JPH0773221A - Design equipment - Google Patents

Abstract

(57) [Summary] [Purpose] It is possible to infer and extract designs that take into account differences such as seating positions with a small database. [Structure] An evaluation term input means 3 for inputting an evaluation term expressing a design image, a design database M for storing a relationship between the evaluation term and a design element, and the design database according to the input evaluation term are suitable for use in the design database. In a design device having an inference means 7 for inferring a design element and an output means 5 for outputting a design as an inference result, the design element affects only a common design element that affects a plurality of attributes or only a specific attribute. Classification means 7 and 17 for classifying non-common design elements, the inference means 7 makes inferences in consideration of a plurality of influencing attributes in the case of common design elements, and non-common design elements. In the case of, the inference is made in consideration of the specific attribute that affects.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a design device used for displaying a vehicle design on a CRT while confirming a human image of the vehicle.

[0002]

2. Description of the Related Art Conventionally, as a design device of this type,
For example, FIG. 1 described in Japanese Patent Laid-Open No. 2-259964.
There is something like 3. The design device 301 includes a keyboard 305 as an input means and a computer 305 for inferring and extracting an optimum design element corresponding to input information.
And a CRT that combines and displays the extracted design elements
309 and.

The computer 305 stores a design database (knowledge database) 307 for storing evaluation terms, design elements, knowledge defining relationships between evaluation terms and design elements, and human attribute terms. It is composed of design selection means for inferring and extracting design elements.

When the user operates the keyboard 303 to input an evaluation term and an attribute term such as a person's gender, age, occupation, nationality, etc., the computer 305 considers the person's attribute from the design database 307. Infer and extract the optimum design element and CR
The image is displayed at T309.

As described above, in this design apparatus, since the reasoning is performed in consideration of the meaning of the evaluation term and the image changing depending on the attribute of the person, even if the attribute of the operator is different, each operator can visually imagine. It is possible to display an image of the optimum design to satisfy.

[0006]

By the way, in such a conventional design apparatus, attributes such as a person's gender, age, occupation, nationality, etc. are commonly considered for all design elements, and inference and extraction are performed. It was supposed to do. For this reason,
When expressing the difference in sitting position, external environment or vehicle interior environment as attributes to be considered, create a design database of the relationship between evaluation terms and design elements for each attribute,
Each design element had to be inferred and extracted using the design database corresponding to each attribute.

Therefore, in order to extract a design considering the difference in seating position and the like by the conventional device, a very large number of databases are required, which results in a long inference time and a high cost. was there.

Therefore, an object of the present invention is to provide a design apparatus capable of extracting a design according to a difference in seating position and the like with a small database.

[0009]

In order to solve the above-mentioned problems, the invention of claim 1 is an evaluation term input means CL1 for inputting an evaluation term expressing a design image as shown in FIG.
A design database CL2 that stores the relationship between the evaluation terms and design elements, an inference means CL3 that infers a suitable design element using the design database CL2 based on the input evaluation terms, and a design as an inference result. The design apparatus including the output unit CL4 for outputting includes a classifying unit CL5 that classifies the design element into a common design element that affects a plurality of attributes or a non-common design element that only affects a specific attribute. However, the inference means CL3 makes an inference in consideration of a plurality of influencing attributes in the case of a common design element, and makes an inference in consideration of a particular attribute influencing in the case of a non-common design element. Characterize.

A second aspect of the present invention is the design apparatus according to the first aspect, wherein the attribute is a difference in seating position in the vehicle interior.

A third aspect of the present invention is the design apparatus according to the first aspect, wherein the attribute is a difference in an external environment such as a brightness state outside the vehicle compartment, a vehicle speed, climate and weather. .

According to a fourth aspect of the present invention, in the design apparatus according to the first aspect, the attribute is a difference in vehicle interior environment such as vehicle interior lighting, human physiological reaction, and passenger's condition. And

[0013]

According to the first aspect of the present invention having the above structure, first, the design elements are classified into common ones that affect a plurality of attributes or non-common ones that only affect a specific attribute.

