CN101930490B - Man-machine function allocation method of civil aircraft cockpit - Google Patents

Abstract

The invention discloses a man-machine function allocation method of a civil aircraft cockpit, which sequentially comprises the following steps: comparing man-machine advantageous capabilities, determining the weight coefficient of each element in a capability advantage set by using an analytic hierarchy process, dividing the automation level of a civil aircraft cockpit system, determining the range of the automation levels based on the comparison of the man-machine advantageous capabilities, setting a function allocation evaluation criterion set, and finally determining the automation level of the man-machine function allocation of the cockpit. The invention can overcome the defect of strong subjectivity in the traditional qualitative analysis method.

Description

Man-machine function allocation method of civil aircraft cockpit

Technical field

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

Background technology

Function is distributed (Function Allocation; FA) notion is proposed in nineteen fifty-one by P.M.Fitts; Be meant and divide the process of tasking people or machine with function in the system (Function) or task (Task), it mainly stresses the Function Decomposition between each constituent of system.This function allocation activities betides the commitment of system synthesis and evaluation procedure, belongs to the research category of systems engineering (Systems Engineering).The function distribution has determined the inside mutual relationship between each key element in the man-machine system on the whole; Thereby influenced the external overall function of total system; Directly relevant with it design of hardware and software and the exploitation of influence of the function of each ingredient of internal system also will have influence on many-sided factors such as training, technical ability, choice of people simultaneously to the functional requirement of people's proposition.Function is distributed and also directly to be affected the design of man-machine interface, and the interaction relationship of people, machine when determining actual executing the task to a great extent.No matter be man-machine system (Human-machine System) research field at the human engineering subject; Or man-machine intelligence system (Man-machine System) research field at the artificial intelligence subject; Or in man-machine integration system (Humachine System) research field; Function is distributed all becomes one of task the most basic in the system design, and it has important effect for the validity, reliability and the efficiency-cost ratio that improve total system.(Human Factors Engineering, one of founder HFE) Alphonse Chapanis are referred to as the function distribution " in man-machine intelligence system's design primarily also is one of sixty-four dollar question " to Human Engineering.At present, function is distributed in the design, exploitation of nuclear power plant's automatic system for monitoring, air traffic control system, manned spacecraft system, UAV control system etc. and has obtained widely using.

Current robotization and intellectualized technology are widely used in the middle of the civil aircraft driving cabin; Though this has alleviated people's working load to a certain extent; But also having caused simultaneously between people's situation perception (Situation Awareness) level decline, automated system and the people can not mutual understanding and to the problems such as transition dependence of system, and irrational The Automation Design will inevitably cause serious threat to whole flight safety.Therefore, when carrying out civil aircraft driving cabin The Automation Design, must at first confirm the robotization grade of system, thereby provide sufficient foundation to carry out into follow-up design of hardware and software and development for the designer through function assigning method.China's large-sized civil transporter project is formally project verification already, and the large-sized civil transporter that development has an independent intellectual property right needs the support of a large amount of correlation techniques, and function is distributed just one of gordian technique wherein.Although function is distributed in and has obtained sufficient application and development abroad,, do not see public reported about its core technology at present in view of the susceptibility of its application background.Domestic method of distributing about function is based on qualitative analysis more, does not form system and complete method.

Summary of the invention

In order to overcome the deficiency of prior art in function is distributed, the present invention proposes the method that a kind of civil aircraft driving cabin function is distributed.The method includes the steps of:

1. carry out man-machine advantageous ability relatively, promptly choose the man-machine advantageous ability project comparatively close, form people, the set of capabilities advantage, be expressed as respectively: H={h with the driving cabin functional relationship ₁, h ₂, h ₃, h ₄, h ₅, h ₆, h ₇, h ₈And M={m ₁, m ₂, m ₃, m ₄, m ₅, m ₆, m ₇, m ₈, each element implication is as shown in table 1.

Table 1 people, the set of capabilities advantage

H M

h ₁=empirical learning ability m ₁=data managing capacity

h ₂=induction ability m ₂=combinatorial problem processing power

h ₃=mode identificating ability m ₃=continuous working ability

h ₄=visually-perceptible ability m ₄=multitasking ability

h ₅=fuzzy message processing power m ₅=quick computing power

h ₆=spatial reasoning ability m ₆=complex mathematical arithmetic capability

h ₇=creativity m ₇=accurate computing power

h ₈=uncertainty event processing power m ₈=simple repetition decision ability

2. adopt analytical hierarchy process (Analytic Hierarchy Process, AHP) confirm capacity superiority gather in the weight coefficient of each element.

