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

Machine tool product based on AHM and Information Entropy manufactures the efficiency evaluation methodology of system

Technical field

The present invention relates to industry lathe product manufacturing system energy consumption monitoring technical field, specifically a kind of based on AHM and entropy The machine tool product of value method manufactures the efficiency evaluation methodology of system.

Background technology

Current manufacturing industry is as mainstay of the national economy industry, and it is createing huge intellectual treasure simultaneously, also same Time consume huge manufacturing recourses, and environment is caused the most serious impact.Energy problem has become as affects society Can and the factor directly perceived of economic development, from the utilization orientation of the energy, energy-conservation had changed into the most important thing.At typical machine Basic constituent element in bed manufacture system can be divided into production environment, produce object, production equipment, four parts of operator.Machine The energy that bed manufacture system consumes in the middle of production process can be divided into directly or indirectly energy, and DIRECT ENERGY manufactures product institute exactly The various Process Energies consumed, the required energy consumed of production environment that indirect energy is contemplated in maintenance manufacturing shop.

How to strengthen the efficiency evaluation of enterprise, the energy efficiency promoting manufacture system has had become as the task of top priority.Efficiency Evaluate, it is simply that enterprise's Energy harvesting situation in the middle of whole production process is evaluated, promote that enterprise improves current pipe Reason mode and production technology, thus the utilization ratio of the significantly more efficient raising energy, reach to save the purpose of the energy.Energy to be promoted The utilization ratio premise in source seeks to the situation of the energy of understanding system own, so the method for research efficiency evaluation and test, it is established that complete Kind energy efficiency evaluation index system is to have very much realistic meaning.

Summary of the invention

It is an object of the invention to provide a kind of machine tool product and manufacture the efficiency evaluation methodology of system, this approach avoid expert The impact of supervisor's factor, turn avoid data not completely time impact, can provide for the overall merit of machine tool product instruct and Foundation.

The technical scheme provided according to the present invention, the method comprises the following steps:

Step one, set up machine tool product manufacture system energy efficiency indexes appraisement system, in system the bottom index constitute Factor of evaluation collection；

Step 2, the subjective weight of application AHM (analytic hierarchy process (AHP)) agriculture products；

Step 3, the objective weight of application Information Entropy agriculture products；

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

Step 5, to manufacture the original of system quantitatively carry out nondimensionalization process；

Step 6, the data processed according to weight and nondimensionalization carry out overall merit, every after nondimensionalization is processed Item data is multiplied with comprehensive weight and obtains last scoring.

Specifically, factor of evaluation collection described in step one includes: ten thousand yuan of product energy consumptions, ten thousand yuan of value added energy consumptions, unit Product comprehensive energy consumption, unit product amount of energy saving, machine tool efficiency, energy transfer efficiency, energy processing conversion efficiency, produce work Artistic skill is imitated, resources of production scheduling nine indexs of efficiency.

In step 2, in order to calculate the relative importance of same interlayer element, it is established that judgment matrix A={a_ij, a in formula_ij =1/a_ji’a_ii=1, wherein a_ijBe according to expertise obtained by importance degree parameter, a_ij∈ { 1,3,5,7,9}；

A={a_ijChanged into by corresponding formula and to estimate matrix

${\mathrm{\μ}}_{ij}=\left\{\begin{array}{cc}\frac{\mathrm{\β}k}{\mathrm{\β}k+1}& a_{ij}=k\\ \frac{1}{\mathrm{\β}k+1}& a_{ij}=\frac{1}{k}\\ 0.5& a_{ij}=1,i\≠j\\ 0& a_{ij}=1,i=j\end{array}\right.$

μ_ijElement in representing measure matrix, in formula, k and β is to seek parameter used when estimating matrix, and k is just being greater than 1 Integer, with specific reference to expertise gained, takes β=1；

Calculate the weight of monolayer index, obtain the bottom index weighting subset relative to upper strata index: W=[W₁, W₂...W₁₀],

$w_i=\frac{2}{n(n-1)}\underset{j=1}{\overset{n}{\Σ}}{\mathrm{\μ}}_{ij},i=1,2,...,n$

$\underset{i=1}{\overset{n}{\Σ}}{w}_i=1,0\≤w_i\≤1,n=10;$

Calculate the combining weights between bottom element

w_j=w_i*w_ij

W in formula_jFor jth sub-goal relative to the combining weights of general objective, w_iFor the combining weights of i-th sub-goal, w_ij For the jth sub-goal weight to i sub-goal, wherein jth sub-goal is positioned at the last layer of jth sub-goal；Described group Close weight to be used to analyze the importance between each index, be not used to calculating below.

