CN104297115B - Detection method for number density of atmospheric particulate matters PM2.5 - Google Patents

Abstract

The invention provides a detection method for the number density of atmospheric particulate matters PM2.5. The detection method comprises the following steps: irradiating gas containing PM2.5 by adopting infrared light outputted by a laser light source, and forming scattered lights due to the scattering effect; converging all the scattered lights by using an optical part to form interference light; measuring the intensity of the interference light by adopting a photoelectric detector; in order to eliminate noise pollution, carrying out signal processing by using a self-adaptive FIR (Finite Impulse Response) filter, and calculating an average value and a variance of the intensity of the interference light; in accordance with the relativity principle that the average value, the variance and the total light intensity of the scattered lights form the total number of the PM2.5 of the scattered lights, finishing the estimation for the intensity of the scattered light and the total number of the PM2.5 according to a system parameter estimation algorithm; and according to the known volume of a testing chamber and the estimated value of the total number of the PM2.5, calculating the number density of the PM2.5 of detected gas to realize detection for the atmospheric PM2.5. The detection method provided by the invention has the advantages that the detection principle is novel and unique, the algorithm is based on the recurrence least square (RLS) principle, and the matrix inversion operation is not needed, so that the operation quantity is small and the performance is stable; and the system is simple in structure, low in cost and high in reliability.

Description

A kind of method of Atmospheric particulates PM2.5 number density detection

Technical field

The present invention relates to Atmospheric particulates detection technique, particularly to a kind of Atmospheric particulates PM2.5 number density detection Method and its device.

Background technology

PM is the english abbreviation of particulate matter (Particulate Matter), is used for referring to air and includes solid and liquid In interior particulate matter.Generally according to the difference of aerodynamic size, PM is divided into PM10 and PM2.5 again, corresponds to air respectively and moves The Atmospheric particulates that 10 μm and less than 2.5 μm of mechanics particle diameter., with respect to PM10, particle diameter is less, toxicity is bigger, transmission distance for PM2.5 Longer from farther, the time of staying；PM2.5 is also referred to as lung particulate matter, carries various harmful substances, enters lung by respiratory tract Bubble, reaches other organs through blood circulation, the infringement to human respiratory and other functions system is very big.

At present, widely used PM2.5 assay method is：Filter membrane weight method, β attenuation sensors, trace oscillating balance method.

Filter membrane weight method：The method in certain traffic sampling, by the particle collection of in the air on high-performance filter membrane, Quality before and after weighing filter membrane sampling, by its particulate matter quality trying to achieve trapping of poor quality, further according to the weight of filter membrane before and after sampling Amount difference and sampled air volume, calculate the mass number density of particulate matter.Its shortcoming is to need to be operated by hand, and impact is surveyed The many factors of accuracy of measurement and operation sequence is numerous and diverse, take room and equipment is more, the sampling time compared with long, apparatus guarantee amount is big, Spend relatively costly etc..Because the method automaticity is low, can only periodically be monitored it is very difficult to be used for on-line monitoring.

β attenuation sensors：Collect the microparticle in air with glass fiber filter paper, before particle sampling, adopt first Pass through the glass fiber filter paper of cleaning with β ray, write down ray attenuation values；Sample gas are allowed to pass through glass fiber filter paper again, complete Become particulate matter collection；After particulate matter collection, β ray is used to pass through same filter paper again；Due to β ray pass through have dirt filter paper when, Transmitted intensity will be decayed, and the decay of its intensity is only relevant with the quality of dirt, according to the absorbed variable quantity of β ray twice, ask The quality of the particulate matter on filter paper must be collected in.

Trace oscillating balance method：Based on conical component vibrate microbalance principle, the frequency of oscillation of balance can be with filter membrane The mass change of the particulate matter of upper collection changes, and obtains the matter of particulate matter collecting by the change of accurate measurement frequency Amount, the sample volume gathering when then according to these particulate matters of collection calculates the number density of sample.

Three of the above main stream approach, monitoring accuracy preferably, is that environmental administration of various countries more adopts.Collected using filter membrane PM2.5, filter membrane is dirty, needs periodically it cleaned, dry, changing etc. with process.Sampling analysis process is loaded down with trivial details it is impossible to realization is grown Phase monitors.

In addition, light scattering method is based on light scattering principle, the particulate of in the air can occur scattering phenomenon, light under the irradiation of light Scattering is relevant with factors such as particle size, optical wavelength, particulate refractive index, particle size distribution and number densitys.Light scattering method is The scattered light signal being sent after measuring particulate matter to be subject to light irradiation, particle diameter distribution and the mass number to calculate particulate matter are close Degree.In recent years, light scattering method because having that measuring speed is fast, high precision, reproducible, and the advantages of be applied to on-line measurement, and Receive extensive concern.

Chinese invention patent " the PM2.5 detection means based on wide-angle Fourier transform " (number of patent application： 201310519249.9), using parts such as laser instrument, collimater, negative pressure generation unit, change lens, diffraction screen and video cameras Composition.Using collimater by laser instrument output light, collimate as required diameter beam, particulate matter under laser beam irradiation, Form diffraction in the back focal plane of change lens, light and dark concentric circles formed by diffraction screen, this concentric circles too little with Grain change of size and change.Concentric circle diagram picture is obtained by video camera, by processing to this concentric circle diagram picture, can obtain The sizes values of grain thing.Therefore, this invention only relates to the detection method of particulate matter diameter, and systematic comparison is complicated.

Content of the invention

In order to solve problem in prior art, the invention provides a kind of Atmospheric particulates PM2.5 number density detection method, It comprises the following steps：

Step 1：Tested air containing multiple particles things ,s, after the filter of different pore size separates, forms one The air-flow containing PM2.5 and more small particle for the stock, and enter gas test room；

Step 2：In gas test room, with the Infrared irradiation tested air-flow of LASER Light Source output, a part of infrared light Scatter with particulate matter in air-flow, form scattered light, another part infrared light do not scattered with particulate matter, be directed through by Survey air-flow, form penetrate light, using optics, scattered light and penetrate light are gathered together；

Step 3：The scattered light being gathered together and penetrate light, interfere effect, and export interference light；

Step 4：Measure the strength signal of above interference light using photodetector, it is strong that it comprises the auto-correlation of each scattered light Cross-correlation intensity between cross-correlation intensity between degree, the auto-correlation intensity of penetrate light, scattered light and penetrate light, each scattered light Four contents, the strength signal of this interference light is random signal, and its probability distributing density function is sub with the particle forming scattered light Total number N is related；

Step 5：With A/D converter to above interference light overall strength signal, with the sampling period as T, carry out discretization and take out Sample, obtains sampled signal sequence y (n), represents the sampled signal in the nT moment with y (n), and n is positive integer；

Step 6：From the sampled signal by noise pollution, recover original interference light intensity signal, adopt

