Regional rural drinking water safety evaluation method based on contact number

Abstract

The invention discloses a regional rural drinking water safety evaluation method based on a connection number, and relates to the technical field of water resource evaluation, and the method comprises the following steps: pre-determining evaluation indexes and samples, screening the evaluation indexes of water quality, water quantity and water supply dimension according to the actual situation of a region, constructing an evaluation index system, and calculating the safety of drinking water. And acquiring rural drinking water safety evaluation sample data of villages and towns in the region as sample index values. According to the method, by dividing evaluation grade standards, constructing single-index connection numbers and comprehensive connection numbers and comprehensively and quantitatively depicting the relative membership degree of sample data and each safety grade, the problems that a traditional method is abstract in result and poor in interpretation are solved, subjective randomness is avoided, in addition, a fuzzy judgment matrix is constructed based on evaluation index sample standard deviation, and the accuracy of evaluation is improved. The index importance is directly reflected through the data fluctuation degree, the matrix consistency is optimized and judged by introducing the accelerated genetic algorithm, and human experience interference is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of water resource evaluation, and in particular to a method for evaluating regional rural drinking water safety based on connection numbers. Background Art

[0002] With rapid economic development and continued population growth, drinking water safety issues in rural areas are becoming increasingly serious. Rural drinking water safety is directly related to residents' health and quality of life. However, due to insufficient infrastructure and difficulty in controlling pollution sources, many rural areas face serious problems with water quality, quantity, and supply. Therefore, how to rationally and accurately assess rural drinking water safety is crucial for ensuring sustainable rural development and achieving rural revitalization.

[0003] Currently, most rural drinking water safety evaluation methods tend to be qualitative, which clearly cannot meet the goal of accurately and quantitatively assessing rural drinking water safety. To achieve this goal, some scholars have proposed quantitative evaluation methods, such as the gray correlation comprehensive evaluation method, to move from qualitative evaluation to quantitative evaluation. However, since rural drinking water safety evaluation involves a system involving many influencing factors such as nature, society, water treatment technology, and water supply projects, this method only reflects the degree of correlation between variables and does not yet address causal relationships. The results obtained are highly abstract and have poor interpretability. In addition, there are still many deficiencies in the evaluation index grading standards and indicator weight determination in quantitative comprehensive evaluation methods. For example, the basis for the evaluation index grading is not unified and often tends to be empirical. The method for determining the evaluation index weight is highly subjective, and the judgment matrix correction model is not unified. This makes it impossible to fully and quantitatively characterize the rural drinking water evaluation system.

[0004] Therefore, how to construct and form a systematic, scientific and applicable rural drinking water safety evaluation method is an urgent problem to be solved.

[0005] Currently, no effective solutions have been proposed for the problems in related technologies. Summary of the Invention

[0006] In response to the problems in the related art, the present invention proposes a regional rural drinking water safety evaluation method based on connection number to overcome the above-mentioned technical problems existing in the existing related art.

[0007] The technical solution of the present invention is achieved as follows:

[0008] In one aspect, the present invention:

[0009] A method for evaluating regional rural drinking water safety based on connection numbers includes the following steps:

[0010] Determine the evaluation indicators and samples in advance, select the evaluation indicators of water quality, water quantity and water supply according to the actual situation of the region, build an evaluation indicator system, and obtain the rural drinking water safety evaluation sample data of each township in the region as the sample indicator value;

[0011] Divide the grade standards and use the mean standard deviation method to determine the evaluation grade standard threshold;

[0012] Calculate the single indicator connection number, match the sample indicator value with the evaluation level, and quantitatively calculate the single indicator connection number;

[0013] Calculate the indicator weights, standardize the indicator data, build a fuzzy judgment matrix based on the sample standard deviation of each evaluation indicator, use the accelerated genetic algorithm to correct the consistency of the judgment matrix, and solve the indicator weights;

[0014] Calculate the comprehensive connection number, combine the single indicator connection number and indicator weight, and calculate the weighted comprehensive connection number;

[0015] Calculate the evaluation grade value and use the grade characteristic value method to calculate the regional rural drinking water safety grade value.

[0016] The threshold values ​​of the grade standards for calculating each indicator are expressed as:

[0017]

[0018] Among them, T M is the threshold value of the level standard (for example, when the security level is divided into 5 levels, it corresponds to the boundary values ​​of "safe", "relatively safe", "basic safe", "relatively unsafe" and "unsafe"), is the sample mean, σ is the standard deviation, and m is the adjustment coefficient (the value is determined according to the number of levels).

