A dynamic noise analysis method, device and medium for urban viaducts

Abstract

The application discloses a dynamic noise analysis method, device and medium for urban viaducts, and belongs to the technical field of noise monitoring and analysis. The method comprises the following steps: acquiring a three-dimensional geographic information model and real-time traffic flow data of a target viaduct; processing the three-dimensional geographic information model based on a sound wave propagation attenuation model to generate a noise propagation path grid; fusing the real-time traffic flow data and a noise source intensity calculation model to generate a dynamic noise source intensity sequence; combining the dynamic noise source intensity sequence and the noise propagation path grid, and calculating the real-time equivalent sound pressure level of each grid unit through a noise superposition algorithm; performing rolling prediction on the real-time equivalent sound pressure level based on a time window sliding mechanism to generate a noise space-time distribution heat map; identifying noise exceeding standard areas according to the noise space-time distribution heat map, and outputting a dynamic noise evaluation report. The application realizes the technical effects of dynamic prediction and analysis of viaduct noise through the above method.

Description

Technical Field

[0001] This application relates to the technical field of noise monitoring and analysis, and in particular to a dynamic noise analysis method, equipment and medium for urban elevated roads. Background Technology

[0002] Urban traffic noise pollution control is a key research area in environmental engineering, especially since elevated roads are major noise sources, making their impact assessment crucial. Existing technologies typically employ fixed monitoring stations combined with standard noise prediction models: sound level meters are deployed to collect noise values ​​at specific locations, a two-dimensional noise propagation path is constructed using a GIS-based map, and then regional noise distribution is estimated based on historical traffic flow data. Some improved solutions incorporate building outline data to correct for sound wave reflection effects, or use short-term mobile monitoring to supplement spatial coverage.

[0003] However, existing technologies largely rely on historical traffic flow data or manually inputted traffic flow parameters, failing to respond in real time to dynamic scenarios such as changes in vehicle speed and sudden congestion. Furthermore, sound propagation models based on two-dimensional maps neglect the three-dimensional structure of elevated roads (such as bridge pier diffraction) and elevation differences among surrounding buildings, resulting in insufficient calculation of complex spatial attenuation effects. Fixed monitoring points are sparsely distributed, requiring manual inspections to supplement data, making it difficult to capture the spatiotemporal evolution of noise in a timely manner. Automation is low: identifying areas exceeding noise standards relies on manual interpretation of heat maps, lacking automatic correlation analysis between traffic flow characteristics and noise causes. Especially in urban elevated road scenarios, with high vehicle type mixing and complex road network structures, existing technologies struggle to achieve dynamic noise assessment with minute-level accuracy, severely hindering the precise implementation of noise reduction measures.

[0004] Therefore, how to dynamically predict and analyze the noise of elevated roads has become an urgent technical problem to be solved. Summary of the Invention

[0005] This application provides a method, device, and medium for dynamic noise analysis of urban elevated roads, in order to solve the following technical problem: how to dynamically predict and analyze the noise of elevated roads.

[0006] In a first aspect, embodiments of this application provide a dynamic noise analysis method for urban elevated roads, characterized in that the method includes: acquiring a three-dimensional geographic information model of the target elevated road and real-time traffic flow data; wherein the three-dimensional geographic information model includes the elevated road structure, surrounding building distribution, and ground elevation data, and the real-time traffic flow data includes vehicle type, vehicle speed, traffic volume, and timestamp sequence; processing the three-dimensional geographic information model based on a sound wave propagation attenuation model to generate a noise propagation path grid; wherein the noise propagation path grid is divided into grid units in multiple propagation directions, starting from the sound source of the elevated road, and each grid unit includes a distance attenuation factor and an obstacle diffraction factor; and fusing real-time traffic flow data with noise... A noise source intensity calculation model is used to generate a dynamic noise source intensity sequence. This model matches a baseline noise value based on vehicle type and dynamically corrects the noise intensity based on vehicle speed. Combining the dynamic noise source intensity sequence with a noise propagation path grid, a noise superposition algorithm is used to calculate the real-time equivalent sound pressure level of each grid cell. This algorithm superimposes the energy of multiple sound source contributions within the same grid cell. A rolling prediction of the real-time equivalent sound pressure level is performed based on a time window sliding mechanism, generating a noise spatiotemporal distribution heatmap. This time window sliding mechanism updates traffic flow data and recalculates the sound pressure level at a preset cycle. Based on the noise spatiotemporal distribution heatmap, areas exceeding noise limits are identified, and a dynamic noise assessment report is output.

[0007] In one implementation of this application, acquiring a three-dimensional geographic information model and real-time traffic flow data of the target elevated road specifically includes: collecting point cloud data of the elevated road and surrounding areas using a lidar scanning device; performing denoising and registration operations on the point cloud data to generate a three-dimensional point cloud model; extracting the elevated road surface, bridge piers, outlines of adjacent buildings, and ground elevation data from the three-dimensional point cloud model; constructing a three-dimensional geographic information model using a triangulation algorithm; deploying Doppler radar sensors at monitoring points on the elevated road to collect real-time data on vehicle speed, vehicle length, and time stamps passing through the monitoring points; classifying vehicle types based on vehicle length and statistically analyzing traffic flow data within a preset time interval; and integrating vehicle type, speed, traffic flow, and time stamp sequences to generate structured real-time traffic flow data.

[0008] In one implementation of this application, a three-dimensional geographic information model is processed based on a sound wave propagation attenuation model to generate a noise propagation path grid. Specifically, this includes: generating multiple sound wave propagation rays radiating outward at preset angle intervals, using the centerline of the elevated road lane as a reference; generating grid cells along each sound wave propagation ray at equal distances, and labeling the straight-line distance from the sound source to each grid cell; calculating the obstacle obstruction rate of the sound wave within the grid cell based on building outlines and surface elevation data in the three-dimensional geographic information model; calculating the obstacle diffraction factor based on the sound wave diffraction physical model and the obstruction rate; calculating the distance attenuation factor based on the straight-line distance according to the spherical diffusion law of sound waves; and associating the distance attenuation factor and obstacle diffraction factor of all grid cells to generate a noise propagation path grid.

[0009] In one implementation of this application, real-time traffic flow data and a noise source intensity calculation model are integrated to generate a dynamic noise source intensity sequence. Specifically, this includes: establishing a mapping table between vehicle type and reference noise value, where small cars, medium-sized cars, and large cars correspond to different reference noise values; dynamically correcting the reference noise value based on vehicle speed in real-time traffic flow data using a linear interpolation algorithm to generate the instantaneous noise intensity of a single vehicle; statistically analyzing all vehicles passing through the same monitoring point within a preset time window, and superimposing the energy of the instantaneous noise intensity of the single vehicle to generate the aggregated noise intensity of the monitoring point; and associating the aggregated noise intensity of all monitoring points with their timestamps to generate a dynamic noise source intensity sequence.

