CN105373692A - Interval two-tuple based flight deck man-machine function distribution method - Google Patents

Abstract

The invention provides an interval two-tuple based flight deck man-machine function distribution method. The method comprises the steps of performing man and machine advantage competence comparison to form a man competence advantage set and a machine competence advantage set; then determining weight coefficients of each element in the man competence advantage set and the machine competence advantage set by adopting a fuzzy analytical hierarchy process; dividing automation levels of a civil aircraft flight deck system, and then determining the range of the automation levels by performing the man and machine advantage competence comparison; and finally determining the function distribution automation level by adopting a multi-attritube group decision making method based on an IT-WAA operator and an IT-WHA operator of the interval two-tuple. The semantic information of decision makers can be fully used to accurately process multi-stage and multi-expert language information so as to avoid information loss and to enable the decision result to be more accurate.

Description

Based on the driving cabin man-machine function allocation method of interval Two-tuple Linguistic Information Processing

Technical field

The present invention relates to a kind of the Automation Design technology of civil aircraft driving cabin.

Background technology

Chinese large-sized transporter project is formally set up the project already, and the large transport airplane that development has an independent intellectual property right needs the support of a large amount of correlation technique, and driving cabin man-machine function allocation is one of gordian technique wherein just.Aircraft cockpit is the main activities place that driver performs aerial mission.Along with driving cabin is gradually to future development that is intelligent, robotization, the relation between man-machine is also correspondingly changing, and decision-making and the management function of driver are strengthened gradually, and handles function and weaken gradually.Although the application of automatic technology in driving cabin reduces the working load of driver to a certain extent, also result in degradation problems under driver's context aware level of consciousness simultaneously.Therefore, in order to work with making man-machine smooth harmonizing, play the maximal efficiency of man-machine system, just must according to the criterion of science and method, consider the many factors such as operational load, robotization reliability, system cost of functional requirement, driver, the function of system is reasonably distributed to driver and machine, makes system efficient, safe, reliable and economical on the whole, for designer provides sufficient foundation to carry out follow-up design of hardware and software and development.Driving cabin man-machine function allocation can regard a typical multi-attribute group decision making problem as.Multi-attribute group decision making problem is an important research field of modern decision science, and its Theories and methods has been widely used in engineering design, city planning, economic management, military affairs and the field such as social.

In actual decision-making, due to driving cabin man-machine function allocation problem self complicacy and ambiguity, the uncertainty of human thinking, during the restriction of, objective factor main by some when ergonomic specialist, often be difficult to describe decision information by the method for quantification, accurately cannot estimate decision attribute, the fuzzy language value distributing relevant evaluation factor with function can only be provided.And this fuzzy language value itself to be ergonomic specialist propose different language evaluation collection according to its people's preference, provide respective Linguistic Assessment Information, and be usually a fuzzy language scope.For addressing this problem, many scholars adopt language qualitatively, and Linguistic Assessment Information is converted into fuzzy number, and carry out Operations of Fuzzy Numbers and analysis according to extension principle.But for the process of Linguistic Assessment Information, too subjective qualitative decision method and very objective quantified decision-making method is adopted to go to describe decision information, namely individual language evaluation information often accurately can not be expressed by the single semantic terms that the semantic evaluation of predefined is concentrated by assembling the group's evaluation information obtained, and an approximation must be had, thus easily cause decision information to lose and distortion, the result of decision is not conformed to the actual conditions.

Summary of the invention

In order to overcome the deficiencies in the prior art, the invention provides a kind of method that driving cabin function based on interval Two-tuple Linguistic Information Processing is distributed.

The technical solution adopted for the present invention to solve the technical problems comprises following steps:

(1) carry out man-machine advantageous ability to compare, form people, capabilities advantage set H={h respectively ₁, h ₂, h ₃, h ₄, h ₅and M={m ₁, m ₂, m ₃, m ₄, m ₅, wherein, h ₁represent Forecast reasoning ability, h ₂represent visual ability, h ₃intermediate scheme recognition capability, h ₄represent empirical learning ability, h ₅represent environment perception, m ₁represent data storage management ability, m ₂represent quick and precisely computing power, m ₃represent rule-based reasoning ability, m ₄represent parallel processing capability, m ₅represent continuous working repetition decision ability;

(2) Fuzzy AHP is adopted to determine the weight coefficient of each element in people, the set of capabilities advantage;

(3) the robotization grade 1 ~ 10 grade of civil aircraft driving cabin system is divided, 1 grade does not provide any help for system, people must complete all decision-makings and manipulation, 2 grades provide a whole set of decision-making or action scheme for system, 3 grades is system reduction scheme range of choice, 4 grades provide a proposed projects for system, if 5 grades is people's agreement, perform this scheme, 6 grades for allow people to veto in limiting time before carrying into execution a plan, 7 grades for automatically performing, only notifier where necessary, if 8 grades need for people, inform him, whether notifier is determined by computing machine entirely, 9 grades determine all work for system, 10 grades of interventions for refusal people,

(4) being determined the scope of robotization grade by man-machine capacity superiority, adopting as given a definition:

Definition 1: establish ( s _k, a _k) and be two Two-tuple Linguistic Information Processing information, wherein s _kit is the PASCAL evaluation collection pre-defined in a kth element, a _k∈ [-0.5,0.5) represent through assembly calculate after obtain language message with press close to most element s _kbetween difference; and then claim ${(s_k,a_k)}^{~}=\[({\underset{\‾}{s}}_k,{\underset{\‾}{a}}_k),({\stackrel{\‾}{s}}_k,{\stackrel{\‾}{a}}_k)\]$ It is an interval Two-tuple Linguistic Information Processing;

Definition 2: for predefined PASCAL evaluation collection, (s _i, a _i), (s _j, a _j) two Two-tuple Linguistic Information Processing information form interval Two-tuple Linguistic Information Processing information [(s _i, a _i), (s _j, a _j)], i≤j, a _i≤ a _jif, [β ₁, β ₂] be PASCAL evaluation collection S ^tthrough assembling the interval real number obtained, β ₁, β ₂∈ [0, T-1], β ₁≤ β ₂, order

$\mathrm{\Δ}\[{\mathrm{\β}}_1,{\mathrm{\β}}_2\]=\[(s_i,a_i),(s_j,a_j)\]=\left\{\begin{array}{cc}{s}_i,& k=round\left({\mathrm{\β}}_1\right)\\ s_j,& k=round\left({\mathrm{\β}}_2\right)\\ a_i={\mathrm{\β}}_1-i,& a_i\∈\[-0.5,0.5)\\ a_j={\mathrm{\β}}_2-j,& a_j\∈\[-0.5,0.5)\end{array}\right.$

Then function Δ is claimed to be interval real number [β ₁, β ₂] transfer function of corresponding interval Two-tuple Linguistic Information Processing information, wherein round is round operator;

Definition 3: make Δ ^-1[(s _i, a _i), (s _j, a _j)]=[i+a _i, j+a _j]=[β ₁, β ₂], then claim Δ ^-1for the inverse function of the Δ of function;

Definition 4: establish (s _k, a _k) ^~, (s _t, a _t) ^~for any two interval Two-tuple Linguistic Information Processings,

Then claim $p\[{(s_k,a_k)}^{~}\≥{(s_t,a_t)}^{~}\]=max\{1-max\[\frac{{\mathrm{\Δ}}^{-1}({\stackrel{\‾}{s}}_i,{\stackrel{\‾}{a}}_i){\mathrm{\Δ}}^{-1}({\underset{\‾}{s}}_k,{\underset{\‾}{a}}_k)}{l_{{(s_k,a_k)}^{~}}+l_{{(s_t,a_t)}^{~}}},0\],0\}$ For (s _k, a _k) ^~>=(s _t, a _t) ^~possibility degree;

