CN109270011A - A kind of plant regulation of absorbing dust capability detection method based on machine learning algorithm - Google Patents
A kind of plant regulation of absorbing dust capability detection method based on machine learning algorithm Download PDFInfo
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- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
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Abstract
The plant regulation of absorbing dust capability detection method based on machine learning algorithm that the present invention relates to a kind of, comprising: obtain plant in the single order inverse of the spectral reflectivity of each wave band and the corresponding amount of laying the dust data；The single order inverse of the spectral reflectivity of each wave band and the relative coefficient of the amount of laying the dust data are calculated, determines wavelength ranges；Using the corresponding spectral reflectivity single order inverse of wavelength ranges as input, using the corresponding amount of the laying the dust data of wavelength ranges as anticipated output, the inverse model that lays the dust is constructed using grid-search algorithm and algorithm of support vector machine；The single order inverse to measuring plants in the spectral reflectivity of wavelength ranges is obtained, and is input in the inverse model that lays the dust, the amount of laying the dust data are obtained.By calculating the single order inverse of the spectral reflectivity of each wave band and the relative coefficient acquisition wavelength ranges of the amount of laying the dust data, the inverse model that lays the dust accurately is constructed in conjunction with grid-search algorithm and algorithm of support vector machine, realizes the quick and precisely acquisition for treating the amount of the laying the dust data of measuring plants.
Description
Technical field
The present invention relates to Environmental Monitoring and Assessment technical fields, more particularly to a kind of plant based on machine learning algorithm
Regulation of absorbing dust capability detection method.
Background technique
With the rapid development of industrialization and urbanization, urban atmospheric pollution is on the rise.Dust is urban atmospheric pollution
In major pollutants, also containing carcinogen and bacterial virus etc., human health is caused in dust in addition to containing heavy metal
Greatly threaten.
Urban greenery plants can effectively in blocks air dust, improve the eco-environmental quality in city.Currently, planting
The dust retention of object has become an important indicator of screening urban greenery plants.Therefore, the amount of laying the dust of plant how has been assessed
Emphasis as current research.
Summary of the invention
Based on this, the object of the present invention is to provide a kind of plant regulation of absorbing dust capability detection side based on machine learning algorithm
Method has the advantages that quick and precisely obtain the amount of the laying the dust data to measuring plants.
A kind of plant regulation of absorbing dust capability detection method based on machine learning algorithm, includes the following steps:
Plant is obtained the single order of the spectral reflectivity of each wave band is reciprocal and corresponding characterization plant regulation of absorbing dust capability
The amount of laying the dust data；
The single order inverse of the spectral reflectivity of each wave band and the relative coefficient of the amount of laying the dust data are calculated, and according to described
Relative coefficient screening obtains wavelength ranges；
Using the corresponding spectral reflectivity single order inverse of wavelength ranges as input, with the corresponding amount of the laying the dust data of wavelength ranges
As anticipated output, the inverse model that lays the dust is constructed using grid-search algorithm and algorithm of support vector machine；
The single order inverse to measuring plants in the spectral reflectivity of wavelength ranges is obtained, and is input in the inverse model that lays the dust,
The acquisition amount of laying the dust data.
The present invention is by calculating the single order inverse of the spectral reflectivity of each wave band and the relative coefficient of the amount of laying the dust data
Realize that quickly screening obtains wavelength ranges, accurately constructs in conjunction with grid-search algorithm and algorithm of support vector machine and lays the dust instead
Model is drilled, and then realizes the quick and precisely acquisition for treating the amount of the laying the dust data of measuring plants.
In one embodiment, the single order inverse and the corresponding amount of laying the dust number of the spectral reflectivity for calculating each wave band
According to relative coefficient mode are as follows:
Wherein, rijFor the spectral reflectivity first derivative of j-th of wave band in i-th of sample；For the spectrum of j-th of wave band
The average value of reflectivity first derivative；xijFor the amount of the laying the dust data of j-th of wave band in i-th of sample；It lays the dust for j-th of wave band
Measure the average value of data；N is the quantity of sample；Each sample includes the single order of the spectral reflectivity of each wave band of each wave band
The reciprocal and corresponding amount of laying the dust data.
