Rural domestic sewage treatment technology decision support method based on combination of AHP and fuzzy relation matrix

Abstract

The invention belongs to the field of decision support and decision theory, and particularly provides a rural domestic sewage treatment technology decision support method based on the combination of AHP and a fuzzy relation matrix, and the method comprises the steps: obtaining sewage treatment technology information, and constructing a database; constructing a rule of a secondary expert scoring system and a membership function, and carrying out quantitative processing on information in the database to obtain a fuzzy relation matrix; performing relative importance evaluation on each level of index, and constructing an evaluation matrix; performing consistency check on the evaluation matrix, and then determining a corresponding weight coefficient of each index in the evaluation process by adopting AHP to form a weight set corresponding to the indexes; and carrying out composite operation on the weight set and the fuzzy relation matrix, wherein the process with the highest score in the calculation result is the optimal decision scheme. According to the method, based on the combination of the AHP and the fuzzy relation matrix, the characteristics of various process technologies and the subjective judgment of a decision maker are considered, so that the decision is more scientific.

Description

Technical Field

[0001] The present invention belongs to the field of decision support and decision theory, and in particular relates to a rural domestic sewage treatment technology decision support method based on the combination of AHP and fuzzy relation matrix. Background Art

[0002] In recent years, with the promotion and development of decentralized sewage treatment technologies, rural domestic sewage treatment has made significant progress. However, due to the complex natural conditions, weak economic foundations, and widely varying characteristics of domestic sewage in rural areas, it is difficult for rural areas to establish a comprehensive standard system like in cities, which has, to a certain extent, hindered the advancement of rural domestic sewage treatment. The lack of scientific and clear guidance in rural areas, unclear treatment technologies, and inconsistent treatment standards have become one of the current difficulties in rural domestic sewage treatment. Faced with the problems existing in rural sewage treatment, how to scientifically and effectively select sewage treatment technologies, integrate various types of information to match the selected solutions with local comprehensive conditions, and optimize the social, economic, and environmental benefits of sewage treatment facility construction has become a key issue that requires consideration.

[0003] Currently, research on wastewater treatment decision support focuses on controlling and optimizing the process parameters of wastewater treatment plant operating systems. The objectives are, first, to ensure the stability of wastewater treatment plant operating systems and compliance with effluent quality standards through precise control and real-time early warning, and second, to achieve energy savings, reduce costs, and maximize the wastewater treatment plant's potential for pollution removal. Other studies have also employed decision-making methods and theories such as weighted methods, entropy weight methods, the analytic hierarchy process, gray correlation analysis, and hesitant fuzzy language terminology to comprehensively evaluate and support wastewater treatment plant site selection. However, research on wastewater treatment technology selection, particularly the selection of appropriate wastewater treatment processes in rural areas, remains limited.

[0004] Chinese patent CN112465337 discloses a sewage treatment plant site selection method based on a hesitant fuzzy language terminology set. This method constructs a language decision matrix based on evaluations by an expert group. A comprehensive evaluation index is calculated based on the distance between the candidate sites and their positive and negative ideal values, and the highest-scoring option is presented to the decision maker. However, when applied to decision support for sewage treatment technology in rural areas, this method struggles to integrate information related to basic conditions and sewage treatment processes, as the indicator systems for technology selection are diverse and the technical solutions provided are also diverse. Chinese patent CN102254102 discloses an ontology-based sewage decision-support system and method. This method constructs a data acquisition layer, a diagnosis layer, and a decision support layer system. The diagnosis layer infers collected data, and the decision support layer provides corresponding ontology classes and diagnostic information based on the diagnostic results, which are then presented to the user for selection. This invention improves the reliability and portability of the decision-support system, but requires the construction of a complex diagnosis layer, and the data collection comes from online sensors. While it can be used for decision support for large sewage treatment plants, it is not suitable for decision support for domestic sewage treatment technology in rural areas. The relevant research materials have different decision-making support objects, but the decision-making theories and methods they apply have certain reference significance.

