Cleanliness automatic control particle counter and monitoring system

Abstract

Disclosed are a particle counter and a monitoring system using same. The particle counter according to one embodiment of the present invention comprises: a particle measurement unit for acquiring a particle-based cleanliness state measured in a clean area; a cleanliness predictive maintenance unit that predicts abnormal phenomena according to cleanliness control prediction conditions on the basis of the acquired cleanliness state and a cleaning process capability state, and automatically controls the operation of an air cleaning device in accordance with the prediction result, and calculates an automatic adjustment optimal condition value for adjusting the target cleanliness by analyzing the cleanliness control result and the control history of the air cleaning device so as to reflect the automatic adjustment optimal condition value when predicting the abnormal phenomenon; and a cleaning device control unit that automatically controls the operation of the air cleaning device according to the prediction result.

Description

Cleanliness Automatic Control Particle Counter and Monitoring System

[0001] The disclosed embodiments relate to a cleanliness automatic control particle counter and monitoring system.

[0002] In the industrial (industrial clean room, ICR) and medical (biological clean room, BCR) fields, cleanliness targets are set for each field and process to prevent problems such as defects caused by foreign substances, and a high-purity clean environment is created and operated.

[0003] In particular, the industrial sector is increasingly demanding that most products be produced in cleaner environments than in the past. This is because, from initial technological development through mass production, controlling foreign substances throughout the entire process—including ensuring a clean environment, utilities, equipment, work methods, consumables, and packaging—is recognized as a key factor in determining product productivity.

[0004] The disclosed embodiments are intended to provide a cleanliness automatic control particle counter and monitoring system monitoring system for measuring the cleanliness status of a clean area in real time and maintaining the cleanliness in a good state.

[0005] Additionally, the disclosed embodiments seek to provide a cleanliness automatic control particle counter and monitoring system for reducing unnecessary energy consumption in maintaining the cleanliness of a clean area in a good state.

[0006] A monitoring system according to one embodiment includes: an air purifier; a plurality of particle counters for predicting an abnormal phenomenon according to a cleanliness control prediction condition based on the particle-based cleanliness status and the cleaning process capability status of the air purifier at each cleanliness measurement reference time within a clean area, and automatically controlling the operation of the air purifier according to the prediction result, and analyzing the cleanliness control result and the control history of the air purifier to calculate an automatic control optimal condition value for target cleanliness control and reflecting the result when predicting the abnormal phenomenon; and an integrated monitoring server for comprehensively monitoring the cleanliness control result collected from each of the plurality of particle counters, the control history of the air purifier, and the cleanliness status, and readjusting the automatic cleanliness control condition within the entire clean area, and controlling the cleanliness status and the status of the air purifier, wherein the plurality of particle counters additionally consider a new target cleanliness and calculate the automatic control optimal condition value and reflect it when predicting and controlling the abnormal phenomenon.

[0007] The above particle counter can predict and control the abnormal phenomenon by multiplying the particle measurement value of the cleanliness state by a preset multiple, or by adding it up for a preset period of time, or by adding a change in particles smaller than the cleanliness standard to a condition when predicting the above abnormal phenomenon.

[0008] The particle counter can predict and control the abnormal phenomenon by reflecting the state of the purification process capability for each output of the air purification device to the automatic control optimal condition value to automatically adjust the cleanliness state so that the target cleanliness and the cleanliness state match preset operating cleanliness conditions when controlling the air purification device.

[0009] The above particle counter can obtain a constant particle-based cleanliness status for at least two or more cleanliness measurement standard times, and can predict and control the above abnormal phenomenon for each of the constant particle-based cleanliness statuses for at least two or more cleanliness measurement standard times.

[0010] The particle counter determines the cleanliness status for each area within the clean area, and controls the operation of the air cleaning device according to the determined cleanliness status and cleanliness control prediction conditions set for each area, and the air cleaning device may include at least one or more of an equipment fan filter unit (EFU), an air cleaning controller, and a fan filter unit (FFU).

[0011] The particle counter can preset an operational cleanliness level for operating the cleanliness (class) within the clean area at a set level of cleanliness compared to the target cleanliness, and can set an output control level of the air cleaning device for each range of measured cleanliness when the measured cleanliness state matches the operational cleanliness level.

[0012] The particle counter can detect the cleanliness status inside a clean room, clean booth, or clean equipment, and operate the air cleaning device according to the detected cleanliness status to automatically control the cleanliness inside the clean room, clean booth, or clean equipment according to the cleanliness control prediction conditions.

[0013] The monitoring system further includes an air cleaning controller that is installed to be communicatively connected between the air cleaning device and the particle counter and controls the operation of the air cleaning device, and the particle counter is communicatively connected to the integrated monitoring server and can transmit and receive the cleanliness status, the cleanliness control result, and the air cleaning device status to and from the integrated monitoring server.

[0014] According to another embodiment, a particle counter may include: a particle measurement unit for obtaining a particle-based cleanliness state measured within a clean area; a cleanliness prediction maintenance unit for predicting an abnormality according to a cleanliness control prediction condition based on the obtained cleanliness state and the cleaning process capability state, and automatically controlling the operation of the air cleaning device according to the prediction result, wherein the cleanliness control result and the control history of the air cleaning device are analyzed to calculate an automatic control optimal condition value for target cleanliness control and reflect the result when predicting the abnormality; and a cleaning device control unit for automatically controlling the operation of the air cleaning device according to the prediction result.

[0015] The above cleanliness prediction and preservation unit can predict the abnormal phenomenon by multiplying the particle measurement value of the cleanliness state by a preset multiple, or by adding it up for a preset period of time, or by adding a change in particles smaller than the cleanliness standard to a condition when predicting the abnormal phenomenon.

[0016] According to the disclosed embodiments, the cleanliness status of a clean area is measured in real time, and based on the measurement results and the cleaning process capability of an air purifier, an abnormal phenomenon is predicted and prevented or reduced using a cleanliness automatic control predictive maintenance algorithm, thereby reducing defects caused by foreign substances and increasing productivity.

[0017] In addition, according to the disclosed embodiments, it is possible to expect a saving effect in operating power energy of the air purifier unit by automatically controlling the installed air purifier to an appropriate output according to the measured cleanliness level.

[0018] In addition, according to the disclosed embodiments, the results of automatic cleanliness control of each particle counter and the control history of the cleanliness and cleaning device section can be monitored through integrated monitoring through an integrated monitoring system, thereby increasing the management efficiency of the entire clean environment and producing energy saving effects, etc.

[0019] Figure 1 is a block diagram illustrating a monitoring system according to one embodiment.

[0020] Figure 2 is a block diagram illustrating a particle counter according to one embodiment.

[0021] Figures 3 to 11 are exemplary diagrams for explaining the setting of cleanliness control prediction conditions and the results of cleanliness automatic adjustment according to one embodiment.

[0022] Figure 12 is a flowchart for explaining the optimal condition setting of cleanliness automatic control predictive maintenance according to one embodiment.

[0023] Figure 13 is a flowchart for explaining a method for setting cleanliness automatic control predictive maintenance conditions according to one embodiment.

[0024] Figure 14 is a flowchart illustrating a process for acquiring purification process capability by output of an air purification device according to one embodiment.

[0025] FIG. 15 is a block diagram illustrating a computing environment including a computing device according to one embodiment.

[0026] Hereinafter, specific embodiments of the present invention will be described with reference to the drawings. The following detailed description is provided to facilitate a comprehensive understanding of the methods, devices, and / or systems described herein. However, these are merely examples and the present invention is not limited thereto.

