CN109270010A - A kind of plant regulation of absorbing dust capability detection method based on random forests algorithm - Google Patents

A kind of plant regulation of absorbing dust capability detection method based on random forests algorithm Download PDF

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CN109270010A
CN109270010A CN201811362049.6A CN201811362049A CN109270010A CN 109270010 A CN109270010 A CN 109270010A CN 201811362049 A CN201811362049 A CN 201811362049A CN 109270010 A CN109270010 A CN 109270010A
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dust
plant
spectral reflectance
band
data
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张晨
周成虎
周霞
荆文龙
王重洋
姜浩
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Guangzhou Institute of Geography of GDAS
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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
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
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    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N2021/8466—Investigation of vegetal material, e.g. leaves, plants, fruits

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Abstract

The plant regulation of absorbing dust capability detection method based on random forests 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 amount of the laying the dust data of corresponding characterization plant regulation of absorbing dust capability；Single order inverse and the corresponding amount of laying the dust data according to plant in the spectral reflectivity of each wave band, using the first random forests algorithm, screening obtains wavelength ranges；According to the wavelength ranges of screening, the corresponding spectral reflectivity single order inverse of wavelength ranges and the corresponding amount of laying the dust data are obtained；The inverse model that lays the dust is constructed using grid-search algorithm and the second random forests algorithm；Spectral reflectivity single order inverse to measuring plants in wavelength ranges is input in the inverse model that lays the dust, the amount of laying the dust data are obtained.Wavelength ranges are quickly and accurately filtered out using the realization of the first random forests algorithm, in conjunction with grid-search algorithm and the second random forests algorithm, establish the inverse model that lays the dust, and then realize and accurately estimate the plant amount of laying the dust.

Description

A kind of plant regulation of absorbing dust capability detection method based on random forests algorithm
Technical field
The present invention relates to Environmental Monitoring and Assessment technical fields, more particularly to a kind of plant based on random forests 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, and " gray haze " weather occurs again and again, The physical condition etc. of normal production and living and human body to the mankind all causes serious influence." gray haze " refers to a large amount of non- The particulate matters even suspensions such as normal tiny drying grit, soot, salt grain become in air so as to cause stuffy, visibility The phenomenon of difference, and the exceeded of the particle contents such as dust is one of the main reason for causing " gray haze ".Therefore, how to effectively control Dust pollution, which has become, improves the main problem that urban air-quality needs to solve.
Plant is a nature " weapon " for alleviating city gray haze, and canopy in great numbers plays the role of reducing wind speed, in air The biggish dust of particle can with wind speed reduce and be deposited to leaves of plants table or ground, to generate dust retention.In addition, The various characteristics on plant leaf blade surface can also promote the absorption of dust particles, and energy after the vanes rain drop erosion of dust covering Again restore dust collection capacity.It can be seen that the dust retention of plant has become an important indicator of screening urban greenery plants. Therefore, the amount of laying the dust for how assessing plant has become the emphasis of 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 random forests algorithm Method has the advantages that quick and precisely to estimate the plant amount of laying the dust.
A kind of plant regulation of absorbing dust capability detection method based on random forests 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；
It is random using first according to the single order inverse of the spectral reflectivity of each wave band of plant and the corresponding amount of laying the dust data Forest algorithm, screening obtain wavelength ranges；
According to the wavelength ranges of screening, it is reciprocal and corresponding lay the dust to obtain the corresponding spectral reflectivity single order of wavelength ranges Measure data；
According to the corresponding spectral reflectivity single order inverse of wavelength ranges and the corresponding amount of laying the dust data, using grid- Search algorithm and the second random forests algorithm construct the inverse model that lays the dust；
Spectral reflectivity single order inverse to measuring plants in wavelength ranges is input in the inverse model that lays the dust, is laid the dust Measure data.
The present invention quickly and accurately filters out wavelength ranges using the realization of the first random forests algorithm, in conjunction with grid- Search algorithm and the second random forests algorithm establish the inverse model that lays the dust, and then realize and accurately estimate the plant amount of laying the dust.
