CN102590473A - Test method and system of suitability of soil heavy metal of agricultural regional plot of land - Google Patents

Abstract

The invention discloses a test method and system of suitability of soil heavy metal of an agricultural regional plot of land, belonging to the technical field of soil test. The method comprises the following steps of: S1, according to a random sampling way, selecting preset number of sampling nodes, obtaining the multivariate data of each sampling node, and recording the coordinate data of all sampling nodes; S2, respectively carrying out data processing for data of each attribute in the multivariate data to eliminate abnormal data; S3, respectively interpolating and fitting the data of each attribute to obtain a multi-index evaluation data set for representing an range of an agricultural regional plot of land to be tested; S4, and determining the soil pollution degree of the range of the agricultural regional plot of land to be tested according to the multi-index evaluation data set. The invention provides a data processing flow method relative to improving the reasonable test for soil heavy metal of the regional plot of land. The data quality can be effectively ensured, meanwhile the data processing accuracy is increased, and therefore the test accuracy of the heavy metal soil of the agricultural regional plot of land is increased.

Description

Farming region plot heavy metal-polluted soil suitability method of testing and system

Technical field

The present invention relates to the soil testing technical field, particularly a kind of farming region plot heavy metal-polluted soil suitability method of testing and system.

Background technology

China's agriculture production environment is along with the transformation of farming pattern, the improving constantly of the adjustment of the structure of rural undertaking and agricultural product production rate of fertilizer application.The pollution situation of agricultural product environment increases the weight of day by day, and especially heavy metal pollution has had influence on agricultural output and quality, can cause having a strong impact on of can not estimating to people's lives with healthy.Therefore for the agricultural product home environment, especially the test of the pollution monitoring of heavy metal-polluted soil and soil suitability just seems even more important.

At present, external and China does not formulate unified standard as yet for the standard of soil testing, and the test process and the method for testing of employing are various.But the harm for heavy metal in soil all has consistent common recognition, has all done deep research for inrichment, spatial variability and the migration mechanism of heavy metal in the soil, and this has established theoretical foundation for the test of soil suitability.Because the complicacy of soil property and composition, and the chaos phenomenon of the interior interaction mechanism of soil, it is not easy to make reasonable and comprehensive better test.Index of single factor method on method of testing, the multi-stress index method returned component analysis, artificial intelligence, methods such as fuzzy mathematics all have application.

Because the judgement determinacy of index of single factor method, composite index law, the not operability of the simplicity of homing method and artificial intelligence causes the measuring accuracy of heavy metal-polluted soil suitability lower, can't satisfy the demands.

Summary of the invention

The technical matters that (one) will solve

The technical matters that the present invention will solve is: how to improve the adaptive test accuracy of heavy metal-polluted soil.

(2) technical scheme

For solving the problems of the technologies described above, the invention provides a kind of farming region plot heavy metal-polluted soil suitability method of testing, said method comprising the steps of:

S1: the sampling node of in the scope of farming region to be measured plot, selecting preset number according to the stochastic sampling mode; The topsoil soils of said sampling node is evenly mixed the mixing sample of back as each sampling node respectively; Said mixing sample is carried out heavy metal-polluted soil to be detected; Obtain the multivariate data of each sampling node, and write down the coordinate data of all sampling node;

S2: the data to every attribute in the said multivariate data are carried out data processing respectively, to get rid of abnormal data;

S3: the data to having got rid of every attribute in the multivariate data behind the abnormal data are carried out interpolation fitting respectively, to obtain many index evaluations data set of the said farming region to be measured of representative plot scope;

S4: the soil pollution degree of confirming said farming region to be measured plot scope according to said many index evaluations data set.

Preferably, step S2 specifically may further comprise the steps:

S201: the data of every attribute in the said multivariate data are carried out ascending order respectively arrange, and calculate the mean value and the standard deviation of the data of every attribute;

S202: according to preset confidence level and said mean value and standard deviation the data of every attribute in the said multivariate data are done the Grubbs data detection, to get rid of abnormal data.

Preferably, the interval of said confidence level is (0,95).

