CN112304997A - Soil heavy metal content detection system and detection method based on space coupling model - Google Patents
Soil heavy metal content detection system and detection method based on space coupling model Download PDFInfo
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Abstract
The invention discloses a soil heavy metal content detection system based on a space coupling model, which comprises a box body and a rotatable soil sample containing table arranged in the box body, wherein a moisture sensor is arranged on a soil sample contact surface of the soil sample containing table, a microprocessor, an X-ray tube, a detector, a positioning module, a memory and a display screen are arranged in the box body and are respectively connected with the microprocessor, the X-ray tube and the detector are respectively aligned with the soil sample containing table, spectrum quantitative analysis models corresponding to different geographical positions are stored in the memory, and the output end of the moisture sensor is connected with the microprocessor. The invention also discloses a method for detecting the heavy metal content in the soil. The detection system provided by the invention is used for carrying out scale correction aiming at the peak value drift phenomenon, compensating aiming at different soil textures and wetting degrees, and selecting the corresponding spectral quantitative analysis model according to the geographical position of the soil sample, so that the detection precision of the heavy metal content in the soil is improved.
Description
Technical Field
The invention belongs to the field of agricultural environment heavy metal detection, and particularly relates to a soil heavy metal content detection system based on a spatial coupling model.
Background
In recent years, toxic heavy metals have increasingly serious harm to the soil environment quality of China, and the pollution problem of cultivated land, agricultural products and the like needs to be solved urgently. Therefore, farmland heavy metal monitoring has become an important task for agricultural product protection and safe production. At present, the soil heavy metal pollution detection method comprises a traditional laboratory detection method and a rapid detection method, wherein the traditional laboratory detection method mainly comprises an atomic absorption and atomic fluorescence spectrometry, an inductively coupled plasma mass spectrometry and the like, the detection result has high accuracy and precision, but the existing method has high requirements on the operation conditions of instruments and is not suitable for on-site rapid judgment; the rapid detection method of heavy metals comprises X-ray fluorescence spectroscopy (XRF), biosensor technology, laser-induced breakdown spectroscopy and the like. Among them, the X-ray fluorescence spectrometer has the advantages of fast detection speed, low cost, simultaneous detection of various elements, and the like, and has been widely applied to detection and analysis of soil heavy metal pollution and agricultural products. Compare laboratory survey method, XRF method earlier stage is handled simply, can carry out effective monitoring and quick screening to main heavy metal concentration, provides scientific foundation to farmland and crops heavy metal pollution's monitoring and prevention and control, formulate reasonable agricultural development planning, nevertheless lacks the XRF heavy metal analysis appearance that can accurate location and accurate prediction concentration.
Disclosure of Invention
The invention aims to solve the problems, and provides a soil heavy metal content detection system based on a spatial coupling model, which compensates different soil textures and wetting degrees, corrects scales according to a peak drift phenomenon, optimizes a wavelength interval of a spectrum of a soil sample, simplifies the wavelength of the spectrum, and improves the soil heavy metal content detection precision.
The technical scheme includes that the soil heavy metal content detection system based on the space coupling model comprises a box body and a rotatable soil sample containing table arranged in the box body, a moisture sensor is arranged on a soil sample contact surface of the soil sample containing table, a microprocessor, an X-ray tube, a detector, a positioning module, a storage, a wireless communication module and a display screen are arranged in the box body and are respectively connected with the microprocessor, the X-ray tube and the detector are respectively aligned with the soil sample containing table, spectrum quantitative analysis models corresponding to different geographical positions are stored in the storage, and an output end of the moisture sensor is connected with the microprocessor; the wireless communication module is in communication connection with the cloud server.
Furthermore, the soil heavy metal content detection system also comprises a spectrum scale correction module and a spectrum quantitative analysis module which operate on the microprocessor. And the spectrum scale correction module is used for re-determining the peak position according to the phenomenon of the peak position drift of the spectrum, calculating an energy scale coefficient and performing scale correction on the spectrum. And the spectrum analysis module is used for selecting a spectrum quantitative analysis model corresponding to the geographical position according to the geographical position information obtained by the positioning module, and analyzing the spectrum of the soil sample to obtain the heavy metal content of the soil.
