CN111024650B - Method and device for detecting gas concentration in glass medicine bottle based on signal sparse reconstruction - Google Patents
Method and device for detecting gas concentration in glass medicine bottle based on signal sparse reconstruction Download PDFInfo
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Abstract
The invention discloses a method and a device for detecting gas concentration in a glass medicine bottle based on signal sparse reconstruction, wherein the method comprises the following steps: step 1, detecting a glass medicine bottle based on a TDLAS/WMS technology to obtain a second harmonic signal S1 of a single period; step 2, carrying out period prolongation on the second harmonic signal S1 to obtain a second harmonic signal S2 containing t periods; step 3, performing discrete Fourier transform on the second harmonic signal S2 to obtain a spectrogram; step 4, extracting the central frequency point of the second harmonic signal and the spectral line components at the adjacent frequency doubling points on the spectrogram, and then performing inverse discrete Fourier transform on the extracted spectral line components to obtain a reconstructed second harmonic signal S3; and 5, obtaining the gas concentration in the glass medicine bottle based on the peak value/peak value of the second harmonic signal S3. The invention can greatly improve the precision and stability of the detection of the gas concentration in the glass medicine bottle.
Description
Technical Field
The invention belongs to the technical field of gas detection, and particularly relates to a method and a device for detecting gas concentration in a glass medicine bottle based on signal sparse reconstruction.
Background
According to the beer Lambert law, the gas to be detected is detected according to a Tunable semiconductor Laser Absorption Spectroscopy (TDLAS/WMS) technology Based on Wavelength Modulation, and the peak value/peak value of a second harmonic signal is demodulated to be in direct proportion to the concentration of the gas to be detected, so that the concentration of the gas in the glass medicine bottle can be detected through the TDLAS/WMS technology. The TDLAS/WMS technology has the advantages of high sensitivity, low cost, non-contact, good real-time performance and the like, and has great potential for detecting the oxygen concentration on site by utilizing the TDLAS/WMS technology. In the pharmaceutical industry, companies exist internationally that apply TDLAS/WMS technology to oxygen concentration measurement in sealed glass vials, such as light source in the united states, beweidy in italy, and the like. However, the task of detecting oxygen concentration on-line of sealing glass medicine bottles (such as penicillin medicine bottles) on a production line needs to be completed in an open light path environment, and the headspace light path of the tested glass medicine bottles is extremely short. Specifically, due to the influences of optical noise, system instrument noise, nonlinear intensity modulation, random free space temperature and humidity and the like caused by the wall of the glass medicine bottle, the second harmonic signal extracted by the TDLAS/WMS technology has background fluctuation, so that the accuracy and the stability of concentration measurement are influenced. Therefore, the problem is essentially a weak signal analysis problem in the context of strong interference, which is extremely challenging. How to realize the weak second harmonic signal noise suppression and sparse reconstruction under the strong interference background is a key premise for completing the gas concentration detection in the high-precision high-reliability glass medicine bottle.
Currently, noise suppression is mainly achieved by filtering methods, such as: average filtering, median filtering, clipping filtering, first-order lag filtering, etc. Patent CN106980491A discloses an improved average filtering algorithm for a/D sampling, which performs bubble sorting on N sampling data in the order from small to large, then removes each N/5 data in the front and back of the sorted array, sums the remaining data, and takes the average value to obtain the final sampling value. The algorithm can effectively remove the value with large difference in the sampling value and can inhibit part of noise. However, in the application of laser detection of gas concentration in a glass vial, the second harmonic signal corresponding to the absorption spectrum of oxygen molecules in the vial to be detected is very weak relative to environmental noise, and the background environmental noise has diversity and time-varying characteristics. It is an urgent technical problem to find a method of combining software and hardware, enhance signal quality from each part of the system, and suppress interference noise of weak detection signal from mechanism, so as to improve positive detection rate of gas concentration in glass bottle.
Disclosure of Invention
The invention aims to provide a method and a device for detecting the concentration of gas in a glass medicine bottle based on signal sparse reconstruction, which can greatly improve the precision and stability of the detection of the concentration of gas in the glass medicine bottle.
The technical scheme provided by the invention is as follows:
a method for detecting gas concentration in a glass medicine bottle based on signal sparse reconstruction comprises the following steps:
and 5, obtaining the gas concentration in the glass medicine bottle based on the peak value/peak value of the second harmonic signal S3.
Further, in the step 1, the glass medicine bottle is detected for the first time based on the TDLAS/WMS technology, and L second harmonic signals of a single period S1 are obtained;
respectively executing the steps 2-3 on the second harmonic signal of each single period to obtain a corresponding spectrogram;
in the step 4, the central frequency point of the second harmonic signal and the spectral line components at the adjacent frequency doubling points are respectively extracted from the L spectrograms, and the extracted spectral line components are averaged according to the frequency point correspondence to obtain 1 average spectrograms corresponding to the L second harmonic signals; and performing inverse discrete Fourier transform on the average spectrogram to obtain a reconstructed second harmonic signal S3. Gaussian white noise disturbance can be further resisted based on an averaging operation, wherein L is an empirical value.
Further, repeating the step 1 to the step 4 for K times to obtain K reconstructed second harmonic signals S3; in the step 5, the gas concentration in the glass medicine bottle is calculated based on the average value of the peak value/peak value of the K reconstructed second harmonic signals S3.
Furthermore, the second harmonic signal center frequency points and the adjacent frequency multiplication points thereof refer to 0 f, 1 f, 2 f, …, h f, wherein f is the second harmonic signal center frequency point, h is the number of the selected adjacent frequency multiplication points, and h is the experience value.
