A method for selecting and ranking industrial computer products based on a multi-scale linguistic evaluation scale

Abstract

The present invention discloses a method for selecting and ranking industrial computer products based on a multi-scale language evaluation scale. The method comprises the following steps: aggregating t evaluation matrices of industrial computer products into an initial probabilistic language decision matrix and calculating the expected value of each probabilistic language term set; utilizing a probabilistic language ratio system to calculate the weighted average of the expected values ​​of all probabilistic language term sets as the ranking value of the probabilistic language ratio system; utilizing a probabilistic language reference point system to obtain the reference point of a criterion using a univariate quadratic score function, and calculating the distance between the probabilistic language term set of each criterion and the reference point using a distance measure, thereby obtaining the ranking value of the probabilistic language reference point system; utilizing a probabilistic language full multiplication model to calculate the weighted geometric mean of the expected values ​​of all criteria as the ranking value of each probabilistic language full multiplication model; and respectively calculating the comprehensive ranking score of each industrial computer product and ranking them. The present invention integrates multiple evaluation systems to obtain a more reasonable final ranking.

Description

Technical Field

[0001] The present invention relates to the field of information science, and in particular to a method for selecting and ranking industrial control computer products based on a multi-scale language evaluation scale. Background Art

[0002] Industrial control computers, commonly known as IPCs, are a core component of industrial control systems. Compared to conventional computers, IPCs offer superior stability, reliability, security, and anti-interference capabilities, making them suitable for long-term operation in harsh environments and stable control of industrial production processes. Therefore, the selection of an IPC is a classic decision-making problem, one that is both critical and requires high accuracy. Commonly used multi-criteria decision-making methods include the ELECTRE method, the MSM method, the TOPSIS method, and the VIKOR method. While multi-criteria decision-making methods typically only consider the opinions of a single decision-maker, multi-criteria group decision-making methods integrate the opinions of multiple decision-makers, resulting in more objective results.

[0003] Because fuzzy theory can well express the uncertainty of decision makers' evaluations, multi-criteria group decision-making methods based on fuzzy information representation can be utilized. Some methods propose intuitionistic fuzzy support functions, risk functions, and credibility functions based on intuitionistic fuzzy sets, and construct an extended intuitionistic fuzzy ranking method, combined with group consensus adaptive search and adjustment methods to solve risk assessment problems. Other methods propose an intuitionistic interval information aggregation operator based on intuitionistic interval numbers and utilize the TOPSIS framework to address multi-criteria group decision-making problems. Other methods utilize the intuitionistic fuzzy multiplicative best-worst method with an intuitionistic fuzzy multiplicative preference relation to resolve the preference relations between criteria.

[0004] Probabilistic linguistic terminology has a good ability to represent uncertain information in the process of dealing with multi-criteria group decision-making problems, and can represent decision information from both qualitative and quantitative aspects. Although multi-criteria group decision-making based on probabilistic linguistic terminology has been widely studied and applied, there are still the following deficiencies: (1) The distance measurement method of probabilistic linguistic terminology is often used in the decision-making problem solving process, but the current distance measurement method still has defects; (2) When representing decision information, the situation that different criteria use different linguistic evaluation scales is ignored, and the current theories and methods in this regard are relatively lacking; (3) There is a lack of research on the framework of probabilistic linguistic multi-criteria group decision-making methods based on multi-scale linguistic evaluation scales. Summary of the Invention

[0005] The purpose of the present invention is to provide an industrial computer product selection and ranking method based on a multi-scale language evaluation scale. The probabilistic language terminology set does not need to be standardized, and the information of the original data will not be lost. A more reasonable final ranking can be obtained by integrating multiple evaluation systems.

[0006] The technical solution adopted in the present invention is:

[0007] A method for selecting and ranking industrial computer products based on a multi-scale language evaluation scale comprises the following steps:

[0008] Step 1: Aggregate the t evaluation matrices of m industrial computer products into an initial probabilistic language decision matrix, and calculate the expected value of each probabilistic language term set in the probabilistic language decision matrix;

[0009] Step 2: Based on the expected value of each probabilistic language term set, the probabilistic language ratio system is used to calculate the weighted average of the expected values ​​of all probabilistic language term sets, which is used as the ranking value of the probabilistic language ratio system. The formula for calculating the probability language ratio system ranking value is as follows:

[0010]

[0011] Among them, w j represents the weight of the jth criterion; E(L ij (p)) is the probabilistic language term set L ij the expected value of (p);

[0012] Specifically, we first calculate the expected value of each probabilistic language term set, and then find the weighted average of the expected values ​​of each criterion of industrial computer products. The larger the weighted average value is, the better the industrial computer product is. Sort the industrial computer products in descending order of value.

