CN103810050A - Embedded computer performance evaluation method based on grey situation decision of AHP - Google Patents

Abstract

The invention provides an embedded computer performance evaluation method based on the grey situation decision based of the AHP. The embedded computer performance evaluation method includes the following steps that firstly, the Delphi expert consultation method is adopted for setting up an embedded computer performance evaluation index system (1-1); secondly, the analytic hierarchy method is adopted for determining a weight coefficient (1-2) of an evaluation index; thirdly, a grey situation decision model (1-3) is set up; fourthly, an evaluation index effect measure (1-4) is calculated; fifthly, a comprehensive effect measure sequence (1-5) is set up; sixthly, the maximization method is adopted for evaluating embedded computer performance (1-6). It is proved by application examples that the embedded computer performance evaluation method based on the grey situation decision of the AHP is reasonable, effective and capable of improving the correctness and the effectiveness of embedded computer performance evaluation. A valuable reference is provided for the evaluation of the embedded computer comprehensive performance in the future.

Description

The embedded computer method of evaluating performance of the grey SITUATION DECISION based on AHP

Technical field

The invention belongs to embedded computer performance evaluation field, be specifically related to the embedded computer method of evaluating performance of the grey SITUATION DECISION based on AHP.

Background technology

From the decades in past, embedded computer still all rolls up in quantity range of application, and embedded computer production firm also increases thereupon.Embedded computer is carried out to synthetic performance evaluation research, is to hold water to use the prerequisite of embedded computer, has important practical significance.

Embedded computer system belongs to complicated system with multi-factors, because its internal feature affects, exist the incomplete problem of parameter information, and gray system theory is processed the tool of this class problem just.Gray system theory is to be founded by professor Deng Julong, this system is to utilize Given information to determine the unknown message of system, and make system become the process of " in vain " from " ash ", and GREY SITUATION DECISION is mainly used in Performance Evaluation, for solving the Performance Evaluation problem in the complete situation of message part, improve accuracy and the versatility of assessment.Because grey trend decision method is to adopt situation to obtain at the mean value of each measure of merit, so just must cause information loss, mean value has flooded the individual character of many measure of merit values, does not make full use of the abundant information that each measure of merit provides.

Summary of the invention

In order to make up the drawback of grey trend decision method in embedded computer performance evaluation, the embedded computer method of evaluating performance of the grey SITUATION DECISION based on AHP is proposed.Idiographic flow is as follows:

Step 1: set up embedded computer Performance Measuring Indicators

The present invention adopts Delphi expert consulting method to choose embedded computer Performance Measuring Indicators from numerous factors that affect embedded computer performance, is shown in Table 1.

Table 1 embedded computer Performance Measuring Indicators

Step 2: the weight of determining embedded computer Performance Evaluating Indexes

The present invention adopts analytical hierarchy process to determine the weight of embedded computer Performance Evaluating Indexes, and concrete steps are as follows:

Step 2.1: compare according to two two indexes relative importances, according to 9 fraction system scorings (in table 2), form judgment matrix A:

Table 2 standards of grading and implication thereof

$A=\left[\begin{array}{cccc}{a}_{11}& a_{12}& ...& a_{1n}\\ a_{21}& a_{22}& ...& a_{2n}\\ .& .& & .\\ .& .& & .\\ .& .& & .\\ a_{m1}& a_{m2}& ...& a_{\mathrm{mn}}\end{array}\right]$

In formula, a _ji=1/a _ij, a _ii=1.

Step 2.2: calculate importance ranking vector, i.e. weight

(λ _maxI-A)w=0

In formula, λ _maxfor the maximum characteristic root of matrix A, w=[w ₁..., w _m] be λ _maxnormalization proper vector, component w _ifor index U _iweight.

Step 2.3: carry out consistency check

$\mathrm{CR}=\frac{{\mathrm{\λ}}_{\max }-n}{\mathrm{RI}(n-1)}$

In formula, CR is judgment matrix random index; RI is mean random coincident indicator, and its index checks in from table 3 according to judgment matrix exponent number.

Table 3 mean random coincidence indicator

In the time of CR≤0.1, judgment matrix A has consistance, illustrates that weight allocation is suitable; Otherwise, need judgment matrix to adjust, to meet conforming requirement.

Step 3: build GREY SITUATION DECISION model

Event a _ioccur, use countermeasure b _jtackle a _iappearance, b _jwith a _ijust form situation s _ij, i.e. s _ij=(a _i, b _j);

Step 4: measure of merit

Make x ⁽⁰⁾=(x ⁽⁰⁾(1), x ⁽⁰⁾(2) ..., x ⁽⁰⁾(n)), make r (k) represent x ⁽⁰⁾(k) measure of merit transformed value.

