A method for calibrating and drawing three-dimensional noise maps based on vertical measured data

Abstract

The present invention relates to the technical field of three-dimensional noise map drawing, and specifically discloses a three-dimensional noise map calibration and drawing method based on vertical measured data, including: S1, obtaining multi-dimensional noise data and noise perception related data in the region, S2, constructing a preliminary three-dimensional noise distribution model, S3, drawing a noise perception contour map, S4, comparing the drawn noise contour map with the measured data and the perception data, identifying errors and S5, generating a risk map, and outputting a three-dimensional noise map; the present invention utilizes a geographic information system and three-dimensional visualization technology, combined with vertical measured data, to construct a preliminary three-dimensional noise distribution model, which can intuitively display the distribution of noise in three-dimensional space. Through a multimodal data fusion algorithm, the spatiotemporal weights of vertical monitoring data are introduced into the model, realizing the comprehensive embodiment of the physical noise level and spatial distribution impact, making the noise map closer to people's reality.

Description

Technical Field

[0001] The present invention belongs to the technical field of three-dimensional noise map drawing, and in particular relates to a three-dimensional noise map calibration and drawing method based on vertical measured data. Background Art

[0002] With the acceleration of urbanization, noise pollution has become increasingly serious and has become an important factor affecting the quality of life of urban residents. In order to effectively manage and control noise pollution, noise maps, as an intuitive and scientific tool, are widely used in urban noise planning, monitoring, evaluation and prevention. However, there are still some shortcomings in the existing technology in drawing and applying noise maps.

[0003] Noise maps are noise value distribution maps on geographic planes and building facades drawn by acoustic simulation software and verified and corrected by actual noise measurement data. It usually uses noise contours, grids and color bands of different colors to represent the noise level in different areas, providing a scientific decision-making basis for urban master planning, traffic development and planning, and noise pollution control measures.

[0004] When constructing noise models, existing technologies only model based on physical noise values, which makes it difficult to fully reflect the impact of noise on human life. During the model calibration process, the calibration process is cumbersome and inefficient due to the large number of parameters and their interrelationships. At the same time, existing technologies lack intelligent calibration methods, making it difficult to quickly and accurately adjust model parameters.

[0005] In view of this, the inventors propose a three-dimensional noise map calibration and drawing method based on vertical measured data to solve the above problems. Summary of the invention

[0006] The object of the present invention is to provide a method for calibrating and drawing a three-dimensional noise map based on vertical measured data to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for calibrating and drawing a three-dimensional noise map based on vertical measured data comprises the following steps:

[0009] S1. Obtain multi-dimensional noise data and regional noise perception-related data, denoise the noise data through a deep learning model, calibrate the measurement errors between different devices; and use big data analysis to convert the perception data into indicators related to physical noise values;

[0010] S2. Using geographic information system and three-dimensional visualization technology, a preliminary three-dimensional noise distribution model is constructed based on the vertical measured data, and the weight of the spatial and temporal joint geographic information difference is introduced into the model by using a multimodal data fusion algorithm in combination with the spatial and temporal joint geographic information data;

[0011] S3, based on the three-dimensional noise model, using the contour line tracking algorithm to generate a three-dimensional noise contour map;

[0012] S4. Compare the drawn noise contour map with the measured data to identify errors; combine the physical propagation model and the noise perception model, and use the parameter optimization algorithm to adjust the model parameters;

[0013] S5. Combine the monitoring data and spatial data to output a three-dimensional noise map.

[0014] Preferably, the three-dimensional noise distribution model is constructed using a noise propagation algorithm, and the noise propagation algorithm expression is:

[0015] Lp=Lw-20log 10 (r)-αr-A

[0016] Lp: noise level at a certain point, dB;

[0017] Lw: sound power level of noise source, dB;

[0018] r: distance between the noise source and the measurement point, m;

[0019] α: air absorption coefficient, dB / m, related to air humidity and temperature;

[0020] A: Additional attenuation caused by obstacles such as the ground and buildings, dB.

