CN116502808A - Multi-drainage-basin scheduling evaluation method and system based on cluster group decision - Google Patents
Multi-drainage-basin scheduling evaluation method and system based on cluster group decision Download PDFInfo
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Abstract
The invention relates to the technical field of drainage basin scheduling data processing, in particular to a multi-drainage basin scheduling evaluation method and system based on cluster group decision. The method comprises the following steps: acquiring scheduling data under different working conditions of a river basin; establishing a multi-target scheduling index system, wherein the multi-target scheduling evaluation index system comprises target layer data, item layer data and index layer data, carrying out data normalization processing, classifying scheduling experts of different categories, giving weights among the categories and weights in the categories to the experts of different categories, improving AHP method weight calculation, thereby obtaining multi-target scheduling weight data, and establishing a multi-target weighted decision matrix. According to the invention, the improved AHP cluster analysis method is adopted to comprehensively calculate the expert weight from two aspects of the category and the category, so that the influence of differences caused by different experts is reduced, the process of picking out a proper scheme by taking the closeness degree of positive and negative ideal solutions of each scheduling method as a standard is adopted, and the decision efficiency is improved.
Description
Technical Field
The invention relates to the technical field of drainage basin scheduling data processing, in particular to a multi-drainage basin scheduling evaluation method and system based on cluster group decision.
Background
The multi-basin scheduling evaluation method is a comprehensive evaluation method aiming at the problems of multi-basin water safety, water resource, water ecology and water environment scheduling, and aims to evaluate, compare and optimize the water resource scheduling schemes in rich, flat and dead water years. The main idea is to establish a comprehensive evaluation system to evaluate the scheduling schemes of the rich, flat and dead water years, and finally obtain a high-quality drainage basin scheduling scheme. The existing scheduling evaluation method often depends on expert or manual decision, and often results in lower decision efficiency of the multi-basin water resource scheduling problem.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a multi-basin scheduling evaluation method and system based on cluster group decision to solve at least one of the above-mentioned problems.
The application provides a multi-basin scheduling evaluation method based on cluster group decision, which comprises the following steps:
step S1: acquiring drainage basin scheduling data under different drainage basin working conditions to establish multi-target scheduling index system data;
step S2: carrying out data normalization processing on the drainage basin scheduling data by utilizing the multi-target scheduling index system data, and classifying scheduling experts of different categories so as to obtain similar drainage basin scheduling experts and drainage basin scheduling experts of different categories;
Step S3: performing improved AHP method weight calculation according to similar drainage basin scheduling experts and different types of drainage basin scheduling experts, so as to obtain multi-target scheduling weight data;
step S4: generating a multi-target weighted decision matrix according to the multi-target scheduling weight data, and performing evaluation calculation on a preset optimized TOPSIS model and drainage basin scheduling data by using the multi-target weighted decision matrix to generate scheduling evaluation data.
According to the method, the scheduling data of the river basin under different working conditions can be obtained through the step S1, and the multi-target scheduling index system data can be established. This helps to fully understand and evaluate the scheduling of the basin and provides an index system that comprehensively considers different objectives. In step S2, data normalization processing is performed on the stream domain scheduling data. The processing can unify the data in different ranges and units to the same scale, eliminates the absolute difference of the data, and enables comparison and trade-off between different indexes. Scheduling expert classification and improved AHP weighting calculation: the knowledge and experience of different specialists can be fully utilized by classifying the scheduling specialists and performing weight calculation (steps S2 and S3) using the improved Analytic Hierarchy Process (AHP), and authority and credibility of the different specialists are considered. This helps to improve the accuracy and reliability of the evaluation result. In step S4, a multi-objective weighted decision matrix is generated based on the multi-objective scheduling weight data, and the evaluation calculation is performed on the drainage basin scheduling data by using the matrix. By comprehensively considering the weights and the importance of different indexes, the method can obtain the comprehensive evaluation result of the flow field scheduling and help a decision maker to make reasonable scheduling decisions.
Preferably, step S1 is specifically:
the method comprises the steps of obtaining river basin scheduling data of different abundant, flat and withered years to establish a multi-target scheduling evaluation index system, wherein the multi-target scheduling evaluation index system comprises target layer data, project layer data and index layer data, water safety, water resources, water ecology and water environment are the project layer data, the next index of the project layer data is the index layer data, and the scheduling data for river basin investigation comprise:
index layer data taking water security defense as a center, including dike overflow degree data, river channel flow rate data, rainwater pipe network jacking quantity data, drainage basin submerged water depth data, drainage basin submerged area data and drainage basin submerged duration data;
index layer data centering on water resource conservation, including water supply amount analysis data of water resources and regional water demand analysis data;
index layer data centering on water ecological restoration, including ecological base flow data, species diversity index data, regional river connectivity data, soil erosion management rate data, zooplankton density data and benthonic animal density data;
index layer data taking water environment protection as a center comprises water quality standard reaching rate data, chromaticity data, pH data, chlorophyll A concentration data and total nitrogen quantity data of a water functional area.
According to the method, a multi-target scheduling evaluation index system can be established by researching the river basin mechanism and collecting the river basin scheduling data of different years. The index system comprises target layer data, project layer data and index layer data, and covers data in aspects of water safety, water resources, water ecology, water environment and the like. This helps to fully consider aspects of the watershed scheduling, thereby evaluating the scheduling effect more fully and accurately. Under each item level data, index level data is further subdivided. For example, in water security defense project layer data, the index layer data includes the degree of embankment overflow, river flow rate, number of rainwater pipe network jacking, basin submerged depth, basin submerged area, and basin submerged duration. Such subdivision may more particularly describe and quantify various metrics, helping to more accurately evaluate the effect of scheduling. By collecting scheduling data of different abundant, flat and dead years and establishing a multi-target scheduling evaluation index system, the effect of drainage basin scheduling can be comprehensively evaluated. The data of different years can reflect the scheduling conditions under different hydrologic conditions, so that the performance of drainage basin scheduling under different scenes can be better known. After the multi-target scheduling evaluation index system is established, comprehensive data support and decision basis can be provided for a decision maker. By evaluating the data of each index, a decision maker can better know the advantages and disadvantages of drainage basin scheduling and make corresponding decisions and adjustments.
