JPH0488559A - Designing device - Google Patents

Abstract

PURPOSE:To consider vehicle exterior information by providing an image data base with correlation to connect the constitution element of the exterior of a design target, etc., to the design element of a meter, etc., in the design device of a vehicle. CONSTITUTION:An intelligence data base(IDB) CL1 is provided with the image data base, an adjective data base, and a rule data base. Furthermore, it is provided with an exterior image data base(EIDB) CL2. An evaluation vocabulary is inputted in step S1. An exterior image(EI) is inputted in step S2. Weight is applied to the IDB CL1 by the EI by using the EIDB CL2 in step S3. The category of an item is inferred and extracted from the IDB CL1 in step S4. In step S5, the image of the entire interior is configured, and a color is allocated to the design element, then, it is displayed as an image.

Description

[Detailed Description of the Invention] [Object of the Invention] (Industrial Application Field) This invention is a design that displays a design on an output means such as a CRT while confirming the image that a person has of a design object such as a vehicle. Regarding equipment.

(Prior art) This type of design device includes, for example, March 1988; published by the Institute of Electronics, Information and Communication Engineers;
There is one as shown in FIG. 7, which is described in No. 71NO, 3pp, 245-247J. This is HULIS (
It shows the configuration diagram of the Human, Livjng System), and the exterior of the house is stored in the computer's memory for each component of the house, such as the entrance, Western-style room, Japanese-style room, kitchen, bath, etc., and the adjective "luxury" expresses the feeling. , evaluation terms such as ``spacious'' and ``spacious'', knowledge that defines the relationships between evaluation terms, and knowledge that defines the relationships between these evaluation terms and design elements, etc., are stored as a knowledge database, and humans can use the keyboard to For example, by operating an evaluation term input means such as ``Japanese-style room,'' which represents a design element that is a component of a house, the user enters a limited term for housing design, such as ``Japanese-style room,'' and also expresses his or her own image of the Japanese-style room. When the evaluation term ``spacious'' is input into the computer, the computer infers and extracts appropriate Japanese-style room design elements from the input evaluation term from the knowledge database, and displays the image on the display means.

By the way, if the technical evaluation terminology of the above-mentioned design device is adapted to the vehicle, it can be put to practical use as a vehicle design device.

For example, as shown in FIG. 7, the knowledge database includes evaluation terms 9 as an adjective database, knowledge 11 that defines relationships between evaluation terms, and
Vehicle design elements 13 as an image database;
Knowledge 15 that defines the relationship between evaluation terms and vehicle design elements, and knowledge 17 that sets combination conditions of vehicle design elements as a rule database are stored respectively.

Furthermore, shape data 19 for each vehicle design element as a shape database and color data 21 for each vehicle design element as a color database are stored in the memory as a graphic database.

The evaluation terms 9 are composed of adjective words as shown between the evaluation terms in FIG.

The knowledge 11 that defines the relationships between evaluation terms is knowledge that is obtained by collecting evaluation terms and analyzing the results of preliminary experiments. This relationship means, for example, that evaluation term 9 can be converted into factors based on human sensations from 1 to 10 using multivariate analysis such as quantification theory type 1 or type Ⅰ.
This is data obtained as factor loadings (numerical values) indicating how much relationship each evaluation term 9 has with each factor. It can be said that terms with similar numerical loadings on each factor are closely related and similar. This knowledge 11 that defines the relationship between evaluation terms selects the most similar term when the input evaluation term is not among the evaluation term group used in the sensory evaluation experiment described later when a design device is required. used for.

As shown in FIG. 9, the vehicle design elements 13 are divided into categories for each item such as the number of meters in the vehicle interior.

Knowledge 15 that defines the relationship between evaluation terms and vehicle design elements is obtained as a result of sensory evaluation experiments. Specifically, items of evaluation terms 9 and vehicle design elements 13 are selected, vehicle design elements corresponding to each item are shown to an unspecified number of people, and each evaluation term 9, which is a group of adjective words, is evaluated. Collecting the feelings received from vehicle design elements and quantifying the results I
It is analyzed as a partial regression coefficient (correlation function) by multivariate analysis such as type or type.

