Noise prediction device and noise prediction method

Abstract

This noise prediction tool flexibly accommodates the usage needs of each function, enabling efficient and highly accurate noise prediction. [Solution] In a noise prediction device 100 equipped with a processor, the processor 103 acquires information on the location of the sound source and the sound receiving point in a model of a building via a display screen of the model drawn by the drawing software 1013. Based on this information and information on the sound emitted by the sound source, the processor predicts the characteristics of the sound at the sound receiving point and executes a process to output output information corresponding to the predicted characteristics. Furthermore, the acquisition of the above information, the prediction of sound characteristics, and the output of output information are all performed via a spreadsheet screen displayed by the execution of the spreadsheet software 1012.

Description

Technical Field

[0005] ,

[0001] The present invention relates to a noise prediction device and a noise prediction method, and relates to a technology that can flexibly respond to the usage needs of each function in a noise prediction tool and support efficient and accurate noise prediction.

Background Art

[0002] When the sound generated at a certain point propagates directly or through a medium and has a noise level above a certain level at the propagation destination, people present at the location are likely to feel "noise". Therefore, for business operators who design or construct apartment buildings where multiple households live together, commercial facilities where many people gather, etc., prior consideration regarding noise becomes important from the perspective of customer satisfaction and the like. As a conventional technology for predicting noise, for example, there has been proposed a noise countermeasure simulation technology (see Patent Document 1) that can accurately separate the positions and directions of a plurality of noise sources even when there are a plurality of noise sources and simulate the sound insulation effect of a sound insulation wall.

[0003] This technology is related to a noise countermeasure simulation method characterized in that when a sound insulation wall is provided between a plurality of noise sources and an observation point, the sound source information of the plurality of noise sources is collected, and the magnitude of the sound reaching the observation point from the plurality of noise sources when the sound insulation wall is provided is calculated, and the sound insulation effect of the sound insulation wall is simulated.

[0004] Also, there has been proposed a technology (see Patent Document 2) that provides a noise propagation prediction method capable of predicting noise propagation at a level close to the actual value under a wide range of conditions, that is, in a situation close to reality.

[0005] <0000This technology relates to a noise propagation prediction method characterized by modeling the components of a structure involved in sound reflection, absorption, and transmission as a set of multiple elements, each with set acoustic physical characteristic coefficients including at least sound absorption coefficient, diffuse reflection coefficient, and transmission loss; determining the total incident energy to each element from the incident energy from the noise source to each element and the amount of energy exchanged between each element, taking into account reflection, multiple diffraction, and sound transmission; and subsequently predicting the noise level at the receiving position from the incident energy from each element to the receiving position, taking into account reflection, multiple diffraction, and sound transmission.

[0006] Furthermore, acoustic analysis techniques for various types of buildings (see Patent Document 3) have also been proposed. This technique relates to an acoustic analysis system comprising: a generation means for generating an analysis model that defines habitable rooms and multiple other room spaces within a building using a BIM model that represents the structure and specifications of the building to be analyzed in three dimensions; a calculation means for extracting parameters from the analysis model that exist along the propagation path through which sound propagates from a predetermined sound source room to a receiving room, and using these parameters to calculate the sound pressure level of the receiving room with at least the sound source room as the sound source; and an output means for outputting the calculation results of the calculation means. [Prior art documents] [Patent Documents]

[0007] [Patent Document 1] Japanese Patent Publication No. 2007-93251 [Patent Document 2] Japanese Patent Publication No. 2003-75244 [Patent Document 3] Japanese Patent Publication No. 2023-50340 [Overview of the project] [Problems that the invention aims to solve]

[0008] Existing noise prediction tools integrate and provide the following functions: 1) determination of the distance between the sound source and various locations in the building, and the dimensions of various structures, components, and equipment; 2) noise prediction calculation based on the determinations in 1); 3) summarization of noise prediction results; and 4) display and output of noise prediction results.

[0009] On the other hand, from a user's perspective, it can be said that it is difficult to individually utilize each of the above-mentioned functions of these noise prediction tools as needed. Such individual utilization is necessary, for example, when introducing new calculation methods for noise prediction, adding or removing sound absorption and reflection conditions, or changing the display and output specifications to meet the needs of the recipient of the prediction results. However, as mentioned above, this is a challenge that is difficult to address with existing noise prediction tools, and this challenge may limit the scope of what users can do in noise prediction work and make it difficult to ensure prediction accuracy.

[0010] These issues become more practical the more specialized knowledge users have regarding noise prediction, and the greater the need for precise noise predictions that meticulously reflect the specific structure and configuration of actual buildings and other structures.

