Noise reduction analysis method and system based on urban green space form

Abstract

The application relates to a noise analysis method, in particular to a noise reduction analysis method and system based on urban green land form. The method comprises the following steps: obtaining three-dimensional data of a target green land, and determining three-dimensional form data corresponding to the target green land according to the three-dimensional data, wherein the three-dimensional form data corresponding to the target green land comprises spatial geometric form of the target green land and vegetation three-dimensional form data corresponding to the target green land; constructing a sound propagation-vegetation attenuation coupling model based on the three-dimensional form data; the sound propagation-vegetation attenuation coupling model is used for predicting expected noise at a target position according to a noise source and three-dimensional form data of the target green land; and noise at the target position is predicted based on the sound propagation-vegetation attenuation coupling model, so as to realize high-precision noise attenuation result prediction according to the three-dimensional form of the urban green land, and provide reliable data support for maintenance or planning of the target green land.

Description

Technical Field

[0001] This disclosure generally relates to noise analysis methods, and specifically to a noise reduction analysis method and system based on urban green space morphology. Background Technology

[0002] Urban green spaces are a type of urban landscape that integrates artificial elements with nature. They serve as a key base for ensuring urban ecological stability and offer ecological, social, and economic benefits. As a crucial component of public service facilities, urban green spaces not only provide aesthetic landscapes and recreational spaces but also enhance the quality of the urban ecological environment by improving air quality, regulating microclimates, and reducing noise, thereby improving residents' quality of life and physical and mental health.

[0003] Among the many ecological and environmental problems in cities, noise pollution is the second leading cause of health problems after air quality, and therefore, the issue of noise pollution is receiving increasing attention. Based on this, providing a reliable noise reduction analysis method based on urban green space morphology to offer a scientific basis for urban planning and green space construction has become an urgent problem to be solved. Summary of the Invention

[0004] In view of the above-mentioned defects or deficiencies in the existing technology, it is desirable to provide a noise reduction analysis method and system based on the morphology of urban green space, so as to achieve high-precision noise attenuation prediction based on the three-dimensional morphology of urban green space, and provide reliable data support for the maintenance or planning of target green space.

[0005] In a first aspect, embodiments of this application provide a noise reduction analysis method based on urban green space morphology, including:

[0006] Acquire three-dimensional data of the target green area, and determine the three-dimensional morphological data corresponding to the target green area based on the three-dimensional data. The three-dimensional morphological data corresponding to the target green area includes the spatial geometric shape of the target green area and the three-dimensional morphological data of the vegetation corresponding to the target green area.

[0007] A sound propagation-vegetation attenuation coupling model is constructed based on the three-dimensional morphological data; the sound propagation-vegetation attenuation coupling model is used to predict the expected noise at the target location based on the noise source and the three-dimensional morphological data of the target green area;

[0008] Based on the sound propagation-vegetation attenuation coupling model, noise prediction is performed at the target location.

[0009] In some embodiments, the vegetation three-dimensional morphological data includes a vegetation volume density index, and determining the three-dimensional morphological data corresponding to the target green space based on the three-dimensional data includes:

[0010] Based on the three-dimensional data of the target green area, the three-dimensional space corresponding to the target green area is divided into multiple three-dimensional voxel grids;

[0011] For each of the three-dimensional voxel grids, determine whether it contains vegetation; if so, count the number of vegetation voxels within the target green area.

[0012] Based on the number of vegetation voxels and the total number of vegetation voxels in the three-dimensional voxel grid, the vegetation volume density index corresponding to the three-dimensional voxel grid is determined.

[0013] In some embodiments, the three-dimensional morphological data of the vegetation includes acoustic propagation depth, and determining the three-dimensional morphological data corresponding to the target green space based on the three-dimensional data includes:

[0014] Simulate the propagation path of noise from the noise source to the target location, and divide the propagation path into multiple path segments;

[0015] For each path segment, obtain the path segment length, average vegetation volume density index, and simulated noise propagation length corresponding to the path segment;

[0016] The acoustic propagation depth corresponding to the path segment is determined based on the path segment length, the average vegetation volume density index, and the simulated propagation length.