Preferably, step S3 specifically may further comprise the steps:

S301: calculate the average theory semivariation value between all sampling node in the scope of said farming region to be measured plot according to the coordinate data of the data of having got rid of every attribute in the multivariate data behind the abnormal data and each sampling node;

S302: adopt semivariance model and said average theory semivariation value to carry out match, to obtain range, base station value and piece gold;

S303: calculate respectively according to said semivariance model, range, base station value and piece gold and to obtain in the scope of said farming region to be measured plot between all sampling node the expansion covariance matrix and to estimate representative point to the expansion covariance vector between each sampling node, said estimation representative point is the geometric center location point in the scope of farming region to be measured plot;

S304: calculate the expansion weight vectors according to said expansion covariance matrix and expansion covariance vector; Obtain the said representative property value of having got rid of every attribute in the multivariate data behind the abnormal data through said expansion weight vectors and the said data computation of having got rid of every attribute in the multivariate data behind the abnormal data, and said representative property value is constituted many index evaluations data set of the said farming region to be measured of representative plot scope.

Preferably, before the step S2, further comprising the steps of:

S101: to the data of every attribute in the said multivariate data value of filling a vacancy respectively;

S102: the data to every attribute in the said multivariate data are removed repetition values respectively.

Preferably, step S4 specifically may further comprise the steps:

S401: set up the degree of membership fuzzy matrix according to said many index evaluations data set, and make up the comprehensive weight vector through the harm of each heavy metal species; S402: the soil pollution degree of confirming said farming region to be measured plot scope through said degree of membership fuzzy matrix and comprehensive weight vector.

The invention also discloses a kind of farming region plot heavy metal-polluted soil suitability test macro, said system comprises:

Detection module; Be used in the scope of farming region to be measured plot selecting the sampling node of preset number according to the stochastic sampling mode; The topsoil soils of said sampling node is evenly mixed the mixing sample of back as each sampling node respectively; Said mixing sample is carried out heavy metal-polluted soil detect, obtain the multivariate data of each sampling node, and write down the coordinate data of all sampling node;

Data processing module is used for the data of every attribute of said multivariate data are carried out data processing respectively, to get rid of abnormal data;

The interpolation fitting module is used for the data of having got rid of every the attribute of multivariate data behind the abnormal data are carried out interpolation fitting respectively, to obtain many index evaluations data set of the said farming region to be measured of representative plot scope;

The pollution level determination module is used for confirming according to said many index evaluations data set the soil pollution degree of said farming region to be measured plot scope.

(3) beneficial effect

The invention provides a cover with respect to improving the regional plot heavy metal-polluted soil flow chart of data processing method of test rationally; When can guarantee the quality of data effectively, thereby improve the test accuracy that the data processing accuracy has improved farming region plot heavy metal soil.

Description of drawings

Fig. 1 is the process flow diagram according to the farming region plot heavy metal-polluted soil suitability method of testing of one embodiment of the present invention.

Embodiment

Below in conjunction with accompanying drawing and embodiment, specific embodiments of the invention describes in further detail.Following examples are used to explain the present invention, but are not used for limiting scope of the present invention.

Fig. 1 is the process flow diagram according to the farming region plot heavy metal-polluted soil suitability method of testing of one embodiment of the present invention; With reference to Fig. 1, the method for this embodiment may further comprise the steps:

S1: the sampling node of in the scope of farming region to be measured plot, selecting preset number according to the stochastical sampling mode; With the topsoil soils of said sampling node (in the present embodiment; Selected depth is the topsoil soils of 0～20cm) the even respectively mixing sample that mixes the back as each sampling node; Said mixing sample is carried out heavy metal-polluted soil detect, obtain the multivariate data of each sampling node, and write down the coordinate data of all sampling node;

S2: the data to every attribute in the said multivariate data are carried out data processing respectively, to get rid of abnormal data; Each metal element content and soil physical chemistry supplemental characteristic be lognormal distribution more since experiment variation function through squared difference with calculate; The existence of exceptional value possibly cause the distortion of experiment variation function, therefore need take the method for rejecting abnormalities value to get rid of its influence to experiment variation function robustness.

S3: the data to having got rid of every attribute in the multivariate data behind the abnormal data are carried out interpolation fitting respectively, to obtain many index evaluations data set of the said farming region to be measured of representative plot scope;

S4: the soil pollution degree of confirming said farming region to be measured plot scope according to said many index evaluations data set.

Preferably, step S2 specifically may further comprise the steps:

S201: the data of every attribute in the said multivariate data are carried out ascending order respectively arrange, and calculate the mean value and the standard deviation of the data of every attribute; The data that define individual event attribute in the said multivariate data are sequence X={ x _i| x _i∈ R}, i=1...n, the data mean value of its individual event attribute does

Standard deviation does X wherein _iBe the single property value of sample i,

Be sample list attribute mean value.