With recursive least-squares RLS algorithm, design the auto-adaptive fir filter of a M rank, to sampling

Signal y (n), is filtered as the following formula：

Wherein, w_iN () is the M rank FIR filter filter factor in nT moment, i=0,1 ..., M-1, W (n)

For nT moment filter coefficient vector：W (n)=[w₀(n),...,w_M-1(n)]^T, and x (n) is filtered letter

Number sequence, x (n) can be considered the original interference light intensity Signal estimation value in the nT moment, and Y (n) is nT

Signal vector corresponding to moment, this vector is made up of M sampled signal, and it is as follows：

Y (n)=[y (n), y (n-1) ..., y (n-M+1)]^T

W^T(n)、Y^TN () is respectively the transposition of W (n) and Y (n)：

W^T(n)=[w₀(n),...,w_M-1(n)], Y^T(n)=[y (n), y (n-1) ..., y (n-M+1)]；

Step 7：If the sequence number pointer of filtered burst x (n) is n₀, from n₀=0 beginning, continuously takes foremost Two segment length is the signal of L, and L is positive integer, uses signal vector X₁、X₂Represent：

X₁=[x (1) ..., x (L)]^T

X₂=[x (L+1) ..., x (2L)]^T

X₁、X₂Correspond in [0-LT], [LT-2LT] the interference light intensity signal in the time respectively, It is signal respectively X₁、X₂Transposed form：

X₁ ^T=[x (1) ..., x (L)]

X₂ ^T=[x (L+1) ..., x (2L)]

As the following formula, calculate X₁、X₂Mean value：

L, M are positive integer, and L ＞ M；

Step 8：As the following formula, calculate X₁、X₂Variance：

σ₁ ²And σ₂ ²Correspond to the fluctuation of the interference light intensity in the time in [0-LT] and [LT-2LT] respectively:

Step 9：According to interference light intensity in mean value in the time of [0-LT], [LT-2LT] and variance： σ₁ ²、σ₂ ², set up following parameter C₁、C₂Matrix equation：

Wherein,C₁=a²,

And a²For the total light intensity of KPT Scatter light, N is the total number of particles mesh forming scattered light, by C₁、C₂Referred to as system ginseng Number it is assumed here that, a²Keep constant with N in time at [0-LT] and [LT-2LT], therefore, within this time, systematic parameter C₁、 C₂It is approximately one group of constant；

Step 10：By the least square solution of above equation, obtain [0-LT] and [LT-2LT] systematic parameter in the time C₁And C₂Estimate：

Wherein, P₀=(A₀ ^TA₀)^-1, A₀ ^TFor matrix A₀Transposition, (A₀ ^TA₀)^-1For A₀ ^TA₀Inverse square Battle array, orderParameter C that will be in [0-LT] and [LT-2LT] in the time₁、C₂, use respectivelyRepresent；

Step 11：Sequence number pointer n of adjustment burst x (n)₀, make n₀=n₀+ 2L, makes sequence number pointer, points to next section Length is the filtered signal of L；

Step 12：From current sequence number pointer n₀Start, take a segment length to be the filtered signal of L：X₃=[x (n₀+ 1),...,x(n₀+L)]^T, X₃Corresponding in [n₀T-(n₀+ L) T] interference light intensity signal in the time, as the following formula, calculate X₃'s Mean valueAnd variances sigma₃ ²：

With newly-generated mean valueAnd variances sigma₃ ², constitute signal update vector d^TAnd b⁽¹⁾：

b⁽¹⁾=1,

Set up in [n₀T-(n₀+ L) T] parameter C in the time₁、C₂Matrix equation：

b⁽¹⁾=1；

Step 13：According to signal update vector d being constituted^TAnd b⁽¹⁾, using recursive least-squares RLS algorithm, press Formula, in [n₀T-(n₀+ L) T] in the time to systematic parameter C₁、C₂Carrying out estimate, and use respectivelyRepresent it：

Wherein, vectorial d and d^TTransposition relation each other,WithCorrespond respectively to current iteration computing In the past with systematic parameter C afterwards₁、C₂；

Step 14：According to above [n₀T-(n₀+ L) T] systematic parameter C in the time₁And C₂, as the following formula, calculate gas test Room within this time, total light intensity a of scattered light²And form the total number of particles mesh N of scattered light:

a²=C₁,

Step 15：Volume V according to the known gas test room and above total number of particles mesh N trying to achieve, can calculate [n₀T-(n₀+ L) T] in the time, particulate count density ρ of tested gas, i.e. PM2.5 number of particles in the gas of unit volume：

Step 16：Required according to recursive least-squares RLS algorithm, as the following formula, reset interative computation each

The initial value of unit：

Step 17：Sequence number pointer n of adjustment burst X₀, make n₀=n₀+ L, makes sequence number pointer point to next segment length to be The filtered signal of L；

Step 18：Repeat step 12-17, so circulates, completes in initial time n₀After=0, random time [n₀T- (n₀+ L) T] in systematic parameter C₁、C₂Estimation, and then, obtain random time [n₀T-(n₀+ L) T] in scattered light total light Strong a²And the total number of particles mesh N of formation scattered light, realize tested gas in initial time n₀After=0, random time [n₀T- (n₀+ L) T] in particulate count density detection, wherein, n₀=0,2L, 3L, 4L, 5L .....

As a further improvement on the present invention, in step 1, tested air in the presence of negative pressure generating unit, through two The filter of level different pore size separates.

As a further improvement on the present invention, in step 2, described optics is convex lens or convex lens group.

As a further improvement on the present invention, in step 6, auto-adaptive fir filter least square RLS algorithm, its feature It is, it comprises the following steps：

Step 1：Initialization：

1) when defining n=-1, W^T(- 1)=[0,0 ..., 0]^T, R (- 1)=δ₁Eye, i.e. R^-1(- 1)=δ₁ ^-1Eye, δ₁ It is the positive number of very little, eye is unit diagonal matrix；

2) define sampled signal vector Y (0) during n=0：Y (0)=[y (0), 0.., 0], Y (0) are M dimensional vector；

3) primary signal estimate d (0) during n=0, d (0)=δ are defined₂, δ₂It is the positive number of very little；

4) select constant λ：0 ＜ λ ＜ 1.

Step 2：By Y (0), R^-1(- 1) and λ, as the following formula, calculates the vectorial K (0) during n=0：

Step 3：As the following formula, calculate automatic adaptation FIR filter coefficient vector W (0) during n=0：

E (0)=d (0)-W^T(- 1) Y (0)=d (0)

W (0)=W (- 1)+K (0) e (0)=K (0) e (0)

Step 4：As the following formula, calculate matrix R during n=0^-1(0)：

Step 5：Make n=n+1, using sampled signal y (n) in current nT moment, update signal vector Y (n)：

Y (n)=[y (n), y (n-1) ..., y (n-M+1)]^T

Step 6：By current nT time-ofday signals vector Y (n) and (n-1) T moment matrix R^-1(n-1), as the following formula, calculate currently The vectorial K (n) in nT moment, matrix R^-1(n)：

Step 7：By following recurrence formula, calculate filter coefficient vector W (n) in current nT moment：

D (n)=W^T(n-1)Y(n-1)

E (n)=d (n)-W^T(n-1)Y(n)

W (n)=W (n-1)+K (n) e (n)

And to sampled signal y (n), carry out automatic adaptation FIR filtering as the following formula：

Step 8：Repeat above step 5-7, so circulate, obtain filtered burst x (n) of any time.

