Odor gas measuring device equipped with a chronological change prediction unit of an odor sensor

Abstract

The present invention aims to provide a real-time measurement value correction technology that enables accurate measurement despite the aging changes of environmental measurement sensors used in conventional environmental measurement. While such technologies have existed in the past, they reflected these aging changes through periodic corrections or periodic inspections. The present invention aims to provide a technology that reflects the performance degradation resulting from the use of such sensors in real time. To this end, the present invention comprises N electrochemical or semiconductor odor gas sensors; and an FTIR gas measuring device capable of measuring an optical wavelength band in the range of 4000 to 600 cm⁻¹; The FTIR odor gas sensor, comprising an odor sensor output function section that generates an output value of the odor sensor from the FTIR gas measurement result by utilizing the measurement values ​​of the odor gas sensor and the FTIR gas measuring device to calculate the gas concentration output value measured by the odor gas sensor using the sample gas concentration measured by the odor gas sensor and the light attenuation value due to light absorption that changes differently depending on the sample gas concentration in the optical wavelength band of 4000–600 cm⁻¹ measured by the FTIR gas measuring device, measures the aging change of the odor gas sensor together with the N odor gas sensors, and constructs an aging change prediction device for the odor gas sensor using this, inputs the output values ​​of the N electrochemical or semiconductor odor gas sensors into the aging change prediction device for the odor gas sensor to predict the aging change of the odor gas sensor, and corrects the output value of the odor gas sensor by the magnitude of the predicted value to determine the odor gas concentration A precision odor gas measuring device is provided, equipped with a prediction unit for the aging change of an odor gas sensor that provides a measurement value. The invention provides a technology that enables accurate environmental measurement throughout the entire usage period of an odor gas sensor for measuring the environment through the configuration of the invention as described above, and has the effect of enabling uninterrupted environmental measurement by indicating the usable period of the sensor based on the sensor's output value.

Description

Technology Field

[0001] The present invention relates to a technology for correcting the measurement value of an environmental sensor in real time by accurately predicting the aging change resulting from the use of the sensor. More specifically, it relates to a technology for correcting the decrease in the output value resulting from the use of the sensor in real time. Background Technology

[0002] Prior art prior to the filing of the present invention discloses a composite gas detection system for detecting odors and harmful gases. This technology includes: a sensing unit comprising a first sensor and a second sensor that output different sensitivities according to the concentration of the collected gas; a concentration calculation unit configured to calculate the gas concentration by applying the sensitivity output by the sensing unit to a concentration calculation formula; and a concentration output unit configured to output the gas concentration calculated by the concentration calculation unit. The concentration output unit is configured to compare the gas concentration calculated by the concentration calculation unit with a preset first concentration and output the gas concentration calculated based on the sensitivity output by any one of the sensors included in the sensing unit.

[0003] Another prior art discloses a temperature-controlled gas detection sensor calibration device. This technology includes: a main body device that detects the external and internal temperatures of a housing, respectively, and controls the internal temperature of the housing; a calibration device that calculates a setting value for controlling the sensitivity of the sensor according to input information transmitted from the outside while the external and internal temperatures of the housing are maintained within a reference range; and a sensor device that detects the temperature and concentration of the odorous gas for a reference time according to the setting value.

[0004] In addition, the paper "Development of Gas Sensor Based on a Light Intensity Analysis of a Webcam Image: Application to Gas Uptake and Diffusivity Measurements in Gas-Charged Polymer Materials" describes the development of a gas sensor designed to quantitatively analyze gas concentration and diffusivity by monitoring changes in water levels within a sealed graduated cylinder containing a gas-charged sample and water. According to this paper, when gas is emitted from the sample, a bright crescent-shaped boundary line forms at the interface between the water and the emitted gas; this is recorded over time using a digital webcam, and the technology enables precise measurement of gas concentration and diffusivity by analyzing changes in pixel brightness over time within the captured image. Prior art literature

[0005] Registered Patent Publication 10-2474878 B1 Registered Patent Publication 10-2021-0155957 A

[0006] Volume 41 Issue 2_1 / Pages.277-289 / 2025 / 1225-6161(pISSN) / 2287-9307(eISSN) Korean Journal of Remote Sensing The problem to be solved

[0007] The present invention aims to provide a real-time measurement value correction technology that enables accurate measurement despite the aging changes of environmental measurement sensors used in conventional environmental measurement. While such technologies have existed in the past, they reflected these aging changes through periodic corrections or periodic inspections. The present invention aims to provide a technology that reflects the performance degradation resulting from the use of such sensors in real time.

[0008] First, let's examine the sensors used for environmental measurement. Environmental measurement sensors include those used for air, water quality, and chimneys. Depending on the measurement target, they include sensors that measure particles such as dust and fine dust, as well as sensors that measure gases such as odors, NOx, SOX, hydrogen sulfide, and ammonia.

[0009] Sensors for such measurements include particulate matter measuring instruments, beta-ray particulate matter measuring instruments, electrochemical sensors, semiconductor sensors, non-dispersive infrared gas sensors, and FTIR sensors for measuring odors and gases.

[0010] Among these sensors, those measuring odors or gases tend to show a decrease in output for the same concentration as exposure time to the odor or gas elapses. Although they are used after periodic calibration, there has been a problem where the difference in measurement values ​​becomes significant as the calibration cycle approaches. The present invention aims to provide a technology that accurately outputs the sensor's output signal by reflecting the aging changes resulting from the sensor's use in real time. means of solving the problem

[0011] The means for solving the problem to solve the above-mentioned problem of the present application invention is as follows.

[0012] N electrochemical or semiconductor odor gas sensors; and

[0013] 4000~600 cm -FTIR gas measuring device capable of measuring an optical wavelength band of the ¹ range; and

[0014] Using the measurement values ​​of the above odor gas sensor and the above FTIR gas measuring device, the sample gas concentration measured by the above odor gas sensor, and

[0015] 4000~600 cm measured by the above FTIR gas measuring device - Using the light attenuation value due to light absorption that changes differently depending on the sample gas concentration in the optical wavelength band of the ¹ range,

[0016] An odor sensor output function is generated to calculate the gas concentration output value measured by the odor gas sensor from the measurement result of the above FTIR gas measuring device,

[0017] The present invention provides an FTIR odor gas sensor characterized by having an FTIR odor sensor output function that generates an output value of the odor sensor from the above FTIR gas measurement result.

[0018] In addition, the present invention provides an FTIR odor gas sensor characterized by further comprising a Mass Flow Controller (MFC) that controls the sample gas at a constant speed and supplies it to the FTIR gas measuring device and the odor gas sensor.

[0019] In addition, the present invention provides an odor gas sensor aging change measuring device characterized by including the construction of a general aging change test DB (Data Base) for measuring the aging change of an odor gas sensor over time by continuously supplying a gas with a concentration corresponding to 80% of the measurable odor concentration of the odor gas sensor to the odor gas sensor and the FTIR odor gas sensor, and comparing the values ​​measured by the odor gas sensor and the FTIR odor gas sensor.

[0020] In addition, the present invention provides an odor gas sensor aging change measuring device characterized by including the construction of an accelerated aging test DB (Data Base) for measuring the aging change of an odor gas sensor over time by continuously supplying a gas with a concentration corresponding to 100% of the measurable odor concentration of the odor gas sensor to the odor gas sensor and the FTIR odor gas sensor, and comparing the values ​​measured by the odor gas sensor and the FTIR odor gas sensor.

[0021] In addition, the above-mentioned general test DB for aging change and the above-mentioned accelerated test DB for aging change include the cumulative measurement time of the odor gas sensor, the output value of the odor gas sensor after 120 seconds of measurement and 180 seconds of purging, the time for the output of the odor gas sensor to reach the concentration of the sample gas after starting measurement at the odor gas sensor, and the number of measurements of the sensor, thereby providing an aging change measuring device for an odor gas sensor.

[0022] In addition, the present invention provides an odor gas sensor aging change prediction device comprising an odor gas usage aging change prediction function that calculates the degree of degradation of the output value of the odor gas sensor according to the exposure time to the odor gas, using the aging change general test DB and the aging change accelerated test DB measured by the odor gas sensor aging change measuring device as inputs.

[0023] In addition, the aging change prediction device of the odor gas sensor is characterized by determining that the service life of the odor gas sensor has expired if the output value of the odor gas sensor does not become zero within a purging time of 180 seconds, and displaying the remaining service life of the odor gas sensor as the remaining usage time until the time at which the output value of the odor gas sensor becomes zero is 180 seconds or more.

[0024] In addition, the present invention provides a precision odor gas measuring device equipped with an odor gas sensor aging change prediction unit, characterized by inputting the output values ​​of the N electrochemical or semiconductor odor gas sensors into an odor gas sensor aging change prediction device to predict the odor gas sensor aging change, and correcting the output value of the odor gas sensor by the magnitude of the predicted value to provide an odor gas concentration measurement value.

[0025] In addition, the present invention provides a precision odor gas measuring device having an aging change prediction unit for an odor gas sensor, characterized by further providing an additional 3-way valve between the sample gas input unit and the MFC, and further providing a calibration gas cylinder to block the input of the sample gas at regular intervals and to read the measurement value of the odor gas sensor using the gas from the calibration gas cylinder as an input to predict the lifespan of the odor gas sensor.

