A method for constructing a usability evaluation index system for civil aircraft cockpit display interface

Abstract

This paper discloses a method for constructing a usability evaluation index system for civil aircraft cockpit display interfaces. First, the EPIC model is used to analyze pilots' human-machine interface interaction cognition, extracting usability indicators in four dimensions. The usability indicators are then preliminarily screened based on the pilots' experience and knowledge, thereby constructing a usability evaluation index system for civil aircraft cockpit display interfaces. Secondly, a hierarchical clustering algorithm and a rough set information entropy algorithm are combined to propose an HC-CEBARKNC algorithm to cluster and simplify the established index system, obtaining a clustered reduced set of indicators for each dimension. Thirdly, the reliability of the clustered reduced set of indicators for each dimension is verified using the SVM classification accuracy, thereby constructing a reasonable usability evaluation index system for civil aircraft cockpit display interfaces. Finally, an improved formula based on rough set conditional information entropy is used to calculate the weights of each indicator in the index system. This method addresses the problem that the index system for civil aircraft cockpit display interface usability research is not comprehensive enough.

Description

Technical Field

[0001] The present invention relates to the field of civil aircraft driving technology, and in particular to a method for constructing a usability evaluation index system for a civil aircraft cockpit display interface. Background Art

[0002] The cockpit display interface of civil aircraft is crucial in civil aviation operations. It is primarily carried by several large-screen color LCDs. It displays information in basic forms such as numbers, symbols, and icons. The primary flight display, navigation display, system display, multi-function control display, and warning and prompt displays provide pilots with systematic and comprehensive flight and status information, ensuring successful mission execution and decision-making. The cockpit display interface of civil aircraft is a crucial human-machine interface between the pilot and the aircraft. With the advancement of modern civil aviation technology, the flight information and tasks pilots must handle are becoming increasingly complex, requiring them to effectively understand and make decisions within a short period of time.

[0003] According to aviation accident statistics, pilots' cognitive errors in display interfaces are the primary cause of errors. In civil aircraft cockpit human-machine interaction systems, the usability of display interfaces directly impacts pilots' cognitive accuracy, and consequently, the performance and safety of the overall flight system. Consequently, research on display interface usability is gaining increasing attention within the aviation industry. Currently, constructing a multidimensional usability evaluation index system for civil aircraft cockpit display interfaces, based on the cognitive interaction process between pilots and display interfaces, has become a major research focus.

[0004] The usability evaluation index system is the key to comprehensive evaluation, and building a reasonable evaluation index system is inseparable from index screening. Indicator screening can reduce information overlap and ensure that each indicator has a significant impact on the evaluation results, thereby improving the efficiency and accuracy of the evaluation.

[0005] Current evaluation index systems primarily focus on qualitative and quantitative screening. First, qualitative index screening relies on expert opinion or subjective experience. Second, quantitative index screening eliminates overlapping indicators by analyzing their correlations. Rough set theory, the most commonly used quantitative index screening method, maintains information classification while utilizing attribute reduction to mine the information provided by the data itself, simplify knowledge, and apply this knowledge to decision-making. Building on classical rough set theory, existing technologies have proposed a series of attribute reduction algorithms based on conditional information entropy, mutual information, and attribute importance. Intelligent optimization algorithms, such as the spider monkey optimization algorithm and genetic algorithm, have also been proposed, inspired by the intelligent behavior of biological populations. However, these intelligent optimization algorithms require large numbers of samples and continuous parameter tuning to achieve the reduction results. However, in practice, large amounts of pilot data are difficult to obtain, making these rough set reduction methods unsuitable for removing redundant indicators from small sample sizes.

[0006] Therefore, it is necessary to improve a method for establishing a usability evaluation index system for civil aircraft cockpit display interfaces and a method for screening indicator data sets suitable for small sample sizes and large numbers of dimensions to solve the problems existing in the above-mentioned existing technologies. Summary of the Invention

[0007] To address the above-mentioned problems, the present invention constructs a new civil aircraft cockpit display interface usability evaluation index system and an index data set screening method suitable for small sample sizes and a large number of dimensions.

[0008] The technical solutions adopted in the present invention are as follows:

[0009] A method for constructing a usability evaluation index system for a civil aircraft cockpit display interface comprises the following steps:

[0010] Step 1: Based on the pilot human-machine interface interaction cognitive process based on the EPIC model, multiple dimensions of civil aircraft cockpit display interface usability indicators are determined, and usability indicators for each dimension are constructed to form an initial set of usability indicators for civil aircraft cockpit display interfaces;

[0011] Step 2: The pilots preliminarily screen the usability indicators obtained in step 1 to obtain a set of screened usability indicators and establish an initial civil aircraft cockpit display interface usability evaluation indicator system;

[0012] Step 3: Use the HC-CEBARKNC algorithm to perform clustering and reduction processing on the indicators in the usability evaluation index system established in step 2 to obtain the clustered and reduced set of indicators in all dimensions;

