A clean room delayed opening interlock control system

Abstract

The present application relates to a kind of clean factory delay opening interlock control system, including non-target area identification module, embolism agent area segmentation module, error embolism index calculation module, target area embolism increment calculation module and strategy gradient reward value generation module, non-target area identification module is through the pressure difference gradient of acquisition buffer room and core area, three-dimensional wind speed vector and micron grade particle concentration data, embolism agent area segmentation module is used to calculate turbulent intensity coefficient;Error embolism index calculation module is used to adaptively adjust access delay parameter, target area embolism increment calculation module continuously verifies environmental stability in countdown period to generate interlock instruction;Strategy gradient reward value generation module is used to simultaneously utilize historical interlock event closed loop optimization decision threshold value.This method solves the problem of traditional fixed delay mechanism due to airflow disturbance resulting in cleanliness out of control or low traffic efficiency, realizes the precise interlock control of dynamic airflow response, and gives consideration to clean factory environment safety and operating efficiency.

Description

Technical Field

[0001] This invention relates to the field of cleanroom safety management technology, specifically to a cleanroom delayed opening interlock control system. Background Technology

[0002] In the operation and management of cleanrooms, access control and interlocking systems are core devices for maintaining a stable cleanroom environment. These systems prevent unclean air from flowing into critical areas by controlling the opening and closing sequence and delay mechanisms of doors between buffer zones and the core area. Traditional delayed-opening interlocking control relies on preset fixed time intervals; that is, after closing one door, a fixed amount of time is waited before opening the next door. This method is based on a static environment assumption and aims to allow airflow to return to a stable state through time buffering, thereby meeting the stringent cleanliness requirements of industries such as pharmaceuticals and electronics manufacturing.

[0003] However, existing technologies have significant limitations: fixed delay mechanisms cannot adapt to dynamic airflow disturbances. In actual operation, cleanrooms often experience severe pressure fluctuations due to equipment start-up and shutdown, personnel movement, or sudden changes in external airflow, causing the environment to remain unstable even after the fixed delay ends. Opening the door at this point allows contaminants to enter, compromising cleanliness; while excessively extending the delay may improve safety, it severely reduces access efficiency. More importantly, existing solutions lack the ability to respond to real-time environmental parameters and cannot dynamically adjust the delay strategy based on key indicators such as turbulence intensity and pressure difference changes. This rigid control logic fundamentally contradicts the complex fluid dynamics of cleanrooms, making it difficult to balance safety and efficiency. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to provide a cleanroom delayed opening interlock control system that can dynamically optimize interlock delay in response to airflow disturbances in real time.

[0005] The objective of this invention is achieved through the following solution:

[0006] This invention provides a delayed-opening interlock control system for cleanrooms, which is configured with the following modules:

[0007] The non-target area identification module is used to synchronously collect real-time differential pressure gradient data, three-dimensional spatial wind speed vector data, and micron-level particle concentration data in the buffer room door and core area entrance of the cleanroom based on differential pressure sensors, three-dimensional ultrasonic anemometers, and particle counters, and generate environmental datasets.

[0008] The embolization agent region segmentation module is used to calculate the turbulence intensity of the environmental dataset. It performs multi-source data weighted fusion of the time derivative results of pressure gradient data, the magnitude derivative results of wind speed vector data, and the time derivative results of particle concentration data to generate the turbulence intensity coefficient.

[0009] The module for calculating the accidental embolism index is used to make dynamic delay decisions on the turbulence intensity coefficient. Based on the preset turbulence intensity threshold, it adjusts the reference delay parameters required for stabilizing the airflow in the cleanroom and generates the actual delay parameters.

[0010] The target area embolism increment calculation module is used to obtain the delayed door closing request triggered by the access control system, perform countdown stability verification based on the actual delay parameters, determine the environmental recovery state based on real-time differential pressure gradient data and turbulence intensity coefficient within the countdown period, generate interlock control commands and send the interlock control commands to the access control actuator. The interlock control commands are used to indicate the locking or unlocking status of the electromagnetic lock.

[0011] The strategy gradient reward value generation module is used to optimize decision parameters based on the historical dataset of interlock events fed back by the access control actuator, analyze the correlation between cleanliness compliance indicators and actual delay parameters, and update the turbulence intensity threshold.

[0012] In one embodiment, the non-target area identification module of the cleanroom delayed opening interlock control system provided by the present invention is configured with the following units:

[0013] The door differential pressure gradient acquisition unit is used to acquire differential pressure gradient data in the edge area of ​​the door gap of the buffer room door. It obtains instantaneous differential pressure change data on both sides of the door through multi-point differential pressure sensors and generates door differential pressure gradient data.

[0014] The three-dimensional spatial wind speed vector acquisition unit is used to acquire wind speed vectors in the three-dimensional spatial area at the entrance of the core area. It obtains the instantaneous wind speed components along the X, Y, and Z axes through an ultrasonic anemometer matrix and generates three-dimensional spatial wind speed vector data.

[0015] The suspended particle concentration acquisition unit is used to acquire the concentration of suspended particles at the entrance of the buffer zone and the core area. It obtains the number of particles with a particle size greater than the preset critical particle size threshold through a laser particle counter and generates microparticle concentration data.

[0016] The multi-source environmental data synchronization unit is used to timestamp and synchronize gate pressure gradient data, three-dimensional spatial wind speed vector data, and particulate concentration data to generate an environmental dataset.

[0017] In one embodiment, the embolic agent area segmentation module of the cleanroom delayed opening interlock control system provided by the present invention is configured with the following units:

[0018] The differential pressure change rate calculation unit is used to perform time differentiation processing on the differential pressure gradient data in the environmental dataset, calculate the absolute value of the differential pressure change per unit time, and generate the differential pressure change rate.

[0019] The wind speed change rate modulus calculation unit is used to perform modulus differentiation processing on the three-dimensional spatial wind speed vector data in the environmental dataset, calculate the wind speed vector change modulus per unit time, and generate the wind speed change rate modulus.

[0020] The concentration change rate calculation unit is used to perform time differentiation processing on the particulate concentration data in the environmental dataset, calculate the concentration change per unit time, and generate the concentration change rate.

[0021] The turbulence intensity coefficient weighted fusion unit is used to perform weighted fusion processing on the rate of change of pressure difference, the modulus of the rate of change of wind speed, and the rate of change of concentration. It calculates the weighted sum according to the preset weight coefficients to generate the turbulence intensity coefficient.

[0022] In one embodiment, the false latching index calculation module of the cleanroom delayed opening interlock control system provided by the present invention is configured with the following units:

[0023] The standardized turbulence deviation calculation unit is used to calculate the deviation of the turbulence intensity coefficient based on a preset turbulence intensity threshold, calculate the difference between the turbulence intensity coefficient at the current moment and the preset turbulence intensity threshold, and generate the standardized turbulence deviation.

[0024] The nonlinear mapping value generation unit is used to perform nonlinear mapping on the standardized turbulence deviation. It converts the standardized turbulence deviation into a gain adjustment coefficient through the sigmoid function and generates a nonlinear mapping value.

