CN106021724A - Energy efficiency evaluation method of machine tool product manufacturing system based on AHM and entropy method - Google Patents

Abstract

The invention provides an energy efficiency evaluation method of a machine tool product manufacturing system based on AHM and entropy method. The method comprises the following steps: one, establishing an energy efficiency index evaluation system of the machine tool product manufacturing system, wherein the index at the bottommost in the system constructs an evaluation factor set; two, determining the subjective weight of the index through the application of an analytic hierarchy process; three, determining the objective weight of the index through the application of the entropy method; four, constructing the comprehensive weight of the evaluation index; five, performing dimensionless method processing on the original quantitative of the manufacturing system; six, comprehensively evaluating according to the weight and data after the dimensionless method processing. Through the adoption of the thought of the combination weighting of the AHM and the entropy method, not only the subjective factor influence of an expert can be avoided, but also the influence of incomplete data is avoided, the energy utilization condition of the enterprise in the while production process is evaluated, thereby effectively improving the utilization efficiency of the energy source, and achieving the aim of saving the energy source.

Description

Energy Efficiency Evaluation Method of Machine Tool Product Manufacturing System Based on AHM and Entropy Value Method

technical field

The invention relates to the technical field of energy consumption monitoring of industrial machine tool product manufacturing systems, in particular to an energy efficiency evaluation method for machine tool product manufacturing systems based on AHM and entropy value methods.

Background technique

Today's manufacturing industry is a pillar industry of the national economy. While creating huge economic wealth, it also consumes huge manufacturing resources and has a very serious impact on the environment. Energy issues have become an intuitive factor affecting social and economic development. Starting from the direction of energy utilization, energy conservation has become a top priority. The basic components in a typical machine tool manufacturing system can be divided into four parts: production environment, production objects, production equipment, and operators. The energy consumed by the machine tool manufacturing system during the production process can be divided into direct and indirect energy. The direct energy is the various process energy consumed by manufacturing products, and the indirect energy is the energy required to maintain the production environment in the manufacturing workshop.

How to strengthen the energy efficiency evaluation of enterprises and improve the energy efficiency of manufacturing systems has become a top priority. Energy efficiency evaluation is to evaluate the energy utilization of enterprises in the entire production process, and promote enterprises to improve current management methods and production processes, so as to more effectively improve energy utilization efficiency and achieve the purpose of saving energy. The premise of improving energy utilization efficiency is to understand the energy consumption of the system itself, so it is of great practical significance to study the methods of energy efficiency evaluation and establish a complete energy efficiency evaluation index system.

Contents of the invention

The purpose of the present invention is to provide an energy efficiency evaluation method for machine tool product manufacturing system. This method avoids the influence of experts' competent factors and the influence of incomplete data, and can provide guidance and basis for comprehensive evaluation of machine tool products.

According to the technical scheme provided by the present invention, the method comprises the following steps:

Step 1. Establish the energy efficiency index evaluation system of the machine tool product manufacturing system, and the lowest index in the system constitutes the evaluation factor set;

Step 2, applying AHM (Analytic Hierarchy Process Method) to determine the subjective weight of the index;

Step 3, applying the entropy method to determine the objective weight of the index;

Step 4, the construction of the comprehensive weight of the evaluation index;

Step five, perform dimensionless processing on the original quantitative of the manufacturing system;

Step 6: Carry out a comprehensive evaluation according to the weight and the dimensionless processed data, and multiply each data after the dimensionless processing by the comprehensive weight to obtain the final score.

Specifically, the set of evaluation factors mentioned in step 1 includes: energy consumption per 10,000 yuan of product, energy consumption per 10,000 yuan of added value, comprehensive energy consumption per unit of product, energy saving per unit of product, energy efficiency of machine tools, energy transmission efficiency, and energy conversion efficiency , energy efficiency of production process, and energy efficiency of production resource scheduling.

