Multi-parameter real-time monitoring based biosafety cabinet double-fan linkage control system

Abstract

The application relates to the technical field of biological safety cabinets, and discloses a biological safety cabinet double-fan linkage control system based on multi-parameter real-time monitoring. The system realizes dynamic determination of a vertical laminar flow state and early prediction of a cleanliness change rate through the collaborative work of a multi-parameter real-time sensing layer and a double-fan intelligent analysis layer, taking the sinking air speed of a work area, the inflow air speed of an operation port and the particle concentration of 0.5 microns as core monitoring variables, taking an airflow model stability analysis module and a cleanliness dynamic trend analysis module as a determination core, and executing millisecond-level closed-loop PID linkage control through a double-fan linkage control decision layer taking a comprehensive environmental health index as a speed regulation basis to realize the technical leap of air speed regulation and cleanliness maintenance from mutual independence to deep coupling, so that the biological safety cabinet realizes dynamic optimal balance among personnel safety protection, sample protection and cleanliness maintenance.

Description

Technical Field

[0001] This invention relates to the field of biosafety cabinet technology, specifically to a dual-fan linkage control system for biosafety cabinets based on real-time monitoring of multiple parameters. Background Technology

[0002] Biosafety cabinets are core protective equipment widely used in medical and health, biopharmaceutical, disease prevention and control, and university research fields. They provide triple protection for operators, experimental samples, and the environment by establishing a stable vertical laminar airflow in the operating area and an inflow airflow through the operating port. With the rapid development of the biopharmaceutical industry and the increasingly stringent requirements of GMP standards for clean environment monitoring, modern biosafety cabinets have gradually evolved from simple physical protective devices into comprehensive experimental platforms integrating environmental monitoring, data analysis, and intelligent control.

[0003] Currently, existing biosafety cabinets still have the following technical shortcomings: First, airflow control and cleanliness monitoring are independent. Most existing equipment treats airflow adjustment as an independent mechanical action and dust particle detection as an independent quality inspection action, lacking real-time data interaction and linkage response mechanisms between the two. When airflow fluctuates due to filter blockage or fan aging, the system cannot proactively predict its potential impact on cleanliness; conversely, when cleanliness is abnormal, the system cannot automatically trace whether it is caused by abnormal airflow, resulting in low problem-solving efficiency. Second, the early warning mechanism is relatively lagging. Existing equipment usually uses a single-parameter static threshold alarm method (such as alarming only when particle concentration exceeds a set value), lacking the ability to dynamically predict risks based on multi-parameter coupled analysis and trend changes. This "better late than never" alarm mode often only issues a warning after contamination or airflow failure has already occurred, making it difficult to give operators time to take preventative measures. Third, data traceability and compliance are insufficient. Most existing equipment lacks audit trail functionality compliant with CFR Part 11 requirements. Key parameters such as wind speed and particle concentration during experiments cannot form a complete and tamper-proof data chain, failing to meet the stringent data integrity requirements of GMP certification. Fourth, operation and maintenance management relies heavily on manual experience. Filter replacement cycles, UV lamp disinfection durations, and cleaning and maintenance of dust particle detection ports all depend on the operator's personal judgment and experience, lacking intelligent operation and maintenance decision support based on real-time monitoring data. This increases the risk of human error and shortens the effective lifespan of core components.

[0004] Therefore, there is an urgent need to build a biosafety cabinet control system that can achieve deep coupling of wind speed control and cleanliness monitoring, has multi-parameter dynamic trend early warning, meets data compliance requirements, and supports intelligent operation and maintenance management, so as to fundamentally solve the shortcomings of the existing technologies and provide a safer, more efficient, and compliant comprehensive solution for the biomedical and laboratory safety fields. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a dual-fan linkage control system for biosafety cabinets based on real-time multi-parameter monitoring. This system features real-time multi-parameter sensing, dynamic determination of airflow model stability, cleanliness trend prediction, millisecond-level linkage control of dual fans, tiered early warning protection, and full-chain data traceability. It solves the problems of existing biosafety cabinets, such as independent airflow control and cleanliness monitoring, delayed early warning mechanisms, lack of multi-parameter linkage response, insufficient data traceability capabilities, and reliance on manual experience for operation and maintenance.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution: a biosafety cabinet dual-fan linkage control system based on multi-parameter real-time monitoring, including a basic physical structure and power execution layer, a multi-parameter real-time sensing and data monitoring layer, a dual-fan intelligent analysis layer, an active safety early warning and protection layer, a dual-fan linkage control decision layer, a data compliance and historical traceability layer, an operation and maintenance disinfection layer, and an optional addition layer.

[0009] The basic physical structure and power execution layer integrate a device base consisting of a fuma wheel leveling system, an antibacterial steel body, a concave plate table, explosion-proof glass, and a waterproof socket. It is also equipped with two sets of standby EC brushless fans, which together constitute the physical and power foundation of the system.