[0013] Step 4: Use the SVM classification accuracy to verify the established index cluster reduction set. If the verification passes, proceed to step 6; if the verification fails, return to step 3;

[0014] Step 5: Based on the index clustering reduction set, re-establish the civil aircraft cockpit display interface usability evaluation index system;

[0015] Step 6: Using the improved formula of rough set conditional information entropy, calculate the attribute importance and normalized weight coefficient of each indicator in the usability evaluation index system established in step 6, and use the normalized weight as the weight of each indicator to obtain the final civil aircraft cockpit display interface usability evaluation index system;

[0016] Step 7: Use the civil aircraft cockpit display interface usability evaluation index system in step 6 to evaluate the usability of the civil aircraft cockpit display interface.

[0017] Furthermore, the primary civil aircraft cockpit display interface usability evaluation index system established in step 2 includes i first-level indicators Yi, corresponding to the four dimensions of flight mission, pilot senses, pilot cognition, and flight interaction, and m second-level indicators c i1 ,c i2 ,...,c im Where i = 1, 2, 3, 4; m = 1, 2, ..., N, and c im ∈Y i They correspond to the availability indicators retained after preliminary screening.

[0018] Furthermore, the specific steps of step 3 include:

[0019] Step 31: Construct the civil aircraft cockpit display interface usability information system S in four dimensions respectively:

[0020] S={U,A,V,f}

[0021] The domain U is a non-empty finite set of objects consisting of pilots; A is a non-empty finite set of attributes that reflect the characteristics of the objects; the information function f:U×A→V is a mapping of attribute values. For any x∈U, a∈A, there is f(x,a)∈V a ; V = ∪ V a is the value range of all attribute values; and A includes the independent condition attribute set C and decision attribute set D;

[0022] Step 32: The m secondary indicators c in the primary indicator Yi corresponding to the i-th dimension i1 ,c i2 ,...,c im As the condition attribute set Ci, n pilots x n The satisfaction of each indicator in Ci is scored independently, with the score value being {0, 1, 2}, representing very dissatisfied, average, and very satisfied respectively;

[0023] Step 33: n pilots score the dimensional decision according to the satisfaction score of each indicator in Ci. The score ranges from {0, 1}, indicating satisfaction and dissatisfaction of the decision, respectively. The n score values ​​are used as the decision attribute set Di, Di = {d i1 ,d i2 ,...,d in};

[0024] Step 34: Based on the four-dimensional information system S, establish the dimension decision table M for each dimension respectively Yi ;

[0025] Step 35: Update M using agglomerative hierarchical clustering Yi , get the clustered dimension decision table M' Yi ;

[0026] Step 36: Eliminate the dimension decision table M' through the rough set conditional entropy algorithm Yi The redundant indicators of the decision attribute set Di that affect the ability to be repeated are used to construct a simplified set R of indicators of all dimensions.

[0027] Furthermore, the specific steps of step 35 include:

[0028] Step 351: Use Euclidean distance to measure the dimension decision table M of each dimension Yi The similarity between the availability indicators of the conditional attribute set Ci is used to establish the similarity matrix Ti of each dimension;

[0029] Step 352: cluster Ti based on the Ward hierarchical clustering method to obtain a cluster dendrogram of the availability index of the i-th dimension;

[0030] Step 353: According to the clustering tree diagram, M Yi The condition attribute set Ci is divided into k condition attribute subsets A ik , and k ≥ 2;

[0031] Step 354: According to A ik Update dimension decision table M Yi , get the updated dimension decision table M' Yi .

[0032] Furthermore, the specific steps of step 36 include:

[0033] Step 361: Define A ik The equivalence class with Di on U is:

[0034] U / A ik ={Q1,Q2,...,Q r}

[0035] U / Di={P1,P2,...,P e}

[0036] Among them, Q x (x=1,2,…,r) and P s (s=1,2,…,e) are all subsets of U; r and e are A ik The number of equivalence classes with Di on U;

[0037] Step 362: Calculate A ik The information entropy H(A ik ):

[0038]

[0039] Among them, p(Q x )=|Q x | / |U|, represents A ik The probability distribution of |Q x | is the set Q x The cardinality of

[0040] Step 363: Calculate A ik The conditional entropy H(Di|A ik ):

[0041]

[0042] Among them, p(Q x |P s )=|Q x ∩P s | / |U|, represents A ik and the conditional probability distribution of Di;

[0043] Step 364: Calculate the decision attribute set Di relative to A in sequence ik Each conditional attribute c ia The conditional entropy H(Di|{c ia}), sorted in descending order according to the calculated conditional entropy value;

[0044] Step 365: Let A ik The initial reduction set is A ik * =A ik , press H(Di|{c ia}) Calculate the decision attribute Di relative index reduction set A in descending order ik * Remove c ia Conditional entropy H'(Di|A ik ):

[0045] H(Di|Aik )=H(Di|A ik * -{c ia});