The method of step 3 is as follows:

Set up the model of hierarchical structure, and build raw data matrix:

X=(X_ij)_m×n

In formula, X represents the matrix of iotave evaluation；X_ijRepresent desired value；M represents the scheme number that band is evaluated；N is the finger evaluated Mark number；

Each index is carried out unison quantization, the index weights of the i-th scheme below calculating jth item index:

$p_{ij}=\frac{x_{ij}}{{\Σ}_{i=1}^m{x}_{ij}}$

Wherein p_ijThe desired value weight of the i-th scheme below expression jth item index；

Calculate the index entropy of jth item

$e_j=-k\underset{i=1}{\overset{m}{\Σ}}{p}_{ij}{\mathrm{lnp}}_{ij}$

Wherein, e_jRepresent the entropy that jth item refers to, e_j>=0, k ＞ 0, k=1/lnm；

The coefficient of the calculating jth item index error opposite sex:

g_j=1-e_j

Wherein, g_jRepresent the coefficient of the jth item index error opposite sex, e_jRepresent the entropy of jth item index；

The index of the calculating bottom relative weighting to upper strata criterion, it is then determined that each layer index is for the weight of general objective:

$w_j=\frac{g_j}{{\mathrm{\Σ}}_{i=1}^{n}gj}$

Wherein, w_jFor every index weights, g_jRepresent the coefficient of the jth item index error opposite sex.

Step 4 to utilizing analytic hierarchy process (AHP) and Information Entropy to obtain subjectivity respectively, the weighted value of objective two aspect indexs enters Row is comprehensive, thus obtains last index weights, finally gives one group of evaluation criterion weight

W=θ w_Ai+(1-θ)w_Bi

In formula, w_AiFor objective weight, w_BiRepresent weight directly perceived, the value condition of θ as the case may be depending on, work as decision-making When tending to the experience of expert, θ ∈ [0.5,1], when objective data are tended in decision-making, θ ∈ [0,0.5]；Finally by meter Calculate and obtain final metrics evaluation weight.

The method of step 5 is:

If the initial data of K item index isThen to be specifically shown in following formula through the process of nondimensionalization, wherein to process After data C_i(k) ∈ (0,1)；

$C_i\left(k\right)=\frac{c_k^i-{\mathrm{minc}}_k^i}{{\mathrm{maxc}}_k^i-{\mathrm{minc}}_k^i}$

In formula, i=1,2...n；K=1,2...m；Wherein, n is optional amount of projects, and m is the quantity of decision index system.

The invention have the advantage that it is contemplated that machine tool product manufactures the efficiency of system evaluates field and provide a kind of comprehensive Efficiency evaluation methodology.Due to the fact that the thought of the combination weighting that have employed AHM and Information Entropy, both can be avoided the master of expert Pipe factor affects, turn avoid data not completely time impact, make enterprise's Energy harvesting feelings in the middle of whole production process Condition is evaluated, thus the utilization ratio of the significantly more efficient raising energy, reach to save the purpose of the energy.

Accompanying drawing explanation

Fig. 1 is the flow chart that efficiency of the present invention is evaluated.

Fig. 2 is the aggregative indicator appraisement system of the present invention.

Detailed description of the invention

Fig. 1 show the overview flow chart of the present invention.

(1) set up machine tool product and manufacture the energy efficiency indexes appraisement system of system.

During machine tool product manufactures the choosing of evaluation index of system it has to be remarked that comprehensive, the purposiveness evaluated, Feasibility and 4 principles of stability, the determination of evaluation index have to be based on actual situation.The present invention chooses product energy The assessment indicator system that effect, economic efficiency, task efficiency and four first class index of energy efficiency of equipment and nine two-level index are set up, tool Body as in figure 2 it is shown, wherein two-level index include: ten thousand yuan of product energy consumption C1, ten thousand yuan of value added energy consumptions C2, unit product comprehensive energy consumption C3, unit product amount of energy saving C4, machine tool efficiency C5, energy transfer efficiency C6, energy processing conversion efficiency C7, production technology Efficiency C8, resources of production scheduling efficiency C9.

(2) the factor of evaluation collection C in energy efficiency indexes appraisement system is determined.

It is C={C that the collection of factor of evaluation is combined into 9 indexs of bottom, the i.e. set of factors of comprehensive assessment₁, C₂...C₉}。

(3) the subjective weight of AHM method agriculture products is applied.