[0021] Preferably, the multi-dimensional noise data is obtained by vertically measuring at different heights and positions within a designated area using noise monitoring equipment;

[0022] The noise perception-related data in the area include noise data collected through automatic noise monitoring equipment, mobile devices, drones and social media.

[0023] Preferably, the model parameters include noise source strength, propagation attenuation coefficient and perception weight coefficient.

[0024] Preferably, the expression of the contour line tracking algorithm is:

[0025]

[0026] Where Lp(x,y,z): three-dimensional distribution function of noise level;

[0027] (x, y, z): spatial coordinates;

[0028] The gradient operator represents the rate of change of the noise level;

[0029] By solving the noise level isosurface, a three-dimensional noise contour map is generated.

[0030] Preferably, the parameter optimization algorithm calibrates the parameters of the noise propagation model and improves the accuracy of the three-dimensional noise map by minimizing the error between the model prediction value and the measured value. The expression is:

[0031]

[0032] in The noise level value predicted by the model;

[0033] Measured noise level value;

[0034] ri: measurement point coordinates;

[0035] Θ: the set of parameters to be optimized, including Lw, α, A;

[0036] n: number of measurement points.

[0037] Preferably, the noise risk model is used to quantify the comprehensive impact of the temporal and spatial distribution of the vertical monitoring data on noise, generate a risk map, and is used for decision-making in noise control and urban planning. The expression is:

[0038]

[0039] Where Rh: risk index.

[0040] Lp: noise level, dB;

[0041] P: noise perception value;

[0042] Eh(t): instantaneous impact function of noise exposure;

[0043] w1,w2,w3: weight coefficients, which are set according to the actual situation of vertical monitoring data;

[0044] T: exposure time.

[0045] Preferably, the calculation expression of the joint difference of space and time is:

[0046]

[0047] Where L(x,y,z,t): the noise value of the target point (x,y,z) at time t;

[0048] L(xi,yi,zi,tj): the noise value of the monitoring point (xi,yi,zi) at time tj;

[0049] wi j: comprehensive weight, considering the impact of space and time:

[0050] di: spatial distance;

[0051] |t-tj|: time difference;

[0052] p,q: spatial and temporal weight parameters.

[0053] Preferably, the calibrated three-dimensional noise map is output in multiple forms such as static visualization images, dynamic interactive platforms and data files.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] (1) This paper uses geographic information system and three-dimensional visualization technology, combined with vertical measured data, to construct a preliminary three-dimensional noise distribution model, which can intuitively display the distribution of noise in three-dimensional space. Through the multimodal data fusion algorithm, the spatiotemporal weights of vertical monitoring data are introduced into the model, realizing the comprehensive reflection of the physical noise level and spatial distribution impact, making the noise map closer to people's actual feelings.

[0056] (2) The three-dimensional noise contour map and noise perception contour map generated in the present invention can clearly show the noise levels in different areas and at different heights, providing a strong basis for noise control and management. The model parameters are adjusted through the parameter optimization algorithm to ensure the accuracy and applicability of the three-dimensional noise map, so that it can better meet the noise monitoring needs in different areas and different time periods. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flow chart of a method for calibrating and drawing a three-dimensional noise map based on vertical measured data of the present invention;

[0058] Figure 2 This is a complex acoustic environment source analysis diagram in Embodiment 3 of the present invention;

[0059] Figure 3 The three-dimensional noise data diagram in the third embodiment of the present invention;

[0060] Figure 4 This is a dynamic noise data distribution diagram in Example 3 of the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] Embodiment 1:

[0063] See also Figure 1 As shown, a method for calibrating and drawing a three-dimensional noise map based on vertical measured data includes the following steps:

[0064] S1. Obtain multi-dimensional noise data and regional noise perception-related data, denoise the noise data through a deep learning model, calibrate the measurement errors between different devices; and use big data analysis to convert the perception data into indicators related to physical noise values;

