Clean room environment monitoring system

Abstract

This invention relates to a cleanroom environment monitoring system for real-time monitoring and automatic control of the cleanroom environment. The system includes: a sensor module comprising three or more wireless sensors arranged adjacent to each other, each measuring the same measurement item; an analysis and control module that receives measurement values ​​from the three or more wireless sensors regarding the same measurement item from the sensor module, corrects the measurement values ​​using an automatic correction algorithm that compares the measurement values, determines the environmental state of the cleanroom based on this, and generates control commands when an abnormal state occurs; and an equipment linkage module that links with one or more devices equipped in the cleanroom and sends control signals to the devices according to the control commands from the analysis and control module. The automatic correction algorithm collects data from the three or more wireless sensors in real-time under the same environment, compares their measurement values, and automatically identifies sensors with deviations.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring and control technology, specifically to a cleanroom environmental monitoring system. Background Technology

[0002] Cleanrooms are typically manufacturing environments that require a high degree of cleanliness. Because environmental factors such as contaminant particles, temperature, humidity, and air pressure have a direct impact on product quality, it is essential to have the technology to consistently maintain and control these environmental factors.

[0003] Traditional cleanroom monitoring systems are mostly wired and based on fixed sensors. This configuration has various problems such as poor installation flexibility, wiring limitations, difficulty in changing sensor positions, and the risk of process interruption during maintenance.

[0004] In particular, when calibrating sensors, the system needs to be manually disassembled or directly connected to external calibration equipment. During this process, the interior of the cleanroom comes into contact with the outside, which may lead to contamination risks and reduced productivity.

[0005] In addition, using reference sensors also has the problems of high initial installation costs and difficulty in maintaining the reliability of the entire sensor network in the long term due to the aging of the reference sensors themselves or location limitations.

[0006] Therefore, there is a growing demand for systems that can maintain accuracy by performing autonomous correction through inter-sensor comparison and correction algorithms without the need for additional reference sensors, and can also achieve automatic control by real-time linkage with cleanroom equipment. Summary of the Invention

[0007] The purpose of this invention is to provide a cleanroom environment monitoring system that can monitor cleanroom environmental data in real time, automatically perform high-precision corrections, and automatically control equipment when abnormal conditions occur.

[0008] To achieve the above objectives, this invention discloses a cleanroom environment monitoring system for real-time monitoring and automatic control of the cleanroom environment. The system includes: The sensor module includes three or more wireless sensors that measure the same measurement item and are arranged adjacent to each other; The analysis and control module receives measurement values ​​for the same measurement item from the sensor module from three or more wireless sensors, corrects the measurement values ​​using an automatic correction algorithm that compares the measurement values, determines the environmental state of the cleanroom based on this, and generates a control command when an abnormal state occurs; and The equipment linkage module is linked with one or more devices equipped in the cleanroom and sends control signals to the devices according to the control commands of the analysis and control module. The automatic calibration algorithm collects data from the three or more wireless sensors in real time under the same environment, compares their measurements, and automatically identifies the sensors that have deviated. When two or more sensor data points are consistent within the allowable deviation value, and one sensor data point exceeds the allowable deviation value, that sensor is considered an outlier, and a correction factor based on the average of the two or more sensor data points is automatically applied to the single sensor for correction. When all the sensor data show a deviation within the allowable deviation value, all the sensors are relatively calibrated based on the average or median value among the sensors.

[0009] Preferably, the automatic correction algorithm further includes a regression correction algorithm based on its own history, an event benchmark exception handling algorithm, and a calibration correction algorithm based on periodic checks.

[0010] Preferably, the regression correction algorithm based on its own history includes: performing linear regression or multinomial regression analysis based on data collected from the sensor to identify the drift tendency over time; and when the accumulated deviation exceeds the baseline error rate, calculating automatic correction coefficients according to the regression model and applying them to the sensor.

[0011] Preferably, the calibration algorithm based on periodic checks connects an external reference measurement device to the sensor during periodic checks 1 to 2 times a year. By comparing the deviation between the sensor's measured value and the reference value obtained from the calibration measurement device, the calibration coefficient is manually or automatically reset.

[0012] Preferably, when an external environmental change occurs within the measurement range of the sensor module, such as door opening, equipment cleaning, or shipping operations, the event benchmark exception handling algorithm detects it as a temporary sudden change signal through inter-sensor comparison or analysis based on historical records, automatically suspends the calibration routine at that time point, and returns to the normal routine after the corresponding event ends.

[0013] Preferably, the sensor module includes at least one of a particle sensor, a temperature sensor, a humidity sensor, a barometric pressure sensor, a differential pressure sensor, an illuminance sensor, an oxygen sensor, and a carbon dioxide sensor.

