Comprehensive benefit evaluation method for FFU-clean bench coverage rate of ISO6-level electronic clean room based on AHP-entropy weight method

Abstract

The invention discloses a comprehensive benefit evaluation method for FFU-clean bench coverage rate of an ISO6-level electronic clean room based on an AHP-entropy weight method, and relates to the technical field of airflow organization of the ISO6-level electronic clean room, and the method comprises the steps: building a judgment matrix of an evaluation layer through employing the AHP method, carrying out the consistency check, and obtaining the weight of each evaluation index of the evaluation layer; establishing a judgment matrix of the scheme layer and carrying out consistency check to obtain the weight of each evaluation index of the scheme layer; combining the weight of each evaluation index of the evaluation layer and the weight of each evaluation index of the scheme layer to obtain the total subjective weight of each scheme under the AHP method; using an entropy weight method to obtain the weight of each scheme of the scheme layer under each evaluation index of the evaluation layer in the index and the total objective weight of each scheme; the AHP method and the entropy weight method are combined, the combined weight of evaluation indexes of the evaluation layer and the combined weight of all schemes of the scheme layer are obtained, the comprehensive benefits of the FFU-clean bench coverage rate of the ISO6-level electronic clean room of the target layer are evaluated, and therefore the optimal FFU-clean bench coverage rate is obtained, and the method has the advantage of being high in objectivity.

Description

Technical Field

[0001] The present invention relates to the technical field of air distribution in ISO Class 6 electronic cleanrooms, specifically to a comprehensive benefit evaluation method for the coverage rate of FFU - clean benches in ISO Class 6 electronic cleanrooms based on the AHP - entropy weight method. Background Technique

[0002] With the development of the high - precision manufacturing industry, higher requirements are inevitably put forward for air purification technology. At the same time, higher production processes also have stricter requirements for cleanroom environment control. The key point for forming the internal flow field in the cleanroom and controlling the cleanroom environment is the air distribution mode. Therefore, the research on cleanroom air distribution is particularly important.

[0003] An electronic cleanroom has a ventilation system with good air distribution. This system not only requires the ability to timely remove pollutants, ensure the indoor thermal and humidity environment and air quality, but also improve the personnel comfort level.

[0004] The common cleanroom air - conditioning systems at the present stage are composed of a fresh air system for cleanrooms (MAU), fan filter units (FFU), and dry coils (DCC). Among them, the air supply of fan filter units (FFU) is the most commonly used air - supply method in ISO Class 6 cleanrooms and has a very important impact on air distribution. By selecting a reasonable FFU - clean bench coverage rate, the indoor environment of the cleanroom can be better controlled, the personnel comfort level can be met, and the requirements of energy conservation and pollution discharge can be achieved.

[0005] The Chinese invention patent with the application number 202111480161.1 proposed a comprehensive evaluation model for the fire - hazard characteristics of building thermal insulation materials. The analytic hierarchy process was used to obtain the relationship between the influencing factors of the fire hazard of building thermal insulation materials, which is beneficial to the comprehensive evaluation of the fire hazard of building thermal insulation materials. However, this evaluation method is more subjective and lacks objective data as a basis and for comparison. The Chinese invention patent with the application number 201410834612.0 proposed a method for optimizing the transmission capacity and wind - fire bundling of ultra - high - voltage channels based on the entropy weight method. By calculating the entropy value of the index and the corresponding weight coefficient, the final index weight is obtained. This evaluation method is more objective and lacks corresponding expert experience and investigations. The Chinese invention patent with the application number 202111413861.9 proposed a substation site - selection method based on AHP - entropy weight method for weighting, which combines the subjective evaluation method and the objective evaluation method, quantitatively analyzes the substation site, and obtains the optimal substation site. However, this method does not consider the preference degree of decision - makers for the subjective evaluation method and the objective evaluation method.