If the classified design elements are common, a plurality of influencing attributes are taken into consideration to make an inference using the design database CL2, and if they are not common, an inference is made considering a specific attribute. do. This inference is performed for each design element, and the result is output by the output means CL4.

Therefore, it is possible to obtain a design corresponding to the difference in the seat position, the external environment or the vehicle interior environment with a small database.

According to the second aspect of the present invention, the design elements are classified by using the difference in the seating position in the vehicle interior as an attribute, and the design is performed in consideration of the difference in the image of the interior design between the driver's seat, the passenger seat and the rear seat. Can reason.

According to the third aspect of the present invention, the design elements are classified by using the difference in the external environment outside the vehicle as an attribute. For example, the difference in the image of the interior design due to the difference in the brightness outside the vehicle, the vehicle speed, the climate, the weather, etc. The design can be inferred in consideration of.

The invention according to claim 4 classifies the design elements by using the difference in the vehicle interior environment as an attribute, and determines the interior design depending on the difference in the vehicle interior environment such as the vehicle interior lighting, the physiological reaction of the human being, and the occupant's situation. Design can be inferred considering the difference in image.

[0019]

Embodiments of the present invention will be described below.

FIG. 2 is a schematic block diagram of an embodiment of the present invention, in which the design apparatus comprises a computer 1, a keyboard 3 and a CRT 5.

The computer 1 is provided with a design database CL2 which is a knowledge database, a memory M as a specific design element selection database (described later) constituting the classification means CL5, and a CPU 7 as the inference means CL3.

In the memory M, evaluation terms 9, knowledge 11 for defining relationships between evaluation terms, vehicle design elements (design elements) 13, knowledge 15 for defining relationships between evaluation terms and vehicle design elements (design database) CL2),
The knowledge 17 (the specific design element selection database) indicating the correspondence between the seat position, the external environment or the vehicle interior environment (seat position in the embodiment) and the design element is stored.

The keyboard 3 is an evaluation term input means CL for inputting an evaluation term 9 expressing a design image.
1, and certain design elements, such as "door thickness",
A selection means for selecting and inputting "instrument height" or the like is configured.

The CPU 7 deduces the design from the design database based on the inputted evaluation terms 9 and attributes such as seat position, external environment or vehicle interior environment.

The CRT 5 constitutes an output means CL4 for outputting the inferred design, and displays the entire design drawing as an image.

Here, the memory M will be further described.

The evaluation term 9 expresses a design image. For example, as shown in the evaluation term column of the chart of FIG. 3, the vehicle interior is defined by adjective words such as "focusable" and "luxurious". There is.

Knowledge 11 defining the relationship between the evaluation terms
Is knowledge that defines a relationship between evaluation terms as shown in FIG. 3 by collecting a plurality of evaluation terms 9 and analyzing the results of preliminary experiments. This relationship is, for example, the evaluation term 9 is divided into factors 1 to 10 based on the human sense by multivariate analysis such as quantification logic type I or type II, and how much each evaluation term is related to each factor. This is the data obtained as the factor loading amount (numerical value). It can be said that the terms with the numbers with close loadings of each factor have a close relationship. The knowledge 11 defining the relationship between the evaluation terms selects the most similar term when the input evaluation terms are not in the evaluation terms group used in the sensory evaluation experiment described later when using the design device. Is used for.

The design element 13 is called an item such as the shape of a meter, instrument, meter cluster or the like that composes the interior of the vehicle, and as shown in the chart of FIG. 4, categories are divided for each item.

Here, the items are, as shown in FIG. 4, the number of meters provided on the instrument panel in the design element 13, the instrument panel and the meter cluster, the meter cluster and the center console, the thickness of the door, and the center cluster. It focuses on overhangs and armrests.

The items focusing on the number of meters are classified into five categories. That is, the first category provided with one large meter, the second category provided with one large meter and two small meters, the third category provided with one large meter and three small meters, and the large meter 2 The fourth category is provided with a small meter and one small meter, and the fifth category is provided with two large meters and two small meters.

