CN110554039B - Laboratory microorganism detection system for realizing laboratory microorganism detection method - Google Patents

Abstract

The invention belongs to the technical field of laboratory detection equipment, and particularly relates to a laboratory microorganism detection system which can realize a laboratory microorganism detection method with more comprehensive detection and higher accuracy, which specifically comprises the following steps: a color table construction module; a spectrum scanning module matrix; a refractive index difference comparison module; an image noise reduction module; and the microbial color table is compared with a confirmation module. The microorganism detection system or the detection method provided by the invention has the advantages that the color contrast data of the conventional environment is constructed as the basis, the detection image is obtained through polarized light scanning, and the abnormity of the laboratory environment is detected through the accurate denoising and contrast method, so that the system has higher precision, wider scanning range and more comprehensive detection.

Description

Laboratory microorganism detection system for realizing laboratory microorganism detection method

Technical Field

The invention belongs to the technical field of laboratory detection equipment, and particularly relates to a laboratory microorganism detection system for realizing a laboratory microorganism detection method.

Background

In the 40's of the 20 th century, the united states established a biological weapons research program and began to implement an "aerosol infection program" that utilized large numbers of pathogens as highly contagious diseases and continued to develop a series of microbiological, weaponry, and field tests. However, due to insufficient protection technology and consciousness of the microbiological experiment at that time, workers are not protected in place during the experiment, so that the infection accidents in the laboratory operation frequently occur. In 1979, anthrax bacillus leakage accidents occurred in anthrax drying plants in weapons research base in the Soviet-Victorloff region during production, which caused inhalation anthrax or skin anthrax to infect a large number of residents downwind of the base, and led to the death of dozens of people. Along with the research of high-risk pathogens, laboratory biosafety accidents continue to continue in this period, developed countries such as English, American, Soviet Union, Canada, Japan and the like successively start the construction of biosafety laboratories to solve the safety problems, and the frequency of the laboratory biosafety accidents is reduced through the operation of different levels of biosafety laboratories and corresponding microorganism operation standard guidelines.

In 1979, the American Occupational Safety and Health Agency (OSHA) issued a pathogenic microorganism classification based on degree of harm, and first proposed the concept of four grades of pathogenic microorganisms and experimental activities thereof. In 1983, in order to reduce the occurrence of laboratory safety accidents, the WHO published the first version of the laboratory biological safety manual, and continuously revised the manual to guide various countries to carry out standardized management on the biological laboratories of the country, and made specific operating rules to guide the safe operation of pathogenic microorganisms. In 2004, after absorbing the laboratory management experience and the safety accident experience training of each country, the newly revised handbook (third edition) made clear requirements for hardware and software including facilities and equipment, personal protection and SOP in the laboratory. Meanwhile, in order to realize safer laboratory management and biosafety control, biological safety laws and regulations of the country are successively set up by each country, laboratory safety supervision and inspection are enhanced, and infection and leakage of pathogenic pathogens in the laboratory are controlled.

In the 80 s of the 20 th century, epidemic hemorrhagic fever laboratory infection accidents occur continuously in the world, and a three-level biosafety level (BSL-3) laboratory made in the first country of China is built by military medical academy of sciences for safely carrying out the experimental research, and relevant operation regulations are made. Subsequently, in order to develop AIDS research, a few BSL-3 laboratories were introduced by the original Chinese academy of preventive medicine, and the construction of the BSL-3 laboratories in China began. Meanwhile, the national biological safety standard is prepared from the end of the last 90 th century in China, and the national standard of biological safety of laboratories in China, namely the universal requirement of biological safety of laboratories (GB19489), is promulgated in 5 2004. On the basis, various departments issue respective laboratory biological safety regulations and rules successively to guide biological safety guarantee of microbial laboratories in various fields.

With the continuous establishment of BSL-3 laboratories and the successive promulgation of relevant standard laws and regulations, the safety of high-risk pathogen experimental work in China is basically guaranteed, but in practical application, the occurrence of biological safety accidents can still be caused by pathogen exposure caused by instrument equipment failure or human accidental factors, for example, in 2004, a certain laboratory researcher in Beijing uses an unverified inactivation method to treat a SARS-CoV sample, then the sample is carried out of the BSL-3 laboratory, and then the experiment is carried out in a common laboratory, so that active viruses are carried out and cause the infection of the laboratory, and then close contacts are infected, and finally 9 people are infected, and 1 person dies. The BSL-3 laboratory ensures the safety of experimental activities, but becomes a risk factor of biological safety, and needs a practitioner to carry out more detailed work, discover the specific risk factor of the laboratory, make scientific and effective control measures and avoid the occurrence of laboratory accidents.

How to invent a laboratory microorganism detection system which can achieve more comprehensive and higher accuracy inside a laboratory by integrally detecting the microorganism environment inside the laboratory and effectively detecting and reducing noise simultaneously becomes an urgent task for solving the problems in the biochemical laboratory at present.

Disclosure of Invention

The invention aims to provide a laboratory microorganism detection system which is more comprehensive in detection and higher in accuracy.

The invention also aims to provide a laboratory microorganism detection method.

The purpose of the invention is realized as follows:

a laboratory microorganism detection system specifically comprises:

the building module is used for building a color table to be confirmed according to the spectral data characteristics of the laboratory;

the system comprises a scanning module, a data acquisition module and a data processing module, wherein the scanning module is used for carrying out optical scanning on the whole environment of a laboratory to obtain a corresponding environment scanning result and converting the environment scanning result into corresponding spectrum data;

the comparison module is used for acquiring the refractive index of a scanning position related to the overall environment of the laboratory scanned by light according to the spectral data acquired by the scanning module; searching and comparing the refractive index difference of each scanning position based on a prestored refractive index mapping table;

the image denoising module is used for denoising the image file to be scanned;

and the confirming module is used for searching a color value which is closest to the refractive index difference of each scanning position searched and compared by the comparison module based on the color table to be confirmed, which is constructed by the construction module, determining the index color corresponding to the image file to be scanned after the noise reduction processing is carried out by the image noise reduction module, and obtaining the microorganism scanning result.

(1) Pre-constructing a color table according to the spectral data characteristics of a laboratory, comprising:

(1.1) constructing a spectral data set Q:

Q＝{Q₁,Q₂,Q₃,…Q_i…,Q_n}

Q_nobtaining the spectrum data for the nth time, wherein n is the total times of obtaining the spectrum data;

(1.2) acquiring a value of the spectral data at each time;

Q_n＝(Q_1,n,Q_2,n,Q_3,n,…Q_j,n,…,Q_m,n)

Q_m,nthe characteristic value is the mth spectral characteristic value when the spectral data is acquired at the nth time, and m is the total characteristic value quantity when the spectral data is acquired each time;

(1.3) carrying out standardization treatment;

Z_m,nfor the purpose of the normalized value of the value,

for the mean, σ Q, of the n-th acquired spectral data_nA variance for the nth acquired spectral data;

(1.4) constructing a confirmation color table;

B_mfor the mth spectral feature value of the constructed color chart, Q _1_mIs the maximum of all the mth spectral characteristic values in the spectral data set Q;

(2) converting environmental scan results into spectral data, comprising the steps of:

the whole environment of the laboratory is optically scanned to obtain a series of polarized light, the light is reflected when entering a detection interface of the laboratory, and the reflected light is as follows:

is the included angle between the incident light of the polarized light and the S-polarization; k_sAnd K_pPredetermined coefficients of S-polarization and P-polarization, respectively, and K_sAnd K_pThe following conditions are satisfied;

k1 is the reflectance of polarized light in a laboratory environment; k2 is the reflection coefficient of the polarized light with the mycoderm, beta is the polarization coefficient of the polarized light, and beta is obtained by the following formula;

lambda is the refractive index of the detection surface;

the spectral data of the emergent light is:

θ_ithe included angle between the polarized light and the normal vector is shown, and F _ C is spectrum data of emergent light;