[0005] Therefore, in order to solve the problem of selecting domestic sewage treatment technology in rural areas, a rural domestic sewage treatment technology decision support method is urgently needed to provide decision makers with comprehensive engineering information comparison and help them optimize decision-making goals by comprehensively considering the technical characteristics of the treatment process, the construction conditions of the actual project, and the treatment requirements that need to be achieved. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a decision support method for rural domestic sewage treatment technology based on the combination of AHP and fuzzy relationship matrix. Based on the combination of AHP and fuzzy relationship matrix, it takes into account the characteristics of various process technologies and the subjective judgment of decision makers, reflects the comprehensive effect of various factors, and makes the decision more scientific.

[0007] To solve the above technical problems, the technical solution adopted by the present invention is: a rural domestic sewage treatment technology decision support method based on the combination of AHP and fuzzy relationship matrix, comprising the following steps: Step 1: Obtain information on sewage treatment technologies applicable to rural areas and build a database; Step 2: Construct the rules of the secondary expert scoring system and membership function, quantify the information in the database, and obtain the fuzzy relationship matrix; Step 3: Indicator evaluation: Evaluate the relative importance of indicators at all levels and construct an evaluation matrix; Step 4: Perform consistency test on the evaluation matrix. If it fails the test, the indicator evaluation is performed again. If it passes the test, AHP is used to determine the corresponding weight coefficient of each indicator in the evaluation process to form a weight set corresponding to the indicator; Step 5: Perform a composite operation on the weight set corresponding to the indicators and the fuzzy relationship matrix. The process with the highest score in the calculation results is the best decision-making solution.

[0008] In a preferred embodiment, the database in step 1 includes treatment performance indicators, technical performance indicators, economic performance indicators and basic condition indicators for each process; the treatment performance indicators include COD treatment rate, SS treatment rate, TN treatment rate, TP treatment rate and ammonia nitrogen treatment rate; the technical performance indicators include implementation difficulty and management difficulty; the economic performance indicators include land area, construction cost and management cost; and the basic condition indicators include pipe network coverage, per capita drainage volume, population density and per capita GDP.

[0009] In a preferred solution, in step 1, all indicators of each process are treated as a separate column and set as columns V1 to Vn, where V1-Vn represent n processes in the database, and then drawn into a processing technology information table.

[0010] In the preferred solution, in step 2, the secondary expert scoring system is constructed as follows: The factor set of the first-level indicator system is as follows: ={processing performance, technical performance, economic performance, basic conditions}; The factor set of the secondary indicators under the primary indicator system is as follows: {COD removal rate, SS removal rate, TN removal rate, TP removal rate, ammonia nitrogen removal rate}; {difficulty of implementation, difficulty of management}; {construction cost, operating cost}; {Pipeline network coverage, per capita drainage volume, population density, per capita GDP}.

[0011] In the preferred solution, in step 2, the rules of the membership function are as follows: For quantitative indicators, including treatment performance indicators, economic performance indicators and basic condition indicators, the membership functions used are as follows: ; in: is the jth secondary indicator under the i-th primary indicator, is the minimum value of the indicator in the database information, is the maximum value of the indicator in the database information, and all indicator values are positive; For qualitative indicators, including technical performance indicators, the membership function used is as follows: ; in: is the jth secondary indicator under the i-th primary indicator, The values are graded according to relative difficulty, and are divided into five levels: easy, relatively easy, medium, relatively difficult, and difficult, corresponding to values 2, 4, 6, 8, and 10 respectively. In addition, there are five more levels between the two levels: 1, 3, 5, 7, and 9, for a total of 10 difficulty levels for evaluation; The indicator information in the technical information table is processed according to the rules of the membership function to form the secondary fuzzy matrices R1, R2, R3, and R4, which are matrices composed of the indicator values of the four scheme layers: processing performance indicators, technical performance indicators, economic performance indicators, and basic condition indicators. The four secondary fuzzy matrices are then vertically spliced to obtain the primary fuzzy matrix R.

[0012] In the preferred solution, in step 3, a 1-9 scaling method is used to determine the relative importance of each pair of indicators, and an evaluation matrix is constructed, including a primary evaluation matrix and a secondary evaluation matrix. The primary evaluation matrix P is an evaluation matrix for the treatment performance indicators, technical performance indicators, economic performance indicators, and basic condition indicators. The secondary evaluation matrix is an evaluation matrix for the secondary indicators in each primary indicator, namely P1, P2, P3, and P4. The evaluation matrix elements are required to meet the following rules: ; ; in, Indicates that under the current evaluation matrix, The element relative to the The importance of the elements, Indicates the The element relative to the The importance of the elements, and Indicates the number of rows and columns of the evaluation matrix.