A kind of Atmospheric particulates PM2.5 number density detection device, it include LASER Light Source, optoisolator, photodetector, A/D converter, signal processing unit, control unit, gas test room, airscoop shroud, negative pressure generating unit, described LASER Light Source Pass sequentially through the first optical fiber, optoisolator, the second optical fiber are connected with gas test room, described photodetector passes through the 3rd light Fibre is connected with gas test room, and the electrical signal of photodetector is connected with A/D converter, at A/D converter and signal Reason unit is connected, and described control unit is connected with signal processing unit, negative pressure generating unit, respectively, and described airscoop shroud passes through air inlet Pipe is connected with gas test room, and described negative pressure generating unit, is connected with gas test room by escape pipe.

As a further improvement on the present invention, the filter of two-stage difference bore, first order filter are set in airscoop shroud The particle of large-size can be filtered, the second level filters the particle that particle diameter is more than 2.5 microns.

As a further improvement on the present invention, described gas test room built-in two groups of optical fiber type GRIN Lens ,s, convex lens Mirror, wherein, first group GRIN Lens pass through the second optical fiber, optoisolator is connected with LASER Light Source, first group GRIN Lens, First convex lens are responsible for launching photon, and second group of GRIN Lens is connected with photodetector by the 3rd optical fiber, and second group certainly Condenser lens, the second convex lens are responsible for receiving photon, and this two groups of GRIN Lens, convex lens are centrally located in same optical axis, The end face of first group GRIN Lens, at the front focus of the first convex lens, the end face of second group of GRIN Lens, positioned at The rear focal point of two convex lens, the first convex lens, the second convex lens each serve as light expand, the effect of light collection；

As a further improvement on the present invention, the line direction between air inlet pipe and escape pipe, built-in with gas test room Two groups of optical fiber type GRIN Lens ,s, convex lens optical axis direction perpendicular, sent out with photon with the flow direction ensureing tested gas Penetrate direction perpendicular.

As a further improvement on the present invention, the line between air inlet pipe and escape pipe, positioned at the first convex lens and second Between convex lens, to ensure the flow region of tested gas, it is concentrated mainly on the region between this two groups of convex lens.

Have the beneficial effect that：

The invention provides a kind of method of Atmospheric particulates PM2.5 number density detection and its device, it adopts laser light The infrared light of source output, irradiates the gas containing PM2.5, and a part of infrared light is scattered with PM2.5 particulate matter in air-flow, shape Become scattered light.Another part infrared light is not scattered with particulate matter, is directed through tested air-flow, forms penetrate light.Using light Department of the Chinese Academy of Sciences's part, scattered light and penetrate light are gathered together.The scattered light being gathered together and penetrate light, interfere effect, and Output interference light.Measure the strength signal of above interference light using photodetector, A/D converter etc., it comprises each scattered light Auto-correlation intensity, the auto-correlation intensity of penetrate light, the cross-correlation intensity between scattered light and penetrate light, between each scattered light Four contents of cross-correlation intensity.This interference light intensity signal is a random signal, its probability distributing density function and PM2.5 air chamber Particulate matter number N related.Theory deduction with regard to its general principles shows (referring to specific embodiment portion below Point)：The mean value of this random signal, variance are related to the total light intensity of scattered light, the PM2.5 total number of formation scattered light. This conclusion is that the general principle of the present invention is located.

According to the general principle of the present invention, use auto-adaptive fir filter first, measurement data is filtered process, from In the sampled signal of noise pollution, recovered original interference light intensity signal filtering.Use filtered measurement data, as The estimation of original interference light intensity signal.This auto-adaptive fir filter, based on recursive least-squares RLS algorithm.The core of algorithm It is that object function is constituted with the estimated error sum of squares of exponential weighting, by this minimization of object function, to determine that FIR filters Coefficient vector：W (n)=[w₀(n),...,w_M-1(n)]^T.This coefficient vector is to realize updating and time-varying by adaptive algorithm.Adaptive The most important characteristics answering wave filter are that it in circumstances not known automatically, effectively work, and can follow the tracks of input letter Number time varying characteristic, the change of adaptive system environment, to obtain Expected Response, make the minimization of object function, complete to unknown The optimal estimation of primary signal.In the case of change of PM2.5 particle concentration or the external interference of tested gas, its interference light Strength signal is the random signal of non-stationary or time-varying state.

And traditional FIR filter, its filter factor is fixed constant, is suitable for processing the random signal under Stationary Random Environments, The random signal of non-stationary environment or time-varying state cannot be processed.

, after completing the filtering process of measurement data, and by filtered measurement data, temporally segmentation, by filtering for the present invention Measurement data (i.e. interference light intensity signal) after ripple, calculates interference light intensity mean value corresponding to each time period, variance.Again PM2.5 total number according to the total light intensity of mean value, variance and scattered light of interference light intensity, formation scattered light is that correlation is former Reason, using systematic parameter algorithm for estimating, estimates to the total light intensity of the scattered light in this time period, PM2.5 total number, and root According to estimate and the known test cabinet volume of PM2.5 total number, calculate the PM2.5 corresponding to this time period of tested gas Number density, i.e. PM2.5 number of particles in the gas of unit volume.Thus realizing the detection of Atmospheric particulates PM2.5 number density.

This systematic parameter algorithm for estimating is also based on recursive least-squares RLS principle, and the core of its algorithm is, to estimate by mistake Difference quadratic sum constitutes object function, by this minimization of object function, to determine systematic parameter C₁And C₂.First with the most initial Two time periods corresponding interference light intensity mean valueAnd variances sigma₁ ²、σ₂ ², as signal vector, by least square Principle, obtains systematic parameter C₁And C₂Estimation initial value；Mean value using corresponding interference light intensity of next time periodAnd Variances sigma₃ ², signal vector is updated；By the recurrence formula derived by the principle of least square, on the basis of estimating initial value, Calculate systematic parameter C corresponding to the new time period₁And C₂.So, complete the random time section after initial paragraph by paragraph paragraph by paragraph Interior parameter C₁And C₂Estimate.

Further according to parameter C₁And C₂Definition：C₁=a²,

Calculate total light intensity a of the scattered light in the random time section after initial²Estimation with PM2.5 total number N Value.