In one embodiment, the single order inverse of the spectral reflectivity according to plant in each wave band and corresponding stagnant Dust quantity data screen the step of obtaining wavelength ranges using the first random forests algorithm, comprising:
Using the single order inverse of each band spectrum reflectivity and the corresponding amount of laying the dust data as input, building first is random gloomy Woods model, and obtain according to first Random Forest model importance score of each wave band；
The importance score of each wave band is sequentially added up according to sequence from big to small, until cumulative importance score Just stop when AccuFI meets AccuFI >=F cumulative；Wherein, F is preset importance threshold value；
Accumulated wave band is screened as wavelength ranges.
In one embodiment, the single order inverse of the spectral reflectivity with each wave band and the corresponding amount of laying the dust data As input, the first Random Forest model is constructed, and is obtained according to the importance that first Random Forest model obtains each wave band The step of dividing, comprising:
It regard the spectral reflectivity first derivative of all wave bands and the corresponding amount of laying the dust as sample set；Wherein, in sample set Each sample standard deviation include each wave band spectral reflectivity single order is reciprocal and the corresponding amount of laying the dust data；
Sampling is used to have the method put back to randomly select n sample from sample set as n sample set；Wherein, not by The sample of extraction is known as the outer data of bag；
To each sample set, original classification tree is constructed, every one tree is all an independent Taxonomy and distribution, The relationship between independent variable and dependent variable is established in each node of every one tree；
Select modeling factors at random at each node and carry out building fractionation, to select optimal case, and then construct with Machine forest model, and seek the prediction result of each tree；
In the case where keeping the single order of spectral reflectivity of its all band reciprocal constant, upset i-th of wave band in sample Spectral reflectivity single order inverse sequence, the single order for then calculating the spectral reflectivity of i-th of wave band of certain one tree again falls Number is disturbed the difference of the prediction error of the outer data of bag of sequence front and back；It calculates each tree and upsets data outside the bag of sequence front and back Predict that the average value of the difference of error obtains the importance score of each wave band with this as the importance score of i-th of wave band.
In one embodiment, described that the inverting that lays the dust is constructed using grid-search algorithm and the second random forests algorithm The step of model, comprising:
Using the spectral reflectivity first derivative of wavelength ranges and the corresponding amount of laying the dust data as sample set；Wherein, sample The each sample concentrated respectively include the spectral reflectivity of one of wavelength ranges single order is reciprocal and the corresponding amount of laying the dust Data.
Sampling is used to have the method put back to randomly select n sample from sample set as n sample set；
To each sample set, original classification tree is constructed, every one tree is all an independent Taxonomy and distribution, The relationship between independent variable and dependent variable is established in each node of every one tree；
It is random gloomy to find second using grid-search algorithm for parameter setting range based on the second random forests algorithm The optimized parameter of woods algorithm, each node obtain the inverse model that lays the dust after carrying out building fractionation according to the optimized parameter.
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 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 random forests 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 screening module, for according to plant in the single order inverse of the spectral reflectivity of each wave band and corresponding The amount of laying the dust data, using the first random forests algorithm, screening obtains wavelength ranges；
Spectral reflectivity single order inverse obtains module, and for the wavelength ranges according to screening, it is corresponding to obtain wavelength ranges Spectral reflectivity single order inverse and the corresponding amount of laying the dust data；
The inverse model that lays the dust constructs module, for reciprocal and corresponding according to the corresponding spectral reflectivity single order of wavelength ranges The amount of laying the dust data, laid the dust inverse model using grid-search algorithm and the building of the second random forests algorithm；
The amount of laying the dust data acquisition module, for the spectral reflectivity single order inverse to measuring plants in wavelength ranges to be input to It lays the dust in inverse model, obtains the amount of laying the dust data.
The present invention quickly and accurately filters out wavelength ranges using the realization of the first random forests algorithm, in conjunction with grid- Search algorithm and the second random forests algorithm establish the inverse model that lays the dust, and then realize and accurately estimate the plant amount of laying the dust.
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 random forests algorithm as described in above-mentioned any one is realized when being executed by processor The step of.
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 random forests algorithm described in item.
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 random forests 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 of present invention screening wavelength ranges；
Fig. 6 is the flow chart that the present invention obtains each wave band importance score；
Fig. 7 is the flow chart that the present invention constructs the inverse model that lays the dust.
Specific embodiment
Specification of the invention, claims and term " first " in attached drawing, " second ", " third " " the 4th " etc. (if present) is to be used to distinguish similar objects, without being used to describe a particular order or precedence order.In addition, term " comprising " and " having " and their any deformation, it is intended that cover not exclusively include, for example, containing a series of steps Rapid or unit process, method, system, product or equipment those of are not necessarily limited to be clearly listed step or unit, but can Including other step or units being not clearly listed or intrinsic for these process, methods, product or equipment.
