Comprehensive evaluation method for oil field waste treatment technology system based on HIEM-TOPSIS technology

Abstract

The invention discloses an oil field waste treatment technology system comprehensive evaluation method based on an HIEM-TOPSIS technology, and the method comprises the steps: constructing an oil field waste treatment technology system comprehensive evaluation index system which comprises a target layer, a criterion layer and a sub-criterion layer; dividing the multi-type data existing in the data information of the upper layers into a plurality of modules according to the data types by utilizing a heterogeneous data modularization method, and constructing a modularization matrix; determining an interval number weight by utilizing a phase separation degree theory; determining a positive ideal solution and a negative ideal solution corresponding to each index of the heterogeneous data modular matrix; and calculating the distance between the screening object and the positive ideal solution and the distance between the screening object and the negative ideal solution, and then calculating the relative closeness to obtain a comprehensive sorting result of the to-be-evaluated oilfield waste treatment technology system. According to the method, various data types are divided into different modules, so that the problem of coexistence of various data types is solved.

Description

Technical Field

[0001] The invention relates to the technical field of oilfield waste treatment, and in particular to a comprehensive evaluation method of an oilfield waste treatment technology system based on the HIEM-TOPSIS technology. Background Art

[0002] The primary principle of treating oilfield waste is to achieve reduction and harmlessness, and then consider resource utilization on this basis. Therefore, the current treatment technologies for oilfield waste include harmless treatment technology and resource treatment technology. In recent years, many oilfield waste disposal methods have been developed, which are of various types and have different characteristics. The carbon emissions, environmental loads, costs, resource utilization rates, etc. of various methods are different. Harmless treatment technologies include incineration, solidification stabilization, oxidation / new oxidation, biological treatment, etc. Resource utilization technologies include physical separation, chemical separation and thermal conversion technology. Physical separation includes mechanical separation, microwave radiation, ultrasonic radiation and freeze-thaw. Chemical separation mainly refers to solvent extraction and surfactant method. Thermal conversion technology mainly refers to pyrolysis.

[0003] Since the recovery rate of petroleum substances in oilfield waste by a single technology is limited, it is difficult to treat oilfield waste with high water content, and some single technologies are costly or occupy a large area, while some single technologies are relatively advantageous. Therefore, different technical combinations are currently developed to treat oilfield waste. There are various combinations of technical systems for oilfield waste treatment and site remediation, but the carbon emissions, environmental pollution loads, costs, and resource recovery rates of different technical systems are different. When selecting a technical system, it is necessary to consider the comprehensive impact of carbon emissions, cost input, resource utilization rate, and other aspects. Therefore, the comprehensive evaluation and optimization of the above-mentioned multiple impacts is a typical multi-attribute decision-making problem.

[0004] Multi-attribute decision making is one of the most widely used technical methods in comprehensive evaluation methods, and is widely used in various fields such as society, economy, and management. Common multi-attribute decision making methods include ELECTRE method, hierarchical analysis method, VIKOR method, TOPSIS method, etc. Multi-attribute decision making is widely used in technology screening. However, the methods currently used do not take into account the processing of heterogeneous information, the determination of the weight of interval numbers, the optimization of the distance calculation between the object and the positive and negative ideal solutions, and are difficult to handle for the comprehensive evaluation of complex data. Summary of the invention

[0005] The purpose of the present invention is to provide a comprehensive evaluation method for oilfield waste treatment technology system based on HIEM-TOPSIS technology to solve the technical problem that the method adopted in the prior art does not simultaneously take into account the processing of heterogeneous information, the weight determination of interval numbers, and the distance calculation optimization between objects and positive and negative ideal solutions, and is difficult to handle in the comprehensive evaluation of complex data.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention provides a comprehensive evaluation method for oilfield waste treatment technology system based on HIEM-TOPSIS technology, comprising:

[0008] Step S1, constructing a comprehensive evaluation index system for oilfield waste treatment technology system, wherein the comprehensive evaluation index system includes a target layer, a criterion layer, and a sub-criterion layer;

[0009] Step S2, constructing a heterogeneous modular decision matrix: using a heterogeneous data modularization method to divide the multi-type data existing in the data information of each layer in step S1 into multiple modules according to the data type, and constructing a modular matrix;

[0010] Step S3, determine the weight of each indicator by entropy weight method: determine the interval number weight by using separation degree theory;

[0011] Step S4, based on steps S2 and S3, determining a positive ideal solution and a negative ideal solution corresponding to each indicator of the heterogeneous data modularization matrix;

[0012] Step S5, calculating the distance between the screening object and the positive ideal solution and the negative ideal solution, and then calculating the relative closeness to obtain the comprehensive ranking result of the oilfield waste treatment technology system to be evaluated.

