A method for constructing a noise data model considering geographical scene characteristics

Abstract

The present application belongs to the technical field of noise prediction, and in particular to a noise data model construction method considering geographical scene characteristics, comprising: data preprocessing of sound source data, geographical data and receiving body data used for constructing a noise scene; constructing a noise scene hierarchical conceptual model, the constructed noise scene hierarchical conceptual model comprising a first hierarchical conceptual model, a second hierarchical conceptual model and a third hierarchical conceptual model, the first hierarchical conceptual model being noise source point data, the second hierarchical conceptual model being a noise continuous field, and the third hierarchical conceptual model being a geographical noise scene; constructing a noise scene space-time data model, establishing a space-time mapping relationship for the three levels, converting each element into space-time data, and storing into a space-time database; screening out required data from the space-time database, and visualizing display according to space-time characteristics to establish a model conforming to reality.

Description

Technical Field

[0001] This invention belongs to the field of noise prediction technology, specifically a method for constructing a noise data model that takes into account geographical scene characteristics. Background Technology

[0002] With the development of modern society, urban noise pollution has become increasingly serious. Various types of noise are not only a major factor in environmental pollution, but also threaten human physical and mental health, even leading to death. Therefore, noise pollution is a pressing social problem that needs to be addressed. Establishing noise scenario models that incorporate geographical context helps to enhance people's understanding of the entire process from noise generation to propagation, providing a scientific basis for urban noise pollution control.

[0003] Noise models are mathematical models used to study the propagation and distribution of noise. Currently, research on noise models has made some progress. Traditional noise models are mainly based on acoustic principles and empirical formulas, predicting noise levels by calculating factors such as the distance from the sound source to the receiver and the sound source intensity, thus reflecting the propagation laws of noise to some extent. Numerical simulation-based noise models can more accurately simulate the propagation and distribution of noise through numerical calculations and simulations; however, both lack consideration of the geographical environment. The propagation and distribution of noise are influenced by various geographical environmental factors, such as topography, buildings, and traffic flow. Therefore, combining noise models with the geographical environment in research has significant scientific importance.

[0004] Existing noise models have the following shortcomings: (1) Traditional noise models neglect the complexity of the geographical environment. Buildings, roads, vegetation, etc., in the geographical environment will affect the propagation and reflection of noise. For example, buildings can act as barriers to reduce the propagation distance of noise; the width of roads and traffic flow will also affect the distribution of noise. Therefore, traditional models have certain limitations when considering these factors. (2) Traditional noise models do not consider the spatiotemporal changes of the geographical environment. The noise level in the geographical environment will change with time and space. For example, the traffic flow during the day and night are different, and the noise level will also be different; the noise level in the city center and the suburbs are also different. Therefore, the prediction results of existing noise models do not match reality. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method for constructing a noise data model that takes into account the characteristics of geographical scenes, so as to solve the problem that the prediction results of existing noise models do not match the actual situation.

[0006] This invention is implemented as follows:

[0007] A method for constructing a noisy data model that takes into account geographical scene characteristics, the method comprising:

[0008] Step (1) Preprocess the sound source data, geographic data and receiver data used in the construction of the noise scene;

[0009] Step (2) Construct a hierarchical conceptual model of the noise scene according to step (1). The constructed hierarchical conceptual model of the noise scene includes a first-level conceptual model, a second-level conceptual model and a third-level conceptual model. The first-level conceptual model is noise source data, the second-level conceptual model is a continuous noise field, and the third-level conceptual model is a geographical noise scene.

[0010] Step (3) Construct a spatiotemporal data model of the noise scene, establish a spatiotemporal mapping relationship for the three levels in the hierarchical conceptual model of the noise scene constructed in step (2), convert each element into spatiotemporal data, and store it in the spatiotemporal database;

[0011] Step (4) Filter out the required data from the spatiotemporal database and visualize it according to the spatiotemporal characteristics.

[0012] Furthermore, step (1) specifically includes:

[0013] Preprocessing of sound source data includes using an adaptive noise filter to exclude irrelevant signals and retain noise data within the noise frequency range corresponding to the sound source information, based on the source of the sound source data and its corresponding noise frequency range.

[0014] Establish a connection between sound source data and geographic data, wherein the geographic data is urban basic geographic data, including urban administrative division data, topographic data, road network data, building data and water system data, wherein roads, buildings and water systems are geographic entities;

[0015] When geographic entities in geographic data are treated as receivers, and since air is an important propagation medium, it is necessary to calculate the sound intensity attenuation coefficient and sound pressure attenuation coefficient in the air and in each geographic entity.