3. divide the robotization grade of civil aircraft driving cabin system, adopt the robotization rank division methods of the man-machine interactive system of people's propositions such as Sheridan, Verplank and Parasuraman, as shown in table 2.

Table 2 robotization rank

The robotization rank is described

1 system does not provide any help, and the people must accomplish all decision-makings and manipulation

2 systems provide a whole set of decision-making or action scheme

3 system's reduction scheme ranges of choice

4 systems provide a proposed projects

If 5 people agree then carry out this scheme

6 allow the people in limiting time, to veto before carrying into execution a plan

7 automatically perform, only notifier where necessary

If 8 people need inform him

9 whether the notifier is determined by computing machine entirely

The work that the decision of 10 systems is all, refusal people's intervention

4. relatively confirm the scope of robotization grade through man-machine advantageous ability.In this step, need use as giving a definition.

Definition 1: establish WAA:R ⁿ→ R, if

${\mathrm{WAA}}_{\mathrm{\ω}}({\mathrm{\α}}_1,{\mathrm{\α}}_2,\mathrm{\Λ},{\mathrm{\α}}_n)=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{\ω}}_j{\mathrm{\α}}_j---\left(1\right)$

ω=(ω wherein ₁, ω ₂, Λ, ω _n) be one group of data (α ₁, α ₂, Λ, α _n) weighing vector, ω _j∈ [0,1], j ∈ N,

Claim that then function WAA is the weighted arithmetic mean operator, is also referred to as the WAA operator.The characteristics of WAA operator are: only to data set (α ₁, α ₂, Λ, α _n) in each data carry out weighting (promptly giving suitable weight) according to the importance of each data, then the data after the weighting are assembled.

The person that considers the system design decision is carrying out qualitatively when estimating, and generally needs suitable language assessment scale, for this reason, can set language assessment scale S={s in advance _α| α=-L, Λ, L}, the term number among the S is generally odd number, like the desirable S={s of language assessment scale _-1, s ₀, s ₁}={ is low, in, height }, S={s _-2, s _-1, s ₀, s ₁, s ₂}={ is very poor, and be poor, general, good, excellent }, S={s _-5, Λ, s ₅}={ extreme difference, very poor, poor, relatively poor, poor slightly, general, good slightly, better, good, fine, fabulous } etc., and satisfy following condition: 1) if α＞β, then s _α＞s _β2) there is negative operator neg (s _α)=s _-α

Define 2: establish EWAA:

if

${\mathrm{EWAA}}_{\mathrm{\ω}}(s_{{\mathrm{\α}}_1},s_{{\mathrm{\α}}_2},\mathrm{\Λ},s_{{\mathrm{\α}}_n})={\mathrm{\ω}}_1{s}_{{\mathrm{\α}}_1}\⊕{\mathrm{\ω}}_2{s}_{{\mathrm{\α}}_2}\⊕\mathrm{\Λ}\⊕{\mathrm{\ω}}_n{s}_{{\mathrm{\α}}_n}=s_{\stackrel{\‾}{\mathrm{\α}}},---\left(2\right)$

Wherein ω=(ω ₁, ω ₂, Λ, ω _n) be language data

Weighing vector, and ω _j∈ [0,1] (j ∈ N),

Then function EWAA is called the weighted arithmetic mean operator of expansion.

Definition 3: establish

s _aAnd s _bBe respectively

The lower limit and the upper limit, then claim

Be uncertain linguistic variable.

Definition 4: establish

And establish l _Ab=b-a, l _Cd=d-c, then

Possibly spend the definition as follows:

$p(\widetilde{\mathrm{\μ}}\≥\widetilde{\mathrm{\υ}})=\max \{1-\max (\frac{d-a}{l_{\mathrm{ab}}+l_{\mathrm{cd}}},0),0\}---\left(3\right)$

Similarly, possibly spending of

defines as follows:

$p(\widetilde{\mathrm{\υ}}\≥\widetilde{\mathrm{\μ}})=\max \{1-\max (\frac{b-c}{l_{\mathrm{ab}}+l_{\mathrm{cd}}},0),0\}---\left(4\right)$

Define 5: establish UEWAA:

if

${\mathrm{UEWAA}}_{\mathrm{\ω}}({\widetilde{\mathrm{\μ}}}_1,{\widetilde{\mathrm{\μ}}}_2,\mathrm{\Λ},{\widetilde{\mathrm{\μ}}}_n)={\mathrm{\ω}}_1{\widetilde{\mathrm{\μ}}}_1\⊕{\mathrm{\ω}}_2{\widetilde{\mathrm{\μ}}}_2\⊕\mathrm{\Λ}\⊕{\mathrm{\ω}}_n{\widetilde{\mathrm{\μ}}}_n---\left(5\right)$

ω=(ω wherein ₁, ω ₂, Λ, ω _n) be uncertain linguistic variable

Weighing vector, and ω _j∈ [0,1] (j ∈ N), Claim that then function U EWAA is uncertain EWAA operator.