It is in one embodiment, described that the step of obtaining wavelength ranges is screened according to relative coefficient, comprising:
The absolute value of the relative coefficient of each wave band is ranked up in the way of from large to small；
The absolute value for screening relative coefficient meets the wave band of the first given threshold as wavelength ranges.
In one embodiment, the step of the single order inverse of spectral reflectivity of the acquisition plant in each wave band, packet
It includes:
The spectral reflectivity of plant is obtained, and spectral reflectivity is pre-processed；
Spectrum transform is carried out to pretreated spectral reflectivity, the single order for obtaining the spectral reflectivity of each wave band falls
Number.
It is in one embodiment, described that pretreated step is carried out to spectral reflectivity, comprising:
The spectral reflectivity of plant is obtained, and rejects the spectral reflectivity that error is more than the first given threshold, will be remained later
Remaining spectral reflectance data is averaging, using average value as the spectral reflectivity of plant；
Actual spectrum of the nearby principle divided by corresponding blank value, as plant is used to the spectral reflectivity of the plant
Reflectivity；
According to the actual spectrum reflectivity of plant, water vapor absorption wave band is obtained, and rejects the spectrum under water vapor absorption wave band
Reflectivity, the curve of spectrum fitting completion further along spectral reflectivity obtain pretreated spectral reflectivity.
The error of DATA REASONING can be reduced by way of rejecting error information and averaging again；By using nearby principle
Divided by the mode of corresponding blank value, the data obtained under different time sections, different experimental conditions can be made to be comparable, and same
When eliminate experimental situation background brought by error；Data processing by rejecting water vapor absorption wave band, after can facilitating.
The plant regulation of absorbing dust capability detection device based on machine learning algorithm that the present invention also provides a kind of, comprising:
Data acquisition module, for obtaining plant in the single order inverse of the spectral reflectivity of each wave band and corresponding table
Levy the amount of the laying the dust data of plant regulation of absorbing dust capability；
Wavelength ranges obtain module, for calculating the single order inverse and the amount of laying the dust data of the spectral reflectivity of each wave band
Relative coefficient, and screened according to the relative coefficient and obtain wavelength ranges；
The inverse model that lays the dust constructs module, is used for using the corresponding spectral reflectivity single order inverse of wavelength ranges as input,
Using the corresponding amount of the laying the dust data of wavelength ranges as anticipated output, using grid-search algorithm and algorithm of support vector machine structure
Build the inverse model that lays the dust；
The amount of laying the dust data acquisition module, it is reciprocal in the single order of the spectral reflectivity of wavelength ranges to measuring plants for obtaining,
And be input in the inverse model that lays the dust, obtain the amount of laying the dust data.
The present invention is by calculating the single order inverse of the spectral reflectivity of each wave band and the relative coefficient of the amount of laying the dust data
Realize that quickly screening obtains wavelength ranges, accurately constructs in conjunction with grid-search algorithm and algorithm of support vector machine and lays the dust instead
Model is drilled, and then realizes the quick and precisely acquisition for treating the amount of the laying the dust data of measuring plants.
The present invention also provides a kind of computer readable storage mediums, store computer program thereon, which is characterized in that should
Realize that the plant based on machine learning algorithm as described in above-mentioned any one lays the dust energy when computer program is executed by processor
The step of power detection method.
The present invention also provides a kind of computer equipments comprising reservoir, processor and is stored in the reservoir
And the computer program that can be executed by the processor, the processor are realized when executing the computer program as above-mentioned any
The step of plant regulation of absorbing dust capability detection method described in one based on machine learning algorithm.
In order to better understand and implement, the invention will now be described in detail with reference to the accompanying drawings.
Detailed description of the invention
Fig. 1 is that the present invention is based on the flow charts of the plant regulation of absorbing dust capability detection method of machine learning algorithm；
Fig. 2 is that the present invention obtains plant in the flow chart of the single order inverse of the spectral reflectivity of each wave band；
Fig. 3 is that the present invention carries out pretreated flow chart to spectral reflectivity；
Fig. 4 is to compare figure before and after the present invention rejects water vapor absorption wave band；
Fig. 5 is the flow chart that the present invention obtains wavelength ranges.