[0046] Step 366: Determine H'(Di|A ik ) and H(Di|A ik ) are equal, if they are equal, then the conditional attribute cia can be simplified, then Aik * =Aik-{cia}; otherwise, the conditional attribute cia cannot be reduced, Aik * No change; Step 367: Calculate A ik After all the conditional attributes are in, delete the reducible conditional attributes, retain the unreduced conditional attributes, and output the index reduction set A ik * ;

[0047] Step 368: Reduce set A based on the obtained index ik * , construct the reduced set of indicators Yi in each dimension * , Yi * ={A i1 * ∪A i2 * ∪...∪A ik *}, based on the indicators of each dimension, the set Yi is simplified * Establish a simplified set R of indicators of all dimensions, R = {Y1 * ∪Y2 * ∪Y3 * ∪Y4 *}.

[0048] Furthermore, the specific steps of step 4 include:

[0049] Step 41: The condition attribute set Ci before clustering reduction of the i-th dimension and the condition attribute set Yi after clustering reduction are * As the training set of SVM, and the decision attribute set Di as the prediction set of SVM;

[0050] Step 42: Set the conditional attribute sets Yi and Yi * Divide the sample into h parts for cross validation, train the SVM, and obtain the trained SVM classifier;

[0051] Step 43: Use Ci and Yi respectively through the trained SVM classifier * Classify and predict the decision attribute set Di, and calculate Yi and Yi respectively * The classification accuracy η i ,ηi * ;

[0052] Step 44: Compare the classification accuracy η of Ci in each dimension i and Ci * The classification accuracy η i * , if η is satisfied i <η i * , then the index reduction set after cluster reduction passes the verification; otherwise, it fails the verification and returns to step 3.

[0053] Furthermore, the specific steps of step 6 include:

[0054] Step 61: Reduce the index set A for the i-th dimension ik * , the decision attribute set Di is calculated relative to A by the following formula ik * Conditional information entropy I(Di|A ik * ):

[0055]

[0056] Where t is A ik * The number of equivalence classes on U; U / Aik*={L1,L2,...,Lt} is A ik * The equivalence class on U, L z is a subset of U;

[0057] Step 62: Calculate A using the following formula ik * The conditional information entropy weight ω(A ik * ):

[0058]

[0059] Step 63: For Calculate c by the following formula ia Improved attribute importance NewSig(c ia ):

[0060] NewSig(c ia )=I(Di|A ik * -{c ia})-I(Di|A ik * )+I(Di|{c ia});

[0061] Step 64: Based on NewSig(c ia ) and ω(A ik * ), calculate c ia The final attribute importance Sig(c ia )':

[0062] Sig(c ia )'=NewSig(c ia )ω(A ik * );

[0063] Step 65: Sig(c ia )' is normalized to obtain Sig(c ia )''s normalized weight is

[0064]

[0065] Step 66: Normalize the weights to As the weight value ω(c ia ),Right now The beneficial effects of the present invention are:

[0066] First, based on the pilot's EPIC cognitive model of the display interface, this paper preliminarily establishes a multi-dimensional civil aircraft cockpit display interface usability evaluation index system, avoiding one-sided and subjective selection of indicators and ensuring the comprehensiveness and integrity of the preliminarily established index system.

[0067] Second, based on the hierarchical clustering algorithm and the rough set conditional entropy algorithm, the present invention proposes the HC-CEBARKNC indicator clustering reduction algorithm, which can screen indicator datasets with few samples and many dimensions for the civil aircraft cockpit display interface usability evaluation index system;

[0068] Third, the present invention uses the SVM to predict the classification accuracy of the four dimensions after clustering simplification of indicators. The conditional attribute set before clustering simplification and the reduced indicator set after clustering simplification are used to classify and predict the decision attribute set. This verifies whether the HC-CEBARKNC algorithm can remove redundant indicators while maintaining the original knowledge classification ability of the usability indicators, thereby ensuring the reliability and rationality of the final civil aircraft cockpit display interface usability evaluation index system. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is a flowchart of the indicator evaluation system construction and indicator clustering simplification proposed in this invention.

[0070] Figure 2 Schematic diagram of the pilot human-machine interface interaction cognitive process based on the EPIC model.

[0071] Figure 3 This is the hierarchical clustering dendrogram of the availability indicators of flight mission dimensions.

[0072] Figure 4 Hierarchical clustering tree diagram for the usability indicators of pilot sensory dimensions.

[0073] Figure 5 This is a hierarchical clustering tree diagram of the availability indicators of pilot cognitive dimensions.

[0074] Figure 6 Hierarchical clustering dendrogram for flight interaction dimension availability indicators.

[0075] Figure 7 The following is a comparison chart of the SVM accuracy distribution in four dimensions before and after clustering reduction using the HC-CEBARKNC algorithm.