[0016] Furthermore, the first-level conceptual model is the noise source point element. The features of the noise source point data used include the noise source data ID, element type, time, sound pressure, sound intensity, longitude and latitude, among which the longitude and latitude features are used for the spatialization of the noise source point data.

[0017] The second-level conceptual model uses a raster model to represent the sound characteristics of the noise continuous field. Two continuous fields are constructed based on the sound pressure and sound intensity of the noise to express the propagation and attenuation characteristics of the noise data. The raster model includes the raster data row and column numbers and feature values.

[0018] The third-level conceptual model of geographic noise scenarios combines noise data with geographic data to express the propagation and attenuation characteristics of noise data considering the geographic environment. The features include: sound pressure and sound intensity of the noise, medium type, sound pressure attenuation coefficient of the medium, and sound intensity attenuation coefficient of the medium.

[0019] Further, step (3) involves constructing a spatiotemporal data model for the noisy scene, specifically including:

[0020] The first-level conceptual model is converted into vector point data with spatiotemporal information and stored in shapefile data format. Based on the temporal characteristics and spatial coordinates of the preprocessed noise source data, the noise source point data table of each time slice is converted into vector point data according to coordinates, becoming spatial point data with spatiotemporal characteristics.

[0021] Based on the noise source data and geographic scene features of the first-level conceptual model, a continuous raster field of geographic noise scene is obtained by interpolation.

[0022] Store spatiotemporal data of geographic noise scenes.

[0023] Furthermore, a continuous noise field based on the spatial point data of the first-level conceptual model is generated, which is then interpolated from the noise source point data to obtain a raster continuous field. Specifically, this includes:

[0024] We perform trend analysis on the sound pressure and sound intensity characteristics of noise source data, explore the data distribution characteristics, remove trends, and determine whether the sound source data characteristics meet the interpolation conditions.

[0025] Based on the exploratory data analysis results of each feature, select the appropriate interpolation model for each feature;

[0026] Input noise source data and the attenuation coefficient of sound waves in the medium;

[0027] Based on the propagation loss and absorption loss of noise, spatial interpolation is performed on the sound pressure and sound intensity characteristics of the noise source data to obtain the noise continuous field. Propagation loss is the noise loss in the air, and absorption loss is the noise loss along its path through various geographical features. The spatial interpolation calculation for sound pressure and sound intensity is as follows: Distances from the unknown point at time t to each noise source point, without considering the medium type:

[0028]

[0029] In the formula, i represents the noise source number, t represents time, and d it x represents the distance from the unknown point at time t to each noise source point. it The longitude of the noise source point numbered i at time t is represented by y. it x represents the latitude of the noise source point numbered i at time t.t Let y be the longitude of the point to be interpolated at time t. t Let be the latitude of the point to be interpolated at time t; considering the propagation distance of each medium type:

[0030]

[0031] In the formula, j represents the medium number of the noise propagation, where j=1 represents air, j≠1 represents the geographic entity type, and d ijt This represents the propagation distance of noise source i at time t in medium j;

[0032] Weights for calculating the noise sound pressure level at an unknown point at time t:

[0033]

[0034] In the formula, ω′ ij The weight α represents the weight used in calculating the noise sound pressure level at an unknown point at time t. ijt This represents the sound pressure attenuation coefficient of noise source point i in medium j at time t;

[0035] Calculate the noise sound pressure level at the unknown point at time t:

[0036]

[0037] In the formula, P 0t P(X) represents the noise sound pressure level at an unknown point at time t. i Y i ) t X represents the sound pressure level of noise source point i at time t. i Y represents the longitude of the noise source point numbered i. i Let i be the latitude of the noise source point numbered i;

[0038] The weighting for calculating the noise intensity at unknown points is:

[0039]

[0040] In the formula, ω″ ijt The weight β represents the noise intensity calculation for unknown points. ijt This represents the sound intensity attenuation coefficient of noise source point i in medium j at time t;

[0041] Calculate the noise intensity attribute value at the unknown point:

[0042]

[0043] In the formula, I0 represents the noise intensity attribute value at the unknown point, and I(X) i Y i) represents the sound intensity value of the noise source point numbered i.

[0044] Compared with the prior art, the beneficial effects of this invention are as follows:

[0045] (1) This invention takes into account the impact of the complex urban geographical environment on noise propagation. Based on the traditional noise propagation mechanism, it combines noise with geographical scene and calculates the attenuation coefficient of different geographical elements on noise propagation to realize the construction of geographical noise scene that takes into account the complex urban environment.