[0065] S2. Using geographic information system and three-dimensional visualization technology, a preliminary three-dimensional noise distribution model is constructed based on the vertical measured data, and the weight of the spatial and temporal joint geographic information difference is introduced into the model by using a multimodal data fusion algorithm in combination with the spatial and temporal joint geographic information data;

[0066] S3, based on the three-dimensional noise model, using the contour line tracking algorithm to generate a three-dimensional noise contour map;

[0067] S4. Compare the drawn noise contour map with the measured data to identify errors; combine the physical propagation model and the noise perception model, and use the parameter optimization algorithm to adjust the model parameters;

[0068] S5. Combine the monitoring data and spatial data to output a three-dimensional noise map.

[0069] As can be seen from the above, using geographic information system and 3D visualization technology, combined with vertical measured data, a preliminary 3D noise distribution model was constructed, which can intuitively display the distribution of noise in 3D space. Through the multimodal data fusion algorithm, the spatiotemporal weights of vertical monitoring data were introduced into the model, realizing the comprehensive reflection of the physical noise level and spatial distribution impact, making the noise map closer to people's actual feelings.

[0070] The generated three-dimensional noise contour map and noise perception contour map can clearly show the noise level in different areas and at different heights and its impact on human perception, providing a strong basis for noise control and management. The model parameters are adjusted through the parameter optimization algorithm to ensure the accuracy and applicability of the three-dimensional noise map, so that it can better meet the noise monitoring needs in different areas and different time periods, and provide a scientific basis for formulating noise control measures and protecting residents.

[0071] In summary, the three-dimensional noise map calibration and drawing method proposed in this invention not only improves the accuracy and reliability of noise data, realizes the comprehensive reflection of physical noise and subjective perception, but also improves the practicality and applicability of noise maps, and provides strong data support for risk assessment and urban planning. The application of this method will help improve the level of urban noise management and promote sustainable urban development.

[0072] Embodiment 2:

[0073] The three-dimensional noise distribution model is constructed using a noise propagation algorithm, and the noise propagation algorithm expression is:

[0074] Lp=Lw-20log 10 (r)-αr-A

[0075] Lp: noise level at a certain point, dB;

[0076] Lw: sound power level of noise source, dB;

[0077] r: distance between the noise source and the measurement point, m;

[0078] α: air absorption coefficient, dB / m, related to air humidity and temperature;

[0079] A: Additional attenuation caused by obstacles such as the ground and buildings, dB;

[0080] This formula is the core of the physical noise model. It is used to preliminarily calculate the attenuation characteristics during noise propagation and generate a basic noise distribution model. Combined with actual measurement data, it can calibrate the noise source intensity and propagation parameters to improve the model accuracy.

[0081] Specifically, the multi-dimensional noise data is obtained by vertically measuring at different heights and positions within a designated area using noise monitoring equipment;

[0082] The noise perception-related data in the area include noise data collected through automatic noise monitoring equipment, mobile devices, drones and social media.

[0083] Specifically, the model parameters include noise source strength, propagation attenuation coefficient and perception weight coefficient.

[0084] Specifically, the expression of the contour line tracking algorithm is:

[0085]

[0086] Where Lp(x,y,z): three-dimensional distribution function of noise level;

[0087] (x, y, z): spatial coordinates;

[0088] The gradient operator represents the rate of change of the noise level;

[0089] By solving the noise level isosurface, a three-dimensional noise contour map is generated. Combined with the perceived noise data, a subjective noise contour map is further generated to provide support for the intuitive display of noise distribution.

[0090] Specifically, the parameter optimization algorithm calibrates the parameters of the noise propagation model and improves the accuracy of the three-dimensional noise map by minimizing the error between the model prediction value and the measured value. The expression is:

[0091]

[0092] in The noise level value predicted by the model;

[0093] Measured noise level value;

[0094] ri: measurement point coordinates;

[0095] Θ: the set of parameters to be optimized, including Lw, α, A;

[0096] n: number of measurement points.