Preferably, the soil heavy metal content detection system based on the spatial coupling model further comprises a soil type correction module running on the microprocessor, and the soil type correction module selects a compensation coefficient of the currently detected soil sample according to the moisture condition of the soil sample obtained by the moisture sensor and the soil texture input by an operator.
Preferably, the soil heavy metal content detection system based on the spatial coupling model further comprises a report generation module which runs on the microprocessor, and the report generation module generates an analysis report according to the concentration, longitude and latitude, time and spectral data of each heavy metal element of the currently detected soil sample.
Preferably, the soil heavy metal content detection system based on the spatial coupling model further comprises a wavelength interval optimization module running on the microprocessor, wherein the wavelength interval optimization module adopts an interval combination optimization algorithm to eliminate useless information and noise wavelength points of a spectrum, and a group of effective wavelength intervals with optimized positions, combinations and widths are obtained.
Preferably, the soil heavy metal content detection system based on the spatial coupling model further comprises a wavelength reduction module running on the microprocessor, the wavelength reduction module adopts a continuous projection algorithm, vector projection analysis is utilized, a variable group containing minimum redundant information is found, co-linearity among wavelengths is eliminated, and the wavelengths of the spectrum are reduced.
Preferably, the soil heavy metal content detection system based on the spatial coupling model further comprises a cloud module running on the microprocessor, the cloud module packs data of the interval combination optimization algorithm or the continuous projection algorithm and sends the data to the cloud server, and the cloud server executes the calculation process of the algorithm and sends the calculation result back to the cloud module; the cloud module sends the analysis report generated by the report generation module to a cloud server to realize cloud storage; the cloud module sends the geographic position information obtained by the positioning module to the cloud server, obtains the latest spectrum quantitative analysis model corresponding to the geographic position information on the cloud server, and stores the latest spectrum quantitative analysis model in the storage.
The detection method adopting the soil heavy metal content detection system comprises the following steps:
step 1: irradiating a soil sample by using X rays, and collecting an X fluorescence spectrum of the soil sample;
step 2: acquiring the geographical position information of a soil sample acquisition point by using a positioning module;
and step 3: correcting the spectral data according to the type of the soil sample and the peak position drift of the spectrum;
step 3.1, determining a compensation coefficient according to the soil texture and the wetting degree of the soil sample, and correcting the spectral data;
step 3.2: the peak position is determined again aiming at the phenomenon of the peak position drift of the spectrum, the energy scale coefficient is calculated, and the spectrum is subjected to scale correction;
and 4, step 4: optimizing the wavelength interval of the spectrum by using a wavelength interval optimization module;
and 5: selecting and simplifying the wavelengths of the spectrum by using a wavelength simplifying module;
step 6: selecting a spectrum quantitative analysis model corresponding to the geographical position information, and analyzing the spectrum of the soil sample to obtain the heavy metal content of the soil;
and 7: and 6, generating an analysis report according to the analysis result of the heavy metal content of the soil, the longitude and latitude of the soil sample, the detection time and the spectrum data of the soil sample, and sending the analysis report to a cloud server.
Compared with the prior art, the invention has the beneficial effects that:
1) the detection system disclosed by the invention is used for carrying out scale correction aiming at the peak value drift phenomenon, and selecting the corresponding spectrum quantitative analysis model according to the geographical position of the soil sample, so that the detection precision of the heavy metal content in the soil is improved;
2) the detection system of the invention compensates for different soil textures and wetting degrees, thereby further improving the detection precision of the heavy metal content in the soil;
3) the detection system provided by the invention optimizes the wavelength interval of the spectrum, simplifies the wavelength of the spectrum, and obtains the heavy metal content of the soil by analyzing the spectrum quantitative analysis model, thereby reducing the influence of noise, detection error and the like on the detection result;
4) according to the detection system, an analysis report is automatically generated according to the concentration, the longitude and latitude, the time and the spectral data of each heavy metal element of the currently detected soil sample, so that the detection system replaces manpower, and is time-saving and labor-saving;
5) the detection method of the invention increases the scientificity of soil heavy metal detection and reduces errors.
Drawings
The invention is further illustrated by the following figures and examples.