Specific embodiment
Referring to Fig. 1, it is the present invention is based on the flow charts of the plant regulation of absorbing dust capability detection method of machine learning algorithm.Institute
The plant regulation of absorbing dust capability detection method based on machine learning algorithm is stated, is included the following steps:
Step S1: it obtains plant and lays the dust in the single order inverse of the spectral reflectivity of each wave band and corresponding characterization plant
The amount of the laying the dust data of ability.
Wherein, the spectral reflectivity of the plant is plant in the luminous flux of the reflection of each wave band and is incident on object
The ratio between luminous flux.The spectral reflectivity of the specific plant described in the present embodiment be the plant in 350nm-2500nm wave band and
Between be divided into the ratio between the reflected flux of 1nm wave band and the incident flux of the wave band, that is, include reflected flux of the plant in 350nm
It is described with the ratio between the incident flux of the wave band, the plant in the ratio between the reflected flux of 351nm and the incident flux of the wave band ...
Plant is in the ratio between the reflected flux of 2500nm and the incident flux of the wave band.
Step S2: calculating the single order inverse of the spectral reflectivity of each wave band and the relative coefficient of the amount of laying the dust data, and
It is screened according to the relative coefficient and obtains wavelength ranges.
Wherein, the absolute value of the relative coefficient is bigger, and the single order of the spectral reflectivity of the corresponding wave band is reciprocal
The plant amount of laying the dust data can more be characterized；
Step S3: corresponding stagnant with wavelength ranges using the corresponding spectral reflectivity single order inverse of wavelength ranges as input
Dust quantity data construct the inverse model that lays the dust as anticipated output, using grid-search algorithm and algorithm of support vector machine；
Step S4: the single order inverse to measuring plants in the spectral reflectivity of wavelength ranges is obtained, and is input to the inverting that lays the dust
In model, the amount of laying the dust data are obtained.
The present invention is by calculating the single order inverse of the spectral reflectivity of each wave band and the relative coefficient of the amount of laying the dust data
Realize that quickly screening obtains wavelength ranges, accurately constructs in conjunction with grid-search algorithm and algorithm of support vector machine and lays the dust instead
Model is drilled, and then realizes the quick and precisely acquisition for treating the amount of the laying the dust data of measuring plants.
Referring to Fig. 2, it obtains plant in the flow chart of the single order inverse of the spectral reflectivity of each wave band for the present invention.
In one embodiment, in step S1, the single order of spectral reflectivity of the acquisition plant in each wave band is reciprocal
The step of, comprising:
Step S11: the spectral reflectivity of plant is obtained, and spectral reflectivity is pre-processed.
Step S12: spectrum transform is carried out to pretreated spectral reflectivity, obtains the spectral reflectivity of each wave band
Single order is reciprocal.
Wherein, by carrying out spectrum transform to pretreated spectral reflectivity, compressible ambient noise is to target information
Influence, improve follow-up data processing accuracy.
Referring to Fig. 3, it carries out pretreated flow chart to spectral reflectivity for the present invention.
In one embodiment, pretreated step is carried out to spectral reflectivity described in step S11, comprising:
Step S111: obtaining the spectral reflectivity of plant, and rejects the spectral reflectivity that error is more than the first given threshold,
Remaining spectral reflectance data is averaging later, using average value as the spectral reflectivity of plant.
In one embodiment, the blade in the multiple orientation of single plant is chosen for a kind of plant, every leaf is chosen solid
Fixed certain point is as measurement point, 5 groups of spectral reflectivities of each measurement point duplicate measurements, then rejects in this 5 groups of spectral reflectivities
It is averaging after the value in the first given threshold, then by remaining spectral reflectivity, the light of this piece leaf is characterized with average value
Reflectivity is composed, and then reduces the error of DATA REASONING, improves the accuracy of DATA REASONING.Multiple orientation therein can plant to be single
The orientation such as the upper and lower, left and right of strain.First given threshold is 5 groups of spectral reflectivities according to measurement, selected by experience
For rejects with other data with large error data threshold value.
