CN109255724A - A kind of pesticide residual contamination evaluation method based on multiple-factor and AHP-E model - Google Patents
A kind of pesticide residual contamination evaluation method based on multiple-factor and AHP-E model Download PDFInfo
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Abstract
The invention discloses a kind of multiple-factor pesticide residual contamination integrated evaluating method based on AHP-E model, more attribute factors in comprehensive detecting result, obtains different agricultural product, pesticide residual contamination degree in different time periods；It include: that a variety of data sets are pre-processed；Overall merit is carried out using analytic hierarchy process (AHP), obtains the residual pollution index of agriculture of specific agricultural product；Overall merit is carried out to the residual pollution of agriculture in each period by Information Entropy, obtains the residual pollution index of agriculture in special time period.The method of the present invention can integrate more attribute factors in detecting result, overall merit is carried out to various agricultural product and pesticide residual contamination situation in different time periods respectively, the prominent sampling time section with the residual pollution condition of abnormal agriculture, the difference of effectively prominent different time sections pesticide residual contamination degree.
Description
Technical field
The present invention relates to data analyses and pollution evaluation technical field, more particularly to one kind to be based on multiple-factor and AHP-E mould
The pesticide residual contamination integrated evaluating method of type.
Background technique
In agricultural planting production process, in order to control disease pest and weed, coordinate plant growth speed, it usually needs in agricultural product
Upper alternate application Multiple Pesticides.Though the use of pesticide helps to improve yield, such as exceeded use will be constituted human health
It threatens, becomes a big hidden danger of food safety.To ensure food safety, the reasonable employment of control and specification pesticide, national governments
Maximum residue limits for pesticide (Maximum has been formulated according to international food code (CAC) food security standard and national conditions
Residue Limit, MRL) standard, and periodically the pesticide residue in agricultural product is detected, grasp pesticide residual contamination feelings
Condition.It urgently to be resolved is asked however, how according to detecting result to carry out comprehensive quantitative evaluation to pesticide residual contamination degree and be one
Topic.
Single index (factor) evaluation method is mainly currently used by the method for pesticide residual contamination degree evaluation to agricultural product,
Recall rate is obtained by information such as statistics agricultural product middle peasant medicine frequency, detection contents, compares and is surpassed with MRL standard
Mark rate, to reflect severity of the agricultural product by pesticide residual contamination, for somewhere Residual Pesticides in Farm Produce situation into
Row monitoring.There are two o'clock deficiencies for such method: 1. using the pesticide frequency of agricultural product, the exceeded frequency, recall rate, exceeding standard rate
Equal single indexes evaluate pesticide residual contamination degree, and the residual pollution condition of agriculture, other categories in detecting result can only be reflected from a certain angle
Sex factor such as detects toxicity of pesticide information and is ignored, and can not be comprehensively analyzed by pesticide residual contamination situation agricultural product
Evaluation；2. the residual detecting of agriculture can be carried out to a variety of agricultural product in the same sampling time, currently to the pesticide residue in each period
Pollution condition is evaluated, when in the hope of being further discovered that the residual pollution distribution of timing agriculture and situation of change, using by each time
The pesticide frequency of a variety of agricultural product, the exceeded frequency are summarized in section, further obtain recall rate, exceeding standard rate as each
The residual pollution evaluation of the agriculture of period as a result, can not protrude certain agricultural product in timing with the residual detection situation of abnormal agriculture with
In the timing distribution of the analysis residual pollution condition of agriculture and the influence of change procedure, and adopting with abnormal pesticide residual contamination situation
The sample period.
Summary of the invention
The present invention is based on analytic hierarchy process (AHP) (Analytic Hierarchy for deficiency existing for existing evaluation method
Process, AHP) and Information Entropy (Entropy Method), propose that a kind of multiple-factor pesticide residue based on AHP-E model is dirty
Integrated evaluating method is contaminated, more attribute factors in detecting result can be integrated, respectively to various agricultural product and in different time periods
Pesticide residual contamination situation carries out overall merit.