[0010] In one implementation of this application, the real-time equivalent sound pressure level of each grid cell is calculated by combining a dynamic noise source intensity sequence with a noise propagation path grid and using a noise superposition algorithm. Specifically, this includes: traversing each grid cell in the noise propagation path grid and retrieving all sound wave propagation rays pointing to that grid cell; obtaining the noise intensity of the sound source corresponding to each sound wave propagation ray at the current location based on the dynamic noise source intensity sequence; multiplying the noise intensity by the distance attenuation factor and obstacle diffraction factor of the corresponding grid cell for each sound wave propagation ray to obtain a single-ray contribution value; performing logarithmic energy summation on all single-ray contribution values ​​within the same grid cell to obtain the real-time equivalent sound pressure level; repeating the above steps until all grid cells are covered.

[0011] In one implementation of this application, the logarithmic energy summation of all single-ray contribution values ​​within the same grid cell is performed to obtain the real-time equivalent sound pressure level. Specifically, this includes: converting the single-ray contribution values ​​from sound pressure units to decibel values; summing the converted decibel values ​​using an energy superposition formula; normalizing the summation result to obtain the equivalent continuous sound pressure level of the grid cell; and storing the equivalent continuous sound pressure level of all grid cells at the current timestamp in a noise distribution database.

[0012] In one implementation of this application, a rolling prediction of the real-time equivalent sound pressure level is performed based on a time window sliding mechanism to generate a noise spatiotemporal distribution heatmap. Specifically, this includes: setting a fixed-length time window and a sliding step size; calculating the equivalent sound pressure level of the entire area grid unit based on the current real-time traffic flow data within the initial time window; inputting the new time period traffic flow data after the sliding step size into the model to replace the data of the earliest time period within the time window; recalculating the equivalent sound pressure level of the entire area grid unit to generate an updated noise distribution matrix; and superimposing the continuously updated noise distribution matrix along the time dimension and converting it into a spatiotemporal distribution heatmap through a color mapping algorithm.

[0013] In one implementation of this application, noise-exceeding areas are identified based on a noise spatiotemporal distribution heatmap, and a dynamic noise assessment report is output. Specifically, this includes: comparing the sound pressure level of each grid cell in the noise spatiotemporal distribution heatmap with a preset threshold; marking grid cells whose sound pressure level continuously exceeds the threshold as noise-exceeding areas; extracting the spatiotemporal range, maximum sound pressure level, and duration of the noise-exceeding areas; associating the traffic flow characteristic data corresponding to the noise-exceeding areas with the traffic flow characteristic data to generate a dynamic noise assessment report containing noise cause analysis.

[0014] Secondly, embodiments of this application also provide a dynamic noise analysis device for urban elevated roads, characterized in that the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: acquire a three-dimensional geographic information model and real-time traffic flow data of the target elevated road; wherein the three-dimensional geographic information model includes elevated road structure, surrounding building distribution, and ground elevation data, and the real-time traffic flow data includes vehicle type, vehicle speed, traffic volume, and timestamp sequence; process the three-dimensional geographic information model based on a sound wave propagation attenuation model to generate a noise propagation path grid; wherein the noise propagation path grid is divided into multiple propagation directions starting from the sound source of the elevated road. The system employs a grid cell approach, where each cell contains a distance attenuation factor and an obstacle diffraction factor. Real-time traffic flow data is fused with a noise source intensity calculation model to generate a dynamic noise source intensity sequence. The noise source intensity calculation model matches a baseline noise value based on vehicle type and dynamically corrects the noise intensity based on vehicle speed. Combining the dynamic noise source intensity sequence with a noise propagation path grid, a noise superposition algorithm calculates the real-time equivalent sound pressure level of each cell. This algorithm superimposes the energy of multiple sound source contributions within the same cell. A rolling prediction of the real-time equivalent sound pressure level is performed based on a time window sliding mechanism, generating a noise spatiotemporal distribution heatmap. This time window sliding mechanism updates traffic flow data and recalculates the sound pressure level at a preset cycle. The system identifies areas exceeding noise limits based on the noise spatiotemporal distribution heatmap and outputs a dynamic noise assessment report.

[0015] Thirdly, embodiments of this application also provide a non-volatile computer storage medium for dynamic noise analysis of urban elevated roads, storing computer-executable instructions, characterized in that the computer-executable instructions are configured to: acquire a three-dimensional geographic information model and real-time traffic flow data of the target elevated road; wherein, the three-dimensional geographic information model includes the elevated road structure, the distribution of surrounding buildings, and surface elevation data, and the real-time traffic flow data includes vehicle type, vehicle speed, traffic volume, and timestamp sequence; process the three-dimensional geographic information model based on a sound wave propagation attenuation model to generate a noise propagation path grid; wherein, the noise propagation path grid is divided into grid units in multiple propagation directions, starting from the sound source of the elevated road, and each grid unit includes a distance attenuation factor and obstacle attenuation factor. The system employs several methods: 1) emission factor analysis; 2) fusing real-time traffic flow data with a noise source intensity calculation model to generate a dynamic noise source intensity sequence; 3) matching a baseline noise value based on vehicle type and dynamically correcting the noise intensity based on vehicle speed; 4) combining the dynamic noise source intensity sequence with a noise propagation path grid to calculate the real-time equivalent sound pressure level of each grid cell using a noise superposition algorithm; 5) superimposing the energy of multiple sound source contributions within the same grid cell; 6) rolling prediction of the real-time equivalent sound pressure level based on a time window sliding mechanism to generate a noise spatiotemporal distribution heatmap; 7) updating traffic flow data and recalculating the sound pressure level at a preset cycle; 8) identifying noise-exceeding areas based on the noise spatiotemporal distribution heatmap and outputting a dynamic noise assessment report.

[0016] The present application provides a method, device, and medium for dynamic noise analysis of urban elevated roads, which has at least the following technical advantages:

[0017] By integrating real-time traffic flow data with a 3D geographic information model, this method effectively solves the prediction bias problem caused by static historical data. The dynamic noise source intensity sequence corrects noise intensity based on real-time matching of vehicle type and speed, and, combined with the 3D obstacle diffraction factor and distance attenuation factor in the noise propagation path grid, significantly improves the physical accuracy of sound pressure level calculation. Compared to traditional 2D models, this method fully considers the influence of elevated road and bridge pier structures, the elevation of surrounding buildings, and surface undulations on sound wave propagation, reducing the simulation error of complex spatial attenuation effects, and ultimately achieving high-fidelity noise field reconstruction in the noise spatiotemporal distribution heatmap.

