An infrared gas concentration sensor and its detection method

Abstract

The present invention belongs to the field of detection technology, and discloses an infrared gas concentration sensor and its detection method, including: collecting the transmittance data of the two channels of the infrared sensor in real time, and simultaneously obtaining the surface temperature of the sensor and the environmental humidity parameter to form a multi-parameter synchronous acquisition data set; establishing a humidity compensation function based on the collected environmental humidity parameter to dynamically correct the transmittance of the main detection channel, and at the same time judging the dew condensation risk according to the change rate of the transmittance of the reference channel, calculating the change rate of the transmittance of the main detection channel, extracting the humidity interference characteristic quantity of the reference channel, and generating a fusion characteristic parameter; performing gas presence determination and triggering a gas presence flag bit; according to the state of the gas presence flag bit, adopting a temperature-adaptive response value calculation method, combining environmental compensation parameters for concentration conversion, and outputting a gas concentration value; through multi-parameter synchronous acquisition and environmental compensation mechanism, effectively solving the problem of environmental interference in high-temperature and high-humidity environments and improving the measurement accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of sensors, and particularly to an infrared gas concentration sensor and a detection method thereof. Background Art

[0002] Gas concentration detection has important application values in fields such as industrial production and environmental monitoring. Among them, infrared sensor technology has become one of the mainstream solutions for gas detection due to its advantages such as non-contact and high sensitivity. However, in some special industrial environments, such as high-temperature and high-humidity working conditions in chemical production and metallurgical processing, traditional gas detection methods often fail to meet the actual requirements. These environments not only have drastic temperature fluctuations but also continuous high humidity, posing severe challenges to the stability and accuracy of sensors.

[0003] In the prior art, infrared gas sensors usually adopt a single-channel detection method, and calculate the gas concentration by measuring the change in the transmittance of the characteristic absorption band of the target gas. However, in a high-temperature and high-humidity environment, this detection method has obvious defects: First, water vapor has multiple absorption peaks in the infrared band, which is easy to overlap and interfere with the absorption peaks of the target gas, resulting in measurement errors; Second, thermal radiation interference and humidity fluctuations in a high-temperature environment will cause baseline drift of the sensor, reducing measurement stability; Finally, the lack of an effective environmental parameter compensation mechanism makes it difficult to meet the accurate measurement requirements under complex working conditions. These technical defects seriously restrict the application effect of infrared sensors in special industrial environments.

[0004] In view of this, it is necessary to improve the gas detection technology of infrared gas sensors in the prior art to solve the technical problems that they are more sensitive to environmental interference and cannot achieve continuous detection. Summary of the Invention

[0005] The purpose of the present invention is to provide an infrared gas concentration sensor and a detection method thereof to solve the above technical problems.

[0006] To achieve this purpose, the present invention adopts the following technical solutions:

[0007] A detection method for an infrared gas concentration sensor includes the following steps:

[0008] Step S1, collect the transmittance data of the two channels of the infrared sensor in real time, and simultaneously obtain the surface temperature of the sensor and the environmental humidity parameter to form a multi-parameter synchronous acquisition data set; the transmittance data includes the transmittance of the main detection channel and the transmittance of the reference channel;

[0009] Step S2: Based on the collected environmental humidity parameters, establish a humidity compensation function to dynamically correct the transmittance of the main detection channel. Meanwhile, judge the condensation risk according to the change rate of the transmittance of the reference channel. When the condensation risk is detected, activate the anti-condensation mode;

[0010] Step S3: Calculate the change rate of the corrected transmittance of the main detection channel, extract the humidity interference characteristic quantity of the reference channel, construct a temperature compensation matrix in combination with the surface temperature of the sensor, and generate a fusion characteristic parameter that integrates environmental compensation;

[0011] Step S4: Based on the fusion characteristic parameter, determine the presence of gas. When the characteristic parameters of multiple consecutive sampling periods meet the preset conditions and the humidity interference characteristic quantity is lower than the threshold, trigger the gas presence flag bit;

[0012] Step S5: According to the status of the gas presence flag bit, adopt a temperature-adaptive response value calculation method, and perform concentration conversion in combination with environmental compensation parameters to output the gas concentration value.

[0013] Optionally, the specific steps of Step S1 include:

[0014] Step S11: Configure a dual-channel infrared detection system. Set the central wavelength of the main detection channel to the characteristic absorption peak of the target gas, which is 4.26 μm; set the central wavelength of the reference channel to the characteristic absorption peak of water vapor, which is 2.7 μm;

[0015] Step S12: Establish a synchronous acquisition timing sequence. Set the sampling period of the main detection channel and the reference channel to 0.5 s, and adopt a time-interleaved sampling method to control the sampling of the two channels within a preset time interval, and collect the transmittance T1 of the main detection channel and the transmittance T2 of the reference channel;

[0016] Step S13: Install a micro-thermocouple array on the surface of the sensor housing. Set 4 temperature measurement points to be evenly distributed around the optical window of the sensor, and use a median filtering algorithm to process the temperature data to output the surface temperature Ts of the sensor.