Here, the items include the size of the meter installed on the instrument panel, the thickness of the instrument, the color of the meter needle, the number of spokes on the steering wheel, the size of the butt of the steering wheel, and the steering wheel as shown in Figure 10. The focus was on the diameter of the door, the storage space in the door, etc.

The size of the meter is classified into three categories: large, medium, and small.

Items focused on the thickness of instruments include large,
Classified into three categories: medium and small.

Items focused on the color of meter needles are divided into three categories: orange, red, and white.

Items focusing on the number of spokes on a steering wheel are classified into three categories: 2, 3, and 4.

Below are items that focus on the diameter of the steering wheel.
Other items are classified into categories as shown in FIG.

Knowledge 17 for setting combination conditions of vehicle design elements is
Based on the opinions of experts in interior design, etc., rules are set for combinations of design elements that cannot be established due to physical or empirical constraints. Then, when inferring the optimal interior, invalid combinations were not inferred. for example,"
Rules such as ``large-diameter steering wheels'' are not compatible with ``sports seats'' will be set.

As shown in FIG. 11, the shape data 19 for each vehicle design element is data for creating several pictures composed of a basic picture and several items of item/category classification. And the picture data for one unit is
The number of patterns is the combination of the number of categories for each item. For example, when considering a single unit called a steering wheel, if the design elements of this steering wheel are composed of three items: number of spokes, size of pads, and diameter of the steering wheel, then the number of spokes is 3. categories (2, 3, 4) and 3 categories of bat size (2, 3, 4).
27
This means that it has image data of a steering wheel with a pattern of (-3x3x3).

The color data 21 for each vehicle design element is data for assigning colors to pictures of the vehicle interior, such as color data such as ``The butt of the steering wheel is gray, the spokes are seal bars, and the rest is dark gray.'' be.

The following description will be made based on the flowchart shown in FIG.

First, in step Sll, by operating an evaluation term input means such as a keyboard and inputting an adjective homonym such as "able to concentrate" as an evaluation term, the knowledge database shows that the partial regression coefficient is the maximum for each item of vehicle design elements. The category (marked with * in Figure 9) is inferred and extracted.

For example, if you enter two evaluation terms, ``allowing concentration'' and ``spacious,'' the partial regression coefficients for each category in each item of the two terms will be added, and the partial regression coefficient will be the highest in the results. The category is inference,
is extracted (step 512).

Next, in step 51B, the optimal combination of items constituting one unit is extracted for each unit from the shape data 19 for each vehicle design element based on the optimal category of each item extracted by inference. Then, by superimposing each unit on the basic picture in the order of steps S21 to S29 as shown in FIG. 12, a picture of the entire interior is constructed. The picture of the entire interior constructed in this way is filled with color data 21 for each vehicle design element.
Assign colors to design elements and create a design drawing.
As shown in FIGS. 13(a) and 13(b), the image is displayed on an output means such as a CRT.

Then, in step S14, if the display image does not match the image that the user has, the items and categories are changed according to the image while checking the display image, and the results are displayed on the CRT again. Ru.

If the user wants to see another design image, the next most offensive design image inferred from the input term is displayed on the CRT.

(Problem to be Solved by the Invention) However, such a design device infers and extracts design elements that match only the input evaluation terms. Can not.

For this reason, although it is possible to obtain a desirable vehicle interior that is inferred and extracted, there is a risk that the interior of the vehicle may not match the image of the vehicle exterior depending on the vehicle type.

Furthermore, there is a risk that some of the extracted designs may not match the user's image.

SUMMARY OF THE INVENTION Therefore, an object of the present invention is to provide a design device that can take into account the correlation between design elements and components of a design object such as a vehicle exterior input by a user.