Present invention provide the technical scheme that
A kind of multiple-factor pesticide residual contamination integrated evaluating method based on AHP-E model.First to a variety of data sets into
Row integration and pretreatment, and therefrom choose the evaluation points that can be used in that pesticide residual contamination situation is described with identification；
Secondly analytic hierarchy process (AHP) is used, by combining in pesticide residue detecting result for describing more attributes of pesticide residual contamination situation
Factor pair Residual Pesticides in Farm Produce pollution condition carries out overall merit, obtains the residual pollution index of agriculture of specific agricultural product；Finally
On this basis, overall merit is carried out to the residual pollution condition of agriculture in each period by Information Entropy, obtains special time period
The interior residual pollution index of agriculture.This method can integrate more attribute factors in detecting result, more fully reflection pesticide residual contamination
Situation, and effectively protrude the gap of different agricultural product, different time sections pesticide residual contamination degree.Specific step is as follows:
A. pesticide residue detecting result data set, pesticide attribute data collection, MRL standard data set are integrated, is obtained
It extracts for carrying out the data set of pesticide residual contamination evaluation, and from more attributes in the data set for carrying out pesticide residue
The evaluation points of Comprehensive Assessment of Pollution；And according to the selection situation of evaluation points, statistical data concentrates agricultural product to correspond to each evaluation
The residual detection situation (such as frequency) of the agriculture of the factor obtains the evaluation points value matrix I=(I of agricultural product1 I2 ... Ii
... In)T, wherein IiIt is the residual detection situation vector of agriculture that i-th kind of agricultural product corresponds to each evaluation points, n is agricultural product to be evaluated
Number；
B. the attribute and structure of the evaluation points according to obtained in A carries out hierarchical structure division, Design hierarchy analysis to it
Structural model, and Judgement Matricies, layer-by-layer Calculation Estimation Factor Weight further obtain evaluation points weight vectors W'；
C. the evaluation points weight matrix vector W' according to obtained in B, the residual pollution of the agriculture that agricultural product are calculated by formula 1 refer to
Manifold Xpro:
Xpro=IW'T(formula 1)
Obtain the residual pollution index collection X of agriculture of agricultural productpro=[x1 x2 ... xi ... xj]T, wherein xiFor i-th kind of agricultural production
The residual pollution index of the agriculture of product, n are agricultural product number.
D. it is calculated according to agriculture residual pollution index of the step A-C to agricultural product different in each period；By each time
Sampling agricultural product in section are as evaluation points, using the residual pollution index of the agriculture of each agricultural product as to the residual pollution of agriculture in each period
The evaluation points value for carrying out overall merit, obtains evaluations matrix；
E. the situation of change for using the residual pollution index of agriculture of the more each agricultural product of Information Entropy in each period, determines
It is in the significance level for being evaluated the residual pollution level of agriculture in the period, i.e. Factor Weight；By its with the evaluation in D because
Subvalue is weighted summation, obtains the residual pollution index of agriculture in special time period.
For the above-mentioned multiple-factor pesticide residual contamination integrated evaluating method based on AHP-E model, further, step B
Specific calculating process is as follows:
B1. the destination layer during determining step analysis, rule layer, solution layer.Wherein destination layer is to agricultural product middle peasant
Medicine residual contamination situation carries out overall merit, and rule layer is evaluation points selected in A, and solution layer is a variety of agricultures to be evaluated
Product；
B2. distinguishing hierarchy is carried out to rule layer according to the property of evaluation points, makes evaluation points uniform amount as far as possible
It is distributed in the rule layer of each level of division, and the evaluation points quantity of each level is less than 9, to reduce error；
B3. for the rule layer of each level, using matrix scale (1~9 scaling law) to evaluation points significance level
(judgment matrix scale is shown in Table 1) is compared two-by-two, obtains judgment matrix B=(bij)n*n, wherein bijFor factor i's and factor j
The ratio between the importance of the ratio between importance, corresponding factor j and factor i bji=1/bij；N is evaluation points quantity.
1 judgment matrix scale of table
B4. the judgment matrix B that each rule layer obtains, the judgment matrix of note rule layer m are Bm, pass through formula
BmWm=λ Wm(formula 2)
Calculate the Factor Weight W of the rule layerm, wherein λ is judgment matrix BmMaximum characteristic root, WmFor corresponding to λ just
The feature vector of ruleization, is denoted as Wm=(w1 w2 ... wi ... wn), WmComponent wiFor the weight of the single sequence of respective element.