[0019] The calculation of the single indicator connection number is expressed as:

[0020] u ij =v ij1 +v ij2 I1+v ij3 I2+v ij4 I3+v ij5 J;

[0021] Among them, u ij is the single indicator connection number of sample i indicator j, and the number of samples is n i , the number of indicators is n j , v ij1 、v ij2 、v ij3 、v ij4 、v ij5are the connection number components of the single indicator value for each level, I1, I2, I3 are the difference coefficients, and J is the opposition coefficient.

[0022] Wherein, solving the indicator weight includes the following steps:

[0023] The indicator data is standardized to calibrate the larger the better (such as the proportion of the population benefiting from centralized water supply) and the smaller the better (such as the proportion of the population with excessive fluoride levels), expressed as:

[0024]

[0025] Where x′ ij is the index value after standardization (value range [0,1]), x ij is the original value of the jth indicator of the i-th sample (such as the proportion of the population with excessive fluoride in a certain township), The maximum / minimum sample value of the jth indicator is used for normalization;

[0026] By x′ ij The fuzzy evaluation matrix R = (x′ ij ) ni×nj Based on the fuzzy evaluation matrix R, the sample standard deviation of each evaluation index is used to construct the fuzzy judgment matrix B, which is expressed as:

[0027]

[0028] Among them, b ij is the relative importance ratio of the i-th index to the j-th index in the fuzzy judgment matrix, s j is the sample standard deviation of the jth indicator (reflecting the degree of data fluctuation; the larger the standard deviation, the more significant the impact of the indicator on the evaluation results), n i is the sample size (such as the number of towns), is the mean value of the jth index after standardization, s min 、s max {s j |j=1,2,…,n j}, the minimum and maximum values ​​of the relative importance parameter value b m =min{9,int[s max / s min +0.5]}, min and int are the minimum function and the integer function respectively;

[0029] Correct the consistency of judgment matrix, and let the correct judgment matrix of B be Y={y ij} nj×nj , the weight value of each element of Y is still recorded as {w j |j=1,2,…,nj}, solve the weight w with the goal of minimizing the consistency index coefficient j , expressed as:

[0030]

[0031] The constraints are:

[0032]

[0033] Among them, the objective function CIC(n j ) is the consistency index coefficient, d is a non-negative parameter that can be selected within [0,0.5], w j is the weight of index j, and the other symbols are the same as before.

[0034] The modified judgment matrix consistency includes: a parent population size of 300, a child population size of 300, a number of excellent individuals of 20, and 100 accelerated cycles.

[0035] The calculation of the comprehensive connection number is expressed as:

[0036]

[0037] Among them, u i is the comprehensive connection number of sample i, v i1 、v i2 、v i3 、v i4 、v i5 It is a comprehensive connection number component that comprehensively and completely reflects the degree of connection between the sample and level k (k = 1, 2, 3, 4, 5).

[0038] The rural drinking water safety level value in the calculation area is expressed as:

[0039]

[0040] Where H is the rural drinking water safety grade, k is the evaluation standard grade (k = 1 is "safe", k = 2 is "relatively safe", ..., k = 5 is "unsafe"), v ik (k=1, 2, 3, 4, 5) is the comprehensive connection number component, which reflects the degree of membership between the sample and each evaluation standard level.

[0041] Another aspect of the present invention is:

[0042] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements a regional rural drinking water safety evaluation method based on a connection number.

[0043] Beneficial effects of the present invention:

[0044] 1. The present invention introduces the connection number theory, and by constructing single-indicator connection numbers and comprehensive connection numbers, it comprehensively and quantitatively characterizes the relative degree of membership of sample data and each security level, solving the problems of abstract results and poor interpretability of traditional methods. At the same time, the mean-standard deviation method is used to replace the traditional empirical value method to divide the grade standards, taking into account the central trend (mean) and dispersion (standard deviation) of the sample data, avoiding subjective arbitrariness. In addition, a fuzzy judgment matrix is ​​constructed based on the standard deviation of the evaluation index sample, which directly reflects the importance of the indicator through the degree of data fluctuation, and an accelerated genetic algorithm is introduced to optimize the consistency of the judgment matrix, avoiding human experience interference.

