KR102540160B1 - Method and Device for automatation of 3D Acoustic Study - Google Patents

Abstract

As a preferred embodiment of the present invention, an automatic output device for a 3D acoustic study includes an extraction unit for extracting shape information and characteristic information of at least one 3D model object; an optimization unit that removes at least one 3D model object that does not meet at least one of a predetermined length, a predetermined area, and a predetermined criterion based on the extracted shape information or characteristic information; a first filtering unit that classifies whether the 3D model entity is an obstacle using the characteristic information; a second filtering unit for classifying whether the 3D model entity is a noise source using the characteristic information; It is characterized in that it includes; a placement unit for arranging the 3D model objects classified as noise sources; and a characteristic input unit for inputting characteristics of the arranged 3D model objects.

Description

3D acoustic study automatic output device and method {Method and Device for automatation of 3D Acoustic Study}

The present invention relates to acoustic studies. In more detail, it relates to a method of automatically performing a three-dimensional acoustic study.

Acoustic Study is to calculate and predict the level of noise in a target space or area. dB) is calculated.

3D acoustic study can bring ground information and even building information through information linkage with GIS or Google Maps, but there is a problem in that it is difficult to input detailed 3D modeling at the EPC level. Accordingly, since small and medium-sized obstacles cannot be reflected and only large or extra-large obstacles are reflected, there is a big problem in that there is a big difference from the actual site, and it takes a long time to create a sound study.

KR 10-2284926 B1

As a preferred embodiment of the present invention, an automatic output device for a 3D acoustic study includes an extraction unit for extracting shape information and characteristic information of at least one 3D model object; an optimization unit that removes at least one 3D model object that does not meet at least one of a predetermined length, a predetermined area, and a predetermined criterion based on the extracted shape information or characteristic information; a first filtering unit that classifies whether the 3D model entity is an obstacle using the characteristic information; a second filtering unit for classifying whether the 3D model entity is a noise source using the characteristic information; It is characterized in that it includes; a placement unit for arranging the 3D model objects classified as noise sources; and a characteristic input unit for inputting characteristics of the arranged 3D model objects.

As a preferred embodiment of the present invention, the automatic 3D acoustic study output device further includes an output unit for outputting a 3D acoustic study document in which the noise source is disposed.

As a preferred embodiment of the present invention, the optimization unit is characterized by removing at least one 3D model object that does not conform to at least one of the predetermined length and the predetermined area based on the shape information.

As a preferred embodiment of the present invention, the first filtering unit is characterized in that it uses, as the predetermined criterion, a conditional sentence defined based on a relation between a characteristic item, a condition, a condition value, and conditions of the characteristic information.

As a preferred embodiment of the present invention, the arranging unit is characterized in that the noise source is automatically generated and arranged using coordinates, height, and direction information of the objects classified as the noise source.

As a preferred embodiment of the present invention, the characteristic input unit is characterized in that it inputs noise characteristic information for each noise source model using the characteristic information of the entities classified as the noise sources.

A preferred embodiment of the present invention proposes a method of automatically outputting a 3D sound study when a 3D model file is input.

As a preferred embodiment of the present invention, an apparatus and method for automatically outputting a 3D acoustic study receive shape information and characteristic information of a 3D model object from 3D modeling software and automatically output a 3D acoustic study result, It has the effect of improving reliability and shortening manufacturing time.

1 shows an internal configuration diagram of a three-dimensional acoustic study automatic output device as a preferred embodiment of the present invention.
2 shows an example of shape information and characteristic information of a 3D model object as a preferred embodiment of the present invention.
FIG. 3 shows an example of using shape information and characteristic information of a 3D model entity in a 3D acoustic study automatic output device as a preferred embodiment of the present invention.
4 shows an example of a user input interface used by an optimization unit as a preferred embodiment of the present invention.
5 and 6 show another example of a user input interface used by the optimization unit as a preferred embodiment of the present invention.
7 shows an example of a user input interface used in the first filtering unit and the first characteristic input unit as a preferred embodiment of the present invention.
8 to 10 show another example of a user input interface used in the second characteristic input unit as a preferred embodiment of the present invention.
FIG. 11 shows a flow chart for automatically generating a 3D acoustic study in a 3D acoustic study automatic output device according to a preferred embodiment of the present invention.