Fig. 1 is a schematic structural diagram of a soil heavy metal content detection system according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart of a soil heavy metal content detection method according to an embodiment of the invention.
Detailed Description
As shown in figure 1, soil heavy metal content detecting system based on space coupling model comprises a box body and a rotatable soil sample containing table arranged in the box body, wherein a moisture sensor is arranged on a soil sample contact surface of the soil sample containing table, a microprocessor, an X-ray tube, a detector, a positioning module, a memory, a wireless communication module and a display screen are arranged in the box body and are respectively connected with the microprocessor, the X-ray tube and the detector are respectively aligned with the soil sample containing table, and the memory stores the soil sample containing tableThe output end of the moisture sensor is connected with the microprocessor; the wireless communication module is in communication connection with the cloud server. In one embodiment, the wireless communication module is a GPRS module, model number SIM 800C. The X-ray tube selects a mini-X micro light tube of America Amptek company, and the main parameters comprise: the silver transmission target is 0.75 mu m, the maximum voltage is 50kV, the maximum power is 4W, the emergence angle is 120 degrees, the high-voltage stability is 0.03 percent, and the use temperature is-10-50 ℃. The detector adopts an Amptek SDD123 silicon drift detector, and the main performance parameters comprise: detector area 25mm2The resolution is less than or equal to 130eV, and the forming time is 0.2-110 mus.
The soil heavy metal content detection system further comprises a spectrum scale correction module, a spectrum quantitative analysis module, a soil type correction module, a wavelength interval optimization module, a wavelength simplification module, a report generation module and a cloud module which are operated on the microprocessor.
And the spectrum scale correction module is used for re-determining the peak position according to the phenomenon of the peak position drift of the spectrum, calculating an energy scale coefficient and performing scale correction on the spectrum.
And the spectrum analysis module is used for selecting a partial least square regression model corresponding to the geographic position according to the geographic position information obtained by the positioning module, and analyzing the spectrum of the soil sample to obtain the heavy metal content of the soil.
The soil type correction module selects a compensation coefficient of the currently detected soil sample according to the moisture condition of the soil sample obtained by the moisture sensor and the soil texture input by the operator.
The wavelength interval optimization module adopts an interval combination optimization algorithm to eliminate useless information and noise wavelength points of the spectrum and obtain a group of effective wavelength intervals with optimized positions, combinations and widths.
The wavelength simplifying module adopts a continuous projection algorithm, utilizes the projection analysis of the vector to find a variable group containing minimum redundant information, eliminates the collinearity among the wavelengths and simplifies the wavelength of the spectrum.
And the report generation module generates an analysis report according to the concentration, longitude and latitude, time and spectral data of each heavy metal element of the currently detected soil sample.
The cloud module packs data of the interval combination optimization algorithm or the continuous projection algorithm and sends the data to the cloud server, and the cloud server executes the calculation process of the algorithm and sends the calculation result back to the cloud module; the cloud module sends the analysis report generated by the report generation module to a cloud server to realize cloud storage; the cloud module sends the geographic position information obtained by the positioning module to the cloud server, obtains the latest spectrum quantitative analysis model corresponding to the geographic position information on the cloud server, and stores the latest spectrum quantitative analysis model in the storage.
The specific process of optimizing the wavelength interval by the interval combination optimization algorithm is as follows:
1) and determining the optimal interval division quantity, dividing the spectrum into a plurality of subintervals with approximately same widths, and respectively establishing a PLS model to predict the heavy metal content. Observing test results of the spectrum divided into different intervals, wherein when the spectrum is divided into s subintervals to obtain the minimum root mean square error value, s is the optimal number of the divided subintervals;
2) and (3) carrying out combined optimization on the wavelength intervals which are not subjected to width optimization:
2.1) generating a submodel, adopting Weighted Bootstrap Sampling (WBS) to generate a subset formed by random combination of M different wavelength intervals, wherein M is Sampling frequency, the initial Sampling weight of each wavelength point is 1, and the probability p that the wavelength i is selected in one Sampling isiThe following were used:
wherein n represents the number of wavelength points, wiA sampling weight representing wavelength i;
2.2) calculating a root mean square error value RMSE corresponding to each interval combination subset by adopting a Partial least squares regression (PLS) algorithm and a 5-fold interactive inspection modeCV；
2.3) extracting the optimal interval combination subset with the proportion of alpha from all the interval combinations and calculating the partial interval groupCombining the subsets to correspond to the RMSECVThe mean value of the values is reported as RMSECVm；
2.4) counting the number of each interval appearing in the optimal interval combination, wherein the calculation formula of the sampling weight corresponding to the ith interval in the next iteration is as follows:
in the formula fiIs the frequency, k, of the ith interval occurring in that portion of the optimal interval combinationbestThe number of the extracted optimal interval combinations;
repeating the steps 2.1) to 2.4) to form an iterative loop until the RMSECVmA rise occurs and the iteration terminates.