Preferably, step S2 is specifically:
step S21: extracting characteristics of the drainage basin scheduling data by utilizing the multi-target scheduling index system data, and carrying out data normalization processing to obtain the characteristic scheduling data;
step S22: and classifying the scheduling specialists in different categories to obtain weights between different categories and in the categories.
Characteristic scheduling data in the invention: and (3) performing feature extraction on the drainage basin scheduling data by utilizing the multi-target scheduling index system data through step S21. This means that characteristic data having representativeness and importance is extracted from the original schedule data. In this way, redundant data and noise can be reduced, focusing on key features, thereby improving the accuracy and reliability of the evaluation. In step S21, data normalization processing is performed on the feature scheduling data. The processing can unify the data in different ranges and units to the same scale, eliminates the absolute difference of the data, and enables comparison and trade-off between different indexes. The data normalization is helpful to eliminate the dimensional influence among indexes, so that the contribution of different indexes to the evaluation result is more uniform and balanced. In step S22, scheduling experts of different categories are classified, and weights between different categories and within the categories are acquired. Through the classified dispatching expert, the knowledge and experience of different experts can be fully utilized, and the authority and credibility of different experts are considered. This helps to accurately assess the importance of different classes of experts on the scheduling metrics and provides a reliable basis for subsequent weight calculation. Through feature extraction, data normalization processing and scheduling expert classification, the accuracy and the credibility of the evaluation result can be improved. The feature extraction and data normalization processing can eliminate redundant information and data difference, extract key features and unify data scales, so that the scheduling effect is accurately evaluated. Meanwhile, the scheduling expert classification and weight acquisition can fully utilize the knowledge and experience of the expert, and the accuracy and the reliability of weight calculation are improved.
Preferably, step S21 is specifically:
dividing the drainage basin scheduling data into positive influence indexes and negative influence indexes, and scoring a j scheduling scheme in the drainage basin scheduling data by utilizing an i scheduling index in the multi-target scheduling index system data, so as to obtain processed drainage basin scheduling data;
and carrying out normalized scheduling index processing on the processing drainage basin scheduling data so as to obtain characteristic scheduling data, wherein the characteristic scheduling data comprises positive and negative influence scheduling indexes, and the positive and negative influence scheduling indexes are processed according to the following formula:
positively affects the scheduling metrics:；
negatively affecting the scheduling metrics:；
wherein the method comprises the steps ofIs the firstiThe scheduling scheme is at the firstjScore of individual scheduling indicators->And +.>Is->Positive and negative influence scheduling indexes of the normalized score are obtained to obtain a normalized decision matrix +.>Wherein->、/>Respectively the firstjMaximum and minimum values of the individual indicator scores.
According to the method, the indexes with positive influence and negative influence on the scheduling scheme can be identified by dividing the drainage basin scheduling data into the positive influence indexes and the negative influence indexes. This helps to better understand the contribution and impact of each index on the scheduling scheme, thereby providing a more comprehensive and accurate assessment result. And scoring the j scheduling scheme in the drainage basin scheduling data by utilizing the i scheduling index in the multi-target scheduling index system data. By comprehensively considering the scores and weights of the indexes, the comprehensive evaluation can be performed on each scheduling scheme, so that the effect and the performance of the comprehensive evaluation are more comprehensively reflected, the normalized scheduling index processing is performed on the processing basin scheduling data in the step S21, and the normalized scores of the positive and negative influence scheduling indexes are obtained. Through normalization processing, scores of different indexes are unified to the same scale, absolute differences of data are eliminated, and comparison and trade-off between different indexes can be performed. This helps to evaluate the goodness of the scheduling scheme more accurately. By dividing the positive influence index and the negative influence index and performing scoring processing and normalized scheduling index processing, the accuracy and comparability of the evaluation result can be improved. The processing mode fully considers the weights and the contributions of different indexes, unifies the scores of the indexes on the same scale, and enables the different scheduling schemes to have comparability, thereby supporting more scientific and comprehensive decision.
Preferably, step S22 is specifically:
acquisition ofhThe individual drainage basin scheduling expert applies a 1-7 scoring method to give a discrimination matrix for comparing different scheduling indexes in pairsPWherein is provided with a firstxBit stream basin scheduling expert and the thyThe discriminant matrix of the bit stream domain scheduling expert is respectively、/>；
Pair discrimination matrixPBy usingPerforming consistency test on the judgment matrix by using the AHP method, if the consistency test is not met, executing the suspension operation, and if the consistency test is met, using a characteristic root method to obtain the final product、/>Obtain the firstx、yThe index weights assigned by the bit expert are +.>、/>Wherein->，/>，/>、/>Respectively the firstx、yBit stream basin expert assignmentjThe weight of the item scheduling indicator is determined,ncalculating the drainage basin scheduling expert (L) by clustering analysis of the drainage basin scheduling expert index weight for the index number>And->Compatibility between:
；
wherein cosine is usedTo express +.>And->Compatibility between->The closer the calculated cosine is to 1, representing +.>And->The more similar the basin scheduling specialists are; otherwise, the two classes belong to different classes;
step S23: by means ofhDerived from a discriminant matrix given by a respective basin scheduling experthIndividual index weights, build a compatibility matrix，/>；
Step S24: at the position ofIn the compatibility matrix, selecting the maximum value of each row and each column, wherein the maximum value is selected without considering the numerical value on the diagonal line +. >，/>Two bit-stream domain scheduling expert corresponding to the value of (2)>、/>Let->，/>Is a class expert;
step S25: removing expert、/>Add->Rearranging the compatibility matrix after class expert, wherein；
Step S26: re-selecting the maximum value of the new compatibility matrix, and merging the drainage basin scheduling experts of the same class;
step S27: separating the classified drainage basin scheduling expert, adding a new class to obtain a new compatibility matrix, and the like until the expert is classified into the same class;
step S28: and drawing a cluster map by using the compatibility value during classification, determining a threshold T, determining the expert with the classification compatibility interval smaller than T as the similar drainage basin scheduling expert, and determining the expert with the classification compatibility interval larger than T as the different drainage basin scheduling expert.