[0013] In a preferred solution, in step 4, the consistency check rule is as follows: Consistency indicators: ; in, is the maximum eigenvalue of the matrix, is the number of rows in the matrix, the consistency index When it is 0, it means the matrix is consistent. The larger it is, the more serious the inconsistency of the matrix; Calculate the consistency ratio of the matrix , the calculation rules are as follows: ; in, is a random consistency indicator. If the diagnostic layer satisfies , it means that the logic is consistent and the next step can be carried out. Otherwise, the logic is contradictory and the system will return to step 3 to re-evaluate the relative importance of each indicator until the constructed evaluation matrix passes the consistency test and enters step 4.

[0014] In the preferred solution, in step 4, the importance of the matrix elements is hierarchically sorted one by one, that is, a weight is assigned to each factor set to form a weight set. The weight set calculation rule is as follows: 1) First, normalize each column of the evaluation matrix P, P1, P2, P3, and P4, namely: ; in, Indicates the first i Liedi j The elements of the row, n is the number of columns of the evaluation matrix; 2) After normalizing the indicators in each evaluation matrix by column, a new matrix is formed, and the elements of each row in the new matrix are added together to obtain the operation matrix after the elements of each row of the evaluation matrix are added together. ,Right now: ; The matrix consists of n rows and 1 column, where n is the number of first-level indicators or the number of second-level indicators corresponding to each first-level indicator; in, For each operation matrix The internal elements of the matrix; 3) The operation matrix obtained in step 2) Normalize again to obtain the final first-level indicator weight matrix W and second-level indicator weight matrices W1, W2, W3, and W4. The calculation formula is as follows: ; ; in, is the first in the operation matrix corresponding to the first-level index j matrix elements; Indicates the i The j-th matrix element in the operation matrix corresponding to the secondary index, is the secondary indicator weight set matrix.

[0015] In the preferred solution, in step 5, the fuzzy matrix is composed of four fuzzy matrices R1, R2, R3, and R4, where R1 is the processing performance index matrix, R2 is the technical performance index matrix, R3 is the economic performance index, and R4 is the basic condition index fuzzy matrix. R1, R2, R3, and R4 are weighted averaged with the weight sets W1, W2, W3, and W4 of the secondary indicators, respectively, to obtain the secondary judgment matrices B1, B2, B3, and B4: ; ; ; ; The calculation expression is: ; in, is the specific weight value of the secondary indicator weight concentration, The values of various indicators after constructing the fuzzy matrix for the current scheme according to the principle of membership function; The four secondary judgment matrices are vertically spliced into a primary judgment matrix B: ; The first-level indicator weight set W and the first-level judgment matrix B are weighted and compounded again, and the calculation result is: .

[0016] In a preferred solution, in step 1, actual engineering case information is collected to construct a reference database; In step five, the process with the highest score in the calculation results is compared with the actual engineering cases in the reference database.

[0017] The present invention provides a decision support method for rural domestic sewage treatment technology based on the combination of AHP and fuzzy relationship matrix, which has the following beneficial effects: 1. This invention, based on the combination of AHP and fuzzy relational matrix, takes into account the characteristics of various process technologies and the subjective judgment of decision makers, reflecting the comprehensive effect of various factors and making the decision more scientific. At the same time, the subjective judgment and preferences of decision makers can also influence the decision-making results to a certain extent. In addition, the comparison of actual engineering case information is added to the decision-making process, thus making the decision-making more flexible.

[0018] 2. The present invention has a reasonable logical structure, comprehensive considerations, and a relatively simple calculation method, which helps decision makers to conduct logical reasoning and analysis, and has strong reproducibility and portability.

[0019] 3. The decision-making operations of the present invention can all be performed in the MATLAB environment, and MATLAB has powerful matrix operation capabilities. By constructing matrices, it helps to improve the efficiency of matrix operations and help decision makers make decisions quickly. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are 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.