[0026] As another embodiment of the present invention,

[0027] N electrochemical or semiconductor odor gas sensors; and

[0028] FTIR gas measuring device capable of measuring an optical wavelength band in the range of 4000~600 cm⁻¹; and

[0029] Using deep learning, a type of artificial intelligence based on learning with the measurement values ​​of the above odor gas sensor and the above FTIR gas measuring device as input,

[0030] The present invention provides an FTIR odor gas sensor characterized by having an odor gas sensor output artificial intelligence inference engine that generates a gas concentration output value measured by the odor gas sensor from the measurement result of the FTIR gas measuring device.

[0031] In addition, the present invention provides an FTIR odor gas sensor characterized by further comprising a Mass Flow Controller (MFC) that controls the sample gas at a constant speed and supplies it to the FTIR gas measuring device and the odor gas sensor.

[0032] In addition, the present invention provides an odor gas sensor aging change measuring device characterized by including the construction of a general aging change test DB (Data Base) for measuring the aging change of an odor gas sensor over time by continuously supplying a gas with a concentration corresponding to 80% of the measurable odor concentration of the odor gas sensor to the odor gas sensor and the FTIR odor gas sensor, and comparing the values ​​measured by the odor gas sensor and the FTIR odor gas sensor.

[0033] In addition, the present invention provides an odor gas sensor aging change measuring device characterized by including the construction of an accelerated aging test DB (Data Base) for measuring the aging change of an odor gas sensor over time by continuously supplying a gas with a concentration corresponding to 100% of the measurable odor concentration of the odor gas sensor to the odor gas sensor and the FTIR odor gas sensor, and comparing the values ​​measured by the odor gas sensor and the FTIR odor gas sensor.

[0034] In addition, the above-mentioned general test DB for aging change and the above-mentioned accelerated test DB for aging change include the cumulative measurement time of the odor gas sensor, the output value of the odor gas sensor after 120 seconds of measurement and 180 seconds of purging, the time for the output of the odor gas sensor to reach the concentration of the sample gas after starting measurement at the odor gas sensor, and the number of measurements of the sensor, thereby providing an aging change measuring device for an odor gas sensor.

[0035] In addition, the present invention provides an odor gas sensor aging change prediction device characterized by including an artificial intelligence aging change prediction unit that predicts the degree of degradation of the output value of the odor gas sensor according to the exposure time of the odor gas sensor to the odor gas sensor by using deep learning, which is a type of artificial intelligence, as input to the aging change general test DB and the aging change accelerated test DB measured by the aging change measuring device of the odor gas sensor.

[0036] In addition, the aging change prediction device of the odor gas sensor is characterized by determining that the service life of the odor gas sensor has expired if the output value of the odor gas sensor does not become zero within a purging time of 180 seconds, and displaying the remaining service life of the odor gas sensor as the remaining usage time until the time at which the output value of the odor gas sensor becomes zero is 180 seconds or more.

[0037] In addition, the present invention provides a precision odor gas measuring device equipped with an artificial intelligence aging change prediction unit for an odor gas sensor, characterized by inputting the output values ​​of the N electrochemical or semiconductor odor gas sensors into an aging change prediction device for the odor gas sensor to predict the aging change of the odor gas sensor, and correcting the output value of the odor gas sensor by the magnitude of the predicted value to provide a measurement value of the odor gas concentration.

[0038] In addition, the present invention provides a precision odor gas measuring device having an aging change prediction unit for an odor gas sensor, characterized by further providing an additional 3-way valve between the sample gas input unit and the MFC, and further providing a calibration gas cylinder to block the input of the sample gas at regular intervals and to read the measurement value of the odor gas sensor using the gas from the calibration gas cylinder as an input to predict the lifespan of the odor gas sensor.

[0039] As another embodiment of the present invention,

[0040] N electrochemical or semiconductor odor gas sensors; and

[0041] 4000~600 cm - FTIR gas measuring device capable of measuring an optical wavelength band of the ¹ range; and

[0042] By utilizing deep learning, a type of artificial intelligence based on learning using the measurement values ​​of the above odor gas sensor and the above FTIR gas measuring device as input, two or more optical wavelengths are selected to represent the measurement values ​​of the above odor gas sensor to configure a non-dispersive infrared gas measuring unit and an artificial intelligence odor sensor concentration inference unit.

[0043] The present invention provides a non-dispersive infrared (NDIR) odor gas sensor characterized by generating a gas concentration output value measured by the odor gas sensor using the non-dispersive infrared gas measuring unit and the artificial intelligence odor sensor concentration inference unit.

[0044] In addition, the present invention provides an FTIR odor gas sensor characterized by further comprising a Mass Flow Controller (MFC) that controls the sample gas at a constant speed and supplies it to the non-dispersive infrared (NDIR) odor gas sensor and the odor gas sensor.

[0045] In addition, the present invention provides an odor gas sensor aging change measuring device characterized by including the construction of a general aging change test DB (Data Base) for measuring the aging change of an odor gas sensor over time by continuously supplying a gas with a concentration corresponding to 80% of the measurable odor concentration of the odor gas sensor to the odor gas sensor and the non-dispersive infrared (NDIR) odor gas sensor, and comparing the values ​​measured by the odor gas sensor and the non-dispersive infrared (NDIR) odor gas sensor.

[0046] In addition, the present invention provides an aging change measuring device for an odor gas sensor characterized by including the construction of an accelerated aging test DB (Data Base) for measuring the aging change of the odor gas sensor over time by continuously supplying a gas with a concentration corresponding to 100% of the measurable odor concentration of the odor gas sensor to the odor gas sensor and the non-dispersive infrared (NDIR) odor gas sensor, and comparing the values ​​measured by the odor gas sensor and the non-dispersive infrared (NDIR) odor gas sensor.

[0047] In addition, the above-mentioned general test DB for aging change and the above-mentioned accelerated test DB for aging change include the cumulative measurement time of the odor gas sensor, the output value of the odor gas sensor after 120 seconds of measurement and 180 seconds of purging, the time for the output of the odor gas sensor to reach the concentration of the sample gas after starting measurement at the odor gas sensor, and the number of measurements of the sensor, thereby providing an aging change measuring device for an odor gas sensor.

[0048] In addition, the present invention provides an odor gas sensor aging change prediction device characterized by including an artificial intelligence aging change prediction unit that predicts the degree of degradation of the output value of the odor gas sensor according to the exposure time of the odor gas sensor to the odor gas sensor by using deep learning, which is a type of artificial intelligence, as input to the aging change general test DB and the aging change accelerated test DB measured by the aging change measuring device of the odor gas sensor.

[0049] In addition, the aging change prediction device of the odor gas sensor is characterized by determining that the service life of the odor gas sensor has expired if the output value of the odor gas sensor does not become zero within a purging time of 180 seconds, and displaying the remaining service life of the odor gas sensor as the remaining usage time until the time at which the output value of the odor gas sensor becomes zero is 180 seconds or more.

[0050] In addition, the present invention provides a precision odor gas measuring device equipped with an artificial intelligence aging change prediction unit for an odor gas sensor, characterized by inputting the output values ​​of the N electrochemical or semiconductor odor gas sensors into an aging change prediction device for the odor gas sensor to predict the aging change of the odor gas sensor, and correcting the output value of the odor gas sensor by the magnitude of the predicted value to provide a measurement value of the odor gas concentration.

[0051] In addition, the present invention provides a precision odor gas measuring device having an aging change prediction unit for an odor gas sensor, characterized by further providing an additional 3-way valve between the sample gas input unit and the MFC, and further providing a calibration gas cylinder to block the input of the sample gas at regular intervals and to read the measurement value of the odor gas sensor using the gas from the calibration gas cylinder as an input to predict the lifespan of the odor gas sensor. Effects of the invention

[0052] The present invention provides a technology that enables accurate environmental measurement throughout the entire usage period of an environmental measuring sensor through the configuration of the invention as described above, and has the effect of enabling uninterrupted environmental measurement by indicating the usable period of the sensor based on the sensor's output value. Brief explanation of the drawing