[0010] The multi-parameter real-time sensing and data monitoring layer starts multi-dimensional data acquisition after the system power-on self-test;

[0011] The dual-fan intelligent analysis layer performs logical analysis based on the collected data and generates fan speed control commands.

[0012] The proactive safety early warning and protection layer performs risk prediction and alarm in parallel throughout the entire system operation process;

[0013] The dual-fan linkage control decision-making layer calculates a comprehensive environmental health index based on collected data. The calculated value is used as the core auxiliary parameter for the linkage of dual fan speeds. After receiving standardized data from the airflow model stability analysis module and the cleanliness dynamic trend analysis module, the comprehensive environmental health index calculation engine is started.

[0014] The data compliance and historical traceability layer is responsible for the synchronous storage of real-time data and alarm events. All stored data is audited and traced in accordance with CFR Part 11 requirements and cannot be tampered with. At the same time, a three-level access control system is set up so that operators / administrators / maintenance personnel have different data viewing and calibration permissions. Finally, log files that meet GMP requirements are exported through the RS485 interface.

[0015] The operation and maintenance disinfection layer serves as the post-processing layer of the system, performing terminal disinfection and maintenance procedures after all experimental operations are completed.

[0016] The optional add-on layer can be flexibly equipped with optional components according to experimental needs.

[0017] Preferably, the bottom four corners of the basic physical structure and the power execution layer are equipped with fusible wheels for leveling and fixing the equipment; the main structure is made of 1.2mm thick antibacterial coated steel plate; the operating table is made of SUS304 stainless steel with a concave design; the front window glass is made of 5° inclined double-layer laminated explosion-proof glass; a waterproof and splash-proof socket is configured on the rear side; the dual fan hardware consists of two independent EC DC brushless maintenance-free fans, which are in standby mode and waiting to receive control commands.

[0018] Preferably, the multi-parameter real-time sensing and data monitoring layer sets up a total of five data monitoring points:

[0019] (1) Monitoring point one: A round bead-shaped micro wind speed sensor is used and deployed in the center and four corners of the work area to collect the average value of multiple points as the measured value of the sinking wind speed in the work area;

[0020] (2) Monitoring point two: The same type of sensor is used and installed at the center line of the front window operating port to collect the measured value of the wind speed flowing into the operating port;

[0021] (3) Monitoring point three: A detachable multi-point particle detection component is used to evenly distribute the points in the working area and cooperate with the universal detection port to collect the measured value of 0.5 micrometer particle concentration;

[0022] (4) Monitoring point four: A micro differential pressure sensor is used, installed at the front and rear ends of the ULPA filter to collect the filter pressure difference ΔP;

[0023] (5) Monitoring point five: An integrated environmental sensor is installed on the side wall of the work area to collect temperature and humidity data.

[0024] Preferably, the intelligent analysis and control decision layer incorporates an airflow model stability analysis module and a cleanliness dynamic trend analysis module; the proactive safety early warning and protection layer incorporates an airflow physical failure risk prediction module and a biological contamination comprehensive risk prediction module.

[0025] Preferably, the airflow model stability analysis module compares the current measured values ​​of the downdraft velocity in the working area and the inflow velocity at the operating inlet in real time to determine whether the vertical laminar flow has been disrupted. The determination criteria must simultaneously meet the following two conditions:

[0026] a. When the measured wind speed flowing into the operating inlet is ≥0.50 m / s;

[0027] When the measured downdraft wind speed in the working area is within the range of 0.36 to 0.54 m / s.

[0028] Preferably, the cleanliness dynamic trend analysis module compares the current measured value of particle concentration with a particle size of 0.5 micrometers with the standard threshold, and calculates the slope of the change in particle concentration over the past 10 minutes.

[0029] Preferably, the airflow physical failure risk prediction module calculates the airflow physical failure risk coefficient based on monitoring data during system operation. The calculation formula is as follows:

[0030]

[0031] In the formula, This indicates the setpoint for the sinking wind speed in the work area. This indicates the real-time monitoring value of the subsidence wind speed in the work area. This indicates the set value of the inlet air velocity. This indicates the real-time monitoring value of the inflow wind speed at the operating inlet.

[0032] Preferably, the comprehensive risk prediction module for biological pollution calculates the comprehensive risk coefficient for biological pollution based on monitoring data during system operation. The calculation formula is as follows:

[0033]

[0034] In the formula, This represents the real-time monitoring value of 0.5-micrometer particle concentration. This represents the standard threshold for the concentration of particles as small as 0.5 micrometers. This represents the slope of the linear change in particle concentration over the past 5 minutes. Indicates the observation time window.