[0045] 2. This invention achieves systematic integration to enhance evaluation effectiveness, forming a complete technical chain from indicator screening and data processing to comprehensive evaluation, resolving the fragmented and logically incoherent nature of traditional methods. It is also highly applicable and scalable, not relying on regional expert experience or historical data. Instead, it utilizes only the actual indicators of the region to obtain screening parameters, completing the evaluation through a standardized process. It is applicable to rural areas with diverse geographical environments and economic levels. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 1 is a flow chart of a method for evaluating regional rural drinking water safety based on connection numbers according to an embodiment of the present invention;

[0048] Figure 2 This is a rural drinking water safety evaluation grade value diagram of a regional rural drinking water safety evaluation method based on connection number according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention are within the scope of protection of the present invention.

[0050] According to an embodiment of the present invention, taking Beipiao City as an example, the regional rural drinking water safety evaluation method based on the connection number provided by the present invention is used to evaluate the water safety situation in Beipiao City.

[0051] like Figure 1As shown, a regional rural drinking water safety evaluation method based on connection number includes the following steps:

[0052] Step S1, determine the evaluation indicators and samples: screen and determine the evaluation indicators of rural drinking water safety in Beipiao City. According to the "Rural Drinking Water Safety Evaluation Criteria" (T / CHES18-2018), and taking into account the actual indicator acquisition, representativeness and scientific nature of Beipiao City, the evaluation indicators of rural drinking water in Beipiao City are determined as the proportion of the population drinking water with excessive fluoride, the proportion of the population drinking bitter and salty water, the proportion of the population drinking seriously polluted surface water, the proportion of the population drinking seriously polluted groundwater, the proportion of the population with substandard water quantity, the proportion of the population with substandard water source guarantee rate, the current per capita water supply of centralized water supply projects, the proportion of the population with substandard drinking water convenience, the proportion of the population benefiting from centralized water supply, the proportion of the population with household water supply, the proportion of the population drinking groundwater and the village coverage rate of centralized water supply projects. The rural drinking water safety evaluation index system of Beipiao City is constructed and expressed as follows: {x j |j=1,2,...,12}, we obtained the rural drinking water safety evaluation samples of 10 towns in Beipiao City (Batuying Township, Heichengzi Town, Wujianfang Town, Shangyuan Town, Daban Town, Xiguanying Town, Taiji Town, Baoguolao Town, Dongguanying Township, Beisijia Township) as the rural drinking water safety evaluation index value x in Beipiao City. ij , expressed as:

[0053] {x ij |i=1,2,...,10;j=1,2,...,12}.

[0054] Step S2, determine the grade standard: reasonably determine the grade standard of Beipiao City's rural drinking water safety evaluation index. Reasonable division of Beipiao City's rural drinking water safety evaluation standard grades is crucial for subsequent single indicator evaluation. At present. The grade standard division method is still not unified and tends to be based on empirical values, which is not scientific enough. Preferably, the present invention proposes to use the mean standard deviation method to divide the evaluation grade standard. This method comprehensively considers the differences in the evaluation samples and takes into account the central trend and discrete degree of the evaluation sample data. The present invention selects 5 evaluation grades for the Beipiao City Rural Drinking Water Safety Grade, and the grade values ​​1, 2, 3, 4, and 5 represent "safe", "relatively safe", "basically safe", "relatively unsafe" and "unsafe", respectively. The corresponding Beipiao City Rural Drinking Water Safety Evaluation Grade Standard thresholds are calculated by the mean standard deviation method, expressed as: {s kj |k=1,2,...,5;j=1,2,...,12}, as shown in Table 1:

[0055] Table 1 Evaluation indicators and grade standards for rural drinking water safety in Beipiao City

[0056]

[0057] Step S3: Calculate the single-indicator connection number: Calculate the single-indicator connection number for the Beipiao City rural drinking water safety evaluation sample. Currently, most rural drinking water safety evaluation methods tend to be qualitative, which clearly cannot meet the evaluation objectives. Some also tend to be quantitative, such as the gray correlation comprehensive evaluation method. However, since rural drinking water safety evaluation involves a system involving many influencing factors such as nature, society, water treatment technology, and water supply projects, this method only reflects the degree of correlation between variables and does not address causal relationships. The results are abstract and difficult to interpret. It is worth mentioning that the connection number method is widely used in the evaluation of uncertain systems such as drought disasters, medical and health care, and water environment because it can comprehensively and quantitatively characterize the degree of membership between sample indicator values ​​and evaluation levels. However, it is rarely used in rural drinking water safety evaluation.