[0013] Step 3: Using the probabilistic language reference point system, obtain the reference point of the criterion through a one-dimensional quadratic scoring function and calculate the distance between the probabilistic language term set of each criterion and the reference point through the distance measure to obtain the ranking value of the probabilistic language reference point system Ranking values ​​of probabilistic language reference point systems;

[0014]

[0015] Among them, w j represents the weight of the jth criterion, represents the optimal probability language term set of the jth criterion, represents the worst probability language term set of the jth criterion, Represents the distance between the best probability language term set and the worst probability language term set; Represents the distance between each data under the criterion and the optimal probability language term set;

[0016] Step 4: Use the probability language full multiplication model to calculate the weighted geometric mean of all criterion expectations as the ranking value of each probability language full multiplication model The formula is as follows:

[0017]

[0018] Among them, w j represents the weight of the jth criterion; E(L ij (p)) is the probabilistic language term set L ij the expected value of (p);

[0019] Specifically, the expected value of the probability language term set is calculated, and then the weighted geometric mean of the expected value of each criterion of the alternative solution is calculated. The larger the average value, the better the solution. Sort the industrial computer products in descending order of value.

[0020] Step 5: Normalize the ranking values ​​of each industrial computer product in different systems to obtain the comprehensive ranking score and rank each industrial computer product according to the comprehensive ranking score. The comprehensive ranking score y i The calculation formula is as follows,

[0021] Furthermore, step 1 specifically includes the following steps:

[0022] Step 1-1: Aggregate t evaluation matrices. The specific expression is as follows:

[0023]

[0024] Among them, i represents the number of alternative options, j represents the number of evaluation criteria, L ij (p) represents the evaluation aggregation result of each expert on the jth criterion of the i-th alternative, represents the evaluation result of the first expert on the jth criterion of the i-th alternative, represents the evaluation result of the t-th expert, ω g represents the weight of the g-th expert; Linguistic terms that express qualitative information; Probability that represents quantitative information; The linguistic term representing the qualitative information given by the g-th expert; represents the probability of the quantitative information given by the g-th expert; represents the evaluation result given by the g-th expert;

[0025] Step 1-2, obtain the initial probability language decision matrix L based on the aggregation results;

[0026]

[0027] L mn (p) represents the aggregated evaluation results of each expert on the nth criterion of the mth product;

[0028] Steps 1-3, and calculate the expected value of each probabilistic linguistic term set in the probabilistic linguistic decision matrix;

[0029]

[0030] Among them, #L(p) represents the number of probabilistic language terms; E(L ij (p)) is the probabilistic language term set L ij The expected value of (p).

[0031] Specifically, the specific steps of step 3 are as follows:

[0032] Step 3-1, first use the quadratic scoring function to find the reference point of the criterion, as follows:

[0033]

[0034] in, represents the optimal probability language term set of the jth criterion, represents the worst probability language term set of the jth criterion; sf(L 1j (p)) indicates L 1j (p) unary quadratic score, L 1j (p) represents the evaluation aggregation result of each expert on the jth criterion of the first alternative, and A linguistic term that represents the qualitative information of the jth criterion of the first alternative; The probability of the quantitative information of the jth criterion of the first alternative; #L 1j (p) indicates L 1j (p) the number of language terms; and so on, sf(L mj (p)) indicates L mj (p) unary quadratic score, L mj (p) represents the evaluation aggregation result of each expert on the jth criterion of the mth alternative, and A linguistic term that represents the qualitative information of the jth criterion of the mth alternative; The probability of the quantitative information of the jth criterion of the mth alternative; #L mj (p) indicates L mj (p) the number of language terms;

[0035] Step 3-2, calculate the distance between the best probability language term set and the worst probability language term set

[0036]

[0037] in, represents the optimal probability language term set of the jth criterion, represents the worst probability language term set of the jth criterion, express The quadratic score of express The unary quadratic score value of ;

[0038] Step 3-3, calculate the distance between each data under the criterion and the optimal probability language term set

[0039]

[0040] Among them, L lj represents the probabilistic language term set of the jth criterion; sf(L lj ) indicates L lj The unary quadratic score value of ;

[0041] Step 3-4, calculate the ranking value of the probabilistic language reference point system;

[0042]

[0043] In the above formula, w j represents the weight of the jth criterion, Represents the distance between the best probability language term set and the worst probability language term set; It represents the distance between each data under the criterion and the optimal probability language term set. The smaller the distance, the better the solution. Therefore, the probability language reference point system is based on Sort the industrial computer products in ascending order of value.