[0014] Preferably, the device includes one or more of the following: an air conditioning unit (AHU), a fan filter unit (FFU), a humidifier, a dehumidifier, a particle removal filter unit, a differential pressure control device, an exhaust filter unit, and an illuminance control unit.

[0015] Preferably, when the measured value received from the sensor module and corrected by the automatic correction algorithm exceeds a preset threshold range or changes drastically, the device linkage module sends the control signal according to the control command of the analysis and control module.

[0016] Preferably, the sensor module includes a LoRa or Zigbee-based wireless communication method and communicates bidirectionally with the analysis and control module directly or through a gateway.

[0017] Preferably, the maintenance of the wireless sensor includes: Remotely confirm the sensor status on the sensor module. If, after automatic application of a correction coefficient based on the average value of data from two or more sensors, the corrected sensor continues to show a deviation exceeding the allowable deviation value, it is determined to be a sensor malfunction. The system sends a notification to the administrator dashboard to determine whether the faulty sensor needs to be replaced. The cleanroom environment monitoring system is designed to operate without interruption.

[0018] Specific details of other embodiments are included in the detailed description and accompanying drawings.

[0019] The cleanroom environment monitoring system disclosed in this invention has the following beneficial effects: First, by using multiple wireless sensors arranged adjacent to each other to measure the same items in a clean room, even without a separate reference sensor, calibration can be automatically performed through mutual comparison and historical data analysis, thereby obtaining highly reliable sensor data and minimizing the intervention of sensor maintenance personnel.

[0020] Secondly, by constructing sensor modules based on wireless communication, the sensor positions can be freely arranged without complex wiring or fixed installation restrictions. Furthermore, automatic location-based identification and mapping can be achieved through gateway or server modules, thus greatly improving installation flexibility and system scalability.

[0021] Third, when the sensor's measured value exceeds the set threshold or changes drastically, the analysis and control module automatically sends a control signal to the linked equipment, thereby ensuring real-time response to abnormal environments and the stability of the cleanroom environment.

[0022] Fourth, it can maximize the flexibility of sensor deployment based on wireless communication, and realize a wireless environmental monitoring structure that overcomes the limitations of wired sensors.

[0023] Fifth, it works in conjunction with an automatic calibration algorithm to respond in real time to sensor drift or abnormal values ​​and maintain accuracy by automatically executing the calibration algorithm.

[0024] Sixth, it can realize an integrated environmental monitoring and control system that can be linked with all equipment, and can be linked with MES / SCADA. Therefore, it can ensure high reliability of sensor data, minimize maintenance personnel intervention, improve equipment reliability, and minimize the risk of cleanroom contamination, thus enabling it to cope with smart factories and high reliability manufacturing. Attached Figure Description

[0025] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a block diagram illustrating the overall structure of a cleanroom environment monitoring system according to an embodiment of the present invention. Figure 2 This is a block diagram of the constituent elements of a sensor module disclosed in an embodiment of the present invention; Figure 3 This is a block diagram of the overall components of a cleanroom environment monitoring system disclosed in an embodiment of the present invention; Figure 4 This is a flowchart illustrating an example of the process by which the analysis and control module executes an automatic correction algorithm, as disclosed in an embodiment of the present invention. Figure 5 This is a reference diagram of an example of the operation interface provided by the status notification module through a display screen, as disclosed in an embodiment of the present invention.

[0026] Attached image labels: 1. Cleanroom 100. Cleanroom Environmental Monitoring System 110. Sensor Module 111. Sensor Unit 111a. Particle sensor 111b. Temperature and humidity sensor 111c, Barometric Pressure Sensor 111d, Illuminance sensor 111e, oxygen and carbon dioxide sensors 113. Wireless communication methods 114. Interface Port 120. Gateway 130. Analysis and Control Module 140. Equipment linkage module 150. Equipment 151. Air conditioning unit 152. Fan filter unit 153. Humidifier 154. Dehumidifier 155. Particle Removal Filter Unit 156. Differential pressure control device 157. Exhaust Filter Unit 158. Illuminance Control Unit 160. Status Notification Module 161. Warning measures 162. Monitor Detailed Implementation Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings to enable those skilled in the art to readily implement them. The present invention can be implemented in many different forms and is not limited to the embodiments described herein.

[0027] The accompanying drawings are schematic and not drawn to scale. They may be exaggerated or reduced for clarity and convenience. The same structural elements or components appearing in more than one drawing use the same reference numerals.

[0028] The embodiments of the present invention specifically illustrate ideal embodiments, but are not limited to a particular form, and also include shape variations caused by manufacturing.

[0029] Figures 1 to 5 A cleanroom environment monitoring system according to the present invention is shown. Specific embodiments of the cleanroom environment monitoring system of the present invention will be described below with reference to the accompanying drawings.