[0013] In some embodiments, in step S1, the target layer is a comprehensive evaluation of the pollution reduction and carbon reduction impact factors of the oilfield waste treatment and disposal technology system, the factors in the criterion layer include environmental level, economic level and technical performance level, and the factors in the sub-criteria layer include carbon emissions, acidification potential, photochemical oxidant formation potential, human health damage potential, resource and energy costs, resource reuse benefits and resource recovery rates.

[0014] In some embodiments, the heterogeneous modular decision matrix constructed in step S2 is:

[0015]

[0016] Among them, m represents the number of technical systems and n represents the number of evaluation indicators.

[0017] In some embodiments, step S3 specifically includes the following steps:

[0018] S301, use the range normalization method based on interval number attribute value to standardize the index, for interval number [a - , a + ], regarded as where a - is the lower limit of the interval number, a+ is the upper limit of the interval number; Unify the decision matrix data form, for interval triangular fuzzy numbers Will As a set of interval numbers, As a set of interval numbers, As a set of interval numbers, the interval triangular fuzzy number matrix is ​​divided into and Divide into three groups and standardize them according to formula (2) (3):

[0019]

[0020] In the formula, X ij is the standardized value of the lower limit of the interval number, is the standardized value of the upper limit of the interval number, is the upper limit of the interval number in row i and column j, x ij is the lower limit of the interval number in the i-th row and j-th column, is the maximum value of the upper limit of the interval number from the first column of the i-th row to the n-th column, is the maximum value of the lower limit of the interval number from the first column of the i-th row to the n-th column, J1 is a cost-type indicator, and J2 is a benefit-type indicator;

[0021] S302, introduce the separation degree theory, and convert the interval number into an exact number according to formula (4):

[0022]

[0023] In the formula, D(a,b) is the separation degree of interval numbers a and b. The standardized interval number matrix is ​​converted into a separation degree matrix. - is the interval number [a - , a + ] lower limit, a + is the interval number [a - , a + ] upper limit, The number of intervals Lower limit, The number of intervals Upper limit;

[0024] S303, normalize the separation degree matrix using the following formula (5) to obtain a normalized decision matrix B = (b ij ) m×n :

[0025]

[0026] In the formula, is the value of the i-th row and j-th column in the separation matrix, is the maximum value among all columns involved in the i-th row in the separation matrix, is the minimum value of all columns involved in the i-th row in the separation matrix;

[0027] S304. Determine the weight of each indicator using the entropy weight method, and calculate the indicator weight according to formulas (6)-(9):

[0028]

[0029] θ=1-E j (8)

[0030]

[0031] In the formula, p ij is the indicator u j The probability of occurrence, θ is the index u j The coefficient of variation, E j is the indicator u j The information entropy of j is the indicator u j The weights of the final weight vector ω=(ω1,ω2,…ω n ) T .

[0032] In some embodiments, in step S4, the positive ideal solution is:

[0033]

[0034] The negative ideal solution is:

[0035]

[0036] In some embodiments, step S5 includes the following steps:

[0037] S501. Calculate the distance between the screening object and the positive ideal solution and the negative ideal solution using the weighted Mahalanobis distance:

[0038] Calculate A from the i-th object to the positive ideal solution + The distance d(Di, A + ) and to the negative ideal solution A - The distance d(D i , A - ), the formula is as follows:

[0039]

[0040] Among them, ∑ -1 is the inverse matrix of correlation coefficient; It is a diagonal matrix composed of each component vector in the weight vector of n indicators;

[0041] S502, after determining the weighted Mahalanobis distance, calculating the relative closeness;

[0042]

[0043] In the formula, C i is the relative closeness of the i-th object, C i The larger it is, the farther the screening object is from the negative ideal solution and the closer it is to the positive ideal solution, indicating that the object is better. The relative closeness results are sorted according to the matrix situation.