[0076] Figure 8 The present invention provides a usability evaluation index system for the cockpit display interface of a civil aircraft. DETAILED DESCRIPTION

[0077] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0078] The core idea of ​​this invention is as follows: first, the pilots' human-machine interface interaction cognition is analyzed through the EPIC model, and the usability indicators of four dimensions are extracted. The usability indicators are preliminarily screened based on the pilots' experience and knowledge, and a new usability evaluation index system for the cockpit display interface of civil aircraft is constructed. Then, based on the hierarchical clustering algorithm and the rough set information entropy algorithm, an HC-CEBARKNC indicator clustering reduction algorithm suitable for datasets with a small number of samples and a large number of dimensions is proposed. The established indicator system is clustered and reduced to obtain a four-dimensional indicator cluster reduction set. The classification accuracy of the SVM is then used to verify the reliability of the indicator cluster reduction set of each dimension, and a reasonable civil aircraft cockpit display interface usability evaluation index system is constructed. Finally, the weight coefficient of each indicator in the indicator system is calculated based on the improved formula of rough set conditional information entropy.

[0079] Example

[0080] The present invention discloses a method for constructing a cockpit display interface usability index system, comprising the following steps:

[0081] Step 1: Preliminary determination of civil aircraft cockpit display interface usability indicators

[0082] S1: Determine the four dimensions of the usability index of the civil aircraft cockpit display interface and obtain the usability index of each dimension in the four dimensions;

[0083] The specific steps include:

[0084] S11: Based on Figure 2 The pilot human-machine interface interaction cognitive process based on the EPIC model is shown, which determines the four dimensions of the usability index of the civil aircraft cockpit display interface: flight mission dimension, pilot sensory dimension, pilot cognitive dimension, and flight interaction dimension;

[0085] The usability index of the flight mission dimension is related to the pilot's task situation and task efficiency as well as the task compatibility of the display interface;

[0086] The usability index of the pilot sensory dimension focuses on the visual visibility and auditory accessibility of the pilot to obtain information on the display interface;

[0087] The usability index of the pilot cognitive dimension is related to the pilot's memory and learning ability;

[0088] The usability indicators of the flight interaction dimension are related to the real-time and predictive nature of the display interface.

[0089] S12: A preliminary set of usability indicators for civil aircraft cockpit display interfaces was constructed from four dimensions. The preliminary set of indicators was supplemented and revised by combining the Nielsen model for general product usability evaluation and the software quality model (ISO 9126). The resulting preliminary set of usability indicators for civil aircraft cockpit display interfaces is shown in Table 1, which contains a total of 43 usability indicators.

[0090] Table 1 Usability indicators of the preliminary selection

[0091]

[0092] Step 2: Preliminary screening of the initial set of availability indicators

[0093] S2: The pilots conduct a preliminary screening of the initial set of availability indicators established in S1;

[0094] It specifically includes the following steps:

[0095] S21: A preliminary screening of the usability indicators shown in Table 1 was conducted using pilots with flight time greater than 30,000 hours. Thirty-five usability indicators were retained, and eight were deleted. The deleted indicators included: mission accuracy and mission completion time from the flight mission dimension; balance, volume adjustability, and audio optimization from the pilot sensory dimension; learnability from the pilot cognitive dimension; and interaction standardization and immediate interaction response from the flight interaction dimension.

[0096] S22: The usability evaluation index set is defined as Y = {Y1, Y2, Y3, Y4}, where Y1 represents the flight mission dimension, Y2 represents the pilot sensory dimension, Y3 represents the pilot cognitive dimension, and Y4 represents the flight interaction dimension. The retained usability indicators and indicator symbols after preliminary screening of the four dimensions Y1, Y2, Y3, and Y4 are shown in Table 2. The usability evaluation index set Y for the civil aircraft cockpit display interface can be described as:

[0097]

[0098] Table 2 Description of symbols for reserved availability indicators

[0099]

[0100] Step 3: Establish an initial usability evaluation index system based on the screened usability indicators

[0101] S3: Establish an initial usability evaluation index system for the usability of civil aircraft cockpit display interfaces;

[0102] It specifically includes the following steps:

[0103] S31: The first level indicator is defined as Y i (i=1, 2, 3, 4), corresponding to flight mission, pilot senses, pilot cognition, and flight interaction, respectively;

[0104] S32: The secondary indicator is defined as c i1 ,c i2 ,...,c im (i=1,2,3,4;m=1,2,…,N), and c im ∈Y i , c im It represents the availability indicators retained after the initial screening of each dimension in Table 2.