Further, in the step 1, the glass vial is detected based on a TDLAS/WMS technology, and after an original second harmonic signal is obtained, a signal conditioning circuit is used to perform filtering processing on the detected original second harmonic signal, so as to obtain a second harmonic signal S1 of a single period;
the signal conditioning circuit comprises an attenuation inverting circuit, a band-limited filter circuit and a level shifting circuit, and is used for sequentially carrying out amplitude attenuation and inverting processing, band-pass filter processing and level shifting processing on the detected original second harmonic signal so as to be matched with the level of the input end of the signal acquisition circuit at the next stage.
Further, a glass vial in which the concentration of gas is known is used as a sample; performing steps 1-4 on the sample to obtain peak value/peak value data of a corresponding second harmonic signal S3; fitting to obtain a relational expression between the gas concentration in the glass medicine bottle and the peak value/peak value of the second harmonic signal S3 based on the gas concentration data in the sample and the peak value/peak value data of the second harmonic signal S3 corresponding to the sample; and (3) executing the steps 1-4 to the glass medicine bottle to be tested to obtain the corresponding peak value/peak value data of the second harmonic signal S3, and substituting the peak value/peak value data into the relational expression obtained by fitting to obtain the gas concentration in the glass medicine bottle to be tested.
The invention also provides a device for detecting the concentration of the gas in the glass medicine bottle based on signal sparse reconstruction, which is used for detecting the concentration of the gas in the glass medicine bottle by adopting the method for detecting the concentration of the gas in the glass medicine bottle based on signal sparse reconstruction;
the device comprises a second harmonic detection circuit and a processing circuit; the second harmonic detection circuit is used for executing the step 1; and the processing circuit is used for executing the steps 2-5.
Further, the second harmonic detection circuit comprises a signal conditioning circuit; the signal conditioning circuit comprises an attenuation inverting circuit, a band-limited filter circuit and a level shifting circuit; after the original second harmonic signal is obtained by detecting the glass medicine bottle based on the TDLAS/WMS technology, the amplitude attenuation and phase inversion processing, the band-pass filtering processing and the level shifting processing are sequentially carried out on the detected original second harmonic signal through the attenuation phase inversion circuit, the band-limit filtering circuit and the level shifting circuit (the level of an output signal is matched with the level of the input end of a signal acquisition circuit in a next-stage processing circuit), and then the second harmonic signal S1 of a single period is obtained.
Has the advantages that:
according to the invention, through a Discrete Fourier Transform (DFT) algorithm, the central frequency point of the measured second harmonic signal and the adjacent frequency multiplication corresponding spectral line thereof are selectively reserved on a signal frequency domain, then the extracted frequency domain components are inversely transformed to a time domain to realize sparse reconstruction of the measured signal, and the peak value/peak value of the reconstructed second harmonic signal is utilized to realize accurate inversion of the gas concentration in the glass medicine bottle. The invention can realize reliable extraction and stable analysis of weak second harmonic signals under the background of strong interference, and can be used for realizing online detection of the sealing integrity of the glass medicine bottle. The invention can eliminate all noises except the selected frequency component, and the signal thinning representation method can also greatly reduce the operation complexity and greatly improve the precision, speed and stability of the gas concentration detection in the glass medicine bottle.
Drawings
Fig. 1 is a block diagram of a signal conditioning circuit.
Fig. 2 is a gain attenuation circuit diagram.
Fig. 3 is an inverter circuit diagram.
Fig. 4 is a high pass filter circuit diagram.
Fig. 5 is a low-pass filter circuit diagram.
Fig. 6 is a voltage shifting circuit diagram.
Fig. 7 is a flow chart of a signal conditioning process.
Fig. 8 is a graph of the second harmonic signal before and after the signal conditioning process.
Fig. 9 is a flowchart of a discrete fourier transform algorithm of the signal sparse reconstruction method of the present invention.
FIG. 10 is a detailed flowchart of the method for detecting the concentration of gas in a glass vial based on signal sparse reconstruction according to the present invention.
FIG. 11 is a flow chart of the steps of the method for detecting the gas concentration in a glass vial based on signal sparse reconstruction according to the present invention and the conventional method for detecting the gas concentration.
FIG. 12 is a graph of an industrial field extracted raw second harmonic signal containing noise pollution.
Fig. 13 is a spectral plot of the original second harmonic signal after a discrete fourier transform.
Fig. 14 is a diagram of a reconstructed second harmonic signal.
Fig. 15 is the average peak/peak of the K raw second harmonic signals detected in glass vials of different oxygen concentrations.
Fig. 16 is the average peak/peak-to-peak of the K reconstructed second harmonic signals in glass vials of different oxygen concentrations.
Detailed Description
In order to facilitate an understanding of the invention, the invention will be described more fully and in detail below with reference to the accompanying drawings and preferred embodiments, but the scope of the invention is not limited to the specific embodiments below.
The embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways as defined and covered by the claims.
Example 1:
the embodiment discloses a method for detecting gas concentration in a glass medicine bottle based on signal sparse reconstruction, which comprises the following steps of:
and 5, obtaining the gas concentration in the glass medicine bottle based on the peak value/peak value of the second harmonic signal S3.