[0053] Figure 1 is a graph comparing sensor age change for predicting age change according to the present invention. Figure 2 is a comparison table of sensor age changes for predicting age changes according to the present invention. FIG. 3 is an explanatory diagram illustrating the concept of a device for measuring the measurement value of an odor sensor corresponding to an odor sensor with no aging change, for measuring the aging change of the odor sensor of the present invention. Figure 4 is a conceptual diagram of a device for constructing a general test DB to measure aging changes at 80% of the maximum measurement range of the odor sensor of the present invention. FIG. 5 is a conceptual diagram of an accelerated test DB construction device for measuring aging changes at 100% of the maximum measurement range of the odor sensor of the present invention to measure aging changes of the odor sensor. FIG. 6 is a conceptual diagram of an aging change prediction device for predicting the aging change of the odor sensor of the present invention. FIG. 7 is a conceptual diagram of an aging change correction device that generates an output value of a sensor by correcting the aging change of the odor sensor in real time in an odor measuring device using the odor sensor of the present invention. FIG. 8 is a conceptual diagram of the development of an AI inference engine for FTIR gas measurement results using artificial intelligence and an FTIR gas sensor as another embodiment of the present invention. FIG. 9 is a conceptual diagram of a general test of aging change of an odor sensor using artificial intelligence and an FTIR gas sensor as another embodiment of the present invention. FIG. 10 is a conceptual diagram of an accelerated aging test of an odor sensor using artificial intelligence and an FTIR gas sensor as another embodiment of the present invention. FIG. 11 is a conceptual diagram of an aging change inference device for an odor sensor using artificial intelligence and an FTIR gas sensor as another embodiment of the present invention. FIG. 12 is a detailed diagram of an aging change inference device for an odor sensor using artificial intelligence and an FTIR gas sensor as another embodiment of the present invention. FIG. 13 is a configuration diagram of an odor measurement system equipped with an aging change correction device for an odor sensor using artificial intelligence and an FTIR gas sensor as another embodiment of the present invention. FIG. 14 is a configuration diagram of an odor measurement system equipped with an additional correction device for aging changes using a calibration gas of an odor sensor using artificial intelligence and an FTIR gas sensor as another embodiment of the present invention. FIG. 15 is a configuration diagram showing the use of FTIR gas measurement results for NDIR wavelength selection in an odor measurement system using artificial intelligence and an NDIR gas sensor as another embodiment of the present invention. FIG. 16 is a configuration diagram of a general test for the aging change of an odor sensor using an odor measurement system utilizing artificial intelligence and an NDIR gas sensor as another embodiment of the present invention. FIG. 17 is a configuration diagram of an accelerated aging test of an odor sensor using an odor measurement system utilizing artificial intelligence and an NDIR gas sensor as another embodiment of the present invention. FIG. 18 is a configuration diagram of an odor sensor aging change inference device using an odor measurement system using artificial intelligence and an NDIR gas sensor as another embodiment of the present invention. FIG. 19 is a configuration diagram of an odor sensor aging change correction device using an odor measurement system utilizing artificial intelligence and an NDIR gas sensor as another embodiment of the present invention. FIG. 20 is a configuration diagram of an odor measurement system equipped with an additional correction device for aging changes using an odor sensor calibration gas, using an odor measurement system utilizing artificial intelligence and an NDIR gas sensor as another embodiment of the present invention. Specific details for implementing the invention

[0054] Air quality is measured to maintain and manage the environment, and for this purpose, the government discloses the values ​​of SO2, NO, NO2, CO, O3, PM10, and PM2.5 on real-time air pollution disclosure systems such as Air Korea (https: / / www.airkorea.or.kr / web).

[0055] In addition, regarding water quality, real-time water pollution disclosure systems such as Water Quality TMS (https: / / www.soosiro.or.kr / index.do) disclose TOC (Total Organic Carbon), TP (Total Phosphorus), TN (Total Nitrogen), pH (acidity and alkalinity of water), and SS (Suspended Solids).

[0056] To explain in detail, TOC (Total Organic Carbon) refers to the total amount of organic carbon compounds dissolved in water. It is used as an indicator of the degree of organic pollution, along with BOD (Biochemical Oxygen Demand) and COD (Chemical Oxygen Demand).

[0057] TP (Total Phosphorus): Refers to the total amount of phosphorus dissolved in water. Phosphorus is a major nutrient that affects the growth of algae, and excessive phosphorus causes eutrophication, which exacerbates water pollution.

[0058] TN (Total Nitrogen): Refers to the total amount of nitrogen dissolved in water. Nitrogen is also a nutrient that affects algal growth, and excessive nitrogen causes eutrophication.

[0059] pH: An indicator of the acidity or alkalinity of a solution. pH ranges from 0 to 14, where 7 is neutral, lower than 7 is acidic, and higher than 7 is alkaline.

[0060] SS (Suspended Solids): Refers to particulate matter floating in water. The higher the SS content, the cloudier the water becomes and the lower the sunlight transmittance, which has an adverse effect on the aquatic ecosystem.

[0061] To explain the relationship between each item and water quality, TOC, TP, and TN values ​​are major causative substances that induce eutrophication in water and are important indicators for evaluating the health of aquatic ecosystems.

[0062] pH indicates the acidity and alkalinity of water and affects the survival of aquatic organisms.

[0063] SS is related to water transparency and is directly linked to the health of the aquatic ecosystem.

[0064] The Water Quality TMS (Tele-Monitoring System) is a system that measures and monitors the discharge of water pollutants in real time. The Water Quality TMS system installs automatic water quality measuring instruments at the final discharge point of a business site to measure BOD, COD, TOC, SS, TN, TP, pH, flow rate, etc., and monitors this 24 hours a day from a control center.

[0065] Chimney TMS systems are also installed and managed on chimneys at industrial sites, just like air and water quality. https: / / www.cleansys.or.kr / index.do It provides data by equipping itself with a real-time disclosure system for chimney pollution levels.

[0066] In the real-time air, water quality, and chimney TMS systems described above, environmental data is collected and provided using environmental measurement sensors equipped with IoT environmental measurement technology.

[0067] The present invention aims to provide a technology capable of reflecting in real time the aging changes resulting from the use of a sensor related to the IoT environment measurement technology.

[0068] To this end, the environmental measurement sensors are as follows.

[0069] 1. Sensor Sensitivity

[0070] If the magnitude of the output signal according to the concentration of the object being measured by the sensor is linear, the slope at this point is the sensitivity of the sensor. If the slope is large, the sensitivity is excellent, and if the slope is small, the sensor is said to have low sensitivity.

[0071] If the sensor's output signal is nonlinear compared to the input concentration, the sensitivity varies depending on the measured concentration value, and the derivative at that concentration becomes the sensitivity.

[0072] 2. Sensor operating range (operating range or span)

[0073] The measurement range refers to the range between the lower and upper limits of the concentration of the target that the sensor can measure. The span is expressed as a number representing the difference between the upper and lower limits. This value is usually expressed in decibels, which are the logarithmic level of power or force. The definition of decibels for power is the form of multiplying the logarithmic ratio of power by 10.

[0074] The aforementioned decibel method is used when there is a significant difference between the minimum and maximum output values, such as with acoustic sensors. However, in cases where the maximum and minimum outputs do not differ significantly, such as with temperature or flow sensors, the span, which is the difference between the maximum and minimum as shown in the figure below, is used.

[0075] 3. Noise

[0076] Noise refers to unwanted irregular signals in measurement, and is an irregularly occurring signal generated from sensor elements, conversion circuits, and power circuits.

[0077] The cause is fluctuating noise, and if it occurs randomly, it may not be possible to eliminate it in principle. Since noise manifests in the output characteristics through various paths, if the response (△y) to the sensor's input change (△x) falls below the noise level, the sensor's measurement value cannot be distinguished from the noise. Furthermore, if the noise entering the sensor increases, even with high sensitivity, the detection of minute input signals becomes impossible, leading to a higher lower limit of measurement. Filters are sometimes used to mitigate this noise. In this way, the lower limit of detection can be improved by enhancing the sensor's signal-to-noise ratio (S / N ratio).

[0078] 4. Sensor Resolution

[0079] Resolution is defined as the smallest input increment that a sensor can detect. In the figure below, as △x decreases, △y also decreases; the △x at which △y is distinguishable from noise is called the resolution. Generally, the smaller the resolution, the better the sensor.

[0080] 5. Sensor Repeatability and Reproducibility

[0081] Repeatability is defined as the change in the sensor's output value when the same sample is measured continuously over a short period under the same conditions and using the same method. The smaller the variation in the sensor's measurement value, the better the repeatability.

[0082] Reproducibility refers to the degree of variation in sensor measurements when the same sample is measured over a long period using the same method, or when different people conduct experiments in different locations.

[0083] Reproducibility is considered excellent if the variation in output values ​​is consistently small. Reproducibility is defined as precision, which is the degree to which the standard deviation of the distribution of measurement values ​​obtained through repeated measurements is small. The smaller the standard deviation, the more precise the sensor is.

[0084] 6. Sensor Accuracy

[0085] Accuracy is the ability to obtain the same value when measured under the same conditions. It is also the error between the indicated value measured by the sensor and the true value.

[0086] The accuracy of a sensor is indicated by the error, and the smaller the error, the higher the accuracy of the sensor.

[0087] 7. Sensor calibration

[0088] Sensor calibration is the process of inputting a sample of a known concentration into the sensor and measuring the output value. The input value used for sensor calibration is called the standard concentration value, and when a function between the input concentration and the output value is generated through calibration, this function is called the calibration function. Ideally, the calibration function should be linear, but it may not be linear; therefore, it is used within the range where it is as linear as possible.

[0089] 8. Sensor response time

[0090] The response time of a sensor corresponds to its temporal characteristic. It refers to the time response characteristic from when the concentration of a sample input to the sensor is changed until that change appears in the output.

[0091] A step function is applied to the input to define the measurement rising time, and in many cases, the time it takes to reach 90% is defined as the response time, or it is defined as the time constant, which is the period it takes to reach 63.2%.

[0092] 9. Frequency Response Characteristics of the Sensor

[0093] It refers to the frequency characteristic at which a sensor can respond when the input is varied sinusoidally (sine or cosine). Generally, the frequency characteristic at which the sensor operates is defined as the range up to the frequency at which the output drops by -3dB relative to the input frequency.

[0094] 10. Sensor output impedance

[0095] When connecting a sensor to an electronic circuit, the relationship between the sensor's output impedance and the circuit's input impedance is important. When the sensor's output is a voltage, the sensor's output impedance is connected in series with the circuit's input impedance Zi; in this case, to minimize distortion of the sensor's output signal, the sensor's output impedance must be smaller than the circuit's input impedance.

