Efficiency analysis method of components of large central air conditioners

Abstract

The present application relates to an efficiency analysis method for components of large central air conditioners. The efficiency analysis method for components of large central air conditioners described in the present application includes the steps of: analyzing efficiency evaluation indicators and establishing a corresponding efficiency indicator evaluation system; processing the input-output data of the candidate components; establishing a model, through which the results of the standardized processing are subjected to self-evaluation and other evaluation, and a cross-efficiency evaluation matrix is ​​formed; measuring the psychological preferences of decision makers through skewness analysis, and adjusting the cross-efficiency evaluation matrix; finding the weights of each candidate component; and using an ordered weighted arithmetic mean operator to obtain the efficiency values ​​of each candidate component. The efficiency analysis method for components of large central air conditioners described in the present application has the advantage of satisfying psychological preferences and thus better serving production practice.

Description

Technical Field

[0001] The present application relates to an efficiency analysis method, and in particular to an efficiency analysis method for components of a large central air conditioner. Background Art

[0002] Large central air conditioners have the characteristics of many product models, a large number of parts, knowledge interweaving, strong linkage effect, and high added value. Although large central air conditioners are integrated with multiple components, not all components are equally important. Some components carry the core technology of manufacturing and represent key functions and capabilities, while other components are redundant or have little impact on the product quality level. The efficiency analysis of key components of large central air conditioners is to select the best component from a group of alternative components. As alternative components, this group of alternative components has similar functions and structures, but their production costs, manufacturing cycles, etc. may be different. In the traditional manufacturing process of large central air conditioners, it is usually difficult for engineers or designers to effectively identify the efficiency of each alternative component. Therefore, in the existing technology, it is not possible to analyze the efficiency of the components of large central air conditioners, which is not conducive to the screening of the components therein. Summary of the invention

[0003] Based on this, the purpose of this application is to provide an efficiency analysis method for components of large central air conditioners, which has the advantages of forming a screening mechanism to select efficient and key components therein and analyze the efficiency and benefits of the output.

[0004] In one aspect of the present application, a method for analyzing the efficiency of components of a large central air conditioner is provided, comprising the steps of:

[0005] Analyze the efficiency evaluation indicators that affect the centrifuge components, and establish a corresponding efficiency index evaluation system based on the "input-output" perspective; through this efficiency index evaluation system, standardize the input-output data of the candidate components;

[0006] Establishing a data envelopment analysis method model, using the data envelopment analysis method model to conduct self-evaluation and other-evaluation on the results of the standardization process, and synthesizing the results to form a cross-efficiency evaluation matrix;

[0007] Measuring the psychological preferences of decision makers through skewness analysis, and adjusting the cross-efficiency evaluation matrix based on the measurement results;

[0008] The weight of each candidate component is obtained through the minimum difference maximization method; and the ordered weighted arithmetic mean operator is used to weighted aggregate each candidate component to obtain the efficiency value of each candidate component.

[0009] Compared with the prior art, the efficiency analysis method of the components of large central air conditioners described in this application takes into account the influence of the subjective preferences of decision makers on the evaluation results during the evaluation process, making the evaluation results more meaningful for the actual decision results. This application takes the components (even key components) of large central air conditioners as the research object, which can effectively help enterprise managers and designers select key components and contribute to product design research.

[0010] Furthermore, the corresponding efficiency index evaluation system is established from the perspective of "input-output", including input index and output index;

[0011] The input indicators include cooling capacity range, comprehensive part load performance coefficient, and centrifuge power distribution;

[0012] The output indicators include energy efficiency index and frequency conversion effect.