[0017] In some embodiments, obtaining the simulated propagation length corresponding to the path segment includes:

[0018] For each path segment, the first part that is not propagated in the vegetation and the second part that is propagated in the vegetation are identified based on the spatial geometry of the target green space;

[0019] For the first part, the distance between the end of the path segment and the boundary of the target green area is taken as the first simulated propagation length;

[0020] For the second part, a simulated path of propagation affected by the vegetation is simulated, and a second simulated propagation length is determined based on the simulated path.

[0021] In some embodiments, constructing the sound propagation-vegetation attenuation coupling model based on the three-dimensional morphological data includes:

[0022] Obtain the acoustic propagation depth corresponding to the target green area;

[0023] Based on the acoustic propagation depth, calculate the noise attenuation corresponding to the target green area;

[0024] Based on the noise attenuation, the sound propagation-vegetation attenuation coupling model is constructed.

[0025] In some embodiments, calculating the noise attenuation corresponding to the target green space based on the acoustic propagation depth includes:

[0026] Obtain vegetation type coefficient and vegetation density parameters;

[0027] The noise attenuation corresponding to the target green space is determined based on the acoustic propagation depth, the vegetation type coefficient, and the vegetation density parameter.

[0028] Secondly, embodiments of this application provide a noise reduction analysis system based on urban green space morphology, including:

[0029] The acquisition module is used to acquire three-dimensional data of the target green area and determine the three-dimensional morphological data corresponding to the target green area based on the three-dimensional data. The three-dimensional morphological data corresponding to the target green area includes the spatial geometric shape of the target green area and the three-dimensional morphological data of the vegetation corresponding to the target green area.

[0030] A construction module is used to construct a sound propagation-vegetation attenuation coupling model based on the three-dimensional morphological data; the sound propagation-vegetation attenuation coupling model is used to predict the expected noise at the target location based on the noise source and the three-dimensional morphological data of the target green area;

[0031] The prediction module is used to predict noise at the target location based on the sound propagation-vegetation attenuation coupling model.

[0032] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in embodiments of this application.

[0033] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in embodiments of this application.

[0034] Fifthly, embodiments of this application provide a computer program product, including a computer program, characterized in that, when the computer program is executed by a processor, it implements the method described in embodiments of this application.

[0035] The noise reduction analysis method and system based on urban green space morphology provided in this application acquires three-dimensional data of the target green space and determines the corresponding three-dimensional morphological data based on the three-dimensional data; constructs a sound propagation-vegetation attenuation coupling model based on the three-dimensional morphological data, and performs noise prediction on the target location based on the sound propagation-vegetation attenuation coupling model, thereby achieving high-precision noise attenuation prediction based on the three-dimensional morphology of urban green space, providing reliable data support for the maintenance or planning of the target green space.

[0036] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0037] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0038] Figure 1 A flowchart illustrating a noise reduction analysis method based on urban green space morphology provided in an embodiment of this application is shown.

[0039] Figure 2 A schematic diagram of the structure of a noise reduction analysis system based on urban green space morphology provided in an embodiment of this application is shown;

[0040] Figure 3 A schematic diagram of the structure of a computer system suitable for implementing an electronic device or server according to embodiments of this application is shown. Detailed Implementation

[0041] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0042] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0043] To further illustrate the technical solutions provided in the embodiments of this application, a detailed description is provided below in conjunction with the accompanying drawings and specific implementation methods. Although the embodiments of this application provide method operation instruction steps as shown in the following embodiments or drawings, the method may include more or fewer operation instruction steps based on conventional or non-creative effort. In steps where there is no logically necessary causal relationship, the execution order of these steps is not limited to the execution order provided in the embodiments of this application. In actual processing or when the device executes the method, it may be executed sequentially or in parallel according to the method shown in the embodiments or drawings.

[0044] Please refer to Figure 1 , Figure 1 A flowchart illustrating a noise reduction analysis method based on urban green space morphology according to an embodiment of this application is shown. Figure 1 As shown, the method includes:

[0045] Step 101: Obtain the three-dimensional data of the target green area, and determine the three-dimensional morphological data corresponding to the target green area based on the three-dimensional data. The three-dimensional morphological data corresponding to the target green area includes the spatial geometric shape of the target green area and the three-dimensional morphological data of the vegetation corresponding to the target green area.