[0025] The actual delay parameter adjustment unit is used to perform delay adjustment processing based on nonlinear mapping values, and to generate actual delay parameters by scaling the preset reference delay parameters according to the gain adjustment coefficient.

[0026] In one embodiment, the calculation formula for the actual delay parameter of the cleanroom delayed opening interlock control system provided by the present invention is as follows:

[0027]

[0028]

[0029] in, This is the actual delay parameter. The preset baseline delay parameter, For the response gain coefficient, For non-linear mapping values, The time decay factor, The time difference between the current time and the closing time. To standardize the turbulence deviation, It is a non-linear adjustment factor.

[0030] In one embodiment, the interlock control command of the cleanroom delayed opening interlock control system method provided by the present invention includes one of an unlocking command and a locking command, and the target area embolism increment calculation module is configured with the following units:

[0031] The door closing request structured parsing unit is used to obtain the delayed door closing request triggered by the access control system, perform door position parsing processing on the delayed door closing request, extract the door identifier and request timestamp, and generate structured door closing request data;

[0032] The countdown initialization unit is used to perform countdown initialization processing on the actual delay parameters, set the countdown initial value according to the actual delay parameters, start the timer, and generate a countdown state object;

[0033] The environmental stability monitoring unit is used to monitor the environmental stability based on the countdown state object. During the countdown period, it performs stability judgment processing on the real-time pressure gradient data and turbulence intensity coefficient. If the absolute value of the pressure gradient is continuously lower than the stability threshold and the turbulence intensity coefficient is less than the turbulence intensity threshold, an environmental recovery compliance mark is generated.

[0034] The interlock state decision unit is used to make interlock state decisions based on the environmental recovery compliance flag. When the environmental recovery compliance flag is valid, it generates an unlock command; when the environmental recovery compliance flag is invalid, it generates a lock command.

[0035] In one embodiment, the strategy gradient reward value generation module of the cleanroom delayed opening interlock control system provided by the present invention is configured with the following units:

[0036] The interlock event data cleaning unit is used to clean the historical dataset of interlock events uploaded by the access control actuator, remove invalid event records and fill in missing fields to generate a standardized event dataset.

[0037] The cleanliness and delay parameter correlation analysis unit is used to perform correlation analysis on the cleanliness compliance indicators and actual delay parameters in the standardized event dataset, calculate the cleanliness compliance rate under different delay parameters, and generate a delay-compliance rate correlation matrix.

[0038] The turbulence intensity threshold optimization unit is used to optimize the parameters of the delay-compliance rate correlation matrix, solve for the turbulence intensity threshold that maximizes the compliance rate and minimizes the average delay, and generate an updated turbulence intensity threshold.

[0039] In summary, the non-target area identification module and embolic agent area segmentation module of the cleanroom delayed opening interlock control system provided by this invention can accurately quantify the airflow disturbance intensity through synchronous acquisition of multi-source environmental data and dynamic calculation of turbulence intensity coefficient, overcoming the shortcomings of traditional fixed delay mechanisms in responding to dynamic environmental changes with lag. The false embolization index calculation module, based on nonlinear mapping and dynamic delay decision, can adaptively adjust the benchmark delay parameter, enabling the access control system to automatically extend the delay period until the environment returns to stability under sudden airflow disturbances, while shortening unnecessary waiting time under normal operating conditions, thereby achieving a dynamic balance between passage efficiency and cleanliness safety. The dual real-time verification mechanism during the countdown period of the target area embolization increment calculation module can effectively identify transient pseudo-steady states, avoid the risk of false locking caused by local airflow stabilization, and significantly reduce the probability of contaminant intrusion. The strategy gradient reward value generation module can continuously optimize the turbulence intensity threshold decision boundary by analyzing the correlation between the cleanliness compliance rate and the actual delay in historical interlock events, enabling the system to have self-learning capabilities to adapt to fluid characteristic drift problems caused by changes in plant layout or equipment aging. This method ultimately achieves intelligent closed-loop management of airflow control in cleanrooms, significantly reducing average passage waiting time while ensuring the cleanliness requirements specified in ISO 14644, and reducing the ineffective energy consumption of the air conditioning system in order to maintain excessive pressure differential, thus meeting the dual stringent requirements of the pharmaceutical, semiconductor and other industries for high cleanliness environment and production cycle.

[0040] To better understand and implement this invention, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description

[0041] Figure 1 A schematic diagram of a cleanroom delayed opening interlock control system provided in this application embodiment;

[0042] Figure 2 This is a schematic diagram of the structure of the module for generating the thrombosis index calculation provided in an embodiment of this application. Detailed Implementation

[0043] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0045] Please see Figure 1 This embodiment illustrates a cleanroom delayed-opening interlock control system provided in this application. This embodiment uses the system's application to a terminal as an example for illustration. It is understood that this system can also be applied to a server, and further to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. Figure 1 As shown, the cleanroom delayed opening interlock control system 100 provided by the present invention includes a non-target area identification module 110, an embolic agent area segmentation module 120, a false embolism index calculation module 130, a target area embolism increment calculation module 140, and a fault diagnosis and strategy gradient reward value generation module 150.

[0046] The non-target area identification module 110 is used to synchronously collect real-time differential pressure gradient data, three-dimensional spatial wind speed vector data, and micron-level particle concentration data in the buffer room door and core area entrance of the cleanroom based on differential pressure sensors, three-dimensional ultrasonic anemometers, and particle counters, and generate an environmental dataset.

[0047] Specifically, this embodiment selects differential pressure sensors, three-dimensional ultrasonic anemometers, and particle counters adapted to the environmental monitoring requirements of cleanrooms. The differential pressure sensors are deployed on both sides of the partition wall between the buffer zone and the core area, the three-dimensional ultrasonic anemometer is deployed at the center of the top of the buffer zone, and the particle counter is deployed inside the entrance of the core area. The deployment positions of each device avoid areas directly affected by local airflow generated by the opening and closing of the doors. The non-target area identification module 110 establishes a communication connection between the sensing devices and the edge computing gateway via an industrial bus. The gateway integrates a timing module to synchronize the time of each sensor, eliminating phase deviations caused by time differences in multi-source data acquisition. The system controls each sensor to collect data according to a preset sampling frequency. The gateway receives the data in real time and performs formatting processing to generate a structured environmental dataset containing sensor identifiers, acquisition timestamps, differential pressure gradient data, three-dimensional wind speed vector data, and particle concentration data. The dataset is stored in real time on the gateway's local storage medium and synchronized to the central control system database.

[0048] The embolizing agent region segmentation module 120 is used to calculate the turbulence intensity of the environmental dataset. It performs multi-source data weighted fusion of the time derivative results of the pressure gradient data, the magnitude derivative results of the wind speed vector data, and the time derivative results of the particle concentration data to generate the turbulence intensity coefficient.