[0017] Optionally, after Step S13, it further includes:

[0018] Step S14: Install a capacitive humidity sensor at the air inlet of the sensor, and use a moving average filter to process the original data to output the environmental humidity Hr;

[0019] Step S15: Design a data preprocessing module to perform outlier rejection and noise filtering on the collected original transmittance data. Among them, the transmittance T1 of the main detection channel and the transmittance T2 of the reference channel are respectively smoothed by an adaptive Kalman filter algorithm, and the filter window width is set to 5 sampling points;

[0020] Step S16, construct a multi-parameter synchronous acquisition data set, align the processed T1, T2, Ts, and Hr data according to the time stamp, and store them in a circular buffer. The buffer capacity is set to 60 groups of data, corresponding to a continuous monitoring duration of 30 s.

[0021] Optionally, step S2 specifically includes:

[0022] Step S21, establish a humidity compensation function library, divide the compensation interval according to the ambient humidity Hr, and select the corresponding compensation function F(Hr);

[0023] Step S22, implement dynamic correction of the transmittance of the main detection channel. Select the corresponding compensation function according to the real-time acquired ambient humidity Hr, and calculate the corrected transmittance of the main detection channel T2 = T1 × F(Hr);

[0024] Step S23, design a dew condensation risk warning mechanism, calculate the instantaneous change rate φ2 of the reference channel transmittance T2 = ΔT2 / Δt, set the change rate threshold to 0.15% / s, and when φ2 in three consecutive sampling periods exceeds the threshold, trigger a dew condensation risk warning;

[0025] Step S24, construct an anti-dew condensation control strategy. When a dew condensation risk is detected, execute the anti-dew condensation mode;

[0026] Step S25, implement real-time evaluation of the compensation effect. By comparing the standard deviation change of the main detection channel transmittance before and after compensation, dynamically adjust the compensation function parameters. When the reduction amplitude of the standard deviation is less than 20%, automatically switch to a higher-order compensation function.

[0027] Optionally, step S3 specifically includes:

[0028] Step S31, calculate the change rate φ1 of the corrected main detection channel transmittance, and use the five-point difference method to calculate the instantaneous change rate φ;

[0029] Step S32, extract the humidity interference characteristic quantity Q, and calculate it using the sliding window integration method: Q = ∫(φ2)dt, where the integration time window is set to the preset time t, and φ2 is the change rate of the reference channel transmittance;

[0030] Step S33, construct a temperature compensation matrix, divide the compensation interval according to the sensor surface temperature Ts, and randomly select the compensation coefficient k according to the compensation interval.

[0031] Optionally, after step S33, it further includes:

[0032] Step S34, design a feature parameter fusion algorithm to generate the feature parameter Φ with environmental compensation, and the calculation formula is: Φ = φ1 / (1 + 0.2Q) × k;

[0033] Step S35: Implement the quality assessment of characteristic parameters, set the effective range of the characteristic parameter Φ. When the Φ value exceeds the range, automatically start the data re-sampling mechanism and mark the abnormal data flag bit;

[0034] Step S36: Establish an optimization mechanism for characteristic parameters. By comparing the standard deviation of the Φ values in 10 consecutive sampling periods, dynamically adjust the weight coefficient of the fusion algorithm. When the standard deviation exceeds 0.02, automatically optimize the weight coefficient of the humidity interference characteristic quantity Q.

[0035] Optionally, the specific steps of step S4 include:

[0036] Step S41: Set the gas presence determination condition, requiring the characteristic parameter Φ in 5 consecutive sampling periods to satisfy: Φ1>0.15%, φ2>0.18%, Φ3>0.20%, Φ4>0.18%, Φ5>0.15%;

[0037] Step S42: Implement humidity interference verification, calculate the average value Q_avg of the humidity interference characteristic quantity Q in the corresponding determination period. When Q_avg<0.5, it is determined that the humidity interference is within the allowable range; otherwise, delay the determination and wait for the next sampling period;

[0038] Step S43: Design a determination result confirmation mechanism. When it is initially determined that gas is present, extend the observation for 2 sampling periods. If the subsequent Φ value continues to be greater than 0.12%, then confirm the gas presence determination result;

[0039] Step S44: Implement classification processing of the determination results, and divide the confidence level according to the number of sampling periods that continuously meet the determination conditions: 5 - 7 periods are low confidence level, 8 - 10 periods are medium confidence level, and more than 10 periods are high confidence level.

[0040] Optionally, the specific steps of step S5 include:

[0041] Step S51: Design a status parsing mechanism for the gas presence flag bit. When the flag bit is 1, start the concentration calculation process; when the flag bit is 0, maintain the standby monitoring state and continuously update the environmental parameters;

[0042] Step S52: Construct a temperature adaptive response value calculation model, and calculate the response value β using a segmented weighted algorithm;

[0043] Step S53: Implement dynamic calibration of the response value, and adjust the response value according to the environmental humidity Hr;

[0044] Step S54: Design a concentration conversion algorithm, and use a non-linear conversion formula: C = α×β1^1.2 / (1 + 0.1β1), where α is the calibration coefficient, and the initial value is set to 1.0;

[0045] Step S55, establish a concentration output smoothing mechanism, and use the exponentially weighted moving average method to process and obtain the output concentration value C_output;

[0046] Step S56, design the data output format, and pack and output the output concentration value C_output, the timestamp, the temperature compensation coefficient k, and the humidity interference characteristic quantity Q to form a complete data packet.