Example 2:
the embodiment provides a device for detecting the concentration of gas in a glass medicine bottle based on signal sparse reconstruction, which is used for detecting the concentration of gas in the glass medicine bottle by adopting the method for detecting the concentration of gas in the glass medicine bottle based on signal sparse reconstruction in the embodiment;
the device comprises a second harmonic detection circuit and a processing circuit; the second harmonic detection circuit is used for executing the step 1; and the processing circuit is used for executing the steps 2-5.
Example 3:
in this embodiment, on the basis of embodiments 1 and 2, in step 1, a glass vial is detected based on a TDLAS/WMS technique, and after an original second harmonic signal is obtained, a signal conditioning circuit is used to filter the detected original second harmonic signal, so as to obtain a second harmonic signal S1 in a single period.
The signal conditioning circuit comprises an attenuation inverting circuit, a band-limiting filter circuit and a level shifting circuit. Referring to fig. 1, the attenuating inverter circuit includes a gain attenuating circuit 10 and an inverter circuit 11. The specific circuit diagram of the gain attenuation circuit refers to fig. 2, the gain attenuation circuit is an active operational amplifier circuit, the active operational amplifier circuit is selected to be a low-noise operational amplifier OPA2277 chip without loss of generality, the active operational amplifier circuit comprises a non-inverting voltage input end, an inverting voltage input end, a positive power supply end, a negative power supply end and a signal output end, the second harmonic signal input end is connected to the ground through resistors R77 and R78 in a voltage dividing mode, the non-inverting voltage input end is connected between the resistors R77 and R78, the inverting voltage input end is connected to the signal output end, the signal output end is fed back to the inverting voltage input end, and the positive power supply end and the negative power supply end are connected to the ground through a bypass capacitor C47 and a bypass capacitor C46 respectively to remove power supply high-frequency noise. The circuit constitutes a gain attenuation circuit, and the voltage gain a1 is R78/(R78+ R77), and without loss of generality, in fig. 2, R78 is 50 kilo-ohms, R77 is 100 kilo-ohms, and the voltage gain a1 is 1/3 times.
Referring to fig. 3, the inverting circuit is an active operational amplifier circuit, and the active operational amplifier circuit is selected as a low-noise operational amplifier OPA2277 chip without loss of generality, and includes a non-inverting voltage input terminal, an inverting voltage input terminal, a positive and negative power supply terminal, and a signal output terminal, the non-inverting voltage input terminal is connected to ground through a resistor R85, one path of the inverting voltage input terminal is connected to an output terminal HS2 of the gain attenuation circuit through a resistor R85, the other path of the inverting voltage input terminal is connected to the signal output terminal through a resistor R83, the positive and negative power supply terminals are respectively connected to voltage sources of +10V and-10V, and the signal output terminal is fed back to the inverting voltage input terminal through a resistor R83. The circuit constitutes an inverse proportional amplifier circuit, and the voltage gain a2 ═ R83/R84, and without loss of generality, in fig. 3, R83 ═ 1 kiloohm, R84 ═ 1 kiloohm, and the voltage gain a1 ═ 1 time, that is, an inverter circuit.
The attenuation reverse circuit carries out amplitude attenuation and reverse phase processing on the second harmonic signals detected in the glass medicine bottle so as to facilitate subsequent signal acquisition and optimization.
A band-limited filter circuit in a signal conditioning circuit carries out band-pass filtering on a harmonic signal after attenuation and phase reversal, referring to FIG. 1, the band-limited filter circuit comprises a high-pass filter circuit 12 and a low-pass filter circuit 13, wherein the specific circuit diagram of the high-pass filter circuit 12 refers to FIG. 4, the high-pass filter circuit is an active high-pass filter circuit, a low-noise operational amplifier OPA2277 chip is selected without loss of generality, the band-limited filter circuit comprises a non-inverting voltage input end, an inverting voltage input end, a positive and negative power supply end and a signal output end, one path of the non-inverting voltage input end is connected to the ground through a resistor R82, the other path of the non-inverting voltage input end is connected to an output end HS3 of the inverting circuit through two capacitors C50 and C51 which are connected in series, the inverting voltage input end is connected to the signal output end through a resistor R87, one path of the signal output end is fed back to the inverting voltage input end through a resistor R87, and the other path of the signal output end is connected between capacitors C50 and C51 through a resistor R86, the cut-off frequency of the high-pass circuit is f1Without loss of generality, fig. 4 shows that R82 is 620 kilo-ohm, C50 is 1 microfarad, and the cutoff frequency f is 0.37/(R82 · C50 · 2 · pi)10.1Hz to isolate the dc signal. The positive and negative power supply terminals are respectively connected to the ground through a bypass capacitor C49 and a bypass capacitor C48 to remove powerHigh frequency noise.
Referring to fig. 5, the specific circuit diagram of the low-pass filter circuit is an active low-pass filter circuit, and a low-noise operational amplifier OPA2277 chip is selected without loss of generality, and includes an in-phase voltage input end, a reverse voltage input end, a positive and negative power supply end, and a signal output end, wherein the in-phase voltage input end is connected with a second-order RC circuit, an input signal of the second-order RC circuit is an output signal HS4 passing through the high-pass filter circuit, and a low-pass cutoff frequency of the second-order RC circuit is f2Without loss of generality, fig. 3 shows that R88 is 1.8 kilo-ohm, C52 is 0.1 microfarad, and the cutoff frequency f is 0.37/(R88 · C52 · 2 · pi)2327Hz to effectively suppress high frequency noise. The inverting voltage input terminal is connected to ground through a resistor R90, the signal output terminal is fed back to the inverting voltage input terminal through a resistor R91, and a secondary voltage gain a2 ═ (1+ R91/R90) is provided, without loss of generality, in fig. 3, R91 ═ R90 ═ 1.8 kilo-ohms, and a secondary voltage gain a2 ═ 2 times. The positive and negative power supply ends are respectively connected to +10V and-10V voltage sources.