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 random forests algorithm.Institute The plant regulation of absorbing dust capability detection method based on random forests 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 is that the spectral reflectivity of the plant is the plant Object is in 350nm-2500nm wave band and is divided into the ratio between the reflected flux of 1nm wave band and the incident flux of the wave band, that is, includes institute Plant is stated in the ratio between the reflected flux of 350nm and the incident flux of the wave band, the plant in the reflected flux of 351nm and the wave The plant is in the ratio between the reflected flux of 2500nm and the incident flux of the wave band for the ratio between the incident flux ... of section.
Step S2: it according to the single order inverse of the spectral reflectivity of each wave band of plant and the corresponding amount of laying the dust data, utilizes First random forests algorithm, screening obtain wavelength ranges.
Step S3: according to the wavelength ranges of screening, it is reciprocal and right to obtain the corresponding spectral reflectivity single order of wavelength ranges The amount of the laying the dust data answered.
Step S4: it according to the corresponding spectral reflectivity single order inverse of wavelength ranges and the corresponding amount of laying the dust data, uses Grid-search algorithm and the second random forests algorithm construct the inverse model that lays the dust；
Step S5: the spectral reflectivity single order inverse to measuring plants in wavelength ranges is input in the inverse model that lays the dust, The acquisition amount of laying the dust data.
The present invention quickly and accurately filters out wavelength ranges using the realization of the first random forests algorithm, in conjunction with grid- Search algorithm and the second random forests algorithm establish the inverse model that lays the dust, and then realize and accurately estimate the plant amount of laying the dust.
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 pair The spectral reflectivity answered, 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；Δ W is a period of time implants blade The amount of laying the dust, 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.
Referring to Fig. 5, its flow chart for present invention screening wavelength ranges.
In one embodiment, the single order inverse of the spectral reflectivity according to plant in each wave band and corresponding stagnant Dust quantity data screen the step of obtaining wavelength ranges using the first random forests algorithm, comprising:
Step S21: using the single order of the spectral reflectivity of each wave band is reciprocal and the corresponding amount of laying the dust data are as inputting, The first Random Forest model is constructed, and obtains the importance score of each wave band according to first Random Forest model；
Step S22: the importance score of each wave band is sequentially added up according to sequence from big to small, until cumulative is important Property score A ccuFI just stop when meeting AccuFI >=F it is cumulative；Wherein, F is preset importance threshold value；
Step S23: accumulated wave band is screened as wavelength ranges.
Please referring to Fig. 6 is the flow chart that the present invention obtains each wave band importance score.
In one embodiment, reciprocal and corresponding stagnant with the single order of the spectral reflectivity of each wave band in step S21 Dust quantity data construct the first Random Forest model as input, and obtain each wave band according to first Random Forest model The step of importance score, comprising:
Step S211: the spectral reflectivity first derivative of all wave bands and the corresponding amount of laying the dust are regard as sample set；Wherein, Each sample standard deviation in sample set includes that the single order of the spectral reflectivity of each wave band is reciprocal and the corresponding amount of laying the dust data；
Step S212: sampling is used to have the method put back to randomly select n sample from sample set as n sample set； Wherein, the sample not being extracted is known as the outer data of bag；
Step S213: to each sample set, original classification tree is constructed, every one tree is all an independent classification With regression tree, the relationship between independent variable and dependent variable is established in each node of every one tree；
Step S214: modeling factors are selected at random at each node and carry out building fractionation, to select optimal case, in turn Random Forest model is constructed, and seeks the prediction result of each tree；
Step S215: in the case where keeping the single order of spectral reflectivity of its all band reciprocal constant, upset in sample The sequence of the single order inverse of the spectral reflectivity of i-th of wave band, then calculates the spectral reflectivity of i-th of wave band of certain one tree again Single order inverse be disturbed sequence front and back the outer data of bag prediction error difference；Calculate the bag that each tree upsets sequence front and back The average value of the difference of the prediction error of outer data obtains the important of each wave band as the importance score of i-th of wave band with this Property score.
Referring to Fig. 7, its flow chart for constructing the inverse model that lays the dust for the present invention.