[0006] Due to the polymorphism and ambiguity of the scheme evaluation indicators, it is very difficult to establish an evaluation system and determine evaluation indicators, and it is difficult to evaluate the advantages and disadvantages of a cleanroom design scheme with an accurate data. So far, when evaluating the layout scheme of the fan filter unit (FFU) in a cleanroom, it always relies on expert experience to judge, and its evaluation results will inevitably be affected by the personal preferences and experience of the judges, and its scientific nature cannot be guaranteed. Therefore, at present, a more reliable and scientific evaluation method for the comprehensive benefits of the FFU-clean bench coverage rate in an ISO Class 6 electronic cleanroom is needed to select the best FFU-clean bench coverage rate. Summary of the Invention

[0007] The purpose of the present invention is to provide an evaluation method for the comprehensive benefits of the FFU-clean bench coverage rate in an ISO Class 6 electronic cleanroom based on the AHP-entropy weight method to solve the problems mentioned in the above background technology.

[0008] To solve the above technical problems, the present invention provides the following technical solutions: An evaluation method for the comprehensive benefits of the FFU-clean bench coverage rate in an ISO Class 6 electronic cleanroom based on the AHP-entropy weight method, including the following steps:

[0009] Step 1, establish an evaluation system for the comprehensive benefits of the FFU-clean bench coverage rate in an ISO Class 6 electronic cleanroom, and divide the evaluation system into three levels: the target level, the evaluation level, and the scheme level;

[0010] Step 2, use the AHP method to establish a judgment matrix for the evaluation level and conduct a consistency test to obtain the weight Q of each evaluation index in the evaluation level Y1 ;

[0011] Step 3, use the AHP method to establish a judgment matrix for the scheme level and conduct a consistency test to obtain the weight Q of each evaluation index in the scheme level Y2 ;

[0012] Step 4, combine the weight Q Y1 and the weight Q Y2 , and obtain the total subjective weight Q of each scheme under the AHP method Y ;

[0013] Step 5, use the entropy weight method to obtain the weight H of each scheme in the scheme level under each evaluation index in the evaluation level and the weight W of each evaluation index in the evaluation level ij and the weight W of each evaluation index in the evaluation level j , and finally obtain the total objective weight R of each scheme Z ;

[0014] Step 6: Combine the AHP method and the entropy weight method to obtain the combined weights of the evaluation indicators in the evaluation layer and the combined weights of each solution in the solution layer, evaluate the comprehensive benefits of the FFU-clean bench coverage rate of the ISO Class 6 electronic cleanroom in the target layer, so as to obtain the optimal FFU-clean bench coverage rate, and the importance ranking of each evaluation indicator can be determined according to the weights of the evaluation indicators in the evaluation layer.

[0015] According to the above technical solution, in the said Step 1, the target layer is established as the evaluation of the comprehensive benefits of the FFU-clean bench coverage rate of the ISO Class 6 electronic cleanroom; the evaluation layer is established as energy utilization efficiency, air distribution characteristics, workspace comfort, TVOC emission efficiency, and ventilation efficiency; the solution layer is established as a coverage rate of 25%, a coverage rate of 50%, a coverage rate of 75%, and a coverage rate of 100%.

[0016] According to the above technical solution, in the said Step 2, the judgment matrix of the evaluation layer is U=(u ij ) n×n ,

[0017] where u ij represents the ratio of the influence of each evaluation indicator in the evaluation layer on the comprehensive benefits of the FFU-clean bench coverage rate in the target layer; n is the order of the judgment matrix U;

[0018] The formula for the consistency test of the judgment matrix of the evaluation layer is:

[0019]

[0020] In the formula, C R is the judgment matrix consistency test coefficient; C I is the judgment matrix consistency index; R I is the average random consistency index; λ max is the maximum eigenvalue of the judgment matrix; n is the order of the judgment matrix; when C R <0.1, it is considered that the judgment matrix passes the consistency test, otherwise the judgment matrix needs to be adjusted again and tested again.

[0021] According to the above technical solution, in the said Step 2, the method for calculating each evaluation indicator in the evaluation layer is: calculate the eigenvector corresponding to the maximum eigenvalue of the judgment matrix of the evaluation layer, and standardize this vector to obtain the weights Q Y1 of each evaluation indicator in the evaluation layer.