Work as x ⁽⁰⁾for maximum value polarity chron, have

r=(r(1),r(2),…,r(n)) （1）

Wherein $r\left(k\right)=\frac{x^{\left(0\right)}\left(k\right)}{\underset{1\≤i\≤n}{\max }\left\{x^{\left(0\right)}\left(i\right)\right\}}$ ；

Work as x ⁽⁰⁾for minimal value polarity chron, have

r=(r(1),r(2),…,r(n)) （2）

Wherein $r\left(k\right)=\frac{\underset{1\≤i\≤n}{\min }\left\{x^{\left(0\right)}\left(i\right)\right\}}{x^{\left(0\right)}\left(k\right)}$ ；

Work as x ⁽⁰⁾for moderate polarity chron, have

r=(r(1),r(2),…,r(n)) （3）

Wherein $r\left(k\right)=\frac{\min \{x_0,x^{\left(0\right)}\left(k\right)\}}{\max \{x_0,x^{\left(0\right)}\left(k\right)\}}$ , x ₀for moderate value

Step 5: synthesis effect measure sequence

Order

for situation s _ijmeasure of merit value under p target, if there is p=1,2 ..., m, claims for s _ijsynthesis effect measure, and if only if

$r_{\mathrm{ij}}^{\left(\mathrm{\Σ}\right)}\left(k\right)=\underset{p=1}{\overset{m}{\mathrm{\Σ}}}{w}_p{r}_{\mathrm{ij}}^{\left(p\right)}\left(k\right).---\left(4\right)$

If have

$r_{{\mathrm{ij}}^{*}}=\underset{j}{\max }\{r_{i1}^{\left(\mathrm{\Σ}\right)}\left(k\right),r_{i2}^{\left(\mathrm{\Σ}\right)}\left(k\right),...,r_{\mathrm{im}}^{\left(\mathrm{\Σ}\right)}\left(k\right)\}---\left(5\right)$

Claim j ^*for tackling a _ibe satisfied with countermeasure.

The present invention has set up the evaluation index system of embedded computer combination property, and GREY SITUATION DECISION algorithm is combined with analytical hierarchy process, traditional GREY SITUATION DECISION algorithm is improved, and be applied in embedded computer synthetic performance evaluation.The method has solved the blindness that GREY SITUATION DECISION is evaluated, and has improved correctness and the validity of embedded computer Performance Evaluation.Exemplary application proves that the method is rationally effective, for the evaluation of embedded computer combination property from now on provides valuable reference.

Accompanying drawing explanation

Fig. 1 is process flow diagram of the present invention;

Fig. 2 is embedded computer Performance Measuring Indicators.

Embodiment

With reference to Fig. 1, process of the present invention comprises: 1) adopt Delphi expert consulting method to set up embedded computer Performance Measuring Indicators (1-1); 2) and adopt analytical hierarchy process to determine the weight coefficient of evaluation index (1-2); 3) build GREY SITUATION DECISION model (1-3); 4) calculate evaluation index measure of merit (1-4); 5) set up synthesis effect measure sequence (1-5); 6) adopt maximization method to evaluate embedded computer performance (1-6).

In order to further illustrate the validity of this invention, take 3 kinds of embedded computers as example, specifically introduce the application of the method below.A type embedded computer is a kind of emerging embedded computer, calculates advanced person, stable performance; Type B embedded computer is the modified of A type embedded computer, has increased storage capacity; C type embedded computer is a kind of old type embedded computer, and each performance is all poor.The algorithm that application the present invention proposes, carries out comprehensive performance evaluation and odds to the embedded computer of these 3 kinds of models, and concrete steps are as follows:

Step 1: set up hierarchy Model

According to embedded computer performance parameter index and the demand that can adapt to FUTURE ENVIRONMENT development, build the assessment indicator system of three layers, as shown in Figure 2.

Step 2: utilize analytical hierarchy process to determine the weight coefficient of each index

$\left[\begin{array}{c}w=[\mathrm{0.081,0.010,0.137,0.034,0.034,0.02,0.025,0.05,0.069,0.069,0.056}\\ \mathrm{0.047,0.09,0.033,0.027,0.019,0.032,0.019,0.023,0.032}]\end{array}\right]$

Step 3: determine event, countermeasure situation, target

Event a: the combination property of 3 kinds of embedded computers is carried out to quality sequence;

Situation: s _a=(a, b _a) A type embedded computer, s _b=(a, b _b) Type B embedded computer, s _c=(a, b _c) the embedded calculating of C type;

Target: all indexs shown in Fig. 2.

