Rapid deployment method for modular pharmaceutical factory

Abstract

The invention discloses a rapid deployment method for a modular pharmaceutical factory. According to the invention, a large amount of verification work is moved forward to a module manufacturing stage, and all necessary verification links are completed before each module leaves a factory by means of the design of the pre-verification black box. The LSTM model is combined to analyze equipment operation time sequence data, potential faults can be recognized in advance, it is ensured that the module is in a stable and compliant state during dispatching, and verification delay or failure caused by factors such as weather and manual errors on site is avoided. According to the mode, the passive situation of traditional field verification is thoroughly changed, the whole deployment process is more controllable, the time window from design to production is greatly compressed, meanwhile, the compliance risk is reduced, an enterprise can more quickly achieve application of product marketing, digital collaborative design and virtual simulation, technological parameters are optimized in combination with an algorithm model, and the technical efficiency is improved. The rationality and compatibility of module design are ensured, and the overall quality and compliance are improved.

Description

Technical Field

[0001] This invention belongs to the field of pharmaceutical technology, specifically a method for the rapid deployment of a modular pharmaceutical factory. Background Technology

[0002] Modular pharmaceutical plants are a modern construction model that breaks down pharmaceutical processes into multiple standardized functional units. These units are pre-designed, manufactured, assembled, and debugged within the factory before being transported to the project site for rapid integration and installation. This "plug-and-play" construction method, through highly integrated modular design, significantly shortens the construction cycle, reduces on-site construction complexity and the risk of cross-contamination, and significantly improves the controllability of project quality. Its flexible modular combination can quickly respond to changes in market demand, easily achieving capacity adjustments or process upgrades. It is particularly suitable for the research and commercial production of innovative drugs such as biopharmaceuticals, cell and gene therapies, and is an important technological path driving the pharmaceutical industry towards agility, intelligence, and flexibility.

[0003] However, while existing modular pharmaceutical technologies can shorten some of the construction cycle, validation still needs to be completed on-site, which makes it impossible to effectively compress the overall cycle. The adaptation of multiple products on the same line requires complex risk assessment, making it difficult to respond quickly to changes in demand. The lack of unified standards for module interfaces increases the workload and uncertainty of on-site integration. Quality control relies on on-site experience and may overlook design flaws. Document management is mainly paper-based, which may lead to version confusion and data inconsistency, making it difficult to meet the needs of rapid and compliant production. Summary of the Invention

[0004] The purpose of this invention is to provide a method for the rapid deployment of a modular pharmaceutical plant in order to solve the problems mentioned above.

[0005] The technical solution adopted in this invention is as follows: a method for rapid deployment of a modular pharmaceutical factory, the method comprising the following steps:

[0006] S1: Collect the product process package provided by the customer, which covers the entire process parameters of API synthesis and formulation production, and convert it into standardized design input documents;

[0007] S2: Based on the design input of S1, BIM technology is used to build a multi-disciplinary integrated model, and a digital twin environment simulation of equipment layout, personnel and material flow and cleanroom zoning is built simultaneously; spatial conflicts or process optimization points found during the simulation will be fed back to the process package adjustment stage of S1 in real time, and the final generated model data will guide the module division work of S3.

[0008] S3: Based on the simulation results of S2, the factory is decomposed into functionally independent physical module units, and a standard process for module pre-verification is formulated.

[0009] S4: In accordance with the S3 division scheme and pre-verification standards, module manufacturing and internal integration are completed in parallel in the controlled workshop; all pre-verifications are completed before the module is shipped, forming a fully functional verification black box;

[0010] S5: Based on the module size and weight of S4, design standardized transport fixing frames and interface protection devices to ensure long-distance transportation safety; at the same time, complete the site foundation leveling and embedded part layout based on the BIM model data of S2 to provide precise support for subsequent module placement.

[0011] S6: Transport the prefabricated modules from S4 to the site, use the BIM model from S2 for laser scanning registration, and quickly complete the standardized interface connection between modules; the verification process directly calls the pre-verification data from S4.

[0012] S7: Based on the digital twin model of S2 and the verification data of S6, launch the remote monitoring and predictive maintenance system; the equipment operation data collected during the operation and maintenance process will be fed back to the module division standard of S3 to continuously optimize the module reuse rate and deployment efficiency, forming a closed loop of the whole process.