[0027] In describing embodiments of the present invention, if a detailed description of a known technology related to the present invention is judged to unnecessarily obscure the gist of the present invention, the detailed description will be omitted. In addition, the terms described below are terms defined in consideration of their functions in the present invention, and this may vary depending on the intention or custom of the user or operator. Therefore, the definitions should be made based on the contents throughout this specification. The terminology used in the detailed description is only for the purpose of describing embodiments of the present invention and should not be limited in any way. Unless clearly used otherwise, the singular form includes the plural form. In this description, expressions such as "comprises" or "having" are intended to indicate certain features, numbers, steps, operations, elements, parts or combinations thereof, and should not be construed to exclude the presence or possibility of one or more other features, numbers, steps, operations, elements, parts or combinations thereof other than those described.

[0028] FIG. 1 is a block diagram illustrating a monitoring system according to one embodiment, and FIG. 2 is a block diagram illustrating a particle counter according to one embodiment.

[0029] Hereinafter, examples for explaining the setting of cleanliness control prediction conditions and the results of automatic cleanliness control according to one embodiment will be described with reference to FIGS. 3 to 11.

[0030] Referring to FIG. 1, the monitoring system (100) includes a particle counter (300) and an integrated monitoring server (400). At this time, the monitoring system (100) can control the cleaning device unit (200).

[0031] The cleaning device unit (200) may include an air cleaning device (210). Additionally, the cleaning device unit (200) may include an air cleaning controller (230), and if the particle counter (300) is directly connected to the air cleaning device (210) and directly controls its operation, the air cleaning controller (230) may be omitted. That is, the particle counter (300) may be controlled through the air cleaning controller (230), or may be directly connected to the air cleaning device (210) and directly controlled without the air cleaning controller (230).

[0032] The components illustrated in FIG. 1 are not essential for implementing the monitoring system (100) according to the present disclosure, and thus, the monitoring system (100) described herein may have more or fewer components than the components listed above. The components illustrated in FIG. 1 may be communicatively connected to each other via a communication network (not shown). In some embodiments, the communication network may include the Internet, one or more local area networks, wire area networks, a serial network, a cellular network, a mobile network, other types of networks, or a combination of these networks.

[0033] The monitoring system (100) disclosed below may be configured to measure the cleanliness status of a clean area in real time and perform predictive maintenance. The clean area may include, but is not limited to, a clean room, a clean booth, and clean equipment.

[0034] Referring to Fig. 1, a plurality of air purification devices (210), a plurality of particle counters (300), and an integrated monitoring server (400) can be connected to each other for each preset area in the entire clean area.

[0035] Specifically, each group of a plurality of air purification devices (210) may be directly connected to the particle counter (300) via a direct interface, or may be connected to the particle counter (300) via an air purification controller (230), and may be connected to the integrated monitoring server (400) via the Internet.

[0036] The particle counter (300) of the disclosed embodiment, unlike a typical mobile particle counter, is implemented to be connected to a cleaning device (200), so that the cleaning state of the cleaning area can be controlled in real time using information (data) measured in real time 365 days a year, and the measurement and control results can be shared with an integrated monitoring server (400) to enable monitoring and management. In this way, the disclosed embodiment can facilitate real-time automatic control of cleanliness compared to a case where a typical cleaning system and a clean air conditioning management system are separated, so that the reliability of automatic control of cleanliness can be relatively high.

[0037] Each of the preset areas described above can be assigned a cleanliness standard (target cleanliness). The cleanliness standard refers to the cleanliness standard that each area within the clean area must maintain, and can be expressed, for example, as a target class.

[0038] The air purification device (210) may include at least one of an equipment fan filter unit (EFU), an air purification controller, and a fan filter unit (FFU).

[0039] The air cleaning controller (230) can drive the air cleaning device (210) according to the operation control information of the air cleaning device transmitted from the particle counter (300), and can check information such as the number of connected air cleaning devices, the power status of the air cleaning device, and the motor rotation speed output (RPM) status of the air cleaning device.

[0040] The air cleaning controller (230) may be omitted when the particle counter (300) is directly connected to the air cleaning device (210) and controls its operation.

[0041] A particle counter (300) is connected to multiple air purification devices (210) to directly measure the cleanliness status within a clean area in real time, and can automatically adjust the cleanliness level within the clean area by directly controlling the air purification devices (210) based on the measured cleanliness status. The particle counter (300) can be implemented in multiple units.

[0042] The particle counter (300) may be configured to obtain a cleanliness state of international standards for clean room cleanliness within a clean area.

[0043] The particle counter (300) may be configured to obtain a constant particle-based cleanliness status and a cleaning process capability status of an air cleaning device at each cleanliness measurement standard time within a clean area.

[0044] The constant particle-based cleanliness status for each of the above cleanliness measurement reference times may include particle measurement values ​​per second or particle measurement values ​​per minute. Specifically, the constant particle-based cleanliness status for each of the above cleanliness measurement reference times expresses particle measurement values ​​per second by adding up to tens of thousands of measurement values ​​per second, and expresses particle measurement values ​​per minute by adding up the sum per second again per minute, and expresses this as cleanliness in accordance with international standards for cleanroom cleanliness, so the automatic cleanliness control prediction conditions may include measurement values ​​per second or per minute.

[0045] The particle counter (300) of the disclosed embodiment can obtain a cleanliness status by time (e.g., by second or minute) based on the particle measurement value measured in the clean area in 0.1 cfm mode. That is, the constant particle-based cleanliness status by cleanliness measurement reference time can include particle measurement values ​​by second and particle measurement values ​​by minute. A detailed description thereof will be provided later.

[0046] The particle counter (300) can automatically control and adjust the output of the air purifier according to the target cleanliness management level and the cleanliness control prediction conditions set based on the cleanliness measured per minute, which is a standard, based on the acquired cleanliness status and the cleanliness process capability of the air purifier. The cleanliness process capability may refer to the process capability of the air purifier.

[0047] The particle counter (300) can predict an abnormal phenomenon based on the constant particle-based cleanliness status at each cleanliness measurement reference time within the clean area and the cleanliness process capability status of the air cleaning device according to the cleanliness control prediction condition, and automatically control the operation of the air cleaning device (210) according to the prediction result. The abnormal phenomenon means a phenomenon in which a corresponding area of ​​the clean area does not satisfy the cleanliness control prediction condition, and may be, for example, a case in which the particle measurement value of the corresponding area exceeds the target cleanliness. At this time, the cleanliness control prediction condition is a criterion for determining whether the cleanliness status of the corresponding area is normally maintained, and may include a condition for predicting an abnormal phenomenon as well as a target cleanliness (target class) preset for the corresponding area. Such a cleanliness control prediction condition may be preset.

[0048] The predictive condition of cleanliness control that predicts and prevents the occurrence of abnormal phenomena in advance is insufficient because real-time control is not possible with cleanliness control per minute, so an algorithm that can predict minute-by-minute information with information measured per second is added, and a predictive condition is set by adding 0.3 um, which is smaller than the particle size (0.5 um), which is the cleanliness evaluation standard, to the per-second or per-minute measurement information, and the air purifier is controlled per second according to the predictive condition, so that it can be included as a condition to prevent the occurrence of abnormal phenomena.

[0049] Referring to FIG. 2, the particle counter (300) measures the cleanliness status to distinguish the particle size, and can predict an abnormality based on the particle measurement values ​​by size in real time (e.g., seconds) and the particle measurement values ​​by sampling time (e.g., minutes).