[0049] Specifically, the embolic agent region segmentation module 120 filters and removes abnormal data in the environmental dataset, using preset criteria to remove data points that deviate from the normal range. Subsequently, it performs moving average filtering on each parameter to eliminate high-frequency fluctuations caused by sensor noise, ensuring the accuracy of differential calculations. Based on the sampling frequency of each sensor, the embolic agent region segmentation module 120 calculates the time derivatives of the pressure gradient data, the wind speed vector magnitude, and the particle concentration data, respectively. The wind speed vector magnitude is obtained through vector synthesis of the three-dimensional wind speed components.

[0050] Preferably, the embolizing agent region segmentation module 120 can use the analytic hierarchy process (AHP) to determine the weighting coefficients of each differential parameter. The weighting coefficients are set based on the degree of correlation between each parameter and the characterization of airflow disturbance. The system sets the maximum allowable threshold for each differential parameter, normalizes each differential parameter, and merges the normalized differential parameters through a weighted summation operation to generate a turbulence intensity coefficient. This coefficient is used to quantitatively characterize the degree of airflow disturbance in the cleanroom, and its value range is within a preset interval.

[0051] The accidental embolism index calculation module 130 is used to make dynamic delay decisions on the turbulence intensity coefficient, adjust the reference delay parameters required for cleanroom airflow stabilization based on the preset turbulence intensity threshold, and generate actual delay parameters.

[0052] Specifically, the thrombosis error calculation module 130 performs a baseline delay parameter calibration process under an ideal steady-state environment. The buffer zone and core area doors are kept closed until the environment stabilizes. Then, the doors are manually opened and closed, simultaneously activating sensor data acquisition to continuously monitor environmental parameters. The time interval between door closure and environmental parameter stabilization is recorded, and the average time interval is obtained through repeated operations as the baseline delay parameter. The thrombosis error calculation module 130 presets multiple levels of turbulence intensity thresholds, each corresponding to different levels of airflow disturbance. The threshold settings are based on a correlation analysis between airflow disturbance levels and cleanliness requirements. The thrombosis error calculation module 130 compares the calculated turbulence intensity coefficient with the preset thresholds and adjusts the baseline delay parameter according to preset rules based on the comparison results to generate the actual delay parameter. When the turbulence intensity coefficient falls within different threshold ranges, the system uses the corresponding adjustment algorithm to modify the baseline delay parameter. In extreme disturbance conditions, the system sets a maximum delay upper limit and simultaneously sends an airflow anomaly alarm signal to the central control system.

[0053] The target area embolism increment calculation module 140 is used to obtain the delayed door closing request triggered by the access control system, perform countdown stability verification based on the actual delay parameters, determine the environmental recovery state according to the real-time differential pressure gradient data and turbulence intensity coefficient within the countdown period, generate interlock control commands and send the interlock control commands to the access control actuator. The interlock control commands are used to indicate the locking or unlocking state of the electromagnetic lock.

[0054] Specifically, the target area embolization increment calculation module 140 detects the door closure status through a door magnetic sensor integrated into the access control system. When the door closes to a preset fit, the door magnetic sensor triggers a delayed closing request. The request signal is transmitted to the edge computing controller via the communication bus, and the signal includes the door identifier, trigger timestamp, and door closure status information. After receiving the request, the controller starts a countdown based on the generated actual delay parameters. During the countdown, the system collects differential pressure gradient data and turbulence intensity coefficient in real time at a preset frequency. The turbulence intensity coefficient is calculated in real time by the embolization agent region segmentation module 120. The system sets environmental stability judgment conditions and continuously verifies whether these conditions are met during the countdown. If multiple consecutive detection results meet the stability judgment conditions, the countdown ends early. If the turbulence intensity coefficient is detected to exceed the preset range, the coefficient is recalculated and the actual delay parameters are updated, resetting the countdown.

[0055] If the countdown ends normally and the stability judgment condition is met, the target area embolism increment calculation module 140 generates an interlock control command. The command is constructed using a preset communication protocol and includes the device address, function code, register address, control value, and check code. The system sends the command to the access control actuator through the communication bus to control the locking or unlocking state of the electromagnetic lock. After the access control actuator executes the command, it returns a response signal to the controller. The controller records the event log. If the execution fails, the command is sent repeatedly. After multiple failures, an access control fault alarm is triggered.

[0056] Preferably, before starting the countdown based on the actual delay parameters, the target area embolism increment calculation module 140 completes the time delay allocation setting for the opening and closing of the interlock door. Based on the numerical characteristics of the actual delay parameters, it divides the door closing delay period and opening delay period according to a preset ratio. The closing delay period corresponds to the interval from when the door is fully closed to when environmental monitoring starts, and the opening delay period corresponds to the interval from when the environment meets the standard to when the door unlocks. The sum of the two durations is consistent with the actual delay parameters. The system records the two types of delay periods into the countdown state object, clearly defining the time boundaries of the closing and opening delay stages. During the closing delay period, the target area embolism increment calculation module 140 continuously monitors the stability of the door's closed state to ensure that the door is not loose or not fully closed. After entering the opening delay period, the system increases the frequency of environmental parameter acquisition, tracking the fluctuations of differential pressure gradient data and turbulence intensity coefficient in real time. If abnormal fluctuations in environmental parameters are detected during the opening delay period, the target area embolism increment calculation module 140 automatically extends the opening delay period until the parameters return to a stable range. Through this time delay allocation and dynamic adjustment mechanism, sufficient buffering is provided for the door state switching and environmental stability transition, avoiding airflow disturbances caused by excessively rapid state switching, and ensuring the safety and rationality of interlock control.

[0057] Furthermore, the system provided in this application embodiment can establish a real-time data interaction link with the factory fire alarm system through a preset communication interface, continuously receiving status signals transmitted by the fire alarm system. These signals include a fire trigger status identifier and alarm area information. When the system detects a fire trigger signal sent by the fire alarm system, it automatically triggers an emergency unlocking mechanism, suspending all conventional interlock control logic and delay parameter related calculations. Based on this, the system generates a global emergency unlocking command, which is synchronously sent to all access control actuators via the communication bus, controlling the electromagnetic locks corresponding to all interlocked doors to de-energize, switching the door locks from the locked state to the open state, completely releasing the interlocking restrictions between the doors. Simultaneously, the system receives door lock status signals from each access control actuator in real time, confirming that all doors are in a valid open state, and records the trigger time of the emergency unlocking event, the source of the fire alarm signal, and the door lock status feedback result, ensuring that personnel can freely pass through without being restricted by the interlocking mechanism in a fire scenario, guaranteeing the unobstructed flow of personnel evacuation routes.

[0058] The strategy gradient reward value generation module 150 is used to optimize decision parameters based on the historical dataset of interlock events fed back by the access control actuator, analyze the correlation between cleanliness compliance indicators and actual delay parameters, and update the turbulence intensity threshold.