[0044] Furthermore, in step 5, according to the ranking score y i The industrial computer products are finally sorted from large to small by value, and the best product is selected based on the sorting.

[0045] The present invention employs the above technical solution and has the following technical advantages over the prior art: 1. The probabilistic language term sets used in prior art calculations are all based on the same language evaluation scale. When probabilistic language term sets based on different language evaluation scales appear in a decision problem, unified calculations cannot be performed. The probabilistic language term sets used in the algorithm proposed in the present invention do not require standardization, and the original data information is not lost. 2. The improved distance measurement method proposed in the present invention uses the difference between univariate quadratic scoring functions to calculate the distance between different probabilistic language term sets, thus avoiding a one-to-one comparison between language terms. This method can calculate the distance between probabilistic language term sets under the same language evaluation scale as well as between probabilistic language term sets under different language evaluation scales. At the same time, because the univariate quadratic scoring function value can compare the advantages and disadvantages of different probabilistic language term sets, other comparison steps can be omitted in the decision-making method, thereby simplifying the calculation process and improving decision-making efficiency. 3. The traditional MULTIMOORA method uses dominance theory to obtain a comprehensive ranking of three models. This process is not only cumbersome but also suffers from the problem of circular reasoning. If the ranking results of the three models are simply combined, some solutions may be ranked side by side. The present invention normalizes the probability language ratio system ranking value, the probability language reference point system ranking value and the probability language full multiplication model ranking value respectively. This process can not only reflect the ranking order of alternative options, but also show the relative differences between different ranking results, thereby obtaining a more reasonable final ranking. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments;

[0047] Figure 1 The figure is a flow chart of a method for selecting and ranking industrial computer products based on a multi-scale language evaluation scale according to the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0049] Current probabilistic linguistic multi-criteria group decision-making methods focus on theories such as distance measurement, correlation measurement, and similarity of probabilistic linguistic term sets, but rarely consider probabilistic linguistic term sets based on multi-scale linguistic evaluation scales, thus ignoring the research on the decision-making method framework required for such problems.

[0050] like Figure 1As shown, the present invention discloses a method for selecting and ranking industrial computer products based on a multi-scale language evaluation scale. In view of the situation where different criteria in group decision-making problems use different language evaluation scales, a semantic scaling function in the form of multiple language terms is introduced to represent the probabilistic language term set under the multi-scale language evaluation scale. With respect to the probabilistic language term set under the multi-scale language evaluation scale, the existing distance measure is analyzed. In order to avoid the distortion of decision maker evaluation information caused by the standardization operation, a univariate quadratic score function and an improved distance measure are proposed, which are respectively used to compare the pros and cons of two different probabilistic language term sets and calculate the distance between them. The specific principles of the present invention are described in detail below:

[0051] The definition of the probabilistic language terminology set is as follows: Let S1 = {s α |α=0,1,…,τ} is a given reference language evaluation scale, then the probabilistic language term set defined on S1 is:

[0052] L(p)={l (k) (p (k) )|l (k) ∈S1,p (k) ≥0,k=1,2,…,#L(p)}

[0053] Among them, l (k) (p (k) ) is a probabilistic language term. Each probabilistic language term consists of two parts: the language term l that represents qualitative information (k) and the probability p representing quantitative information (k) , #L(p) represents the number of probabilistic language terms.

[0054] In order to solve the problem of operating on probabilistic language term sets with different language evaluation scales, we first introduce the following semantic scaling function: f:s α →δ α ,δ α ∈[0,1], because f is a strictly monotonically increasing function, so f -1 :δ α →s α ,δ α ∈[0,1]. Therefore, the semantic scaling function of the above form is given below.