With the sampling element mercury is the method that example is explained this embodiment, and that establishes sampling node adds up to 10, obtains attribute mercury sequence X _HgThe descriptive statistics following table:

S202: according to preset confidence level and said mean value and standard deviation the data of every attribute in the said multivariate data are done the Grubbs data detection, to get rid of abnormal data.In this embodiment, be specially: the formula of definition statistic obtains statistical value according to preset confidence level, mean value and standard deviation; Relatively critical value is given up greater than critical value, keeps less than critical value; Giving up value replaces with upside critical value or downside critical value respectively; In this embodiment, the interval of said confidence level is (0,95).

Definition statistic G=(average-less dubious value)/standard deviation and (big dubious value-average)/standard deviation; I.e. definition

as statistic, is carried out the eliminating of abnormal data.Preferably, step S3 specifically may further comprise the steps:

S301: calculate the average theory semivariation value between all sampling node in the scope of said farming region to be measured plot according to the coordinate data of the data of having got rid of every attribute in the multivariate data behind the abnormal data and each sampling node; If Z (x) is a regionalized variable, satisfy second-order stationary and intrinsic hypothesis, its mathematical expectation is unknown, and covariance function c (d) and variation function (d) exist.Suppose total n eyeball, i.e. x in the neighborhood of treating regional plot estimation point (x) ₁, x ₂..., x _n, its sample value is Z (x _i).If the representative attribute data in regional plot is Z ^*(x), the plot geometric center point is got in its locus, obtains center point coordinate x=37.418705, y=37.418705.

Wherein, distance does between all sampling node

Variance between all sampling node is S _Ij=(Z _i-Z _j) ², theoretical semivariation value does

Average theory semivariation value does $\mathrm{\γ}\left(d\right)=\frac{1}{2}E{[Z\left(x\right)-Z(x+h)]}^2=E\left[\mathrm{\γ}{\left(d\right)}_{\mathrm{Ij}}\right].$

With mercury Hg sample is example, establish get rid of 1 abnormal data after, 9 sampling node are calculated.

S302: adopt semivariance model and said average theory semivariation value to carry out match, to obtain range (range), base station value (sill) and piece gold (nugget); Adopt its semivariance model of sphere model Spherical to be:

$\mathrm{\γ}\left(d\right)=\left\{\begin{array}{c}{c}_o+c[3d/2a)-(d^3/{2a}^3)],d\≤a\\ c_o+c,d>a\end{array}\right.$

Wherein, D is two distances between the sampling node, and

Its covariance c (d) is c (d)=σ ²-γ (d) is through calculating corresponding range a, base station value c _o+ c, piece gold number c _oBe respectively 44.3751,0.0781,0.0001.

S303: calculate respectively according to said semivariance model, range, base station value and piece gold and to obtain in the scope of said farming region to be measured plot between all sampling node the expansion covariance matrix and to estimate that representative point is to the expansion covariance vector between each sampling node; Calculate sampling point respectively to a covariance c (x according to the semivariance model _i, x _j) expansion covariance matrix c ₊And the estimation representative point is to the expansion covariance vector c of the covariance c between the sample point (x) ₊(x), said estimation representative point is the geometric center location point in the scope of farming region to be measured plot.

S304: calculate the expansion weight vectors according to said expansion covariance matrix and expansion covariance vector; Obtain the said representative property value of having got rid of every attribute in the multivariate data behind the abnormal data through said expansion weight vectors and the said data computation of having got rid of every attribute in the multivariate data behind the abnormal data, and said representative property value is constituted many index evaluations data set of the said farming region to be measured of representative plot scope.Earlier through computes expansion weight vectors λ ₊(x),

${\mathrm{\λ}}_{+}\left(x\right)=C_{+}^{-1}{C}_{+}\left(x\right)$

The computing formula of then expanding covariance vector is:

$Z^{*}\left(x\right)=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{\λ}}_{i}Z\left(x_i\right)$

Wherein, Z ^*(x) be the representative attribute data in regional plot, the geometric center point mean value Z of Hg ^*(x)=0.278, realize regional plot represented property value comparatively really.

Reduce the data that cause because of test error or the intrinsic changeability of data and depart from for improving the quality of data, preferably, before the step S2, further comprising the steps of:

S101: to the data of every attribute in the said multivariate data value of filling a vacancy respectively; In this embodiment, fill up this vacancy value with the mean value of this property value of this property value of last record of vacancy value record place record row and a back record.