In order to calculate the relative importance of same interlayer element, it is established that judgment matrix A={a_ij, a in formula_ij=1/a_ji’a_ii =1.Wherein a_ijBe according to expertise obtained by importance degree parameter, a_ij∈ { 1,3,5,7,9}.

A={a_ijChanged into by corresponding formula and to estimate matrix

${\mathrm{\μ}}_{ij}=\left\{\begin{array}{cc}\frac{\mathrm{\β}k}{\mathrm{\β}k+1}& a_{ij}=k\\ \frac{1}{\mathrm{\β}k+1}& a_{ij}=\frac{1}{k}\\ 0.5& a_{ij}=1,i\≠j\\ 0& a_{ij}=1,i=j\end{array}\right.$

μ_ijElement in representing measure matrix, in formula, k and β is to seek parameter used when estimating matrix, and k is just being greater than 1 Integer, with specific reference to expertise gained, takes β=1 here.

Calculate the weight of monolayer index, obtain bottom index when the weighting subset than upper layer index: W=[W₁, W₂...W₁₀],

$w_i=\frac{2}{n(n-1)}\underset{j=1}{\overset{n}{\Σ}}{\mathrm{\μ}}_{ij},i=1,2,...,n$

$\underset{i=1}{\overset{n}{\Σ}}{w}_i=1,0\≤w_i\≤1,n=10.$

Calculate the combining weights between bottom element.

w_j=w_i*w_ij

W in formula_jFor jth sub-goal relative to the combining weights of general objective, w_iFor the combining weights of i-th sub-goal, w_ij For jth sub-goal to i sub-goal weight, wherein jth sub-goal is positioned at the last layer of jth sub-goal.Wherein Combining weights is used to analyze the importance between each index, is not used to calculating below.

(4) objective weight of Information Entropy agriculture products is applied.

Set up the model of hierarchical structure, and build raw data matrix:

X=(X_ij)_m×n

In formula, X represents the matrix of iotave evaluation；X_ijRepresent desired value；M represents the scheme number that band is evaluated；N is the finger evaluated Mark number.

Each index is carried out unison quantization, the index weights of the i-th scheme below calculating jth item index:

$p_{ij}=\frac{x_{ij}}{{\Σ}_{i=1}^m{x}_{ij}}$

Wherein p_ijThe desired value weight of the i-th scheme below expression jth item index.

Calculate the index entropy of jth item

$e_j=-k\underset{i=1}{\overset{m}{\Σ}}{p}_{ij}{\mathrm{lnp}}_{ij}$

Wherein, e_jRepresent the entropy that jth item refers to, e_j>=0, k ＞ 0, k=1/lnm.

The coefficient of the calculating jth item index error opposite sex:

g_j=1-e_j

Wherein, g_jRepresent the coefficient of the jth item index error opposite sex, e_jRepresent the entropy of jth item index.

The index of the calculating bottom relative weighting to upper strata criterion, it is then determined that each layer index is for the weight of general objective:

$w_j=\frac{g_j}{{\mathrm{\Σ}}_{i=1}^{n}gj}$

Wherein, w_jFor every index weights, g_jRepresent the coefficient of the jth item index error opposite sex.

(5) structure of the comprehensive weight of evaluation index.

Utilize AHM and Information Entropy to obtain subjectivity, the weighted value of objective two aspect indexs respectively, utilize the Information Entropy can be with Based on objective data, overcome and affected by expert's subjective factors, but be also easily subject to the impact of sample data.Utilize AHM method, can well utilize the experience of expert, but it is the biggest to be affected by artificial impact, it is impossible to objectively reflected sample weight. So two kinds of methods are carried out comprehensively, thus obtain last index weights, finally give one group of evaluation criterion weight.

W=θ w_Ai+(1-θ)w_Bi

In formula, w_AiFor objective weight, w_BiRepresent weight directly perceived, the value condition of θ as the case may be depending on, work as decision-making When tending to the experience of expert, θ ∈ [0.5,1], when objective data are tended in decision-making, θ ∈ [0,0.5]；Finally by meter Calculate and obtain final metrics evaluation weight.

(6) manufacture the original of system is quantitatively carried out nondimensionalization process.

For quantitative index, the measurement unit of each index, magnitude are different.Also need to carry out immeasurable to initial data The process of guiding principle, reduces the interference of random factor.If the initial data of K item index isThen will be through the place of nondimensionalization Reason, is specifically shown in following formula.Data C after wherein processing_i(k) ∈ (0,1).