(3) converting the refractive index of the scanned position according to the spectral data; mapping index is carried out on the data of each position, the refractive index of each position is used for mapping with a refractive index comparison table, the comparison table is compared, and the refractive index difference is detected;

the imaging system outputs an image of the object as:

c is the imaging system channel number; f. of_c(x) For the C-th channel in the imaging systemThe xth position of the track outputs a response value; r_c(mu) is the spectral integrated response curve of the C channel; sigma_c(μ) is the response curve of the light source spectrum of the C-th channel; d_c(mu) spectral power curve of light source of C channel, P_c(μ) is the spectral transmittance curve of the pre-channel filter for the C-th channel, μ is the integrated parameter, with no practical meaning, x is the imaging system output object image position, C is 1, 2, 3 … … q, q is the total number of channels;

the output images of all channels are represented in a discrete matrix manner:

f (x) is the output image of all channels,

the spectral reflectance of the light-scanned image surface;

r (mu) is a diagonal matrix formed by the spectral comprehensive response curves of all channels;

σ (μ) is a diagonal matrix formed by response curves of the light source spectra of all channels;

d (mu) is a diagonal matrix formed by spectral power curves of light sources of all channels

P (mu) is a diagonal matrix formed by the spectral transmittance curves of the channel front filters of all channels;

calculating the mapping relation between the light scanning image and the color table through the inverse transformation of the positive image model of the spectral imaging system;

(5) denoising the scanned image file; comprises the following steps of;

(4.1) acquiring a scanned image file W;

(4.2) dividing the scanned image W into N blocks, the image W being N partial images, i.e., W ═ W₁,W₂,W₃,…,W_i,…,W_NAnd arbitrary local image W_NAll satisfy the following formula;

W_N＝CS_N+LB_N

CS_Npixel matrix, LB, of the original image of the Nth partial image_NA filtered pixel matrix for an nth image;

(4.3) solving for said W_NThe singular characteristic quantity WV of the pixel of_NAnd said W_NVariance σ of_N；

(4.4) calculating an energy function of the local image;

wherein, fCS_NIs the energy function of the local image, | W_N-CS_N||_FIs W_N-CS_NF-norm, | | CS_N||_w*Is a weighted norm and meets the following requirements;

wherein j belongs to m and is a value of j, and m is a singular characteristic quantity WV_NThe number of values contained, d is the sum of the local image W_NThe Euclidean distance of less than 0.1, K is a preset value, | SV_N|_jThe original singular value is obtained by the following formula;

wherein, | WV_N|_jIs WV_NThe jth value of (d);

(4.5) obtaining LB by the energy function of the partial image_N；

The CS can be obtained by constructing an equation system by using the formulas in (4.5) and (4.2)_NI.e. to the local part W_NRemoving the filtered image, and removing the filtering of all the N local images to obtain the image from which the filtering is removed;

the pre-constructing of the color table according to the spectral data characteristics of the laboratory comprises:

(1.1) extracting a color table of the image in the first clustering:

Q₁＝{z₁、z₂、…z_n}；

z_ncollecting color points of a laboratory environment image, wherein n is the number of the color points;

(1.2) clustering for the second time to obtain an extended color table;

wherein σ is a spreading factor; and the secondary clustering refines the color table in the block. The association of colors and the diversity of color combinations within a block are maintained.

(1.3) carrying out standardization treatment;

z_ij ^*is a normalized value, z_ijTo expandColor point values in the color table; alpha is the mean value of all sample data; β is the standard deviation of all sample data;

(1.4) carrying out brightness self-adaptation;

It_lis the mean value of brightness, Ct, of the color chart_lIs the original brightness value, Pt, of the color chart_lIs the mean value of the brightness, Im, of the color chart_lAs the mean value of the brightness, Cm, of the image_lIs the original brightness value, Pm, of the image_lIs the contrast luminance mean of the image.

Scanning the whole environment of laboratory is a series of polarized light, and the reflection takes place when light incides laboratory detection interface, and the reverberation is:

the included angle between the polarization direction of the polarizer and the S polarization component is formed; r is_sAnd r_pPolarization coefficients for the S and P polarization components, respectively;

r₀₁the polarization reflection coefficients of the laboratory environment and air; r is₁₂The polarization reflection coefficients of the laboratory environment and the bacteria-carrying membrane are obtained;

delta is the polarized light phase difference;

n_iis the refractive index of the detection surface; theta_iAn incident angle of the detection light for the detection plane;

the spectral data of the emergent light is:

is the angle between the polarized light and the long axis.

Converting the refractive index of the scanned position according to the spectral data; mapping and indexing the data of each position, mapping the refractive index of each position and a refractive index comparison table, comparing the comparison table, and detecting the refractive index difference, including;

the imaging system outputs an image of the object as:

c_k＝∫_λr_k(λ)ρ_k(λ)s_k(λ)o(λ)dλ；

k is the imaging system channel number; c. C_kIs the output response of the k channel in the imaging system; r (λ) is the spectral response curve; s (lambda) is a response curve of the light source spectrum; o (λ) is the spectral power spectrum of the light source, ρ_k(λ) the spectral transmittance of the front filter of each channel;

expressed in a discrete matrix manner as:

Cn＝(Rnρn)^TSnRn；

dn is a multi-channel image output by the imaging system; sn is the spectral reflectivity of the surface of the optical scanning image; is a diagonal matrix with diagonal elements r (λ); the elements on the diagonal are the spectral responsivity of the imaging system at each wavelength;

tn is the spectral transmittance of the imaging system;

sn is a diagonal matrix taking the spectral power distribution of the light source as diagonal elements;

cn is an erect image model of the spectral imaging system;

and calculating the mapping relation between the light scanning image and the color table through the inverse transformation of the positive image model of the spectral imaging system.

Denoising the scanned image file; comprises the following steps of;

(4.1) all image information documents g (t) { g ═ g₁,g₂,g₃,…,g_NConstructed as an m × n-order document matrix, g_iThe ith image information document being the font, image information document set g (t);

(4.2) setting the dimension n of the document matrix;

(4.3) extracting { g ] from G (t)₁,g₂,g₃,…,g_nAs the first row of the matrix;

(4.4) sequentially delaying backward by one document until the last signal of n lines is G_NAs the last row of the matrix;

the matrix formed is:

X_m×nan m ﹡ n-dimensional matrix constructed for the original signal; v_m×nAn m ﹡ n-dimensional matrix constructed for the noise signal; if N is an even number, m is N/2+1, and N is N/2; if N is an odd number, m and N are both (N + 1)/2;

(4.5) performing singular value decomposition on H;

H＝ΣWR^T

w is unitary matrix with dimension m ﹡ m; r is a unitary matrix of n ﹡ n dimension, namely a left and right singular matrix of H, and T represents a transposed matrix of the matrix; element alpha of major diagonal line of m ﹡ n-dimensional diagonal matrix of sigma_iIs a non-zero singular value of H to increase the orderOrder, i.e. alpha₁≥α₂≥α₃≥…≥α_i；

(4.6) determining the effective rank of sigma, namely the first l maximum singular values; approximating matrix Y for reconstruction H_l；

W_lIs the left singular vector corresponding to the first l largest singular values; r_lIs the right singular vector corresponding to the first l largest singular values; sigma_lThe diagonal matrix corresponding to the first l maximum singular values;

(4.7) mixing Y_lReconstructing the document components matched with the image information into a document matrix:

ε＝min(l,γ-ε+l)，γ＝max(n,k)；

w is the image information after denoising.

Searching the closest color value in the microorganism color table, and determining the index color corresponding to the scanned image to obtain a microorganism scanning result;

(5.1) dividing the low-contrast points;

χ＝(x,y,σ)o(λ)

x and y are pixel coordinate values of the scanned image, and sigma is a scale parameter of the scanned image layer;

acquiring an extreme value Cn (x);

computing

If it is

If the point belongs to the ground contrast point, deleting the point;

(5.2) eliminating edge points;

constructing a scanning image HESSIAN matrix as follows:

C_ijis the value of the point Cn with pixel coordinates (i, j);

calculating the determinant and the like of the matrix;

Sr(H)＝C_xx+C_yy＝α+β；

Bet(H)＝C_xxC_yy-C_xy ²＝αβ；

let a be θ β,

if the pixel point can not satisfy the formula, rejecting the pixel point;

and searching the closest color value of the pixel of the scanned image in the color table to determine the corresponding index color and determine the result of scanning the microorganism.