According to the method, the drainage basin scheduling expert can be classified by analyzing the discrimination matrix and the compatibility matrix, and the scheduling expert of the same class and different classes can be identified. This helps to determine the weight and impact of different experts in the evaluation and decision. The weight distribution of each scheduling expert to the index can be calculated through an AHP method and a characteristic root method. These weights reflect how important the expert is to the index and can provide a reference in the evaluation and decision. Through drawing the cluster map, the relationship among the experts and the classification result can be intuitively displayed. This helps to better understand the similarity and variability between experts, providing clearer guidance for decision making. By selecting the experts with the consistency intervals smaller than the threshold value to be classified as the same class, the scheduling experts can be effectively classified and divided in practical application. This helps the organization expert team to make more targeted evaluations and decisions.
Preferably, step S3 is specifically:
step S31: according to step 2, finally obtainqClass basin scheduling expert, the firstx、yBit stream basin scheduling expert、/>Calculating the consistency weight difference value of the x bit stream domain scheduling expert and other experts by using the index weight obtained by the AHP method>；
；
Step S32: set the firstqThe number of class basin scheduling experts is，/>Is the firstqWeight difference value between class expert and non-class expert>Refer tohBelonging to the first expertqExpert of class;
；
step S33: is provided withIs the firstqExpert weight coefficient among class expert class, can get the basin scheduling expert class weight +.>；
，/>；
；
Step S34: is arranged at the firstqThe number of experts among class specialists is，/>Is the first in the categoryxBit expert judgment matrixIdentity ratio of (1), thenxExpert in the first placeqWeight coefficient in expert class in class expert +.>The method comprises the following steps:
；
wherein the method comprises the steps ofbFor the assumed scale factor, the weight coefficient in expert class is determinedIs an important factor in (a) and (b),bthe larger the consistency ratioCRWeight coefficient within expert class +.>The more the middle-warmer is embodied, i.e.)>The greater the phase difference, the more generallybFor real numbers greater than zero, reference is made herein to experiencebTaking 10;
step S35: combining and considering drainage basin scheduling expert weight value to obtainWherein- >Is the firstxComprehensive weight of a bit expert, modifying weight of an AHP method by using the expert weight, and obtaining final weight of scheduling indexes of different flow domains>Wherein the multi-objective scheduling weight data is the final weight of the scheduling indexes of different flow domains;
expert comprehensive weight: ；
final weight of the index: wherein->The final weights of the metrics are scheduled for different flow domains.
The expert weight difference is considered in the invention: by calculating the consistency weight difference value and the weight difference value between the categories, the method can accurately reflect the difference of the expert in weight distribution. This helps to identify inconsistent situations of authoritative specialists and weight distribution, improving the accuracy of decisions. And (5) adjusting weight distribution: by calculating the expert weight coefficient and the weight coefficient in the category, the method can properly adjust the weight according to the consistency ratio among the experts and the weight difference among the categories. This helps to more accurately reflect the weight distribution of different experts and categories. And (3) comprehensive weight calculation: by calculating the comprehensive weight and the final weight, the method can combine the opinion and the weight values of a plurality of experts to obtain more comprehensive and accurate decision index weight. This helps to improve the scientificity and reliability of the decision.
Preferably, step S4 is specifically:
step S41: is provided withnThe weight of each index isWherein->Is the firstjWeights of the individual indicators, weights of the indicators to be determinedWAdding to decision matrixRIn a decision matrix composing a weighting +.>The method comprises the following steps:
；
wherein the method comprises the steps ofIs the firstiThe first sewage treatment plant isjAdding attribute values of index weights of the individual indexes;
step S42: computing a positive ideal solution for a drainage basin scheduling index by a weighted decision matrixAnd negative ideal solution->；
；
；
Step S43: calculation ofmThe Euclidean distance between each scheme and ideal solution in each drainage basin scheduling scheme is set、/>Respectively the firstiThe Euclidean distance between the individual basin scheduling scheme and the positive and negative ideal solutions;
；
；
step S44: calculating relative closeness of different flow field scheduling schemes and ideal solutionsObtaining the relative closeness of the scheduling schemes of different abundant, flat and dead water years, when +.>When the value is closer to 1, the scheme is closer to an ideal solution, and the scheme is better, wherein the scheduling evaluation data is the relative closeness of scheduling schemes with different abundant, flat and withered water years;
wherein->Is the relative proximity.
Index weights are considered in the present invention: through the weighted decision matrix, the method can carry out weighted processing on the decisions according to the index weights, so that the importance of different indexes is accurately reflected. This helps ensure that the decision process is more objective and accurate. By calculating the positive ideal solution and the negative ideal solution, the method can determine the ideal value of each index under the condition of maximization and minimization. This helps to define decision targets and provides a reference criterion for assessing the goodness of different schemes. By calculating the Euclidean distance and the relative closeness, the method can quantitatively evaluate the difference between different schemes and ideal solutions. This helps determine the relative merits of the solution and provides a basis for decision making.
Preferably, a multi-basin scheduling evaluation system based on cluster group decision, the system comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform a cluster-decision-based multi-basin scheduling assessment method as claimed in any one of the above.
The invention has the beneficial effects that: (1) And establishing a drainage basin multi-target scheduling comprehensive evaluation model, and a multi-level evaluation system of 4 secondary indexes and 22 tertiary indexes.
(2) And the expert weight is comprehensively calculated from two aspects of the category and the category by adopting a cluster analysis method, so that the index weight determined by the AHP method is corrected, and the influence of differences caused by different experts is reduced.
(3) And using a TOPSIS method, and taking the closeness degree of positive and negative ideal solutions of each scheduling method as a standard for judging the multi-target scheduling scheme. The process of picking out a proper scheme under the complex factors is simplified, and the decision efficiency is improved.
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FIG. 1 shows a flow chart of steps of a cluster-decision-based multi-basin scheduling assessment method according to an embodiment.
Detailed Description
The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.
It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
The present application provides an embodiment in which the TOPSIS method is a common and effective method for solving the multi-objective decision problem, and uses the closeness of each scheme to the positive and negative ideal solutions as the basis for comparing the advantages and disadvantages of each scheme. The multi-objective scheduling can use a TOPSIS method to take importance degrees of different indexes into consideration in a form of weights, and a decision matrix and an evaluation model are established.