[0046] (2) This invention takes into account the characteristics of noise propagation, the characteristics of geographical elements and their spatiotemporal attributes. First, it constructs a hierarchical conceptual model of noise scene. On this basis, it converts the hierarchical conceptual model of noise scene into a spatiotemporal data model and realizes the visualization of the spatiotemporal data model of geographical noise scene. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the hierarchical conceptual model structure of a noise scene provided in an embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of the spatiotemporal data model structure for noise scenarios provided in an embodiment of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0050] See Figure 1 Combination Figure 2 As shown, this invention discloses a method for constructing a noisy scene data model based on multi-layer geographic feature extraction, the specific steps of which are as follows:

[0051] Step (1) Noise scene data preprocessing: Preprocess the sound source data, geographical data and receiver data used in the construction of the noise scene respectively;

[0052] Step (2) Construct a hierarchical conceptual model of the noise scene: Based on the data preprocessed in step (1), construct a hierarchical conceptual model of the noise scene. The constructed hierarchical conceptual model of the noise scene contains three levels: a first-level conceptual model, a second-level conceptual model, and a third-level conceptual model. The first-level conceptual model is the noise source data, the second-level conceptual model is the noise continuous field, and the third-level conceptual model is the geographical noise scene.

[0053] Step (3) Construct a spatiotemporal data model for the noise scene: Establish a spatiotemporal mapping relationship for the three levels in the hierarchical conceptual model of the noise scene, convert each element into spatiotemporal data, and store it in the spatiotemporal database;

[0054] Step (4) Filter out the required data from the spatiotemporal database and visualize it according to the spatiotemporal characteristics.

[0055] Step (1) Noise Scene Data Preprocessing. The main data used in constructing the noise scene includes sound source data, geographical data, and receiver data. Data preprocessing is performed on these three types of data, and the specific steps are as follows:

[0056] Step (1.1) Sound source data preprocessing. The main purpose of sound source data preprocessing is to extract noise data. Based on the source of the sound source data and its corresponding noise frequency range, an adaptive noise filter is used to exclude irrelevant signals and retain noise data within the noise frequency range corresponding to the sound source information;

[0057] Step (1.2) Geographic data preprocessing. Geographic data refers to basic urban geographic data. The basic geographic data required by this invention includes urban administrative division data, topographic data, road network data, building data, and water system data. This step establishes the connection between sound source data and geographic data, where roads, buildings, and water systems are geographic entities.

[0058] Step (1.3) Receiver data preprocessing. The geographic entities in step (1.2) are taken as receivers. Since air is an important propagation medium, it is necessary to calculate the sound intensity attenuation coefficient and sound pressure attenuation coefficient in the air and in each geographic entity.

[0059] Step (2) Construct a hierarchical conceptual model of the noise scene. The specific steps are as follows:

[0060] Step (2.1) Construct the first-level conceptual model of the noise scene. The first-level conceptual model is the noise source point element. The features of the noise source point data used include the ID, element type, time, sound pressure, sound intensity, longitude and latitude of the noise source data. Among them, the longitude and latitude features are used for the spatialization of the noise source point data.

[0061] Step (2.2) involves constructing the second-level conceptual model of the noise scene. The second-level conceptual model is a continuous noise field, using a raster model to represent sound characteristics, with each raster element possessing a unique attribute value. Therefore, this invention addresses the two main characteristics of noise: sound pressure and sound intensity. Based on these characteristics, continuous fields with spatiotemporal properties are constructed, namely, a sound pressure field and a sound intensity field, to express the propagation and attenuation characteristics of noise data. Their attributes are the raster data row and column numbers and feature values.

[0062] Step (2.3) constructs the third-level conceptual model of the noise scene. The third-level conceptual model is the geographic noise scene, which combines noise data with geographic data to express the propagation and attenuation characteristics of noise data considering the geographic environment. Features include the two major noise features (sound pressure and sound intensity) in step (2.2), medium type, sound intensity attenuation coefficient of the medium, and sound pressure attenuation coefficient of the medium.