[0011] Therefore, the present invention has been made in view of the above problems, and its objective is to provide a technology that can flexibly respond to the usage needs of each function in a noise prediction tool and support efficient and accurate noise prediction. [Means for solving the problem]

[0012] The above problem is solved by a noise prediction device equipped with a processor, wherein the processor performs the following processes: receiving a specification regarding the location of a sound source and a receiving point in a model of a building; acquiring first input information regarding the specifications and structure of the building and second input information regarding the sound emitted by the sound source; predicting the characteristics of the sound at the receiving point based on the location of the sound source and the receiving point, the first input information and the second input information; and outputting output information corresponding to the predicted characteristics, wherein the process of receiving a specification regarding the location of the sound source and the receiving point is performed via a display screen of the model drawn by drawing software, and the processes of acquiring the first input information and the second input information, predicting the characteristics of the sound, and outputting the output information are performed via a spreadsheet screen displayed by the execution of spreadsheet software.

[0013] The noise prediction device of the present invention, configured as described above, can flexibly respond to the usage needs of each function in a noise prediction tool and support efficient and accurate noise prediction.

[0014] Furthermore, in the above-described noise prediction device, if a plurality of sound sources are specified and the second input information is acquired for the plurality of sound sources, it is preferable that the processor performs the following: a process of calculating the sound characteristics of each frequency for each of the sound sources in the spreadsheet software; a process of accepting a selection of which sound source's sound characteristics to use from among the sound characteristics of each frequency calculated for each sound source; and, based on the sound characteristics of each frequency calculated for the sound source for which the selection has been accepted, the spreadsheet software predicts the sound characteristics at the sound receiving point.

[0015] With the above configuration, for example, users can freely select sound sources based on their knowledge, while flexibly performing simulations to achieve noise levels in accordance with laws and regulations. In turn, it becomes possible to flexibly respond to the usage needs of each function in the noise prediction tool and support more efficient and accurate noise prediction.

[0016] Furthermore, in the noise prediction device described above, it is preferable that the processor, each time it receives a selection, re-executes the prediction of the sound characteristics corresponding to that selection in the spreadsheet software.

[0017] With the above configuration, the characteristics of the sound (noise level, etc.) can be predicted each time the user selects or rejects sound sources. In turn, this allows for flexible response to the usage needs of each function in the noise prediction tool, supporting more efficient and accurate noise prediction.

[0018] Furthermore, in the above-described noise prediction device, it is preferable that the processor performs a process of receiving input regarding the setting of the diffraction path of sound emitted by the sound source, taking into account the location of the sound source, the sound receiving point, and the structure of the building, and then identifies an expression to be used when predicting the characteristics of the sound from various expressions that are pre-held in the spreadsheet software according to the characteristics of the diffraction path, in accordance with the received input regarding the setting of the diffraction path.

[0019] The above configuration allows for flexible setting of diffraction paths according to user knowledge and needs, moving away from the fixed diffraction path concepts used in existing noise prediction tools. Ultimately, this enables flexible adaptation to the usage needs of each function in the noise prediction tool, supporting more efficient and accurate noise prediction.

[0020] Also, according to the noise prediction method of the present invention, in a noise prediction device including a processor, the processor performs a process of receiving a specification regarding the position of a sound source and a sound reception point in a model of a building, a process of acquiring first input information regarding the specifications and structure of the building, and second input information regarding the sound emitted by the sound source, and a process of predicting the characteristics of the sound at the sound reception point based on the position and sound reception point of the sound source, the first input information, and the second input information, and a process of outputting output information corresponding to the predicted characteristics. The process of receiving the specification regarding the position and sound reception point of the sound source is performed via a display screen of the model drawn by drawing software, and each process of acquiring the first input information and the second input information, predicting the characteristics of the sound, and outputting the output information is performed through a spreadsheet screen displayed by executing spreadsheet software. This solves the problem.

[0021] According to the above noise prediction method, it is possible to flexibly respond to the usage needs of each function in the noise prediction tool and support efficient and accurate noise prediction.

Effects of the Invention

[0022] According to the noise prediction device and the noise prediction method of the present invention, it is possible to flexibly respond to the usage needs of each function in the noise prediction tool and support efficient and accurate noise prediction. [[ID=I2]]