In one embodiment, described to be constructed using grid-search algorithm and the second random forests algorithm in step S4 Lay the dust inverse model the step of, comprising:
Step S41: using the spectral reflectivity first derivative of wavelength ranges and the corresponding amount of laying the dust data as sample set；Its In, the single order that each sample in sample set respectively includes the spectral reflectivity of one of wavelength ranges is reciprocal and corresponding The amount of laying the dust data.
Step S42: sampling is used to have the method put back to randomly select n sample from sample set as n sample set；
Step S43: to each sample set, original classification tree is constructed, every one tree is all an independent classification With regression tree, the relationship between independent variable and dependent variable is established in each node of every one tree；
Step S44: the parameter setting range based on the second random forests algorithm finds the using grid-search algorithm The optimized parameter of two random forests algorithms, each node obtain the inverting mould that lays the dust after carrying out building fractionation according to the optimized parameter Type.
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.It lays the dust instead in the application for rapid build Model is drilled, according to the optimized parameter for the second random forests algorithm that grid-search algorithm obtains specifically: the number of iterations n_ The candidate parameter collection of estimators are as follows: 20,40,60,80,100,120,140,160,180,200.
For the precision for verifying inverse model, the actual measured value for the amount of laying the dust is compared with predicted value.The precision of model Verifying is measured using quantitative accuracy evaluation index, specially determines coefficient (the coefficient of Determination, R2), root-mean-square error (the root-mean-square error, RMSE), mean absolute error (the mean absolute error, MAE), average deviation (Bias), calculation formula is as follows.
Wherein, xiFor the actual measurement amount of laying the dust of i-th of sample, xi' for i-th of sample model prediction the amount of laying the dust,For institute There is the average value of the actual measurement amount of laying the dust,For the average value of all model prediction amounts of laying the dust, N is the quantity of sample.
R2Indicate the linearly related degree between two data, MAE and RMSE are used to the integral level of evaluated error, and Bias is anti- Estimation data and the departure degree of measured data numerically are reflected.
The result shows that the verifying precision RMSE of the amount of laying the dust appraising model is respectively less than 0.3g/m2, efficiency of inverse process is good.
In one embodiment, after the step of amount of laying the dust data are obtained described in step S5, 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 random forests 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 screening module, for according to plant in the single order inverse of the spectral reflectivity of each wave band and corresponding The amount of laying the dust data, using the first random forests algorithm, screening obtains wavelength ranges；
Spectral reflectivity single order inverse obtains module, and for the wavelength ranges according to screening, it is corresponding to obtain wavelength ranges Spectral reflectivity single order inverse and the corresponding amount of laying the dust data；
The inverse model that lays the dust constructs module, for reciprocal and corresponding according to the corresponding spectral reflectivity single order of wavelength ranges The amount of laying the dust data, laid the dust inverse model using grid-search algorithm and the building of the second random forests algorithm；
The amount of laying the dust data acquisition module, for the spectral reflectivity single order inverse to measuring plants in wavelength ranges to be input to It lays the dust in inverse model, obtains the amount of laying the dust data.
The present invention quickly and accurately filters out wavelength ranges using the realization of the first random forests algorithm, in conjunction with grid- Search algorithm and the second random forests algorithm establish the inverse model that lays the dust, and then realize and accurately estimate the plant amount of laying the dust.
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.