[0030] Figure 1 This is a block diagram illustrating the overall structure of a cleanroom environment monitoring system according to an embodiment of the present invention.

[0031] Reference Figure 1 The cleanroom environment monitoring system 100 disclosed in one embodiment of the present invention may include a sensor module 110, an analysis and control module 130, and an equipment linkage module 140.

[0032] The sensor module 110 may include sensor units 111 of three or more wireless sensors that measure the same measurement items and are arranged adjacent to each other. Multiple such sensor units 111 may be arranged according to different measurement items inside the clean room 1.

[0033] That is, refer to Figure 1 Sensor units 111 consisting of three or more particle sensors 111a, three or more temperature and humidity sensors 111b, and three or more air pressure sensors 111c can be installed in the clean room 1.

[0034] However, such sensor units 111 can be installed one in each space of the cleanroom 1, or multiple sensor units 111 for the same measurement item can be installed in different locations. In addition, if the cleanroom 1 is divided into multiple zones, each zone can be equipped with its own sensor unit 111, or only the necessary sensor units 111 can be selectively installed.

[0035] Sensor module 110 is a core component for continuously monitoring the internal environment of cleanroom 1. The sensors in sensor module 110 convert the surrounding physical / chemical environmental information into digital signals and transmit them to analysis and control module 130.

[0036] The sensors equipped in the sensor module 110 may include at least one of the following: particle sensor 111a, temperature and humidity sensor 111b, air pressure sensor 111c, illuminance sensor 111d, and oxygen and carbon dioxide sensor 111e.

[0037] Particle sensor 111a is a sensor used to measure suspended particles (dust, fine contaminants, etc.) in cleanroom 1 and monitor whether they meet cleanliness standards.

[0038] The temperature and humidity sensor 111b is a component configured to maintain a constant temperature and humidity inside the cleanroom 1 to prevent problems such as static electricity and equipment malfunctions, and to ensure operation under optimal temperature and humidity conditions. The temperature inside the cleanroom 1 is preferably maintained within 22°C ± 2°C, i.e., within the range of 20°C to 24°C; this range can be considered a critical threshold range. If the temperature measured by the temperature and humidity sensor 111b exceeds this range, it can be determined that the critical threshold range has been exceeded, and an abnormality in the temperature inside the cleanroom 1 is identified.

[0039] The humidity inside cleanroom 1 is preferably maintained within 50%RH ± 10%RH, that is, within the range of 40%RH to 60%RH. This range can be used as the critical threshold range. If the humidity measured by the temperature and humidity sensor 111b exceeds this range, it can be determined that the critical threshold range has been exceeded, and it can be determined that the humidity inside cleanroom 1 is abnormal.

[0040] A pressure sensor (111c, Differential Pressure Sensor) is commonly referred to as a differential pressure sensor. The 111c pressure sensor is used to detect the pressure difference between a clean area and the outside or other areas to control airflow direction and prevent contamination from entering. Here, the clean area refers to the interior of cleanroom 1, and the differential pressure between it and the outside or potentially contaminated areas is important.

[0041] The pressure measured by pressure sensor 111c typically requires the internal pressure of cleanroom 1 to be maintained at least +10 Pa higher than the external pressure to prevent contamination from flowing in. Furthermore, a differential pressure of at least +5 Pa should be maintained at the boundaries between areas of different cleanliness levels. The operating standard between areas within cleanroom 1 is usually set within the range of 15 Pa to 45 Pa, which can be considered a threshold range. In addition, the external reference pressure can be considered 0 Pa at this time. Based on this standard, when the measured value of differential pressure sensor 111c is lower than the set threshold range or fluctuates significantly, it can be identified as a contamination risk situation within cleanroom 1 and judged as an abnormal state.

[0042] An illuminance sensor (Illuminance Sensor 111d) is used to sense illuminance levels and uniformity to ensure a stable field of vision during operation, detect abnormal lighting conditions, and support process quality management. Illuminance sensors 111d are typically essential for maintaining illuminance levels so that operators can perform precision tasks without visual errors. In cleanroom environments, insufficient or significantly skewed lighting can lead to decreased work accuracy and process quality.

[0043] The reference illuminance value measured by the illuminance sensor 111d is 800 lux ± 200 lux, and the recommended illuminance range can be set to 600 to 1000 lux, which can be used as a threshold range. This corresponds to the illuminance level required in cleanroom environments such as those used in semiconductor and precision electronics manufacturing.

[0044] Furthermore, illuminance is not simply a matter of brightness level; illumination uniformity is also an important factor. Illumination uniformity represents the consistency of illuminance distribution within a space, and a baseline uniformity is preferably maintained above 0.75. The illuminance sensor 111d also senses this uniformity condition, thereby enabling it to proactively identify partial lighting failures, shadow formation, and other imbalances in the working environment and determine abnormal conditions.

