CN105488772B - Sensor signal peak value detection method - Google Patents
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Abstract
The invention discloses a signal peak value detection method for atmospheric particulate counting, which comprises the steps of filtering an original signal detected by a sensor to remove high-frequency noise, deriving the filtered signal through first-order difference, smoothing the signal after difference to further eliminate the influence caused by the noise, monitoring the zero crossing point of the signal after smoothing to obtain an initial peak value set, and finally merging the initial peak value set to obtain a final peak value set. The invention inhibits the influence of noise on the signal, improves the quality of the signal, eliminates the influence of false peak values on peak detection counting, reduces the false detection rate and has good robustness.
Description
Technical Field
The invention belongs to the field of signal processing, and particularly relates to a sensor signal peak value detection method.
Background
At present, the methods for detecting the mass concentration of atmospheric particulates at home and abroad mainly adopt an off-line filter membrane weighing method, an on-line beta-ray method, a micro-oscillation balance method and the like. The filter membrane weighing method is characterized in that air with a quantitative volume is extracted at a constant speed, after particle size screening is carried out on particles, the particles with the selected particle size section in the air are intercepted on the filter membrane, the weight of the filter membrane before and after sampling is weighed by balance, and the mass concentration of the particles is calculated according to the difference value and the sampling volume. The filter membrane weighing method is the internationally recognized quality and concentration detection reference method for the atmospheric particulates at present, and has the defects of time and labor waste and poor timeliness. Collecting the particulate matters on a filter membrane by a beta-ray method, irradiating the filter membrane by beta rays, and calculating the mass concentration of the particulate matters according to the attenuation of the rays after the rays pass through the filter paper and the particulate matters and the sampling volume; and (3) trapping the particles on the filter membrane by using a micro-oscillation balance method, weighing by using the micro-oscillation balance, and calculating the mass concentration of the particles according to the weight gain and the sampling volume of the filter membrane. The beta-ray method and the micro-oscillation balance method can realize real-time and automatic monitoring, and have the defects of high production cost, large equipment volume and inapplicability to outdoor flow measurement and dense point distribution.
The light scattering particle sensor based on the mie scattering theory is recently favored by people due to the advantages of low cost, low power consumption, miniaturization, small maintenance amount and the like. The sensor converts scattered light energy signals of single particles into an electric pulse signal by a photoelectric detector, the amplitude of the electric pulse signal reflects the particle size of the particles, the specific number of the particles can be calculated according to the number of the pulse signals as long as only one particle flows through the measuring area at any moment, and a peak value of the signal represents the flow of one particle. However, when the peak value of the light scattering electric pulse signal of the atmospheric particulates is detected, the signal fluctuation or the phenomenon of false peak value can be caused due to the interference of the circuit and the external environment noise, and the particle counting can be greatly deviated due to the problem.
Disclosure of Invention
Technical problem to be solved
In view of the above problems, the present invention provides a method for detecting a peak value of a sensor signal, which solves the problem that environmental noise interference can cause signal fluctuation or false peak value.
(II) technical scheme
The invention provides a sensor signal peak value detection method, which comprises the following steps:
s1, performing band-pass filtering on the original signal detected by the sensor to obtain a filtered signal;
s2, the filtering signal is differentiated to obtain a differential signal;
s3, smoothing the differential signal to obtain a smoothed signal;
s4, carrying out zero crossing point detection on the smoothed signal to obtain an initial peak value set of the signal;
and S5, performing reduction on the initial peak set to remove the false peaks, thereby obtaining a final peak set.
(III) advantageous effects
The invention provides a robust sensor signal peak value detection method, which is used for smoothing the differential signal, further inhibiting the influence of noise on the signal, improving the quality of the signal, eliminating the influence of a false peak value on peak value detection counting by combining adjacent peak values, greatly improving the detection precision and reducing the false detection rate. The invention can be applied to a light scattering sensor for detecting the mass concentration of atmospheric particulates.
Drawings
Fig. 1 is a flow chart of a method for detecting a peak value of a sensor signal according to the present invention.
Fig. 2 is a flow chart of signal filtering provided by an embodiment of the invention.
Fig. 3 is a schematic diagram of an original signal provided by an embodiment of the present invention.
Fig. 4 is a schematic diagram of a filtered signal provided by an embodiment of the invention.
Fig. 5 is a schematic diagram of a detected initial set of peaks provided by an embodiment of the present invention.
Fig. 6 is a schematic diagram of a reduced final set of peaks provided by an embodiment of the present invention.