In step 2, in order to calculate the relative importance of elements in the same layer, a judgment matrix A={a _ij } is established, where a _ij =1/a _ji' a _ii =1, where a _ij is obtained based on expert knowledge The importance parameter of a _ij ∈ {1, 3, 5, 7, 9};

Convert A={a _ij } into a measure matrix through the corresponding formula

${\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\frac{\mathrm{<span\; class="notranslate">\; \&beta;</span>}\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; \&beta;</span>}\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}& {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; k</span>}\\ \frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&beta;</span>}\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}& {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; k</span>}}\\ \mathrm{<span\; class="notranslate">\; 0.5</span>}& {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&NotEqual;</span>\mathrm{<span\; class="notranslate">\; j</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; j</span>}\end{array}\right.$

μ _ij represents the elements in the measure matrix, where k and β are the parameters used when seeking the measure matrix, and k is a positive integer greater than 1, specifically according to expert experience, take β=1;

Calculate the weight of the single-layer indicators to obtain the weighted subset of the bottom-level indicators relative to the upper-level indicators: W=[W ₁ , W ₂ ...W ₁₀ ],

${\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; ...</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}$

$\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; ;</span>$

Computes combined weights between underlying elements

w _j =w _i *w _ij

In the formula, w _j is the combined weight of the j-th sub-goal relative to the total target, w _i is the combined weight of the i-th sub-goal, w _ij is the weight of the j-th sub-goal to the i-th sub-goal, and the j-th sub-goal is located in the j-th sub-goal The upper layer of the target; the combined weight is used to analyze the importance of each indicator, and is not used for subsequent calculations.

The method of step three is as follows:

Model the hierarchy and build the original data matrix:

X＝(X _ij ) _m×n

In the formula, X represents the original evaluation matrix; X _ij represents the index value; m represents the number of plans with evaluation; n is the number of evaluation indexes;

Quantify each index at the same time, and calculate the index weight of the i-th scheme under the j-th index:

${\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}}{{<span\; class="notranslate">\; \&Sigma;</span>}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}}$

Where p _ij represents the index value weight of the i-th scheme under the j-th index;

Calculate the index entropy value of item j

${\mathrm{<span\; class="notranslate">\; e</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; k</span>}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}{\mathrm{<span\; class="notranslate">\; lnp</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}$

Among them, e _j represents the entropy value of the j item, e _j ≥ 0, k>0, k=1/lnm;

Calculate the coefficient of the j-th index difference:

g _j =1-e _j

Among them, g _j represents the coefficient of the j index index difference, and e _j represents the entropy value of the j index index;

Calculate the relative weight of the underlying indicators to the upper-level criteria, and then determine the weight of each layer's indicators for the overall goal:

${\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}}{{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}\mathrm{<span\; class="notranslate">\; g</span>}\mathrm{<span\; class="notranslate">\; j</span>}}$

Among them, w _j is the index weight of each item, and g _j represents the coefficient of the index difference of the jth item.

Step 4 Synthesize the weight values of the subjective and objective indicators obtained by using the AHP and the entropy method respectively, so as to obtain the final index weights, and finally obtain a set of evaluation index weights

W＝θw _Ai +(1-θ)w _Bi

In the formula, w _Ai is the objective weight, w _Bi represents the intuitive weight, and the value of θ depends on the specific situation. When the decision is inclined to the experience of experts, θ∈[0.5, 1], when the decision is inclined to the objective data , θ∈[0, 0.5]; Finally, the final index evaluation weight is obtained by calculation.

The method of step five is:

Suppose the original data of the K-th indicator is Then it needs to go through dimensionless processing, see the following formula for details, where the processed data C _i (k)∈(0,1);

${\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; minc</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; maxc</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; minc</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; i</span>}}}$

In the formula, i=1, 2...n; k=1, 2...m; wherein, n is the number of options available, and m is the number of decision-making indicators.

The advantage of the present invention is that: the present invention aims at providing a comprehensive energy efficiency evaluation method in the field of energy efficiency evaluation of machine tool product manufacturing systems. Because the present invention adopts the idea of combined weighting of AHM and entropy value method, it can not only avoid the influence of experts’ supervisory factors, but also avoid the influence of incomplete data, so that the energy utilization of enterprises in the whole production process can be evaluated , so as to more effectively improve the efficiency of energy utilization and achieve the purpose of saving energy.

Description of drawings

Fig. 1 is a flow chart of the energy efficiency evaluation of the present invention.

Fig. 2 is the comprehensive index evaluation system of the present invention.

detailed description

Figure 1 shows the overall flow chart of the present invention.

(1) Establish the energy efficiency index evaluation system of the machine tool product manufacturing system.

