JPH04259070A - Design device - Google Patents

Abstract

PURPOSE:To grasp the relationship between an input evaluation term and a design element shown in an image on the same screen. CONSTITUTION:When an input evaluation term 13 is inputted with operation of a keyboard 1, a computer 16 infers and extracts a car design element 11 most suited to the term 13 out of a knowledge data base 2. At the same time, the computer 16 extracts the image knowledge 12 that prescribes the relationship between an evaluation term related to the term 13 and the element 11 out of the base 2. These extracted element 11 and the knowledge 12 are simultaneously shown on the same screen of 8 CRT 55.

Description

[Detailed description of the invention]

0001

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a design device that displays a design on a display device such as a CRT while confirming the image that a person has.

0002

[Prior Art] This type of design device includes, for example, March 1988; Published by the Institute of Electronics, Information and Communication Engineers.
There are some listed in 47. This is stored in the computer's memory, including the appearance of the house, house design elements for each component such as the entrance, Western-style room, Japanese-style room, kitchen, bath, etc., evaluation terms such as luxury, spaciousness, and spaciousness as adjectives that express the feeling.
Knowledge that defines the relationship between evaluation terms, knowledge that defines the relationship between these evaluation terms and housing design elements, etc. is stored as a knowledge database, and a human being can operate an input means such as a keyboard to input, for example, a housing design element. By inputting a limited term for residential design, ``Japanese-style room,'' which describes the design elements that are the constituent parts, and inputting into the computer an evaluation term, ``spacious,'' which describes the feeling that one has of a Japanese-style room, The computer is configured to infer and extract appropriate Japanese-style room design elements based on evaluation terms input from a knowledge database, and display the images on the display means.

0003

[Problems to be Solved by the Invention] Generally, the meanings and feelings of input evaluation terms vary from person to person and change over time. Further, the above-mentioned design device only displays an image of the optimal design element inferred according to a preset control procedure based on input evaluation terms. Therefore, even if the input evaluation term and the design element displayed in the image do not match the image that the person who entered the evaluation term has, the degree of influence of the input evaluation term on the design element displayed in the image is shown. There is a disadvantage that information cannot be recognized at all.

In particular, when a plurality of evaluation terms are input and a weight is input that determines the strength of the relationship between these evaluation terms, the evaluation term synthesized from the plurality of input evaluation terms is There is also the problem that information on the degree of influence of design elements is not included in the knowledge database.

[0005]

[Means for Solving the Problems] Accordingly, in the first invention, as shown in FIG. and a display means 5 such as a CRT. The knowledge database 2 has evaluation terms 9, adjective knowledge 10, design elements 11, and image knowledge 12. The adjective knowledge 10 defines the relationship between evaluation terms. Image knowledge 12 defines the relationship between evaluation terms and design elements. The inference means 3 uses the evaluation terms (hereinafter referred to as
(referred to as input evaluation term) 13, the knowledge database 2
The optimum design element 11 is inferred and extracted from the above and outputted to the display means 5. The image knowledge selection means 4 extracts the image knowledge 12 related to the input evaluation term 13 from the knowledge database 2 using the input evaluation term 13 and outputs it to the display means 5.

In the second invention, the image knowledge selection means 4 is composed of a weighting calculation means 6 and a data conversion means 7. The weighting calculation means 6 calculates a plurality of input evaluation terms 1 from the knowledge database 2 by using a plurality of input evaluation terms 13 and a plurality of weights 14 input corresponding thereto.
3 is extracted, and the strength of the relationship between the plurality of evaluation terms 9 and the design elements 11 is calculated based on the extracted image knowledge 12. The data conversion means 7 combines the calculation results from the weighting calculation means 6, and outputs this combination result to the display means 5 as the extracted image knowledge 12.

[0007]

[Operation] In the first invention, when a human inputs the input evaluation term 13 by operating the input means 1, the inference means 3 infers and extracts the optimal design element 11 from the knowledge database 2,
Image knowledge selection means 4 extracts image knowledge 12 regarding input evaluation terms 13 from knowledge database 2, and display means 5 displays these extracted design elements 11 and image knowledge 12 together on the same screen.

In the second invention, when a human inputs a plurality of input evaluation terms 13 and a plurality of weights 14 corresponding thereto by operating the input means 1, the weight calculation means 6 inputs a plurality of input evaluation terms from the knowledge database 2. The image knowledge 12 regarding the term 13 is extracted, and the image knowledge 12 is multiplied by the weight 14 to obtain the strength of the relationship between the plurality of evaluation terms 9 and the design element 11, and the data conversion means 7 calculates the strength of this relationship. The synthesized result is output to the inference means 3 and the display means 5, and the inference means 3
uses the synthesis result as image knowledge 12 to infer and extract the optimal design element 11 from the knowledge database 2, and displays this design element 11 and the synthesis result together on the same screen on the display means 5.