Define 6: establish ULHA:

if

${\mathrm{ULHA}}_{\mathrm{\ω},w}({\widetilde{\mathrm{\μ}}}_1,{\widetilde{\mathrm{\μ}}}_2,\mathrm{\Λ},{\widetilde{\mathrm{\μ}}}_n)=w_1{\widetilde{\mathrm{\υ}}}_1\⊕w_2{\widetilde{\mathrm{\υ}}}_2\⊕\mathrm{\Λ}\⊕w_n{\widetilde{\mathrm{\υ}}}_n---\left(6\right)$

W=(w wherein ₁, w ₂, Λ, w _n) be the weighing vector (position vector) that is associated with ULHA, w _j∈ [0,1] (j ∈ N),

It is the uncertain linguistic variable group of weighting

In the big element of j, ω=(ω here ₁, ω ₂, A, ω _n) be uncertain linguistic variable group

Weighing vector, ω _j∈ [0,1] (j ∈ N), And n is a balance factor, claims that then function U LHA is that uncertain language mixes assembly (ULHA) operator.

On above-mentioned definition basis, provide based on the robotization rate range of UEWAA operator and confirm method, detailed process is following:

A): establish X and represent function collection to be allocated, H, M are respectively the capacity superiority item set of people, machine, and both weight vectors are respectively ω=(ω ₁, ω ₂, Λ, ω _m) and ξ=(ξ ₁, ξ ₂, Λ, ξ _l), and satisfy ω _j>=0 (j ∈ M),

ξ _j>=0 (j ∈ L),

The decision maker provides people, each capacity superiority h of machine respectively _j∈ H and m _j∈ M treats distribution function x _iThe uncertain language assessment value of ∈ X influence degree

And obtain evaluating matrix

$\tilde{Q}={\left({\tilde{q}}_{\mathrm{Ij}}\right)}_{n\×l},$ And ${\tilde{r}}_{\mathrm{Ij}},{\tilde{q}}_{\mathrm{Ij}}\∈\tilde{S}.$

B): utilize the UEWAA operator respectively to evaluating matrix

With

In the capable language assessment information of i assemble, obtain the people, capabilities is treated distribution function x _iThe comprehensive assessment result of ∈ X With

${\tilde{y}}_i\left(\mathrm{\ω}\right)={\mathrm{UEWAA}}_{\mathrm{\ω}}({\tilde{r}}_{i1},{\tilde{r}}_{i2},\mathrm{\Λ},{\tilde{r}}_{\mathrm{im}})={\mathrm{\ω}}_1{\tilde{r}}_{i1}\⊕{\mathrm{\ω}}_2{\tilde{r}}_{i2}\⊕\mathrm{\Λ}\⊕{\mathrm{\ω}}_m{\tilde{r}}_{\mathrm{im}},i\∈N---\left(7\right)$

${\tilde{z}}_i\left(\mathrm{\ξ}\right)={\mathrm{UEWAA}}_{\mathrm{\ξ}}({\tilde{q}}_{i1},{\tilde{q}}_{i2},\mathrm{\Λ},{\tilde{q}}_{\mathrm{il}})={\mathrm{\ξ}}_1{\tilde{q}}_{i1}\⊕{\mathrm{\ξ}}_2{\tilde{q}}_{i2}\⊕\mathrm{\Λ}\⊕{\mathrm{\ξ}}_l{\tilde{q}}_{\mathrm{il}},i\∈N---\left(8\right)$

C): utilize (3) formula, calculate the people, capabilities is treated distribution function x _iThe comprehensive assessment result of ∈ X

With

Between possibly spend

Obtain to spend vectorial P={p ₁, p ₂, Λ, p _n.0≤p wherein _i≤1.