[0045] The oxygen and carbon dioxide sensor (111e, O2 / CO2Sensor) is used to measure the oxygen and carbon dioxide concentration in indoor air, i.e., inside a clean room, to determine the health status of operators and any abnormalities in the ventilation of the space.

[0046] The oxygen sensor in oxygen and carbon dioxide sensor 111e is used to prevent the risk of operator hypoxia due to lack of oxygen in the cleanroom 1. The oxygen concentration should typically be maintained within the range of 20.9% ± 0.5%, with an permissible measurement range of 20.4% to 21.4%, which can be considered a threshold range. Exceeding this range may pose a safety risk due to hypoxia or excessive oxygen supply.

[0047] The carbon dioxide sensor in oxygen and carbon dioxide sensor 111e is used to detect abnormal conditions inside cleanroom 1, such as insufficient ventilation, overcrowding, or process gas leakage, which may be caused by poor airtightness. Its normal range is 400 to 800 ppm, which can be used as a threshold range. In particular, a measured value exceeding 1000 ppm can be considered a hazard warning baseline.

[0048] The carbon dioxide sensor in oxygen and carbon dioxide sensor 111e is particularly important for cleanrooms 1 where operators are resident, as it can serve as crucial data for generating control signals for ventilation systems or air conditioning units. Furthermore, the analysis and control module 130 can use these concentration measurements to determine abnormal conditions and, if necessary, implement ventilation improvement measures through air conditioning equipment control.

[0049] Figure 2 This is a block diagram of the constituent elements of a sensor module disclosed in an embodiment of the present invention.

[0050] Reference Figure 2 The sensor module 110 may also contain a microcontroller unit 112 (MCU), a wireless communication means 113, and an interface port 114. The microcontroller unit 112 collects the measurement values ​​of the various sensors (111a, 111b, 111c, 111d, 111e) contained in the sensor module 110, and preprocesses the measurement values ​​or sends them to the analysis and control module 130 via the wireless communication means 113.

[0051] The sensor nodes of the sensor module 110 are configured in star and mesh topologies, enabling bidirectional communication with the gateway and allowing for free change of sensor positions. The gateway can automatically identify sensor nodes and perform automatic identification and mapping based on their positions.

[0052] The microcontroller unit 112 can communicate with an external reference device via interface port 114. The microcontroller unit 112 can receive reference values ​​from the external reference device during periodic checks and transmit the reference values ​​to the analysis and control module 130.

[0053] Furthermore, the microcontroller unit 112 can receive correction coefficients calculated from the analysis and control module 130 to generate correction data. Typically, the correction coefficients are calculated by the analysis and control module 130, and the correction data is also generated by the analysis and control module 130. However, if necessary, the correction coefficients calculated by the analysis and control module 130 can be sent to the microcontroller unit 112, where the correction data is generated.

[0054] Furthermore, the wireless communication means 113 enables real-time data transmission and reception between the sensor module 110 and the analysis and control module 130. The wireless communication means 113 of the sensor module 110 can be based on LoRa or Zigbee.

[0055] The LoRa (Long Range) protocol ensures indoor communication distances of hundreds of meters or more, enabling stable connections even in large spaces like cleanroom 1. In shaded areas not covered by LoRa or in environments with complex layouts, a ZigBee-based mesh network can be used for inter-sensor data relay. Each sensor module 110 can also be designed to integrate both LoRa and ZigBee wireless communication methods 113, automatically switching according to network conditions.

[0056] All wireless data of wireless communication means 113 is transmitted using AES-128-based encryption and simultaneously performs CRC-based integrity verification. To prevent collisions when multiple sensors transmit simultaneously, a time-slot division transmission structure based on TDMA (Time Division Multiple Access) may be included.

[0057] This wireless communication method enables long-distance communication without the need for separate wiring inside the cleanroom 1, and its low power consumption facilitates long-term operation of the sensor module 110. Furthermore, the communication network configuration can be easily updated when changing sensor locations or adding new installations, thus offering advantages in terms of scalability and maintenance.

[0058] Furthermore, interface port 114 is a terminal for connecting external reference devices and sensor module 110. During periodic inspections, reference values ​​can be input from external reference devices to set or automatically update calibration coefficients without disassembling the sensors (111a, 111b, 111c, 111d, 111e). This allows calibration to be performed without stopping cleanroom 1, improving maintenance efficiency and system reliability.

[0059] Reference Figure 1 and Figure 2 The sensor module 110 can communicate bidirectionally with the analysis and control module 130 directly or through the gateway 120. Depending on the structure of the cleanroom 1, communication distance, noise environment, etc., a suitable connection method can be selected, either a direct connection or a repeater-based structure, thereby ensuring data stability and system flexibility.