[0046] It should be noted that the three-dimensional data of the target green space can be obtained by collecting information and simulating the actual target green space, or it can be obtained by simulating the virtual target green space.

[0047] In other words, this application can be used to conduct noise reduction assessments and predictions for green spaces that have already been planted with vegetation, or it can be used during the municipal planning stage to conduct noise reduction assessments and predictions for green spaces that have not yet been planted with vegetation. It should be understood that for green spaces that have not yet been planted with vegetation, noise reduction assessments and predictions can be conducted using three-dimensional simulation data. Based on the results of the noise reduction assessment and predictions, suitable vegetation types can be selected for planting, or target green spaces can be planned to achieve the public service objective of noise reduction. For green spaces that have already been planted with vegetation, noise reduction assessments and predictions can be conducted based on the actual three-dimensional data of the target green space to determine maintenance strategies or vegetation replacement strategies for the planted vegetation.

[0048] The three-dimensional morphological data corresponding to the target green space includes the spatial geometry of the target green space, such as the boundary and elevation undulation of the target green space, as well as the data corresponding to the three-dimensional morphological indicators of the vegetation. The three-dimensional morphological data of vegetation is used to characterize the three-dimensional morphological index parameters of the target green space, including but not limited to vegetation volume density index, leaf area volume density, vertical stratification index, plant area index, canopy roughness, and acoustic propagation depth.

[0049] In one feasible embodiment, the three-dimensional morphological data of vegetation is the vegetation volume density index. Based on the three-dimensional data, the three-dimensional morphological data corresponding to the target green space is determined, including: dividing the three-dimensional space corresponding to the target green space into multiple three-dimensional voxel grids based on the three-dimensional data of the target green space; for each three-dimensional voxel grid, determining whether it contains vegetation, and if so, counting the number of vegetation voxels within the target green space; and determining the vegetation volume density index corresponding to the three-dimensional voxel grid based on the number of vegetation voxels and the total number of vegetation voxels in the three-dimensional voxel grid.

[0050] It should be noted that a voxel is a volume element. In the embodiments of this application, a voxel is a spatial point where vegetation is planted in the target green space.

[0051] In other words, the 3D data of the target green space is divided into multiple uniformly sized 3D voxel grids. Then, spatial points representing the vegetation of the green space, such as branches and leaves, are obtained from each voxel grid. The number of spatial points representing the vegetation is taken as the number of vegetation voxels within the 3D voxel grid. Then, based on the number of vegetation voxels and the total number of vegetation voxels in the 3D voxel grid, the vegetation volume density index corresponding to the 3D voxel grid is determined.

[0052] In some embodiments, the vegetation volume density index corresponding to the three-dimensional voxel grid is determined based on the number of vegetation voxels and the total number of vegetation voxels in the three-dimensional voxel grid, including: for each three-dimensional voxel grid, the vegetation volume density index corresponding to the three-dimensional voxel grid is used as the ratio of the product of the number of vegetation voxels and the voxel volume in the three-dimensional voxel grid to the total volume of the three-dimensional voxel grid.

[0053] In other words, in some embodiments, for example, when the target green space is a green space that has been planted with vegetation, the voxels in the three-dimensional voxel grid of the target green space are three-dimensional point cloud data. The three-dimensional point cloud data usually only represents the location of the vegetation and cannot represent the volume of the vegetation. Therefore, it is necessary to multiply the voxels by the voxel volumes to obtain the volume of the vegetation in the three-dimensional voxel grid, and then use the ratio of the volume to the total volume of the three-dimensional voxel grid as the vegetation volume density index corresponding to the three-dimensional voxel grid.

[0054] In one feasible embodiment, the three-dimensional morphological data of vegetation is the acoustic propagation depth. Based on the three-dimensional data, the three-dimensional morphological data corresponding to the target green space is determined, including: simulating the propagation path of noise from the noise source to the target location, and dividing the propagation path into multiple path segments; for each path segment, obtaining the path segment length, average vegetation volume density index, and simulated propagation length of the noise propagation corresponding to the path segment; and determining the acoustic propagation depth corresponding to the path segment based on the path segment length, average vegetation volume density index, and simulated propagation length.