D): according to spending p as a result _iConfirm function x to be allocated _iThe robotization grade A scope of ∈ X.Specifically rule is as follows:

$\left\{\begin{array}{c}\mathrm{floor}((1-p_i)\×10)-1\≤A\≤\mathrm{floor}((1-p_i)\×10)+1\\ A\∈\{\mathrm{1,2},\mathrm{\Λ},10\}\end{array}\right.---\left(9\right)$

Wherein, floor (x) is Gauss's bracket function.For example: if p _i=1, A=1 then explains that this decision making function should be accomplished by the operator fully; If possible spend p as a result _i=0.35,5≤A≤7 then.

5. given function is distributed assessment level (attribute) set U={u ₁, u ₂, u ₃, u ₄, u ₅, its element is five main assessment levels of the man-machine function distribution of corresponding driving cabin respectively, u ₁---mental load; u ₂---the situation perception; u ₃---reliability; u ₄---risk of policy making; u ₅---system cost.

6. after function distributed the robotization rate range to confirm, the different schemes that promptly given some functions are distributed also need distribute assessment level from allocative decision, to select optimal case according to function, finally confirms the robotization grade that the man-machine function of driving cabin is distributed.In the actual assessment process, normally according to assessment level different schemes is marked, to reduce the subjective deviation of assessment experts by the multidigit expert.Employing is confirmed function distribution robotization grade based on the multiattribute groups Decision Method of UEWAA operator and ULHA operator, and detailed process is following:

A): for a certain function assignment problem, establish X, U and D are respectively scheme set, property set (assessment level) and decision maker (expert) collection.The weight vectors of attribute weight is ω=(ω ₁, ω ₂, Λ, ω _m), and its vector is ω _j>=0 (j ∈ M),

Decision maker's weight vectors is λ=(λ ₁, λ ₂, Λ, λ _t), λ _k>=0 (k=1,2, Λ, t),

Decision maker d _k∈ D provides scheme x _i∈ X is at attribute u _jUncertain language assessment value under the ∈ U

And obtain evaluating matrix ${\tilde{R}}_k={\left({{\tilde{r}}_{\mathrm{Ij}}}^{\left(k\right)}\right)}_{n\×m},$ And ${{\tilde{r}}_{\mathrm{Ij}}}^{\left(k\right)}\∈\tilde{S}.$

B): utilize the UEWAA operator to evaluating matrix

In the capable uncertain language assessment information of i assemble, obtain decision maker d _kThe allocative decision x that provides _iThe synthesized attribute assessed value

${\tilde{z}}_i^{\left(k\right)}\left(\mathrm{\ω}\right)={\mathrm{UEWAA}}_{\mathrm{\ω}}({{\tilde{r}}_{i1}}^{\left(k\right)},{{\tilde{r}}_{i2}}^{\left(k\right)},\mathrm{\Λ},{{\tilde{r}}_{\mathrm{im}}}^{\left(k\right)})$

$={\mathrm{\ω}}_1{{\tilde{r}}_{i1}}^{\left(k\right)}\⊕{\mathrm{\ω}}_2{{\tilde{r}}_{i2}}^{\left(k\right)}\⊕\mathrm{\Λ}\⊕{\mathrm{\ω}}_m{{\tilde{r}}_{\mathrm{im}}}^{\left(k\right)},i\∈N,k=\mathrm{1,2},\mathrm{\Λ},t.---\left(10\right)$

C): the allocative decision x that utilizes the ULHA operator that t bit decisions person is provided again _iThe synthesized attribute assessed value

Assemble, obtain allocative decision x _iColony's synthesized attribute assessed value

$(i\∈N):$ ${\tilde{z}}_i(\mathrm{\λ},w^{\′})={\mathrm{ULHA}}_{\mathrm{\λ},w^{\′}}({{\tilde{r}}_i}^{\left(1\right)},{{\tilde{r}}_i}^{\left(2\right)},\mathrm{\Λ},{{\tilde{r}}_i}^{\left(t\right)})$

$=w_1^{\′}{{\widetilde{\mathrm{\υ}}}_i}^{\left(1\right)}\⊕w_2^{\′}{{\widetilde{\mathrm{\υ}}}_i}^{\left(2\right)}\⊕\mathrm{\Λ}\⊕w_t^{\′}{{\widetilde{\mathrm{\υ}}}_i}^{\left(t\right)},i\∈N,---\left(11\right)$

Wherein is the weighing vector of ULHA operator;

is the big element of k in the uncertain linguistic variable

of one group of weighting, and t is a balance factor.