$C_i\left(k\right)=\frac{c_k^i-{\mathrm{minc}}_k^i}{{\mathrm{maxc}}_k^i-{\mathrm{minc}}_k^i}$

In formula, i=1,2...n, k=1,2...m, wherein, n is optional amount of projects, and m is the quantity of decision index system.

(7) data processed according to weight and nondimensionalization carry out overall merit.

The data of each after no quantization process are multiplied with comprehensive weight, obtain last scoring.

Below in conjunction with embodiment, the present invention is elaborated further.

Choosing the first and second two machine-tool industries to be estimated as evaluation object, each achievement data see table.

Calculate weight based on AHM:

Monolayer index weights:

W=[0.251 0.263 0,244 0.242]

w₁=[0.6 0.4] w₂=[0.440 0.56] w₃=[0.491 0.302 0.207] w₄=[0.392 0.608]

Combining weights:

$\begin{array}{cccc}\stackrel{\‾}{w_1}=\[\begin{array}{cc}0.128& 0.109\end{array}\]& \stackrel{\‾}{w_1}=\[\begin{array}{cc}0.122& 0.129\end{array}\]& \stackrel{\‾}{w_3}=\[\begin{array}{ccc}0.117& 0.113& 0.046\end{array}\]& \stackrel{\‾}{w_4}=\[\begin{array}{cc}0.107& 0.129\end{array}\]\end{array}$

Calculate weight based on Information Entropy:

Original matrix is:

$x=\left[\begin{array}{ccccccccc}2.5& 3.7& 8.9& 4.9& 0.4& 67& 60& 0.5& 0.6\\ 3.2& 5.1& 9.2& 2.6& 0.5& 55& 65& 0.8& 0.6\end{array}\right]$

The matrix of unison quantization is:

$p=\left[\begin{array}{ccccccccc}0.439& 0.420& 0.492& 0.653& 0.440& 0.549& 0.480& 0.385& 0.500\\ 0.561& 0.580& 0.508& 0.347& 0.560& 0.451& 0.520& 0.615& 0.500\end{array}\right]$

Entropy after calculating is:

e_j=[0.989 0.981 0.999 0.931 0.989 0.993 0.998 0.961 1]

Diversity coefficient after calculating is:

g_j=[0.011 0.019 0.001 0.069 0.011 0.007 0.002 0.039 0.000]

The weight of general objective is:

w_j=[0.069 0.119 0.006 0.434 0.069 0.045 0.012 0.246 0.000]

Calculating comprehensive weight:

Obtain subjective weight evaluation index and objective weight evaluation index respectively by AHM and Information Entropy, calculate synthetic weights Weight, according to formula w=θ w_Ai+(1-θ)w_Bi, taking θ=0.62, result is partial to objective weight, and comprehensive weight is

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

Each item data of first machine tool plant is carried out nondimensionalization process:

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

Each item data of second machine tool plant is carried out nondimensionalization process:

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

It is multiplied with comprehensive weight finally according to the data of each after no quantization process, obtains last scoring:

According to can be calculated, the efficiency of first machine tool plant is 0.56826, and the efficiency of second lathe length is 0.5097.

Claims (6)

1. machine tool product based on AHM and Information Entropy manufactures the efficiency evaluation methodology of system, it is characterized in that, comprises the following steps:

Step one, set up machine tool product manufacture system energy efficiency indexes appraisement system, in system the bottom index constitute evaluate Set of factors；

Step 2, the subjective weight of application level analytic process agriculture products；

Step 3, the objective weight of application Information Entropy agriculture products；

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

Step 5, to manufacture the original of system quantitatively carry out nondimensionalization process；

Step 6, the data processed according to weight and nondimensionalization carry out overall merit, the every item number after nondimensionalization is processed Last scoring is obtained according to being multiplied with comprehensive weight.

2. machine tool product based on AHM and Information Entropy as claimed in claim 1 manufactures the efficiency evaluation methodology of system, its feature It is, in step 2, in order to calculate the relative importance of same interlayer element, it is established that judgment matrix A={a_ij, a in formula_ij=1/ a_ji’a_ii=1, wherein a_ijBe according to expertise obtained by importance degree parameter, a_ij∈ { 1,3,5,7,9}；

A={a_ijChanged into by corresponding formula and to estimate matrix

${\mathrm{\μ}}_{ij}=\left\{\begin{array}{cc}\frac{\mathrm{\β}k}{\mathrm{\β}k+1}& a_{ij}=k\\ \frac{1}{\mathrm{\β}k+1}& a_{ij}=\frac{1}{k}\\ 0.5& a_{ij}=1,i\≠j\\ 0& a_{ij}=1,i=j\end{array}\right.$