[0050] When predicting an abnormal phenomenon, the particle counter (300) can predict and control the abnormal phenomenon by multiplying the particle measurement value in the cleanliness state by a preset multiple, or by summing it up for a preset period of time, or by adding a change in particles smaller than the cleanliness standard to a condition. For example, the value multiplied by the preset multiple may be data in the multiplication by 10 (x10) method described below, and the value summed up for the preset period of time may be data in the 10-minute sum (sum 10 minutes) method described below.

[0051] The above-mentioned change in particles may refer to a phenomenon in which the particle measurement value changes per second or per minute. The above phenomenon is a measurement value (number) and may appear as data in which the number increases or decreases. In places where high cleanliness is required, the standard 0.5 μm particle is often measured as almost 0, so it may be inadequate for predicting abnormal phenomena. In this case, it is measured about 2.5 to 10 times more than 0.5 μm, and it is measured before 0.5 μm, so if a small 0.3 μm particle is included in the prediction condition and an algorithm is applied, the RPM of the air purifier (210) can be increased before the abnormal phenomenon occurs, thereby preventing the abnormal phenomenon.

[0052] When controlling an air purifier, the particle counter (300) can predict and control abnormal phenomena by reflecting the purification process capability status for each output of the air purifier to the automatic control optimal condition value to automatically adjust the cleanliness status so that the target cleanliness and cleanliness status match the preset operating cleanliness conditions.

[0053] The particle counter (300) can obtain a constant particle-based cleanliness status for at least two or more cleanliness measurement reference times, and can predict the above abnormal phenomenon for each of the constant particle-based cleanliness status for at least two or more cleanliness measurement reference times. Specifically, referring to FIGS. 3A to 3D , the disclosed embodiment can apply a particle counter (300) in 0.1 cfm (cubic feet per minute) mode to automatically adjust cleanliness through control of the air cleaning device (210). In the case of the particle counter (300) in 0.1 cfm mode, two data (e.g., hourly cleanliness status) can be obtained. At this time, the two data can include data in a multiplication by 10 (x10) format and data in a 10-minute sum (sum 10 minutes) format. The above x10 method data means multiplying the particle measurement value by 10 times, and is relatively suitable for predicting abnormal phenomena, and the level of predictive maintenance can be determined by adjusting the multiplication factor. The above sum 10-minute method data means adding up the particle measurement values ​​for 10 minutes, and can be relatively suitable for expressing cleanliness.

[0054] The large or small flow rate of the particle counter (300) may mean that the characteristics expressed by the measurement result data are different for each flow rate. If the particle counter (300) has the same performance, when the flow rate is large, the cleanliness converges to the average value and the dispersion is small, and when the flow rate is small, the dispersion is large and the measurement is sensitive. In other words, the smaller the flow rate, the larger the dispersion of the measurement and the more sensitive the measurement may be.

[0055] In an optical particle counter (300) of the same performance, the difference in flow rate between a particle counter in 1 cfm mode and a particle counter in 0.1 cfm mode determines the speed of the air velocity to be measured, so that measurements at slow velocity are more sensitive to changes and thus the dispersion is relatively large, making it suitable for prediction.

[0056] The particle counter (300) of the disclosed embodiment can improve prediction performance because it uses particle measurement values ​​in 0.1 cfm mode to predict abnormal phenomena that change in real time (e.g., per second). In addition, the particle counter (300) can improve the efficiency of cleanliness class expression and energy saving by performing energy saving per minute and expression of cleanliness class based on the sum of particle measurement values ​​for 10 minutes.

[0057] Through the above-described implementation, the disclosed embodiment applies a particle counter (300) in 0.1 cfm mode when predicting the cleanliness state of the FED standard, so that prediction performance and preservation performance can be improved by applying particle measurement values ​​in more diverse flow rate modes compared to using only one type of particle measurement value obtained from a particle counter in 1 cfm (28.3 liter flow rate per minute) mode.

[0058] The prediction of the above-described abnormal phenomenon utilizes particle measurement values ​​per second to increase the control response speed for the air purification device (210), and can primarily utilize particle (dust) measurements that serve as the standard for the cleanliness class and relatively small particle measurements. At this time, the particle size applied when predicting the abnormal phenomenon can be changed according to the customer's needs.

[0059] In general, the occurrence of abnormal phenomena varies depending on the implementation level of the cleaning device (200), but under normal management conditions, they occur at a level of approximately 5% or less, and more than 80% of them can be measured as a cleanliness state significantly lower than the target class. In cases where the occurrence of abnormal phenomena is approximately 5% or less, it is easy to find phenomena occurring at a level several to ten times higher than the target class.

[0060] Even if abnormal phenomena cannot be completely eliminated by predicting and controlling them, reducing the number and level of abnormal phenomena can help prevent serious problems caused by foreign substances, and reducing abnormal phenomena can save energy by operating air purifiers (210) that are operating excessively by about 80% at an appropriately low output.

[0061] For example, in the case of Company A, 24 EFUs are operated at 800 RPM to maintain the target cleanliness level of 100 Class with the air conditioning control unit. As a result, it can be confirmed that in the results of three consecutive days of measurement, 94.86% are 0 Class, 99.26% are 100 Class, but 0.74% occurs up to 1330 Class. The fact that 0 Class is 94.86% of the target cleanliness level of 100 Class means that the air purifier (210) is being operated with excessive power, wasting energy, and an abnormal phenomenon is occurring in which 0.74% occurs up to 1330 Class even though it is being operated so that 0 Class becomes 94.86%. Since the disclosed embodiment enables real-time prediction of abnormal phenomena in a clean area and control of an air purifier (210), the output of the air purifier is increased when the above-mentioned 0.74% abnormal phenomenon is expected to occur, and the output of the air purifier is lowered (for example, lowered from 800 RPM to 300 RPM) when the 0 Class is 94.86%, thereby preventing abnormal phenomena and saving energy.

[0062] In addition, equipment within a clean environment is not operated 24 hours a day, 365 days a year due to various issues, and air purification devices within a clean environment must generally be operated at all times even when process equipment is not in operation. Therefore, when process equipment is not in operation, the cleanliness level is relatively very low, so the energy saving effect of automatic cleanliness control through predictive maintenance can be even greater.

[0063] Table 1 below may show the results of cleanliness measurements when the output of Company A's air purifier is 800 RPM.

[0064] Zone1234AverageSum7007701,330670 Number of data6,2484,4756,2576,256 Average(number)0.1120.1720.2130.107 Class097.375%91.240%94.183%96.627%94.86%1097.999%95.732%96.148%97.826%96.93%2098.624%97.095%97.187%98.370%97.82%3 098.944%97.520%97.810%98.769%98.26%4099.120%97.564%98.274%98.993%98.49%5099.344%97.788%98.594%99.345%98.77%6099.39 2%97.966%98.849%99.169%98.84%7099.600%98.190%99.025%99.281%99.02%8099.632%98.279%99.169%99.345%99.11%9099.648%98.503%99.313%99.361%99.21%10099.712%98.547%99.377%99.409%99.26%MAX7007701330670868 or moreDATA0.288%1.453%0.623%0.591%0.74%

[0065] Meanwhile, referring to [Table 2], the unit cost for maintaining cleanliness by target class of semiconductor standard clean room in the Journal of the Korean Society of Mechanical Engineers is presented as 7,000,000 won per m2 to maintain 100 Class.

[0066] Coas Comparison by Clean Room Class (Semiconductor Standard) (Unit: 10,000 won / ㎡) Cleanliness Class 0.1 Class 1 Class 10 Class 100 Class 1000 Note Unit Price 2,500 1,500 1,000 700 400 Varies depending on ventilation frequency, facility size, building structure, vibration, required specifications, etc.