[0055] Definition 1 (1) When the language evaluation scale is an unbalanced language evaluation scale, the semantic scaling function is:

[0056] f(s α )=α / 2τ,α∈[0,2τ]

[0057] (2) When the language evaluation scale is a balanced language evaluation scale, the semantic scaling function is:

[0058] f(s α )=(α+τ) / 2τ,α∈[-τ,τ]

[0059] (3) When the language evaluation scale is an unbalanced form of a non-uniform language evaluation scale with gradually increasing deviations from the center to both sides, the semantic scaling function is:

[0060]

[0061] Among them, the μ value is used to control the degree of gradual increase of the deviation. The larger μ is, the greater the degree of increase of the deviation is.

[0062] (4) When the language evaluation scale is a balanced form of a non-uniform language evaluation scale with gradually increasing deviations from the center to both sides, the semantic scaling function is:

[0063]

[0064] Here, the μ value has the same meaning as above.

[0065] (5) When the language evaluation scale is an unbalanced form of a non-uniform language evaluation scale with a gradually decreasing deviation from the center to both sides, the semantic scaling function is:

[0066]

[0067] The γ value is used to control the degree to which the deviation gradually decreases. The larger the γ is, the greater the degree to which the deviation decreases.

[0068] (6) When the language evaluation scale is a balanced form of a non-uniform language evaluation scale with a gradually decreasing deviation from the center to both sides, the semantic scaling function is:

[0069]

[0070] Since the current operation of the probability language term set requires normalization of the probability, such as normalizing L(p)={s3(0.7)} to become It is obvious that normalization changes the decision maker's evaluation information, and to a certain extent, loses the authenticity of the decision information. To solve the above problem, the present invention proposes a new expected value and deviation function, so that the decision process no longer performs probabilistic normalization on the probabilistic language term set, but directly calculates the original probabilistic language term set.

[0071] Definition 2 Assume that L(p)={l (k) (p (k) )|l (k) ∈S,p (k) ≥0,k=1,2,…,#L(p)} is a set of probabilistic language terms, where S can be any form of language evaluation scale, and its expected value is defined as follows:

[0072]

[0073] Definition 3 Assume that \(L(p)=\{l (k) (p (k) )|l (k) \in S, p (k) \geq0, k = 1, 2, \ldots, \#L(p)\}\) is a probabilistic linguistic term set, where \(S\) can be any form of linguistic evaluation scale, and its deviation value is defined as follows:

[0074]

[0075] Using the expected value and deviation value to compare the sizes of probabilistic linguistic term sets, for any two probabilistic linguistic term sets \(L1(p)\) and \(L2(p)\):

[0076] If \(E(L1(p))>E(L2(p))\), then \(L1(p)>L2(p)\);

[0077] If \(E(L1(p))<E(L2(p))\), then \(L1(p)<L2(p)\);

[0078] If \(E(L1(p)) = E(L2(p))\), then if \(\sigma(L1(p))>\sigma(L2(p))\) then \(L1(p)>L2(p)\), if \(\sigma(L1(p))<\sigma(L2(p))\) then \(L1(p)<L2(p)\), if \(\sigma(L1(p)) = \sigma(L2(p))\) then \(L1(p)=L2(p)\).

[0079] To address the problem that the existing distance measures of probabilistic linguistic term sets cannot handle unbalanced linguistic evaluation scales, the present invention proposes a quadratic scoring function, which is defined as follows:

[0080] Definition 4 Assume that \(L(p)=\{l (k) (p (k) )|l (k) \in S, p (k) \geq0, k = 1, 2, \ldots, \#L(p)\}\) is a probabilistic linguistic term set, where \(S\) can be any form of linguistic evaluation scale. Its quadratic scoring function formula is as follows:

[0081]

[0082] Integrate the concepts of expectation and variance into one formula. By calculating the unary quadratic score value, the advantages and disadvantages of two probabilistic linguistic term sets can be directly judged, which is more concise than the method of judging advantages and disadvantages by comparing the score function and the deviation value. The judgment rule is as follows: For any two probabilistic linguistic term sets, if sf(L1(p)) > sf(L2(p)), then L1(p) > L2(p); if sf(L1(p)) < sf(L2(p)), then L1(p) < L2(p); if sf(L1(p)) = sf(L2(p)), then L1(p) = L2(p).