[0022] According to the above technical solution, in the said Step 3, the judgment matrix of the solution layer is V=(v ij ) t×t ,

[0023] where v ijIt represents the ratio of the influence of each evaluation index at the solution layer on the comprehensive benefit of the FFU-clean bench coverage rate at the evaluation layer; t is the order of the judgment matrix V;

[0024] Perform a consistency test on the judgment matrix of the solution layer. If it fails, the judgment matrix needs to be readjusted and tested again.

[0025] According to the above technical solution, in the third step, the method for calculating each evaluation index in the solution layer is as follows: calculate the eigenvector corresponding to the maximum eigenvalue of the judgment matrix of the solution layer, and standardize this vector to obtain the weight Q of each evaluation index in the solution layer Y2 。

[0026] According to the above technical solution, in the fourth step, the method for calculating the total subjective weight of each solution under the AHP method is as follows: combine the weight Q Y1 and the weight Q Y2 to obtain the total subjective weight of each solution under the AHP method: Q Y＝ Q Y1 ×Q Y2 。

[0027] According to the above technical solution, in the fifth step, the method for calculating the weight of each evaluation index and the total objective weight of each solution using the entropy weight method is as follows:

[0028] (1) List the original data of the solutions based on statistical data and relevant standards;

[0029] (2) Perform dimensionless processing on the original data and positive processing on the data of each index. Positive processing means the larger the index, the better:

[0030]

[0031] Among them, x ij represents the value of the i-th index under the j-th index; X ij represents the normalized data.

[0032] (3) Calculate the contribution degree of the j-th factor in the i-th case:

[0033]

[0034] (4) Calculate the information entropy E of each factor j :

[0035]

[0036] (5) Calculate the weight Wj of each evaluation index according to the information entropy of each factor:

[0037]

[0038] (6) Calculate the objective weight Rzi of each scheme under the entropy weight method:

[0039]

[0040] According to the above technical solution, in step six, the AHP-entropy weight method is combined to obtain the combined weights of the evaluation layer and the scheme layer:

[0041]

[0042] Among them, θ is the relative importance of the decision maker's preference, 0.3 < θ < 0.7; r is the number of types of weighting methods, taking r = 2; λ is the preference of the decision maker for the weighting method, where the preference of the decision maker for the analytic hierarchy process is λ1, 0 < λ1 < 1; the preference of the decision maker for the entropy weight method is λ2, 0 < λ2 < 1, and λ1 + λ2 = 1; W (s) is the weight of this evaluation index under a certain weighting method; β is the consistency coefficient of the weighting method. Since there are only two weighting methods, the values are β1 = β2 = 0.5.

[0043] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: The evaluation method for the comprehensive benefit of the FFU-clean bench coverage rate in the ISO 6-class electronic cleanroom based on the AHP-entropy weight method combines the subjective experience of experts and the objectively obtained index data, establishes an evaluation system for the FFU-clean bench coverage rate in the ISO 6-class electronic cleanroom, ranks the importance of each evaluation index, and makes the selection of the FFU-clean bench coverage rate more scientific and effective. The present invention combines the AHP method and the entropy weight method and applies them to the selection of the FFU-clean bench coverage rate in the ISO 6-class electronic cleanroom, enabling the cleanroom to have a ventilation system with better air flow organization, which is beneficial to improving the cleanroom environment and promoting the development of clean technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation to the present invention. In the drawings:

[0045] Attached Figure 1 is a schematic diagram of the evaluation system for the comprehensive benefit of the FFU-clean bench coverage rate in the embodiment of the present invention.

[0046] Attached Figure 2 is a flowchart of the evaluation process for the comprehensive benefit of the FFU-clean bench coverage rate in the embodiment of the present invention.

[0047] Attached Figure 3 is a flowchart of the process for calculating the subjective weight by the AHP method in the embodiment of the present invention.