[0060] As previously described, the analysis and control module 130 receives measurement values ​​from multiple sensors in the sensor module 110 that measure the same measurement item and are arranged adjacent to each other, from the microcontroller unit 112, corrects the measurement values, and determines the environmental state of the cleanroom 1 based on these values. Furthermore, the analysis and control module 130 can generate control commands for the devices 150 related to the measurement values ​​when an abnormal state occurs in the cleanroom 1, based on the correction data.

[0061] For example, the analysis and control module 130 receives the measurement values ​​from multiple temperature and humidity sensors, generates calibration data after calibration, and then determines the abnormal state of the clean room 1, thereby generating control commands for the humidifier and dehumidifier.

[0062] On the other hand, the analysis and control module 130 may include an automatic correction algorithm. The automatic correction algorithm may include one or more of the following: an algorithm that identifies and corrects abnormal sensors by mutual comparison, a regression correction algorithm based on its own history, an event benchmark exception handling algorithm, and a periodic check benchmark calibration correction algorithm.

[0063] First, the automatic calibration algorithm can identify abnormal sensors by comparing them with each other, and then correct the measurements of the abnormal sensors to generate calibration data.

[0064] The automatic calibration algorithm receives measurement values ​​from the sensor module 110, which contains three or more wireless sensors arranged adjacent to each other and measuring the same measurement item, within the cleanroom 1, and can compare the measurement values ​​with each other.

[0065] Specifically, the automatic calibration algorithm collects data from three or more wireless sensors in real time under the same environment and automatically identifies the sensor with deviation by comparing their measurements. In one embodiment, when the data from two or more sensors are consistent within the allowable deviation value, and the data from one sensor exceeds the allowable deviation value, that sensor is considered an outlier, and a correction coefficient based on the average of the data from the two or more sensors can be automatically applied to that sensor for calibration. The allowable deviation value can be a value set by the user based on the cleanroom environment and sensor characteristics.

[0066] For example, the measured values ​​of the three temperature and humidity sensors 111b in the sensor module 110 can be compared. Here, if there is a temperature and humidity sensor 111b that shows a deviation greater than the allowable deviation value compared to the measured values ​​of at least two or more temperature and humidity sensors 111b that show a deviation within the allowable deviation value, then the temperature and humidity sensor 111b can be identified as an abnormal sensor.

[0067] The automatic calibration algorithm calculates a calibration coefficient based on the average value of the temperature and humidity sensor 111b that shows a deviation within the allowable deviation value, and applies the calculated calibration coefficient to the measurement value of the temperature and humidity sensor 111b that is identified as an abnormal sensor to generate calibration data.

[0068] Therefore, even without a separate reference sensor, highly reliable data can be ensured simply by comparing the sensors with each other.

[0069] Alternatively, when all sensor data show deviations within the allowable deviation value, all sensors can be relatively calibrated based on the average or median value among the sensors.

[0070] For example, if the allowable deviation is 1℃, and the five temperature sensors measure 23.5℃, 24℃, 24.1℃, 24.4℃, and 24.3℃ respectively, then the average value of 24.06℃ can be used as a benchmark to calculate a correction factor for each sensor. Here, the correction factor can be calculated as the average value minus the sensor measurement value.

[0071] This method is useful during initial installation or when process baselines are well-defined, offering higher predictability and stability than automatic calibration. The resulting calibration coefficients are then applied to multiple sensor measurements for the same measurement item, converting them into reliable calibration data. The application can be in the form of calibration value = original measured value + calibration coefficient, following a simple linear calibration model.

[0072] The advantage of this calibration method is that it can maintain overall measurement accuracy through relative reliability analysis among all sensors without the need for a separate reference sensor.

[0073] Furthermore, the automatic calibration algorithm can execute an event baseline exception handling algorithm that pauses the generation of calibration data based on changes in the external environment. Specifically, when the sensors included in the sensor module 110 are measuring, events occurring inside the cleanroom 1 caused by changes in the external environment can be detected.

[0074] The event baseline exception handling algorithm detects changes in the external environment, such as door opening, equipment cleaning, or shipping operations, within the sensor module's measurement range as temporary abrupt changes in sensor comparisons or historical data analysis. It automatically pauses the calibration routine at that point in time and returns to the normal routine after the corresponding event ends.

[0075] In other words, when an event caused by a change in the external environment is detected, the generation of correction data is paused. When the event ends, the correction algorithm is executed again to generate correction data with the correction coefficient applied.

[0076] At this point, the occurrence and termination of the event can be automatically determined by the analysis and control module 130 based on the measurement values ​​collected from the multiple sensors included in the sensor module 110.

[0077] In addition, the automatic correction algorithm may include a regression correction algorithm based on its own history that performs regression analysis and applies the calculated correction coefficients to generate correction data.