B5. consistency check is carried out to judgment matrix, specific formula is as follows:
CI=(λ-n)/(n-1) (formula 3)
CR=CI/RI (formula 4)
In formula, CR is consistency ratio；CI is coincident indicator；N is the evaluation points number of the rule layer, and RI is random
Coincident indicator is determined that the table of comparisons of RI and n are as shown in table 2 by n；As CR < 0.1, judgment matrix B meets consistency check,
Then the corresponding characteristic vector W of λ can be used as the weight vector of overall merit, otherwise need to readjust judgment matrix.
2 RI of table and the n table of comparisons
B6. rule layer has multi-layer framework, passes through formula
W=Wn T Wn-1....W1(formula 5)
Calculate whole evaluation points weight matrix W, W in formula1For evaluation points weight vector in rule layer 1, WnFor rule layer
Evaluation points weight vector in n.In order to facilitate the calculating of subsequent step, evaluation points weight matrix W is arranged as evaluation points
Weight vector W'.
For the above-mentioned multiple-factor pesticide residual contamination integrated evaluating method based on AHP-E model, further, step D
According to the calculated result of step A and step C, evaluations matrix is obtained are as follows:
Wherein TiRefer to i-th of period evaluated, xijRefer in sampling time TiThe residual pollution of the agriculture of j-th of agricultural product refers to
Number, m are time hop counts, and n, which refers to, samples agricultural product species number in this time, i.e., for pesticide residual contamination situation in the period
Carry out the agricultural product number of overall merit.
For the above-mentioned multiple-factor pesticide residual contamination integrated evaluating method based on AHP-E model, further, step E
Specific calculating process are as follows:
E1. according to the situation of change of the residual pollution index of agricultural product agriculture each in different time sections, each agricultural product is calculated and are being made
For to the information content contribution degree in the period when evaluation points of pesticide residual contamination situation progress overall merit, calculation formula
Are as follows:
P in formulaijIndicate j-th of factor for period TiCarry out the contribution degree of overall merit.
E2. according to factor contributions degree, coefficient of variation between the factor is calculated.Calculation formula is as follows:
K=1/ln m (formula 8)
dj=1-ej(formula 9)
E in formulajFor the entropy of j-th of evaluation points, meet ej∈ [0,1], djFor the coefficient of variation of j-th of factor.
E3. according to factor difference coefficient, Calculation Estimation Factor Weight, formula are as follows:
Wherein djWhen=0, indicate that j-th of factor is negligible for evaluation procedure influence degree, then its weight wj=
0。
E4. each evaluation points weight vectors W=[w is obtained by step E1-E31 w2 ... wj ... wn", by its with
Evaluations matrix X items weighted sum E=XWT=[E1 E2 ... Ei ... Em]TTo get to sampling time TiThe residual pollution of agriculture
Index Ei。
Compared with prior art, the beneficial effects of the present invention are:
The relevant more attribute factors of the comprehensive Practice for Pesticide Residue in Agricultural Products detecting result of the present invention, construct based on AHP-E model
Multiple-factor pesticide residual contamination integrated evaluating method carries out quantitative synthesis to all kinds of Residual Pesticides in Farm Produce pollution conditions and comments
Valence obtains the residual pollution index of agriculture of specific agricultural product.The residual pollution index of agriculture in each special time period can further be obtained.
Compared with analyzing with existing common single attribute appraisement method pesticide residual contamination situation, it can integrate in detecting result
More attribute factors, more fully reflection Practice for Pesticide Residue in Agricultural Products pollution condition；Agricultural production is detected by measuring in each period
The situation of change of the residual pollution level of product agriculture, the prominent sampling time section with the residual pollution condition of abnormal agriculture, when effectively protruding different
Between section pesticide residual contamination degree difference.
Detailed description of the invention:
Fig. 1 is the method flow block diagram of pesticide residual contamination comprehensive evaluation model AHP-E；
Wherein, (a) is process of data preprocessing；(b) agricultural product are obtained to carry out overall merit using analytic hierarchy process (AHP)
The residual pollution index of agriculture；(c) it to carry out overall merit to the residual pollution condition of agriculture in each period by Information Entropy, obtains specific
The residual pollution index of agriculture in period.
Fig. 2 is evaluation points analytic hierarchy structure figure in pesticide residual contamination comprehensive evaluation model.
Fig. 3 is the residual pollution index of agriculture of each specific agricultural product in the embodiment of the present invention (by taking in May, 2013 as an example).
Fig. 4 is the residual pollution index of agriculture in the city the A 3-6 month in 2013 each special time period in the embodiment of the present invention.