S102: the data to every attribute in the said multivariate data are removed repetition values respectively; Generic data are compared analysis, and based on the recognizer of distance, promptly whether two character strings of research are equivalent under the certain situation of error, if equivalence is then with one of them removal.

Preferably, step S4 specifically may further comprise the steps:

S401: set up the degree of membership fuzzy matrix based on said many index evaluations data set; And through the harm structure comprehensive weight of each heavy metal species vectorial (structure of said comprehensive weight vector is done weighting standardization with both weights and is obtained with reference to the daily body weight for humans metal intake standard of hankason toxicity response coefficient and FAO/WHO formulation);

S402: the soil pollution degree of confirming said farming region to be measured plot scope through said degree of membership fuzzy matrix and comprehensive weight vector.

Confirm the domain and the model element of evaluation model: set of factors U, the evaluation index content of decision set V and R.

Set set of factors U={x ₁..., x _i... x _n, x wherein _iBe n kind plot heavy metal attribute factor, the toxic hazard weight vectors of its each factor is A=(a ₁..., a _n).

According to the soil types and the soil background in corresponding area, and critical value and relevant pollution index national standard, corresponding fuzzy comprehensive evaluation decision set formulated, decision set V={v ₁..., v _j... v _m, v wherein _jGrade for multifactorial evaluation.

With reference to shantung soil background value and standard GB 15618 multifactorial evaluation table of gradings, as shown in the table:

Annotate: 1 grade be not comtaminated, and 2 grades light are polluted, pollution in 3 grades, 4 grades of heavily contaminateds, 5 grades of severe contaminations, the mg/kg of unit

Factor is passed judgment on matrix R=(r _Ij) _{N * m}, factor attribute and the relation of passing judgment between the grade can be represented with fuzzy relationship matrix r through setting up subordinate function.

Make FUZZY MAPPING

By FUZZY MAPPING

Can induce fuzzy relation: ${\underset{~}{R}}_f(u_i,v_j)=\underset{~}{f}\left(u_i\right)\left(v_j\right)=r_{\mathrm{Ij}},$ R representes by fuzzy matrix, wherein

$R=\left[\begin{array}{cccc}{r}_{11}& r_{12}& ...& r_{1m}\\ r_{21}& r_{22}& ...& r_{2m}\\ .& .& & .\\ .& .& & .\\ .& .& & .\\ r_{n1}& r_{n2}& ...& r_{\mathrm{nm}}\end{array}\right]$

Carry out fuzzy operation, need to confirm membership function, setting soil environment comprehensive evaluation grade is m grade.According to single-factor contamination index definition, rise half trapezoidal and the membership function that falls half trapezoidal profile statement different stage respectively.Then:

1 grade of degree of membership: fall half trapezoidal profile

$r\left(x\right)=\left\{\begin{array}{cc}1& (x_i\&le;S_{\mathrm{ij}})\\ \frac{S_{\mathrm{ij}+1}-x_i}{S_{\mathrm{ij}+1}-S_{\mathrm{ij}}}& (S_{\mathrm{ij}}<x_i\&le;S_{\mathrm{ij}+1})\\ 0& (x_i>S_{\mathrm{ij}+1})\end{array}\right.$

2 grades～m-1 level degree of membership: half trapezoidal profile of going up and down

$r\left(x\right)=\left\{\begin{array}{cc}0& (x_i\&le;S_{\mathrm{ij}})\\ \frac{x_i-S_{\mathrm{ij}-1}}{S_{\mathrm{ij}}-S_{\mathrm{ij}-1}}& (S_{\mathrm{ij}-1}<x_i\&le;S_{\mathrm{ij}})\\ \frac{S_{\mathrm{ij}+1}-x_i}{S_{\mathrm{ij}+1}-S_{\mathrm{ij}}}& (S_{\mathrm{ij}}<x_i\&le;S_{\mathrm{ij}+1})\\ 0& (x_i>S_{\mathrm{ij}+1})\end{array}\right.$

M level degree of membership: fall half trapezoidal profile

$r\left(x\right)=\left\{\begin{array}{cc}0& (x_i\&le;S_{\mathrm{ij}-1})\\ \frac{x_i-S_{\mathrm{ij}-1}}{S_{\mathrm{ij}}-S_{\mathrm{ij}-1}}& (S_{\mathrm{ij}-1}<x_i\&le;S_{\mathrm{ij}})\\ 1& (x_i>S_{\mathrm{ij}})\end{array}\right.$

X wherein _iBe that property value is represented, S in i kind element plot _IjIt is the j level judgment criteria of i kind element.