[0078] Specifically, the regression correction algorithm based on its own history analyzes the changing trend over a certain period of time based on the cumulative measurement values ​​of multiple sensors in each sensor module 110 that measure the same measurement item, and performs regression analysis on the tendency of the measurement value to change over time.

[0079] Regression analysis uses linear or multinomial regression analysis based on measurements stored over a certain period to identify drift tendencies over time. For example, if the cumulative deviation of a particular sensor's measurements exceeds the cumulative error rate, a correction coefficient is calculated using a regression analysis model, and the calculated correction coefficient is applied to the sensor's measurements to generate correction data.

[0080] This regression-based automatic calibration is unaffected by short-term instantaneous deviations. Instead, it corrects for changes in sensor sensitivity or drift based on long-term trends, thus maintaining the overall reliability of the system. Furthermore, it can be executed in parallel with real-time calibration routines.

[0081] As mentioned above, the analysis and control module 130 uses an automatic correction algorithm to determine the abnormal state of cleanroom 1 based on the corrected data and generates a control command. This control command is then transmitted to the equipment linkage module 140, which sends control signals to the corresponding equipment.

[0082] The automatic calibration algorithm may include a periodic check benchmark calibration correction algorithm, which compares the deviation between the benchmark value obtained from the external benchmark device and the measured value during periodic checks, and manually or automatically resets the correction coefficients based on the deviation.

[0083] Regular inspections, performed once or twice a year, are a management procedure to maintain the long-term accuracy of the sensor. During this process, an external reference device is connected via the interface port 114 of the sensor module 110, and the difference between the accurate reference value input from the reference device and the sensor measurement value is compared to calculate the error.

[0084] The calculated error is converted into a correction factor, which can be manually entered by the administrator or automatically updated by the algorithm, depending on the system settings. This method allows calibration to be performed without separating the sensor or interrupting the system, and has the advantage of enabling uninterrupted operation of the cleanroom.

[0085] Therefore, the calibration and correction algorithm based on periodic checks differs from the autonomous correction performed in routine operations by anomaly sensor detection algorithms that compare with each other, regression correction algorithms based on their own history, and event benchmark exception handling algorithms. It utilizes a highly reliable external benchmark device and is capable of precisely initializing or fine-tuning the correction coefficients.

[0086] Figure 3 This is a block diagram of all the constituent elements of a cleanroom environment monitoring system disclosed in an embodiment of the present invention.

[0087] Reference Figure 3 The equipment linkage module 140 is linked with one or more devices 150 equipped in the clean room 1, and sends control signals to the devices 150 according to the control commands of the analysis and control module 130.

[0088] The device 150 may include one or more of the following: an air conditioning unit (151, AHU), a fan filter unit (152, FFU), a humidifier 153, a dehumidifier 154, a particle removal filter unit 155, a differential pressure control device 156, an exhaust filter unit 157, and an illuminance control unit 158.

[0089] These devices 150 respectively play a role in regulating the main environmental parameters of cleanroom 1, such as temperature, humidity, pressure, illuminance, particle count, and gas concentration, and can be automatically started or stopped according to the control commands of the analysis and control module.

[0090] The device linkage module 140 and the device 150 can be connected via industrial standard communication protocols such as Modbus RTU, Modbus TCP or OPC-UA interface.

[0091] The device linkage module 140 is configured such that when the measured value received from the sensor module 110 exceeds the preset threshold range or changes drastically, the analysis and control module 130 judges the corresponding abnormal state and generates a control command, and then sends a control signal to the device 150 according to the command.

[0092] For example, when the temperature and humidity sensor 111b of the sensor module 110 detects that the internal temperature of the clean room 1 has risen above a certain temperature and the humidity has increased, the analysis and control module 130 sends a cooling command to the air conditioning unit (AHU) and a dehumidification command to the dehumidifier; conversely, when the temperature and humidity inside the clean room 1 are detected to decrease, a start signal can be sent to the heater operation command and humidifier operation command of the air conditioning unit.

[0093] The device linkage module 140 confirms whether the device 150 responds in real time and records the result status of the device 150 after control (e.g., running / stopping, alarm status, etc.) as a log, which can be confirmed through the status notification module 160.

[0094] This structure enables real-time automatic control of the cleanroom environment, helping to prevent contamination from entering or equipment malfunctions, and maintaining a stable production process environment.

[0095] Reference Figure 3 The cleanroom environment monitoring system 100 may also include a status notification module 160.

[0096] The status notification module 160 is configured to notify operators or administrators in real time whether an abnormal state of cleanroom 1 has occurred. Specifically, the status notification module 160 is a module that provides users with visual and auditory information about the environmental status of cleanroom 1 or the measurement status of sensors (111a, 111b, 111c, 111d, 111e), and may include warning means 161 and a display 162.