[0044] Based on the above technical solution, the embodiments of the present invention can at least produce the following technical effects:

[0045] (1) The comprehensive evaluation method of oilfield waste treatment technology system based on HIEM-TOPSIS technology provided by the present invention is improved in three stages: data processing, indicator weighting and distance calculation, and combines heterogeneous data modularization, interval number entropy weight, weighted Mahalanobis distance and TOPSIS method, while solving the problems of modularization, weight calculation and accurate sorting of complex indicator data sets in the evaluation of oilfield waste treatment and disposal technology.

[0046] (2) The comprehensive evaluation method of oilfield waste treatment technology system based on HIEM-TOPSIS technology provided by the present invention divides various data types into different modules based on the TOPSIS framework combined with the heterogeneous modularization method, solving the problem of coexistence of multiple data types. Secondly, the concept of phase separation is introduced to calculate the weight of the interval number; then the weighted Mahalanobis distance is used instead of the Euclidean distance, which not only maintains the accuracy of the original index, but also eliminates the interference of the correlation between the indicators. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0048] Figure 1 It is a flow chart of the steps of the comprehensive evaluation method of the oilfield waste treatment technology system of the present invention;

[0049] Figure 2 A schematic diagram of a comprehensive screening index system for oilfield waste treatment technology system constructed by the present invention;

[0050] Figure 3 It is a technical roadmap for the comprehensive evaluation method of the oilfield waste treatment technology system of the present invention. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0052] The objective of the present invention is achieved through the following technical solutions:

[0053] See also Figure 1 and Figure 3 The present invention proposes a comprehensive evaluation method for oilfield waste treatment technology system based on HIEM-TOPSIS technology, which includes the following steps:

[0054] Step S1: Construct a comprehensive evaluation index system for oilfield waste treatment technology system. Figure 2 The comprehensive evaluation index system includes the target layer, the criterion layer, and the sub-criterion layer; the target layer is the ultimate goal of the decision or the problem to be solved in the end, the criterion layer is the decision-making criteria or standards that need to be considered in order to achieve the target layer, and the elements of the criterion layer are the decomposition of the target layer, which are the key factors that must be considered to achieve the goal. The target layer is a comprehensive evaluation of the pollution reduction and carbon reduction factors of the oilfield waste treatment and disposal technology system. The factors in the criterion layer include the environmental level, the economic level, and the technical performance level. The factors in the sub-criterion layer include carbon emissions, acidification potential, photochemical oxidant formation potential, human health damage potential, resource energy cost, resource reuse benefit, and resource recovery rate.

[0055] Specifically, first determine the purpose and object of the evaluation, which is the starting point for building the index system; secondly, according to the evaluation objectives, select indicators that can fully reflect the characteristics of the evaluation object, and build a system structure including the target layer, criterion layer and indicator layer. Follow the principles of systematicity, scientificity and operability in establishing the index system, screen indicators at the criterion layer and sub-criterion layer, and finally determine the comprehensive evaluation index system of the oilfield waste treatment technology system.

[0056] Step S2, constructing a heterogeneous modular decision matrix: using a heterogeneous data modularization method to divide the multi-type data existing in the data information of each level in step S1 into multiple modules according to the data type, and constructing a modular matrix;

[0057] Where m represents the number of technical systems and n represents the number of evaluation indicators.