[0047] The present invention also provides a gas concentration infrared sensor, which realizes detection by using the detection method of the gas concentration infrared sensor as described above. The gas concentration infrared sensor includes:

[0048] A dual-channel infrared detection module, including a main detection channel and a reference channel, which are respectively used to detect the target gas absorption peak and the water vapor characteristic absorption peak, and are equipped with a spectroscope prism to realize beam separation;

[0049] An environmental parameter acquisition module, including a micro-thermocouple array installed on the surface of the sensor and a capacitive humidity sensor at the air inlet, which is used to collect the surface temperature of the sensor and the environmental humidity in real time;

[0050] A signal processing module, which is used to filter, compensate, and calculate characteristic parameters for the collected transmittance data;

[0051] A gas determination module, which determines the presence of gas based on the fused characteristic parameters, and includes a determination condition setting unit and a confidence evaluation unit;

[0052] A concentration calculation module, which adopts a temperature-adaptive response value calculation method and combines environmental compensation parameters for concentration conversion.

[0053] Compared with the prior art, the present invention has the following beneficial effects: First, the transmittance data of the main detection channel and the reference channel are synchronously collected by the dual channels of the infrared sensor, and the environmental temperature and humidity parameters are obtained at the same time to form a multi-parameter data set; Subsequently, a compensation function is established based on the humidity parameter to dynamically correct the data of the main channel, and the reference channel data is used to judge the risk of condensation, and the anti-condensation mode is started in a timely manner; Then, calculate the change rate of the transmittance of the corrected main channel, extract the humidity interference characteristic quantity, combine the temperature parameter to construct a compensation matrix, and generate a characteristic parameter that integrates environmental compensation; Then, based on the fused characteristic parameters, determine the presence of gas. When multiple consecutive sampling periods meet the preset conditions and the humidity interference is lower than the threshold, trigger the gas presence flag bit; Finally, according to the status of the flag bit, adopt a temperature-adaptive response value calculation method, combine environmental compensation parameters for concentration conversion, and output the final gas concentration value; This method effectively solves the interference problem of infrared gas detection in high-temperature and high-humidity environments through multi-parameter synchronous acquisition and environmental compensation mechanisms, improves the measurement accuracy, and realizes high-precision, high-reliability, and fast response of gas concentration detection, and is especially suitable for continuous online monitoring of high-temperature and high-humidity working conditions. Brief Description of the Drawings

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0055] The structures, proportions, sizes, etc. shown in the drawings of this specification are only used to cooperate with the content disclosed in the specification for those who are familiar with this technology to understand and read, and are not used to limit the conditions for the implementation of the present invention. Therefore, they do not have substantial technical significance. Any modification of the structure, change in the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope that can be covered by the technical content disclosed in the present invention.

[0056] Figure 1 One of the flow diagrams of the detection method of the gas concentration infrared sensor in the first embodiment.

[0057] Figure 2 Another flow diagram of the detection method of the gas concentration infrared sensor in the first embodiment. Detailed Embodiments

[0058] In order to make the objectives, features, and advantages of the present invention more obvious and understandable, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0059] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "upper", "lower", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. It should be noted that when a component is considered to be "connected" to another component, it can be directly connected to the other component or there may be intermediate components present at the same time.

[0060] The following will further illustrate the technical solutions of the present invention in conjunction with the drawings and through specific embodiments.

[0061] Embodiment 1:

[0062] Combined with Figure 1 and Figure 2 As shown, an embodiment of the present invention provides a detection method for an infrared gas concentration sensor, including the following steps:

[0063] Step S1, collect the transmittance data of the two channels of the infrared sensor in real time, and at the same time obtain the surface temperature of the sensor and the environmental humidity parameters to form a multi-parameter synchronous acquisition data set; the transmittance data includes the main detection channel transmittance and the reference channel transmittance; the transmittance data includes the main detection channel transmittance in the absorption peak band of the target gas and the reference channel transmittance in the characteristic absorption band of water vapor. The synchronous acquisition of environmental temperature and humidity provides key parameters for subsequent environmental compensation. The core of this step lies in multi-parameter synchronous acquisition, ensuring the time consistency and integrity of the data, and laying a foundation for subsequent accurate calculations.

[0064] Step S2, establish a humidity compensation function based on the collected environmental humidity parameters, dynamically correct the main detection channel transmittance, and at the same time judge the dew condensation risk according to the change rate of the reference channel transmittance, and start the anti-dew condensation mode when the dew condensation risk is detected;

[0065] Based on the collected environmental humidity parameters, establish a humidity compensation function to dynamically correct the transmittance of the main detection channel to eliminate the influence of the water vapor absorption band on the detection of the target gas. At the same time, judge whether there is a dew condensation risk in the sensor according to the change rate of the reference channel transmittance. When the dew condensation risk is detected, start the anti-dew condensation mode (such as increasing the heater power or starting the purging device) to prevent the sensor performance from degrading. The key to this step lies in real-time environmental compensation and dew condensation risk prevention and control, ensuring the stability and reliability of the detection.