Definition 5: establish IT-WAA; If be a class interval Two-tuple Linguistic Information Processing information, j=1,2 ..., n, ω=(ω ₁, ω ₂..., ω _n) ^tfor corresponding weight, and ω _j∈ [0,1], j=(1,2 ..., n), ${\mathrm{\Φ}}_{\mathrm{\ω}}\[{(s_1,a_1)}^{~},{(s_2,a_2)}^{~},...,{(s_n,a_n)}^{~}\]=\{\mathrm{\Δ}\[\underset{j=1}{\overset{n}{\Σ}}{\mathrm{\ω}}_j{\mathrm{\Δ}}^{-1}({\underset{\‾}{s}}_j,{\underset{\‾}{a}}_j)\],\mathrm{\Δ}\[\underset{j=1}{\overset{n}{\Σ}}{\mathrm{\ω}}_j{\mathrm{\Δ}}^{-1}({\stackrel{\‾}{s}}_j,{\stackrel{\‾}{a}}_j)\]\}$ Then claim Φ _wfor interval Two-tuple Linguistic Information Processing weighted arithmetic mean operator;

Definition 6: establish IT-WHA: if be a class interval Two-tuple Linguistic Information Processing information, ω=(ω ₁, ω ₂..., ω _n) ^tfor corresponding weight, and ω _j∈ [0,1], ${\mathrm{\Φ}}_{\mathrm{\ω},w}\[{(s_1,a_1)}^{~},{(s_2,a_2)}^{~},...,{(s_n,a_n)}^{~}\]=\{\mathrm{\Δ}\[\underset{j=1}{\overset{n}{\Σ}}{w}_j{\mathrm{\Δ}}^{-1}({\underset{\‾}{s}}_j,{\underset{\‾}{a}}_j)\],\mathrm{\Δ}\[\underset{j=1}{\overset{n}{\Σ}}{w}_j{\mathrm{\Δ}}^{-1}({\stackrel{\‾}{s}}_j,{\stackrel{\‾}{a}}_j)\]\},$ Wherein w=(w ₁, w ₂..., w _n) ^tthe weighing vector be associated, and w _j∈ [0,1], $v_j=\[({\underset{\‾}{s}}_{\mathrm{\π}\left(j\right)},{\underset{\‾}{a}}_{\mathrm{\π}\left(j\right)}),({\stackrel{\‾}{s}}_{\mathrm{\π}\left(j\right)},{\stackrel{\‾}{a}}_{\mathrm{\π}\left(j\right)})\]$ It is the Two-tuple Linguistic Information Processing set of variables of weighting $({\widetilde{\mathrm{\μ}}}_1,{\widetilde{\mathrm{\μ}}}_2,...,{\widetilde{\mathrm{\μ}}}_n)({\widetilde{\mathrm{\μ}}}_j={\mathrm{n\ω}}_j{(s_j,a_j)}^{~})$ The large element of jth, and n is balance factor, then claim Φ _{ω, w}for interval Two-tuple Linguistic Information Processing mixed weighting operator;

Definition 7: be located at PASCAL evaluation collection under the interval version Evaluations matrix that obtains be $\tilde{R}={(\[{\underset{\‾}{r}}_{ij},{\stackrel{\‾}{r}}_{ij}\])}_{m\×n},$ Wherein for property value; Setting basic language evaluation collection is $S^T=\{s_i^T|i\∈\{0,1,...,T-1\left\}\right\},$ Employing transfer function ζ will be converted to basic language evaluation collection S ^tinterval Two-tuple Linguistic Information Processing Evaluations matrix under representing

In formula ${\stackrel{\‾}{a}}_{ij}\∈\[-0.5,0.5);$

On above-mentioned definition basis, provide the robotization rate range defining method based on IT-WAA operator, detailed process is as follows:

A): decision maker's set is D={d ₁, d ₂..., d _k..., d _t, t bit decisions person altogether; Every bit decisions person d _kprovide people respectively, interval version assessed value that capabilities advantage treats distribution function with k=1,2 ..., t, and obtain evaluating matrix ${\tilde{H}}_k={(\[{\underset{\‾}{h}}_{ij}^k,{\stackrel{\‾}{h}}_{ij}^k\])}_{n\×m},{\tilde{Q}}_k={(\[{\underset{\‾}{q}}_{ij}^k,{\stackrel{\‾}{q}}_{ij}^k\])}_{n\×l};$

B): setting basic language evaluation collection S ^t, will with change into based on S ^tthe interval Two-tuple Linguistic Information Processing Evaluations matrix of the unification represented with iT-WAA operator is utilized to incite somebody to action with assemble for the interval Two-tuple Linguistic Information Processing Evaluations matrix of group with

C): according to IT-WAA operator and people, capabilities advantage weight will with τ with assemble by row, calculate people, the interval Two-tuple Linguistic Information Processing comprehensive evaluation value of machine scheme with calculate people, comprehensive assessment result that capabilities treats distribution function with between possibility degree

D): the robotization rate range determining function to be allocated according to possibility degree result p:

$\left\{\begin{array}{c}ceil(p\×5)A-1\≤LOA\≤ceil(p\×5)+1\\ LOA\∈\{1,2,...,10\}\end{array}\right.$

Wherein, ceil (x) is flow in upper plenum;

(5) employing distributes robotization grade based on the IT-WAA operator of interval Two-tuple Linguistic Information Processing and the Multiple Attribute Group Decision determination function of IT-WHA operator, and detailed process is as follows:

For certain function Multiple Attribute Decision Problems, if A={a ₁, a ₂..., a _{n '}scheme collection, the n' kind situation of its element representation robotization grade; Given function distributes assessment level set G={g ₁, g ₂..., g _{m '}, five main assessment levels of the corresponding driving cabin man-machine function allocation of its element difference, g ₁for Situation Awareness; g ₂for Mental Workload; g ₃for risk of policy making; g ₄for reliability; g ₁for system cost; The weight vectors of attribute weight is η=(η ₁, η ₂..., η _{m '}), and its vector is η _l>=0 (l=1,2 ..., m '),

A) weight vectors of t bit decisions person is decision maker d _k∈ D provides scheme a _i∈ A is at attribute g _jinterval version assessed value under ∈ G and obtain evaluating matrix

B) will change into based on basic language evaluation collection S ^{t '}the interval Two-tuple Linguistic Information Processing Evaluations matrix of the unification represented utilize IT-WAA operator pair in the language assessment information of the i-th row assemble, obtain decision maker d _kto scheme a _isynthesized attribute assessed value i=1,2 ..., n ';

C) utilize IT-WHA operator to the synthesized attribute assessed value of t bit decisions person assemble, obtain about scheme a _icolony's comprehensive assessment value wherein, w=(w ₁, w ₂..., w _t) be the weighing vector of IT-WHA operator, w _k∈ [0,1] (k=1,2 ..., t) and it is the Two-tuple Linguistic Information Processing set of variables of weighting the large element of kth, λ=(λ ₁, λ ₂..., λ _t) ^tfor corresponding weight, and λ _k∈ [0,1], and t is balance factor;

D) scheme a is calculated _icomprehensive assessment value γ ⁱ(λ, w) and a _jcomprehensive assessment value γ ^jpossibility degree p ' between (λ, w) _ij=p [γ ⁱ(λ, w)>=γ ^j(λ, w)], thus obtain Possibility Degree Matrix

E) the ordering vector v=(v of Possibility Degree Matrix p ' is obtained ₁, v ₂..., v _{n '}), and by its component size, scheme is sorted, namely obtain optimal case; Wherein finally by its component size, scheme is sorted, namely obtain optimum function allocative decision.