Fig. 5 is conclusion control group in the embodiment of the present invention --- single pollution assessment Practice for Pesticide Residue in Agricultural Products detects situation.
Fig. 6 is conclusion control group in the embodiment of the present invention --- pesticide residue detects situation in the single pollution assessment period.
Specific embodiment
With reference to the accompanying drawing, the present invention, the model of but do not limit the invention in any way are further described by embodiment
It encloses.
The present invention provides a kind of multiple-factor pesticide residual contamination integrated evaluating method based on AHP-E model.AHP-E model
Method framework is integrated and is pre-processed to a variety of data sets first as shown in Figure 1, including three parts, and therefrom choosing can
For pesticide residual contamination situation being described the evaluation points with identification；Secondly analytic hierarchy process (AHP) is used, by combining agriculture
Feelings are polluted to Residual Pesticides in Farm Produce for describing more attribute factors of pesticide residual contamination situation in medicine residual detecting result
Condition carries out overall merit, obtains the residual pollution index of agriculture of specific agricultural product；Finally on this basis, by Information Entropy to it is each when
Between the residual pollution condition of agriculture in section carry out overall merit, obtain the residual pollution index of agriculture in special time period.This method can integrate
More attribute factors in detecting result, more fully reflection pesticide residual contamination situation, and effectively protrude different agricultural product, difference
The gap of period pesticide residual contamination degree.
More attribute factors of the comprehensive Practice for Pesticide Residue in Agricultural Products detecting result of the present invention, building based on AHP-E model mostly because
Sub- pesticide residual contamination integrated evaluating method carries out quantitative overall merit to all kinds of Residual Pesticides in Farm Produce pollution conditions.
The residual Comprehensive Assessment of Pollution of agriculture and the comparing result in each period can further be obtained.It is used with solving single attribute appraisement method
There are problems that one-sidedness during carrying out pesticide residual contamination evaluation.
Method concrete operation step are as follows:
A. the selection of data fusion and evaluation points.
In specific implementation, pesticide residue detecting result data set, pesticide attribute data collection, MRL normal data have been merged
Collection, obtains the data set for carrying out pesticide residual contamination evaluation.Data sample is as shown in table 3:
3 detecting result raw data table (part) of table
Therefrom select toxicity of pesticide relevant to pesticide attribute and the residual class of pollution of agriculture relevant with detected level as commenting
The valence factor.Wherein detection toxicity of pesticide is divided into four classes, respectively less toxic (L), poisoning (M), high poison (H), hypertoxic (V)；Pesticide is residual
It stays the class of pollution to compare MRL standard by the residual detected level of agriculture to obtain, is divided into 3 grades, be 1 grade of pollution (the residual content >=MRL of agriculture), 2 grades
Pollute (the residual content < MRL of 0.1MRL≤agriculture), 3 grades of pollutions (the residual content < 0.1MRL of agriculture).12 evaluation points such as table 4 is obtained
It is shown:
4 evaluation points table of table
Data set is integrated and counted according to evaluation points, obtains evaluation points value table, as shown in table 5.Table
The middle digital representation agricultural product correspond to the frequency under evaluation points classification, that is, detect such pesticide number
5 evaluation points value table of table
B. the weight of evaluation points obtained in step A is calculated.
According to evaluation points Design hierarchy structural model (as shown in Figure 2), the power of layer-by-layer each evaluation points of calculation criterion layer
Weight.According to the judgment matrix B (toxic) of the rule layer 1 of toxicity of pesticide design, and according to the design of pesticide residual contamination grade
The judgment matrix B (grade) of rule layer 2 is respectively
Wherein, V is severe toxicity；H is high poison；M is poisoning；L is low toxicity.The judgement square of m layers of rule layer is calculated by formula (2)
Battle array BmCorresponding characteristic vector Wm, i.e. weight vector.And consistency check is carried out by formula (3) and (4), formula is as follows:
BmWm=λ Wm(formula 2)
CI=(λ-n)/(n-1) (formula 3)
CR=CI/RI (formula 4)
λ is judgment matrix B in formula (2)mMaximum characteristic root, WmFor the normalized feature vector corresponding to λ, it is denoted as Wm
=(w1 w2 ... wi ... wn), WmComponent wiFor the weight of the single sequence of respective element.CR is consistency ratio in formula (3) (4)
Rate, CI are coincident indicator, and n is the evaluation points number of this layer, and RI is random index, determine (pair of RI and n by n
It is shown in Table 2) according to table.