[0059] Specifically, the strategy gradient reward value generation module 150 constructs a historical dataset of interlocking events. This dataset is stored in an industrial time-series database. Each record includes an event ID, trigger time, trigger scenario, actual delay parameters, environmental parameters during the countdown, particle concentration data after unlocking, cleanliness compliance markers, and the electromagnetic lock's execution status. The data is retained according to a preset period and backed up periodically. The strategy gradient reward value generation module 150 preprocesses the historical dataset, removing abnormal event data such as sensor malfunctions and emergency alarms, retaining only valid data samples.

[0060] Preferably, the strategy gradient reward value generation module 150 employs a combination of statistical analysis and machine learning to calculate the correlation between the cleanliness compliance index and the actual delay parameter and turbulence intensity coefficient. It then selects core influencing factors, constructs a logistic regression model, and trains the model using the cleanliness compliance marker as the dependent variable and the turbulence intensity coefficient and actual delay parameter as independent variables to obtain the optimal decision boundary. The strategy gradient reward value generation module 150 uses the cleanliness compliance rate meeting preset requirements and the shortest average actual delay as its optimization objective. It solves for the optimal turbulence intensity threshold using a grid search method, sets a threshold update mechanism, and updates the threshold regularly according to a preset cycle, while triggering immediate updates under special circumstances. The updated threshold is stored in the controller's non-volatile memory and synchronized to the central control system database. The strategy gradient reward value generation module 150 verifies the effectiveness of the updated threshold through simulation tests and on-site measurements. If the target is met, the threshold is retained; otherwise, it rolls back to the previous version and re-analyzes the data.

[0061] In summary, the non-target area identification module 110 and embolic agent area segmentation module 120 of the cleanroom delayed opening interlock control system provided by this invention can accurately quantify the intensity of airflow disturbance through synchronous acquisition of multi-source environmental data and dynamic calculation of turbulence intensity coefficient, overcoming the defect of traditional fixed delay mechanism in responding to dynamic environmental changes with lag; the false embolization index calculation module 130, based on nonlinear mapping dynamic delay decision, can adaptively adjust the reference delay parameter, so that the access control system automatically extends the delay period until the environment returns to stability under sudden airflow disturbance, while under normal operating conditions... The method shortens unnecessary waiting time to achieve a dynamic balance between passage efficiency and cleanliness and safety. The dual real-time verification mechanism during the countdown period of the target area embolism increment calculation module 140 effectively identifies transient pseudo-steady states, avoiding the risk of false locking due to local airflow stabilization and significantly reducing the probability of contaminant intrusion. The strategy gradient reward value generation module 150 continuously optimizes the turbulence intensity threshold decision boundary by analyzing the correlation between cleanliness compliance rate and actual delay in historical interlock events, enabling the system to have self-learning capabilities to adapt to fluid characteristic drift problems caused by changes in plant layout or equipment aging. This method ultimately achieves intelligent closed-loop management of cleanroom airflow control, significantly shortening the average passage waiting time while ensuring the cleanliness requirements specified in ISO 14644, and reducing the ineffective energy consumption of the air conditioning system to maintain excessive pressure differential, meeting the stringent requirements of the pharmaceutical and semiconductor industries for both high cleanliness and production cycle time.

[0062] In one embodiment, the non-target area identification module 110 of the cleanroom delayed opening interlock control system provided by the present invention is configured with the following units:

[0063] The door differential pressure gradient acquisition unit is used to acquire differential pressure gradient data in the edge area of ​​the door gap of the buffer room door. It obtains instantaneous differential pressure change data on both sides of the door through multi-point differential pressure sensors and generates door differential pressure gradient data.

[0064] Specifically, in this embodiment, multiple differential pressure acquisition components are deployed along the edge of the door seam of the buffer room door. These components are evenly distributed along the edge of the door seam, covering its entire length. The deployment positions maintain a preset distance from the door's opening and closing trajectory to avoid physical contact or direct airflow interference with the acquisition components during door movement. After the door differential pressure gradient acquisition unit is activated, all differential pressure sensors are simultaneously activated. Each sensor continuously acquires pressure data from both sides of the door according to a uniform preset sampling period, forming an instantaneous differential pressure change sequence. The door differential pressure gradient acquisition unit verifies the data output from each monitoring point point by point. Based on preset data rationality judgment rules, it identifies and eliminates abnormal data points caused by poor sensor contact or instantaneous electromagnetic interference.

[0065] The door differential pressure gradient acquisition unit employs a data fusion algorithm to integrate verified multi-point differential pressure data. Through spatial weight allocation and statistical calculation of the data at each point, the scattered multi-point data is transformed into unified data that reflects the overall differential pressure distribution characteristics of the door gap edge, generating door differential pressure gradient data. After this data is generated, the system associates and stores it with sensor identification and acquisition time information, providing accurate basic parameters for differential pressure changes in subsequent turbulence intensity calculations.

[0066] The three-dimensional spatial wind speed vector acquisition unit is used to acquire wind speed vectors in the three-dimensional spatial area at the entrance of the core area. It obtains the instantaneous wind speed components along the X, Y, and Z axes through an ultrasonic anemometer matrix and generates three-dimensional spatial wind speed vector data.

[0067] Specifically, this embodiment determines the deployment scheme of the ultrasonic anemometer matrix based on the spatial structure and size characteristics of the core area entrance. The deployment density of the anemometer matrix is ​​adjusted according to the complexity of airflow movement in the entrance space to ensure that each monitoring point can form a continuous airflow monitoring network without blind spots. The three-dimensional spatial wind speed vector acquisition unit sets a unified spatial coordinate system for the anemometer matrix. The X-axis is consistent with the door opening direction, the Y-axis is perpendicular to the door plane, and the Z-axis is perpendicular to the ground. The definitions of each axis are fixed by preset parameters in the system to ensure the uniformity of the measurement direction of all anemometers. The three-dimensional spatial wind speed vector acquisition unit controls the ultrasonic anemometer matrix to start acquisition at a synchronous sampling frequency. Each anemometer simultaneously acquires the instantaneous wind speed components along the X, Y, and Z axes at its location. The component data is transmitted to the data processing unit in real time through a preset communication link.

[0068] Furthermore, the three-dimensional spatial wind speed vector acquisition unit performs consistency verification on the received axial wind speed component data, compares the measurement results of adjacent anemometers within the same spatial area, and eliminates abnormal component data that deviates from the overall measurement trend. The three-dimensional spatial wind speed vector acquisition unit uses a vector synthesis algorithm to calculate the X, Y, and Z axial components at the same monitoring time, integrating them to form three-dimensional spatial wind speed vector data that can fully characterize the three-dimensional airflow motion state at the core area entrance at that moment. After data generation, the acquisition points and time information are simultaneously marked, providing comprehensive basic wind speed data for airflow disturbance analysis.

[0069] The suspended particle concentration acquisition unit is used to acquire the concentration of suspended particles at the entrance of the buffer zone and the core area. It obtains the number of particles with a particle size larger than a preset critical particle size threshold through a laser particle counter and generates microparticle concentration data.