D): utilize (3) formula, calculate scenarios synthesized attribute value

Between possibly spend

And set up and to spend matrix P=(p _Ij) _{N * n}

E): obtain the ordering vector v=(v that possibly spend matrix P ₁, v ₂, Λ, v _n), wherein:

$v_i=\frac{1}{n(n-1)}(\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{p}_{\mathrm{ij}}+\frac{n}{2}-1),i\∈N---\left(12\right)$

By its component size scheme is sorted at last, promptly obtain optimum function allocative decision.

Adopt said method that the man-machine automated system of civil aircraft driving cabin is carried out the function distribution and can overcome the subjective shortcoming of method for qualitative analysis in the past.

Below in conjunction with embodiment the present invention is further specified.

Embodiment

Be the practical implementation process that example explanation above-mentioned functions is distributed with a function of civil aircraft driving cabin " fault diagnosis (Fault Diagnosis, FD) " below, promptly adopt the method that is proposed to confirm the optimum robotization grade of FD.

1. confirm people, machine advantageous ability set H={h ₁, h ₂, h ₃, h ₄, h ₅, h ₆, h ₇, h ₈And M={m ₁, m ₂, m ₃, m ₄, m ₅, m ₆, m ₇, m ₈, each element implication is as shown in table 1.

2. the weight vectors that utilizes analytical hierarchy process to obtain element among H and the M is respectively:

ω＝(0.287，0.106，0.081，0.142，0.119，0.019，0.193，0.053)

ξ＝(0.163，0.097，0.228，0.126，0.154，0.106，0.062，0.064)

3. its FD function is divided into 10 robotization ranks, is respectively from low to high by automaticity:

x ₁: system does not provide any help, and the pilot must accomplish all decision-makings and manipulation

x ₂: system provides a whole set of decision-making or action scheme

x ₃: system's reduction scheme range of choice

x ₄: system provides a proposed projects

x ₅: if the pilot agrees then carries out this scheme

x ₆: before carrying into execution a plan, allow the pilot in limiting time, to veto

x ₇: automatically perform, only notify the pilot where necessary

x ₈If: the pilot need then inform him

x ₉: whether notify the pilot to determine by computing machine entirely

x ₁₀: the work that system's decision is all, refusal pilot's intervention

4. set up the language assessment scale:

S={s _α| α=-5, Λ, 5}={ is minimum, and is very little, little, less, slightly little, general, big slightly, bigger, big, very big, greatly }.

By the expert people, capabilities advantage element are assessed FD function effect (contribution) degree, are obtained assessment result and be respectively:

$\tilde{R}=\left(\right[s_2,s_3],[s_0,s_2],[s_3,s_4],[s_1,s_3],[s_0,s_2],[s_2,s_3],[s_{-2},s_0],[s_0,s_2\left]\right)$

$\tilde{Q}=\left(\right[s_0,s_2],[s_2,s_4],[s_0,s_1],[s_2,s_3],[s_2,s_4],[s_3,s_4],[s_1,s_3],[s_{-3},s_{-1}\left]\right)$

According to formula (7) and (8); Utilize the UEWAA operator that assessment result

is assembled, obtain the people, capabilities to the comprehensive assessment result of FD function does

$\tilde{y}\left(\mathrm{\ω}\right)=0.287\×[s_2,s_3]\⊕0.106\×[s_0,s_2]\⊕0.081\×[s_3,s_4]\⊕0.142\×[s_1,s_3]\⊕$

$0.119\×[s_0,s_2]\⊕0.019\×[s_2,s_3]\⊕0.193\×[s_{-2},s_0]\⊕0.053\×[s_0,s_2]$

$=[s_{1.138},s_{2.224}],$

$\tilde{z}\left(\mathrm{\ξ}\right)=0.163\×[s_0,s_2]\⊕0.097\×[s_2,s_4]\⊕0.228\×[s_0,s_1]\⊕0.126\×[s_2,s_3]\⊕$

$0.154\×[s_2,s_4]\⊕0.106\×[s_3,s_4]\⊕0.062\×[s_1,s_3]\⊕0.064\×[s_{-3},s_{-1}]$

$=[s_{0.942},s_{2.482}].$

(3) formula of utilization, possibly spending for

of trying to achieve

can get FD robotization rate range 3≤A≤5 by (9) formula again.

5. set up function and distribute assessment level (attribute) set U={u ₁, u ₂, u ₃, u ₄, u ₅, its element is respectively: u ₁---mental load; u ₂---the situation perception; u ₃---reliability; u ₄---risk of policy making; u ₅---system cost.