[0018] The rolling prediction architecture based on a sliding time window mechanism enables the system to continuously respond to minute-level changes in traffic flow. By periodically updating traffic flow data and recalculating the sound pressure level across the entire area, this method overcomes the limitation of insufficient spatial coverage of fixed monitoring points, achieving continuous capture of the spatiotemporal evolution of noise. This mechanism not only ensures real-time tracking of the dynamic characteristics of elevated road noise but also provides traffic management departments with continuously updated decision-making data, significantly improving the timeliness of noise pollution early warning.

[0019] The automatic identification and dynamic assessment report generation of noise-exceeding areas completely replaces the traditional manual interpretation of heat maps. The system automatically marks areas exceeding noise standards by comparing them to sound pressure level thresholds and correlates them with traffic flow characteristic data to generate noise cause analysis, providing precise data support for noise reduction measures. This automated process significantly shortens the assessment cycle, reduces the cost of manual intervention, and to a certain extent ensures the scientific and timely nature of urban noise control decisions.

[0020] This method integrates LiDAR 3D modeling, Doppler radar traffic monitoring, sound propagation physical models, and intelligent algorithms into a unified framework, forming an engineering-ready solution. Through standardized processing procedures, it ensures stable system operation in complex urban elevated road environments, providing a scalable technical foundation for the construction of intelligent traffic noise management platforms. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0022] Figure 1 A flowchart illustrating a dynamic noise analysis method for urban elevated roads, provided as an embodiment of this application;

[0023] Figure 2 This is a schematic diagram of the internal structure of a dynamic noise analysis device for urban elevated roads, provided as an embodiment of this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] This application provides a method, device, and medium for dynamic noise analysis of urban elevated roads, in order to solve the following technical problem: how to dynamically predict and analyze the noise of elevated roads.

[0026] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0027] Figure 1 This application provides a flowchart for dynamic noise analysis of urban elevated roads, as illustrated in its embodiments. Figure 1As shown in the embodiment of this application, a dynamic noise analysis method for urban elevated roads is provided, which specifically includes the following steps:

[0028] Step 1: Obtain the 3D geographic information model and real-time traffic flow data of the target elevated road; the 3D geographic information model includes the elevated road structure, the distribution of surrounding buildings and the ground elevation data, and the real-time traffic flow data includes vehicle type, vehicle speed, traffic volume and timestamp sequence.

[0029] Step 1.1: Collect point cloud data of the elevated road and surrounding area using a lidar scanning device, and perform noise reduction and registration operations on the point cloud data to generate a three-dimensional point cloud model.

[0030] LiDAR scanning equipment: an active remote sensing device based on the principle of laser ranging, which obtains the three-dimensional coordinate information of the target surface by emitting laser pulses and receiving reflected signals.

[0031] Point cloud data: A collection of discrete three-dimensional spatial points collected by a device. Each point contains spatial coordinates (X, Y, Z) and reflection intensity information.

[0032] Denoising operation: Use statistical filtering algorithms (such as radius filtering) to remove outlier noise points, such as deleting outliers with spatial density below a threshold.

[0033] Registration operation: The point clouds of multiple scanning stations are aligned to a unified coordinate system by using the Iterative Closest Point (ICP) algorithm to eliminate device pose error.

[0034] A vehicle-mounted LiDAR system was deployed in the target elevated road area A, moving and scanning along a predetermined route at a speed of 20 km / h to collect raw point clouds covering the elevated road surface, bridge piers, and a surrounding area of ​​200 meters. A radius filtering algorithm (neighborhood radius 0.5 m, minimum number of neighboring points 10) was used to remove flying point noise. Based on ground control point B, the ICP algorithm was used to register the point clouds from 12 scanning stations to the local coordinate system, generating a complete 3D point cloud model.

[0035] In a specific example: for the C area of ​​pier A on elevated road, LiDAR scanning acquired raw data containing 2.3 million points. After radius filtering, 8,500 noise points (0.37%) were removed, and point cloud registration was achieved using 4 ground control points, ultimately generating a 3D point cloud model with a coordinate system error of <5cm.

[0036] Step 1.2: Extract the elevated road surface, bridge piers, outlines of nearby buildings, and ground elevation data from the 3D point cloud model, and construct a 3D geographic information model using a triangulation algorithm.

[0037] Triangulation algorithm: A surface reconstruction method that converts point clouds into continuous triangular patches (such as Delaunay triangulation).

[0038] Ground elevation data: Z-axis coordinate dataset of surface point cloud separated by filtering algorithm.

[0039] The Random Sample Consensus (RANSAC) algorithm was used to fit the elevated road surface plane and extract continuous point sets with a slope of <5°; point cloud clusters of bridge piers with a diameter >1m were identified based on a cylindrical model; and the outer contour polygons of buildings were extracted using the AlphaShape algorithm.

[0040] The extracted features were imported into MeshLab software, and a triangular mesh model with a resolution of 0.2m was generated using the Poisson surface reconstruction algorithm. The model was then saved as a 3D geographic information model in OBJ format.

[0041] In a specific example: for the X segment (300m long) of elevated road A in the point cloud model, 12 bridge piers (average diameter 1.2m), the outlines of 5 adjacent buildings (maximum height 40m) and 210,000 ground elevation points were extracted.

[0042] Step 1.3: Deploy Doppler radar sensors at monitoring points on elevated roads to collect real-time data on vehicle speed, vehicle length, and timestamps as they pass through the monitoring points.

[0043] Doppler radar sensor: a microwave velocity measurement device based on the Doppler effect principle, operating at a frequency of 24 GHz.

[0044] Vehicle length calculation: calculated by multiplying the vehicle speed by the time difference between entering and leaving the radar beam.

[0045] Install radar sensor arrays (vertical installation height 6m, depression angle 15°) at the entrance / exit of elevated road A. When a vehicle enters the monitoring area:

[0046] The real-time vehicle speed V (unit: km / h) is calculated using the frequency shift; the vehicle length is calculated using the formula L=V×3.6×Δt based on the radar beam coverage time Δt; and the time of passage is recorded to the millisecond level timestamp.

[0047] In a specific example: The radar at monitoring point D recorded vehicle E: speed 72km / h, entry time into the beam 10:05:23.115, exit time 10:05:23.893, and calculated vehicle length L = 72 × 3.6 × 0.778 = 4.2m.

[0048] Step 1.4: Classify vehicle types based on vehicle length and collect traffic flow data within a preset time interval;

[0049] Vehicle classification rules:

[0050] Small cars: L≤4.5m;

[0051] Mid-size vehicle: 4.5m <L≤9m;

[0052] Large vehicles: L>9m;

[0053] Traffic flow statistics: The number of standard vehicle equivalents (PCUs) passing through the monitoring point per unit time.

