition, and the material of the partition can be stainless steel, aluminum alloy, high-pressure sealed plate (HPL), or other composite materials. Each partition can be quickly installed or removed from the partitions through connecting components, such as sealing strips, tenons, or connecting clips. Each cleanroom's interior space is an airtight space. Maintaining positive pressure and laminar flow within this airtight space ensures that the cleanroom achieves a predetermined cleanliness level. The predetermined cleanliness level can be Class 4 (i.e., conforming to ISO 8 standards). In practice, the materials used for partitions and connecting components are not limited to this and can be adjusted and designed according to user needs, production requirements, or specifications.

[0025] Because modular cleanrooms can be assembled using movable partitions, the space utilization efficiency within the cell factory can be improved. The space required for each schedule can be quickly and flexibly planned according to production needs, and multiple internal areas can be rapidly assembled from multiple partitions, thereby enhancing the overall flexibility and production efficiency of the cell factory. Furthermore, regarding the operating environment of the robotic arm, digital twin simulation technology can be used to monitor the performance of the robotic arm under different environmental parameters in real time, ensuring its accuracy and stability under different cleanroom conditions. Further, in this specific embodiment, the partitions with rapid assembly and disassembly characteristics allow each cleanroom to be an independent and isolated space. Therefore, the digital twin equipment monitoring system for modular cleanrooms provided by this invention, when applied to a CDMO cell factory, can adjust the number of cleanrooms or move them to different locations for maintenance or production scheduling, even when the equipment within the cell factory requires annual maintenance. In addition, depending on the independence of the equipment inside each cleanroom or the need for rotational annual rest, that is, when the cell factory arranges an annual rest or a production schedule, the cleanroom can use quick-release movable partitions to conform to the annual rest or production schedule for specific cleanrooms.

[0026] The digital twin equipment monitoring system 1 applied to modular cleanrooms in this specific embodiment can combine various machine learning models to improve the accuracy and automation of equipment monitoring, anomaly prediction, maintenance optimization, and contamination risk control in modular cleanrooms. Among these, equipment anomaly detection and prediction models (such as LSTM and random forests) can identify potential faults in advance based on equipment operating data, avoiding unplanned downtime; optimal maintenance scheduling models (such as reinforcement learning and Bayesian networks) dynamically adjust maintenance cycles based on real-time data, extending equipment lifespan. For the motion control of the robotic arm, the system in this specific embodiment uses deep reinforcement learning and CNN models to optimize clamping force and motion trajectory, reducing the impact of shear force during cell culture. Furthermore, through environmental control and contamination risk assessment models (such as decision trees and graph neural networks), the system can monitor airflow, temperature, humidity, and particulate concentration in the cleanroom in real time, predict contamination risks, and adjust operating strategies to avoid cross-contamination. Regarding consumable management and supply chain optimization, through LSTM and XGBoost models, the system can predict the replacement timing of consumables such as culture media and filter membranes, ensuring supply chain stability. Finally, by combining digital twin simulations and operational optimization models (such as GANs and simulation reinforcement learning), the system in this specific embodiment can predict the optimal production process, further improving the operational efficiency and production stability of cleanroom equipment and ensuring high-standard execution of cell culture and biopharmaceutical processes. However, in practice, the types and forms of machine learning models are not limited to this. 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 effects.

[0027] Please refer to Figure 2, which is a functional block diagram of a digital twin device monitoring system 2 applied to a modular cleanroom according to a specific embodiment of the present invention. The difference between this specific embodiment and the aforementioned specific embodiments is that the sensing data processing module 12 in this specific embodiment further includes at least one sensing device 121. The sensing device 121 is disposed on partitions, the device body, airflow ducts, and key areas of the cleanroom, and is used to detect the operating parameters of the device and the consumable usage parameters in real time, 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 placement of the sensing device 121 is not limited to those mentioned above, and can be flexibly adjusted and optimized according to the parameters the user wishes to monitor, the operating characteristics of the device, environmental conditions, and measurement requirements. The sensing device 121 in this specific embodiment may include, but is not limited to, the following types:

[0027] 1. Temperature and humidity sensor: monitors changes in temperature and humidity inside and outside the equipment to ensure a stable cell culture environment.