[0048] Attached Figure 4This is the flowchart for calculating the objective weight by the entropy weight method in the embodiments of the present invention. Specific embodiments

[0049] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0050] Please refer to Figures 1 - 4 , the present invention provides a technical solution: an integrated benefit evaluation method for the FFU-clean bench coverage rate in an ISO Class 6 electronic cleanroom based on the AHP-entropy weight method, including the following steps:

[0051] Step 1: Establish an integrated benefit evaluation system for the FFU-clean bench coverage rate in an ISO Class 6 electronic cleanroom, and divide the evaluation system into three levels: the target layer, the evaluation layer, and the scheme layer;

[0052] Step 2: Use the AHP method to establish a judgment matrix for the evaluation layer and perform a consistency test to obtain the weight Q of each evaluation index in the evaluation layer Y1 ;

[0053] Step 3: Use the AHP method to establish a judgment matrix for the scheme layer and perform a consistency test to obtain the weight Q of each evaluation index in the scheme layer Y2 ;

[0054] Step 4: Combine the weight Q Y1 and the weight Q Y2 to obtain the total subjective weight Q of each scheme under the AHP method Y ;

[0055] Step 5: Use the entropy weight method to obtain the weight H of each scheme in the scheme layer under each evaluation index in the evaluation layer and the weight W of each evaluation index in the evaluation layer ij and finally obtain the total objective weight R of each scheme j ; Z ;

[0056] Step 6: Combine the AHP method and the entropy weight method to obtain the combined weight of the evaluation indexes in the evaluation layer and the combined weight of each scheme in the scheme layer, evaluate the integrated benefit of the FFU-clean bench coverage rate in the target layer ISO Class 6 electronic cleanroom, so as to obtain the optimal FFU-clean bench coverage rate, and the importance ranking of each evaluation index can be determined according to the weight of the evaluation indexes in the evaluation layer;

[0057] In Step 1, the target layer is established as the evaluation of the comprehensive benefits of the FFU-clean bench coverage rate in the ISO Class 6 electronic cleanroom; the evaluation layer is established as energy utilization efficiency, air distribution characteristics, workspace comfort, TVOC emission efficiency, and ventilation efficiency; the scheme layer is established as coverage rates of 25%, 50%, 75%, and 100%.

[0058] In Step 2, the judgment matrix of the evaluation layer is U = (u ij ) n×n ,

[0059] where u ij represents the ratio of the influence of each evaluation index in the evaluation layer on the comprehensive benefits of the FFU-clean bench coverage rate in the target layer; n is the order of the judgment matrix U;

[0060] The formula for conducting a consistency test on the judgment matrix of the evaluation layer is:

[0061]

[0062] In the formula, C R is the judgment matrix consistency test coefficient; C I is the judgment matrix consistency index; R I is the average random consistency index; λ max is the maximum eigenvalue of the judgment matrix; n is the order of the judgment matrix; when C R < 0.1, it is considered that the judgment matrix passes the consistency test, otherwise the judgment matrix needs to be readjusted and tested again;

[0063] In Step 2, the method for calculating each evaluation index in the evaluation layer is: calculate the eigenvector corresponding to the maximum eigenvalue of the judgment matrix of the evaluation layer, and standardize this vector to obtain the weights Q Y1 of each evaluation index in the evaluation layer;

[0064] In Step 3, the judgment matrix of the scheme layer is V = (v ij ) t×t ,

[0065] where v ij represents the ratio of the influence of each evaluation index in the scheme layer on the comprehensive benefits of the FFU-clean bench coverage rate in the evaluation layer; t is the order of the judgment matrix V;

[0066] Conduct a consistency test on the judgment matrix of the scheme layer. If it cannot pass, the judgment matrix needs to be readjusted and tested again;

[0067] In Step 3, the method for calculating each evaluation index in the scheme layer is: calculate the eigenvector corresponding to the maximum eigenvalue of the judgment matrix of the scheme layer, and standardize this vector to obtain the weights Q Y2;

[0068] In step 4, the method for calculating the total subjective weight of each plan under the AHP method is: combining the weight Q Y1 and weight Q Y2 Obtain the total subjective weight of each plan under the AHP method: Q Y＝ Q Y1 ×Q Y2 ;

[0069] In step 5, the entropy weight method is used to calculate the weight of each evaluation indicator and the total objective weight of each solution as follows:

[0070] (1) List the original data of the plan based on statistical data and relevant standards;

[0071] (2) The original data is dimensionless and each indicator data is positively processed. Positive processing means that the larger the indicator, the better:

[0072]

[0073] Among them, x ij represents the value of the i-th indicator under the j-th indicator; X ij Represents the normalized data.