The invention has the beneficial effects as follows: adopt said method to carry out function distribution to the man-machine automated system of civil aircraft driving cabin and can overcome previous methods easily causes information loss and distortion shortcoming when carrying out the assembly of many grain size intervals Linguistic Assessment Information and computing.Said method can make full use of the semantic information of decision maker, accurately processes the language message of multistage, multi-expert, can avoid information dropout well, makes the result of decision more accurate.

Embodiment

Below in conjunction with embodiment, the present invention is further described, the present invention includes but be not limited only to following embodiment.

The present invention comprises following steps:

(1) carry out man-machine advantageous ability to compare, namely choose the man-machine advantage comparatively close with driving cabin functional relationship, composition people, the set of capabilities advantage, be expressed as: H={h ₁, h ₂, h ₃, h ₄, h ₅and M={m ₁, m ₂, m ₃, m ₄, m ₅, each element implication is as shown in table 1.

Table 1 people, the set of capabilities advantage

(2) Fuzzy AHP (FuzzyAnalyticHierarchyProcess, FAHP) is adopted to determine the weight coefficient of each element in people, the set of capabilities advantage.

(3) the robotization grade of civil aircraft driving cabin system is divided, the robotization partition of the level method of the man-machine interactive system adopting the people such as Sheridan, Verplank and Parasuraman to propose, as shown in table 2.

Table 2 robotization rank

(4) scope of robotization grade is determined by man-machine capacity superiority.Need in this step to use as given a definition.

Definition 1: establish ( s _k, a _k) and be two Two-tuple Linguistic Information Processing information, wherein s _kit is the PASCAL evaluation collection pre-defined in a kth element, a _k∈ [-0.5,0.5) represent through assembly calculate after obtain language message with press close to most element s _kbetween difference. s _k, a _k, and then claim ${(s_k,a_k)}^{~}=\[({\underset{\‾}{s}}_k,{\underset{\‾}{a}}_k),({\stackrel{\‾}{s}}_k,{\stackrel{\‾}{a}}_k)\]$ It is an interval Two-tuple Linguistic Information Processing.

Definition 2: for predefined PASCAL evaluation collection, (s _i, a _i), (s _j, a _j(i≤j, a _i≤ a _j) two Two-tuple Linguistic Information Processing information form interval Two-tuple Linguistic Information Processing information [(s _i, a _i), (s _j, a _j]).If [β ₁, β ₂] (β ₁, β ₂∈ [0, T-1], β ₁≤ β ₂) be PASCAL evaluation collection S ^tthrough assembling the interval real number obtained, order

$\mathrm{\Δ}\[{\mathrm{\β}}_1,{\mathrm{\β}}_2\]=\[(s_i,a_i),(s_j,a_j)\]=\left\{\begin{array}{cc}{s}_i,& k=round\left({\mathrm{\β}}_1\right)\\ s_j,& k=round\left({\mathrm{\β}}_2\right)\\ a_i={\mathrm{\β}}_1-i,& a_i\∈\[-0.5,0.5)\\ a_j={\mathrm{\β}}_2-j,& a_j\∈\[-0.5,0.5)\end{array}\right.---\left(1\right)$

Then function Δ is claimed to be interval real number [β ₁, β ₂] transfer function of corresponding interval Two-tuple Linguistic Information Processing information, wherein round is round operator.

Definition 3: order

Δ ^-1[(s _i,a _i),(s _j,a _j)]＝[i+a _i,j+a _j]＝[β ₁,β ₂](2)

Then claim Δ ^-1for the inverse function of the Δ of function.

Definition 4: establish (s _k, a _k) ^~, (s _t, a _t) ^~for any two interval Two-tuple Linguistic Information Processings, then claim

$p\[{(s_k,a_k)}^{~}\≥{(s_t,a_t)}^{~}\]=max\{1-max\[\frac{{\mathrm{\Δ}}^{-1}({\stackrel{\‾}{s}}_t,{\stackrel{\‾}{a}}_t)-{\mathrm{\Δ}}^{-1}({\underset{\‾}{s}}_k,{\underset{\‾}{a}}_k)}{l_{{(s_k,a_k)}^{~}}+l_{{(s_t,a_t)}^{~}}},0\],0\}---\left(3\right)$

For (s _k, a _k) ^~>=(s _t, a _t) ^~possibility degree.

Definition 5: establish IT-WAA: if ${\mathrm{\μ}}_j={(s_j,a_j)}^{~}=\[({\underset{\‾}{s}}_j,{\underset{\‾}{a}}_j),({\stackrel{\‾}{s}}_j,{\stackrel{\‾}{a}}_j)\](j=1,2,...,n)$ Be a class interval Two-tuple Linguistic Information Processing information, ω=(ω ₁, ω ₂..., ω _n) ^tfor corresponding weight, and ω _j∈ [0,1], j=(1,2 ..., n),

${\mathrm{\Φ}}_{\mathrm{\ω}}\[{(s_1,a_1)}^{~},{(s_2,a_2)}^{~},...,{(s_n,a_n)}^{~}\]=\{\mathrm{\Δ}\[\underset{j=1}{\overset{n}{\Σ}}{\mathrm{\ω}}_j{\mathrm{\Δ}}^{-1}({\underset{\‾}{s}}_j,{\underset{\‾}{a}}_j)\],\mathrm{\Δ}\[\underset{j=1}{\overset{n}{\Σ}}{\mathrm{\ω}}_j{\mathrm{\Δ}}^{-1}({\stackrel{\‾}{s}}_j,{\stackrel{\‾}{a}}_j)\]\}---\left(4\right)$

Then claim Φ _wfor interval Two-tuple Linguistic Information Processing weighted arithmetic mean (IT-WAA) operator.

Definition 6: establish IT-WHA: if ${\mathrm{\μ}}_j={(s_j,a_j)}^{~}=\[({\underset{\‾}{s}}_j,{\underset{\‾}{a}}_j),({\stackrel{\‾}{s}}_j,{\stackrel{\‾}{a}}_j)\](j=1,2,...,n)$ Be a class interval Two-tuple Linguistic Information Processing information, ω=(ω ₁, ω ₂..., ω _n) ^tfor corresponding weight, and ω _j∈ [0,1],

${\mathrm{\Φ}}_{\mathrm{\ω},w}\[{(s_1,a_1)}^{~},{(s_2,a_2)}^{~},...,{(s_n,a_n)}^{~}\]=\{\mathrm{\Δ}\[\underset{j=1}{\overset{n}{\Σ}}{w}_j{\mathrm{\Δ}}^{-1}({\underset{\‾}{s}}_j,{\underset{\‾}{a}}_j)\],\mathrm{\Δ}\[\underset{j=1}{\overset{n}{\Σ}}{w}_j{\mathrm{\Δ}}^{-1}({\stackrel{\‾}{s}}_j,{\stackrel{\‾}{a}}_j)\]\}---\left(5\right)$

Wherein w=(w ₁, w ₂..., w _n) ^tthe weighing vector (position vector) be associated, and w _j∈ [0,1], $v_j=\[({\underset{\‾}{s}}_{\mathrm{\π}\left(j\right)},{\underset{\‾}{a}}_{\mathrm{\π}\left(j\right)}),({\stackrel{\‾}{s}}_{\mathrm{\π}\left(j\right)},{\stackrel{\‾}{a}}_{\mathrm{\π}\left(j\right)})\]$ It is the Two-tuple Linguistic Information Processing set of variables of weighting $({\widetilde{\mathrm{\μ}}}_1,{\widetilde{\mathrm{\μ}}}_2,...,{\widetilde{\mathrm{\μ}}}_n)({\widetilde{\mathrm{\μ}}}_j={\mathrm{n\ω}}_j{(s_j,a_j)}^{~})$ The large element of jth, and n is balance factor, then claim Φ _{ω, w}for interval Two-tuple Linguistic Information Processing mixed weighting (IT-WHA) operator.

