CN114565215A - High-end equipment evaluation method and system based on product life cycle - Google Patents

Abstract

The invention provides a high-end equipment evaluation method and system based on the full life cycle of a product, and relates to the technical field of high-end equipment evaluation. The system of the invention includes an evaluation data acquisition module, a multi-dimensional quantitative data acquisition module, a quantitative index prediction interval acquisition module, a rule conversion module, a fusion module and a sorting module. Wherein, the rule conversion module is used to convert the multiple measurement values of the quantitative indicators into the evaluation form of the confidence distribution under the unified identification framework between adjacent schemes according to the conversion rules, so as to obtain the evaluation of each quantitative indicator between the adjacent evaluation schemes The invention converts the quantitative indicators into a pairwise comparison mode through the rule conversion module, which greatly improves the evaluation efficiency while ensuring the stability of the results, improves the multi-attribute evaluation system, and makes the high-end equipment multi-attribute based on the full life cycle. Evaluation questions are more realistic.

Description

High-end equipment evaluation method and system based on product life cycle

technical field

The invention relates to the technical field of high-end equipment evaluation, in particular to a high-end equipment evaluation method and system based on the full life cycle of a product.

Background technique

High-end equipment manufacturing industry has the characteristics of high technology content, high-end value chain and core link in the industry chain, involving multi-disciplinary and multi-field intersection, with high added value, and its level directly determines the competitiveness of the entire industry chain . Product life cycle is a product-centered, life-cycle-oriented strategic thinking method that connects various product stakeholders in the entire life cycle of a product from raw material acquisition to scrapping. The whole life cycle of a product generally includes stages such as design, raw material acquisition, manufacturing, logistics, use, after-sales, and scrapping. The research on high-end equipment evaluation methods based on the product life cycle can realize the collaborative management of all links, businesses and elements of the product life cycle, reduce costs, improve manufacturing capabilities, management capabilities, and green environmental protection capabilities, and realize the optimal decision-making of enterprises. Therefore, it has important theoretical and practical significance to study the evaluation of high-end equipment based on the product life cycle.

The existing high-end equipment evaluation based on the whole product life cycle generally evaluates all schemes individually by using a multi-attribute evaluation method, which is inefficient.

SUMMARY OF THE INVENTION

(1) Technical problems solved

Aiming at the deficiencies of the prior art, the present invention provides a high-end equipment evaluation method and system based on the full life cycle of the product, and solves the technical problem of low efficiency of the existing evaluation method through the comparative evaluation method.

(2) Technical solutions

To achieve the above purpose, the present invention is achieved through the following technical solutions:

In a first aspect, the present invention provides a high-end equipment evaluation system based on the full life cycle of a product, including:

The evaluation data acquisition module is used to acquire multiple plans to be evaluated for high-end equipment, evaluation levels, and a multi-dimensional evaluation index system based on the full life cycle of high-end equipment;

The multi-dimensional quantitative data acquisition module is used to acquire the measurement data of each quantitative index at different stages in the whole life cycle based on the multi-dimensional evaluation index system;

The quantitative index prediction interval obtaining module is used to obtain the historical measurement values of the quantitative indicators in the multiple to-be-evaluated schemes obtained based on the multi-dimensional evaluation index system, and obtain the prediction interval of each quantitative index;

The rule conversion module is used to convert the multiple measurement values of the quantitative indicators into the evaluation form of the confidence distribution under the unified identification framework between adjacent schemes according to the conversion rules, and obtain the evaluation information of each quantitative indicator between the adjacent evaluation schemes ;

The fusion module is used to fuse the evaluation information of each quantitative index to obtain a comprehensive evaluation of each quantitative index compared between adjacent evaluation schemes;

The sorting module is used to combine the utility of each evaluation level to convert the comprehensive evaluation of each quantitative index in the comparison between adjacent evaluation schemes into the interval utility value of the comparison between the schemes; convert the interval utility value into the preference possibility; based on the preference possibility and The chain ratio calculates the comprehensive importance and arranges the plans in full order.

Preferably, the quantitative index prediction interval obtaining module is further configured to: obtain the prediction interval of each quantitative index according to industry regulations.

Preferably, the evaluation level includes:

A set of discrete linguistic variables representing the degree of preference among alternatives, from H ₁ to H _N representing an increase in the degree of preference, H ₁ representing no preference at all, H _N representing complete preference, and H _(N+1)/2 representing no preference difference, N is an odd number.

Preferably, the quantitative indicators include:

R&D expenses, product specifications, product life, core technology ratio, proportion of R&D personnel, raw material price, processing cost, raw material transportation distance, raw material transportation time, manufacturing automation rate, workpiece specifications, assembly time, product qualification rate, precision operation and maintenance rate, user satisfaction, energy consumption, core component reuse and scrap recycling costs.

Preferably, the conversion rules include:

Quantitative indicators are divided into income indicators, cost indicators, deviation indicators and fixed indicators;

The conversion rules for revenue indicators include:

即Pre_n与H_n正向一一对应，Pre_n∈[-1,1]且均匀分布；a. For profit indicators

That is, Pre _n and H _n have a positive one-to-one correspondence, and Pre _n ∈ [-1,1] and are uniformly distributed;

b. Obtain the prediction interval [FIL _l , FIH _l ], (l=1,2,...,L);

和

计算指标偏好度c. Obtain the exact values of the adjacent evaluation schemes A _i and A _i+1 on the quantitative index a _l

and

Calculate index preference

d. If Pre _n ≤Pre(l) _i,i+1 <Pre _n+1 , convert the comparative evaluation of quantitative indicators on adjacent evaluation schemes into unified identification through the index preference degree Pre(l) _i,i+1 The confidence distribution under the framework, expressed as:

d(a _l (A _i,i+1 ))={(H _n ,β _n,l (A _i,i+1 )),(H _n+1 ,β _n+1,l (A _{i,i +1} ))}

(l=1,2,...,L)

β_n+1,l(A_i,i+1)＝1-β_n,l(A_i,i+1)。in:

β _n+1,1 (A _i,i+1 )=1−β _n,1 (A _i,i+1 ).

d(a _l (A _i,i+1 )) represents the comparative evaluation of the adjacent evaluation schemes A _i and A _i+1 obtained by the dimension c _k on the quantitative index a _l through a specific transformation rule, β _n,l ( A _i,i+1 ) and β _n+1,l (A _i,i+1 ) represent the confidence levels assigned to H _n and H _n+1 levels, respectively;