[0105] Step 4: Simplify the availability indicator system to obtain the index cluster reduction set

[0106] S4: Based on the HC-CEBARKNC algorithm, cluster reduction calculations are performed on the availability evaluation index system initially established in step S3 to obtain a cluster reduction set of indicators in four dimensions;

[0107] This example uses the flight mission dimension as an example to describe the indicator clustering and simplification process of the flight mission dimension:

[0108] S41: Construct a four-dimensional civil aircraft cockpit display interface usability information system S, where S = {U, A, V, f}, where U represents a non-empty finite set of objects consisting of pilots, and A represents a non-empty finite set of attributes reflecting object characteristics, including a mutually independent conditional attribute set C and a decision attribute set D, satisfying C∪D=A and C∩D=φ; V=∪V a Represents the range of all attribute values; the function f:U×A→V is a mapping of attribute values. For any x∈U, a∈A, there is f(x,a)∈V a ;

[0109] S42: The m availability indicators c included in Yi i1 ,c i2 ,...,c im As the condition attribute set Ci, invite n pilots x n The usability indicators contained in the conditional attribute set Ci are scored independently, with scores ranging from {0, 1, 2}, where a score of 0 indicates a very unsatisfactory indicator, a score of 1 indicates an average indicator, and a score of 2 indicates a very satisfactory indicator. The data is standardized and discretized using the scoring values.

[0110] S43: The pilot's satisfaction with the usability index of the civil aircraft cockpit display interface in the i-th dimension is used as the decision attribute set Di, whose value is {0,1}: the decision value 0 indicates dissatisfaction and the decision value 1 indicates satisfaction;

[0111] S44: Construct dimensional decision table M based on the four-dimensional information system S Yi (i=1,2,3,4), define the conditional attribute set Ci of the i-th dimension={c i1 ,c i2 ,...,c im}, the decision attribute set is Di={di1,di2,...,din}, and the domain is n pilots (x n ), that is, U = {x1, x2, ..., xn};

[0112] This embodiment takes the flight mission (i=1) as an example, and constructs the dimension decision table M Y1 , as shown in Table 3.

[0113] Table 3 Dimensional decision table of flight mission

[0114]

[0115]

[0116] S45: Use the agglomerative hierarchical clustering method to analyze the i-th dimension decision table M Yi Update and get the clustered dimension decision table M'Yi , the specific steps are:

[0117] 1) Use Euclidean distance to measure the dimension decision table M Yi The similarity between the two availability indicators contained in the conditional attribute set Ci in , and form a similarity matrix Ti;

[0118] 2) Use Ward hierarchical clustering method to cluster the similarity matrix Ti and obtain the clustering tree diagram of the availability index of the i-th dimension. The clustering tree diagram of the flight mission dimension (i=1) is as follows: Figure 3 As shown;

[0119] 3) According to the clustering tree diagram, the decision table M Yi The conditional attribute set Ci is divided into k conditional attribute subsets, k≥2, that is, Ci={Ai1∪Ai2∪...∪Aik}, and c ia ∈A ik ;

[0120] Taking the flight mission as an example, when k=3, Yi is divided into Y1={A 11 ,A 12 ,A 12}(c 1a ∈A 1k ,), where: A 11 ={c 16 ,c 17 ,c 19}, A 12 ={c 11 ,c 12 ,c 14}, A 13 ={c 13 ,c 15 ,c 110};

[0121] 4) Update the dimension decision table M according to the hierarchical clustering results of the previous step Yi , get the updated dimension decision table M' Yi ;

[0122] The updated dimension decision table of the flight mission is shown in Table 4.

[0123] Table 4 Dimension decision table of updated flight mission

[0124]

[0125]

[0126] S46: Use the rough set conditional entropy CEBARKNC algorithm to screen the i-th dimension condition attribute set Ci again, and eliminate the dimension decision table M' in turnYi The redundant indicators of the ability to influence the decision attribute set Di are repeated;

[0127] Taking a flight mission as an example, the specific steps include:

[0128] 1)A 11 The equivalence class with D1 on U is U / A 11 ={{x1},{x2},{x3,x4,x6},{x5},{x7}}, the equivalence class derived from D1 is U / D1={{x2,x3,x6},{x1,x4,x5,x7}};

[0129] 2) Calculate A 11 The information entropy H(A 11 )=2.1281;

[0130] 3) Calculate A 11 The conditional entropy H(D1|A 11 )=0.5714;

[0131] 4) Calculate H(D1|{c ia}), each conditional attribute c ia According to H(D1|{c ia The values ​​of}) are arranged in descending order, and the calculation results are shown in Table 5;

[0132] 5) Let A 11 The initial reduction set A 11 * =A 11 , press H(D1|{c ia}) Calculate the decision attribute D1 relative index reduction set A in descending order 11 * Remove c ia The conditional entropy H'(D1|A 11 ), the calculation results are shown in Table 5;

[0133] 6) If H'(D1|A 11 )==H(D1|A 11 ), it means that the conditional attribute c can be simplified ia , at this time A 11 * =A 11 -{cia}; otherwise, the conditional attribute cia cannot be reduced, A 11 * Unchanged; calculation condition attribute subset A ik All c ia After that, delete the availability indicator c 16 、c 17 , retain the availability index c 19, the final output index reduction set A 11 * ={c 19};

[0134] Table 5 Conditional entropy calculation results

[0135]