And scoring m scheduling methods by an expert for n indexes, and normalizing the data. The method of normalization is also different for different indexes. Is provided withScore at the j index for the i-th scheduling method,/for the j-th index>Is->Normalized score, positive and negative indexes are processed according to the following formula:
positive index: ；
inverse index: ；
then a normalized decision matrix is obtained. Wherein->、/>The maximum and minimum of the j-th index score, respectively. Let the weights of n indexes be +.>Wherein->Is the weight of the j index. Adding the determined index weight W into a decision matrix R to form a weighted decision matrix +.>The method comprises the following steps:
wherein->And adding the attribute value of the index weight to the jth index for the ith scheduling method.
Let the weights of n indexes beWherein- >Is the weight of the j index. Adding the determined index weight W into a decision matrix R to form a weighted decision matrix +.>The method comprises the following steps:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein->And adding the attribute value of the index weight to the jth index for the ith scheduling method.
The positive ideal solution is the solution that the attribute value of each index in the weighted decision matrix reaches the optimum, and the negative ideal solution is the opposite. Is provided with、/>Positive ideal solution and negative ideal solution of the weighted decision matrix V, respectively, +.>，Wherein->、/>The optimal attribute value and the worst attribute value of the j index are respectively:
；
；
calculating Euclidean distance between each scheme and ideal solution in m scheduling methods, and setting、/>The Euclidean distance between the ith scheduling method and the positive and negative ideal solutions is respectively:
；/> ；
calculating relative closeness of different schemes and ideal solutions：/> The method comprises the steps of carrying out a first treatment on the surface of the When->The closer the value is to 1, the closer the solution is to the ideal solution, and the better the solution.
The traditional AHP method does not consider that the effectiveness of the judgment matrix is different due to the fact that the knowledge structure and the cognition level of the expert are different. Based on the AHP method, the multi-objective scheduling classifies the experts by using a systematic clustering method, and gives different weight values in and among different classes to different expert classes, so that the index weight is corrected. The AHP method for cluster group decision multi-attribute determines the weight:
The h experts apply the 1-9 scale method to give the judgment matrix P with indexes compared pairwise, and then the consistency test of the judgment matrix is carried out according to the AHP method. Set the firstExpert and->The judgment matrix of the bit expert is +.>、/>And->、/>Pass the consistency check.
By applying characteristic root methods、/>Find the->、/>The index weights assigned by the bit expert are +.>、/>Wherein，/>，/>、/>Respectively +.>、/>Expert gives->Item indexWeight of->The number of indexes.
By cluster analysis, calculationAnd->Compatibility between them is>；
Cosine of vector angleTo express +.>And->Compatibility between->. The closer the compatibility is to 1, the vector +.>And->The more similar the two; otherwise, the more distant. Reuse->The judgment matrix given by the expert gives +.>The index weights are used for establishing a compatibility matrix>，/>；
The expert is classified by means of the compatibility as follows:
1) At the position ofSelecting the maximum value of all elements except diagonal line in matrixDefinitions->Corresponding two experts->、/>Is a new category->，/>；
2) Removing expert、/>Add->The compatibility matrix is then rearranged. Wherein->；
3) Selecting the maximum value among all elements of the new matrix except the diagonalThe corresponding expert is combined with the expert of the last class.
4) Removing the classified expert and adding a new category to obtain a new compatibility matrix;
5) Repeating 3, 4 until all experts are incorporated into the same category;
6) And drawing a cluster map by using the compatibility value during classification, determining a threshold T, and classifying the experts with the classification compatibility interval smaller than T into one category and the experts with the classification compatibility interval larger than T into different categories respectively to finish classification.
Classifying the h experts into Q classes by using a clustering method, and setting、/>And respectively applying an AHP method to obtain index weights for the x-th expert and the y-th expert. Then the x-th expert is consistent with other experts for weight difference value +.>The method comprises the following steps: /> Let the number of class q experts be +.>，/>The value of the consistency weight difference value between the class of the q-th expert and the class of the non-native expert is the consistency weight difference value of the class q expert>Arithmetic mean of (c): /> Wherein->Refer toThe h experts belong to the q-th class of experts. Is provided with->The model of the inter-class expert weight can be obtained as follows for the inter-class expert weight coefficient of the q-th class expert:
，/>the method comprises the steps of carrying out a first treatment on the surface of the The function is guided, and Lagrane function solution is introduced: />The method comprises the steps of carrying out a first treatment on the surface of the For->And->And (3) solving deviation guide, and finishing to obtain:
；
among the same class of specialists, the consistency of the judgment matrix of each specialist is different, and the representativeness and the credibility of the judgment matrix are also different. The smaller the consistency coefficient is considered to be, the more representative, and the larger the weight factor given. The number of the experts is set in the q-th class of experts ，/>Judging matrix for x-th expert in the class>In the expert class of the expert of the q-th class +.>The method comprises the following steps: />Wherein b is a hypothetical scale factor, which determines the weighting factor in the expert class +.>Is an important factor of (a). b is larger, the difference of the consistency ratio CR weights the coefficients +.>The more the middle-warmer is embodied, i.e.)>The greater the phase difference. Typically b is a real number greater than zero.
Combining expert weight coefficients among different categories with expert weight coefficients in the categories to obtain expert comprehensive weightWherein->Is the comprehensive weight of the x-th expert. Correcting the weight of AHP method with the expert weight to obtain final weight of different indexes of the project>. Expert comprehensive weight: /> Final weight of the index: /> Wherein->The final weights of the metrics are scheduled for different flow domains.
The drainage basin multi-target scheduling evaluation system starts from three dimensions of a target layer, a project layer and an index layer, and is as follows:
basin multi-objective scheduling comprehensive evaluation: after the multi-target scheduling method is determined, according to a multi-level evaluation system of 4 secondary indexes and 22 tertiary indexes in the table, expert weights are comprehensively calculated from two aspects of the category and the category by adopting a clustering analysis method, the index weights determined by an AHP method are corrected, and the multi-target scheduling method is compared by combining a TOPSIS model, so that the optimal multi-target scheduling method is optimized.