The conversion rules for cost indicators include:

即Pre_n与H_n反向一一对应，Pre_n∈[-1,1]且均匀分布；a. For cost indicators

That is, Pre _n and H _n have a reverse one-to-one correspondence, and Pre _n ∈ [-1,1] and are uniformly distributed;

b. Obtain the prediction interval [FIL _l , FIH _l ], (l=1,2,...,L);

和

计算指标偏好度c. Obtain the exact values of the adjacent evaluation schemes A _i and A _i+1 on the quantitative index a _l

and

Calculate index preference

d. If Pre _n ≤Pre(l) _i,i+1 ≤Pre _n+1 , convert the comparative evaluation of quantitative indicators on adjacent evaluation schemes into unified identification through the index preference degree Pre(l) _i,i+1 The confidence distribution under the framework, expressed as:

d(a _l (A _i,i+1 ))={(H _N+1-n ,β _N+1-n,l (A _i,i+1 )),(H _Nn ,β _Nn,l ( A _i,i+1 ))}

(l=1,2,...,L)

in:

β_N-n,l(A_i,i+1)＝1-β_N+1-n,l(A_i,i+1)。

β _Nn,1 (A _i,i+1 )=1−β _N+1−n,1 (A _i,i+1 ).

β _N+1-n,l (A _i,i+1 ) represents the confidence level assigned to the sum H _N+1-n level, β _Nn,l (A _i,i+1 ) represents the confidence level assigned to the sum H _Nn level confidence;

The conversion rules for deviating indicators include:

即Pre_n与H_n正向一一对应，Pre_n∈[-1,1]且均匀分布；a. For deviation indicators

That is, Pre _n and H _n have a positive one-to-one correspondence, and Pre _n ∈ [-1,1] and are uniformly distributed;

b. Obtain the prediction interval [FIL _l , FIH _l ], (l=1,2,...,L);

和

计算指标偏好度c. Obtain the exact values of the adjacent evaluation schemes A _i and A _i+1 on the qualitative index a _l

and

Calculate index preference

d. If Pre _n ≤Pre(l) _i,i+1 <Pre _n+1 , convert the comparative evaluation of quantitative indicators on adjacent evaluation schemes into unified identification through the index preference degree Pre(l) _i,i+1 The confidence distribution under the framework, expressed as:

d(a _l (A _i,i+1 ))={(H _n ,β _n,l (A _i,i+1 )),(H _n+1 ,β _n+1,l (A _{i,i +1} ))}

(l=1,2,...,L)

in:

β_n+1,l(A_i,i+1)＝1-β_n,l(A_i,i+1)。

β _n+1,1 (A _i,i+1 )=1−β _n,1 (A _i,i+1 ).

β _n,l (A _i,i+1 ) and β _n+1,1 (A _i,i+1 ) represent the confidence levels assigned to H _n and H _n+1 levels, respectively;

The conversion rules for fixed indicators include:

即Pre_n与H_n反向一一对应，Pre_n∈[-1,1]且均匀分布；a. For ordinary income indicators

That is, Pre _n and H _n have a reverse one-to-one correspondence, and Pre _n ∈ [-1,1] and are uniformly distributed;

b. Obtain the prediction interval [FIL _l , FIH _l ], (l=1,2,...,L);

和

计算指标偏好度：c. Obtain the exact values of the adjacent evaluation schemes A _i and A _i+1 on the quantitative index a _l

and

Calculate index preference:

d. If Pre _n ≤Pre(l) _i,i+1 <Pre _n+1 , convert the comparative evaluation of quantitative indicators on adjacent evaluation schemes into unified identification through the index preference degree Pre(l) _i,i+1 The confidence distribution under the framework, expressed as:

d(a _l (A _i,i+1 ))={(H _N+1-n ,β _N+1-n,l (A _i,i+1 )),(H _Nn ,β _Nn,l ( A _i,i+1 ))}

(l=1,2,...,L)

in:

β_N-n,l(A_i,i+1)＝1-β_N+1-n,l(A_i,i+1)。

β _Nn,1 (A _i,i+1 )=1−β _N+1−n,1 (A _i,i+1 ).

β _N+1-n,1 (A _i,i+1 ) and β _Nn,1 (A _i,i+1 ) represent the confidence levels assigned to the H _N+1-n and H _Nn ranks, respectively.

Preferably, the integration of each quantitative index evaluation information to obtain a comprehensive evaluation of each quantitative index compared between adjacent evaluation schemes, including:

The evaluation information of multiple quantitative indicators given by different dimensions under each pair of adjacent schemes is fused, and expressed as a comprehensive evaluation of the comparison between adjacent evaluation schemes, namely:

d(A _i,i+1 )={(H _n ,β _n (A _i,i+1 )),n=1,2,...,N; (Ω,β _Ω (A _i,i+1 ) )}

(l=1,2,...,L)

in:

d(A _i,i+1 ) represents the comprehensive evaluation made by comparing all qualitative indicators of adjacent evaluation schemes A _i and A _i+1 , β _n (A _i,i+1 ) and β _Ω (A _{i,i+ 1} ) represent the confidence assigned to the _Hn level and global ignorance, respectively.

Preferably, in combination with the utility of each evaluation level, the comprehensive evaluation of each quantitative index compared between adjacent evaluation schemes is converted into an interval utility value compared between the schemes, including

Among them, S(A _i,i+1 ) ^- and S(A _i,i+1 ) ⁺ represent the lower and upper limits of the utility value of the comprehensive evaluation interval comparing adjacent evaluation schemes A _i and A _i+1 , and S(A _i,i+1 ) ^- = -S(A _i+1,i ) ⁺ , S(A _i,i+1 ) ⁺ = -S(A _i+1,i ) ^- .

Preferably, the converting the interval utility value into the preference possibility includes:

表示区间[S(A_i,i+1)^-,S(A_i,i+1)⁺]和区间[S(A_i+1,i)^-,S(A_i+1,i)⁺]重叠的长度。Among them, pi _,i+1 represents the preference degree when comparing adjacent schemes A _i and A _i+1 , and pi _,i+1 ∈[0,1]; pi _,i+1 =0.5 represents no preference , p _i,i+1 <0.5 indicates that A _i+1 is preferred, and p _i,i+1 >0.5 indicates that A _i is preferred;

Represents the interval [S(A _i,i+1 ) ^- ,S(A _i,i+1 ) ⁺ ] and the interval [S(A _i+1,i ) ^- ,S(A _i+1,i ) ⁺ ] length of overlap.