[0097] Specifically, the noise risk model is used to quantify the comprehensive impact of noise and generate a risk map for decision-making in noise control and urban planning. Its expression is:

[0098]

[0099] Where Rh: risk index.

[0100] Lp: noise level, dB;

[0101] P: noise perception value;

[0102] Eh(t): instantaneous impact function of noise exposure;

[0103] w1,w2,w3: weight coefficients, which are set according to the actual situation on site;

[0104] T: exposure time.

[0105] Specifically, the calibrated three-dimensional noise map is output in multiple forms such as static visualization images, dynamic interactive platforms and data files.

[0106] As can be seen from the above, by combining the physical noise propagation model with the human perception model, this method can more accurately assess the impact of noise on the environment and residents. Traditional noise assessment methods rely on physical noise data and ignore differences in human perception. For example, the same noise level may have completely different effects on different groups of people. This method introduces a noise perception model and takes into account the differences in perception of different groups of people, making the noise map more in line with the actual noise experience of humans, thereby providing a more scientific basis for urban noise management.

[0107] Embodiment three:

[0108] See also Figures 2 to 4 As shown, this embodiment shows how to implement a three-dimensional noise map calibration and drawing system based on vertical measured data, noise perception model and optimization algorithm. The system is applied to traffic noise monitoring and control in a certain city in my country, especially the noise pollution around the main roads in the city. The goal is to optimize urban planning and noise prevention measures through accurate noise maps, and improve the quality of life of residents.

[0109] Implementation steps and parameter description

[0110] Step S1: Data collection

[0111] Region Selection:

[0112] like Figure 2 As shown in the complex sound environment source analysis diagram, the area around the city's main roads was selected as the noise monitoring target, covering an area of ​​2 square kilometers, which includes multiple residential areas, commercial areas and transportation hubs.

[0113] Monitoring equipment deployment:

[0114] Deploy 20 noise monitoring devices evenly distributed in the area. The equipment configuration is as follows:

[0115] Equipment type: High-precision noise meter (model: Brue l&Kjaer 2250).

[0116] Measuring height: 0 m (ground), 1.5 m (height of human ear), 10 m (upper floor of building).

[0117] Measurement frequency: Sampling once every 5 seconds, measurement time is 24 hours continuous.

[0118] Collection parameters: A-weighted noise level (dB(A)), maximum sound level, minimum sound level, and average sound level.

[0119] Perception data collection:

[0120] Obtain complaint data related to noise perception (such as noise distress and sleep quality reports) through social media platforms and citizen feedback. Data collection includes the following:

[0121] Residents interviewed: Feedback from 500 residents.

[0122] Feedback items: noise nuisance level (1-5 ratings), sleep quality (1-5 ratings), and changes in perception due to long-term exposure (perception decline or aggravation).

[0123] Data integration: Build a noise perception level model based on social media feedback and survey data.

[0124] Step S2: Data preprocessing

[0125] Data cleaning and denoising:

[0126] De-noise the collected noise data. Use a deep learning generative model (such as a variational autoencoder) to filter out the noise from the raw data and eliminate errors caused by atypical data and equipment failures.

[0127] Data calibration:

[0128] Different noise monitoring equipment is calibrated to correct the deviation between equipment. The equipment error is within ±2dB(A), and linear regression and genetic algorithm are used to correct the deviation to ensure the consistency and accuracy of the data.

[0129] Perception data conversion:

[0130] Using the pre-trained noise perception model, based on the residents’ feedback data, the noise perception value P is calculated and mapped with the physical noise level Lp to establish the relationship between physical noise and perceived noise. The perception coefficient is:

[0131] Ch=1.2(Sensitive to the elderly)

[0132] Cp=0.8 (applicable to urban residents).