[0027] 2. Airflow sensor: detects the airflow speed and direction in the clean room to ensure that the sterile conditions are maintained at the best.

[0027] 3. Vibration and pressure sensors: Real-time monitoring of vibration and stress changes during the operation of the robotic arm to ensure the stability of the cell manipulation process.

[0027] 4. Image and optical sensors: Through high-resolution cameras and optical sensing technology, monitor cell culture status, equipment operation status and abnormal changes.

[0027] 5. Gas and pollutant sensors: monitor CO and O concentrations and the number of airborne particles in the clean room to ensure that the environmental quality meets the standards of the biological process.

[0027] 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 transmitted in real time to the digital twin module 13 for subsequent analysis and optimization of equipment operation strategies. This method not only improves the stability of the cell culture process, but also reduces the need for manual monitoring and lowers production risks through data-driven approaches. Please note that the other modules, models, and corresponding functions in the digital twin equipment monitoring system 2 applied to the modular cleanroom in this specific embodiment are largely the same as the corresponding modules in the aforementioned specific embodiments, and therefore will not be described again here. Please refer to Figure 3, which is a functional block diagram of the digital twin equipment monitoring system 3 applied to the modular cleanroom according to a specific embodiment of the present invention. The digital twin module 13 in the digital twin equipment monitoring system 3 applied to the modular cleanroom in this specific embodiment further includes a data processing and analysis unit 131. The data processing and analysis unit 131 is used to predict the optimal maintenance cycle and optimal consumable replacement time of the equipment based on equipment status data and consumable replacement data through a machine learning model, and to provide equipment operation optimization suggestions. In practice, the digital twin device monitoring system 3 for modular cleanrooms in this specific embodiment can dynamically analyze and optimize the operation of the robotic arm. It adjusts its operating strategy through AI technology, for example, by using AI analysis to optimize mechanical gripping force, motion trajectory, and media exchange mode to ensure low shear force impact during cell culture operations and reduce the possibility of cell damage. Furthermore, the system in this specific embodiment can also detect real-time temperature changes, vibration frequency, and operational accuracy of the robotic arm, ensuring long-term operational stability and predicting potential abnormalities to avoid affecting culture results due to equipment deviations. Please note that the other modules, models, and corresponding functions in the digital twin device monitoring system 3 for modular cleanrooms in this specific embodiment are largely the same as those in the aforementioned specific embodiments, and therefore will not be described again here.

[0029] In another specific embodiment, the digital twin module 13 is further used to perform historical data analysis on the equipment status data to establish an equipment operation failure model, and to provide equipment maintenance and replacement strategy suggestions based on the equipment operation failure model. The equipment consumable replacement data further includes consumable lifespan, real-time consumption rate, and replacement cycle information, and an optimal replacement schedule is established through the digital twin module to reduce unplanned equipment downtime. In addition, the system of this specific embodiment can also use accumulated data to establish a predictive model to further optimize the equipment lifespan and maintenance strategy, ensure long-term stable operation of the equipment, and reduce the risk of sudden failures.

[0030] In another specific embodiment, the visual display module 14 further includes a mobile device. The mobile device may further include a mobile phone, computer, or tablet, and receives maintenance suggestion data via wireless communication, providing users with real-time control and remote monitoring. In practical applications, the visual display module 14 may also include a multi-device monitoring interface and have AR / VR remote monitoring capabilities to display the real-time status of all cleanroom equipment, including the operation status of incubators, operating tables, robotic arms, and other equipment. In addition, through AR / VR technology, the system can provide remote monitoring and operation simulation, enabling operators to adjust equipment parameters in real time, reducing the need for personnel to enter the cleanroom, and further reducing the risk of cross-contamination. In this specific embodiment, the 3D model building module 11, the perception data processing module 12, the digital twin module 13, and the visual display module 14 can synchronize data via wireless communication (Wi-Fi, Zigbee, 5G) or wired communication (Ethernet, RS-485) to provide real-time operation monitoring and ensure the accuracy and timeliness of equipment data.