Preferably, the comprehensive importance is calculated based on the preference probability and the chain ratio and the schemes are ranked in total order, including:

Then convert the preference probability into the importance ratio between adjacent comparison schemes, namely:

Among them: r _i,i+1 represents the importance ratio when comparing adjacent evaluation schemes A _i and A _i+1 , and r _max represents the maximum importance ratio between adjacent comparison schemes;

According to the importance ratio value, the comprehensive importance of each scheme is calculated in reverse order of scheme serial number, namely:

Where: Z _i represents the comprehensive importance of the scheme A _i , _ki represents the comprehensive score value of the scheme A _i , and k _I =1;

According to the comprehensive importance of the scheme, the total order of high-end equipment schemes based on the product life cycle is obtained.

In a second aspect, the present invention also provides a high-end equipment evaluation method based on the full life cycle of a product, the method comprising:

Obtain multiple plans to be evaluated for high-end equipment, evaluation levels, and a multi-dimensional evaluation index system based on the full life cycle of high-end equipment;

Obtain measurement data of various quantitative indicators at different stages in the full life cycle of the multi-dimensional evaluation indicator system;

Obtain the prediction interval of each quantitative indicator based on the historical measurement values of the quantitative indicators in the multiple to-be-evaluated schemes obtained by the multi-dimensional evaluation indicator system;

Converting a plurality of measured values of the quantitative index into an evaluation form of confidence distribution under the unified identification framework between adjacent schemes according to the conversion rule, to obtain evaluation information of each quantitative index between adjacent evaluation schemes;

Integrate the evaluation information of each quantitative index to obtain a comprehensive evaluation of each quantitative index compared between adjacent evaluation schemes;

Combined with the utility of each evaluation level, the comprehensive evaluation of each quantitative index of the comparison between adjacent evaluation schemes is converted into the interval utility value of the comparison between the schemes, and it is converted into a preference probability. Finally, the comprehensive importance is calculated based on the preference probability and the chain ratio. degree and arrange the plans in full order.

(3) Beneficial effects

The invention provides a high-end equipment evaluation method and system based on the whole life cycle of the product. Compared with the prior art, it has the following beneficial effects:

The invention includes an evaluation data acquisition module, a multi-dimensional quantitative data acquisition module, a quantitative index prediction interval acquisition module, a rule conversion module, a fusion module and a sorting module. The evaluation data acquisition module is used to acquire multiple plans to be evaluated, the evaluation level and the multi-dimensional evaluation index system based on the full life cycle of high-end equipment; the multi-dimensional quantitative data acquisition module is used to acquire the multi-dimensional evaluation index system based on the whole life cycle The measurement data of each quantitative index at different stages; the quantitative index prediction interval acquisition module is used to obtain the historical measurement values of the quantitative indicators in multiple schemes to be evaluated based on the multi-dimensional evaluation index system, and obtain the prediction interval of each quantitative index; the rule conversion module It is used to convert the multiple measurement values of the quantitative indicators into the evaluation form of the confidence distribution under the unified identification framework between adjacent schemes according to the conversion rules, so as to obtain the evaluation information of each quantitative index between the adjacent evaluation schemes; the fusion module uses It is used to fuse the evaluation information of various quantitative indicators to obtain a comprehensive evaluation of each quantitative indicator compared between adjacent evaluation schemes; the sorting module is used to combine the utility of each evaluation level, and convert the comprehensive evaluation of each quantitative indicator compared between adjacent evaluation schemes into one between the schemes. Compare the interval utility value and convert it into preference probability. Finally, based on preference probability and chain ratio, the comprehensive importance is calculated and the plans are ranked in total order. The invention converts the quantitative indicators into a pairwise comparison mode through the rule conversion module, which greatly improves the evaluation efficiency while ensuring the stability of the results, improves the multi-attribute evaluation system, and also solves the problem of multi-attribute evaluation of high-end equipment based on the full life cycle. more realistic.

Description of drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative effort.

FIG. 1 is a block diagram of a high-end equipment evaluation system based on the full life cycle of a product in an embodiment of the present invention.

Detailed ways

In order to make the purposes, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the implementations. example. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

The embodiment of the present application solves the problem of low efficiency of the existing evaluation method by providing a high-end equipment evaluation method and system based on the full life cycle of a product, and greatly improves the evaluation efficiency while ensuring the stability of the results.

The technical solutions in the embodiments of the present application are to solve the above-mentioned technical problems, and the general idea is as follows:

The existing high-end equipment evaluation methods mainly have the following problems: separate evaluation of all schemes leads to long evaluation time and high cost; the conversion rules of quantitative indicators are not perfect, resulting in a large amount of information loss; the form of distributed pairwise comparison is not used to express Preference information, there is uncertainty information. The embodiment of the present invention can save the evaluation time and cost by adopting the evaluation mode of pairwise comparison between adjacent evaluation schemes instead of evaluating all the schemes individually; adopting the distributed form to express the reasonable uncertainty information of the preference information, and improving the accuracy of the evaluation sex.

In order to better understand the above technical solutions, the above technical solutions will be described in detail below with reference to the accompanying drawings and specific embodiments.

An embodiment of the present invention provides a high-end equipment evaluation system based on the full life cycle of a product, as shown in FIG. 1 , including:

The evaluation data acquisition module is used to acquire multiple plans to be evaluated for high-end equipment, evaluation levels, and a multi-dimensional evaluation index system based on the full life cycle of high-end equipment;

The multi-dimensional quantitative data acquisition module is used to acquire the measurement data of each quantitative index at different stages in the whole life cycle based on the multi-dimensional evaluation index system;

The quantitative index prediction interval obtaining module is used to obtain the historical measurement values of the quantitative indicators in the multiple to-be-evaluated schemes obtained based on the multi-dimensional evaluation index system, and obtain the prediction interval of each quantitative index;

The rule conversion module is used to convert the multiple measurement values of the quantitative indicators into the evaluation form of the confidence distribution under the unified identification framework between adjacent schemes according to the conversion rules, and obtain the evaluation information of each quantitative indicator between the adjacent evaluation schemes ;

The fusion module is used to fuse the evaluation information of each quantitative index to obtain a comprehensive evaluation of each quantitative index compared between adjacent evaluation schemes;

The sorting module is used to combine the utility of each evaluation level, convert the comprehensive evaluation of each quantitative index of the comparison between adjacent evaluation schemes into the interval utility value of the comparison between the schemes, and convert it into a preference probability. Finally, based on the preference probability and The chain ratio calculates the comprehensive importance and arranges the plans in full order.