Brief Description of the Drawings

[0023] [Figure 1] It is a diagram showing an example of a network configuration including the noise prediction device of the present embodiment. [Figure 2] It is a diagram showing an example of the hardware configuration of the noise prediction device in the present embodiment. [Figure 3] It is a diagram showing an example of BIM data in the present embodiment. [Figure 4] It is a diagram showing an example of sound source data in the present embodiment. [Figure 5] It is a diagram showing an example of a diffraction path in the present embodiment. [Figure 6]This figure shows an example of the prediction results in this embodiment. [Figure 7] This figure shows an example of a flow chart of the noise prediction method in this embodiment. [Figure 8] This figure shows an example of a flow chart of the noise prediction method in this embodiment. [Figure 9] This figure shows an example of a diffraction path in this embodiment. [Figure 10] This figure shows an example of a mathematical formula in this embodiment. [Figure 11] This figure shows an example of a flow chart of the noise prediction method in this embodiment. [Figure 12] This figure shows an example of a flow chart of the noise prediction method in this embodiment. [Figure 13] This figure shows an example of the output in this embodiment. [Figure 14] This figure shows an example of the output in this embodiment. [Figure 15] This figure shows an example of the output in this embodiment. [Figure 16] This figure shows an example of the output in this embodiment. [Figure 17] This figure shows an example of the output in this embodiment. [Modes for carrying out the invention]

[0024] <<System configuration including the noise prediction device of this embodiment>> The configuration of the noise prediction device 100 and the methods performed by the noise prediction device 100 will be described below, with reference to the attached drawings, using one embodiment of the present invention (hereinafter referred to as "this embodiment") as an example. However, the embodiment described below is merely an example given to facilitate understanding of the present invention and does not limit the present invention. That is, the present invention can be modified or improved from the embodiment described below without departing from its spirit. Naturally, the present invention also includes equivalents thereof.

[0025] Furthermore, the screen examples shown in the diagrams referenced in the following explanation are merely examples, and the screen configuration, the content of the displayed information, and the GUI (Graphical User Interface) can be freely designed and modified according to the system design specifications and user preferences.

[0026] Furthermore, in this specification, "device," "equipment," "system," or "terminal" includes not only a single device that performs a predetermined function on its own, but also multiple devices that are separate from each other but cooperate to perform a predetermined function.

[0027] First, the noise prediction device 100 of this embodiment will be described. Figure 1 is a diagram showing an example of a network configuration including the noise prediction device 100 in this embodiment. This noise prediction device 100 is configured to be communicatively connected to a user terminal 200, a design information management system 300, and an external system 400 via a network N. The noise prediction device 100 is an information processing device that performs each process corresponding to the noise prediction method of the present invention, and more specifically, it is a server device that exchanges data with the user terminal 200 and the design information management system 300 as needed and performs noise prediction. As shown in Figure 1, the noise prediction device 100 can be the main component of a group of devices connected via network N and working together, which can be called a noise prediction system 10.

[0028] The noise prediction device 100 of this embodiment is a noise prediction device operated, for example, by a housing manufacturer or a vendor that supports their operations. Such a noise prediction device 100 receives instructions to perform noise prediction from, for example, a design engineer of the housing manufacturer, either directly through a user interface or via a user terminal 200. The noise prediction device 100 also acquires data as appropriate from the user terminal 200 or the design information management system 300 in response to the instructions, and performs noise prediction at the sound receiving points in the building targeted for noise prediction, and outputs the results. The "design engineer" mentioned above is an example of a "user" in this invention. The "noise prediction results" are information on the expected noise levels at various locations (sound receiving points) in the building that the housing manufacturer is targeting for construction and sale.

[0029] Furthermore, the noise prediction device 100 in this embodiment is capable of handling and utilizing various data in the so-called BIM (Building Information Modeling) format with respect to the above-mentioned building. The BIM data 1015 (described later in Figure 3) can be obtained and used from an appropriate management system, such as a design information management system 300 operated by a housing manufacturer or the like.

[0030] In this case, the noise prediction device 100 executes its own BIM software 1013 (Figure 2), or calls an external BIM software function provision service via the network N, to perform various processes associated with the execution of the noise prediction method of this embodiment, such as drawing a three-dimensional or two-dimensional model of the building based on the BIM data 1015, and receiving the specification of sound sources and sound receiving points on the model.

[0031] Furthermore, the noise prediction device 100 performs noise prediction using spreadsheet software 1012 (Figure 2) based on information such as sound sources and receiving points specified by the design engineer via the BIM software 1013, as well as information such as the specifications and structure of the building (e.g., retrieved from the design information management system 300 at the instruction of the design engineer, or retrieved from information entered by the design engineer from the user terminal 200). This spreadsheet software 1012 has various formulas for noise prediction (described later in Figure 10) stored in advance, and calculates the noise level value at each receiving point using the values ​​indicated by the information provided by the noise prediction device 100 (necessary information such as the sound sources, receiving points, and building specifications and structure) as input values.