[0035] Preferably, the dual-fan linkage control decision layer calculates the comprehensive environmental health index based on the collected data. The calculation formula is as follows:

[0036]

[0037] In the formula, This indicates the setpoint for the sinking wind speed in the work area. This indicates the real-time monitoring value of the subsidence wind speed in the work area. This indicates the set value of the inlet air velocity. This indicates the real-time monitoring value of the inflow air velocity at the operating inlet. This represents the real-time monitoring value of 0.5-micrometer particle concentration. This represents the standard threshold for the concentration of particles as small as 0.5 micrometers. , , These represent the weighting coefficients for sinking wind speed protection, inflow wind speed safety protection, and cleanliness penalty, respectively, and represent the weighting of wind speed protection and cleanliness penalty. > > .

[0038] Preferably, the maintenance and disinfection layer provides filter maintenance reminders based on the cumulative value of the pressure difference ΔP between the front and rear ends of the ULPA filter collected at monitoring point four, which is used to intelligently determine the filter's clogging status and perform the following operations:

[0039] (1) When When the pressure difference is ≤1.5 times the initial differential pressure, the filter operates normally, only recording the current differential pressure value without any prompts.

[0040] (2) When the initial pressure difference × 1.5 < When the initial pressure difference is ≤2.0 × 2.0, the filter is slightly clogged. The touch screen will display a prompt to clean or replace the inlet pre-filter.

[0041] (3) When >When the initial pressure difference is 2.0, a severe filter blockage will trigger a mandatory alarm and intelligently prompt you to immediately replace the ULPA or inlet pre-filter.

[0042] Compared with existing technologies, this invention provides a dual-fan linkage control system for biosafety cabinets based on real-time monitoring of multiple parameters, which has the following advantages:

[0043] 1. This invention, through the collaborative work of a multi-parameter real-time sensing layer and a dual-fan intelligent analysis layer, uses the sinking wind speed in the working area, the inflow wind speed at the operating inlet, and the 0.5-micron particle concentration as core monitoring variables. It employs an airflow model stability analysis module and a cleanliness dynamic trend analysis module as the judgment center, achieving dynamic judgment of the vertical laminar flow state and early prediction of the cleanliness change rate. Furthermore, through a dual-fan linkage control decision layer, it integrates the environmental health index. The system employs millisecond-level closed-loop PID linkage control to achieve speed regulation, enabling a technological leap from independent to deeply coupled system airflow regulation and cleanliness maintenance. This allows the biosafety cabinet to achieve a dynamic optimal balance among personnel safety, sample protection, and cleanliness maintenance.

[0044] 2. This invention calculates the airflow physical failure risk coefficient through an active safety early warning system and an airflow physical failure risk prediction module built into the protective layer. Using this as a means, it serves as a quantitative evaluation index for the degree of wind speed deviation, enabling early identification and graded warning of airflow barrier failure risks; simultaneously, a comprehensive biocontamination risk prediction module is used to calculate the comprehensive biocontamination risk coefficient. Using this method, the static concentration ratio is coupled with the dynamic trend slope as a quantitative evaluation index of pollution risk, realizing the upgrade of the system from static threshold alarm to dynamic trend early warning, thereby gaining valuable advance response time for operators and effectively avoiding personnel exposure and sample loss accidents caused by sudden airflow failure or pollution.

[0045] 3. This invention, through a data compliance and historical traceability layer, performs tamper-proof audit tracking and storage of all operating parameters and alarm events, such as wind speed, particle concentration, differential pressure, temperature, and humidity, in accordance with CFR Part 11 requirements. Combined with a three-level access control system, it enables tiered data access and system management for operators, administrators, and maintenance personnel. Simultaneously, through an operation and maintenance disinfection layer, it intelligently determines filter blockage status based on the cumulative differential pressure value collected from the DP-04 monitoring point and provides tiered maintenance prompts. Combined with timed UV sterilization and independent cleaning and disinfection of the detachable particle detection components, the system achieves full-chain data compliance and traceability, and intelligent early warning throughout the operation and maintenance process. This provides complete data support for GMP certification and effectively extends the service life of core components such as filters. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating the control process of the structural system of this invention.

[0047] Figure 2 This is a visual representation of the biosafety cabinet of the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Please see Figures 1-2 The biosafety cabinet dual-fan linkage control system based on multi-parameter real-time monitoring includes a basic physical structure and power execution layer, a multi-parameter real-time sensing and data monitoring layer, a dual-fan intelligent analysis layer, an active safety early warning and protection layer, a dual-fan linkage control decision layer, a data compliance and historical traceability layer, an operation and maintenance disinfection layer, and an optional addition layer, and includes the following functions and roles:

[0050] The basic physical structure and the bottom four corners of the power execution layer use casters for equipment leveling and fixation, ensuring stable operation of the entire machine. The main structure uses 1.2mm thick antibacterial coated steel plates, possessing excellent corrosion resistance and surface antibacterial capabilities. The operating table uses a concave design of SUS304 stainless steel (SUS316 optional), effectively preventing accidental liquid spills. The front window uses 5° inclined double-layer laminated explosion-proof glass, reducing the risk of glass shattering and splashing, while also meeting ergonomic operating requirements. A waterproof and splash-proof socket is provided at the rear to ensure electrical safety. Dual fan hardware is ready: Two independent EC DC brushless maintenance-free fans are in place, both equipped with overheat protection, overvoltage protection, and soft-start functions, and are in standby mode, awaiting control commands.