Therefore, this parameter estimation algorithm, the estimates of parameters of each of which time period, is by the interference light of current slot Strong mean value, variance and previous time parameter estimate, to be together decided on by recurrence formula.Its algorithm is transported without matrix inversion Calculate, have that operand is low, the feature of stable performance.

The photodetector that the present invention adopts, is optical fiber type point detecting element, belongs to non-face image-forming component.It is with low cost, Without parts such as diffraction screen, ccd video cameras.Relatively conventional technology, system architecture is succinct, low cost, and reliability is higher.

Gas test room built-in two groups of optical fiber type GRIN Lens ,s, convex lens of the present invention.Wherein, first group of self-focusing is saturating Mirror, convex lens, are responsible for transmitting photon and light expands；Second group of GRIN Lens, convex lens, responsible light collection and receiving light Son.

From the infrared light of LASER Light Source, enter through optical fiber after first group GRIN Lens, by the end of this GRIN Lens Face goes out light, and the light that goes out of this end face is had angle with optical axis, and its whole light is radiated on the first convex lens, because of this end face just position At the front focus of the first convex lens, light, after the first convex lens, forms the directional light parallel to optical axis expanding.

Due to the line direction between air inlet pipe and escape pipe, two group optical fiber type self-focusings built-in with gas test room are saturating Mirror, convex lens optical axis direction perpendicular, the directional light that this expands can be made perpendicular with the flow direction of tested gas.Meanwhile, Due to the line between air inlet pipe and escape pipe, between the first convex lens and the second convex lens, make the flowing of tested gas Region, is concentrated mainly on the region between this two groups of convex lens.This directional light, after effectively irradiating tested gas, is imitated because scattering Should, the scattering light of formation, can effectively reach on the second convex lens, the second convex lens, by this scattering light, converge at thereafter Focal point.Due to the back focus position of this second convex lens, overlap with the end face of second group of GRIN Lens.So, converge it Scattering light afterwards, by the end coupling of second group of GRIN Lens, enters the 3rd optical fiber, and through the 3rd optical fiber by light electrical resistivity survey Survey device to receive.

Above-mentioned design principle and structure, both can guarantee that infrared light fully irradiated tested gas, also can guarantee that infrared light and quilt Survey gas all PM2.5 particle to scatter, its scattering effect is more abundant.Simultaneously it is ensured that scattering light, had by photodetector The reception of effect.Favourable to the measurement performance improving system.

Brief description

Fig. 1 is a kind of present invention Atmospheric particulates PM2.5 number density detection device structural representation.

Specific embodiment

The present invention will be further described below in conjunction with the accompanying drawings.

A kind of Atmospheric particulates PM2.5 number density detection device, it include LASER Light Source, optoisolator, photodetector, A/D converter, signal processing unit, control unit, gas test room, airscoop shroud, negative pressure generating unit, described LASER Light Source Pass sequentially through the first optical fiber, optoisolator, the second optical fiber are connected with gas test room, described photodetector passes through the 3rd light Fibre is connected with gas test room, and the electrical signal of photodetector is connected with A/D converter, at A/D converter and signal Reason unit is connected, and described control unit is connected with signal processing unit, negative pressure generating unit, respectively, and described airscoop shroud passes through air inlet Pipe is connected with gas test room, the filter of setting two-stage difference bore in airscoop shroud, and first order filter can filter larger chi Very little particle, the second level filters the particle that particle diameter is more than 2.5 microns.Negative pressure generating unit, passes through escape pipe and gas test room phase Even.

Gas test room built-in two groups of optical fiber type GRIN Lens ,s, convex lens, wherein, first group GRIN Lens pass through the Two optical fiber, optoisolator are connected with LASER Light Source, first group GRIN Lens, the first convex lens be responsible for launch photon, second group GRIN Lens is connected with photodetector by the 3rd optical fiber, and receiving light is responsible for by second group of GRIN Lens, second convex lens Son, this two groups of GRIN Lens, convex lens are centrally located in same optical axis, the end face of first group GRIN Lens, positioned at At the front focus of one convex lens, the end face of second group of GRIN Lens, positioned at the rear focal point of the second convex lens, the first convex lens Mirror, the second convex lens each serve as light expand, the effect of light collection.

1st, the general principle of the Atmospheric particulates number density detection method of the present invention

Tested air containing multiple particles things ,s, in the presence of negative pressure generating unit, through two grades of different pore sizes Filter separate after, form one and contain PM2.5 and the air-flow of more small particle, and enter gas test room.Survey in gas Try the Infrared irradiation tested air-flow in room, being exported with LASER Light Source, a part of infrared light is scattered with particulate matter in air-flow, Form so-called scattered light.Another part infrared light is not scattered with particulate matter, is directed through tested air-flow, is formed so-called Penetrate light.Using optics, scattered light and penetrate light are gathered together.The scattered light being gathered together and penetrate light, send out Raw interference effect, and export interference light.

If it is assumed that the present granule thing number in gas test room is N, ignore the energy loss of scattering process, and assume The polarization direction that each particle forms scattered light is consistent, and amplitude is same, and the phase place of all KPT Scatter lights is at (- π, π) It is uniformly distributed, and separate.Then the electric field of the total scattering light that N number of particle is formed is

Wherein,For the scattering optical electric field vector of i-th particle, ignore its vector characteristics, use scalarTable Show,For the scattered light electric field amplitude of i-th particle, φ_iScattered light electric field phase for i-th particle.

Here, the scattering optical electric field vector E to each particle_i, introduce scale factorIt is in order in number of particles When tending to very big, the energy summation of its all scattered light keeps limited.I.e.：

a²Total light intensity for KPT Scatter light.

WithRepresent the electric field vector of printing opacity, use E_TAnd φ_TRepresent amplitude and the phase place penetrating optical electric field vector.Work as scattering LightAnd penetrate lightIt is gathered together, interferes effect, form interference light, the total electric field vector of this interference light

If it is assumed that scattered lightWith penetrate lightPolarization direction consistent, then the total electric field of interference light is write as scalar shape Formula：

Measure overall strength I of interference light using photodetector, because photodetector is square law device, therefore I=E_I* E_I ^*(3)

Wherein, E_I ^*Total electric field E for interference light_IComplex conjugate.Formula (2) is substituted into (3)：

Launch above formula, obtain (4) formula:

Because

Formula (5) is substituted into (4)：

φ_T-φ_iPenetrate light and the phase difference of scattered light, φ_i-φ_kFor the phase difference between the scattered light of different particles.This In a²、E_T、φ_TCan be approximated to be constant.

Can be seen that from formula (6)：

Section 1 is the auto-correlation intensity of penetrate light, and Section 2 is the auto-correlation intensity of scattered light；Section 3 is scattered light And the cross-correlation intensity of penetrate light, Section 4 are the cross-correlation intensities between each scattered light；The statistics of overall strength I of interference light is put down Average

E () represents that according with the stochastic variable in () to bracket does statistical average.