[0074] (3) Calculate the contribution of the jth factor in the i-th case:

[0075]

[0076] (4) Calculate the information entropy E of each factor j :

[0077]

[0078] (5) Calculate the weight W of each evaluation index based on the information entropy of each factor j :

[0079]

[0080] (6) Calculate the objective weight R of each scheme under the entropy weight method zi :

[0081]

[0082] In step 6, the AHP-entropy weight method is combined to obtain the combined weights of the evaluation layer and the solution layer:

[0083]

[0084] Among them, θ is the relative importance of the decision maker's preference degree, 0.3 < θ < 0.7; r is the number of types of weight assignment methods, taking r = 2; λ is the preference degree of the decision maker for the weight assignment method, where the preference degree of the decision maker for the analytic hierarchy process is λ1, 0 < λ1 < 1; the preference degree of the decision maker for the entropy weight method is λ2, 0 < λ2 < 1, and λ1 + λ2 = 1; W (s) is the weight of this evaluation index under a certain weight assignment method; β is the consistency coefficient of the weight assignment method. Since there are only two weight assignment methods, the values are β1 = β2 = 0.5.

[0085] 1. Methods and steps for calculating the subjective weight of indicators by the AHP method (1) Establish the judgment matrix of the evaluation layer. According to the judgment matrix, calculate the importance of all nodes in this layer associated with a node in the upper layer for the upper layer. The present invention needs to establish two judgment matrices of the evaluation layer for the target layer and the scheme layer for the evaluation layer. When constructing the judgment matrix, the consistent matrix method is used. Instead of comparing all factors together, they are compared pairwise. The relative scale is used for comparison to minimize the difficulty of comparing factors with different natures as much as possible and improve the accuracy. The index evaluation criteria are shown in Table 1.

[0086] Table 1 Index evaluation criteria

[0087]

[0088] Among them, the judgment matrix of the evaluation layer is:

[0089]

[0090] where uij represents the ratio of the influence of each evaluation index in the evaluation layer on the comprehensive benefit of the target layer FFU-clean bench coverage rate; n is the order of the judgment matrix U.

[0091] (2) Calculate the maximum eigenvalue λ of the judgment matrix max and the corresponding eigenvector, and conduct a consistency test to obtain the weights QY1 of each evaluation index in the evaluation layer.

[0092] Among them, the formula for conducting a consistency test on the judgment matrix of the evaluation layer is:

[0093]

[0094] In the formula, C R is the judgment matrix consistency test coefficient; C I is the judgment matrix consistency index; R I is the average random consistency index; λ max is the maximum eigenvalue of the judgment matrix; n is the order of the judgment matrix; when C RWhen it is less than 0.1, it is considered that the judgment matrix passes the consistency test; otherwise, the judgment matrix needs to be adjusted again and tested again. The average random consistency index values for orders 1-10 are shown in Table 2.

[0095] Table 2 Average random consistency index values for orders 1-10

[0096]

[0097] (3) Establish the judgment matrix for the scheme layer. Calculate the maximum eigenvalue λ max of the judgment matrix and the corresponding eigenvector, and conduct a consistency test to obtain the weights QY2 of each evaluation index in the evaluation layer.

[0098] Among them, the judgment matrix for the scheme layer is:

[0099]

[0100] where vij represents the ratio of the influence of each evaluation index in the scheme layer on the comprehensive benefit of the FFU-clean bench coverage rate in the evaluation layer; t is the order of the judgment matrix V.

[0101] (4) The steps to calculate the total subjective weight of each scheme under the AHP method are as follows:

[0102] Combining the weight QY1 and the weight QY2, the total subjective weight QY of each scheme under the AHP method is obtained, as shown in Table 5.

[0103] Among them, the total subjective weight of each scheme is: QY = QY1 × QY2.

[0104] 2. AHP method weight calculation results

[0105] Based on expert experience, questionnaires, and relevant materials, establish a judgment matrix and conduct a consistency test as follows:

[0106] Evaluation layer: The content of the evaluation layer is as attached Figure 1 shown, which is Z1, Z2, Z3, Z4, Z5. Establish its judgment matrix as:

[0107]

[0108] where λ max = 5.194, CR = 0.0432 < 0.1, passing the consistency verification.