[0066] Step S3, calculate the change rate of the corrected main detection channel transmittance, extract the humidity interference characteristic quantity of the reference channel, combine the surface temperature of the sensor to construct a temperature compensation matrix, and generate a fusion characteristic parameter that integrates environmental compensation;

[0067] Further process and analyze the corrected data. First, calculate the change rate of the corrected main detection channel transmittance, extract the humidity interference characteristic quantity of the reference channel, and quantify the influence degree of water vapor on the detection result. Then combine the surface temperature of the sensor to construct a temperature compensation matrix, and generate a fusion characteristic parameter that integrates environmental compensation. The core of this step lies in eliminating the cross-interference of temperature and humidity through multi-parameter fusion, and generating high-quality data that can accurately reflect the characteristics of the target gas.

[0068] Step S4, based on the fusion characteristic parameter, determine the presence of gas. When the characteristic parameters of multiple consecutive sampling periods meet the preset conditions and the humidity interference characteristic quantity is lower than the threshold, trigger the gas presence flag bit;

[0069] Based on the fusion feature parameters, combined with preset determination conditions (such as the variation law of feature parameters in consecutive sampling periods), it is judged whether there is a target gas in the air. At the same time, through the threshold judgment of the humidity interference feature quantity, the false judgment caused by the environmental humidity fluctuation is excluded. When all the determination conditions are met, the gas presence flag bit is triggered. The core of this step lies in improving the accuracy and anti-interference ability of gas detection through multi-condition joint determination.

[0070] Step S5: According to the status of the gas presence flag bit, adopt a temperature-adaptive response value calculation method, combine with environmental compensation parameters for concentration conversion, and output the gas concentration value.

[0071] According to the status of the gas presence flag bit, adopt a temperature-adaptive response value calculation method, combine with environmental compensation parameters for concentration conversion, and output the final gas concentration value. The core of this step lies in ensuring that accurate concentration values can be output at different environmental temperatures through the design of a temperature-adaptive algorithm. At the same time, combining environmental compensation parameters further improves the detection accuracy. The finally output concentration value is subjected to smoothing processing and range verification.

[0072] The working principle of the present invention is as follows: First, the transmittance data of the main detection channel and the reference channel are synchronously collected through the infrared sensor's dual channels, and at the same time, the environmental temperature and humidity parameters are obtained to form a multi-parameter data set; then, a compensation function is established based on the humidity parameter to dynamically correct the data of the main channel, and the reference channel data is used to judge the risk of condensation, and the anti-condensation mode is started in a timely manner; then, the change rate of the transmittance of the corrected main channel is calculated, the humidity interference feature quantity is extracted, a compensation matrix is constructed in combination with the temperature parameter, and a feature parameter with integrated environmental compensation is generated; then, based on the fusion feature parameter, the gas presence is determined. When consecutive sampling periods meet the preset conditions and the humidity interference is lower than the threshold, the gas presence flag bit is triggered; finally, according to the status of the flag bit, a temperature-adaptive response value calculation method is adopted, combined with environmental compensation parameters for concentration conversion, and the final gas concentration value is output; this method effectively solves the interference problem of infrared gas detection in high-temperature and high-humidity environments through multi-parameter synchronous collection and environmental compensation mechanism, improves the measurement accuracy, and realizes high-precision, high-reliability and fast response of gas concentration detection, and is especially suitable for continuous on-line monitoring of high-temperature and high-humidity working conditions.

[0073] In this embodiment, specifically, step S1 specifically includes:

[0074] Step S11: Configure a dual-channel infrared detection system, set the central wavelength of the main detection channel to the characteristic absorption peak of the target gas at 4.26 μm; set the central wavelength of the reference channel to the characteristic absorption peak of water vapor at 2.7 μm; wherein, the optical paths of the two channels are separated by a beam splitter prism to make the light intensity consistency error less than 1%.

[0075] The central wavelength of the main detection channel is set to the characteristic absorption peak of the target gas at 4.26 μm for accurately detecting the concentration of the target gas; the central wavelength of the reference channel is set to the characteristic absorption peak of water vapor at 2.7 μm for monitoring the interference of environmental humidity on the detection result. Through the dual-channel design, the separation of the target gas signal and the humidity interference signal is achieved, providing a basis for subsequent environmental compensation.

[0076] Step S12, establish a synchronous acquisition timing sequence, set the sampling periods of the main detection channel and the reference channel to 0.5 s, adopt a time-interleaved sampling method, and control the sampling of the two channels within a preset time interval to collect the transmittance T1 of the main detection channel and the transmittance T2 of the reference channel.