The embodiment of the present invention converts the quantitative indicators into a pairwise comparison mode through the rule conversion module, which greatly improves the evaluation efficiency while ensuring the stability of the results, improves the multi-attribute evaluation system, and also enables the multi-attribute high-end equipment based on the full life cycle. Evaluation questions are more realistic.

Each module is described in detail below:

Evaluation data acquisition module:

In the specific implementation process, the evaluation data acquisition module is used to obtain multiple plans to be evaluated for high-end equipment, evaluation levels, and a multi-dimensional evaluation index system based on the full life cycle of high-end equipment.

For example: I scheme A={A ₁ ,A ₂ ,...,A _I }, L quantitative indicators a={a ₁ ,a ₂ ,...,a _L }, N evaluation levels Ω={H ₁ ,H ₂ ,...,H _N }, where A _i , a _l , and H _n represent the i, l, and nth evaluation schemes, quantitative indicators, and evaluation grades, respectively; the evaluation grades are a set of discrete linguistic variables, representing the comparison between schemes. The degree of preference, from H ₁ to H _N represents the increase in the degree of preference, H ₁ means no preference at all, H _N means complete preference, H _(N+1)/2 means no difference (N is an odd number); u(H _n ) represents the utility corresponding to the evaluation level H _n , and −1≤u(H _n )≤1, u(H _n )≤u(H _n+1 ).

For example, the whole life cycle of a certain high-end equipment includes: product design stage, raw material acquisition and processing stage, manufacturing and assembly stage, operation and maintenance stage, and scrapping and recycling stage. Each stage includes a number of quantitative indicators, as shown in Table 1:

Table 1 The multi-dimensional evaluation index system of the whole life cycle of a certain high-end equipment

Multidimensional quantitative data acquisition module:

In the specific implementation process, the multi-dimensional quantitative data acquisition module is used to acquire the measurement data of each quantitative indicator at different stages in the whole life cycle on the basis of the constructed multi-dimensional evaluation index system.

To obtain the measurement data of each quantitative index at different stages of the whole life cycle, different methods can be adopted according to the nature of the index. For example, in the manufacturing and assembly stage, the method of assembling sensors on the production line can be adopted to measure the relevant data of a certain part at different times or different stages to obtain the measurement value of the product specification; in the operation and maintenance stage, the user can be obtained by means of data crawling For the user satisfaction comments of products, sentiment analysis is performed on the comments using relevant machine learning and deep learning methods to obtain the measurement value of user satisfaction; some quantitative indicators that require manual statistics, such as raw material prices, can also be manually entered. method to obtain.

Quantitative indicator prediction interval acquisition module:

In the process of multi-dimensional quantitative data acquisition, because the measurement data of different quantitative indicators at different stages are affected by the nature of their own indicators and external factors, different data intervals will be generated. The measured historical data can be used as the prediction interval of a qualitative evaluation indicator. In the specific operation process, the prediction interval of some quantitative indicators can also be determined by the interval specified by the industry. For example, the national sixth stage motor vehicle pollutant emission standard stipulates that the engine emission standard is: carbon monoxide emission is 0 ~ 700mg, The non-methane hydrocarbon emission content is 0 ~ 68mg, etc.; China's automobile classification standard stipulates that the length of the minibus is less than or equal to 3.5m, and the length of the small bus is less than 6m. The quantitative index prediction interval acquisition module is used to determine the prediction interval of each quantitative index, which lays a foundation for the operation of the rule conversion module.

Rule conversion module:

In the specific implementation process, the rule conversion module is configured to convert the multiple measurement values of the quantitative indicators into the evaluation form of the confidence distribution under the unified identification framework between adjacent schemes according to the conversion rules, and obtain the relationship between the adjacent evaluation schemes. Each quantitative indicator evaluates information.

Quantitative indicators can first be classified according to the attributes of the indicators themselves, and can be divided into income indicators, cost indicators, deviation indicators and fixed indicators. For the convenience of explanation, assuming that the evaluation level N is an odd number, a set of index preference degrees Pre={Pre ₁ , Pre ₂ ,..., Pre _N } representing the degree of preference, where the index preference degree Pre _n and the evaluation level H _n are positive or negative. One-to-one correspondence, Pre _n ∈ [-1,1] and uniform distribution.

The following will convert quantitative indicators from four aspects:

(1) Profitable indicators:

即Pre_n与H_n正向一一对应；a. For profit indicators

That is, there is a one-to-one positive correspondence between Pre _n and H _n ;

b. Obtain the prediction interval [FIL _l , FIH _l ], (l=1,2,...,L);

和

计算指标偏好度c. Obtain the exact values of the adjacent evaluation schemes A _i and A _i+1 on the quantitative index a _l

and

Calculate index preference

d. If Pre _n ≤Pre(l) _i,i+1 <Pre _n+1 , convert the comparative evaluation of quantitative indicators on adjacent evaluation schemes into unified identification through the index preference degree Pre(l) _i,i+1 The confidence distribution under the framework, expressed as:

d(a _l (A _i,i+1 ))={(H _n ,β _n,l (A _i,i+1 )),(H _n+1 ,β _n+1,l (A _{i,i +1} ))}

(l=1,2,...,L)

β_n+1,l(A_i,i+1)＝1-β_n,l(A_i,i+1)。in:

β _n+1,1 (A _i,i+1 )=1−β _n,1 (A _i,i+1 ).

d(a _l (A _i,i+1 )) represents the comparative evaluation of the adjacent evaluation schemes A _i and A _i+1 obtained by the dimension c _k on the quantitative index a _l through a specific transformation rule, β _n,l ( A _i,i+1 ) and β _n+1,l (A _i,i+1 ) represent the confidence levels assigned to H _n and H _n+1 levels, respectively.