[0067] If the target cleanliness (target class) fails to reduce abnormal phenomena, a change to a higher cleanliness level that is relatively lower than the actual required cleanliness may be required. For example, if there is a process with a target cleanliness level of 100, abnormal phenomena may occur at a level of 1000 or higher, which is more than 10 times higher. According to this principle, if the number and magnitude of abnormal phenomena are reduced, the above-described process may be possible even at a level of 500. The prediction and prevention of abnormal phenomena is a technology that normalizes the cleanliness standard of the process to an effective standard. If the prevention of abnormal phenomena is possible, the operation of the cleaning device (200) (clean air conditioning system) can be achieved with an appropriate power, thereby enabling energy savings. The technology that normalizes the above-described cleanliness standard to an effective standard may mean setting the target class within the clean area to an appropriate level rather than setting it relatively excessively to prepare for abnormal phenomena. The disclosed embodiment detects the cleanliness status in real time, controls the operation of an air purifier based on the detected result, and monitors the result after the operation control to reflect it in the cleanliness standard, so that the cleanliness standard can be set to an appropriate level.

[0068] The particle counter (300) has real measurement data for each particle size measured in real time, so it can be used to transform this into data suitable for two or more purposes in units of seconds or minutes and to use it for cleanliness prediction and maintenance.

[0069] In addition, the particle counter (300) can use particles of 0.5 um size, which are the standard for the cleanliness class, as data, and is not limited thereto. In addition to particles of 0.5 um size, the prediction effect can be increased by using particles of a small size (e.g., 0.3 um size) that are measured about 2 to 10 times more than 0.5 um.

[0070] The above-mentioned cleanliness standards refer to the cleanroom cleanliness standards that each area within the clean zone must maintain. The cleanroom cleanliness can be expressed in accordance with the standards of the international cleanliness standard ISO-14844-1 and the US Federal Standard Fed. Std. 209D. The particle counter (300) can express the measured values ​​in both the ISO and Fed. formats.

[0071] Cleanroom cleanliness class standards are expressed in the Fed. method, and 1 CFM can be said to be a cleanliness level determined by the maximum allowable number of particles larger than 0.5㎛ contained in 1 cubic foot (1 ft3, CFT) of air with a length, width, and height of 30cm. For example, a clean space with 1 or less particles larger than 0.5㎛ in 1 cubic foot of air can be called Class 1, a clean space with 100 or fewer particles larger than 0.5㎛ in air can be called Class 100, and a clean space with 1,000 or fewer particles can be called Class 1,000.

[0072] When predicting an abnormal phenomenon, the particle counter (300) can multiply the particle measurement value in the cleanliness state by a preset multiple, or can use the value added over a preset period of time to predict the abnormal phenomenon. For example, the value multiplied by the preset multiple can be data in the multiplication by 10 (x10) method described below, and the value added over the preset period of time can be data in the 10-minute sum (sum 10 minutes) / 1-minute unit display method described below.

[0073] Fig. 3a illustrates a case where 0.5 um-sized particles are used as data in a multiplication by 10 (x10) manner, and Fig. 3b may illustrate a case where 0.5 um-sized particles are used as data in a 10-minute sum (sum 10 minutes) manner. Fig. 3c may illustrate a case where 0.3 um-sized particles are used as data in a multiplication by 10 (x10) manner, and Fig. 3d may illustrate a case where 0.3 um-sized particles are used as data in a 10-minute sum (sum 10 minutes) manner.

[0074] Specifically, when the particle data measured for 1 minute in a particle counter (300) in 0.1 cfm mode is expressed by multiplying the measurement value by 10 to express it according to the class standard, a large dispersion appears in the phenomenon as in FIG. 3a, so that it can be easy to predict an abnormal phenomenon. When the particle counter (300) in 0.1 cfm mode expresses the sum of the particle measurement values ​​for 10 minutes in 1-minute units as in FIG. 3b, it can obtain a value similar to the particle counter measurement value in 1 cfm mode, and in the automatic control of cleanliness, the condition that the target cleanliness range must be satisfied for 10 minutes in 1-minute units becomes possible, so that stricter cleanliness management can be possible at low cleanliness levels. By this principle, the 0.1 cfm mode particle counter (300) of the disclosed embodiment can obtain and utilize both the advantages of the two flow rates obtained through the 1 cfm mode particle counter and the 0.1 cfm mode particle counter. The prediction of the above-described abnormal phenomenon can increase the control response speed for the air purifier (210) by utilizing the particle measurement values ​​per second obtained in real time by the particle counter (300) and the measurement values ​​of small particles that have relatively many measurement values ​​compared to the particles (dust) that serve as the standard for the cleanliness class, thereby increasing the efficiency of predictive maintenance, and can increase the stability of the automatic cleanliness control with an algorithm for the measurement values ​​per minute.

[0075] Through the disclosed embodiment, the particle counter (300) can automatically control the cleanliness level by automatically controlling the cleanliness device unit (200) through predictive maintenance using the particle measurement unit (310), cleanliness prediction and maintenance unit (330), and cleanliness device control unit (350) described below, and the results or history can be monitored through the particle counter (300) or the integrated monitoring server (400).

[0076] The disclosed embodiment automatically adjusts the speed of an air purification device (e.g., EFU) (210) by utilizing the cleanliness status per minute (particle measurement value), and since the cleanliness status per minute becomes relatively stable as predictive maintenance for abnormalities is performed, energy saving efficiency can be relatively improved.

[0077] In the disclosed embodiment, the operator can set a target class (cleanliness standard class) and a management level for each particle size, and the level of prevention of abnormal phenomena and energy saving can be determined based on the set level.

[0078] The disclosed embodiment sets and executes abnormal phenomenon prediction conditions according to the class of automatic cleanliness control, and the results can be immediately monitored by a particle counter (300), and the cleanliness control efficiency can be improved by modifying parameter input prediction conditions through monitoring of the cleanliness control results. The cleanliness control results can include at least one of a measured cleanliness state (particle measurement value), a cleanliness control result, a control history of an air purifier, and a new target cleanliness.

[0079] The particle counter (300) can monitor information generated after automatically adjusting the cleanliness of the air purifier (210) according to the cleanliness status, and can apply the monitoring results to the subsequent predictive maintenance of the cleanliness status. The particle counter (300) can set in advance the operation control information of the air purifier according to the acquired cleanliness status. For example, the operation control information of the air purifier can include information such as adjusting the rotation speed of the air purifier (210) located in the corresponding area to xx when the cleanliness status is 200 class. To this end, the disclosed embodiments can be transmitted, received, or managed by matching information such as the cleanliness status, the operation control information of the air purifier, and the like with each identification information (e.g., identification information of an area within a clean area, identification information of the air purifier, identification information of the particle counter).

[0080] In the embodiment disclosed in Fig. 4a, the cleanliness automatic control predictive maintenance conditions of Fig. 13 can be set through the particle counter (300). In the disclosed embodiment, it is possible to set a target cleanliness including small particles, an air purifier output control step, a cleaning process capability measurement for each air purifier output, a cleanliness control level for each air purifier output, a multiplication coefficient for increasing the effect of predicting abnormal phenomena, a time required for controlling the output change of the air purifier, a maintenance time of the output control, etc.

[0081] Fig. 4b may represent the results of automatic cleanliness control through a particle counter (300) according to the disclosed embodiment. Specifically, the graph may represent the results of setting the target cleanliness to class 100 and executing the automatic cleanliness control algorithm according to the disclosed embodiment for 1.3 days. The cleanliness status of the first graph (R1) according to the disclosed embodiment is compared to the second graph (R2) to which the general algorithm is applied, and it can be confirmed that the target cleanliness (less than class 100) is maintained, specifically, the average is 33 classes, the maximum is 81 classes, and the minimum is 2 classes.