[0058]

[0059] Step S3, determine the weight of each indicator by entropy weight method: determine the weight of interval number by using separation degree theory;

[0060] S301, use the range normalization method based on interval number attribute value to standardize the index, for interval number [a - , a + ], regarded as where a - is the lower limit of the interval number, a + is the upper limit of the interval number; The decision matrix data form is unified, for interval triangular fuzzy numbers Will As a set of interval numbers, As a set of interval numbers, As a set of interval numbers, the interval triangular fuzzy number matrix is ​​divided into and Divide into three groups and standardize them according to formula (2) (3):

[0061]

[0062] In the formula, X ij is the standardized value of the lower limit of the interval number, is the standardized value of the upper limit of the interval number, is the upper limit of the interval number in row i and column j, x ij is the lower limit of the interval number in the i-th row and j-th column, is the maximum value of the upper limit of the interval number from the first column of the i-th row to the n-th column, is the maximum value of the lower limit of the interval number from the first column of the i-th row to the n-th column, J1 is a cost-type indicator, and J2 is a benefit-type indicator;

[0063] S302, introduce the separation degree theory, and convert the interval number into an exact number according to formula (4):

[0064]

[0065] In the formula, D(a,b) is the separation degree of interval numbers a and b. The standardized interval number matrix is ​​converted into a separation degree matrix. - is the interval number [a - , a + ] lower limit, a + is the interval number [a - , a + ] upper limit, The number of intervals Lower limit, The number of intervals Upper limit;

[0066] S303, normalize the separation degree matrix using the following formula (5) to obtain a normalized decision matrix B = (b ij ) m×n :

[0067]

[0068] In the formula, is the value of the i-th row and j-th column in the separation matrix, is the maximum value among all columns involved in the i-th row in the separation matrix, is the minimum value of all columns involved in the i-th row in the separation matrix;

[0069] S304. Determine the weight of each indicator using the entropy weight method, and calculate the indicator weight according to formulas (6)-(9):

[0070]

[0071] θ=1-E j (8)

[0072]

[0073] In the formula, p ij is the indicator u j The probability of occurrence, θ is the index u j The coefficient of variation, E j is the indicator u j The information entropy of j is the indicator u j The weights of the final weight vector ω=(ω1,ω2,…ω n ) T .

[0074] Step S4: Based on steps S2 and S3, determine the positive and negative ideal solutions corresponding to each indicator of the heterogeneous data modularization matrix;

[0075] Positive ideal solution:

[0076]

[0077] Negative ideal solution:

[0078]

[0079] Step S5: Obtain the comprehensive ranking results of the oilfield waste treatment technology systems to be evaluated.

[0080] S501. Calculate the distance between the screening object and the positive ideal solution and the negative ideal solution using the weighted Mahalanobis distance:

[0081] Calculate A from the i-th object to the positive ideal solution + The distance d(Di, A + ) and to the negative ideal solution A - The distance d(D i , A - ), the formula is as follows:

[0082]

[0083] Among them, ∑ -1 is the inverse matrix of correlation coefficient; It is a diagonal matrix composed of each component vector in the weight vector of n indicators;

[0084] S502, after determining the weighted Mahalanobis distance, calculating the relative closeness;

[0085]

[0086] In the formula, C i is the relative closeness of the i-th object, C i The larger it is, the farther the screening object is from the negative ideal solution and the closer it is to the positive ideal solution, indicating that the object is better. The relative closeness results are sorted according to the matrix situation.

[0087] The present invention is further described below by a specific embodiment:

[0088] The types of oilfield wastes vary with the depth of drilling. Water-based drill cuttings are produced when the drilling depth is less than 6 km, and oil-based drill cuttings are produced when the drilling depth is greater than 6 km. Different treatment and disposal methods are usually used for different types of waste. Typical treatment technologies for water-based drill cuttings include drying landfill, solidification, etc., and typical treatment technologies for oil-based drill cuttings include thermal desorption, incineration, cement kiln co-disposal, etc. It is assumed that these oilfield wastes are collected and treated separately. Therefore, the technical system consists of water-based drill cuttings treatment technology and oil-based drill cuttings treatment technology.

[0089] After a preliminary field investigation, two water-based drill cuttings treatment technologies and six oil-based drill cuttings treatment technologies were considered in the technology system in consideration of technology representativeness and data availability. Wastewater treatment technologies include cement kiln co-treatment and dry landfill. Oil-based drill cuttings treatment technologies include cement kiln co-treatment, pyrolysis, thermal desorption-landfill, thermal desorption-cement kiln co-treatment, incineration-landfill, and incineration-cement kiln co-treatment. Therefore, the technology system to be evaluated is a free combination of the above-mentioned various treatment technologies, totaling 12 treatment technology systems.