[0009]

[Embodiment] FIG. 2 shows an embodiment of the present invention, and the design device of this embodiment is a vehicle design device that designs the interior of a vehicle interior using computer graphics. The computer 16 is shown as an example, and is composed of a computer 16, a keyboard serving as input means 1, and a CRT serving as output means 5. A knowledge database 2 is stored in the memory of the computer 16. A reasoning means 3 and an image knowledge selection means 4 are built into the CPU of the computer 16.

[0010] The keyboard 1 allows a human to operate keys.
Input evaluation terms 13 and weights 14 are input to the computer 16.

The knowledge database 2 includes evaluation terms 9, adjective knowledge 10, and vehicle design elements 1 as design elements.
1 and image knowledge 12.

Evaluation terms 9 are composed of adjective words as shown in the evaluation term column of Table 1.

[0013] As shown in Table 1, the adjective knowledge 10 is the knowledge obtained by collecting the evaluation terms 9 and analyzing the results of preliminary experiments, which defines the relationship between the evaluation terms. For example, the evaluation term 9 is divided into a plurality of factors by multivariate analysis such as factor analysis, and the degree of relationship between each evaluation term 9 and each factor is obtained as a factor loading amount (numerical value). This adjective knowledge 10 is used to select the most similar evaluation term 9 when the input evaluation term 13 is not among the 9 groups of evaluation terms used in the sensory evaluation experiment described later when using the design device. used.

The design elements 12 are as shown in Table 2.
It is divided into items, which are large frames, and categories, which are small frames.

Image knowledge 12 is as shown in Table 2.
This was obtained as a result of a sensory evaluation experiment. Specifically, items of evaluation terms 9 and vehicle design elements 11 are selected, vehicle design elements 11 corresponding to each item are shown to an unspecified number of people, and one of the evaluation terms 9, which is an adjective word group, is selected. The feelings received from the vehicle design elements 11 for each vehicle are collected, and the collected results are analyzed as partial regression coefficients (correlation coefficients) by multivariate analysis using quantification logic type 1 or 2.

[0016] Here, the items are as shown in Table 2.
Vehicle design elements 11 include the number of meters provided on the instrument panel, the instrument panel and the meter cluster, and, although omitted from Table 2, the meter cluster and center console, the thickness of the door, the overhang of the center cluster, Each of these focuses on armrests, etc.

[0017]Then, items focusing on the number of meters are:
As shown in Table 2, they are classified into five categories. That is, a category with one large meter, a category with one large meter and two small meters, a category with one large meter and three small meters, and a category with two large meters and one small meter. One category has two large meters and two small meters.

Items focusing on the instrument panel and meter cluster are classified into two categories as shown in Table 2. That is, there is a category in which the instrument panel and meter cluster are integrated, and a category in which the instrument panel and meter cluster are separated.

Items focusing on the meter cluster and center console are omitted from Table 2, but for example, there is a category where the meter cluster and center console are integrated, and a category where the meter cluster and center console are separated. It is divided into categories.

Items focusing on the thickness of the door are omitted from Table 2, but are classified into a thick door category and a thin door category.

Items focused on the overhang of the center cluster are omitted from Table 2, but are classified into categories with an overhang of the center cluster and categories with no overhang of the center cluster.

[0022] Also, items focusing on armrests,
In addition, other items of interest are classified into multiple categories.

[0023] The inference means 3 uses the input evaluation term 13 to
According to the flowchart shown in FIG. 3 or 4, the optimum vehicle design element 11 is inferred and extracted from the knowledge database 2 and the image is displayed on the CRT 5.

The image knowledge selection means 4 selects Tables 3, 4, and 5 from the knowledge database 2 according to the flowchart shown in FIG. 3 or 4 using the input evaluation terms 13.
The image knowledge 12 related to the input evaluation term 13 as shown in Table 6 is extracted, and the extracted image knowledge 1
2 to the vehicle design element 1 whose image was displayed on the CRT 5.
The images are displayed together on the same screen as 1. This image knowledge selection means 4 includes a weighting calculation means 6
and data conversion means 7.

The weighting calculation means 6 calculates a plurality of inputs from the knowledge database 2 as shown in Tables 3 and 4 using a plurality of input evaluation terms 13 and a plurality of correspondingly input weights 14. By extracting the image knowledge 12 related to the evaluation term 13 and multiplying each of the extracted image knowledge 12 by the weight 14, a plurality of evaluation terms 9
The strength of the relationship between the vehicle design element 11 and the vehicle design element 11 is calculated.