[0013] In a preferred embodiment, in step S1, the product process package provided by the customer is comprehensively collected, covering the entire process parameters of active pharmaceutical ingredient synthesis, formulation production, purified water distribution, and HVAC unit, including equipment model, process parameter range, cleanliness level requirements, and material compatibility standards. This information is then converted into standardized design input documents that comply with GMP specifications.

[0014] In a preferred embodiment, in step S2, based on the design input file of S1, a multi-disciplinary integrated model of architecture, structure, electromechanical, and process equipment is constructed using tools. Simultaneously, a digital twin environment simulation of equipment layout, personnel and material flow, and cleanroom zoning is built. The pressure differential gradient in the cleanroom is maintained at ≥10Pa and the airflow organization uniformity meets GMP requirements through CFD simulation. Spatial conflicts and cross-contamination risks are identified by simulating the personnel and material flow paths. Problems found during the simulation are fed back to S1 in real time to adjust the process package content. The final generated model data must include the dimensions, interface locations, equipment parameters, and cleanliness level information of each module.

[0015] In a preferred embodiment, in step S3, based on the simulation results of S2, the factory is decomposed into functionally independent physical module units, including a raw material synthesis module, a solid dosage form production module, a purified water distribution module, and an HVAC unit module. The module layout adopts a U-shaped or I-shaped design to optimize logistics and cleanroom zoning. At the same time, a standard process for module pre-validation is established. The standard must clearly define the verification of equipment model for installation confirmation, installation location verification, pipeline connection sealing requirements, the range of no-load operating parameters for equipment for operational confirmation, the static pressure difference for cleanroom airtightness testing ≥15Pa, and the leakage rate for HEPA filter integrity testing ≤0.01%.

[0016] In a preferred embodiment, in step S4, module manufacturing and internal integration are carried out in parallel in a controlled cleanroom; the module adopts an integrated design of steel frame and cleanroom panel, and the joints are sealed with silicone to ensure airtightness; the internal integration includes the installation of sealed mixing tanks, ASME BPE standard pipe connections, and electrical wiring for microcomputer protection devices.

[0017] In a preferred embodiment, in step S4, during the pre-verification operation confirmation stage of the module, in order to ensure that there are no potential faults before the equipment is shipped, an LSTM model is used to analyze the time-series operation data of the equipment. In the OQ test of the motor of the mixing tank of the closed granulation module, the vibration acceleration and bearing temperature time-series data of the motor are collected, and the LSTM model is used to predict whether there will be any abnormal trend of the motor in the next 24 hours.

[0018] In a preferred embodiment, in step S4, the LSTM cell state update formula is:

[0019] ;

[0020] In the formula:

[0021] C t This represents the motor operating state captured by the model at the current moment; f t Indicates the output of the forget gate; C t−1 Indicates the motor's operating state at the previous moment; i t Indicates the input gate output; C~ t Indicates a candidate state; ⊙ represents element-wise multiplication;

[0022] The formula for the LSTM input gate is:

[0023] ;

[0024] In the formula:

[0025] σ represents the sigmoid activation function;

[0026] W iRepresents the input gate weight matrix;

[0027] h t−1 This indicates the previously hidden state;

[0028] x t This represents the motor vibration acceleration data at the current moment;

[0029] b i Indicates the input gate bias term;

[0030] The formula for the LSTM forget gate is:

[0031] ;

[0032] In the formula:

[0033] W f Forget gate weight matrix; b f Forget gate bias term;

[0034] The formula for LSTM candidate cell states is:

[0035] ;

[0036] In the formula:

[0037] tanh represents the hyperbolic tangent activation function; W c Table: Candidate cell state weight matrix; b c Table of candidate cell state bias terms;

[0038] The formula for the hidden state of LSTM is:

[0039] ;

[0040] In the formula:

[0041] h t Indicates the hidden state at the current moment. t This indicates the output of the output gate.

[0042] In a preferred embodiment, in step S5, a standardized transport frame is designed based on the module size and weight in S4. The frame adopts an adjustable clamp and elastic band structure, meets the seismic requirements of UN38.3 certification, and has a maximum load capacity of ≥20 tons. At the same time, an interface protection device is designed, using a sealing system with airtight, watertight, and fireproof functions, and the thickness of the buffer material is ≥50mm. On-site foundation preparation requires the completion of foundation leveling, with an error control within ≤±5mm, and the deviation of the embedded parts layout position within ≤±10mm. Laser scanning is used to scan the actual data on site, with a scanning resolution of ≥6mm and a point density of ≥1 million points / m².

