CN106339536B - Comprehensive Evaluation of Water Quality based on water pollution index's method and cloud model - Google Patents

Comprehensive Evaluation of Water Quality based on water pollution index's method and cloud model Download PDF

Info

Publication number
CN106339536B
CN106339536B CN201610694207.2A CN201610694207A CN106339536B CN 106339536 B CN106339536 B CN 106339536B CN 201610694207 A CN201610694207 A CN 201610694207A CN 106339536 B CN106339536 B CN 106339536B
Authority
CN
China
Prior art keywords
cloud
value
index
evaluation
wpi
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Fee Related
Application number
CN201610694207.2A
Other languages
Chinese (zh)
Other versions
CN106339536A (en
Inventor
刘永
招国栋
张志军
彭洁
李国辉
贺桂成
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
University of South China
Original Assignee
University of South China
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by University of South China filed Critical University of South China
Priority to CN201610694207.2A priority Critical patent/CN106339536B/en
Publication of CN106339536A publication Critical patent/CN106339536A/en
Application granted granted Critical
Publication of CN106339536B publication Critical patent/CN106339536B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00Computer-aided design [CAD]
    • G06F30/20Design optimisation, verification or simulation
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02ATECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A20/00Water conservation; Efficient water supply; Efficient water use
    • Y02A20/152Water filtration

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

A kind of Comprehensive Evaluation of Water Quality based on water pollution index's method and cloud model is to improve water pollution index's method using cloud model on the basis of water pollution index's method.Cloud model is combined with the WPI value in water pollution index's method, WPI value evaluation cloud model is obtained by cloud parameter determination method, single-factor WPI value cloud model is obtained by backward cloud generator, WPI value evaluation cloud atlas and single-factor WPI value cloud atlas are generated with normal state cloud generator, intuitively finds out the pollution situation of each single-factor;In conjunction with weight, comprehensive WPI value cloud model is obtained by comprehensive cloud algorithm, and showed with water dust formal intuition, determine Water Quality Evaluation grade, effectively solves the problems, such as not considering whole pollution factors in the contribution in comprehensive water quality assessment and the comparison being difficult to realize between different section in water pollution index's method.

Description

基于水污染指数法和云模型的水质综合评价方法Comprehensive evaluation method of water quality based on water pollution index method and cloud model

技术领域technical field

本发明属于信息处理技术领域,涉及一种基于水污染指数法和云模型的水质综合评价方法。The invention belongs to the technical field of information processing, and relates to a comprehensive water quality evaluation method based on a water pollution index method and a cloud model.

背景技术Background technique

水环境系统是一个集模糊性和随机性于一体的复杂系统,要想保证水质评价结果的合理性,必须综合考虑评价过程中存在的模糊性和随机性。The water environment system is a complex system integrating fuzziness and randomness. In order to ensure the rationality of water quality evaluation results, the fuzziness and randomness in the evaluation process must be comprehensively considered.

现有文献:《中国环境监测》,2013年03期公开了一篇《水污染指数法在河流水质评价中的应用研究》,该篇文章公开了水污染指数法评价思路:Existing literature: "China Environmental Monitoring", 2013 03 published an article "The Application of Water Pollution Index Method in River Water Quality Evaluation", the article disclosed the evaluation idea of water pollution index method:

基于单因子评价法的评价原理,依据水质类别与WPI值(水污染指数值)对应表(见表3),用内插方法计算得出某一断面每个参加水质评价项目的WPI值,取最高WPI值作为该断面的WPI值。Based on the evaluation principle of the single-factor evaluation method, according to the corresponding table of water quality category and WPI value (water pollution index value) (see Table 3), the WPI value of each water quality evaluation project of a certain section is calculated by interpolation method, The highest WPI value is taken as the WPI value of the section.

表3水质类别与WPI值对应表Table 3 Corresponding table of water quality category and WPI value

水质类别Water quality category Ⅰ类Class I Ⅱ类Class II Ⅲ类Class III Ⅳ类Class IV Ⅴ类Class V 劣Ⅴ类Inferior Class V WPI范围WPI range WPI=20WPI=20 20<WPI≤4020<WPI≤40 40<WPI≤6040<WPI≤60 60<WPI≤8060<WPI≤80 80<WPI≤10080<WPI≤100 WPI>100WPI>100

水污染指数法计算步骤如下:The calculation steps of the water pollution index method are as follows:

(1)水污染指数未超过Ⅴ类水限值时指标WPI值计算方法:(1) Calculation method of index WPI value when the water pollution index does not exceed the limit of Class V water:

Cl(i)<C(i)≤Ch(i)C l (i)<C(i)≤C h (i)

式中,C(i)为第i个水质指标的实际监测值,Cl(i)、Ch(i)分别为第i个水质指标所在类别标准的下限值和上限值,WPIl(i)、WPIh(i)分别为第i个水质指标所在类别标准的下限值和上限值所对应的指数值,WPI(i)为第i个水质指标所对应的指数值。In the formula, C(i) is the actual monitoring value of the i-th water quality indicator, C l (i) and C h (i) are the lower and upper limit values of the category standard of the i-th water quality indicator, respectively, WPI l (i), WPI h (i) are the index values corresponding to the lower limit value and upper limit value of the category standard of the i-th water quality indicator, respectively, and WPI(i) is the index value corresponding to the i-th water quality indicator.

此外,当6<pH<9时,In addition, when 6<pH<9,

WPI(pH)=20WPI(pH)=20

(2)超过Ⅴ类水限值时指标WPI值计算方法:(2) Calculation method of indicator WPI value when it exceeds the limit of Class V water:

式中,C5(i)为第i项目水质类别中Ⅴ类标准浓度限值。In the formula, C 5 (i) is the standard concentration limit of Class V in the water quality category of the i project.

此外,当pH<6时,Furthermore, when pH < 6,

WPI(pH)=100+6.67×(6-pH)WPI(pH)=100+6.67×(6-pH)

当pH>9时,When pH>9,

WPI(pH)=100+8.00×(pH-9)WPI(pH)=100+8.00×(pH-9)

(3)综合水质WPI值的确定(3) Determination of comprehensive water quality WPI value

WPI=MAX(WPI(i))WPI=MAX(WPI(i))

与单因子评价法相比,水污染指数法虽延续了单因子评价法以污染最严重的指标作为判断水质类别的思想,但其能够将水质状况进行量化。根据量化结果,不仅能够直观判断水质类别,更能反映水质的时空变化情况。根据水污染指数法与其他4种水质评价方法的应用对比情况得知,水污染指数法能够同时满足水质定量评价、主要污染指标识别、水质类别评价以及劣Ⅴ类水体水质比较的需要,对比结果见表4。Compared with the single-factor evaluation method, although the water pollution index method continues the single-factor evaluation method with the most polluted index as the judgment of the water quality category, it can quantify the water quality status. According to the quantitative results, not only can the water quality category be judged intuitively, but also the temporal and spatial changes of water quality can be reflected. According to the application of the water pollution index method and the other four water quality evaluation methods, it is known that the water pollution index method can meet the needs of quantitative water quality evaluation, identification of main pollution indicators, water quality category evaluation and comparison of water quality inferior to Class V. The comparison results See Table 4.

表4 5种水质评价方法的应用对比Table 4 Application comparison of five water quality evaluation methods

综合来看,现有技术具有以下缺点:Taken together, the prior art has the following disadvantages:

(1)评价过程中对污染最重因子赋以100%权重,未考虑多个不同污染指标对水质影响程度的差异,水质监测信息未充分利用。(1) In the evaluation process, 100% weight is assigned to the most polluted factor, the difference in the degree of influence of different pollution indicators on water quality is not considered, and the water quality monitoring information is not fully utilized.

(2)以污染最重指标的WPI值作为该断面的WPI值,当主要污染指标不同时,不同断面的WPI值可比性显得不是很强。(2) The WPI value of the most polluted index is used as the WPI value of the section. When the main pollution index is different, the comparability of WPI values of different sections is not very strong.

(3)在水质类别评价方面,未考虑水环境中客观存在的模糊性和不确定性,其水质评价结果劣于综合评价方法得到的水质类别。(3) In the evaluation of water quality category, the objective ambiguity and uncertainty in the water environment are not considered, and the water quality evaluation result is inferior to the water quality category obtained by the comprehensive evaluation method.

发明内容SUMMARY OF THE INVENTION

本发明的目的是提供一种基于水污染指数法和云模型的水质综合评价方法,将云模型与水污染指数法中的WPI值相结合,由云参数确定方法得到WPI值评价云模型,由逆向云发生器得到单因子WPI值云模型,运用正态云发生器生成WPI值评价云图和单因子WPI值云图,直观地看出各单因子的污染状况;结合权重,由综合云算法得到综合WPI值云模型,并以云滴形式直观地展现,判定水质综合评价等级,有效解决水污染指数法中未考虑全部污染因子在综合水质评价中的贡献及难以实现不同断面之间的比较的问题。The purpose of the present invention is to provide a comprehensive water quality evaluation method based on the water pollution index method and the cloud model, which combines the cloud model with the WPI value in the water pollution index method, and obtains the WPI value evaluation cloud model by the cloud parameter determination method. The single-factor WPI value cloud model is obtained by the reverse cloud generator, and the WPI value evaluation cloud map and the single-factor WPI value cloud map are generated by the normal cloud generator, and the pollution status of each single factor can be seen intuitively; The WPI value cloud model is visually displayed in the form of cloud droplets to determine the comprehensive water quality evaluation level, which effectively solves the problem that the water pollution index method does not consider the contribution of all pollution factors in the comprehensive water quality evaluation and it is difficult to achieve comparison between different sections. .

本发明所采用的技术方案是,一种基于水污染指数法和云模型的水质综合评价方法,按照以下步骤进行:The technical scheme adopted in the present invention is a comprehensive evaluation method of water quality based on the water pollution index method and cloud model, which is carried out according to the following steps:

步骤1,确定评价的因素集、WPI值集及权重集;Step 1, determine the factor set, WPI value set and weight set for evaluation;

步骤2,建立WPI值评价云模型;Step 2, establish a WPI value evaluation cloud model;

步骤3,得到单因子WPI值云模型;Step 3, obtain a single factor WPI value cloud model;

步骤4,得到综合WPI值云模型;Step 4, obtain the comprehensive WPI value cloud model;

步骤5,确定水质评价等级。Step 5: Determine the water quality evaluation grade.

