Vehicle appearance multifunctional design method fusing eye movement tracking analysis and A-KANO model

Abstract

The invention provides a special vehicle shape design method based on eye movement data and an A-KANO model, and relates to the technical field of special vehicle shape design, and the method comprises the following steps: firstly, constructing a target vehicle model database, and creating a high-fidelity VR simulation battlefield environment; visual attention data in the user experience process are collected through an eye tracker to serve as preposed subjective evaluation. Then, guiding a user to carry out perceptual description on a region of interest, collecting keywords to construct a functional demand pool and design an A-KANO survey questionnaire, and calculating keyword frequency and weight at the same time; thirdly, fusing questionnaire results and keyword weights, calculating Bet-Worse coefficients of all functional requirements by adopting an improved A-KANO model, constructing a requirement quantization table according to the Bet-Worse coefficients, and mapping perceptual requirements of users into specific functional engineering indexes according to priorities; and finally, executing three-stage shape topological optimization according to a priority sequence of necessary, expected and charm requirements on the basis of quantitative indexes, and carrying out step-by-step iteration to generate a final design scheme.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of special vehicle shape design, in particular, especially relates to a vehicle shape multifunctional design method combining eye movement tracking analysis and A-KANO model. BACKGROUND

[0002] Traditional special vehicle shape design has problems such as unclear demand conversion and low efficiency in solving function conflicts. In the shape design process, a large amount of experience of engineers is relied on, there is a lack of quantitative analysis of user (special operation / rescue personnel) demand, and there are problems such as function differentiation and poor compatibility. For the composite requirements of stealth, protection and mobility, the existing method lacks a systematic conflict resolution mechanism, and it is difficult to realize effective coordination of multi-target performance.

[0003] At present, vehicle shape design mostly focuses on aerodynamic performance optimization, fluid mechanics analysis, and mostly optimizes the shape of the vehicle for single function requirements. And a large number of theoretical simulation researches lack correlation with the real needs of users. For example, patent CN 112883487 A provides a vehicle shape design optimization system based on big data, which optimizes and adjusts the main axis and main point of the vehicle model through the shape optimization module, adjusts the parameters according to the actual resistance data through the shape test module, and the shape optimized by using a large amount of collected data can withstand smaller air resistance, optimizing the aerodynamic performance of the vehicle shape.

[0004] At present, the user participation is limited, and the feedback of terminal users such as special operation personnel and rescue team members is not deeply integrated into the design closed loop. The function configuration and the adaptability to actual combat scene still have optimization space, and the traditional experience-driven mode often needs repeated trial and error, prolonging the development cycle. SUMMARY

[0005] According to the technical problems mentioned in the above background art, a vehicle shape multifunctional design method combining eye movement tracking analysis and A-KANO model is provided. The purpose of the present application is to provide a vehicle shape multifunctional design method combining eye movement tracking analysis and A-KANO model, which can consider user demand in special vehicle shape design and a method of fusing various function requirements.

[0006] The technical means adopted by the present application are as follows: A vehicle shape multifunctional design method combining eye movement tracking analysis and A-KANO model, characterized in that it comprises the following steps: Step S01: constructing a target vehicle design data library, and constructing a VR simulation scene on a virtual server, while embedding the target vehicle design data library in the simulation scene; a user wearing a VR device with an eye tracker experiences a simulated battlefield environment in the simulation scene, and acquires eye movement feedback data for a pre-subjective evaluation; Step S02: corresponding the eye movement feedback data pre-subjective evaluation with a plurality of subjective feeling keywords for describing the subjective feeling when focusing on a high-frequency attention area or a long-time gaze area; collecting the subjective feeling keywords and constructing a special vehicle function demand pool according to the subjective feeling keywords; constructing a user survey questionnaire according to the special vehicle function demand pool and the A-KANO model, and calculating the frequency and weight of the appearance of the subjective feeling keywords ; Step S03: calculating a Better-Worse coefficient of the user subjective feeling keywords corresponding to the demand according to the results of the user survey questionnaire, the frequency and weight of the appearance of the subjective feeling keywords ; and constructing a demand quantification table according to the priority mapping of the size of the Better-Worse coefficient; Step S04: sequentially performing three-stage shape topology optimization according to the engineering parameter indexes mapped by the constructed demand quantification table; the three-stage shape topology optimization firstly inputs the necessary demand layer parameters into a topology optimization software to generate a basic vehicle body configuration meeting the necessary demand indexes; after the indexes of the layer are verified, the desired demand layer parameters are then imported for secondary design iteration; after the indexes of the two layers are verified, the final optimization of the function fusion is finally completed according to the charm demand layer indexes, and a special vehicle shape design scheme integrating multiple demand characteristics is formed.