[0097] Warning method 161 is to issue an alarm sound, or output warning lights, vibration or voice messages, etc., when an abnormal condition occurs, so as to guide the operator to recognize the composition of the situation in a timely manner.

[0098] For example, when the temperature or particle value in a specific sensor rises above a set benchmark, a corresponding alarm can be triggered based on the judgment of the analysis and control module 130.

[0099] The display 162 is a structure that visualizes and provides the user with the overall environmental status of the clean room 1, the real-time measurement values ​​of each sensor module 110, whether the calibration is performed, and the results of abnormal judgment. It can be implemented as a network-based dashboard.

[0100] In addition, the display 162 may include functions such as displaying the overall or partial environmental status of the cleanroom 1, outputting warning messages for abnormal detection items, displaying calibration history or status, and providing an interface for administrator setting input or adjustment.

[0101] The display 162 can be implemented as a GUI (graphical user interface) based touch screen, a regular display, or a remotely accessible web page, and can also provide system operation and maintenance information such as sensor failures, communication errors, and the necessity of periodic checks.

[0102] Therefore, the status notification module 160 according to the present invention is not limited to simple alarm output, but is configured to intuitively grasp the overall operating status of the system and support rapid response to abnormal states and maintenance plan formulation.

[0103] Figure 4 This is a flowchart of an example of the analysis and control module 130 executing an automatic correction algorithm according to an embodiment of the present invention.

[0104] Reference Figure 4The analysis and control module 130 first receives measurement values ​​from multiple sensors measuring the same measurement item (step S110), and then calculates the deviation between the sensors (step S115). The multiple sensors measuring the same measurement item can be three or more wireless sensors arranged adjacent to each other.

[0105] In this context, deviation refers to the difference between the values ​​measured by each sensor, and is used as an important benchmark for accurately monitoring changes in the cleanroom environment. The allowable deviation value can be a value set by the user based on the cleanroom environment and sensor characteristics.

[0106] When all sensor data show a deviation within the allowable deviation value (step S120), all sensors can be relatively calibrated based on the average or median value among the sensors (step S125).

[0107] Alternatively, if two or more sensor data points are consistent within the allowable deviation value, and one sensor data point exceeds the allowable deviation value, that sensor is considered an anomaly (step S130). A correction coefficient based on the average of the two or more sensor data points is generated, and this generated correction coefficient is automatically applied to the sensor showing a deviation exceeding the allowable deviation value for correction. That is, the sensor is identified as an abnormal sensor, and abnormal sensor correction can be performed (step S135).

[0108] Furthermore, when all sensor data show deviations exceeding the allowable deviation value, and it cannot be determined that the sensor is faulty; or when all sensor data are within the allowable deviation value but show values ​​exceeding the threshold, an environmental anomaly or exception will be determined (step S140). For example, temporary environmental changes such as door opening, equipment cleaning, or shipping operations can be considered exceptions, and in such cases, corrections will be temporarily retained (step S145).

[0109] Barring exceptional circumstances, the analysis and control module 130 can perform historical-based calibration on the corresponding sensors (step S150). This involves performing regression analysis on the cumulative measurements of the corresponding sensors, calculating calibration coefficients, and generating calibration data.

[0110] After the above process generates the calibration data, it is confirmed again whether the deviation has been reduced to an acceptable level (step S155). If the deviation is sufficiently reduced, the calibration process ends. However, if the standard is still not met, a calibration failure warning is sent to the administrator (step S160), and manual calibration using an external reference device can be switched to performing periodic calibration (step S165).

[0111] However, periodic calibration (step S165) can be performed approximately 1 to 2 times per year without the automatic calibration algorithm. In this case, if multiple sensor measurements are received, periodic calibration can be performed immediately.

[0112] The sequential execution structure of this automatic correction algorithm, by comprehensively considering sensor malfunctions and environmental conditions, can achieve optimal correction and make a significant contribution to maintaining the environmental state of cleanroom 1 in real time with precision and stability.

[0113] Furthermore, after generating correction data through the aforementioned automatic correction algorithm, the generated correction data is used to determine whether the cleanroom 1 is abnormal. If the correction data confirms that the environment of the cleanroom 1 is abnormal, the equipment 150 can be controlled through the equipment linkage module 140 to restore the environment of the cleanroom 1 to a normal state.

[0114] Reference Figure 5 , Figure 5 An example of an operating interface screen provided by display 162 in a cleanroom environment monitoring system is shown.

[0115] The display 162 is configured to visually differentiate the environmental status of each cleanroom zone. For example, it can be configured to simultaneously monitor environmental status information of different spatial units such as zone 1000 and zone 100.

[0116] The items displayed for each zone can include real-time measured temperature information, relative humidity values, ambient lighting brightness, indoor air oxygen concentration, indoor air carbon dioxide concentration, and pressure difference between clean rooms and outdoors or between zones.