[0070] Specifically, in this embodiment, laser particle counting components are deployed inside the buffer room and at the entrance of the core area. The number of components is determined based on the size of the monitored area to ensure that the monitoring ranges of each component are interconnected, covering all areas where the opening and closing of the door may cause particle diffusion. The suspended particle concentration acquisition unit pre-sets the critical particle size threshold for suspended particles according to the cleanliness level requirements of the cleanroom. This threshold serves as the screening standard for target particles and is stored in the system control unit.

[0071] After the laser particle counting component is activated, it extracts air samples from the monitoring area at a preset sampling flow rate. The samples pass through the optical detection channel inside the component, and the suspended particle concentration acquisition unit identifies the particle size in the sample using optical detection principles, filtering out target particles with a diameter larger than the critical particle size threshold, and counting the number of target particles in real time. During the counting process, the suspended particle concentration acquisition unit verifies the stability of the counting results at preset time intervals, eliminating counting deviations caused by fluctuations in sample airflow or instantaneous contamination of optical components. The suspended particle concentration acquisition unit correlates the verified particle count results with the monitoring area identifier and collection time information to generate microparticle concentration data that reflects the real-time cleanliness status of the monitoring area, providing a direct basis for determining whether cleanliness standards are met.

[0072] The multi-source environmental data synchronization unit is used to timestamp and synchronize gate pressure gradient data, three-dimensional spatial wind speed vector data, and particulate concentration data to generate an environmental dataset.

[0073] Specifically, after receiving gate pressure gradient data, three-dimensional spatial wind speed vector data, and particulate concentration data, the multi-source environmental data synchronization unit initiates the multi-source data synchronization processing flow and calls the time synchronization function module. This module establishes a connection with a standard time source using a preset time synchronization protocol to obtain a unified time reference. The multi-source environmental data synchronization unit adds a timestamp to each piece of collected data according to a preset format. The precision setting of the timestamp is adapted to the sampling frequency of various types of data, ensuring that different types of data at the same monitoring time can be accurately correlated through timestamps.

[0074] The multi-source environmental data synchronization unit compares the timestamps of the three types of data one by one, identifies time difference phase deviations caused by data transmission delays or differences in sensor responses, and adjusts the data timestamps through a preset deviation correction algorithm to ensure a strict one-to-one correspondence between the pressure difference, wind speed, and particle concentration data at the same monitoring time. The multi-source environmental data synchronization unit then performs structured processing on the synchronized multi-source data, integrating sensor identifiers, acquisition timestamps, gate pressure gradient data, three-dimensional spatial wind speed vector data, and particulate concentration data according to preset data fields to generate a complete environmental dataset.

[0075] In one embodiment, the embolic agent area segmentation module 120 of the cleanroom delayed opening interlock control system provided by the present invention is configured with the following units:

[0076] The differential pressure change rate calculation unit is used to perform time differentiation processing on the differential pressure gradient data in the environmental dataset, calculate the absolute value of the differential pressure change per unit time, and generate the differential pressure change rate.

[0077] Specifically, the differential pressure change rate calculation unit extracts the gate differential pressure gradient data from the environmental dataset. During the extraction process, it compares the data continuity point by point according to the time series, identifies missing items in the data transmission or storage process according to preset rules, and uses a linear interpolation algorithm to fill in the data gaps, ensuring the integrity of the differential pressure gradient data sequence. The differential pressure change rate calculation unit sorts the verified differential pressure gradient data according to the chronological order of the collection timestamps, clarifies the time interval between two adjacent valid data points, and calculates the differential pressure change rate based on the time differential algorithm. The core calculation formula is:

[0078]

[0079] in, This represents the difference in pressure gradient data between two adjacent valid data points. This represents the time interval between the collection of two adjacent valid data points. This represents the generated differential pressure change rate. The differential pressure change rate calculation unit associates the calculation results with the corresponding acquisition timestamp and sensor identifier to generate differential pressure change rate data.

[0080] The wind speed change rate modulus calculation unit is used to perform modulus differentiation processing on the three-dimensional spatial wind speed vector data in the environmental dataset, calculate the wind speed vector change modulus per unit time, and generate the wind speed change rate modulus.

[0081] Specifically, the wind speed change rate modulus calculation unit extracts three-dimensional spatial wind speed vector data from the environmental dataset, simultaneously verifying the validity of the X, Y, and Z axial wind speed components at each monitoring time. By comparing the wind speed component change trends at multiple consecutive times from the same data collection point, abnormal data deviating from the overall change pattern is eliminated. The wind speed change rate modulus calculation unit first calculates the wind speed vector modulus at each time point using a vector modulus synthesis algorithm. The calculation formula is as follows:

[0082]

[0083] in, These represent the instantaneous wind speed components along the X, Y, and Z axes in three-dimensional space, respectively. This represents the synthesized wind speed vector magnitude, forming a continuous wind speed magnitude data sequence. The wind speed change rate magnitude calculation unit arranges the wind speed magnitude data sequence in timestamp order, determines the time interval between adjacent data points, and uses the same time differential operation logic as the pressure gradient data to calculate the wind speed change rate magnitude using a formula. :

[0084]

[0085] in, This represents the difference in wind speed modulus between two adjacent valid data points. Represents a time interval. This represents the modulus of wind speed change.

[0086] The concentration change rate calculation unit is used to perform time differentiation processing on the particulate concentration data in the environmental dataset, calculate the concentration change per unit time, and generate the concentration change rate.

[0087] Specifically, the concentration change rate calculation unit extracts particulate concentration data from the environmental dataset, sets reasonable range judgment rules based on the concentration range corresponding to the cleanliness level, identifies and eliminates abnormal values ​​caused by fluctuations in the sampling airflow or instantaneous interference from the detection equipment, and supplements missing items in the data sequence using a linear interpolation algorithm to ensure the continuity and integrity of the concentration data sequence. The concentration change rate calculation unit arranges the verified concentration data in chronological order of the collection timestamps, defines the time interval between two adjacent valid data points, and calculates the concentration change rate through time differentiation. The core calculation formula is:

[0088]

[0089] in, This represents the difference in particle concentration data between two adjacent valid data points. This represents the time interval between the collection of adjacent data points. This represents the generated concentration change rate. The concentration change rate calculation unit associates the generated concentration change rate with the corresponding monitoring area identifier and collection timestamp, and stores it in a dedicated data storage unit. During storage, data redundancy backup is performed to avoid data loss due to storage media failure. This data directly reflects the dynamic change trend of the number of suspended particles in the monitoring area.

[0090] The turbulence intensity coefficient weighted fusion unit is used to perform weighted fusion processing on the rate of change of pressure difference, the modulus of the rate of change of wind speed, and the rate of change of concentration. It calculates the weighted sum according to the preset weight coefficients to generate the turbulence intensity coefficient.