μ_ijElement in representing measure matrix, in formula, k and β is to seek parameter used when estimating matrix, and k is greater than the positive integer of 1, With specific reference to expertise gained, take β=1；

Calculate the weight of monolayer index, obtain the bottom index weighting subset relative to upper strata index: W=[W₁, W₂...W₁₀],

$w_i=\frac{2}{n(n-1)}\underset{j=1}{\overset{n}{\Σ}}{\mathrm{\μ}}_{ij},i=1,2,...,n$

$\underset{i=1}{\overset{n}{\Σ}}{w}_i=1,0\≤w_i\≤1,n=10;$

Calculate the combining weights between bottom element

w_j=w_i*w_ij

W in formula_jFor jth sub-goal relative to the combining weights of general objective, w_iFor the combining weights of i-th sub-goal, w_ijFor jth The individual sub-goal weight to i sub-goal, wherein jth sub-goal is positioned at the last layer of jth sub-goal；Described combining weights It is used to analyze the importance between each index, is not used to calculating below.

3. machine tool product based on AHM and Information Entropy as claimed in claim 1 manufactures the efficiency evaluation methodology of system, its feature It is that the method for step 3 is as follows:

Set up the model of hierarchical structure, and build raw data matrix:

X=(X_ij)_m×n

In formula, X represents the matrix of iotave evaluation；X_ijRepresent desired value；M represents the scheme number that band is evaluated；N is the index evaluated Number；

Each index is carried out unison quantization, the index weights of the i-th scheme below calculating jth item index:

$p_{ij}=\frac{x_{ij}}{{\Σ}_{i=1}^m{x}_{ij}}$

Wherein p_ijThe desired value weight of the i-th scheme below expression jth item index；

Calculate the index entropy of jth item

$e_j=-k\underset{i=1}{\overset{m}{\Σ}}{p}_{ij}\ln p_{ij}$

Wherein, e_jRepresent the entropy that jth item refers to, e_j>=0, k ＞ 0, k=1/lnm；

The coefficient of the calculating jth item index error opposite sex:

g_j=1-e_j

Wherein, g_jRepresent the coefficient of the jth item index error opposite sex, e_jRepresent the entropy of jth item index；

The index of the calculating bottom relative weighting to upper strata criterion, it is then determined that each layer index is for the weight of general objective:

$w_j=\frac{g_j}{{\Σ}_{i=1}^{n}gj}$

Wherein, w_jFor every index weights, g_jRepresent the coefficient of the jth item index error opposite sex.

4. machine tool product based on AHM and Information Entropy as claimed in claim 1 manufactures the efficiency evaluation methodology of system, its feature Be, step 4 to utilizing analytic hierarchy process (AHP) and Information Entropy to obtain subjectivity respectively, the weighted value of objective two aspect indexs is combined Close, thus obtain last index weights, finally give one group of evaluation criterion weight

W=θ w_Ai+(1-θ)w_Bi

In formula, w_AiFor objective weight, w_BiRepresent weight directly perceived, the value condition of θ as the case may be depending on, when decision-making is tended to During the experience of expert, θ ∈ [0.5,1], when objective data are tended in decision-making, θ ∈ [0,0.5]；Finally by being calculated Final metrics evaluation weight.

5. machine tool product based on AHM and Information Entropy as claimed in claim 1 manufactures the efficiency evaluation methodology of system, its feature It is that the method for step 5 is:

If the initial data of K item index isThen to be specifically shown in following formula through the process of nondimensionalization, after wherein processing Data C_i(k) ∈ (0,1)；

$C_i\left(k\right)=\frac{c_k^i-\min c_k^i}{{\mathrm{maxc}}_k^i-{\mathrm{minc}}_k^i}$

In formula, i=1,2...n；K=1,2...m；Wherein, n is optional amount of projects, and m is the quantity of decision index system.

6. machine tool product based on AHM and Information Entropy as claimed in claim 1 manufactures the efficiency evaluation methodology of system, its feature It is that factor of evaluation collection described in step one includes: ten thousand yuan of product energy consumptions, ten thousand yuan of value added energy consumptions, unit product comprehensive energy consumption, single Position product amount of energy saving, machine tool efficiency, energy transfer efficiency, energy processing conversion efficiency, production technology efficiency, the resources of production Scheduling nine indexs of efficiency.