(2) Cost-based indicators:

即Pre_n与H_n反向一一对应；a. For cost indicators

That is, there is a one-to-one reverse correspondence between Pre _n and H _n ;

b. Obtain the prediction interval [FIL _l , FIH _l ], (l=1,2,...,L);

和

计算指标偏好度c. Obtain the exact values of the adjacent evaluation schemes A _i and A _i+1 on the quantitative index a _l

and

Calculate index preference

d. If Pre _n ≤Pre(l) _i,i+1 ≤Pre _n+1 , convert the comparative evaluation of quantitative indicators on adjacent evaluation schemes into unified identification through the index preference degree Pre(l) _i,i+1 The confidence distribution under the framework, expressed as:

d(a _l (A _i,i+1 ))={(H _N+1-n ,β _N+1-n,l (A _i,i+1 )),(H _Nn ,β _Nn,l ( A _i,i+1 ))}

(l=1,2,...,L)

in:

β_N-n,l(A_i,i+1)＝1-β_N+1-n,l(A_i,i+1)。

β _Nn,1 (A _i,i+1 )=1−β _N+1−n,1 (A _i,i+1 ).

β _N+1-n,l (A _i,i+1 ) represents the confidence level assigned to the sum H _N+1-n level, β _Nn,l (A _i,i+1 ) represents the confidence level assigned to the sum H _Nn level confidence.

(3) Deviation index:

Deviation indicators refer to the existence of a deviation value (or worst value) when evaluating a quantitative indicator. Whether the actual evaluation value is greater or less than the deviation value, it means that the actual situation is better than the worst case. When the actual evaluation value is greater than the deviation value, the performance of the quantitative index becomes better as the actual value increases; when the actual evaluation value is smaller than the deviation value, the performance of the quantitative index becomes better as the actual value decreases.

即Pre_n与H_n正向一一对应；a. For deviation indicators

That is, there is a one-to-one positive correspondence between Pre _n and H _n ;

b. Obtain the prediction interval [FIL _l , FIH _l ], (l=1,2,...,L). For the convenience of explanation, the prediction interval is symmetrical about the deviation value.

和

计算指标偏好度c. Obtain the exact values of the adjacent evaluation schemes A _i and A _i+1 on the qualitative index a _l

and

Calculate index preference

d. If Pre _n ≤Pre(l) _i,i+1 <Pre _n+1 , convert the comparative evaluation of quantitative indicators on adjacent evaluation schemes into unified identification through the index preference degree Pre(l) _i,i+1 The confidence distribution under the framework, expressed as:

d(a _l (A _i,i+1 ))={(H _n ,β _n,l (A _i,i+1 )),(H _n+1 ,β _n+1,l (A _{i,i +1} ))}

(l=1,2,...,L)

in:

β_n+1,l(A_i,i+1)＝1-β_n,l(A_i,i+1)。

β _n+1,1 (A _i,i+1 )=1−β _n,1 (A _i,i+1 ).

β _n,l (A _i,i+1 ) and β _n+1,1 (A _i,i+1 ) represent the confidence levels assigned to H _n and H _n+1 levels, respectively.

(4) Fixed indicators:

Fixed index means that when evaluating a quantitative index, there is a fixed value (or the best value), no matter whether the actual evaluation value is greater or less than this fixed value, it means that the actual situation is worse than the optimal situation. . When the actual evaluation value is greater than the fixed value, the performance of the quantitative index deteriorates with the increase of the actual value; when the actual evaluation value is less than the fixed value, the performance of the quantitative index deteriorates as the actual value decreases.

即Pre_n与H_n反向一一对应；a. For ordinary income indicators

That is, there is a one-to-one reverse correspondence between Pre _n and H _n ;

b. Obtain the prediction interval [FIL _l , FIH _l ], (l=1,2,...,L). For the convenience of explanation, the prediction interval is symmetric about the fixed value.

和

计算指标偏好度c. Obtain the exact values of the adjacent evaluation schemes A _i and A _i+1 on the quantitative index a _l

and

Calculate index preference

d. If Pre _n ≤Pre(l) _i,i+1 <Pre _n+1 , convert the comparative evaluation of quantitative indicators on adjacent evaluation schemes into unified identification through the index preference degree Pre(l) _i,i+1 The confidence distribution under the framework, expressed as:

d(a _l (A _i,i+1 ))={(H _N+1-n ,β _N+1-n,l (A _i,i+1 )),(H _Nn ,β _Nn,l ( A _i,i+1 ))}

(l=1,2,...,L)

in:

β_N-n,l(A_i,i+1)＝1-β_N+1-n,l(A_i,i+1)。

β _Nn,1 (A _i,i+1 )=1−β _N+1−n,1 (A _i,i+1 ).

β _N+1-n,1 (A _i,i+1 ) and β _Nn,1 (A _i,i+1 ) represent the confidence levels assigned to the H _N+1-n and H _Nn ranks, respectively.

After the above rule conversion module operation, the initial evaluation matrix can be obtained:

d(a(A _1,2 )) d(a(A _2,3 ))…d(a(A _i,i+1 ))…d(a(A _I-1,I ))

a ₁ d(a ₁ (A _1,2 )) d(a ₁ (A _2,3 ))…d(a ₁ (A _i,i+1 ))…d(a ₁ (A _I-1,I ))

a ₂ d(a ₂ (A _1,2 )) d(a ₂ (A _2,3 ))…d(a ₂ (A _i,i+1 ))…d(a ₂ (A _I-1,I ))

………………

a _l d(a _l (A _1,2 )) d(a _l (A _2,3 ))…d(a _l (A _i,i+1 ))…d(a _l (A _I-1,I ))

………………

a _L-1 d(a _L-1 (A _1,2 )) d(a ₃ (A _2,3 ))…d(a _L-1 (A _i,i+1 ))…d(a _{L- 1} (A _I-1,I )

a _L d(a _L (A _1,2 )) d(a ₄ (A _2,3 ))…d(a _L (A _i,i+1 ))…d(a _L (A _I-1,I ))

和

转换为相邻方案A_i和A_i+1在定量指标a_l上的对比评估。融合模块：Among them, d(a _l (A _i,i+1 )) represents the operation of the rule conversion module, and the exact value of the scheme A _i and A _i+1 on the quantitative index a _l

and

Converted to the comparative evaluation of the adjacent schemes A _i and A _i+1 on the quantitative index a _l . Fusion module:

In the specific implementation process, the fusion module is used to fuse the evaluation information of multiple quantitative indicators to obtain a comprehensive evaluation of each quantitative indicator compared between adjacent evaluation schemes.