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 random forests 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 random forests 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)

1.一种基于随机森林算法的植物滞尘能力检测方法，其特征在于，包括如下步骤：A method for detecting a dust retention capability of a plant based on a random forest algorithm, comprising the steps of: 获取植物在各个波段的光谱反射率的一阶倒数以及对应的表征植物滞尘能力的滞尘量数据；Obtaining the first-order reciprocal of the spectral reflectance of the plant in each band and the corresponding dust-dissipating data characterizing the dust-retaining ability of the plant; 根据植物各个波段的光谱反射率的一阶倒数和对应的滞尘量数据，利用第一随机森林算法，筛选获得反演波段；According to the first-order reciprocal of the spectral reflectance of each band of the plant and the corresponding dust-dissipation data, the first random forest algorithm is used to screen and obtain the inversion band; 根据筛选的反演波段，获得反演波段对应的光谱反射率一阶倒数以及对应的滞尘量数据；Obtaining the first-order inverse of the spectral reflectance corresponding to the inversion band and the corresponding dust-dissipation data according to the selected inversion band; 根据反演波段对应的光谱反射率一阶倒数以及对应的滞尘量数据，采用grid-search算法和第二随机森林算法构建滞尘反演模型；According to the first-order inverse of the spectral reflectance corresponding to the inversion band and the corresponding dust-dissipation data, the grid-search algorithm and the second random forest algorithm are used to construct the dust-reversing inversion model; 将待测植物在反演波段的光谱反射率一阶倒数输入到滞尘反演模型中，获得滞尘量数据。The first-order reciprocal of the spectral reflectance of the plant to be tested in the inversion band is input into the dust reversal inversion model to obtain the dust retention data. 2.根据权利要求1所述的基于随机森林算法的植物滞尘能力检测方法，其特征在于，所述根据植物在各个波段的光谱反射率的一阶倒数和对应的滞尘量数据，利用第一随机森林算法，筛选获得反演波段的步骤，包括：2 . The method according to claim 1 , wherein the method according to the first-order reciprocal of the spectral reflectance of the plant in each wavelength band and the corresponding dust-retaining amount data is utilized. A random forest algorithm that screens for the inversion band, including: 以各波段光谱反射率的一阶倒数和对应的滞尘量数据作为输入，构建第一随机森林模型，并根据所述第一随机森林模型获取各波段的重要性得分；Constructing a first random forest model with the first-order reciprocal of the spectral reflectance of each band and the corresponding dust-retaining data as input, and obtaining the importance scores of each band according to the first random forest model; 将各波段的重要性得分按照从大到小的顺序依序累加，直至累加的重要性得分AccuFI满足AccuFI≥F时才停止累加；其中，F为预设的重要性阈值；The importance scores of the respective bands are sequentially accumulated in descending order until the accumulated importance score AccuFI satisfies AccuFI ≥ F; wherein F is a preset importance threshold; 筛选参与累加的波段作为反演波段。Filter the bands participating in the accumulation as the inversion band. 3.根据权利要求2所述的基于随机森林算法的植物滞尘能力检测方法，其特征在于，所述以各个波段的光谱反射率的一阶倒数和对应的滞尘量数据作为输入，构建第一随机森林模型，并根据所述第一随机森林模型获取各波段的重要性得分的步骤，包括：3 . The method according to claim 2 , wherein the first-order inverse of the spectral reflectance of each band and the corresponding dust-retaining amount data are used as inputs, and the first method is constructed. a random forest model and steps for obtaining importance scores for each band based on the first random forest model, including: 将所有波段的光谱反射率一阶导数和对应的滞尘量作为样本集；其中，样本集中的每个样本均包括各个波段的光谱反射率的一阶倒数以及对应的滞尘量数据；Taking the first derivative of the spectral reflectance of all the bands and the corresponding dross amount as a sample set; wherein each sample in the sample set includes a first-order reciprocal of the spectral reflectance of each band and a corresponding dross amount data; 用抽样有放回的方法从样本集中随机抽取n个样本作为n个样本子集；其中，未被抽取的样本称为袋外数据；Randomly extract n samples from the sample set as n subsets of samples by sampling back; wherein the unsampled samples are called out-of-bag data; 对每一个样本子集，构建原始的分类树，每一棵树都是一个独立的分类与回归树，在每一棵树的每一个节点内建立自变量与因变量之间的关系；For each sample subset, construct the original classification tree, each tree is an independent classification and regression tree, and establish the relationship between the independent variable and the dependent variable in each node of each tree; 在每个节点处随机选出建模因子进行构建拆分，以选出最优方案，进而构建出随机森林模型，并求取每棵树的预测结果；A modeling factor is randomly selected at each node to construct a split to select an optimal solution, and then a random forest model is constructed, and the predicted result of each tree is obtained; 在保持其他波段的光谱反射率的一阶倒数不变的情况下，打乱样本中第i个波段的光谱反射率的一阶倒数的顺序，然后再计算某一棵树第i个波段的光谱反射率的一阶倒数被打乱顺序前后的袋外数据的预测误差的差值；计算每棵树打乱顺序前后的袋外数据的预测误差的差值的平均值，作为第i个波段的重要性得分，以此获得各波段的重要性得分。