[0032] The noise prediction device 100 processes the noise prediction results from the spreadsheet software 1012 into an output format specified by the recipient of the noise prediction results, such as a government agency, and generates output data such as a list showing the noise level at each receiving point and various graphs mapping the noise level between the sound source and each receiving point. In generating this output data, the noise prediction device 100 adds the noise prediction results to output software 1014 (Figure 2), such as mapping software, to generate the list and various graphs.

[0033] The noise prediction device 100 may also obtain predefined information regarding the output format of the noise prediction results from an external system 400 and prepare for outputting the noise prediction results. The external system 400 is a system operated by government agencies, etc., that publishes information on network N. Examples of output formats specified by the above predefined information include tables, two-dimensional contours (Figure 13), two-dimensional propagation diagrams (Figure 14), two-dimensional scatter plots (Figure 15), three-dimensional contours (Figure 16), and three-dimensional scatter plots (Figure 17). The external system 400 may also be the destination for the transmission (submission) of the above output data generated by the noise prediction device 100.

[0034] In addition to server equipment, the specific implementation form of the noise prediction device 100 can also include PCs (Personal Computers), tablet devices, smartphones, etc., as long as they have the necessary functions and specifications to perform the noise prediction method.

[0035] Furthermore, the user terminal 200 is a terminal that, for example, accesses the noise prediction device 100 or, in response to a request from the noise prediction device 100, performs various processes such as identifying and displaying BIM data 1015 related to the building to be noise predicted, accepting diffraction path specifications from the design engineer, and displaying noise prediction results (noise levels) for viewing by the design engineer. In this embodiment, the user terminal 200 may access the noise prediction device 100 via the network N, or the noise prediction device 100 may provide its own user interface (console).

[0036] Furthermore, the external system 400 holds at least predefined information regarding the output format of noise prediction results and distributes this information to the noise prediction device 100 in response to requests from the noise prediction device 100 or user terminal 200. Note that, if the predefined information is subject to authentication management, the noise prediction device 100 and user terminal 200 accessing the external system 400 may, for example, pre-store various access information such as authentication information and access destinations (endpoint URLs) necessary for referencing the predefined information. The endpoint URL is the access destination in the Web API, i.e., the address to which the external system 400 provides the web service.

[0037] It should be noted that this network configuration is merely an example, and various other configurations are possible and are not limited to the present invention. These include a configuration in which the noise prediction device 100 is integrated with a user terminal 200, a design information management system 300, and an external system 400, or a configuration in which at least one of the user terminal 200, the design information management system 300, or the external system 400 provides all or part of the configuration and functions of the noise prediction device 100 to execute the noise prediction method of the present invention.

[0038] <Hardware configuration of the noise prediction device> The noise prediction device 100 in this embodiment is a computer device that primarily performs each process in the noise prediction method of the present invention. Therefore, it has the hardware configuration shown in Figure 2. The noise prediction device 100 has the configuration of a general information processing device and includes an auxiliary storage device 101, a main memory device 102, a processor 103, and a communication device 104.

[0039] In this embodiment, the noise prediction device 100 may consist of a single information processing device as shown in the figure, or it may consist of multiple parallel distributed information processing devices. Alternatively, the noise prediction device 100 may consist of an information processing device for ASP (Application Service Provider), SaaS (Software as a Service), PaaS (Platform as a Service), or IaaS (Infrastructure as a Service).

[0040] Here, assuming that the noise prediction device 100 in this embodiment is composed of a single information processing device, the diagram illustrates a configuration in which the auxiliary storage device 101, main storage device 102, processor 103, and communication device 104 are connected by a bus.

[0041] Of the above configurations, the auxiliary storage device 101 is implemented using a non-volatile storage device or storage medium such as an HDD (Hard Disk Drive), SSD (Solid State Drive), flash memory, FD (Flexible Disc), MO disk (Magneto-Optical disc), CD (Compact Disc), DVD (Digital Versatile Disc), SD card (Secure Digital card), or USB memory (Universal Serial Bus memory).

[0042] Furthermore, the auxiliary storage device 101 may be configured to be built into the housing of the noise prediction device 100, or it may be configured to be connected to the noise prediction device 100 in an external form. In addition, the auxiliary storage device 101 may be configured as another computer or the like that is connected to the noise prediction device 100 in a communicative manner. As a technology for recording various data, a distributed ledger technology such as blockchain may be used to avoid unauthorized data tampering. In this embodiment, the auxiliary storage device 101 stores a program 1011 that leads and manages the execution of the noise prediction method. The auxiliary storage device 101 also has spreadsheet software 1012, BIM software 1013, and output software 1014 as tools used by the program 1011. Details of these tools will be described later. The auxiliary storage device 101 also holds BIM data 1015, sound source data 1016, diffraction path data 1017, and prediction results 1018, which will be described in detail later.