In one embodiment, the spectral reflectivity of plant is obtained using the measurement of 3 spectrometer of ASD Field-Spec, by
It is .asp format in the data format of measurement, the data of this format read and handle, therefore, measuring with being unable to direct convenience
Before carrying out data processing after the spectral reflectivity of acquisition plant, View Spectral Pro software spectral reflectivity is also utilized
Data Format Transform be .txt format.
Step S112: use nearby principle divided by corresponding blank value the spectral reflectivity of the plant, as plant
Actual spectrum reflectivity.
To be comparable the data obtained under different time sections, different experimental conditions, and experimental situation is eliminated simultaneously
Error brought by background, section obtains different blank values to the application in different times, hence in some period
The spectral reflectivity of the plant of acquisition then needs for the spectral reflectivity of the plant to be in the blank value of the period, as place
The spectral reflectivity of plant after reason.
Step S113: it according to the actual spectrum reflectivity of plant, determines water vapor absorption wave band, and rejects water vapor absorption wave band
Corresponding spectral reflectivity, the curve of spectrum fitting completion further along spectral reflectivity obtain pretreated spectral reflectivity.
Due to determining instrument of spectral reflectivity itself, the spectral reflectivity determined can be due to the absorption of steam
It is affected to the spectral reflectivity of plant, and the wavelength band studying also without too big meaning, therefore to vegetation spectrum
Data processing by rejecting water vapor absorption wave band, after can facilitating.Referring to Fig. 4, it rejects water vapor absorption wave for the present invention
Compare figure in the front and back of section.Specifically, the actual spectrum reflectivity data of plant is fitted to spectral reflectivity curve, pass through observation
Are there is abnormal wave band as water vapor absorption wave band by spectral reflectivity curve in data in spectral reflectivity curve；Again by the water
Spectral reflectivity in vapour absorption bands is rejected, and the curve of spectrum fitting completion acquisition further along spectral reflectivity is pretreated
Spectral reflectivity.
In one embodiment, spectrum transform is carried out to pretreated spectral reflectivity described in step S22, obtained each
The calculation of the single order inverse of the spectral reflectivity of a wave band are as follows:
Wherein, λi+1, λi, λi-1For adjacent wavelength, dR (λi) it is wavelength XiFirst derivative spectrum, R (λi+1), R (λi), R
(λi-1) be respectively wavelength be λi+1, λi, λi-1The reflectivity at place.
In one embodiment, the calculation of the amount of the laying the dust data of the plant regulation of absorbing dust capability of acquisition characterization described in step S1
Are as follows:
D=Δ W/S
In above-mentioned formula, D is the amount of the laying the dust data for characterizing plant regulation of absorbing dust capability；The amount of laying the dust data are bigger, then regulation of absorbing dust capability
It is bigger；Δ W is the amount of laying the dust of a period of time implants blade, and S is blade area.
Wherein, the amount of the laying the dust data of each blade of plant are consistent, i.e., the spectrum of each wave band is anti-in each blade
The single order corresponding amount of the laying the dust data reciprocal for penetrating rate are identical, it can think the corresponding sample of each blade, each sample
The single order inverse of the spectral reflectivity of each wave band including the blade and the corresponding amount of laying the dust data.
In one embodiment, the single order inverse of the spectral reflectivity of each wave band of calculating described in step 2 and corresponding
The mode of the relative coefficient of the amount of laying the dust data are as follows:
Wherein, rijFor the spectral reflectivity first derivative of j-th of wave band in i-th of sample；For the spectrum of j-th of wave band
The average value of reflectivity first derivative；xijFor the amount of the laying the dust data of j-th of wave band in i-th of sample；It lays the dust for j-th of wave band
Measure the average value of data；N is the quantity of sample.
Above-mentioned formula can measure the spectral reflectivity of some wave band single order inverse and the corresponding amount of laying the dust data the two
The linear index of variable, value range are [- 1,1].And the absolute value of the calculated relative coefficient of above-mentioned formula is bigger,
The correlation of the single order inverse and the amount of laying the dust data that indicate the spectral reflectivity of the wave band is stronger, i.e., the wave band can more characterize plant
The amount of laying the dust data, the correlation intensity of two variables can be judged by following value range, referring specifically to table 1- correlation
Coefficient table corresponding with correlation intensity.