[0043] In a preferred embodiment, in step S6, after the prefabricated module from S4 is transported to the site, laser scanning technology is used to acquire actual site data, which is then registered with the BIM model. The registration error is controlled within ≤±15mm to guide the precise placement of the module. The connection between modules adopts standardized interfaces. The mechanical interface conforms to the ASME BPE standard, the electrical interface follows the IEC61158 protocol, and the data interface adopts the industrial Ethernet and OPC UA protocols.

[0044] In a preferred embodiment, in step S7, based on the digital twin model of S2 and the verification data of S6, the continuous operation support system is started, and the vibration, temperature and pressure operation data of the equipment are collected in real time through IoT sensors, with a data sampling frequency of ≥1 time / second; the equipment failure trend is analyzed by combining the LSTM prediction model, and the predictive maintenance trigger threshold is set; the equipment operation data and module performance data collected during the operation and maintenance process will be fed back to the module division standard of S3.

[0045] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0046] 1. In this invention, by moving a significant amount of verification work forward to the module manufacturing stage and utilizing a pre-verification black box design, each module completes all necessary verification steps before leaving the factory. Combined with LSTM model analysis of device runtime sequence data, potential faults can be identified in advance, ensuring that modules are in a stable and compliant state upon shipment, avoiding verification delays or failures caused by weather, human error, or other factors. This model completely changes the passive nature of traditional on-site verification, making the entire deployment process more controllable, significantly shortening the time window from design to production, reducing compliance risks, and enabling companies to bring products to market faster.

[0047] 2. In this invention, the application of digital collaborative design and virtual simulation, combined with the optimization of process parameters using algorithm models, ensures the rationality and compatibility of the module design, improving overall quality and compliance. The modular design also makes the factory highly replicable; different product or capacity requirements can be quickly adapted through modules, significantly reducing the cost and time required for new factory construction and providing strong support for pharmaceutical companies to quickly respond to market demands. Attached Figure Description

[0048] Figure 1 This is a schematic diagram illustrating the process principle of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0050] Reference Figure 1 A method for rapid deployment of a modular pharmaceutical plant, comprising the following steps:

[0051] S1: Collect the product process package provided by the customer, which covers the entire process parameters from API synthesis to formulation production, and convert it into standardized design input documents;

[0052] S2: Based on the design input of S1, BIM technology is used to build a multi-disciplinary integrated model, and a digital twin environment simulation of equipment layout, personnel and material flow and cleanroom zoning is built simultaneously. Spatial conflicts or process optimization points found during the simulation will be fed back to the process package adjustment stage of S1 in real time. The final generated model data will guide the module division work of S3.

[0053] S3: Based on the simulation results of S2, the factory is decomposed into functionally independent physical module units. At the same time, standard procedures for module pre-verification are formulated. These standards will be directly applied to the module prefabrication process in S4 to ensure the consistency of pre-verification data.

[0054] S4: In accordance with the division scheme and pre-verification standards of S3, module manufacturing and internal integration are completed in parallel in the controlled workshop; all pre-verifications are completed before the modules are shipped to form a fully functional verification black box. The pre-verification data will be synchronized to the system verification stage of S6 to avoid repeated verification on site.

[0055] S5: Based on the module size and weight of S4, design standardized transport fixing frames and interface protection devices to ensure long-distance transportation safety; at the same time, complete the site foundation leveling and embedded part layout based on the BIM model data of S2 to provide precise support for subsequent module placement.

[0056] S6: Transport the prefabricated modules from S4 to the site, use the BIM model from S2 for laser scanning registration, and quickly complete the standardized interface connection between modules; in the verification phase, directly call the pre-verification data from S4, focusing on confirming the linkage logic and process stability between modules, and significantly shorten the on-site verification cycle.

[0057] S7: Based on the digital twin model of S2 and the verification data of S6, launch the remote monitoring and predictive maintenance system; the equipment operation data collected during the operation and maintenance process will be fed back to the module division standard of S3 to continuously optimize the module reuse rate and deployment efficiency, forming a closed loop of the whole process.