[0021] Figure 1 is a decision flow chart of the present invention; Figure 2 Classification diagram of the secondary expert rating system; Figure 3 This is the logic diagram of the secondary indicator system of the present invention; Figure 4 A decision diagram provided after the diagnosis layer decision of the present invention; Figure 5 Screenshot of the consistency check code in MATLAB environment; Figure 6 Screenshot of the code for weight set calculation in MATLAB environment. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] Example 1: like Figure 1Figure 2 shows a decision support method for rural domestic sewage treatment technology based on the combination of AHP and fuzzy relational matrices. The solution layer constructs a solution database by capturing the process characteristics, construction requirements, and treatment capabilities of rural sewage treatment technologies. The actual project case information collected at the solution layer serves as an independent reference database. This database does not participate in the diagnostic layer's calculations but serves as comparison information for the diagnostic layer's decision output.

[0025] The diagnostic layer combines database information with decision-maker evaluations to establish a membership function, quantifying the information in the solution layer database. A secondary expert scoring system is constructed, and matrix operations are performed on the quantified database information to arrive at a selection of possible decisions. The diagnostic layer includes establishing rules for the membership function, a secondary expert scoring system, and matrix scoring and operation rules.

[0026] The target layer provides the decision-making process and results of the diagnosis layer to the decision-makers in a visual form, giving them clear reference information directly.

[0027] like Figure 1 As shown, the following steps are included: Step 1: Obtain information on sewage treatment technologies applicable to rural areas and build a database.

[0028] The database includes processing performance indicators, technical performance indicators, economic performance indicators and basic condition indicators for each process.

[0029] The treatment performance indicators include COD treatment rate, SS treatment rate, TN treatment rate, TP treatment rate, and ammonia nitrogen treatment rate; the technical performance indicators include implementation difficulty and management difficulty; the economic performance indicators include land area, construction cost, and management cost; the basic conditions indicators include pipeline network coverage, per capita drainage volume, population density, and per capita GDP.

[0030] All indicators of each process are taken as a separate column and set as columns V1 to Vn, where V1-Vn represent n processes in the database, and then drawn into a processing technology information table, as shown in Table 1:

[0031] V1-Vn represent n processes in the database. Each time a process is added to the database, it is given a unique identifier and can be imported into the processing technology information table as a new column.

[0032] Step 2: Construct the rules of the secondary expert scoring system and membership function, quantify the information in the database, and obtain the fuzzy relationship matrix R.

[0033] like Figure 2As shown, the secondary expert scoring system includes four primary indicators: processing performance indicators, technical performance indicators, economic performance indicators, and basic conditions indicators.

[0034] The four first-level indicators contain different second-level indicators respectively, and a multi-factor second-level indicator system with 14 indicators in four aspects, namely, processing performance, technical performance, economic performance and basic conditions, has been established. The logic diagram of the second-level indicator system is shown in Figure 3 shown The secondary expert scoring system is constructed as follows: The factor set of the first-level indicator system is as follows: ={processing performance, technical performance, economic performance, basic conditions}; The factor set of the secondary indicators under the primary indicator system is as follows: {COD removal rate, SS removal rate, TN removal rate, TP removal rate, ammonia nitrogen removal rate}; {difficulty of implementation, difficulty of management}; {construction cost, operating cost}; {Pipeline network coverage, per capita drainage volume, population density, per capita GDP}.

[0035] The rules of the membership function are as follows: For quantitative indicators, including treatment performance indicators, economic performance indicators and basic condition indicators, the membership functions used are as follows: ; in: is the jth secondary indicator under the i-th primary indicator, is the minimum value of the indicator in the database information, is the maximum value of the indicator in the database information, and all indicator values are positive; For qualitative indicators, including technical performance indicators, the membership function used is as follows: ; in: is the jth secondary indicator under the i-th primary indicator, The values are graded according to relative difficulty, and are divided into five levels: easy, relatively easy, medium, relatively difficult, and difficult, corresponding to values 2, 4, 6, 8, and 10 respectively. In addition, there are five more levels between the two levels: 1, 3, 5, 7, and 9, for a total of 10 difficulty levels for evaluation; The indicator information in the technical information table is processed according to the rules of the membership function to form a fuzzy matrix R. The fuzzy matrix R is constructed by connecting the four first-level indicators, namely, processing performance indicators, technical performance indicators, economic performance indicators, and basic condition indicators, in rows.

[0036] The fuzzy matrix R is composed of four fuzzy matrices R1, R2, R3, and R4. R1 is the processing performance index matrix, R2 is the technical performance index matrix, R3 is the economic performance index, and R4 is the basic condition index fuzzy matrix.