The evaluation information of multiple quantitative indicators given by different dimensions under each pair of adjacent schemes is fused, and expressed as a comprehensive evaluation of the comparison between adjacent evaluation schemes, namely:

d(A _i,i+1 )={(H _n ,β _n (A _i,i+1 )),n=1,2,...,N; (Ω,β _Ω (A _i,i+1 ) )}

(l=1,2,...,L)

in:

d(A _i,i+1 ) represents the comprehensive evaluation made by comparing all qualitative indicators of adjacent evaluation schemes A _i and A _i+1 , β _n (A _i,i+1 ) and β _Ω (A _{i,i+ 1} ) represent the confidence assigned to the _Hn level and global ignorance, respectively. Global ignorance Ω indicates that there are several confidence levels that have not been assigned to any evaluation level. This is because there may be some evaluation indicators that cannot be measured due to various external factors. This lack of information is expressed as the confidence level assigned to the global ignorance Ω. is 1, so after the evaluation information of multiple qualitative indicators is integrated, information loss will occur in the comprehensive evaluation.

Regarding the method of quantitative index evaluation information fusion, Evidence Reasoning (ER) method is adopted to deal with this uncertain evaluation information. The ER approach is unique in its ability to represent incompleteness and ignorance in evaluation information in a systematic and consistent manner. The aggregation function based on the ER method can be expressed as:

d(A _i,i+1 )=γ(d(a ₁ (A _i,i+1 )),d(a ₂ (A _i,i+1 )),…,d(a _L (A _{i, i+1} )))

Among them, γ is the ER aggregation function, and the fusion process can be expressed as: m _n,l =ω _l ·β _n,i (A _i,i+1 ), (n=1,...N; l=1,...L)

m_n、

K都表示为融合过程中的中间变量，ω_l表示定量指标a_l的权重。where m _n,l , m _Ω,l ,

m _n ,

K are all represented as intermediate variables in the fusion process, and ω _l represents the weight of the quantitative index a _l .

The sorting module is used to combine the utility of each evaluation level, convert the comprehensive evaluation of the comparison between adjacent evaluation schemes into the interval utility value of the comparison between the schemes, and convert it into a preference probability, and finally calculate based on the preference probability and the chain ratio. Synthesize the importance and rank the plans in full order.

Based on the comparative comprehensive evaluation between adjacent evaluation schemes, combined with the utility of each evaluation level, it is converted into a score interval, namely:

Among them, S(A _i,i+1 ) ^- and S(A _i,i+1 ) ⁺ represent the lower and upper limits of the utility value of the comprehensive evaluation interval comparing adjacent evaluation schemes A _i and A _i+1 , and S(A _i,i+1 ) ^- = -S(A _i+1,i ) ⁺ , S(A _i,i+1 ) ⁺ = -S(A _i+1,i ) ^- .

Convert the above interval utility value into the preference probability between adjacent contrast schemes, namely:

表示区间[S(A_i,i+1)^-,S(A_i,i+1)⁺]和区间[S(A_i+1,i)^-,S(A_i+1,i)⁺]重叠的长度。Among them, pi _,i+1 represents the degree of preference when comparing adjacent schemes A _i and A _i+1 , and pi _,i+1 ∈[0,1]; pi _,i+1 =0.5 represents no preference , p _i,i+1 <0.5 indicates that A _i+1 is preferred, and p _i,i+1 >0.5 indicates that A _i is preferred;

Represents the interval [S(A _i,i+1 ) ^- ,S(A _i,i+1 ) ⁺ ] and the interval [S(A _i+1,i ) ^- ,S(A _i+1,i ) ⁺ ] length of overlap.

Then convert the preference probability into the importance ratio between adjacent comparison schemes, namely:

Among them, ri _,i+1 represents the ratio of importance degree when comparing adjacent evaluation schemes A _i and A _i+1 , and r _max represents the maximum ratio of importance degree between adjacent comparison schemes.

Finally, according to the importance ratio value, the comprehensive importance of each scheme is calculated in reverse order of scheme serial number, namely:

k_i＝r_i,i+1·k_i+1

k _i =r _i,i+1 ·ki ₊₁

Wherein Z _i represents the comprehensive importance of the scheme A _i , _ki represents the comprehensive score value of the scheme A _i , and k _I =1.

Finally, according to the comprehensive importance of the scheme, the full order of high-end equipment schemes based on the product life cycle is obtained.

The embodiment of the present invention also provides a high-end equipment evaluation method based on the whole life cycle of the product, and the method includes:

Obtain multiple plans to be evaluated for high-end equipment, evaluation levels, and a multi-dimensional evaluation index system based on the full life cycle of high-end equipment;

Obtain measurement data of various quantitative indicators at different stages in the full life cycle of the multi-dimensional evaluation indicator system;

Obtain the prediction interval of each quantitative indicator based on the historical measurement values of the quantitative indicators in the multiple to-be-evaluated schemes obtained by the multi-dimensional evaluation indicator system;

Converting a plurality of measured values of the quantitative index into an evaluation form of confidence distribution under the unified identification framework between adjacent schemes according to the conversion rule, to obtain evaluation information of each quantitative index between adjacent evaluation schemes;

Integrate the evaluation information of each quantitative index to obtain a comprehensive evaluation of each quantitative index compared between adjacent evaluation schemes;

Combined with the utility of each evaluation level, the comprehensive evaluation of each quantitative index of the comparison between adjacent evaluation schemes is converted into the interval utility value of the comparison between the schemes, and converted into the preference probability, and finally the comprehensive importance is calculated based on the preference probability and the chain ratio. degree and arrange the plans in full order.

It is understood that the high-end equipment evaluation method based on the product life cycle provided by the embodiment of the present invention corresponds to the above-mentioned high-end equipment evaluation system based on the product life cycle. The corresponding content in the high-end equipment evaluation system of the product life cycle will not be repeated here.

To sum up, compared with the prior art, it has the following beneficial effects:

1. The embodiment of the present invention adopts the mode of pairwise comparison between the schemes, which saves the evaluation cost, greatly improves the evaluation efficiency while ensuring the stability of the results, improves the multi-attribute evaluation system, and also makes the high-end equipment based on the whole life cycle more abundant. Attribute evaluation questions are more realistic.

2. The embodiment of the present invention uses the whole life cycle theory to evaluate the high-end equipment, realizes the coordinated management of the elements of each link of the high-end equipment, reduces the cost and improves the efficiency.