In the selection process of the evaluation index of the machine tool product manufacturing system, we must pay attention to the four principles of comprehensiveness, purpose, feasibility and stability of the evaluation, and the determination of the evaluation index must be based on the actual situation. The present invention selects four first-level indicators of product energy efficiency, economic energy efficiency, task energy efficiency, and equipment energy efficiency to establish an evaluation index system, as shown in Figure 2, wherein the second-level indicators include: energy consumption per ten thousand yuan product C1 , energy consumption per 10,000 yuan added value C2, comprehensive energy consumption per unit product C3, energy saving per unit product C4, machine tool equipment energy efficiency C5, energy transmission efficiency C6, energy processing conversion efficiency C7, production process energy efficiency C8, production resource scheduling energy efficiency C9.

(2) Determine the evaluation factor set C in the energy efficiency index evaluation system.

The set of evaluation factors is the bottom 9 indicators, that is, the set of comprehensive evaluation factors is C={C ₁ , C ₂ ...C ₉ }.

(3) Apply the AHM method to determine the subjective weight of the index.

In order to calculate the relative importance of elements in the same layer, a judgment matrix A={a _ij } is established, where a _ij =1/a _ji' a _ii =1. Where a _ij is the importance parameter obtained according to expert knowledge, a _ij ∈ {1, 3, 5, 7, 9}.

Convert A={a _ij } into a measure matrix through the corresponding formula

${\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\frac{\mathrm{<span\; class="notranslate">\; \&beta;</span>}\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; \&beta;</span>}\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}& {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; k</span>}\\ \frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&beta;</span>}\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}& {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; k</span>}}\\ \mathrm{<span\; class="notranslate">\; 0.5</span>}& {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&NotEqual;</span>\mathrm{<span\; class="notranslate">\; j</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; j</span>}\end{array}\right.$

μ _ij represents the elements in the measure matrix, where k and β are the parameters used in calculating the measure matrix, and k is a positive integer greater than 1, which is specifically obtained according to expert experience, where β=1 is taken.

Calculate the weight of the single-layer index, and get the weighted subset of the bottom index when comparing the upper layer index: W=[W ₁ , W ₂ ...W ₁₀ ],

${\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; ...</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}$

$\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 10.</span>}$

Computes combined weights between underlying elements.

w _j =w _i *w _ij

In the formula, w _j is the combined weight of the j-th sub-goal relative to the total target, w _i is the combined weight of the i-th sub-goal, w _ij is the weight of the j-th sub-goal to the i-th sub-goal, and the j-th sub-goal is located in the j-th sub-goal The upper layer of a sub-goal. The combined weight is used to analyze the importance of each indicator, and is not used for subsequent calculations.

(4) Apply the entropy method to determine the objective weight of the index.

Model the hierarchy and build the original data matrix:

X＝(X _ij ) _m×n

In the formula, X represents the original evaluation matrix; X _ij represents the index value; m represents the number of programs with evaluation; n is the number of evaluation indexes.

Quantify each index at the same time, and calculate the index weight of the i-th scheme under the j-th index:

${\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}}{{<span\; class="notranslate">\; \&Sigma;</span>}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}}$

Among them, p _ij represents the index value weight of the i-th scheme under the j-th index.

Calculate the index entropy value of the jth item

${\mathrm{<span\; class="notranslate">\; e</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; k</span>}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}{\mathrm{<span\; class="notranslate">\; lnp</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}$

Wherein, e _j represents the entropy value of the jth item, e _j ≥ 0, k>0, k=1/lnm.

Calculate the coefficient of the j-th index difference:

g _j =1-e _j

Among them, g _j represents the coefficient of the j-th index difference, and e _j represents the entropy value of the j-th index.

Calculate the relative weight of the bottom-level indicators to the upper-level criteria, and then determine the weight of each layer's indicators for the overall goal:

${\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}}{{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}\mathrm{<span\; class="notranslate">\; g</span>}\mathrm{<span\; class="notranslate">\; j</span>}}$

Among them, w _j is the index weight of each item, and g _j represents the coefficient of the index difference of the jth item.

(5) The construction of the comprehensive weight of the evaluation index.