The degree of membership fuzzy matrix of confirming is:

$R=\left[\begin{array}{ccccc}0.448& 0.552& 0& 0& 0\\ 0& 0.36& 0.64& 0& 0\\ 1& 0& 0& 0& 0\\ 0.263& 0.737& 0& 0& 0\\ 0.629& 0.371& 0& 0& 0\\ 1& 0& 0& 0& 0\\ 0& 0.965& 0.035& 0& 0\end{array}\right]$

Consider the Heavy Metal Ecological pollution toxicity and to the harm toxic degree of health, definition n item heavy metal grade is f ₁..., f _nSet up the weight factor vector A=(a of corresponding heavy metal toxicity coefficient as comprehensive evaluation ₁..., a _n).Wherein do the normalization of weight standard, constraint condition does

And

Daily human intaking amount's standard according to potential ecological hazard response toxic factor and FAO/WHO formulation; Consider of the harm of the various heavy metals of comprehensive embodiment, confirm that heavy metal grade cadmium Cd, mercury Hg, arsenic As, copper Cu, plumbous Pb, chromium Cr, zinc Zn are respectively: f environment and health _Hg=4, f _Cd=f _Pb=3, f _As=f _Cr=2, f _Cu=f _Zn=1.

Toxic hazard weight vectors cadmium Cd, mercury Hg, arsenic As, copper Cu, plumbous Pb, chromium Cr, zinc Zn are A=(0.1875,0.25,0.125,0.0625,0.1875,0.125,0.0625).

Do multifactorial evaluation by comprehensive weight vector A and single-factor fuzzy matrix R and utilize weighted average model M (,+) that all factors are taken into account according to the weight size equalization, be applicable to the acting situation of consideration various factors.Can get multifactorial evaluation: B=A ° R, promptly B is multiple-factor multifactorial evaluation result, B=(b ₁..., b _n) the vector set.Wherein:

$b_i=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{a}_i\&CenterDot;r_{\mathrm{ij}}=\min \{1,\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{a}_i\&CenterDot;r_{\mathrm{ij}}\}$

Obtain plot comprehensive evaluation vector B=(0.455005,0.38029,0.164705; 0; 0), what obtain the employing of soil pollution degree in the method for this embodiment is the fuzzy evaluation method, shows tendency and the approximate range that pollutes aggregate level according to passing judgment on proportion; See overall level of pollution near the slight pollution critical value according to said plot comprehensive evaluation vector, and have the heavy metal level of intermediate pollution.

A kind of farming region plot heavy metal-polluted soil suitability method of testing that is provided in this embodiment; One cover is provided with respect to improving the regional plot heavy metal-polluted soil flow chart of data processing method of test rationally; When can guarantee the quality of data effectively, thereby improve the test accuracy that the data processing accuracy has improved farming region plot heavy metal soil.

Employing is carried out the data pre-service to the plot sampled data, gets rid of abnormal data, represents the match of property value to extract, the data processing method of regional plot pollutant integration test, and final the realization improved the accuracy of target plot integration test.At first obtain certain plot multiple spot pollutant attribute sampled data.Carry out the data pre-service to obtaining data, carry out statistical test and get rid of abnormal data.Carry out the geo-statistic match according to said sampling individual event attribute data and seek suitable match mathematical model, property value is represented in the geometric center match individual event that obtains corresponding plot.Do similar operations according to the sampling element of corresponding pollutant heavy metal and obtain each item attribute typical value.Heavy metal background value and critical value according to the regional soil types of correspondence; Formulate corresponding discrimination standard grade; Set up rational membership function and create the test fuzzy matrix; As the weight test vector, realize integration test system in conjunction with Heavy Metal Ecological toxic hazard and human health damage to regional plot heavy metal-polluted soil.Reach a conclusion according to test result at last.

The invention also discloses a kind of farming region plot heavy metal-polluted soil suitability test macro, said system comprises:

Detection module; Be used in the scope of farming region to be measured plot selecting the sampling node of preset number according to the stochastic sampling mode; The topsoil soils of said sampling node is evenly mixed the mixing sample of back as each sampling node respectively; Said mixing sample is carried out heavy metal-polluted soil detect, obtain the multivariate data of each sampling node, and write down the coordinate data of all sampling node;

Data processing module is used for the data of every attribute of said multivariate data are carried out data processing respectively, to get rid of abnormal data;

The interpolation fitting module is used for the data of having got rid of every the attribute of multivariate data behind the abnormal data are carried out interpolation fitting respectively, to obtain many index evaluations data set of the said farming region to be measured of representative plot scope;

The pollution level determination module is used for confirming according to said many index evaluations data set the soil pollution degree of said farming region to be measured plot scope.