[0096] On the other hand, for a current sensor whose output is current, the sensor's output is connected in parallel to the electronic circuit, and in this case, the input impedance of the electronic circuit must be smaller than the output impedance of the sensor.

[0097] 11. Influence of the sensor measurement environment

[0098] External variables that affect the performance of a sensor, such as temperature, humidity, light conditions, and mechanical vibration, are called environmental variables. Changes in sensitivity or output levels caused by environmental factors are called drift. In most cases, temperature and humidity affect almost all sensors. The output characteristics of a sensor due to environmental influence can be considered as cases where the sensor's sensitivity changes, cases where an offset occurs in the sensor's output value, and cases where both sensitivity and offset occur simultaneously; such fluctuations in the sensor's output characteristics due to environmental influence can be accurately measured through the calibration process described above.

[0099] Electrochemical and semiconductor sensors are widely used for environmental measurement. This paper examines their operating principles and provides a method for calibration utilizing these characteristics.

[0100] Voltametry: A compound word of voltmetry and amperometry, it is a method of measuring the potential difference between two electrodes placed in a solution.

[0101] Electrical conductivity measurement: This is a method of measuring the ion concentration in a solution by passing alternating current through an electrode and utilizing the characteristic that the magnitude of the alternating current is proportional to the ion concentration in the solution.

[0102] Potential difference measurement: This is a method of measurement that utilizes the phenomenon where the potential difference between electrodes changes according to the concentration of the solution using electrodes such as silver-silver chloride electrodes.

[0103] Potential difference measurement: A method that uses two electrodes and an additional reference electrode.

[0104] Semiconductor gas sensor: A sensor that utilizes changes in the density of conduction electrons on a semiconductor surface caused by chemical interactions between air components and the semiconductor surface.

[0105] - Pre-adsorption step of oxygen on the semiconductor surface

[0106] - Adsorption stage of the target gas

[0107] Oxidation reaction stage of target gas and oxygen

[0108] Desorption step of the target gas

[0109] The concentration of the target gas in the sample gas is measured while repeating the process.

[0110] Specifically, the exchange of electrons occurs during the oxidation reaction between the target gas and oxygen, and this is measured. The electrical conductivity of the semiconductor sensor decreases when the adsorbed air particles act as acceptors and increases when they act as donors.

[0111] Most semiconductor sensors utilize changes in electrical conductivity that occur when a gas comes into contact with the semiconductor surface; they operate by heating, and metal-ceramic oxides that are stable at high temperatures are used. Among these, it becomes an n-type semiconductor when metal atoms are in excess (oxygen deficiency), and a p-type semiconductor when metal atoms are deficient.

[0112] These semiconductor gas sensors are primarily used to measure toxic and flammable gases; however, semiconductor gas sensors with excellent selectivity capable of selectively measuring only the target gas are very rare. Therefore, they are used as sensors that respond to a wide range of flammable and toxic gases, such as combustible and toxic gases. Metal oxides used in semiconductor gas sensors include SnO2, ZnO, and Fe2O3, with SnO2 being the most widely used.

[0113] The semiconductor properties of SnO2 ceramics arise from crystal imperfections. The causes of crystal imperfections include extra atoms being interstitial, atoms forming a periodic scale being absent at some locations (vacancy), and atoms having a different valence from the constituent atoms being substituted at some locations (substitutial).

[0114] SnO2 ceramics used in semiconductor sensors correspond to cases where atoms forming a periodic scale are absent at certain positions (vacancy), and are in the form of oxygen vacancies (Vo) where oxygen atoms are lacking. When thermal energy is applied from the outside, electrons in oxygen vacancies, acting as electron donors, move to the conduction band and act as carriers, exhibiting n-type semiconductor characteristics. The operating temperature of ceramic semiconductors acts as a factor that changes sensor characteristics because it alters electrical conductivity by changing the number and mobility of carriers moving from the donor level to the conduction band, and also changes gas adsorption.

[0115] Ceramic semiconductor gas sensors utilize the reaction between a solid surface and a gas; the rate of gas adsorption and the selectivity of the adsorbed gas are influenced not only by the sensor's operating temperature but also by the catalyst component, its quantity, and the surrounding environment. When thermal energy is supplied to SnO2 crystals, the number of free electrons increases. When oxygen gas is adsorbed, these free electrons are captured by the oxygen gas on the particle surface. Consequently, a potential barrier forms at the SnO2 particle interface, lowering the electrical conductivity between particles. Since reducing or combustible gases oxidize upon contact with oxygen, their presence removes the oxygen adsorbed on the SnO2 surface. Consequently, the free electrons captured by the oxygen gas enter the SnO2 particles, lowering the potential barrier and increasing the electrical conductivity between particles. Ultimately, the amount of oxygen adsorbed and desorbed determines the sensor's sensitivity. Fundamentally, to maximize oxygen adsorption, the specific surface area of ​​the SnO2 powder must be large, and the temperature must be raised to the point where oxygen adsorption is maximized. The adsorption temperature of each gas varies depending on the SnO2 temperature. At temperatures below 100°C, CO is adsorbed in the order of hydrogen, alcohol, isobutane, oxygen, and methane as the temperature rises.

[0116] In this case, the use of a catalyst accelerates the adsorption rate of these gases through its action and changes the adsorption temperature and amount; consequently, it increases the sensor's response speed and sensitivity at the same operating temperature and imparts gas selectivity. The most commonly used catalysts are Pd or PdO; a small amount of catalyst results in high sensitivity to alcohol, while a large amount results in high sensitivity to methane gas. Furthermore, a low sensor temperature leads to high sensitivity to hydrogen and alcohol, but low sensitivity to methane. Conversely, a high temperature increases sensitivity to methane. By adjusting the sensor's heater resistance or controlling the voltage applied to the heater according to the target gas, appropriate gases can be selected to enhance measurement sensitivity. To improve the sensor's lifespan and reliability, other additives are sometimes mixed with the Pd catalyst.

[0117] Semiconductor gas sensors are classified into sintered and thin-film types based on their shape, and into direct heating and indirect heating types based on the heating method. Although semiconductor gas sensors come in various shapes formed by mixing precious metal catalysts with various ceramic powders, most are of the sintered type.

[0118] Platinum wire with a diameter of 0.1 mm is used as the electrode and heating type heater, and the electrode and heater are embedded during molding. The gas detection paste used in the sintered gas sensor is made by mixing 0.5 to 5 wt% of precious metal catalysts such as Pt and Pd with SnO2 powder, which is the main raw material, and adding alumina silica, water, and a small amount of binder.

[0119] There are thin-film gas sensors in the form of other semiconductor gases. Thin-film gas sensors are fabricated using sputtering or vacuum deposition methods. Alumina is used as the substrate, and the electrodes are typically made by depositing Au at intervals ranging from 0.5 mm to several mm. As for the materials used to detect gas, for example, in the case of SnO2, there are methods of fabricating it by depositing metallic Sn and then oxidizing it to form SnO2, or by using organotin.

[0120] Generally, gas sensors utilizing physical or chemical adsorption, desorption, or chemical reactions often operate at high temperatures, leading to significant degradation of their characteristics over time. Since SnO2 ceramics do not sinter well compared to other materials, grain boundary growth hardly occurs even under high-temperature conditions during prolonged use. This is precisely why SnO2 gas sensors have a longer lifespan and higher reliability than gas sensors made from other materials. However, the catalyst added to the sensor degrades during long-term use due to the influence of atmospheric moisture and other harmful gases, resulting in changes in sensor resistance and sensitivity. Generally, SnO2 gas sensors tend to show a decrease in resistance and an increase in sensitivity over time compared to their initial state.

[0121] The present invention aims to improve the measurement precision of sensors by reflecting changes in performance resulting from the use of the aforementioned sensors in real time, and also includes a function to calculate the sensor replacement cycle using this method and notify the user. Furthermore, it includes a function capable of diagnosing sensor failures, etc., based on changes in sensor measurement values.

[0122] Based on the functions of the invention described above, it is equipped with a data correction function that improves measurement accuracy, removes noise from data collected by the sensor, and corrects errors caused by various environmental conditions (temperature, humidity, etc.) to enhance measurement accuracy. Furthermore, it is further equipped with a function that detects the possibility of internal contamination of the measuring device, including the sensor, at an early stage when a sample with a measurement value outside the normal range is introduced based on past measurement data, and processes the device to prevent contamination by high-concentration samples.

[0123] In addition, the system is designed to enable more accurate measurements by fusing temperature and humidity measurement data with the odor sensor data, in addition to the odor sensor data. Furthermore, it is equipped with a self-diagnostic function that automatically detects abnormalities in individual sensors to maintain measurement accuracy. This self-diagnostic function analyzes measurement values ​​to predict signs of sensor malfunction and alerts the user, allowing for immediate response. Moreover, the function incorporates sensor replacement cycles and consumable management, enabling users and administrators to efficiently manage the measurement system. The self-diagnostic function also includes a sensor lifespan management feature to maximize sensor usage, ensuring that sensors can be utilized for as long as possible.

[0124] In the present invention, FTIR and / or NDIR gas measurement sensors were used to measure reference values ​​that do not undergo aging changes due to the use of the sensor. These technologies are described as follows.