Referring to fig. 1, the present application provides a multi-basin scheduling evaluation method based on cluster group decision, which includes the following steps:
step S1: acquiring drainage basin scheduling data under different drainage basin working conditions to establish multi-target scheduling index system data;
step S2: carrying out data normalization processing on the drainage basin scheduling data by utilizing the multi-target scheduling index system data, and classifying scheduling experts of different categories so as to obtain similar drainage basin scheduling experts and drainage basin scheduling experts of different categories;
step S3: performing improved AHP method weight calculation according to similar drainage basin scheduling experts and different types of drainage basin scheduling experts, so as to obtain multi-target scheduling weight data;
step S4: generating a multi-target weighted decision matrix according to the multi-target scheduling weight data, and performing evaluation calculation on a preset optimized TOPSIS model and drainage basin scheduling data by using the multi-target weighted decision matrix to generate scheduling evaluation data.
According to the method, the scheduling data of the river basin under different working conditions can be obtained through the step S1, and the multi-target scheduling index system data can be established. This helps to fully understand and evaluate the scheduling of the basin and provides an index system that comprehensively considers different objectives. In step S2, data normalization processing is performed on the stream domain scheduling data. The processing can unify the data in different ranges and units to the same scale, eliminates the absolute difference of the data, and enables comparison and trade-off between different indexes. Scheduling expert classification and improved AHP weighting calculation: the knowledge and experience of different specialists can be fully utilized by classifying the scheduling specialists and performing weight calculation (steps S2 and S3) using the improved Analytic Hierarchy Process (AHP), and authority and credibility of the different specialists are considered. This helps to improve the accuracy and reliability of the evaluation result. In step S4, a multi-objective weighted decision matrix is generated based on the multi-objective scheduling weight data, and the evaluation calculation is performed on the drainage basin scheduling data by using the matrix. By comprehensively considering the weights and the importance of different indexes, the method can obtain the comprehensive evaluation result of the flow field scheduling and help a decision maker to make reasonable scheduling decisions.
Preferably, step S1 is specifically:
the method comprises the steps of obtaining river basin scheduling data of different abundant, flat and withered years to establish a multi-target scheduling evaluation index system, wherein the multi-target scheduling evaluation index system comprises target layer data, project layer data and index layer data, water safety, water resources, water ecology and water environment are the project layer data, the next index of the project layer data is the index layer data, and the scheduling data for river basin investigation comprise:
index layer data taking water security defense as a center, including dike overflow degree data, river channel flow rate data, rainwater pipe network jacking quantity data, drainage basin submerged water depth data, drainage basin submerged area data and drainage basin submerged duration data;
index layer data centering on water resource conservation, including water supply amount analysis data of water resources and regional water demand analysis data;
index layer data centering on water ecological restoration, including ecological base flow data, species diversity index data, regional river connectivity data, soil erosion management rate data, zooplankton density data and benthonic animal density data;
index layer data taking water environment protection as a center comprises water quality standard reaching rate data, chromaticity data, pH data, chlorophyll A concentration data and total nitrogen quantity data of a water functional area.
According to the method, a multi-target scheduling evaluation index system can be established by researching the river basin mechanism and collecting the river basin scheduling data of different years. The index system comprises target layer data, project layer data and index layer data, and covers data in aspects of water safety, water resources, water ecology, water environment and the like. This helps to fully consider aspects of the watershed scheduling, thereby evaluating the scheduling effect more fully and accurately. Under each item level data, index level data is further subdivided. For example, in water security defense project layer data, the index layer data includes the degree of embankment overflow, river flow rate, number of rainwater pipe network jacking, basin submerged depth, basin submerged area, and basin submerged duration. Such subdivision may more particularly describe and quantify various metrics, helping to more accurately evaluate the effect of scheduling. By collecting scheduling data of different abundant, flat and dead years and establishing a multi-target scheduling evaluation index system, the effect of drainage basin scheduling can be comprehensively evaluated. The data of different years can reflect the scheduling conditions under different hydrologic conditions, so that the performance of drainage basin scheduling under different scenes can be better known. After the multi-target scheduling evaluation index system is established, comprehensive data support and decision basis can be provided for a decision maker. By evaluating the data of each index, a decision maker can better know the advantages and disadvantages of drainage basin scheduling and make corresponding decisions and adjustments.
Preferably, step S2 is specifically:
step S21: extracting characteristics of the drainage basin scheduling data by utilizing the multi-target scheduling index system data, and carrying out data normalization processing to obtain the characteristic scheduling data;
step S22: and classifying the scheduling specialists in different categories to obtain weights between different categories and in the categories.
Characteristic scheduling data in the invention: and (3) performing feature extraction on the drainage basin scheduling data by utilizing the multi-target scheduling index system data through step S21. This means that characteristic data having representativeness and importance is extracted from the original schedule data. In this way, redundant data and noise can be reduced, focusing on key features, thereby improving the accuracy and reliability of the evaluation. In step S21, data normalization processing is performed on the feature scheduling data. The processing can unify the data in different ranges and units to the same scale, eliminates the absolute difference of the data, and enables comparison and trade-off between different indexes. The data normalization is helpful to eliminate the dimensional influence among indexes, so that the contribution of different indexes to the evaluation result is more uniform and balanced. In step S22, scheduling experts of different categories are classified, and weights between different categories and within the categories are acquired. Through the classified dispatching expert, the knowledge and experience of different experts can be fully utilized, and the authority and credibility of different experts are considered. This helps to accurately assess the importance of different classes of experts on the scheduling metrics and provides a reliable basis for subsequent weight calculation. Through feature extraction, data normalization processing and scheduling expert classification, the accuracy and the credibility of the evaluation result can be improved. The feature extraction and data normalization processing can eliminate redundant information and data difference, extract key features and unify data scales, so that the scheduling effect is accurately evaluated. Meanwhile, the scheduling expert classification and weight acquisition can fully utilize the knowledge and experience of the expert, and the accuracy and the reliability of weight calculation are improved.