[0058] In step S1, the product process package provided by the customer is comprehensively collected, covering the entire process parameters such as API synthesis, formulation production, purified water distribution, and HVAC units. This includes equipment models, process parameter ranges, cleanliness level requirements, and material compatibility standards. This information is then transformed into standardized design input documents that comply with GMP regulations. These documents must clearly define key information such as the spatial requirements of each functional unit, material flow paths, cleanroom zoning boundaries, and equipment installation accuracy requirements. These documents will directly serve as the core basis for the digital collaborative design in step S2, ensuring that the subsequent design direction is fully aligned with the customer's needs and avoiding any deviations from requirements.

[0059] In step S2, based on the design input file of S1, BIM tools are used to construct an integrated model of multiple disciplines including architecture, structure, MEP, and process equipment. Simultaneously, a digital twin environment simulation of equipment layout, personnel and material flow, and cleanroom zoning is built. CFD simulation is used to maintain the pressure gradient of the cleanroom at ≥10Pa and ensure that the airflow organization uniformity meets GMP requirements. By modeling and simulating the personnel and material flow paths, spatial conflicts and cross-contamination risks are identified. Problems found during the simulation are fed back to S1 in real time to adjust the process package content. The final generated model data must include information such as the size, interface location, equipment parameters, and cleanliness level of each module, directly guiding the module division work in S3 and ensuring the rationality of the module division.

[0060] In step S3, based on the simulation results of S2, the factory is decomposed into functionally independent physical module units, including API synthesis module, solid dosage form production module, purified water distribution module, HVAC unit module, etc. The module layout adopts U-shaped or I-shaped optimization of logistics and cleanroom zoning. At the same time, a standard process for module pre-validation is established. The standard needs to clearly define the verification of equipment model for installation confirmation, installation location verification, pipeline connection sealing requirements, no-load operating parameter range of equipment for operational confirmation, static pressure difference ≥15Pa for cleanroom airtightness test, and leakage rate ≤0.01% for HEPA filter integrity test, etc. These standards will be directly applied to the module prefabrication process in S4 to ensure the consistency and compliance of pre-validation data.

[0061] In step S4, module manufacturing and internal integration are carried out in parallel within a controlled cleanroom. The modules feature an integrated design of steel frame and cleanroom panels, with joints sealed with silicone to ensure airtightness. Internal integration includes the installation of sealed mixing tanks, ASME BPE standard piping connections, and electrical wiring for microprocessor-based protection devices. Before shipment, each module undergoes full pre-validation: Installation Qualification (IQ) verifies the equipment model against the design drawings; Operational Qualification (OQ) tests the equipment's no-load operating parameters and ensures cleanliness standards are met through airtightness and HEPA filter integrity tests. To ensure the reliability of pre-validation data, an LSTM model is used in the OQ phase to analyze the equipment's time-series operating data and predict potential failures. All pre-validation data generates an electronic report forming a "validation black box," which is synchronized to the system validation stage in S6 to avoid redundant validation.

[0062] In step S4, during the pre-verification operation confirmation stage of the module, in order to ensure that there are no potential faults before the equipment is shipped, an LSTM model is used to analyze the time-series operation data of the equipment. In the OQ test of the motor of the agitator in the closed granulation module, the vibration acceleration (unit: m / s²) and bearing temperature (unit: ℃) of the motor are collected in time series. The LSTM model is used to predict whether there will be any abnormal trend of the motor in the next 24 hours.

[0063] In step S4, the LSTM cell state update formula is:

[0064] ;

[0065] In the formula:

[0066] C t This represents the motor's operating state captured by the model at the current moment (reflecting the presence of potential wear); f t The output of the forget gate (determines the proportion of the previous state to be retained, such as the weight for retaining the temperature trend from the previous hour); C t−1 Indicates the motor's operating state at the previous moment; i t Indicates the input gate output (determines the influence weight of the current vibration data); C~ t ⊙ represents a candidate state (a new state calculated based on current vibration and temperature data); ⊙ represents element-wise multiplication.

[0067] The formula for the LSTM input gate is:

[0068] ;

[0069] In the formula:

[0070] σ represents the sigmoid activation function;

[0071] W iThis represents the input gate weight matrix (obtained by training historical fault data of the mixing tank motor).