Table 2- relative coefficient table corresponding with correlation intensity
| Relative coefficient (absolute value) | Strength of correlation |
| 0.8-1.0 | Extremely strong correlation |
| 0.6-0.8 | Strong correlation |
| 0.4-0.6 | Moderate correlation |
| 0.2-0.4 | Weak correlation |
| 0.0-0.2 | It is extremely weak related or without correlation |
Referring to Fig. 5, its flow chart for obtaining wavelength ranges for the present invention.
In one embodiment, the step of obtaining wavelength ranges, is screened according to relative coefficient described in step 2, comprising:
Step 21: the absolute value of the relative coefficient of each wave band is ranked up in the way of from large to small；
Step 22: the absolute value for screening relative coefficient meets the wave band of the first given threshold as wavelength ranges.
First given threshold can be the specific value about relative coefficient, and such as the first given threshold is correlation system
Several absolute values is greater than 0.8, i.e. wave band of the absolute value of relative coefficient greater than 0.8 is as wavelength ranges；First setting
Threshold value may be ranking value, and such as the first given threshold is that the absolute value of relative coefficient sorts within preceding 10, i.e. correlation
The sequence of the absolute value of coefficient from large to small is correlation using corresponding wave band as wavelength ranges or the first given threshold 10
The absolute value sequence quantity of property coefficient accounts for the 10% of all relative coefficient quantity using corresponding wave band as wavelength ranges etc..
The basic thought of support vector machines is to be divided into input data according to spacing value is maximized using a hyperplane
In the feature space of n dimension, specifically, in one embodiment, the corresponding spectral reflectivity one of wavelength ranges described in step S4
Rank inverse is as input, using the corresponding amount of the laying the dust data of wavelength ranges as anticipated output, using grid-search algorithm and branch
It holds vector machine algorithm and constructs the step of laying the dust inverse model, comprising:
Step S41: the corresponding spectral reflectivity single order inverse of wavelength ranges and the corresponding amount of laying the dust data are formed into sample
Collection；
Step S42: the parameter setting range based on algorithm of support vector machine is found using grid-search algorithm and is supported
The optimized parameter of vector machine algorithm.
Wherein grid-search algorithm uses the means of exhaustive search, i.e., concentrates in all candidate parameters, by following
Ring traversal, attempts each possibility, the parameter to behave oneself best is exactly final result.
Step S43: substituting into algorithm of support vector machine for the optimized parameter, and according to maximizing, spacing value searching one is optimal
Hyperplane sample set is split, and sample set is divided into the feature space of m dimension, so establish it is optimal lay the dust it is anti-
Drill model.
In one embodiment, the maximization spacing value is obtained by solving constraint quadratic function, specific formula for calculation
It is as follows:
Wherein, W (α) indicates to maximize spacing value；K(xi, xj) it is kernel function；α indicates Lagrange multiplier；xiAnd xjPoint
Not Biao Shi i-th and the corresponding spectral reflectivity single order of j-th of sample it is reciprocal, yiAnd yjRespectively indicate i-th and j-th of sample
The corresponding amount of laying the dust data；The quantity of n expression sample.
The calculation of the hyperplane are as follows:
Wherein, wjIndicate the weight vector in Hilbert space (Hilbert space)；gj(x) the non-thread of jth dimension is indicated
Property transformation, wherein j=1,2 ... m, m indicate the feature space of m dimension；B is bias term.
It lays the dust inverse model in the application for rapid build, is calculated according to the support vector machines that grid-search algorithm obtains
The optimized parameter of method specifically: the candidate parameter collection of the gamma coefficient of radial basis function are as follows: 2-6, 2-5, 2-4, 2-3, 2-2, 2-1, 1,21,
22, 23, 24, 25, 26.The candidate parameter collection of punishment parameter C is 20,40,60,80,100,150,200,220,250,280,300.
In one embodiment, after the step of amount of laying the dust data are obtained described in step S4, further includes: obtain multiple groups and wait for
The amount of the laying the dust data of measuring plants, and corresponding plant is planted in setting regions according to the amount of the laying the dust data.Specifically, according to stagnant
Dust quantity data are bigger, illustrate that the regulation of absorbing dust capability to measuring plants is stronger, and such as there is the area of sandstorm can plant according to setting regions
Regulation of absorbing dust capability is stronger to measuring plants.