[0013] Furthermore, the input-output data of the candidate components are standardized, including classifying the input-output data into multiple types, specifically including income type, cost type and fixed type;

[0014] Among them, the standardized calculation formula for income type is:

[0015]

[0016] The standardized calculation formula for cost type is

[0017]

[0018] The fixed standardized calculation formula is

[0019]

[0020] Furthermore, the data envelopment analysis method model is established, and the result of the standardization processing is self-evaluated and evaluated by others through the data envelopment analysis method model, including:

[0021] Establish and use a data envelopment analysis method model with constant economies of scale to conduct self-evaluation on the standardized processing results of alternative components; and establish and use an adversarial data envelopment analysis method model to conduct peer evaluation on the standardized processing structure of alternative components; or establish and use a benevolent data envelopment analysis method model to conduct peer evaluation on the standardized processing structure of alternative components.

[0022] Furthermore, the calculation formula of the data envelopment analysis method model with constant scale benefits is:

[0023]

[0024] The model needs to meet the following conditions:

[0025] Condition 1.

[0026] Condition 2.

[0027] Condition 3: i=1,...,n; r=1,...,n;

[0028] Condition 4: k = 1, ..., m; j = 1, ..., s;

[0029] Among them, x ij and ik Respectively represent input-output indicators, v ij and u ik Respectively represent the weights of input and output indicators, E ii is the self-assessed efficiency value of the decision-making unit;

[0030] The calculation formula of the adversarial data envelopment analysis method model is:

[0031]

[0032] The model needs to meet the following conditions:

[0033] Condition 1.

[0034] Condition 2.

[0035] Condition 3.

[0036] Condition 4: u≥0; v≥0;

[0037] Condition 5: i=1,...,n; r=1,...,n;

[0038] Condition 6: k = 1, ..., m; j = 1, ..., s;

[0039] The calculation formula of the adversarial data envelopment analysis method model is:

[0040]

[0041] The model needs to meet the following conditions:

[0042] Condition 1.

[0043] Condition 2.

[0044] Condition 3.

[0045] Condition 4: u≥0; v≥0;

[0046] Condition 5: i=1,...,n; r=1,...,n;

[0047] Condition six: k = 1, ..., m; j = 1, ..., s.

[0048] Furthermore, the form of the cross efficiency evaluation matrix is ​​as follows:

[0049]

[0050] Furthermore, the measuring of the decision maker's psychological preference through skewness analysis includes:

[0051] The decision maker's psychological preferences are statistically analyzed through skewness, and the expression of skewness is:

[0052]

[0053] In the above formula, φ r is the standard deviation, which is calculated as

[0054]

[0055] Furthermore, the adjusting the cross efficiency evaluation matrix using the measurement results includes:

[0056] If the skewness of all decision makers is positive, the normalized calculation formula of the result is

[0057]

[0058] If there are one or more negative values ​​in the skewness of all decision makers, the normalized calculation formula of the result is

[0059]

[0060]

[0061]

[0062] The normalized result is introduced into the matrix, and the values ​​in the matrix are processed. The expression of the correction equation is:

[0063]

[0064] Furthermore, the step of adjusting the cross-efficiency evaluation matrix using the measurement results further includes:

[0065] Considering the influence of sorting, the priority coefficient is introduced; the expression of the priority coefficient is

[0066]

[0067] Among them, θ ir It represents the ranking of the i-th decision unit, and its expression is

[0068]

[0069] Introducing the priority coefficient into the modified equation of the matrix, its expression is

[0070]

[0071] The corresponding cross-efficiency matrix with preference is obtained, which is as follows

[0072]

[0073] Furthermore, the weights of the candidate components are obtained by the minimum difference maximization method; and the ordered weighted arithmetic mean operator is used to weight the candidate components to obtain the efficiency values ​​of the candidate components, including:

[0074] The maximum-minimum deviation model is used to determine the weights that maximize the preference differences of the decision-making units. The weights are the weights of the ordered weighted arithmetic mean operator. The expression of this model is:

[0075] max min|E″ ir -E″ lr |·w r ;

[0076] The following conditions must be met

[0077] Condition 1.

[0078] Condition 2. 1≥w r ≥0;

[0079] Condition 3, i=1,...,n;

[0080] Condition 4: r = 1, ..., n;

[0081] Condition 5: l = 1, ..., n;

[0082] Then, assuming that the minimum deviation of two different decision units is represented by λ, its expression is

[0083] λ=min|E″ ir -E″ lr |·w r ;

[0084] It can be understood that λ≤min|E″ ir -E″ lr |·wr ;

[0085] The maximum-minimum deviation model is modified to Eq.