[0077] The sampling periods of the main detection channel and the reference channel are set to 0.5 s, and a time-interleaved sampling method is adopted to ensure that the data acquisition of the two channels is completed within a preset time interval. This design not only ensures the time synchronization of the data but also avoids the mutual interference between channels. By precisely controlling the sampling timing sequence, the transmittance T1 of the main detection channel and the transmittance T2 of the reference channel collected have a high degree of consistency.

[0078] Step S13, install a micro-thermocouple array on the surface of the sensor housing, set 4 temperature measurement points evenly distributed around the optical window of the sensor, and use a median filtering algorithm to process the temperature data and output the surface temperature Ts of the sensor.

[0079] Install a micro-thermocouple array on the surface of the sensor housing, set 4 temperature measurement points evenly distributed around the optical window to ensure the representativeness and accuracy of temperature measurement. Use a median filtering algorithm to process the temperature data to effectively eliminate the interference of outliers and output a stable surface temperature Ts of the sensor. This step provides reliable data support for subsequent temperature compensation.

[0080] Step S14, install a capacitive humidity sensor at the air inlet of the sensor, set the humidity sampling period to 1 s, the measurement range to 0 - 100%RH, the accuracy to ±2%RH, and use a moving average filter to process the original data and output the environmental humidity Hr.

[0081] Install a capacitive humidity sensor at the air inlet of the sensor to monitor the environmental humidity in real time. Use a moving average filter to process the original data to smooth the humidity fluctuations and output a stable environmental humidity Hr. This step provides key parameters for the establishment of the humidity compensation function and the judgment of the dew condensation risk.

[0082] Step S15: Design a data preprocessing module to perform outlier rejection and noise filtering on the collected original transmittance data. Among them, the transmittance T1 of the main detection channel and the transmittance T2 of the reference channel are respectively smoothed using the adaptive Kalman filtering algorithm. The width of the filtering window is set to 5 sampling points.

[0083] Perform outlier rejection and noise filtering on the collected original transmittance data. Among them, the transmittance T1 of the main detection channel and the transmittance T2 of the reference channel are respectively smoothed using the adaptive Kalman filtering algorithm. The width of the filtering window is set to 5 sampling points to ensure data smoothness while retaining the characteristics of effective signals.

[0084] Step S16: Construct a multi-parameter synchronous acquisition data set. Align the processed T1, T2, Ts, and Hr data according to the time stamp and store them in a circular buffer. The capacity of the buffer is set to 60 groups of data, corresponding to a continuous monitoring duration of 30 s. This design not only ensures the temporal consistency of the data but also provides sufficient data support for subsequent real-time analysis and processing.

[0085] In this embodiment, specifically, step S2 specifically includes:

[0086] Step S21: Establish a humidity compensation function library and select the corresponding compensation function F(Hr) according to the environmental humidity Hr to divide the compensation interval. Specifically: it is divided into three compensation intervals. When Hr < 60%, the linear compensation function F1(Hr) = 1 + 0.02×(Hr - 40) / 20 is used; when 60% ≤ Hr < 80%, the quadratic compensation function F2(Hr) = 1 + 0.05 + 0.001×(Hr - 60)^2 is used; when Hr ≥ 80%, the exponential compensation function F3(Hr) = 1.1×exp(0.005×(Hr - 80)) is used.

[0087] Step S22: Implement dynamic correction of the transmittance of the main detection channel. Select the corresponding compensation function according to the real-time collected environmental humidity Hr, and calculate the corrected transmittance of the main detection channel T2 = T1×F(Hr).

[0088] This step effectively eliminates the interference of humidity on the detection of the target gas by dynamically adjusting the compensation function, improving the accuracy of the detection result. The corrected transmittance T2 more truly reflects the absorption characteristics of the target gas.

[0089] Step S23: Design a dew condensation risk warning mechanism. Calculate the instantaneous change rate φ2 of the transmittance of the reference channel T2 = ΔT2 / Δt, and set the change rate threshold to 0.15% / s. When φ2 exceeds the threshold for 3 consecutive sampling periods, trigger a dew condensation risk warning.

[0090] Step S24, construct an anti-condensation control strategy. When condensation risk is detected, execute the anti-condensation mode. The anti-condensation mode specifically involves the following operations: increase the power of the sensor heater to 120% of the rated value, shorten the sampling interval to 0.3 s, and simultaneously start the optical window purging device with a purging air flow rate of 0.5 m / s and a duration of 30 s;

[0091] These measures can effectively prevent condensation on the sensor window, ensuring the reliability and stability of the detection data. The implementation of the anti-condensation mode significantly improves the adaptability of the sensor in high-temperature and high-humidity environments.

[0092] Step S25, implement real-time evaluation of the compensation effect. By comparing the standard deviation changes of the transmittance of the main detection channel before and after compensation, dynamically adjust the compensation function parameters. When the reduction amplitude of the standard deviation is less than 20%, automatically switch to a higher-order compensation function.

[0093] When the reduction amplitude of the standard deviation is less than 20%, it indicates that the effect of the current compensation function is insufficient, and the system automatically switches to a higher-order compensation function. This step realizes the dynamic optimization of the compensation parameters, ensuring the accuracy and adaptability of humidity compensation.