Thus the results are shown in Table 6 for two judgment matrixs obtain single layer Factor Weight and consistency check.It is all satisfied one
Cause property is examined.
6 single layer Factor Weight of table
Whole evaluation points weights are calculated according to formula (5):
W=W2 T W1(formula 5)
W in formula1For evaluation points weight vector in rule layer 1, W2For evaluation points weight vector in rule layer 2, W is layer
Secondary total weight order matrix, it is corresponding with 4 evaluation points table structure of table.Weight vectors W' is obtained after arrangement, i.e. two rule layers are total
The weight of 12 evaluation points is as shown in table 7.
7 evaluation points weight table of table
C. using Factor Weight obtained in step B, by the detection result of its evaluation points corresponding with agricultural product in table 5 into
Row weighted sum obtains the pesticide residual contamination index of each agricultural product.With the data instance in May, each specific agricultural product are obtained
The residual pollution index of agriculture as shown in table 8 (corresponding diagram 3).
The residual pollution index of agriculture (in May, 2013) of the specific agricultural product of table 8
D. each residual pollution index of agricultural product agriculture in each period is calculated by step A-C, the residual pollution of building period agriculture is comprehensive
Close evaluations matrix.
E. the residual pollution index of agriculture in each special time period is calculated.
3 to June whole Practice for Pesticide Residue in Agricultural Products comprehensive evaluation results are substituted into formula (6) to (10), obtain each agricultural production
Product are carrying out the weight in the period in pesticide residual contamination Process of Comprehensive Assessment, as shown in table 9.
K=1/ln m (formula 8)
dj=1-ej(formula 9)
P in formula 6ijIndicate j-th of factor for period TiCarry out the contribution degree of overall merit.The e into formula 8 of formula 7jFor
The entropy of j-th of evaluation points, meets ej∈ [0,1], djFor the coefficient of variation of j-th of factor.In formula 9, if djIt is indicated when=0
J-th of factor is negligible for evaluation procedure influence degree, then its weight wj=0.
The weight of the upper residual Comprehensive Assessment of Pollution factor of agriculture of each period of table 9
The residual pollution index of agriculture of agricultural product in agricultural product weight each in table 9 and different time sections is weighted summation, is obtained
The residual pollution index of agriculture in each special time period, (corresponding diagram 4) as shown in table 10:
The residual pollution index of agriculture in each special time period of table 10
By aforesaid operations, the residual pollution index of agriculture (as shown in table 8 and Fig. 3) of specific agricultural product is finally obtained, and each
The residual pollution index of agriculture in special time period (as shown in table 10 and Fig. 4).
It is had the advantage that show that the method for the present invention is compared with traditional single attribute (factor) evaluation method, in the present embodiment
The evaluation result of single attribute appraisement method is provided for same data set, the evaluation with multi-parameter assessment method of the invention
As a result it compares.Select two kinds of residual recall rate of agriculture, the residual exceeding standard rate of agriculture single pollution assessment methods as control group, the residual inspection of middle peasant
Extracting rate refers in the agricultural product sample of sampling, examines the agricultural product percentage for being found to have pesticide residue phenomenon；The residual exceeding standard rate of agriculture refers to
The agricultural product percentage that persticide residue is more than country's MRL standard is detected in the agricultural product sample of sampling.Each agricultural product pesticide is residual
The single pollution assessment result of detection situation is stayed as shown in figure 5, the single-factor of pesticide residue detection situation is commented in each sampling time section
Valence result is as shown in Figure 6.
Single attribute (factor) evaluation result (Fig. 5, Fig. 6) and Synthesis factors evaluation result (Fig. 3, Fig. 4) are compared.