[0043] Furthermore, the main memory 102 may be composed of volatile semiconductor memory such as ROM (Read Only Memory) and RAM (Random Access Memory). In this embodiment, the main memory 102 holds the program 1011 (and the software corresponding to the above tools) including the OS (Operating System) that the processor 103 has read from the auxiliary memory 101 for execution.

[0044] The OS implements the control and basic functions of the noise prediction device 100 itself. Under its control, the processor 103 calls and executes program 1011, calling each tool in a procedure corresponding to the noise prediction method and implementing the necessary functions. The processor 103 may be composed of a CPU (Central Processing Unit), MPU (Micro-Processing Unit), MCU (Micro Controller Unit), GPU (Graphics Processing Unit), DSP (Digital Signal Processor), TPU (Tensor Processing Unit), or ASIC (Application Specific Integrated Circuit), etc.

[0045] Furthermore, if the network N is configured as a wired LAN, the communication device 104 is implemented using a network interface card compatible with the protocol of the wired LAN. If the network N is configured as a wireless LAN, the communication device 104 is implemented using a network interface card compatible with the Wi-Fi®-based wireless LAN protocol. Alternatively, if the network N is configured as a mobile phone network, the communication device 104 is implemented using a communication chipset compatible with, for example, 3G to 5G or later generations of mobile communication protocols, or the LTE (Long Term Evolution) protocol. Of course, the configuration of the network N and the corresponding implementation of the communication device 104 are merely examples, and other devices may be used depending on the type of network N.

[0046] In addition to the above configuration, the noise prediction device 100 may also be equipped with input and output devices. Input devices mainly include keyboards, mice, and touch panels, but may also include other appropriate input devices such as microphones. On the other hand, output devices include displays, printers, and speakers, but may also include other output devices.

[0047] <Data structure> Next, the various data held and used for processing by the noise prediction device 100 of this embodiment will be described. Figure 3 shows an example of the data structure of BIM data 1015. This BIM data 1015 is a database that stores and manages BIM data for buildings designed and sold by the above-mentioned housing manufacturer, etc. BIM data 1015 is a collection of records that associates at least the following values ​​with an ID that uniquely identifies the building: the part of the building, the No. of the component constituting that part, the specifications, size, and installation position of the component (e.g., coordinate values ​​in the three-dimensional space drawn in the BIM data). Of these, "specifications" are the specifications of each component used in each part of the building, and include at least information such as material, thickness, and structure necessary for noise prediction. "Size" is information that indicates the size of the component, for example, in terms of length, width, and area.

[0048] These BIM data 1015 are obtained either by the noise prediction device 100 acquiring data that the design engineer has individually entered by operating the user terminal 200 and storing it in the auxiliary storage device 101, or by the design information management system 300 acquiring the data in response to a request from the user terminal 200 and storing it in the auxiliary storage device 101.

[0049] Figure 4 shows an example of the data structure of the sound source data 1016 in this embodiment. The sound source data 1016 in this embodiment is one of the data that the noise prediction device 100 adds to the spreadsheet software 1012 and uses for noise prediction. The sound source data 1016 shown in Figure 4 is a collection of records that associate values ​​such as the sound source name, sound power level, and location of the sound source with a sound source No. that uniquely identifies the sound source as the key.

[0050] Of these, the "sound power level" value represents the output from the sound source as a level, and its unit is dB (decibels). The "position" value is, for example, the coordinate value in the coordinate space of a 3D model of a building. This coordinate value is the coordinate value of the location of the sound source specified by the design engineer operating the user terminal 200 on the drawn 3D model. The noise prediction device 100 will then assign the "sound power level" and "position" values ​​(second information of the present invention) of each sound source indicated by this sound source data 1016 to the spreadsheet software 1012.

[0051] Figure 5 shows an example of the data structure of the diffraction path data 1017 in this embodiment. The diffraction path data 1017 in this embodiment is one of the data that the noise prediction device 100 adds to the spreadsheet software 1012 and uses for noise prediction. The diffraction path data 1017 shown in Figure 5 is a collection of records that associates values ​​such as the diffraction direction of the sound emitted from the sound source, the coordinates of each diffraction point (e.g., diffraction point 1, diffraction point 2, ...) at the edges of objects (e.g., buildings and various equipment) present in the propagation space (medium) of the sound, the straight-line distance between the sound source and the receiving point, the straight-line distance between the sound source and diffraction point 1, the straight-line distance between diffraction point 1 and diffraction point 2, the straight-line distance between diffraction point 2 and the receiving point, and acceptance or rejection, with the sound source No. which uniquely identifies the sound source as the key.