Therefore, the band-limited filter circuit formed by the high-pass filter circuit 12 and the low-pass filter circuit 13 is substantially a band-pass filter circuit, and has a bandwidth f1To f2Without loss of generality, under the parameter configuration of the embodiment of fig. 4 and 5, the cut-off frequency range is 0.1Hz to 327Hz, and noise outside the bandwidth is limited.
The level shifting circuit 14 in the signal conditioning part shifts the positive and negative balanced second harmonic signals which are subjected to band limitation and take 0 as a direct current component to 0-3.3V which takes 1.024 as the direct current component, so that the signals can be smoothly acquired by a signal acquisition circuit powered by a single power supply. Referring to fig. 1, a specific circuit diagram of the level shifter circuit 14 referring to fig. 6, the level reference chip of the level shifter circuit 14 is selected as LM4140BCM-1.024 without loss of generality, and includes a ground terminal 1, a signal input terminal, an enable terminal, a ground terminal 2, a free terminal, a reference voltage input terminal, a ground terminal 3, and a ground terminal 4, all of the ground terminals 1 to 4 are grounded, the signal input terminal and the enable terminal are connected to a 3.3V voltage source through an inductor L8, and a bypass capacitor C54 is connected to ground to remove high-frequency noise of the power supply. The suspended end is suspended, the reference voltage output end is provided with 2 bypass capacitors C55 and C56 to remove power supply high-frequency noise, the reference voltage output end is connected to R96 after being subjected to voltage division through two resistors R94 and R93 and then is input to a non-inverting voltage input end of OPA2277, the resistance value of a resistor R96 is equal to that of a resistor R92 connected with an inverting input end of OPA2277 to balance direct current bias of an arithmetic circuit, the chip OPA2277 comprises a non-inverting voltage input end, an inverting voltage input end, a positive power supply end and a negative power supply end and a signal output end, one of the inverting voltage input ends is connected with an output end HS5 in a low-pass filter circuit through a resistor R92, the other end is connected with a signal output end through a parallel circuit of a resistor R95 and a capacitor C58, the positive power supply end is connected to a 3.3V power supply and is provided with a bypass capacitor C59 to remove power supply high-frequency noise, and the signal output end is connected with a non-inverting voltage input end of another OPA2277 operational amplifier, the reverse voltage input end of the OPA2277 is connected with the signal output end to form an output driving circuit to output a final signal-conditioned second harmonic signal, and the positive power supply end is connected to a 3.3V power supply through L9 to remove power supply high-frequency noise. And the signal output by the signal output end is the second harmonic signal after signal conditioning.
Referring to fig. 7, the signal conditioning process of the present invention comprises the steps of:
in step U001, the demodulated second harmonic signal i is processedf(t) performing gain attenuation.
Specifically, referring to fig. 2, the second harmonic signal i obtained after demodulation is processedf(t) operating by an active operational amplifier circuit:
outputting a second harmonic signal i with the amplitude reduced to 1/3 times of the original amplitudef1(t)。
In step U002, the second harmonic signal i is processedf1(t) inverting.
Specifically, referring to fig. 3, the second harmonic signal i having its amplitude reduced to 1/3 timesf1(t) inversion by active operational amplifiersAnd (3) operation:
outputting the second harmonic signal i after phase inversionf2(t)。
In step U003, the inverted second harmonic signal i is processedf2(t) high-pass filtering.
Specifically, referring to fig. 4, the inverted second harmonic signal if2(t) obtaining a second harmonic signal i through an active high-pass filter circuitf3(t) cutoff frequency f of the active high-pass filter circuit1F is calculated according to the relation among R82, C50 and C5110.1Hz, isolating the dc signal.
In step U004, the high-pass filtered second harmonic signal i is processedf3(t) low pass filtering.
Specifically, referring to FIG. 5, the high-pass filtered second harmonic signal if3(t) obtaining a second harmonic signal i through an active low-pass filter circuitf4(t) cutoff frequency f of the active low-pass filter circuit2F is calculated according to the relationship among R88, R89, C52 and C532327 Hz. Since the dominant frequency component of the second harmonic signal is within 150Hz, the cut-off frequency f1High frequency noise is suppressed.
Therefore, the band-limited filter circuit formed by the high-pass filter circuit 12 and the low-pass filter circuit 13 is substantially a band-pass filter circuit, and has a bandwidth f1To f2Without loss of generality, under the parameter configuration of the embodiment of fig. 4 and 5, the cut-off frequency range is 0.1Hz to 327Hz, and the second harmonic signal i is limitedf4(t) noise outside the bandwidth.
In step U005, the second harmonic signal if4(t) stably moving to the interval of 0V to 3.3V.
Specifically, referring to fig. 6, the circuit provides a bias voltage of 1.024V, and a second harmonic signal i with a direct current component of 0 and positive and negative equalizationf4(t) moving to 0-3 with 1.024 as DC componentThe second harmonic signal after 3V is i2f(t) so as to be successfully acquired by the signal acquisition circuit powered by the single power supply.
Referring to FIG. 8, the signal conditioned second harmonic signal i2f(t) the original second harmonic signal i has been significantly suppressedfDue to random interference noise in (t), the waveform becomes smoother, but still has a waveform distortion phenomenon, such as asymmetry of left and right valleys, so that further digital signal filtering (i.e., signal sparse reconstruction in subsequent steps) is required.