[0037] Step 3: Indicator evaluation: Evaluate the relative importance of indicators at all levels and construct an evaluation matrix.

[0038] The diagnostic layer evaluates the matrix elements based on the five factors and the corresponding judgment matrices P, P1, P2, P3 and P4. The 1-9 scale is used to assign values based on the relative importance of the two indicators. 1, 3, 5, 7, and 9 indicate that the former indicator is equally, slightly, significantly, strongly, and extremely important to the latter indicator, respectively. 2, 4, 6, and 8 represent the median value of the two adjacent indicators.

[0039] By determining the relative importance between each pair of indicators, an evaluation matrix is constructed, including a first-level evaluation matrix and a second-level evaluation matrix. The first-level evaluation matrix is an evaluation matrix P constructed for processing performance indicators, technical performance indicators, economic performance indicators, and basic condition indicators. The second-level evaluation matrix is an evaluation matrix constructed for the second-level indicators in each first-level indicator, which are P1, P2, P3, and P4 respectively.

[0040] The expression of the evaluation matrix is as follows: ; ; ; ; ; The evaluation matrices P, P1, P2, P3, and P4 are square matrices of order 4, order 5, order 2, order 2, and order 4, respectively. The matrix elements satisfy the following rules: ; ; in, Indicates that under the current evaluation matrix, The element relative to the The importance of the elements, Indicates the The element relative to the The importance of the elements, and Indicates the number of rows and columns of the evaluation matrix.

[0041] Step 4: Perform consistency test on the evaluation matrix. If it fails the test, perform indicator evaluation again. If it passes the test, use AHP to determine the corresponding weight coefficient of each indicator in the evaluation process to form the weight set corresponding to the indicator.

[0042] By performing operations on each matrix, the maximum eigenvector value is determined , and perform consistency check. The consistency check rules are as follows: Consistency indicators: ; in, is the maximum eigenvalue of the matrix, is the number of rows in the matrix, the consistency index When it is 0, it means the matrix is consistent. The larger it is, the more serious the inconsistency of the matrix.

[0043] The diagnostic layer calculates the consistency ratio of the matrix , used to determine the permissible range of matrix inconsistency and calculate the consistency ratio of the matrix , the calculation rules are as follows: ; in, is a random consistency indicator, and its numerical description is shown in Table 2.

[0044]

[0045] If the diagnostic layer is calculated to meet , it means that the logic is consistent and the next step can be carried out. Otherwise, the logic is contradictory and the system will return to step 3 to re-evaluate the relative importance of each indicator until the constructed evaluation matrix passes the consistency test and enters step 4.

[0046] The operating environment of the diagnostic layer is in MATLAB environment, and its judgment code is as follows Figure 5 shown.

[0047] The importance of the matrix elements is ranked hierarchically one by one, that is, weights are assigned to each factor set to form weight sets W, W1, W2, W3, and W4. The calculation rules for the weight sets are as follows: 1) First, normalize each column of the evaluation matrix P, P1, P2, P3, and P4, namely: ; in, Indicates the first i Liedij The elements of the row, n is the number of columns of the evaluation matrix; 2) After normalizing the indicators in each evaluation matrix by column, a new matrix is formed, and the elements of each row in the new matrix are added together to obtain the operation matrix after the elements of each row of the evaluation matrix are added together. ,Right now: ; The matrix consists of n rows and 1 column, where n is the number of first-level indicators or the number of second-level indicators corresponding to each first-level indicator; in, For each operation matrix The internal elements of the matrix; 3) The operation matrix obtained in step 2) Normalize again to obtain the final first-level indicator weight matrix W and second-level indicator weight matrices W1, W2, W3, and W4. The calculation formula is as follows: ; ; in, is the first in the operation matrix corresponding to the first-level index j matrix elements; Indicates the i The j-th matrix element in the operation matrix corresponding to the secondary index, is the secondary indicator weight set matrix.

[0048] The weight vector can be calculated in the MATLAB environment. The calculation code is as follows: Figure 6 shown.

[0049] Step 5: Perform a composite operation on the weight set matrix and the fuzzy relationship matrix. The process with the highest score in the calculation results is the best decision-making solution.