[0058] The present invention introduces the connection number method into the rural drinking water safety evaluation, and uses the Beipiao City rural drinking water safety evaluation index value x collected and sorted in step S1 ij The evaluation standard level s in step S2 kj Perform matching analysis and quantitatively calculate the single indicator connection number u of rural drinking water safety evaluation in Beipiao City ijk , expressed as:

[0059]

[0060] Among them, the positive (negative) indicator means that as the indicator value increases (decreases), the safety of rural drinking water decreases (increases), and the corresponding rural drinking water safety evaluation level increases (decreases); 0j , s 1j}、{s 1j , s 2j}、{s 2j , s 3j}、{s 3j , s 4j}、{s 4j , s 5j} are the thresholds of evaluation criteria level 1, level 2, level 3, level 4, and level 5, respectively, and are expressed as: i = 1, 2, ..., 10, j = 1, 2, ..., 12. Connection number u ijk As the sample value x for the safety evaluation of rural drinking water in Beipiao City ij and evaluation criteria grades kj A relative difference function of the variable fuzzy relationship of closeness between the two, the corresponding relative membership is:

[0061]

[0062] Among them, the single indicator connection coefficient component v of the rural drinking water safety evaluation sample in Beipiao City can be obtained after normalization ijk , expressed as:

[0063]

[0064] Then, the single indicator connection number u of Beipiao City rural drinking water safety evaluation can be formed ij , expressed as:

[0065] u ij =v ij1 +v ij2 I1+v ij3 I2+v ij4 I3+v ij5 J;

[0066] Among them, I1, I2, and I3 are the difference coefficients, and J is the opposition coefficient.

[0067] Step S4: Calculate the index weights: Calculate the weights of the rural drinking water safety evaluation indexes in Beipiao City. The index weights are the necessary parameters required to transfer the single index evaluation results in step S3 to the comprehensive evaluation. However, the current weight determination methods are highly subjective. For example, the hierarchical analysis method that constructs a judgment matrix based on expert scoring relies on expert experience to determine the judgment matrix, and the correction mode of the judgment matrix is ​​inconsistent, often only correcting individual elements in the matrix, which will undoubtedly seriously affect the comprehensive evaluation results and cause distortion of the evaluation results. Therefore, the present invention introduces a method for constructing a judgment matrix directly based on the sample standard deviation of the rural drinking water safety evaluation index in Beipiao City.

[0068] Specifically, in order to eliminate the dimensional effect of each indicator and maintain the actual physical meaning of each indicator, it is necessary to standardize the values ​​of each indicator. The larger the better and smaller the better indicators are standardized using the following formulas, expressed as:

[0069] r(i,j)=x(i,j) / (x max (j)+x min (j));

[0070] r(i,j)=(x max (j)+x min (j)-x(i,j)) / (x max (j)+x min (j));

[0071] Among them, x max (j), x min(j) are the maximum and minimum values ​​of the jth indicator in the Beipiao City rural drinking water safety evaluation index data set. r(i,j) is the standardized evaluation index value, which is also the relative membership value of the jth evaluation index of the i-th evaluation sample to the Beipiao City rural drinking water safety level. The fuzzy evaluation matrix R = (r(i,j)) for the Beipiao City rural drinking water single indicator evaluation can be formed by r(i,j) as the elements. 12×12 .

[0072] According to the fuzzy evaluation matrix R = (r(i,j)) 12×12 Construct a judgment matrix B for determining the weight of each evaluation index = (b ij ) 12×12 Based on the fuzzy evaluation matrix, the sample standard deviation s(j) of each evaluation index can be used to reflect the degree of influence of each evaluation index on the comprehensive evaluation, and is used to construct the judgment matrix B. The calculation formula of s(j) is expressed as:

[0073]

[0074] Among them, s min 、s max are the minimum and maximum values ​​of {s(j)|j=1,2,...,12} respectively; the relative importance parameter value b m =min{9,int[smax / smin+0.5]}, where min and int are the minimum function and the integer function respectively.