[0136]

[0137] 7) Reduce set A according to the index 11 * , and obtain the flight mission dimension index reduction set Y1 * :

[0138] Y1*={A 11 *∪A 12 *∪A 13 *}={{c 19}∪{c 12}∪{c 13 ,c 15 ,c 110}};

[0139] S47: Repeat step S45 to obtain the hierarchical clustering dendrograms of the usability indicators of the pilot sensory dimension, the pilot cognitive dimension, and the flight interaction dimension, as shown in FIG. Figure 4 、 Figure 5 and Figure 6 Repeat step S46 to calculate the four-dimensional index reduction set Yi of the civil aircraft cockpit display interface usability evaluation index system * , establish a simplified set R of indicators of all dimensions:

[0140]

[0141] Step 5: Verify whether the cluster reduction is reasonable. If not, rebuild the decision table.

[0142] S5: Use the SVM classification accuracy to verify the rationality of the cluster reduction of the four-dimensional indicators. If the SVM classification accuracy verification of a certain dimension is unreasonable, it is necessary to return to step S3 to reconstruct the decision table of each dimension;

[0143] The specific steps include:

[0144] S51: In the i dimension, the condition attribute set Ci before clustering simplification and the condition attribute set Yi after clustering simplification are * As the training set of SVM, the decision attribute set Di is used as the prediction set of SVM;

[0145] S52: To verify the effectiveness of cluster reduction using the HC-CEBARKNC algorithm, the SVM classifier is trained using the Gaussian radial basis kernel function of the libsvm toolkit in MATLAB software, and the conditional attribute set Yi and the index reduction set Yi are transformed into * Divide into h samples for cross validation, the Gaussian radial basis kernel function K(σ i ,σ x )for:

[0146]

[0147] Among them, δ is the scale parameter;

[0148] S53: Use SVM to predict the classification accuracy, using the conditional attribute set Yi before clustering reduction and the index reduction set Yi after clustering reduction. * Classify and predict the decision attribute set Di;

[0149] The classification accuracy is calculated by the classification accuracy calculation formula, and the classification accuracy η of the conditional attribute set Yi is output. i , index reduction set * The classification accuracy η i * ;

[0150] The calculation formula for the classification accuracy is:

[0151]

[0152] Among them, h is the total number of samples, h1 is the number of samples whose training results of the training set samples are the same as the actual category, and h2 is the number of samples whose training results of the prediction set samples are the same as the actual category;

[0153] The classification accuracy of the four dimensions is calculated by the above formula:

[0154] η1=51.23,η1 * =76.52(η1<η1 * )

[0155] η2=53.46,η2 * =81.66(η2<η2 * )

[0156] η3=62.18,η3 * =78.29(η3<η3 * )

[0157] η4=58.15,η4 * =80.01(η4<η4 * )

[0158] The SVM accuracy distribution comparison diagram of the four dimensions in this embodiment before and after the HC-CEBARKNC algorithm cluster reduction is as follows: Figure 7 As shown;

[0159] S54: Determine η of each dimension i Whether η is satisfied i <η i * , if satisfied, the clustering reduction is reasonable;

[0160] From the above classification accuracy calculation results, we can see that the η of the four dimensions i Are greater than or equal to η i * ,It is proved that the usability indicators retained after cluster simplification are ,relatively strong and can be used to construct a usability evaluation ,index system for civil aircraft cockpit display interface.

[0161] Step 6: Construct a new usability evaluation index system based on index clustering and reduction set transformation

[0162] S6: Based on the four dimensions and the cluster reduction set of indicators in each dimension, establish a usability evaluation index system for the cockpit display interface of civil aircraft;

[0163] It specifically includes the following steps:

[0164] S61: The four dimensions Yi (i = 1, 2, 3, 4) are still used as the first-level indicators, including: flight mission, pilot senses, pilot cognition, and flight interaction;

[0165] S62: Yi after clustering simplification * The indicators included in the are as secondary indicators, including: task orientation, satisfaction, task accuracy, information matching, task complexity, interface layout, interface information, interface color, voice warning prompts, understanding, minimum memory load, simplicity, consistency, information real-time, information accuracy, interactive feedback, setting change, and collision warning;

[0166] Based on the above-mentioned primary and secondary indicators, a civil aircraft cockpit display interface usability evaluation index system is constructed. The evaluation index system established is as follows: Figure 8 shown.