Hereinafter, it will be described with reference to the drawings.

1 shows an internal configuration diagram of a three-dimensional acoustic study automatic output device as a preferred embodiment of the present invention. FIG. 3 shows an example of using shape information and characteristic information of a 3D model entity in a 3D acoustic study automatic output device as a preferred embodiment of the present invention.

As a preferred embodiment of the present invention, the 3D acoustic study automatic output device 100 utilizes shape information and characteristic information of a 3D model to automatically detect noise sources in acoustic study software that can be implemented in a terminal through an acoustic study technology or processor. You can classify, arrange, and enter characteristics.

In a preferred embodiment of the present invention, the 3D acoustic study automatic output device 100 includes an extraction unit 110, an optimization unit 120, a first filtering unit 130 and a first characteristic input unit 132. . In addition, a processing unit 162 and an output unit 170 may be further included. In a preferred embodiment of the present invention, the first filtering unit 130 or the second filtering unit 140 is implemented to integrate a filtering function and a function of inputting characteristics, or to classify a filtering function and a function of inputting characteristics. It can be implemented to do so. 1, the first filtering unit 130 or the second filtering unit 140 performs a filtering function, and the first characteristic input unit 132 and the second characteristic input unit 160 perform a function of inputting characteristics. An example is shown.

In another preferred embodiment of the present invention, the 3D acoustic study automatic output device 100 further includes a second filtering unit 140, a placement unit 150 and a second characteristic input unit 160.

In another preferred embodiment of the present invention, the 3D acoustic study automatic output device 100 further includes a third filtering unit 142. FIG. 3 shows some of the embodiments disclosed in FIG. 1, and components corresponding to the configuration of FIG. 1 perform substantially the same or similar functions.

The extraction unit 110 extracts shape information and characteristic information of at least one 3D model object. The extraction unit 110 may receive at least one 3D model entity from sound study software or a sound study program executable in the 3D sound study automatic output device. In a preferred embodiment of the present invention, it is assumed that each entity in the 3D model has shape information and characteristic information. 2 shows an example of shape information 210 and characteristic information 220 of a 3D model object 200 as a preferred embodiment of the present invention.

The shape information includes geometric information such as points, lines, and planes constituting the shape, and material and texture information such as color, reflectance, and transparency. Characteristic information includes information such as position, direction, and layer of an object automatically input by 3D modeling software, etc., or information such as object name, tag number, noise source model, noise source output, etc. manually input by a user. include

As a preferred embodiment of the present invention, the 3D acoustic study automatic output device 100 converts a 3D model object into an obstacle object through the first filtering unit 130, and the noise source through the second filtering unit 140. It can be created or converted into an object, and converted into a ground object through the third filtering unit 142.

The 3D acoustic study automatic output device 100 includes a first filtering unit 130, a second filtering unit 140 to a second filtering unit 130 based on the shape information and characteristic information of at least one 3D model object extracted by the extraction unit 110. Using at least one of the third filtering unit 142, a 3D model object may be converted into at least one of an obstacle, a noise source, and a ground object. Through a control unit (not shown), the first filtering unit 130, the second filtering unit 140 to the third filtering unit 142 may be used selectively or in parallel. An example implementation is shown below. However, this is only an example of selective or parallel use of the first filtering unit 130, the second filtering unit 140 to the third filtering unit 142 in the 3D acoustic study automatic output device 100 and is limited thereto. It should be noted that it is not

As a preferred embodiment of the present invention, the 3D acoustic study automatic output device 100 uses the third filtering unit 142 based on the shape information and characteristic information of at least one 3D model object extracted by the extraction unit 110. At least one 3D model object is converted into a ground object through the second filtering unit 140, and at least one 3D model object is converted or generated and placed into a noise source object through the second filtering unit 140. After inputting characteristics, the optimization unit Optimization is performed in 120, at least one 3D model object is converted into an obstacle object through the first filtering unit 130, the obstacle type is classified, and the characteristic is input through the first characteristic input unit 132. can

As another preferred embodiment of the present invention, the 3D acoustic study automatic output device 100 is based on the shape information and characteristic information of at least one 3D model object extracted by the extraction unit 110, the second filtering unit ( 140) converts or creates at least one 3D model object into a noise source object, arranges it, and inputs characteristics. Thereafter, at least one 3D model object is converted into a ground object through the third filtering unit 142, optimization is performed in the optimization unit 120, and at least one 3D model object is converted through the first filtering unit 130. Objects can be converted into obstacle objects, and characteristics can be entered by classifying obstacle types.