[0054] Set a 5-minute statistical period. For each vehicle: match the vehicle type (small / medium / large) according to its body length; calculate PCU by vehicle type weight (small: 1.0, medium: 1.5, large: 2.2); and accumulate the total traffic flow based on the PCU value of all vehicles within the period.

[0055] In a specific example:

[0056] Between 10:00 and 10:05, monitoring point D passed through:

[0057] 15 small cars (PCU=15×1.0=15);

[0058] 8 medium-sized vehicles (PCU=8×1.5=12);

[0059] Three large vehicles (PCU=3×2.2=6.6);

[0060] Total traffic volume = 15 + 12 + 6.6 = 33.6 PCU.

[0061] Step 1.5: Integrate vehicle type, vehicle speed, traffic volume and timestamp sequence to generate structured real-time traffic flow data.

[0062] Construct a time series database table with the following fields:

[0063] Timestamp (ms): Vehicle ID: Vehicle Model: Speed ​​(km / h): Time Period;

[0064] 1637124323115: VE001: Small: 72:10:00-10:05.

[0065] In a specific example:

[0066] Data from three monitoring points on Elevated Road A between 10:00 and 11:00 on June 15, 2025, were integrated to generate a structured CSV file containing 10,000 records.

[0067] Step 2: Process the 3D geographic information model based on the sound wave propagation attenuation model to generate a noise propagation path grid; wherein, the noise propagation path grid is divided into grid units in multiple propagation directions, with the elevated road sound source as the starting point, and each grid unit contains a distance attenuation factor and an obstacle diffraction factor.

[0068] Step 2.1: Using the center line of the elevated road lane as a reference, generate multiple sound wave propagation rays outward at preset angle intervals.

[0069] Extract the centerline vector of lane A of the elevated road (accuracy 0.1m). Based on the centerline, generate 24 rays at 15° intervals (0°-360° omnidirectional coverage), with a ray extension length of 1000m.

[0070] In a specific example:

[0071] For the centerline of elevated road A, which is 500m long, sound source points are set every 50m, and each point generates 24 rays (a total of 240 rays), with a maximum coverage radius of 1km.

[0072] Step 2.2: Divide each sound wave propagation ray into equal parts to generate grid cells, and mark the straight-line distance from the sound source for each grid cell.

[0073] The rays are divided into grid cells at 50m intervals.

[0074] Each cell records attributes:

[0075] {Ray ID, Starting distance (m), Ending distance (m), Center point coordinates, Linear distance from sound source (m)}

[0076] In a specific example:

[0077] Ray R001 (azimuth 0°) is divided into 20 grid cells: cell 1 (0-50m), cell 2 (50-100m)... cell 20 (950-1000m).

[0078] Step 2.3: Based on the building outlines and surface elevation data in the 3D geographic information model, calculate the obstacle occlusion rate of sound waves within the grid cells.

[0079] Construct the line of sight (LOS) from the sound source to the center of the unit in the three-dimensional geographic information model.

[0080] Calculate the proportion of LOS length obscured by buildings / terrain: Obscuration rate = Total length of obscured segment / Total length of LOS.

[0081] In a specific example: the LOS of unit P (300m from the sound source, 20m elevation) is blocked by building B for 120m, the total LOS length is 320m, and the blocking rate is 120 / 320=0.375.

[0082] Step 2.4: Calculate the obstacle diffraction factor based on the physical model of acoustic wave diffraction and the occlusion rate.

[0083] The ISO9613-2 diffraction model was adopted: diffraction factor = 10^(-0.1×ΔL).

[0084] The diffraction attenuation ΔL = K × d × f × (occlusion rate).

[0085] (K: material coefficient, d: barrier thickness, f: center frequency)

[0086] In a specific example: the occlusion rate of unit P is 0.375, the concrete building (K=0.7), d=15m, f=500Hz, and the calculated ΔL=3.9dB, the diffraction factor=10^(-0.39)=0.41.

[0087] Step 2.5: Based on the spherical diffusion law of sound waves, calculate the distance attenuation factor based on the straight-line distance.

[0088] According to the spherical diffusion law of sound energy: distance attenuation factor = (reference distance / actual distance)^2; where (the reference distance is usually taken as 10m).

[0089] In a specific example: Unit Q is 250m away from the sound source, and the attenuation factor = (10 / 250)^2 = 0.0016.

[0090] Step 2.6: Associate the distance attenuation factor and obstacle diffraction factor of all grid cells to generate a noise propagation path grid.

[0091] Construct a spatial grid database with the following structure:

[0092] Grid ID: Source ID: Distance (m): Attenuation factor: Diffraction factor: Composite attenuation value;

[0093] G001: S001: 150: 0.0044: 0.53: -36.2dB.

[0094] Step 3: Integrate real-time traffic flow data with the noise source intensity calculation model to generate a dynamic noise source intensity sequence; wherein, the noise source intensity calculation model matches the baseline noise value according to the vehicle type and dynamically corrects the noise intensity based on the vehicle speed.

[0095] Step 3.1: Establish a mapping table between vehicle type and reference noise value, where small cars, medium cars, and large cars correspond to different reference noise values.

[0096] Mapping table: A structured data mapping table that defines standard noise reference values ​​for different vehicle models;

[0097] Reference noise value: Typical noise intensity measured under standard test conditions (e.g., vehicle speed 60km / h, flat road surface);

[0098] Small cars: Passenger vehicles (such as sedans) with a body length ≤ 4.5m;

[0099] Medium-sized vehicles: Commercial vehicles with a body length of 4.5-9m (such as minibuses);

[0100] Large vehicles: Heavy vehicles (such as trucks) with a body length > 9m;

[0101] Implementation method:

[0102] Create a mapping configuration file in JSON format:

[0103] "vehicle_classification":[

[0104] {"type":"small","max_length":4.5,"base_noise":68},

[0105] {"type":"medium","min_length":4.5,"max_length":9,"base_noise":73},

[0106] {"type":"large","min_length":9,"base_noise":79} ]

[0108] The data comes from the measured average value of the national standard "Limits for Exterior Noise of Automobiles During Acceleration" (GB1495-2002).

[0109] In a specific example:

[0110] In the noise prediction system for elevated road A, the mapping table is configured as follows: 68dB for small vehicles, 73dB for medium-sized vehicles, and 79dB for large vehicles. When the Doppler radar measures a vehicle length of 8.2m, it is matched as a medium-sized vehicle, using a reference value of 73dB.

[0111] Step 3.2: Based on the vehicle speed in the real-time traffic flow data, a linear interpolation algorithm is used to dynamically correct the reference noise value and generate the instantaneous noise intensity of a single vehicle.