[0086] maxλ;

[0087] The following conditions must be met

[0088] Condition 1.

[0089] Condition 2. λ≤|E″ ir -E″ lr |w r ;

[0090] Condition 3. 0≤w r ≤1;

[0091] Condition 4: i = 1, ..., n;

[0092] Condition 5: r = 1, ..., n;

[0093] Condition six: l=1,...,n.

[0094] For better understanding and implementation, the present application is described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] Figure 1 A flowchart of an exemplary method for analyzing the efficiency of components of a large central air conditioner according to the present application;

[0096] Figure 2 The present invention is a flowchart of another exemplary method for analyzing the efficiency of components of a large central air conditioner. DETAILED DESCRIPTION

[0097] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. In the description of the present application, unless otherwise specified, "multiple" means two or more.

[0098] The efficiency analysis of key components of large central air conditioners is to select the best component from a group of candidate components. As candidate components, this group of candidate components have similar functions and structures, but their production costs, manufacturing cycles, etc. may be different. In the traditional manufacturing process of large central air conditioners, it is usually difficult for engineers or designers to effectively identify the efficiency of each candidate component. Therefore, in order to achieve the reliability of product quality and the economy of production costs, it is crucial to select the best components, which helps managers and designers make production decisions and strategies.

[0099] The efficiency analysis of key components is essentially a ranking problem. Decision makers need to select the best winner (or optimal solution) from the alternative components that best meets the requirements of influencing factors such as product quality and functional requirements. Existing research results on key component analysis mainly focus on combinatorial optimization or resource allocation problems, which may not be able to describe the priority of a single component. In addition, efficiency analysis covers two aspects: self-evaluation or other evaluation by decision making units (DMUs), and the evaluation itself is subjective; therefore, the efficiency analysis of components should also consider the impact of decision makers' psychological preferences on decision results. In the prior art, only the components of large air conditioners are objectively evaluated, and the impact of subjective factors cannot be fully considered, so that the product is not excellent enough. Based on this, there is an urgent need for an efficiency analysis method for key components of large central air conditioners that can solve the missing links of the prior art.

[0100] Based on the above problems and inventive concepts, the following technical solutions are proposed.

[0101] See also Figure 1 and Figure 2 , an exemplary method for analyzing the efficiency of a component of a large central air conditioner of the present application comprises the steps of:

[0102] S10, analyzing the efficiency evaluation indexes that affect the centrifuge components, and establishing a corresponding efficiency index evaluation system from the "input-output" perspective; through the efficiency index evaluation system, the input-output data of the candidate components are standardized.

[0103] In some preferred embodiments, the corresponding efficiency index evaluation system is established from the perspective of "input-output", including input index and output index;

[0104] The input indicators include cooling capacity range, comprehensive part load performance coefficient, and centrifuge power distribution;

[0105] The output indicators include energy efficiency index and frequency conversion effect.

[0106] In some preferred embodiments, the input-output data of the candidate components are standardized, including classifying the input-output data into multiple types, specifically including income type, cost type and fixed type.

[0107] The input-output data of each candidate component is standardized using the following formula according to the indicator type.

[0108] Among them, the standardized calculation formula for income type is:

[0109]

[0110] The standardized calculation formula for cost type is

[0111]

[0112] The fixed standardized calculation formula is

[0113]

[0114] S20, establishing a data envelopment analysis method model, using the data envelopment analysis method model to perform self-evaluation and other-evaluation on the results of the standardization processing, and synthesizing the results to form a cross-efficiency evaluation matrix.

