Interference signal noise elimination method in ambient air non-dispersive infrared monitoring system

Abstract

The invention discloses an interference signal noise elimination method in an ambient air non-dispersive infrared monitoring system. The interference signal noise elimination method in the ambient air non-dispersive infrared monitoring system comprises the steps of eliminating high-frequency noise in a preprocessing process and eliminating low-frequency noise through a sub-band structure of a filter bank in a post-processing process. High-frequency noise is eliminated in the preprocessing process, and the method comprises the steps that optical signals are converted into initial digital signals, each initial digital signal comprises an output signal of a reference cell and an output signal of a measuring cell, and the output signal of the reference cell and the output signal of the measuring cell are a pair of pulse signals; classifying the plurality of initial digital signals; and by taking a preset pulse signal reference line as a reference, performing moving averaging on the pulse signals of the classified initial digital signals to eliminate high-frequency noise.

Description

Technical Field

[0001] The present application relates to the field of gas analysis, and in particular to a method for eliminating interference signal noise in an ambient air non-dispersive infrared monitoring system. Background Art

[0002] With the implementation of national environmental protection policies over the years, the state and the public have put forward higher and higher requirements for environmental quality. The concentration of greenhouse gases (such as CO, CO2, CH4, N2O, etc.) in the ambient air has been rising year by year, and the measurement technology based on non-dispersive infrared (ND infrared) analysis has been widely used and developed. In order to achieve the measurement of low-concentration pollutant gases and real-time synchronous measurement of multiple pollutant gas components, the multi gas filter correlation wheel (multi GFC wheel) based on ND infrared technology has been developed and applied. In the process of gas analysis based on ND infrared technology, it is necessary to distinguish the different measured signals with the help of signal detection algorithms. However, the measured signal belongs to a very low-level analog signal, so the additional noise will cause many problems in the signal analysis process. Therefore, the analog processing unit used to process the analog signal needs to be equipped with a low-drift, low-noise amplifier with stable characteristics, and the digital signal processing unit used to process the digital signal needs to consider noise elimination and signal separation based on the signal characteristics.

[0003] Among the noise elimination methods, adaptive methods that do not rely on signal or noise characteristics, methods using wavelet transform, independent component analysis, and blind methods have been studied in depth. The noise elimination method using adaptive algorithms is usually most effective when the reference signal and the primary signal are measured by two or more sensors, while the performance of noise elimination will be greatly reduced when the signal is measured by only one sensor. In addition, the independent component analysis method and the blind method require a large amount of calculation, so it is not conducive to the actual hardware implementation in the development of multiple pollutant measurement equipment that requires real-time processing. The noise elimination method based on wavelet transform uses mother wavelets of finite intervals to effectively decompose and synthesize the signal. Compared with the noise elimination method based on Fourier transform, it has the advantage of multi-resolution decomposition of the signal and can improve the resolution in the frequency domain and time domain. However, due to the complexity of the wavelet function, the noise elimination method based on wavelet transform makes it difficult to implement in hardware. Summary of the invention

[0004] One advantage of the present application is that it provides a method for eliminating interference signal noise in an ambient air non-dispersive infrared monitoring system, wherein the method for eliminating interference signal noise in an ambient air non-dispersive infrared monitoring system improves the accuracy and precision of noise elimination at the algorithm level, provides algorithm support for gas analysis equipment based on non-dispersive infrared analysis, and has relatively loose requirements on hardware, facilitating the implementation of hardware adapted thereto.

[0005] Another advantage of the present application is that it provides a method for eliminating interference signal noise in an ambient air non-dispersive infrared monitoring system, wherein the method for eliminating interference signal noise in an ambient air non-dispersive infrared monitoring system specifically eliminates different noises during pre-processing and post-processing, thereby improving the progress and accuracy of non-dispersive infrared measurement.

[0006] Another advantage of the present application is that it provides a method for eliminating interference signal noise in an ambient air non-dispersive infrared monitoring system, wherein the method for eliminating interference signal noise in an ambient air non-dispersive infrared monitoring system eliminates rapidly changing high-frequency noise in the preprocessing process of time domain characteristics, and can obtain strong resistance to rapidly changing high-frequency noise. In the post-processing process of considering the power ratio through the sub-band structure of the filter group, the sub-band noise elimination technology of the filter group is used to eliminate the slowly changing low-frequency noise, which can improve the resistance to slowly changing low-frequency noise.

[0007] According to one aspect of the present application, a method for eliminating interference signal noise in an ambient air non-dispersive infrared monitoring system is provided, which comprises the steps of:

[0008] Eliminating high-frequency noise in a preprocessing process includes the following steps: converting an optical signal into an initial digital signal, wherein each of the initial digital signals includes an output signal of a reference cell and an output signal of a measuring cell, and the output signal of the reference cell and the output signal of the measuring cell are a pair of pulse signals; classifying a plurality of the initial digital signals; eliminating high-frequency noise by moving average the pulse signals of the classified initial digital signals based on a preset pulse signal baseline; and

[0009] In the post-processing process, low-frequency noise is eliminated through the sub-band structure of the filter bank.