Items focusing on the instrument panel and the meter cluster are classified into two categories. That is, a first category in which the instrument panel and the meter cluster are integrated, and a second category in which the instrument panel and the meter cluster are separated.

Items focused on the meter cluster and the center console are classified into two categories.
That is, the first category in which the meter cluster and the center console are integrated, and the second category in which the meter cluster and the center console are separated.

Items focusing on the thickness of the door are classified into two categories. That is, the first category has thick doors and the second category has thin doors.

Items focused on the overhang of the center cluster are classified into two categories. That is, the first category has a center cluster overhang, and the second category has no center cluster overhang.

Below, items focusing on the armrest,
Other items are classified into categories.

The knowledge 15 defining the relationship between the evaluation terms and the design elements is obtained as a result of the sensory evaluation experiment as shown in FIG. Specifically, the items of the evaluation terms 9 and the design elements 13 are selected, the designs corresponding to the respective items are shown to an unspecified number of people, and the fee received from the design for each of the evaluation terms 9 which is an adjective word group. The rings are collected, and the accumulation results are analyzed as partial regression coefficients (correlation coefficients) by multivariate analysis such as quantification theory type I or type II.

The knowledge 17 indicating the correspondence between the seat position, the external environment or the vehicle interior environment and the design element is obtained as shown in FIG. 5, for example. FIG. 5 shows an example of knowledge showing correspondence between seat positions and design elements. Specifically, items of design elements are selected, and each of the items is selected as a driver seat, a front passenger seat, and a rear seat. It is an analysis of the relationship with. Here, the items are "indoor color", "door thickness", "meters", and "spokes".
The items related to the driver's seat, the passenger's seat, and the rear seat are indicated by solid circles.

The operation of this embodiment will be described below conceptually with reference to the flowchart of FIG. 6 and then with reference to FIG. Note that here, the seat position is considered as an attribute.

First, in step S1 of FIG.
An evaluation term 9 is input for each seat position such as a passenger seat and a rear seat.

In step S2, a specific design element in the design target is selected, and the selected design element is a common one that affects a plurality of seat positions or a non-common one that only affects a specific seat position. Is classified. Then, if the classified design elements are common, in step S3, the category is inferred and extracted from the design database by the evaluation terms for the plurality of influential seat positions. If the classified design elements are not common, the category is inferred from the design database by the evaluation term 9 corresponding to the specific seat position in step S4,
To be extracted. This inference and extraction are based on all design elements (i
= 1: ... N).

In step S5, the design based on the optimum combination of categories inferred and extracted in steps 3 and 4 is displayed as an image.

FIG. 7 shows a specific example in which the image display is performed by the CRT 5. However, FIG. 7 shows a case where “sporty driver's seat”, “smart passenger seat”, and “loose rear seat” are input as evaluation terms for each seat position.

Next, as the evaluation terms for each seat position, "driver's seat for runners", "luxury passenger seat", and "spacious rear seat" are input, and more concretely by the flowchart of FIG. Explained.

First, in step S11, "driver's seat for runner", "luxury passenger seat", and "extensive rear seat" are input as the evaluation terms 9 for each seat position by operating the keyboard 3.

In step S12, a specific design element such as "door thickness", "instrument height", "number of meters", etc. is selected and input by operating the keyboard 3, and this design element is designed as shown in FIG. The element selection database classifies common design elements that affect multiple seat positions or non-common design elements that affect only a single seat position. For example, "door thickness" is classified as a common item that affects "driver's seat" and "passenger seat," and "instant height" is a common item that affects "driver's seat,""passengerseat," and "rear seat." Is classified as an empty item (step S1)
3). Further, the "number of meters" is classified as a non-common one that affects only the "driver's seat" (step S14).

In step S15, for the non-common design element 13 that affects only a single seat position, the partial regression coefficient corresponding to the evaluation term 9 of that seat position shows the relationship between the evaluation term and the design element shown in FIG. It is inferred from the defining knowledge 15 (design database). For example, with respect to the "number of meters" that affects only the driver's seat, the partial regression coefficient corresponding to the driver's evaluation term "for runners" is inferred as circled in FIG.