Definition 7: be located at PASCAL evaluation collection under the interval version Evaluations matrix that obtains be $\tilde{R}={(\[{\underset{\‾}{r}}_{ij},{\stackrel{\‾}{r}}_{ij}\])}_{m\×n},$ Wherein for property value.Setting basic language evaluation collection is $S^T=\{s_i^T|i\∈\{0,1,...,T-1\left\}\right\},$ Employing transfer function ζ will be converted to basic language evaluation collection S ^tinterval Two-tuple Linguistic Information Processing Evaluations matrix under representing

In formula a _ij, ${\stackrel{\‾}{a}}_{ij}\∈\[-0.5,0.5).$

On above-mentioned definition basis, provide the robotization rate range defining method based on IT-WAA operator, detailed process is as follows:

A): decision maker's set is D={d ₁, d ₂..., d _k..., d _t, t bit decisions person altogether.Every bit decisions person d _k(k=1,2 ..., t) provide people respectively, interval version assessed value that capabilities advantage treats distribution function with and obtain evaluating matrix ${\tilde{H}}_k={(\[{\underset{\‾}{h}}_{ij}^k,{\stackrel{\‾}{h}}_{ij}^k\])}_{n\×m},{\tilde{Q}}_k={(\[{\underset{\‾}{q}}_{ij}^k,{\stackrel{\‾}{q}}_{ij}^k\])}_{n\×l}.$

B): setting basic language evaluation collection S ^t, will according to (6) formula with change into based on S ^tthe interval Two-tuple Linguistic Information Processing Evaluations matrix of the unification represented with iT-WAA operator is utilized to incite somebody to action with assemble for the interval Two-tuple Linguistic Information Processing Evaluations matrix of group with

C): according to IT-WAA operator and people, capabilities advantage weight will with τ with assemble by row, calculate people, the interval Two-tuple Linguistic Information Processing comprehensive evaluation value of machine scheme with (3) formula of utilization, calculates people, comprehensive assessment result that capabilities treats distribution function with between possibility degree

D): robotization grade (LevelOfAutomation, the LOA) scope determining function to be allocated according to possibility degree result p.

$\left\{\begin{array}{c}ceil(p\×5)-1\≤LOA\≤ceil(p\×5)+1\\ LOA\∈\{1,2,...,10\}\end{array}\right.---\left(7\right)$

Wherein, ceil (x) is flow in upper plenum.

(5) employing distributes robotization grade based on the IT-WAA operator of interval Two-tuple Linguistic Information Processing and the Multiple Attribute Group Decision determination function of IT-WHA operator, and detailed process is as follows:

Function is distributed after robotization rate range determines, the different schemes that namely given function is distributed, and also needs to distribute assessment level according to function and select optimal case from allocative decision, finally determine the robotization grade of driving cabin man-machine function allocation.For certain function Multiple Attribute Decision Problems, if A={a ₁, a ₂..., a _{n '}scheme collection, the n' kind situation of its element representation robotization grade.Given function is distributed assessment level (attribute) and is gathered G={g ₁, g ₂..., g _{m '}, five main assessment levels of the corresponding driving cabin man-machine function allocation of its element difference, g ₁-Situation Awareness; g ₂-Mental Workload; g ₃-risk of policy making; g ₄-reliability; g ₁-system cost.The weight vectors of attribute weight is η=(η ₁, η ₂..., η _{m '}), and its vector is η _l>=0 (l=1,2 ..., m '),

A): the weight vectors of t bit decisions person is decision maker d _k∈ D provides scheme a _i∈ A is at attribute g _jinterval version assessed value under ∈ G and obtain evaluating matrix

B): will according to (6) formula change into based on basic language evaluation collection S ^{t '}the interval Two-tuple Linguistic Information Processing Evaluations matrix of the unification represented utilize IT-WAA operator pair in the language assessment information of the i-th row assemble, obtain decision maker d _k(k=1,2 ..., t) to scheme a _i(i=1,2 ..., n ') synthesized attribute assessed value

C): utilize IT-WHA operator to the synthesized attribute assessed value of t bit decisions person assemble, obtain about scheme a _icolony comprehensive assessment value γ ⁱ(λ, w):

${\mathrm{\γ}}^i(\mathrm{\λ},w)=w_1{\tilde{v}}_1\⊕w_2{\tilde{v}}_2\⊕...\⊕w_t{\tilde{v}}_t---\left(8\right)$

Wherein, w=(w ₁, w ₂..., w _t) be the weighing vector of IT-WHA operator, w _k∈ [0,1] (k=1,2 ..., t) and $\underset{k=1}{\overset{t}{\Σ}}{w}_k=1.$ it is the Two-tuple Linguistic Information Processing set of variables of weighting $({\widetilde{\mathrm{\μ}}}_1,{\widetilde{\mathrm{\μ}}}_2,...,{\widetilde{\mathrm{\μ}}}_t)({\widetilde{\mathrm{\μ}}}_k={\mathrm{t\λ}}_k{\mathrm{\γ}}_k^i(\mathrm{\η}\left)\right)$ The large element of kth, λ=(λ ₁, λ ₂..., λ _t) ^tfor corresponding weight, and λ _k∈ [0,1], and t is balance factor.

D): utilize formula (3), calculate scheme a _i(i=1,2 ..., n ') and comprehensive assessment value γ ⁱ(λ, w) and a _j(j=1,2 ..., n ') and comprehensive assessment value γ ^jpossibility degree p ' between (λ, w) _ij=p [γ ⁱ(λ, w)>=γ ^j(λ, w)], thus obtain Possibility Degree Matrix $p^{\′}={\left(p_{ij}^{\′}\right)}_{n^{\′}\×n^{\′}}.$

E): the ordering vector v=(v obtaining Possibility Degree Matrix p ' ₁, v ₂..., v _{n '}), and by its component size, scheme is sorted, namely obtain optimal case.Wherein

$v_i=\frac{1}{n^{\′}(n^{\′}-1)}(\underset{j=1}{\overset{n^{\′}}{\Σ}}{p}_{ij}^{\′}+\frac{n^{\′}}{2}-1),i=1,2,...,n^{\′}---\left(9\right)$

Finally by its component size, scheme is sorted, namely obtain optimum function allocative decision.

Below with ground proximity warning system (GroundProximityWarningSystem in civil aircraft driving cabin, GPWS) be example, describe the specific implementation process based on the driving cabin function assigning method of interval Two-tuple Linguistic Information Processing in detail, namely adopt the method proposed to determine the optimum robotization grade of GPWS.

1. determine people, machine advantageous ability set H={h ₁, h ₂, h ₃, h ₄, h ₅and M={m ₁, m ₂, m ₃, m ₄, m ₅, each element implication is as shown in table 1.

2. utilize Fuzzy AHP to obtain the weight vectors of element in H and M to be respectively:

τ＝(0.1425,0.1714,0.2873,0.1519,0.2469)。

3. its GPWS function is divided into 10 robotization ranks, and its automaticity is as shown in table 2.