3. The embodiment of the present invention discloses four different quantitative index conversion rules for different attribute categories of quantitative indicators, so that quantitative indicators can express evaluation information under a unified framework, facilitate information fusion, and improve the credibility of indicator information. and reliability;

4. The embodiment of the present invention adopts distributed preference information, which can better handle the incompleteness and inaccuracy of information, and make the expression of evaluation information more scientific and reasonable.

It should be noted that, from the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the above-mentioned technical solutions can be embodied in the form of software products in essence or the parts that make contributions to the prior art, and the computer software products can be stored in computer-readable storage media, such as ROM/RAM, magnetic A disc, an optical disc, etc., includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the methods described in various embodiments or some parts of the embodiments.

In this document, relational terms such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such existence between these entities or operations. The actual relationship or sequence. Moreover, the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device comprising a list of elements includes not only those elements, but also includes not explicitly listed or other elements inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.

The above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: The recorded technical solutions are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. A product full lifecycle based high-end equipment evaluation system, comprising:

the evaluation data acquisition module is used for acquiring a plurality of schemes to be evaluated, evaluation grades and a multi-dimensional evaluation index system based on the full life cycle of the high-end equipment;

the multidimensional quantitative data acquisition module is used for acquiring the measurement data of each quantitative index at different stages in the whole life cycle based on the multidimensional evaluation index system;

the quantitative index prediction interval obtaining module is used for obtaining the prediction interval of each quantitative index based on the historical measured values of the quantitative indexes in the multiple schemes to be evaluated, which are obtained by the multi-dimensional evaluation index system;

the rule conversion module is used for converting the plurality of measured values of the quantitative indexes into an evaluation form of confidence distribution under a unified identification framework between adjacent schemes according to a conversion rule to obtain evaluation information of each quantitative index between the adjacent evaluation schemes;

the fusion module is used for fusing the quantitative index evaluation information to obtain the comprehensive evaluation of the quantitative indexes compared between the adjacent evaluation schemes;

the sequencing module is used for combining the utility of each evaluation grade and converting the comprehensive evaluation of each quantitative index compared between adjacent evaluation schemes into an interval utility value compared between the schemes; converting the interval utility value into a preference possibility; and calculating comprehensive importance degree based on the preference possibility degree and the interlinkage ratio and carrying out full-order arrangement on the schemes.

2. The product full lifecycle-based high-end equipment evaluation system of claim 1, wherein the quantitative index prediction interval acquisition module is further to: and acquiring the prediction interval of each quantitative index according to the industry regulation.

3. The product full lifecycle-based high-end equipment evaluation system of claim 1, wherein the evaluation rating comprises:

a set of discrete variables representing the preference for inter-solution comparisons, from H₁To H_NRepresenting an increase in the degree of preference, H₁Indicates complete non-preference, H_NIndicates complete preference, H_(N+1)/2Representing no difference, N is an odd number.

4. The product full lifecycle-based high-end equipment evaluation system of claim 1, wherein the quantitative indicators comprise:

research and development expense, product specification, product life, core technology ratio, research and development personnel proportion, raw material price, processing cost, raw material transportation distance, raw material transportation time, manufacturing automation rate, workpiece specification, assembly time, product qualification rate, operation and maintenance precision rate, user use satisfaction degree, energy consumption, core component reutilization rate and scrap recovery cost.

5. The product full lifecycle-based high-end equipment evaluation system of claim 1, wherein the conversion rules comprise:

dividing the quantitative index into a profit type index, a cost type index, a deviation type index and a fixed type index;

the conversion rule of the profit type indicator includes:

a. for revenue type index

I.e. Pre_nAnd H_nForward one-to-one correspondence, Pre_n∈[-1,1]And are uniformly distributed;

b. obtaining a prediction Interval [ FIL ]_l,FIH_l]，(l＝1,2,…,L)；

c. Obtaining a neighbor evaluation scenario A_iAnd A_i+1At a quantitative index a_lUpper precise value

And

calculating index preference

d. If Pre_n≤Pre(l)_i,i+1＜Pre_n+1Preference by index Pre (l)_i,i+1And converting the comparative evaluation of the quantitative indexes on the adjacent evaluation schemes into confidence distribution under a unified recognition framework, wherein the confidence distribution is expressed as:

d(a_l(A_i,i+1))＝{(H_n,β_n,l(A_i,i+1)),(H_n+1,β_n+1,l(A_i,i+1))}

(l＝1,2,…,L)

wherein:

β_n+1,l(A_i,i+1)＝1-β_n,l(A_i,i+1)；

d(a_l(A_i,i+1) Represents dimension c)_kAt a quantitative index a_lAbove adjacent evaluation scheme A by a specific transformation rule_iAnd A_i+1Comparative evaluation of (1), beta_n,l(A_i,i+1) And beta_n+1,l(A_i,i+1) Respectively represent assignment to H_nAnd H_n+1Confidence of the level;

the conversion rules of the cost-type index include:

a. aiming at cost type index

I.e. Pre_nAnd H_nReverse one-to-one correspondence, Pre_n∈[-1,1]And are uniformly distributed;

b. obtaining a prediction Interval [ FIL ]_l,FIH_l]，(l＝1,2,…,L)；

c. Obtaining a neighbor evaluation scenario A_iAnd A_i+1At a quantitative index a_lUpper precise value

And

calculating index preference

d. If Pre_n≤Pre(l)_i,i+1≤Pre_n+1Preference by index Pre (l)_i,i+1Pairing of quantitative indicators on adjacent evaluation schemesThe ratio evaluation is converted into a confidence distribution under a unified recognition framework, which is expressed as:

d(a_l(A_i,i+1))＝{(H_N+1-n,β_N+1-n,l(A_i,i+1)),(H_N-n,β_N-n,l(A_i,i+1))}

(l＝1,2,…,L)

wherein:

β_N-n,l(A_i,i+1)＝1-β_N+1-n,l(A_i,i+1)；

β_N+1-n,l(A_i,i+1) Representative assignment to sum H_N+1-nConfidence of the grade, β_N-n,l(A_i,i+1) Representative assignment to sum H_N-nConfidence of the level;

the conversion rule of the deviation type index comprises the following steps:

a. for deviation index

I.e. Pre_nAnd H_nForward one-to-one correspondence, Pre_n∈[-1,1]And are uniformly distributed;

b. obtaining a prediction Interval [ FIL ]_l,FIH_l],(l＝1,2,…,L)；

c. Obtaining a neighbor evaluation scenario A_iAnd A_i+1In qualitative index a_lUpper precise value