Using the AHM and entropy method to obtain the weight values of subjective and objective indicators respectively, the entropy method can be based on objective data and overcome the influence of subjective factors of experts, but it is also easily affected by sample data. Using the AHM method can make good use of the experience of experts, but it is greatly affected by human beings and cannot objectively reflect the weight of the sample. Therefore, the two methods are combined to obtain the final index weights, and finally a set of evaluation index weights is obtained.

W＝θw _Ai +(1-θ)w _Bi

In the formula, w _Ai is the objective weight, w _Bi represents the intuitive weight, and the value of θ depends on the specific situation. When the decision is inclined to the experience of experts, θ∈[0.5, 1], when the decision is inclined to the objective data , θ∈[0, 0.5]; Finally, the final index evaluation weight is obtained by calculation.

(6) Dimensionless treatment of the original quantification of the manufacturing system.

For quantitative indicators, the measurement units and magnitudes of each indicator are different. It is also necessary to perform dimensionless processing on the original data to reduce the interference of random factors. Suppose the original data of the K-th indicator is It needs to go through dimensionless processing, see the following formula for details. The processed data C _i (k) ∈ (0, 1).

${\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; minc</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; maxc</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; minc</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; i</span>}}}$

In the formula, i=1, 2...n, k=1, 2...m, where n is the number of options available, and m is the number of decision-making indicators.

(7) Carry out comprehensive evaluation according to weight and dimensionless processed data.

The final score is obtained by multiplying the unquantified processed data of each item with the comprehensive weight.

The present invention will be described in further detail below in conjunction with the examples.

Two machine tool manufacturing industries, A and B, are selected as the evaluation objects for evaluation, and the data of each index are shown in the table below.

Compute weights based on AHM:

Single-layer index weight:

w=[0.251 0.263 0244 0.242]

w ₁ =[0.6 0.4]w ₂ =[0.440 0.56]w ₃ =[0.491 0.302 0.207]w ₄ =[0.392 0.608]

Combination weight:

$\begin{array}{cccc}\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; \&lsqb;</span>\begin{array}{cc}\mathrm{<span\; class="notranslate">\; 0.128</span>}& \mathrm{<span\; class="notranslate">\; 0.109</span>}\end{array}<span\; class="notranslate">\; \&rsqb;</span>& \stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; \&lsqb;</span>\begin{array}{cc}\mathrm{<span\; class="notranslate">\; 0.122</span>}& \mathrm{<span\; class="notranslate">\; 0.129</span>}\end{array}<span\; class="notranslate">\; \&rsqb;</span>& \stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; \&lsqb;</span>\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 0.117</span>}& \mathrm{<span\; class="notranslate">\; 0.113</span>}& \mathrm{<span\; class="notranslate">\; 0.046</span>}\end{array}<span\; class="notranslate">\; \&rsqb;</span>& \stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; \&lsqb;</span>\begin{array}{cc}\mathrm{<span\; class="notranslate">\; 0.107</span>}& \mathrm{<span\; class="notranslate">\; 0.129</span>}\end{array}<span\; class="notranslate">\; \&rsqb;</span>\end{array}$

Compute entropy-based weights:

The original matrix is:

$\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; =</span>\left[\begin{array}{ccccccccc}\mathrm{<span\; class="notranslate">\; 2.5</span>}& \mathrm{<span\; class="notranslate">\; 3.7</span>}& \mathrm{<span\; class="notranslate">\; 8.9</span>}& \mathrm{<span\; class="notranslate">\; 4.9</span>}& \mathrm{<span\; class="notranslate">\; 0.4</span>}& \mathrm{<span\; class="notranslate">\; 67</span>}& \mathrm{<span\; class="notranslate">\; 60</span>}& \mathrm{<span\; class="notranslate">\; 0.5</span>}& \mathrm{<span\; class="notranslate">\; 0.6</span>}\\ \mathrm{<span\; class="notranslate">\; 3.2</span>}& \mathrm{<span\; class="notranslate">\; 5.1</span>}& \mathrm{<span\; class="notranslate">\; 9.2</span>}& \mathrm{<span\; class="notranslate">\; 2.6</span>}& \mathrm{<span\; class="notranslate">\; 0.5</span>}& \mathrm{<span\; class="notranslate">\; 55</span>}& \mathrm{<span\; class="notranslate">\; 65</span>}& \mathrm{<span\; class="notranslate">\; 0.8</span>}& \mathrm{<span\; class="notranslate">\; 0.6</span>}\end{array}\right]$