4. determine the robotization rate range of function

Decision maker's set is D={d ₁, d ₂, d ₃, the PASCAL evaluation collection of three bit decisions persons preference is separately:

Three bit decisions persons are respectively people, the assessment result of capabilities advantage element to GPWS function effect degree according to respective language preference:

${\tilde{H}}_1=\{\[s_4^7,s_5^7\],\[s_3^7,s_4^7\],\[s_2^7,s_3^7\],\[s_3^7,s_6^7\],\[s_4^7,s_6^7\]\},$

${\tilde{H}}_2=\{\[s_2^5,s_4^5\],\[s_2^5,s_3^5\],\[s_1^5,s_2^5\],\[s_3^5,s_4^5\],\[s_1^5,s_3^5\]\},$

${\tilde{H}}_3=\{\[s_6^{11},s_9^{11}\],\[s_3^{11},s_7^{11}\],\[s_2^{11},s_8^{11}\],\[s_5^{11},s_9^{11}\],\[s_6^{11},s_9^{11}\]\},{\tilde{Q}}_3=\{\[s_2^{11},s_8^{11}\],\[s_6^{11},s_9^{11}\],\[s_3^{11},s_8^{11}\],\[s_5^{11},s_7^{11}\],\[s_7^{11},s_9^{11}\]\}.$

According to the maximum principle of PASCAL evaluation collection granularity, select as basic language evaluation collection.Will according to (6) formula be converted into and collected by basic language evaluation the interval Two-tuple Linguistic Information Processing evaluation vector of the unification represented

${\stackrel{\‾}{\tilde{H}}}_1=\{\[(s_7^{11},-0.33),(s_8^{11},0.33)\],\[(s_5^{11},0),(s_7^{11},-0.33)\],\[(s_3^{11},0.33),(s_5^{11},0)\],\[(s_5^{11},0),(s_{10}^{11},0)\],\[(s_7^{11},-0.33),(s_{10}^{11},-0)\]\}$

${\stackrel{\‾}{\tilde{H}}}_2=\{\[(s_5^{11},0),(s_{10}^{11},0)\],\[(s_5^{11},0),(s_8^{11},-0.5)\],\[(s_3^{11},-0.5),(s_5^{11},0)\],\[(s_8^{11},-0.5),(s_{10}^{11},0)\],\[(s_3^{11},-0.5),(s_8^{11},-0.5)\]\}$

${\stackrel{\‾}{\tilde{H}}}_3=\{\[(s_6^{11},0),(s_9^{11},0)\],\[(s_3^{11},0),(s_7^{11},0)\],\[(s_2^{11},0),(s_8^{11},0)\],\[(s_5^{11},0),(s_9^{11},0)\],\[(s_6^{11},0),(s_9^{11},0)\]\}$

${\stackrel{\‾}{\tilde{Q}}}_1=\{\[(s_3^{11},0.33),(s_5^{11},0)\],\[(s_7^{11},-0.33),(s_{10}^{11},0)\],\[(s_3^{11},0.33),(s_7^{11},-0.33)\],\[(s_2^{11},-0.33),(s_5^{11},0)\],\[(s_8^{11},0.33),(s_{10}^{11},0)\]\}$

${\stackrel{\‾}{\tilde{Q}}}_2=\{\[(s_3^{11},-0.5),(s_5^{11},0)\],\[(s_8^{11},-0.5),(s_{10}^{11},0)\],\[(s_5^{11},0),(s_{10}^{11},0)\],\[(s_3^{11},-0.5),(s_5^{11},0)\],\[(s_5^{11},0),(s_8^{11},-0.5)\]\}$

Utilize IT-WAA operator to assemble three decision maker's assessment results, obtain people, each element of capabilities to the assessment result of GPWS function:

$\begin{array}{c}\stackrel{\‾}{\tilde{H}}=\{\[(s_6^{11},-0.1089),(s_9^{11},0.1089)\],\[(s_4^{11},0.32),(s_7^{11},0.0561)\],\[(s_3^{11},-0.3961),\\ (s_6^{11},-0.02)\],\[(s_6^{11},-0.175),(s_{10}^{11},-0.34)\],\[(s_5^{11},0.0661),(s_9^{11},-0.165)\]\}\end{array}$

$\begin{array}{c}\tilde{Q}={\text{{\[(s}}_3^{11}\text{,-0}{\text{.3961),(s}}_6^{11}\text{,0}{\text{.02)\],\[(s}}_7^{11}\text{,-0}{\text{.2839),(s}}_{10}^{11}\text{,-0}{\text{.34)\],\[(s}}_4^{11}\text{,-0}\text{.2311),}\\ {\text{(s}}_8^{11}\text{,0}{\text{.2211)\],\[(s}}_3^{11}\text{,0}{\text{.0761),(s}}_6^{11}\text{,-0}{\text{.32)\],\[(s}}_7^{11}\text{,-0}{\text{.2211),(s}}_9^{11}\text{,-0}\text{.165)\]}}\end{array}$

According to people, capabilities advantage weight and τ, utilize IT-WAA operator to assemble and obtain people, capabilities to the assessment synthesis result of GPWS function:

$\tilde{L}(\stackrel{\‾}{\tilde{Q}},\mathrm{\τ})=\[(s_5^{11},0.1249),(s_8^{11},-0.0804)\].$

(3) formula of utilization can be calculated possibility degree be basis (7) formula can obtain the robotization rate range of GPWS is again 2≤LOA≤4.

5. set up function and distribute assessment level (attribute) set

After determining the robotization rate range of GPWS function, 3 kinds of different allocative decisions are equivalent to given, if GPWS allocative decision set A={ a ₂, a ₃, a ₄, what its element representation GPWS function allocative decision adopted is three kinds of situations of 2,3,4 grades of robotizations.If distribute assessment level (attribute) to gather G={g ₁, g ₂, g ₃, g ₄, g ₅, five important assessment level: g that it represents people, machine function is distributed ₁-Situation Awareness; g ₂-Mental Workload; g ₃-risk of policy making; g ₄-reliability; g ₁-system cost.Fuzzy AHP is utilized to obtain weight vectors η=(0.2447,0.3581,0.1423,0.1869,0.068) of attribute equally.

For three scheme a _i(i=2,3,4) are about five attribute g _jestimating of (j=1,2,3,4,5), the interval version appreciation information that three bit decisions persons provide is respectively

${\tilde{R}}_1=\left[\begin{array}{ccccc}\[s_3^7,s_4^7\]& \[s_1^7,s_2^7\]& \[s_0^7,s_1^7\]& \[s_5^7,s_6^7\]& \[s_4^7,s_6^7\]\\ \[s_5^7,s_6^7\]& \[s_3^7,s_4^7\]& \[s_2^7,s_3^7\]& \[s_1^7,s_3^7\]& \[s_3^7,s_5^7\]\\ \[s_2^7,s_3^7\]& \[s_5^7,s_6^7\]& \[s_2^7,s_4^7\]& \[s_2^7,s_4^7\]& \[s_3^7,s_4^7\]\end{array}\right]$

${\tilde{R}}_2=\left[\begin{array}{ccccc}\[s_2^5,s_3^5\]& \[s_1^5,s_2^5\]& \[s_0^5,s_1^5\]& \[s_2^5,s_4^5\]& \[s_2^5,s_4^5\]\\ \[s_3^5,s_4^5\]& \[s_2^5,s_3^5\]& \[s_1^5,s_4^5\]& \[s_1^5,s_3^5\]& \[s_1^5,s_3^5\]\\ \[s_1^5,s_3^5\]& \[s_2^5,s_4^5\]& \[s_2^5,s_3^5\]& \[s_2^5,s_4^5\]& \[s_1^5,s_2^5\]\end{array}\right]$

${\tilde{R}}_3=\left[\begin{array}{ccccc}\[s_5^{11},s_7^{11}\]& \[s_7^{11},s_9^{11}\]& \[s_2^{11},s_4^{11}\]& \[s_4^{11},s_5^{11}\]& \[s_6^{11},s_{10}^{11}\]\\ \[s_1^{11},s_4^{11}\]& \[s_3^{11},s_5^{11}\]& \[s_3^{11},s_5^{11}\]& \[s_6^{11},s_7^{11}\]& \[s_5^{11},s_9^{11}\]\\ \[s_3^{11},s_6^{11}\]& \[s_4^{11},s_6^{11}\]& \[s_9^{11},s_{10}^{11}\]& \[s_3^{11},s_7^{11}\]& \[s_3^{11},s_8^{11}\]\end{array}\right]$