[0091] Specifically, the turbulence intensity coefficient weighted fusion unit extracts data on pressure difference rate of change, wind speed rate of change modulus, and concentration rate of change from the data buffer and dedicated storage unit, respectively. Following a preset data alignment rule, it precisely matches the three types of data based on the acquisition timestamp, ensuring a strict correspondence between the three types of rate of change data at the same monitoring time and eliminating deviations in the data's temporal dimension. The turbulence intensity coefficient weighted fusion unit first normalizes the matched three types of data. Taking the pressure difference rate of change as an example, the normalization formula is:

[0092]

[0093] in, This represents the rate of change of the original pressure difference. This represents the preset maximum allowable rate of change of differential pressure. This represents the normalized rate of change of pressure difference. The modulus of the rate of change of wind speed and the rate of change of concentration are normalized using the same logic. The turbulence intensity coefficient weighted fusion unit calls the preset weight coefficients. , , These coefficients, based on the influence of various rates of change on the airflow turbulence state, are determined through statistical analysis to set their proportions, and their sum is 1. They are then stored in the system control unit. The turbulence intensity coefficient weighted fusion unit performs a weighted summation calculation using the following formula:

[0094]

[0095] in, , These represent the normalized modulus of wind speed change and the rate of change of concentration, respectively. This represents the turbulence intensity coefficient.

[0096] In one embodiment, the false latching index calculation module 130 of the cleanroom delayed opening interlock control system provided by the present invention is configured with the following units:

[0097] The standardized turbulence deviation calculation unit is used to calculate the deviation of the turbulence intensity coefficient based on a preset turbulence intensity threshold, calculate the difference between the turbulence intensity coefficient at the current moment and the preset turbulence intensity threshold, and generate the standardized turbulence deviation.

[0098] Specifically, the standardized turbulence deviation calculation unit extracts the current turbulence intensity coefficient and a preset turbulence intensity threshold from the storage unit. The preset turbulence intensity threshold is set based on the cleanroom airflow stabilization requirements and is stored in the non-volatile storage area of ​​the system control unit, maintaining consistency with the calculation dimension of the turbulence intensity coefficient. The standardized turbulence deviation calculation unit obtains the difference between the current turbulence intensity coefficient and the preset turbulence intensity threshold through numerical calculation. The core calculation formula is:

[0099]

[0100] in, Represents the turbulence intensity coefficient generated at the current moment. This represents the preset turbulence intensity threshold. This represents the standardized turbulence deviation. During the calculation process, the standardized turbulence deviation calculation unit performs a dimensional consistency check on the two data points involved in the calculation, ensuring that they are completely matched in terms of data format and quantization standards, thus avoiding calculation deviations caused by dimensional differences. The standardized turbulence deviation calculation unit associates the generated standardized turbulence deviation with the corresponding monitoring time and area identifier, stores it in a dedicated data buffer, and simultaneously performs data range verification to ensure that the deviation conforms to the preset logical range.

[0101] The nonlinear mapping value generation unit is used to perform nonlinear mapping on the standardized turbulence deviation. It converts the standardized turbulence deviation into a gain adjustment coefficient through the sigmoid function to generate the nonlinear mapping value.

[0102] Specifically, the nonlinear mapping value generation unit extracts the standardized turbulence deviation from the data buffer and calls the preset sigmoid function to transform the deviation. The core calculation formula for the nonlinear mapping is:

[0103]

[0104] in, Represents the standardized turbulence deviation. This represents a non-linear adjustment factor used to adjust the slope of the sigmoid function curve. This represents the generated nonlinear mapping value, i.e., the gain adjustment coefficient. The nonlinear mapping value generation unit is invoked according to preset parameters. The value of this parameter is set based on the airflow disturbance response sensitivity requirements and is stored in the system control unit. During the calculation process, the nonlinear mapping value generation unit performs accuracy verification on the exponential operation results and the summation results to ensure the accuracy of the numerical calculation. The generated nonlinear mapping value ranges between 0 and 1, directly reflecting the degree of influence of the standardized turbulence deviation on the gain of the delay adjustment.

[0105] The actual delay parameter adjustment unit is used to perform delay adjustment processing based on nonlinear mapping values, and to generate actual delay parameters by scaling the preset reference delay parameters according to the gain adjustment coefficient.

[0106] Specifically, the actual delay parameter adjustment unit extracts preset baseline delay parameters, response gain coefficient, nonlinear mapping value, and time decay factor from the storage unit. Simultaneously, it calculates the time difference between the current time and the closing time, obtained through the timestamp difference of the system clock module, accurately reflecting the cumulative time elapsed after the door closes. Based on these parameters, the actual delay parameter adjustment unit performs delay adjustment processing, scaling the baseline delay parameters using a preset formula. The calculation formula for the actual delay parameters is as follows:

[0107]

[0108] in, This is the actual delay parameter. The preset baseline delay parameter, For the response gain coefficient, For non-linear mapping values, The time decay factor, The time difference between the current time and the closing time. To standardize turbulence deviation, the actual delay parameter adjustment unit performs numerical consistency checks on all parameters in the formula, ensuring that all parameters involved in the calculation conform to the operational logic in terms of dimensions and numerical range. It then performs exponential, multiplicative, and addition operations sequentially according to the formula to generate the actual delay parameters. After the calculation is complete, the actual delay parameter adjustment unit performs data accuracy checks to ensure that the actual delay parameters meet the time quantization requirements of access control. It then associates and stores these parameters with the corresponding monitoring time, area identifier, and turbulence state information, and synchronously transmits them to the delayed door closing request processing stage, providing core delay control parameters for the generation of interlock control commands.

[0109] In one embodiment, the interlock control command of the cleanroom delayed opening interlock control system provided by the present invention includes one of an unlocking command and a locking command, and the target area embolism increment calculation module 140 is configured with the following units:

[0110] The door closing request structured parsing unit is used to obtain delayed door closing requests triggered by the access control system, perform door position parsing processing on the delayed door closing requests, extract the door identifier and request timestamp, and generate structured door closing request data.

[0111] Specifically, the door closing request structured parsing unit receives delayed door closing requests triggered by the access control system through a preset communication link. The communication link employs an industry-standard communication protocol to ensure the reliability and standardization of data transmission. The door closing request structured parsing unit performs data format verification on the received delayed door closing requests, checking each of the door location-related fields, timestamp fields, and status identifier fields in the request data according to preset field rules, and eliminating invalid data with missing fields or incorrect formats. The system extracts the door identifier and request timestamp from the valid request data. The door identifier is a character sequence that uniquely identifies the door corresponding to the buffer zone and the core area, and the request timestamp is a time code recording the time the request was triggered. The door closing request structured parsing unit integrates and extracts information according to a preset data structure. The expression for the structured door closing request data is:

[0112]

[0113] in, Represents the gate identifier. This represents a request for a timestamp. This indicates the validity of the request.

[0114] The countdown initialization unit is used to perform countdown initialization processing on the actual delay parameters. It sets the initial countdown value according to the actual delay parameters, starts the timer, and generates a countdown state object.