[0133] Step S3: 3D modeling

[0134] Preliminary 3D model construction:

[0135] like Figure 3 As shown in the three-dimensional noise data diagram, GIS software (such as ArcGIS Pro) is used to import geographic information data such as buildings, highways, and green belts in the area to build a three-dimensional geographic model. According to the location of the noise source (such as the traffic flow on the highway), the noise level distribution is preliminarily calculated.

[0136] Combined with sensory data:

[0137] In the three-dimensional noise model, the noise level of each area is adjusted in combination with the human perception model to generate a perceived noise distribution map. The physical noise level at a certain location is 75dB(A), but due to the high noise sensitivity of residents in the area, the perceived noise value may be 82dB(A).

[0138] Step S4: Noise contour drawing

[0139] Noise contour map generation:

[0140] The gradient calculation formula

[0141] Generate a noise contour map. The noise distribution of the area is as follows:

[0142] The noise level in the city center is 85dB(A).

[0143] The noise level in residential areas is 65dB(A).

[0144] The noise level in areas far from transportation hubs is 50dB(A).

[0145] Perceived noise contour plot:

[0146] like Figure 4 The dynamic noise data distribution map shows that the perception contour map is generated based on the perceived noise value. The impact of perceived noise on residents in high-density residential areas is more significant, and the perceived noise contour line in this area is 88dB(A) instead of 75dB(A).

[0147] Step S5: Map calibration

[0148] Error comparison:

[0149] Compare the measured data with the model prediction data to verify the accuracy of the noise contour map. For example, the noise level in a residential area is measured to be 70dB(A), while the model predicts 68dB(A), with an error of 2dB(A).

[0150] Parameter optimization:

[0151] The noise source intensity, attenuation coefficient and other parameters were adjusted through the optimization algorithm to minimize the error. After the adjustment, the error between the predicted value and the measured value was reduced to ±1dB(A).

[0152] Step S6: Impact assessment and decision support

[0153] risk assessment:

[0154] Based on the risk assessment formula, the risk value of each residential area in the region is calculated by combining the noise level, perceived noise and exposure time. For example, the risk value of a residential area is 0.75, indicating that the noise in this area has a greater impact on residents.

[0155] Noise reduction measures simulation:

[0156] By simulating the effect of different noise reduction measures (such as green belts or sound barriers), the noise reduction effect of the measures was evaluated. After the installation of the sound barrier, the noise level in a certain area was reduced from 80dB(A) to 70dB(A).

[0157] Step S7: Map output

[0158] Static map output:

[0159] Generate 3D noise maps, including noise level contour maps and perceived noise maps. Graphically display the degree of noise pollution in various areas within the region.

[0160] Dynamic interactive platform:

[0161] Through a web platform or virtual reality application, decision makers and residents can intuitively view noise distribution and simulate the effects of different noise reduction measures.

[0162] Regionally accurate noise distribution:

[0163] This method combines physical noise and human perception to generate a more accurate and realistic noise map. The introduction of perceived noise makes the map closer to the actual feelings of residents, especially in high-density residential areas.

[0164] Optimized noise reduction measures:

[0165] Through simulation evaluation, the effectiveness of noise reduction measures can be evaluated and scientific urban noise management decisions can be supported. For example, the installation of sound barriers effectively reduces residents’ noise exposure.

[0166] Improve the quality of life of residents:

[0167] By implementing noise control, noise pollution has been reduced and the quality of life of residents has been improved. After implementation, the noise distress and psychological stress reported by residents have been improved.

[0168] Efficient urban planning support:

[0169] Noise maps provide a scientific basis for urban planners, helping them make more reasonable noise prevention and control policies and urban layout decisions.