The plant regulation of absorbing dust capability detection device based on machine learning algorithm that the present invention also provides a kind of, comprising:
Data acquisition module, for obtaining plant in the single order inverse of the spectral reflectivity of each wave band and corresponding table
Levy the amount of the laying the dust data of plant regulation of absorbing dust capability；
Wavelength ranges obtain module, for calculating the single order inverse and the amount of laying the dust data of the spectral reflectivity of each wave band
Relative coefficient, and screened according to the relative coefficient and obtain wavelength ranges；
The inverse model that lays the dust constructs module, is used for using the corresponding spectral reflectivity single order inverse of wavelength ranges as input,
Using the corresponding amount of the laying the dust data of wavelength ranges as anticipated output, using grid-search algorithm and algorithm of support vector machine structure
Build the inverse model that lays the dust；
The amount of laying the dust data acquisition module, it is reciprocal in the single order of the spectral reflectivity of wavelength ranges to measuring plants for obtaining,
And be input in the inverse model that lays the dust, obtain the amount of laying the dust data.
The present invention is by calculating the single order inverse of the spectral reflectivity of each wave band and the relative coefficient of the amount of laying the dust data
Realize that quickly screening obtains wavelength ranges, accurately constructs in conjunction with grid-search algorithm and algorithm of support vector machine and lays the dust instead
Model is drilled, and then realizes the quick and precisely acquisition for treating the amount of the laying the dust data of measuring plants.
In one embodiment, the data acquisition module includes:
Preprocessing module is pre-processed for obtaining the spectral reflectivity of plant, and to spectral reflectivity.
Spectrum transform module obtains the light of each wave band for carrying out spectrum transform to pretreated spectral reflectivity
The single order for composing reflectivity is reciprocal.
Wherein, the preprocessing module includes:
Mean value calculation module, for obtaining the spectral reflectivity of plant, and rejecting error is more than the first given threshold
Remaining spectral reflectance data is averaging by spectral reflectivity later, using average value as the spectral reflectivity of plant.
Actual spectrum reflectivity computing module, for the spectral reflectivity to the plant using nearby principle divided by correspondence
Blank value, the actual spectrum reflectivity as plant.
Water vapor absorption wave band rejects module, for the actual spectrum reflectivity according to plant, obtains water vapor absorption wave band, and
Reject water vapor absorption wave band under spectral reflectivity, further along spectral reflectivity the curve of spectrum fitting completion pre-processed after
Spectral reflectivity.
In one embodiment, described that spectrum transform is carried out to pretreated spectral reflectivity, obtain each wave band
The calculation of the single order inverse of spectral reflectivity are as follows:
Wherein, λi+1, λi, λi-1For adjacent wavelength, dR (λi) it is wavelength XiFirst derivative spectrum, R (λi+1), R (λi), R
(λi-1) be respectively wavelength be λi+1, λi, λi-1The reflectivity at place.
In one embodiment, the calculation of the amount of the laying the dust data for obtaining characterization plant regulation of absorbing dust capability are as follows:
D=Δ W/S
In above-mentioned formula, D is the amount of the laying the dust data for characterizing plant regulation of absorbing dust capability；Δ W is a period of time implants blade
Regulation of absorbing dust capability, S are blade area.
Wherein, the amount of the laying the dust data of each blade of plant are consistent, i.e., the spectrum of each wave band is anti-in each blade
The single order corresponding amount of the laying the dust data reciprocal for penetrating rate are identical, it can think the corresponding sample of each blade, each sample
The single order inverse of the spectral reflectivity of each wave band including the blade and the corresponding amount of laying the dust data.
In one embodiment, the single order inverse and the corresponding amount of laying the dust number of the spectral reflectivity for calculating each wave band
According to relative coefficient mode are as follows:
Wherein, rijFor the spectral reflectivity first derivative of j-th of wave band in i-th of sample；For the spectrum of j-th of wave band
The average value of reflectivity first derivative；xijFor the amount of the laying the dust data of j-th of wave band in i-th of sample；It lays the dust for j-th of wave band
Measure the average value of data；N is the quantity of sample.