[Structure of the Invention] (Means for Solving the Problems) In order to achieve the above object, the present invention, as shown in FIG. comprising means CL4 for inputting a term, means CL3 for inferring a design from the knowledge database CL1 based on the input evaluation term, and means CL7 for outputting the inferred design,
An image database CL2 of correlations that strongly connect design images between the constituent elements of the design object and the design elements, a means CL5 for inputting the constituent elements of the design object, and an input method for this constituent element input means CL5. In this case, the design inference means CL3 performs the design inference taking into account the image database CL2.

(Operation) When an evaluation term is input through the input means CL4, a design is inferred from the knowledge database CLI by the input evaluation term by the design inference means CL3, and this inferred design is inferred by the output means CL7. When a component such as an exterior is input by the component input means CL5, the design inference means CL3 performs design inference taking into account the image database CL2.

(Example) Embodiments of the present invention will be described below based on the drawings.

FIG. 2 shows a schematic configuration diagram of a design device according to an embodiment of the present invention, and this design device is composed of a microcomputer 1, a keyboard 3, and a CRT 5.

The microcomputer 1 includes a knowledge database CL1, a memory M as an image database CL2, and a CPU 7 as a design inference means CL3.

Here, the memory M stores evaluation terms 9, knowledge 11 that defines relationships between evaluation terms, vehicle design elements 13, knowledge 15 that defines relationships between evaluation terms and vehicle design elements, and knowledge that sets combination conditions for vehicle design elements. 17, shape pigs 19 for each vehicle design element and color data 21 for each vehicle design element are stored.

Each of the above data is configured in the same manner as described above, and therefore, redundant explanation will be omitted.

Further, the memory stores knowledge 23 as an image database of a correlation between an exterior image, which is one of the constituent elements of the design object, and a vehicle design element, which strongly connects the design image.

The knowledge 23 that defines the relationship between the exterior image and vehicle design elements is obtained from the results of sensory evaluation experiments and the empirical rules of experts as shown in FIG. Specifically, we selected items for exterior images and vehicle design elements, showed the vehicle design elements corresponding to each item to an unspecified number of people, and asked them to express the feelings they received from the vehicle design elements for each of the exterior images. Each exterior image and each item/category is quantified by applying a multivariate analysis method such as quantification theory type I or type II to the accumulated results.

The keyboard 3 is an input means CL for inputting evaluation terms 9.
In this embodiment, it also serves as means CL5 for inputting the constituent elements of the design object. A means for inputting this exterior image may be provided separately.

The CPU 7 infers a design from the knowledge database based on the input evaluation terms 9, and weights vehicle design elements from the image database based on the input exterior image.

The CRT 5 constitutes an output means CL7 for outputting the inferred design and displays the design as an image.

According to the above configuration, the evaluation terms selected in advance, the knowledge 11 that defines the relationship between the evaluation terms obtained in the preliminary experiment, the vehicle design elements 13, and the evaluation terms and vehicle design elements obtained in the sensory evaluation experiment. knowledge 15 that defines the relationship between vehicle design elements, knowledge 17 that sets combination conditions of vehicle design elements, shape data 19 for each vehicle design element, color data 21 for each vehicle design element, and vehicle design such as the exterior and meter that are design objects. Knowledge 23 and the like that define the correlation of elements are stored in the memory M of the computer 1. In this state, use the keyboard 3 to select the evaluation term 9.
When you input an adjective word as , computer 1's CP
U7 infers and extracts each vehicle design element 13 from the knowledge database, and further extracts a shape and color design that matches the input term from the graphic database, and displays the results on the CRT 5 as an image. Then, when an exterior image is input by operating the keyboard 3, weighting is performed from the exterior image database to the knowledge database by the function of the CPU 7.
A design corresponding to the exterior image is inferred and extracted, and the image is displayed on the CRT5.

The following description will be made based on the flowchart shown in FIG.

First, in step S1, an adjective word such as "fashionable" is input as an evaluation term by operating the keyboard 3. Subsequently, in step S2, the keyboard 3 is operated to input, for example, "popular sports car" as an exterior image.

This exterior image can be a vehicle concept such as a luxury sports car, a popular sports car, or a luxury car, as shown in Figure 5 (0), or a photograph of an actual vehicle that represents the vehicle concept, as shown in Figure 5 (b). This is input by .