[0170] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0171] In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0172] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for calibrating and drawing a three-dimensional noise map based on vertical measured data, characterized in that: The following steps are involved: S1. Obtain multi-dimensional noise data and regional noise perception-related data, denoise the noise data through a deep learning model, calibrate the measurement errors between different devices; and use big data analysis to convert the perception data into indicators related to physical noise values; S2. Using geographic information system and three-dimensional visualization technology, a preliminary three-dimensional noise distribution model is constructed based on the vertical measured data, and the weight of the spatial and temporal joint geographic information difference is introduced into the model by using a multimodal data fusion algorithm in combination with the spatial and temporal joint geographic information data; S3, based on the three-dimensional noise model, using the contour line tracking algorithm to generate a three-dimensional noise contour map; S4. Compare the drawn noise contour map with the measured data to identify errors; combine the physical propagation model and the noise perception model, and use the parameter optimization algorithm to adjust the model parameters; S5. Combine the monitoring data and spatial data to output a three-dimensional noise map.

2. The method for calibrating and drawing a three-dimensional noise map based on vertical measured data according to claim 1, characterized in that: The three-dimensional noise distribution model is constructed by using a noise propagation algorithm, and the noise propagation algorithm expression is: Lp=Lw-20log 10 (r)-αr-A Lp: noise level at a certain point, dB; Lw: sound power level of noise source, dB; r: distance between the noise source and the measurement point, m; α: air absorption coefficient, dB / m, related to air humidity and temperature; A: Additional attenuation caused by ground and building obstacles, dB.

3. The method for calibrating and drawing a three-dimensional noise map based on vertical measured data according to claim 1, characterized in that: The multi-dimensional noise data is obtained by vertically measuring at different heights and positions within a designated area using noise monitoring equipment; The noise perception-related data in the area include noise data collected through automatic noise monitoring equipment, mobile devices, drones and social media.

4. The method for calibrating and drawing a three-dimensional noise map based on vertical measured data according to claim 1, characterized in that: The model parameters include noise source strength, propagation attenuation coefficient and perception weight coefficient.

5. The method for calibrating and drawing a three-dimensional noise map based on vertical measured data according to claim 1, characterized in that: The expression of the contour line tracking algorithm is: Where Lp(x,y,z): three-dimensional distribution function of noise level; (x, y, z): spatial coordinates; The gradient operator represents the rate of change of the noise level; By solving the noise level isosurface, a three-dimensional noise contour map is generated.

6. The method for calibrating and drawing a three-dimensional noise map based on vertical measured data according to claim 5, characterized in that: The parameter optimization algorithm calibrates the parameters of the noise propagation model and improves the accuracy of the three-dimensional noise map by minimizing the error between the model prediction value and the measured value. The expression is: in The noise level value predicted by the model; Measured noise level value; ri: measurement point coordinates; Θ: the set of parameters to be optimized, including Lw, α, A; n: number of measurement points.

7. The method for calibrating and drawing a three-dimensional noise map based on vertical measured data according to claim 1, characterized in that: The noise risk model is used to quantify the comprehensive impact of the temporal and spatial distribution of the vertical monitoring data on noise, generate a risk map, and is used for decision-making in noise control and urban planning. Its expression is: Where Rh: risk index; Lp: noise level, dB; P: noise perception value; Eh(t): instantaneous impact function of noise exposure; w1,w2,w3: weight coefficients, set according to the research; T: exposure time.

8. The method for calibrating and drawing a three-dimensional noise map based on vertical measured data according to claim 1, characterized in that: The calculation expression of the joint difference of space and time is: Where L(x,y,z,t): the noise value of the target point (x,y,z) at time t; L(xi,yi,zi,tj): the noise value of the monitoring point (xi,yi,zi) at time tj; wij: comprehensive weight, considering the impact of space and time: di: spatial distance; |t-tj|: time difference; p,q: spatial and temporal weight parameters.

9. The method for calibrating and drawing a three-dimensional noise map based on vertical measured data according to claim 1, characterized in that: The calibrated 3D noise map can be output in the form of static visualization images, dynamic interactive platforms and data files.

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