2.5) in the last iteration, RMSECVThe set of wavelength intervals with the smallest value is considered as the final selected wavelength interval;
3) and (3) flexible optimization of interval boundaries:
and optimizing the width of each wavelength interval selected in the step 2.5) by adopting a local search strategy. In the strategy, wavelength points adjacent to the edge of each selected wavelength interval are included or excluded one by one for modeling, and a model RMSE is determined according to the wavelength pointsCVThe influence of the values was evaluated. Incorporation of an adjacent wavelength point would cause RMSE of the modelCVIf the value is decreased, the wavelength point is selected, otherwise, the wavelength point is rejected. Repeatedly conducting local search on each optimized section until there is no new wavelength point to enable RMSE for the modelCVThe value has an impact, and the interval optimized at this time can be considered as the final optimal characteristic wavelength subinterval.
The specific process of the wavelength simplification module for simplifying the characteristic wavelength by adopting a continuous projection algorithm is as follows:
1) for the correction set spectral matrix XN×MOptionally, a column of vectors x in the spectral matrixjIs marked as xk(0)The set of other remaining column variable positions is denoted as S,
wherein N represents the number of training samples, M represents the number of spectral wavelengths, and xjA column vector corresponding to the wavelength point j; k (0) is the initial position of the selected variable, and H represents the number of the maximum characteristic variables needing to be selected;
2) computing a residual column vector xi(i ∈ S) at xk(n-1)The projection value in the orthogonal space of (2) is calculated as follows
In the formula, xiA column vector corresponding to the wavelength point i; p is a projection operator;
3) finding out the wavelength point with the maximum projection value, and assigning values to k (n)
k(n)＝arg(max||Pxi||,i∈S) (5)
Incorporating the wavelength point into a wavelength combination;
4) setting N to be N +1, if N is less than N, executing the step 2), and if not, ending;
5) to obtain a wavelength combination of
{k(n)；n＝0,...,N-1} (6)
Iteratively obtaining a group of wavelength combinations by taking each wavelength point as a starting point, and then calculating the RMSE (remote management Environment) corresponding to each wavelength combination by adopting a Multiple Linear Regression (MLR) algorithmCVValue, then RMSECVThe wavelength combination corresponding to the value is regarded as the optimal wavelength combination, then the MLR algorithm is adopted to calculate the absolute value of the regression coefficient corresponding to each wavelength point in the optimal wavelength combination for wavelength sorting, the wavelengths with larger regression coefficients are brought into the MLR model one by one, and the corresponding RMSE is recordedCVThe value, the number of optimal wavelengths to be finally retained is determined by comparing the RMSECVThe manner of the value is determined.
The continuous projection algorithm of the embodiment refers to a continuous projection algorithm recorded in a thesis paper of three novel wavelength selection methods in near infrared spectrum quantitative analysis in Song dynasty published in 2017.