[0055] It should be noted that acoustic propagation depth is used to characterize the actual length of the noise's path through the target green space. Sound propagates through the vibration of a medium, while noise in an urban green space environment propagates through the vibration of air. During propagation, noise will collide with branches and leaves in the target green space, thus reducing or altering the propagation path. Based on this, it is necessary to simulate the propagation path of noise from the noise source to the target location. It should be understood that acoustic simulation software can be used to simulate the propagation path, and the simulated propagation path can be one or multiple; this application does not impose any specific limitations. After obtaining the propagation path, the propagation path is divided into multiple path segments with consistent straight-line lengths. In other words, the distance between the noise source and the target location is divided into multiple segments according to a certain length (i.e., the path segment length). The propagation path in each segment is then used as the path segment, and the length of the simulated path corresponding to each path segment is the simulated propagation length.

[0056] In one specific embodiment, obtaining the simulated propagation length corresponding to the path segment includes: for each path segment, identifying a first part of the path segment that does not propagate in the vegetation and a second part that propagates in the vegetation based on the spatial geometry of the target green space; for the first part, taking the distance between the end of the path segment and the boundary of the target green space as the first simulated propagation length; for the second part, simulating the propagation path affected by the vegetation, and determining the second simulated propagation length based on the simulated path.

[0057] It should be understood that the noise source and target location are usually at a certain distance from the target green space. For example, there is a certain distance between the green belt set up next to the highway and the road, or there are intervals such as walkways in the middle of the target green space. Therefore, the simulated path will pass through vegetation in some parts and not in others. In this case, it is necessary to calculate the simulated propagation length separately according to the different areas traversed by the simulated sound propagation path.

[0058] Specifically, for the first portion that does not propagate through vegetation, the length of the noise that does not propagate through vegetation can be directly used as the first simulated propagation length. For example, if a path segment first does not propagate through vegetation before entering a vegetated area, the distance from the starting point of the path segment to the boundary where the noise enters the target green area is used as the first simulated propagation length; conversely, the distance from the boundary where the noise leaves the target green area to the ending point of the path segment is used as the first simulated propagation length. It should be understood that if the path segment is long enough that the entire path segment does not propagate through vegetation, the length of the path segment is directly used as the simulated propagation length of that path segment.

[0059] For the second part, it is necessary to simulate the noise propagation path affected by vegetation, that is, the new propagation path formed after the noise is reflected by, for example, branches and leaves, and determine the second simulated propagation length based on the simulated path. It should be understood that the second simulated propagation length is usually greater than the path segment length.

[0060] Then, the sum of the first and second simulated propagation lengths corresponding to the path segments is taken as the corresponding simulated propagation length.

[0061] For each path segment, the average vegetation volume density index is obtained. The path segment may exist in at least one of the aforementioned 3D voxel grids. For example, it may propagate from one 3D voxel grid to another. In this case, the average vegetation volume density index is the average of the vegetation volume density indices of at least two 3D voxel grids along the path. Alternatively, the entire path segment may be located within the same 3D voxel grid due to continuous changes in the propagation direction caused by collisions with vegetation branches and leaves. In this case, the average vegetation volume density index is the vegetation volume density index of that 3D voxel grid.

[0062] For example, the acoustic propagation depth corresponding to a path segment can be determined based on the path segment length, average vegetation volume density index, and simulated propagation length using the following formula:

[0063]

[0064] in, To transmit depth information acoustically, Provide path segment length information. The average vegetation volume density index information for the j-th path segment. Provide the simulated propagation length information of the j-th path segment in the vegetation. For the simulated propagation length information of the propagation path, The attenuation coefficient is... The total number of segments for the path.