[0083] Based on the above score function, an improved distance measure method is proposed as follows:

[0084] Definition 5 Suppose there are two probabilistic linguistic term sets L1(p) = {l1 (k) (p1 (k) ) | l1 (k) ∈ S 1 , p (k) ≥ 0, k = 1, 2, …, #L1(p)} and L2(p) = {l2 (k) (p2 (k) ) | l2 (k) ∈ S 2 , p2 (k) ≥ 0, k = 1, 2, …, #L2(p)}, where SS 1 and S 2 are reference linguistic evaluation scales in any form. The improved distance measure formula is as follows:

[0085] d(L1(p), L2(p)) = |sf(L1(p)) - sf(L2(p))|

[0086] Among them,

[0087]

[0088] Since Definition 5 uses the unary quadratic function f(x) = 2x - x 2 , which is strictly monotonically increasing in (0, 1), and f(0) = 0, f(1) = 1. Therefore, the improved distance measure satisfies the following properties:

[0089] (1) Boundedness, 0 ≤ d(L1(p), L2(p)) ≤ 1;

[0090] (2) Reflexivity, d(L1(p), L1(p)) = 0;

[0091] (3) Symmetry, d(L1(p), L2(p)) = d(L2(p), L1(p)).

[0092] The present invention includes three ranking systems: a probabilistic language ratio model, a probabilistic language reference point system, and a probabilistic language full multiplication model. The three ranking systems can obtain three rankings, and the final ranking of the scheme is obtained by combining the three rankings. Compared with the existing probabilistic language MULTIMOORA method, the method used in the present invention expands the calculation range of the probabilistic language term set and improves the probabilistic language reference point system, so it can solve more complex decision problems. In order to more intuitively show the decision steps of the probabilistic language MULTIMOORA method based on the multi-scale language evaluation scale, a method flow chart is given, as shown in FIG. Figure 1 shown.

[0093] The evaluation criteria in the decision-making process include benefit-based and cost-based. In the traditional MULTIMOORA method, the two types of criterion data need to be calculated separately. However, in the method used in the present invention, the calculation of the two types of criteria can be unified by setting different language evaluation scales. For benefit-based criteria, a positive language evaluation scale is set, that is, the larger the subscript of the language term, the higher the evaluation, which means "high benefit"; for cost-based criteria, a negative language evaluation scale is set, that is, the larger the subscript of the language term, the lower the evaluation, which means "low cost". In summary, the larger the subscript of the language term, the better the result.

[0094] The above method is applied to the selection problem of industrial control computers. Suppose a company wants to purchase a batch of industrial control computers, and there are m products (A1, A2, ..., A m ) are available, and the enterprise invites t experts in related fields (D1, D2, ..., D t ) from n aspects (C1, C2, ..., C n ) (such as price, reliability, safety and other criteria) to conduct a comprehensive evaluation of industrial computer products. The expert group decided through consultation that the weight of each criterion is w=(w1,w2,…,w n ) T Each expert gives the probability language evaluation data of each criterion according to the given language evaluation scale information, and obtains t probability language evaluation matrices in

[0095] Step 1: Aggregate the t evaluation matrices into an initial probabilistic language decision matrix. The aggregation method is as follows:

[0096]

[0097] Among them, i represents the number of alternative options, j represents the number of evaluation criteria, L ij (p) represents the evaluation aggregation result of each expert on the jth criterion of the i-th alternative, represents the evaluation result of the first expert on the jth criterion of the i-th alternative, represents the evaluation result of the t-th expert, ω g Indicates the weight of the g-th expert, the default is ω g =1 / t, and different weights can also be assigned according to the identity of the expert; Linguistic terms that express qualitative information; Probability that represents quantitative information; The linguistic term representing the qualitative information given by the g-th expert; represents the probability of the quantitative information given by the g-th expert; represents the evaluation result given by the g-th expert;

[0098] Then the initial probability language decision matrix L is obtained.

[0099]

[0100] L mn (p) represents the aggregated evaluation result of each expert on the nth criterion of the mth product.

[0101] Step 2: Use the probability language ratio system to calculate the comprehensive ratio value of each industrial computer product. The calculation formula is as follows:

[0102]

[0103] Among them, w j Represents the weight of the jth criterion. First, use the formula in Definition 2 to calculate the expected value of each probabilistic language term set, and then find the weighted average of the expected values ​​of each criterion of the industrial computer product. The larger the weighted average value is, the better the industrial computer product is. Therefore, according to Sort the industrial computer products in descending order of value.