In one embodiment, the wavelength ranges obtain the step of module, comprising:
Sorting module, for arranging the absolute value of the relative coefficient of each wave band in the way of from large to small
Sequence；
Screening module, the absolute value for screening relative coefficient meet the wave band of the first given threshold as inverting wave
Section.
First given threshold can be the specific value about relative coefficient, and such as the first given threshold is correlation system
Several absolute values is greater than 0.8, i.e. wave band of the absolute value of relative coefficient greater than 0.8 is as wavelength ranges；First setting
Threshold value may be ranking value, and such as the first given threshold is that the absolute value of relative coefficient sorts within preceding 10, i.e. correlation
The sequence of the absolute value of coefficient from large to small is correlation using corresponding wave band as wavelength ranges or the first given threshold 10
The absolute value sequence quantity of property coefficient accounts for the 10% of all relative coefficient quantity using corresponding wave band as wavelength ranges etc..
The present invention also provides a kind of computer readable storage mediums, store computer program thereon, the computer program
The plant regulation of absorbing dust capability detection method based on machine learning algorithm as described in above-mentioned any one is realized when being executed by processor
The step of.
It wherein includes storage medium (the including but not limited to disk of program code that the present invention, which can be used in one or more,
Memory, CD-ROM, optical memory etc.) on the form of computer program product implemented.Computer-readable storage media packet
Permanent and non-permanent, removable and non-removable media is included, can be accomplished by any method or technique information storage.Letter
Breath can be computer readable instructions, data structure, the module of program or other data.The example packet of the storage medium of computer
Include but be not limited to: phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM),
Other kinds of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory
(EEPROM), flash memory or other memory techniques, read-only disc read only memory (CD-ROM) (CD-ROM), digital versatile disc
(DVD) or other optical storage, magnetic cassettes, tape magnetic disk storage or other magnetic storage devices or any other non-biography
Defeated medium, can be used for storage can be accessed by a computing device information.
The present invention also provides a kind of computer equipment, including reservoir, processor and it is stored in the reservoir simultaneously
The computer program that can be executed by the processor, the processor are realized when executing the computer program as above-mentioned any one
The step of plant regulation of absorbing dust capability detection method based on machine learning algorithm described in item.
The embodiments described above only express several embodiments of the present invention, and the description thereof is more specific and detailed, but simultaneously
It cannot therefore be construed as limiting the scope of the patent.It should be pointed out that coming for those of ordinary skill in the art
It says, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to protection of the invention
Range.
Claims (10)
1. a kind of plant regulation of absorbing dust capability detection method based on machine learning algorithm, which comprises the steps of:
Plant is obtained in the single order inverse of the spectral reflectivity of each wave band and laying the dust for corresponding characterization plant regulation of absorbing dust capability
Measure data；
The single order inverse of the spectral reflectivity of each wave band and the relative coefficient of the amount of laying the dust data are calculated, and according to the correlation
Property coefficient screening obtains wavelength ranges；
Using the corresponding spectral reflectivity single order inverse of wavelength ranges as input, using the corresponding amount of the laying the dust data of wavelength ranges as
Anticipated output constructs the inverse model that lays the dust using grid-search algorithm and algorithm of support vector machine；
The single order inverse to measuring plants in the spectral reflectivity of wavelength ranges is obtained, and is input in the inverse model that lays the dust, is obtained
The amount of laying the dust data.
2. the plant regulation of absorbing dust capability detection method according to claim 1 based on machine learning algorithm, which is characterized in that institute
State the mode of the relative coefficient of the single order inverse for calculating the spectral reflectivity of each wave band and the corresponding amount of laying the dust data are as follows:
Wherein, rijFor the spectral reflectivity first derivative of j-th of wave band in i-th of sample；For the spectral reflectance of j-th of wave band
The average value of rate first derivative；xijFor the amount of the laying the dust data of j-th of wave band in i-th of sample；For j-th of the wave band amount of laying the dust number
According to average value；N is the quantity of sample；Each sample includes that the single order of the spectral reflectivity of each wave band is reciprocal and corresponding
The amount of laying the dust data.