[0052] The "Acceptance / Rejection" value indicates, for example, the diffraction path selected by the design engineer by operating the user terminal 200 in the coordinate space including the three-dimensional model of the building shown by the BIM software 1013 (described later based on Figure 9), or the diffraction path selected by the design engineer by operating the user terminal 200 in the diffraction path data 1017 displayed by the spreadsheet software 1012 (output target for the spreadsheet screen; the same applies hereinafter) with "○" (accepted) or "-" (rejected) in the "Acceptance / Rejection" column.

[0053] Figure 6 is a diagram showing an example of the data structure of the prediction result 1018 in this embodiment. The prediction result 1018 in this embodiment shows the characteristics of the sound at the receiving point, obtained by the noise prediction device 100 adding the above-mentioned BIM data 1015 (first input information relating to the specifications and structure of the building) and sound source data (second input information relating to the sound emitted by the sound source, including the location of the sound source and the receiving point in the building model) to the spreadsheet software 1012.

[0054] The prediction result 1018 shown in Figure 6 is a collection of records that use the sound source No., which uniquely identifies the sound source, as the key, and associate it with values ​​such as the sound pressure level (for each frequency) that will be observed at the receiving point due to the sound emitted from that sound source, the AP (all-purpose) sound pressure level related to A-weighting (indicated as AP(A) in the figure), whether or not the sound source is accepted, and the combined value of the sound pressure level at the receiving point, the A-weighting correction value, the A-weighting sound pressure level at the receiving point, and the noise level at the receiving point.

[0055] The "Acceptance / Rejection" value indicates, for example, the sound source selected by the design engineer using the user terminal 200 in the prediction result 1018 displayed by the spreadsheet software 1012, where "○" means accepted and "-" means rejected. "A-weighting" is a value that weights the sound pressure level at the receiving point according to the frequency range that humans can hear (e.g., frequencies around 1000 Hz). Each time the design engineer sets the value in the "Acceptance / Rejection" column of the prediction result 1018 using the user terminal 200, the "Noise level at the receiving point" value is recalculated.

[0056] <Noise prediction method flow: Main flow> Next, an example of the noise prediction method in this embodiment will be described. Figure 7 is a diagram showing an example of the noise prediction method in this embodiment. Here, we assume that a design engineer wants to perform noise prediction for a building he is in charge of, and accesses the noise prediction device 100 from a user terminal 200. On the other hand, we assume that the noise prediction device 100, upon receiving the above access, responds to the user terminal 200 with an inquiry about the building to be predicted for noise.

[0057] Under the above circumstances, the processor 103 of the noise prediction device 100 receives a specification regarding the location of the sound source and the receiving point in the model of the building to be noise predicted (S10). This process of receiving the specification regarding the location of the sound source and the receiving point is performed via the display screen of the building model drawn by the BIM software 1013 (software that draws a three-dimensional model based on the BIM data 1015 of the building).

[0058] Therefore, the processor 103 receives a designation of a building to be noise predicted from the user terminal 200, for example, as shown in the flow chart of Figure 8 (S100). The processor 103 then identifies the BIM data 1015 of the building received in S100 using the auxiliary storage device 101 and assigns it to the BIM software 1013 (S101). The BIM software 1013 then draws a three-dimensional model of the building from the assigned BIM data 1015 (S102).

[0059] The designer will view the three-dimensional model on the user terminal 200 and operate a predetermined user interface, such as the mouse cursor, on this three-dimensional model to specify the location of the sound source and the sound receiving point. The BIM software 1013 then obtains the coordinates of the location where such a specification operation (e.g., a mouse click) was performed (S103). The processor 103 obtains these coordinate values ​​from the BIM software 1013 (S104).

[0060] The processor 103 also accepts input regarding the setting of the diffraction path of sound emitted by the sound source, taking into account the location of the sound source, the receiving point, and the structure of the building (S11). In this case, the processor 103 instructs the BIM software 1013, for example, to draw the sound source and receiving point specified in S10 on a three-dimensional model including the building drawn based on the BIM data 1015. Figure 9 shows an example in which the sound source and receiving point are drawn together with the building G1A as models G1 and G2.

[0061] Figure 9 shows a comparison between the diffraction path model G1 in an existing noise prediction tool and the diffraction path model G2 from the noise prediction device 100 in this embodiment. In the existing noise prediction tool, a predetermined diffraction path (the "left path" and "right path" in the example in Figure 9) is automatically selected, leaving no room for selection by the designer. On the other hand, in the BIM software 1013 of the noise prediction device 100 in this embodiment, as shown in Figure 9, "right path 2" is drawn as a selectable option in place of or in addition to "right path 1," which is the "right path" in the existing tool.