Fig. 5 carries out ranking to agricultural product according to the residual recall rate of agriculture, and recall rate is followed successively by cucumber, apple, tomato, green pepper, celery from high to low
Dish, leek；In conjunction with the residual exceeding standard rate discovery of agriculture, though cucumber, apple, tomato, the residual recall rate of four kinds of agricultural product agricultures of green pepper are high, not
There are the residual exceeded situation of agriculture, instead the lower celery of the residual recall rate of agriculture, leek exceeding standard rate be more than remaining four kinds of agricultural product, two kinds
Different single index evaluation methods obtain entirely different evaluation result.And the comprehensive more attribute factors of the method for the present invention carry out synthesis and comment
(Fig. 3) each residual pollution index of agricultural product agriculture is from high to low successively as the result is shown for valence are as follows: leek, celery, cucumber, green pepper, tomato, apple
Fruit.In the ranking result, there are the leek of the residual over-standard phenomenon of agriculture, celery and the higher cucumber of the residual recall rate of agriculture are in the top,
The ranking generation greatly variation of height, the leek of highly toxic pesticide, celery is especially detected, this variation can also give data point
Analysis personnel and supervisor are to warn.
Result in Fig. 6 is compared with Fig. 4 result.According to the residual recall rate of agriculture and the residual exceeding standard rate of agriculture to different time sections
The residual pollution condition of agriculture be ranked up, discovery obtain consistent ranking results, i.e., from high to low be May, April, March, June, respectively
Recall rate (exceeding standard rate) difference is smaller between month.Method carries out the residual pollution level ranking results (Fig. 4) of agriculture and list in the present invention
Attribute evaluation result is consistent, but the residual pollution index of agriculture in May is much higher than other months, this is because May, agricultural chemical recall rate was higher,
And part agricultural product are compared compared with other months and there is repeatedly high, highly toxic pesticide detection situation, the i.e. residual detection abnormal conditions of agriculture, need
Supervisor pays special attention to.
Integrated evaluating method of the invention is for commenting the pesticide residual contamination detecting result with more attribute factors
Valence.Include detecting time, detection area, detecting agricultural product, detection pesticide, pesticide detection in pesticide residue detecting result data set
The information of amount is expanded detecting result data set by introducing pesticide attribute data collection and MRL standard data set, is obtained
Toxicity of pesticide and the residual class of pollution of agriculture.It is residual seriously polluted by agriculture to agricultural product comprising can be used in the statistic analysis result
More attribute factors that degree is qualitatively or quantitatively described, such as frequency, detection toxicity of pesticide, the residual class of pollution of agriculture.Pass through
Comprehensive a variety of attributes design AHP-E model based on the means of analytic hierarchy process (AHP) and entropy standard measure comprehensive analysis, to agricultural product,
And each sampling time section is carried out comprehensive, quantitative evaluation by the serious conditions of pesticide residual contamination.In addition, right in the present invention
The method that each sampling time is carried out overall merit by pesticide residual contamination situation, is equally applicable to residual by pesticide to each detection area
Pollution condition is stayed to carry out overall merit.
It should be noted that the purpose for publicizing and implementing example is to be to help to further understand the present invention, but this field
Technical staff is understood that；It is not departing from the present invention and spirit and scope of the appended claims, various substitutions and modifications are all
It is possible.Therefore, the present invention should not be limited to embodiment disclosure of that, and the scope of protection of present invention is wanted with power
Subject to the range for asking book to define.
Claims (6)
1. a kind of multiple-factor pesticide residual contamination integrated evaluating method based on AHP-E model integrates more categories in detecting result
Sex factor obtains different agricultural product, pesticide residual contamination degree in different time periods；It include: that a variety of data sets are located in advance
Reason；Overall merit is carried out using analytic hierarchy process (AHP), obtains the residual pollution index of agriculture of specific agricultural product；By Information Entropy to it is each when
Between the residual pollution of agriculture in section carry out overall merit, obtain the residual pollution index of agriculture in special time period；Specific step is as follows:
A. a variety of data sets are pre-processed: is marked using pesticide residue detecting result data set, pesticide attribute data collection, MRL
Quasi- data set obtains the multiattribute data collection for carrying out pesticide residual contamination evaluation, extracts from the data set for carrying out
The evaluation points of pesticide residual contamination overall merit；Further obtain the evaluation points value matrix I=(I of agricultural product1 I2 ...