3. the plant regulation of absorbing dust capability detection method according to claim 2 based on machine learning algorithm, which is characterized in that institute
It states and the step of obtaining wavelength ranges is screened according to relative coefficient, comprising:
The absolute value of the relative coefficient of each wave band is ranked up in the way of from large to small；
The absolute value for screening relative coefficient meets the wave band of the first given threshold as wavelength ranges.
4. the plant regulation of absorbing dust capability detection method according to claim 3 based on machine learning algorithm, which is characterized in that institute
It states and obtains plant in the step of the single order inverse of the spectral reflectivity of each wave band, comprising:
The spectral reflectivity of plant is obtained, and spectral reflectivity is pre-processed；
Spectrum transform is carried out to pretreated spectral reflectivity, the single order for obtaining the spectral reflectivity of each wave band is reciprocal.
5. the plant regulation of absorbing dust capability detection method according to claim 4 based on machine learning algorithm, which is characterized in that institute
It states and pretreated step is carried out to spectral reflectivity, comprising:
The spectral reflectivity of plant is obtained, and rejects the spectral reflectivity that error is more than the first given threshold, it later will be remaining
Spectral reflectance data is averaging, using average value as the spectral reflectivity of plant；
Use nearby principle divided by corresponding blank value the spectral reflectivity of the plant, the actual spectrum as plant reflects
Rate；
According to the actual spectrum reflectivity of plant, water vapor absorption wave band is determined, and it is anti-to reject the corresponding spectrum of water vapor absorption wave band
Rate is penetrated, the curve of spectrum fitting completion further along spectral reflectivity obtains pretreated spectral reflectivity.
6. the plant regulation of absorbing dust capability detection method according to claim 5 based on machine learning algorithm, which is characterized in that institute
State the mode for obtaining the single order inverse of the spectral reflectivity of each wave band are as follows:
Wherein, λi+1, λi, λi-1For adjacent wavelength, dR (λi) it is wavelength XiFirst derivative spectrum, R (λi+1), R (λi), R (λi-1)
Be respectively wavelength be λi+1, λi, λi-1The reflectivity at place.
The calculation of the amount of the laying the dust data for obtaining characterization plant regulation of absorbing dust capability are as follows:
D=Δ W/S
Wherein, D is the amount of the laying the dust data for characterizing plant regulation of absorbing dust capability；Δ W is the amount of laying the dust of a period of time implants blade；S is
Blade area.
7. the plant regulation of absorbing dust capability detection method according to claim 1 based on machine learning algorithm, which is characterized in that institute
After the step of stating the acquisition amount of laying the dust data, further includes: obtain multiple groups and wait for the amount of the laying the dust data of measuring plants, and laid the dust according to described
It measures data and plants corresponding plant in setting regions.
8. a kind of plant regulation of absorbing dust capability detection device based on machine learning algorithm characterized by comprising
Data acquisition module is planted for obtaining plant in the single order inverse of the spectral reflectivity of each wave band and corresponding characterization
The amount of the laying the dust data of object regulation of absorbing dust capability；
Wavelength ranges obtain module, related to the amount of laying the dust data for calculating the single order inverse of spectral reflectivity of each wave band
Property coefficient, and screened according to the relative coefficient and obtain wavelength ranges；
The inverse model that lays the dust constructs module, is used for using the corresponding spectral reflectivity single order inverse of wavelength ranges as input, with anti-
The corresponding amount of the laying the dust data of wave band are drilled as anticipated output, are constructed using grid-search algorithm and algorithm of support vector machine stagnant
Dirt inverse model；
The amount of laying the dust data acquisition module, it is reciprocal and defeated in the single order of the spectral reflectivity of wavelength ranges to measuring plants for obtaining
Enter into the inverse model that lays the dust, obtains the amount of laying the dust data.
9. a kind of computer readable storage medium, stores computer program thereon, which is characterized in that the computer program is located
It manages and realizes the plant regulation of absorbing dust capability inspection based on machine learning algorithm as claimed in any of claims 1 to 7 in one of claims when device executes
The step of survey method.
10. a kind of computer equipment, which is characterized in that including reservoir, processor and be stored in the reservoir and can
The computer program executed by the processor, the processor realize such as claim 1 to 7 when executing the computer program
Any one of described in the plant regulation of absorbing dust capability detection method based on machine learning algorithm the step of.
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