[0062] Therefore, the BIM software 1013 will perform a drawing that presents all the paths through which sound diffracts at the edges of obstacles (such as buildings) between the sound source and the receiving point. In this case, the designer will operate the user terminal 200 and select a diffraction path by clicking on "Right Path 2" among the paths between the sound source and the receiving point displayed on the three-dimensional model as shown in Figure 9. At this time, the BIM software 1013 will calculate the straight-line distance from the sound source to diffraction point G1B (diffraction point 1), the straight-line distance from diffraction point G1B (diffraction point 1) to diffraction point G1C (diffraction point 2), the straight-line distance from diffraction point G1C (diffraction point 2) to the receiving point, and the straight-line distance from the sound source to the receiving point for the selected diffraction path.

[0063] This calculation is performed using a general method for determining the distance between the sound source, the receiving point, diffraction point 1, diffraction point 2, and the coordinate values ​​of the receiving point (the values ​​in the "Position" column of the sound source data 1016 in Figure 4, and the values ​​in the "Coordinate" column of the diffraction path data 1017 in Figure 5). Each of the straight-line distances thus calculated is stored in the corresponding column in the diffraction path data 1017.

[0064] Next, the processor 103 identifies an equation to be used when predicting sound characteristics from the equations pre-stored in the spreadsheet software 1012 according to the characteristics of the diffraction path, in accordance with the input regarding the diffraction path settings received from the designer in S11 (S12). An example of this equation is shown in Figure 10. Specifically, the processor selects either equation M1 or M2, which are defined for sound propagation calculation, attenuation term, and diffraction due to a wall, according to the shape of the diffraction path, etc., as specified for each standard adopted when performing noise prediction. This selection may be made by the processor 103 using spreadsheet software, or the processor may obtain the equation specified by the designer on the user terminal 200 as the selection result.

[0065] Next, the processor 103 acquires the information regarding the specifications and structure of the building (first input information) obtained in S100 to S101, and the information regarding the sound emitted by the sound source (second input information) obtained through S103 to S104 and S12 (S13). The sound power level value of the sound source (sound source data 1016) can be obtained by acquiring the value entered by the design engineer via the user terminal 200, or by using the value that the BIM data 1015 has previously stored for each sound source.

[0066] Furthermore, the processor 103 provides the information obtained in S13 to the spreadsheet software 1012 to predict the sound characteristics at the receiving point (S14). Here, the processor 103 provides the information obtained in S13 for each of the multiple sound sources specified by the designer to the spreadsheet software 1012 to calculate the sound characteristics of each frequency for each sound source (S140). The sound characteristics calculated here include values ​​such as the sound pressure level for each frequency at the receiving point, the AP (all-purpose) sound pressure level related to A-weighting, the combined value of the sound pressure levels at the receiving point, the A-weighting correction value, and the A-weighting sound pressure level at the receiving point (see prediction result 1018 in Figure 6).

[0067] Furthermore, the processor 103 distributes the prediction results from S14 to the user terminal 200 for display, and accepts the design engineer's selection from the sound characteristics of each frequency calculated for each sound source in S14, via the user terminal 200 (S141). The sound source selection result received here is stored in the "Acceptance / Rejection" column of the prediction result 1018, with a value of "○" if selected and "-" if not selected.

[0068] Furthermore, the processor 103 predicts the noise level at the receiving point as a characteristic of the sound, based on the sound characteristics of each frequency calculated for the sound sources selected in S141, using the spreadsheet software 1012 (S142). In the example of the prediction result 1018 shown in Figure 6, the noise level at the receiving point is predicted to be "49.7" [dB] as a result of selecting sound sources "01" and "02". The noise level calculation method itself can be an existing method, such as appropriately summing the A-weighted sound pressure levels at the receiving point for each frequency for each sound source. The processor 103 will re-execute the prediction of sound characteristics in the spreadsheet software 1012 each time it receives the above selection from the designer.

[0069] Next, the processor 103 obtains the information on the sound characteristics predicted in S14, i.e., the prediction result 1018, from the spreadsheet software 1012 and adds it to the output software 1014 to generate output information in the output format specified by government agencies, etc. (S15). In this case, the processor 103 (or the output software 1014) receives, for example, a specification regarding the destination for submitting the output information from the user terminal 200 (S150).

[0070] Furthermore, the processor 103 (or output software 1014) identifies the output format corresponding to the specification received in S150 based on the list information of output formats that is pre-stored in the auxiliary storage device 101 for each destination of the output information (S151). The processor 103 also calls the output software 1014 from among the output software 1014 stored in the auxiliary storage device 101 that corresponds to the output format identified in S151, and generates output information by adding the characteristics of the sound to it (S152). For this reason, the output software 1014 is managed in the noise prediction device 100 for each output format.