[0104] Step 3: Use the probabilistic language reference point system to calculate the ranking value of each industrial computer product. First, use the unary quadratic score function to find the reference point of the criterion, as shown below.

[0105]

[0106] in, represents the optimal probability language term set of the jth criterion, Denotes the worst probabilistic language term set of the jth criterion. Then, the distance between the probabilistic language term sets is calculated using the formula in Definition 5 to obtain the ranking value.

[0107]

[0108] In the above formula, wj represents the weight of the jth criterion, Represents the distance between the best probability language term set and the worst probability language term set. It represents the distance between each data under the criterion and the optimal probability language term set. The smaller the distance, the better the solution. Therefore, the probability language reference point system is based on Sort the industrial computer products in ascending order of value.

[0109] Step 4: Use the probability language full multiplication model to calculate the comprehensive ratio value of each industrial computer product. The formula is as follows:

[0110]

[0111] Among them, w j This method uses the formula in Definition 2 to calculate the expected value of the probability language term set, and then calculates the weighted geometric mean of the expected values ​​of each criterion of the alternative scheme. The larger the average value, the better the scheme, so according to Sort the industrial computer products in descending order of value.

[0112] Step 5: Use the following formula to sort the industrial computer products.

[0113]

[0114] In the above formula, They are the ranking values ​​of the probabilistic language ratio system, the probabilistic language reference point system and the probabilistic language full multiplication model respectively. is the maximum value of the probability language ratio system ranking value, is the minimum value of the probability language ratio system ranking value, and the other two are the same. Finally, according to y i The industrial computer products are finally sorted from large to small by value, and the best product is selected based on the sorting.

[0115] The present invention employs the above technical solution and has the following technical advantages over the prior art: 1. The probabilistic language term sets used in prior art calculations are all based on the same language evaluation scale. When probabilistic language term sets based on different language evaluation scales appear in a decision problem, unified calculations cannot be performed. The probabilistic language term sets used in the algorithm proposed in the present invention do not require standardization, and the original data information is not lost. 2. The improved distance measurement method proposed in the present invention uses the difference between univariate quadratic scoring functions to calculate the distance between different probabilistic language term sets, thus avoiding a one-to-one comparison between language terms. This method can calculate the distance between probabilistic language term sets under the same language evaluation scale as well as between probabilistic language term sets under different language evaluation scales. At the same time, because the univariate quadratic scoring function value can compare the advantages and disadvantages of different probabilistic language term sets, other comparison steps can be omitted in the decision-making method, thereby simplifying the calculation process and improving decision-making efficiency. 3. The traditional MULTIMOORA method uses dominance theory to obtain a comprehensive ranking of three models. This process is not only cumbersome but also suffers from the problem of circular reasoning. If the ranking results of the three models are simply combined, some solutions may be ranked side by side. The present invention normalizes the probability language ratio system ranking value, the probability language reference point system ranking value and the probability language full multiplication model ranking value respectively. This process can not only reflect the ranking order of alternative options, but also show the relative differences between different ranking results, thereby obtaining a more reasonable final ranking.

[0116] Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. In the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present application is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

Claims

1. A method for selecting and ranking industrial computer products based on a multi-scale language evaluation scale, characterized by: The following steps are involved: Step 1: Aggregate the t evaluation matrices of m industrial computer products into an initial probabilistic language decision matrix, and calculate the expected value of each probabilistic language term set in the probabilistic language decision matrix; Step 2: Using the probability language ratio system, calculate the weighted average of the expected values ​​of all probability language term sets as the ranking value of the probability language ratio system The formula for calculating the probability language ratio system ranking value is as follows: Among them, w j represents the weight of the jth criterion; E(L ij (p)) is the probabilistic language term set L ij the expected value of (p); Step 3: Using the probabilistic language reference point system, obtain the reference point of the criterion through a one-dimensional quadratic score function, and calculate the distance between the probabilistic language term set of each criterion and the reference point through the distance measure, and then obtain the ranking value of the probabilistic language reference point system The formula for calculating the ranking value of the probabilistic language reference point system is as follows: Among them, w j represents the weight of the jth criterion, represents the optimal probability language term set of the jth criterion, represents the worst probability language term set of the jth criterion, Represents the distance between the best probability language term set and the worst probability language term set; Represents the distance between each data under the criterion and the optimal probability language term set; Step 4: Using the probability language full multiplication model, calculate the weighted geometric mean of all criterion expectations as the ranking value of each probability language full multiplication model The formula is as follows: Among them, w j represents the weight of the jth criterion; E(L ij (p)) is the probabilistic language term set L ij the expected value of (p); Step 5: Normalize the ranking values ​​of each industrial computer product in different systems to obtain a comprehensive ranking score and rank each industrial computer product according to the comprehensive ranking score. The comprehensive ranking score y i The calculation formula is as follows, in, They are the ranking values ​​of the probability language ratio system, the probability language reference point system and the probability language full multiplication model respectively; is the maximum value of the probability language ratio system ranking value, The minimum value of the probability language ratio system ranking value; is the maximum value of the probability language reference point system ranking value, The minimum value of the probability language reference point system ranking value; is the maximum value of the probability language full multiplication model ranking value, Minimum value for the probabilistic language full multiplication model ranking value.