[0094] In this embodiment, specifically, step S3 specifically includes:

[0095] Step S31, calculate the change rate φ1 of the corrected transmittance of the main detection channel, and calculate the instantaneous change rate φ using the five-point difference method.

[0096] Step S32, extract the humidity interference characteristic quantity Q, and calculate it using the sliding window integration method: Q = ∫(φ2)dt, where the integration time window is set to the preset time t, and φ2 is the change rate of the transmittance of the reference channel.

[0097] Step S33, construct a temperature compensation matrix, divide the compensation interval according to the sensor surface temperature Ts, and select the compensation coefficient k according to the compensation interval.

[0098] Specifically: when Ts < 100 °C, the compensation coefficient k = 1.0; when 100 °C ≤ Ts < 150 °C, k = 0.95 + 0.001 × (Ts - 100); when Ts ≥ 150 °C, k = 0.90 + 0.0005 × (Ts - 150).

[0099] Step S34, design a feature parameter fusion algorithm to generate the feature parameter Φ for fused environmental compensation. The calculation formula is: Φ = φ1 / (1 + 0.2Q) × k.

[0100] Step S35, implement the quality evaluation of the feature parameters, set the effective range of the feature parameter Φ. When the Φ value exceeds the range, automatically start the data re-sampling mechanism and mark the abnormal data flag bit.

[0101] Step S36, establish a feature parameter optimization mechanism. By comparing the standard deviation of the Φ values in 10 consecutive sampling periods, dynamically adjust the weight coefficients of the fusion algorithm. When the standard deviation exceeds 0.02, automatically optimize the weight coefficient of the humidity interference feature quantity Q.

[0102] In this embodiment, specifically, step S4 specifically includes:

[0103] Step S41, set the gas presence determination condition, requiring the feature parameters Φ in 5 consecutive sampling periods to satisfy: Φ1 > 0.15%, φ2 > 0.18%, Φ3 > 0.20%, Φ4 > 0.18%, Φ5 > 0.15%, and the change rate between adjacent sampling points does not exceed ±20%.

[0104] Step S42, perform humidity interference verification, calculate the average value Q_avg of the humidity interference feature quantity Q in the corresponding determination period. When Q_avg < 0.5, determine that the humidity interference is within the allowable range; otherwise, delay the determination and wait for the next sampling period.

[0105] Step S43, design a determination result confirmation mechanism. When it is initially determined that gas is present, extend the observation for 2 sampling periods. If the subsequent Φ value continues to be greater than 0.12%, then confirm the gas presence determination result.

[0106] Step S44, perform hierarchical processing of the determination results. Divide the confidence level according to the number of sampling periods that continuously meet the determination conditions: 5 - 7 periods are low confidence level, 8 - 10 periods are medium confidence level, and more than 10 periods are high confidence level.

[0107] In this embodiment, specifically, step S5 specifically includes:

[0108] Step S51, design a gas presence flag bit status parsing mechanism. When the flag bit is 1, start the concentration calculation process; when the flag bit is 0, maintain the standby monitoring state and continuously update the environmental parameters.

[0109] Step S52, construct a temperature adaptive response value calculation model, and calculate the response value β using a segmented weighted algorithm; specifically: when Ts < 100°C, β = ΣΦ / n; when 100°C ≤ Ts < 150°C, β = 0.95×ΣΦ / n; when Ts ≥ 150°C, β = 0.90×ΣΦ / n, where Φ is the feature parameter and n is the number of effective sampling points.

[0110] Step S53, perform dynamic calibration of the response value, and adjust the response value according to the environmental humidity Hr; specifically: when Hr < 60%, β1 = β×1.0; when 60% ≤ Hr < 80%, β1 = β×0.98; when Hr ≥ 80%, β1 = β×0.95.

[0111] Step S54, design a concentration conversion algorithm, and adopt a non-linear conversion formula: C = α × β1^1.2 / (1 + 0.1β1), where α is a calibration coefficient, and its initial value is set to 1.0.

[0112] Step S55, establish a concentration output smoothing mechanism, and use the exponential weighted moving average method to process and obtain the output concentration value C_output; specifically: C_output = 0.7 × C_prev + 0.3 × C_current, where C_prev is the previous output value and C_current is the current calculated value.

[0113] Step S56, design a data output format, and pack and output the output concentration value C_output, the timestamp, the temperature compensation coefficient k, and the humidity interference characteristic quantity Q to form a complete data packet.

[0114] Embodiment 2:

[0115] The present invention also provides an infrared gas concentration sensor, which uses the detection method of the infrared gas concentration sensor in Embodiment 1 to implement detection. The infrared gas concentration sensor includes:

[0116] A dual-channel infrared detection module, including a main detection channel and a reference channel, which are respectively used to detect the absorption peak of the target gas and the characteristic absorption peak of water vapor, and is equipped with a spectroscope prism to achieve beam separation.