Assume the phase of all KPT Scatter lights_iIt is to be uniformly distributed at (- π, π), and separate.And a²、E_T、 φ_TIt is approximately constant, therefore：

As i ≠ k:

Formula (8a), (8b) are substituted into (7), obtains the assembly average of I：

Using formula (6) and (9), calculate

According to formula (10), rightCarry out statistical average calculating, obtain the variances sigma of overall strength I of interference light²：

Statistical average is done to formula (10), obtains：

Section 1：

Because as i ≠ k:

E[cos(φ_T-φ_i)cos(φ_T-φ_l)]=E [cos (φ_T-φ_i)]*E[cos(φ_T-φ_l)]=0；

Therefore, Section 1：

Section 2：

Section 3：

Section 4：

Because working as l ≠ i, k ≠ i, l ≠ m, during m ≠ k：

Work as l=i, m=k, k ≠ i, during l ≠ m：

Therefore, Section 4：

(12), (13), (14) and (15) are substituted into (11), obtains the variances sigma of overall strength I of interference light²：

By formula (9) above：Substitution formula (16), therefore variances sigma²Write as：

(17) formula shows, the variances sigma of overall strength I of interference light², mean valueTotal light intensity, formation scattered light with scattered light PM2.5 total number be related.

Because overall strength I of interference light is measurement data, the variances sigma of I²And mean valueCan be calculated by measurement data Come.Therefore, obtaining variances sigma²And mean valueAfterwards, (17) formula can be utilized, to total number of particles N forming scattered light and scattered light Total light intensity a²Estimated.Further according to the volume of known measurement air chamber, the particulate count density of tested air-flow can be calculated, that is, Particles contained quantity in the gas of unit volume.

The general principle of the Atmospheric particulates number density detection method of conclusions, the as present invention.

2nd, the parameter estimation algorithm of the airborne particulate analyte detection of the present invention

With A/D converter to above interference light overall strength signal, with the sampling period as T, carry out discretization sampling, obtain Sampled signal sequence Y, is represented with signal vector：Y=[y (1) ..., y (n) ...]^T,Y^TFor the transposed form of signal Y, wherein y N () is the sampled signal in the nT moment, n positive integer.

In order to eliminate the impact of outside noise, from the sampled signal by noise pollution, recover original interference light intensity Signal, using an adaptive M rank FIR filter, is filtered to measurement signal Y processing：Filtered signal is X：X= [x(1),...,x(n),...]^T；

If initial time is n₀T, from n₀=0 beginning, continuously takes the signal for L for the two segment length of foremost：

X₁=[x (1) ..., x (L)]^T

X₂=[x (L+1) ..., x (2L)]^T

X₁、X₂Correspond in [0-LT], [LT-2LT] the interference light intensity signal in the time respectively, as the following formula, calculate X₁,X₂ Mean value, variance：

n₀, L is positive integer；

By (17) formula：

Wherein a₁ ²、N₁And a₂ ²、N₂Correspond to respectively and dissipate in the general power of [0-LT] and the scattered light of [LT-2LT] time, formation Penetrate the total number of particles mesh of light.

By (18), (19) formula equal sign both sides, it is respectively divided by σ₁ ²、σ₂ ²：

If it is assumed that, the general power of the scattered light in two time adjacent segments, the approximate phase of total number of particles of formation scattered light Deng that is,：

a₁ ²=a₂ ²=a²、N₁=N₂=N

Then (20) formula is write as matrix equation：

Order

Defined parameters C₁And C₂：

Then (21) formula can be write as：

WithIt is expressed as parameter C in the time in [0-LT] and [LT-2LT]₁、C₂：

By the least square solution of above equation, obtain

Wherein, A₀ ^TFor matrix A₀Transposition, P₀=(A₀ ^TA₀)^-1,(A₀ ^TA₀)^-1For A₀ ^TA₀Inverse matrix.

By adjusting sequence number pointer n of burst X₀, make n₀=n₀+ 2L, making sequence number pointer point to next segment length is L Filtered signal；From current sequence number pointer n₀Start, take a segment length to be the filtered signal of L：X₃=[x (n₀+ 1),...,x(n₀+L)]^T.X₃Corresponding in [n₀T-(n₀+ L) T] interference light intensity signal in the time, as the following formula, and calculate X₃ Mean valueAnd variances sigma₃ ²：

In the same manner, according to (17) formula：

Wherein, a₃ ²、N₃Correspond in [n respectively₀T-(n₀+ L) T] general power of scattered light in the time, form the grain of scattered light Sub- total number.

By (28) formula equal sign both sides, it is respectively divided by σ₃ ²：

(28) formula is write as matrix form：

Definition signal renewal vector d^TAnd b⁽¹⁾：

With respect to aboveData vector d^TAnd b⁽¹⁾From up-to-date hits According to therefore, by d^TAnd b⁽¹⁾Referred to as signal update vector.

According to parameter C₁And C₂Definition, formula (23) is it is known that in [n₀T-(n₀+ L) T] C in the time₁And C₂：

Then (29) can be write as：

Because matrix dd^T：

dd^TFor unusual square formation, can not invert it is impossible to by formula (25), seek the least square solution of formula (32).Therefore by formula (32) with Formula (25) is combined into following matrix equation：

Will be in [n₀T-(n₀+ L) T] parameter C in the time₁、C₂, use respectivelyRepresent it, by solving (33) Least square solution：

Order

Therefore (34) formula is write as：

By formula (25), have：

By above formula equal sign both sides, it is multiplied by P together₀ ^-1：

Formula (37) is substituted into formula (36)：

According to matrix inversion lemma：

[A+UV]^-1=A^-1-A^-1U[eye+VA^-1U]^-1VA^-1(39)

Wherein, eye is unit diagonal matrix, and matrix A is nonsingular square matrix, and matrix U, and V meets, and makes eye+VA^-1U is non- Singular matrix.

Using above matrix inversion lemma, and substitute into formula (35)：

Using P₀=[A₀ ^TA₀]^-1, have：

Comprehensive above (41) formula, (38) formula, constitute stepping type least-squares algorithm (Recursive Least-Squares Algorithm,RLS)：

In sum, by RLS stepping type least-squares algorithm, realize parameter C₁And C₂The process estimated is summarized as follows：

First, according to two sections of the most initial measurement data X₁,X₂, calculate its mean valueAnd variances sigma₁ ²,σ₂ ²；

2nd, calculating matrix A₀,P₀,b⁽⁰⁾

P₀=(A₀ ^TA₀)^-1；

3rd, calculate X₁,X₂Corresponding parameter C₁And C₂：

4th, using new one section of measurement data X arriving₃, calculate its mean valueAnd variances sigma₃ ², and calculate renewal vector d^T And b⁽¹⁾：

b⁽¹⁾=1

5th, calculate measurement data X newly arriving₃Corresponding parameter C₁And C₂：

6th, as the following formula, reset the initial value of interative computation each unit：

7th, continuously acquire measurement data X of renewal₃, repeat above four to six, carry out new round iteration, piecewise recursion, directly To realizing corresponding parameter C of all measurement data₁And C₂Estimation.