As a preferred embodiment of the present invention, an example of converting a 3D model object into an obstacle object will be described. In order to transform a 3D model object into an obstacle object, the optimization unit 120 has at least one of a predetermined length, a predetermined area, and a predetermined characteristic information criterion based on the shape information or characteristic information extracted from the extraction unit 110. At least one 3D model object that does not match one is removed first.

An example of performing optimization based on a predefined length in the optimizer 120 is as follows. The optimizer 120 performs optimization by deleting an object smaller than a user-defined size among at least one 3D model object. For this, refer to the description of FIG. 4 .

As a preferred embodiment of the present invention, an example of converting or generating a 3D model object into a noise source object will be described. The second filtering unit 140 converts the 3D model object into a noise source such as a plane or a line. However, when converting a 3D object into a point noise source, it is difficult to correctly set the position or direction of the point noise source because the 3D model object is in the form of a plane. In this case, the second filtering unit 140 newly creates and arranges a 3D model object as a point noise source through the arranging unit.

As a preferred embodiment of the present invention, an example of converting a 3D model object into a ground object object will be described. The third filtering unit 142 determines whether the 3D model object is ground using the characteristic information of the 3D model object and converts it into a ground object. For example, if the 'System Path' characteristic information includes a condition value of 'Road&Paving', the object can be classified as a ground object.

4 shows an example of a user input interface used by the optimization unit 120 as a preferred embodiment of the present invention.

A user may input a horizontal (X-Y axis) length reference value 401 and a vertical (Z-axis) length reference value 403 through a user input interface. And, the unit 405 of the input value can be designated. The optimizer 120 deletes a 3D model object having a length less than or equal to a predetermined length defined by the user in the optimizer 120 among at least one input 3D model object. Referring to FIG. 4 , all objects whose X-axis end point length and Y-axis end point length are less than 1.5 m and whose Z-axis end point length is less than 0.15 m are all deleted.

An example of performing optimization based on a predetermined area in the optimization unit 120 is as follows. The optimizer 120 performs optimization by deleting an object that does not include any vertex inside the 3D hexahedron region defined by the user among at least one 3D model object. An example in which a user defines a 3D hexahedral area will be described with reference to FIG. 5 .

5 shows another example of a user input interface used by the optimization unit 120 as a preferred embodiment of the present invention.

As a preferred embodiment of the present invention, when the user activates the Current Screen View 510 button, coordinate values of both ends of a region visible on a screen displaying at least one 3D model object are automatically assigned. (530). In this case, only X1, X2, Y1, and Y2 are automatically input, and values of Z1 and Z2 are input by the user.

As another preferred embodiment of the present invention, when the user activates the mouse input 520 button, the coordinates of the point where the mouse is clicked on the screen are automatically entered in the X1, Y1, Z1 or X2, Y2, Z2 input fields ( 530) is assigned to. By pressing the left mouse input button and clicking one end point of the area on the screen, (X1, Y1, Z1) coordinates are automatically assigned to the X1, Y1, Z1 input cells. Also, by clicking the opposite end point of the area on the screen after pressing the right mouse button, (X2, Y2, Z2) coordinates are automatically assigned to the X2, Y2, Z2 input fields.

However, if the activated screen is a 2D view (Top View), only the horizontal coordinate values X1, X2, Y1, Y2 are automatically entered except for the Z1, Z2 values corresponding to the height. If the screen is a 3D view, the horizontal Coordinate values X1, X2, Y1, Y2 and vertical coordinate values Z1, Z2 are all entered automatically.

As another preferred embodiment of the present invention, the user can directly input coordinate values and units corresponding to X1, X2, Y1, Y2, Z1, and Z2.

An example of performing optimization based on a preset characteristic information criterion in the optimization unit 120 is as follows. The optimization unit 120 performs optimization by selectively deleting only objects that match the characteristic information predefined by the user among the input characteristic information values of at least one 3D model object. This will be described with reference to FIG. 6 .