The data conversion means 7 includes the weighting calculation means 6
The combined image knowledge 12 as shown in Table 5 is created by adding together the weighted plurality of image knowledge 12 as the calculation results from The data is converted and output as the extracted image knowledge 12 to the CRT 5, and the image is displayed on the same screen as the vehicle design element 11 displayed as the image.

In other words, the CPU of the computer 16 infers and extracts the vehicle design element 11 from the knowledge database 2 from the input evaluation term 13 according to the flowchart shown in FIG. 3 or FIG.
The image knowledge 12 related to the input evaluation term 13 is extracted from the input evaluation term 13, and the extracted vehicle design elements 11 and image knowledge 12 are displayed together on the same screen on the CRT 5.

In the CRT 5 shown in FIG. 2, an image is displayed for the inputted evaluation term 13 such as "warm", but the image knowledge 12 regarding "warm" is based on the data. There are a large number of vehicle design elements 11, and when they are displayed together with the image display of the vehicle design elements 11, they cannot be displayed on the same screen at once. Therefore, FIG.
All the vehicle design elements 11 and all the image knowledge 12 related to the input evaluation term 13 "warm" can be viewed on the same screen.

According to the above embodiment structure, first, the evaluation terms 9 selected in advance and the adjective knowledge 10 obtained in the preliminary experiment are
, preselected vehicle design elements 11, image knowledge 13 obtained through sensory experiments, etc. are stored in the memory M of the computer 16.
Let me remember it. In this state, when a person operates the keyboard 1 and inputs an adjective as the evaluation term 13, the knowledge database 2 is
Optimal vehicle design elements 11 and image knowledge 1
2 is inferred and extracted using adjective processing.

Here, assuming that the evaluation term 13 to be input is "warm", the above operation will be specifically explained based on the flowchart of FIG.

In step 101, execution of the process begins by starting the computer 16, and step 1
Proceed to 02.

In step 102, the input evaluation term 13 "warm" is input. Then, the evaluation term 9 "warm" is displayed as an image on the CRT 5, and the process proceeds to step 103.

In step 103, evaluation term 9 most closely related to "warm" is selected from Table 1 as adjective knowledge 10 of knowledge database 2, and "warm" of evaluation term 9 selected from Table 1 is used as a standard. , evaluation term 9 in Table 2 as image knowledge 12 in knowledge database 2 and the corresponding partial regression coefficient are extracted. That is, the image knowledge 12 as shown in Table 3 for the input evaluation term 13 is extracted, and the process proceeds to step 104.

In step 104, the vehicle design element 11 of the category corresponding to the first partial regression coefficient among the partial regression coefficients for the item "warm" in the image knowledge 12 extracted in step 103 is stored in the knowledge database 2.
This is inferred and extracted from, and outputted to the CRT 5, and the process proceeds to step 105. Note that if a plurality of input evaluation terms 13 are input in step 102 above, image knowledge 12 for the input evaluation terms 13 is extracted in step 103,
In step 104, the partial regression coefficients of each category in the item are added up for a plurality of input evaluation terms 13, and the category in which the partial regression coefficient of each item in the result is the maximum is extracted.

In step 105, the image knowledge 12 extracted in step 103 is converted into data in an easy-to-read format, and from this data-converted image knowledge 12, for example, the portions marked with notes in Table 3 are extracted and output to the CRT 5. The process then proceeds to step 106.

[0036] In step 106, the vehicle design elements 11 extracted in step 104 and the image knowledge 12 data-converted in step 105 are combined as shown in FIG.
The images are displayed together on the same screen, and the process advances to step 107.

In step 107, one cycle of design display ends.

Next, as the plurality of input evaluation terms 13, "warm" and "colorful", and these input evaluation terms 1
80% (0.8) and 20% as weight 14 corresponding to 3
(0.2), the above operation will be specifically explained based on the flowchart of FIG.

Step 201 is the same as step 101 above.

In step 202, the input evaluation terms 13 of "warm" and "colorful" are input, and
Enter 0.8 representing a weight of 14 for "warm" and 0.2 representing a weight of 14 for "colorful". Then, evaluation terms 9 such as "warm" and "colorful" and weights 14 such as "80%" and "20%" are displayed as images on the CRT 5. Then, the process advances to step 203.

[0041] In step 203, the evaluation term 9 most closely related to "warm" and "colorful" is selected from Table 1 as adjective knowledge 10 of the knowledge database 2, and the evaluation term 9 selected from this Table 1 is selected. Based on "warm" and "colorful" in Table 2 as image knowledge 12 in knowledge database 2, "warm" and "colorful" in Table 2 and their corresponding partial regression coefficients are extracted. . That is, image knowledge 12 as shown in Tables 3 and 4 for a plurality of input evaluation terms 13 is extracted, and step 204
Proceed to.