[0010] In one embodiment of the method for eliminating interference signal noise in the ambient air non-dispersive infrared monitoring system described in the present application, the optical signal is converted into an initial digital signal, including the steps of: converting the optical signal into an analog electrical signal through a non-dispersive infrared detector; amplifying the analog electrical signal through a preamplifier to form an amplified analog signal; converting the amplified analog signal through an analog-to-digital converter to form the initial digital signal.

[0011] In one embodiment of the interference signal noise elimination method in the ambient air non-dispersive infrared monitoring system described in the present application, multiple initial digital signals are classified, including the steps of: generating a trigger signal based on the initial digital signal; counting the number of pulses in the continuous pulse signal based on the threshold of the trigger signal; wherein, if the number of pulses in the pulse signal of the (nl)th to nth pulse signal in the initial digital signal is less than the preset pulse signal baseline, the number of target pulses is recorded as 0; if the number of pulses in the pulse signal of the (nl)th to nth pulse signal in the initial digital signal is less than the preset pulse signal baseline and the number of target pulses is recorded as 0, the measurement pool position expression value used to express the position of the measurement pool or the reference pool position expression value used to express the position of the reference pool is increased by 1; l is a set parameter used to select the output value of the number of pulses that is not affected by high-frequency noise and is greater than the threshold of the trigger signal.

[0012] In one embodiment of the interference signal noise elimination method in the ambient air non-dispersive infrared monitoring system described in the present application, the pulse signal after moving average is calculated by the following formula: sig MA (k+CNT)=avg γ {sig(n)},10≤γ≤20; where sig MA represents the pulse signal after moving average; k is the index of the external trigger; γ represents a constant that depends on fs; avg γ {sig(n)} represents the average value of the first γ% after sig(n) is arranged in descending order.

[0013] In one embodiment of the method for eliminating interference signal noise in the ambient air non-dispersive infrared monitoring system described in the present application, low-frequency noise is eliminated through the sub-band structure of the filter group in the post-processing process, including the steps of: estimating the power of each sub-band structure of the filter group; estimating the power of the full frequency band based on the estimated value of the power of each sub-band structure of the filter group; and removing noise based on the estimated value of the power of the full frequency band.

[0014] In one embodiment of the interference signal noise elimination method in the ambient air non-dispersive infrared monitoring system according to the present application, the power of each sub-band structure of the filter bank is estimated by the following formula: i(n) = (1-α) x Di (n-1)+α x Di (n); where x Di (n) = x i (n2 i-1 ); i = 1, 2, 3,…, n.

[0015] In one embodiment of the interference signal noise elimination method in the ambient air non-dispersive infrared monitoring system according to the present application, the power of each sub-band structure of the filter bank is estimated by the following formula: i (n) = (1-α) x i (n-1)+αx i (n); where x i (n) represents the output signal of the i-th frequency band at time n; i = 1, 2, 3,…, n.

[0016] In one embodiment of the interference signal noise elimination method in the ambient air non-dispersive infrared monitoring system according to the present application, the power of the full frequency band is estimated by the following formula:

[0017] In one embodiment of the method for eliminating interference signal noise in the ambient air non-dispersive infrared monitoring system according to the present application, the noise is removed by the following formula: i (n) = βγ i (n)x i (n).

[0018] Further objectives and advantages of the present application will be fully reflected through understanding of the following description and drawings.

[0019] These and other objects, features and advantages of the present application are fully reflected in the following detailed description, drawings and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or.

[0021] Figure 1 The figure illustrates a flowchart schematic diagram of a method for eliminating interference signal noise in an ambient air non-dispersive infrared monitoring system according to an embodiment of the present application.

[0022] Figure 2The figure illustrates a flowchart schematic diagram of signal processing for performing multiple pollutant analysis by non-dispersive infrared analysis method in a method for eliminating interference signal noise in an ambient air non-dispersive infrared monitoring system according to an embodiment of the present application.

[0023] Figure 3 A flowchart schematic diagram of a signal processing detection method for distinguishing signals between measurement items in a method for eliminating interference signal noise in an ambient air non-dispersive infrared monitoring system according to an embodiment of the present application is illustrated.

[0024] Figure 4 A schematic diagram of the structure of a filter bank with a uniform tree structure is shown.

[0025] Figure 5 The figure shows a schematic diagram of the structure of a non-uniform tree structure filter bank.

[0026] Fig. 6A The diagram illustrates a schematic diagram of a non-dispersive detector output and a trigger signal in a simulation verification of an interference signal noise elimination method in a non-dispersive infrared monitoring system for ambient air according to an embodiment of the present application.

[0027] Figure 6B The diagram illustrates a schematic diagram of a gas signal measured in a pulse signal during simulation verification of an interference signal noise elimination method in an ambient air non-dispersive infrared monitoring system according to an embodiment of the present application.

[0028] Fig. 7A The diagram illustrates a state diagram of a method for eliminating interference signal noise in a non-dispersive infrared monitoring system for ambient air according to an embodiment of the present application, in which a baseline level includes noise in a simulation verification.