In the case of keeping the first-order reciprocal of the spectral reflectance of other bands constant, the order of the first-order reciprocal of the spectral reflectance of the i-th band in the sample is disturbed, and then the spectrum of the i-th band of a tree is calculated. The first-order reciprocal of the reflectivity is the difference of the prediction error of the out-of-bag data before and after the disordered order; the average value of the difference of the prediction error of the out-of-bag data before and after the disordered order of each tree is calculated as the i-th band The importance score is used to obtain the importance score for each band. 4.根据权利要求1所述的基于随机森林算法的植物滞尘能力检测方法，其特征在于，所述采用grid-search算法和第二随机森林算法构建滞尘反演模型的步骤，包括：The method for detecting a dust retention capability of a plant based on a random forest algorithm according to claim 1, wherein the step of constructing a dust reversal inversion model by using a grid-search algorithm and a second random forest algorithm comprises: 将反演波段的光谱反射率一阶导数和对应的滞尘量数据作为样本集；其中，样本集中的每个样本分别包括其中一个反演波段的光谱反射率的一阶倒数以及对应的滞尘量数据。Taking the first derivative of the spectral reflectance of the inversion band and the corresponding dross amount data as a sample set; wherein each sample in the sample set includes a first-order reciprocal of the spectral reflectance of one of the inversion bands and corresponding dust retention Volume data. 用抽样有放回的方法从样本集中随机抽取n个样本作为n个样本子集；Randomly extract n samples from the sample set as n subsets of samples by sampling back. 对每一个样本子集，构建原始的分类树，每一棵树都是一个独立的分类与回归树，在每一棵树的每一个节点内建立自变量与因变量之间的关系；For each sample subset, construct the original classification tree, each tree is an independent classification and regression tree, and establish the relationship between the independent variable and the dependent variable in each node of each tree; 基于第二随机森林算法的参数设置范围，采用grid-search算法寻找第二随机森林算法的最优参数，每个节点根据所述最优参数进行构建拆分后获得滞尘反演模型。Based on the parameter setting range of the second random forest algorithm, the grid-search algorithm is used to find the optimal parameters of the second random forest algorithm, and each node is constructed and split according to the optimal parameters to obtain the dust-reversing inversion model. 5.根据权利要求1所述的基于随机森林算法的植物滞尘能力检测方法，其特征在于，所述获取植物在各个波段的光谱反射率的一阶倒数的步骤，包括：The method according to claim 1, wherein the step of obtaining a first-order reciprocal of spectral reflectance of each wavelength band of the plant comprises: 获取植物的光谱反射率，并对光谱反射率进行预处理；Obtaining the spectral reflectance of the plant and pretreating the spectral reflectance; 对预处理后的光谱反射率进行光谱变换，获得各个波段的光谱反射率的一阶倒数；Performing spectral transformation on the spectral reflectance after pretreatment to obtain a first-order reciprocal of the spectral reflectance of each band; 其中，所述对光谱反射率进行预处理的步骤，包括：The step of pre-treating the spectral reflectance includes: 获取植物的光谱反射率，并剔除误差超过第一设定阈值的光谱反射率，之后将剩余的光谱反射率数据求平均，以平均值作为植物的光谱反射率；Obtaining the spectral reflectance of the plant, and rejecting the spectral reflectance of the error exceeding the first set threshold, and then averaging the remaining spectral reflectance data, using the average as the spectral reflectance of the plant; 对所述植物的光谱反射率采用就近原则除以对应的白板值，作为植物的实际光谱反射率；The spectral reflectance of the plant is divided by the nearest whiteboard value as the actual spectral reflectance of the plant; 根据植物的实际光谱反射率，获得水汽吸收波段，并剔除水汽吸收波段下的光谱反射率，再沿着光谱反射率的光谱曲线拟合补全获得预处理后的光谱反射率。According to the actual spectral reflectance of the plant, the water vapor absorption band is obtained, and the spectral reflectance under the water vapor absorption band is removed, and the spectral reflectance after pretreatment is obtained along the spectral curve fitting complement of the spectral reflectance. 