[0112] Linear interpolation algorithm: A mathematical method for adjusting noise values ​​proportionally based on changes in vehicle speed;

[0113] Dynamic correction: Noise levels increase linearly with vehicle speed, with an increase of approximately 1 dB for every 10 km / h increase;

[0114] Instantaneous noise intensity of a single vehicle: The noise radiation intensity of a single vehicle at the current moment.

[0115] Correction formula: Corrected noise value = Baseline noise value + (Actual vehicle speed - Baseline vehicle speed) × 0.1;

[0116] The reference speed is 60 km / h, and the coefficient is 0.1 dB / (km / h), which comes from the "Acoustic Design Specification for Road Noise Barriers".

[0117] In a specific example:

[0118] A medium-sized vehicle (reference value 73dB, reference speed 60km / h) passes the monitoring point at 82km / h:

[0119] Correction value = 73 + (82 - 60) × 0.1 = 75.2 dB;

[0120] Real-time generation of instantaneous noise intensity records for the vehicle:

[0121] Timestamp 1677825400, Vehicle ID: VH_3827, Noise intensity: 75.2dB.

[0122] Step 3.3: Statistically count all vehicles passing through the same monitoring point within the preset time window, and superimpose the energy of the instantaneous noise intensity of a single vehicle to generate the aggregated noise intensity of the monitoring point.

[0123] Preset time window: a fixed statistical period (standard setting is 1 minute);

[0124] Energy superposition: Logarithmic summation based on the principle of acoustic energy additivity;

[0125] Aggregate noise intensity: The combined noise radiation intensity of multiple vehicles within a given time period.

[0126] In a specific example:

[0127] Monitoring point B passed through within 1 minute:

[0128] Two small cars: 69.4 dB and 71.2 dB;

[0129] One medium-sized vehicle: 75.2 dB;

[0130] One large vehicle: 80.5dB.

[0131] Calculation process:

[0132] 69.4dB → Energy value 8710;

[0133] 71.2dB → Energy value 13183;

[0134] 75.2dB → Energy value 33113;

[0135] 80.5dB → Energy value 112202.

[0136] Total energy = 8710 + 13183 + 33113 + 112202 = 167208;

[0137] L_sum=10×log10(167208)=82.2dB.

[0138] That is, the aggregate noise intensity at this monitoring point is 82.2 dB.

[0139] Step 3.4: Associate the aggregated noise intensity and timestamp of all monitoring points to generate a dynamic noise source intensity sequence.

[0140] Building a time series database:

[0141] Timestamp (s): Monitoring Point ID: Aggregation Intensity (dB): Vehicle Model Distribution;

[0142] 1677825400: B: 82.2: Small cars: 2, Medium cars: 1, Large cars: 1.

[0143] Data is updated every 5 seconds, and data from the last 2 hours is retained.

[0144] In a specific example:

[0145] Dynamic sequences were generated at three monitoring points along Elevated Road A between 10:00 and 10:15:

[0146] Time point 10:00:05 - Monitoring point C: 79.6dB (dominated by medium-sized vehicles);

[0147] Time point 10:07:33 - Monitoring point D: 85.1dB (Large convoy passing through);

[0148] Time point 10:14:20 - Monitoring point E: 76.8dB (mainly small vehicles).

[0149] Step 4: Combine the dynamic noise source strength sequence with the noise propagation path grid, and calculate the real-time equivalent sound pressure level of each grid cell using the noise superposition algorithm; wherein, the noise superposition algorithm performs energy superposition on the contribution values ​​of multiple sound sources within the same grid cell.

[0150] Step 4.1: Traverse each grid cell in the noise propagation path grid and retrieve all sound wave propagation rays pointing to that grid cell.

[0151] Noise propagation path grid: A spatially divided grid system in which each cell records the noise propagation characteristics;

[0152] Sound wave propagation ray: A virtual propagation path radiating outward from the sound source on the elevated road;

[0153] Traversal: Access all grid cells in a specific order (e.g., from near field to far field).

[0154] A grid spatial indexing system is established, starting with the grid cell closest to the elevated road (e.g., the 0-50 meter area). Based on pre-stored spatial topology relationships, all acoustic rays passing through that cell are queried. The index relationships are stored in the grid attribute table: each cell records the associated ray number, direction angle, and distance range.

[0155] In a specific example: grid cell GA-1025 (location: 120 meters south of elevated road A) is associated with 3 sound wave propagation rays: ray RA-15 (azimuth 25°), ray RA-42 (155°), and ray RA-87 (295°). The system retrieves these ray records by their numbers.

[0156] Step 4.2: Based on the dynamic noise source intensity sequence, obtain the noise intensity of the sound source corresponding to each sound wave propagation ray at the current position.

[0157] Dynamic noise source intensity sequence: Noise intensity data of each monitoring point stored in time series;

[0158] Sound source location: The location of the elevated road lane corresponding to the starting point of each ray;

[0159] By matching the coordinates of the ray origin to the nearest monitoring point (e.g., within 500 meters), the latest noise value corresponding to the timestamp is extracted from the real-time updated noise source intensity sequence. Linear interpolation is used to handle data missing with a time deviation of less than 2 seconds.

[0160] In a specific case: the monitoring point MB corresponding to the starting point of ray RA-15, the noise intensity was found to be 83.5 dB at the timestamp of 10:05:30 on June 15, 2023. This data comes from the aggregated calculation value from 10:05:28 to 10:05:32.

[0161] Step 4.3: For each sound wave propagation ray, multiply the noise intensity by the distance attenuation factor and obstacle diffraction factor of the corresponding grid cell to obtain the single-ray contribution value.

[0162] Distance attenuation factor: The proportionality coefficient by which sound waves attenuate as the propagation distance increases;

[0163] Obstacle diffraction factor: The energy loss coefficient caused by sound wave diffraction due to obstacles such as buildings.

[0164] The energy value (sound pressure unit) is converted by multiplying the attenuation factor (distance attenuation factor × obstacle diffraction factor) stored in the grid cells by the sound source intensity. The calculation process conforms to the ISO 9613 acoustic standard.

[0165] In a specific example: Grid cell GA-1025 attributes: distance attenuation factor 0.023, obstacle diffraction factor 0.61. For ray RA-15 (sound source intensity 83.5dB): First, convert the decibel value to an energy value (10^8.35), multiply by the attenuation coefficient (0.023×0.61=0.01403), and the resulting value of 1.17 (sound pressure unit) is the single-ray contribution value.

[0166] Step 4.4: Perform logarithmic energy summation on the contribution values ​​of all single rays within the same grid cell to obtain the real-time equivalent sound pressure level.

[0167] Step 4.4.1: Convert the single-ray contribution value from sound pressure unit to decibel value.