[0029] Figure 7B The diagram illustrates a state diagram of a method for eliminating interference signal noise in a non-dispersive infrared monitoring system for ambient air according to an embodiment of the present application after noise is removed by a noise elimination algorithm with a sub-band structure in a simulation verification.

[0030] Fig. 8A The diagram illustrates a schematic diagram of the comparison results of a noisy reference unit signal of NO and a denoised reference unit signal in a simulation verification of an interference signal noise elimination method in an ambient air non-dispersive infrared monitoring system according to an embodiment of the present application.

[0031] Figure 8B The diagram illustrates a schematic diagram of the comparison result between another NO noisy reference unit signal and a denoised reference unit signal in a simulation verification of the interference signal noise elimination method in the ambient air non-dispersive infrared monitoring system according to an embodiment of the present application.

[0032] Fig. 9AThe figure shows a schematic diagram of the comparison result of the noisy reference unit signal of CO and the denoised reference unit signal in the simulation verification of the interference signal noise elimination method in the ambient air non-dispersive infrared monitoring system according to an embodiment of the present application.

[0033] Fig. 9B The figure shows a schematic diagram of the comparison result between a noisy reference unit signal of another CO and a denoised reference unit signal in a simulation verification of the interference signal noise elimination method in the ambient air non-dispersive infrared monitoring system according to an embodiment of the present application.

[0034] Fig. 10A The figure shows a schematic diagram of the comparison result between the noisy reference unit signal of SO2 and the denoised reference unit signal in the simulation verification of the interference signal noise elimination method in the ambient air non-dispersive infrared monitoring system according to an embodiment of the present application.

[0035] Fig. 10B The diagram illustrates a comparison result diagram of a noisy reference unit signal of another SO2 and a denoised reference unit signal in a simulation verification of the interference signal noise elimination method in the ambient air non-dispersive infrared monitoring system according to an embodiment of the present application.

[0036] Fig.11A The diagram illustrates a schematic diagram of the comparison results between the noisy reference unit signal and the measurement unit signal of NO in the simulation verification of the interference signal noise elimination method in the ambient air non-dispersive infrared monitoring system according to an embodiment of the present application.

[0037] Fig. 11B The diagram illustrates a schematic diagram of comparison results of a noisy reference unit signal and a measurement unit signal of another NO in a simulation verification of a method for eliminating interference signal noise in a non-dispersive infrared monitoring system for ambient air according to an embodiment of the present application.

[0038] Fig. 12A The figure shows a schematic diagram of the comparison result between the noisy reference unit signal and the measurement unit signal of CO in the simulation verification of the interference signal noise elimination method in the ambient air non-dispersive infrared monitoring system according to an embodiment of the present application.

[0039] Fig. 12B The figure shows a schematic diagram of the comparison result of a noisy reference unit signal and a measurement unit signal of another CO in a simulation verification of the interference signal noise elimination method in an ambient air non-dispersive infrared monitoring system according to an embodiment of the present application.

[0040] Fig.13A The diagram illustrates a schematic diagram of the comparison result between the noisy reference unit signal and the measurement unit signal of SO2 in the simulation verification of the interference signal noise elimination method in the ambient air non-dispersive infrared monitoring system according to an embodiment of the present application.

[0041] Fig. 13B The diagram shows a schematic diagram of comparison results of a noisy reference unit signal and a measurement unit signal of another SO2 in a simulation verification of a method for eliminating interference signal noise in a non-dispersive infrared monitoring system for ambient air according to an embodiment of the present application. DETAILED DESCRIPTION

[0042] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0043] It is understood that the term "one" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple, and the term "one" cannot be understood as a limitation on the number. "Multiple" means greater than or equal to two.

[0044] Although ordinals such as "first," "second," and the like will be used to describe various components, those components are not limited herein. The term is used only to distinguish one component from another. For example, a first component may be referred to as a second component, and likewise, a second component may be referred to as a first component without departing from the teachings of the present application. The term "and / or" as used herein includes any and all combinations of one or more associated listed items.

[0045] The terms used herein are only used for the purpose of describing various embodiments and are not intended to be limiting. As used herein, the singular form is intended to include the plural form, unless the context clearly indicates an exception. It will also be understood that the terms "including" and / or "having" when used in this specification specify the presence of the described features, numbers, operations, components, elements, or combinations thereof, without excluding the presence or addition of one or more other features, numbers, operations, components, elements, or combinations thereof.

[0046] like Figures 1 to 13BAs shown, the interference signal noise elimination method in the non-dispersive infrared monitoring system of ambient air according to the embodiment of the present application is explained. Taking into account that the gas analysis equipment based on the non-dispersive infrared (ND infrared) analysis method currently has certain limitations in hardware in improving the accuracy and precision of noise elimination due to the constraints of factors such as the overall manufacturing level and the working principle of the device, the present application improves the accuracy and precision of noise elimination at the algorithm level, and provides algorithm support for the gas analysis equipment based on the non-dispersive infrared analysis method. Furthermore, when designing the algorithm, the present application takes into account the hardware feasibility of the gas analysis equipment based on the non-dispersive infrared (ND infrared) analysis method currently, so that the interference signal noise elimination method in the non-dispersive infrared monitoring system of ambient air of the present application has relatively loose requirements on hardware, which is convenient for the implementation of hardware adapted thereto.