[0125] An FTIR (Fourier Transform Infrared Spectroscopy) gas measurement sensor is a device that measures the type and concentration of a gas using infrared spectroscopy. By passing infrared light of a specific wavelength through a gas sample and measuring the amount of absorbed light, it identifies and quantifies the gas by analyzing the unique infrared absorption spectrum of the gas molecules. An FTIR gas measurement sensor consists of a light source, an interferometer, a gas cell, a detector, and a data processing unit. The light source generates infrared light, and the interferometer splits the light passing through the light source into two streams and combines them to create an interference pattern, which varies depending on the type of gas being measured.

[0126] The gas cell is a space through which the infrared light passes. The detector senses the interference pattern generated by the interferometer and converts it into an electrical signal. The data processing device is a device that converts the electrical signal obtained from the detector into an infrared spectrum by performing a Fourier transform, and calculates the type and concentration of the gas by analyzing it.

[0127] FTIR gas measuring devices can measure multiple gases simultaneously and, since they utilize the unique infrared absorption spectrum of gas molecules, can detect even trace amounts of gas.

[0128] An NDIR (Non-Dispersive Infrared) gas measuring device is a device that measures gas concentration using similar principles and scientific methods. An NDIR sensor measures gas concentration by utilizing the principle that a specific gas absorbs infrared light. Although it differs slightly from FTIR gas sensors, which use light of various wavelengths, it calculates the gas concentration by detecting the amount of light remaining after specific wavelengths of light are absorbed as light emitted from an infrared light source passes through the gas being measured.

[0129] FTIR sensors and NDIR sensors are characterized by high accuracy, stability, and long lifespan, and unlike semiconductor gas sensors or electrochemical gas sensors, they do not undergo aging changes. The present invention aims to provide a device that utilizes these characteristics to measure the aging changes of a semiconductor gas sensor or electrochemical gas sensor using an FTIR gas sensor or an NDIR gas sensor, and uses this to predict, correct, and output the aging changes of the semiconductor gas sensor or electrochemical gas sensor in real time.

[0130] The data used in the statistical method for compensating for the aging change of the sensor used in the present invention is the speed at which the output value corresponding to the measured concentration is reached based on the output of the odor gas sensor, the time for the sensor value to return to normal through purging, and the degree of decrease in the sensor output value as the exposure time to the gas increases.

[0131] Among various statistical models, the present invention developed a model for predicting the aging of a sensor using a drift modeling (aging change modeling) method. An exponential function model was used to model changes in the sensor output value, and the results of this modeling were utilized to correct for the decrease in the output value due to the sensor's aging.

[0132] The deep learning artificial intelligence training method used in the present invention is as follows. In order to predict aging and degradation by learning time-series data of a sensor, an artificial intelligence model capable of detecting gradual performance degradation or abnormal signs over time is required.

[0133] 1. Recurrent Neural Networks (RNN) family models

[0134] LSTM (Long Short-Term Memory) / GRU (Gated Recurrent Unit) models are suitable for processing time series data and identifying long-term dependencies.

[0135] Gradual changes in sensors can be captured in long-term data (e.g., predicting how sensor output has changed over several months / years).

[0136] 2. Temporal Convolutional Networks (TCN) family models

[0137] It is faster and more stable than RNNs. It processes sensor inputs from various time points in parallel and is useful for tracking aging changes.

[0138] 3. Autoencoder-based anomaly detection / aging estimation

[0139] It learns from normal-state sensor data and determines aging by comparing the input with the reconstruction error. It is a model that identifies sensor aging or performance degradation based on the increasing error.

[0140] In the present invention, the learning factors include the speed at which an output value corresponding to the measured concentration is reached based on the output of an odor gas sensor, the time for the sensor value to return to normal through purging, and the decrease in the sensor output value as the exposure time to the gas increases.

[0141] Although the above-mentioned model could be used, a Recurrent Neural Network (RNN) family model was used to predict the sensor's service life. To compensate for sensor aging, a Temporal Convolutional Network (TCN) model was used to process sensor inputs from various time points in parallel, and the correction value for the sensor output at the current measurement point was calculated.

[0142] In addition, the present invention provides a technology that allows the output value of an odor sensor to be corrected in real-time within the usable period so that it decreases as usage time increases, and notifies the user when the usage time of the sensor has elapsed.

[0143] The effects of the present invention are explained using the drawings as follows.

[0144] Figure 1 is a graph comparing sensor age change for predicting age change according to the present invention.

[0145] This graph illustrates the development of the technology of the present application invention, showing the measurement of odors corresponding to 80% of the total measurement range of a newly used odor gas sensor and an odor gas sensor used for 100 hours. The odor-causing gas measured in the above odor gas measurement graph is ammonia (NH3). The maximum measured concentration is 200 ppm, and the above graph is the result of measuring an ammonia gas concentration of 160 ppm. As usage time elapses, the time required for the measured concentration value to stop increasing increases. In other words, a measurement time delay (Tmd) occurs. There is an output value reduction (Merr) error where the output value of the sensor used for a longer period becomes smaller for the same sample gas concentration. Additionally, the preparation time required for the sensor to be ready for measurement again increases. The time required for the sensor to be exposed to ammonia gas and then purged so that the sensor's output value returns to zero and becomes ready for measurement again increases.

[0146] Figure 2 compares the characteristics of the odor sensor of the present invention as they change over time. When viewed from the graph, the measurement graph of the sensor used for a long time became smaller and extended further to the right. Looking at the table, it can be seen that the output decreases for the same concentration, with final concentration measurements of 0.8 and 0.77, and the time required for the sensor to be ready after purging increases from 80 seconds to 120 seconds. Additionally, it can be seen that the measurement time increases by 30 seconds, from 200 seconds to 230 seconds.

[0147] Air Korea (examined earlier) https: / / www.airkorea.or.kr / web Environmental measurements on the ) site are updated every 5 minutes. Therefore, if the time for the sensor to be ready to measure again due to measurement time and fuzzing exceeds 300 seconds, the sensor's measurement value is the aforementioned Air Korea ( https: / / www.airkorea.or.kr / web Cannot connect to the site. We intend to provide a technology that diagnoses the lifespan of an odor sensor using these usage conditions and independently determines whether the measurement value is accurate.

[0148] FIG. 3 is an explanatory diagram illustrating the concept of a device for measuring the measurement value of an odor sensor corresponding to an odor sensor that does not undergo aging change, for measuring the aging change of the odor sensor of the present invention.

[0149] The concentration of the sample gas is controlled by an MFC (a fluid flow control device that controls the concentration and supplies the gas), and the sample gas of the same concentration is supplied to the FTIR gas measuring device and the odor sensor of the present application. The odor sensor undergoes aging changes over time, whereas the FTIR sensor can maintain stable measurements without aging changes even as time passes, thanks to an internal calibration device, etc.

[0150] The process of generating an FTIR gas sensor function that matches the output value of each of the N odor sensors with the measurement result of the FTIR gas measuring device by supplying sample gases of various concentrations and comparing the output value of the FTIR gas measuring device with the output value of the odor sensor is illustrated.

[0151] FIG. 4 is a conceptual diagram of a device for constructing a general test DB to measure the aging change of the odor sensor of the present invention at a concentration of 80% of the maximum measurement range of the odor sensor. It illustrates a device for collecting data on the degradation of the odor sensor's output as usage time increases. The use of 80% concentration was prepared because the actual odor sensor operates at a concentration below 80%. Since the output value of the previously developed FTIR gas sensor function does not undergo aging change, while the odor sensor does, this is utilized for measurement. The measurement items are the cumulative measurement time of the odor gas sensor, the output value of the odor gas sensor after 180 seconds of purging following a 120-second measurement, the time it takes for the output of the odor gas sensor to reach the concentration of the sample gas after the start of measurement, and the number of measurements by the sensor. These are stored in a DB and used for real-time calibration of the odor gas sensor.

[0152] FIG. 5 is a conceptual diagram of a device for constructing an accelerated test DB to measure aging changes at 100% of the maximum measurement range of the odor sensor of the present invention. In order to rapidly induce aging of the odor gas sensor by measuring under a high load condition, the concentration of the sample gas was set to 100% of the measurement range of the odor gas sensor, and data was collected to create an accelerated test DB. Although using a sample gas concentration of 100% or higher can reflect rapid aging of the odor gas sensor, it is difficult to predict the aging speed and the output value of the odor gas sensor, so the accelerated test was performed using a concentration of 100%.

[0153] FIG. 6 is a conceptual diagram of an aging change prediction device for predicting the aging change of an odor sensor according to the present invention. An aging change prediction device for an odor gas sensor was developed that can correct the output value of the sensor, which changes according to the usage time of the odor gas sensor in real time, by using a sample gas at 80% concentration of the entire measurable range and a sample gas at 100% concentration of the entire measurable range test DB, and by using means such as correlation analysis and principal component analysis.

[0154] FIG. 7 is a conceptual diagram of an aging change correction device that generates a sensor output value by correcting the aging change of the odor sensor in real time in an odor measuring device using the odor sensor of the present invention. By providing the previously developed odor gas sensor aging change prediction device, the output value of the odor gas sensor is corrected in real time and output using information such as the time the odor gas sensor measures the value, the time it is ready to measure again after purging, and the usage time and number of uses of the sensor. By doing so, accurate odor gas monitoring is possible.