Will according to (6) formula be converted into and collected by basic language evaluation the interval Two-tuple Linguistic Information Processing evaluation vector of the unification represented

$\begin{array}{c}{\stackrel{\‾}{\tilde{R}}}_1=\left[\begin{array}{ccc}\[\left(\begin{array}{cc}{s}_5^{11},& 0\end{array}\right),(s_7^{11},-0.33)\]& \[\left(\begin{array}{cc}{s}_2^{11},& 0.33\end{array}\right),\left(\begin{array}{cc}{s}_3^{11},& 0.33\end{array}\right)\]& \[\left(\begin{array}{cc}{s}_0^{11},& 0\end{array}\right),\\ \[(s_8^{11},0.33),\left(\begin{array}{cc}{s}_{10}^{11},& 0\end{array}\right)\]& \[\left(\begin{array}{cc}{s}_5^{11},& 0\end{array}\right),(s_7^{11},-0.33)\]& \[(s_3^{11},0.33),\\ \[(s_3^{11},0.33),\left(\begin{array}{cc}{s}_5^{11},& 0\end{array}\right)\]& \[\left(\begin{array}{cc}{s}_8^{11},& 0\end{array}.33\right),\left(\begin{array}{cc}{s}_{10}^{11},& 0\end{array}\right)\]& \[(s_3^{11},0.33),\end{array}\right.\\ \left.\begin{array}{ccc}(s_2^{11},-0.33)\]& \[\left(\begin{array}{cc}{s}_8^{11},& 0\end{array}.33\right),\left(\begin{array}{cc}{s}_{10}^{11},& 0\end{array}\right)\]& \[(s_7^{11},-0.33),\left(\begin{array}{cc}{s}_{10}^{11},& 0\end{array}\right)\]\\ \left(\begin{array}{cc}{s}_5^{11},& 0\end{array}\right)\]& \[(s_2^{11},-0.33),\left(\begin{array}{cc}{s}_5^{11},& 0\end{array}\right)\]& \[\left(\begin{array}{cc}{s}_5^{11},& 0\end{array}\right),\left(\begin{array}{cc}{s}_8^{11},& 0.33\end{array}\right)\]\\ (s_7^{11},-0.33)\]& \[\left(\begin{array}{cc}{s}_3^{11},& 0.33\end{array}\right),(s_7^{11},-0.33)\]& \[\left(\begin{array}{cc}{s}_5^{11},& 0\end{array}\right),(s_7^{11},-0.33)\]\end{array}\right]\end{array}$

$\begin{array}{c}{\stackrel{\‾}{\tilde{R}}}_2=\left[\begin{array}{ccc}\[\left(\begin{array}{cc}{s}_5^{11},& 0\end{array}\right)(s_8^{11},-0.5)\]& \[(s_3^{11},-0.5),\left(\begin{array}{cc}{s}_5^{11},& 0\end{array}\right)\]& \[\left(\begin{array}{cc}{s}_0^{11},& 0\end{array}\right),\\ \[(s_8^{11},-0.5),\left(\begin{array}{cc}{s}_{10}^{11},& 0\end{array}\right)\]& \[\left(\begin{array}{cc}{s}_5^{11},& 0\end{array}\right),(s_8^{11},-0.5)\]& \[(s_3^{11},-0.5),\\ \[(s_3^{11},-0.5),(s_8^{11},-0.5)\]& \[\left(\begin{array}{cc}{s}_5^{11},& 0\end{array}\right),\left(\begin{array}{cc}{s}_{10}^{11},& 0\end{array}\right)\]& \[\left(\begin{array}{cc}{s}_5^{11},& 0\end{array}\right),\end{array}\right.\\ \left.\begin{array}{ccc}(s_3^{11},-0.5)\]& \[\left(\begin{array}{cc}{s}_5^{11},& 0\end{array}\right),\left(\begin{array}{cc}{s}_{10}^{11},& 0\end{array}\right)\]& \[\left(\begin{array}{cc}{s}_5^{11},& 0\end{array}\right),\left(\begin{array}{cc}{s}_{10}^{11},& 0\end{array}\right)\]\\ \left(\begin{array}{cc}{s}_{10}^{11},& 0\end{array}\right)\]& \[(s_3^{11},-0.5),(s_8^{11},-0.5)\]& \[(s_3^{11},-0.5),(s_8^{11},-0.5)\]\\ (s_8^{11},-0.5)\]& \[\left(\begin{array}{cc}{s}_5^{11},& 0\end{array}\right),\left(\begin{array}{cc}{s}_{10}^{11},& 0\end{array}\right)\]& \[(s_3^{11},-0.5),\left(\begin{array}{cc}{s}_5^{11},& 0\end{array}\right)\]\end{array}\right]\end{array}$

${\stackrel{\‾}{\tilde{R}}}_3=\left[\begin{array}{ccccc}\[(s_5^{11},0),(s_7^{11},0)\]& \[(s_7^{11},0),(s_9^{11},0)\]& \[(s_2^{11},0),(s_4^{11},0)\]& \[(s_4^{11},0),(s_5^{11},0)\]& \[(s_6^{11},0),(s_{10}^{11},0)\]\\ \[(s_1^{11},0),(s_4^{11},0)\]& \[(s_3^{11},0),(s_5^{11},0)\]& \[(s_3^{11},0),(s_5^{11},0)\]& \[(s_6^{11},0),(s_7^{11},0)\]& \[(s_5^{11},0),(s_9^{11},0)\]\\ \[(s_3^{11},0),(s_6^{11},0)\]& \[(s_4^{11},0),(s_6^{11},0)\]& \[(s_9^{11},0),(s_{10}^{11},0)\]& \[(s_3^{11},0),(s_7^{11},0)\]& \[(s_3^{11},0),(s_8^{11},0)\]\end{array}\right]$

Utilize IT-WAA operator pair in the language assessment information of the i-th row assemble, obtain decision maker d _k(k=1,2,3 to) scheme a _ithe synthesized attribute assessed value of (i=2,3,4)

${\mathrm{\γ}}_1^2\left(\mathrm{\η}\right)=\[(s_4^{11},-0.168),(s_6^{11},-0.3887)\];$

${\mathrm{\γ}}_1^3\left(\mathrm{\η}\right)=\[(s_5^{11},-0.0452),(s_7^{11},0.048)\];$

${\mathrm{\γ}}_1^4\left(\mathrm{\η}\right)=\[(s_5^{11},0.2341),(s_7^{11},0.4538)\];$

${\mathrm{\γ}}_2^2\left(\mathrm{\η}\right)=\[(s_3^{11},0.3933),(s_7^{11},-0.4695)\];$

${\mathrm{\γ}}_2^3\left(\mathrm{\η}\right)=\[(s_5^{11},-0.3812),(s_8^{11},0.4675)\];$

${\mathrm{\γ}}_2^4\left(\mathrm{\η}\right)=\[(s_4^{11},0.2182),(s_9^{11},-0.3075)\];$

${\mathrm{\γ}}_3^2\left(\mathrm{\η}\right)=\[(s_5^{11},0.1704),(s_7^{11},0.1195)\];$

${\mathrm{\γ}}_3^3\left(\mathrm{\η}\right)=\[(s_3^{11},0.2073),(s_5^{11},0.4011)\];$

${\mathrm{\γ}}_3^4\left(\mathrm{\η}\right)=\[(s_4^{11},0.2119),(s_7^{11},-0.1079)\].$

By the weight vectors of n=3 and three bit decisions persons be obtain:

$3{\mathrm{\λ}}_1{\mathrm{\γ}}_1^2\left(\mathrm{\η}\right)=\[(s_4^{11},-0.2063),(s_6^{11},-0.4448)\];$