[0047] The following is a more detailed description of the principle and implementation of the interference signal noise elimination method in the ambient air non-dispersive infrared monitoring system of the present application.

[0048] The interference signal noise elimination method in the ambient air non-dispersive infrared monitoring system eliminates different noises in a targeted manner during the pre-processing process and the post-processing process, and can improve the progress and accuracy of the non-dispersive infrared measurement. Specifically, the interference signal noise elimination method in the ambient air non-dispersive infrared monitoring system eliminates the rapidly changing high-frequency noise during the pre-processing process of the time domain characteristics, and can obtain strong resistance to the rapidly changing high-frequency noise. In the post-processing process of considering the power ratio through the sub-band structure of the filter group, the sub-band noise elimination technology of the filter group is used to eliminate the slowly changing low-frequency noise, and can improve the resistance to the slowly changing low-frequency noise.

[0049] Accordingly, the method for eliminating interference signal noise in the ambient air non-dispersive infrared monitoring system includes step S110, eliminating high-frequency noise in a pre-processing process; and step S120, eliminating low-frequency noise in a post-processing process.

[0050] Specifically, the gas analysis equipment based on the non-dispersive infrared (ND) analysis method includes a non-dispersive infrared detector, wherein the non-dispersive infrared detector includes a multi-path optical absorption chamber (MOA), an optical interference filter and an infrared sensor. The multi-path optical absorption chamber includes a multi gas filter correlation wheel (multiGFC wheel) and a reflector. The multi-gas correlation filter wheel is connected to a reference cell filled with a high-concentration standard gas and a measuring cell filled with nitrogen by alternating switching, so as to offset the influence of interfering components, thereby eliminating the influence of other interfering substances during measurement.

[0051] The wide-wavelength infrared light emitted from the light source passes through the rotating multi-gas correlation filter wheel and is alternately transmitted through the reference cell and the measuring cell. In this way, the generated optical signal forms a reference pulse and a measuring pulse in the multi-path light absorption cavity. During the period when the measuring pulse occurs, the measuring cell filled with nitrogen basically does not absorb the infrared light, and the multi-path light absorption cavity will absorb the infrared light corresponding to the concentration of the measuring gas. On the contrary, during the period when the reference pulse occurs, since the high-concentration standard gas in the measuring cell absorbs most of the infrared light of a specific wavelength, the absorption degree generated by the measuring gas with a lower concentration than the standard gas in the multi-path light absorption cavity can be ignored.

[0052] The light emitted from the multi-path light absorption cavity passes through an optical interference filter, wherein the optical interference filter allows only light of a preset wavelength to enter the infrared sensor. The light entering the infrared sensor is converted into a voltage signal and then amplified. The degree of light absorption depends on the concentration of the substance; the concentration and absorbance follow the Beer-Lambert Law, and the formula is as follows: A(λ)=ε(λ)LC, wherein A(λ) represents the absorbance at wavelength λ, ε(λ) represents the absorption coefficient at wavelength λ, L represents the transmission distance of light (the transmission distance of light is equal to the light path length in the multi-path light absorption cavity), and C represents the gas concentration. It can be seen from the Beer-Lambert Law that the absorbance is proportional to the length of the multi-path light absorption cavity and the gas concentration. Therefore, when the absorption coefficient ε is large enough and the target gas is measured by monochromatic light, the concentration of the target gas can be calculated according to A(λ)=ε(λ)LC by measuring the intensity of the transmitted light without being interfered by other gases. By calculating the absorbance difference (differential absorption) between the reference cell output signal and the measurement cell output signal corresponding to the infrared light of each channel, the concentration of the measured gas can be obtained. The non-dispersive infrared detector alternately detects the light passing through a pair of reference cells and measurement cells, so the output signal of the non-dispersive infrared detector presents a pulse form modulated by the reference pulse and the measurement pulse.

[0053] In step S110, the signal processing of the analysis of various pollutants is performed by the non-dispersive infrared (ND) analysis method. The optical signal passes through the non-dispersive infrared detector to form an analog electrical signal; the analog electrical signal formed after passing through the non-dispersive infrared detector has a low level; the analog electrical signal formed after passing through the non-dispersive infrared detector is amplified by a preamplifier to form an amplified analog signal; the amplified analog signal is converted by an analog-to-digital converter to form an initial digital signal. In other words, the optical signal is converted into an initial digital signal by the non-dispersive infrared detector, the preamplifier, and the analog-to-digital converter in sequence.

[0054] Since the initial digital signals of multiple measurement items are continuous, it is necessary to accurately distinguish each of the initial digital signals to determine which measurement item each initial digital signal corresponds to, so as to facilitate subsequent analysis. To this end, an external optical trigger sensor outside the rotating multi-gas correlation filter wheel is used to classify the multiple initial digital signals.

[0055] Specifically, each of the initial digital signals includes an output signal of a reference cell and an output signal of a measuring cell, and is in a form similar to a pulse signal sequence. Corresponding to each measurement item, the output signal of the reference cell and the output signal of the measuring cell are a pair of pulse signals.