The matrix of spectrum scanning modules includes a scanning circuit,

the scanning circuit includes: a first resistor R1, a second resistor R2, a third resistor R3, a fourth resistor R4, a fifth resistor R5, a sixth resistor R6, a seventh resistor R7, an eighth resistor R8, a ninth resistor R9, a tenth resistor R10, an eleventh resistor R11, a twelfth resistor R12, a thirteenth resistor R13, a first capacitor C1, a second capacitor C2, a third capacitor C3, a fourth capacitor C4, a first diode L1, a second diode L2, a third diode L3, a first NPN transistor Q1, a second NPN transistor Q2, a third NPN transistor Q3, a PNP transistor P1, a power source VCC, a ground GND, a scanner S1, and an integrator S2;

the power supply VCC is respectively connected with one ends of a third resistor R3, a first capacitor C1, a fourth resistor R4 and a fifth resistor R5, and is also connected with a first end of an integrator S2, the other end of the third resistor R3 is respectively connected with a first end and a second end of a scanner S1, a third end of the scanner S1 is respectively connected with one end of the first resistor R1 and a third end of the integrator S2, and a fourth end of the scanner S1 is respectively connected with one end of the second resistor R2 and a fourth end of the integrator S2;

a second end of the integrator S2 is connected to one end of an eighth resistor R8, the other end of the eighth resistor R8 is connected to a base of a first NPN transistor Q1, and a fifth end of the integrator S2 is connected to the other end of a fourth resistor R4 and one end of a sixth resistor R6, respectively;

the other end of the fifth resistor R5 is connected to one end of a second capacitor C2, a seventh resistor R7 and a ninth resistor R9, and is further connected to the anode of the first diode L1 and the collector of the PNP transistor P1; the other end of the seventh resistor R7 is connected to the anode of a third diode L3, and the cathode of the third diode L3 is connected to the emitter of the first NPN transistor Q1 and the cathode of the first diode L1, respectively;

the base electrode of the PNP transistor P1 is connected to one end of a tenth resistor R10 and one end of a third capacitor C3, the other ends of the tenth resistor R10 and the third capacitor C3 are connected to one end of an eleventh resistor R11, the other end of the eleventh resistor R11 is connected to the base electrode of a third NPN transistor Q3, and the collector electrode of the third NPN transistor Q3 is connected to the negative electrode of a second diode L2;

an emitter of the PNP transistor P1 is connected to a collector of the second NPN transistor Q2, a base of the second NPN transistor Q2 is connected to the other end of the ninth resistor R9, one end of the fourth capacitor C4, one end of the twelfth resistor R12, and one end of the thirteenth resistor R13, respectively, and the other end of the twelfth resistor R12 is connected to an anode of the second diode L2;

the other ends of the first capacitor C1, the first resistor R1, the second resistor R2, the sixth resistor R6, the second capacitor C2, the fourth capacitor C4 and the thirteenth resistor R13 are grounded to GND, and the emitters of the first NPN transistor Q1, the second NPN transistor Q2 and the third NPN transistor Q3 are also grounded to GND.

A laboratory microorganism detection method specifically comprises the following steps:

(1) pre-constructing a color table according to the spectral data characteristics of a laboratory, comprising:

(1.1) constructing a spectral data set Q:

Q＝{Q₁,Q₂,Q₃,…Q_i…,Q_n}

Q_nobtaining the spectrum data for the nth time, wherein n is the total times of obtaining the spectrum data;

(1.2) acquiring a value of the spectral data at each time;

Q_n＝(Q_1,n,Q_2,n,Q_3,n,…Q_j,n,…,Q_m,n)

Q_m,nthe characteristic value is the mth spectral characteristic value when the spectral data is acquired at the nth time, and m is the total characteristic value quantity when the spectral data is acquired each time;

(1.3) carrying out standardization treatment;

Z_m,nfor the purpose of the normalized value of the value,

for the mean, σ Q, of the n-th acquired spectral data_nA variance for the nth acquired spectral data;

(1.4) constructing a confirmation color table;

B_mfor the mth spectral feature value of the constructed color chart, Q _1_mIs the maximum of all the mth spectral characteristic values in the spectral data set Q;

(2) converting environmental scan results into spectral data, comprising the steps of:

the whole environment of the laboratory is optically scanned to obtain a series of polarized light, the light is reflected when entering a detection interface of the laboratory, and the reflected light is as follows:

is the included angle between the incident light of the polarized light and the S-polarization; k_sAnd K_pPredetermined coefficients of S-polarization and P-polarization, respectively, and K_sAnd K_pThe following conditions are satisfied;

k1 is the reflectance of polarized light in a laboratory environment; k2 is the reflection coefficient of the polarized light with the mycoderm, beta is the polarization coefficient of the polarized light, and beta is obtained by the following formula;

lambda is the refractive index of the detection surface;

the spectral data of the emergent light is:

θ_iis the polarized light and normal vectorF _ C is the spectrum data of the emergent light;

(3) converting the refractive index of the scanned position according to the spectral data; mapping index is carried out on the data of each position, the refractive index of each position is used for mapping with a refractive index comparison table, the comparison table is compared, and the refractive index difference is detected;

the imaging system outputs an image of the object as:

c is the imaging system channel number; f. of_c(x) Outputting a response value for the xth position of the C channel in the imaging system; r_c(mu) is the spectral integrated response curve of the C channel; sigma_c(μ) is the response curve of the light source spectrum of the C-th channel; d_c(mu) spectral power curve of light source of C channel, P_c(μ) is the spectral transmittance curve of the pre-channel filter for the C-th channel, μ is the integrated parameter, with no practical meaning, x is the imaging system output object image position, C is 1, 2, 3 … … q, q is the total number of channels;

the output images of all channels are represented in a discrete matrix manner:

f (x) is the output image of all channels,

the spectral reflectance of the light-scanned image surface;

r (mu) is a diagonal matrix formed by the spectral comprehensive response curves of all channels;

σ (μ) is a diagonal matrix formed by response curves of the light source spectra of all channels;

d (mu) is a diagonal matrix formed by spectral power curves of light sources of all channels

P (mu) is a diagonal matrix formed by the spectral transmittance curves of the channel front filters of all channels;

calculating the mapping relation between the light scanning image and the color table through the inverse transformation of the positive image model of the spectral imaging system;

(5) denoising the scanned image file; comprises the following steps of;

(4.1) acquiring a scanned image file W;

(4.2) dividing the scanned image W into N blocks, the image W being N partial images, i.e., W ═ W₁,W₂,W₃,…,W_i,…,W_NAnd arbitrary local image W_NAll satisfy the following formula;

W_N＝CS_N+LB_N

CS_Npixel matrix, LB, of the original image of the Nth partial image_NA filtered pixel matrix for an nth image;

(4.3) solving for said W_NThe singular characteristic quantity WV of the pixel of_NAnd said W_NVariance σ of_N；

(4.4) calculating an energy function of the local image;

wherein CS_NIs the energy function of the local image, | W_N-CS_N||_FIs W_N-CS_NF-norm, | | CS_N||_w*Is a weighted norm and meets the following requirements;

wherein j belongs to m and is a value of j, and m is a singular characteristic quantity WV_NThe number of values contained, d is the sum of the local image W_NThe Euclidean distance of less than 0.1, K is a preset value, | SV_N|_jThe original singular value is obtained by the following formula;

wherein, | WV_N|_jIs WV_NThe jth value of (d);

(4.5) obtaining LB by the energy function of the partial image_N；

The CS can be obtained by constructing an equation system by using the formulas in (4.5) and (4.2)_NI.e. to the local part W_NRemoving the filtered image, and removing the filtering of all the N local images to obtain the image from which the filtering is removed;

(1) the pre-constructing of the color table according to the spectral data characteristics of the laboratory comprises:

(1.1) extracting a color table of the image in the first clustering:

Q₁＝{z₁、z₂、…z_n}；

z_ncollecting color points of a laboratory environment image, wherein n is the number of the color points;

(1.2) clustering for the second time to obtain an extended color table;

wherein σ is a spreading factor; and the secondary clustering refines the color table in the block. The association of colors and the diversity of color combinations within a block are maintained.

(1.3) carrying out standardization treatment;

z_ijis a normalized value, z_ijExpanding the color point value in the color table; alpha is the mean value of all sample data; β is the standard deviation of all sample data;

(1.4) carrying out brightness self-adaptation;

It_lis the mean value of brightness, Ct, of the color chart_lIs the original brightness value, Pt, of the color chart_lIs the mean value of the brightness, Im, of the color chart_lAs the mean value of the brightness, Cm, of the image_lIs the original brightness value, Pm, of the image_lIs the contrast luminance mean of the image.