Ii ... In)T, wherein IiIt is the residual detection situation vector of agriculture that i-th kind of agricultural product corresponds to each evaluation points, n is agriculture to be evaluated
Product number；
B. the attribute and structure of the evaluation points according to obtained in A carries out hierarchical structure division, Design hierarchy point to evaluation points
Structural model is analysed, and Judgement Matricies, layer-by-layer Calculation Estimation Factor Weight further obtain evaluation points weight vectors
W'；Including operating as follows:
B1. the destination layer during determining step analysis, rule layer, solution layer；Wherein destination layer is used for pesticide in agricultural product
Residual contamination situation carries out overall merit, and rule layer is evaluation points selected in A, and solution layer is a variety of agricultural productions to be evaluated
Product；
B2. according to the property of evaluation points, distinguishing hierarchy is carried out to rule layer, makes the distribution of evaluation points uniform amount as far as possible
In each rule layer；
B3. for each rule layer, evaluation points significance level is compared two-by-two using matrix scale, obtains judging square
Battle array B=(bij)n*n, wherein bijFor the ratio between the importance of factor i and factor j, the importance of corresponding factor j and factor i
The ratio between bji=1/bij；N is evaluation points quantity；
B4. the judgment matrix B that each rule layer obtains, the judgment matrix of note rule layer m are Bm, which is calculated by formula 2
Factor Weight Wm:
BmWm=λ Wm(formula 2)
Wherein λ is judgment matrix BmMaximum characteristic root, WmFor the normalized feature vector corresponding to λ, it is denoted as Wm=(w1 w2
... wi ... wn), WmComponent wiFor the weight of the single sequence of respective element；
B5. by formula 3~4, consistency check is carried out to judgment matrix:
CI=(λ-n)/(n-1) (formula 3)
CR=CI/RI (formula 4)
In formula, CR is consistency ratio；CI is coincident indicator；N is the evaluation points number of the rule layer；RI is random consistent
Property index, is determined by n；As CR < 0.1, judgment matrix B meets consistency check, and the corresponding characteristic vector W of λ can be used as synthesis
The weight vector of evaluation；Otherwise it needs to readjust judgment matrix B；
B6. rule layer has multi-layer framework, calculates whole evaluation points weight matrix W by formula 5:
W=Wn T Wn-1....W1(formula 5)
Calculate whole evaluation points weight matrix W, W in formula1For evaluation points weight vector in rule layer 1, WnFor in rule layer n
Evaluation points weight vector；
For convenient for calculating, evaluation points weight matrix W is converted to evaluation points weight vector W'；
C. by each evaluation corresponding with agricultural product each in pesticide residual contamination detecting result of evaluation points weight obtained in step B because
The lower residual detection situation of agriculture of son is weighted summation, obtains the residual pollution index of agriculture of each agricultural product；
D. the residual pollution index of agriculture to agricultural product different in each period is calculated according to step A-C；It will be in each period
Sampling agricultural product as evaluation points, carried out using the residual pollution index of the agriculture of each agricultural product as to the residual pollution of agriculture in each period
The evaluation points value of overall merit, obtains evaluations matrix；
E. the situation of change of agriculture residual pollution index of the more each agricultural product of Information Entropy in each period, certainty factor are used
Weight；Evaluation points value in Factor Weight and D is weighted summation, obtains the residual pollution index of agriculture in special time period；Tool
Body process are as follows:
E1. according to the situation of change of the residual pollution index of agricultural product agriculture each in different time sections, each agricultural product is calculated by formula 6 and are existed
Information content contribution degree when as the evaluation points for carrying out overall merit to pesticide residual contamination situation in the period:
In formula, PijIndicate j-th of factor for period TiCarry out the contribution degree of overall merit；xijRefer in sampling time TiThe
The residual pollution index of the agriculture of j agricultural product, m are time hop counts；
E2. according to factor contributions degree, pass through coefficient of variation between the calculating factor of formula 7~9:
K=1/ln m (formula 8)
dj=1-ej(formula 9)
In formula, ejFor the entropy of j-th of evaluation points, ej∈[0,1]；djFor the coefficient of variation of j-th of factor；
E3. according to factor difference coefficient, evaluation points weight is calculated by formula 10:
Wherein, work as djWhen=0, j-th of factor ignores for evaluation procedure influence degree, weight wj=0；
E4. each evaluation points weight vectors are obtained by step E1-E3By itself and evaluations matrix
X items weighted sum E=XWT=[E1 E2 ... Ei ... Em]TTo get to sampling time TiThe residual pollution index E of agriculturei；
Through the above steps, the multiple-factor pesticide residual contamination overall merit based on AHP-E model is realized.
2. the multiple-factor pesticide residual contamination integrated evaluating method based on AHP-E model as described in claim 1, characterized in that
In step B2, the evaluation points quantity of each rule layer is less than 9.