[0051] The advantages are: stable equipment operation is ensured by leveling and fixing with a fuma wheel; a corrosion-resistant and leak-proof operating environment is constructed with an antibacterial coated steel plate body and a concave plate platform; explosion-proof glass and waterproof sockets ensure the safety of operators and electricity use; and the two EC brushless fans are reliably on standby under overheat / overpressure protection. Thus, a solid physical base and power hardware foundation are provided for the entire system, ensuring that the upper functional modules operate in an orderly manner on a safe and stable structure.

[0052] The multi-parameter real-time sensing and data monitoring layer sets up five core data monitoring points:

[0053] (1) Monitoring point 1 (DP-01) uses a bead-shaped micro wind speed sensor, which is deployed in the center and four corners of the work area to collect the average value of multiple points as the measured value of the sinking wind speed in the work area (V sinking measured).

[0054] (2) Monitoring point 2 (DP-02) uses the same type of sensor, which is installed on the center line of the front window operating port to collect the measured value of the wind speed flowing into the operating port (V inflow measured).

[0055] (3) Monitoring point three (DP-03) adopts a detachable multi-point particle detection component, which is evenly distributed in the working area and used with a universal detection port to collect the measured value of 0.5 micrometer particle concentration (C particle measured).

[0056] (4) Monitoring point four (DP-04) uses a micro differential pressure sensor, which is installed at the front and rear ends of the ULPA filter to collect the filter pressure difference (ΔP).

[0057] (5) Monitoring point five (DP-05) uses an integrated environmental sensor, which is installed on the side wall of the work area to collect temperature and humidity (T / H). All monitoring point data are synchronously refreshed to the 10-inch touch screen in real time and stored in a temporary buffer for subsequent analysis.

[0058] By deploying bead-shaped micro-wind speed sensors at the center and four corners of the work area, a similar sensor installed at the center line of the front window operating port, detachable multi-point particle detection components evenly distributed throughout the work area, micro-differential pressure sensors installed at the front and rear ends of the ULPA filter, and integrated environmental sensors installed on the side walls of the work area, five core parameters are collected: measured values ​​of the sinking wind speed in the work area, measured values ​​of the inflow wind speed at the operating port, measured values ​​of the 0.5-micron particle concentration, filter differential pressure, and temperature and humidity. A multi-point averaging and uniform distribution strategy is used to improve data representativeness and measurement accuracy. The data from each sensor is synchronously and in real time refreshed to the 10-inch touchscreen and stored in a temporary cache area, thereby providing comprehensive, accurate, and real-time data support for the intelligent analysis layer, early warning layer, and control decision-making layer, ensuring that subsequent analysis, judgment, and execution at each level are based on evidence and are accurate and reliable.

[0059] The intelligent analysis and control decision-making layer incorporates an airflow model stability analysis module and a cleanliness dynamic trend analysis module.

[0060] The airflow model stability analysis module compares the current measured values ​​of the downdraft velocity in the working area with the inflow velocity at the operating inlet in real time to determine whether the vertical laminar flow has been disrupted. The determination criteria require that the following two conditions be met simultaneously:

[0061] a. When the measured inflow velocity at the operating inlet (V_inflow measured) is ≥ 0.50 m / s;

[0062] When the measured subsidence wind speed (V_subsidence) in the working area is within the range of 0.36 to 0.54 m / s;

[0063] (1) When conditions a and b are met at the same time: the vertical laminar flow is stable and normal, the airflow model is qualified, the system continues to operate normally, and the touch screen displays "Airflow status: normal" (green indicator).

[0064] (2) When either condition a or b is not met: the vertical laminar flow state is disrupted or there is a risk of deviation, the system immediately triggers the calculation of the airflow physical failure risk coefficient and issues a yellow warning or a red severe alarm according to the risk level, and at the same time notifies the dual-fan linkage control decision layer to intervene in speed regulation.

[0065] By comparing the measured values ​​of the sinking wind speed in the working area with the measured values ​​of the inflow wind speed at the operating inlet in real time, the judgment standard (inflow wind speed ≥ 0.50 m / s and sinking wind speed within the range of 0.36 to 0.54 m / s) is used as the quantitative basis for whether the vertical laminar flow has been disrupted. This enables the system to immediately trigger the risk factor calculation and notify the control decision layer to intervene and adjust the speed when the laminar flow state is abnormal. This ensures that the operating area is always under the airflow protection that meets the standard, effectively protecting the safety of both the operators and the experimental samples.