[0090] Since the oilfield system data is relatively complex, the method adopted by the present invention can solve the situation where different categories of data exist at the same time, and can solve the weight calculation problem caused by the complexity of data categories, and at the same time solves the problem of accurate distance calculation.

[0091] Under the condition that the ratio of water-based drill cuttings to oil-based drill cuttings in oilfield waste is constant, due to the change of daily incineration processing volume, its carbon emissions and costs are presented in the form of fuzzy numbers. According to the survey, the ratio of water-based drill cuttings to oil-based drill cuttings fluctuates within a certain range, resulting in the existence of interval numbers and interval triangular fuzzy numbers in the data. The specific data information is subjected to modular steps, and the modular decision matrix information is as shown in Table 1: as shown in Table 1 (in the table, R1 is cement kiln co-treatment-cement kiln co-treatment, R2 is cement kiln co-treatment-thermal cracking, R3 is cement kiln co-treatment-thermal desorption landfill, R4 is cement kiln co-treatment-thermal desorption cement kiln co-treatment, R5 is cement kiln co-treatment-incineration landfill, R6 is cement kiln co-treatment-incineration cement kiln co-treatment, R7 is drying landfill-cement kiln co-treatment, R8 is drying landfill-thermal cracking, R9 is drying landfill-thermal desorption landfill, R10 is drying landfill-thermal desorption cement kiln co-treatment, R11 is drying landfill-incineration landfill, and R12 is drying landfill-incineration cement kiln co-treatment):

[0092] Table 1 Modular decision matrix of the technology system to be evaluated

[0093]

[0094]

[0095]

[0096] After the matrix is ​​standardized, three standardized matrices are obtained according to the data grouping. One of the cases is taken for illustration. The standardized matrix of the fuzzy lower limit is shown in Table 2:

[0097] Table 2 Standardized matrix of fuzzy lower bounds

[0098]

[0099]

[0100] After the separation degree transformation, the interval number matrix is ​​transformed into the separation degree matrix (precise number matrix), and the obtained separation degree matrix can be expressed as Table 3:

[0101] Table 3 The separation degree matrix of fuzzy lower bound

[0102]

[0103] The separation degree matrix is ​​normalized again, and the weight is determined by the entropy weight method. The weights of each indicator are shown in Table 4:

[0104]

[0105]

[0106] through Calculation of Ω yields:

[0107]

[0108] The positive and negative ideal solutions and negative ideal solutions of the object to be evaluated are obtained from the modular and grouped decision matrix. After the weighted Mahalanobis distance is calculated and the relative closeness is calculated, the relative closeness and ranking results of each technical system are as follows:

[0109] serial number Relative closeness Sorting R1 0.171674256 11 R2 0.790159051 2 R3 0.333427189 5 R4 0.331487317 6 R5 0.20770616 9 R6 0.186359924 10 R7 0.167299369 12 R8 0.918178898 1 R9 0.375842761 3 R10 0.353074374 4 R11 0.299004961 7 R12 0.284317694 8

[0110] The 12 technical systems are ranked according to relative closeness. The greater the relative closeness, the higher the ranking, which means that the technical system is more worthy of recommendation based on its comprehensive indicators.

[0111] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements fall within the scope of the present invention to be protected. The scope of the present invention to be protected is defined by the attached claims and their equivalents.

Claims

1. A comprehensive evaluation method for oilfield waste treatment technology system, characterized in that: include: Step S1, constructing a comprehensive evaluation index system for oilfield waste treatment technology system, wherein the comprehensive evaluation index system includes a target layer, a criterion layer, and a sub-criterion layer; Step S2, constructing a heterogeneous modular decision matrix: using a heterogeneous data modularization method to divide the multi-type data existing in the data information of each layer in step S1 into multiple modules according to the data type, and constructing a modular matrix; Step S3, determine the weight of each indicator by entropy weight method: determine the interval number weight by using separation degree theory; Step S4, based on steps S2 and S3, determining the positive ideal solution and the negative ideal solution corresponding to each indicator of the heterogeneous data modularization matrix; Step S5, calculating the distance between the screening object and the positive ideal solution and the negative ideal solution, and then calculating the relative closeness to obtain the comprehensive ranking result of the oilfield waste treatment technology system to be evaluated.