[0050] The fuzzy matrix R is composed of four fuzzy matrices R1, R2, R3, and R4. R1 is the processing performance index matrix, R2 is the technical performance index matrix, R3 is the economic performance index, and R4 is the basic condition index fuzzy matrix. R1, R2, R3, and R4 are weighted averaged with the weight sets W1, W2, W3, and W4 of the secondary indicators respectively to obtain the first-level judgment matrices B1, B2, B3, and B4: ; ; ; ; The calculation expression is: in, is the specific weight value of the secondary indicator weight concentration, The values of various indicators after constructing the fuzzy matrix for the current scheme according to the principle of membership function.

[0051] The four secondary judgment matrices are vertically spliced into a primary judgment matrix B: ; The first-level indicator weight set W and the first-level judgment matrix B are weighted and compounded again, and the calculation result is: , and obtain the comprehensive evaluation index of each process.

[0052] Furthermore, after comprehensively evaluating each process according to the diagnosis layer, the target layer displays the results calculated by the diagnosis layer in a visual form. The visualization form can be a bar chart or a line chart, and the graph can be selected to display data.

[0053] The goal layer provides not only visual representations but also the decision-making process steps to assist decision makers in verification. The highest-scoring process is accompanied by actual engineering case studies to help decision makers process the solution and determine whether corrections and changes are necessary.

[0054] In step 1, actual engineering case information is collected to construct a reference database. In step 5, the highest-scoring processes from the calculation results are compared with actual engineering cases in the reference database. The reference database provides information on the application of the selected highest-scoring processes in actual engineering cases (such as treatment effectiveness, operating costs, management difficulty, and economic benefits). This information helps decision makers determine whether revisions and changes are necessary, and if not, retain the strategy.

[0055] Example 2: In this embodiment, it is assumed that five relevant sewage treatment technologies applicable to rural areas are collected, numbered V1, V2, V3, V4, and V5 respectively, and their specific process parameters are constructed into a treatment technology information table, see Table 3 below.

[0056]

[0057] The indicators are sorted according to the membership function rules and drawn into a fuzzy matrix R.

[0058] To facilitate understanding, here we take COD removal rate as a quantitative indicator and implementation difficulty as a qualitative indicator for calculation examples for demonstration.

[0059] The COD removal rate index is processed using the following membership function: ; The COD removal rates of treatment technologies V1-V5 after membership function processing are: V1: ; V2: ; V3: ; V4: ; V5: ; The difficulty index processing is implemented using the following membership function: ; The implementation difficulty of processing technologies V1-V5 is processed using membership functions: V1: ; V2: ; V3: ; V4: ; V5: ; The remaining indicators are calculated using the above method to form a fuzzy relationship matrix R with 14 rows and 5 columns. The elements of the matrix R1, the number of matrix rows The elements of the matrix R2, the number of matrix rows The elements of the matrix R3, the number of rows of the matrix The elements of form the matrix R4.

[0060] After calculation, the fuzzy relationship matrix obtained is: ; ; ; ; The fuzzy matrix R after operation is saved as data in the database.

[0061] Decision makers conduct secondary indicator evaluation according to the indicator evaluation rules, use the 1 to 9 scale method to determine the relative importance of each indicator, and construct an evaluation matrix.

[0062] The first-level evaluation matrix is a judgment matrix P constructed by processing performance indicators, technical performance indicators, economic performance indicators, and basic condition indicators. According to the judgment of this embodiment, the matrix P is as follows: ; according to Figure 3The secondary evaluation indicators shown are used to construct the secondary evaluation matrices P1, P2, P3, and P4.

[0063] ; ; ; ; according to Figure 1 As shown, the evaluation matrix needs to be checked for consistency. If the matrix passes the consistency check, the weight set of the judgment matrix is calculated. In the above description, the judgment matrix is checked in the MATLAB environment, and the calculation results are shown in Table 4.

[0064]

[0065] According to the calculation results, the weight set of this embodiment is as follows: ; ; ; ; ; According to the calculation rules described in Implementation 1, the weight sets W1, W2, W3, and W4 corresponding to the secondary indicators are compounded with the fuzzy matrices R1, R2, R3, and R4.