Then, in step S21, the category having the largest partial regression coefficient is extracted from each category.
For example, as for the “number of meters”, the category of “two large meters and one small meter” surrounded by a square frame in FIG. 4 is extracted.

In step S16, the knowledge 15 that the partial regression coefficient corresponding to the evaluation term 9 of each seat position for the common design element 13 that affects a plurality of seat positions defines the relationship between the evaluation term and the design element shown in FIG. Inferred and extracted from (design database). Here, the partial regression coefficient PD (i) of the target design element i with respect to the driver's input term "for a runner" is extracted from the design database. Similarly, "passive" for the passenger seat
The partial regression coefficient PA (i) of the design element is extracted. For example, taking the design element "door thickness" as an example, the partial regression coefficient corresponding to the evaluation term "for runners" of the driver seat and the partial regression coefficient corresponding to the evaluation term "luxurious" of the passenger seat are It is inferred and extracted as circled in the diagram of FIG.

In step S17, the partial regression coefficients extracted in step S16 are added for each category to obtain the total of partial regression coefficients for each category. For example, for “door thickness”, partial regression coefficients corresponding to categories “thick” and “thin” (numerical values circled in the chart of FIG. 9)
Is added for each of the categories “thick” and “thin”, the total sum of the partial regression coefficients (the numerical value shown in the total sum column in FIG. 9) is obtained.

In step S18, the total of partial regression coefficients for each category obtained in step S17 is divided by the number of seat positions corresponding to the category, and the average value of the partial regression coefficients for the category is calculated. That is, considering the above example, since the number of seat positions is 2, the partial regression coefficient of the target design element i is 0.5 × (PD (i) + PA
(I)). This gives, for example,
Regarding the "door thickness", the sum of the partial regression coefficients of the categories "thick" and "thin" shown in the total sum column of FIG. 9 is divided by 2 to obtain the average value of the partial regression coefficients (see FIG. 9). The numerical value shown in the average value column) is obtained.

Then, in step S21, the category showing the maximum average value among the average values of the partial regression coefficients of each category is extracted. That is, as for the “door thickness”, the category “thick” surrounded by a square frame in FIG. 9 is extracted.

Further, it is conceivable that the common design element 13 that affects a plurality of seat positions is assigned a weight depending on the difference in visibility from each seat position.

In this case, the process proceeds from step S16 to step S19. In step S19, step S16
The partial regression coefficient extracted for each category is multiplied by the weight given in the seat position weight database shown in FIG. 10 to obtain the weighted partial regression coefficient. For example, for the "instrument shape", the weight for each seat position is 2: 5: 0 for the driver's seat: passenger's seat: rear seat as shown in FIG. 10, so the evaluation term "circled" in the diagram of FIG. Suitable for runners "
And the partial regression coefficient corresponding to the evaluation term “luxurious” is multiplied by 5.

The seat position weight database shown in FIG. 10 is obtained as a result of the sensory evaluation experiment. Specifically, the design element 13 is selected, the design element 13 is shown to an unspecified number of people from each seat position, and the difference in the visibility and influence of the design element 13 with respect to each seat position is collected. The result of the accumulation is analyzed as a correlation coefficient. Alternatively, the weighting in FIG. 10 can be determined by physically determining the visibility of each design element at each seat position, instead of this.

Then, the process proceeds to step S17, the weighted partial regression coefficients calculated in step S19 are added for each category, and the sum of the weighted partial regression coefficients for each category is calculated.

In step S20, the sum of the weighted partial regression coefficients for each category obtained in step S17 is divided by the weighted total for each seat position, and the average value of the weighted partial regression coefficients is calculated. Considering the above example, since the weighted sum is 7, the weighted partial regression coefficient is {2.
It can be obtained by PD (i) + 5 · PA (i)} / 7.