Next, in step S3, the knowledge database is weighted using the exterior image database based on the input exterior image.

Here, for example, enter "stylish" as an evaluation term,
When "popular sports car" is input as an exterior image, first, as shown in Figure 6 (a), the data of the vehicle design element corresponding to "Shiyareta" is extracted from the knowledge database, and then the As shown in Figure (b), data on vehicle design elements corresponding to a "popular sports car" is extracted from the exterior image database.

Next, as shown in FIG. 6(C), the knowledge database is weighted by multiplying the numerical values of the same item/category of the above two data. For example, in the case of the item "steering wheel diameter size", the category "
The category "small" is weighted 1.0 times, the category "medium" is weighted 1.0 times, the category "large" is weighted 0 times, etc., and the weighted knowledge database shown in FIG. 6(C) is calculated.

Then, in step S4, from the weight knowledge database, the item with the largest partial regression coefficient is selected from * in Fig. 6 (C).
Infer and extract the marked categories.

Next, in step S5, the shape data 1 for each vehicle design element is extracted from the optimal category of each item extracted by inference.
9, the optimal combination of items constituting one unit is extracted for each unit, and these are sequentially superimposed on the basic picture to construct a picture of the entire interior. Then, a color is assigned to the design element from the color data 21 for each vehicle design element, and the image is displayed on the CRT 5 as a design image.

Then, in step S6, if the displayed image does not match the image that the user has, the item/category is changed according to the image while looking at this displayed image, and the result is displayed on the CRT 5 again. Ru.

If the user wishes to see another design, the CRT 5 displays the next most appropriate design image inferred from the input term.

Therefore, the user can complete an interior design that corresponds to the exterior image.

Note that the present invention is not limited to the above embodiments. For example, the image database can be composed of colors, design elements, relationships between steering wheel and meters, etc. Further, the present invention can be applied to the design of vehicle exteriors, other ships, aircraft, etc.

[Effects of the Invention] As is clear from the above description, according to the configuration of the present invention, it is possible to associate a design that matches an evaluation term with the constituent elements of a design object through input from a user, etc., and to create a more qualified design. You can output the design.

[Brief explanation of drawings]

Fig. 1 is a block diagram of the present invention, Fig. 2 is a block diagram of an embodiment of the present invention, Fig. 3 is a table relating to exterior image data, and Fig. 4 is a database based on the block diagram of Fig. 2. The flowchart shown in FIG. 5(a),
6(b) is a diagram showing an example of an exterior image input method; FIGS. 6(a), 6(b), and 6(C) are diagrams showing the inference process; FIG. Figure 8 is a table of knowledge that defines the relationship between evaluation terms, Figure 9 is a table of sensory evaluation experiment results,
Figure 10 is a table showing items and categories, Figure 11 is a diagram showing the inference process using the shape database, Figure 12 is a flowchart of the inference process in Figure 11, and Figure 13 (a).
. (b) is a diagram showing an example of the output of a CRT. CLl...Knowledge database Cl3...Image database CL3...Design inference means Cl3...Evaluation term input means Cl5...Component input means Cl3...Output means Fig. 2 Wc1 Fig. 4 Exterior Image input method Fig. 5 (a) Fig. 6 (a) Fig. 6 (b) (Item category] ``I) - Part of the list) @ Fig. 10 Fig. 11 Fig. 1 4!') 4- Basics 12th ward display example σ) J') If you input F5 J, 13th
Figure (a) Chest 13 (b)

Claims (1)

[Claims] A knowledge database of evaluation terms expressing a design and the relationship between design elements, means for inputting the evaluation terms, means for inferring a design from the knowledge database using the input evaluation terms, and means for outputting the inferred design. an image database of correlations that connects design images in relation to constituent elements such as the exterior of the design object and design elements such as meters; and means for inputting the constituent elements of the design object;
A design apparatus characterized in that the image database is taken into consideration in design inference by the design inference means when an input is made by the component input means.