进一步的,所述步骤1按照以下步骤进行:Further, the step 1 is carried out according to the following steps:

步骤a,选取评价指标,确定因素集;Step a, select the evaluation index, and determine the factor set;

首先进行初始指标选取;然后采用主成分-相关性分析方法进行指标筛选,运用主成分分析删除冗余指标,再运用相关性分析删除重复性指标,既保证了保留指标对评价结果有显著影响,又保证了指标之间信息重叠程度比较低;最终筛选后的评价指标即作为因素集;First select the initial indicators; then use the principal component-correlation analysis method to screen the indicators, use the principal component analysis to delete redundant indicators, and then use the correlation analysis to delete the repetitive indicators, which not only ensures that the retained indicators have a significant impact on the evaluation results, It also ensures that the information overlap between the indicators is relatively low; the final selected evaluation indicators are used as factor sets;

初始指标的选取根据评价对象而定,可通过水质监测报告或是以往已有的评价指标进行选取;The selection of the initial indicators depends on the evaluation object, and can be selected through the water quality monitoring report or the existing evaluation indicators;

主成分分析的基本模型为:The basic model of principal component analysis is:

式中,xi表示第i个指标(i=1,2,…,p);zj表示第j个主成分(j=1,2,…,m);lij表示第j个主成分中第i个指标对应的主成分载荷;p表示指标个数;m表示主成分的个数;In the formula, x i represents the ith index (i=1,2,...,p); z j represents the jth principal component (j=1,2,...,m); l ij represents the jth principal component The principal component load corresponding to the ith index in

主成分分析的步骤如下:The steps of principal component analysis are as follows:

1:计算指标标准化数据的相关系数矩阵R;1: Calculate the correlation coefficient matrix R of the standardized data of the indicators;

式中,rij为第i个指标和第j个指标的相关系数,xki和xkj分别为第k个评价对象第i和j个指标的值,分别为第i和j个指标的平均值;In the formula, r ij is the correlation coefficient between the i-th index and the j-th index, x ki and x kj are the values of the i-th index and the j-th index of the k-th evaluation object, respectively, and are the average values of the i-th and j-th indicators, respectively;

2:计算相关系数矩阵R的特征值λi及特征向量ei(i=1,2,…,p)、方差贡献率ωi及累计贡献率G(m);2: Calculate the eigenvalue λ i of the correlation coefficient matrix R, the eigenvector e i (i=1,2,...,p), the variance contribution rate ω i and the cumulative contribution rate G(m);

λi表示第i个主成分所解释的原始指标数据的总方差,第i个主成分对原始指标数据的方差贡献率ωi为:λ i represents the total variance of the original indicator data explained by the i-th principal component, and the variance contribution rate ω i of the i-th principal component to the original indicator data is:

累计贡献率G(m)为The cumulative contribution rate G(m) is

3:根据特征值或累计贡献率选择主成分,确定主成分个数m;3: Select principal components according to eigenvalues or cumulative contribution rates, and determine the number m of principal components;

主成分选取准则:(1)取特征值λi>1对应的主成分;(2)取累计贡献率G(k)≥85%对应的主成分;Principal component selection criteria: (1) take the principal component corresponding to the eigenvalue λ i >1; (2) take the principal component corresponding to the cumulative contribution rate G(k) ≥ 85%;

4:计算主成分因子载荷lij4: Calculate the principal component factor loading l ij ;

设eij为第i个指标的特征向量ei的第j个分量,因子载荷计算公式为Let e ij be the jth component of the eigenvector e i of the ith index, and the factor loading formula is

5:根据主成分上因子载荷的绝对值筛选指标;5: Screen the indicators according to the absolute value of the factor loadings on the principal components;

因子载荷绝对值越大说明指标对评价结果的影响越明显,越应当保留;因子载荷绝对值越小则说明指标对评价结果的影响越弱,越应当剔除;The larger the absolute value of the factor load, the more obvious the influence of the index on the evaluation results, and the more it should be retained; the smaller the absolute value of the factor load, the weaker the influence of the index on the evaluation results, the more it should be eliminated;

相关性分析的具体步骤如下:The specific steps of correlation analysis are as follows:

1:计算各个评价指标之间的相关系数;1: Calculate the correlation coefficient between each evaluation index;

设rij为第i个指标和第j个指标的相关系数,xki和xkj分别为第k个评价对象第i和j个指标的值,分别为第i和j个指标的平均值;Let r ij be the correlation coefficient between the i-th index and the j-th index, x ki and x kj be the values of the i-th index and the j-th index of the k-th evaluation object, respectively, and are the average values of the i-th and j-th indicators, respectively;

2:给定一个临界值P(0<P<1),判定指标的去留;2: Given a critical value P (0<P<1), determine the removal and retention of indicators;

当|rij|<P时,则同时保留两个评价指标;当|rij|>P时,若两个指标之间的影响含义相近,则可以根据重要性的判断删除其中的一个评价指标,若指标影响含义差别较大,则两个指标都保留;When |r ij |<P, the two evaluation indicators are kept at the same time; when |r ij |>P, if the impact between the two indicators is similar, one of the evaluation indicators can be deleted according to the judgment of importance , if the impact and meaning of the indicators are quite different, both indicators are retained;

步骤b,确定评语集,即WPI值集;Step b, determine the comment set, that is, the WPI value set;

结合已有的水质评价标准,确定水质评价的评语集,并给出对应评语集中各评价等级的WPI值范围,确定WPI值集;Combined with the existing water quality evaluation standards, determine the water quality evaluation comment set, and give the WPI value range of each evaluation level corresponding to the comment set, and determine the WPI value set;

步骤c,确定权重集;Step c, determine the weight set;

基于AHP-CRITIC的组合赋权方法确定权重,运用AHP法确定主观权重,运用CRITIC法确定客观权重,运用组合权重确定公式得到评价指标的组合权重,确定权重集;The combination weighting method based on AHP-CRITIC determines the weight, uses the AHP method to determine the subjective weight, uses the CRITIC method to determine the objective weight, uses the combination weight determination formula to obtain the combined weight of the evaluation indicators, and determines the weight set;

AHP法确定权重的具体步骤如下:The specific steps of the AHP method to determine the weight are as follows:

1:确定水质评价指标;1: Determine the water quality evaluation indicators;

2:构造判断矩阵;2: Construct judgment matrix;

判断矩阵中的元素值是各元素相对重要性判断的定量化指标,一般采用1~9尺度法;判断矩阵中各因素的数值是通过人对各因素相对重要程度作出判断,然后根据一定的比率标度将判断定量化而获得的;The element value in the judgment matrix is a quantitative index for judging the relative importance of each element, and the 1-9 scale method is generally used; the value of each factor in the judgment matrix is judged by people on the relative importance of each factor, and then according to a certain ratio The scale is obtained by quantifying judgments;

3:计算判断矩阵的最大特征值及其特征向量,确定权重向量;3: Calculate the maximum eigenvalue of the judgment matrix and its eigenvector, and determine the weight vector;

目前,对于特征值及特征向量的计算方法有很多,常用的有方根法、和法、特征根法、最小二乘法等;At present, there are many calculation methods for eigenvalues and eigenvectors, such as square root method, sum method, eigenroot method, least square method, etc.;

4:一致性检验;4: Consistency check;

为了保证结论的合理性,需要对判断矩阵进行一致性检验,步骤如下:In order to ensure the rationality of the conclusion, the judgment matrix needs to be checked for consistency. The steps are as follows:

(1)计算一致性检验指标CI;(1) Calculate the consistency test index CI;

设λmax判断矩阵最大特征值,则Let λ max judge the maximum eigenvalue of the matrix, then

(2)根据表1查找相应的平均随机一致性指标RI,其中n表示判断矩阵的阶数;(2) Find the corresponding average random consistency index RI according to Table 1, wherein n represents the order of the judgment matrix;

表1平均随机一致性指标Table 1 Average Stochastic Consistency Index

nn 11 22 33 44 55 66 77 88 99 1010 1111 RIRI 00 00 0.580.58 0.900.90 1.121.12 1.241.24 1.321.32 1.411.41 1.451.45 1.491.49 1.511.51

(3)计算随机一致性比率CR,计算公式为(3) Calculate the random consistency ratio CR, the calculation formula is

当CR<0.10时,即认为判断矩阵具有满意的一致性,否则就需要调整判断矩阵,使之具有满意的一致性;When CR<0.10, it is considered that the judgment matrix has satisfactory consistency, otherwise, it is necessary to adjust the judgment matrix to make it have satisfactory consistency;

CRITIC法确定权重的具体步骤如下:The specific steps for determining the weight by the CRITIC method are as follows:

1:计算指标样本的标准差;1: Calculate the standard deviation of the indicator sample;

令σj为第j个指标的标准差,标准差的计算公式为:Let σ j be the standard deviation of the jth indicator, and the formula for calculating the standard deviation is:

其中,N为样本个数,xi为样本值,为样本均值;Among them, N is the number of samples, x i is the sample value, is the sample mean;

步骤2:计算 Step 2: Calculate

步骤3:计算指标所包含的信息量;Step 3: Calculate the amount of information contained in the indicator;

令Cj为第j个指标所包含的的信息量,其计算公式为:Let C j be the amount of information contained in the jth index, and its calculation formula is:

Cj越大,则第j个指标所包含的信息量越大,该指标的相对重要性也就越大;The larger the C j , the greater the amount of information contained in the jth index, and the greater the relative importance of the index;

4:计算指标的客观权重;4: Calculate the objective weight of the indicator;

令ωj为第j个指标的客观权重,其计算公式为:Let ω j be the objective weight of the jth index, and its calculation formula is:

组合权重确定公式如下:The formula for determining the combined weight is as follows:

式中,Wj为第j个指标的组合权重,为利用AHP法得到的第j个指标的主观权重,为利用CRITIC法得到的第j个指标的客观权重。In the formula, W j is the combined weight of the j-th indicator, is the subjective weight of the jth index obtained by the AHP method, is the objective weight of the jth index obtained by the CRITIC method.