Detailed Description
The invention provides a signal peak value detection method for atmospheric particulate counting, which comprises the steps of filtering an original signal detected by a sensor to remove high-frequency noise, deriving the filtered signal through first-order difference, smoothing the signal after difference to further eliminate the influence caused by the noise, monitoring the zero crossing point of the signal after smoothing to obtain an initial peak value set, and finally merging the initial peak value set to obtain a final peak value set. The invention inhibits the influence of noise on the signal, improves the quality of the signal, eliminates the influence of false peak values on peak value detection counting, and greatly improves the detection precision, thereby reducing the false detection rate.
According to one embodiment of the present invention, a sensor signal peak detection method includes:
s1, performing band-pass filtering on the original signal detected by the sensor to obtain a filtered signal;
s2, the filtering signal is differentiated to obtain a differential signal;
s3, smoothing the differential signal to obtain a smoothed signal;
s4, carrying out zero crossing point detection on the smoothed signal to obtain an initial peak value set of the signal;
and S5, performing reduction on the initial peak set to remove the false peaks, thereby obtaining a final peak set.
According to an embodiment of the present invention, step S1 includes:
s11, carrying out Fourier transform on the original signal to obtain a frequency domain signal;
s12, multiplying the frequency domain signal by a band-pass truncation signal to obtain a frequency domain truncation signal;
and S13, performing inverse Fourier transform on the frequency domain truncation signal to obtain a filtering signal after band-pass filtering.
According to one embodiment of the present invention, step S2 includes deriving the filtered signal by a first order difference pair to obtain a difference signal d (t):
d(t)＝x′(t)-x′(t-n)，
where t represents time, x' (t) is the filtered signal, and n is the differential time interval.
According to one embodiment of the present invention, in step S3, the formula for smoothing the differential signal is:
s(t)＝[d(t)+d(t-1)+d(t-2)+…+d(t-m)]/m，
where s (t) is the smoothed signal and m is the smoothing time interval.
According to one embodiment of the present invention, step S4 includes comparing the smoothed signal S (t) with a threshold value α, and if S (t) < α, determining that the smoothed signal S (t) is a zero-crossing signal at time t, and combining all the zero-crossing signals into an initial peak set.
According to one embodiment of the present invention, step S5 includes, in the initial peak value set, connecting a plurality of zero-crossing signals S (t)1),s(t2)...s(tk) Combining to obtain corresponding combined signal s (t)0) Wherein, in the step (A),s(tk) Represents tkZero-crossing signal of time, k being the number of successive zero-crossing signals, t1<t2<...<tkAnd t isk-t1<And beta are time threshold values, and all combined signals and non-combined zero-crossing point signals form a final peak value set.
According to one embodiment of the invention, the signal s (t) is combined0) The expression of (a) is:
s(t0)＝[s(t1)+s(t2)+...+s(tk)]k, and, t0＝(t1+t2+...+tk)/k。
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
The embodiment of the invention applies the signal peak value detection method to the atmospheric particulate matter light scattering sensor, can determine the particle size of the atmospheric particulate matter according to the amplitude of the peak value, and can determine the number of the atmospheric particulate matter according to the number of the peak value, thereby determining the mass concentration of the atmospheric particulate matter. As shown in fig. 1, the signal peak detection method of the light scattering sensor includes:
s1, as shown in fig. 2, performing fourier transform on the original signal to obtain a frequency domain signal, multiplying the frequency domain signal by a band-pass truncated signal to obtain a frequency domain truncated signal, and performing inverse fourier transform on the frequency domain truncated signal to obtain a band-pass filtered signal, where the original signal is as shown in fig. 3, the signal has various noises and pseudo peaks, and the band-pass filtered signal is as shown in fig. 4;
s2, deriving the filtered signal by a first order difference pair to obtain a difference signal d (t):
d(t)＝x′(t)-x′(t-n)，
where t represents time, x' (t) is the filtered signal, and n is the differential time interval;
s3, smoothing the differential signal to obtain a smoothed signal S (t), wherein: s (t) ([ d (t)) + d (t-1) + d (t-2) + … + d (t-m) ]/m, where m is the smoothing interval.