(2) The whole environment of the optical scanning laboratory converts the detection signals into spectral data;

scanning the whole environment of laboratory is a series of polarized light, and the reflection takes place when light incides laboratory detection interface, and the reverberation is:

is the polarization direction of a polarizerThe angle with the S polarization component; r is_sAnd r_pPolarization coefficients for the S and P polarization components, respectively;

r₀₁the polarization reflection coefficients of the laboratory environment and air; r is₁₂The polarization reflection coefficients of the laboratory environment and the bacteria-carrying membrane are obtained;

delta is the polarized light phase difference;

n_iis the refractive index of the detection surface; theta_iAn incident angle of the detection light for the detection plane;

the spectral data of the emergent light is:

is the angle between the polarized light and the long axis.

(3) Converting the refractive index of the scanned position according to the spectral data; mapping and indexing the data of each position, mapping the refractive index of each position and a refractive index comparison table, comparing the comparison table and detecting the refractive index difference;

the imaging system outputs an image of the object as:

c_k＝∫_λr_k(λ)ρ_k(λ)s_k(λ)o(λ)dλ；

k is the imaging system channel number; c. C_kIs the output response of the k channel in the imaging system; r (λ) is the spectral response curve; s (lambda) is a response curve of the light source spectrum; o (λ) is the spectral power spectrum of the light source, ρ_k(λ) the spectral transmittance of the front filter of each channel;

expressed in a discrete matrix manner as:

Cn＝(Rnρn)^TSnRn；

dn is a multi-channel image output by the imaging system; sn is the spectral reflectivity of the surface of the optical scanning image; is a diagonal matrix with diagonal elements r (λ); the elements on the diagonal are the spectral responsivity of the imaging system at each wavelength;

tn is the spectral transmittance of the imaging system;

sn is a diagonal matrix taking the spectral power distribution of the light source as diagonal elements;

cn is an erect image model of the spectral imaging system;

and calculating the mapping relation between the light scanning image and the color table through the inverse transformation of the positive image model of the spectral imaging system.

(4) Denoising the scanned image file; comprises the following steps of;

(4.1) all image information documents g (t) { g ═ g₁,g₂,g₃,…,g_NConstructed as an m × n-order document matrix, g_iThe ith image information document being the font, image information document set g (t);

(4.2) setting the dimension n of the document matrix;

(4.3) extracting { g ] from G (t)₁,g₂,g₃,…,g_nAs the first row of the matrix;

(4.4) sequentially delaying backward by one document until the last signal of n lines is G_NAs the last row of the matrix；

The matrix formed is:

X_m×nan m ﹡ n-dimensional matrix constructed for the original signal; v_m×nAn m ﹡ n-dimensional matrix constructed for the noise signal; if N is an even number, m is N/2+1, and N is N/2; if N is an odd number, m and N are both (N + 1)/2;

(4.5) performing singular value decomposition on H;

H＝ΣWR^T

w is unitary matrix with dimension m ﹡ m; r is a unitary matrix of n ﹡ n dimension, namely a left and right singular matrix of H, and T represents a transposed matrix of the matrix; element alpha of major diagonal line of m ﹡ n-dimensional diagonal matrix of sigma_iNon-zero singular values of H, arranged in increasing order, i.e. alpha₁≥α₂≥α₃≥…≥α_i；

(4.6) determining the effective rank of sigma, namely the first l maximum singular values; approximating matrix Y for reconstruction H_l；

W_lIs the left singular vector corresponding to the first l largest singular values; r_lIs the right singular vector corresponding to the first l largest singular values; sigma_lThe diagonal matrix corresponding to the first l maximum singular values;

(4.7) mixing Y_lReconstructing the document components matched with the image information into a document matrix:

ε＝min(l,γ-ε+l)，γ＝max(n,k)；

w is the image information after denoising.

(5) Searching the closest color value in the microorganism color table, and determining the index color corresponding to the scanned image to obtain a microorganism scanning result;

(5.1) dividing the low-contrast points;

χ＝(x,y,σ)o(λ)

x and y are pixel coordinate values of the scanned image, and sigma is a scale parameter of the scanned image layer;

acquiring an extreme value Cn (x);

computing

If it is

If the point belongs to the ground contrast point, deleting the point;

(5.2) eliminating edge points;

constructing a scanning image HESSIAN matrix as follows:

C_ijis the value of the point Cn with pixel coordinates (i, j);

calculating the determinant and the like of the matrix;

Sr(H)＝C_xx+C_yy＝α+β；

Bet(H)＝C_xxC_yy-C_xy ²＝αβ；

let a be θ β,

if the pixel point can not satisfy the formula, rejecting the pixel point;

and searching the closest color value of the pixel of the scanned image in the color table to determine the corresponding index color and determine the result of scanning the microorganism.

The invention has the beneficial effects that: the microorganism detection system or the detection method provided by the invention has the advantages that the color contrast data of the conventional environment is constructed as the basis, the detection image is obtained through polarized light scanning, and the abnormity of the laboratory environment is detected through the accurate denoising and contrast method, so that the system has higher precision, wider scanning range and more comprehensive detection.

Drawings

FIG. 1 is a schematic diagram of a laboratory microorganism detection system according to the present invention;

FIG. 2 is a schematic diagram of a laboratory microorganism detection method according to the present invention;

FIG. 3 is a circuit diagram of a scanning circuit of the laboratory microorganism detection system of the present invention.

Detailed Description

The invention is further described below with reference to fig. 1-2.

A laboratory microorganism detection system specifically comprises:

the building module is used for building a color table to be confirmed according to the spectral data characteristics of the laboratory;

the system comprises a scanning module, a data acquisition module and a data processing module, wherein the scanning module is used for carrying out optical scanning on the whole environment of a laboratory to obtain a corresponding environment scanning result and converting the environment scanning result into corresponding spectrum data;

the comparison module is used for acquiring the refractive index of a scanning position related to the overall environment of the laboratory scanned by light according to the spectral data acquired by the scanning module; searching and comparing the refractive index difference of each scanning position based on a prestored refractive index mapping table;

the image denoising module is used for denoising the image file to be scanned;

and the confirming module is used for searching a color value which is closest to the refractive index difference of each scanning position searched and compared by the comparison module based on the color table to be confirmed, which is constructed by the construction module, determining the index color corresponding to the image file to be scanned after the noise reduction processing is carried out by the image noise reduction module, and obtaining the microorganism scanning result.

(1) Pre-constructing a color table according to the spectral data characteristics of a laboratory, comprising:

(1.1) constructing a spectral data set Q:

Q＝{Q₁,Q₂,Q₃,…Q_i…,Q_n}

Q_nobtaining the spectrum data for the nth time, wherein n is the total times of obtaining the spectrum data;

(1.2) acquiring a value of the spectral data at each time;

Q_n＝(Q_1,n,Q_2,n,Q_3,n,…Q_j,n,…,Q_m,n)

Q_m,nthe characteristic value is the mth spectral characteristic value when the spectral data is acquired at the nth time, and m is the total characteristic value quantity when the spectral data is acquired each time;

(1.3) carrying out standardization treatment;

Z_m,nfor the purpose of the normalized value of the value,

for the mean, σ Q, of the n-th acquired spectral data_nA variance for the nth acquired spectral data;

(1.4) constructing a confirmation color table;

B_mfor the mth spectral feature value of the constructed color chart, Q _1_mIs the maximum of all the mth spectral characteristic values in the spectral data set Q;

by using the above technology, a color table can be constructed through the acquired spectral data, and in the process of constructing the color table, the value ranges of the characteristic values of different positions of the spectral data are different, which may cause a large value range in the calculation process and greatly affect the result, so that the value range of the characteristic value causes a large error to the result, and in the process, the data is standardized, thereby determining the value ranges of the characteristic values of different positions of the spectral data to be a fixed range.