[0066] The cleanliness dynamic trend analysis module compares the current measured value of particle concentration with a particle size of 0.5 micrometers with the Class 3 threshold (≤102 particles / cubic meter) in the ISO 14644.1 standard, and calculates the slope of the change in particle concentration (upward or downward trend) over the past 10 minutes to determine the rate of cleanliness deterioration or improvement.

[0067] By comparing the current measured value of 0.5 micrometer particle concentration with the Class 3 threshold (≤102 particles / cubic meter) in ISO 14644.1 in real time, and using the linear change slope of particle concentration over the past 10 minutes as the basis for trend judgment, the system can dynamically capture the rate of deterioration or improvement of cleanliness. This allows for early identification and prediction before the risk of contamination exceeds the threshold, providing valuable time for early warning and decision-making layers to intervene in advance, and effectively avoiding experimental failures and sample losses caused by exceeding cleanliness standards.

[0068] The active safety early warning and protective layer incorporates a built-in airflow physical failure risk prediction module and a comprehensive biological contamination risk prediction module.

[0069] The airflow physical failure risk prediction module calculates the airflow physical failure risk coefficient based on monitoring data during system operation. The calculation formula is as follows:

[0070]

[0071] In the formula, This indicates the setpoint for the sinking wind speed in the work area (e.g., 0.45 m / s). This indicates the real-time monitoring value of the subsidence wind speed in the work area. This indicates the set value of the inlet air velocity (e.g., 0.55 m / s). This indicates the real-time monitoring value of the inflow air velocity at the operating inlet;

[0072] When the airflow physical failure risk coefficient A value ≥0.15 will automatically trigger a yellow warning, indicating that the current wind speed deviation is large, and it is recommended to inspect the seals or filters.

[0073] When the airflow physical failure risk coefficient If the value is ≥0.30, a red critical alarm will be automatically triggered, indicating that the current airflow has failed and personnel and samples are at risk of exposure. Operation must be stopped immediately.

[0074] The advantage is that it calculates the airflow physical failure risk coefficient. The degree of deviation between the measured values ​​of the sinking wind speed in the work area and the inflow wind speed at the operating inlet and the set value is used as a risk quantification indicator, enabling the system to automatically trigger a yellow warning before the wind speed deviation accumulates to a dangerous threshold. ≥0.15) or red critical alarm ( (≥0.30), thereby enabling graded identification and early intervention of airflow barrier failure risks, effectively avoiding operator exposure and sample contamination accidents caused by airflow protection failure, and comprehensively improving the safety of biosafety cabinet operation.

[0075] The comprehensive risk prediction module for biological contamination calculates the comprehensive risk coefficient for biological contamination based on monitoring data during system operation. The calculation formula is as follows:

[0076]

[0077] In the formula, This represents the real-time monitoring value of 0.5-micrometer particle concentration. This represents the standard threshold for the concentration of particles as small as 0.5 micrometers. This represents the slope of the linear change in particle concentration over the past 5 minutes. Indicates the observation time window (fixed at 5 minutes);

[0078] Based on the comprehensive risk coefficient of biological pollution The specific rules for risk identification are as follows:

[0079] (1) When the comprehensive risk coefficient of biological pollution If the value is ≥1.0 and remains so for more than 10 seconds, an abnormal cleanliness warning will be automatically triggered (this may be due to filter penetration or turbulence at the operating port).

[0080] (2) When the comprehensive risk coefficient of biological pollution If the concentration is ≥1.5, a severe pollution alarm will be automatically triggered, and the operator will be reminded to perform the disinfection procedure.

[0081] The advantage is that it calculates the comprehensive risk coefficient of biological pollution. The ratio of real-time particle concentration to a standard threshold is used as the basic risk measure. Simultaneously, the slope of particle concentration change over the past 5 minutes is introduced as a trend correction factor. This allows the system to not only assess the current contamination level but also dynamically predict the deterioration trend of cleanliness. Therefore, even if the particle concentration has not yet exceeded the threshold but the trend is clearly deteriorating, the system can still provide a corrective measure. An abnormal cleanliness warning is automatically triggered when the contamination level is ≥1.0 and lasts for more than 10 seconds. ≥1.5) Automatically triggers a severe pollution alarm and forces interlocking reminders for disinfection, completely realizing the upgrade from static threshold alarm to dynamic trend early warning, effectively avoiding the scrapping of the entire batch of samples and damage to personnel health due to sudden pollution.