[0066] ; ; ; ; According to the calculation rules in the above description, the four secondary judgment matrices are vertically spliced into a primary judgment matrix B. The resulting judgment matrix is as follows: ; The first-level indicator weight set W and the first-level judgment matrix B are weighted and compounded again, and the calculation result is: ; The calculation result T indicates that among the five technologies provided in this embodiment, the comprehensive evaluation scores V1-V5 are 49.975, 38.486, 55.675, 36.639, and 46.985 points respectively.

[0067] The diagnostic layer outputs the calculation results as a bar chart or line chart, such as Figure 4The system database will output the engineering case information of the process and provide it to the decision maker in text form to help the decision maker process the decision plan and determine whether it needs to be revised or changed.

[0068] This embodiment does not provide a decision reference portion of the engineering case information, and thus in this embodiment, the best decision solution selected is the V3 process.

[0069] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A decision support method for rural domestic sewage treatment technology based on the combination of AHP and fuzzy relation matrix, characterized by: The following steps are involved: Step 1: Obtain information on sewage treatment technologies applicable to rural areas and build a database; Step 2: Construct the rules of the secondary expert scoring system and membership function, quantify the information in the database, and obtain the fuzzy relationship matrix; Step 3: Indicator evaluation: Evaluate the relative importance of indicators at all levels and construct an evaluation matrix; Step 4: Perform consistency test on the evaluation matrix. If it fails the test, the indicator evaluation is performed again. If it passes the test, AHP is used to determine the corresponding weight coefficient of each indicator in the evaluation process to form a weight set corresponding to the indicator; Step 5: Perform a composite operation on the weight set corresponding to the indicators and the fuzzy relationship matrix. The process with the highest score in the calculation results is the best decision-making solution.

2. The rural domestic sewage treatment technology decision support method based on the combination of AHP and fuzzy relationship matrix according to claim 1 is characterized in that: The database in step one includes treatment performance indicators, technical performance indicators, economic performance indicators and basic condition indicators for each process; the treatment performance indicators include COD treatment rate, SS treatment rate, TN treatment rate, TP treatment rate and ammonia nitrogen treatment rate; the technical performance indicators include implementation difficulty and management difficulty; the economic performance indicators include land area, construction cost and management cost; the basic condition indicators include pipe network coverage, per capita drainage volume, population density and per capita GDP.

3. The rural domestic sewage treatment technology decision support method based on the combination of AHP and fuzzy relationship matrix according to claim 2 is characterized in that: In the step 1, all indicators of each process are taken as a separate column and set as columns V1 to Vn, where V1-Vn represent n processes in the database, and then drawn into a processing technology information table.

4. The rural domestic sewage treatment technology decision support method based on the combination of AHP and fuzzy relation matrix according to claim 2 is characterized in that: In step 2, the secondary expert scoring system is constructed as follows: The factor set of the first-level indicator system is as follows: ={processing performance, technical performance, economic performance, basic conditions}; The factor set of the secondary indicators under the primary indicator system is as follows: {COD removal rate, SS removal rate, TN removal rate, TP removal rate, ammonia nitrogen removal rate}; {difficulty of implementation, difficulty of management}; {construction cost, operating cost}; {Pipeline network coverage, per capita drainage volume, population density, per capita GDP}.

5. The rural domestic sewage treatment technology decision support method based on the combination of AHP and fuzzy relationship matrix according to claim 4 is characterized in that: In step 2, the rules of the membership function are as follows: For quantitative indicators, including treatment performance indicators, economic performance indicators and basic condition indicators, the membership functions used are as follows: ； in: is the jth secondary indicator under the i-th primary indicator, is the minimum value of the indicator in the database information, is the maximum value of the indicator in the database information, and all indicator values are positive; For qualitative indicators, including technical performance indicators, the membership function used is as follows: ； in: is the jth secondary indicator under the i-th primary indicator, The values are graded according to relative difficulty, and are divided into five levels: easy, relatively easy, medium, relatively difficult, and difficult, corresponding to values 2, 4, 6, 8, and 10 respectively. In addition, there are five more levels between the two levels: 1, 3, 5, 7, and 9, for a total of 10 difficulty levels for evaluation; The indicator information in the technical information table is processed according to the rules of the membership function to form the secondary fuzzy matrices R1, R2, R3, and R4, which are matrices composed of the indicator values of the four scheme layers: processing performance indicators, technical performance indicators, economic performance indicators, and basic condition indicators. The four secondary fuzzy matrices are then vertically spliced to obtain the primary fuzzy matrix R.