6 shows another example of a user input interface used by the optimization unit 120 as a preferred embodiment of the present invention.

The user uses the user input interface to enter the 3D model characteristic item input unit (Property, 620), condition input unit (Condition, 630), condition value input unit (Value, 640), additional setting input unit (Not, Match Case, Wild Card) ( 650) can be selected and input to create one conditional row.

The 3D model property item input unit (Property, 620) is implemented to select and input property items such as Name, Tag Number, Layer, Material, Description, System Path, Folder Path, Location, and Direction.

The condition input unit (Condition, 630) is an item that defines the conditional relationship between a property and a condition value, and includes condition items such as Equals, Contains, >, >=, <, <=, Defined, and Undefined. It is implemented so that it can be selected and entered. Each condition item works as follows.

Equals : 선택된 특성 항목 값이 조건값과 일치

Equals: The value of the selected property item matches the condition value

Contains : 선택된 특성 항목 값이 조건값을 포함

Contains: The value of the selected attribute item contains the condition value

> : 선택된 특성 항목 값이 조건값보다 큼

> : The value of the selected property item is greater than the condition value

>= : 선택된 특성 항목 값이 조건값보다 크거나 같음

>= : the value of the selected property item is greater than or equal to the condition value

< : 선택된 특성 항목 값이 조건값보다 작음

<: The value of the selected property item is less than the condition value

<= : 선택된 특성 항목 값이 조건값보다 작거나 같음

<= : The value of the selected property item is less than or equal to the condition value

Defined : 선택된 특성 항목 값이 정의되어 있음(해당 항목 값이 존재함)

Defined: The value of the selected property item is defined (the item value exists)

Undefined : 선택된 특성 항목 값이 정의되어 있지 않음(해당 항목 값이 없음)

Undefined : The value of the selected property item is not defined (there is no value for that item)

In the condition value input unit (Value, 640), the user can define the application of the corresponding setting by using check boxes corresponding to Not, Match Case, and Wild Card. Considering the application of Match Case setting and Wild Card setting, case sensitive or wild card characters (*, ?) can be considered for input.

Not : 설정한 조건에 반대되는 조건을 정의함, Equals 조건을 선택하고 Not 설정 적용 시, 선택된 특성 항목 값이 조건값과 일치하지 않는 조건을 정의

Not: Defines a condition opposite to the set condition. When selecting the Equals condition and applying the Not setting, defines a condition in which the selected property item value does not match the condition value

Match Case : 조건 적용 시 대소문자 구분 여부를 정의

Match Case: Defines case sensitivity when applying conditions

Wild Card : Wild card 문자(*, ) 가 조건값에 포함될 경우 이를 Wild card 문자로 인식할 것인지, 단순 문자로 인식할 것인지 여부를 정의

Wild Card: Defines whether wild card characters (*, ) are recognized as wild card characters or simple characters when they are included in the condition value.

In addition, one or more conditional statements may be defined by combining the generated conditional lines using logical operators (New, And, Or) 610. The functions of logical operators are as follows.

NEW : 기존의 조건문과 결합되지 않는 신규 조건의 시작을 선언. NEW 연산자를 가진 조건부터 다음 NEW 연산자 조건이 나오기까지 나열된 조건들은 AND 혹은 OR 연산자를 통해 하나의 조건으로 결합됨

NEW: Declare the start of a new condition that is not combined with an existing conditional statement. The conditions listed from the condition with the NEW operator to the next NEW operator condition are combined into one condition through the AND or OR operator.

AND : 해당 조건행을 기존 조건행에 AND 연산으로 결합됨

AND: The condition row is combined with the existing condition line by AND operation.

OR : 해당 조건행을 기존 조건행에 OR 연산으로 결합됨

OR: The condition row is combined with the existing condition row by OR operation.

The first filtering unit 130 determines whether the 3D model object is an obstacle using the characteristic information of the 3D model object, converts it into an obstacle object, and classifies the type of the obstacle. In addition, predefined obstacle characteristics are input to the obstacles classified by the first characteristic input unit 132 . This will be described with reference to FIGS. 3 and 7 . FIG. 3 shows an example of using shape information and characteristic information of a 3D model entity in a 3D acoustic study automatic output device as a preferred embodiment of the present invention.