[0082] The dual-fan linkage control decision-making layer calculates the comprehensive environmental health index based on the collected data. The calculation formula is as follows:

[0083]

[0084] In the formula, This indicates the setpoint for the sinking wind speed in the work area (e.g., 0.45 m / s). This indicates the real-time monitoring value of the subsidence wind speed in the work area. This indicates the set value of the inlet air velocity (e.g., 0.55 m / s). This indicates the real-time monitoring value of the inflow air velocity at the operating inlet. This represents the real-time monitoring value of 0.5-micrometer particle concentration. This represents the standard threshold for the concentration of particles as small as 0.5 micrometers. , , These represent the weighting coefficients for sinking wind speed protection, inflow wind speed safety protection, and cleanliness penalty, respectively, and represent the weighting of wind speed protection and cleanliness penalty. > >

[0085] Based on the comprehensive environmental health index Perform speed regulation logic determination at the control layer:

[0086] (1) When the comprehensive environmental health index If the overall environmental health index is <0.85 and the overall environmental health index has severely deteriorated, prioritize increasing the speed of the inflow fan (to quickly restore personnel protective barriers), then supplement by increasing the speed of the sinking fan until the overall environmental health index is reached. Rebound;

[0087] (2) 0.85≤Comprehensive Environmental Health Index ≤0.94 In slightly unfavorable environments, moderately adjust the PWM duty cycle of the dual fans to prioritize ensuring that the inflow air velocity meets the standard;

[0088] (3) 0.95≤Comprehensive Environmental Health Index ≤1.05 steady-state optimal range maintain the current dual fan speed (enter constant wind speed / constant flow energy saving mode);

[0089] (4) 1.05 < Comprehensive Environmental Health Index ≤1.10 Environmental conditions are good (wind is strong), no adjustments will be made for now, observe and maintain.

[0090] (5) Comprehensive Environmental Health Index >1.10 The environment is extremely favorable and the particle count is extremely low. The dual fan speeds are simultaneously reduced to the lowest effective value (entering a silent, energy-saving, and noise-reducing mode) to reduce energy consumption and filter load.

[0091] After speed adjustment, the system refreshes all monitoring data through the multi-parameter real-time sensing layer within 3 seconds and sends it back to the dual-fan intelligent analysis layer and the dual-fan linkage control decision layer for a new round of Ey calculation, forming a millisecond-level closed-loop PID linkage control.

[0092] The advantage is that it calculates the comprehensive environmental health index. A dynamic balance model is constructed between wind speed protection and cleanliness penalty, enabling the system to adjust accordingly. The five-level range judgment of the value (severe deterioration, slightly poor, steady-state best, good to high, excellent energy saving) implements a differentiated dual-fan linkage speed regulation strategy, completing data refresh and speed closed-loop adjustment within 3 seconds, thereby ensuring personnel safety protection ( Priority), sample protection ( (Balance) and maintenance of cleanliness ( Achieving an optimal balance among the three (auxiliary, auxiliary, and auxiliary) significantly reduces energy consumption and filter load, ensuring that the biosafety cabinet always operates in a state of optimal overall performance.

[0093] The operation and maintenance disinfection layer, as the system's post-processing layer, performs terminal disinfection and maintenance procedures after all experimental operations are completed. The specific details are as follows:

[0094] (1) UV lamp scheduled sterilization: According to the weekly calendar set by the dual fan linkage control decision layer, the system automatically turns on the 254nm UV lamp after the experiment to carry out terminal disinfection and sterilization for 30 minutes (the UV lamp also has a scheduled on / off function and supports on-demand timed start-up).

[0095] (2) Cleaning of detachable components: Disassemble the multi-point particle detection port components from the work area and perform high-pressure steam sterilization or disinfectant immersion sterilization treatment separately according to the type of pollutants in the experiment (the detachable design greatly facilitates the deep cleaning of the components and avoids cross-contamination).

[0096] (3) Filter maintenance reminder: Based on the cumulative value of the pressure difference (ΔP) between the front and rear ends of the ULPA filter collected by the DP-04 monitoring point, the system intelligently judges the clogging status of the filter and performs the following operations:

[0097] If the filter operates normally and only records the current differential pressure value, without any prompts, the output will be ≤1.5 of the initial differential pressure.

[0098] Initial pressure difference × 1.5 < If the filter is slightly clogged (≤initial differential pressure × 2.0), the touchscreen will display a message suggesting cleaning or replacing the inlet pre-filter.

[0099] > Initial differential pressure × 2.0: Severe filter blockage triggers a mandatory alarm and intelligent prompts to immediately replace the ULPA or inlet pre-filter;

[0100] (4) Optional Additions Finally, the following optional components can be flexibly added according to experimental needs to expand the system functions: U15 high-efficiency filter (providing a higher level of cleanliness), side wall valve (gas / water / vacuum interface), camera (for monitoring the experimental process), stainless steel rod with hook (for auxiliary suspension equipment), fluorescent lamp (white / yellow light optional), etc.