[0168] The logarithmic transformation formula is used: decibel value = 10 × log10 (energy value), where the energy value is the output of step 4.3.

[0169] In a specific case: the contribution of RA-15 radiation is 1.17 (sound pressure unit) → 10×log10(1.17)≈0.68 dB.

[0170] Step 4.4.2: Use the energy superposition formula to sum the converted decibel values.

[0171] Convert the decibel values ​​of all rays into energy values;

[0172] Find the total energy.

[0173] Convert the sum back to decibels.

[0174] In a specific example:

[0175] Three rays, decibel values:

[0176] RA-15: 0.68 dB → Energy value 1.17;

[0177] RA-42: 0.52 dB → Energy value 1.12;

[0178] RA-87: 0.73 dB → Energy value 1.18.

[0179] Total energy value = 3.47 → Equivalent sound pressure level = 10 × log10(3.47) ≈ 5.4 dB.

[0180] Step 4.4.3: Normalize the summation result to obtain the equivalent continuous sound pressure level of the grid cell.

[0181] Adding a fixed background noise correction (typically 40 dB): Final SPL = 10 × log10(10^(superimposed value / 10) + 10^(background noise / 10)); In a specific case: 5.4 dB superimposed value + 40 dB background noise → final SPL ≈ 40.0003 dB (simplified to 40.0 dB).

[0182] Step 4.4.4: Store the equivalent continuous sound pressure level of all grid cells at the current timestamp to the noise distribution database.

[0183] Stored in the time-series database InfluxDB, data structure: |timestamp|grid ID|equivalent sound pressure level|sound source type distribution|In a specific case, the stored record is: 2023-06-15T10:05:30Z, GA-1025, 40.0, small car: 65%.

[0184] Step 4.5: Repeat the above steps until all grid cells are covered.

[0185] Step 5: Based on the time window sliding mechanism, perform rolling prediction of the real-time equivalent sound pressure level and generate a heat map of the spatiotemporal distribution of noise; wherein, the time window sliding mechanism updates traffic flow data and recalculates the sound pressure level at a preset period.

[0186] Step 5.1: Set a fixed-length time window and a sliding step size.

[0187] Time window: The time range for analysis (e.g., 10 minutes);

[0188] Slide step: The time interval between window movements.

[0189] In a specific example: the elevated road noise prediction system is configured with the following settings: window = 10 minutes, step = 1 minute.

[0190] Step 5.2: Within the initial time window, calculate the equivalent sound pressure level of the entire area grid cell based on the current real-time traffic flow data.

[0191] Obtain traffic data for the previous 10 minutes, execute step 4 to calculate the sound pressure level of the entire grid, and generate the initial noise matrix.

[0192] In a specific example: Based on the traffic flow from 09:50 to 10:00, the noise distribution matrix DA-0 (120×90 grid) of area A of the elevated road is generated.

[0193] Step 5.3: Input the new time period traffic flow data after the sliding step size into the model, replacing the data of the earliest time period within the time window.

[0194] Remove old data from the earliest time period in non-overlapping areas and add real-time data for the new time period.

[0195] In a specific example, at 10:01:

[0196] Remove data from 09:50 to 09:51 (accounting for 10%).

[0197] Add real-time data from 10:00 to 10:01.

[0198] Step 5.4: Recalculate the equivalent sound pressure level of the entire grid cells to generate the updated noise distribution matrix.

[0199] An incremental calculation mechanism is adopted: only the grid area affected by the new traffic flow (within 500 meters of the sound source) is updated to avoid global recalculation.

[0200] In a specific case: a sudden change in traffic flow at monitoring point MC (a large convoy of vehicles passing through) only requires recalculating 1,200 grid cells within its coverage area to generate an update matrix DA-1.

[0201] Step 5.5: Overlay the continuously updated noise distribution matrix along the time dimension and convert it into a spatiotemporal distribution heatmap using a color mapping algorithm.

[0202] Color mapping algorithm: Maps sound pressure level to a color spectrum (blue < 55dB → green < 65dB → yellow < 75dB → red ≥ 75dB);

[0203] Spatiotemporal distribution heatmap: visualization of noise intensity in time and space;

[0204] The continuous time slice matrices are combined into a three-dimensional data cube (X, Y, time), which is then converted into a video stream using the OpenCV library, with time information labeled for each frame.

[0205] In a specific example: In the heat map sequence generated between 10:00 and 10:10, grid GB-3075 showed red (peak 78 dB) between 10:05 and 10:07, indicating that the noise level was seriously exceeded during this period.

[0206] Step 6: Identify areas with excessive noise based on the spatiotemporal distribution heatmap of noise and output a dynamic noise assessment report.

[0207] Step 6.1: Compare the sound pressure level of each grid cell in the noise spatiotemporal distribution heatmap with the preset threshold.

[0208] Step 6.2: Mark the grid cells whose sound pressure level continuously exceeds the threshold as noise over-limit areas.

[0209] Step 6.3: Extract the spatiotemporal range, maximum sound pressure level, and duration of the area exceeding the standard.

[0210] Step 6.4: Associate the traffic flow characteristic data corresponding to the areas exceeding the standard, and generate a dynamic noise assessment report that includes noise cause analysis.

[0211] In a specific example: School area B was required to have a sound pressure level of ≤55 dB at 10:05 AM, but the grid GB-3075 recorded a value of 78 dB → exceeding the limit. Maximum sound pressure level: 62 dB (grid GC-5105), duration: 8 minutes, affected area: 150×80 meter rectangular area.

[0212] Dynamic Noise Assessment Report [No.: NA-20250615-001]

[0213] 1. Summary of the event exceeding the standard:

[0214] Location: South side of residential area C;

[0215] Exceeding the limit: 10:00-10:08 (lasting 8 minutes);

[0216] Peak noise: 62 dB (7 dB above the standard).

[0217] 2. Causal Analysis:

[0218] Related traffic flow: Medium-sized vehicles accounted for 42% (15% higher than the normal value);

[0219] Speed ​​distribution: Average 78 km / h (18% over the speed limit);

[0220] Road conditions: Slippery road surfaces increase tire noise.

[0221] 3. Suggestions for improvement:

[0222] Install additional sound barriers (recommended height 4 meters);

[0223] Strengthen speed monitoring on this section of the road.

[0224] The above are embodiments of the method proposed in this application. Based on the same inventive concept, embodiments of this application also provide a dynamic noise analysis device for urban elevated roads, the structure of which is as follows: Figure 2 As shown.