[0115] In some preferred embodiments, the establishment of a data envelopment analysis (DEA) model, and the self-evaluation and the peer-evaluation of the results of the standardization processing by the data envelopment analysis (DEA) model, respectively, include:

[0116] Establish and use a data envelopment analysis method model with constant scale benefits to conduct self-evaluation of the standardized processing results of candidate components. It also includes establishing and using a confrontational data envelopment analysis method model to conduct peer evaluation of the standardized processing structure of candidate components; or establishing and using a benevolent data envelopment analysis method model to conduct peer evaluation of the standardized processing structure of candidate components. According to the actual situation and specific problems, one of the models is selected for peer evaluation.

[0117] Let us assume that there are n decision making units (DMU i ,i=1,...,n). DMU i There are j input indicators and m output indicators, which are x ij (j=1,...,s) and y ik (k=1,...,m). The ratio of input to output can be calculated by the following formula

[0118]

[0119] In some preferred embodiments, the calculation formula of the data envelopment analysis method model with constant scale benefits is:

[0120]

[0121] The model needs to meet the following conditions:

[0122] Condition 1.

[0123] Condition 2.

[0124] Condition 3: i=1,...,n; r=1,...,n;

[0125] Condition 4: k = 1, ..., m; j = 1, ..., s;

[0126] Among them, x ij and ik Represent input index and output index respectively, v ij and u ik They represent the input index weight and output index weight respectively, E ii is the self-evaluated efficiency value of the decision-making unit, E ir It is the efficiency value of the decision making unit.

[0127] when and is the optimal solution of the above formula, so the self-evaluated efficiency value is expressed as This is the best self-assessed efficiency value.

[0128] Correspondingly, the calculation formula of crossover efficiency can be expressed as

[0129]

[0130] Among them, E ir The larger the value, the better the efficiency value of other evaluations and the worse the efficiency value of self evaluations. In the cross-efficiency model, E ii is the first object, E ir is the second object.

[0131] The calculation formula of the adversarial data envelopment analysis method model is:

[0132]

[0133] The model needs to meet the following conditions:

[0134] Condition 1.

[0135] Condition 2.

[0136] Condition 3.

[0137] Condition 4: u≥0; v≥0;

[0138] Condition 5: i=1,...,n; r=1,...,n;

[0139] Condition 6: k = 1, ..., m; j = 1, ..., s;

[0140] The calculation formula of the adversarial data envelopment analysis method model is:

[0141]

[0142] The model needs to meet the following conditions:

[0143] Condition 1.

[0144] Condition 2.

[0145] Condition 3.

[0146] Condition 4: u≥0; v≥0;

[0147] Condition 5: i=1,...,n; r=1,...,n;

[0148] Condition six: k = 1, ..., m; j = 1, ..., s.

[0149] In some preferred embodiments, the cross-efficiency evaluation matrix is ​​in the form of:

[0150]

[0151] S30, measuring the psychological preference of the decision maker through skewness analysis, and adjusting the cross efficiency evaluation matrix based on the measurement results.

[0152] In some preferred embodiments, the measuring of the decision maker's psychological preference by skewness analysis includes:

[0153] The decision maker's psychological preferences are statistically analyzed through skewness, and the expression of skewness is:

[0154]

[0155] In the above formula, φ r is the standard deviation, which is calculated as

[0156]

[0157] In some preferred embodiments, adjusting the cross-efficiency evaluation matrix using the measurement results includes:

[0158] If the skewness of all decision makers is positive, the normalized calculation formula of the result is

[0159]

[0160] If there are one or more negative values ​​in the skewness of all decision makers, the normalized calculation formula of the result is

[0161]

[0162]

[0163]

[0164] The normalized result is introduced into the matrix, and the values ​​in the matrix are processed. The expression of the correction equation is:

[0165]

[0166] in, represents the decision maker’s preference for each decision unit, δ i Represents the priority coefficient of the decision-making unit.