In step 204, the plurality of image knowledge items 12 extracted in step 203 are weighted. That is, the partial correlation coefficient and partial regression coefficient of image knowledge 12 related to "warm" shown in Table 3 are each multiplied by 0.8, and the partial correlation coefficient of image knowledge 12 related to "colorful" shown in Table 4 is multiplied by 0.8. The number and partial regression coefficient are each multiplied by 0.2, and the process proceeds to step 205.

In step 205, the image knowledge 12 of "warm" and "colorful" weighted in step 204 is added to synthesize image knowledge 12 as shown in Table 5, and the process proceeds to step 206. Here, Table 3
, the leftmost number in each row of Tables 4 and 6 is the partial correlation coefficient,
This means that each row's items (such as the color of the instrument) are
It shows how strong the relationship is with evaluation term 9. In addition, the numbers starting from the second from the left in each row indicate the strength of the relationship between each category (for example, if the item is an instrumental color, colors such as black, brown, blue, red, etc.) and evaluation term 9. There is. "Warm" and "Colorful"
The fourth and fifth numbers from the left in the rows of items A, B, C, D, F, ..., N are "0", which means that categories C and D do not exist. It is shown that.

In step 206, the vehicle design element 11 of the category corresponding to the first partial regression coefficient among the partial regression coefficients of the items of the image knowledge 12 synthesized in step 205 is inferred and extracted from the knowledge database 2 and displayed on the CRT 5. The process then proceeds to step 207.

In step 207, the image knowledge 12 synthesized in step 206 is converted into an easily viewable form as shown in Table 6. Table 6 shows that "quite warm and somewhat colorful" has a strong relationship with the category whose bar graph extends to the left. Furthermore, since the synthesized partial correlation is also displayed, items having a strong relationship with the synthesized image knowledge 12 are also displayed. For example, extract the part marked in the notes of Table 6 and
Output to RT5 and proceed to step 208.

In step 208, the vehicle design elements 11 and image knowledge 12 extracted in steps 206 and 207 are displayed together on the same screen, and the process proceeds to step 209.

In step 209, one cycle of design display ends.

Note that the present invention is not limited to the above-mentioned embodiments, and although not shown in the drawings, for example, it can be applied to an interior including a stereo operation section, a built-in TV/V, an air conditioner outlet and an operation section, etc. Images can also be displayed of vehicle interior design, which consists of a combination of parts, or vehicle exterior design, which consists of a combination of exterior parts, including wheels, wheel covers, front, rear, and side spoilers, fog lamps, drive lamps, special lamps, etc.
Of course, it can be used for vehicle image enhancement.

Furthermore, by changing the vehicle design elements 11 and evaluation terms 9 stored in the knowledge database 2,
It can be applied as a design device that can be used in fields other than vehicles, such as a housing design device or a machine design device.

Furthermore, the number of items and the number of categories for each item are not limited to those in the above embodiment.

[0051]

[Table 1]

[0052]

[Table 2]

[0053]

[Table 3]

[0054]

[Table 4]

[0055]

[Table 5]

[0056]

[Table 6]

[0057]

As described above, according to the present invention, by inputting evaluation terms, the strength of the relationship between the evaluation terms and design elements and the optimal design can be determined from the knowledge database.
Since the inferences and extractions are made and displayed together on the same screen, it is possible to recognize at a glance the strength of the influence of the input evaluation term on the image-displayed design element.

[Brief explanation of the drawing]

FIG. 1 is a claim correspondence diagram of the present invention.

FIG. 2 is an overall configuration diagram showing an embodiment of the present invention.

FIG. 3 is a flowchart of the same embodiment.

FIG. 4 is a different flowchart of the same embodiment.

[Explanation of symbols]

1...Input means (keyboard) 2...Knowledge database 3...Inference means 4...Image knowledge selection means 5...Display means (CRT) 9...Evaluation terms 10...Adjective knowledge 11...Vehicle design elements (design elements) 12...Image knowledge

Claims (2)

[Claims] Claim 1. A design device that infers and extracts optimal vehicle design elements from a knowledge database and displays an image based on input of an evaluation term, wherein the input evaluation term is used to infer and extract an optimal vehicle design element from a knowledge database. A design device comprising image selection means for extracting image knowledge that defines the relationship between evaluation terms and design elements and displaying the extracted image knowledge as an image together with the image-displayed design elements. . 2. The image selection means uses a plurality of input evaluation terms and a plurality of corresponding input weights to determine the relationship between the plurality of evaluation terms and design elements regarding the plurality of evaluation terms. A weighting calculation means for calculating the strength and a calculation result of this weighting calculation means are combined,
2. The design apparatus according to claim 1, further comprising data conversion means for displaying the synthesis result as the image knowledge together with the image-displayed design elements.