进一步的,所述步骤2按照以下步骤进行:Further, the step 2 is carried out according to the following steps:

步骤a,云参数的确定;Step a, the determination of cloud parameters;

云模型用期望Ex、熵En和超熵He这3个数字特征来整体表征一个定性概念;The cloud model uses the three numerical features of expectation Ex, entropy En and super entropy He to represent a qualitative concept as a whole;

期望Ex:云滴在论域空间中分布的期望,是概念在论域空间中的中心值,是最能够代表定性概念的点,其隶属度为1,即100%地隶属于该定性概念;Expectation Ex: The expectation of the distribution of cloud droplets in the universe of discourse space is the central value of the concept in the universe of discourse space, and it is the point that can best represent the qualitative concept, and its membership degree is 1, that is, it belongs to the qualitative concept 100%;

熵En:是定性概念不确定性的度量,是由定性概念的随机性和模糊性共同决定的;En是定性概念随机性的度量,反映了能代表这个定性概念的云滴的离散程度;同时,En又体现了定性概念亦此亦彼性的裕度,反映了论域空间中可被定性概念接受的云滴的取值范围,是对定性概念模糊性的度量;En越大,定性概念所接受的云滴的取值范围也就越大,定性概念也就越模糊;用同一个数宇特征来反映随机性和模糊性,也必然反映了它们之间的关联性;Entropy En: It is a measure of the uncertainty of a qualitative concept, which is jointly determined by the randomness and ambiguity of the qualitative concept; En is a measure of the randomness of the qualitative concept, reflecting the discrete degree of cloud droplets that can represent the qualitative concept; , En also reflects the margin of the qualitative concept and the other, and reflects the value range of cloud droplets in the universe space that can be accepted by the qualitative concept, which is a measure of the ambiguity of the qualitative concept; The larger the accepted value range of cloud droplets, the more ambiguous the qualitative concept; the use of the same digital feature to reflect randomness and ambiguity must also reflect the correlation between them;

超熵He:是熵的不确定性的度量,即熵的熵,由熵的随机性和模糊性共同决定,反映了在论域空间代表该语言值的所有点的不确定度的凝聚性,它的大小可以表示云的离散度以及厚度;Hyperentropy He: is a measure of the uncertainty of entropy, that is, the entropy of entropy, which is jointly determined by the randomness and ambiguity of entropy, and reflects the cohesion of the uncertainty of all points representing the language value in the universe of discourse space, Its size can represent the dispersion and thickness of the cloud;

在计算评语云的数字特征时,采用双边约束[Cmin,Cmax]来确定云参数,其计算公式如下:When calculating the digital features of the comment cloud, the bilateral constraints [C min , C max ] are used to determine the cloud parameters, and the calculation formula is as follows:

Ex=(Cmin+Cmax)/2Ex=(C min +C max )/2

En=(Cmax-Cmin)/6En=(C max -C min )/6

He=kHe=k

其中,k是根据评语本身情况设定的常数,体现了评语的模糊程度;[Cmin,Cmax]表示评语集中各评价等级对应的WPI值范围;Among them, k is a constant set according to the comment itself, which reflects the ambiguity of the comment; [C min , C max ] represents the WPI value range corresponding to each evaluation level in the comment set;

当只有单边约束的评语时,云参数的确定需要结合数据的上下限,确定其缺省边界的参数,再参照上式进行计算;在上述公式的基础上,得到的云参数确定方法如下:When there are only unilaterally constrained comments, the determination of cloud parameters needs to combine the upper and lower limits of the data to determine the parameters of its default boundaries, and then calculate with reference to the above formula; on the basis of the above formula, the obtained cloud parameter determination method is as follows:

1)评价区间1(0,a)对应的云参数:1) Cloud parameters corresponding to evaluation interval 1 (0, a):

Ex1=0Ex1=0

En1=a/3En1=a/3

He1=kHe1=k

2)评价区间i(Cmin,Cmax)(0<i<n)对应的云参数:2) Cloud parameters corresponding to the evaluation interval i (C min , C max ) (0<i<n):

Exi=(Cmin+Cmax)/2Ex i =(C min +C max )/2

Eni=(Cmax-Cmin)/6En i =(C max -C min )/6

Hei=kHe i = k

3)评价区间n(m,+∞)对应的云参数:3) The cloud parameters corresponding to the evaluation interval n(m, +∞):

Ex=Cmin′+CmaxEx= Cmin '+ Cmax '

En=Cmin′/3En= Cmin '/3

He=kHe=k

Cmin′、Cmax′分别为评价区间n-1的上下限值;C min ' and C max ' are the upper and lower limits of the evaluation interval n-1, respectively;

以具有六个评价区间(0,a]、(a,b]、(b,c]、(c,d]、(d,e]、[e,+∞)的评价指标为例,其评价云模型参数的确定如表2所示;Taking the evaluation index with six evaluation intervals (0, a], (a, b], (b, c], (c, d], (d, e], [e, +∞)) as an example, its evaluation The parameters of the cloud model are determined as shown in Table 2;

表2云模型参数(Ex,En,He)的确定方法Table 2 Determination method of cloud model parameters (Ex, En, He)

CloudCloud ExEx EnEn HeHe C1C1 Ex1=0Ex1=0 En1=a/3En1=a/3 kk C2C2 Ex2=(a+b)/2Ex2=(a+b)/2 En2=(b-a)/6En2=(b-a)/6 kk C3C3 Ex3=(b+c)/2Ex3=(b+c)/2 En3=(c-b)/6En3=(c-b)/6 kk C4C4 Ex4=(c+d)/2Ex4=(c+d)/2 En4=(d-c)/6En4=(d-c)/6 kk C5C5 Ex5=(d+e)/2Ex5=(d+e)/2 En5=(e-d)/6En5=(e-d)/6 kk C6C6 Ex6=d+eEx6=d+e En6=d/3En6=d/3 kk

步骤b,云模型的生成;Step b, the generation of cloud model;

根据已确定的WPI值评价云模型参数Ex、En、He,运用正态云发生器,生成对应的WPI值评价云图;Evaluate the cloud model parameters Ex, En, He according to the determined WPI value, and use the normal cloud generator to generate the corresponding WPI value evaluation cloud map;

具体过程如下:The specific process is as follows:

输入:云模型数字特征(Ex、En、He)及生成的云滴个数n;Input: cloud model digital features (Ex, En, He) and the number of cloud droplets generated n;

输出:n个云滴定量数据xi及其对应定性概念的确定度yi(i=1,2,…,n);Output: n cloud titration data x i and the certainty y i of the corresponding qualitative concept (i=1,2,...,n);

算法步骤:Algorithm steps:

1:生成以En为期望值,He2为方差的一个正态随机数Enn;1: Generate a normal random number Enn with En as the expected value and He 2 as the variance;

2:生成以Ex为期望值,Enn2为方差的一个正态随机数xi,即xi为定性概念A在其相应定量论域上的一次具体量化实现,称为云滴定量数据;2: Generate a normal random number xi with Ex as the expected value and Enn 2 as the variance, that is, xi is a specific quantitative realization of the qualitative concept A in its corresponding quantitative universe, which is called cloud titration data;

3:计算 3: Calculation

定义yi为xi属于定性概念A的确定度,(xi,yi)为云滴;Define y i as the degree of certainty that xi belongs to qualitative concept A, and ( xi , y i ) as cloud droplets;

4:重复以上步骤,直到生成n个云滴(xi,yi)(i=n)为止。4: Repeat the above steps until n cloud droplets (x i , y i ) (i=n) are generated.

进一步的,所述步骤3按照以下步骤进行:Further, the step 3 is carried out according to the following steps:

步骤a,基于水污染指数法,计算各水质评价因子对应的WPI值;Step a, based on the water pollution index method, calculate the WPI value corresponding to each water quality evaluation factor;

水污染指数法基于单因子评价法的评价原理,依据水质类别与WPI值对应表(见表3),用内插方法计算得出某一断面每个参加水质评价项目的WPI值,取最高WPI值作为该断面的WPI值;The water pollution index method is based on the evaluation principle of the single-factor evaluation method. According to the corresponding table of water quality category and WPI value (see Table 3), the WPI value of each water quality evaluation project in a certain section is calculated by interpolation method, and the highest WPI value is obtained. value as the WPI value of the section;

表3水质类别与WPI值对应表Table 3 Corresponding table of water quality category and WPI value

水质类别Water quality category Ⅰ类Class I Ⅱ类Class II Ⅲ类Class III Ⅳ类Class IV Ⅴ类Class V 劣Ⅴ类Inferior Class V WPI范围WPI range WPI=20WPI=20 20<WPI≤4020<WPI≤40 40<WPI≤6040<WPI≤60 60<WPI≤8060<WPI≤80 80<WPI≤10080<WPI≤100 WPI>100WPI>100

各水质评价因子WPI值计算公式如下:The formula for calculating the WPI value of each water quality evaluation factor is as follows:

1)水污染指数未超过Ⅴ类水限值时指标WPI值计算方法:1) The calculation method of the WPI value of the indicator when the water pollution index does not exceed the limit of Class V water:

Cl(i)<C(i)≤Ch(i)C l (i)<C(i)≤C h (i)

式中,C(i)为第i个水质指标的实际监测值,Cl(i)、Ch(i)分别为第i个水质指标所在类别标准的下限值和上限值,WPIl(i)、WPIh(i)分别为第i个水质指标所在类别标准的下限值和上限值所对应的指数值,WPI(i)为第i个水质指标所对应的指数值;In the formula, C(i) is the actual monitoring value of the i-th water quality indicator, C l (i) and C h (i) are the lower and upper limit values of the category standard of the i-th water quality indicator, respectively, WPI l (i), WPI h (i) are the index values corresponding to the lower limit value and the upper limit value of the category standard of the i-th water quality indicator, respectively, and WPI(i) is the index value corresponding to the i-th water quality indicator;

此外,当6<pH<9时,In addition, when 6<pH<9,

WPI(pH)=20WPI(pH)=20

WIP(pH)即为指标pH值所对应的WPI值;WIP (pH) is the WPI value corresponding to the indicator pH value;

2)超过Ⅴ类水限值时指标WPI值计算方法:2) Calculation method of indicator WPI value when it exceeds the limit of Class V water:

式中,C5(i)为第i项目水质类别中Ⅴ类标准浓度限值;In the formula, C 5 (i) is the standard concentration limit of Class V in the water quality category of the i project;

此外,当pH<6时,Furthermore, when pH < 6,

WPI(pH)=100+6.67×(6-pH)WPI(pH)=100+6.67×(6-pH)

当pH>9时,When pH>9,

WPI(pH)=100+8.00×(pH-9)WPI(pH)=100+8.00×(pH-9)

步骤b,生成单因子WPI值云模型;Step b, generating a single-factor WPI value cloud model;

利用基于X信息的逆向云算法计算云模型的参数,仅仅是利用云滴X(xi)的定量数值来还原云的三个参数,不需要确定度Y(yi)的值,过程如下:Using the inverse cloud algorithm based on X information to calculate the parameters of the cloud model, just use the quantitative values of cloud droplets X(x i ) to restore the three parameters of the cloud, without the need for the value of the degree of certainty Y(y i ), the process is as follows:

输入:样本点xi(i=1,2,…,n);Input: sample point x i (i=1,2,...,n);

输出:这n个云滴对应定性概念的云模型数字特征(Ex、En、He);Output: The cloud model numerical features (Ex, En, He) of the n cloud droplets corresponding to qualitative concepts;

算法步骤:Algorithm steps:

1:根据xi计算这组数据的样本均值一阶样本绝对中心距样本方差 1: Calculate the sample mean of this set of data based on xi First-order sample absolute center distance sample variance

2:计算期望 2: Calculate expectations

3:计算熵 3: Calculate entropy

4:计算超熵 4: Calculate super entropy

算法中的云滴xi指的是在实际应用中所收集的水质评价指标实际监测数据对应的WPI值。The cloud drop xi in the algorithm refers to the WPI value corresponding to the actual monitoring data of water quality evaluation indicators collected in practical applications.