[0167] In some preferred embodiments, the step of adjusting the cross-efficiency evaluation matrix using the measurement results further includes:

[0168] Considering the influence of sorting, the priority coefficient is introduced; the expression of the priority coefficient is

[0169]

[0170] Among them, θ ir It represents the ranking of the i-th decision unit, and its expression is

[0171]

[0172] Introducing the priority coefficient into the modified equation of the matrix, its expression is

[0173]

[0174] The corresponding cross-efficiency matrix with preference is obtained, which is as follows

[0175]

[0176] S40. Calculate the weight of each candidate component by using the minimum difference maximization method; and use the ordered weighted arithmetic mean operator to perform weighted aggregation on each candidate component to obtain the efficiency value of each candidate component.

[0177] In some preferred embodiments, the weights of the candidate components are obtained by the minimum difference maximization method; and the ordered weighted arithmetic mean operator is used to weight the candidate components to obtain the efficiency values ​​of the candidate components, including:

[0178] The maximum-minimum deviation model is used to determine the weights that maximize the preference differences of the decision-making units. The weights are the weights of the ordered weighted arithmetic mean operator. The expression of this model is:

[0179] max min|E″ ir -E″ lr |·w r ;

[0180] The following conditions must be met

[0181] Condition 1.

[0182] Condition 2. 1≥w r ≥0;

[0183] Condition 3, i=1,...,n;

[0184] Condition 4: r = 1, ..., n;

[0185] Condition 5: l = 1, ..., n;

[0186] Among them, w r is the weight of each candidate component;

[0187] Then, assuming that the minimum deviation of two different decision units is represented by λ, its expression is

[0188] λ=min|E″ ir -E″ lr |·w r ;

[0189] It can be understood that λ≤min|E″ ir -E″ lr |·w r ;

[0190] The maximum-minimum deviation model is modified to Eq.

[0191] maxλ;

[0192] The following conditions must be met

[0193] Condition 1.

[0194] Condition 2, λ≤|E″ ir -E″ lr |w r ;

[0195] Condition 3. 0≤w r ≤1;

[0196] Condition 4: i = 1, ..., n;

[0197] Condition 5: r = 1, ..., n;

[0198] Condition six: l=1,...,n.

[0199] Furthermore, an ordered weighted averaging (OWA) operator is used to perform weighted aggregation on each candidate component to obtain the evaluation efficiency of each candidate component.

[0200] The expression is

[0201]

[0202] This application proposes a DEA and OWA operator that takes into account the decision maker's preference psychology to obtain a method for the efficiency of key components of large central air conditioners. It plays a key role in product improvement and supporting new product design and development, its effectiveness and efficiency, and also includes the following effects:

[0203] (1) Compared with the prior art, this application takes into account the impact of the decision maker's subjective preference on the evaluation results during the evaluation process, making the evaluation results more meaningful for the actual decision results.

[0204] (2) This application targets components (even key components) of large central air conditioners, which can effectively help business managers and designers select key components and facilitate product design research.

[0205] The following is an application example in a specific example.

[0206] Taking the magnetic levitation DC variable frequency centrifugal water-cooled chiller and its eight candidate centrifuges as examples, performance evaluation is carried out.

[0207] Step 1: Analyze the efficiency evaluation indicators that affect the centrifuge components, and establish an efficiency indicator evaluation system from the "input-output" perspective. The input indicators include: cooling capacity range, comprehensive part load performance coefficient, centrifuge power distribution power; output indicators include: energy efficiency index, frequency conversion effect, and standardize the input-output data of each alternative component.

[0208] Step 2: Use the DEA model with constant scale benefits to obtain the self-evaluated efficiency of each candidate centrifuge. The basic model is as follows:

[0209]

[0210]

[0211]

[0212] i=1,...,n;r=1,...,n;

[0213] k=1,...,m;j=1,...,s.

[0214] Among them, x ij and ik Respectively represent input-output indicators, v ij and u ik Respectively represent the weights of input and output indicators, E ii is the self-evaluated efficiency value of the decision-making unit.

[0215] Self-evaluation efficiency cannot clearly distinguish the pros and cons of the candidate centrifuges. Further, the adversarial DEA model is used to solve the other-evaluation efficiency value of each candidate centrifuge. The basic model is as follows:

[0216]

[0217]

[0218]

[0219]

[0220] u≥0;v≥0;

[0221] i=1,...,n;r=1,...,n;

[0222] k=1,...,m;j=1,...,s.