The first filtering unit 130 may automatically classify the 3D model object as a specific type of obstacle according to the criteria defined by the user using the characteristic information 304 of the 3D model object. Then, the first characteristic input unit 132 assigns an obstacle characteristic information value corresponding to a predefined obstacle type to the converted obstacle entity.

Referring to FIG. 7 , the user creates a row for each type of obstacle and inputs a name 710 . In addition, obstacle characteristic information 720 is input for each type of created obstacle. Examples of obstacle property information include Obstacle Type, Material, Attenuation Factor, and Reflection Loss. If you press the Setup button in the Condition column 730 of each obstacle classification row, a new window for setting the corresponding obstacle classification condition appears, and the condition setting method can be implemented similarly to the embodiment of FIG. 6 .

The first filtering unit 130 may automatically classify the 3D model object as a specific type of obstacle using obstacle classification and definition information previously defined by the user as shown in FIGS. 6 and 7 .

For example, if 'Layer' property information includes the condition value 'Building' and 'Material' property information includes 'Concrete' at the same time, the object is classified as a Building Type 2 obstacle.

The first characteristic input unit 132 inputs the characteristic of the obstacle entity. This will be described with reference to FIG. 7 . The first characteristic input unit 132 inputs predefined obstacle characteristic information 720 according to the obstacle classification (type) classified by the first filtering unit 130 . Examples of obstacle characteristic information include Obstacle Type, Material, Attenuation Factor, and Reflection Loss. Referring to FIG. 7 , characteristic information 720 to be input for each obstacle category 710 may be input. Obstacle Type and Material property information can be selected from values that can be selected from the corresponding property items of the sound study software or sound study program. Attenuation Factor, Reflection Loss, etc. are defined by directly inputting characteristic values for each obstacle category. For example, if the obstacle object classified by the first filtering unit 130 corresponds to 'Building Type 2', the first characteristic input unit 132 inputs noise source characteristic information corresponding to 'Building Type 2' to the corresponding obstacle object. . An example of input characteristic information with reference to FIG. 7 may be Obstacle Type: Building with flat roof, Material: Concrete, Attenuation Factor: -2dB, and Reflection Loss: 0.79.

The second filtering unit 140 determines whether the 3D model object is a noise source using the characteristic information of the 3D model object, and classifies the type if it is determined to be a noise source. The second filtering unit 140 determines a corresponding 3D model object as a noise source according to a criterion predefined by a user using the 3D model characteristic information value, and classifies the type of the noise source.

For example, the second filtering unit 140 has a value of 'Sound (dB), Day' characteristic information, and 'Model No.' If the value of the characteristic information matches 'DSP_15Ex', the object can be identified as the 'Speaker1' noise source.

The arranging unit 150 converts or newly creates a 3D model object classified as a noise source into a noise source object of sound study software or sound study program and arranges it. The placement unit 150 places the 3D model object classified as a noise source in the sound study software or sound study program. In this case, if the 'Source Type' characteristic value of the 3D model object classified as a noise source is not 'Point Source' (eg, Line Source, Area Source, etc.), the arranging unit 150 determines the corresponding 3D model object After copying to the same location, it is converted into a noise source object and placed, and when the 'Source Type' is 'Point Source', the corresponding 3D model in the 'Location' (820, Fig. 8) characteristic information among the characteristic information of the 3D model object Using the coordinate and height information of the object, the object's X-axis, Y-axis, and Z-axis rotation angle information is called from the 'Direction' (Fig. 8, 820) characteristic information, and a noise source is created and placed in the same position and direction.

For example, when the arrangement unit 150 arranges a noise source object on sound study software or a sound study program, in the case of an object whose Source Type characteristic value is classified as a 'Point Source' noise source, the characteristic information of the corresponding 3D model object. (E:1,000,000 mm, N:1,000,000 mm, EL:3,000mm) coordinates and (X-axis rotation angle: 90 degrees, Y-axis rotation angle: 0 degrees, Z-axis rotation angle: 0 Fig.) By inputting the direction, the noise source object of the sound study software or sound study program is automatically created and arranged.