[0072] h t−1 This indicates the hidden state at the previous moment (representing the previous motor operating mode);

[0073] x t This represents the motor vibration acceleration data at the current moment;

[0074] b i Indicates the input gate bias term;

[0075] The formula for the LSTM forget gate is:

[0076] ;

[0077] In the formula:

[0078] W f Forget gate weight matrix; b f Forget gate bias term;

[0079] The formula for LSTM candidate cell states is:

[0080] ;

[0081] In the formula:

[0082] tanh represents the hyperbolic tangent activation function; W c Table: Candidate cell state weight matrix; b c Table of candidate cell state bias terms;

[0083] The formula for the hidden state of LSTM is:

[0084] ;

[0085] In the formula:

[0086] h t This indicates the current hidden state (output to the subsequent fault diagnosis module). t Indicates the output of the output gate;

[0087] In step S5, a standardized transport frame is designed based on the module dimensions and weight from S4. The frame uses adjustable clamps and elastic bands, meeting the seismic requirements of UN38.3 certification, with a maximum load capacity of ≥20 tons. Simultaneously, an interface protection device is designed, employing a sealing system with airtightness, watertightness, and fire resistance, and a buffer material thickness of ≥50mm. On-site foundation preparation requires leveling the ground with an error control of ≤±5mm, and the deviation of the embedded parts' placement ≤±10mm. Laser scanning of actual on-site data is used, with a scanning resolution ≥6mm and a point density ≥1 million points / m², comparing it with the BIM model to guide precise installation and provide stable support for subsequent module placement.

[0088] In step S6, after the prefabricated modules from S4 are transported to the site, laser scanning technology is used to acquire actual site data, which is then registered with the BIM model. The registration error is controlled within ≤±15mm to guide the precise positioning of the modules. Standardized interfaces are used for module connections. Mechanical interfaces conform to ASME BPE standards, electrical interfaces follow the IEC61158 protocol, and data interfaces use industrial Ethernet and OPC UA protocols to quickly complete the connection of mechanical, electrical, and data links. The system-level verification process directly calls the pre-verification data from S4, eliminating the need to repeat the IQ / OQ of individual devices. The focus is on confirming the linkage logic between modules, including the smoothness of material transfer paths, the synchronization accuracy of process parameters, and the stability of pressure difference in the clean area. The process verification PQ requires the production of at least three batches of products that meet the quality standards to verify the stability and repeatability of the process.

[0089] In step S7, based on the digital twin model in S2 and the verification data in S6, the continuous operation support system is launched. The system collects real-time operating data such as equipment vibration, temperature, and pressure through IoT sensors, with a data sampling frequency of ≥1 time / second. The system analyzes equipment failure trends using an LSTM prediction model and sets predictive maintenance trigger thresholds. The equipment operation data and module performance data collected during the operation and maintenance process are fed back into the module division criteria in S3 to optimize module design, improve module reuse rate to ≥80%, and form a closed loop from design to operation and maintenance, thereby reducing the construction cost of subsequent new factories.

[0090] In this application, the abbreviations and English names and their meanings are explained as follows:

[0091] BIM: Building Information Modeling, used to build multi-disciplinary integrated models, simulate equipment layout, personnel and material flow and cleanroom zoning, and is the core technology of modular factory design.

[0092] LSTM: Long Short-Term Memory Network, a deep learning model used to analyze the timing data of equipment (such as motor vibration and temperature), predict potential faults, and ensure the stability of modules before shipment.

[0093] GMP: Good Manufacturing Practice for Pharmaceuticals, the core compliance standard in the pharmaceutical industry, all design and validation processes must comply with its requirements.

[0094] CFD: Computational Fluid Dynamics, used to simulate airflow organization in clean areas to ensure that pressure gradient (≥10Pa) and airflow uniformity meet GMP requirements.

[0095] ASME BPE: American Society of Mechanical Engineers standard for bioprocessing equipment, a design standard for mechanical interfaces between modules to ensure the sealing and compatibility of pipe connections.

[0096] UN38.3: The United Nations standard for the safety of transporting lithium batteries. Transport fixtures must comply with this standard to ensure the shock resistance and safety of modules during long-distance transport.

[0097] IEC 61158: International Electrotechnical Commission Fieldbus Standard, a communication protocol standard for electrical interfaces between modules to ensure the compatibility of electrical systems.

[0098] OPC UA: Open platform unified communication architecture, communication protocol standard for data interfaces between modules, supporting data transmission over industrial Ethernet.

[0099] IQ: Installation Validation, one of the pre-verification steps for modules, checks whether the equipment model, installation location, etc., meet the design requirements.