[0167] Step 7: Calculate the weight of each indicator in the established evaluation index system and normalize it

[0168] S7: Assume that the weights of the first-level indicators of the indicator system are the same, and use the improved formula of rough set conditional information entropy to calculate the attribute importance and normalized weight coefficient of each indicator in the indicator system;

[0169] Taking the flight mission dimension as an example, the specific steps include:

[0170] S71: Reduced set A for flight mission dimensions 1k * , the decision attribute set Di relative to A is calculated by the conditional entropy calculation formula 1k * The conditional information entropy I(D1|A 1k * );

[0171] The conditional information entropy calculation formula is:

[0172]

[0173] The calculated conditional information entropy is:

[0174] I(D1|A 11 * )=8 / 49;

[0175] I(D1|A 12 * )=2 / 49;

[0176] I(D1|A 13 * )=4 / 49;

[0177] S72: Since the condition attribute Y1 is divided into k condition attribute sets, A is calculated according to the condition information entropy weight calculation formula. 1k * The conditional information entropy weight ω(A 1k * );

[0178] The calculation formula of the conditional information entropy weight is:

[0179]

[0180] The calculated weights are:

[0181] ω(A 11 * )=0.5714;

[0182] ω(A 12 * )=0.1429;

[0183] ω(A 13 * )=0.2857;

[0184] S73: For Indicator c ia The improved attribute importance calculation formula based on rough set information entropy is:

[0185] NewSig(c ia )=I(Di|A ik * -{c ia})-I(Di|A ik * )+I(Di|{c ia});

[0186] The calculated improved attribute importance NewSig(c ia )for:

[0187] NewSig(c 19 )=0.2449;

[0188] NewSig(c 12 )=0.1225;

[0189] NewSig(c 13 )=0.4081;

[0190] NewSig(c 15 )=0.2041;

[0191] NewSig(c 110 )=0.2041;

[0192] S74: For indicators By setting the indicator c ia The improved attribute importance of NewSig(c ia ) and the conditional information entropy weight ω(A 1k * ) is weighted to calculate the final attribute importance Sig(c ia )';

[0193] The final attribute importance calculation formula is:

[0194] Sig(c ia )'=NewSig(c ia )ω(A ik * );

[0195] The final attribute importance calculated is:

[0196] Sig(c 19 )'=ω(A 11 * )×NewSig(c 19 )=0.1399;

[0197] Sig(c 12)'=ω(A 12 * )×NewSig(c 12 )=0.0175;

[0198] Sig(c 13 )'=ω(A 13 * )×NewSig(c 13 )=0.1166;

[0199] Sig(c 15 )'=ω(A 13 * )×NewSig(c 15 )=0.0583;

[0200] Sig(c 110 )'=ω(A 13 * )×NewSig(c 110 )=0.0583;

[0201] S75: The final attribute importance Sig(c ia )', since the weights of the four dimensions of the civil aircraft cockpit display interface usability index evaluation system are the same, the final attribute importance Sig(c ia )' perform normalization to obtain any index c ia ∈R’s final attribute importance Sig(c ia )''s normalized weight use The weight values ​​of all indicators in the index reduction set R can be determined, where The calculation formula is:

[0202]

[0203] S76: Set any indicator c ia The normalized weight of ∈R is As the indicator weight ω(c ia ),Right now The weight values ​​ω(c ia )The calculation results are shown in Table 6.

[0204] Table 6 Calculation results of indicator weights

[0205]

[0206] Step 8: Evaluate the usability of the civil aircraft cockpit display interface based on the usability evaluation index system established in step 7.

[0207] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for constructing a usability evaluation index system for a civil aircraft cockpit display interface, characterized in that: The following steps are involved: Step 1: Based on the pilot human-machine interface interaction cognitive process based on the EPIC model, multiple dimensions of civil aircraft cockpit display interface usability indicators are determined, and usability indicators for each dimension are constructed to form an initial set of usability indicators for civil aircraft cockpit display interfaces. Step 2: The pilots preliminarily screen the usability indicators obtained in step 1 to obtain a set of screened usability indicators and establish an initial civil aircraft cockpit display interface usability evaluation indicator system; Step 3: Based on the hierarchical clustering algorithm and the rough set conditional entropy algorithm, the HC-CEBARKNC indicator clustering reduction algorithm is constructed. The HC-CEBARKNC algorithm is used to cluster and reduce the indicators in the usability evaluation indicator system established in step 2 to obtain the clustered and reduced set of indicators in all dimensions. Step 4: Use the SVM classification accuracy to verify the established index cluster reduction set. If the verification passes, proceed to step 5; if the verification fails, return to step 3; Step 5: Based on the index clustering reduction set, re-establish the civil aircraft cockpit display interface usability evaluation index system; Step 6: Using the improved formula of rough set conditional information entropy, calculate the attribute importance and normalized weight coefficient of each indicator in the usability evaluation index system established in step 5, and use the normalized weight as the weight of each indicator to obtain the final civil aircraft cockpit display interface usability evaluation index system; Step 7: Use the civil aircraft cockpit display interface usability evaluation index system in step 6 to evaluate the usability of the civil aircraft cockpit display interface; Among them, the primary civil aircraft cockpit display interface usability evaluation index system established in step 2 includes: i First-level indicators Yi , corresponding to the four dimensions of flight mission, pilot senses, pilot cognition, and flight interaction, and m Secondary indicators in, and They correspond to the availability indicators retained after preliminary screening.