[0056] In order to distinguish the initial digital signal of the corresponding measurement item, the trigger signal and threshold value provided by the optical coupler attached to the multi-gas correlation filter wheel are used to accurately find the synchronization of a cycle of the multi-gas correlation filter wheel. Based on the threshold value of the trigger signal, the number of pulses in the continuous pulse signal is calculated to find the output signal of the reference pool and the output signal of the measurement pool of the corresponding measurement item. In the process of calculating the number of pulses in the continuous pulse signal based on the threshold value of the trigger signal to find the output signal of the reference pool and the output signal of the measurement pool of the corresponding measurement item, the baseline level of the pulse signal will be affected by the high-frequency noise caused by various analog components and mechanical rotation, as well as the drifting low-frequency noise caused by the temperature characteristics and thermal noise of the non-dispersive infrared detector. Due to the high-frequency noise added to the pulse signal amplitude, errors may occur when using a fixed threshold value THCNT to calculate the number of pulses, so the following method is used to count the number of pulses in the pulse signal:

[0057]

[0058] Among them, l is a set parameter used to select the output value of the number of pulses that is not affected by high-frequency noise and is greater than a preset threshold; if the number of pulses in the pulse signal of the (nl)th to nth pulse signals in the initial digital signal is less than the preset pulse signal baseline, the number of target pulses is recorded as 0; if the number of pulses in the pulse signal of the (nl)th to nth pulse signals in the initial digital signal is less than the preset pulse signal baseline and the number of target pulses is recorded as 0, the measurement pool position expression value used to express the position of the measurement pool or the reference pool position expression value used to express the position of the reference pool is increased by 1.

[0059] When the concentration of the measurement item to be measured (eg, SO2) is high, the absorption of the infrared light source in the measuring cell increases, resulting in a decrease in the amplitude of the output signal of the measuring cell. Therefore, the signal-to-noise ratio decreases, and difficulty arises in counting the number of pulses.

[0060] In addition, when obtaining valid data from the pulse signal corresponding to the classified measurement items, the influence of high-frequency noise on the peak value of the pulse signal must also be considered. In order to obtain valid data from the peak value of the pulse signal contaminated by high-frequency noise, the pulse signal of the classified initial digital signal is moved averaged (MA) based on the preset pulse signal baseline to eliminate high-frequency noise.

[0061] sig MA (k+CNT)=avg γ {sig(n)},10≤γ≤20;

[0062] Among them, sig MA represents the pulse signal after moving average; k is the index of the external trigger; γ represents a constant that depends on fs; avg γ {sig(n)} represents the average value of the first γ% after sig(n) is arranged in descending order.

[0063] After the signals of each measurement item are classified, the reference voltage of the corresponding signal is predicted. The measurement value is determined by taking the average value (mean) of the first γ% of the data above the predicted reference voltage. As a noise removal technology in the pre-processing process, it can quickly remove rapidly changing high-frequency noise. The value of γ can be selected according to the needs, for example, 20.

[0064] Correspondingly, step S110 includes the steps of: S111, converting the optical signal into an initial digital signal, wherein each of the initial digital signals includes an output signal of a reference cell and an output signal of a measuring cell, and the output signal of the reference cell and the output signal of the measuring cell are a pair of pulse signals; S112, classifying the multiple initial digital signals; S113, eliminating high-frequency noise by performing moving averaging on the pulse signals of the classified initial digital signals based on a preset pulse signal baseline.

[0065] Step S111, converting the optical signal into an initial digital signal, includes the following steps: S1111, converting the optical signal into an analog electrical signal through a non-dispersive infrared detector; S1112, amplifying the analog electrical signal through a preamplifier to form an amplified analog signal; S1113, converting the amplified analog signal through an analog-to-digital converter to form the initial digital signal.

[0066] Step S112, classifying the plurality of said initial digital signals, specifically, using a light trigger sensor to classify the plurality of said initial digital signals, wherein the light trigger sensor is outside the rotating multi-gas related filter wheel. Step S112, includes step S1121, generating a trigger signal based on the initial digital signal; S1122, counting the number of pulses in the continuous pulse signal based on the threshold of the trigger signal; wherein, if the number of pulses in the pulse signal of the (nl)th to nth pulse signal in the initial digital signal is less than the preset pulse signal baseline, the number of target pulses is recorded as 0; if the number of pulses in the pulse signal of the (nl)th to nth pulse signal in the initial digital signal is less than the preset pulse signal baseline and the number of target pulses is recorded as 0, the measurement pool position expression value used to express the position of the measurement pool or the reference pool position expression value used to express the position of the reference pool is increased by 1; l is a set parameter, which is used to select the output value of the number of pulses that is not affected by high-frequency noise and is greater than the threshold of the trigger signal.

[0067] In step S113, sig MA (k+CNT)=avg γ {sig(n)},10≤γ≤20; where sig MA represents the pulse signal after moving average; k is the index of the external trigger; γ represents a constant that depends on fs; avg γ {sig(n)} represents the average value of the first γ% after sig(n) is arranged in descending order.