[0109] Scheme layer: (1) Under the Z1 index, for Scheme 1, Scheme 2, Scheme 3, and Scheme 4, establish their judgment matrix as:

[0110]

[0111] where λ max=4.124, CR=0.03697<0.1, passed the consistency verification.

[0112] (2) Under the Z2 index, the judgment matrix of Scheme 1, Scheme 2, Scheme 3 and Scheme 4 is as follows:

[0113]

[0114] where λ max =4.066, CR=0.01965<0.1, passed the consistency verification.

[0115] (3) Under the Z3 index, the judgment matrix of Scheme 1, Scheme 2, Scheme 3 and Scheme 4 is established as follows:

[0116]

[0117] where λ max =4.169, CR=0.05052<0.1, passed the consistency verification.

[0118] (4) Under the Z4 index, the judgment matrix of Scheme 1, Scheme 2, Scheme 3 and Scheme 4 is as follows:

[0119] where λ max =4.254, CR=0.07579<0.1, passed the consistency verification.

[0120] (5) Under the Z5 index, the judgment matrix of Scheme 1, Scheme 2, Scheme 3 and Scheme 4 is established as follows:

[0121] where λ max =4.114, CR=0.03419<0.1, passed the consistency verification.

[0122] Table 3 Summary of the weights of each evaluation index in the AHP evaluation layer

[0123]

[0124] Table 4 Summary of evaluation indicators of AHP scheme layer

[0125]

[0126]

[0127] Table 5 Summary of the total objective weights of each scheme

[0128]

[0129] In Table 3, the weights of the evaluation indicators in the evaluation layer can be directly obtained from the judgment matrix among the five indicators; in Table 4, the weights of the schemes in the scheme layer can be obtained from the judgment matrix of each scheme under each evaluation indicator in the evaluation layer; in Table 5, the total weight of each scheme is obtained by multiplying the weight of the scheme under the evaluation indicator in the evaluation layer by the sum of the weights of the evaluation indicators.

[0130] Step five, as Figure 4 shown, using the entropy weight method, first obtain the weight Hij of each scheme in the scheme layer under each evaluation indicator in the evaluation layer, then obtain the weight Wj of each evaluation indicator in the evaluation layer; finally, obtain the total objective weight RZ of each scheme.

[0131] 3. Methods and steps for calculating the objective weights of indicators by the entropy weight method

[0132] (1) List the original data of the schemes according to the statistical data and relevant standards, as shown in Table 6.

[0133] (2) Perform dimensionless processing on the original data and positive processing on the data of each indicator. Positive processing means that the larger the indicator, the better:

[0134]

[0135] Among them, xij represents the value of the i-th indicator under the j-th indicator; Xij represents the normalized data. (3) Calculate the contribution degree of the j-th factor in the i-th case, as shown in Table 7:

[0136]

[0137] (4) Calculate the information entropy Ej of each factor, as shown in Table 8:

[0138]

[0139] (5) Calculate the weight Wj of each evaluation indicator according to the information entropy of each factor, as shown in Table 8:

[0140] (6) Calculate the objective weight Rzi of each scheme under the entropy weight method, as shown in Table 9:

[0141]

[0142] 4. Results of weight calculation by the entropy weight method

[0143] Table 6 Original data table

[0144]

[0145]

[0146] Table 7 Contribution degree Hij of each indicator in each scheme

[0147]

[0148] Table 8 Summary Table of Index Information Entropy and Weights of Each Evaluation Index

[0149]

[0150] Table 9 Summary Table of Weights of Each Scheme

[0151]

[0152] Step 6: Combine the AHP method and the entropy weight method to obtain the combined weights of the evaluation indexes in the evaluation layer and the combined weights of each scheme in the scheme layer, evaluate the comprehensive benefit of the ISO 6-class electronic cleanroom FFU-clean bench coverage rate in the target layer, so as to obtain the optimal FFU-clean bench coverage rate, and the importance ranking of each evaluation index can be determined according to the weights of the evaluation indexes in the evaluation layer.