[0031] Please refer to Figure 4, which is a functional block diagram of a digital twin device monitoring system 4 applied to a modular cleanroom according to a specific embodiment of the present invention. The digital twin device monitoring system applied to a modular cleanroom in this specific embodiment further includes an early warning module 15, coupled to a digital twin module 13 and a visual display module 14, to monitor the equipment operating status in real time and provide abnormal warnings and maintenance suggestions. When the early warning module 15 receives equipment status data showing that the equipment may malfunction (such as excessive temperature, abnormal airflow, abnormal vibration, or abnormal consumable usage) or when consumable replacement data reaches a preset threshold, it can send real-time warnings and maintenance suggestions to the user through the visual display module 14. In addition, the early warning module 15 can also send notifications to mobile devices (such as mobile phones, tablets, and computers), enabling operators to perform remote control to ensure the stability and continuity of the equipment.

[0032] Furthermore, the system of the present invention can correspond to the actual cell factory production line process. Through continuous data monitoring, it proactively identifies, discovers, or detects anomalies or differences, and classifies and evaluates these anomalies through built-in data analysis and machine learning algorithms. When the system detects an anomaly, the early warning module 15 can issue an immediate warning and provide diagnostic information, such as the possible causes of the anomaly, the scope of its impact, and suggested solutions, to assist operators in subsequent control and decision-making. For example, during cell culture, when the robotic arm performs medium replacement, its operating parameters (clamping force, movement trajectory, shear force influence) will be monitored. If an abnormality in medium replacement or uneven replacement of the culture medium is detected, the system can immediately send a notification and suggest adjusting the robotic arm parameters or calibrating the equipment to ensure the stability of the culture process. The application of this technology can not only improve the stability of the internal operating environment of the clean room, but also establish accurate predictive models through data analysis, optimize equipment maintenance cycles, reduce unplanned downtime, and reduce production risks. Please note that the other modules, models, and corresponding functions in the digital twin equipment monitoring system 4 applied to the modular cleanroom in this specific embodiment are largely the same as the corresponding modules in the aforementioned specific embodiments, and therefore will not be described again here.

[0033] Compared to conventional technologies, the digital twin equipment monitoring system of this invention, applied to modular cleanrooms, can establish a virtual cell culture environment through digital twin technology to simulate and monitor the operating status of equipment in multiple cleanrooms in real time. Furthermore, by incorporating machine learning models, this system predicts equipment anomalies and optimizes maintenance, adjusting maintenance cycles based on actual equipment operating data to extend equipment lifespan and reduce the risk of production interruptions. In addition, by integrating AI-driven robotic arm simulation and learning functions, this system analyzes the impact of robotic arm operations on cell culture results, ensuring the standardization and stability of the culture process. Data analysis is used to optimize mechanical clamping force, motion trajectory, and fluid replacement patterns, reducing the risk of cell damage. This system can further integrate data on equipment status, culture parameters, and consumable replacement, ensuring that all key equipment and production processes within the cell factory can be monitored in real time. Data analysis is used to optimize equipment operation strategies to reduce contamination and resource waste. Furthermore, by using sensing devices to monitor cleanroom environmental parameters (such as temperature, humidity, airflow, vibration, and gas concentration) in real time, dynamic environmental control is achieved, ensuring stable production conditions. This invention further establishes a multi-cleanroom contamination risk assessment and control mechanism. By simulating the operation of robotic arms and equipment in different cleanrooms using digital twins, it prevents cross-contamination. Through data accumulation and analysis, it provides optimal cleanroom configuration and contamination control strategies to reduce production risks and improve efficiency. Furthermore, by combining AR / VR remote monitoring technology with real-time control functions, this system further enhances the automation level of cell factories, allowing operators to remotely monitor equipment status and adjust operating parameters in real time. This reduces the need for personnel to enter and exit cleanrooms, effectively lowering contamination risks while improving production efficiency and operational stability. In summary, the digital twin equipment monitoring system for modular cleanrooms provided by this invention can comprehensively optimize the operation mode of cell factories, improve equipment management efficiency, ensure that the production environment meets high cleanliness standards, and promote the development of intelligent and automated cell culture technology.