In the case of an object classified as a noise source (eg, Line Source, Area Source, etc.) whose Source Type characteristic value is not 'Point Source', copy the 3D model object to the same location and then use sound study software or sound study program is converted and placed into a noise source object of

The second characteristic input unit 160 inputs the characteristics of the individual noise source. This will be described with reference to FIGS. 8 to 10 . In order to perform the acoustic study, there is characteristic information that must be input to each of the arranged noise sources. The second characteristic input unit 160 inputs predetermined noise source characteristic information 830 to the noise source object arranged in the arrangement unit 150 according to the noise source classification (type) classified by the second filtering unit 140. . Examples of noise source characteristic information include Source Type, Directivity, Emission Day, Emission Night, Events Per Hour, and Duration. Referring to FIGS. 8 to 10 , characteristic information 830 to be input for each noise source classification 810 may be input. Source Type (910) and Directivity (920) characteristic information can be selected from values that can be selected from corresponding characteristic items of sound study software or sound study programs. For Emission Day (1010) and Emission Night (1020) characteristic information, corresponding characteristic values of each noise source object may be obtained by selecting an item corresponding to noise source output information among 3D model characteristic items. Events Per Hour, Duration, etc. are defined by directly inputting characteristic values for each noise source category. For example, if the noise source object arranged in the arrangement unit 150 corresponds to 'Speaker1' according to the classification of the second filtering unit 140, The second characteristic input unit 160 inputs noise source characteristic information corresponding to 'Speaker1' to the corresponding noise source entity. An example of input characteristic information with reference to FIG. 8 may be Source Type: Point Source, Directivity: DSP_15Ex, Emmission Day: 124, Emmission Night: 110, Events Per Hour: 1, and Duration (Min): 60. Here, the emission day and emission night characteristic information are entered to correspond to the 'Sound (dB), Day' and 'Sound (dB), Night) items of the 3D model characteristic information, respectively, so that the 3D model characteristics of the corresponding noise source Among the items, the characteristic values entered in the 'Sound (dB), Day' item and the 'Sound (dB), Night' item are input as the emission day and emission night characteristic values of the noise source object. Here, the 'Sound (dB), Day' characteristic value of the 3D model object is exemplified as 124 and the 'Sound (dB), Night' characteristic value as 110.

8 is an example in which a user inputs noise source arrangement information 820 and characteristic information 830 for each of the arranged noise sources, or imports and inputs characteristic information of a 3D model object corresponding to the arranged noise source from acoustic study software. shows Pressing the Setup button in the Condition column 840 brings up a new window for setting the noise source classification criterion conditions of the corresponding type, and the condition setting method is similar to that of the embodiment of FIG. 6 .

The user may classify and input the name 810 for each of the arranged noise sources. In addition, a 3D model characteristic item corresponding to the location information and direction information 820 of each of the arranged noise sources may be selected.

In order to perform the acoustic study, there is characteristic information that must be input to each of the arranged noise sources. Referring to FIGS. 9 to 10 , the user may select and input characteristic information 920 and 1010 to be input for each noise source 910 . Examples of characteristic information include Source Type, Directivity, Emission Day, Emission Night, Events Per Hour, and Duration.

The output unit 170 outputs a three-dimensional acoustic study document in which noise sources are arranged and characteristic information values of the noise sources are input. Calculated values may be set and processed through the processing unit 362 of FIG. 3 before the output unit 170 outputs the final 3D acoustic study result.

11 is a flowchart for automatically generating a 3D acoustic study in a 3D acoustic study automatic output device according to a preferred embodiment of the present invention.

The extraction unit extracts shape information and characteristic information of at least one 3D model object (S1110). The third filtering unit classifies whether the 3D model object is a ground object using the characteristic information (S1120). The second filtering unit classifies whether the 3D model entity is a noise source using the characteristic information (S1130). The arranging unit arranges the 3D model objects classified as noise sources (S1140). The second characteristic input unit inputs the characteristics of the arranged noise source entity (S1150). The first filtering unit classifies whether the 3D model object is an obstacle using the characteristic information (S1160). And the first characteristic input unit inputs the characteristic of the classified obstacle entity (S1170).