[0115] Specifically, the countdown initialization unit obtains the actual delay parameters from the dynamic delay decision-making stage through a data interaction interface, performs validity checks on these parameters, and determines whether the parameters are within a reasonable range based on preset data range rules. If the parameters exceed the range, preset backup parameters are activated to ensure continuous process operation. The countdown initialization unit uses structured closing request data... Using the base time point, the actual delay parameter is assigned the initial countdown value, completing the timer initialization configuration. The countdown initialization unit starts the timer, and the timer's timing period is consistent with the environmental parameter acquisition period to ensure that the timing progress is synchronized with the update frequency of environmental monitoring data. The expression for calculating the remaining countdown time is:

[0116]

[0117] in, Represents the current remaining time. Represents the actual delay parameter. Represents the current system time. The system generates a countdown state object, which contains... , , , Core information such as state objects are updated in real time according to a timer cycle. It is also synchronized to the environmental stability monitoring stage through the data transmission channel, providing accurate basis for determining the time nodes in the monitoring process.

[0118] The environmental stability monitoring unit is used to monitor the environmental stability based on the countdown state object. During the countdown period, it performs stability judgment processing on the real-time differential pressure gradient data and turbulence intensity coefficient. If the absolute value of the differential pressure gradient is continuously lower than the stability threshold and the turbulence intensity coefficient is less than the turbulence intensity threshold, an environmental recovery compliance mark is generated.

[0119] Specifically, the environmental stability monitoring unit acquires real-time differential pressure gradient data and turbulence intensity coefficients from the environmental monitoring data transmission channel at a preset frequency within a countdown period. The real-time differential pressure gradient data, after being acquired by sensors, undergoes a preprocessing procedure to remove noise interference. The turbulence intensity coefficient is calculated in real-time using a previously set multi-source data fusion algorithm. The environmental stability monitoring unit takes the absolute value of the real-time differential pressure gradient data and compares it with a preset stability threshold, while simultaneously comparing the real-time turbulence intensity coefficient with the preset turbulence intensity threshold. The logical expression for stability determination is:

[0120]

[0121] in, This represents the stability determination result. Represents real-time differential pressure gradient data. This represents the preset stability threshold. Represents the real-time turbulence intensity coefficient. This represents the preset turbulence intensity threshold. This represents the duration during which both conditions are simultaneously satisfied. This represents the preset minimum duration requirement. When When true, the environmental stability monitoring unit generates an environmental restoration compliance indicator for an effective state; when... When the value is false, an invalid state flag is generated, and the qualified flag is synchronized to the interlock state decision-making process through a real-time communication mechanism.

[0122] The interlock state decision unit is used to make interlock state decisions based on the environmental recovery compliance flag. When the environmental recovery compliance flag is valid, it generates an unlock command; when the environmental recovery compliance flag is invalid, it generates a lock command.

[0123] Specifically, the interlock state decision unit receives environmental recovery compliance flags in real time through the data receiving port, determines the flag status, and clarifies whether the current environment has met the requirements for stable recovery. When the flag is valid, the interlock state decision unit generates an unlock command; when the flag is invalid, the interlock state decision unit generates a lock command. The interlock state decision unit constructs control commands according to a preset command format, and the command expression is as follows:

[0124]

[0125] in, Represents the gate identifier. Represents the control status (1 for unlocked, 0 for locked). The timestamp representing the generation of the instruction. This represents verification information. The command is calculated using a hash algorithm to ensure it is not tampered with during transmission and to guarantee transmission integrity. The interlock status decision unit performs format verification on the generated control command, checking whether each field conforms to the access control actuator's receiving protocol requirements. If a format error is found, it is immediately regenerated. The interlock status decision unit transmits the verified control command to the corresponding communication port of the access control actuator, using a two-way communication mechanism to perform real-time feedback verification, and receives the command reception confirmation signal returned by the access control actuator to ensure successful command delivery, providing accurate and reliable control basis for the locking or unlocking operation of the door.

[0126] In one embodiment, the strategy gradient reward value generation module 150 of the cleanroom delayed opening interlock control system provided by the present invention is configured with the following units:

[0127] The interlock event data cleaning unit is used to clean the historical dataset of interlock events uploaded by the access control actuator, remove invalid event records and fill in missing fields to generate a standardized event dataset.

[0128] Specifically, the interlock event data cleaning unit receives historical datasets of interlock events uploaded by the access control actuators. This dataset contains multiple fields, including event ID, actual delay parameters, cleanliness compliance markers, execution status, and timestamps. The interlock event data cleaning unit filters valid records using an integrity verification formula:

[0129]

[0130] in, The data integrity coefficient. This represents the number of records where no field is missing. For the total number of records in the dataset, when Records falling below a preset standard are deemed invalid and removed. The interlocking event data cleaning unit completes the missing fields of the remaining records, using corresponding filling rules based on field type: statistical filling for numeric fields and pattern filling for categorical fields. The interlocking event data cleaning unit standardizes field formats according to preset data specifications, clarifying the data type, value range, and encoding rules of each field, generating a standardized event dataset containing complete and valid interlocking event information. This dataset is stored in a time-series database, providing a high-quality data foundation for subsequent correlation analysis.

[0131] The cleanliness and delay parameter correlation analysis unit is used to perform correlation analysis on the cleanliness compliance indicators and actual delay parameters in the standardized event dataset, calculate the cleanliness compliance rate under different delay parameters, and generate a delay-compliance rate correlation matrix.

[0132] Specifically, the cleanliness and delay parameter correlation analysis unit extracts cleanliness compliance indicators and actual delay parameters from the standardized event dataset. It then divides the dataset into several continuous intervals based on the value range of the actual delay parameters, with each interval containing a preset number of event records. The cleanliness and delay parameter correlation analysis unit calculates the cleanliness compliance rate for each interval using the compliance rate calculation formula:

[0133]

[0134] in, To achieve the cleanliness standard compliance rate, The number of events within the interval that meet the cleanliness standard are marked as valid. This represents the total number of events within the interval. The cleanliness and delay parameter correlation analysis unit constructs a delay-compliance rate correlation matrix. The row dimension of the matrix corresponds to the actual delay parameter interval, and the column dimension corresponds to the cleanliness compliance rate and the average delay within that interval. The matrix elements are the compliance rate value and the average delay value corresponding to each interval. The cleanliness and delay parameter correlation analysis unit performs data verification on the correlation matrix, ensuring consistency by comparing the matrix elements with the statistical results of the original data. The generated correlation matrix clearly presents the correspondence between different delay parameters and the cleanliness compliance rate, providing a quantitative basis for parameter optimization.

[0135] The turbulence intensity threshold optimization unit is used to optimize the parameters of the delay-compliance rate correlation matrix, solve for the turbulence intensity threshold that maximizes the compliance rate and minimizes the average delay, and generate an updated turbulence intensity threshold.

[0136] Specifically, the cleanliness and delay parameter correlation analysis unit sets a dual-objective optimization function based on the delay-compliance rate correlation matrix:

[0137]

[0138] in, To comprehensively optimize the target value, The compliance rate is the weighting coefficient. This is the average delay weighting coefficient. To achieve the cleanliness standard compliance rate, The average actual delay for the corresponding interval is determined by weighting coefficients based on the cleanroom's operational requirements, and their sum is 1. The cleanliness and delay parameter correlation analysis unit solves this function using an optimization algorithm, selecting a set of Pareto optimal solutions that maximize the compliance rate and minimize the average delay. From the optimal solution set, the cleanliness and delay parameter correlation analysis unit extracts the corresponding actual delay parameter interval, maps it inversely to the corresponding turbulence intensity coefficient range, and, combined with the original turbulence intensity threshold's hierarchical logic, determines the updated turbulence intensity threshold value that simultaneously optimizes both objectives.