3. the multiple-factor pesticide residual contamination integrated evaluating method based on AHP-E model as described in claim 1, characterized in that
In step B3, for each rule layer, matrix scale is specifically carried out using 1~9 scaling law.
4. the multiple-factor pesticide residual contamination integrated evaluating method based on AHP-E model as described in claim 1, characterized in that
Step C is specifically: according to evaluation points weight matrix vector W', the residual pollution index collection of agriculture that agricultural product are calculated by formula 1
Xpro:
Xpro=IW'T(formula 1)
Wherein, the residual pollution index collection X of the agriculture of agricultural productpro=[x1 x2 ... xi ... xj]T, xiFor the agriculture of i-th kind of agricultural product
Residual pollution index, n are agricultural product number.
5. the multiple-factor pesticide residual contamination integrated evaluating method based on AHP-E model as described in claim 1, characterized in that
In step B2, specifically, the first rule layer is designed according to toxicity of pesticide, corresponding judgment matrix is B (toxic)；According to pesticide
Residual contamination grade designs the second rule layer, and corresponding judgment matrix is B (grade)；It respectively indicates are as follows:
Wherein, V is severe toxicity；H is high poison；M is poisoning；L is low toxicity.
6. the multiple-factor pesticide residual contamination integrated evaluating method based on AHP-E model as described in claim 1, characterized in that
The evaluations matrix that step D is obtained is to indicate are as follows:
Wherein, TiRefer to i-th of period evaluated, xijRefer in sampling time TiThe residual pollution index of the agriculture of j-th of agricultural product, m
It is time hop counts, n, which refers to, samples agricultural product species number in this time, i.e., for carrying out to pesticide residual contamination situation in the period
The agricultural product number of overall merit.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112766739A (en) * | 2021-01-22 | 2021-05-07 | 北京工商大学 | BWM-E model-based evaluation method for heavy metal pollution in meat products |
| CN114154857A (en) * | 2021-12-03 | 2022-03-08 | 江苏省农业科学院 | Pesticide residue pollutant tracing system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103577888A (en) * | 2013-09-05 | 2014-02-12 | 西安电子科技大学 | Improved entropy weight AHP and application thereof |
| CN103606031A (en) * | 2013-11-04 | 2014-02-26 | 清华大学 | Evaluation method of kitchen waste feed product safety |
| CN106204154A (en) * | 2016-07-20 | 2016-12-07 | 武汉斗鱼网络科技有限公司 | User based on analytic hierarchy process (AHP) and Information Entropy is worth marking system and method thereof |
| US20160371596A1 (en) * | 2014-03-10 | 2016-12-22 | Nec Corporation | Evaluation system, evaluation method, and computer-readable storage medium |
| CN107679703A (en) * | 2017-09-08 | 2018-02-09 | 天津大学 | A kind of coastal ecological safety evaluation method |
-
2018
- 2018-09-13 CN CN201811068430.1A patent/CN109255724B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103577888A (en) * | 2013-09-05 | 2014-02-12 | 西安电子科技大学 | Improved entropy weight AHP and application thereof |
| CN103606031A (en) * | 2013-11-04 | 2014-02-26 | 清华大学 | Evaluation method of kitchen waste feed product safety |
| US20160371596A1 (en) * | 2014-03-10 | 2016-12-22 | Nec Corporation | Evaluation system, evaluation method, and computer-readable storage medium |
| CN106204154A (en) * | 2016-07-20 | 2016-12-07 | 武汉斗鱼网络科技有限公司 | User based on analytic hierarchy process (AHP) and Information Entropy is worth marking system and method thereof |
| CN107679703A (en) * | 2017-09-08 | 2018-02-09 | 天津大学 | A kind of coastal ecological safety evaluation method |
Non-Patent Citations (1)
| Title |
|---|
| 孙璐 等: "基于模糊熵和AHP的山区公路交通气象安全评价", 《公路交通科技》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112766739A (en) * | 2021-01-22 | 2021-05-07 | 北京工商大学 | BWM-E model-based evaluation method for heavy metal pollution in meat products |
| CN112766739B (en) * | 2021-01-22 | 2023-08-11 | 北京工商大学 | Method for evaluating heavy metal pollution in meat product based on BWM-E model |
| CN114154857A (en) * | 2021-12-03 | 2022-03-08 | 江苏省农业科学院 | Pesticide residue pollutant tracing system |
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