[0223] Among them, E ir It is the efficiency value of the decision making unit.

[0224] Step 3: The efficiency evaluation process of the components is a subjective evaluation process of the decision maker. Therefore, the preference priority is used to measure the psychological preference of the decision maker, and the initial cross-efficiency matrix is ​​corrected. The new cross-efficiency evaluation matrix is ​​a new cross-efficiency matrix with psychological preference.

[0225] Step 4: In order to maximize the difference between the quality of the alternative centrifuges, use OWA to aggregate the information of the modified cross efficiency matrix. Before aggregating the evaluation information, the idea of ​​maximizing the minimum difference is used to obtain the weight of each alternative centrifuge, w = (w1, w2, ..., w n ).

[0226] Arrange each row of the corrected cross-efficiency matrix in descending order, expressed as in represents the r-th largest evaluation value in the i-th row,

[0227] Furthermore, the ordered weighted arithmetic average (OWA) operator is used to perform weighted aggregation on the candidate components to obtain the efficiency value of each candidate component.

[0228] Specifically:

[0229] (1) Establish an input-output indicator evaluation system and collect evaluation data for 8 candidate centrifuges.

[0230] (2) The DEA model was used to obtain the initial cross-efficiency evaluation values ​​of 8 alternative centrifuges. The calculation results are shown in Table 1.

[0231] Table 1 Evaluation values ​​of initial cross-efficiency of alternative centrifuges

[0232]

[0233] (3) Considering the impact of decision makers’ psychology on efficiency evaluation results, the initial cross-efficiency value is adjusted using skewness to obtain the cross-efficiency value with psychological preference. The measurement results are shown in Table 2.

[0234] Table 2 Cross-efficiency values ​​of psychological preferences

[0235]

[0236] (4) Based on the idea of ​​maximizing the minimum difference, 8 alternative centrifuge weights are obtained.

[0237] w=(0.0026,0.0562,0.0062,0.0159,0.3370,0.0101,0.1226,0.4494)

[0238] (5) The OWA operator is used to perform weighted aggregation on the cross-evaluation information to obtain the cross-efficiency scores of the eight candidate centrifuges, which are then ranked. The results are shown in Table 3.

[0239] Table 3 Final scores and rankings

[0240]

[0241] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent application. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A method for analyzing the efficiency of components of a large central air conditioner, characterized in that: Includes steps: Analyze the efficiency evaluation indicators that affect the centrifuge components, and establish a corresponding efficiency index evaluation system from the "input-output" perspective; through this efficiency index evaluation system, standardize the input-output data of the candidate components; Establishing a data envelopment analysis method model, using the data envelopment analysis method model to conduct self-evaluation and other-evaluation on the results of the standardization process, and synthesizing the results to form a cross-efficiency evaluation matrix; Measuring the psychological preferences of decision makers through skewness analysis, and adjusting the cross-efficiency evaluation matrix based on the measurement results; The weights of the candidate components are calculated by the minimum difference maximization method; and the ordered weighted arithmetic mean operator is used to perform weighted aggregation on the candidate components to obtain the efficiency values ​​of the candidate components; The method of measuring the psychological preferences of decision makers through skewness analysis includes: The decision maker's psychological preferences are statistically analyzed through skewness, and the expression of skewness is: In the above formula, φ r is the standard deviation, which is calculated as The step of adjusting the cross-efficiency evaluation matrix using the measurement results includes: If the skewness of all decision makers is positive, the normalized calculation formula of the result is If there are one or more negative values ​​in the skewness of all decision makers, the normalized calculation formula of the result is The normalized result is introduced into the matrix, and the values ​​in the matrix are processed. The expression of the correction equation is: The adjusting the cross efficiency evaluation matrix by the measurement results also includes: Considering the influence of sorting, the priority coefficient is introduced; the expression of the priority coefficient is Among them, θ ir represents the preference ranking of the i-th decision-making unit, and its expression is Introducing the priority coefficient into the modified equation of the matrix, its expression is The corresponding cross-efficiency matrix with preference is obtained, which is as follows 2. The efficiency analysis method of components of a large central air conditioner according to claim 1, characterized in that: The above-mentioned establishment of a corresponding efficiency index evaluation system based on the "input-output" perspective includes input indexes and output indexes; The input indicators include cooling capacity range, comprehensive part load performance coefficient, and centrifuge power distribution power; The output indicators include energy efficiency index and frequency conversion effect.