[0068] In order to improve the accuracy of the measured data, the synchronization of the external synchronization signal (trigger signal) and the target signal detection algorithm is carried out continuously, and the number of data samples and the estimation of the reference voltage of each measurement item are repeatedly confirmed and executed. As a noise removal technology in this pre-processing process, the fast-changing high-frequency noise generated by the detection part including the sensor and the pre-processing amplifier and the mechanical part including the multi-gas correlation filter wheel can be effectively removed, but the slowly changing low-frequency noise generated by the driving circuit of the sensor part and the stepper motor (step motor) driving the multi-gas correlation filter wheel is more difficult to remove. For this reason, the sub-band noise removal technology based on the filter group is adopted as a post-processing method. By passing the noisy signal through each sub-band decomposition filter group, the noise is removed from the power of the obtained sub-band signal. In general, unlike the noise that is uniformly distributed throughout the frequency range, the signal is distributed in a limited frequency range, so after passing through the decomposition filter group, the frequency of the signal will appear in the corresponding frequency interval. When the signal-to-noise ratio (SNR) is high, the power of the signal is large relative to the power of the added noise, so the power of the sub-band containing the signal will be larger than that of other sub-bands. Noise removal is performed by utilizing the frequency characteristics and power relationship between the signal and the noise. As Figure 4 The filter bank with a uniform tree structure shown in the figure has the same resolution in each frequency band, so even if the actual signal is contained in any frequency band, the noise removal performance will not be reduced. In addition, each sub-band has the same decomposition / synthesis filter and down / up sampling number, so there is no need to consider the delay difference caused by the down sampling rate and sub-band filtering during hardware implementation, which facilitates the implementation of the system.

[0069] Generally speaking, the actual target signal is distributed in a frequency band lower than the low-frequency noise. Figure 4 Each frequency band has uniform resolution, Figure 5 The low-frequency band contained in the actual signal is decomposed into a uniform and uneven frequency band structure, thereby improving the resolution of the low-frequency band. Since the frequency components of the actual signal are mostly distributed in the low-frequency band, it will not cause a decrease in noise removal performance, and the hardware cost can be significantly reduced. If the frequency components of the signal are in the high-frequency band, a structure that repeatedly divides the high-frequency band instead of the low-frequency band can be used. However, when the target signal is distributed in both low-frequency and high-frequency areas, the degradation of noise removal performance may be very serious, and the hardware implementation needs to accurately calculate the downsampling rate of each sub-band and the delay difference caused by sub-band filtering, so it faces certain difficulties. Therefore, it is necessary to appropriately select the detection signal according to its characteristics. Figure 4 or Figure 5 structure.

[0070] The subband power estimation method used for noise removal on each subband is as follows:

[0071] P i =σ i 2 ; Where Pi represents the estimated value of the subband power of the filter bank; σ i 2 Represents the variance of the signal output from the i-th frequency band.

[0072] Only when the statistical characteristics of the output signals of each sub-band are fully understood can the P i =σ i 2 It is impossible to directly obtain these characteristics in order to realize sub-band power estimation. The leakage degree parameter α between the sub-band output signal power at control time n and the past power can be used to perform the following recursive estimation:

[0073] P i (n) = (1-α) x i (n-1)+αx i (n).

[0074] Figure 5 The unevenly decomposed sub-band structures shown in FIG. 1 need to consider the number of downsampling times of each frequency band due to the inconsistent downsampling rates of the signal sequences, and recursively calculate the sub-band output signals in the following way:

[0075] P i (n) = (1-α) x D i (n-1)+α x Di (n); where x Di (n) = x i (n2 i-1 ), where i = 1, 2, 3, ..., xi(n) represents the output signal of the i-th frequency band at time n.

[0076] When using Figure 4 When the uniform tree structure is used, since there is no need to consider the number of downsampling, the subband output signal is recursively calculated in the following way:

[0077] P i (n) = (1-α) x i (n-1)+αx i (n), i = 1, 2, 3, ...; where x i (n) represents the output signal of the ith frequency band at time n. Using the sub-band power, the power of the full frequency band can be estimated as follows: The power ratio is calculated based on the ratio of the signal power to the noise power in the corresponding sub-band power as follows:

[0078]

[0079] The method for noise elimination based on power ratio of the present application is similar to the soft threshold value selection method in noise elimination using threshold. Generally speaking, the method using soft threshold has better performance than the hard threshold method, but it is more difficult to implement. However, although the method for noise elimination based on power ratio of the present application is similar to the soft threshold method, it has the advantage of being simpler to implement.

[0080] By v i (n) = βγ i (n)x i (n) Each subband signal after noise removal is restored by up sampling and synthesis filter. In order for the non-uniform tree structure and the uniform tree structure to obtain the same subband decomposition capability, the same level of subband decomposition is required. In the case of 4 layers, the number of subbands in the uniform tree structure is 16, while the number of subbands in the non-uniform tree structure is 8. Therefore, the uniform tree structure will lead to a significant increase in system cost compared to the non-uniform tree structure. However, when using a non-uniform structure, it is necessary to solve the problem of severe degradation of noise eliminator performance and delay mismatch caused by the difference in the number of downsampling / upsampling and decomposition / synthesis filters during hardware implementation. Therefore, a noise eliminator based on a uniform tree structure is adopted.