Preferably, step S21 is specifically:
dividing the drainage basin scheduling data into positive influence indexes and negative influence indexes, and scoring a j scheduling scheme in the drainage basin scheduling data by utilizing an i scheduling index in the multi-target scheduling index system data, so as to obtain processed drainage basin scheduling data;
and carrying out normalized scheduling index processing on the processing drainage basin scheduling data so as to obtain characteristic scheduling data, wherein the characteristic scheduling data comprises positive and negative influence scheduling indexes, and the positive and negative influence scheduling indexes are processed according to the following formula:
positively affects the scheduling metrics:；
negatively affecting the scheduling metrics:；
wherein the method comprises the steps ofIs the firstiThe scheduling scheme is at the firstjScore of individual scheduling indicators->And +.>Is->Positive and negative influence scheduling indexes of the normalized score are obtained to obtain a normalized decision matrix +.>Wherein->、/>Respectively the firstjMaximum and minimum values of the individual indicator scores.
According to the method, the indexes with positive influence and negative influence on the scheduling scheme can be identified by dividing the drainage basin scheduling data into the positive influence indexes and the negative influence indexes. This helps to better understand the contribution and impact of each index on the scheduling scheme, thereby providing a more comprehensive and accurate assessment result. And scoring the j scheduling scheme in the drainage basin scheduling data by utilizing the i scheduling index in the multi-target scheduling index system data. By comprehensively considering the scores and weights of the indexes, the comprehensive evaluation can be performed on each scheduling scheme, so that the effect and the performance of the comprehensive evaluation are more comprehensively reflected, the normalized scheduling index processing is performed on the processing basin scheduling data in the step S21, and the normalized scores of the positive and negative influence scheduling indexes are obtained. Through normalization processing, scores of different indexes are unified to the same scale, absolute differences of data are eliminated, and comparison and trade-off between different indexes can be performed. This helps to evaluate the goodness of the scheduling scheme more accurately. By dividing the positive influence index and the negative influence index and performing scoring processing and normalized scheduling index processing, the accuracy and comparability of the evaluation result can be improved. The processing mode fully considers the weights and the contributions of different indexes, unifies the scores of the indexes on the same scale, and enables the different scheduling schemes to have comparability, thereby supporting more scientific and comprehensive decision.
Preferably, step S22 is specifically:
acquisition ofhThe individual drainage basin scheduling expert applies a 1-7 scoring method to give a discrimination matrix for comparing different scheduling indexes in pairsPWherein is provided with a firstxBit stream basin scheduling expert and the thyThe discriminant matrix of the bit stream domain scheduling expert is respectively、/>；
Pair discrimination matrixPPerforming consistency test on the judgment matrix by using an AHP method, if the consistency test is not met, executing a suspension operation, and if the consistency test is met, using a characteristic root method to obtain a result、/>Obtain the firstx、yThe index weights assigned by the bit expert are +.>、/>Wherein->，/>，/>、/>Respectively the firstx、yBit stream basin expert assignmentjThe weight of the item scheduling indicator is determined,ncalculating the drainage basin scheduling expert (L) by clustering analysis of the drainage basin scheduling expert index weight for the index number>And->Compatibility between:
；
wherein cosine is usedTo express +.>And->Compatibility between->The closer the calculated cosine is to 1, representing +.>And->BasinThe more similar the scheduling specialists are; otherwise, the two classes belong to different classes;
step S23: by means ofhDerived from a discriminant matrix given by a respective basin scheduling experthIndividual index weights, build a compatibility matrix，/>；
Step S24: at the position ofIn the compatibility matrix, selecting the maximum value of each row and each column, wherein the maximum value is selected without considering the numerical value on the diagonal line +. >，/>Two bit-stream domain scheduling expert corresponding to the value of (2)>、/>Let->，/>Is a class expert;
step S25: removing expert、/>Add->Rearranging the compatibility matrix after class expert, wherein；
Step S26: re-selecting the maximum value of the new compatibility matrix, and merging the drainage basin scheduling experts of the same class;
step S27: separating the classified drainage basin scheduling expert, adding a new class to obtain a new compatibility matrix, and the like until the expert is classified into the same class;
step S28: and drawing a cluster map by using the compatibility value during classification, determining a threshold T, determining the expert with the classification compatibility interval smaller than T as the similar drainage basin scheduling expert, and determining the expert with the classification compatibility interval larger than T as the different drainage basin scheduling expert.
According to the method, the drainage basin scheduling expert can be classified by analyzing the discrimination matrix and the compatibility matrix, and the scheduling expert of the same class and different classes can be identified. This helps to determine the weight and impact of different experts in the evaluation and decision. The weight distribution of each scheduling expert to the index can be calculated through an AHP method and a characteristic root method. These weights reflect how important the expert is to the index and can provide a reference in the evaluation and decision. Through drawing the cluster map, the relationship among the experts and the classification result can be intuitively displayed. This helps to better understand the similarity and variability between experts, providing clearer guidance for decision making. By selecting the experts with the consistency intervals smaller than the threshold value to be classified as the same class, the scheduling experts can be effectively classified and divided in practical application. This helps the organization expert team to make more targeted evaluations and decisions.
Preferably, step S3 is specifically:
step S31: according to step 2, finally obtainqClass basin scheduling expert, the firstx、yBit stream basin scheduling expert、/>Calculating the consistency weight difference value of the x bit stream domain scheduling expert and other experts by using the index weight obtained by the AHP method>；
；
Step S32: set the firstqThe number of class basin scheduling experts is，/>Is the firstqWeight difference value between class expert and non-class expert>Refer tohBelonging to the first expertqExpert of class;
；
step S33: is provided withIs the firstqExpert weight coefficient among class expert class, can get the basin scheduling expert class weight +.>；
，/>；
；
Step S34: is arranged at the firstqThe number of experts among class specialists is，/>Is the first in the categoryxBit expert judgment matrixIdentity ratio of (1), thenxExpert in the first placeqWeight coefficient in expert class in class expert +.>The method comprises the following steps:
；
wherein the method comprises the steps ofbFor the assumed scale factor, the weight coefficient in expert class is determinedIs an important factor in (a) and (b),bthe larger the consistency ratioCRWeight coefficient within expert class +.>The more the middle-warmer is embodied, i.e.)>The greater the phase difference, the more generallybFor real numbers greater than zero, reference is made herein to experiencebTaking 10;
step S35: combining and considering drainage basin scheduling expert weight value to obtainWherein- >Is the firstxComprehensive weight of bit expert, and weight of AHP method is corrected by using expert weight to obtain non-dataFinal weight of the same-basin scheduling indicator +.>Wherein the multi-objective scheduling weight data is the final weight of the scheduling indexes of different flow domains;
expert comprehensive weight: ；/>
final weight of the index: wherein->The final weights of the metrics are scheduled for different flow domains.