Example 4:
referring to fig. 10, the method for detecting gas concentration in a glass vial based on signal sparse reconstruction provided by the present embodiment includes Discrete Fourier Transform (DFT) B101, second harmonic signal reconstruction B102, and group peak/peak comparison B103.
Referring to fig. 9, the discrete fourier transform algorithm of the signal sparse reconstruction method of the present invention includes a Discrete Fourier Transform (DFT) unit B101 and a second harmonic signal reconstruction unit B102, and includes the following steps:
in step U101, the original second harmonic signal of one cycle is extracted.
Specifically, the laser wavelength sweep frequency is set to F, i.e., the frequency of the detected second harmonic signal is set to F, and the sampling frequency F is setsAnd acquiring original second harmonic signals (generally polluted by noise) of an industrial field, wherein each second harmonic signal contains alpha sampling points. Without loss of generality, in the embodiment of the invention, F is set to be 25Hz, FsSet to 12800Hz, then α ═ Fs12800/25-512. In particular, the original second harmonic signal of one of the cycles is shown in fig. 12.
In step U102, one period of the original second harmonic signal is extended to contain t periods of the original second harmonic signal to ensure that the discrete fourier transform is performed with a sufficiently high frequency resolution Δ f. Specifically, the data length of the second harmonic signal after the period extension is N ═ t × α, and the frequency resolution of the discrete fourier transform is Δ F ═ Fsand/N. Preferably, N is selected such that Δ f ≦ f, but the smaller Δ fThe larger the calculation amount of the overhead required by DFT is, without loss of generality, in the embodiment of the present invention, N is 1024, and t is N/α is 2, that is, Δ f is 0.5f is 12.5Hz, so that accurate spectrum analysis of the detected second harmonic signal with the frequency f can be achieved, and the calculation amount of DFT can be accommodated by a general embedded chip (e.g., ARM, DSP, FPGA).
In step U103, Discrete Fourier Transform (DFT) is performed on the second harmonic signal with the data length N after the period extension.
Specifically, discrete fourier transform is performed on the original second harmonic signal after the period extension, and the second harmonic signal with the data length of N is transformed from the time domain to the frequency domain to obtain a spectrogram of a discrete fourier transform domain.
Specifically, the calculation formula of the discrete fourier transform is as follows:
where x (k) is data after discrete fourier transform, x (N) is a second harmonic signal data point after period extension, and N is the total number of sampling points of discrete fourier transform, i.e. the length of the second harmonic signal data after period extension, equation (1) can be further expanded as follows:
wherein the values of the real part Re (X (k)) and the imaginary part Im (X (k)) are as follows:
calculating the amplitude of each sine wave component according to the real part value and the imaginary part value, wherein the calculation formula is as follows:
further, obtaining a spectrogram of the original second harmonic signal after discrete fourier transform, see fig. 13;
in step U104, spectral components at the 0 × f, 1 × f, 2 × f, …, h × f frequency points in the spectrogram are extracted;
in particular, due to the complex and large amount of noise, such as optical interference noise in industrial fields, environmental noise, and system instrument noise, the noise may be randomly distributed over various frequency bands. And selectively reserving the central frequency points of the measured second harmonic signals and the spectral line components corresponding to adjacent frequency multiplication, namely the spectral line components at the frequency points of 0 x f, 1 x f, 2 x f, … and h x f in the spectrogram. Preferably, in the embodiment of the present invention, h is 3, that is, spectral line components at 0Hz, 25Hz, 50Hz, and 75Hz frequency points in the extracted spectrogram are taken. From the perspective of signal processing, spectral line components except the main component of the signal spectrum are environment noise or system noise with high probability, the main component of the signal spectrum is reserved, and the noise components are discarded, so that noise suppression and sparse reconstruction of the original second harmonic signal can be realized.
In step U105, the thinned spectrogram is reconstructed into a second harmonic signal through Inverse Discrete Fourier Transform (IDFT).
Specifically, referring to fig. 6, preferably, the spectral line components at the frequency points 0 × f, 1 × f, 2 × f, …, h × f are reconstructed into a second harmonic signal through inverse discrete fourier transform, that is, the spectral line components at the frequency points 0Hz, 25Hz, 50Hz, and 75Hz are subjected to inverse discrete fourier transform to obtain a completely new reconstructed second harmonic signal. Comparing the reconstructed second harmonic signal (fig. 14) with the original second harmonic signal (fig. 12) shows that the second harmonic signal after signal sparsity reconstruction is smoother, and the environmental noise or system noise is eliminated.
Aiming at the application of laser detection of gas concentration in a glass medicine bottle, the invention uses discrete Fourier transform to process a second harmonic signal containing noise pollution in an industrial field, and adopts the principle of signal sparse reconstruction to selectively reserve a central frequency point of the detected second harmonic signal and a spectral line component corresponding to adjacent frequency multiplication on a signal frequency domain, thereby realizing the robust suppression of random noise.
Example 5:
referring to fig. 10 and 11 in combination, with reference to fig. 10, the method for detecting gas concentration in a glass vial based on signal sparse reconstruction according to the present embodiment includes Discrete Fourier Transform (DFT) B101, second harmonic signal reconstruction B102, and group peak/peak comparison B103.