6. The rural domestic sewage treatment technology decision support method based on the combination of AHP and fuzzy relation matrix according to claim 4 is characterized in that: In the step 3, the 1-9 scaling method is used to determine the relative importance of each pair of indicators and construct an evaluation matrix, including a primary evaluation matrix and a secondary evaluation matrix. The primary evaluation matrix P is an evaluation matrix for the treatment performance indicators, technical performance indicators, economic performance indicators, and basic condition indicators. The secondary evaluation matrix is an evaluation matrix for the secondary indicators in each primary indicator, which are P1, P2, P3, and P4 respectively. The evaluation matrix elements are required to meet the following rules: ； ； in, Indicates that under the current evaluation matrix, The element relative to the The importance of the elements, Indicates the The element relative to the The importance of the elements, and Indicates the number of rows and columns of the evaluation matrix.

7. The rural domestic sewage treatment technology decision support method based on the combination of AHP and fuzzy relation matrix according to claim 1 is characterized in that: In step 4, the consistency check rules are as follows: Consistency indicators: ; in, is the maximum eigenvalue of the matrix, is the number of rows in the matrix, the consistency index When it is 0, it means the matrix is consistent. The larger it is, the more serious the inconsistency of the matrix; Calculate the consistency ratio of the matrix , the calculation rules are as follows: ； in, is a random consistency indicator. If the diagnostic layer satisfies , it means that the logic is consistent and the next step can be carried out. Otherwise, the logic is contradictory and the system will return to step 3 to re-evaluate the relative importance of each indicator until the constructed evaluation matrix passes the consistency test and enters step 4.

8. The rural domestic sewage treatment technology decision support method based on the combination of AHP and fuzzy relation matrix according to claim 1 is characterized in that: In step 4, the importance of the matrix elements is ranked hierarchically one by one, that is, a weight is assigned to each factor set to form a weight set. The weight set calculation rule is as follows: 1) First, normalize each column of the evaluation matrix P, P1, P2, P3, and P4, namely: ； in, Indicates the first i Liedi j The elements of the row, n is the number of columns of the evaluation matrix; 2) After normalizing the indicators in each evaluation matrix by column, a new matrix is formed, and the elements of each row in the new matrix are added together to obtain the operation matrix after the elements of each row of the evaluation matrix are added together. ,Right now: ； The matrix consists of n rows and 1 column, where n is the number of first-level indicators or the number of second-level indicators corresponding to each first-level indicator; in, For each operation matrix The internal elements of the matrix; 3) The operation matrix obtained in step 2) Normalize again to obtain the final first-level indicator weight matrix W and second-level indicator weight matrices W1, W2, W3, and W4. The calculation formula is as follows: ； ； in, is the first in the operation matrix corresponding to the first-level index j matrix elements; Indicates the i The j-th matrix element in the operation matrix corresponding to the secondary index, is the secondary indicator weight set matrix.

9. The rural domestic sewage treatment technology decision support method based on the combination of AHP and fuzzy relationship matrix according to claim 1 is characterized in that: In step 5, the fuzzy matrix is composed of four fuzzy matrices R1, R2, R3, and R4. R1 is the processing performance index matrix, R2 is the technical performance index matrix, R3 is the economic performance index, and R4 is the basic condition index fuzzy matrix. R1, R2, R3, and R4 are weighted averaged with the weight sets W1, W2, W3, and W4 of the secondary indicators respectively to obtain the secondary judgment matrices B1, B2, B3, and B4: ； ； ； ； The calculation expression is: ； in, is the specific weight value of the secondary indicator weight concentration, The values of various indicators after constructing the fuzzy matrix for the current scheme according to the principle of membership function; The four secondary judgment matrices are vertically spliced into a primary judgment matrix B: ； The first-level indicator weight set W and the first-level judgment matrix B are weighted and compounded again, and the calculation result is: .

10. The rural domestic sewage treatment technology decision support method based on the combination of AHP and fuzzy relation matrix according to claim 1 is characterized in that: In the step 1, actual engineering case information is collected to build a reference database; In step five, the process with the highest score in the calculation results is compared with the actual engineering cases in the reference database.

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