S4, comparing the smoothed signal S (t) with a threshold α, if S (t) < α, determining that the smoothed signal S (t) is a zero-crossing signal at time t, and combining all the zero-crossing signals into an initial peak set, as shown in fig. 5;
s5, as shown in FIG. 6, in the initial peak value set, a plurality of zero-crossing signals S (t) are continuously set1),s(t2)...s(tk) Combining to obtain corresponding combined signal s (t)0) As shown in fig. 6, the two zero-crossing signals on the left side in fig. 5 are combined into one combined signal, and the three zero-crossing signals on the right side are combined into another combined signal, where s (t) isk) Represents tkZero-crossing signal of time, k being the number of successive zero-crossing signals, t1<t2<...<tkAnd t isk-t1<Beta and beta are a time threshold value, all combined signals and non-combined zero-crossing point signals are combined into a final peak value set, wherein the combined signals s (t) are combined0) The expression of (a) is:
s(t0)＝[s(t1)+s(t2)+...+s(tk)]k, i.e. averaging the amplitudes of the multiple zero crossing signals, and t0＝(t1+t2+...+tk) And/k, namely averaging the time values of a plurality of zero-crossing point signals.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like 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 sensor signal peak value detection method is applied to an atmospheric particulate matter light scattering sensor, and is characterized by comprising the following steps:
s1, performing band-pass filtering on the original signal detected by the sensor to obtain a filtered signal;
s2, performing derivation on the filtering signal to obtain a differential signal;
s3, smoothing the differential signal to obtain a smoothed signal;
s4, carrying out zero crossing point detection on the smooth processing signal to obtain an initial peak value set of the signal;
s5, reducing the initial peak value set to remove false peak values so as to obtain a final peak value set, determining the particle size of the atmospheric particulates according to the amplitude of the peak values, determining the number of the atmospheric particulates according to the number of the peak values so as to determine the mass concentration of the atmospheric particulates,
wherein the step S5 includes, in the initial peak value set, connecting a plurality of zero-crossing signals S (t)1)，s(t2)...s(tk) Combining to obtain corresponding combined signal s (t)0) Wherein s (t)k) Represents tkZero-crossing signal of time, k being the number of successive zero-crossing signals, t1＜t2＜...＜tkAnd t isk-t1Beta is a time threshold value, and all the combined signals and the non-combined zero-crossing point signals form a final peak value set;
wherein the combined signal s (t)0) The expression of (a) is: s (t)0)＝[s(t1)+s(t2)+...+s(tk)]K, and, t0＝(t1+t2+...+tk)/k。
2. The method according to claim 1, wherein the step S1 includes:
s11, carrying out Fourier transform on the original signal to obtain a frequency domain signal;
s12, multiplying the frequency domain signal by a band-pass truncation signal to obtain a frequency domain truncation signal;
and S13, performing inverse Fourier transform on the frequency domain truncation signal to obtain a filtering signal after band-pass filtering.
3. The method according to claim 2, wherein the step S2 includes deriving the filtered signal by a first order difference to obtain a difference signal d (t):
d(t)＝x′(t)-x′(t-n)，
where t represents time, x' (t) is the filtered signal, and n is the differential time interval.
4. The method according to claim 3, wherein in step S3, the formula for smoothing the differential signal is as follows:
s(t)＝[d(t)+d(t-1)+d(t-2)+...+d(t-m)]/m，
where s (t) is the smoothed signal and m is the smoothing time interval.
5. The method according to claim 4, wherein the step S4 comprises comparing the smoothed signal S (t) with a threshold value α, and if S (t) < α, determining that the smoothed signal S (t) is a zero-crossing signal at time t, and combining all zero-crossing signals into an initial peak value set.
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| CN110187294A (en) * | 2019-04-17 | 2019-08-30 | 安徽建筑大学 | Method for diagnosing faults and device towards piecewise linearity constant-current driving LED light source |
| CN110327036B (en) * | 2019-07-24 | 2021-11-30 | 东南大学 | Method for extracting respiratory signal and respiratory frequency from wearable electrocardiogram |
| CN111141809B (en) * | 2020-01-20 | 2022-04-29 | 中国科学院合肥物质科学研究院 | Soil nutrient ion content detection method based on non-contact type conductivity signal |
| CN113986711A (en) * | 2021-12-28 | 2022-01-28 | 云智慧（北京）科技有限公司 | Time series data peak value detection method, device and equipment |
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| CN102480455B (en) * | 2010-11-26 | 2014-10-22 | 联芯科技有限公司 | Detecting method and detecting device for primary synchronization signals in long-term evolution system |
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| US5808962A (en) * | 1996-06-03 | 1998-09-15 | The Trustees Of The University Of Pennsylvania | Ultrasparse, ultrawideband arrays |
| CN102480455B (en) * | 2010-11-26 | 2014-10-22 | 联芯科技有限公司 | Detecting method and detecting device for primary synchronization signals in long-term evolution system |
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