In step U201, the glass vial is tested 1 time in the industrial field, and L original second harmonic signals are obtained for the averaging operation in step U304 to again resist Gaussian white noise disturbance. When L is too small, the difference between the secondary harmonic signal individuals cannot be effectively compensated in the subsequent steps, the accuracy of the peak value/peak value of the secondary harmonic signal is influenced, and the gas concentration detection precision is reduced; and the excessive L increases the calculation amount of the algorithm and reduces the real-time property of gas concentration detection. Preferably, in the embodiment of the invention, L is 20.
In step U202, the L original second harmonic signals are extended t times cycle by cycle to obtain L original second harmonic signals containing t cycles.
In step U203, Discrete Fourier Transform (DFT) is sequentially performed on the L original second harmonic signals including t periods extended in step U202 to obtain L spectrograms.
In step U204, spectral line components at frequency points 0 × f, 1 × f, 2 × f, …, h × f in the L spectrograms are extracted, and 1 average spectrogram corresponding to L original second harmonic signals is obtained according to the corresponding average of the frequency points.
Specifically, spectral line components at frequency points 0 × f, 1 × f, 2 × f, …, and h × f are extracted from the L spectrograms output in step U203, spectral line components at other frequency points are discarded, and then the L spectrograms are averaged over the spectral amplitudes at the frequency points. Specifically, the spectral amplitudes at all 0 × f in the L spectrograms are averaged and placed at 0 × f of the averaged spectrogram; averaging the spectrum amplitudes at all 1 x f positions in the L spectrograms, and placing the average spectrograms at 1 x f positions; repeating the above operations; and finally, averaging the spectrum amplitudes at all h & ltf & gt in the L spectrograms, and placing the average spectrograms at h & ltf & gt. Preferably, in the embodiment of the present invention, h is 3, that is, an average spectrogram of the line components of the L spectrograms output in step U203 at the frequency points of 0Hz, 25Hz, 50Hz, and 75Hz is obtained.
In step U205, the average spectrogram output by U304 is reconstructed into a second harmonic signal through Inverse Discrete Fourier Transform (IDFT), and the reconstructed second harmonic signal can be used as the decision data for detecting the gas concentration.
In step U206, the peak value/peak-to-peak value of the reconstructed second harmonic signal output by U205 is calculated, and the reconstructed second harmonic signal peak value/peak-to-peak value can be used as the decision basis for the gas concentration detection.
In the determination D201, it is determined whether the number of times of accumulated detections for the glass medicine bottle with the gas concentration to be measured reaches K?
Specifically, if the number of detection times is less than K, the above steps U201 to U206 are repeated; if the number of detections is equal to K, K reconstructed second harmonic signal peaks/peak-to-peak values in K times of gas concentration detections are output (step U207). Without loss of generality, in the embodiment, K is 90, which is used for demonstrating the beneficial effects achieved by the invention.
Referring to fig. 11, to further show the advantages of the embodiment of the present invention, a left side B301 of fig. 11 shows a graphical illustration of the flow from the step U201 to the step U206 in the method for detecting a concentration of a gas in a vial based on signal sparse reconstruction according to the present embodiment; fig. 11 right side B302 shows a graphical illustration of a conventional gas concentration detection method implementation flow. Specifically, in B301: performing signal sparse reconstruction operation in the flow from the step U201 to the step U206 on the L original second harmonic signals to obtain 1 reconstructed second harmonic signal peak value which is used as a judgment basis for the gas concentration detection; in B302: and directly (or after time domain filtering) obtaining L peak values from the L original second harmonic signals, and calculating an average value of the L peak values to be used as a judgment basis for gas concentration detection.
In order to verify the stability of the performance provided by the invention, the gas concentration of the glass medicine bottle to be detected for K times is detected, the original second harmonic signals of L periods are collected each time, and the period number of the original second harmonic signals counted in fig. 11 is M-KL. Preferably, in the embodiment of the present invention, when K is 90, M is 1800. Further, a comparison between the gas concentration detection method based on signal sparse reconstruction in the glass vial of the present invention and the conventional gas concentration detection method is shown in B303.
Specifically, fig. 15 shows the output performance of B302 of fig. 11 (conventional method), and fig. 16 shows the output performance of B301 of fig. 11 (present invention).
Specifically, fig. 15 and 16 relate to 2 glass drugs to be tested, wherein the bottle a is a standard glass drug bottle with 0% oxygen concentration, and the bottle B is a standard glass drug bottle with 5% oxygen concentration.
Referring to fig. 15, after 90 consecutive measurements are performed, the peak-to-peak distances corresponding to the a-bottle and the B-bottle in the conventional gas concentration detection method are short, and it is difficult to define a threshold line to distinguish the two bottles from each other with a positive detection rate of 100%. Specifically, the threshold line is defined as 1.04 x 10 in fig. 94The bottle B is still wrongly judged as the bottle A4 times.
Referring to fig. 16, after 90 consecutive measurements are performed, the peak-to-peak distance between the bottle a and the bottle B is increased by the method for detecting the gas concentration in the glass medicine bottle based on signal sparse reconstruction according to the present invention, and a threshold line can be defined to obtain a higher positive detection rate. Specifically, the threshold line is defined to be 0.995 × 10 in fig. 164The bottles A and B were distinguished by a positive detection rate of 100%.