$3{\mathrm{\λ}}_1{\mathrm{\γ}}_1^3\left(\mathrm{\η}\right)=\[(s_5^{11},-0.0947),(s_7^{11},-0.0225)\];$

$3{\mathrm{\λ}}_1{\mathrm{\γ}}_1^4\left(\mathrm{\η}\right)=\[(s_5^{11},0.1818),(s_7^{11},0.3793)\];$

$3{\mathrm{\λ}}_2{\mathrm{\γ}}_2^2\left(\mathrm{\η}\right)=\[(s_3^{11},0.3594),(s_6^{11},0.4652)\];$

$3{\mathrm{\λ}}_2{\mathrm{\γ}}_2^3\left(\mathrm{\η}\right)=\[(s_5^{11},-0.4274),(s_8^{11},0.3828)\];$

$3{\mathrm{\λ}}_2{\mathrm{\γ}}_2^4\left(\mathrm{\η}\right)=\[(s_4^{11},0.1760),(s_9^{11},-0.3944)\];$

$3{\mathrm{\λ}}_3{\mathrm{\γ}}_3^2\left(\mathrm{\η}\right)=\[(s_5^{11},0.2738),(s_7^{11},0.2619)\];$

$3{\mathrm{\λ}}_3{\mathrm{\γ}}_3^3\left(\mathrm{\η}\right)=\[(s_3^{11},0.2714),(s_6^{11},-0.4909)\];$

$3{\mathrm{\λ}}_3{\mathrm{\γ}}_3^4\left(\mathrm{\η}\right)=\[(s_4^{11},0.2961),(s_7^{11},0.0299)\].$

If the position weighing vector w=(0.25,0.50,0.25) of IT-WHA operator, utilize IT-WHA operator to assemble to the comprehensive assessment value of each scheme three bit decisions persons, obtain γ ⁱ(λ, w) (i=2,3,4):

$\begin{array}{c}{\mathrm{\γ}}^2(\mathrm{\λ},w)=0.25\×\[(s_5^{11},0.2738),(s_7^{11},0.2619)\]\⊕0.50\×\[(s_3^{11},0.3594),(s_6^{11},0.4652)\]\\ \⊕0.25\×\[(s_4^{11},-0.2063),(s_6^{11},-0.4448)\]\\ =\[(s_4^{11},-0.0534),(s_6^{11},0.4369)\]\end{array}$

$\begin{array}{c}{\mathrm{\γ}}^3(\mathrm{\λ},w)=0.25\×\[(s_5^{11},-0.4274),(s_8^{11},0.3828)\]\⊕0.50\×(s_5^{11},-0.0947),(s_7^{11},-0.0225)\]\\ \⊕0.25\×\[(s_3^{11},0.2714),(s_6^{11},-0.4909)\]\\ =\[(s_4^{11},0.4137),(s_7^{11},-0.0383)\]\end{array}$

(3) formula of utilization, calculates scheme a _i(i=2,3,4) comprehensive assessment value γ ⁱ(λ, w) and a _j(j=2,3,4) comprehensive assessment value γ ^jpossibility degree between (λ, w) thus obtain Possibility Degree Matrix

$P^{\′}=\left[\begin{array}{ccc}0.5000& 0.4016& 0.3212\\ 0.5984& 0.5000& 0.4176\\ 0.6788& 0.5824& 0.5000\end{array}\right]$

The ordering vector that can obtain Possibility Degree Matrix P ' according to (9) formula is

v＝(0.2871,0.3360,0.3796)

It can thus be appreciated that a ₄> a ₃> a ₂, scheme a ₄for optimal case, therefore, the final robotization grade of GWPS function is 4.

Claims (1)

1., based on a driving cabin man-machine function allocation method for interval Two-tuple Linguistic Information Processing, it is characterized in that comprising the steps:

(1) carry out man-machine advantageous ability to compare, form people, capabilities advantage set H={h respectively ₁, h ₂, h ₃, h ₄, h ₅and M={m ₁, m ₂, m ₃, m ₄, m ₅, wherein, h ₁represent Forecast reasoning ability, h ₂represent visual ability, h ₃intermediate scheme recognition capability, h ₄represent empirical learning ability, h ₅represent environment perception, m ₁represent data storage management ability, m ₂represent quick and precisely computing power, m ₃represent rule-based reasoning ability, m ₄represent parallel processing capability, m ₅represent continuous working repetition decision ability;

(2) Fuzzy AHP is adopted to determine the weight coefficient of each element in people, the set of capabilities advantage;

(3) the robotization grade 1 ~ 10 grade of civil aircraft driving cabin system is divided, 1 grade does not provide any help for system, people must complete all decision-makings and manipulation, 2 grades provide a whole set of decision-making or action scheme for system, 3 grades is system reduction scheme range of choice, 4 grades provide a proposed projects for system, if 5 grades is people's agreement, perform this scheme, 6 grades for allow people to veto in limiting time before carrying into execution a plan, 7 grades for automatically performing, only notifier where necessary, if 8 grades need for people, inform him, whether notifier is determined by computing machine entirely, 9 grades determine all work for system, 10 grades of interventions for refusal people,

(4) being determined the scope of robotization grade by man-machine capacity superiority, adopting as given a definition:

Definition 1: establish with be two Two-tuple Linguistic Information Processing information, wherein s _kit is the PASCAL evaluation collection pre-defined in a kth element, a _k∈ [-0.5,0.5) represent through assembly calculate after obtain language message with press close to most element s _kbetween difference; and then claim ${(s_k,a_k)}^{~}=\[({\underset{\‾}{s}}_k,{\underset{\‾}{a}}_k),({\stackrel{\‾}{s}}_k,{\stackrel{\‾}{a}}_k)\]$ It is an interval Two-tuple Linguistic Information Processing;

Definition 2: for predefined PASCAL evaluation collection, (s _i, a _i), (s _j, a _j) two Two-tuple Linguistic Information Processing information form interval Two-tuple Linguistic Information Processing information [(s _i, a _i), (s _j, a _j)], i≤j, a _i≤ a _jif, [β ₁, β ₂] be PASCAL evaluation collection S ^tthrough assembling the interval real number obtained, β ₁, β ₂∈ [0, T-1], β ₁≤ β ₂, order

$\mathrm{\Δ}\[{\mathrm{\β}}_1,{\mathrm{\β}}_2\]=\[\left(s_i,a_i\right),\left(s_j,a_j\right)\]=\left\{\begin{array}{cc}{s}_i,& k=round\left({\mathrm{\β}}_1\right)\\ s_j,& k=round\left({\mathrm{\β}}_2\right)\\ a_i={\mathrm{\β}}_1-i,& a_i\∈\[-0.5,0.5)\\ a_j={\mathrm{\β}}_2-j,& a_j\∈\[-0.5,0.5)\end{array}\right.$

Then function Δ is claimed to be interval real number [β ₁, β ₂] transfer function of corresponding interval Two-tuple Linguistic Information Processing information, wherein round is round operator;

Definition 3: make Δ ^-1[(s _i, a _i), (s _j, a _j)]=[i+a _i, j+a _j]=[β ₁, β ₂], then claim Δ ^-1for the inverse function of the Δ of function;

Definition 4: establish (s _k, a _k) ^~, (s _t, a _t) ^~for any two interval Two-tuple Linguistic Information Processings,

Then claim $p\[{\left(s_k,a_k\right)}^{~}\≥{\left(s_t,a_t\right)}^{~}\]=\max \left\{1-\max \[\frac{{\mathrm{\Δ}}^{-1}\left({\stackrel{\‾}{s}}_t,{\stackrel{\‾}{a}}_t\right)-{\mathrm{\Δ}}^{-1}\left({\underset{\‾}{s}}_k,{\underset{\‾}{a}}_k\right)}{l_{{\left(s_k,a_k\right)}^{~}}+l_{{\left(s_t,a_t\right)}^{~}}},0\],0\right\}$ For (s _k, a _k) ^~>=(s _t, a _t) ^~possibility degree;