The operation of the method according to the embodiment of the present invention can be implemented as a computer readable program or code on a computer readable recording medium. A computer-readable recording medium includes all types of recording devices in which information that can be read by a computer system is stored. In addition, computer-readable recording media may be distributed to computer systems connected through a network to store and execute computer-readable programs or codes in a distributed manner.

In addition, the computer-readable recording medium may include hardware devices specially configured to store and execute program instructions, such as ROM, RAM, and flash memory. The program command may include high-level language codes that can be executed by a computer using an interpreter or the like as well as machine code generated by a compiler.

Although the above has been described with reference to preferred embodiments of the present invention, those skilled in the art can variously modify and change the present invention without departing from the spirit and scope of the present invention described in the claims below. You will understand that you can.

Claims (15)

an extraction unit for extracting shape information and characteristic information of at least one 3D model object;
an optimization unit that removes at least one 3D model entity that does not meet at least one of a predefined length, a predefined area, and a predefined characteristic information criterion based on the extracted shape information or characteristic information;
a first filtering unit for classifying whether the 3D model entity is an obstacle using the characteristic information and converting the 3D model entity into an obstacle entity;
a second filtering unit for converting or generating a 3D model entity into a noise source entity using the characteristic information of the 3D model entity extracted from the extraction unit;
a placement unit for automatically arranging the noise source objects using coordinate, height, and direction information;
A first characteristic input unit for inputting obstacle characteristics according to the classified obstacle types; And
A three-dimensional acoustic study automatic output device comprising a; second characteristic input unit for inputting the characteristics of the noise source entities. delete According to claim 1,
The 3D acoustic study automatic output device further comprising a third filtering unit for converting the 3D model entity into a ground entity using the characteristic information of the 3D model entity extracted from the extraction unit. The method of claim 1, wherein the optimization unit
3D acoustic study automatic, characterized in that for removing at least one 3D model object that does not conform to at least one of the predefined length, the predefined area and the predefined characteristic information based on the shape information output device. The method of claim 1, wherein the first filtering unit
A three-dimensional acoustic study automatic output device characterized in that a conditional sentence defined on the basis of a characteristic item of the characteristic information, a condition, a condition value, and a relationship between conditions is used as a criterion for the predefined characteristic information. delete The method of claim 1, wherein the characteristic information
A three-dimensional acoustic study automatic output device comprising characteristic information of a three-dimensional model object automatically input through a three-dimensional modeling software or characteristic information of a three-dimensional model object input by a user. As a method of automatically performing a three-dimensional acoustic study in a three-dimensional acoustic study automatic output device,
Extracting shape information and characteristic information of at least one 3D model object in an extractor;
removing at least one 3D model entity that does not meet at least one of a predefined length, a predefined area, and a predefined characteristic information criterion based on the shape information or characteristic information extracted by the optimization unit; and
Classifying whether or not the 3D model object is an obstacle by using the characteristic information in a first filtering unit and converting it into an obstacle entity, classifying the obstacle type and inputting the obstacle characteristic through a first characteristic input unit;
converting or generating a 3D model object into a noise source object in a second filtering unit using the characteristic information of the 3D model entity extracted by the extraction unit, automatically arranging it, and inputting the characteristic through a second characteristic input unit; A method comprising a. The method of claim 8, wherein the optimization unit
and removing at least one 3D model entity that does not conform to at least one of the predefined length and the predefined area based on the shape information. The method of claim 8, wherein the first filtering unit
A method characterized in that a conditional sentence defined based on a relation between a characteristic item, a condition, a condition value, and conditions of the characteristic information is used as a criterion for the predefined characteristic information. delete According to claim 8,
and converting the 3D model entity into a ground entity in a third filtering unit using the characteristic information of the 3D model entity extracted in the extraction unit. The method of claim 8, wherein the characteristic information
A method characterized by including characteristic information of a 3D model entity automatically input through 3D modeling software or characteristic information of a 3D model entity input by a user. The method of claim 8, wherein the characteristic information
Including information such as the location, direction, layer, etc. of an object automatically input by 3D modeling software or information such as the object name (Name), tag number, noise source model, noise source output, etc. manually input by the user How to characterize. A computer-readable recording medium storing a program implementing a method of automatically performing a 3-dimensional acoustic study according to any one of claims 8 to 10 and 12 to 14.

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