进一步的,所述步骤4按照以下步骤进行:运用综合云算法,结合权重,综合各单因子WPI值云模型生成综合WPI值云模型,综合云算法公式为:Further, the step 4 is carried out according to the following steps: using the comprehensive cloud algorithm, combining the weights, synthesizing each single factor WPI value cloud model to generate a comprehensive WPI value cloud model, and the comprehensive cloud algorithm formula is:

进一步的,所述步骤5按照以下步骤进行:利用正态云发生器,生成WPI值评价云图及综合WPI值云图,并进行比较,最终确定水质综合评价等级。Further, the step 5 is performed according to the following steps: using a normal cloud generator to generate a WPI value evaluation cloud map and a comprehensive WPI value cloud map, and compare them to finally determine the comprehensive water quality evaluation level.

本发明的有益效果是对评价语言进行模糊处理,对具有随机性和模糊性特点的指标进行定量化,通过云模型的3个数字特征来表征水质评价因子的模糊性和随机性,不仅可以比较直观的看出各个单因子的污染状态,并以云滴形式直观地展现渗水的综合水质等级,使评价结果更符合实际,能有效弥补了水污染指数法所存在的缺点和不足,为水质评价方法的研究提供更优化的模型或新的思路。The beneficial effects of the invention are that the evaluation language is fuzzified, the indexes with randomness and fuzziness are quantified, and the fuzziness and randomness of the water quality evaluation factors are represented by the three digital features of the cloud model, which can not only compare Intuitively see the pollution status of each single factor, and visually display the comprehensive water quality grade of seepage in the form of cloud droplets, so that the evaluation results are more realistic, which can effectively make up for the shortcomings and deficiencies of the water pollution index method. The study of methods provides more optimized models or new ideas.

附图说明Description of drawings

为了更清楚地说明本发明实施例或现有技术中的技术方案,下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本发明的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative efforts.

图1是本发明中水质综合评价方法的流程图。Fig. 1 is a flow chart of the comprehensive evaluation method of water quality in the present invention.

图2是WPI值评价云图。Figure 2 is a cloud map of WPI value evaluation.

图3是单因子WPI值云图。Figure 3 is a single factor WPI value cloud map.

其中图3a是pH的WPI值云模型;图3b是U的WPI值云模型;图3c是Ra的WPI值云模型;图3d是∑β的WPI值云模型;图3e是NH4-N的WPI值云模型;图3f是Mn的WPI值云模型;图3g是F离子的WPI值云模型;图3h是Zn的WPI值云模型。Figure 3a is the cloud model of the WPI value of pH; Figure 3b is the cloud model of the WPI value of U; Figure 3c is the cloud model of the WPI value of Ra; Figure 3d is the cloud model of the WPI value of Σβ; Figure 3e is the cloud model of NH 4 -N WPI value cloud model; Figure 3f is the WPI value cloud model of Mn; Figure 3g is the WPI value cloud model of F ions; Figure 3h is the WPI value cloud model of Zn.

图4是综合WPI值云图。Figure 4 is a comprehensive WPI value cloud map.

其中图4a是坝段A渗水综合WPI值云图;图4b是坝段B渗水综合WPI值云图;图4c是坝段C渗水综合WPI值云图;图4d是坝段D渗水综合WPI值云图。Figure 4a is the cloud map of the comprehensive WPI value of seepage in dam section A; Figure 4b is the cloud map of the comprehensive WPI value of water seepage in dam section B; Figure 4c is the cloud map of the comprehensive WPI value of water seepage in dam section C; Figure 4d is the cloud map of the comprehensive WPI value of water seepage in dam section D.

具体实施方式Detailed ways

下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

一种基于水污染指数法和云模型的水质综合评价方法,具体按照以下步骤进行:A comprehensive water quality evaluation method based on water pollution index method and cloud model, which is specifically carried out according to the following steps:

以退役铀尾矿库渗水水质评价为例,在给出的退役铀尾矿库渗水水质评价指标体系及指标权重的基础上,以云模型改进水污染指数法来进行水质综合评价,具体步骤如图1所示。Taking the evaluation of seepage water quality of decommissioned uranium tailings pond as an example, on the basis of the given index system and index weight of seepage water quality of decommissioned uranium tailings pond, the cloud model is used to improve the water pollution index method to conduct comprehensive evaluation of water quality. The specific steps are as follows: Figure 1.

1)因素集、评语集及权重集的确定1) Determination of factor set, comment set and weight set

根据现有退役铀尾矿库环境监测报告,确定退役铀尾矿库渗水水质评价初选指标有12个:pH、U、Ra、∑α、∑β、230Th、210Po、210Pb、NH4-N、Mn、F-、Zn,基于主成分-相关性分析法进行指标筛选,确定最终的渗水水质评价指标为pH、U、Ra、∑β、NH4-N、Mn、F-、Zn。According to the existing environmental monitoring reports of decommissioned uranium tailings ponds, it is determined that there are 12 primary indicators for evaluating the seepage water quality of decommissioned uranium tailings ponds: pH, U, Ra, ∑α, ∑β, 230 Th, 210 Po, 210 Pb, NH 4 -N, Mn, F-, Zn, based on the principal component-correlation analysis method for index screening, determine the final seepage water quality evaluation index is pH, U, Ra, ∑β, NH 4 -N, Mn, F - , Zn.

基于AHP-CRITIC组合赋权法,运用AHP法确定主观权重为(0.0605,0.2822,0.1760,0.1760,0.049,0.0605,0.0386,0.1032),运用CRITIC法确定客观权重为(0.1088,0.0783,0.1049,0.1808,0.0998,0.1731,0.0910,0.1632),将主观权重和客观权重代入组合权重确定公式,确定其权重集为(0.0534,0.1793,0.1498,0.2582,0.049,0.1450,0.0285,0.1369)。Based on the AHP-CRITIC combination weighting method, the subjective weight is determined by the AHP method as (0.0605, 0.2822, 0.1760, 0.1760, 0.049, 0.0605, 0.0386, 0.1032), and the objective weight is determined by the CRITIC method as (0.1088, 0.0783, 0.1049, 0.1808, 0.0998, 0.1731, 0.0910, 0.1632), substitute the subjective weight and the objective weight into the combined weight determination formula, and determine its weight set as (0.0534, 0.1793, 0.1498, 0.2582, 0.049, 0.1450, 0.0285, 0.1369).

结合GB8978《污水综合排放标准》,在已有的水质污染指数分级标准(见表5)的基础上,将渗水水质划分为六个等级,即评语集为{“清洁”、“尚清洁”、“轻度污染”、“中度污染”、“重度污染”、“严重污染”},分别记为Ⅰ、Ⅱ、Ⅲ、Ⅳ、Ⅴ、Ⅵ;根据已有的水污染指数法中的WPI值评价范围(见表3),确定WPI值集为{[0,20]、(20,40]、(40,60]、(60,80]、(80,100]、(100,+∞)};最终确定的退役铀尾矿库渗水水质评价标准见表6。Combined with GB8978 "Comprehensive Sewage Discharge Standard", on the basis of the existing water quality pollution index classification standard (see Table 5), the seepage water quality is divided into six grades, that is, the comment set is {"clean", "still clean", "Mild pollution", "moderate pollution", "severe pollution", and "severe pollution"}, respectively recorded as Ⅰ, Ⅱ, Ⅲ, Ⅳ, Ⅴ, Ⅵ; according to the WPI value in the existing water pollution index method Evaluation range (see Table 3), determine the WPI value set as {[0, 20], (20, 40], (40, 60], (60, 80], (80, 100], (100, +∞)) }; See Table 6 for the finalized evaluation criteria for seepage water quality of decommissioned uranium tailings ponds.

表5已有的水质污染指数分级标准Table 5 The existing water pollution index classification standards

水质指数water quality index 级别level 分级依据Grading Basis <0.2<0.2 清洁clean 多项项目未检出Multiple items not checked out 0.2~0.40.2~0.4 尚清洁still clean 检出值均在标准值内The detected values are all within the standard value 0.4~0.70.4~0.7 轻度污染light pollution 1项检出值超过标准1 detected value exceeds the standard 0.7~1.00.7~1.0 中度污染Moderately polluted 2项检出值超过标准2 detected values exceeded the standard 1.0~2.01.0~2.0 重度污染heavy pollution 全部或大部分项目超标All or most items exceed the standard >2.0>2.0 严重污染heavily polluted 全部或大部分项目超标>1倍All or most items exceed the standard > 1 times

表6退役铀尾矿库渗水水质分级标准Table 6 Classification standard of seepage water quality of decommissioned uranium tailings pond

2)WPI值评价云模型的建立2) Establishment of WPI value evaluation cloud model

(1)云参数的确定(1) Determination of cloud parameters

在计算评语云的数字特征时,采用双边约束[Cmin,Cmax]来确定云参数,其计算公式如下:When calculating the digital features of the comment cloud, the bilateral constraints [C min , C max ] are used to determine the cloud parameters, and the calculation formula is as follows:

Ex=(Cmin+Cmax)/2Ex=(C min +C max )/2

En=(Cmax-Cmin)/6En=(C max -C min )/6

He=kHe=k

其中,k是根据评语本身情况设定的常数,体现了评语的模糊程度;[Cmin,Cmax]表示评语集中各评价等级对应的WPI值范围;Among them, k is a constant set according to the comment itself, which reflects the ambiguity of the comment; [C min , C max ] represents the WPI value range corresponding to each evaluation level in the comment set;

当只有单边约束的评语时,云参数的确定需要结合数据的上下限,确定其缺省边界的参数,再参照上式进行计算;在上述公式的基础上,得到的云参数确定方法如下:When there are only unilaterally constrained comments, the determination of cloud parameters needs to combine the upper and lower limits of the data to determine the parameters of its default boundaries, and then calculate with reference to the above formula; on the basis of the above formula, the obtained cloud parameter determination method is as follows:

1)评价区间1(0,a)对应的云参数:1) Cloud parameters corresponding to evaluation interval 1 (0, a):

Ex1=0Ex1=0

En1=a/3En1=a/3

He1=kHe1=k

2)评价区间i(Cmin,Cmax)(0<i<n)对应的云参数:2) Cloud parameters corresponding to the evaluation interval i (C min , C max ) (0<i<n):

Exi=(Cmin+Cmax)/2Ex i =(C min +C max )/2

Eni=(Cmax-Cmin)/6En i =(C max -C min )/6

Hei=kHe i = k

3)评价区间n(m,+∞)对应的云参数:3) The cloud parameters corresponding to the evaluation interval n(m, +∞):

Ex=Cmin′+CmaxEx= Cmin '+ Cmax '

En=Cmin′/3En= Cmin '/3

He=kHe=k

Cmin′、Cmax′分别为评价区间n-1的上下限值。C min ' and C max ' are the upper and lower limits of the evaluation interval n-1, respectively.