The method and the device for detecting the concentration of the gas in the glass medicine bottle based on signal sparse reconstruction are used for solving the technical problem that judgment performance is declined due to serious noise interference when the TDLAS/WMS technology is adopted for detecting the concentration of the gas in the glass-packaged medicine industrial production environment, so that reliable extraction and stable analysis of weak second harmonic signals under the background of strong interference are supported, and further the concentration of the gas in the glass medicine bottle is accurately detected. The method adopts discrete Fourier transform to reconstruct the second harmonic signal containing noise pollution in the industrial field, selectively reserves the central frequency point of the measured second harmonic signal and the adjacent frequency multiplication corresponding spectral line component thereof on the signal frequency domain, and then inversely transforms the extracted frequency domain principal component to the time domain to realize the noise suppression and sparse reconstruction of the measured signal. The method for detecting the concentration of the gas in the glass medicine bottle based on signal sparse reconstruction can eradicate all noise on frequency bands except selected frequency components, reduces the characteristic dimension and the operation complexity of signal processing, and greatly improves the stability and the real-time performance of the detection of the concentration of the gas in the glass medicine bottle.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A method for detecting gas concentration in a glass medicine bottle based on signal sparse reconstruction is characterized by comprising the following steps:
step 1, detecting a glass medicine bottle based on a TDLAS/WMS technology to obtain a second harmonic signal S1 of a single period;
step 2, carrying out period prolongation on the second harmonic signal S1 to obtain a second harmonic signal S2 containing t periods;
the data length of the second harmonic signal after the period extension is N ═ t × α, the second harmonic signal S1 contains α sampling points, the frequency of the second harmonic signal is F, and the sampling frequency F is setsThen the frequency resolution of the discrete fourier transform is Δ F ═ FsN, delta f is required to be less than or equal to f;
step 3, performing discrete Fourier transform on the second harmonic signal S2 to obtain a spectrogram;
step 4, extracting the central frequency point of the second harmonic signal and the spectral line components at the adjacent frequency doubling points on the spectrogram, and then performing inverse discrete Fourier transform on the extracted spectral line components to obtain a reconstructed second harmonic signal S3;
step 5, obtaining the gas concentration in the glass medicine bottle based on the peak value/peak value of the second harmonic signal S3;
in the step 1, the glass medicine bottle is subjected to primary detection based on the TDLAS/WMS technology, and L single-period second harmonic signals S1 are obtained;
respectively executing the steps 2-3 on the second harmonic signal of each single period to obtain a corresponding spectrogram;
in the step 4, the central frequency point of the second harmonic signal and the spectral line components at the adjacent frequency doubling points are respectively extracted from the L spectrograms, and the extracted spectral line components are averaged according to the frequency point correspondence to obtain 1 average spectrograms corresponding to the L second harmonic signals; performing inverse discrete Fourier transform on the average spectrogram to obtain a reconstructed second harmonic signal S3;
the second harmonic signal central frequency points and the adjacent frequency doubling points thereof refer to 0 f, 1 f, 2 f, … and h f, wherein f is the second harmonic signal central frequency point, and h is the selected number of adjacent frequency doubling points.
2. The method for detecting the concentration of the gas in the glass medicine bottle based on the signal sparse reconstruction as claimed in claim 1, wherein the steps 1 to 4 are repeated K times to obtain K reconstructed second harmonic signals S3; in the step 5, the gas concentration in the glass vial is obtained based on the average value of the peak value/peak value of the K reconstructed second harmonic signals S3.
3. The method for detecting the concentration of gas in the glass medicine bottle based on signal sparse reconstruction as claimed in claim 1, wherein in the step 1, the glass medicine bottle is detected based on a TDLAS/WMS technology, after an original second harmonic signal is obtained, a signal conditioning circuit is used for filtering the detected original second harmonic signal, and then a second harmonic signal S1 of a single period is obtained;
the signal conditioning circuit comprises an attenuation inverting circuit, a band-limited filter circuit and a level shifting circuit, and is used for sequentially carrying out amplitude attenuation and inverting processing, band-pass filter processing and level shifting processing on the detected original second harmonic signal so as to be matched with the level of the input end of the signal acquisition circuit at the next stage.
4. The method for detecting the gas concentration in the glass medicine bottle based on signal sparse reconstruction as claimed in any one of claims 1 to 3, wherein the glass medicine bottle with known gas concentration is used as a sample; performing steps 1-4 on the sample to obtain peak value/peak value data of a corresponding second harmonic signal S3; fitting to obtain a relational expression between the gas concentration in the glass medicine bottle and the peak value/peak value of the second harmonic signal S3 based on the gas concentration data in the sample and the peak value/peak value data of the second harmonic signal S3 corresponding to the sample; and (3) executing the steps 1-4 to the glass medicine bottle to be detected to obtain the corresponding peak value/peak value data of the second harmonic signal S3, substituting the peak value/peak value data into the relational expression obtained by fitting, and calculating to obtain the concentration of the gas in the glass medicine bottle to be detected.