[0063] Step (3) involves constructing a spatiotemporal data model for the noise scene. A spatiotemporal mapping relationship is established for the three levels of the hierarchical conceptual model of the noise scene, and each element is converted into spatiotemporal data and stored in a spatiotemporal database. The specific steps are as follows:

[0064] Step (3.1) converts the first-level conceptual model into vector point data with spatiotemporal information and stores it in shapefile data format. Based on the temporal characteristics and spatial coordinates of the noise source data obtained in step (1.1), the noise source point data tables of each time slice are converted into vector point data according to coordinates, becoming spatial point data with spatiotemporal characteristics.

[0065] Step (3.2) Generation of a noise continuum model based on a geographic spatiotemporal scene. The noise continuum model based on a geographic spatiotemporal scene refers to a raster continuum obtained by interpolating noise source data and geographic scene features. The specific steps are as follows:

[0066] Step (3.2.1) Exploratory data analysis: Perform trend analysis on the sound pressure and sound intensity characteristics of the noise source data in step (3.1) and explore the data distribution characteristics. Remove the trend and determine whether the sound source data characteristics meet the interpolation conditions.

[0067] Step (3.2.2) Selecting a spatial interpolation model: Based on the exploratory data analysis results of each feature in step (3.2.1), select an interpolation model suitable for each feature.

[0068] Step (3.2.3) Input the noise source data and the attenuation coefficient of the sound wave in the medium;

[0069] Step (3.2.4) Spatial Interpolation: For each feature, use the applicable spatial interpolation model from step (3.2.2) to perform interpolation, considering the propagation loss and absorption loss of noise. Perform spatial interpolation on the sound pressure and sound intensity features of the noise source data to obtain the noise continuous field. Propagation loss is the noise loss in the air, and absorption loss is the loss of noise as it propagates through various geographical elements. The calculation of spatial interpolation for the sound pressure and sound intensity features is as follows:

[0070] Distances from the unknown point at time t to each noise source point, disregarding the medium type:

[0071]

[0072] In the formula, i represents the noise source number, t represents time, and d it x represents the distance from the unknown point at time t to each noise source point. it The longitude of the noise source point numbered i at time t is represented by y. it x represents the latitude of the noise source point numbered i at time t. t Let y be the longitude of the point to be interpolated at time t. t Let be the latitude of the point to be interpolated at time t; considering the propagation distance of each medium type:

[0073]

[0074] In the formula, j represents the medium number of the noise propagation, where j=1 represents air, j≠1 represents the geographic entity type, and d ijt This represents the propagation distance of noise source i at time t in medium j.

[0075] Weights for calculating the noise sound pressure level at an unknown point at time t:

[0076]

[0077] In the formula, ω′ ij The weight α represents the weight used in calculating the noise sound pressure level at an unknown point at time t. ijt It represents the sound pressure attenuation coefficient of noise source point i in medium j at time t.

[0078]

[0079] In the formula, P 0t P(X) represents the noise sound pressure level at an unknown point at time t. i Y i ) t This represents the sound pressure level of noise source point i at time t.

[0080]

[0081] In the formula, ω″ ijt The weight β represents the noise intensity calculation for unknown points. ijt It represents the sound intensity attenuation coefficient of noise source point i in medium j at time t.

[0082] Calculate the noise intensity attribute value at the unknown point:

[0083]

[0084] In the formula, I0 represents the noise intensity attribute value at the unknown point, and I(X) i Yi ) represents the sound intensity value of the noise source point numbered i.

[0085] (3.3) Spatiotemporal data storage for noise scenarios. The stored features include time, longitude, latitude, sound pressure, sound intensity, medium number, medium type, propagation loss coefficient, and absorption loss coefficient, which are expressed in XML format.

[0086] (4) Filter the required data from the spatiotemporal database and visualize it according to spatiotemporal characteristics. This includes data retrieval. Filter the required data from the spatiotemporal database and visualize it according to spatiotemporal characteristics.