6. confirmed after the robotization rate range of FD function, be equivalent to 3 kinds of different allocative decisions given, established FD allocative decision set X={x ₃, x ₄, x ₅, its element representation robotization grade is in 3,4,5 three kind of situation, distributes assessment level (attribute) set U={u ₁, u ₂, u ₃, u ₄, u ₅, establish its weight vectors and be:

ω＝(0.351，0.227，0.284，0.037，0.074)

Decision maker's set is D={d ₁, d ₂, d ₃, supposing that its weight vectors is λ=(0.33,0.33,0.34), three bit decisions persons utilize the language assessment scale:

S={s _α| α=-5, Λ, the 5}={ extreme difference, very poor, poor, relatively poor, poor slightly, general, good slightly, better, good, fine, fabulous

The uncertain language assessment matrix that provides is shown in table 3～table 5.

Table 3 decision matrix

Table 4 decision matrix

Table 5 decision matrix

Table 3～table 5 representes that respectively three system designers are the decision matrix that function Decision of Allocation person provides according to the language assessment scale

U wherein ₁The mental load of expression; u ₂The perception of expression situation; u ₃The expression reliability; u ₄The expression risk of policy making; u ₅The expression system cost.x ₃The presentation function allocative decision adopts the 3rd level robotization that is; x ₄The presentation function allocative decision adopts the 4th grade of robotization that is; x ₅The presentation function allocative decision adopts the 5th grade of robotization that is;

At first utilize the UEWAA operator to evaluating matrix In the capable language assessment information of i assemble, obtain decision maker d _kThe allocative decision x that provides _iThe synthesized attribute assessed value

${\tilde{z}}_3^{\left(1\right)}\left(\mathrm{\ω}\right)=[s_{0.335},s_{1.98}],$ ${\tilde{z}}_4^{\left(1\right)}\left(\mathrm{\ω}\right)=[s_{1.037},s_{3.371}],$ ${\tilde{z}}_5^{\left(1\right)}\left(\mathrm{\ω}\right)=[s_{1.41},s_{3.282}],$

${\tilde{z}}_3^{\left(2\right)}\left(\mathrm{\ω}\right)=[s_{-0.033},s_{1.224}],$ ${\tilde{z}}_4^{\left(2\right)}\left(\mathrm{\ω}\right)=[s_{0.753},s_{1.953}],$ ${\tilde{z}}_5^{\left(2\right)}\left(\mathrm{\ω}\right)=[s_{0.321},s_{1.682}],$

${\tilde{z}}_3^{\left(3\right)}\left(\mathrm{\ω}\right)=[s_{1.785},s_{2.758}],$ ${\tilde{z}}_4^{\left(3\right)}\left(\mathrm{\ω}\right)=[s_{1.702},s_{3.063}],$ ${\tilde{z}}_5^{\left(3\right)}\left(\mathrm{\ω}\right)=[s_{0.004},s_{1.686}].$

The allocative decision x that utilizes the ULHA operator that 3 bit decisions persons are provided again _iThe synthesized attribute assessed value Before assembling, need provide the vectorial w of weighting (position) of ULHA, establish w=(0.243,0.514,0.243).

By λ, t and find the solution

$3{\mathrm{\λ}}_1{\tilde{z}}_3^{\left(1\right)}\left(\mathrm{\ω}\right)=[s_{0.221},s_{1.307}],$ $3{\mathrm{\λ}}_1{\tilde{z}}_4^{\left(1\right)}\left(\mathrm{\ω}\right)=[s_{0.684},s_{2.225}],$ $3{\mathrm{\λ}}_1{\tilde{z}}_5^{\left(1\right)}\left(\mathrm{\ω}\right)=[s_{0.931},s_{2.166}],$

$3{\mathrm{\λ}}_2{\tilde{z}}_3^{\left(2\right)}\left(\mathrm{\ω}\right)=[s_{-0.022},s_{0.808}],$ $3{\mathrm{\λ}}_2{\tilde{z}}_4^{\left(2\right)}\left(\mathrm{\ω}\right)=[s_{0.497},s_{1.289}],$ $3{\mathrm{\λ}}_2{\tilde{z}}_5^{\left(2\right)}\left(\mathrm{\ω}\right)=[s_{0.212},s_{1.110}],$

$3{\mathrm{\λ}}_3{\tilde{z}}_3^{\left(3\right)}\left(\mathrm{\ω}\right)=[s_{1.214},s_{1.875}],$ $3{\mathrm{\λ}}_3{\tilde{z}}_4^{\left(3\right)}\left(\mathrm{\ω}\right)=[s_{1.157},s_{2.083}],$ $3{\mathrm{\λ}}_3{\tilde{z}}_5^{\left(3\right)}\left(\mathrm{\ω}\right)=[s_{0.003},s_{1.146}].$