以具有六个评价区间(0,a]、(a,b]、(b,c]、(c,d]、(d,e]、[e,+∞)的评价指标为例,其评价云模型参数的确定如表2所示。Taking the evaluation index with six evaluation intervals (0, a], (a, b], (b, c], (c, d], (d, e], [e, +∞)) as an example, its evaluation The parameters of the cloud model are determined as shown in Table 2.

表2云模型参数(Ex,En,He)的确定方法Table 2 Determination method of cloud model parameters (Ex, En, He)

根据表5中的水质分级标准,采用表2中的方法确定WPI值评价云模型的参数,分别为(0,6.67,1)、(30,3.33,1)、(50,3.33,1)、(70,3.33,1)、(90,3.33,1)、(180,26.67,1)。According to the water quality classification standard in Table 5, the method in Table 2 is used to determine the parameters of the WPI value evaluation cloud model, which are (0, 6.67, 1), (30, 3.33, 1), (50, 3.33, 1), (70,3.33,1), (90,3.33,1), (180,26.67,1).

(2)WPI值评价云模型的生成(2) Generation of WPI value evaluation cloud model

根据已确定的WPI值评价云模型参数Ex、En、He,运用正态云发生器,生成对应的WPI值评价云图。过程如下:The cloud model parameters Ex, En, and He are evaluated according to the determined WPI value, and the corresponding WPI value evaluation cloud map is generated by using the normal cloud generator. The process is as follows:

输入:云模型数字特征(Ex、En、He)及生成的云滴个数n。Input: cloud model digital features (Ex, En, He) and the number n of cloud droplets generated.

输出:n个云滴定量数据xi及其对应定性概念的确定度yi(i=1,2,…,n)。Output: n cloud titration data x i and their corresponding qualitative concepts y i (i=1,2,...,n).

算法步骤:Algorithm steps:

1:生成以En为期望值,He2为方差的一个正态随机数Enn;1: Generate a normal random number Enn with En as the expected value and He 2 as the variance;

2:生成以Ex为期望值,Enn2为方差的一个正态随机数xi,即xi为定性概念A在其相应定量论域上的一次具体量化实现,称为云滴定量数据;2: Generate a normal random number xi with Ex as the expected value and Enn 2 as the variance, that is, xi is a specific quantitative realization of the qualitative concept A in its corresponding quantitative universe, which is called cloud titration data;

3:计算 3: Calculation

4:定义yi为xi属于定性概念A的确定度,(xi,yi)为云滴;4: Define y i as the degree of certainty that x i belongs to the qualitative concept A, and (x i , y i ) are cloud droplets;

5:重复以上步骤,直到生成n个云滴为止。5: Repeat the above steps until n cloud droplets are generated.

由正态云发生器生成WPI值评价云图见图2。The WPI value evaluation cloud map generated by the normal cloud generator is shown in Figure 2.

3)获取单因子WPI值云模型3) Obtain the single factor WPI value cloud model

(1)各水质评价因子对应的WPI值的计算(1) Calculation of WPI value corresponding to each water quality evaluation factor

根据各水质评价因子的实测数据,运用WPI值计算公式,得到各水质评价因子对应的WPI值。According to the measured data of each water quality evaluation factor, using the WPI value calculation formula, the WPI value corresponding to each water quality evaluation factor is obtained.

根据2012年(1-12月)四个坝段渗水的指标监测数据,运用WPI值计算公式得到各坝段各指标对应的WPI值,表7给出了坝段A渗水指标的WPI值。According to the monitoring data of the water seepage indicators of the four dam sections in 2012 (January to December), the WPI value corresponding to each index of each dam section was obtained by using the WPI value calculation formula.

表7 2012年坝段A渗水指标的WPI值Table 7 WPI value of seepage index of dam section A in 2012

(2)单因子WPI值云模型生成(2) Single-factor WPI value cloud model generation

利用基于X信息的逆向云新算法计算云模型的参数,仅仅是利用云滴X的定量数值来还原云的三个参数,不需要确定度Y的值,过程如下:Using the new reverse cloud algorithm based on X information to calculate the parameters of the cloud model, just use the quantitative value of the cloud droplet X to restore the three parameters of the cloud, without the value of the degree of certainty Y, the process is as follows:

输入:样本点xi(i=1,2,…,n)。Input: sample points x i (i=1,2,...,n).

输出:这n个云滴对应定性概念的云模型数字特征(Ex、En、He)。Output: These n cloud droplets correspond to the cloud model numerical features (Ex, En, He) of qualitative concepts.

算法步骤:Algorithm steps:

1:根据xi计算这组数据的样本均值一阶样本绝对中心距样本方差 1: Calculate the sample mean of this set of data based on x i First-order sample absolute center distance sample variance

2:计算期望 2: Calculate expectations

3:计算熵 3: Calculate entropy

4:计算超熵 4: Calculate super entropy

算法中的云滴xi指的是在实际应用中所收集的水质评价指标实际监测数据对应的WPI值。The cloud drop xi in the algorithm refers to the WPI value corresponding to the actual monitoring data of the water quality evaluation index collected in practical applications.

根据渗水指标的WPI值,按照基于X信息的逆向云新算法计算出8项指标对应的单因子WPI值云模型的参数,如表8所示。According to the WPI value of the water seepage index, and according to the new reverse cloud algorithm based on X information, the parameters of the single-factor WPI value cloud model corresponding to the eight indicators are calculated, as shown in Table 8.

表8单因子WPI值云模型Table 8 One-factor WPI value cloud model

以坝段A为例,根据所得的单因子WPI值云模型,运用正态云发生器,生成8个单因子所对应的WPI值云图,并与WPI值评价云图进行比较,如图3所示。图中,灰色为单因子WPI值云模型,纯黑色为WPI值评价云模型。Taking dam section A as an example, according to the obtained single-factor WPI value cloud model, the normal cloud generator is used to generate the WPI value cloud map corresponding to 8 single factors, and compare with the WPI value evaluation cloud map, as shown in Figure 3 . In the figure, gray is the single-factor WPI value cloud model, and pure black is the WPI value evaluation cloud model.

4)综合WPI值云模型的确定4) Determination of comprehensive WPI value cloud model

综合云算法公式为:The comprehensive cloud algorithm formula is:

根据已确定的单因子WPI值云模型,结合渗水指标的权重,代入综合云运算公式,得到各坝段渗水的综合WPI值云模型,见表9,并利用正态云发生器生成各坝段渗水的综合WPI值云图,如图4所示,红色为各坝段渗水的综合WPI值云图,蓝色为WPI值评价云图。According to the determined single-factor WPI value cloud model, combined with the weight of the seepage index, and substituted into the comprehensive cloud calculation formula, the comprehensive WPI value cloud model of the seepage of each dam section is obtained, as shown in Table 9, and the normal cloud generator is used to generate each dam section. The comprehensive WPI value cloud map of seepage is shown in Figure 4. The red is the comprehensive WPI value cloud map of each dam section, and the blue is the WPI value evaluation cloud map.

表9综合WPI值云模型Table 9 Comprehensive WPI value cloud model

坝段Dam section 综合WPI值云模型Comprehensive WPI Value Cloud Model 坝段ADam Section A (62.6947,5.5986,4.2850)(62.6947, 5.5986, 4.2850) 坝段BDam section B (130.6272,19.0690,14.8482)(130.6272, 19.0690, 14.8482) 坝段CDam section C (69.1420,7.2551,1.0598)(69.1420,7.2551,1.0598) 坝段DDam section D (151.1792,21.3879,0.8707)(151.1792, 21.3879, 0.8707)

5)综合评价结果及分析5) Comprehensive evaluation results and analysis

通过综合WPI值云图与WPI值评价云图的比较,确定各坝段渗水的水质评价等级结果见表10。Through the comparison of the comprehensive WPI value cloud map and the WPI value evaluation cloud map, the water quality evaluation results of the seepage water in each dam section are determined in Table 10.

表10综合评价结果Table 10 Comprehensive evaluation results

坝段Dam section 综合WPI值云模型Comprehensive WPI Value Cloud Model 评价结果Evaluation results 主要污染物质major pollutants 坝段ADam Section A (62.6947,5.5986,4.2850)(62.6947, 5.5986, 4.2850) Mn(119)、pH(108)Mn(119), pH(108) 坝段BDam section B (130.6272,19.0690,14.8482)(130.6272, 19.0690, 14.8482) VI Mn(229)、pH(110)Mn(229), pH(110) 坝段CDam section C (69.1420,7.2551,1.0598)(69.1420,7.2551,1.0598) NH<sub>4</sub>-N(158)、Mn(112)NH<sub>4</sub>-N(158), Mn(112) 坝段DDam section D (151.1792,21.3879,0.8707)(151.1792, 21.3879, 0.8707) VI Mn(248)、pH(102)、NH<sub>4</sub>-N(81)Mn(248), pH(102), NH<sub>4</sub>-N(81)

结果分析:Result analysis:

(1)单因子污染等级:从图3中可以直观的看出坝段A渗水水质评价中的单因子污染等级,其中pH和Mn的污染等级为“严重污染”,U、Ra、∑β、F离子、Zn的污染等级为“清洁”,NH4-N的污染等级为“尚清洁”,所以可以确定坝段A渗水中主要的污染指标为pH和Mn。同时,利用正态云来表现单因子污染等级,可以进行同等级的不同污染指标污染程度之间的比较,坝段A渗水中指标pH、Mn的污染等级均为“严重污染”,比较其WPI值期望及云滴离散程度,Mn明显高于pH,即可得出,pH的污染程度低于Mn。(1) Single-factor pollution level: From Figure 3, we can intuitively see the single-factor pollution level in the evaluation of seepage water quality of dam section A, where the pollution levels of pH and Mn are "severe pollution", U, Ra, ∑β, The pollution level of F ions and Zn is "clean", and the pollution level of NH 4 -N is "still clean", so it can be determined that the main pollution indicators in the seepage water of dam section A are pH and Mn. At the same time, using the normal cloud to represent the single-factor pollution level, it is possible to compare the pollution levels of different pollution indicators of the same level. It can be concluded that the pollution degree of pH is lower than that of Mn.