Then, in step S21, the category showing the maximum average value among the average values of the weighted partial regression coefficients of each category is extracted.

In short, in the memory M of the computer 1,
Evaluation terms for expressing preset design images 9,
Knowledge that defines the relationship between evaluation terms obtained in preliminary experiments 1
1, vehicle design elements 13, knowledge that defines the relationship between vehicle design elements and evaluation terms obtained in sensory evaluation experiments 1
5. The knowledge 17 indicating the correspondence between the vacant seat position, the external environment or the vehicle interior environment and the design element is stored.

Then, when the operator operates the keyboard 3 to input the evaluation terms 9 for each seat position and select and input a specific design element, first, the CPU 7 operates so that the selected design element is a plurality of seats. It is classified as either common, which affects position, or non-common, which affects only a single seat position.

If the classified design elements are common, an inference is made in consideration of a plurality of seat positions that influence, and if they are not common, an inference is made in consideration of a single seat position.

This inference is performed on each design element, and the result is displayed on the CRT 5 as a design.

Therefore, according to this embodiment, the design according to the difference in the seat position can be obtained by inputting the evaluation term which matches the image of each seat position held by the user. Moreover, the design database does not need to be provided for each attribute, that is, for each seat position, and inference and extraction can be performed in consideration of each attribute with a small database.

Further, in the above embodiment, when inferring the optimum category from the selected design element 13,
It is also possible to add the priority of the design element 13 at each seat position. That is, the driver's seat has a steering shape →
Number of meters → Indoor color ..., instrument shape for passenger seat → Indoor color →
The order of priority, such as instrument height, is set in advance.
The same applies to the rear seats.

This priority can be input by the keyboard 3 or can be determined in advance as a database by rank or numerical value.

FIG. 11 is a flow chart showing another embodiment of the present invention.

In this embodiment, the optimum design element is inferred in accordance with the difference in external environment such as outside brightness such as night and day as an example of attribute, climate and weather. First, in step S31, the evaluation term 9 is input to the external environment such as external brightness, climate, and weather.
For example, enter "gorgeous when bright".

In step S32, a specific design element in the design target is selected, and the selected design element is a common one that affects a plurality of external environments or a non-common one that only affects a specific external environment. Is classified.

In step S33, in the case of a common design element, a category is inferred and extracted from the design database according to the evaluation terms for a plurality of influential external environments. In step S34, if the design elements are not common, the category is inferred and extracted from the design database by the evaluation term corresponding to the specific external environment.

In step S35, the design based on the optimum combination of categories inferred and extracted in steps S33 and S34 is displayed as an image with the external environment as a background.

In short, in this embodiment, the keyboard 3
Operate to enter the evaluation term 9 for each external environment, and select and enter specific design elements.
By the action of the CPU 7, the selected design elements are classified into common ones that affect a plurality of external environments or non-common ones that only affect a specific external environment.

If the classified design elements are common, an inference is made in consideration of a plurality of influential external environments, and if they are not common, an inference is made in consideration of a specific external environment.

This inference is performed for each design element, and the result is displayed on the CRT 5 as a design.

Therefore, according to this embodiment, it is possible to obtain a design according to the difference in the external environment.

FIG. 12 is a flow chart showing still another embodiment of the present invention.

In this embodiment, the optimum design element is inferred in accordance with the interior lighting as an example of the attribute, other occupants (whether VIP or friend), or the physiological condition of a person. Is. The human condition is detected from biological signals such as heartbeat and skin potential.

First, in step S41, the evaluation term 9 is input to the vehicle interior environment such as the vehicle interior lighting and the occupant's condition. For example, "passenger is friend and suitable for all" is input.

In step S42, a specific design element in the design object is selected, and the selected design element is a common one that affects a plurality of vehicle interior environments or a non-common one that affects only a specific vehicle interior environment. Things are classified.

In step S43, in the case of a common design element, a category is inferred and extracted from the design database by the evaluation terms for a plurality of in-vehicle environment that influence. In step S44, if the design elements are not common, the category is inferred and extracted from the design database by the evaluation term corresponding to the specific vehicle interior environment.