Step 4: determine target polarity, do measure of merit conversion

First analyze the polarity of each index, recycle corresponding measure of merit transformation for mula index is converted.For example, to evaluation index U ₁₁, U ₄₃, U ₅₁do measure of merit conversion.

Target U ₁₁(dominant frequency)

Target U ₁₁belong to maximum value polarity, be respectively 533MHZ, 1.6GHZ and 200MHZ according to the dominant frequency of A, B, tri-kinds of embedded computers of C, the measure of merit that is obtained this target by formula (1) is

$r^{11}=(r_A^{11},r_B^{11},r_C^{11})=(\frac{533}{1600},\frac{1600}{1600},\frac{200}{1600})=\left(\mathrm{0.3331,1,0.125}\right)$

Target U ₄₃(mean time between failures)

Mean time between failures belongs to maximum value polarity, is respectively 50000 hours, 60000 hours and 45000 hours according to the mean time between failures of A, B, tri-kinds of embedded computers of C, obtains target U by formula (1) ₄₃measure of merit be

$r^{43}=(r_A^{43},r_B^{43},r_c^{43})=(\frac{50000}{60000},\frac{60000}{60000},\frac{45000}{60000})=(0.8333,\mathrm{1,0.75})$

Target U ₅₁(working temperature)

Working temperature belongs to moderate polarity, be respectively-10 ℃～60 ℃ according to A, B, tri-kinds of embedded computer working temperatures of C,-10 ℃～75 ℃ and-10 ℃～50 ℃, be all-10 ℃ because three kinds of embedded computer working temperatures are minimum, therefore this index can be considered as to maximum value polarity, obtain target U by formula (1) ₅₁measure of merit be

$r^{51}=(r_A^{51},r_B^{51},r_C^{51})=(\frac{60}{75},\frac{75}{75},\frac{50}{75})=\left(\mathrm{0.8,1,0.6667}\right)$ 。

In like manner, can obtain the measure of merit of other indexs

(ij represents the designator of index), as shown in table 4.

The measure of merit value of each index of table 4

Step 5: find out and be satisfied with situation

By the weight w of evaluation index _ij, i=1,2 ..., 6, j=1,2 ..., n _iwith table 3, obtain the synthesis effect measure of A, B, tri-kinds of embedded computers of C according to formula (4).Wherein, the synthesis effect measure of A type embedded computer is:

$\begin{array}{c}{r}_A^{\left(\mathrm{\Σ}\right)}\left(k\right)=0.081\×0.3331+0.010\×1+0.137\×0.75\\ +0.034\×1+0.034\×0.78+0.02\×1+0.025\×0.65\\ +0.05\×0.89+0.069\×0.9+0.069\×0.89+0.056\×0.8\\ 0.047\×0.9+0.09\×0.8333+0.033\×0.7+0.027\×0.8\\ +0.019\×0.8+0.032\×0.7+0.019\×1+0.023\×0.9+0.032\×0.85\\ =0.7158\end{array}$

In like manner, solving Type B embedded computer synthesis effect measure is

, the synthesis effect measure of C type embedded computer is .The synthesis effect measure of comprehensive A, B, tri-kinds of embedded computers of C, as shown in table 5.

Table 5 embedded computer synthesis effect measure evaluation result

This shows that, in A, B, tri-kinds of embedded computers of C, the combination property optimum of Type B embedded computer, is secondly A type embedded computer, is finally C type embedded computer.Assessment result conforms to the performance performance in actual use.

Claims (2)

1. the embedded computer method of evaluating performance of the grey SITUATION DECISION based on AHP, its concrete steps are as follows: 1) adopt Delphi expert consulting method to set up embedded computer Performance Measuring Indicators (1-1); 2) and adopt analytical hierarchy process to determine the weight coefficient of evaluation index (1-2); 3) build GREY SITUATION DECISION model (1-3); 4) calculate evaluation index measure of merit (1-4); 5) set up synthesis effect measure sequence (1-5); 6) adopt maximization method to evaluate embedded computer performance (1-6).

2. the embedded computer method of evaluating performance of the grey SITUATION DECISION based on AHP according to claim 1, is characterized in that application level analytic approach determines the weight of evaluation index.

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