2. The method for selecting and ranking industrial computer products based on a multi-scale language evaluation scale according to claim 1, characterized in that: Step 1 specifically includes the following steps: Step 1-1: Aggregate t evaluation matrices. The specific expression is as follows: Among them, i represents the number of alternative options, j represents the number of evaluation criteria, L ij (p) represents the evaluation aggregation result of each expert on the jth criterion of the i-th alternative, represents the evaluation result of the first expert on the jth criterion of the i-th alternative, represents the evaluation result of the t-th expert, ω g represents the weight of the g-th expert; Linguistic terms that express qualitative information; Probability that represents quantitative information; The linguistic term representing the qualitative information given by the g-th expert; represents the probability of the quantitative information given by the g-th expert; represents the evaluation result given by the g-th expert; Step 1-2, obtain the initial probability language decision matrix L based on the aggregation results; L mn (p) represents the aggregated evaluation results of each expert on the nth criterion of the mth product; Steps 1-3, and calculate the expected value of each probabilistic linguistic term set in the probabilistic linguistic decision matrix; Among them, #L(p) represents the number of probabilistic language terms; E(L ij (p)) is the probabilistic language term set L ij The expected value of (p).

3. The method for selecting and ranking industrial computer products based on a multi-scale language evaluation scale according to claim 2, characterized in that: The weight ω of the g-th expert g =1 / t; or, assign different weights according to the identity of the expert.

4. The method for selecting and ranking industrial computer products based on a multi-scale language evaluation scale according to claim 1, characterized in that: The specific steps of step 3 are as follows: Step 3-1, first use the quadratic scoring function to find the reference point of the criterion, as follows: in, represents the optimal probability language term set of the jth criterion, represents the worst probability language term set of the jth criterion; sf(L 1j (p)) indicates L 1j (p) is a quadratic score, L 1j (p) represents the evaluation aggregation result of each expert on the jth criterion of the first alternative, and A linguistic term that represents the qualitative information of the jth criterion of the first alternative; The probability of the quantitative information of the jth criterion of the first alternative; #L 1j (p) indicates L 1j (p) the number of language terms; and so on, sf(L mj (p)) indicates L mj (p) is a quadratic score, L mj (p) represents the evaluation aggregation result of each expert on the jth criterion of the mth alternative, and A linguistic term that represents the qualitative information of the jth criterion of the mth alternative; The probability of the quantitative information of the jth criterion of the mth alternative; #L mj (p) indicates L mj (p) the number of language terms; Step 3-2, calculate the distance between the best probability language term set and the worst probability language term set in, represents the optimal probability language term set of the jth criterion, represents the worst probability language term set of the jth criterion, express The quadratic score of express The unary quadratic score value of ; Step 3-3, calculate the distance between each data under the criterion and the optimal probability language term set Among them, L lj represents the probabilistic language term set of the jth criterion; sf(L lj ) indicates L lj The unary quadratic score value of ; Step 3-4, calculate the ranking value of the probabilistic language reference point system; Among them, w j represents the weight of the jth criterion, Represents the distance between the best probability language term set and the worst probability language term set; Represents the distance between each data under the criterion and the optimal probability language term set.

5. The method for selecting and ranking industrial computer products based on a multi-scale language evaluation scale according to claim 1, characterized in that: In step 5, according to the sorting score y i The industrial computer products are finally sorted from large to small by value, and the best product is selected based on the sorting.

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