(2)水质综合评价等级:从表9可以看出,坝段A渗水和坝段C渗水的水质等级为“中度污染”,坝段B渗水和坝段D渗水的水质等级为“严重污染”。从图4可以直观的看出,坝段A渗水和坝段C渗水的综合WPI值云模型均位于“轻度污染”和“中度污染”两朵云之间,坝段C渗水的综合WPI值期望基本完全接近“中度污染”的WPI值评价期望,明显高于坝段A渗水的综合WPI值云模型;坝段B渗水和坝段D渗水的综合WPI值云模型均位于“重度污染”和“严重污染”两朵云之间,并且更加靠近“严重污染”,坝段B渗水的综合WPI值期望比坝段D渗水的综合WPI值期望低,但坝段B渗水的WPI值云滴离散程度远远高于坝段D渗水的WPI值云滴离散程度。综合比较得出:坝段B渗水的水质污染程度最高,其次是坝段D渗水、坝段C渗水,坝段A渗水的水质污染程度最低。(2) Comprehensive evaluation grade of water quality: It can be seen from Table 9 that the water quality grade of seepage in dam section A and dam section C is "moderately polluted", and the water quality grade of seepage in dam section B and dam section D is "severely polluted" ". It can be seen intuitively from Fig. 4 that the comprehensive WPI value cloud models of water seepage in dam section A and dam section C are both located between the two clouds of "light pollution" and "moderate pollution", and the comprehensive WPI value of seepage in dam section C is located between the two clouds. The value expectation is basically close to the WPI value evaluation expectation of "moderate pollution", which is significantly higher than the comprehensive WPI value cloud model of seepage in dam section A; the comprehensive WPI value cloud model of seepage in dam section B and dam section D are both located in "severe pollution". " and "severely polluted", and closer to "severely polluted", the expected comprehensive WPI value of seepage in dam section B is lower than the expected comprehensive WPI value of seepage in dam section D, but the WPI value cloud of seepage in dam section B The dispersion degree of droplets is much higher than the dispersion degree of cloud droplets in the WPI value of seepage in dam section D. The comprehensive comparison shows that the water pollution degree of seepage water in dam section B is the highest, followed by the water seepage in dam section D and the water seepage in dam section C, and the water seepage degree in dam section A is the lowest.

以上所述仅为本发明的较佳实施例而已,并非用于限定本发明的保护范围。凡在本发明的精神和原则之内所作的任何修改、等同替换、改进等,均包含在本发明的保护范围内。The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims (5)