[0100] OQ: Operational Qualification, one of the pre-verification steps for modules, tests the no-load operating parameters of the equipment to ensure that cleanliness and performance meet the standards.

[0101] PQ: Performance Qualification, one of the on-site verification steps, involves producing at least three batches of qualified products to verify process stability.

[0102] Agent modeling: Intelligent agent modeling is used to simulate the flow paths of people and materials, and to identify spatial conflicts and cross-contamination risks.

[0103] From the above, we can conclude that:

[0104] In this invention, by moving a significant amount of verification work forward to the module manufacturing stage and utilizing a pre-verification black box design, each module completes all necessary verification steps before leaving the factory. Combined with LSTM model analysis of device runtime sequence data, potential faults can be identified in advance, ensuring that modules are shipped in a stable and compliant state, avoiding verification delays or failures caused by weather, human error, or other factors. This model completely changes the passive nature of traditional on-site verification, making the entire deployment process more controllable, significantly shortening the time window from design to production, reducing compliance risks, and enabling companies to bring products to market faster.

[0105] In this invention, the application of digital collaborative design and virtual simulation, combined with algorithmic models to optimize process parameters, ensures the rationality and compatibility of the module design, improving overall quality and compliance. The modular design also makes the factory highly replicable; different product or capacity requirements can be quickly adapted through modules, significantly reducing the cost and time required for new factory construction and providing strong support for pharmaceutical companies to respond quickly to market demands.

[0106] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" or any other variations thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0107] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for rapid deployment of a modular pharmaceutical plant, characterized in that: The method includes the following steps: S1: Collect the product process package provided by the customer, which covers the entire process parameters of API synthesis and formulation production, and convert it into standardized design input documents; S2: Based on the design input of S1, BIM technology is used to build a multi-disciplinary integrated model, and a digital twin environment simulation of equipment layout, personnel and material flow and cleanroom zoning is built simultaneously; spatial conflicts or process optimization points found during the simulation will be fed back to the process package adjustment stage of S1 in real time, and the final generated model data will guide the module division work of S3. S3: Based on the simulation results of S2, the factory is decomposed into functionally independent physical module units, and a standard process for module pre-verification is formulated. S4: In accordance with the S3 division scheme and pre-verification standards, module manufacturing and internal integration are completed in parallel in the controlled workshop; all pre-verifications are completed before the module is shipped, forming a fully functional verification black box; S5: Based on the module size and weight of S4, design standardized transport fixing frames and interface protection devices to ensure long-distance transportation safety; at the same time, complete the site foundation leveling and embedded part layout based on the BIM model data of S2 to provide precise support for subsequent module placement. S6: Transport the prefabricated modules from S4 to the site, use the BIM model from S2 for laser scanning registration, and quickly complete the standardized interface connection between modules; the verification process directly calls the pre-verification data from S4. S7: Based on the digital twin model of S2 and the verification data of S6, launch the remote monitoring and predictive maintenance system; the equipment operation data collected during the operation and maintenance process will be fed back to the module division standard of S3 to continuously optimize the module reuse rate and deployment efficiency, forming a closed loop of the whole process.

2. The rapid deployment method for a modular pharmaceutical factory as described in claim 1, characterized in that: In step S1, the product process package provided by the customer is collected in its entirety, covering the entire process parameters of API synthesis, formulation production, purified water distribution, and HVAC unit, including equipment model, process parameter range, cleanliness level requirements, and material compatibility standards. This information is then transformed into standardized design input documents that comply with GMP regulations.

3. The rapid deployment method for a modular pharmaceutical factory as described in claim 1, characterized in that: In step S2, based on the design input file of S1, tools are used to construct a multi-disciplinary integrated model of architecture, structure, electromechanical, and process equipment. Simultaneously, a digital twin environment simulation of equipment layout, personnel and material flow, and cleanroom zoning is built. CFD simulation is used to maintain the pressure gradient of the cleanroom at ≥10Pa and ensure that the airflow organization uniformity meets GMP requirements. By modeling and simulating the personnel and material flow paths, spatial conflicts and cross-contamination risks are identified. Problems found during the simulation are fed back to S1 in real time to adjust the process package content. The final generated model data must include the dimensions, interface locations, equipment parameters, and cleanliness level information of each module.