[0087] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for constructing a noise data model considering geographical scene characteristics, characterized in that, The method includes: Step (1) involves preprocessing the sound source data, geographic data, and receiver data used in constructing the noise scene; Step (1) specifically includes: Preprocessing of sound source data includes using an adaptive noise filter to exclude irrelevant signals and retain noise data within the noise frequency range corresponding to the sound source information, based on the source of the sound source data and its corresponding noise frequency range. Establish a connection between sound source data and geographic data, wherein the geographic data is urban basic geographic data, including urban administrative division data, topographic data, road network data, building data and water system data, wherein roads, buildings and water systems are geographic entities; When geographic entities in geographic data are treated as receivers, and since air is an important propagation medium, it is necessary to calculate the sound intensity attenuation coefficient and sound pressure attenuation coefficient in the air and in each geographic entity. Step (2) Construct a hierarchical conceptual model of the noise scene according to step (1). The constructed hierarchical conceptual model of the noise scene includes a first-level conceptual model, a second-level conceptual model, and a third-level conceptual model. The first-level conceptual model is noise source data, the second-level conceptual model is a continuous noise field, and the third-level conceptual model is a geographic noise scene. The first-level conceptual model is noise source elements. The features of the noise source data used include the ID, element type, time, sound pressure, sound intensity, longitude, and latitude of the noise source data. Among them, the longitude and latitude features are used for the spatialization of the noise source data. The second-level conceptual model uses a raster model to represent the sound characteristics of the noise continuous field. Based on the sound pressure and sound intensity characteristics of the noise, a continuous field with spatiotemporal characteristics is constructed, namely the sound pressure field and the sound intensity field, which are used to express the propagation and attenuation characteristics of the noise data. The raster model includes the raster data row and column numbers and feature values. The third-level conceptual model of geographic noise scenarios combines noise data with geographic scene features to express the propagation and attenuation characteristics of noise data considering the geographic environment. The features include: sound pressure and sound intensity of the noise, medium type, sound pressure attenuation coefficient of the medium, and sound intensity attenuation coefficient of the medium. Step (3) Construct a spatiotemporal data model of the noise scene, establish spatiotemporal mapping relationships for the three levels in the hierarchical conceptual model of the noise scene constructed in step (2), convert each element into spatiotemporal data, and store it in the spatiotemporal database; Step (3) Constructing a spatiotemporal data model of the noise scene specifically includes: The first-level conceptual model is converted into vector point data with spatiotemporal information and stored in shapefile data format. Based on the temporal characteristics and spatial coordinates of the preprocessed noise source data, the noise source point data table of each time slice is converted into vector point data according to the coordinates, becoming spatial point data with spatiotemporal characteristics. Step (4) Filter out the required data from the spatiotemporal database and visualize it according to the spatiotemporal characteristics.

2. The method for constructing a noise data model that takes into account geographical scene characteristics according to claim 1, characterized in that, Based on the spatial point data and geographic scene features of the first-level conceptual model, a continuous noise field based on the geographic spatiotemporal scene is generated. That is, the noise source point data is spatially interpolated to obtain a raster continuous field that takes into account the geographic scene features, specifically including: We perform trend analysis on the sound pressure and sound intensity characteristics of noise source data, explore the data distribution characteristics, remove trends, and determine whether the sound source data characteristics meet the interpolation conditions. Based on the exploratory data analysis results of each feature, select the appropriate interpolation model for each feature; Input noise source data and the attenuation coefficient of sound waves in the medium; According to the propagation loss and the absorption loss of the noise, the sound pressure and the sound intensity of the noise source point data are spatially interpolated to obtain a continuous noise field, wherein the propagation loss is the loss of the noise in the air, the absorption loss is the loss of the noise when passing through each geographical element, and the calculation formula for spatially interpolating the sound pressure and the sound intensity is as follows: not considering the medium type The distance from the unknown point to each noise source point at the moment （1）， In the formula, Represents the noise source number. Representing a moment, represent The distance from the unknown point in time to each noise source. represent Time number The longitude of the noise source point, represent Time number The latitude of the noise source point, for The longitude of the point that needs to be interpolated at any given time. for The latitude of the point that needs to be interpolated at any given time; considering the propagation distance of various media types. for: （2）， In the formula, The medium number representing noise propagation, where The time represents the medium as air. Represents geographic entity type, represent Time number The noise source is in the medium The propagation distance in; Weights for noise sound pressure calculation at unknown time points: （3）， In the formula, represent Weights for calculating noise sound pressure at unknown time points. express Noise source at any time In medium The sound pressure attenuation coefficient in the middle; calculate Noise sound pressure level at an unknown time point: （4）， In the formula, express Noise sound pressure level at an unknown time point. express Time number The sound pressure level at the noise source point, using Indicates the number is The longitude of the noise source point, For the number The latitude of the noise source; The weighting for calculating the noise intensity at unknown points is: （5）， In the formula, The weights used in calculating the noise intensity at unknown points. express Noise source at any time In medium The sound intensity attenuation coefficient in the middle; Calculate the noise intensity attribute value at the unknown point: （6）， In the formula, This represents the noise intensity attribute value at an unknown point. Indicates number The sound intensity value at the noise source point; Store spatiotemporal data of geographic noise scenes.

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