[0139] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0140] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0141] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A cleanroom delayed-opening interlock control system, characterized in that, The system is configured with the following modules: The non-target area identification module is used to synchronously collect real-time differential pressure gradient data, three-dimensional spatial wind speed vector data, and micron-level particle concentration data in the buffer room door and core area entrance of the cleanroom based on differential pressure sensors, three-dimensional ultrasonic anemometers, and particle counters, and generate environmental datasets. The embolization agent region segmentation module is used to calculate the turbulence intensity of the environmental dataset. It performs multi-source data weighted fusion of the time derivative results of pressure gradient data, the magnitude derivative results of wind speed vector data, and the time derivative results of particle concentration data to generate a turbulence intensity coefficient. The accidental embolization index calculation module is used to make dynamic delay decisions on the turbulence intensity coefficient, adjust the reference delay parameters required for cleanroom airflow stabilization based on the preset turbulence intensity threshold, and generate actual delay parameters. The target area embolism increment calculation module is used to obtain the delayed door closing request triggered by the access control system, perform countdown stability verification based on the actual delay parameters, determine the environmental recovery state according to the real-time differential pressure gradient data and turbulence intensity coefficient within the countdown period, generate interlock control instructions and send the interlock control instructions to the access control actuator, and the interlock control instructions are used to indicate the locking or unlocking state of the electromagnetic lock. The strategy gradient reward value generation module is used to optimize decision parameters based on the historical dataset of interlock events fed back by the access control actuator, analyze the correlation between cleanliness compliance indicators and actual delay parameters, and update the turbulence intensity threshold.

2. The system according to claim 1, characterized in that, The non-target region identification module is configured with the following units: The door differential pressure gradient acquisition unit is used to acquire differential pressure gradient data in the edge area of ​​the door gap of the buffer room door. It obtains instantaneous differential pressure change data on both sides of the door through multi-point differential pressure sensors and generates door differential pressure gradient data. The three-dimensional spatial wind speed vector acquisition unit is used to acquire wind speed vectors in the three-dimensional spatial area at the entrance of the core area. It obtains the instantaneous wind speed components along the X, Y, and Z axes through an ultrasonic anemometer matrix and generates three-dimensional spatial wind speed vector data. The suspended particle concentration acquisition unit is used to acquire the concentration of suspended particles at the entrance of the buffer zone and the core area. It obtains the number of particles with a particle size greater than the preset critical particle size threshold through a laser particle counter and generates microparticle concentration data. The multi-source environmental data synchronization unit is used to synchronize the gate pressure gradient data, the three-dimensional spatial wind speed vector data, and the particulate concentration data with timestamps to generate an environmental dataset.

3. The system according to claim 1, characterized in that, The embolic agent region segmentation module is configured with the following units: The pressure difference change rate calculation unit is used to perform time differentiation processing on the pressure difference gradient data in the environmental dataset, calculate the absolute value of the pressure difference change per unit time, and generate the pressure difference change rate. The wind speed change rate modulus calculation unit is used to perform modulus differentiation processing on the three-dimensional spatial wind speed vector data in the environmental dataset, calculate the wind speed vector change modulus per unit time, and generate the wind speed change rate modulus. The concentration change rate calculation unit is used to perform time differentiation processing on the particulate concentration data in the environmental dataset, calculate the concentration change per unit time, and generate the concentration change rate. The turbulence intensity coefficient weighted fusion unit is used to perform weighted fusion processing on the pressure difference change rate, the wind speed change rate modulus and the concentration change rate, and calculate the weighted sum according to the preset weight coefficients to generate the turbulence intensity coefficient.

4. The system according to claim 1, characterized in that, The accidental embolism index calculation module is configured with the following units: The standardized turbulence deviation calculation unit is used to calculate the deviation of the turbulence intensity coefficient based on a preset turbulence intensity threshold, calculate the difference between the turbulence intensity coefficient at the current moment and the preset turbulence intensity threshold, and generate a standardized turbulence deviation. The nonlinear mapping value generation unit is used to perform nonlinear mapping on the standardized turbulence deviation, converting the standardized turbulence deviation into a gain adjustment coefficient through the sigmoid function, and generating a nonlinear mapping value. The actual delay parameter adjustment unit is used to perform delay adjustment processing based on the nonlinear mapping value, and to scale the preset reference delay parameter according to the gain adjustment coefficient to generate the actual delay parameter.

5. The system according to claim 4, characterized in that, The formula for calculating the actual delay parameter is as follows: in, This is the actual delay parameter. The preset baseline delay parameter, For the response gain coefficient, For non-linear mapping values, The time decay factor, The time difference between the current time and the closing time. To standardize the turbulence deviation, It is a non-linear adjustment factor.

6. The system according to claim 1, characterized in that, The interlock control command includes one of an unlock command and a lock command, and the target area embolization increment calculation module is configured with the following units: The door closing request structured parsing unit is used to obtain the delayed door closing request triggered by the access control system, perform door position parsing processing on the delayed door closing request, extract the door identifier and request timestamp, and generate structured door closing request data. The countdown initialization unit is used to perform countdown initialization processing on the actual delay parameters, set the countdown initial value according to the actual delay parameters, start the timer, and generate a countdown state object; An environmental stability monitoring unit is used to monitor environmental stability based on the countdown state object. During the countdown period, it performs stability judgment processing on real-time pressure gradient data and turbulence intensity coefficient. If the absolute value of the pressure gradient is continuously lower than the stability threshold and the turbulence intensity coefficient is less than the turbulence intensity threshold, an environmental recovery compliance mark is generated. The interlock state decision unit is used to make interlock state decisions based on the environmental recovery compliance flag. When the environmental recovery compliance flag is valid, an unlock command is generated, and when the environmental recovery compliance flag is invalid, a lock command is generated.

7. The system according to any one of claims 1-6, characterized in that, The strategy gradient reward value generation module is configured with the following units: The interlock event data cleaning unit is used to clean the historical dataset of interlock events uploaded by the access control actuator, remove invalid event records and fill in missing fields to generate a standardized event dataset. The cleanliness and delay parameter correlation analysis unit is used to perform correlation analysis on the cleanliness compliance index and the actual delay parameter in the standardized event dataset, calculate the cleanliness compliance rate under different delay parameters, and generate a delay-compliance rate correlation matrix. The turbulence intensity threshold optimization unit is used to perform parameter optimization processing on the delay-compliance rate correlation matrix, solve for the turbulence intensity threshold that maximizes the compliance rate and minimizes the average delay, and generate an updated turbulence intensity threshold.

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