Above embodiment only is used to explain the present invention; And be not limitation of the present invention; The those of ordinary skill in relevant technologies field under the situation that does not break away from the spirit and scope of the present invention, can also be made various variations and modification; Therefore all technical schemes that are equal to also belong to category of the present invention, and scope of patent protection of the present invention should be defined by the claims.

Claims (7)

1. a farming region plot heavy metal-polluted soil suitability method of testing is characterized in that, said method comprising the steps of:

S1: the sampling node of in the scope of farming region to be measured plot, selecting preset number according to the stochastic sampling mode; The topsoil soils of said sampling node is evenly mixed the mixing sample of back as each sampling node respectively; Said mixing sample is carried out heavy metal-polluted soil to be detected; Obtain the multivariate data of each sampling node, and write down the coordinate data of all sampling node;

S2: the data to every attribute in the said multivariate data are carried out data processing respectively, to get rid of abnormal data;

S3: the data to having got rid of every attribute in the multivariate data behind the abnormal data are carried out interpolation fitting respectively, to obtain many index evaluations data set of the said farming region to be measured of representative plot scope;

S4: the soil pollution degree of confirming said farming region to be measured plot scope according to said many index evaluations data set.

2. the method for claim 1 is characterized in that, step S2 specifically may further comprise the steps:

S201: the data of every attribute in the said multivariate data are carried out ascending order respectively arrange, and calculate the mean value and the standard deviation of the data of every attribute;

S202: according to preset confidence level and said mean value and standard deviation the data of every attribute in the said multivariate data are done the Grubbs data detection, to get rid of abnormal data.

3. method as claimed in claim 2 is characterized in that, the interval of said confidence level is (0,95).

4. the method for claim 1 is characterized in that, step S3 specifically may further comprise the steps:

S301: calculate the average theory semivariation value between all sampling node in the scope of said farming region to be measured plot according to the coordinate data of the data of having got rid of every attribute in the multivariate data behind the abnormal data and each sampling node;

S302: adopt semivariance model and said average theory semivariation value to carry out match, to obtain range, base station value and piece gold;

S303: calculate respectively according to said semivariance model, range, base station value and piece gold and to obtain in the scope of said farming region to be measured plot between all sampling node the expansion covariance matrix and to estimate representative point to the expansion covariance vector between each sampling node, said estimation representative point is the geometric center location point in the scope of farming region to be measured plot;

S304: calculate the expansion weight vectors according to said expansion covariance matrix and expansion covariance vector; Obtain the said representative property value of having got rid of every attribute in the multivariate data behind the abnormal data through said expansion weight vectors and the said data computation of having got rid of every attribute in the multivariate data behind the abnormal data, and said representative property value is constituted many index evaluations data set of the said farming region to be measured of representative plot scope.

5. the method for claim 1 is characterized in that, and is before the step S2, further comprising the steps of:

S101: to the data of every attribute in the said multivariate data value of filling a vacancy respectively;

S102: the data to every attribute in the said multivariate data are removed repetition values respectively.

6. the method for claim 1 is characterized in that, step S4 specifically may further comprise the steps:

S401: set up the degree of membership fuzzy matrix according to said many index evaluations data set, and make up the comprehensive weight vector through the harm of each heavy metal species; S402: the soil pollution degree of confirming said farming region to be measured plot scope through said degree of membership fuzzy matrix and comprehensive weight vector.

7. farming region plot heavy metal-polluted soil suitability test macro is characterized in that said system comprises:

Detection module; Be used in the scope of farming region to be measured plot selecting the sampling node of preset number according to the stochastic sampling mode; The topsoil soils of said sampling node is evenly mixed the mixing sample of back as each sampling node respectively; Said mixing sample is carried out heavy metal-polluted soil detect, obtain the multivariate data of each sampling node, and write down the coordinate data of all sampling node;

Data processing module is used for the data of every attribute of said multivariate data are carried out data processing respectively, to get rid of abnormal data;

The interpolation fitting module is used for the data of having got rid of every the attribute of multivariate data behind the abnormal data are carried out interpolation fitting respectively, to obtain many index evaluations data set of the said farming region to be measured of representative plot scope;

The pollution level determination module is used for confirming according to said many index evaluations data set the soil pollution degree of said farming region to be measured plot scope.

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