2. The comprehensive evaluation method for oilfield waste treatment technology system according to claim 1 is characterized in that: In step S1, the target layer is a comprehensive evaluation of the pollution reduction and carbon reduction factors of the oilfield waste treatment and disposal technology system. The factors in the criterion layer include the environmental level, the economic level and the technical performance level. The factors in the sub-criterion layer include carbon emissions, acidification potential, photochemical oxidant formation potential, human health damage potential, resource and energy costs, resource reuse benefits and resource recovery rates.

3. The comprehensive evaluation method for oilfield waste treatment technology system according to claim 1 is characterized in that: The heterogeneous modular decision matrix constructed in step S2 is: Among them, m represents the number of technical systems and n represents the number of evaluation indicators.

4. The comprehensive evaluation method for oilfield waste treatment technology system according to claim 1 is characterized in that: The step S3 specifically comprises the following steps: S301, use the range normalization method based on interval number attribute value to standardize the index, for interval number [a - , a + ], regarded as where a - is the lower limit of the interval number, a + is the upper limit of the interval number; Unify the decision matrix data form, for interval triangular fuzzy numbers Will As a set of interval numbers, As a set of interval numbers, As a set of interval numbers, the interval triangular fuzzy number matrix is ​​divided into and Divide into three groups and standardize them according to formula (2) (3): In the formula, X ij is the standardized value of the lower limit of the interval number, is the standardized value of the upper limit of the interval number, is the upper limit of the interval number in row i and column j, x ij is the lower limit of the interval number in the i-th row and j-th column, is the maximum value of the upper limit of the interval number from the first column of the i-th row to the n-th column, is the maximum value of the lower limit of the interval number from the first column of the i-th row to the n-th column, J1 is a cost-type indicator, and J2 is a benefit-type indicator; S302, introduce the separation degree theory, and convert the interval number into an exact number according to formula (4): In the formula, D(a,b) is the separation degree of interval numbers a and b. The standardized interval number matrix is ​​converted into a separation degree matrix. - is the interval number [a - , a + ] lower limit, a + is the interval number [a - , a + ] upper limit, The number of intervals Lower limit, The number of intervals Upper limit; S303, normalize the separation degree matrix using the following formula (5) to obtain a normalized decision matrix B = (b ij ) m×n : In the formula, is the value of the i-th row and j-th column in the separation matrix, is the maximum value among all columns involved in the i-th row in the separation matrix, is the minimum value of all columns involved in the i-th row in the separation matrix; S304. Determine the weight of each indicator using the entropy weight method, and calculate the indicator weight according to formulas (6)-(9): In the formula, p ij is the indicator u j The probability of occurrence, θ is the index u j The coefficient of variation, E j is the indicator u j The information entropy of j is the indicator u j The weights of the final weight vector ω=(ω1,ω2,…ω n ) T .

5. The comprehensive evaluation method for oilfield waste treatment technology system according to claim 1 is characterized in that: In step S4, the positive ideal solution is: The negative ideal solution is:

6. The comprehensive evaluation method for oilfield waste treatment technology system according to claim 1 is characterized in that: Step S5 includes the following steps: S501. Calculate the distance between the screening object and the positive ideal solution and the negative ideal solution using the weighted Mahalanobis distance: Calculate A from the i-th object to the positive ideal solution + The distance d(Di, A + ) and to the negative ideal solution A - The distance d(D i , A - ), the formula is as follows: Among them, ∑ -1 is the inverse matrix of correlation coefficient; It is a diagonal matrix composed of each component vector in the weight vector of n indicators; S502, after determining the weighted Mahalanobis distance, calculating the relative closeness; In the formula, C i is the relative closeness of the i-th object, C i The larger it is, the farther the screening object is from the negative ideal solution and the closer it is to the positive ideal solution, indicating that the object is better. The relative closeness results are sorted according to the matrix situation.

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