The same quantized matrix is:

$\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; =</span>\left[\begin{array}{ccccccccc}\mathrm{<span\; class="notranslate">\; 0.439</span>}& \mathrm{<span\; class="notranslate">\; 0.420</span>}& \mathrm{<span\; class="notranslate">\; 0.492</span>}& \mathrm{<span\; class="notranslate">\; 0.653</span>}& \mathrm{<span\; class="notranslate">\; 0.440</span>}& \mathrm{<span\; class="notranslate">\; 0.549</span>}& \mathrm{<span\; class="notranslate">\; 0.480</span>}& \mathrm{<span\; class="notranslate">\; 0.385</span>}& \mathrm{<span\; class="notranslate">\; 0.500</span>}\\ \mathrm{<span\; class="notranslate">\; 0.561</span>}& \mathrm{<span\; class="notranslate">\; 0.580</span>}& \mathrm{<span\; class="notranslate">\; 0.508</span>}& \mathrm{<span\; class="notranslate">\; 0.347</span>}& \mathrm{<span\; class="notranslate">\; 0.560</span>}& \mathrm{<span\; class="notranslate">\; 0.451</span>}& \mathrm{<span\; class="notranslate">\; 0.520</span>}& \mathrm{<span\; class="notranslate">\; 0.615</span>}& \mathrm{<span\; class="notranslate">\; 0.500</span>}\end{array}\right]$

The calculated entropy value is:

e _j =[0.989 0.981 0.999 0.931 0.989 0.993 0.998 0.961 1]

The calculated variance coefficient is:

g _j =[0.011 0.019 0.001 0.069 0.011 0.007 0.002 0.039 0.000]

The weight of the overall goal is:

w _j =[0.069 0.119 0.006 0.434 0.069 0.045 0.012 0.246 0.000]

Calculate the composite weight:

The subjective weight evaluation index and the objective weight evaluation index are respectively obtained by AHM and entropy method, and the comprehensive weight is calculated. According to the formula w=θw _Ai +(1-θ)w _Bi , θ=0.62, the result is biased towards the objective weight, and the comprehensive weight for

w=[0.091 0.115 0.050 0.318 0.080 0.070 0.024 0.193 0.059]

Dimensionless processing of various data of A machine tool factory:

c＝[0.36 0.13 0.77 0.85 0.5 0.75 0.5 0.25 1]

Dimensionless processing of various data of B machine tool factory:

c＝[0.58 0.64 0.62 0.036 1 0.24 0.9 1 0.5]

Finally, the final score is obtained by multiplying the data of each item processed without quantification and the comprehensive weight:

According to the calculation, the energy efficiency of A machine tool factory is 0.56826, and the energy efficiency of B machine tool manager is 0.5097.

Claims (6)