[0117] The current date, time, and day of the week will be displayed at the top of the screen, providing real-time baseline information to operators or administrators.

[0118] In particular, the display 162 simultaneously outputs the status of each area of ​​the cleanroom 1 on the same screen, thereby enabling simultaneous monitoring of multiple areas. When the set threshold range is exceeded or an abnormal state is detected, it is designed in conjunction with visual warning methods such as icon color changes, flashing, and warning signs to guide the rapid implementation of measures.

[0119] Furthermore, since the display 162 outputs the latest environmental status based on the calibration data received from the analysis and control module 130, it has the characteristic of reflecting accurate status information that includes automatic calibration in real time.

[0120] The embodiments of the present invention have been described above with reference to the accompanying drawings, but those skilled in the art will understand that the present invention can be implemented in other specific forms.

[0121] Therefore, the above embodiments should be understood as exemplary and not restrictive in all respects, and the scope of the invention is indicated by the claims. All modifications or variations derived from the meaning, scope and equivalent concepts of the claims should be interpreted as being included within the scope of the invention.

Claims

1. A cleanroom environment monitoring system for real-time monitoring and automatic control of the cleanroom environment, characterized in that, The system includes: The sensor module includes three or more wireless sensors that measure the same measurement item and are arranged adjacent to each other; The analysis and control module receives measurement values ​​for the same measurement item from the sensor module from three or more wireless sensors, corrects the measurement values ​​using an automatic correction algorithm that compares the measurement values, determines the environmental state of the cleanroom based on this, and generates a control command when an abnormal state occurs; and The equipment linkage module is linked with one or more devices equipped in the cleanroom and sends control signals to the devices according to the control commands of the analysis and control module. The automatic calibration algorithm collects data from the three or more wireless sensors in real time under the same environment, compares their measurements, and automatically identifies the sensors that have deviated. When two or more sensor data points are consistent within the allowable deviation value, and one sensor data point exceeds the allowable deviation value, that sensor is considered an outlier, and a correction factor based on the average of the two or more sensor data points is automatically applied to the single sensor for correction. When all the sensor data show a deviation within the allowable deviation value, all the sensors are relatively calibrated based on the average or median value among the sensors.

2. The system according to claim 1, characterized in that, The automatic correction algorithm also includes a regression correction algorithm based on its own history, an event benchmark exception handling algorithm, and a calibration correction algorithm based on periodic checks.

3. The system according to claim 2, characterized in that, The regression correction algorithm based on its own history includes: Linear or multinomial regression analysis is performed based on the data collected from the sensors to identify drift tendencies over time. When the accumulated deviation exceeds the baseline error rate, an automatic correction coefficient is calculated based on the regression model and applied to the sensor.

4. The system according to claim 2, characterized in that, The calibration algorithm based on periodic checks connects an external reference measurement device to the sensor via a calibration port during 1 to 2 periodic checks per year. By comparing the deviation between the sensor's measured value and the reference value obtained from the calibration measurement device, the calibration coefficient is manually or automatically reset.

5. The system according to claim 2, characterized in that, When an external environmental change occurs within the measurement range of the sensor module, such as door opening, equipment cleaning, or shipping operations, the event benchmark exception handling algorithm detects it as a temporary sudden change signal through inter-sensor comparison or analysis based on historical records, automatically suspends the calibration routine at that time point, and returns to the normal routine after the corresponding event ends.

6. The system according to claim 1, characterized in that, The sensor module includes at least one of the following: a particle sensor, a temperature sensor, a humidity sensor, a barometric pressure sensor, a differential pressure sensor, an illuminance sensor, an oxygen sensor, and a carbon dioxide sensor.

7. The system according to claim 1, characterized in that, The equipment includes one or more of the following: an air conditioning unit (AHU), a fan filter unit (FFU), a humidifier, a dehumidifier, a particle removal filter unit, a differential pressure control device, an exhaust filter unit, and an illuminance control unit.

8. The system according to claim 1, characterized in that, When the measured value received from the sensor module and corrected by the automatic correction algorithm exceeds a preset threshold range or changes drastically, the device linkage module sends the control signal according to the control command of the analysis and control module.

9. The system according to claim 1, characterized in that, The sensor module includes a wireless communication method based on LoRa or Zigbee, and communicates bidirectionally with the analysis and control module directly or through a gateway.

10. The system according to claim 1, characterized in that, The maintenance of the wireless sensor includes: Remotely confirm the sensor status on the sensor module. If, after automatic application of a correction coefficient based on the average value of data from two or more sensors, the corrected sensor continues to show a deviation exceeding the allowable deviation value, it is determined to be a sensor malfunction. The system sends a notification to the administrator dashboard to determine whether the faulty sensor needs to be replaced. The cleanroom environment monitoring system is designed to operate without interruption.

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