2. The method for constructing a usability evaluation index system for a civil aircraft cockpit display interface according to claim 1, characterized in that: The specific steps of step 3 include: Step 31: Construct four dimensions of civil aircraft cockpit display interface usability information system S : Among them, the domain U is a non-empty finite set of objects consisting of pilots; A is a non-empty finite set of attributes that reflects the characteristics of the object; information function is a mapping of attribute values, for any , both is the range of all attribute values; and A Includes independent sets of conditional attributes C and decision attribute set D ; Step 32: i The first-level indicators corresponding to the dimensions Yi in m Secondary indicators As a conditional attribute set Ci ,Depend on n pilots x n right Ci The satisfaction of each indicator in the score is scored independently, and the score is , indicating very dissatisfied, average, and very satisfied respectively; Step 33: By n The pilots were Ci The satisfaction scores of each indicator in the dimensional decision are used to score the dimension decision, and the score value is , indicating satisfaction with the decision and dissatisfaction with the decision, respectively, and n The scoring value is used as the decision attribute set Di , ; Step 34: Information system based on four dimensions S , respectively establish the dimension decision table for each dimension ; Step 35: Update using agglomerative hierarchical clustering , get the dimension decision table after clustering ; Step 36: Eliminate dimension decision table using rough set conditional entropy algorithm Decision attribute set Di Redundant indicators that affect capability duplication, building a simplified set of indicators for all dimensions R .

3. The method for constructing a usability evaluation index system for a civil aircraft cockpit display interface according to claim 2, characterized in that: The specific steps of step 35 include: Step 351: Use Euclidean distance to measure the dimension decision table of each dimension With conditional attribute set Ci The similarity between the two usability indicators is used to establish the similarity matrix of each dimension. Ti ; Step 352: Based on the Ward hierarchical clustering method Ti Perform clustering and obtain Cluster dendrogram of availability indicators of dimensions; Step 353: According to the cluster dendrogram, Conditional attribute set Ci Divided into Conditional attribute subset ,and ; Step 354: According to Update dimension decision table , get the updated dimension decision table .

4. The method for constructing a usability evaluation index system for a civil aircraft cockpit display interface according to claim 3, characterized in that: The specific steps of step 36 include: Step 361: Definition and Di exist U The equivalence classes on are: in, and Both U A subset of r and e They are and Di exist U the number of equivalence classes on ; Step 362: Calculation Information entropy : in, ,express The probability distribution of For collection The cardinality of Step 363: Calculation For decision attribute set Di Conditional entropy : in, ,express and Di The conditional probability distribution of ; Step 364: Calculate the decision attribute set in sequence Di relatively Each conditional attribute Conditional entropy , sorted in descending order according to the calculated conditional entropy value; Step 365: Make The initial reduction set is ,according to Calculate decision attributes in descending order Di Relative Index Reduced Set Remove Conditional entropy : ； Step 366: Judgment Are they equal? ​​If they are equal, then the conditional attribute can be simplified, then ; Otherwise, the conditional attribute Cannot be reduced, No change; Step 367: Calculate After all the conditional attributes are in the index, the reducible conditional attributes are deleted, the unreduced conditional attributes are retained, and the index reduction set is output. ; Step 368: Reduce the set based on the obtained index , construct a reduced set of indicators for each dimension , according to the simplified set of indicators of each dimension Create a simplified collection of indicators for all dimensions R , .

5. The method for constructing a usability evaluation index system for a civil aircraft cockpit display interface according to claim 4, characterized in that: The specific steps of step 4 include: Step 41: i The conditional attribute set before cluster reduction of dimensions Ci And the conditional attribute set after clustering reduction As the training set of SVM, and the decision attribute set Di As the prediction set of SVM; Step 42: Set the conditional attribute Yi and Divide h Cross-validate the samples, train the SVM, and obtain the trained SVM classifier; Step 43: Use the trained SVM classifier to Ci 、 For decision attribute set Di Perform classification predictions and calculate the Yi and The classification accuracy of ; Step 44: Compare the dimensions Ci The classification accuracy of and The classification accuracy of , if satisfied , then the index reduction set after cluster reduction passes the verification; otherwise, it fails the verification and returns to step 3.

6. The method for constructing a usability evaluation index system for a civil aircraft cockpit display interface according to claim 5, characterized in that: The specific steps of step 6 include: Step 61: Reduce the index set for the i-th dimension , the decision attribute set is calculated by the following formula Di Relative to Conditional information entropy : in, t for exist U the number of equivalence classes on ; for exist U The equivalence class on for U A subset of Step 62: Calculate using the following formula Conditional information entropy weight of each conditional attribute subset : ； Step 63: For , calculated by the following formula Improved attribute importance : ； Step 64: Based on and ,calculate The final attribute importance : ； Step 65: Perform normalization and obtain The normalized weight of : ； Step 66: Normalize the weights to As the weight value of all indicators in the index reduction set R ,Right now .

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