1. The energy efficiency evaluation method of the machine tool product manufacturing system based on AHM and entropy value method, it is characterized in that, comprises the following steps: Step 1. Establish the energy efficiency index evaluation system of the machine tool product manufacturing system, and the lowest index in the system constitutes the evaluation factor set; Step 2, applying the AHP to determine the subjective weight of the indicators; Step 3, applying the entropy method to determine the objective weight of the index; Step 4, the construction of the comprehensive weight of the evaluation index; Step five, perform dimensionless processing on the original quantitative of the manufacturing system; Step 6: Carry out a comprehensive evaluation according to the weight and the dimensionless processed data, and multiply each data after the dimensionless processing by the comprehensive weight to obtain the final score. 2. the energy efficiency evaluation method of the machine tool product manufacturing system based on AHM and entropy value method as claimed in claim 1, it is characterized in that, in step 2, in order to calculate the relative importance of element between the same layer, set up judgment matrix A= {a _ij }, where a _ij =1/a _ji 'a _ii =1, where a _ij is the importance parameter obtained from expert knowledge, a _ij ∈ {1, 3, 5, 7, 9}; Convert A={a _ij } into a measure matrix through the corresponding formula ${\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\frac{\mathrm{<span\; class="notranslate">\; \&beta;</span>}\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; \&beta;</span>}\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}& {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; k</span>}\\ \frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&beta;</span>}\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}& {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; k</span>}}\\ \mathrm{<span\; class="notranslate">\; 0.5</span>}& {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&NotEqual;</span>\mathrm{<span\; class="notranslate">\; j</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; j</span>}\end{array}\right.$ μ _ij represents the elements in the measure matrix, where k and β are the parameters used when seeking the measure matrix, and k is a positive integer greater than 1, specifically according to expert experience, take β=1; Calculate the weight of the single-layer indicators to obtain the weighted subset of the bottom-level indicators relative to the upper-level indicators: W=[W ₁ , W ₂ ...W ₁₀ ], ${\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; ...</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}$ $\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; ;</span>$ Computes combined weights between underlying elements w _j =w _i *w _ij In the formula, w _j is the combined weight of the j-th sub-goal relative to the total target, w _i is the combined weight of the i-th sub-goal, w _ij is the weight of the j-th sub-goal to the i-th sub-goal, and the j-th sub-goal is located in the j-th sub-goal The upper layer of the target; the combined weight is used to analyze the importance of each indicator, and is not used for subsequent calculations. 3. the energy efficiency evaluation method of the machine tool product manufacturing system based on AHM and entropy value method as claimed in claim 1, it is characterized in that, the method of step 3 is as follows: Model the hierarchy and build the original data matrix: X＝(X _ij ) _m×n In the formula, X represents the original evaluation matrix; X _ij represents the index value; m represents the number of plans with evaluation; n is the number of evaluation indexes; Quantify each index at the same time, and calculate the index weight of the i-th scheme under the j-th index: ${\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}}{{<span\; class="notranslate">\; \&Sigma;</span>}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}}$ Where p _ij represents the index value weight of the i-th scheme under the j-th index; Calculate the index entropy value of item j ${\mathrm{<span\; class="notranslate">\; e</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; k</span>}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}\mathrm{<span\; class="notranslate">\; ln</span>}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}$ Among them, e _j represents the entropy value of the j item, e _j ≥ 0, k>0, k=1/lnm; Calculate the coefficient of the j-th index difference: g _j =1-e _j Among them, g _j represents the coefficient of the j index index difference, and e _j represents the entropy value of the j index index; Calculate the relative weight of the underlying indicators to the upper-level criteria, and then determine the weight of each layer's indicators for the overall goal: ${\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}}{{<span\; class="notranslate">\; \&Sigma;</span>}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}\mathrm{<span\; class="notranslate">\; g</span>}\mathrm{<span\; class="notranslate">\; j</span>}}$ Among them, w _j is the index weight of each item, and g _j represents the coefficient of the index difference of the jth item. 4. the energy efficiency evaluation method of the machine tool product manufacturing system based on AHM and entropy value method as claimed in claim 1, it is characterized in that, step 4 is to utilizing Analytic Hierarchy Process and entropy value method to respectively obtain subjective and objective two aspect indexes The weight values are integrated to obtain the final index weight, and finally a set of evaluation index weights W＝θw _Ai +(1-θ)w _Bi In the formula, w _Ai is the objective weight, w _Bi represents the intuitive weight, and the value of θ depends on the specific situation. When the decision is inclined to the experience of experts, θ∈[0.5, 1], when the decision is inclined to the objective data , θ∈[0, 0.5]; Finally, the final index evaluation weight is obtained by calculation. 5. the energy efficiency evaluation method of the machine tool product manufacturing system based on AHM and entropy value method as claimed in claim 1, it is characterized in that, the method of step five is: Suppose the original data of the K-th indicator is Then it needs to go through dimensionless processing, see the following formula for details, where the processed data C _i (k)∈(0,1); ${\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; min</span>}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; maxc</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; minc</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; i</span>}}}$ In the formula, i=1, 2...n; k=1, 2...m; wherein, n is the number of options available, and m is the number of decision-making indicators. 6. The energy efficiency evaluation method of the machine tool product manufacturing system based on AHM and entropy value method as claimed in claim 1, it is characterized in that, the evaluation factor set described in step 1 includes: energy consumption of ten thousand yuan product, energy consumption of ten thousand yuan added value Consumption, comprehensive energy consumption per unit product, energy saving per unit product, energy efficiency of machine tool equipment, energy transmission efficiency, energy processing conversion efficiency, production process energy efficiency, and production resource scheduling energy efficiency.