[0101] The advantages are: 30-minute terminal disinfection of the work area is performed via scheduled UV lamp sterilization; cross-contamination is avoided by high-pressure sterilization or disinfectant immersion of the detachable particle detection port components; intelligent management of the entire filter lifecycle is achieved through the cumulative filter differential pressure value collected by the DP-04 monitoring point (ΔP≤ initial differential pressure × 1.5: normal operation only recorded; initial differential pressure × 1.5<ΔP≤ initial differential pressure × 2.0: slight blockage indicating cleaning or replacement of the pre-filter; ΔP> initial differential pressure × 2.0: severe blockage triggering an alarm indicating replacement of the ULPA or pre-filter). Furthermore, the system's functionality can be expanded by optional additions such as U15 high-efficiency filters, side-wall valves, cameras, stainless steel rods with hooks, and fluorescent lights, ensuring a thorough cleaning and disinfection loop after each experiment. Modular configuration allows for flexible adaptation to the diverse needs of different experimental scenarios, effectively extending the lifespan of core components and reducing maintenance costs.

[0102] Example (Application of biosafety cabinet based on real-time monitoring of multiple parameters in long-term continuous experiments)

[0103] A third-party testing laboratory used this system to analyze environmental monitoring samples for 8 consecutive hours.

[0104] Six hours into the experiment, the cleanliness dynamic trend analysis module detected that the measured particle concentration gradually increased from 42 particles / m³ to 78 particles / m³, and the slope of the particle concentration change over the past 10 minutes was positive (upward trend). The module predicted that the cleanliness might continue to deteriorate. The biological contamination comprehensive risk prediction module calculated... =(78 / 102)×(1+0.8×5)=0.765×5.0=3.825, If the concentration is ≥1.5, a severe pollution alarm will be automatically triggered, and the operator will be forced to stop the operation immediately and carry out the disinfection process.

[0105] The operators immediately stopped the experiment and found that the petri dish had accidentally tipped over, causing a small amount of liquid to spill out. After cleaning and emergency disinfection with ultraviolet lamps for 30 minutes, the system was restarted, and the particle concentration returned to 35 particles / m³, and the cleanliness returned to normal.

[0106] In summary, this system achieves a fully automated intelligent linkage control closed loop through seven levels of collaborative work, from physical readiness to sensing and data acquisition, intelligent analysis, safety early warning, linkage control, data traceability, and maintenance and disinfection. This significantly improves the operational safety, ease of use, and data compliance of biosafety cabinets in scenarios such as cell culture, virus experiments, and environmental monitoring.

[0107] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A biosafety cabinet dual-fan linkage control system based on real-time monitoring of multiple parameters, characterized in that, It includes the basic physical structure and power execution layer, the multi-parameter real-time perception and data monitoring layer, the dual-fan intelligent analysis layer, the proactive safety early warning and protection layer, the dual-fan linkage control decision layer, the data compliance and historical traceability layer, the operation and maintenance disinfection layer, and the optional addition layer; The basic physical structure and power execution layer integrate a device base consisting of a fuma wheel leveling system, an antibacterial steel body, a concave plate table, explosion-proof glass, and a waterproof socket. It is also equipped with two sets of standby EC brushless fans, which together constitute the physical and power foundation of the system. The multi-parameter real-time sensing and data monitoring layer starts multi-dimensional data acquisition after the system power-on self-test; The dual-fan intelligent analysis layer performs logical analysis based on the collected data and generates fan speed control commands. The proactive safety early warning and protection layer performs risk prediction and alarm in parallel throughout the entire system operation process; The dual-fan linkage control decision-making layer calculates a comprehensive environmental health index based on collected data. The calculated value is used as the core auxiliary parameter for the linkage of dual fan speeds. After receiving standardized data from the airflow model stability analysis module and the cleanliness dynamic trend analysis module, the comprehensive environmental health index calculation engine is started. The data compliance and historical traceability layer is responsible for the synchronous storage of real-time data and alarm events. All stored data is audited and traced in accordance with CFR Part 11 requirements and cannot be tampered with. At the same time, a three-level access control system is set up so that operators / administrators / maintenance personnel have different data viewing and calibration permissions. Finally, log files that meet GMP requirements are exported through the RS485 interface. The operation and maintenance disinfection layer serves as the post-processing layer of the system, performing terminal disinfection and maintenance procedures after all experimental operations are completed. The optional add-on layer can be flexibly equipped with optional components according to experimental needs.

2. The biosafety cabinet dual-fan linkage control system based on multi-parameter real-time monitoring according to claim 1, characterized in that, The basic physical structure and the bottom four corners of the power execution layer are equipped with fuma wheels for leveling and fixing the equipment; the main structure is made of 1.2mm thick antibacterial coated steel plate; the operating table is made of SUS304 stainless steel concave design; the front window glass is made of 5° inclined double-layer laminated explosion-proof glass; a waterproof and splash-proof socket is configured on the rear side; the dual fan hardware consists of two independent EC DC brushless maintenance-free fans, which are in standby mode and waiting to receive control commands.