(2) Converting environmental scan results into spectral data, comprising the steps of:

the whole environment of the laboratory is optically scanned to obtain a series of polarized light, the light is reflected when entering a detection interface of the laboratory, and the reflected light is as follows:

is the included angle between the incident light of the polarized light and the S-polarization; k_sAnd K_pPredetermined coefficients of S-polarization and P-polarization, respectively, and K_sAnd K_pThe following conditions are satisfied;

k1 is the reflectance of polarized light in a laboratory environment; k2 is the reflection coefficient of the polarized light with the mycoderm, beta is the polarization coefficient of the polarized light, and beta is obtained by the following formula;

lambda is the refractive index of the detection surface;

the spectral data of the emergent light is:

θ_ithe included angle between the polarized light and the normal vector is shown, and F _ C is spectrum data of emergent light;

by utilizing the technology, the environment scanning result can be automatically converted into the spectrum data corresponding to the environment through the polarized light, the incident light and the reflected light, so that the environment scanning result can be digitalized and the quantitative analysis can be better carried out.

(3) Converting the refractive index of the scanned position according to the spectral data; mapping index is carried out on the data of each position, the refractive index of each position is used for mapping with a refractive index comparison table, the comparison table is compared, and the refractive index difference is detected;

the imaging system outputs an image of the object as:

c is the imaging system channel number; f. of_c(x) Outputting a response value for the xth position of the C channel in the imaging system; r_c(mu) is the spectral integrated response curve of the C channel; sigma_c(μ) is the response curve of the light source spectrum of the C-th channel; d_c(mu) spectral power curve of light source of C channel, P_c(μ) is the spectral transmittance curve of the pre-channel filter for the C-th channel, μ is the integrated parameter, with no practical meaning, x is the imaging system output object image position, C is 1, 2, 3 … … q, q is the total number of channels;

the output images of all channels are represented in a discrete matrix manner:

f (x) is the output image of all channels,

the spectral reflectance of the light-scanned image surface;

r (mu) is a diagonal matrix formed by the spectral comprehensive response curves of all channels;

σ (μ) is a diagonal matrix formed by response curves of the light source spectra of all channels;

d (mu) is a diagonal matrix formed by spectral power curves of light sources of all channels

P (mu) is a diagonal matrix formed by the spectral transmittance curves of the channel front filters of all channels;

calculating the mapping relation between the light scanning image and the color table through the inverse transformation of the positive image model of the spectral imaging system;

(5) denoising the scanned image file; comprises the following steps of;

(4.1) acquiring a scanned image file W;

(4.2) dividing the scanned image into WDividing into N blocks, and forming the image W into N partial images, namely W ═ W₁,W₂,W₃,…,W_i,…,W_NAnd arbitrary local image W_NAll satisfy the following formula;

W_N＝CS_N+LB_N

CS_Npixel matrix, LB, of the original image of the Nth partial image_NA filtered pixel matrix for an nth image;

(4_.3) solving for the W_NThe singular characteristic quantity WV of the pixel of_NAnd said W_NVariance σ of_N；

(4.4) calculating an energy function of the local image;

wherein, fCS_NIs the energy function of the local image, | W_N-CS_N||_FIs W_N-CS_NF-norm, | | CS_N||_w*Is a weighted norm and meets the following requirements;

wherein j belongs to m and is a value of j, and m is a singular characteristic quantity WV_NThe number of values contained, d is the sum of the local image W_NThe Euclidean distance of less than 0.1, K is a preset value, | SV_N|_jThe original singular value is obtained by the following formula;

wherein, | WV_N|_jIs WV_NThe jth value of (d);

(4.5) obtaining LB by the energy function of the partial image_N；

The CS can be obtained by constructing an equation system by using the formulas in (4.5) and (4.2)_NI.e. to the local part W_NRemoving the filtered image, and removing the filtering of all the N local images to obtain the image from which the filtering is removed;

with the above technique: the filtering of the image can be intelligently reduced, because the more similar matrixes are, the larger the singular value of the image is, the larger the information content of the image is, and the less the probability that the value is filtered is when the filtering matrix is calculated, so that more information can be reserved, and the original information is more reliable.

The pre-constructing of the color table according to the spectral data characteristics of the laboratory comprises:

(1.1) extracting a color table of the image in the first clustering:

Q₁＝{z₁、z₂、…z_n}；

z_ncollecting color points of a laboratory environment image, wherein n is the number of the color points;

(1.2) clustering for the second time to obtain an extended color table;

wherein σ is a spreading factor; and the secondary clustering refines the color table in the block. The association of colors and the diversity of color combinations within a block are maintained.

(1.3) carrying out standardization treatment;

z_ijis a normalized value, z_ijExpanding the color point value in the color table; alpha is the mean value of all sample data; beta is all sample dataStandard deviation of (d);

(1.4) carrying out brightness self-adaptation;

It_lis the mean value of brightness, Ct, of the color chart_lIs the original brightness value, Pt, of the color chart_lIs the mean value of the brightness, Im, of the color chart_lAs the mean value of the brightness, Cm, of the image_lIs the original brightness value, Pm, of the image_lIs the contrast luminance mean of the image.

Scanning the whole environment of laboratory is a series of polarized light, and the reflection takes place when light incides laboratory detection interface, and the reverberation is:

the included angle between the polarization direction of the polarizer and the S polarization component is formed; r is_sAnd r_pPolarization coefficients for the S and P polarization components, respectively;

r₀₁the polarization reflection coefficients of the laboratory environment and air; r is₁₂The polarization reflection coefficients of the laboratory environment and the bacteria-carrying membrane are obtained;

delta is the polarized light phase difference;

n_iis the refractive index of the detection surface; theta_iAn incident angle of the detection light for the detection plane;

the spectral data of the emergent light is:

is the angle between the polarized light and the long axis.

Converting the refractive index of the scanned position according to the spectral data; mapping and indexing the data of each position, mapping the refractive index of each position and a refractive index comparison table, comparing the comparison table, and detecting the refractive index difference, including;

the imaging system outputs an image of the object as:

c_k＝∫_λr_k(λ)ρ_k(λ)s_k(λ)o(λ)dλ；

k is the imaging system channel number; c. C_kIs the output response of the k channel in the imaging system; r (λ) is the spectral response curve; s (lambda) is a response curve of the light source spectrum; o (λ) is the spectral power spectrum of the light source, ρ_k(λ) the spectral transmittance of the front filter of each channel;

expressed in a discrete matrix manner as:

Cn＝(Rnρn)^TSnRn；

dn is a multi-channel image output by the imaging system; sn is the spectral reflectivity of the surface of the optical scanning image; is a diagonal matrix with diagonal elements r (λ); the elements on the diagonal are the spectral responsivity of the imaging system at each wavelength;

tn is the spectral transmittance of the imaging system;

sn is a diagonal matrix taking the spectral power distribution of the light source as diagonal elements;

cn is an erect image model of the spectral imaging system;

and calculating the mapping relation between the light scanning image and the color table through the inverse transformation of the positive image model of the spectral imaging system.

Denoising the scanned image file; comprises the following steps of;

(4.1) all image information documents g (t) { g ═ g₁,g₂,g₃,…,g_NConstructed as an m × n-order document matrix, g_iThe ith image information document being the font, image information document set g (t);

(4.2) setting the dimension n of the document matrix;

(4.3) extracting { g ] from G (t)₁,g₂,g₃,…,g_nAs the first row of the matrix;

(4.4) sequentially delaying backward by one document until the last signal of n lines is G_NAs the last row of the matrix;

the matrix formed is:

X_m×nan m ﹡ n-dimensional matrix constructed for the original signal; v_m×nAn m ﹡ n-dimensional matrix constructed for the noise signal; if N is an even number, m is N/2+1, and N is N/2; if N is an odd number, m and N are both (N + 1)/2;

(4.5) performing singular value decomposition on H;

H＝ΣWR^T

w is unitary matrix with dimension m ﹡ m; r is a unitary matrix of n ﹡ n dimension, namely a left and right singular matrix of H, and T represents a transposed matrix of the matrix; element alpha of major diagonal line of m ﹡ n-dimensional diagonal matrix of sigma_iNon-zero singular values of H, arranged in increasing order, i.e. alpha₁≥α₂≥α₃≥…≥α_i；

(4.6) determining the effective rank of sigma, namely the first l maximum singular values; approximating matrix Y for reconstruction H_l；

W_lIs the left singular vector corresponding to the first l largest singular values; r_lIs the right singular vector corresponding to the first l largest singular values; sigma_lThe diagonal matrix corresponding to the first l maximum singular values;

(4.7) mixing Y_lReconstructing the document components matched with the image information into a document matrix:

ε＝min(l,γ-ε+l)，γ＝max(n,k)；

w is the image information after denoising.