[0065] It should be understood that, since noise propagates through the branches and leaves of green spaces, the denser the branches and leaves, the more scattering surfaces the sound waves encounter when propagating within them, resulting in faster noise attenuation. Conversely, sparse woodlands and grasslands have limited noise reduction effects. Based on this, this application proposes using vegetation volume density index and acoustic propagation depth as three-dimensional morphological data, which can better reflect the propagation of noise in the target green space, so as to better analyze the noise attenuation in the future.

[0066] Step 102: Construct a sound propagation-vegetation attenuation coupling model based on three-dimensional morphological data; the sound propagation-vegetation attenuation coupling model is used to predict the expected noise at the target location based on the three-dimensional morphological data of the noise source and the target green area.

[0067] It should be noted that the sound propagation-vegetation attenuation coupling model adds a vegetation attenuation component to the traditional sound attenuation model, that is, the noise attenuation caused by the target green space, so as to achieve complete noise attenuation result prediction.

[0068] In one feasible embodiment, the acoustic propagation depth corresponding to the target green space is obtained; based on the acoustic propagation depth, the noise attenuation corresponding to the target green space is calculated; based on the noise attenuation, a sound propagation-vegetation attenuation coupling model is constructed.

[0069] The calculation of noise attenuation corresponding to the target green space based on acoustic propagation depth includes: obtaining vegetation type coefficient and vegetation density parameters; and determining the noise attenuation corresponding to the target green space based on acoustic propagation depth, vegetation type coefficient and vegetation density parameters.

[0070] It should be noted that the vegetation type coefficient is a key empirical parameter, closely related to the species and community structure of the vegetation planted in the target green space. In some embodiments, it can be obtained through multiple linear regression calculation based on other three-dimensional morphological data of the target green space, besides the acoustic propagation depth. For example, a polynomial can be constructed based on the vegetation volume density, vertical stratification index, and canopy roughness of the target green space, and the polynomial coefficients can be calculated using known data through multiple linear regression. Then, the vegetation type coefficient can be calculated based on the known polynomial coefficients and the three-dimensional morphological data of the target green space.

[0071] Vegetation density is used to characterize the sound energy blocking efficiency of vegetation per unit depth. The higher the vegetation density parameter, the shorter the propagation path required for noise attenuation in the target green space. In some embodiments, similar to the vegetation type coefficient, it can be obtained through multiple linear regression calculation based on other three-dimensional morphological data beyond the acoustic propagation depth of the target green space.

[0072] For example, the noise attenuation can be expressed using the following formula:

[0073]

[0074] in, Information on noise attenuation corresponding to the target green space. The vegetation type coefficient corresponding to the target green space. The vegetation density parameter corresponding to the target green space. This refers to the set of three-dimensional morphological data of the target green space, excluding acoustic propagation depth. To transmit depth information acoustically, This represents the noise frequency.

[0075] It should be understood that plant materials have a stronger absorption and scattering effect on mid-to-high frequency sounds, and traffic noise has a wider spectrum, including mid-to-high frequency components that are more disturbing to people. Adding noise frequencies to the noise attenuation model can better ensure the model's evaluation of the target green space's ability to reduce noise of different frequencies.

[0076] Furthermore, This characterizes the nonlinear saturation effect of vegetation noise reduction, meaning that the noise reduction in the early stages of propagation within vegetation increases rapidly with increasing propagation depth, while the marginal noise reduction benefit diminishes with further increases in propagation depth. It should be understood that this concept can be used to rationally design the vegetation cover width (i.e., sound propagation depth) of a target green space before vegetation is planted, thereby reducing greening costs while still meeting noise reduction requirements.

[0077] Furthermore, based on the noise attenuation, a sound propagation-vegetation attenuation coupling model is constructed, including: constructing a sound propagation attenuation model, adding the noise attenuation corresponding to the target green area to the sound propagation attenuation model, and obtaining the sound propagation-vegetation attenuation coupling model.

[0078] In the embodiments of this application, the following improved sound propagation-vegetation attenuation coupling model can be adopted:

[0079]

[0080] in, Noise prediction information at target location r. For noise source power level, For directional index information, It is a geometrically divergent decay term. This is the air absorption attenuation term. This provides noise attenuation information for the target green space.