Obtain decision scheme x thus _iColony's synthesized attribute assessed value

${\tilde{z}}_3(\mathrm{\λ},w)=0.243\×[s_{1.214},s_{1.875}]\⊕0.514\×[s_{0.221},s_{1.307}]\⊕0.243\×[s_{-0.022},s_{0.808}]$

$=[s_{0.403},s_{1.324}],$

${\tilde{z}}_4(\mathrm{\λ},w)=0.243\×[s_{1.157},s_{2.083}]\⊕0.514\×[s_{0.684},s_{2.225}]\⊕0.243\×[s_{0.497},s_{1.289}]$

$=[s_{0.753},s_{1.963}],$

${\tilde{z}}_5(\mathrm{\λ},w)=0.243\×[s_{0.931},s_{2.166}]\⊕0.514\×[s_{0.212},s_{1.110}]\⊕0.243\×[s_{0.003},s_{1.146}]$

$=[s_{0.336},s_{1.375}].$

(3) formula of utilization is calculated possibly spend

and also setting up between the scenarios synthesized attribute assessed value

and possibly spent matrix

$P=\left[\begin{array}{ccc}0.5& 0.268& 0.504\\ 0.732& 0.5& 0.723\\ 0.496& 0.277& 0.5\end{array}\right]$

Can get the ordering vector that possibly spend matrix P by formula (12) is:

v＝(0.295，0.409，0.296)。

Sort by its component size,

x ₄φx ₅φx ₃

Therefore optimal case is x ₄, promptly the robotization grade of FD function get 4 the most suitable.

Claims (1)

1. man-machine function allocation method of civil aircraft cockpit is characterized in that comprising the steps:

1) carries out man-machine advantageous ability relatively, promptly choose the man-machine advantageous ability project comparatively close, form people, the set of capabilities advantage, be expressed as respectively: H={h with the driving cabin functional relationship ₁, h ₂, h ₃, h ₄, h ₅, h ₆, h ₇, h ₈And M={m ₁, m ₂, m ₃, m ₄, m ₅, m ₆, m ₇, m ₈, each element implication is as shown in table 1;

Table 1 people, the set of capabilities advantage

H M h ₁=empirical learning ability m ₁=data managing capacity h ₂=induction ability m ₂=combinatorial problem processing power h ₃=mode identificating ability m ₃=continuous working ability h ₄=visually-perceptible ability m ₄=multitasking ability h ₅=fuzzy message processing power m ₅=quick computing power h ₆=spatial reasoning ability m ₆=complex mathematical arithmetic capability h ₇=creativity m ₇=accurate computing power h ₈=uncertainty event processing power m ₈=simple repetition decision ability

2) adopt analytical hierarchy process to confirm the weight coefficient of each element in the capacity superiority set;

3) the robotization grade of division civil aircraft driving cabin system, the robotization rank division methods of employing man-machine interactive system, as shown in table 2;

Table 2 robotization rank

The robotization rank Describe 1 System does not provide any help, and the people must accomplish all decision-makings and manipulation 2 System provides a whole set of decision-making or action scheme 3 System's reduction scheme range of choice 4 System provides a proposed projects 5 If the people agrees then carries out this scheme 6 Before carrying into execution a plan, allow the people in limiting time, to veto 7 Automatically perform, only notifier where necessary 8 If the people need inform him 9 Whether the notifier is determined by computing machine entirely 10 The work that system's decision is all, refusal people's intervention

4) relatively confirm the scope of robotization grade through man-machine advantageous ability;

Definition 1: establish WAA:R ⁿ→ R, if

Wherein Be one group of data (α ₁, α ₂..., α _n) weighing vector, J ∈ N,

Claim that then function WAA is the weighted arithmetic mean operator, is also referred to as the WAA operator; The characteristics of WAA operator are: only

To data set (α ₁, α ₂..., α _n) in each data carry out weighting, then the data after the weighting are assembled;

The person that considers the system design decision is carrying out qualitatively when estimating, and generally needs suitable language assessment scale, for this reason, can set language assessment scale S={s in advance _α| α=-L ..., L}, the term number among the S is an odd number, and satisfies following condition: 1) if α＞β, then s _α＞s _β2) there is negative operator neg (s _α)=s _-α

Define 2: establish EWAA:

if

Wherein

is the weighing vector of language data , and

then function EWAA be called the weighted arithmetic mean operator of expansion;