3. The efficiency analysis method of components of a large central air conditioner according to claim 2, characterized in that: The input-output data of the candidate components are standardized, including classifying the input-output data into multiple types, specifically including income type, cost type and fixed type; Among them, the standardized calculation formula for income type is: The standardized calculation formula for cost type is The fixed standardized calculation formula is 4. The efficiency analysis method of components of a large central air conditioner according to claim 1, characterized in that: The data envelopment analysis method model is established, and the results of the standardization processing are self-evaluated and evaluated by others through the data envelopment analysis method model, including: Establish and use a data envelopment analysis method model with constant economies of scale to conduct self-evaluation on the standardized processing results of alternative components; and establish and use an adversarial data envelopment analysis method model to conduct peer evaluation on the standardized processing structure of alternative components; or establish and use a benevolent data envelopment analysis method model to conduct peer evaluation on the standardized processing structure of alternative components.

5. The efficiency analysis method of components of a large central air conditioner according to claim 4, characterized in that: The calculation formula of the data envelopment analysis method model with constant scale benefits is: The model needs to meet the following conditions: Condition 1, Condition two: Condition 3: i=1,...,n; r=1,...,n; Condition 4: k = 1, ..., m; j = 1, ..., s; Among them, x ij and ik Respectively represent input-output indicators, v ij and u ik Respectively represent the weights of input and output indicators, E ii is the self-assessed efficiency value of the decision-making unit; The calculation formula of the adversarial data envelopment analysis method model is: The model needs to meet the following conditions: Condition 1, Condition two: Condition three: Condition 4: u≥0; v≥0; Condition 5: i=1,...,n; r=1,...,n; Condition 6: k = 1, ..., m; j = 1, ..., s; The calculation formula of the adversarial data envelopment analysis method model is: The model needs to meet the following conditions: Condition 1, Condition two: Condition three: Condition 4: u≥0; v≥0; Condition 5: i=1,...,n; r=1,...,n; Condition six: k = 1, ..., m; j = 1, ..., s.

6. The efficiency analysis method of components of a large central air conditioner according to claim 5, characterized in that: The form of the cross-efficiency evaluation matrix is ​​as follows 7. The efficiency analysis method of components of a large central air conditioner according to any one of claims 1 to 6, characterized in that: The weight of each candidate component is obtained by the minimum difference maximization method; and the ordered weighted arithmetic average operator is used to weight the candidate components to obtain the efficiency value of each candidate component, including: The maximum-minimum deviation model is used to determine the weight that maximizes the preference difference of the decision-making unit. The weight is the weight of the ordered weighted arithmetic mean operator. The expression of this model is max min|E″ ir -E″ lr |·w r ; The following conditions must be met Condition 1, Condition 2: 1 ≥ w r ≥0; Condition 3, i=1,...,n; Condition 4: r = 1, ..., n; Condition 5: l = 1, ..., n; Then, assuming that the minimum deviation of two different decision units is represented by λ, its expression is λ=min|E″ ir -E″ lr |·w r ; It can be understood that λ≤min|E″ ir -E″ lr |·w r ; The maximum-minimum deviation model is modified to Eq. maxλ; The following conditions must be met Condition 1, Condition 2: λ ≤ |E″ ir -E″ lr |w r ; Condition 3: 0 ≤ w r ≤ 1; Condition 4: i = 1, ..., n; Condition 5: r = 1, ..., n; Condition six: l=1,...,n.

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