[0082] FIG. 5 may illustrate the results of implementing automatic cleanliness control through a particle counter (300) according to the disclosed embodiment into a product. As illustrated, when the target cleanliness is Class 100, it can be confirmed that particles with a size of 0.5 μm are controlled to Class 6 to Class 25, below Class 100.

[0083] FIG. 6 provides a graph of the average speed and speed change history of an air purification device (210) (e.g., EFU) along with a graph of measured cleanliness and hourly cleanliness change, enabling visual management of automatic cleanliness control.

[0084] Fig. 7 allows for checking and manually adjusting the speed of air purifiers (210) connected to a particle counter (300). For example, this is a function that enables manual adjustment when automatic adjustment must be stopped for maintenance of the particle counter (300).

[0085] Fig. 8 may represent the integrated monitoring results for the cleanliness measurement and automatic adjustment results of particle counters (300). At this time, the monitoring may be monitoring of cleanliness, energy saving, speed control history of the air cleaning device (210) (e.g., EFU), energy saving effect, etc.

[0086] The control history of the air purifier described above may be, but is not limited to, the rotation speed (RPM) control history of the air purifier or energy-saving power values. The new target cleanliness refers to, but is not limited to, the target cleanliness of the relevant area arbitrarily determined by the operator.

[0087] Fig. 9a shows the change in the cleanliness status over time in a general clean area, and may represent a cleanliness status distribution map by time zone in the clean area (clean room layout). Fig. 9b shows the change in the cleanliness status over time in a clean area according to the disclosed embodiment, and may represent a cleanliness status distribution map by time zone in the clean area (clean room layout). At this time, it can be confirmed that the class level in the case where the cleanliness automatic control method according to the disclosed embodiment is applied is a relatively significantly lower class compared to the case where the general cleanliness control method is applied, and is maintained below the target class (for example, class 100 based on particles with a size of 0.5 μm).

[0088] The particle counter (300) presets an operational cleanliness level for operating the cleanliness (class) within the clean area at a set level of cleanliness compared to the target cleanliness, and when the measured cleanliness state matches the operational cleanliness level, the output control level of the air cleaning device can be set for each range of the measured cleanliness.

[0089] Below, a detailed description will be given of how the particle counter (300) predicts and maintains the cleanliness status of the clean area.

[0090] For example, the particle counter (300) can determine the cleanliness status of each area within the clean area, and control the operation of the air cleaning device (210) according to the determined cleanliness status and the cleanliness control prediction conditions set for each area. For example, if the target cleanliness of area A-1 within clean area A is class 100 and the acquired cleanliness status (particle measurement value) is class 200, the particle counter (300) can extract the corresponding control information from the operation control information of the air cleaning device for each preset cleanliness status, and control the operation of the air cleaning device (210) using the same.

[0091] In the above-described process, the particle counter (300) can determine the hourly cleanliness status (e.g., particle measurement values ​​based on second-by-second data, minute-by-minute data, multiplied by 10 (x10) data, and 10-minute sum (sum 10 minutes) data). This principle can be applied to all disclosed embodiments.

[0092] As another example, the particle counter (300) can determine the cleanliness status of an area where the range of change in cleanliness within a clean area exceeds a preset range of change, and control the operation of the air purification device (210) to reduce the range of change in cleanliness according to the determined cleanliness status. At this time, the particle counter (300) can match and store operation control information of the air purification device according to the range of change in cleanliness in advance.

[0093] As another example, the particle counter (300) can determine the cleanliness status of an area subject to intensive management, and control the operation of the air cleaning device (210) so that the cleanliness class of the area matches the target class based on the determined cleanliness status. The above-described area subject to intensive management may include an area arbitrarily set by the operator for reasons such as a process that is relatively sensitive to particles or an area that requires a relatively important process. In addition, the area subject to intensive management may include an area where the cleanliness status measured in the past matches a condition requiring attention.

[0094] The above-described embodiments can be implemented singly or in combination.

[0095] Referring to FIGS. 10 and 11, the particle counter (300) can detect the cleanliness status inside a clean room, clean booth (600), or clean equipment (500), and operate the air cleaning device (210) according to the detected cleanliness status to automatically adjust the cleanliness inside the clean room, clean booth (600), or clean equipment (500) according to cleanliness control prediction conditions.

[0096] Referring to Fig. 10, the cleaning equipment (500) of the disclosed embodiment can be installed in a clean room. In this structure, the particle counter (300) is connected to the cleaning device unit (200) (e.g., the air cleaning device (210)), and automatically adjusts the rotation speed (RPM) of the air cleaning device (210) according to the real-time change in cleanliness by time zone inside the clean equipment (500), thereby maintaining the cleanliness state inside the clean equipment (500) at a constant level according to the target cleanliness.

[0097] Referring to FIG. 11, the particle counter (300) is connected to the cleaning device unit (200) (e.g., the air cleaning device (210)) and automatically controls the rotation speed (RPM) of the air cleaning device (210) according to the change in the cleanliness level of the clean booth (600) over time, so that the cleanliness level inside the clean booth (600) can be automatically controlled so that it can be maintained within the target cleanliness level.

[0098] The particle counter (300) analyzes the cleanliness control results and the control history of the air purifier to calculate the optimal automatic control condition value for target cleanliness control, which can be reflected when predicting an abnormal phenomenon.

[0099] In addition, multiple particle counters (300) can additionally consider a new target cleanliness level to calculate an optimal automatic adjustment condition value and reflect it when predicting and controlling an abnormal phenomenon.

[0100] The control history of the air purifier described above may be, but is not limited to, the control history of the rotation speed (RPM) of the air purifier. The new target cleanliness refers to, but is not limited to, the target cleanliness of the corresponding area arbitrarily determined by the operator. The automatic adjustment optimal condition value refers to an input condition for predictive maintenance of the particle counter (300) to maintain the cleanliness status of the corresponding area at a management level desired by the operator, and may be reflected when updating the cleanliness control predictive condition.

[0101] The particle counter (300) is connected to the integrated monitoring server (400) in communication with the integrated monitoring server (400) and can transmit and receive the cleanliness status, cleanliness control results, and air purification device status to and from the integrated monitoring server (400).

[0102] The particle counter (300) can store and monitor particle measurement values, purifier control history, predictive maintenance conditions, and the purifier unit (200) communication protocol in a database, and can communicate and transmit this information to the integrated monitoring server (400) for integrated monitoring.

[0103] Referring to FIG. 2, the particle counter (300) includes a particle measurement unit (310), a cleanliness prediction and preservation unit (330), and a clean device control unit (350), and transmits and receives result information with the integrated monitoring server (400), and can monitor and control the devices of the clean device unit (200).

[0104] The particle measurement unit (310) may be configured to obtain a particle-based cleanliness status measured within a clean area.

[0105] The particle measurement unit (310) continuously measures the state of cleanliness in real time, and can transmit information on the size of the measured particles to the cleanliness prediction and maintenance unit (330) in real time in units of seconds or minutes or sampling time.

[0106] The cleanliness prediction maintenance unit (330) may be configured to predict an abnormal phenomenon according to a cleanliness control prediction condition based on the acquired cleanliness status and the cleaning process capability status, and automatically control the operation of the air cleaning device according to the prediction result. However, the cleanliness control result and the control history of the air cleaning device may be analyzed to calculate an automatic control optimal condition value for target cleanliness control, and reflect it when predicting an abnormal phenomenon.

[0107] The above cleanliness prediction and preservation unit (330) can predict an abnormal phenomenon by multiplying the particle measurement value of the cleanliness state by a preset multiple, or adding it up for a preset period of time, or adding a change in particles smaller than the cleanliness standard to a condition when predicting an abnormal phenomenon.