Definition 3: establish

s _aAnd s _bBe respectively

The lower limit and the upper limit, then claim

Be uncertain linguistic variable;

Definition 4: establish

And establish l _Ab=b-a, l _Cd=d-c, then

Possibly spend the definition as follows:

Similarly, possibly spending of

defines as follows:

Define 5: establish UEWAA:

if

Wherein

is the weighing vector of uncertain linguistic variable , and

claims that then function U EWAA is uncertain EWAA operator;

Define 6: establish ULHA:

if

W=(w wherein ₁, w ₂..., w _n) be the weighing vector (position vector) that is associated with ULHA, w _j∈ [0,1] (j ∈ N),

It is the uncertain linguistic variable group of weighting

In the big element of j, here

It is uncertain linguistic variable group

Weighing vector,

And n is a balance factor, claims that then function U LHA is that uncertain language mixes assembly (ULHA) operator;

On above-mentioned definition basis, provide based on the robotization rate range of UEWAA operator and confirm method, detailed process is following:

A): establish X and represent function collection to be allocated, H, M are respectively the capacity superiority item set of people, machine, and both weight vectors are respectively

And ξ=(ξ ₁, ξ ₂..., ξ _l), m and l are respectively the number of the capacity superiority item of people, machine, and satisfy

ξ j>=0 (j ∈ l), Provide people, each capacity superiority h of machine respectively _j∈ H and m _j∈ M treats distribution function x _iThe uncertain language assessment value of ∈ X influence degree

And obtain evaluating matrix

And

B): utilize the UEWAA operator respectively to evaluating matrix

With

In the capable language assessment information of i assemble, obtain the people, capabilities is treated distribution function x _iThe comprehensive assessment result of ∈ X With

C): utilize (3) formula, calculate the people, capabilities is treated distribution function x _iThe comprehensive assessment result of ∈ X

With Between possibly spend

Obtain to spend vectorial P={p ₁, p ₂, Λ, p _n; 0≤p wherein _i≤1;

D): according to spending p as a result _iConfirm function x to be allocated _iThe robotization grade A scope of ∈ X, specifically rule is as follows:

Wherein, floor (x) is Gauss's bracket function;

5) given function is distributed assessment level set U={u ₁, u ₂, u ₃, u ₄, u ₅, its element is five main assessment levels of the man-machine function distribution of corresponding driving cabin respectively, u ₁---mental load; u ₂---the situation perception; u ₃---reliability; u ₄---risk of policy making; u ₅---system cost;

6) after function distributed the robotization rate range to confirm, the different schemes that promptly given some functions are distributed also need distribute assessment level from allocative decision, to select optimal case according to function, finally confirms the robotization grade that the man-machine function of driving cabin is distributed; Employing is confirmed function distribution robotization grade based on the multiattribute groups Decision Method of UEWAA operator and ULHA operator, and detailed process is following:

A): for a certain function assignment problem, establish X, U and D are respectively scheme set, property set and decision maker's collection; The weight vectors of attribute weight is ω=(ω ₁, ω ₂..., ω _m), and its vector is ω _j>=0 (j ∈ M),

Decision maker's weight vectors is λ=(λ ₁, λ ₂..., λ _t), λ _k>=0 (k=1,2 ..., t), Decision maker d _k∈ D provides scheme x _i∈ X is at attribute u _jUncertain language assessment value under the ∈ U

And obtain evaluating matrix And

B): utilize the UEWAA operator to evaluating matrix In the capable uncertain language assessment information of i assemble, obtain decision maker d _kThe allocative decision x that provides _iThe synthesized attribute assessed value

(i ∈ N, k=1,2 ..., t)

i∈N，k＝1，2，…，t；?(10)

C): the allocative decision x that utilizes the ULHA operator that t bit decisions person is provided again _iThe synthesized attribute assessed value

(k=1,2 ..., t) assemble, obtain allocative decision x _iColony's synthesized attribute assessed value

i∈N， (11)

Wherein w '=(w ' ₁, w ' ₂..., w ' _t) be the weighing vector of ULHA operator, w ' _k∈ [0,1] (k=1,2 ..., t),

It is the uncertain linguistic variable of one group of weighting

In the big element of k, t is a balance factor;

D): utilize (3) formula, calculate scenarios synthesized attribute value

Between possibly spend And set up and to spend matrix P=(p _Ij) _{N * n}

E): obtain the ordering vector v=(v that possibly spend matrix P ₁, v ₂..., v _n), wherein:

By its component size scheme is sorted at last, promptly obtain optimum function allocative decision.