[0153] 5. Combine the AHP-entropy weight method to obtain the combined weights of the evaluation layer and the scheme layer:

[0154]

[0155] Among them, θ is the relative importance of the decision maker's preference degree, 0.3 < θ < 0.7; r is the number of types of weighting methods, taking r = 2; λ is the preference degree of the decision maker for the weighting method, where the preference degree of the decision maker for the analytic hierarchy process is λ1, 0 < λ1 < 1; the preference degree of the decision maker for the entropy weight method is λ2, 0 < λ2 < 1, and λ1 + λ2 = 1; W (s) is the weight of this evaluation index under a certain weighting method; β is the consistency coefficient of the weighting method. Since there are only two weighting methods, the values are β1 = β2 = 0.5.

[0156] 6. Calculation Results of Combined Weights

[0157] The combined weights of each evaluation index in the evaluation layer and the combined weights of each scheme are shown in Tables 10 and 11:[[]]END

[0158] Table 10 Combined Weights of Each Evaluation Index

[0159]

[0160]

[0161] Table 11 Combined Weights of Each Scheme

[0162]

[0163] ​7. It can be calculated by combining the AHP method and the entropy weight method that when the FFU-clean bench coverage rate is 100%, the comprehensive benefit is the best, followed by the coverage rates of 50% and 75%. When the coverage rate is 25%, the comprehensive benefit is the worst. If low energy consumption is required, a coverage rate of 50% should be selected; if high cleanliness is required, a coverage rate of 100% should be selected; if high personnel comfort is required, a coverage rate of 25% should be selected.

[0164] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent in such process, method, article or device.

[0165] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A comprehensive benefit evaluation method for the coverage rate of FFU - clean benches in ISO Class 6 electronic cleanrooms based on the AHP - entropy weight method, characterized in that: It includes the following steps: Step 1: Establish an integrated benefit evaluation system for the FFU-clean bench coverage rate in an ISO Class 6 electronic cleanroom, and divide the evaluation system into three levels: the target level, the evaluation level, and the solution level; Step 2: Use the AHP method to establish the judgment matrix of the evaluation layer and conduct a consistency test to obtain the weights Q of each evaluation index in the evaluation layer Y1 ; Step 3: Use the AHP method to establish the judgment matrix of the scheme layer and conduct a consistency test to obtain the weights Q of each evaluation index of the scheme layer Y2 ; Step 4: Combine the weight Q Y1 and the weight Q Y2 , and obtain the total subjective weight Q of each solution under the AHP method Y ; Step 5: Use the entropy weight method to obtain the weight H of each solution in the solution layer under each evaluation index in the evaluation layer ij and the weight W of each evaluation index in the evaluation layer j , and finally obtain the total objective weight R of each solution Z ; Step 6: Combine the AHP method and the entropy weight method to obtain the combined weights of the evaluation indicators at the evaluation level and the combined weights of each solution at the solution level, and evaluate the comprehensive benefit of the FFU-clean bench coverage rate of the ISO Class 6 electronic cleanroom at the target level, so as to obtain the optimal FFU-clean bench coverage rate, and the importance ranking of each evaluation indicator can be determined according to the weights of the evaluation indicators at the evaluation level.

2. The comprehensive benefit evaluation method for the coverage rate of FFU - clean benches in an ISO Class 6 electronic cleanroom based on the AHP - entropy weight method according to claim 1, wherein: In the said Step 1, the target level is established as the evaluation of the comprehensive benefit of the FFU-clean bench coverage rate in the ISO Class 6 electronic cleanroom; the evaluation level is established as energy utilization efficiency, air distribution characteristics, workspace comfort, TVOC emission efficiency, and ventilation efficiency; the solution level is established as a coverage rate of 25%, a coverage rate of 50%, a coverage rate of 75%, and a coverage rate of 100%.