3. The biosafety cabinet dual-fan linkage control system based on multi-parameter real-time monitoring according to claim 1, characterized in that, The multi-parameter real-time sensing and data monitoring layer sets up a total of five data monitoring points: (1) Monitoring point one: A round bead-shaped micro wind speed sensor is used and deployed in the center and four corners of the work area to collect the average value of multiple points as the measured value of the sinking wind speed in the work area; (2) Monitoring point two: The same type of sensor is used and installed at the center line of the front window operating port to collect the measured value of the wind speed flowing into the operating port; (3) Monitoring point three: A detachable multi-point particle detection component is used to evenly distribute the points in the working area and cooperate with the universal detection port to collect the measured value of 0.5 micrometer particle concentration; (4) Monitoring point four: A micro differential pressure sensor is used, installed at the front and rear ends of the ULPA filter to collect the filter pressure difference ΔP; (5) Monitoring point five: An integrated environmental sensor is installed on the side wall of the work area to collect temperature and humidity data.

4. The biosafety cabinet dual-fan linkage control system based on multi-parameter real-time monitoring according to claim 1, characterized in that, The intelligent analysis and control decision layer incorporates an airflow model stability analysis module and a cleanliness dynamic trend analysis module; the proactive safety early warning and protection layer incorporates an airflow physical failure risk prediction module and a biological contamination comprehensive risk prediction module.

5. The biosafety cabinet dual-fan linkage control system based on multi-parameter real-time monitoring according to claim 4, characterized in that, The airflow model stability analysis module compares the current measured values ​​of the downdraft velocity in the working area and the inflow velocity at the operating inlet in real time to determine whether the vertical laminar flow has been disrupted. The determination criteria require that the following two conditions be met simultaneously: a. When the measured wind speed flowing into the operating inlet is ≥0.50 m / s; When the measured downdraft wind speed in the working area is within the range of 0.36 to 0.54 m / s.

6. The biosafety cabinet dual-fan linkage control system based on multi-parameter real-time monitoring according to claim 4, characterized in that, The cleanliness dynamic trend analysis module compares the current measured value of particle concentration with a particle size of 0.5 micrometers with the standard threshold, and calculates the slope of the change in particle concentration over the past 10 minutes.

7. The biosafety cabinet dual-fan linkage control system based on multi-parameter real-time monitoring according to claim 4, characterized in that, The airflow physical failure risk prediction module calculates the airflow physical failure risk coefficient based on monitoring data during system operation. The calculation formula is as follows: In the formula, This indicates the setpoint for the sinking wind speed in the work area. This indicates the real-time monitoring value of the subsidence wind speed in the work area. This indicates the set value of the inlet air velocity. This indicates the real-time monitoring value of the inflow wind speed at the operating inlet.

8. The biosafety cabinet dual-fan linkage control system based on multi-parameter real-time monitoring according to claim 4, characterized in that, The comprehensive risk prediction module for biological pollution calculates the comprehensive risk coefficient for biological pollution based on monitoring data during system operation. The calculation formula is as follows: In the formula, This represents the real-time monitoring value of 0.5-micrometer particle concentration. This represents the standard threshold for the concentration of particles as small as 0.5 micrometers. This represents the slope of the linear change in particle concentration over the past 5 minutes. Indicates the observation time window.

9. The biosafety cabinet dual-fan linkage control system based on multi-parameter real-time monitoring according to claim 1, characterized in that, The dual-fan linkage control decision-making layer calculates a comprehensive environmental health index based on collected data. The calculation formula is as follows: In the formula, This indicates the setpoint for the sinking wind speed in the work area. This indicates the real-time monitoring value of the subsidence wind speed in the work area. This indicates the set value of the inlet air velocity. This indicates the real-time monitoring value of the inflow air velocity at the operating inlet. This represents the real-time monitoring value of 0.5-micrometer particle concentration. This represents the standard threshold for the concentration of particles as small as 0.5 micrometers. , , These represent the weighting coefficients for sinking wind speed protection, inflow wind speed safety protection, and cleanliness penalty, respectively, and represent the weighting of wind speed protection and cleanliness penalty. > > .

10. The biosafety cabinet dual-fan linkage control system based on multi-parameter real-time monitoring according to claim 1, characterized in that, The maintenance and disinfection layer provides filter maintenance reminders based on the cumulative value of the pressure difference ΔP between the front and rear ends of the ULPA filter collected at monitoring point four. This is used to intelligently determine the filter's clogging status and perform the following operations: (1) When When the pressure difference is ≤1.5 times the initial differential pressure, the filter operates normally, only recording the current differential pressure value without any prompts. (2) When the initial pressure difference × 1.5 < When the initial pressure difference is ≤2.0 × 2.0, the filter is slightly clogged. The touch screen will display a prompt to clean or replace the inlet pre-filter. (3) When >When the initial pressure difference is 2.0, a severe filter blockage will trigger a mandatory alarm and intelligently prompt you to immediately replace the ULPA or inlet pre-filter.

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