The expert weight difference is considered in the invention: by calculating the consistency weight difference value and the weight difference value between the categories, the method can accurately reflect the difference of the expert in weight distribution. This helps to identify inconsistent situations of authoritative specialists and weight distribution, improving the accuracy of decisions. And (5) adjusting weight distribution: by calculating the expert weight coefficient and the weight coefficient in the category, the method can properly adjust the weight according to the consistency ratio among the experts and the weight difference among the categories. This helps to more accurately reflect the weight distribution of different experts and categories. And (3) comprehensive weight calculation: by calculating the comprehensive weight and the final weight, the method can combine the opinion and the weight values of a plurality of experts to obtain more comprehensive and accurate decision index weight. This helps to improve the scientificity and reliability of the decision.
Preferably, step S4 is specifically:
step S41: is provided withnThe weight of each index isWherein->Is the firstjWeights of the individual indicators, weights of the indicators to be determinedWAdding to decision matrixRIn a decision matrix composing a weighting +.>The method comprises the following steps:
；
wherein the method comprises the steps ofIs the firstiThe first sewage treatment plant isjAdding attribute values of index weights of the individual indexes;
step S42: computing a positive ideal solution for a drainage basin scheduling index by a weighted decision matrixAnd negative ideal solution->；
；
；
Step S43: calculation ofmThe Euclidean distance between each scheme and ideal solution in each drainage basin scheduling scheme is set、/>Respectively the firstiThe Euclidean distance between the individual basin scheduling scheme and the positive and negative ideal solutions;
；
；
step S44: calculating relative closeness of different flow field scheduling schemes and ideal solutionsObtaining the relative closeness of the scheduling schemes of different abundant, flat and dead water years, when +.>When the value is closer to 1, the scheme is closer to an ideal solution, and the scheme is better, wherein the scheduling evaluation data is the relative closeness of scheduling schemes with different abundant, flat and withered water years;
wherein->Is the relative proximity.
Index weights are considered in the present invention: through the weighted decision matrix, the method can carry out weighted processing on the decisions according to the index weights, so that the importance of different indexes is accurately reflected. This helps ensure that the decision process is more objective and accurate. By calculating the positive ideal solution and the negative ideal solution, the method can determine the ideal value of each index under the condition of maximization and minimization. This helps to define decision targets and provides a reference criterion for assessing the goodness of different schemes. By calculating the Euclidean distance and the relative closeness, the method can quantitatively evaluate the difference between different schemes and ideal solutions. This helps determine the relative merits of the solution and provides a basis for decision making.
Preferably, a multi-basin scheduling evaluation system based on cluster group decision, the system comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform a cluster-decision-based multi-basin scheduling assessment method as claimed in any one of the above.
The invention has the beneficial effects that: (1) And establishing a drainage basin multi-target scheduling comprehensive evaluation model, and a multi-level evaluation system of 4 secondary indexes and 22 tertiary indexes.
(2) And the expert weight is comprehensively calculated from two aspects of the category and the category by adopting a cluster analysis method, so that the index weight determined by the AHP method is corrected, and the influence of differences caused by different experts is reduced.
(3) And using a TOPSIS method, and taking the closeness degree of positive and negative ideal solutions of each scheduling method as a standard for judging the multi-target scheduling scheme. The process of picking out a proper scheme under the complex factors is simplified, and the decision efficiency is improved.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A multi-drainage-basin scheduling evaluation method based on cluster group decision is characterized by comprising the following steps:
step S1: acquiring drainage basin scheduling data under different drainage basin working conditions to establish multi-target scheduling index system data;
step S2: carrying out data normalization processing on the drainage basin scheduling data by utilizing the multi-target scheduling index system data, and classifying scheduling experts of different categories so as to obtain similar drainage basin scheduling experts and drainage basin scheduling experts of different categories;
step S3: performing improved AHP method weight calculation according to similar drainage basin scheduling experts and different types of drainage basin scheduling experts, so as to obtain multi-target scheduling weight data;
Step S4: generating a multi-target weighted decision matrix according to the multi-target scheduling weight data, and performing evaluation calculation on a preset optimized TOPSIS model and drainage basin scheduling data by using the multi-target weighted decision matrix to generate scheduling evaluation data.
2. The method according to claim 1, wherein step S1 is specifically:
the method comprises the steps of obtaining river basin scheduling data of different abundant, flat and withered years to establish a multi-target scheduling evaluation index system, wherein the multi-target scheduling evaluation index system comprises target layer data, project layer data and index layer data, water safety, water resources, water ecology and water environment are the project layer data, the next index of the project layer data is the index layer data, and the scheduling data for river basin investigation comprise:
index layer data taking water security defense as a center, including dike overflow degree data, river channel flow rate data, rainwater pipe network jacking quantity data, drainage basin submerged water depth data, drainage basin submerged area data and drainage basin submerged duration data;
index layer data centering on water resource conservation, including water supply amount analysis data of water resources and regional water demand analysis data;
index layer data centering on water ecological restoration, including ecological base flow data, species diversity index data, regional river connectivity data, soil erosion management rate data, zooplankton density data and benthonic animal density data;
Index layer data taking water environment protection as a center comprises water quality standard reaching rate data, chromaticity data, pH data, chlorophyll A concentration data and total nitrogen quantity data of a water functional area.
3. The method according to claim 2, wherein step S2 is specifically:
step S21: extracting characteristics of the drainage basin scheduling data by utilizing the multi-target scheduling index system data, and carrying out data normalization processing to obtain the characteristic scheduling data;
step S22: and classifying the scheduling specialists in different categories to obtain weights between different categories and in the categories.