1.一种基于水污染指数法和云模型的水质综合评价方法,其特征在于,按照以下步骤进行:1. a comprehensive evaluation method for water quality based on water pollution index method and cloud model, is characterized in that, carry out according to the following steps: 步骤1,确定评价的因素集、WPI值集及权重集;Step 1, determine the factor set, WPI value set and weight set for evaluation; 步骤2,建立WPI值评价云模型;Step 2, establish a WPI value evaluation cloud model; 步骤3,得到单因子WPI值云模型;Step 3, obtain a single factor WPI value cloud model; 步骤4,得到综合WPI值云模型;Step 4, obtain the comprehensive WPI value cloud model; 步骤5,确定水质评价等级;Step 5, determine the water quality evaluation grade; 所述步骤1按照以下步骤进行:Said step 1 is carried out according to the following steps: 步骤a,选取评价指标,确定因素集;Step a, select the evaluation index, and determine the factor set; 首先进行初始指标选取;然后采用主成分-相关性分析方法进行指标筛选,运用主成分分析删除冗余指标,再运用相关性分析删除重复性指标,最终筛选后的评价指标即作为因素集;First select the initial index; then use the principal component-correlation analysis method to screen the index, use the principal component analysis to delete the redundant index, and then use the correlation analysis to delete the repetitive index, and the final selected evaluation index is used as the factor set; 初始指标的选取根据评价对象而定,通过水质监测报告或是以往已有的评价指标进行选取;The selection of the initial indicators is determined according to the evaluation object, and is selected through the water quality monitoring report or the existing evaluation indicators; 主成分分析的基本模型为:The basic model of principal component analysis is: 式中,xi表示第i个指标(i=1,2,…,p);zj表示第j个主成分(j=1,2,…,m);lij表示第j个主成分中第i个指标对应的主成分载荷;p表示指标个数;m表示主成分的个数;In the formula, x i represents the ith index (i=1,2,...,p); z j represents the jth principal component (j=1,2,...,m); l ij represents the jth principal component The principal component load corresponding to the ith index in 主成分分析的步骤如下:The steps of principal component analysis are as follows: 1:计算指标标准化数据的相关系数矩阵R;1: Calculate the correlation coefficient matrix R of the standardized data of the indicators; 式中,rij为第i个指标和第j个指标的相关系数,xki和xkj分别为第k个评价对象第i和j个指标的值,分别为第i和j个指标的平均值;In the formula, r ij is the correlation coefficient between the i-th index and the j-th index, x ki and x kj are the values of the i-th index and the j-th index of the k-th evaluation object, respectively, and are the average values of the i-th and j-th indicators, respectively; 2:计算相关系数矩阵R的特征值λi及特征向量ei(i=1,2,…,p)、方差贡献率ωi及累计贡献率G(m);2: Calculate the eigenvalue λ i of the correlation coefficient matrix R, the eigenvector e i (i=1,2,...,p), the variance contribution rate ω i and the cumulative contribution rate G(m); λi表示第i个主成分所解释的原始指标数据的总方差,第i个主成分对原始指标数据的方差贡献率ωi为:λ i represents the total variance of the original indicator data explained by the i-th principal component, and the variance contribution rate ω i of the i-th principal component to the original indicator data is: 累计贡献率G(m)为The cumulative contribution rate G(m) is 3:根据特征值或累计贡献率选择主成分,确定主成分个数m;3: Select principal components according to eigenvalues or cumulative contribution rates, and determine the number m of principal components; 主成分选取准则:(1)取特征值λi>1对应的主成分;(2)取累计贡献率G(k)≥85%对应的主成分;Principal component selection criteria: (1) take the principal component corresponding to the eigenvalue λ i >1; (2) take the principal component corresponding to the cumulative contribution rate G(k) ≥ 85%; 4:计算主成分因子载荷lij4: Calculate the principal component factor loading l ij ; 设eij为第i个指标的特征向量ei的第j个分量,因子载荷计算公式为Let e ij be the jth component of the eigenvector e i of the ith index, and the factor loading formula is 5:根据主成分上因子载荷的绝对值筛选指标;5: Screen the indicators according to the absolute value of the factor loadings on the principal components; 因子载荷绝对值越大说明指标对评价结果的影响越明显,越应当保留;因子载荷绝对值越小则说明指标对评价结果的影响越弱,越应当剔除;The larger the absolute value of the factor load, the more obvious the influence of the index on the evaluation result, and the more it should be retained; the smaller the absolute value of the factor load, the weaker the influence of the index on the evaluation result, and the more it should be eliminated; 相关性分析的具体步骤如下:The specific steps of correlation analysis are as follows: 1:计算各个评价指标之间的相关系数;1: Calculate the correlation coefficient between each evaluation index; 设rij为第i个指标和第j个指标的相关系数,xki和xkj分别为第k个评价对象第i和j个指标的值,分别为第i和j个指标的平均值;Let r ij be the correlation coefficient between the i-th index and the j-th index, x ki and x kj be the values of the i-th index and the j-th index of the k-th evaluation object, respectively, and are the average values of the i-th and j-th indicators, respectively; 2:给定一个临界值P(0<P<1),判定指标的去留;2: Given a critical value P (0<P<1), determine the removal and retention of indicators; 当|rij|<P时,则同时保留两个评价指标;当|rij|>P时,若两个指标之间的影响含义相近,则可以根据重要性的判断删除其中的一个评价指标,若指标影响含义差别较大,则两个指标都保留;When |r ij |<P, the two evaluation indicators are kept at the same time; when |r ij |>P, if the impact between the two indicators is similar, one of the evaluation indicators can be deleted according to the judgment of importance , if the impact and meaning of the indicators are quite different, both indicators are retained; 步骤b,确定评语集,即WPI值集;Step b, determine the comment set, that is, the WPI value set; 结合已有的水质评价标准,确定水质评价的评语集,并给出对应评语集中各评价等级的WPI值范围,确定WPI值集;Combined with the existing water quality evaluation standards, determine the water quality evaluation comment set, and give the WPI value range of each evaluation level corresponding to the comment set, and determine the WPI value set; 步骤c,确定权重集;Step c, determine the weight set; 基于AHP-CRITIC的组合赋权方法确定权重,运用AHP法确定主观权重,运用CRITIC法确定客观权重,运用组合权重确定公式得到评价指标的组合权重,确定权重集;The combination weighting method based on AHP-CRITIC determines the weight, uses the AHP method to determine the subjective weight, uses the CRITIC method to determine the objective weight, uses the combination weight determination formula to obtain the combined weight of the evaluation indicators, and determines the weight set; 所述AHP法确定主观权重的具体步骤如下:The specific steps of determining the subjective weight by the AHP method are as follows: 1:确定水质评价指标;1: Determine the water quality evaluation indicators; 2:构造判断矩阵;2: Construct judgment matrix; 判断矩阵中的元素值是各元素相对重要性判断的定量化指标,采用1~9尺度法;判断矩阵中各因素的数值是通过人对各因素相对重要程度作出判断,然后根据比率标度将判断定量化获得;The element value in the judgment matrix is a quantitative index for judging the relative importance of each element, and the 1-9 scale method is used; the value of each factor in the judgment matrix is judged by people on the relative importance of each factor, and then according to the ratio scale. Judgment and quantification; 3:计算判断矩阵的最大特征值及其特征向量,确定权重向量;3: Calculate the maximum eigenvalue of the judgment matrix and its eigenvector, and determine the weight vector; 4:一致性检验;4: Consistency check; 对判断矩阵进行一致性检验,步骤如下:To check the consistency of the judgment matrix, the steps are as follows: (1)计算一致性检验指标CI;(1) Calculate the consistency test index CI; 设λmax判断矩阵最大特征值,则Let λ max judge the maximum eigenvalue of the matrix, then (2)根据表1查找相应的平均随机一致性指标RI,其中n表示判断矩阵的阶数;(2) Find the corresponding average random consistency index RI according to Table 1, wherein n represents the order of the judgment matrix; 表1平均随机一致性指标Table 1 Average Stochastic Consistency Index nn 11 22 33 44 55 66 77 88 99 1010 1111 RIRI 00 00 0.580.58 0.900.90 1.121.12 1.241.24 1.321.32 1.411.41 1.451.45 1.491.49 1.511.51
(3)计算随机一致性比率CR,计算公式为(3) Calculate the random consistency ratio CR, the calculation formula is 当CR<0.10时,即认为判断矩阵具有满意的一致性,否则就需要调整判断矩阵,使之具有满意的一致性;When CR<0.10, it is considered that the judgment matrix has satisfactory consistency, otherwise, it is necessary to adjust the judgment matrix to make it have satisfactory consistency; CRITIC法确定权重的具体步骤如下:The specific steps for determining the weight by the CRITIC method are as follows: 1:计算指标样本的标准差;1: Calculate the standard deviation of the indicator sample; 令σj为第j个指标的标准差,标准差的计算公式为:Let σ j be the standard deviation of the jth indicator, and the formula for calculating the standard deviation is: 其中,N为样本个数,xi为样本值,为样本均值;Among them, N is the number of samples, x i is the sample value, is the sample mean; 步骤2:计算 Step 2: Calculate 步骤3:计算指标所包含的信息量;Step 3: Calculate the amount of information contained in the indicator; 令Cj为第j个指标所包含的的信息量,其计算公式为:Let C j be the amount of information contained in the jth index, and its calculation formula is: Cj越大,则第j个指标所包含的信息量越大,该指标的相对重要性也就越大;The larger the C j , the greater the amount of information contained in the jth index, and the greater the relative importance of the index; 4:计算指标的客观权重;4: Calculate the objective weight of the indicator; 令ωj为第j个指标的客观权重,其计算公式为:Let ω j be the objective weight of the jth index, and its calculation formula is: 组合权重确定公式如下:The formula for determining the combined weight is as follows: 式中,Wj为第j个指标的组合权重,为利用AHP法得到的第j个指标的主观权重,为利用CRITIC法得到的第j个指标的客观权重。In the formula, W j is the combined weight of the j-th indicator, is the subjective weight of the jth index obtained by the AHP method, is the objective weight of the jth index obtained by the CRITIC method.
2.根据权利要求1所述的一种基于水污染指数法和云模型的水质综合评价方法,其特征在于,所述步骤2按照以下步骤进行:2. a kind of water quality comprehensive evaluation method based on water pollution index method and cloud model according to claim 1, is characterized in that, described step 2 is carried out according to the following steps: 步骤a,云参数的确定;Step a, the determination of cloud parameters; 在计算评语云的数字特征时,采用双边约束[Cmin,Cmax]来确定云参数,其计算公式如下:When calculating the digital features of the comment cloud, the bilateral constraints [C min , C max ] are used to determine the cloud parameters, and the calculation formula is as follows: Ex=(Cmin+Cmax)/2Ex=(C min +C max )/2 En=(Cmax-Cmin)/6En=(C max -C min )/6 He=kHe=k 其中,in, 期望Ex:云滴在论域空间中分布的期望,是概念在论域空间中的中心值,是最能够代表定性概念的点,其隶属度为1,即100%地隶属于该定性概念;Expectation Ex: The expectation of the distribution of cloud droplets in the universe of discourse space is the central value of the concept in the universe of discourse space, and it is the point that can best represent the qualitative concept, and its membership degree is 1, that is, it belongs to the qualitative concept 100%; 熵En:是定性概念不确定性的度量,是由定性概念的随机性和模糊性共同决定的;En是定性概念随机性的度量,反映了能代表这个定性概念的云滴的离散程度;同时,En又体现了定性概念亦此亦彼性的裕度,反映了论域空间中可被定性概念接受的云滴的取值范围,是对定性概念模糊性的度量;En越大,定性概念所接受的云滴的取值范围也就越大,定性概念也就越模糊;用同一个数宇特征来反映随机性和模糊性,也必然反映了它们之间的关联性;Entropy En: It is a measure of the uncertainty of a qualitative concept, which is jointly determined by the randomness and ambiguity of the qualitative concept; En is a measure of the randomness of the qualitative concept, reflecting the discrete degree of cloud droplets that can represent the qualitative concept; , En also reflects the margin of the qualitative concept and the other, and reflects the value range of cloud droplets in the universe space that can be accepted by the qualitative concept, which is a measure of the ambiguity of the qualitative concept; The larger the accepted value range of cloud droplets, the more ambiguous the qualitative concept; the use of the same digital feature to reflect randomness and ambiguity must also reflect the correlation between them; 超熵He:是熵的不确定性的度量,即熵的熵,由熵的随机性和模糊性共同决定,反映了在论域空间代表特定语言值的所有点的不确定度的凝聚性,它的大小可以表示云的离散度以及厚度;Hyperentropy He: is a measure of the uncertainty of entropy, namely the entropy of entropy, which is jointly determined by the randomness and ambiguity of entropy, and reflects the cohesion of the uncertainty of all points representing a specific language value in the universe of discourse space, Its size can represent the dispersion and thickness of the cloud; k是根据评语本身情况设定的常数,体现了评语的模糊程度;[Cmin,Cmax]表示评语集中各评价等级对应的WPI值范围;k is a constant set according to the comment itself, which reflects the ambiguity of the comment; [C min , C max ] represents the WPI value range corresponding to each evaluation level in the comment set; 当只有单边约束的评语时,云参数的确定需要结合数据的上下限,确定其缺省边界的参数,再参照上式进行计算;在上述公式的基础上,得到的云参数确定方法如下:When there are only unilaterally constrained comments, the determination of cloud parameters needs to combine the upper and lower limits of the data to determine the parameters of its default boundaries, and then calculate with reference to the above formula; on the basis of the above formula, the obtained cloud parameter determination method is as follows: 1)评价区间1(0,a)对应的云参数:1) Cloud parameters corresponding to evaluation interval 1 (0, a): Ex1=0Ex1=0 En1=a/3En1=a/3 He1=kHe1=k 2)评价区间i(Cmin,Cmax)(0<i<n)对应的云参数:2) Cloud parameters corresponding to the evaluation interval i (C min , C max ) (0<i<n): Exi=(Cmin+Cmax)/2Ex i =(C min +C max )/2 Eni=(Cmax-Cmin)/6En i =(C max -C min )/6 Hei=kHe i = k 3)评价区间n(m,+∞)对应的云参数:3) The cloud parameters corresponding to the evaluation interval n(m, +∞): Ex=Cmin′+CmaxEx= Cmin '+ Cmax ' En=Cmin′/3En= Cmin '/3 He=kHe=k Cmin′、Cmax′分别为评价区间n-1的上下限值;C min ' and C max ' are the upper and lower limits of the evaluation interval n-1, respectively; 步骤b,云模型的生成;Step b, the generation of cloud model; 根据已确定的WPI值评价云模型参数Ex、En、He,运用正态云发生器,生成对应的WPI值评价云图;Evaluate the cloud model parameters Ex, En, He according to the determined WPI value, and use the normal cloud generator to generate the corresponding WPI value evaluation cloud map; 具体操作过程如下:The specific operation process is as follows: 输入:云模型数字特征(Ex、En、He)及生成的云滴个数n;Input: cloud model digital features (Ex, En, He) and the number of cloud droplets generated n; 输出:n个云滴定量数据xi及其对应定性概念的确定度yi(i=1,2,…,n);Output: n cloud titration data x i and the certainty y i of the corresponding qualitative concept (i=1,2,...,n); 具体算法步骤为:The specific algorithm steps are: 1:生成以En为期望值,He2为方差的一个正态随机数Enn;1: Generate a normal random number Enn with En as the expected value and He 2 as the variance; 2:生成以Ex为期望值,Enn2为方差的一个正态随机数xi,即xi为定性概念A在其相应定量论域上的一次具体量化实现,称为云滴定量数据;2: Generate a normal random number xi with Ex as the expected value and Enn 2 as the variance, that is, xi is a specific quantitative realization of the qualitative concept A in its corresponding quantitative universe, which is called cloud titration data; 3:计算 3: Calculation 定义yi为xi属于定性概念A的确定度,(xi,yi)为云滴;Define y i as the degree of certainty that xi belongs to qualitative concept A, and ( xi , y i ) as cloud droplets; 4:重复以上步骤,直到生成n个云滴(xi,yi)(i=n)为止。4: Repeat the above steps until n cloud droplets (x i , y i ) (i=n) are generated. 3.根据权利要求1所述的一种基于水污染指数法和云模型的水质综合评价方法,其特征在于,所述步骤3按照以下步骤进行:3. a kind of water quality comprehensive evaluation method based on water pollution index method and cloud model according to claim 1, is characterized in that, described step 3 is carried out according to the following steps: 步骤a,基于水污染指数法,计算各水质评价因子对应的WPI值;Step a, based on the water pollution index method, calculate the WPI value corresponding to each water quality evaluation factor; 水污染指数法基于单因子评价法的评价原理,依据水质类别与WPI值对应表,用内插方法计算得出某一断面每个参加水质评价项目的WPI值,取最高WPI值作为该断面的WPI值;The water pollution index method is based on the evaluation principle of the single-factor evaluation method, and according to the corresponding table of water quality categories and WPI values, the WPI value of each water quality evaluation project of a certain section is calculated by interpolation method, and the highest WPI value is taken as the section's WPI value. WPI value; 各水质评价因子WPI值计算公式如下:The formula for calculating the WPI value of each water quality evaluation factor is as follows: 1)水污染指数未超过Ⅴ类水限值时指标WPI值计算方法:1) The calculation method of the WPI value of the indicator when the water pollution index does not exceed the limit of Class V water: Cl(i)<C(i)≤Ch(i)C l (i)<C(i)≤C h (i) 式中,C(i)为第i个水质指标的实际监测值,Cl(i)、Ch(i)分别为第i个水质指标所在类别标准的下限值和上限值,WPIl(i)、WPIh(i)分别为第i个水质指标所在类别标准的下限值和上限值所对应的指数值,WPI(i)为第i个水质指标所对应的指数值;In the formula, C(i) is the actual monitoring value of the i-th water quality indicator, C l (i) and C h (i) are the lower and upper limit values of the category standard of the i-th water quality indicator, respectively, WPI l (i), WPI h (i) are the index values corresponding to the lower limit value and the upper limit value of the category standard of the i-th water quality indicator, respectively, and WPI(i) is the index value corresponding to the i-th water quality indicator; 此外,当6<pH<9时,In addition, when 6<pH<9, WPI(pH)=20;WPI(pH)=20; WIP(pH)即为指标pH值所对应的WPI值;WIP (pH) is the WPI value corresponding to the indicator pH value; 2)超过Ⅴ类水限值时指标WPI值计算方法:2) Calculation method of indicator WPI value when it exceeds the limit of Class V water: 式中,C5(i)为第i项目水质类别中Ⅴ类标准浓度限值;In the formula, C 5 (i) is the standard concentration limit of Class V in the water quality category of the i project; 此外,当pH<6时,Furthermore, when pH < 6, WPI(pH)=100+6.67×(6-pH)WPI(pH)=100+6.67×(6-pH) 当pH>9时,When pH>9, WPI(pH)=100+8.00×(pH-9);WPI(pH)=100+8.00×(pH-9); 步骤b,生成单因子WPI值云模型;Step b, generating a single-factor WPI value cloud model; 利用基于X信息的逆向云算法计算云模型的参数,仅仅是利用云滴X(xi)的定量数值来还原云的三个参数,不需要确定度Y(yi)的值,操作过程如下:Using the inverse cloud algorithm based on X information to calculate the parameters of the cloud model, just use the quantitative value of the cloud droplet X( xi ) to restore the three parameters of the cloud, without the value of the degree of certainty Y( yi ), the operation process is as follows : 输入:样本点xi(i=1,2,…,n);Input: sample point x i (i=1,2,...,n); 输出:这n个云滴对应定性概念的云模型数字特征(Ex、En、He);Output: The cloud model numerical features (Ex, En, He) of the n cloud droplets corresponding to qualitative concepts; 算法步骤为:The algorithm steps are: 1:根据xi计算这组数据的样本均值一阶样本绝对中心距样本方差 1: Calculate the sample mean of this set of data based on xi First-order sample absolute center distance sample variance 2:计算期望 2: Calculate expectations 3:计算熵 3: Calculate entropy 4:计算超熵 4: Calculate super entropy 云滴xi指的是在实际应用中所收集的水质评价指标实际监测数据对应的WPI值。Cloud drop xi refers to the WPI value corresponding to the actual monitoring data of water quality evaluation indicators collected in practical applications. 4.根据权利要求1所述的一种基于水污染指数法和云模型的水质综合评价方法,其特征在于,所述步骤4按照以下步骤进行:运用综合云算法,结合权重,综合各单因子WPI值云模型生成综合WPI值云模型,综合云模型的公式为:4. a kind of water quality comprehensive evaluation method based on water pollution index method and cloud model according to claim 1, is characterized in that, described step 4 is carried out according to the following steps: use comprehensive cloud algorithm, combine weight, synthesize each single factor The WPI value cloud model generates a comprehensive WPI value cloud model. The formula of the comprehensive cloud model is: 5.根据权利要求1所述的一种基于水污染指数法和云模型的水质综合评价方法,其特征在于,所述步骤5按照以下步骤进行:利用正态云发生器,生成WPI值评价云图及综合WPI值云图,并进行比较,最终确定水质综合评价等级。5. a kind of water quality comprehensive evaluation method based on water pollution index method and cloud model according to claim 1, is characterized in that, described step 5 is carried out according to the following steps: utilize normal cloud generator, generate WPI value evaluation cloud map And comprehensive WPI value cloud map, and compare, and finally determine the water quality comprehensive evaluation grade.
CN201610694207.2A 2016-08-19 2016-08-19 Comprehensive Evaluation of Water Quality based on water pollution index's method and cloud model Expired - Fee Related CN106339536B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201610694207.2A CN106339536B (en) 2016-08-19 2016-08-19 Comprehensive Evaluation of Water Quality based on water pollution index's method and cloud model