[0071] The processor 103 transmits the output information generated in S13 to the user terminal 200 or an external system 400 (a system of a government agency, etc.) via the network N (S16), and terminates this flow. Examples of the above output information are shown in Figures 13 to 17. Figure 13 is a two-dimensional contour map showing the direction of sound propagation and sound pressure using arcs and colors (regions with the same sound pressure are the same color) when the sound source is placed in the center of the left edge of the graph. Figure 14 is a two-dimensional propagation map showing the direction of sound propagation and sound pressure using the direction and length of arrows (arrows of the same length indicate the same sound pressure) when the sound source is placed in the center of the left edge of the graph. Figure 15 is a two-dimensional scatter plot showing the sound pressure at various discrete locations using colors (regions with the same sound pressure are the same color) when the sound source is placed in the center of the left edge of the graph. Figure 16 is a three-dimensional contour map showing the direction of sound propagation and sound pressure using mesh sections and colors (regions with the same sound pressure are the same color), with the sound source placed at the center of the left edge of the graph. Figure 17 is a three-dimensional scatter plot showing the sound pressure at various discrete locations in three dimensions using colors (regions with the same sound pressure are the same color), with the sound source placed at the center of the left edge of the rectangular plane on the graph.

[0072] Although one embodiment of the noise prediction device and noise prediction method of the present invention has been described above, the above embodiment is merely an example to facilitate understanding of the present invention and does not limit it. In other words, the present invention can be modified and improved without departing from its spirit. Furthermore, it goes without saying that the present invention includes equivalents thereof. [Explanation of Symbols]

[0073] N Network 10. Noise prediction system 100 Noise prediction device 101 Auxiliary storage 1011 Program 1012 Spreadsheet Software 1013 BIM software (drawing software) 1014 Output Software 1015 BIM data 1016 Audio Data 1017 Diffraction path 1018 Prediction Results 102 Main storage 103 Processors 104 Communication equipment 200 user terminals 300 Design Information Management System 400 External Systems

Claims

1. A noise prediction device equipped with a processor, The aforementioned processor, A process for receiving specifications regarding the location of sound sources and sound receiving points in a building model, A process for acquiring first input information relating to the specifications and structure of the building and second input information relating to the sound emitted by the sound source, A process for predicting the characteristics of the sound at the sound receiving point based on the position of the sound source and the sound receiving point, and the first input information and the second input information, The process is executed to output output information corresponding to the predicted features, The process of receiving the specifications regarding the position of the sound source and the sound receiving point is performed via the display screen of the model drawn by the drawing software. The acquisition of the first and second input information, the prediction of the sound characteristics, and the output of the output information are all performed through a spreadsheet screen displayed by the execution of spreadsheet software. A noise prediction device characterized by the following features.

2. The aforementioned processor, If multiple sound sources are specified and the second input information is obtained for each of those multiple sound sources, The process involves calculating the sound characteristics of each frequency in each of the aforementioned sound sources using the spreadsheet software, The process is executed to accept a selection of which sound source's sound characteristics to use from among the sound characteristics of each frequency calculated for each sound source. Based on the sound characteristics of each frequency calculated for the sound source selected, the spreadsheet software predicts the sound characteristics at the receiving point. The noise prediction device according to claim 1.

3. The aforementioned processor, Each time the aforementioned selection is received, the spreadsheet software re-executes the prediction of the sound characteristics corresponding to that selection. The noise prediction device according to claim 2.

4. The aforementioned processor, A process for receiving input regarding the setting of the diffraction path of sound emitted by the sound source, taking into account the location of the sound source, the receiving point, and the structure of the building, In the aforementioned spreadsheet software, from among the formulas pre-stored according to the characteristics of the diffraction path, a formula to be used when predicting the characteristics of the sound is identified in accordance with the input regarding the settings of the diffraction path received. The noise prediction device according to claim 1.

5. In a noise prediction device equipped with a processor, The aforementioned processor, A process for receiving specifications regarding the location of sound sources and sound receiving points in a building model, A process for acquiring first input information relating to the specifications and structure of the building and second input information relating to the sound emitted by the sound source, A process for predicting the characteristics of the sound at the sound receiving point based on the position of the sound source and the sound receiving point, and the first input information and the second input information, The process is executed to output output information corresponding to the predicted features, The process of receiving the specifications regarding the position of the sound source and the sound receiving point is performed via the display screen of the model drawn by the drawing software. The acquisition of the first and second input information, the prediction of the sound characteristics, and the output of the output information are all performed through a spreadsheet screen displayed by the execution of spreadsheet software. A noise prediction method characterized by the following features.

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