[0117] An environmental parameter acquisition module, including a micro-thermocouple array installed on the surface of the sensor and a capacitive humidity sensor at the air inlet, which is used to collect the surface temperature of the sensor and the environmental humidity in real time.

[0118] A signal processing module, which is used to filter, compensate, and calculate characteristic parameters for the collected transmittance data.

[0119] A gas determination module, which determines the existence of gas based on the fused characteristic parameters, and includes a determination condition setting unit and a confidence evaluation unit.

[0120] A concentration calculation module, which adopts a temperature-adaptive response value calculation method and combines environmental compensation parameters for concentration conversion.

[0121] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting gas concentration using an infrared sensor, characterized in that, It includes the following steps: Step S1, collect the transmittance data of the two channels of the infrared sensor in real time, and at the same time obtain the sensor surface temperature and environmental humidity parameters to form a multi-parameter synchronous acquisition data set; the transmittance data includes the main detection channel transmittance and the reference channel transmittance; Step S2, establish a humidity compensation function based on the collected environmental humidity parameters to dynamically correct the main detection channel transmittance, and at the same time judge the condensation risk according to the change rate of the reference channel transmittance. When the condensation risk is detected, start the anti-condensation mode; Step S3, calculate the instantaneous change rate of the corrected main detection channel transmittance, extract the humidity interference characteristic quantity of the reference channel, construct a temperature compensation matrix in combination with the sensor surface temperature, and generate a fusion characteristic parameter with environmental compensation fusion; Step S4, determine the presence of gas based on the fusion characteristic parameter. When the fusion characteristic parameters of multiple consecutive sampling periods meet the preset conditions and the humidity interference characteristic quantity is lower than the threshold, trigger the gas presence flag bit; Step S5, according to the state of the gas presence flag bit, adopt a temperature-adaptive response value calculation method, combine the environmental humidity parameters for concentration conversion, and output the gas concentration value; The specific content of step S1 includes: set the central wavelength of the reference channel to the water vapor characteristic absorption peak of 2.7 μm; collect the main detection channel transmittance T1 and the reference channel transmittance T2; The specific content of step S3 includes: Step S31, calculate the instantaneous change rate φ1 of the corrected main detection channel transmittance, and use the five-point difference method to calculate the instantaneous change rate; Step S32, extract the humidity interference characteristic quantity Q, and calculate it by using the sliding window integration method: Q = ∫(φ2)dt, and the integration time window is set to the preset time t, where φ2 is the change rate of the reference channel transmittance; Step S33, construct a temperature compensation matrix, divide the compensation interval according to the sensor surface temperature Ts, and randomly select the temperature compensation coefficient k according to the compensation interval; when Ts < 100 °C, k = 1.0; when 100 °C ≤ Ts < 150 °C, k = 0.95 + 0.001×(Ts - 100); when Ts ≥ 150 °C, k = 0.90 + 0.0005×(Ts - 150); After that, it also includes: Step S34, design a feature parameter fusion algorithm to generate a fusion feature parameter Φ with environmental compensation fusion, and the calculation formula is: Φ = φ1 / (1 + 0.2Q) × k; Step S35, implement the quality evaluation of the feature parameter, set the effective range of the fusion feature parameter Φ. When the Φ value exceeds the range, automatically start the data re-acquisition mechanism and mark the abnormal data flag bit.

2. The method for detecting gas concentration using an infrared sensor according to claim 1, wherein The step S1 also includes: Step S11, configure a two-channel infrared detection system, and set the central wavelength of the main detection channel to the target gas characteristic absorption peak of 4.26 μm; Step S12, establish a synchronous acquisition timing, set the sampling periods of the main detection channel and the reference channel to 0.5 s, and adopt a time-interleaved sampling method to control the sampling of the two channels within the preset time interval; Step S13: Install a micro-thermocouple array on the surface of the sensor housing. Set 4 temperature measurement points evenly distributed around the optical window of the sensor. Process the temperature data using the median filtering algorithm and output the surface temperature Ts of the sensor.

3. The method for detecting gas concentration using an infrared sensor according to claim 2, characterized in that, After the said step S13, it further includes: Step S14: Install a capacitive humidity sensor at the air inlet of the sensor. Process the raw data using the moving average filtering and output the ambient humidity Hr. Step S15: Design a data preprocessing module to perform outlier removal and noise filtering on the collected raw transmittance data. Among them, the transmittance T1 of the main detection channel and the transmittance T2 of the reference channel are smoothed using the adaptive Kalman filtering algorithm, and the width of the filtering window is set to 5 sampling points. Step S16: Construct a multi-parameter synchronous acquisition data set. Align the processed data of T1, T2, Ts, and Hr according to the time stamp and store them in a circular buffer. The capacity of the buffer is set to 60 groups of data, corresponding to a continuous monitoring duration of 30 s.