[0225] Figure 2 This is a schematic diagram of the internal structure of a dynamic noise analysis device for urban elevated roads, provided as an embodiment of this application. Figure 2 As shown, the device includes:

[0226] At least one processor 201;

[0227] And a memory 202 that is communicatively connected to at least one processor;

[0228] The memory 202 stores instructions executable by at least one processor, which are executed by at least one processor 201 to enable at least one processor 201 to:

[0229] The process involves acquiring a 3D geographic information model and real-time traffic flow data for the target elevated road. The 3D geographic information model includes the elevated road structure, surrounding building distribution, and ground elevation data. The real-time traffic flow data includes vehicle type, speed, traffic volume, and timestamp sequences. The 3D geographic information model is processed based on a sound wave propagation attenuation model to generate a noise propagation path grid. This grid is divided into multiple grid cells along multiple propagation directions, starting from the sound source on the elevated road. Each grid cell includes a distance attenuation factor and an obstacle diffraction factor. Finally, the real-time traffic flow data is fused with the noise source intensity calculation model to generate a dynamic noise source intensity sequence. The noise source intensity calculation model matches a baseline noise value based on vehicle type and dynamically corrects the noise intensity based on vehicle speed. Combining the dynamic noise source intensity sequence with the noise propagation path grid, the model calculates the real-time equivalent sound pressure level of each grid cell using a noise superposition algorithm. The noise superposition algorithm performs energy superposition on the contribution values ​​of multiple sound sources within the same grid cell. Based on a time window sliding mechanism, the model performs rolling prediction of the real-time equivalent sound pressure level and generates a noise spatiotemporal distribution heatmap. The time window sliding mechanism updates traffic flow data and recalculates the sound pressure level at a preset period. Based on the noise spatiotemporal distribution heatmap, the model identifies areas where noise exceeds the standard and outputs a dynamic noise assessment report.

[0230] Some embodiments of this application provide corresponding to Figure 1 A non-volatile computer storage medium for dynamic noise analysis of urban elevated roads, storing computer-executable instructions, wherein the computer-executable instructions are configured as follows:

[0231] The process involves acquiring a 3D geographic information model and real-time traffic flow data for the target elevated road. The 3D geographic information model includes the elevated road structure, surrounding building distribution, and ground elevation data. The real-time traffic flow data includes vehicle type, speed, traffic volume, and timestamp sequences. The 3D geographic information model is processed based on a sound wave propagation attenuation model to generate a noise propagation path grid. This grid is divided into multiple grid cells along multiple propagation directions, starting from the sound source on the elevated road. Each grid cell includes a distance attenuation factor and an obstacle diffraction factor. Finally, the real-time traffic flow data is fused with the noise source intensity calculation model to generate a dynamic noise source intensity sequence. The noise source intensity calculation model matches a baseline noise value based on vehicle type and dynamically corrects the noise intensity based on vehicle speed. Combining the dynamic noise source intensity sequence with the noise propagation path grid, the model calculates the real-time equivalent sound pressure level of each grid cell using a noise superposition algorithm. The noise superposition algorithm performs energy superposition on the contribution values ​​of multiple sound sources within the same grid cell. Based on a time window sliding mechanism, the model performs rolling prediction of the real-time equivalent sound pressure level and generates a noise spatiotemporal distribution heatmap. The time window sliding mechanism updates traffic flow data and recalculates the sound pressure level at a preset period. Based on the noise spatiotemporal distribution heatmap, the model identifies areas where noise exceeds the standard and outputs a dynamic noise assessment report.

[0232] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for IoT devices and media are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0233] The systems, media, and methods provided in this application are one-to-one correspondences. Therefore, the systems and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be repeated here.

[0234] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0235] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0236] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0237] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0238] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0239] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0240] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0241] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0242] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A dynamic noise analysis method for urban elevated roads, characterized in that, The method includes: Acquire a three-dimensional geographic information model and real-time traffic flow data of the target elevated road; wherein, the three-dimensional geographic information model includes the elevated road structure, the distribution of surrounding buildings and the ground elevation data, and the real-time traffic flow data includes vehicle type, vehicle speed, traffic volume and timestamp sequence; The three-dimensional geographic information model is processed based on the sound wave propagation attenuation model to generate a noise propagation path grid; wherein, the noise propagation path grid is divided into grid cells in multiple propagation directions, with the elevated road sound source as the starting point, and each grid cell contains a distance attenuation factor and an obstacle diffraction factor. By integrating the real-time traffic flow data with the noise source intensity calculation model, a dynamic noise source intensity sequence is generated; wherein, the noise source intensity calculation model matches a reference noise value according to the vehicle type and dynamically corrects the noise intensity based on the vehicle speed; Combining the dynamic noise source strength sequence with the noise propagation path grid, the real-time equivalent sound pressure level of each grid cell is calculated using a noise superposition algorithm; wherein, the noise superposition algorithm performs energy superposition on the contribution values ​​of multiple sound sources within the same grid cell; The real-time equivalent sound pressure level is predicted using a time window sliding mechanism to generate a heat map of the spatiotemporal distribution of noise; wherein the time window sliding mechanism updates traffic flow data and recalculates the sound pressure level at a preset period. The noise exceedance areas are identified based on the spatiotemporal distribution heatmap, and a dynamic noise assessment report is generated.

2. The dynamic noise analysis method for urban elevated roads according to claim 1, characterized in that, Obtain a 3D geographic information model and real-time traffic flow data of the target elevated road, specifically including: Point cloud data of the elevated road and surrounding area are collected by a lidar scanning device, and the point cloud data is denoised and registered to generate a three-dimensional point cloud model. Extract the elevated road surface, bridge piers, outlines of adjacent buildings, and ground elevation data from the 3D point cloud model, and construct a 3D geographic information model using a triangulation algorithm; Doppler radar sensors are deployed at monitoring points on elevated roads to collect real-time data on vehicle speed, vehicle length, and timestamps of passing through the monitoring points. Vehicle types are classified based on vehicle length, and traffic flow data is collected within a preset time interval. By integrating vehicle type, speed, traffic volume, and timestamp sequences, structured real-time traffic flow data is generated.

3. The dynamic noise analysis method for urban elevated roads according to claim 1, characterized in that, The three-dimensional geographic information model is processed based on the sound wave propagation attenuation model to generate a noise propagation path grid, specifically including: Based on the center line of the elevated road lane, multiple sound wave propagation rays are generated outward at preset angle intervals. Grid cells are generated along each sound wave propagation ray, with the straight-line distance from the sound source marked for each grid cell; Based on the building outlines and surface elevation data in the 3D geographic information model, the obstacle occlusion rate of sound waves in the grid cell is calculated. Based on the physical model of acoustic wave diffraction, the obstacle diffraction factor is calculated in combination with the occlusion rate. Based on the spherical diffusion law of sound waves, the distance attenuation factor is calculated based on the straight-line distance; By associating the distance attenuation factor of all grid cells with the obstacle diffraction factor, a noise propagation path grid is generated.