[0075] The judgment matrix B for rural drinking water safety evaluation in Beipiao City is obtained and expressed as:

[0076]

[0077] In fact, the judgment matrix constructed by the standard deviation of the evaluation index samples reflects the degree of influence of each evaluation index on the comprehensive evaluation. However, due to the complexity of the rural drinking water evaluation system, the consistency condition of the judgment matrix B is not fully satisfied. This is an objective existence and cannot be completely eliminated in practical applications. Therefore, the judgment matrix needs to be modified and should meet w j >0 and The following steps are involved:

[0078] The correction judgment matrix of calibration B is Y={y ij} 12×12 , the weight value of each element of Y is still recorded as {w j |j=1,2,...,12}, then the smallest Y matrix is ​​the optimal consistency judgment matrix of B, which is expressed as:

[0079]

[0080] Among them, the objective function CIC(nj ) is the consistency index coefficient; d is a non-negative parameter, and d=0.2 is selected here. It is a problem of solving the minimum function value of a nonlinear function. It is essentially a nonlinear optimization problem, which is difficult to handle with conventional methods. There are 78 optimization variables, including the weight value {w j |j=1,2,...,12} a total of 12 and the modified judgment matrix Y={y ij} 12×12 The upper triangular matrix of has 66 elements in total, and the constraint condition is st. Here, an accelerated genetic algorithm simulating the rules of biological selection, hybridization, and mutation survival of the fittest is used to solve the minCIC(n j ) optimization problem, is simple, effective, and highly applicable. Using a genetic algorithm accelerated 100 times to optimize the judgment matrix B, the calculated weights for indicators 1 to 12 of the Beipiao rural drinking water safety evaluation were 0.052, 0.162, 0.103, 0.133, 0.143, 0.052, 0.058, 0.042, 0.143, 0.042, 0.026, and 0.044, respectively, with a consistency index coefficient of 0.0872 (<0.10). The fuzzy analytic hierarchy process, based on an accelerated genetic algorithm, combines weight calculation with consistency testing of the judgment matrix. Given a fixed judgment matrix, the weights are derived with the goal of minimizing the consistency index coefficient.

[0081] Step S5: Calculate the comprehensive connection number: Calculate the comprehensive connection number u of the rural drinking water safety evaluation sample i in Beipiao City i Based on the single indicator connection number of Beipiao City Rural Drinking Water Safety Evaluation in step S3 and the weight of each indicator in step S4, the sample comprehensive connection number u can be obtained by weighting i , expressed as:

[0082]

[0083] The results are shown in Table 2, as follows:

[0084] Table 2. Number of connections of samples for rural drinking water safety evaluation in Beipiao City

[0085]

[0086]

[0087] Step S6: Calculate the rural drinking water safety evaluation grade value of Beipiao City. To fully utilize and deeply explore the information of the comprehensive connection number of regional rural drinking water safety samples, the grade eigenvalue method is used to calculate the rural drinking water safety grade value of Beipiao City based on the comprehensive connection number components of the rural drinking water safety evaluation samples of Beipiao City, expressed as:

[0088]

[0089] The results are shown in Table 3 and Figure 2 , as follows:

[0090] Table 3 Connection number and grade value of rural drinking water safety evaluation samples in Beipiao City

[0091]

[0092] Depend on Figure 2 As shown in Table 3: (1) The rural drinking water safety level of the four towns of Beipiao City, namely Batuying Township, Xiguanying Town, Baoguo Lao Town and Beisijia Township, is between relatively safe (level 2) and basically safe (level 3), while the six towns of Heichengzi Town, Wujianfang Town, Shangyuan Town, Daban Town, Taiji Town and Dongguanying Township are between relatively unsafe (level 4) and basically safe (level 3); from the perspective of the entire city, the average safety level of the 10 towns is 3.0, that is, the rural drinking water in the entire city is basically safe, which is consistent with the research results of relevant literature, indicating that the rural drinking water safety evaluation method of Beipiao City based on the connection number constructed by the present invention is reasonable and reliable. (2) The method constructed by the present invention is more sensitive, the evaluation results are more refined, the discrimination is good and it is consistent with the actual situation of Beipiao City, while the results obtained in the relevant literature are integer level values ​​with weak discrimination and cannot objectively and truly reflect the drinking water quality of various towns in Beipiao City. (3) Analysis of the above reasons is mainly due to the fact that the regional rural drinking water evaluation method based on the connection number constructed by the present invention uses the mean standard deviation to scientifically divide the evaluation grade standard, which can reflect the concentration trend and dispersion degree of the sample data; secondly, the present invention calculates the fuzzy judgment matrix based on the principle that the greater the degree of change of the indicator data, the more important the indicator is to the comprehensive evaluation result, and optimizes the Beipiao City rural drinking water safety evaluation indicator weight based on the accelerated genetic algorithm, overcoming the erroneous evaluation results that may be caused by the subjective weighting of the traditional hierarchical analysis method; finally, the novel uncertainty method of the connection number is introduced into the rural drinking water safety evaluation of Beipiao City, facing the comprehensive evaluation goal, adhering to the physical meaning of the indicator evaluation, and comprehensively and quantitatively characterizing the relative degree of affiliation between the rural drinking water safety evaluation indicator data and the evaluation standard grade. Obviously, this is superior to the traditional evaluation method, providing a new way for regional rural drinking water safety evaluation, and further enriching the regional rural drinking water safety evaluation method.