4. The method for detecting gas concentration by using an infrared sensor according to claim 1, wherein The said step S2 specifically includes: Step S21: Establish a humidity compensation function library. Select the corresponding compensation function F(Hr) according to the ambient humidity Hr to divide the compensation interval. Specifically: it is divided into three compensation intervals. When Hr < 60%, use the linear compensation function F1(Hr) = 1 + 0.02×(Hr - 40) / 20; when 60% ≤ Hr < 80%, use the quadratic compensation function F2(Hr) = 1 + 0.05 + 0.001×(Hr - 60)^2; when Hr ≥ 80%, use the exponential compensation function F3(Hr) = 1.1×exp(0.005×(Hr - 80)). Step S22: Implement dynamic correction of the transmittance of the main detection channel. Select the corresponding compensation function according to the real-time collected ambient humidity Hr, and calculate the corrected transmittance T1' of the main detection channel = T1×F(Hr). Step S23: Design a dew condensation risk warning mechanism. Calculate the instantaneous change rate φ2 of the transmittance T2 of the reference channel = ΔT2 / Δt. Set the change rate threshold to 0.15% / s. When φ2 exceeds the threshold for 3 consecutive sampling periods, trigger the dew condensation risk warning. Step S24: Construct an anti-dew condensation control strategy. When the dew condensation risk is detected, execute the anti-dew condensation mode. Step S25: Implement real-time evaluation of the compensation effect. Dynamically adjust the parameters of the compensation function by comparing the standard deviation change of the transmittance of the main detection channel before and after compensation. When the reduction amplitude of the standard deviation is less than 20%, automatically switch to a higher-order compensation function.

5. The method for detecting gas concentration by using an infrared sensor according to claim 1, characterized in that After the said step S35, it further includes: Step S36: Establish a fusion feature parameter optimization mechanism. Dynamically adjust the weight coefficient of the fusion algorithm by comparing the standard deviation of the Φ values in 10 consecutive sampling periods. When the standard deviation exceeds 0.02, automatically optimize the weight coefficient of the humidity interference feature quantity Q.

6. The method for detecting gas concentration using an infrared sensor according to claim 5, characterized in that, The said step S4 specifically includes: Step S41: Set the gas presence determination condition, requiring that the fusion feature parameters Φ in 5 consecutive sampling periods satisfy: Φ1 > 0.15%, φ2 > 0.18%, Φ3 > 0.20%, Φ4 > 0.18%, Φ5 > 0.15%. Step S42: Implement humidity interference verification, calculate the mean value Q_avg of the humidity interference characteristic quantity Q for the corresponding determination period. When Q_avg < 0.5, it is determined that the humidity interference is within the allowable range; otherwise, the determination is delayed and waiting for the next sampling period. Step S43: Design a determination result confirmation mechanism. When it is preliminarily determined that the gas exists, observe for 2 more sampling periods. If the subsequent Φ value continues to be greater than 0.12%, then confirm the determination result that the gas exists. Step S44: Implement hierarchical processing of the determination results. Divide the confidence level according to the number of sampling periods continuously meeting the determination conditions: 5 - 7 periods are low confidence level, 8 - 10 periods are medium confidence level, and more than 10 periods are high confidence level.

7. The method for detecting gas concentration using an infrared sensor according to claim 1, wherein The specific steps of step S5 include: Step S51: Design a gas presence flag bit status parsing mechanism. When the flag bit is 1, start the concentration calculation process; when the flag bit is 0, maintain the standby monitoring state and continuously update the environmental parameters. Step S52: Construct a temperature adaptive response value calculation model, and use a segmented weighted algorithm to calculate the response value β. Step S53: Implement dynamic calibration of the response value, and adjust the response value according to the environmental humidity Hr. Step S54: Design a concentration conversion algorithm, and use a non - linear conversion formula: C = α×β1^1.2 / (1 + 0.1β1), where α is the calibration coefficient, and the initial value is set to 1.0, and β1 is the adjusted response value. Step S55: Establish a concentration output smoothing mechanism, and use the exponential weighted moving average method to process and obtain the output concentration value C_output; specifically: C_output = 0.7×C_prev + 0.3×C_current, where C_prev is the previous output value and C_current is the current calculated value. Step S56: Design the data output format, and pack and output the output concentration value C_output together with the timestamp, temperature compensation coefficient k, and humidity interference characteristic quantity Q to form a complete data packet.

8. An infrared gas concentration sensor, characterized in that, The detection is implemented by using the method for detecting gas concentration using an infrared sensor as described in any one of claims 1 to 7. The gas concentration infrared sensor includes: A dual - channel infrared detection module, including a main detection channel and a reference channel, which are respectively used to detect the target gas absorption peak and the water vapor characteristic absorption peak, and is equipped with a beam splitting prism to achieve beam separation. An environmental parameter acquisition module, including a micro - thermocouple array installed on the surface of the sensor and a capacitive humidity sensor at the air inlet, which is used to collect the sensor surface temperature and environmental humidity in real - time. A signal processing module, which is used to filter, compensate, and calculate the fusion characteristic parameters for the collected transmittance data. A gas determination module, which determines the presence of gas based on the fusion characteristic parameters, and includes a determination condition setting unit and a confidence level evaluation unit. A concentration calculation module, which uses a temperature - adaptive response value calculation method and combines the environmental temperature parameter for concentration conversion.

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