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201610694207.2A CN106339536B (en) 2016-08-19 2016-08-19 Comprehensive Evaluation of Water Quality based on water pollution index's method and cloud model

Publications (2)

Publication Number Publication Date
CN106339536A CN106339536A (en) 2017-01-18
CN106339536B true CN106339536B (en) 2019-04-16

Family

ID=57825200

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201610694207.2A Expired - Fee Related CN106339536B (en) 2016-08-19 2016-08-19 Comprehensive Evaluation of Water Quality based on water pollution index's method and cloud model

Country Status (1)

Country Link
CN (1) CN106339536B (en)

Families Citing this family (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107967534B (en) * 2017-11-20 2021-06-04 华北电力大学 A method for improving prediction accuracy by constructing a sample library of typical operating conditions of operating data
CN108470234A (en) * 2018-02-02 2018-08-31 中国海洋大学 A kind of comprehensive quality evaluation method
CN108536911B (en) * 2018-03-12 2020-12-25 中国南方电网有限责任公司超高压输电公司检修试验中心 Converter transformer state evaluation method based on center distance and sample characteristics
CN108665004B (en) * 2018-05-15 2020-09-08 安徽理工大学 Improved water quality evaluation method of Nemerow pollution index based on principal component analysis
CN108898275B (en) * 2018-05-30 2020-12-29 北京农业信息技术研究中心 A method and system for evaluating the comfort level of dairy cattle breeding environment based on cloud model
CN108805456A (en) * 2018-06-19 2018-11-13 西南科技大学 A kind of assessment method of the air major pollutants based on principal component analysis
CN109115970A (en) * 2018-07-12 2019-01-01 天津理工大学 Comprehensive water quality assessment method based on conventional index and Biological indicators
CN109115675A (en) * 2018-08-02 2019-01-01 贵州电网有限责任公司 A kind of Evaluating Soil Corrosivity method based on principle component analysis
CN109061086A (en) * 2018-08-15 2018-12-21 浙江海洋大学 A kind of water quality monitoring and early warning system applied to shrimp culture pond
CN109489978B (en) * 2018-10-30 2020-07-31 中国汽车技术研究中心有限公司 Multi-source data correlation analysis method of diesel locomotive multi-emission detection method based on V-a working condition
CN109934514A (en) * 2019-04-03 2019-06-25 南京林业大学 An artificial intelligence comprehensive evaluation method of forest soil fertility based on cloud model
CN110163537B (en) * 2019-06-25 2021-05-14 北京工商大学 Water eutrophication evaluation method based on trapezoidal cloud model
CN110850049A (en) * 2019-08-15 2020-02-28 清华大学 Water quality monitoring and water sensory pleasure degree evaluation method
CN111240318A (en) * 2019-12-24 2020-06-05 华中农业大学 A Robotic Person Discovery Algorithm
CN111461484A (en) * 2020-02-29 2020-07-28 西安理工大学 Comprehensive evaluation method for rainfall and runoff water quality
CN113486502B (en) * 2021-06-24 2023-04-07 天津大学 Cruise ship glass curtain wall risk analysis method based on composite empowerment cloud model
CN113791186B (en) * 2021-08-12 2024-06-25 北京金水永利科技有限公司 Method and system for selecting water quality abnormality alarm monitoring factors
CN114548212A (en) * 2021-12-28 2022-05-27 南京河海南自水电自动化有限公司 A method and system for evaluating water quality
CN116882634A (en) * 2023-07-25 2023-10-13 安徽清洛数字科技有限公司 Sewage pipe network water quality comprehensive evaluation and prediction method based on main component indexes
CN117035514B (en) * 2023-08-08 2024-04-12 上海东振环保工程技术有限公司 Comprehensive sewage treatment management and control system based on cloud platform
CN119398572A (en) * 2024-09-12 2025-02-07 中国环境科学研究院 Water environment quality evaluation method based on physical and chemical indicators of CRITIC method

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102590889A (en) * 2012-02-17 2012-07-18 中国石油化工股份有限公司 Log multi-parameter oil-gas interpretation method based on radar map and cloud model
CN104102762A (en) * 2014-04-29 2014-10-15 兰州交通大学 Application of cloud model fuzzy analytical hierarchy process in risk analysis of railway signal system

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014164567A1 (en) * 2013-03-10 2014-10-09 Edulock, Inc. Multi-layered education based locking of computing devices

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102590889A (en) * 2012-02-17 2012-07-18 中国石油化工股份有限公司 Log multi-parameter oil-gas interpretation method based on radar map and cloud model
CN104102762A (en) * 2014-04-29 2014-10-15 兰州交通大学 Application of cloud model fuzzy analytical hierarchy process in risk analysis of railway signal system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
"基于WPI模型的重庆市水资源安全分析";张喆等;《长江科学院院报》;20160430;第33卷(第4期);第1-5+15页
"基于云模型的退役铀尾矿库环境质量评价方法";彭洁等;《南华大学学报(自然科学版)》;20160630;第30卷(第2期);第14-20页

Also Published As

Publication number Publication date
CN106339536A (en) 2017-01-18

Similar Documents

Publication Publication Date Title
CN106339536B (en) Comprehensive Evaluation of Water Quality based on water pollution index&#39;s method and cloud model
CN111950918B (en) Market risk assessment method based on power transaction data
CN107146009B (en) A method for evaluating the operation status of water supply network
CN103177301A (en) Typhoon disaster risk estimate method
CN112132371A (en) An urban flood risk assessment method based on coupled entropy weight-fuzzy clustering algorithm
CN108665004B (en) Improved water quality evaluation method of Nemerow pollution index based on principal component analysis
CN118569631A (en) Electrical fire risk assessment method based on fuzzy comprehensive evaluation-BP neural network
CN112183935A (en) River water quality comprehensive evaluation method and system
CN105550515B (en) A kind of method that Multilateral Comprehensive Judge is carried out to air quality data
CN110782130A (en) A Comprehensive Evaluation Method of Regional Voltage Quality Based on Multi-attribute Decision Making
CN113688458B (en) Foundation scheme optimization method based on analytic hierarchy process
CN109118082A (en) Plant-grid connection systems fatigue reliability grey close value assessment models
CN116823067B (en) Method and device for determining water quality cleaning state of pipe network and electronic equipment
CN110728409A (en) A Flood Process Type Similarity Mining and Fast Prediction Method
CN110611334A (en) A method for output correlation of multiple wind farms based on Copula-garch model
CN114881374B (en) Multi-element heterogeneous energy consumption data fusion method and system for building energy consumption prediction
CN111932081A (en) Method and system for evaluating running state of power information system
CN110210154A (en) The method of measuring point characterization dam performance similarity is judged using dam safety monitoring measuring point data
CN106713322A (en) Fuzzy measurement method for network equipment information security evaluation
CN118134011A (en) Combined analysis prediction method for foundation pit multi-measuring-point deformation
CN113962456B (en) A medium- and long-term load forecasting method taking into account industry correlation
CN110674471A (en) Debris flow susceptibility prediction method based on GIS and Logistic regression model
CN117196398A (en) A distributed new energy consumption impact assessment method based on improved combination weighting and gray correlation method
CN113469563A (en) Method for determining background area of surface water environment of drainage basin
CN112184076A (en) Energy Internet clean and low-carbon development index index system

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20190416

Termination date: 20210819

CF01 Termination of patent right due to non-payment of annual fee