Definition 5: establish IT-WAA; If be a class interval Two-tuple Linguistic Information Processing information, j=1,2 ..., n, ω=(ω ₁, ω ₂..., ω _n) ^tfor corresponding weight, and ω _j∈ [0,1], j=(1,2 ..., n), ${\mathrm{\Φ}}_{\mathrm{\ω}}\[{(s_1,a_1)}^{~},{(s_2,a_2)}^{~},...,{(s_n,a_n)}^{~}\]=\{\mathrm{\Δ}\[\underset{j=1}{\overset{n}{\Σ}}{\mathrm{\ω}}_j{\mathrm{\Δ}}^{-1}({\underset{\‾}{s}}_j,{\underset{\‾}{a}}_j)\],\mathrm{\Δ}\[\underset{j=1}{\overset{n}{\Σ}}{\mathrm{\ω}}_j{\mathrm{\Δ}}^{-1}({\stackrel{\‾}{s}}_j,{\stackrel{\‾}{a}}_j)\]\}$ Then claim Φ _wfor interval Two-tuple Linguistic Information Processing weighted arithmetic mean operator;

Definition 6: establish IT-WHA: if be a class interval Two-tuple Linguistic Information Processing information, ω=(ω ₁, ω ₂..., ω _n) ^tfor corresponding weight, and ω _j∈ [0,1], ${\mathrm{\Φ}}_{\mathrm{\ω},w}\[{(s_1,a_1)}^{~},{(s_2,a_2)}^{~},...,{(s_n,a_n)}^{~}\]=\{\mathrm{\Δ}\[\underset{j=1}{\overset{n}{\Σ}}{w}_j{\mathrm{\Δ}}^{-1}({\underset{\‾}{s}}_j,{\underset{\‾}{a}}_j)\],\mathrm{\Δ}\[\underset{j=1}{\overset{n}{\Σ}}{w}_j{\mathrm{\Δ}}^{-1}({\stackrel{\‾}{s}}_j,{\stackrel{\‾}{a}}_j)\]\},$ Wherein

W=(w ₁, w ₂..., w _n) ^tthe weighing vector be associated, and w _j∈ [0,1],

$v_j=\[\left({\underset{\‾}{s}}_{\mathrm{\π}\left(j\right)},{\underset{\‾}{a}}_{\mathrm{\π}\left(j\right)}\right),\left({\stackrel{\‾}{s}}_{\mathrm{\π}\left(j\right)},{\stackrel{\‾}{a}}_{\mathrm{\π}\left(j\right)}\right)\]$ It is the Two-tuple Linguistic Information Processing set of variables of weighting $({\widetilde{\mathrm{\μ}}}_1,{\widetilde{\mathrm{\μ}}}_2,...,{\widetilde{\mathrm{\μ}}}_n)({\widetilde{\mathrm{\μ}}}_j={\mathrm{n\ω}}_j{\left(s_j,a_j\right)}^{~})$ The large element of jth, and n is balance factor, then claim Φ _{ω, w}for interval Two-tuple Linguistic Information Processing mixed weighting operator;

Definition 7: be located at PASCAL evaluation collection under the interval version Evaluations matrix that obtains be wherein for property value; Setting basic language evaluation collection is employing transfer function ζ will be converted to basic language evaluation collection S ^tinterval Two-tuple Linguistic Information Processing Evaluations matrix under representing

In formula ${\stackrel{\‾}{a}}_{ij}\∈\[-0.5,0.5);$

On above-mentioned definition basis, provide the robotization rate range defining method based on IT-WAA operator, detailed process is as follows:

A): decision maker's set is D={d ₁, d ₂..., d _k..., d _t, t bit decisions person altogether; Every bit decisions person d _kprovide people respectively, interval version assessed value that capabilities advantage treats distribution function with k=1,2 ..., t, and obtain evaluating matrix ${\tilde{H}}_k={(\[{\underset{\‾}{h}}_{ij}^k,{\stackrel{\‾}{h}}_{ij}^k\])}_{n\×m},{\tilde{Q}}_k={(\[{\underset{\‾}{q}}_{ij}^k,{\stackrel{\‾}{q}}_{ij}^k\])}_{n\×l};$

B): setting basic language evaluation collection S ^t, will with change into based on S ^tthe interval Two-tuple Linguistic Information Processing Evaluations matrix of the unification represented with iT-WAA operator is utilized to incite somebody to action with assemble for the interval Two-tuple Linguistic Information Processing Evaluations matrix of group with

C): according to IT-WAA operator and people, capabilities advantage weight will with τ with assemble by row, calculate people, the interval Two-tuple Linguistic Information Processing comprehensive evaluation value of machine scheme with calculate people, comprehensive assessment result that capabilities treats distribution function with between possibility degree

D): the robotization rate range determining function to be allocated according to possibility degree result p:

$\left\{\begin{array}{c}ceil(p\×5)-1\≤LOA\≤ceil(p\×5)+1\\ LOA\∈\{1,2,...,10\}\end{array}\right.$

Wherein, ceil (x) is flow in upper plenum;

(5) employing distributes robotization grade based on the IT-WAA operator of interval Two-tuple Linguistic Information Processing and the Multiple Attribute Group Decision determination function of IT-WHA operator, and detailed process is as follows:

For certain function Multiple Attribute Decision Problems, if A={a ₁, a ₂..., a _{n '}scheme collection, the n' kind situation of its element representation robotization grade; Given function distributes assessment level set G={g ₁, g ₂..., g _{m '}, five main assessment levels of the corresponding driving cabin man-machine function allocation of its element difference, g ₁for Situation Awareness; g ₂for Mental Workload; g ₃for risk of policy making; g ₄for reliability; g ₁for system cost; The weight vectors of attribute weight is η=(η ₁, η ₂..., η _{m '}), and its vector is η _l>=0 (l=1,2 ..., m '),

A) weight vectors of t bit decisions person is decision maker d _k∈ D provides scheme a _i∈ A is at attribute g _jinterval version assessed value under ∈ G and obtain evaluating matrix

B) will change into based on basic language evaluation collection S ^{t '}the interval Two-tuple Linguistic Information Processing Evaluations matrix of the unification represented utilize IT-WAA operator pair in the language assessment information of the i-th row assemble, obtain decision maker d _kto scheme a _isynthesized attribute assessed value i=1,2 ..., n ';

C) utilize IT-WHA operator to the synthesized attribute assessed value of t bit decisions person assemble, obtain about scheme a _icolony's comprehensive assessment value wherein, w=(w ₁, w ₂..., w _t) be the weighing vector of IT-WHA operator, w _k∈ [0,1] (k=1,2 ..., t) and it is the Two-tuple Linguistic Information Processing set of variables of weighting the large element of kth, λ=(λ ₁, λ ₂..., λ _t) ^tfor corresponding weight, and λ _k∈ [0,1], and t is balance factor;

D) scheme a is calculated _icomprehensive assessment value γ ⁱ(λ, w) and a _jcomprehensive assessment value γ ^jpossibility degree p ' between (λ, w) _ij=p [γ ⁱ(λ, w)>=γ ^j(λ, w)], thus obtain Possibility Degree Matrix p '=(p ' _ij) _{n ' × n '};

E) the ordering vector v=(v of Possibility Degree Matrix p ' is obtained ₁, v ₂..., v _{n '}), and by its component size, scheme is sorted, namely obtain optimal case; Wherein finally by its component size, scheme is sorted, namely obtain optimum function allocative decision.