Searching the closest color value in the microorganism color table, and determining the index color corresponding to the scanned image to obtain a microorganism scanning result;

(5.1) dividing the low-contrast points;

χ＝(x,y,σ)o(λ)

x and y are pixel coordinate values of the scanned image, and sigma is a scale parameter of the scanned image layer;

acquiring an extreme value Cn (x);

computing

If it is

If the point belongs to the ground contrast point, deleting the point;

(5.2) eliminating edge points;

constructing a scanning image HESSIAN matrix as follows:

C_ijis the value of the point Cn with pixel coordinates (i, j);

calculating the determinant and the like of the matrix;

Sr(H)＝C_xx+C_yy＝α+β；

Bet(H)＝C_xxC_yy-C_xy ²＝αβ；

let a be θ β,

if the pixel point can not satisfy the formula, rejecting the pixel point;

and searching the closest color value of the pixel of the scanned image in the color table to determine the corresponding index color and determine the result of scanning the microorganism.

The spectral scan module matrix includes a scan circuit, as shown in figure 3,

the scanning circuit includes: a first resistor R1, a second resistor R2, a third resistor R3, a fourth resistor R4, a fifth resistor R5, a sixth resistor R6, a seventh resistor R7, an eighth resistor R8, a ninth resistor R9, a tenth resistor R10, an eleventh resistor R11, a twelfth resistor R12, a thirteenth resistor R13, a first capacitor C1, a second capacitor C2, a third capacitor C3, a fourth capacitor C4, a first diode L1, a second diode L2, a third diode L3, a first NPN transistor Q1, a second NPN transistor Q2, a third NPN transistor Q3, a PNP transistor P1, a power source VCC, a ground GND, a scanner S1, and an integrator S2;

the power supply VCC is respectively connected with one ends of a third resistor R3, a first capacitor C1, a fourth resistor R4 and a fifth resistor R5, and is also connected with a first end of an integrator S2, the other end of the third resistor R3 is respectively connected with a first end and a second end of a scanner S1, a third end of the scanner S1 is respectively connected with one end of the first resistor R1 and a third end of the integrator S2, and a fourth end of the scanner S1 is respectively connected with one end of the second resistor R2 and a fourth end of the integrator S2;

a second end of the integrator S2 is connected to one end of an eighth resistor R8, the other end of the eighth resistor R8 is connected to a base of a first NPN transistor Q1, and a fifth end of the integrator S2 is connected to the other end of a fourth resistor R4 and one end of a sixth resistor R6, respectively;

the other end of the fifth resistor R5 is connected to one end of a second capacitor C2, a seventh resistor R7 and a ninth resistor R9, and is further connected to the anode of the first diode L1 and the collector of the PNP transistor P1; the other end of the seventh resistor R7 is connected to the anode of a third diode L3, and the cathode of the third diode L3 is connected to the emitter of the first NPN transistor Q1 and the cathode of the first diode L1, respectively;

the base electrode of the PNP transistor P1 is connected to one end of a tenth resistor R10 and one end of a third capacitor C3, the other ends of the tenth resistor R10 and the third capacitor C3 are connected to one end of an eleventh resistor R11, the other end of the eleventh resistor R11 is connected to the base electrode of a third NPN transistor Q3, and the collector electrode of the third NPN transistor Q3 is connected to the negative electrode of a second diode L2;

an emitter of the PNP transistor P1 is connected to a collector of the second NPN transistor Q2, a base of the second NPN transistor Q2 is connected to the other end of the ninth resistor R9, one end of the fourth capacitor C4, one end of the twelfth resistor R12, and one end of the thirteenth resistor R13, respectively, and the other end of the twelfth resistor R12 is connected to an anode of the second diode L2;

the other ends of the first capacitor C1, the first resistor R1, the second resistor R2, the sixth resistor R6, the second capacitor C2, the fourth capacitor C4 and the thirteenth resistor R13 are grounded to GND, and the emitters of the first NPN transistor Q1, the second NPN transistor Q2 and the third NPN transistor Q3 are also grounded to GND.

The scanning circuit has the following beneficial effects: the purpose of spectrum scanning is conveniently achieved by arranging the scanner S1 and the integrator S2, the PNP transistor P1 and the second NPN transistor Q2 are arranged, the scanning circuit is conveniently protected, the possibility of damaging the scanning circuit or a device is reduced in the process of supplying power to the circuit, in addition, the scanned signal is further processed through a branch formed by the sixth resistor R6 and the fifth resistor R5, and the stability of the scanned signal is ensured.

A laboratory microorganism detection method specifically comprises the following steps:

(1) pre-constructing a color table according to the spectral data characteristics of a laboratory, comprising:

(1.1) constructing a spectral data set Q:

Q＝{Q₁,Q₂,Q₃,…Q_i…,Q_n}

Q_nobtaining the spectrum data for the nth time, wherein n is the total times of obtaining the spectrum data;

(1.2) acquiring a value of the spectral data at each time;

Q_n＝(Q_1,n,Q_2,n,Q_3,n,…Q_j,n,…,Q_m,n)

Q_m,nthe characteristic value is the mth spectral characteristic value when the spectral data is acquired at the nth time, and m is the total characteristic value quantity when the spectral data is acquired each time;

(1.3) carrying out standardization treatment;

Z_m,nfor the purpose of the normalized value of the value,

for the mean, σ Q, of the n-th acquired spectral data_nA variance for the nth acquired spectral data;

(1.4) constructing a confirmation color table;

B_mfor the mth spectral feature value of the constructed color chart, Q _1_mIs the maximum of all the mth spectral characteristic values in the spectral data set Q;

(2) converting environmental scan results into spectral data, comprising the steps of:

the whole environment of the laboratory is optically scanned to obtain a series of polarized light, the light is reflected when entering a detection interface of the laboratory, and the reflected light is as follows:

is the included angle between the incident light of the polarized light and the S-polarization; k_sAnd K_pPredetermined coefficients of S-polarization and P-polarization, respectively, and K_sAnd K_pThe following conditions are satisfied;

k1 is the reflectance of polarized light in a laboratory environment; k2 is the reflection coefficient of the polarized light with the mycoderm, beta is the polarization coefficient of the polarized light, and beta is obtained by the following formula;

lambda is the refractive index of the detection surface;

the spectral data of the emergent light is:

θ_ithe included angle between the polarized light and the normal vector is shown, and F _ C is spectrum data of emergent light;

(3) converting the refractive index of the scanned position according to the spectral data; mapping index is carried out on the data of each position, the refractive index of each position is used for mapping with a refractive index comparison table, the comparison table is compared, and the refractive index difference is detected;

the imaging system outputs an image of the object as:

c is the imaging system channel number; f. of_c(x) Outputting a response value for the xth position of the C channel in the imaging system; r_c(mu) is the spectral integrated response curve of the C channel; sigma_c(μ) is the response curve of the light source spectrum of the C-th channel; d_c(mu) spectral power curve of light source of C channel, P_c(μ) is the spectral transmittance curve of the pre-channel filter for the C-th channel, μ is the integrated parameter, with no practical meaning, x is the imaging system output object image position, C is 1, 2, 3 … … q, q is the total number of channels;

the output images of all channels are represented in a discrete matrix manner:

f (x) is the output image of all channels,

the spectral reflectance of the light-scanned image surface;

r (mu) is a diagonal matrix formed by the spectral comprehensive response curves of all channels;

σ (μ) is a diagonal matrix formed by response curves of the light source spectra of all channels;

d (mu) is a diagonal matrix formed by spectral power curves of light sources of all channels

P (mu) is a diagonal matrix formed by the spectral transmittance curves of the channel front filters of all channels;

calculating the mapping relation between the light scanning image and the color table through the inverse transformation of the positive image model of the spectral imaging system;

(5) denoising the scanned image file; comprises the following steps of;

(4.1) acquiring a scanned image file W;

(4.2) dividing the scanned image W into N blocks, the image W being N partial images, i.e., W ═ W₁,W₂,W₃,…,W_i,…,W_NAnd arbitrary local image W_NAll satisfy the following formula;

W_N＝CS_N+LB_N

CS_Npixel matrix, LB, of the original image of the Nth partial image_NAs the Nth imageThe filtered pixel matrix of (2);

(4.3) solving for said W_NThe singular characteristic quantity WV of the pixel of_NAnd said W_NVariance σ of_N；