8th, according to parameter C₁And C₂, as the following formula, calculate PM2.5 air chamber within this time, total light intensity a of scattered light²And shape Become the total number of particles mesh N of scattered light:

a²=C₁,

9th, the volume V according to known gas test room and total number of particles mesh N, and then calculate the particle of tested gas Thing number density ρ, i.e. PM2.5 total number of particles amount in the gas of unit volume：

3rd, the RLS of automatic adaptation FIR filter

Traditional FIR filter, its filter factor is fixed constant, is suitable for processing the random signal under Stationary Random Environments.Place Random signal under reason non-stationary or time-varying situation, frequently with so-called auto-adaptive fir filter.Self adaptation according to the present invention FIR filter, based on RLS (Recursive Least-Squares Algorithm, RLS).

Assume that y (n) is the sampled signal in the nT moment, n is positive integer, y (n) is the signal after outside noise pollution.For From the sampled signal by noise pollution, the primary signal that recovers, using FIR filter, y (n) is filtered process：

X (n) is nT moment filtered signal.

Introduce nT moment filter coefficient vector W (n)：

W (n)=[w₀(n),...,w_M-1(n)]^T(44-1)

Introduce nT moment sampled signal vector Y (n)：

Then (43) are to be write as：

X (n)=W^T(n) Y (n)=Y^T(n)W(n) (44-3)

Wherein, W^T(n) and Y^T(n)Y^TIt is respectively the transposition of W (n) and Y (n)：

W^T(n)=[w₀(n),...,w_M-1(n)],Y^T(n)=[y (n) ..., y (n-M+1)]

If setting the desired value as the unpolluted primary signal in nT moment for the d (n), filtered signal x (n) is considered as The estimate of nT moment d (n).By primary signal d (n) and its difference ε (n) of estimating between x (n), it is defined as evaluated error：

ε (n)=d (n)-x (n)=d (n)-W^T(n) Y (n)=d (n)-Y^T(n)W(n) (45)

The core of the RLS of automatic adaptation FIR filter is, with the estimated error sum of squares structure of exponential weighting Become object function, by this minimization of object function, to determine FIR filter coefficient vector W (n)：

In formula, exponential weighting factor 0 ＜ λ ＜ 1 is referred to as forgetting factor, its effect be to from the n moment more close to error add ratio Larger weight, and to from the n moment more away from error add smaller weight, that is, give old data from new data with different power Weight values.

Derivative minimizing criterion J (n) is equal to zero to the derivative of W (n), that is,

$\frac{\∂J\left(n\right)}{\∂W}=-2\underset{i=0}{\overset{n}{\Σ}}{\mathrm{\λ}}^{n-i}\[d\left(n\right)-Y^T\left(n\right)W\left(n\right)\]Y\left(n\right)=0---\left(47\right)$

After collated, obtain normal equation

Definition,

Then normal equation, formula (48) is write as, R (n) W (n)=q (n) (50)

The solution of the equation is, W (n)=R^-1(n)q(n) (51)

Here R^-1N () is the inverse matrix of square formation R (n).

According to definition (49), have

Then, above two formulas, are write as following recursive form：

R (n)=λ R (n-1)+Y (n) Y^T(n) (52-c)

Q (n)=λ q (n-1)+d (n) Y (n) (52-d)

By matrix inversion lemma, [A+UV]^-1=A^-1-A^-1U[I+VA^-1U]^-1VA^-1(39) act on (52-c)：

Definition vector K (n)：

Formula (54) is substituted into formula (53), then

Formula (55) is substituted into formula (51), has

Because the definition of K (n), (54) formula, then

Formula (57) is substituted into formula (56), obtains the recurrence formula of adaptive-filtering coefficient vector：

W (n)=W (n-1)+K (n) e (n) (59)

In (59) formula, define e (n)：

E (n)=d (n)-W^T(n-1) Y (n)=d (n)-Y^T(n)W(n-1) (60)

E (n) is referred to as prior estimate error

According to the definition of evaluated error ε (n) above, i.e. formula (45)：

ε (n)=d (n)-x (n)=d (n)-W^T(n) Y (n)=d (n)-Y^T(n)W(n) (45)

Contrast (60) understand, e (n) is different from evaluated error ε (n), specific as follows with formula (45)：

(60) in formula, W^T(n-1) Y (n) be nT moment automatic adaptation FIR filter output, using be to filter in (n-1) T moment Coefficient vector W (n-1), uses W^T(n-1) Y (n) estimates primary signal d (n) in nT moment.Its evaluated error e (n) producing, claims For prior estimate error.

And in formula (45), W^T(n) Y (n) be nT moment automatic adaptation FIR filter output, but using be to filter in the nT moment Coefficient vector W (n).Use W^TPrimary signal d (n) in (n) Y (n) estimation nT moment, evaluated error e (n) of generation, also referred to as Posterior estimator error.

Due to primary signal d (n) in nT moment cannot be known exactly which, therefore use W^T(n-1) Y (n-1), to estimate d (n), uses D (n) represents it：

D (n)=W^T(n-1)Y(n-1) (61)

D (n) in formula (60) is replaced with d (n), then the recurrence formula of adaptive-filtering coefficient vector：

In sum, automatic adaptation FIR filter algorithm is as follows：

First, initialize：

(1) when defining n=-1, W^T(- 1)=[0,0 ..., 0]^T, R (- 1)=δ₁Eye, i.e. R^-1(- 1)=δ₁ ^-1Eye, δ₁ It is the positive number of very little, eye is unit diagonal matrix；

(2) define sampled signal vector Y (0) during n=0：Y (0)=[y (0), 0.., 0], Y (0) are M dimensional vector；

(3) primary signal estimate d (0) during n=0, d (0)=δ are defined₂, δ₂It is the positive number of very little；

(4) select constant λ：0 ＜ λ ＜ 1.

2nd, by Y (0), R^-1(- 1) and λ, according to formula (45), calculates the vectorial K (0) during n=0：

3rd, according to formula (62), calculate automatic adaptation FIR filter coefficient vector W (0) during n=0：

E (0)=d (0)-W^T(- 1) Y (0)=d (0)

W (0)=W (- 1)+K (0) e (0)=K (0) e (0)

4th, according to formula (55), calculate matrix R during n=0^-1(0)：

5th, make n=n+1, using sampled signal y (n) in current nT moment, update signal vector Y (n)：

Y (n)=[y (n), y (n-1) ..., y (n-M+1)]^T

6th, by current nT time-ofday signals vector Y (n) and (n-1) T moment matrix R^-1(n-1), according to formula (54), (55), meter Calculate vectorial K (n), the matrix R in current nT moment^-1(n)：

7th, by recurrence formula (62) formula, calculate filter coefficient vector W (n) in current nT moment：

D (n)=W^T(n-1)Y(n-1)

E (n)=d (n)-W^T(n-1)Y(n)

W (n)=W (n-1)+K (n) e (n)

And to sampled signal y (n), carry out automatic adaptation FIR filtering as the following formula：

Repeat above five to seven, so circulate, obtain filtered burst x (n) of any time.