[0081] It should be noted that the noise source power level Directivity index is used to characterize the intensity of a noise source. In urban scenarios, it can be differentiated based on attributes such as road grade, traffic volume, and vehicle speed. Used to describe the spatial distribution direction of sound source energy. For traffic noise, it is usually assumed to be a line source, and its directivity helps to more accurately simulate the attenuation differences of noise in the longitudinal and transverse directions of the road. Geometric divergence attenuation term. Air absorption attenuation term is used to characterize the natural attenuation of sound with increasing distance in a free field, which is the fundamental physical process of noise propagation. It is related to the air temperature and humidity between the noise source and the target location, and plays a significant role in high-frequency noise attenuation. Since the target green space may change the nearby air temperature, humidity and other indicators, it needs to be taken into consideration.

[0082] Step 103: Based on the sound propagation-vegetation attenuation coupling model, noise prediction is performed at the target location.

[0083] In other words, after obtaining the sound propagation-vegetation attenuation coupling model, the sound propagation-vegetation attenuation coupling model can be used to predict the noise at the target location, that is, to predict the noise value at the target location and obtain the predicted noise value at the target location.

[0084] When the target green space is already planted with vegetation, the noise prediction value can be used to determine whether the target green space meets the noise reduction plan. If it does not meet the plan, measures such as increasing the planting density can be taken. When the target green space is not yet planted with vegetation, suitable vegetation species can be selected based on the noise prediction value, such as dense vegetation or sparse woodland and grassland.

[0085] Therefore, the noise reduction analysis method based on urban green space morphology provided in this application obtains the three-dimensional data of the target green space and determines the corresponding three-dimensional morphological data of the target green space based on the three-dimensional data; constructs a sound propagation-vegetation attenuation coupling model based on the three-dimensional morphological data, and performs noise prediction on the target location based on the sound propagation-vegetation attenuation coupling model, thereby achieving high-precision noise attenuation prediction based on the three-dimensional morphology of urban green space, providing reliable data support for the maintenance or planning of the target green space.

[0086] It should be noted that although the operation of the method of the present invention is described in a specific order in the accompanying drawings, this does not require or imply that the operations must be performed in that specific order, or that all the operations shown must be performed in order to achieve the desired result.

[0087] Figure 2 A schematic diagram of the structure of a noise reduction analysis system based on urban green space morphology provided in an embodiment of this application is shown.

[0088] like Figure 2 As shown, the noise reduction analysis system 10 based on urban green space morphology includes:

[0089] The acquisition module 11 is used to acquire the three-dimensional data of the target green area and determine the three-dimensional morphological data corresponding to the target green area based on the three-dimensional data. The three-dimensional morphological data corresponding to the target green area includes the spatial geometric shape of the target green area and the three-dimensional morphological data of the vegetation corresponding to the target green area.

[0090] Module 12 is used to construct a sound propagation-vegetation attenuation coupling model based on the three-dimensional morphological data; the sound propagation-vegetation attenuation coupling model is used to predict the expected noise at the target location based on the noise source and the three-dimensional morphological data of the target green area;

[0091] The prediction module 13 is used to predict noise at the target location based on the sound propagation-vegetation attenuation coupling model.

[0092] In some embodiments, the vegetation three-dimensional morphological data includes a vegetation volume density index, and the acquisition module 11 is specifically used for:

[0093] Based on the three-dimensional data of the target green area, the three-dimensional space corresponding to the target green area is divided into multiple three-dimensional voxel grids;

[0094] For each of the three-dimensional voxel grids, determine whether it contains vegetation; if so, count the number of vegetation voxels within the target green area.

[0095] Based on the number of vegetation voxels and the total number of vegetation voxels in the three-dimensional voxel grid, the vegetation volume density index corresponding to the three-dimensional voxel grid is determined.

[0096] In some embodiments, the vegetation three-dimensional morphological data includes acoustic propagation depth, and the acquisition module 11 is specifically used for:

[0097] Simulate the propagation path of noise from the noise source to the target location, and divide the propagation path into multiple path segments;

[0098] For each path segment, obtain the path segment length, average vegetation volume density index, and simulated noise propagation length corresponding to the path segment;

[0099] The acoustic propagation depth corresponding to the path segment is determined based on the path segment length, the average vegetation volume density index, and the simulated propagation length.