[0155] FIG. 8 is a conceptual diagram of the development of an AI inference engine for FTIR gas measurement results using artificial intelligence and an FTIR gas sensor as another embodiment of the present invention. It illustrates the process of developing an FTIR gas sensor AI inference engine that uses artificial intelligence (AI) to supply sample gases of various concentrations, compares the output value of the FTIR gas measuring device with the output value of the odor sensor, and matches the output value of each of N odor sensors with the measurement result of the FTIR gas measuring device.

[0156] Among available artificial intelligence techniques, the long-range dependency learning method among Transformer-based models is a learning method that can be used when there is a correlation between measurement peaks that are far apart in FTIR spectrum data for the same gas, and is an artificial intelligence learning method useful in cases where a single gas exhibits peaks at multiple wavelengths, such as in FTIR. For example, if absorption peaks at wavenumbers 1000 cm⁻¹ and 2500 cm⁻¹ appear together for a specific gas in the FTIR spectrum, a dependency exists between these two points. For this reason, the present invention developed an FTIR gas sensor artificial intelligence inference engine using the long-range dependency learning method among Transformer-based models among artificial intelligence learning methods.

[0157] FIG. 9 is a conceptual diagram of a general test for the aging change of an odor sensor using artificial intelligence and an FTIR gas sensor as another embodiment of the present invention. It is an explanatory diagram illustrating the concept of a device for measuring the measurement value of a corresponding odor sensor using an FTIR gas sensor that exhibits no aging change, for measuring the aging change of the odor sensor of the present invention. It illustrates a device for collecting data in which the output of the odor sensor degrades as the usage time increases. The use of an 80% concentration is prepared because the actual odor sensor operates at a concentration of 80% or lower. Since the output value of the previously developed FTIR gas sensor function exhibits no aging change, while the odor sensor exhibits aging change, this is utilized for measurement. The measurement items are the cumulative measurement time of the odor gas sensor, the output value of the odor gas sensor after 180 seconds of purging following a 120-second measurement, the time it takes for the output of the odor gas sensor to reach the concentration of the sample gas after the measurement begins, and the number of measurements by the sensor. These are stored in a database and used for real-time calibration of the odor gas sensor.

[0158] FIG. 10 is a conceptual diagram of an accelerated aging test of an odor sensor using artificial intelligence and an FTIR gas sensor as another embodiment of the present invention. In order to rapidly induce aging of the odor gas sensor by measuring under a heavy load on the odor gas sensor, the concentration of the sample gas was set to 100% of the measurement range of the odor gas sensor, and data was collected to create an accelerated test DB. Although using a sample gas concentration of 100% or higher can reflect rapid aging of the odor gas sensor, it is difficult to predict the aging rate and the output value of the odor gas sensor, so the accelerated test was performed using a concentration of 100%.

[0159] FIG. 11 is a conceptual diagram of an aging change inference device for an odor sensor using artificial intelligence and an FTIR gas sensor as another embodiment of the present invention. An artificial intelligence inference engine was developed using an AI deep learning method, utilizing a database of sample gases at 80% concentration of the total measurable range and sample gases at 100% concentration of the total measurable range. As previously mentioned, an aging change model was developed using an LSTM (Long Short-Term Memory) / GRU (Gated Recurrent Unit) model among Recurrent Neural Networks (RNN) series models. Since this model is suitable for processing time-series data and identifying long-term dependencies, it can capture gradual changes in the sensor from long-term data (used for real-time aging change correction).

[0160] As an additional model, Temporal Convolutional Networks (TCN) were used to process sensor inputs from various time points in parallel and served as a validation tool to compensate for inter-age variations.

[0161] An autoencoder-based anomaly detection and aging estimation learning model was used to estimate the replacement time for sensors due to failure or aging. This model learns from normal-state sensor data, and when a signal resulting from sensor usage is input, it calculates the reconstruction error and compares it to determine the presence of aging and the timing for sensor replacement. The replacement time or remaining usage time of the sensor is determined if the magnitude of the reconstruction error exceeds a set threshold. The remaining usage count is provided by converting the difference between the reconstruction error magnitude and the set threshold into the number of measurements taken by the sensor. This conversion is calculated by using the number of uses of the sensor that has reached the end of its lifespan, based on the difference between the reconstruction error in the normal state and the reconstruction error of the sensor that has reached the end of its lifespan.

[0162] That is, in the present invention, the three artificial intelligence models mentioned above were used simultaneously to correct the real-time odor sensor output and to determine the timing for replacing the sensor.

[0163] A method was used to generate a correction value for the odor gas sensor using the above LSTM (Long Short-Term Memory) / GRU (Gated Recurrent Unit) model, verify the sensor correction value using the above Temporal Convolutional Networks (TCN) model, perform correction if the verification error is within 5%, and regenerate the correction value if it is greater.

[0164] The present invention provides an aging change prediction device for an odor gas sensor, characterized in that the replacement time of the odor gas sensor is determined as the lifespan of the odor gas sensor has elapsed if the output value of the odor gas sensor does not become zero within a purging time of 180 seconds, and the remaining lifespan of the odor gas sensor is displayed as the remaining usage time until the time for the output value of the odor gas sensor to become zero is 180 seconds or more.

[0165] FIG. 12 is a detailed diagram of an aging change inference device for an odor sensor using artificial intelligence and an FTIR gas sensor as another embodiment of the present invention. The sensor's age of use is calculated (based on reconstruction error) using data used for training the artificial intelligence model, such as cumulative gas exposure, cumulative usage time, and cumulative high-concentration shock time (gas concentration of 80% or more of the gas measurable range), and the sensor output value is corrected using this.

[0166] FIG. 13 is a configuration diagram of an odor measurement system equipped with an aging change correction device for an odor sensor using artificial intelligence and an FTIR gas sensor as another embodiment of the present invention.

[0167] When an odor is measured by an odor gas sensor, an output value is generated. Simultaneously measured parameters, such as the speed at which the measurement value is reached and the preparation time for re-measurement due to purging, are input into an aging change inference device of the odor sensor. A correction value is generated from this input, and this value is used to correct the output value of the odor gas sensor to output a sensor value. Therefore, it is also possible to connect and use the output value of the present invention to the odor gas input section of an existing odor gas system.

[0168] FIG. 14 is a configuration diagram of an odor measurement system equipped with an additional correction device for aging changes using a calibration gas of an odor sensor utilizing artificial intelligence and an FTIR gas sensor, as another embodiment of the present invention. In the measurement device equipped with the odor gas sensor aging change prediction unit of the present application, if there is an error in the correction value, the user may suffer damage as it appears that the odor being emitted is excessive. To solve this problem, a method is used to periodically measure the measurement value of the odor sensor using a calibration gas of known concentration and to increase the accuracy of the odor gas sensor aging change prediction unit.

[0169] FIG. 15 is a configuration diagram showing the use of FTIR gas measurement results for selecting the NDIR wavelength of an odor measurement system using artificial intelligence and an NDIR gas sensor, as another embodiment of the present invention. In addition to FTIR gas measurement, NDIR gas measurement methods are used as a technique to measure the type and concentration of gas using gas absorption in the infrared region. The NDIR gas measurement method is a more cost-effective method for measuring the type and concentration of gas. By utilizing this, the aging change prediction unit of the odor sensor can be configured in a more cost-effective manner.

[0170] However, in the NDIR gas measurement method, a wavelength selection process is required to select a specific wavelength or wavelength band and connect it to the output of the odor gas sensor. To this end, an artificial intelligence deep learning method was used to configure the NDIR gas measurement sensor by selecting as few infrared light wavelengths as possible. The method used utilized the long-range dependency learning method among the Transformer-based models mentioned earlier. It learned by using the correlation between measurement peaks that are far apart in the FTIR spectrum data and merged the light wavelengths with high correlation, thereby enabling the configuration of an NDIR odor gas measurement system using a minimum number of infrared light wavelength filters.

[0171] FIG. 16 is a configuration diagram for a general test of aging change in an odor sensor using an odor measurement system utilizing artificial intelligence and an NDIR gas sensor as another embodiment of the present invention. It is an explanatory diagram illustrating the concept of a device for measuring the measurement value of an odor sensor corresponding to an NDIR gas sensor that exhibits no aging change, for measuring the aging change of the odor sensor of the present invention. It illustrates a device for collecting data in which the output of the odor sensor degrades as usage time increases. The use of an 80% concentration is prepared because the actual odor sensor operates at a concentration of 80% or lower. Since the output value of the previously developed NDIR gas sensor function exhibits no aging change, while the odor sensor exhibits aging change, this is utilized for measurement. The measurement items are the cumulative measurement time of the odor gas sensor, the output value of the odor gas sensor after 180 seconds of purging following a 120-second measurement, the time it takes for the output of the odor gas sensor to reach the concentration of the sample gas after starting the measurement from the odor gas sensor, and the number of measurements by the sensor. These are stored in a database and used for real-time calibration of the odor gas sensor.

[0172] FIG. 17 is a configuration diagram of an accelerated aging test for an odor sensor using an odor measurement system utilizing artificial intelligence and an NDIR gas sensor as another embodiment of the present invention. In order to rapidly induce aging of the odor gas sensor by measuring under a heavy load on the odor gas sensor, the concentration of the sample gas was set to 100% of the measurement range of the odor gas sensor, and data was collected to create an accelerated test DB. Although using a sample gas concentration of 100% or higher can reflect rapid aging of the odor gas sensor, it is difficult to predict the aging rate and the output value of the odor gas sensor, so the accelerated test was performed using a concentration of 100%.