Above content is to further describe it is impossible to assert with reference to specific preferred embodiment is made for the present invention Being embodied as of the present invention is confined to these explanations.For general technical staff of the technical field of the invention, On the premise of present inventive concept, some simple deduction or replace can also be made, all should be considered as belonging to the present invention's Protection domain.

Claims (4)

1. a kind of Atmospheric particulates PM2.5 number density detection method is it is characterised in that it comprises the following steps：

Step 1：Tested air containing multiple particles things ,s, after the filter of different pore size separates, forms one and contains There are PM2.5 and the air-flow of more small particle, and enter gas test room；

Step 2：In gas test room, with Infrared irradiation tested air-flow, a part of infrared light and the gas of LASER Light Source output In stream, particulate matter scatters, and forms scattered light, another part infrared light is not scattered with particulate matter, is directed through tested gas Stream, forms penetrate light, using optics, scattered light and penetrate light is gathered together；

Step 3：The scattered light being gathered together and penetrate light, interfere effect, and export interference light；

Step 4：Using photodetector measure above interference light strength signal, its comprise the auto-correlation intensity of each scattered light, Cross-correlation intensity four between cross-correlation intensity between the auto-correlation intensity of penetrate light, scattered light and penetrate light, each scattered light Item content, the strength signal of this interference light is random signal, and its probability distributing density function is total with particle forming scattered light Number N is related；

Step 5：With A/D converter to above interference light overall strength signal, with the sampling period as T, carry out discretization sampling, obtain To sampled signal sequence y (n), represent the sampled signal in the nT moment with y (n), n is positive integer；

Step 6：In order to from the sampled signal by noise pollution, recover original interference light intensity signal, minimum using recursion Two take advantage of RLS algorithm, the auto-adaptive fir filter of one M rank of design, to sampled signal y (n), are filtered as the following formula：

$x\left(n\right)=\underset{i=0}{\overset{M-1}{\mathrm{\Σ}}}{w}_i\left(n\right)y(n-i)=W^T\left(n\right)Y\left(n\right)=Y^T\left(n\right)W\left(n\right)$

Wherein, w_iN () is the M rank FIR filter filter factor in nT moment, i=0, and 1 ..., M-1, W (n) filter system for the nT moment Number vector：W (n)=[w₀(n),...,w_M-1(n)]^T, and x (n) is filtered burst, x (n) can be considered in the nT moment Original interference light intensity Signal estimation value, Y (n) is the signal vector corresponding to the nT moment, and this vector is by M sampled signal group Become, it is as follows：

Y (n)=[y (n), y (n-1) ..., y (n-M+1)]^T

W^T(n)、Y^TN () is respectively the transposition of W (n) and Y (n)：

W^T(n)=[w₀(n),...,w_M-1(n)], Y^T(n)=[y (n), y (n-1) ..., y (n-M+1)]；

Step 7：If the sequence number pointer of filtered burst x (n) is n₀, from n₀=0 beginning, continuously takes two sections of foremost Length is the signal of L, and L is positive integer, uses signal vector X₁、X₂Represent：

X₁=[x (1) ..., x (L)]^T

X₂=[x (L+1) ..., x (2L)]^T

X₁、X₂Correspond in [0-LT], [LT-2LT] the interference light intensity signal in the time respectively, It is signal X respectively₁、X₂ Transposed form：

X₁ ^T=[x (1) ..., x (L)]

X₂ ^T=[x (L+1) ..., x (2L)]

As the following formula, calculate X₁、X₂Mean value：

$\stackrel{\‾}{X_1}=\left(\underset{k=1}{\overset{L}{\Σ}}x\right(k\left)\right)/L$

$\stackrel{\‾}{X_2}=\left(\underset{k=1}{\overset{L}{\mathrm{\Σ}}}x\right(L+k\left)\right)/L$

L, M are positive integer, and L ＞ M；

Step 8：As the following formula, calculate X₁、X₂Variance：

${{\mathrm{\σ}}_1}^2=\underset{k=1}{\overset{L}{\Σ}}{\[x\left(k\right)-\stackrel{\‾}{X_1}\]}^2$

${{\mathrm{\σ}}_2}^2=\underset{k=1}{\overset{L}{\Σ}}{\[x(L+k)-\stackrel{\‾}{X_2}\]}^2$

σ₁ ²And σ₂ ²Correspond to the fluctuation of the interference light intensity in the time in [0-LT] and [LT-2LT] respectively:

Step 9：According to interference light intensity in mean value in the time of [0-LT], [LT-2LT] and variance：

σ₁ ²、σ₂ ², set up following parameter C₁、C₂Matrix equation：

Wherein,

a²For the total light intensity of KPT Scatter light, N is the total number of particles mesh forming scattered light, by C₁、C₂Referred to as systematic parameter, here It is assumed that a²Keep constant with N in time at [0-LT] and [LT-2LT], therefore, within this time, systematic parameter C₁、C₂It is approximately One group of constant；

Step 10：By the least square solution of above equation, obtain [0-LT] and [LT-2LT] systematic parameter C in the time₁And C₂ Estimate：

Wherein, P₀=(A₀ ^TA₀)^-1, A₀ ^TFor matrix A₀Transposition, (A₀ ^TA₀)^-1For A₀ ^TA₀Inverse matrix, orderSystematic parameter C that will be in [0-LT] and [LT-2LT] in the time₁、C₂, use respectivelyRepresent；

Step 11：Sequence number pointer n of adjustment burst x (n)₀, make n₀=n₀+ 2L, makes sequence number pointer, points to next segment length Filtered signal for L；

Step 12：From current sequence number pointer n₀Start, take a segment length to be the filtered signal of L：X₃=[x (n₀+1),...,x (n₀+L)]^T, X₃Corresponding in [n₀T-(n₀+ L) T] interference light intensity signal in the time, as the following formula, calculate X₃Mean value And variances sigma₃ ²：

$\begin{array}{c}\stackrel{\‾}{X_3}=\left(\underset{k=1}{\overset{L}{\mathrm{\Σ}}}x\right(n_0+k\left)\right)/L\\ {{\mathrm{\σ}}_3}^2=\underset{k=1}{\overset{L}{\Σ}}{\[x\left(n_0+k\right)-\stackrel{\‾}{X_3}\]}^2\end{array},$