4. The dynamic noise analysis method for urban elevated roads according to claim 1, characterized in that, By integrating the real-time traffic flow data with the noise source intensity calculation model, a dynamic noise source intensity sequence is generated, specifically including: Establish a mapping table between vehicle type and reference noise value, where small cars, medium cars, and large cars correspond to different reference noise values; Based on vehicle speed in real-time traffic flow data, a linear interpolation algorithm is used to dynamically correct the baseline noise value and generate the instantaneous noise intensity of a single vehicle. All vehicles passing through the same monitoring point within a preset time window are statistically analyzed, and the instantaneous noise intensity of each vehicle is superimposed to generate the aggregated noise intensity of the monitoring point. By associating the aggregated noise intensity of all monitoring points with their timestamps, a dynamic noise source intensity sequence is generated.

5. The dynamic noise analysis method for urban elevated roads according to claim 1, characterized in that, Combining the dynamic noise source intensity sequence with the noise propagation path grid, the real-time equivalent sound pressure level of each grid cell is calculated using a noise superposition algorithm, specifically including: Traverse each grid cell in the noise propagation path grid and retrieve all sound wave propagation rays pointing to that grid cell; Based on the dynamic noise source intensity sequence, the noise intensity of the sound source corresponding to each sound wave propagation ray at the current position is obtained; For each sound wave propagation ray, the noise intensity is multiplied by the distance attenuation factor and obstacle diffraction factor of the corresponding grid cell to obtain the single-ray contribution value; Logarithmic energy summation is performed on the contribution values ​​of all single rays within the same grid cell to obtain the real-time equivalent sound pressure level. Repeat the above steps until all grid cells are covered.

6. The dynamic noise analysis method for urban elevated roads according to claim 5, characterized in that, The real-time equivalent sound pressure level is obtained by logarithmically summing the contribution values ​​of all single rays within the same grid cell, specifically including: Convert the single-ray contribution value from sound pressure units to decibels; The converted decibel values ​​are summed using the energy superposition formula. The summation result is normalized to obtain the equivalent continuous sound pressure level of the grid cell; Store the equivalent continuous sound pressure level of all grid cells at the current timestamp into the noise distribution database.

7. The dynamic noise analysis method for urban elevated roads according to claim 1, characterized in that, Based on a time window sliding mechanism, a rolling prediction is performed on the real-time equivalent sound pressure level to generate a noise spatiotemporal distribution heatmap, specifically including: Set a fixed-length time window and sliding step size; Within the initial time window, the equivalent sound pressure level of the entire area grid unit is calculated based on the current real-time traffic flow data; Input the new time period traffic flow data after the sliding step size into the model, replacing the data of the earliest time period within the time window; The equivalent sound pressure level of all grid cells in the entire region is recalculated to generate an updated noise distribution matrix; The continuously updated noise distribution matrix is ​​superimposed along the time dimension and converted into a spatiotemporal distribution heatmap using a color mapping algorithm.

8. The dynamic noise analysis method for urban elevated roads according to claim 1, characterized in that, Based on the spatial and temporal distribution heatmap of noise, areas exceeding noise standards are identified, and a dynamic noise assessment report is generated, including: Compare the sound pressure level of each grid cell in the noise spatiotemporal distribution heatmap with the preset threshold. Grid cells whose sound pressure levels consistently exceed the threshold are marked as noise-exceeding regions; Extract the spatiotemporal range, maximum sound pressure level, and duration of the area exceeding the standard; By associating traffic flow characteristic data with areas exceeding the standard, a dynamic noise assessment report containing noise cause analysis is generated.

9. A dynamic noise analysis device for urban elevated roads, characterized in that, The device includes: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to: Acquire a three-dimensional geographic information model and real-time traffic flow data of the target elevated road; wherein, the three-dimensional geographic information model includes the elevated road structure, the distribution of surrounding buildings and the ground elevation data, and the real-time traffic flow data includes vehicle type, vehicle speed, traffic volume and timestamp sequence; The three-dimensional geographic information model is processed based on the sound wave propagation attenuation model to generate a noise propagation path grid; wherein, the noise propagation path grid is divided into grid cells in multiple propagation directions, with the elevated road sound source as the starting point, and each grid cell contains a distance attenuation factor and an obstacle diffraction factor. By integrating the real-time traffic flow data with the noise source intensity calculation model, a dynamic noise source intensity sequence is generated; wherein, the noise source intensity calculation model matches a reference noise value according to the vehicle type and dynamically corrects the noise intensity based on the vehicle speed; Combining the dynamic noise source strength sequence with the noise propagation path grid, the real-time equivalent sound pressure level of each grid cell is calculated using a noise superposition algorithm; wherein, the noise superposition algorithm performs energy superposition on the contribution values ​​of multiple sound sources within the same grid cell; The real-time equivalent sound pressure level is predicted using a time window sliding mechanism to generate a heat map of the spatiotemporal distribution of noise; wherein the time window sliding mechanism updates traffic flow data and recalculates the sound pressure level at a preset period. The noise exceedance areas are identified based on the spatiotemporal distribution heatmap, and a dynamic noise assessment report is generated.

10. A non-volatile computer storage medium for dynamic noise analysis of urban elevated roads, storing computer-executable instructions, characterized in that, The computer-executable instructions are set as follows: Acquire a three-dimensional geographic information model and real-time traffic flow data of the target elevated road; wherein, the three-dimensional geographic information model includes the elevated road structure, the distribution of surrounding buildings and the ground elevation data, and the real-time traffic flow data includes vehicle type, vehicle speed, traffic volume and timestamp sequence; The three-dimensional geographic information model is processed based on the sound wave propagation attenuation model to generate a noise propagation path grid; wherein, the noise propagation path grid is divided into grid cells in multiple propagation directions, with the elevated road sound source as the starting point, and each grid cell contains a distance attenuation factor and an obstacle diffraction factor. By integrating the real-time traffic flow data with the noise source intensity calculation model, a dynamic noise source intensity sequence is generated; wherein, the noise source intensity calculation model matches a reference noise value according to the vehicle type and dynamically corrects the noise intensity based on the vehicle speed; Combining the dynamic noise source strength sequence with the noise propagation path grid, the real-time equivalent sound pressure level of each grid cell is calculated using a noise superposition algorithm; wherein, the noise superposition algorithm performs energy superposition on the contribution values ​​of multiple sound sources within the same grid cell; The real-time equivalent sound pressure level is predicted using a time window sliding mechanism to generate a heat map of the spatiotemporal distribution of noise; wherein the time window sliding mechanism updates traffic flow data and recalculates the sound pressure level at a preset period. The noise exceedance areas are identified based on the spatiotemporal distribution heatmap, and a dynamic noise assessment report is generated.

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