[0093] The above analysis results show that: (1) the introduction of the novel uncertainty analysis method of connection number into the regional rural drinking water safety evaluation can comprehensively and completely quantitatively characterize the uncertainty and fuzziness between the rural drinking water safety evaluation indicators and the evaluation grade standards, providing a new idea for the regional rural drinking water safety evaluation and enriching the regional rural drinking water quantitative evaluation method; (2) the use of the mean standard deviation method to divide the rural drinking water evaluation index grade standards takes into account the central trend and discrete degree of the evaluation sample data, which is more scientific than the empirical value method to divide the evaluation grade standards; (3) the judgment matrix is ​​directly constructed according to the standard deviation of the rural drinking water evaluation index sample, which is relatively Compared with the judgment matrix constructed based on the expert scoring method, it is more objective. At the same time, when correcting the consistency of the judgment matrix, introducing the accelerated genetic algorithm to deal with nonlinear complex optimization problems is simple and effective. In addition, the judgment matrix is ​​directly constructed according to the standard deviation of the evaluation index sample and the judgment matrix is ​​corrected by the accelerated genetic algorithm. This processing method has strong applicability; (4) The present invention integrates the determination of evaluation grade standards, the calculation of evaluation index weights, the quantitative evaluation of single indicators, and the fuzzy comprehensive evaluation of samples, forming a set of scientific, complete, and highly applicable regional rural drinking water safety evaluation technology, which can also provide a technical reference for subsequent complex system comprehensive evaluation problems and has good application prospects.

[0094] In summary, with the help of the above technical solution of the present invention, the following effects can be achieved:

[0095] 1. This paper introduces the novel uncertainty analysis method of connection number into the regional rural drinking water safety assessment. It can comprehensively and completely quantitatively characterize the uncertainty and fuzziness between rural drinking water safety assessment indicators and evaluation standard levels, providing a new approach to regional rural drinking water safety assessment and enriching the regional rural drinking water quantitative assessment method.

[0096] 2. The present invention adopts the mean standard deviation method to divide the rural drinking water evaluation index grade standards, taking into account the central tendency and dispersion degree of the evaluation sample data. Compared with the empirical value method, it is more scientific to divide the evaluation grade standards;

[0097] 3. The present invention directly constructs a judgment matrix based on the sample standard deviation of rural drinking water evaluation indicators, which is more objective than constructing a judgment matrix based on the expert scoring method. At the same time, when correcting the consistency of the judgment matrix, introducing an accelerated genetic algorithm to deal with nonlinear complex optimization problems is simple and effective. In addition, the judgment matrix is ​​directly constructed according to the sample standard deviation of the evaluation indicators and the judgment matrix is ​​corrected using an accelerated genetic algorithm. This processing method has strong applicability.

[0098] 4. The present invention integrates the determination of evaluation grade standards, calculation of evaluation index weights, quantitative evaluation of single indicators, and fuzzy comprehensive evaluation of samples, forming a set of scientific, complete, and highly applicable regional rural drinking water safety evaluation technologies with good application prospects.

[0099] The foregoing is merely a preferred embodiment of the present invention and is not intended to limit the present invention. A person skilled in the art will readily appreciate other embodiments of the present invention after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses, or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered merely exemplary, and the true scope and spirit of the present invention are indicated by the claims.