(4.4) calculating an energy function of the local image;

wherein, fCS_NIs the energy function of the local image, | W_N-CS_N||_FIs W_N-CS_NF-norm, | | CS_N||_w*Is a weighted norm and meets the following requirements;

wherein j belongs to m and is a value of j, and m is a singular characteristic quantity WV_NThe number of values contained, d is the sum of the local image W_NThe Euclidean distance of less than 0.1, K is a preset value, | SV_N|_jThe original singular value is obtained by the following formula;

wherein, | WV_N|_jIs WV_NThe jth value of (d);

(4.5) obtaining LB by the energy function of the partial image_N；

The CS can be obtained by constructing an equation system by using the formulas in (4.5) and (4.2)_NI.e. to the local part W_NRemoving the filtered image, and removing the filtering of all the N local images to obtain the image from which the filtering is removed;

a laboratory microorganism detection method specifically comprises the following steps:

(1) the pre-constructing of the color table according to the spectral data characteristics of the laboratory comprises:

(1.1) extracting a color table of the image in the first clustering:

Q₁＝{z₁、z₂、…z_n}；

z_ncollecting color points of a laboratory environment image, wherein n is the number of the color points;

(1.2) clustering for the second time to obtain an extended color table;

wherein σ is a spreading factor; and the secondary clustering refines the color table in the block. The association of colors and the diversity of color combinations within a block are maintained.

(1.3) carrying out standardization treatment;

z_ijis a normalized value, z_ijExpanding the color point value in the color table; alpha is the mean value of all sample data; β is the standard deviation of all sample data;

(1.4) carrying out brightness self-adaptation;

It_lis the mean value of brightness, Ct, of the color chart_lIs the original brightness value, Pt, of the color chart_lIs the mean value of the brightness, Im, of the color chart_lAs the mean value of the brightness, Cm, of the image_lIs the original brightness value, Pm, of the image_lIs the contrast luminance mean of the image.

(2) The whole environment of the optical scanning laboratory converts the detection signals into spectral data;

scanning the whole environment of laboratory is a series of polarized light, and the reflection takes place when light incides laboratory detection interface, and the reverberation is:

the included angle between the polarization direction of the polarizer and the S polarization component is formed; r is_sAnd r_pPolarization coefficients for the S and P polarization components, respectively;

r₀₁the polarization reflection coefficients of the laboratory environment and air; r is₁₂The polarization reflection coefficients of the laboratory environment and the bacteria-carrying membrane are obtained;

delta is the polarized light phase difference;

n_iis the refractive index of the detection surface; theta_iAn incident angle of the detection light for the detection plane;

the spectral data of the emergent light is:

is the angle between the polarized light and the long axis.

(3) Converting the refractive index of the scanned position according to the spectral data; mapping and indexing the data of each position, mapping the refractive index of each position and a refractive index comparison table, comparing the comparison table and detecting the refractive index difference;

the imaging system outputs an image of the object as:

c_k＝∫_λr_k(λ)ρ_k(λ)s_k(λ)o(λ)dλ；

k is the imaging system channel number; c. C_kIs the output response of the k channel in the imaging system; r (λ) is the spectral response curve; s (lambda) is a response curve of the light source spectrum; o (λ) is the spectral power spectrum of the light source, ρ_k(λ) the spectral transmittance of the front filter of each channel;

expressed in a discrete matrix manner as:

Cn＝(Rnρn)^TSnRn；

dn is a multi-channel image output by the imaging system; sn is the spectral reflectivity of the surface of the optical scanning image; is a diagonal matrix with diagonal elements r (λ); the elements on the diagonal are the spectral responsivity of the imaging system at each wavelength;

tn is the spectral transmittance of the imaging system;

sn is a diagonal matrix taking the spectral power distribution of the light source as diagonal elements;

cn is an erect image model of the spectral imaging system;

and calculating the mapping relation between the light scanning image and the color table through the inverse transformation of the positive image model of the spectral imaging system.

(4) Denoising the scanned image file; comprises the following steps of;

(4.1) all image information textGear g (t) { g₁,g₂,g₃,…,g_NConstructed as an m × n-order document matrix, g_iThe ith image information document being the font, image information document set g (t);

(4.2) setting the dimension n of the document matrix;

(4.3) extracting { g ] from G (t)₁,g₂,g₃,…,g_nAs the first row of the matrix;

(4.4) sequentially delaying backward by one document until the last signal of n lines is G_NAs the last row of the matrix;

the matrix formed is:

X_m×nan m ﹡ n-dimensional matrix constructed for the original signal; v_m×nAn m ﹡ n-dimensional matrix constructed for the noise signal; if N is an even number, m is N/2+1, and N is N/2; if N is an odd number, m and N are both (N + 1)/2;

(4.5) performing singular value decomposition on H;

H＝ΣWR^T

w is unitary matrix with dimension m ﹡ m; r is a unitary matrix of n ﹡ n dimension, namely a left and right singular matrix of H, and T represents a transposed matrix of the matrix; element alpha of major diagonal line of m ﹡ n-dimensional diagonal matrix of sigma_iNon-zero singular values of H, arranged in increasing order, i.e. alpha₁≥α₂≥α₃≥…≥α_i；

(4.6) determining the effective rank of sigma, namely the first l maximum singular values; approximating matrix Y for reconstruction H_l；

W_lIs the left singular vector corresponding to the first l largest singular values; r_lIs the right singular vector corresponding to the first l largest singular values; sigma_lThe diagonal matrix corresponding to the first l maximum singular values;

(4.7) mixing Y_lReconstructing the document components matched with the image information into a document matrix:

ε＝min(l,γ-ε+l)，γ＝max(n,k)；

w is the image information after denoising.

(5) Searching the closest color value in the microorganism color table, and determining the index color corresponding to the scanned image to obtain a microorganism scanning result;

(5.1) dividing the low-contrast points;

χ＝(x,y,σ)o(λ)

x and y are pixel coordinate values of the scanned image, and sigma is a scale parameter of the scanned image layer;

acquiring an extreme value Cn (x);

computing

If it is

If the point belongs to the ground contrast point, deleting the point;

(5.2) eliminating edge points;

constructing a scanning image HESSIAN matrix as follows:

C_ijis the value of the point Cn with pixel coordinates (i, j);

calculating the determinant and the like of the matrix;

Sr(H)＝C_xx+C_yy＝α+β；

Bet(H)＝C_xxC_yy-C_xy ²＝αβ；

let a be θ β,

if the pixel point can not satisfy the formula, rejecting the pixel point;

and searching the closest color value of the pixel of the scanned image in the color table to determine the corresponding index color and determine the result of scanning the microorganism.

The microorganism detection system or the detection method provided by the invention has the advantages that the color contrast data of the conventional environment is constructed as the basis, the detection image is obtained through polarized light scanning, and the abnormity of the laboratory environment is detected through the accurate denoising and contrast method, so that the system has higher precision, wider scanning range and more comprehensive detection.

The above description is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (2)

1. A laboratory microorganism detection system for realizing a laboratory microorganism detection method is characterized by specifically comprising:

the building module is used for building a color table to be confirmed according to the spectral data characteristics of the laboratory;

the system comprises a scanning module, a data acquisition module and a data processing module, wherein the scanning module is used for carrying out optical scanning on the whole environment of a laboratory to obtain a corresponding environment scanning result and converting the environment scanning result into corresponding spectrum data;

the comparison module is used for acquiring the refractive index of a scanning position related to the overall environment of the laboratory scanned by light according to the spectral data acquired by the scanning module; searching and comparing the refractive index difference of each scanning position based on a prestored refractive index mapping table;

the image denoising module is used for denoising the image file to be scanned;

the confirmation module is used for searching a color value which is closest to the refractive index difference of each scanning position searched and compared by the comparison module based on the color table to be confirmed constructed by the construction module, determining an index color corresponding to the image file to be scanned after the noise reduction processing is carried out by the image noise reduction module, and obtaining a microorganism scanning result;

a laboratory microorganism detection method specifically comprises the following steps:

(1) pre-constructing a color table according to the spectral data characteristics of a laboratory, comprising:

(1.1) constructing a spectral data set Q:

Q＝{Q₁,Q₂,Q₃,…Q_i…,Q_n}

Q_nobtaining the spectrum data for the nth time, wherein n is the total times of obtaining the spectrum data;