4. The method according to claim 2, wherein step S21 is specifically:
dividing the drainage basin scheduling data into positive influence indexes and negative influence indexes, and scoring a j scheduling scheme in the drainage basin scheduling data by utilizing an i scheduling index in the multi-target scheduling index system data, so as to obtain processed drainage basin scheduling data;
and carrying out normalized scheduling index processing on the processing drainage basin scheduling data so as to obtain characteristic scheduling data, wherein the characteristic scheduling data comprises positive and negative influence scheduling indexes, and the positive and negative influence scheduling indexes are processed according to the following formula:
Positively affects the scheduling metrics:；
negatively affecting the scheduling metrics:；
wherein the method comprises the steps ofIs the firstiThe scheduling scheme is at the firstjScore of individual scheduling indicators->And +.>Is->Positive and negative influence scheduling indexes of the normalized score are obtained to obtain a normalized decision matrix +.>Wherein、/>Respectively the firstjMaximum and minimum values of the individual indicator scores.
5. The method according to claim 2, wherein step S22 is specifically:
acquisition ofhThe individual drainage basin scheduling expert applies a 1-7 scoring method to give a discrimination matrix for comparing different scheduling indexes in pairsPWherein is provided with a firstxBit stream basin scheduling expert and the thyThe discriminant matrix of the bit stream domain scheduling expert is respectively、/>；
Pair discrimination matrixPPerforming consistency test on the judgment matrix by using an AHP method, if the consistency test is not met, executing a suspension operation, and if the consistency test is met, using a characteristic root method to obtain a result、/>Obtain the firstx、yThe index weights assigned by the bit expert are respectively、/>Wherein->，/>，/>、/>Respectively the firstx、yBit stream basin expert assignmentjThe weight of the item scheduling indicator is determined,ncalculating the drainage basin scheduling expert (L) by clustering analysis of the drainage basin scheduling expert index weight for the index number>And->Compatibility between:
；
wherein cosine is usedTo express +. >And->Compatibility between->The closer the calculated cosine is to 1, representing +.>And->The more similar the basin scheduling specialists are; otherwise, the two classes belong to different classes;
step S23: by means ofhDerived from a discriminant matrix given by a respective basin scheduling experthIndividual index weights, build a compatibility matrix，/>；
Step S24: at the position ofIn the compatibility matrix, selecting the maximum value of each row and each column, wherein the maximum value is selected without considering the numerical value on the diagonal line +.>，/>Two bit-stream domain scheduling expert corresponding to the value of (2)>、/>Order-making，/>Is a class expert;
step S25: removing expert、/>Add->Rearranging the compatibility matrix after class expert, wherein；
Step S26: re-selecting the maximum value of the new compatibility matrix, and merging the drainage basin scheduling experts of the same class;
step S27: separating the classified drainage basin scheduling expert, adding a new class to obtain a new compatibility matrix, and the like until the expert is classified into the same class;
step S28: and drawing a cluster map by using the compatibility value during classification, determining a threshold T, determining the expert with the classification compatibility interval smaller than T as the similar drainage basin scheduling expert, and determining the expert with the classification compatibility interval larger than T as the different drainage basin scheduling expert.
6. A method according to claim 3, wherein step S3 is specifically:
Step S31: according to step 2, finally obtainqClass basin scheduling expert, the firstx、yBit stream basin scheduling expert、/>Calculating the consistency weight difference value of the x bit stream domain scheduling expert and other experts by using the index weight obtained by the AHP method>；
；
Step S32: set the firstqThe number of class basin scheduling experts is，/>Is the firstqWeight difference value between class expert and non-class expert>Refer tohBelonging to the first expertqExpert of class;
；
step S33: is provided withIs the firstqExpert weight coefficient among class expert class, can get the basin scheduling expert class weight +.>；
，/>；
；
Step S34: is arranged at the firstqThe number of experts among class specialists is，/>Is the first in the categoryxBit expert judgment matrix->Identity ratio of (1), thenxExpert in the first placeqWeight coefficient in expert class in class expert +.>The method comprises the following steps:
；
wherein the method comprises the steps ofbFor the assumed scale factor, the weight coefficient in expert class is determinedIs an important factor in (a) and (b),bthe larger the consistency ratioCRWeight coefficient within expert class +.>The more the middle-warmer is embodied, i.e.)>The greater the phase difference, the more generallybFor real numbers greater than zero, reference is made herein to experiencebTaking 10;
step S35: combining and considering drainage basin scheduling expert weight value to obtainWherein->Is the firstxComprehensive weight of a bit expert, modifying weight of an AHP method by using the expert weight, and obtaining final weight of scheduling indexes of different flow domains >Wherein the multi-objective scheduling weight data is the final weight of the scheduling indexes of different flow domains;
expert comprehensive weight: ；
final weight of the index: wherein->The final weights of the metrics are scheduled for different flow domains.
7. The method according to claim 4, wherein step S4 is specifically:
step S41: is provided withnThe weight of each index isWherein->Is the firstjWeights of the individual indicators, weights of the indicators to be determinedWAdding to decision matrixRIn a decision matrix composing a weighting +.>The method comprises the following steps:
；
wherein the method comprises the steps ofIs the firstiThe first sewage treatment plant isjAdding attribute values of index weights of the individual indexes;
step S42: computing a positive ideal solution for a drainage basin scheduling index by a weighted decision matrixAnd negative ideal solution->；
；
；
Step S43: calculation ofmThe Euclidean distance between each scheme and ideal solution in each drainage basin scheduling scheme is set、/>Respectively the firstiThe Euclidean distance between the individual basin scheduling scheme and the positive and negative ideal solutions;
；
；
step S44: calculating relative closeness of different flow field scheduling schemes and ideal solutionsObtaining the relative closeness of the scheduling schemes of different abundant, flat and dead water years, when +.>When the value is closer to 1, the scheme is closer to an ideal solution, and the scheme is better, wherein the scheduling evaluation data is the relative closeness of scheduling schemes with different abundant, flat and withered water years;
Wherein->Is the relative proximity.
8. A multi-basin scheduling and evaluation system based on cluster group decision, the system comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform a cluster group decision-based multi-basin scheduling assessment method according to any one of claims 1 to 7.
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