In step S45, the design based on the optimum combination of categories inferred and extracted in steps S43 and S44 is displayed as an image.

In short, in this embodiment, the keyboard 3
By operating and inputting the evaluation term 9 for each vehicle interior environment and selecting and inputting specific design elements, first,
By the action of the CPU 7, the selected design elements are classified into common ones that affect a plurality of vehicle interior environments or non-common ones that only affect a specific vehicle interior environment.

If the classified design elements are common, an inference is made in consideration of a plurality of influential vehicle interior environments, and if they are not common, an inference is made in consideration of a specific vehicle interior environment.

This inference is performed on each design element, and the result is displayed on the CRT 5 as a design.

Therefore, according to this embodiment, it is possible to obtain a design corresponding to the difference in the vehicle interior environment.

In each of the above embodiments, when the overall design of the vehicle interior corresponding to the seat position, the external environment, the vehicle interior environment, etc. is inferred and extracted, a combination of design elements is unnatural or physically difficult. Can be avoided by setting a conflict check rule.

The present invention is not limited to the above embodiment, but can be applied to the vehicle exterior, the vehicle appearance, or the design other than the vehicle. Also,
It can be used not only by a designer but also by a general user when a designer or the like decides a design.

[0087]

As is apparent from the above description, according to the invention of claim 1, it is possible to obtain a suitable design in consideration of differences in attributes such as seat position, external environment or vehicle interior environment with a small database. it can.

According to the second aspect of the invention, a suitable design can be obtained irrespective of the difference in the seating positions by inferring the design in consideration of the difference in the seating positions in the vehicle compartment.

According to the invention of claim 3, a suitable design can be obtained regardless of the difference in the external environment outside the vehicle interior.

According to the invention of claim 4, a suitable design can be obtained regardless of the difference in the vehicle interior environment.

[Brief description of drawings]

FIG. 1 is a configuration diagram of the present invention.

FIG. 2 is a schematic configuration diagram of an embodiment.

FIG. 3 is a diagram of knowledge defining relationships between evaluation terms.

FIG. 4 is a diagram of knowledge defining relationships between evaluation terms and vehicle design elements.

FIG. 5 is a diagram of knowledge showing correspondence between seat positions and design elements as an example of a specific design element selection database.

FIG. 6 is a flowchart of an example.

FIG. 7 is an explanatory diagram showing an example of image display.

FIG. 8 is a flowchart showing a specific example of one embodiment.

FIG. 9 is a chart showing an inference process according to a specific example of the embodiment.

FIG. 10 is a chart showing a seat position weight database as an example of a weight database.

FIG. 11 is a flowchart of another embodiment.

FIG. 12 is a flowchart of yet another embodiment.

FIG. 13 is a block diagram according to a conventional example.

[Explanation of symbols]

 CL1 input means CL2 design database CL3 inference means CL4 output means CL5 classification means

Claims (4)

[Claims] 1. An evaluation term input means for inputting an evaluation term expressing a design image, a design database for storing a relationship between the evaluation term and a design element, and the design database according to the input evaluation term. In a design device having an inference means for inferring various design elements and an output means for outputting a design as an inference result, the design element affects only a common design element that affects a plurality of attributes or only a specific attribute. A non-common design element, the inference means makes a deduction in consideration of a plurality of influential attributes in the case of a common design element, and an inference in the case of a non-common design element. A design device characterized by inferring in consideration of a specific attribute to be performed. 2. The design apparatus according to claim 1, wherein
The design apparatus, wherein the attribute is a seating position difference in a vehicle compartment. 3. The design apparatus according to claim 1, wherein:
The design device characterized in that the attribute is a difference in external environment such as a brightness condition outside the vehicle, a vehicle speed, climate and weather. 4. The design apparatus according to claim 1, wherein:
The design device, wherein the attribute is a difference in a vehicle interior environment such as a vehicle interior lighting, a human physiological reaction, and a passenger's condition.