6.根据权利要求1所述的基于随机森林算法的植物滞尘能力检测方法，其特征在于，The method according to claim 1, wherein the method for detecting dust retention capability of a plant based on a random forest algorithm is characterized in that: 所述各个波段的光谱反射率的一阶倒数的计算方式为：The first-order reciprocal of the spectral reflectance of each band is calculated as: 其中，λi+1，λi，λi-1为相邻波长，dR(λi)为波长λi的一阶导数光谱，R(λi+1)，R(λi)，R(λi-1)分别是波长为λi+1，λi，λi-1处的反射率。Where λ i+1 , λ i , λ i-1 are adjacent wavelengths, dR(λ i ) is the first derivative spectrum of wavelength λ i , R(λ i+1 ), R(λ i ), R( λ i-1 ) is the reflectance at wavelengths λ i+1 , λ i , λ i-1 , respectively. 所述获取表征植物滞尘能力的滞尘量数据的计算方式为：The calculation method for obtaining the dust retention data for characterizing the dust retention ability of the plant is: D＝ΔW/SD=ΔW/S 其中，D为表征植物滞尘能力的滞尘量数据；ΔW为一段时间内植物叶片的滞尘量；S为叶片面积。Among them, D is the dust retention data to characterize the dust retention ability of plants; ΔW is the dust retention of plant leaves in a period of time; S is the leaf area. 7.根据权利要求1所述的基于随机森林算法的植物滞尘能力检测方法，其特征在于，所述获得滞尘量数据的步骤之后，还包括：获得多组待测植物的滞尘量数据，并根据所述滞尘量数据在设定区域种植对应的植物。The method according to claim 1, wherein the step of obtaining the dust retention data further comprises: obtaining dust collection data of the plurality of plants to be tested. And planting corresponding plants in the set area according to the dust amount data. 8.一种基于随机森林算法的植物滞尘能力检测装置，其特征在于，包括：A device for detecting a dust-retaining ability of a plant based on a random forest algorithm, comprising: 数据获取模块，用于获取植物在各个波段的光谱反射率的一阶倒数以及对应的表征植物滞尘能力的滞尘量数据；a data acquisition module, configured to obtain a first-order reciprocal of the spectral reflectance of the plant at each wavelength band and a corresponding dust-dissipation data indicative of the dust-retaining ability of the plant; 反演波段筛选模块，用于根据植物在各个波段的光谱反射率的一阶倒数和对应的滞尘量数据，利用第一随机森林算法，筛选获得反演波段；The inversion band screening module is configured to use the first random forest algorithm to select and obtain the inversion band according to the first-order reciprocal of the spectral reflectance of the plant in each band and the corresponding dust-retaining amount data; 光谱反射率一阶倒数获取模块，用于根据筛选的反演波段，获得反演波段对应的光谱反射率一阶倒数以及对应的滞尘量数据；a spectral first-order reciprocal acquisition module for obtaining a first-order inverse of the spectral reflectance corresponding to the inversion band and corresponding dust-dissipation data according to the selected inversion band; 滞尘反演模型构建模块，用于根据反演波段对应的光谱反射率一阶倒数以及对应的滞尘量数据，采用grid-search算法和第二随机森林算法构建滞尘反演模型；The dust-reversing inversion model building module is configured to construct a dust-reversing inversion model by using a grid-search algorithm and a second random forest algorithm according to the first-order inverse of the spectral reflectance corresponding to the inversion band and the corresponding dust-retaining data; 滞尘量数据获取模块，用于将待测植物在反演波段的光谱反射率一阶倒数输入到滞尘反演模型中，获得滞尘量数据。The dust collection data acquisition module is configured to input the first-order reciprocal of the spectral reflectance of the plant to be tested in the inversion band into the dust reversal inversion model to obtain the dust retention data. 9.一种计算机可读存储介质，其上储存有计算机程序，其特征在于，该计算机程序被处理器执行时实现如权利要求1至7中任意一项所述的基于随机森林算法的植物滞尘能力检测方法的步骤。A computer readable storage medium having stored thereon a computer program, wherein the computer program is executed by a processor to implement a random forest algorithm based plant stagnation according to any one of claims 1 to 7. The steps of the dust capacity detection method. 10.一种计算机设备，其特征在于，包括储存器、处理器以及储存在所述储存器中并可被所述处理器执行的计算机程序，所述处理器执行所述计算机程序时实现如权利要求1至7中任意一项所述的基于随机森林算法的植物滞尘能力检测方法的步骤。10. A computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable by the processor, the processor implementing the computer program as claimed The method of the method for detecting a dust retention ability of a plant based on a random forest algorithm according to any one of claims 1 to 7.
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