[0007] Further, the eye movement feedback data includes a user's high-frequency attention area, a gaze movement path, and a gaze duration.

[0008] Further, the user pre-subjective evaluation includes a gaze hotspot map, a saccade path, and a gaze duration.

[0009] Further, the user survey questionnaire designed based on the user pre-subjective evaluation is a standardized setting; The user survey questionnaire designed based on the user pre-subjective evaluation includes a two-way demand degree evaluation and a function importance weight score; Let each respondent be , and the survey data results of the functions corresponding to the collected user subjective evaluation keywords are ; wherein ; the asymmetric setting score is ; wherein, the satisfaction degree when the function is provided the value of the function, representing the dissatisfaction when the function is missing, representing the importance weight of the function; the average satisfaction when the function is available the average dissatisfaction when the function is missing is: ; ; ; The user satisfaction feedback of each function requirement can be represented by the Better_Worse coefficient as :

[0010] ; The frequency and weight of the keywords in the user's pre-subjective evaluation are introduced The final coefficient is represented as: .

[0011] Further, the mapping rule contained in the requirement quantification table is: for The greater the coefficient of the requirement, the more stringent the response indicator should be.

[0012] Further, in step S04, the three-stage shape topology optimization includes: essential requirement layer, expected requirement layer and charm requirement layer; The three-stage shape topology optimization is respectively provided with a constraint condition; the constraint condition is: A, essential requirement layer: strict boundary constraint; B, expected requirement layer: elastic boundary constraint; C, charm requirement layer: no mandatory constraint.

[0013] Compared with the prior art, the present application has the following advantages: 1. The special vehicle shape design method based on eye movement data and A-KANO model provided by the present application realizes accurate quantitative conversion of user sensory requirements to engineering design language by combining user subjective eye movement data and A-KANO model, effectively improving the multi-objective collaboration and user satisfaction of special vehicle shape design.

[0014] 2. The special vehicle shape design method based on eye movement data and A-KANO model provided by the present application highlights the guiding and correcting effect of behavior data on subjective expression by using eye movement data as pre-subjective evaluation.

[0015] 3. The application provides a special vehicle shape design method based on eye movement data and A-KANO model, which clearly improves the core of the A-KANO model by introducing the quantitative weight of perceptual vocabulary by fusing questionnaire results and keyword weight.

[0016] In conclusion, the technical scheme of the application solves the problem that it is difficult to quantify perceptual demand and to convert the perceptual demand of users into engineering design indexes when designing the shape of a vehicle, especially a special vehicle. The application forms a quantitative conversion mechanism for mapping "sensory language" into "engineering indexes" by frequency and weight of perceptual vocabulary and combining the frequency and weight with the structured analysis of the A-KANO model, and performs shape design according to the mapped engineering indexes, so that the perceptual demand can be accurately guided and verified in the design. Therefore, the technical scheme of the application solves the problems of fuzzy demand conversion and low efficiency in solving function conflicts in the existing shape design.

[0017] Based on the above reasons, the application can be widely promoted in the fields of special vehicles and high-end equipment manufacturing. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical scheme in the embodiments of the application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0019] Figure 1 The whole design method flowchart of the application.

[0020] Figure 2 The user research questionnaire example diagram for function 1 demand in the application.

[0021] Figure 3 The example keyword set collected based on the user's pre-subjective evaluation in the application.

[0022] Figure 4 The demand quantification representation example diagram in the application.

[0023] Figure 5 The topology optimization design flowchart in step S04 of the application. DETAILED DESCRIPTION

[0024] In order to make the person skilled in the art better understand the technical scheme of the present application, the technical scheme in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by the person skilled in the art without creative labor should belong to the protection scope of the present application.

[0025] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0026] As shown in Figure 1 The present application discloses a vehicle shape multifunctional design method combining eye tracking analysis and A-KANO model, comprising the following steps: Step S01: Collect domestic and foreign 5-year target vehicle protection level, shape, weight and three-dimensional size data, etc., to construct a target vehicle design data library. Build a high-fidelity VR simulation scene on a virtual server, and embed the vehicle design data library. Users wearing VR equipment with an eye tracker experience simulated battlefield environment in the simulation scene. Record the user's high-frequency attention area, eye movement path and gaze duration, and use these eye movement feedback data as pre-subjective evaluation.