[0100] In some embodiments, obtaining the simulated propagation length corresponding to the path segment includes:

[0101] For each path segment, the first part that is not propagated in the vegetation and the second part that is propagated in the vegetation are identified based on the spatial geometry of the target green space;

[0102] For the first part, the distance between the end of the path segment and the boundary of the target green area is taken as the first simulated propagation length;

[0103] For the second part, a simulated path of propagation affected by the vegetation is simulated, and a second simulated propagation length is determined based on the simulated path.

[0104] In some embodiments, the construction module 12 is configured to:

[0105] Obtain the acoustic propagation depth corresponding to the target green area;

[0106] Based on the acoustic propagation depth, calculate the noise attenuation corresponding to the target green area;

[0107] Based on the noise attenuation, the sound propagation-vegetation attenuation coupling model is constructed.

[0108] In some embodiments, the construction module 12 is configured to:

[0109] Obtain vegetation type coefficient and vegetation density parameters;

[0110] The noise attenuation corresponding to the target green space is determined based on the acoustic propagation depth, the vegetation type coefficient, and the vegetation density parameter.

[0111] It should be understood that the modules or modules described in the noise reduction analysis system 10 based on urban green space morphology are similar to those in the reference system. Figure 1 The steps in the described method correspond accordingly. Therefore, the operations and features described above for the method are also applicable to the noise reduction analysis system 10 based on urban green space morphology and its included modules, and will not be repeated here. The noise reduction analysis system 10 based on urban green space morphology can be pre-implemented in the browser or other secure applications of an electronic device, or it can be loaded into the browser or its secure applications of an electronic device through download or other means. The corresponding modules in the noise reduction analysis system 10 based on urban green space morphology can cooperate with the modules in the electronic device to implement the solution of the embodiments of this application.

[0112] The division of modules or units mentioned in the detailed description above is not mandatory. In fact, according to the embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0113] The following is for reference. Figure 3 , Figure 3 A schematic diagram of the structure of a computer system suitable for implementing the embodiments of this application is shown.

[0114] like Figure 3 As shown, the computer system 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 302 or programs loaded from storage section 308 into random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the system's operating instructions. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0115] The following components are connected to I / O interface 305: an input section 306 including a keyboard, mouse, etc.; an output section 307 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card, modem, etc. The communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0116] Specifically, according to embodiments of this application, the flowchart above refers to... Figure 2 The described process can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such an embodiment, the computer program contains program code for performing the methods shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the functions defined in the system of this application.

[0117] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0118] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operational instructions of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two connected blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified functions or operational instructions, or using a combination of dedicated hardware and computer instructions.

[0119] The units or modules described in the embodiments of this application can be implemented in software or hardware. The described units or modules can also be housed in a processor; for example, a processor can be described as including an acquisition module, a construction module, and a prediction module. The names of these units or modules do not necessarily limit the specific unit or module itself. For example, an acquisition module can also be described as "acquiring three-dimensional data of a target green space and determining the corresponding three-dimensional morphological data of the target green space based on the three-dimensional data."

[0120] In another aspect, this application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments, or may exist independently and not assembled into the electronic device. The aforementioned computer-readable storage medium stores one or more programs that, when used by one or more processors, execute the noise reduction analysis method based on urban green space morphology described in this application.

[0121] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A noise reduction analysis method based on urban green space morphology, characterized in that, include: Acquire three-dimensional data of the target green space, and determine the three-dimensional morphological data corresponding to the target green space based on the three-dimensional data. The three-dimensional morphological data corresponding to the target green space includes the spatial geometric shape of the target green space and the three-dimensional morphological data of the vegetation corresponding to the target green space. The vegetation three-dimensional morphological data includes the vegetation volume density index calculated by dividing the three-dimensional voxel grid and the acoustic propagation depth obtained by segmented simulation of the noise propagation path. A sound propagation-vegetation attenuation coupling model is constructed based on the three-dimensional morphological data; the sound propagation-vegetation attenuation coupling model is used to predict the expected noise at the target location based on the noise source and the three-dimensional morphological data of the target green area; Based on the sound propagation-vegetation attenuation coupling model, noise prediction is performed at the target location; The construction of the sound propagation-vegetation attenuation coupling model based on the three-dimensional morphological data includes: Obtain the acoustic propagation depth corresponding to the target green area; Based on the acoustic propagation depth, calculate the noise attenuation corresponding to the target green area; Based on the noise attenuation, the sound propagation-vegetation attenuation coupling model is constructed.