[0173] FIG. 18 is a configuration diagram of an odor sensor aging change inference device using an odor measurement system using artificial intelligence and an NDIR gas sensor as another embodiment of the present invention.

[0174] An AI inference engine was developed using an AI deep learning method, utilizing a database of sample gases at 80% and 100% concentrations of the total measurable range. As mentioned above, an inter-aging model was developed using LSTM (Long Short-Term Memory) / GRU (Gated Recurrent Unit) models from the Recurrent Neural Network (RNN) family. Since this model is suitable for processing time-series data and identifying long-term dependencies, it can capture gradual changes in the sensor from long-term data (used for real-time inter-aging correction).

[0175] As an additional model, Temporal Convolutional Networks (TCN) were used to process sensor inputs from various time points in parallel and served as a validation tool to compensate for inter-age variations.

[0176] An autoencoder-based anomaly detection and aging estimation learning model was used to estimate the replacement time for sensors due to failure or aging. This model learns from normal-state sensor data, and when a signal resulting from sensor usage is input, it calculates the reconstruction error and compares it to determine the presence of aging and the timing for sensor replacement. The replacement time or remaining usage time of the sensor is determined if the magnitude of the reconstruction error exceeds a set threshold. The remaining usage count is provided by converting the difference between the reconstruction error magnitude and the set threshold into the number of measurements taken by the sensor. This conversion is calculated by using the number of uses of the sensor that has reached the end of its lifespan, based on the difference between the reconstruction error in the normal state and the reconstruction error of the sensor that has reached the end of its lifespan.

[0177] That is, in the present invention, the three artificial intelligence models mentioned above were used simultaneously to correct the real-time odor sensor output and to determine the timing for replacing the sensor.

[0178] A method was used to generate a correction value for the odor gas sensor using the above LSTM (Long Short-Term Memory) / GRU (Gated Recurrent Unit) model, verify the sensor correction value using the above Temporal Convolutional Networks (TCN) model, perform correction if the verification error is within 5%, and regenerate the correction value if it is greater.

[0179] The present invention provides an aging change prediction device for an odor gas sensor, characterized in that the replacement time of the odor gas sensor is determined as the lifespan of the odor gas sensor has elapsed if the output value of the odor gas sensor does not become zero within a purging time of 180 seconds, and the remaining lifespan of the odor gas sensor is displayed as the remaining usage time until the time for the output value of the odor gas sensor to become zero is 180 seconds or more.

[0180] FIG. 19 is a configuration diagram of an odor sensor aging change correction device using an odor measurement system utilizing artificial intelligence and an NDIR gas sensor as another embodiment of the present invention. When an odor is measured by an odor gas sensor, an output value of the odor gas sensor is generated. At this time, the speed at which the measurement value is reached and the preparation time for re-measurement due to purging, which are measured together, are input into an aging change inference device of the odor sensor. A correction value is generated therefrom, and this is used to correct the output value of the odor gas sensor to output a sensor value. Therefore, it is also possible to use the output value of the present application by connecting it to the odor gas input of an existing odor gas system.

[0181] FIG. 20 is a configuration diagram of an odor measurement system equipped with an additional correction device for aging changes using an odor sensor calibration gas, utilizing an odor measurement system utilizing artificial intelligence and an NDIR gas sensor as another embodiment of the present invention. In the measurement device equipped with the odor gas sensor aging change prediction unit of the present application, if there is an error in the correction value, the user may suffer damage as it appears that the odor being emitted is excessive. To solve this problem, a method is used to periodically measure the measurement value of the odor sensor using a calibration gas with a known concentration, and to also increase the accuracy of the odor gas sensor aging change prediction unit.

[0182] The composition of the invention for demonstrating the effects of the invention as described above is as follows.

[0183] N electrochemical or semiconductor odor gas sensors; and

[0184] 4000~600 cm - FTIR gas measuring device capable of measuring an optical wavelength band of the ¹ range; and

[0185] Using the measurement values ​​of the above odor gas sensor and the above FTIR gas measuring device, the sample gas concentration measured by the above odor gas sensor, and

[0186] 4000~600 cm measured by the above FTIR gas measuring device - Using the light attenuation value due to light absorption that changes differently depending on the sample gas concentration in the optical wavelength band of the ¹ range,

[0187] An odor sensor output function is generated to calculate the gas concentration output value measured by the odor gas sensor from the measurement result of the above FTIR gas measuring device,

[0188] The present invention provides an FTIR odor gas sensor characterized by having an FTIR odor sensor output function that generates an output value of the odor sensor from the above FTIR gas measurement result.

[0189] In addition, the present invention provides an FTIR odor gas sensor characterized by further comprising a Mass Flow Controller (MFC) that controls the sample gas at a constant speed and supplies it to the FTIR gas measuring device and the odor gas sensor.

[0190] In addition, the present invention provides an odor gas sensor aging change measuring device characterized by including the construction of a general aging change test DB (Data Base) for measuring the aging change of an odor gas sensor over time by continuously supplying a gas with a concentration corresponding to 80% of the measurable odor concentration of the odor gas sensor to the odor gas sensor and the FTIR odor gas sensor, and comparing the values ​​measured by the odor gas sensor and the FTIR odor gas sensor.

[0191] In addition, the present invention provides an odor gas sensor aging change measuring device characterized by including the construction of an accelerated aging test DB (Data Base) for measuring the aging change of an odor gas sensor over time by continuously supplying a gas with a concentration corresponding to 100% of the measurable odor concentration of the odor gas sensor to the odor gas sensor and the FTIR odor gas sensor, and comparing the values ​​measured by the odor gas sensor and the FTIR odor gas sensor.

[0192] In addition, the above-mentioned general test DB for aging change and the above-mentioned accelerated test DB for aging change include the cumulative measurement time of the odor gas sensor, the output value of the odor gas sensor after 120 seconds of measurement and 180 seconds of purging, the time for the output of the odor gas sensor to reach the concentration of the sample gas after starting measurement at the odor gas sensor, and the number of measurements of the sensor, thereby providing an aging change measuring device for an odor gas sensor.

[0193] In addition, the present invention provides an odor gas sensor aging change prediction device comprising an odor gas usage aging change prediction function that calculates the degree of degradation of the output value of the odor gas sensor according to the exposure time to the odor gas, using the aging change general test DB and the aging change accelerated test DB measured by the odor gas sensor aging change measuring device as inputs.

[0194] In addition, the aging change prediction device of the odor gas sensor is characterized by determining that the service life of the odor gas sensor has expired if the output value of the odor gas sensor does not become zero within a purging time of 180 seconds, and displaying the remaining service life of the odor gas sensor as the remaining usage time until the time at which the output value of the odor gas sensor becomes zero is 180 seconds or more.

[0195] In addition, the present invention provides a precision odor gas measuring device equipped with an odor gas sensor aging change prediction unit, characterized by inputting the output values ​​of the N electrochemical or semiconductor odor gas sensors into an odor gas sensor aging change prediction device to predict the odor gas sensor aging change, and correcting the output value of the odor gas sensor by the magnitude of the predicted value to provide an odor gas concentration measurement value.

[0196] In addition, the present invention provides a precision odor gas measuring device having an aging change prediction unit for an odor gas sensor, characterized by further providing an additional 3-way valve between the sample gas input unit and the MFC, and further providing a calibration gas cylinder to block the input of the sample gas at regular intervals and to read the measurement value of the odor gas sensor using the gas from the calibration gas cylinder as an input to predict the lifespan of the odor gas sensor.

[0197] As another embodiment of the present invention,

[0198] N electrochemical or semiconductor odor gas sensors; and

[0199] 4000~600 cm - FTIR gas measuring device capable of measuring an optical wavelength band of the ¹ range; and

[0200] Using deep learning, a type of artificial intelligence based on learning with the measurement values ​​of the above odor gas sensor and the above FTIR gas measuring device as input,

[0201] The present invention provides an FTIR odor gas sensor characterized by having an odor gas sensor output artificial intelligence inference engine that generates a gas concentration output value measured by the odor gas sensor from the measurement result of the FTIR gas measuring device.

[0202] In addition, the present invention provides an FTIR odor gas sensor characterized by further comprising a Mass Flow Controller (MFC) that controls the sample gas at a constant speed and supplies it to the FTIR gas measuring device and the odor gas sensor.

[0203] In addition, the present invention provides an odor gas sensor aging change measuring device characterized by including the construction of a general aging change test DB (Data Base) for measuring the aging change of an odor gas sensor over time by continuously supplying a gas with a concentration corresponding to 80% of the measurable odor concentration of the odor gas sensor to the odor gas sensor and the FTIR odor gas sensor, and comparing the values ​​measured by the odor gas sensor and the FTIR odor gas sensor.

[0204] In addition, the present invention provides an odor gas sensor aging change measuring device characterized by including the construction of an accelerated aging test DB (Data Base) for measuring the aging change of an odor gas sensor over time by continuously supplying a gas with a concentration corresponding to 100% of the measurable odor concentration of the odor gas sensor to the odor gas sensor and the FTIR odor gas sensor, and comparing the values ​​measured by the odor gas sensor and the FTIR odor gas sensor.

[0205] In addition, the above-mentioned general test DB for aging change and the above-mentioned accelerated test DB for aging change include the cumulative measurement time of the odor gas sensor, the output value of the odor gas sensor after 120 seconds of measurement and 180 seconds of purging, the time for the output of the odor gas sensor to reach the concentration of the sample gas after starting measurement at the odor gas sensor, and the number of measurements of the sensor, thereby providing an aging change measuring device for an odor gas sensor.