[0027] Step S02: Correspond the eye movement feedback data pre-subjective evaluation with a number of subjective feeling keywords used to describe the high-frequency attention area or long-time gaze area; collect subjective feeling keywords and construct a special vehicle function demand pool according to the subjective feeling keywords; construct a user survey questionnaire according to the special vehicle function demand pool and A-KANO model, and calculate the frequency and weight of the appearance of the subjective feeling keywords .

[0028] Step S03: According to the results of the user survey questionnaire, the frequency and weight of the appearance of the subjective feeling keywords A Better-Worse coefficient of a user subjective feeling word corresponding demand is calculated; a demand quantification table is constructed according to a priority mapping of the size of the Better-Worse coefficient; Step S04: according to the engineering parameter index mapped by the constructed demand quantification table, sequentially performing three-stage shape topology optimization; the three-stage shape topology optimization firstly inputs the necessary demand layer parameter into the topology optimization software to generate a basic vehicle body configuration meeting the necessary demand index; after the index of this layer is verified, the expected demand layer parameter is then imported for secondary design iteration; after the indexes of the two layers are verified, finally, the final optimization of function fusion is completed according to the charm demand layer index, and a special vehicle shape design scheme with multiple demand characteristics is formed.

[0029] In the embodiment, step S01 first collects user eye movement feedback data as a pre-subjective evaluation, and based on the function content design in the demand pool, a user research questionnaire for demand 1 is designed as shown in the table. Figure 2 The A-KANO model is used to add the user eye high-frequency attention area, gaze movement path and gaze duration in the user research questionnaire, and the eye movement feedback data is added to the A-KANO model to cooperatively calculate the Better_Worse coefficient.

[0030] As a preferred embodiment, the fusion of eye movement tracking analysis and the A-KANO model includes adding the frequency and weight of keywords appearing in the user pre-subjective evaluation The high-fidelity VR simulation scene includes a battlefield, a disaster area, a mountainous area and other special operation environments, and the user experience simulation battlefield environment includes tasks such as performing rapid identification of vehicles and evaluating concealment. The user pre-subjective evaluation includes a gaze hotspot map (high-frequency attention area, such as vehicle body edges and protective structures), a saccade path (visual movement logic reflecting information acquisition priority), and a gaze duration (judging the attractiveness or cognitive difficulty of a design element).

[0031] As a preferred embodiment, designing a user research questionnaire based on user pre-subjective evaluation includes setting each respondent as , and the research data results corresponding to the user subjective evaluation keywords collected are , wherein The questionnaire design form adopts a standardized setting, each function facility is briefly explained, and three questions are set below, including a bidirectional demand degree evaluation of whether the demand is possessed and a function importance weight scoring. The degree of customer satisfaction or dissatisfaction is defined by using a numerical value. Since positive and positive answers are considered to have more value than negative answers, in order to reduce the impact of negative answers, the numerical proportion is not symmetrical, and the weight value is normalized to a number between 0 and 1, i.e. . , wherein Satisfaction level when the representative function is available Pick, 'w' represents dissatisfaction when a feature is missing, and 'w' represents the weight of the feature's importance. From this, the mean satisfaction level when the feature is available can be calculated. Mean dissatisfaction when features are missing : ; ; ; Ultimately, user satisfaction feedback for each feature requirement can be represented by the Better_Worse coefficient: ; ; The improved A-KANO model incorporates the frequency and weight of keywords from users' pre-subjective evaluations. The final indicators were obtained Coefficient representation: ; In this embodiment, step S03 includes mapping rules based on the demand quantification table: for The larger the coefficient, the more stringent the corresponding response metrics should be.

[0032] In step S04, the three-stage shape topology optimization includes: the essential requirement layer, the desired requirement layer, and the attractive requirement layer; The three-stage shape and topology optimization is each subject to constraints; the constraints are as follows: A. Essential Requirements Layer: Strict boundary constraints; B. Expected Demand Layer: Flexible Boundary Constraints; C. Attraction Demand Layer: No mandatory constraints.

[0033] That is to say, the result calculated in step S02 The higher the value, the more important the requirement. Therefore, the corresponding specifications should be more stringent in the exterior design to ensure that essential requirements are met first. In this application, different functions have different stringent specifications. For example, the stringent specifications for "stealth" performance require… "Strict" can be defined from several aspects: 1. In terms of numerical constraints, performance parameters with a narrower numerical range and higher target values; 2. In terms of subsequent topology optimization, it has a higher priority and is placed in the essential requirements layer for optimization design; 3. In subsequent topology optimization, strict boundary condition constraints should be applied.