(1.2) acquiring a value of the spectral data at each time;

Q_n＝(Q_1,n,Q_2,n,Q_3,n,…Q_j,n,…,Q_m,n)

Q_m,nthe characteristic value is the mth spectral characteristic value when the spectral data is acquired at the nth time, and m is the total characteristic value quantity when the spectral data is acquired each time;

(1.3) carrying out standardization treatment;

Z_m,nfor the purpose of the normalized value of the value,

for the mean, σ Q, of the n-th acquired spectral data_nA variance for the nth acquired spectral data;

(1.4) constructing a confirmation color table;

B_mfor the mth spectral feature value of the constructed color chart, Q _1_mIs the maximum of all the mth spectral characteristic values in the spectral data set Q;

(2) converting environmental scan results into spectral data, comprising the steps of:

the whole environment of the laboratory is optically scanned to obtain a series of polarized light, the light is reflected when entering a detection interface of the laboratory, and the reflected light is as follows:

is the included angle between the incident light of the polarized light and the S-polarization; k_sAnd K_pPredetermined coefficients of S-polarization and P-polarization, respectively, and K_sAnd K_pThe following conditions are satisfied;

k1 is the reflectance of polarized light in a laboratory environment; k2 is the reflection coefficient of the polarized light with the mycoderm, beta is the polarization coefficient of the polarized light, and beta is obtained by the following formula;

lambda is the refractive index of the detection surface;

the spectral data of the emergent light is:

θ_ithe included angle between the polarized light and the normal vector is shown, and F _ C is spectrum data of emergent light;

(3) converting the refractive index of the scanned position according to the spectral data; mapping index is carried out on the data of each position, the refractive index of each position is used for mapping with a refractive index comparison table, the comparison table is compared, and the refractive index difference is detected;

the imaging system outputs an image of the object as:

c is the imaging system channel number; f. of_c(x) Outputting a response value for the xth position of the C channel in the imaging system; r_c(mu) is the spectral integrated response curve of the C channel; sigma_c(μ) is the response curve of the light source spectrum of the C-th channel; d_c(mu) spectral power curve of light source of C channel, P_c(μ) is the spectral transmittance curve of the pre-channel filter for the C-th channel, μ is the integrated parameter, with no practical meaning, x is the imaging system output object image position, C is 1, 2, 3 … … q, q is the total number of channels;

the output images of all channels are represented in a discrete matrix manner:

f (x) is the output image of all channels,

the spectral reflectance of the light-scanned image surface;

r (mu) is a diagonal matrix formed by the spectral comprehensive response curves of all channels;

σ (μ) is a diagonal matrix formed by response curves of the light source spectra of all channels;

d (mu) is a diagonal matrix formed by spectral power curves of light sources of all channels

P (mu) is a diagonal matrix formed by the spectral transmittance curves of the channel front filters of all channels;

calculating the mapping relation between the light scanning image and the color table through the inverse transformation of the positive image model of the spectral imaging system;

(4) denoising the scanned image file; comprises the following steps of;

(4.1) acquiring a scanned image file W;

(4.2) dividing the scanned image W into N blocks, wherein the image W is N partial images，W＝{W₁,W₂,W₃,…,W_i,…,W_NAnd arbitrary local image W_NAll satisfy the following formula;

W_N＝CS_N+LB_N

CS_Npixel matrix, LB, of the original image of the Nth partial image_NA filtered pixel matrix for an nth image;

(4_.3) solving for the W_NThe singular characteristic quantity WV of the pixel of_NAnd said W_NVariance σ of_N；

(4.4) calculating an energy function of the local image;

wherein, fCS_NIs the energy function of the local image, | W_N-CS_N||_FIs W_N-CS_NF-norm, | | CS_N||_w*Is a weighted norm and meets the following requirements;

wherein j belongs to m and is a value of j, and m is a singular characteristic quantity WV_NThe number of values contained, d is the sum of the local image W_NThe Euclidean distance of less than 0.1, K is a preset value, | SV_N|_jThe original singular value is obtained by the following formula;

wherein, | WV_N|_jIs WV_NThe jth value of (d);

(4.5) obtaining LB by the energy function of the partial image_N；

The CS can be obtained by constructing an equation system by using the formulas in (4.5) and (4.2)_NI.e. to the local part W_NRemoving the filtered image, and removing the filtering of all the N local images to obtain the image from which the filtering is removed;

(5) searching the closest color value in the microorganism color table, and determining the index color corresponding to the scanned image to obtain a microorganism scanning result;

(5.1) dividing the low-contrast points;

χ＝(x,y,σ)o(λ)

x and y are pixel coordinate values of the scanned image, and sigma is a scale parameter of the scanned image layer;

acquiring an extreme value Cn (x);

computing

If it is

If the point belongs to the ground contrast point, deleting the point;

(5.2) eliminating edge points;

constructing a scanning image HESSIAN matrix as follows:

C_ijis a point with pixel coordinate (i, j)A Cn value;

calculating the determinant and the like of the matrix;

Sr(H)＝C_xx+C_yy＝α+β；

Bet(H)＝C_xxC_yy-C_xy ²＝αβ；

let a be θ β,

if the pixel point can not satisfy the formula, rejecting the pixel point;

and searching the closest color value of the pixel of the scanned image in the color table to determine the corresponding index color and determine the result of scanning the microorganism.

2. The system of claim 1, wherein the spectral scanning module matrix comprises a scanning circuit,

the scanning circuit includes: a first resistor R1, a second resistor R2, a third resistor R3, a fourth resistor R4, a fifth resistor R5, a sixth resistor R6, a seventh resistor R7, an eighth resistor R8, a ninth resistor R9, a tenth resistor R10, an eleventh resistor R11, a twelfth resistor R12, a thirteenth resistor R13, a first capacitor C1, a second capacitor C2, a third capacitor C3, a fourth capacitor C4, a first diode L1, a second diode L2, a third diode L3, a first NPN transistor Q1, a second NPN transistor Q2, a third NPN transistor Q3, a PNP transistor P1, a power source VCC, a ground GND, a scanner S1, and an integrator S2;

the power supply VCC is respectively connected with one ends of a third resistor R3, a first capacitor C1, a fourth resistor R4 and a fifth resistor R5, and is also connected with a first end of an integrator S2, the other end of the third resistor R3 is respectively connected with a first end and a second end of a scanner S1, a third end of the scanner S1 is respectively connected with one end of the first resistor R1 and a third end of the integrator S2, and a fourth end of the scanner S1 is respectively connected with one end of the second resistor R2 and a fourth end of the integrator S2;

a second end of the integrator S2 is connected to one end of an eighth resistor R8, the other end of the eighth resistor R8 is connected to a base of a first NPN transistor Q1, and a fifth end of the integrator S2 is connected to the other end of a fourth resistor R4 and one end of a sixth resistor R6, respectively;

the other end of the fifth resistor R5 is connected to one end of a second capacitor C2, a seventh resistor R7 and a ninth resistor R9, and is further connected to the anode of the first diode L1 and the collector of the PNP transistor P1; the other end of the seventh resistor R7 is connected to the anode of a third diode L3, and the cathode of the third diode L3 is connected to the emitter of the first NPN transistor Q1 and the cathode of the first diode L1, respectively;

the base electrode of the PNP transistor P1 is connected to one end of a tenth resistor R10 and one end of a third capacitor C3, the other ends of the tenth resistor R10 and the third capacitor C3 are connected to one end of an eleventh resistor R11, the other end of the eleventh resistor R11 is connected to the base electrode of a third NPN transistor Q3, and the collector electrode of the third NPN transistor Q3 is connected to the negative electrode of a second diode L2;

an emitter of the PNP transistor P1 is connected to a collector of the second NPN transistor Q2, a base of the second NPN transistor Q2 is connected to the other end of the ninth resistor R9, one end of the fourth capacitor C4, one end of the twelfth resistor R12, and one end of the thirteenth resistor R13, respectively, and the other end of the twelfth resistor R12 is connected to an anode of the second diode L2;

the other ends of the first capacitor C1, the first resistor R1, the second resistor R2, the sixth resistor R6, the second capacitor C2, the fourth capacitor C4 and the thirteenth resistor R13 are grounded to GND, and the emitters of the first NPN transistor Q1, the second NPN transistor Q2 and the third NPN transistor Q3 are also grounded to GND.

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