[0108] The cleanliness prediction maintenance unit (330) controls the devices of the cleaning device unit (200) in units of seconds or minutes according to the cleanliness prediction maintenance conditions set using the information (Data) received from the particle measurement unit (310) in units of seconds or minutes, so that the cleanliness can be automatically adjusted to the target cleanliness and management level, and the cleanliness measured by the minimum to maximum output (rotational speed: RPM) of the air cleaning device (210) that is already installed can be automatically or manually measured to obtain the cleaning process capability of the air cleaning device (210).

[0109] The cleanliness predictive maintenance condition of the cleanliness predictive maintenance unit (330) predicts and prevents in advance the phenomenon in which particles are measured in excess of a given limit value, thereby preventing defects caused by foreign substances in the cleaning process and increasing productivity, or by adjusting the output of the air purifier (210) to an appropriate output according to the cleanliness level measured in real time, thereby implementing energy savings. The cleanliness predictive maintenance unit (330) reflects an algorithm for predicting and preventing abnormal phenomena in the measured cleanliness in advance. An abnormal phenomenon in cleanliness generally refers to a phenomenon of less than about 5% that occurs several to ten times higher than the target cleanliness class, but in addition, about 80% of cases in which a cleanliness class that is significantly lower than the target class can also be considered an abnormal phenomenon in terms of energy. The cleaning device control unit (350) may be configured to automatically control the operation of the air purifier (210) according to the prediction result.

[0110] The air purifier control unit (350) can manually monitor or control the air purifier unit (200) to respond to various changes such as maintenance of the air purifier unit (200) or particle counter (300), and can be implemented to create a database of communication protocols of the controller (230) or air purifier (210) by manufacturer or model for controlling various air purifiers and to select them.

[0111] The integrated monitoring server (400) obtains all information measured and controlled by all connected particle counters (300), analyzes the cleanliness level, cleanliness control history, and status of the cleaning device for each installed location, and enables various improvements such as modifying cleanliness prediction and preservation conditions, changing the cleaning device, and checking the location of occurrence of abnormal phenomena in the overall cleanroom layout.

[0112] The integrated monitoring server (400) may be configured to monitor the cleanliness control results collected from each of a plurality of particle counters (300), the control history of the air purifier, and the cleanliness status, thereby readjusting the automatic cleanliness control conditions within the entire clean area and controlling the cleanliness status and the status of the air purifier. In addition, the integrated monitoring server (400) may also monitor matters related to energy saving effects through the integrated monitoring described above.

[0113] The integrated monitoring server (400) analyzes the status of the plurality of particle counters (300) according to preset monitoring criteria based on the cleanliness control results collected from each of the plurality of particle counters (300), the control history of the air purifier, and the cleanliness status, and can reflect the analysis results when adjusting the automatic control optimal condition value.

[0114] Fig. 12 is a flowchart illustrating optimal condition setting for predictive maintenance through automatic cleanliness control according to one embodiment. The method illustrated in Fig. 12 may be performed, for example, by the aforementioned monitoring system (100). While the illustrated flowchart describes the method as divided into multiple steps, at least some of the steps may be performed in reverse order, combined with other steps and performed together, omitted, divided into substeps, or performed with one or more additional steps not illustrated.

[0115] Referring to FIG. 12, at step 1100, the monitoring system (100) can determine a cleanliness control prediction condition composed of multiple conditions including a target cleanliness level, a management level, a cleaning process capability, a cleanliness control level, and a prediction count per second / minute, through a particle counter (300). At this time, the monitoring system (100) can determine a cleanliness control level according to the degree of optimization of the cleanliness control prediction condition.

[0116] At step 1200, the monitoring system (100) obtains a preset particle-based cleanliness status within the clean area by time through the particle counter (300), and reflects this in the second-by-second abnormality prediction algorithm to predict an abnormality.

[0117] Specifically, when the particle counter (300) receives the cleanliness status by particle size measured in units of seconds, it can use this to predict an abnormal phenomenon according to the cleanliness control prediction conditions that reflect the second-by-second abnormal phenomenon prediction algorithm. At this time, the particle counter (300) can control the cleaning device unit (200) by predicting in units of seconds whether the cleanliness status will increase or decrease. The information in the second-by-second cleanliness status used for prediction can be implemented in various ways, such as a multiplication coefficient applied value or a sum value in a manner suitable for the target cleanliness level, particle counter flow rate, particle status smaller than the standard, etc., and the value of particles smaller than the cleanliness standard.

[0118] At step 1300, the particle counter (300) can automatically control the air purifier (210) based on the prediction result to perform predictive maintenance. At this time, the operation of step 1300 can automatically control the air purifier (210) through automatic control of the purifier control unit (350) within the particle counter (300).

[0119] At step 1400, the particle counter (300) can predict an abnormality according to a cleanliness control prediction condition that reflects a minute-by-minute abnormality prediction algorithm using the acquired cleanliness status.

[0120] Specifically, the particle counter (300) can obtain cleanliness information by particle size measured in units of minutes and apply a minute-by-minute abnormal phenomenon prediction algorithm. At this time, the particle counter (300) can control the cleaning device unit (200) by predicting in units of minutes whether the cleanliness status will increase or decrease. The information in the minute-by-minute cleanliness status used for prediction can be implemented in various ways, such as a multiplication coefficient applied value or a sum value in a manner suitable for a target cleanliness level, a particle counter flow rate, a particle status smaller than a standard, etc., and a value of a particle smaller than the cleanliness standard.

[0121] At step 1500, the particle counter (300) can automatically control the air purifier (210) based on the prediction result to perform predictive maintenance. At this time, the operation of step 1500 can automatically control the air purifier (210) through automatic control of the purifier control unit (350) within the particle counter (300).

[0122] At step 1600, the particle counter (300) is run in accordance with the cleanliness control predictive maintenance conditions set at step 1100, and the state of cleanliness control is confirmed and analyzed through the cleanliness control result, so that the cleanliness control predictive conditions at step 1100 can be corrected to optimal conditions.

[0123] The cleanliness control prediction conditions include target cleanliness, management level, control step, and count per second / minute, and in particular, by varying each output of the installed air cleaning device (210) from maximum to minimum and measuring the cleanliness for each output, the information obtained can be applied to the cleanliness control prediction conditions as an accurate value through actual measurement as the cleaning process capability of the air cleaning device, thereby optimizing the prediction conditions.

[0124] In step 1700, the monitoring system (100) can analyze the status of the plurality of particle counters according to preset monitoring criteria based on the cleanliness control results collected from each of the plurality of particle counters (300) and the control history of the air purification device (210) through the integrated monitoring server (400). At this time, the integrated monitoring server (400) can additionally apply the status of the air purification device (210) when analyzing the status of the plurality of particle counters.

[0125] Fig. 13 is a flowchart illustrating a method for setting cleanliness automatic control predictive maintenance conditions according to one embodiment. The method illustrated in Fig. 13 may be performed, for example, by the particle counter (300) described above. Although the method is described in the illustrated flowchart as being divided into a plurality of steps, at least some of the steps may be performed in a different order, combined with other steps and performed together, omitted, divided into sub-steps and performed, or one or more steps not illustrated may be added and performed.

[0126] At step 2100, the particle counter (300) can set the target cleanliness and management level through the predictive maintenance input screen.