[0034] The detailed description of the preferred embodiments above is intended to more clearly illustrate the features and spirit of the present invention, and is not intended to limit the scope of the present invention by the preferred embodiments disclosed above. Rather, the aim is to cover various modifications and equivalent arrangements within the scope of the patent claims made by the present invention. Therefore, the scope of the patent claims made by the present invention should be interpreted in the broadest possible sense based on the foregoing description, so as to cover all possible modifications and equivalent arrangements.

Claims

1. A digital twin equipment monitoring system for a modular cleanroom, the system comprising: a 3D model building module for building a 3D model of a modular cleanroom and an equipment, wherein the modular cleanroom includes a cleanroom containing a plurality of partitions; a sensing data processing module coupled to the 3D model building module, the sensing data processing module for sensing an operating parameter and a consumable usage parameter of the equipment in the modular cleanroom to generate corresponding equipment status data and consumable replacement data; a digital twin module coupled to the 3D model building module and the sensing data processing module for performing a simulation calculation on the equipment status data and the consumable replacement data, and predicting the maintenance time of the equipment and the timing of consumable replacement based on a machine learning model to generate maintenance suggestion data; and a visual display module coupled to the digital twin module for visually presenting the equipment status data, the consumable replacement data, and the maintenance suggestion data to provide a user with immediate alerts and maintenance suggestions; wherein... The sensing data processing module further includes at least one sensing device disposed on the partitions and the device, for real-time detection of the device's operating parameters and consumable usage parameters, and for generating sensing data based on the operating parameters and consumable usage parameters, and transmitting the sensing data to the digital twin module.

2. The digital twin equipment monitoring system for modular cleanrooms as described in claim 1, wherein the digital twin module further includes: a data processing and analysis unit for predicting an optimal maintenance cycle and an optimal consumable replacement time for the equipment based on the equipment status data and the consumable replacement data through the machine learning model, and providing an equipment operation optimization suggestion.

3. The digital twin equipment monitoring system for modular cleanrooms as described in claim 1, wherein the visual display module further includes: a mobile device, which further includes one of a mobile phone, a computer, and a tablet, and receives the maintenance suggestion data via wireless communication, and provides the user with real-time control and remote monitoring.

4. The digital twin equipment monitoring system for modular cleanrooms as described in claim 1, wherein the digital twin module is further used to perform historical data analysis on the equipment status data to establish an equipment operation failure model, and to provide equipment maintenance and replacement strategy recommendations based on the equipment operation failure model.

5. The digital twin device monitoring system for modular cleanrooms as described in claim 1, wherein the device further comprises one or more of a cell culture incubator, a cell sterile operating table, a conveyor belt, a turntable, and a robotic arm.

6. The digital twin device monitoring system for modular cleanrooms as described in claim 1, wherein the digital twin device monitoring system for modular cleanrooms is applied to a cell factory, the modular cleanroom further comprising a plurality of cleanrooms, each cleanroom comprising a plurality of partitions, the partitions being used for rapid assembly to form an internal space and an external area, and the cell factory further being a commissioned development and manufacturing service (CDMO) cell factory, each of the partitions being a movable partition, the internal space of each of the cleanrooms being an airtight space, the airtight space being maintained at positive pressure and laminar flow to achieve a predetermined cleanliness level for the internal space of the cleanroom.

7. The digital twin equipment monitoring system for modular cleanrooms as described in claim 6, wherein when the cell factory schedules a year-end or a production schedule, the cleanrooms adjust the specific cleanrooms within the cleanrooms according to the year-end or the production schedule by quickly disassembling the movable partitions, thereby conforming to the year-end or the production schedule.

8. The digital twin device monitoring system for modular cleanrooms as described in claim 1 further includes: an early warning module coupled to the digital twin module and the visual display module, for sending an immediate warning to the user and providing maintenance suggestions through the visual display module when the early warning module receives data showing that the device may malfunction or that the consumable replacement data reaches a preset threshold.

9. The digital twin device monitoring system for modular cleanrooms as described in claim 1, wherein the consumable replacement data of the device further includes a consumable lifespan, a real-time consumption rate, and a replacement cycle information, and an optimal replacement schedule is established through the digital twin module.

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