2. The noise reduction analysis method based on urban green space morphology according to claim 1, characterized in that, The vegetation three-dimensional morphological data includes a vegetation volume density index. Determining the three-dimensional morphological data corresponding to the target green space based on the three-dimensional data includes: Based on the three-dimensional data of the target green area, the three-dimensional space corresponding to the target green area is divided into multiple three-dimensional voxel grids; For each of the three-dimensional voxel grids, determine whether it contains vegetation; if so, count the number of vegetation voxels within the target green area. Based on the number of vegetation voxels and the total number of vegetation voxels in the three-dimensional voxel grid, the vegetation volume density index corresponding to the three-dimensional voxel grid is determined.

3. The noise reduction analysis method based on urban green space morphology according to claim 1, characterized in that, The vegetation three-dimensional morphological data includes acoustic propagation depth. Determining the three-dimensional morphological data corresponding to the target green space based on the three-dimensional data includes: Simulate the propagation path of noise from the noise source to the target location, and divide the propagation path into multiple path segments; For each path segment, obtain the path segment length, average vegetation volume density index, and simulated noise propagation length corresponding to the path segment; The acoustic propagation depth corresponding to the path segment is determined based on the path segment length, the average vegetation volume density index, and the simulated propagation length.

4. The noise reduction analysis method based on urban green space morphology according to claim 3, characterized in that, Obtaining the simulated propagation length corresponding to the path segment includes: For each path segment, the first part that is not propagated in the vegetation and the second part that is propagated in the vegetation are identified based on the spatial geometry of the target green space; For the first part, the distance between the end of the path segment and the boundary of the target green area is taken as the first simulated propagation length; For the second part, a simulated path of propagation affected by the vegetation is simulated, and a second simulated propagation length is determined based on the simulated path.

5. The noise reduction analysis method based on urban green space morphology according to claim 1, characterized in that, The calculation of the noise attenuation corresponding to the target green area based on the acoustic propagation depth includes: Obtain vegetation type coefficient and vegetation density parameters; The noise attenuation corresponding to the target green space is determined based on the acoustic propagation depth, the vegetation type coefficient, and the vegetation density parameter.

6. A noise reduction analysis system based on urban green space morphology, characterized in that, include: The acquisition module is used to acquire three-dimensional data of the target green area and determine the three-dimensional morphological data corresponding to the target green area based on the three-dimensional data. The three-dimensional morphological data corresponding to the target green area includes the spatial geometric shape of the target green area and the three-dimensional morphological data of the vegetation corresponding to the target green area. The three-dimensional morphological data of the vegetation includes the vegetation volume density index calculated by dividing the three-dimensional voxel mesh and the acoustic propagation depth obtained by segmented simulation based on the noise propagation path. A construction module is used to construct a sound propagation-vegetation attenuation coupling model based on the three-dimensional morphological data; the sound propagation-vegetation attenuation coupling model is used to predict the expected noise at the target location based on the noise source and the three-dimensional morphological data of the target green area; The prediction module is used to predict noise at the target location based on the sound propagation-vegetation attenuation coupling model. The construction of the sound propagation-vegetation attenuation coupling model based on the three-dimensional morphological data includes: Obtain the acoustic propagation depth corresponding to the target green area; Based on the acoustic propagation depth, calculate the noise attenuation corresponding to the target green area; Based on the noise attenuation, the sound propagation-vegetation attenuation coupling model is constructed.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the noise reduction analysis method based on urban green space morphology as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the noise reduction analysis method based on urban green space morphology as described in any one of claims 1-5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the noise reduction analysis method based on urban green space morphology as described in any one of claims 1-5.

Images