As shown in fig. 2, the detection method using the soil heavy metal content detection system includes the following steps:
step 1: irradiating a soil sample by using X rays, and collecting an X fluorescence spectrum of the soil sample;
step 2: acquiring the geographical position information of a soil sample acquisition point by using a positioning module;
and step 3: correcting the spectral data according to the type of the soil sample and the peak position drift of the spectrum;
step 3.1: determining a compensation coefficient according to the soil texture and the wetting degree of the soil sample, and correcting the spectral data;
step 3.2: the peak position is determined again aiming at the phenomenon of the peak position drift of the spectrum, the energy scale coefficient is calculated, and the spectrum is subjected to scale correction;
and 4, step 4: optimizing the wavelength interval of the spectrum by using a wavelength interval optimization module;
and 5: selecting and simplifying the wavelengths of the spectrum by using a wavelength simplifying module;
step 6: selecting a partial least squares regression model corresponding to the geographical position information, and analyzing the spectrum of the soil sample to obtain the heavy metal content of the soil;
and 7: and 6, generating an analysis report according to the analysis result of the heavy metal content of the soil, the longitude and latitude of the soil sample, the detection time and the spectrum data of the soil sample, and sending the analysis report to a cloud server.
The type and the degree of wetting of the soil have a great influence on the measurement of heavy metals, and examples of the soil selected include the classes 9 of partially dry sandy soil, partially dry loam, partially dry clay, medium sandy soil, medium loam, medium clay, partially wet sandy soil, partially wet loam and partially wet clay. And the spectral data are corrected according to the type and the wetting degree of the soil so as to improve the accuracy of the measurement result. And selecting standard slightly dry loam as a reference model, and performing dynamic compensation coefficient correction on the other 8 soils in different conditions. Measuring the true value of the standard sample of the partially dry loam, collecting the spectrum of the standard sample, analyzing the heavy metal content by using a partial least square regression model, adjusting the parameters of the partial least square regression model, and setting a reference compensation coefficient to be 1.00; and respectively selecting samples from the other 8 kinds of soils with different conditions to measure the real concentration values of the soils, and comparing the real concentration values with the reference values to obtain respective compensation coefficients. In the examples, the soil compensation factors determined through a number of experiments and comparative analyses are shown in table 1.
TABLE 1 soil Compensation coefficient Table
Although the set parameters of different spectrometers are completely the same, the different spectrometers have a little difference, especially the difference of gain, so that the peak positions of the spectrograms obtained by different instruments are slightly shifted, and therefore, the accurate peak positions are searched again to correct the spectrum scales.
The calibration utilizes the K alpha peak of Fe and the K alpha peak of Ag in a soil measurement spectrogram, when the peak positions drift, the Fe peak and the Ag peak positions can move for several to more than ten times, the accurate peak positions are searched again in the range of 10 times about the original peak positions, and then energy calibration is carried out.
In the examples, taking calibration of the K α peak of Fe and the K α peak of Ag as an example, the specific process of calibration is as follows:
the method comprises the following steps: calculating Fe peak values among 950-1050 channels;
nTempChnFe＝FindPeak(950,1050)
wherein nTempChnFe represents the position of the Fe peak, and FindPeak () is a peak searching function;
step two: calculating Ag peak values among 3340-3440 channels;
nTempChnAg＝FindPeak(3340,3440)
wherein nTempChnAg represents the Ag peak position;
step three: energy scale corresponding to calculated peak value
Cl＝CalEn(nTempChnFe,c)
Cr＝CalEn(nTempChnAg,c)
In the formula, Cl represents a characteristic X-ray spectral line energy value corresponding to a Fe peak position, Cr represents a characteristic X-ray spectral line energy value corresponding to an Ag peak position, CalEn () is an energy scale calculation function, c is a peak searching range parameter, and in the embodiment, c is 10;
step four: calculating a scale correction factor
ESFactorA＝(Er-El)/(Cr-Cl)；
In the formula, ESFactorA represents a scale correction coefficient, and El represents a characteristic X-ray spectral line energy reference value of Fe element; er represents the energy reference value of the characteristic X-ray spectral line of Ag element;
step six: calculating the calibration offset
ESFactorB＝Er-ESFactorA*Cr；
Where esfactory b represents the scale correction offset.