[0127] The above management level is the condition of automatic cleanliness control, which is how many steps the output of the air purifier will be controlled in for automatic cleanliness control and at what % of the target cleanliness level for each step to operate. In a process where it is important to prevent abnormal phenomena, the output of the air purifier (210) can be set to a small number of steps and a lower % compared to the target cleanliness level, and in cases where there are many particles or energy saving is important, the output of the air purifier (210) can be set to several steps compared to the previous case and a higher %. For example, in the case of 100 Class, the output of the air purifier (210) can be adjusted in three steps and when it is set to 50% of the target cleanliness level and when it is set to 70% of the target cleanliness level, the cleanliness level at 50% is automatically controlled to a lower cleanliness level than that at 70%. For example, in the case of 1000 Class, if the output of the air purifier (210) is set to be adjusted in 3 stages and in 5 stages, the 5 stages can increase energy saving efficiency compared to the 3 stages.

[0128] The above target cleanliness is based on 0.5 um in the Fed. cleanroom standard, but it can also include the limit value of the measurement value for small dust particles that are measured 3 to 10 times higher than 0.5 um. The disclosed embodiment that considers even relatively small dust particles like this increases the predictive effect of abnormal phenomena, and allows the management level of the target cleanliness to be input, so that the level for reducing abnormal phenomena and the level of energy saving can be determined according to the management level. For example, if the management level is set to 70% in a place managed as 100 Class, the air purifier (210) is controlled to the set maximum output when it becomes 70 Class or higher, which is 70% of 100 Class.

[0129] At step 2200, the particle counter (300) can measure the cleanliness status of each output of the air purifier (210) to obtain the cleaning process capability status of the air purifier (210). The cleaning process capability status can be reflected in the output setting conditions of the air purifier for each cleanliness status at step 2400, which will be described later. Accordingly, when the target measured cleanliness is within the target cleanliness level, the air purifier (210) can be controlled to an appropriate output accordingly to save energy.

[0130] At step 2300, the particle counter (300) can set the output control stage of the air purifier (210). At this time, the output control stage can be set to be adjusted to a relatively low stage when the target cleanliness level is low or the main purpose is to prevent abnormal phenomena, and to be adjusted to a relatively high stage when the target cleanliness level is high or the main purpose is to save energy. This is because, in a place where a low Class (e.g., 10 Class) cleanliness state must be maintained, the level of automatic cleanliness control should be controlled to a low stage of 2 or 3 among stages 1 (low stage) to 5 (high stage) to maintain a stable cleanliness level and save energy, and in a high Class (e.g., 10000 Class), the output of the air purifier (210) should be adjusted to a high stage of 4 or 5 to increase the efficiency of energy efficiency.

[0131] In step 2400, the particle counter (300) can set the cleanliness management level for each output of the air purifier (210). When the cleanliness measured based on the cleanliness process capability obtained for each output of the air purifier (210) in step 2200 is within the target cleanliness level, the target cleanliness level is divided into cleanliness levels so that the output of the air purifier can be set appropriately for each level.

[0132] As a result of the automatic control of cleanliness, even if the cleanliness level is within the target cleanliness, there may be a percentage of the target cleanliness that the current cleanliness is. For example, if the cleanliness is at 10% of the target cleanliness, the output (RPM) of the air purifier (210) may be lowered, and if it is at 90%, the output may need to be increased by 10%. In other words, dividing the target cleanliness level into cleanliness levels and setting the appropriate output of the air purifier for each level may mean that the RPM of each air purifier (210) can be set according to the percentage of the target cleanliness that the cleanliness is. In other words, when the cleanliness is within the target cleanliness, energy can be saved by setting the appropriate output of the air purifier (210) that is appropriate for the current cleanliness.

[0133] The output of the above air purifier can refer to the motor's rotational speed. Increasing the output can mean increasing the motor's rotational speed (RPM), while decreasing it can mean decreasing the RPM. As the motor's rotational speed increases, both voltage and current increase, which can lead to increased power consumption.

[0134] At step 2500, the particle counter (300) can set a prediction coefficient to enable prediction per second / minute to prevent abnormal phenomena or save energy.

[0135] For example, a particle counter (300) with a flow rate of 1 cfm can be applied with a relatively simple algorithm to control the air cleaning device (210) according to the measured value and the cleanliness control prediction condition, but a particle counter (300) with a flow rate of 0.1 cfm can over-operate in a relatively low class (e.g., 10 Class) when the measured value is converted by multiplying by 10. To prevent this, the multiplication coefficient can be adjusted by class, minute, and second.

[0136] In a relatively low cleanliness class of 0.1 cfm, a multiplication factor of 10 may not be suitable for automatic cleanliness control. For example, in the case of Class 10, even if only 2 particles are measured, they may be displayed as 20. Since the disclosed embodiment can apply a cleanliness control prediction condition by calculating a 10-minute measurement sum value in 1-minute units in a relatively low cleanliness class, the cleanliness can be automatically controlled so that the 10-minute cleanliness value does not exceed the limit target class, and thus, stricter cleanliness management can be achieved compared to 1 cfm. On the other hand, since it is difficult to respond to an instantaneous change if a 10-minute sum value is applied in the event of an instantaneous abnormality, the disclosed embodiment can predict and take action on a change in the cleanliness status from the measurement value per second / minute by applying an arbitrary multiplication factor to the 1-minute measurement value.

[0137] At step 2600, in addition to the above cleanliness control prediction conditions, the particle counter (300) can set other conditions, including the length of time to maintain the output when the output of the air purifier (210) is changed, the change speed between outputs, the prediction algorithm application time per second, etc.

[0138] In step 2700, the particle counter (300) can automatically adjust the cleanliness state according to the conditions set in step 2600. At this time, information including the cleanliness of the execution result, the measurement values ​​by particle size, the control history of the air purification device, and the cleaning process capability, etc. can be stored in the database of the particle counter (300). The cleanliness of the execution result is expressed based on 0.5 um, and the measurement values ​​by particle size can mean a state in which measurement information for various sizes of 0.3, 1, 5, 10, and 25 um are included in addition to 0.5 um. The monitoring system (100) can analyze the information stored in the database through the particle counter (300) to readjust and optimize the cleanliness control prediction conditions, thereby increasing the efficiency of the automatic cleanliness control.

[0139] Fig. 14 is a flowchart illustrating a process for acquiring purification process capability by output of an air purification device according to one embodiment.

[0140] The method illustrated in Fig. 14 may be performed, for example, through the cleanliness prediction and preservation unit (330) in the aforementioned particle counter (300). In the illustrated flowchart, the method is described by dividing it into a plurality of steps, but at least some of the steps may be performed in a different order, combined with other steps and performed together, omitted, divided into sub-steps and performed, or one or more steps not illustrated may be added and performed.

[0141] At step 3100, the particle counter (300) can determine the output and measurement time of the air purifier through the cleanliness prediction maintenance unit (330).

[0142] Specifically, the particle counter (300) can be set to automatically measure the maximum and minimum outputs of the air purifier (210) at the above-described step 2200 for a set period of time at a set interval from the maximum to the minimum output within a desired range. Referring to FIG. 14, the measured output (measured output) can mean the output obtained by subtracting the step output from the maximum output.

[0143] If the output of the air purifier (210) can be adjusted from 0 to 1000 RPM, and the measurement is set to 200 RPM, 400 RPM, 600 RPM, 800 RPM, and up to 1000 RPM, and if each is measured for 1 hour, the step (GAP) output can be 200 RPM. In this case, the cleanliness at 200 RPM, 400 RPM, 600 RPM, 800 RPM, and 1000 RPM can be output as data. The cleanliness implemented for each output (motor rotation speed RPM) of the air purifier (210) can be known, and this can be expressed as the air purifier purification “process capability.” In other words, automatic cleanliness control can appropriately adjust the RPM only when the cleanliness implemented according to the RPM rotation speed of the air purifier is known, and this can be confirmed through actual measurement.