[0206] In addition, the present invention provides an odor gas sensor aging change prediction device characterized by including an artificial intelligence aging change prediction unit that predicts the degree of degradation of the output value of the odor gas sensor according to the exposure time of the odor gas sensor to the odor gas sensor by using deep learning, which is a type of artificial intelligence, as input to the aging change general test DB and the aging change accelerated test DB measured by the aging change measuring device of the odor gas sensor.

[0207] In addition, the aging change prediction device of the odor gas sensor is characterized by determining that the service life of the odor gas sensor has expired if the output value of the odor gas sensor does not become zero within a purging time of 180 seconds, and displaying the remaining service life of the odor gas sensor as the remaining usage time until the time at which the output value of the odor gas sensor becomes zero is 180 seconds or more.

[0208] In addition, the present invention provides a precision odor gas measuring device equipped with an artificial intelligence aging change prediction unit for an odor gas sensor, characterized by inputting the output values ​​of the N electrochemical or semiconductor odor gas sensors into an aging change prediction device for the odor gas sensor to predict the aging change of the odor gas sensor, and correcting the output value of the odor gas sensor by the magnitude of the predicted value to provide a measurement value of the odor gas concentration.

[0209] In addition, the present invention provides a precision odor gas measuring device having an aging change prediction unit for an odor gas sensor, characterized by further providing an additional 3-way valve between the sample gas input unit and the MFC, and further providing a calibration gas cylinder to block the input of the sample gas at regular intervals and to read the measurement value of the odor gas sensor using the gas from the calibration gas cylinder as an input to predict the lifespan of the odor gas sensor.

[0210] As another embodiment of the present invention,

[0211] N electrochemical or semiconductor odor gas sensors; and

[0212] 4000~600 cm - FTIR gas measuring device capable of measuring an optical wavelength band of the ¹ range; and

[0213] By utilizing deep learning, a type of artificial intelligence based on learning using the measurement values ​​of the above odor gas sensor and the above FTIR gas measuring device as input, two or more optical wavelengths are selected to represent the measurement values ​​of the above odor gas sensor to configure a non-dispersive infrared gas measuring unit and an artificial intelligence odor sensor concentration inference unit.

[0214] The present invention provides a non-dispersive infrared (NDIR) odor gas sensor characterized by generating a gas concentration output value measured by the odor gas sensor using the non-dispersive infrared gas measuring unit and the artificial intelligence odor sensor concentration inference unit.

[0215] In addition, the present invention provides an FTIR odor gas sensor characterized by further comprising a Mass Flow Controller (MFC) that controls the sample gas at a constant speed and supplies it to the non-dispersive infrared (NDIR) odor gas sensor and the odor gas sensor.

[0216] In addition, the present invention provides an odor gas sensor aging change measuring device characterized by including the construction of a general aging change test DB (Data Base) for measuring the aging change of an odor gas sensor over time by continuously supplying a gas with a concentration corresponding to 80% of the measurable odor concentration of the odor gas sensor to the odor gas sensor and the non-dispersive infrared (NDIR) odor gas sensor, and comparing the values ​​measured by the odor gas sensor and the non-dispersive infrared (NDIR) odor gas sensor.

[0217] In addition, the present invention provides an aging change measuring device for an odor gas sensor characterized by including the construction of an accelerated aging test DB (Data Base) for measuring the aging change of the odor gas sensor over time by continuously supplying a gas with a concentration corresponding to 100% of the measurable odor concentration of the odor gas sensor to the odor gas sensor and the non-dispersive infrared (NDIR) odor gas sensor, and comparing the values ​​measured by the odor gas sensor and the non-dispersive infrared (NDIR) odor gas sensor.

[0218] In addition, the above-mentioned general test DB for aging change and the above-mentioned accelerated test DB for aging change include the cumulative measurement time of the odor gas sensor, the output value of the odor gas sensor after 120 seconds of measurement and 180 seconds of purging, the time for the output of the odor gas sensor to reach the concentration of the sample gas after starting measurement at the odor gas sensor, and the number of measurements of the sensor, thereby providing an aging change measuring device for an odor gas sensor.

[0219] In addition, the present invention provides an odor gas sensor aging change prediction device characterized by including an artificial intelligence aging change prediction unit that predicts the degree of degradation of the output value of the odor gas sensor according to the exposure time of the odor gas sensor to the odor gas sensor by using deep learning, which is a type of artificial intelligence, as input to the aging change general test DB and the aging change accelerated test DB measured by the aging change measuring device of the odor gas sensor.

[0220] In addition, the aging change prediction device of the odor gas sensor is characterized by determining that the service life of the odor gas sensor has expired if the output value of the odor gas sensor does not become zero within a purging time of 180 seconds, and displaying the remaining service life of the odor gas sensor as the remaining usage time until the time at which the output value of the odor gas sensor becomes zero is 180 seconds or more.

[0221] In addition, the present invention provides a precision odor gas measuring device equipped with an artificial intelligence aging change prediction unit for an odor gas sensor, characterized by inputting the output values ​​of the N electrochemical or semiconductor odor gas sensors into an aging change prediction device for the odor gas sensor to predict the aging change of the odor gas sensor, and correcting the output value of the odor gas sensor by the magnitude of the predicted value to provide a measurement value of the odor gas concentration.

[0222] In addition, the present invention provides a precision odor gas measuring device having an aging change prediction unit for an odor gas sensor, characterized by further providing an additional 3-way valve between the sample gas input unit and the MFC, and further providing a calibration gas cylinder to block the input of the sample gas at regular intervals and to read the measurement value of the odor gas sensor using the gas from the calibration gas cylinder as an input to predict the lifespan of the odor gas sensor.

Claims

Claim 1 N electrochemical or semiconductor odor gas sensors; and 4000~600 cm - FTIR gas measuring device capable of measuring an optical wavelength band of the ¹ range; and using the measurement values ​​of the odor gas sensor and the FTIR gas measuring device, the sample gas concentration measured by the odor gas sensor and the 4000~600 cm⁻¹ measured by the FTIR gas measuring device - An odor gas sensor comprising an FTIR odor sensor output function unit that generates an output value of the odor gas sensor from the FTIR gas measurement result by utilizing a light reduction value due to light absorption that changes differently depending on the sample gas concentration in a range of optical wavelength bands, thereby generating an odor sensor output function that calculates a gas concentration output value measured by the odor gas sensor from the measurement result of the FTIR gas measurement device, and comprises a Mass Flow Controller (MFC) that controls the sample gas at a constant speed and supplies it to the FTIR gas measurement device and the odor gas sensor, and continuously supplies gas at a concentration corresponding to 80% of the measurable odor concentration of the odor gas sensor to the odor gas sensor and the FTIR odor gas sensor, and by comparing the values ​​measured by the odor gas sensor and the FTIR odor gas sensor, measures the occurrence of aging change in the odor gas sensor over time, and includes the construction of a general test DB (Data Base) for the aging change of the odor gas sensor. Claim 2 delete Claim 3 delete Claim 4 An odor gas sensor aging change measuring device according to claim 1, characterized by including the construction of an accelerated aging test DB (Database) for measuring the occurrence of aging change in the odor gas sensor over time by continuously supplying gas at a concentration corresponding to 100% of the measurable odor concentration of the odor gas sensor to the odor gas sensor and the FTIR odor gas sensor, and comparing the values ​​measured by the odor gas sensor and the FTIR odor gas sensor. Claim 5 An odor gas sensor aging change measuring device according to claim 4, wherein the above-mentioned general aging change test DB and the above-mentioned accelerated aging change test DB include the cumulative measurement time of the odor gas sensor, the output value of the odor gas sensor after 120 seconds of measurement and 180 seconds of purging, the time for the output of the odor gas sensor to reach the concentration of the sample gas after starting measurement at the odor gas sensor, and the number of measurements of the sensor. Claim 6 An odor gas sensor aging change measuring device according to claim 5, characterized by including an aging change prediction function according to odor gas usage that calculates the degree of degradation of the output value of the odor gas sensor according to the exposure time to the odor gas, using the aging change general test DB and the aging change accelerated test DB measured by the odor gas sensor aging change measuring device as inputs. Claim 7 In claim 6, the aging change prediction device of the odor gas sensor determines that the service life of the odor gas sensor has expired if the output value of the odor gas sensor does not become zero within a purging time of 180 seconds, and the remaining service life of the odor gas sensor is displayed as the remaining usage time until the time at which the output value of the odor gas sensor becomes zero is 180 seconds or more. Claim 8 An odor gas sensor aging change measuring device according to claim 7, characterized in that the output values ​​of the N electrochemical or semiconductor odor gas sensors are input into an odor gas sensor aging change prediction device to predict the odor gas sensor aging change, and the output values ​​of the odor gas sensors are corrected by the magnitude of the predicted values ​​to provide an odor gas concentration measurement value. Claim 9 An aging change measuring device for an odor gas sensor according to claim 8, characterized in that it further comprises an additional 3-way valve between the sample gas input section and the MFC, and further comprises a calibration gas cylinder, thereby blocking the input of the sample gas at regular time intervals and using the gas from the calibration gas cylinder as an input to read the measurement value of the odor gas sensor, thereby predicting the lifespan of the odor gas sensor.

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