[0100] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for evaluating regional rural drinking water safety based on connection number, characterized in that: The following steps are involved: Determine the evaluation indicators and samples in advance, select the evaluation indicators of water quality, water quantity and water supply according to the actual situation of the region, build an evaluation indicator system, and obtain the rural drinking water safety evaluation sample data of each township in the region as the sample indicator value; Divide the grade standards and use the mean standard deviation method to determine the evaluation grade standard threshold; Calculate the single indicator connection number, match the sample indicator value with the evaluation level, and quantitatively calculate the single indicator connection number; Calculate the indicator weights, standardize the indicator data, build a fuzzy judgment matrix based on the standard deviation of each evaluation indicator sample, use the accelerated genetic algorithm to correct the consistency of the judgment matrix, and solve the indicator weights; Calculate the comprehensive connection number, combine the single indicator connection number and indicator weight, and calculate the weighted comprehensive connection number; Calculate the evaluation grade value and use the grade characteristic value method to calculate the regional rural drinking water safety grade value.

2. The regional rural drinking water safety evaluation method based on connection number according to claim 1 is characterized in that: The threshold values ​​of the grade standards for calculating each indicator are expressed as: Among them, T M is the grade standard threshold, is the sample mean, σ is the standard deviation, and m is the adjustment coefficient.

3. The regional rural drinking water safety evaluation method based on connection number according to claim 1 is characterized in that: The calculation of the single indicator connection number is expressed as: you ij =v ij1 +v ij2 I1+v ij3 I2+v ij4 I3+v ij5 J; Among them, u ij is the single indicator connection number of sample i indicator j, and the number of samples is n i , the number of indicators is n j , v ij1 、v ij2 、v ij3 、v ij4 、v ij5 are the connection number components of the single indicator value for level k (k = 1, 2, 3, 4, 5), I1, I2, I3 are the difference coefficients, and J is the opposition coefficient.

4. The method for evaluating regional rural drinking water safety based on connection number according to claim 1, characterized in that: The method of solving the indicator weight includes the following steps: The indicator data is standardized and the larger the better indicator and the smaller the better indicator are calibrated, which can be expressed as: Where x′ ij is the normalized index value, x ij is the original value of the jth indicator of the i-th sample, The maximum / minimum sample value of the jth indicator is used for normalization; By x′ ij The fuzzy evaluation matrix R = (x′ ij ) ni×nj , based on the fuzzy evaluation matrix R, the sample standard deviation of each evaluation index is used to construct the fuzzy judgment matrix B, which is expressed as: Among them, b ij is the relative importance ratio of the i-th index to the j-th index in the fuzzy judgment matrix, s j is the sample standard deviation of the j-th indicator, is the mean of the jth index after standardization, s min 、s max {s j |j=1,2,…,n j }, the minimum and maximum values ​​of the relative importance parameter value b m =min{9,int[s max / s min +0.5]}, min and int are the minimum function and the integer function respectively; Correct the consistency of judgment matrix, and let the correct judgment matrix of B be Y={y ij } nj×nj , the weight value of each element of Y is still recorded as {w j |j=1,2,…,n j }, solve the weight w with the goal of minimizing the consistency index coefficient j , expressed as: The constraints are: Among them, the objective function CIC(n j ) is the consistency index coefficient, d is a non-negative parameter that can be selected within [0,0.5], w j is the weight of index j, and the other symbols are the same as before.

5. The regional rural drinking water safety evaluation method based on connection number according to claim 1 is characterized in that: The modified judgment matrix consistency includes: a parent population size of 300, a child population size of 300, a number of excellent individuals of 20, and 100 accelerated cycles.

6. The regional rural drinking water safety evaluation method based on connection number according to claim 1 is characterized in that: The calculated comprehensive connection number is expressed as: Among them, u i is the comprehensive connection number of sample i, v i1 、v i2 、v i3 、v i4 、v i5 It is a comprehensive connection number component that comprehensively and completely reflects the degree of connection between the sample and level k (k = 1, 2, 3, 4, 5).

7. The method for evaluating regional rural drinking water safety based on connection number according to claim 6, characterized in that: The rural drinking water safety level value in the calculation area is expressed as: Among them, H is the rural drinking water safety level value.

8. A computer-readable storage medium, characterized in that A computer program is stored, and when the program is executed by a processor, the regional rural drinking water safety evaluation method based on connection number as described in any one of claims 1 to 7 is implemented.

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