With newly-generated mean valueAnd varianceConstitute signal update vector d^TAnd b⁽¹⁾：

$d^T=\left[\begin{array}{cc}\frac{2\stackrel{\‾}{X_3}}{{{\mathrm{\σ}}_3}^2}& \frac{-2}{{{\mathrm{\σ}}_3}^2}\end{array}\right],b^{\left(1\right)}=1,$

Set up in [n₀T-(n₀+ L) T] systematic parameter C in the time₁、C₂Matrix equation：

$d^T\left[\begin{array}{c}{C}_1\\ C_2\end{array}\right]=b^{\left(1\right)},b^{\left(1\right)}=1;$

Step 13：According to signal update vector d being constituted^TAnd b⁽¹⁾, using recursive least-squares RLS algorithm, as the following formula, complete In [n₀T-(n₀+ L) T] in the time to systematic parameter C₁、C₂Carrying out estimate, and use respectivelyRepresent it：

$\begin{array}{c}{P}_1=P_0-\frac{P_0{\mathrm{dd}}^T{P}_0}{1+d^T{P}_{0}d}\\ \left[\begin{array}{c}{C}_1^{\left(1\right)}\\ C_2^{\left(1\right)}\end{array}\right]=\left[\begin{array}{c}{C}_1^{\left(0\right)}\\ C_2^{\left(0\right)}\end{array}\right]+\left(b^{\left(1\right)}-d^T\left[\begin{array}{c}{C}_1^{\left(0\right)}\\ C_2^{\left(0\right)}\end{array}\right]\right)P_{1}d\end{array},$

Wherein, vectorial d and d^TTransposition relation each other,WithBefore corresponding respectively to current iteration computing With parameter C afterwards₁、C₂；

Step 14：According to above [n₀T-(n₀+ L) T] systematic parameter C in the time₁And C₂, as the following formula, calculate gas test room and exist In this time, total light intensity a of scattered light²And form the total number of particles mesh N of scattered light:

$a^2=C_1,N=\frac{{C_1}^2}{C_2};$

Step 15：Volume V according to the known gas test room and above total number of particles mesh N trying to achieve, can calculate in [n₀T- (n₀+ L) T] in the time, particulate count density ρ of tested gas, i.e. PM2.5 number of particles in the gas of unit volume：

$\mathrm{\ρ}=\frac{N}{V}$

Step 16：Required according to recursive least-squares RLS algorithm, as the following formula, reset the initial value of interative computation each unit：

$\begin{array}{c}{P}_0=P_1\\ \left[\begin{array}{c}{C}_1^{\left(0\right)}\\ C_2^{\left(0\right)}\end{array}\right]=\left[\begin{array}{c}{C}_1^{\left(1\right)}\\ C_2^{\left(1\right)}\end{array}\right]\end{array};$

Step 17：Sequence number pointer n of adjustment burst X₀, make n₀=n₀+ L, making sequence number pointer point to next segment length is L's Filtered signal；

Step 18：Repeat step 12-17, so circulates, completes in initial time n₀After=0, random time [n₀T-(n₀+ L) T] in systematic parameter C₁、C₂Estimation, and then, obtain random time [n₀T-(n₀+ L) T] in scattered light total light intensity a² And the total number of particles mesh N of formation scattered light, realize tested gas in initial time n₀After=0, random time [n₀T-(n₀+ L) T] in particulate count density detection, wherein, n₀=0,2L, 3L, 4L, 5L .....

2. a kind of Atmospheric particulates PM2.5 number density detection method according to claim 1 it is characterised in that：Step 1 In, tested air, in the presence of negative pressure generating unit, separates through the filter of two grades of different pore sizes.

3. a kind of Atmospheric particulates PM2.5 number density detection method according to claim 1 it is characterised in that：Step 2 In, described optics is convex lens or convex lens group.

4. a kind of Atmospheric particulates PM2.5 number density detection method according to claim 1 it is characterised in that：Step 6 In, auto-adaptive fir filter least square RLS algorithm is it is characterised in that it comprises the following steps：

Step 1：Initialization：

1) when defining n=-1, W^T(- 1)=[0,0 ..., 0]^T, R (- 1)=δ₁Eye, i.e. R^-1(- 1)=δ₁ ^-1Eye, δ₁It is very little Positive number, eye be unit diagonal matrix；

2) define sampled signal vector Y (0) during n=0：Y (0)=[y (0), 0.., 0], Y (0) are M dimensional vector；

3) primary signal estimate d (0) during n=0, d (0)=δ are defined₂, δ₂It is the positive number of very little；

4) select constant λ：0 ＜ λ ＜ 1；

Step 2：By Y (0), R^-1(- 1) and λ, as the following formula, calculates the vectorial K (0) during n=0：

$K\left(0\right)=\frac{R^{-1}(-1)Y\left(0\right)}{\mathrm{\λ}+Y^T\left(0\right)R^{-1}(-1)Y\left(0\right)}$

Step 3：As the following formula, calculate automatic adaptation FIR filter coefficient vector W (0) during n=0：

E (0)=d (0)-W^T(- 1) Y (0)=d (0)

W (0)=W (- 1)+K (0) e (0)=K (0) e (0)

Step 4：As the following formula, calculate matrix R during n=0^-1(0)：

$R^{-1}\left(0\right)=\frac{1}{\mathrm{\λ}}\[R^{-1}(-1)-K\left(0\right)Y^T{R}^{-1}(-1)\]$

Step 5：Make n=n+1, using sampled signal y (n) in current nT moment, update signal vector Y (n)：

Y (n)=[y (n), y (n-1) ..., y (n-M+1)]^T

Step 6：By current nT time-ofday signals vector Y (n) and (n-1) T moment matrix R^-1(n-1) when, as the following formula, calculating current nT The vectorial K (n) at quarter, matrix R^-1(n)：

$K\left(n\right)=\frac{R^{-1}(n-1)Y\left(n\right)}{\mathrm{\λ}+Y^T\left(n\right)R^{-1}(n-1)Y\left(n\right)}$

$R^{-1}\left(n\right)=\frac{1}{\mathrm{\λ}}\[R^{-1}(n-1)-K\left(n\right)Y^T{R}^{-1}(n-1)\]$

Step 7：By following recurrence formula, calculate filter coefficient vector W (n) in current nT moment：

D (n)=W^T(n-1)Y(n-1)

E (n)=d (n)-W^T(n-1)Y(n)

W (n)=W (n-1)+K (n) e (n)

And to sampled signal y (n), carry out automatic adaptation FIR filtering as the following formula：

$x\left(n\right)=\underset{i=0}{\overset{M-1}{\mathrm{\Σ}}}{w}_i\left(n\right)y(n-i)=W^T\left(n\right)Y\left(n\right)$

Step 8：Repeat above step 5-7, so circulate, obtain filtered burst x (n) of any time.