And

calculating index preference

d. If Pre_n≤Pre(l)_i,i+1＜Pre_n+1Preference by index Pre (l)_i,i+1And converting the comparative evaluation of the quantitative indexes on the adjacent evaluation schemes into confidence distribution under a unified recognition framework, wherein the confidence distribution is expressed as:

d(a_l(A_i,i+1))＝{(H_n,β_n,l(A_i,i+1)),(H_n+1,β_n+1,l(A_i,i+1))}

(l＝1,2,…,L)

wherein:

β_n+1,l(A_i,i+1)＝1-β_n,l(A_i,i+1)；

β_n,l(A_i,i+1) And beta_n+1,l(A_i,i+1) Respectively represent assignment to H_nAnd H_n+1Confidence of the level;

the conversion rule of the fixed index includes:

a. aiming at the general income index

I.e. Pre_nAnd H_nReverse one-to-one correspondence, Pre_n∈[-1,1]And are uniformly distributed;

b. obtaining a prediction Interval [ FIL ]_l,FIH_l],(l＝1,2,…,L)；

c. Obtaining a neighbor evaluation scenario A_iAnd A_i+1At a quantitative index a_lUpper precise value

And

calculating the index preference:

d. if Pre_n≤Pre(l)_i,i+1＜Pre_n+1Preference by index Pre (l)_i,i+1And converting the comparative evaluation of the quantitative indexes on the adjacent evaluation schemes into confidence distribution under a unified recognition framework, wherein the confidence distribution is expressed as:

d(a_l(A_i,i+1))＝{(H_N+1-n,β_N+1-n,l(A_i,i+1)),(H_N-n,β_N-n,l(A_i,i+1))}

(l＝1,2,…,L)

wherein:

β_N-n,l(A_i,i+1)＝1-β_N+1-n,l(A_i,i+1)；

β_N+1-n,l(A_i,i+1) And beta_N-n,l(A_i,i+1) Respectively represent assignment to H_N+1-nAnd H_N-nThe confidence of the level.

6. The product full-life-cycle-based high-end equipment evaluation system as claimed in any one of claims 1 to 5, wherein the fusion of the evaluation information of each quantitative index to obtain the comprehensive evaluation of each quantitative index for comparison between adjacent evaluation schemes comprises:

fusing a plurality of quantitative index evaluation information given by different dimensions under each pair of adjacent schemes, and expressing the fused quantitative index evaluation information as the comprehensive evaluation of the comparison between the adjacent evaluation schemes, namely:

d(A_i,i+1)＝{(H_n,β_n(A_i,i+1)),n＝1,2,…,N；(Ω,β_Ω(A_i,i+1))}

(l＝1,2,…,L)

wherein:

d(A_i,i+1) Representing comparative neighboring evaluation scheme A_iAnd A_i+1Comprehensive evaluation of all qualitative indicators, beta_n(A_i,i+1) And beta_Ω(A_i,i+1) Respectively represent assignment to H_nLevel and globally unknown confidence.

7. The product full-life-cycle-based high-end equipment evaluation system of claim 6, wherein the comprehensive evaluation of each quantitative index of the comparison between adjacent evaluation schemes is converted into an interval utility value of the comparison between schemes in combination with the utility of each evaluation level, comprising

Wherein, S (A)_i,i+1)^-And S (A)_i,i+1)⁺Representing comparative neighboring evaluation scheme A_iAnd A_i+1Comprehensively evaluating the lower limit and the upper limit of the interval utility value, and S (A)_i,i+1)^-＝-S(A_i+1,i)⁺,S(A_i,i+1)⁺＝-S(A_i+1,i)^-。

8. The product full lifecycle-based high-end equipment evaluation system of claim 7, wherein the converting the interval utility value to a preference likelihood comprises:

wherein p is_i,i+1Representing comparative neighbor scheme A_iAnd A_i+1Degree of preference of time, and p_i,i+1∈[0,1]；p_i,i+10.5 denotes no preference, p_i,i+1< 0.5 indicates that A is more preferable_i+1，p_i,i+1> 0.5 indicates a greater preference for A_i；

Represents the interval [ S (A)_i,i+1)^-,S(A_i,i+1)⁺]And interval [ S (A) ]_i+1,i)^-,S(A_i+1,i)⁺]The length of the overlap.

9. The product full lifecycle-based high-end equipment evaluation system of claim 8, wherein computing a composite importance level and ranking the schema in full order based on preference likelihoods and link ratios comprises:

and then converting the preference possibility into an importance degree ratio value between adjacent contrast schemes, namely:

wherein: r is_i,i+1Representing comparative neighboring evaluation scheme A_iAnd A_i+1Ratio of importance of time, r_maxRepresenting the maximum ratio of importance between adjacent contrast schemes;

and sequentially calculating the comprehensive importance degree of each scheme according to the importance degree ratio value and the sequence direction of the scheme sequence numbers, namely:

wherein: z_iRepresents scheme A_iOf overall importance of, k_iRepresents scheme A_iAnd k is the total score value of_I＝1；

And obtaining the full-order arrangement of the high-end equipment scheme based on the product full life cycle according to the comprehensive importance degree of the scheme.

10. A method for high-end equipment assessment based on a product full lifecycle, the method comprising:

acquiring a plurality of schemes to be evaluated and evaluation grades of high-end equipment and a multi-dimensional evaluation index system based on the full life cycle of the high-end equipment;

acquiring measurement data of each quantitative index at different stages in the whole life cycle based on a multi-dimensional evaluation index system;

acquiring a prediction interval of each quantitative index based on historical measurement values of the quantitative indexes in a plurality of schemes to be evaluated, which are acquired by a multi-dimensional evaluation index system;

converting the plurality of measured values of the quantitative indexes into an evaluation form of confidence distribution under a unified identification framework between adjacent schemes according to a conversion rule to obtain evaluation information of each quantitative index between the adjacent evaluation schemes;

fusing each quantitative index evaluation information to obtain each quantitative index comprehensive evaluation compared between adjacent evaluation schemes;

and comprehensively evaluating each quantitative index of the comparison between adjacent evaluation schemes by combining the utility of each evaluation grade to convert the quantitative index into an interval utility value of the comparison between the schemes, converting the interval utility value into preference possibility, calculating the comprehensive importance degree based on the preference possibility and the interlinkage ratio, and completely arranging the schemes.

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