5. A device for detecting the concentration of gas in a glass medicine bottle based on signal sparse reconstruction is characterized in that the method for detecting the concentration of gas in the glass medicine bottle based on signal sparse reconstruction is adopted to detect the concentration of gas in the glass medicine bottle according to any one of claims 1 to 3;
the device comprises a second harmonic detection circuit and a processing circuit; the second harmonic detection circuit is used for executing the step 1; the processing circuit is used for executing the steps 2-5;
the second harmonic detection circuit comprises a signal conditioning circuit; the signal conditioning circuit comprises an attenuation inverting circuit, a band-limited filter circuit and a level shifting circuit; after detecting a glass medicine bottle based on a TDLAS/WMS technology to obtain an original second harmonic signal, sequentially carrying out amplitude attenuation and phase inversion processing, band-pass filtering processing and level shifting processing on the detected original second harmonic signal through an attenuation phase inversion circuit, a band-limiting filter circuit and a level shifting circuit to further obtain a second harmonic signal S1 in a single period;
the level reference chip in the level shifting circuit comprises a grounding terminal 1, a signal input terminal, an enabling terminal, a grounding terminal 2, a free terminal, a reference voltage input terminal, a grounding terminal 3 and a grounding terminal 4, wherein the grounding terminals 1 to 4 are all grounded, the signal input terminal and the enabling terminal are connected to a 3.3V voltage source through an inductor L8, and a bypass capacitor C54 is arranged to be connected to the ground to remove high-frequency noise of the power supply; the suspension end is suspended; the reference voltage output end is provided with 2 bypass capacitors C55 and C56 to remove power supply high-frequency noise, the reference voltage output end is connected to R96 after being divided by two resistors R94 and R93 and then is input to a non-inverting voltage input end of an OPA2277, the resistance value of a resistor R96 is equal to that of a resistor R92 connected with an inverting input end of the OPA2277 to balance direct current bias of an arithmetic circuit, the chip OPA2277 comprises a non-inverting voltage input end, an inverting voltage input end, a positive power supply end, a negative power supply end and a signal output end, one of the inverting voltage input ends is connected with an output end HS5 in a low-pass filter circuit through a resistor R92, the other end is connected with a signal output end through a parallel circuit of a resistor R95 and a capacitor C58, the positive power supply end is connected to a 3.3V voltage source and is provided with a bypass capacitor C59 to remove power supply high-frequency noise, the signal output end is connected with a non-inverting voltage input end of another OPA2277 operational amplifier, the inverting voltage input end of the OPA2277 is connected with the signal output end, the output driving circuit is formed to output the final signal-conditioned second harmonic signal, and the positive power supply terminal is connected to a 3.3V voltage source through L9 to remove power supply high-frequency noise.
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| CN115112604A (en) * | 2022-07-21 | 2022-09-27 | 湖南五凌电力科技有限公司 | Method for measuring ethane in transformer oil, computer equipment and storage medium |
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Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103033481A (en) * | 2012-12-21 | 2013-04-10 | 重庆川仪自动化股份有限公司 | FFT-based second harmonic filtering method for laser analyzer |
Family Cites Families (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| SG43422A1 (en) * | 1995-10-10 | 1997-10-17 | Air Liquide | Method for stabilizing the wavelength in a laser spectrometer system |
| CN100504309C (en) * | 2007-09-30 | 2009-06-24 | 南京大学 | Brillouin optical time domain reflection measuring method based on quick fourier transform |
| CN101545856A (en) * | 2009-04-30 | 2009-09-30 | 山东省科学院激光研究所 | Fibre optic gas detector for fire hazard of coal gab area |
| CN102590138B (en) * | 2012-01-16 | 2013-11-27 | 清华大学 | Gas concentration on-line measurement method based on laser absorption spectrum |
| US9176050B2 (en) * | 2012-05-03 | 2015-11-03 | Lighthouse Instruments, Llc. | Method and apparatus for increased purge efficacy in optical absorption spectroscopic measurements of gases in sealed containers |
| CN104089699B (en) * | 2014-06-20 | 2016-08-24 | 国家电网公司 | A kind of substation equipment speech reconstruction algorithm |
| CN105548075A (en) * | 2016-01-08 | 2016-05-04 | 楚天科技股份有限公司 | Device and method for detecting oxygen content in glass medicine bottle |
| CN106788714B (en) * | 2016-12-05 | 2019-01-18 | 重庆工商大学 | A kind of sparse solution mixing method based on optical computing |
| CN107643252B (en) * | 2017-09-18 | 2020-02-04 | 中南大学 | Real-time background-deduction nonlinear correction method for WMS detection of oxygen concentration in bottle |
| CN108918964B (en) * | 2018-07-11 | 2020-06-26 | 国网江苏省电力有限公司宿迁供电分公司 | Harmonic analysis method for sparsity enhancement |
| CN109765616B (en) * | 2019-01-10 | 2020-10-16 | 中国海洋石油集团有限公司 | Amplitude-preserving wave field continuation correction method and system |
| CN109738392B (en) * | 2019-01-29 | 2021-03-02 | 中南大学 | Compressed sensing reconstruction method and system for TDLAS (tunable diode laser absorption Spectroscopy) online detection of oxygen concentration in bottle |
| CN109959637B (en) * | 2019-04-04 | 2021-06-01 | 中南大学 | Etalon effect inhibition method and device for detecting residual oxygen of glass medicine bottle |
| CN110044844B (en) * | 2019-05-29 | 2020-03-27 | 中南大学 | Gas concentration inversion method and system based on multiple harmonic joint analysis |
| CN110160976B (en) * | 2019-06-06 | 2020-05-26 | 中南大学 | Gas concentration detection method and system based on second harmonic interference envelope compensation |
| CN110286093B (en) * | 2019-07-17 | 2020-04-17 | 中南大学 | Method for detecting gas concentration in glass bottle with dynamically adjusted threshold value |
-
2019
- 2019-12-10 CN CN201911260200.XA patent/CN111024650B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103033481A (en) * | 2012-12-21 | 2013-04-10 | 重庆川仪自动化股份有限公司 | FFT-based second harmonic filtering method for laser analyzer |
Non-Patent Citations (1)
| Title |
|---|
| 封装西林药瓶残留氧气检测中的谐波基线校正和去噪方法;刘永胜;《光谱学与光谱分析》;20170831;第３７卷(第８期);全文 * |
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