Claims (8)
1. The soil heavy metal content detection system based on the spatial coupling model is characterized by comprising a box body and a rotatable soil sample containing table arranged in the box body, wherein a moisture sensor is arranged on a soil sample contact surface of the soil sample containing table, a microprocessor, an X-ray tube, a detector, a positioning module, a storage, a wireless communication module and a display screen are arranged in the box body and are respectively connected with the microprocessor, the X-ray tube and the detector are respectively aligned with the soil sample containing table, spectral quantitative analysis models corresponding to different geographical positions are stored in the storage, and the output end of the moisture sensor is connected with the microprocessor; the wireless communication module is in communication connection with the cloud server;
the soil heavy metal content detection system also comprises a spectrum scale correction module and a spectrum analysis module which run on the microprocessor,
the spectrum scale correction module is used for re-determining the peak position according to the phenomenon of peak position drift of the spectrum, calculating an energy scale coefficient and performing scale correction on the spectrum;
and the spectrum analysis module selects a spectrum quantitative analysis model corresponding to the geographical position according to the geographical position information obtained by the positioning module, and analyzes the spectrum of the soil sample to obtain the heavy metal content of the soil.
2. The soil heavy metal content detection system based on the spatial coupling model as claimed in claim 1, further comprising a soil type correction module running on the microprocessor, wherein the soil type correction module selects the compensation coefficient of the currently detected soil sample according to the moisture condition of the soil sample obtained by the moisture sensor and the soil texture input by the operator.
3. The soil heavy metal content detection system based on the spatial coupling model according to claim 2, further comprising a wavelength interval optimization module operating on the microprocessor, wherein the wavelength interval optimization module adopts an interval combination optimization algorithm to eliminate useless information and noise wavelength points of a spectrum, and obtains a group of effective wavelength intervals with optimized positions, combinations and widths.
4. The soil heavy metal content detection system based on the spatial coupling model as claimed in claim 3, further comprising a wavelength reduction module running on the microprocessor, wherein the wavelength reduction module adopts a continuous projection algorithm, and utilizes vector projection analysis to find a variable group containing minimum redundant information, eliminate co-linearity between wavelengths, and reduce the wavelengths of the spectrum.
5. The soil heavy metal content detection system based on the spatial coupling model as claimed in claim 4, further comprising a report generation module operating on the microprocessor, wherein the report generation module generates an analysis report according to the concentration of each heavy metal element of the currently detected soil sample, the longitude and latitude of the soil sample, the detection time, and the spectral data of the soil sample.
6. The soil heavy metal content detection system based on the spatial coupling model according to claim 5, further comprising a cloud module running on the microprocessor, wherein the cloud module packs and sends data of the interval combination optimization algorithm or the continuous projection algorithm to a cloud server, and the cloud server executes a calculation process of the algorithm and sends a calculation result back to the cloud module; the cloud module sends the analysis report generated by the report generation module to a cloud server to realize cloud storage; the cloud module sends the geographic position information obtained by the positioning module to the cloud server, obtains the latest spectrum quantitative analysis model corresponding to the geographic position information on the cloud server, and stores the latest spectrum quantitative analysis model in the storage.
7. The detection method of the soil heavy metal content detection system according to any one of claims 4, 5 or 6, characterized by comprising the following steps:
step 1: irradiating a soil sample by using X rays, and collecting an X fluorescence spectrum of the soil sample;
step 2: acquiring the geographical position information of a soil sample acquisition point by using a positioning module;
and step 3: correcting the spectral data according to the type of the soil sample and the peak position drift of the spectrum;
step 3.1, determining a compensation coefficient according to the soil texture and the wetting degree of the soil sample, and correcting the spectral data;
step 3.2: the peak position is determined again aiming at the phenomenon of the peak position drift of the spectrum, the energy scale coefficient is calculated, and the spectrum is subjected to scale correction;
and 4, step 4: optimizing the wavelength interval of the spectrum by using a wavelength interval optimization module;
and 5: selecting and simplifying the wavelengths of the spectrum by using a wavelength simplifying module;
step 6: and selecting a spectrum quantitative analysis model corresponding to the geographical position information, and analyzing the spectrum of the soil sample to obtain the heavy metal content of the soil.
8. The detection method according to claim 7, further comprising the step of 7: and 6, generating an analysis report according to the analysis result of the heavy metal content of the soil, the longitude and latitude of the soil sample, the detection time and the spectrum data of the soil sample, and sending the analysis report to a cloud server.
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| CN113567652A (en) * | 2021-07-27 | 2021-10-29 | 中国地质调查局水文地质环境地质调查中心 | Cr element soil moisture content correction method based on XRF detection technology |
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