4. The rapid deployment method for a modular pharmaceutical factory as described in claim 1, characterized in that: In step S3, based on the simulation results of S2, the factory is decomposed into functionally independent physical module units, including a raw material synthesis module, a solid dosage form production module, a purified water distribution module, and an HVAC unit module. The module layout adopts a U-shaped or I-shaped design to optimize logistics and cleanroom zoning. At the same time, a standard process for module pre-validation is established. The standard must clearly define the verification of equipment model for installation confirmation, installation location verification, pipeline connection sealing requirements, the range of no-load operating parameters for equipment for operational confirmation, the static pressure difference for cleanroom airtightness testing ≥15Pa, and the leakage rate for HEPA filter integrity testing ≤0.01%.

5. The rapid deployment method for a modular pharmaceutical factory as described in claim 1, characterized in that: In step S4, module manufacturing and internal integration are carried out in parallel in a controlled cleanroom; the module adopts an integrated design of steel frame and cleanroom panel, and the joints are sealed with silicone to ensure airtightness; the internal integration includes the installation of sealed mixing tank, ASME BPE standard pipe connection, and electrical wiring of microcomputer protection device.

6. The method for rapid deployment of a modular pharmaceutical plant as described in claim 1, characterized in that: In step S4, during the pre-verification operation confirmation stage of the module, in order to ensure that there are no potential faults before the equipment is shipped, an LSTM model is used to analyze the time-series operation data of the equipment. In the OQ test of the motor of the agitator in the closed granulation module, the vibration acceleration and bearing temperature time-series data of the motor are collected, and the LSTM model is used to predict whether there will be any abnormal trends in the motor in the next 24 hours.

7. The method for rapid deployment of a modular pharmaceutical plant as described in claim 1, characterized in that: In step S4, the LSTM cell state update formula is: ； In the formula: C t This represents the motor operating state captured by the model at the current moment; f t Indicates the output of the forget gate; C t−1 Indicates the motor's operating state at the previous moment; i t Indicates the input gate output; C~ t Indicates a candidate state; ⊙ represents element-wise multiplication; The formula for the LSTM input gate is: ； In the formula: σ represents the sigmoid activation function; W i Represents the input gate weight matrix; h t−1 Indicates the previously hidden state; x t This represents the motor vibration acceleration data at the current moment; b i Indicates the input gate bias term; The formula for the LSTM forget gate is: ； In the formula: W f Forget gate weight matrix; b f Forget gate bias term; The formula for LSTM candidate cell states is: ； In the formula: tanh represents the hyperbolic tangent activation function; W c Table: Candidate cell state weight matrix; b c Table of candidate cell state bias terms; The formula for the hidden state of LSTM is: ； In the formula: h t Indicates the hidden state at the current moment. t This indicates the output of the output gate.

8. The method for rapid deployment of a modular pharmaceutical plant as described in claim 1, characterized in that: In step S5, a standardized transport frame is designed based on the module size and weight in S4. The frame uses an adjustable clamp and elastic band structure, meets the seismic requirements of UN38.3 certification, and has a maximum load capacity of ≥20 tons. At the same time, an interface protection device is designed, using a sealing system with airtight, watertight, and fireproof functions, and the thickness of the buffer material is ≥50mm. On-site foundation preparation requires the completion of foundation leveling, with an error control of ≤±5mm, and the deviation of the embedded parts layout position ≤±10mm. Laser scanning is used to scan the actual data on site, with a scanning resolution of ≥6mm and a point density of ≥1 million points / m².

9. The method for rapid deployment of a modular pharmaceutical plant as described in claim 1, characterized in that: In step S6, after the prefabricated module from S4 is transported to the site, laser scanning technology is used to acquire actual site data and register it with the BIM model. The registration error is controlled within ≤±15mm to guide the precise placement of the module. The connection between modules adopts standardized interfaces. The mechanical interface conforms to the ASME BPE standard, the electrical interface follows the IEC61158 protocol, and the data interface adopts the industrial Ethernet and OPC UA protocols.

10. The method for rapid deployment of a modular pharmaceutical plant as described in claim 1, characterized in that: In step S7, based on the digital twin model of S2 and the verification data of S6, the continuous operation support system is started. The system collects equipment vibration, temperature and pressure operation data in real time through IoT sensors, with a data sampling frequency of ≥1 time / second. The system analyzes equipment failure trends by combining the LSTM prediction model and sets the predictive maintenance trigger threshold. The equipment operation data and module performance data collected during the operation and maintenance process will be fed back to the module division standard of S3.

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