[0081] Accordingly, in step S120, low-frequency noise is eliminated by the subband structure of the filter bank. Step S120 includes the steps of: S121, estimating the power of each subband structure of the filter bank; S122, estimating the power of the full frequency band based on the estimated value of the power of each subband structure of the filter bank; S123, removing noise based on the estimated value of the power of the full frequency band.

[0082] In step S121, by P i (n) = (1-α) x Di (n-1)+α x Di (n) or P i (n) = (1-α) x i (n-1)+αx i (n), i = 1, 2, 3, ... Estimate the power of each subband structure of the filter bank: P i (n) = (1-α) x i (n-1)+αx i (n); where x Di (n) = x i (n2 i-1 ), i = 1, 2, 3, ..., n; x i(n) represents the output signal of the i-th frequency band at time n.

[0083] In step S122, by Estimate the power of the full frequency band.

[0084] In step S123, by v i (n) = βγ i (n)x i (n) Remove noise.

[0085] In the present application, the interference signal noise elimination method in the non-dispersive infrared monitoring system of ambient air is verified by computer simulation to evaluate the application effect of the interference signal noise elimination method in the non-dispersive infrared monitoring system of ambient air. In the process of computer simulation verification of the interference signal noise elimination method in the non-dispersive infrared monitoring system of ambient air, the standard mixed gas used is shown in Table 1, and the flow rate is maintained at 1000 [cc / min] by using an orifice.

[0086] Table 1. Standard gas mixtures used in the experiments

[0087]

[0088] Fig. 6A and Figure 6B The measurement order of the output signal of the ND infrared detector, the external trigger signal and the pulse signal measured within 20 [seconds] is shown. In the multi-signal detection algorithm, the rotation speed of multiple GFC wheels is K = 0.655 [cycle / sec], the sampling frequency is fs = 1 [kHz], and in order to remove high-frequency noise, the upper data processing parameter γ = 20 is used. The analysis filter bank used for the post-processing process in the simulation is a length 2 analysis filter bank obtained based on the Hadamard transform, using a uniform 4 and 16 subband structure.

[0089] Fig. 6A The non-dispersive detector output and trigger signal are shown; Figure 6B The gas signals measured in the pulse signal are shown (a = sample.HCI, b = reference.CO, c = sample.CO, d = reference.NO, e = sample.NO, f = reference.SO2, g = sample.SO2, h = reference.H2O, i = sample.H2O, j = none, k = none, l = reference.HCI). Fig. 7A and Figure 7B The states of the reference level including noise and the states after the noise is removed by the noise elimination algorithm with 4 and 16 subband structures are shown. The change of the reference level will affect the measured values ​​of each pollutant item in the infrared measurement signal.

[0090] Fig. 8A and Figure 8B The comparison results of the noisy reference unit signal and the denoised reference unit signal of NO are shown. Fig. 9A and Fig. 9B The comparison results of the noisy reference unit signal and the denoised reference unit signal of CO are shown. Fig. 10A and Fig. 10B The comparison results of the noisy reference unit signal and the denoised reference unit signal of SO2 are shown. The subband structure used for denoising is a uniform tree filter bank with 4 and 16 subband structures. It can be seen that the changes in the measured values ​​of each pollutant item depend on the changes in the reference level in Figure 6. In addition, the noise removal performance improves with the increase in the number of subband decompositions. From these results, it can be easily inferred that the increase in resolution with the increase in the number of subbands can more accurately remove the noise signal.

[0091] Fig.11A and Fig. 11B The comparison results of the noisy reference cell signal and the measured cell signal of NO are shown. Fig. 12A and Fig. 12B The comparison results of the noisy reference cell signal and the measurement cell signal of CO are shown. Fig.13A and Fig. 13B The comparison results of the noisy reference cell signal and the measurement cell signal of SO2 are shown. As can be seen from the figure, the change trends of the reference cell signal and the measurement cell signal of each gas item are similar. The GFC method cancels the influence of interfering components by alternately switching between the reference cell filled with high-concentration standard gas and the measurement cell filled with nitrogen, thereby removing the influence of other interfering substances during measurement. Even if the reference level changes, the infrared signal measurement value of each gas item depends on the change of the reference level, but the influence of drift can be reduced by the mutual cancellation effect.

[0092] The analysis filter of the subband filter bank used in the experiment is a coefficient of length 2 obtained by Hadamard transform. At this time, although the cutoff characteristics of each analysis filter are reduced, in order to obtain higher subband resolution, more decomposition will not cause delays caused by the length of the analysis filter and multiplication operations in the convolution required for subband decomposition. After downsampling at the maximum downsampling rate, the processing of each filter will not overlap, so noise elimination is performed and restored by a shorter length synthesis filter (since the length of the synthesis filter should be the same as the length of the analysis filter, the length of the synthesis filter is 2). The reason why noise elimination appears in the form of block processing within a certain time interval is that noise elimination is performed after downsampling after subband decomposition occurs. However, due to the reduction in sampling rate caused by downsampling, this reduces the amount of calculation and memory when implementing the noise eliminator, etc., which has the advantage of reducing hardware cost and making the implementation of low-specification systems possible.