[0034] like Figure 5As shown, in this embodiment, step S04 sequentially performs three-stage shape topology optimization based on the engineering parameter indicators mapped by the demand quantification table. First, the essential demand layer parameters are input into the topology optimization software to generate a basic vehicle body configuration that meets the essential demand indicators; after the indicators of this layer are met, the expected demand layer parameters are then imported for secondary design iteration; after the current two layers of demand indicators are verified to be passed, the final optimization of functional integration is completed based on the attractive demand layer indicators, forming a special vehicle shape design scheme that integrates multiple demand characteristics.

[0035] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. In the above embodiments of the present invention, the descriptions of each embodiment have their own emphasis; parts not described in detail in a certain embodiment can be referred to in the relevant descriptions of other embodiments. It should be understood that the disclosed technical content in the several embodiments provided in this application can be implemented in other ways.

[0036] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-functional design method for vehicle exterior shape integrating eye-tracking analysis and A-KANO model, characterized in that, Includes the following steps: Step S01: Construct a target vehicle design data database and build a VR simulation scene on a virtual server, while embedding the target vehicle design data database into the simulation scene; Users wear VR devices equipped with eye trackers to experience simulated battlefield environments in simulated scenarios, and obtain eye-tracking feedback data for pre-subjective evaluation; Step S02: Pre-evaluate the eye-tracking feedback data with several keywords describing subjective feelings during high-frequency or prolonged fixation areas; collect these subjective feelings keywords and construct a special vehicle functional requirement pool based on them; construct a user survey questionnaire based on the special vehicle functional requirement pool and the A-KANO model, and simultaneously calculate the frequency and weight of the subjective feelings keywords. ; Step S03: Based on the results of the user survey questionnaire, the frequency and weight of the keywords related to subjective feelings. Calculate the Better-Worse coefficient of the demand corresponding to the user's subjective feeling words; construct a demand quantification table based on the priority mapping according to the magnitude of the Better-Worse coefficient; Step S04: Based on the engineering parameter indicators mapped by the constructed requirement quantification table, perform three-stage shape topology optimization sequentially; the three-stage shape topology optimization first inputs the essential requirement layer parameters into the topology optimization software to generate a basic vehicle body configuration that meets the essential requirement indicators; after the indicators of this layer are met, the expected requirement layer parameters are then imported for secondary design iteration; after the current two layers of requirement indicators are verified to be passed, the final optimization of functional integration is completed based on the attractive requirement layer indicators, forming a special vehicle shape design scheme that integrates multiple requirement characteristics.

2. The multi-functional vehicle exterior design method integrating eye-tracking analysis and A-KANO model according to claim 1, characterized in that, The eye-tracking feedback data includes: the user's high-frequency focus areas, the path of eye movement, and the duration of fixation.

3. The multi-functional vehicle exterior design method integrating eye-tracking analysis and A-KANO model according to claim 1, characterized in that, The user's pre-evaluation includes: gaze heatmap, saccade path, and gaze duration.

4. The multi-functional vehicle exterior design method integrating eye-tracking analysis and A-KANO model according to claim 1, characterized in that, The user survey questionnaire designed based on users' prior subjective evaluations is a standardized setting. The user survey questionnaire designed based on users' prior subjective evaluation includes: two-way demand evaluation and weighted scoring of the importance of functions; Let each respondent be The survey data results corresponding to the keywords in the collected user subjective evaluations are as follows: ;in, Asymmetrical score setting: ； in, Indicates satisfaction when the function is available. The value of , This indicates dissatisfaction when a feature is missing. The weighting represents the importance of the functions; The average satisfaction level when the function is available was calculated. Mean dissatisfaction when features are missing for: ； ； ； User satisfaction feedback for each feature requirement can be represented by the Better_Worse coefficient. : ； Introducing the frequency and weight of keywords in user pre-subjective evaluations The final indicators were obtained The coefficient is expressed as: 。 5. The multi-functional vehicle exterior design method integrating eye-tracking analysis and A-KANO model according to claim 1, characterized in that, The mapping rules included in the demand quantification table are as follows: For The larger the coefficient of demand, the more stringent the corresponding response metrics should be.

6. The multi-functional vehicle exterior design method integrating eye-tracking analysis and A-KANO model according to claim 1, characterized in that, In step S04, the three-stage shape topology optimization includes: the essential requirement layer, the expected requirement layer, and the attractive requirement layer. The three-stage shape and topology optimization is each subject to constraints; the constraints are as follows: A. Essential Requirements Layer: Strict boundary constraints; B. Expected Demand Layer: Flexible Boundary Constraints; C. Attraction Demand Layer: No mandatory constraints.

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