[0144] At step 3200, the particle counter (300) can change the output of the air purifier (210) as set at step 3100 described above, measure the cleanliness status for a set period of time, and store it in the database of the particle counter (300) and the monitoring system (100).

[0145] At step 3300, the particle counter (300) can analyze the process capability status of each output of the air purifier (210).

[0146] Specifically, the particle counter (300) can identify the process capability status by deleting and analyzing abnormal data and information on variable output points from the information stored in the database at step 3200. For example, if the particle counter (300) measures for one hour for each output of the air purifier (210), it can identify the process capability status by deleting the initial measurement value and abnormal data from the measured data and securing about 30 pieces of data.

[0147] At step 3400, the monitoring system (100) can reflect the information analyzed at step 3300 through the particle counter (300) when determining the cleanliness control prediction conditions at step 1100 described above.

[0148] FIG. 15 is a block diagram illustrating a computing environment including a computing device according to one embodiment. In the illustrated embodiment, each component may have different functions and capabilities other than those described below, and may include additional components other than those described below.

[0149] The illustrated computing environment (10) includes a computing device (12). The computing device (12) may be one or more components included in each of the monitoring system (100), the cleaning device unit (200), the particle counter (300), and the integrated monitoring server (400) according to one embodiment.

[0150] A computing device (12) includes at least one processor (14), a computer-readable storage medium (16), and a communication bus (18). The processor (14) may cause the computing device (12) to operate according to the exemplary embodiments mentioned above. For example, the processor (14) may execute one or more programs stored in the computer-readable storage medium (16). The one or more programs may include one or more computer-executable instructions, which, when executed by the processor (14), may be configured to cause the computing device (12) to perform operations according to the exemplary embodiments.

[0151] A computer-readable storage medium (16) is configured to store computer-executable instructions or program code, program data, and / or other suitable forms of information. A program (20) stored in the computer-readable storage medium (16) includes a set of instructions executable by the processor (14). In one embodiment, the computer-readable storage medium (16) may be a memory (volatile memory such as random access memory, non-volatile memory, or a suitable combination thereof), one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, any other form of storage medium that can be accessed by the computing device (12) and store desired information, or a suitable combination thereof.

[0152] A communication bus (18) interconnects various other components of the computing device (12), including the processor (14) and computer-readable storage media (16).

[0153] The computing device (12) may also include one or more input / output interfaces (22) that provide interfaces for one or more input / output devices (24) and one or more network communication interfaces (26). The input / output interfaces (22) and the network communication interfaces (26) are connected to the communication bus (18). The input / output devices (24) may be connected to other components of the computing device (12) via the input / output interfaces (22). Exemplary input / output devices (24) may include input devices such as pointing devices (such as a mouse or a trackpad), a keyboard, a touch input device (such as a touchpad or a touchscreen), a voice or sound input device, various types of sensor devices and / or photographing devices, and / or output devices such as display devices, printers, speakers and / or network cards. The exemplary input / output devices (24) may be included within the computing device (12) as a component constituting the computing device (12), or may be connected to the computing device (12) as a separate device distinct from the computing device (12).

[0154] The disclosed embodiments may be implemented in the form of a recording medium storing computer-executable instructions. The instructions may be stored in the form of program code, and when executed by a processor, may generate program modules to perform the operations of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

[0155] While representative embodiments of the present invention have been described in detail above, those skilled in the art will appreciate that various modifications to the above-described embodiments are possible without departing from the scope of the present invention. Therefore, the scope of the present invention should not be limited to the described embodiments, but should be defined not only by the claims set forth below but also by equivalents thereof.

Claims

1. Air purifier; A plurality of particle counters that predict abnormal phenomena based on the particle-based cleanliness status and the cleaning process capability status of the air cleaning device at each cleanliness measurement reference time within the clean area according to cleanliness control prediction conditions, and automatically control the operation of the air cleaning device according to the prediction result, and analyze the cleanliness control result and the control history of the air cleaning device to calculate the optimal automatic control condition value for target cleanliness control and reflect it when predicting the abnormal phenomenon; and The cleanliness control results collected from each of the plurality of particle counters, the control history of the air purifier, and the cleanliness status are integrated and monitored, and an integrated monitoring server is included for readjusting the cleanliness automatic control conditions in the entire clean area and controlling the cleanliness status and the status of the air purifier. A monitoring system in which the above plurality of particle counters additionally consider a new target cleanliness level to calculate the above automatic adjustment optimal condition value and reflect it when predicting and controlling the above abnormal phenomenon.

2. In claim 1, The above particle counter is, A monitoring system that predicts and controls the abnormal phenomenon by multiplying the particle measurement value of the cleanliness state by a preset multiple, or by adding it for a preset period of time, or by adding a change in particles smaller than the cleanliness standard to a condition when predicting the above abnormal phenomenon.

3. In claim 1, The above particle counter is, A monitoring system that predicts and controls the abnormal phenomenon by reflecting the state of the purification process capability for each output of the air purifier device to the automatic control optimal condition value to automatically adjust the state of the cleanliness so that the target cleanliness and the state of the cleanliness match preset operating cleanliness conditions when controlling the air purifier device.

4. In claim 1, The above particle counter is, A monitoring system that obtains a constant particle-based cleanliness status for at least two or more cleanliness measurement reference times, and predicts and controls the abnormal phenomenon for each of the constant particle-based cleanliness statuses for the at least two or more cleanliness measurement reference times.

5. In claim 1, The above particle counter is, The cleanliness status is identified for each area within the above clean area, and the operation of the air cleaning device is controlled based on the identified cleanliness status and the cleanliness control prediction conditions set for each area. A monitoring system, wherein the air purification device comprises at least one of an equipment fan filter unit (EFU), an air purification controller, and a fan filter unit (FFU).

6. In claim 1, The above particle counter is, A monitoring system that presets an operational cleanliness level for operating the cleanliness (class) within the above clean area at a set level of cleanliness compared to the target cleanliness, and sets the output control level of the air cleaning device according to the range of measured cleanliness when the measured cleanliness status matches the above operating cleanliness level.

7. In claim 1, The above particle counter is a clean area A monitoring system that detects the cleanliness status inside a clean room, clean booth or clean equipment, and operates the air cleaning device according to the detected cleanliness status, thereby automatically controlling the cleanliness inside the clean room, clean booth or clean equipment according to the cleanliness control prediction conditions.

8. In claim 1, Further comprising an air cleaning controller, which is installed to be connected to each of the air cleaning device and the particle counter to control the operation of the air cleaning device, A monitoring system in which the particle counter is connected to the integrated monitoring server for communication, and transmits and receives the cleanliness status, the cleanliness control result, and the air purification device status with the integrated monitoring server.

9. A particle measuring unit for obtaining a particle-based cleanliness status measured within a clean area; A cleanliness prediction maintenance unit that predicts an abnormal phenomenon based on the acquired cleanliness status and the cleaning process capability status according to the cleanliness control prediction conditions and automatically controls the operation of the air cleaning device according to the prediction result, and analyzes the cleanliness control result and the control history of the air cleaning device to calculate the optimal automatic control condition value for target cleanliness control and reflects it when predicting the abnormal phenomenon; and A particle counter comprising a purifier control unit that automatically controls the operation of the air purifier according to the prediction result.

10. In claim 9, The above cleanliness prediction and preservation department is, A particle counter that predicts the above abnormal phenomenon by multiplying the particle measurement value of the cleanliness state by a preset multiple, or by summing them up for a preset period of time, or by adding a change in particles smaller than the cleanliness standard to a condition.

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