[0093] In summary, the interference signal noise elimination method in the ambient air non-dispersive infrared monitoring system is explained. The interference signal noise elimination method in the ambient air non-dispersive infrared monitoring system improves the accuracy and precision of noise elimination at the algorithm level, provides algorithm support for gas analysis equipment based on non-dispersive infrared analysis, and has relatively loose requirements on hardware, which facilitates the implementation of hardware adapted thereto. Specifically, the interference signal noise elimination method in the ambient air non-dispersive infrared monitoring system eliminates different noises in a targeted manner during the pre-processing process and the post-processing process, effectively removing low-frequency and high-frequency noises of different natures.

[0094] The present application and its implementation methods are described above, and such description is not restrictive. The drawings show only one implementation method of the present application, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it and design structural methods and embodiments similar to the technical solution without creative design without departing from the inventive purpose of the present application, they should all fall within the protection scope of the present application.

Claims

1. A method for eliminating interference signal noise in an ambient air non-dispersive infrared monitoring system, characterized in that: Includes steps: Eliminating high-frequency noise in a preprocessing process includes the following steps: converting an optical signal into an initial digital signal, wherein each of the initial digital signals includes an output signal of a reference cell and an output signal of a measuring cell, and the output signal of the reference cell and the output signal of the measuring cell are a pair of pulse signals; classifying a plurality of the initial digital signals; eliminating high-frequency noise by moving average the pulse signals of the classified initial digital signals based on a preset pulse signal baseline; and In the post-processing process, low-frequency noise is eliminated through the sub-band structure of the filter bank.

2. The method for eliminating interference signal noise in the ambient air non-dispersive infrared monitoring system according to claim 1, characterized in that: Converting the optical signal into an initial digital signal includes the following steps: The optical signal is converted into an analog electrical signal through a non-dispersive infrared detector; Amplifying the analog electrical signal through a preamplifier to form an amplified analog signal; The amplified analog signal is converted through an analog-to-digital converter to form the initial digital signal.

3. The method for eliminating interference signal noise in the ambient air non-dispersive infrared monitoring system according to claim 2, characterized in that: The plurality of initial digital signals are classified, comprising the steps of: generating a trigger signal based on the initial digital signal; The number of pulses in a continuous pulse signal is counted based on a threshold of a trigger signal; wherein, if the number of pulses in a pulse signal from the (nl)th to the nth pulse signal in the initial digital signal is less than a preset pulse signal baseline, the number of target pulses is recorded as 0; if the number of pulses in a pulse signal from the (nl)th to the nth pulse signal in the initial digital signal is less than a preset pulse signal baseline and the number of target pulses is recorded as 0, a measurement pool position expression value for expressing the position of a measurement pool or a reference pool position expression value for expressing the position of a reference pool is increased by 1; l is a set parameter for selecting an output value of the number of pulses that is not affected by high-frequency noise and is greater than the threshold of a trigger signal.

4. The method for eliminating interference signal noise in the ambient air non-dispersive infrared monitoring system according to claim 3, characterized in that: The pulse signal after moving average is calculated by the following formula: sig MA (k+CNT)=avg γ {sig(n)},10≤γ≤20; where sig MA represents the pulse signal after moving average; k is the index of the external trigger; γ represents a constant that depends on fs; avg γ {sig(n)} represents the average value of the first γ% after sig(n) is arranged in descending order.

5. The method for eliminating interference signal noise in the ambient air non-dispersive infrared monitoring system according to claim 1, characterized in that: In the post-processing process, low-frequency noise is eliminated through the sub-band structure of the filter bank, including the steps: Estimating the power of each subband structure of the filter bank; estimating the power of the entire frequency band based on the estimated values ​​of the power of each subband structure of the filter bank; Noise is removed based on an estimate of the power of the entire frequency band.

6. The method for eliminating interference signal noise in the ambient air non-dispersive infrared monitoring system according to claim 5, characterized in that: The power of each subband structure of the filter bank is estimated by the following formula: P i (n)=(1-α)x Di (n-1)+αx Di (n); Among them, x Di (n) = x i (n2 i-1 ); i = 1, 2, 3,…, n.

7. The method for eliminating interference signal noise in the ambient air non-dispersive infrared monitoring system according to claim 5, characterized in that: The power of each subband structure of the filter bank is estimated by the following formula: P i (n) = (1-α) x i (n-1)+αx i (n); where x i (n) represents the output signal of the i-th frequency band at time n; i = 1, 2, 3,…, n.

8. The method for eliminating interference signal noise in the ambient air non-dispersive infrared monitoring system according to claim 5, characterized in that: The power of the full frequency band is estimated by:

9. The method for eliminating interference signal noise in the ambient air non-dispersive infrared monitoring system according to claim 8, characterized in that: The noise is removed by: i (n) = βγ i (n)x i (n).

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