3. The comprehensive benefit evaluation method for the coverage rate of FFU-clean benches in an ISO Class 6 electronic cleanroom based on the AHP-entropy weight method according to claim 2, wherein: In the second step, the judgment matrix of the evaluation layer is U = (u ij ) n×n , Among them, u ij represents the ratio of the influence of each evaluation index in the evaluation layer on the comprehensive benefit of the FFU-clean bench coverage rate in the target layer; n is the order of the judgment matrix U; The formula for performing a consistency test on the judgment matrix at the evaluation level is: Where, C R is the consistency test coefficient of the judgment matrix; C I is the consistency index of the judgment matrix; R I is the average random consistency index; λ max is the maximum eigenvalue of the judgment matrix; n is the order of the judgment matrix; when C R <0.1, it is considered that the judgment matrix passes the consistency test, otherwise the judgment matrix needs to be readjusted and tested again.

4. The comprehensive benefit evaluation method for the coverage rate of FFU - clean benches in an ISO 6 - level electronic cleanroom based on the AHP - entropy weight method according to claim 3, characterized in that: In the second step, the method for calculating each evaluation index in the evaluation layer is as follows: calculate the eigenvector corresponding to the maximum eigenvalue of the judgment matrix of the evaluation layer, and standardize this vector to obtain the weights Q of each evaluation index in the evaluation layer Y1 .

5. The comprehensive benefit evaluation method for the FFU-clean bench coverage rate of an ISO Class 6 electronic cleanroom based on the AHP-entropy weight method according to claim 4, wherein: In the third step, the judgment matrix of the scheme layer is V = (v ij ) t×t , where v ij represents the ratio of the influence of each evaluation index at the solution layer on the comprehensive benefit of the FFU-clean bench coverage rate at the evaluation layer; t is the order of the judgment matrix V; Perform a consistency test on the judgment matrix at the solution level. If it cannot pass, the judgment matrix needs to be readjusted and tested again.

6. The comprehensive benefit evaluation method for the coverage rate of FFU-clean benches in an ISO Class 6 electronic cleanroom based on the AHP-entropy weight method according to claim 5, wherein: In the third step, the method for calculating each evaluation index in the scheme layer is as follows: calculate the eigenvector corresponding to the maximum eigenvalue of the judgment matrix in the scheme layer, and standardize this vector to obtain the weight Q of each evaluation index in the scheme layer Y2 .

7. The comprehensive benefit evaluation method of the FFU-clean bench coverage rate in the ISO 6-level electronic cleanroom based on the AHP-entropy weight method according to claim 6, wherein: In the fourth step, the method for calculating the total subjective weight of each scheme under the AHP method is as follows: combining the weight Q Y1 and the weight Q Y2 to obtain the total subjective weight of each scheme under the AHP method: Q Y＝ Q Y1 ×Q Y2 .

8. The comprehensive benefit evaluation method for the coverage rate of FFU - clean benches in an ISO Class 6 electronic cleanroom based on the AHP - entropy weight method according to claim 7, characterized in that: In the said Step 5, the method for calculating the weights of each evaluation indicator and the total objective weights of each solution using the entropy weight method is: (1) List the original data of the solutions based on statistical data and relevant standards; (2) Perform dimensionless processing on the original data, and perform positive normalization on the data of each indicator. Positive normalization means that the larger the indicator, the better: where x ij represents the value of the i-th index under the j-th index; X ij represents the normalized data; (3) Calculate the contribution degree of the jth factor in the ith case: (4) Calculate the information entropy Ej of each factor: (5) Calculate the weight Wj of each evaluation indicator according to the information entropy of each factor: (6) Calculate the objective weight Rzi of each solution under the entropy weight method:

9. The comprehensive benefit evaluation method for the coverage rate of FFU-clean benches in an ISO Class 6 electronic cleanroom based on the AHP-entropy weight method according to claim 8, characterized in that: In the said Step 6, combine the AHP-entropy weight method to obtain the combined weights of the evaluation level and the solution level: Among them, θ is the relative importance of the decision maker's preference degree, where 0.3 < θ < 0.7; r is the number of types of weighting methods, taking r = 2; λ is the preference degree of the decision maker for the weighting methods, where the preference degree of the decision maker for the analytic hierarchy process is λ1, 0 < λ1 < 1; the preference degree of the decision maker for the entropy weight method is λ2, 0 < λ2 < 1, and λ1 + λ2 = 1; W (s) is the weight of this evaluation index under a certain weighting method; β is the consistency coefficient of the weighting method. Since there are only two weighting methods, the values are taken as β1 = β2 = 0.5.

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