A noise map fast calculation method and device based on spatial autocorrelation

Abstract

This invention discloses a method for rapid calculation of noise maps based on spatial autocorrelation, comprising: determining an initial road traffic noise map based on road network data and building vector data; traversing road segments in the initial road traffic noise map and determining the calculation boundary of the initial noise sub-map based on noise propagation threshold and equivalent sound level; selecting receiver points from the initial road traffic noise map according to the calculation boundary to form a first noise sub-map; calculating a preset attenuation term for the receiver points in the first noise sub-map and then cropping the first noise sub-map based on the noise propagation threshold to obtain a second noise sub-map; dividing the receiver points in the second noise sub-map into a prediction set and an interpolation set, performing attenuation and interpolation calculations based on the prediction set and the interpolation set, fusing and reconstructing the two calculation results to obtain a complete noise sub-map; and fusing the complete noise sub-map of each road segment with the initial road traffic noise map to generate a target road traffic noise map.

Description

Technical Field

[0001] This invention relates to the field of noise map algorithm technology, and in particular to a method, apparatus and device for fast calculation of noise maps based on spatial autocorrelation. Background Technology

[0002] In the field of traffic noise, noise maps are crucial tools for assessing traffic noise distribution, and their importance is self-evident. Through deep integration with GIS (Geographic Information System), researchers can intuitively grasp the noise distribution within a specific area and the impact range of traffic noise sources. Furthermore, with the help of relevant interfaces, noise maps can also be used to evaluate the actual effectiveness of measures to block noise propagation and mitigate noise hazards. However, in urban scenarios, the generation of noise maps faces significant computational challenges due to the dense distribution of buildings, the uniformity of their spatial distribution, and the topological complexity of their forms. Specifically, the high-density distribution of buildings means that some noise sub-maps require handling a large number of complex polygon intersection calculations, causing the computation time to increase exponentially, thus severely impacting the overall computational efficiency of the road traffic noise map for the target area. Traditional noise map calculation methods often struggle to complete computational tasks within a reasonable timeframe in urban scenarios, limiting their widespread adoption and use in practical applications. Summary of the Invention

[0003] In view of this, the purpose of this invention is to propose a method, apparatus and device for fast calculation of noise maps based on spatial autocorrelation, which aims to solve the problems of how to improve the calculation efficiency of road traffic noise maps in urban scenarios, so as to reduce the calculation time of the algorithm in urban scenarios and its dependence on computing resources, and ensure the accuracy of the calculation results.

[0004] To achieve the above objectives, this invention provides a method for fast calculation of noise maps based on spatial autocorrelation, the method comprising:

[0005] The initial road traffic noise map and receiver set for the target area are determined based on the acquired road network data and building vector data. Traverse each road segment in the initial road traffic noise map and determine the calculation boundary of the initial noise sub-map based on the preset noise propagation threshold and equivalent sound level. Based on the calculation boundary, receiving points are selected from the initial road traffic noise map to form a first noise sub-map. After calculating the receiving points in the first noise sub-map using a preset attenuation term, the first noise sub-map is cropped in conjunction with the noise propagation threshold to obtain a second noise sub-map. The receiver points in the second noise sub-map are divided into a prediction set and an interpolation set. Building occlusion attenuation is calculated based on the receiver points in the prediction set to obtain a noise attenuation result. The noise attenuation result is interpolated based on the receiver points in the interpolation set to obtain an interpolation result. The noise attenuation result and the interpolation result are fused and reconstructed to obtain a complete noise sub-map. Based on spatial mapping relationships, the complete noise sub-map corresponding to each road segment is fused with the initial road traffic noise map to generate a target road traffic noise map.

[0006] Preferably, determining the initial road traffic noise map and receiver point set of the target area based on the acquired road network data and building vector data includes: Based on the coordinates of the road network data and the building vector data, the maximum and minimum horizontal and vertical coordinates are obtained, and the boundary range of the initial road traffic noise map is determined based on the maximum and minimum horizontal and vertical coordinates. The number of receiving points participating in the calculation in the initial road traffic noise map is determined within the boundary range according to the preset grid size, and the receiving point set is obtained.

[0007] Preferably, determining the calculation boundary of the initial noise sub-map based on a preset noise propagation threshold and equivalent sound level includes: The difference is calculated based on the noise propagation threshold and the equivalent sound level. The noise propagation distance of the corresponding road segment is determined by traversing the target calculation file based on the difference. The target calculation file is determined in the calculation file set based on the preset temperature and humidity. The calculation file set is constructed based on the atmospheric absorption coefficient of different frequencies and the geometric attenuation formula. The calculation boundary of the initial noise sub-map is determined based on the noise propagation distance and the center coordinates of the corresponding road segment.

[0008] Preferably, the step of calculating a preset attenuation term for the receiver points in the first noise sub-map and then cropping the first noise sub-map in conjunction with the noise propagation threshold to obtain a second noise sub-map includes: The geometric attenuation, atmospheric absorption attenuation, and finite length correction of road noise sources are calculated for the receiving points in the first noise sub-map to obtain preliminary attenuation results; The first noise sub-map is cropped based on the preliminary attenuation result and the noise propagation threshold to obtain the second noise sub-map.

[0009] Preferably, the step of cropping the first noise sub-map based on the preliminary attenuation result and the noise propagation threshold to obtain the second noise sub-map includes: The receiving points of the first noise sub-map are searched row by row and column by column toward the center. The coordinates of the first receiving point whose equivalent sound level is greater than the propagation attenuation threshold are determined as the effective boundary point. The first noise sub-map is cropped based on the effective boundary points to obtain the second noise sub-map.

[0010] Preferably, the step of calculating building obstruction attenuation based on the receiver points in the prediction set to obtain noise attenuation results, and then performing interpolation calculations on the noise attenuation results based on the receiver points in the interpolation set to obtain interpolation results, includes: The receivers in the prediction set are traversed, the building density attenuation and the occlusion attenuation of the front buildings are calculated, and the total attenuation value and the attenuation result of each receiver in the prediction set are obtained as the noise attenuation result. The receiving points in the interpolation set are traversed, and spatial interpolation is used to calculate the total attenuation and attenuation result for each receiving point in the interpolation set based on the noise attenuation result.

[0011] Preferably, the step of fusing and reconstructing the noise attenuation result and the interpolation result to obtain a complete noise sub-map includes: The noise attenuation results and the interpolation results are arranged sequentially according to the row and column coordinates of the receiving points in the second noise sub-map to obtain the complete noise sub-map corresponding to each road segment.

[0012] Preferably, the step of fusing the complete noise sub-map corresponding to each road segment with the initial road traffic noise map based on spatial mapping relationships to generate a target road traffic noise map includes: Based on the spatial correspondence between the top left corner coordinates of the complete noise sub-map and the initial road traffic noise map, the receiving points in the complete noise sub-map are mapped to the initial road traffic noise map to obtain the mapping result; If the receiving point in the initial road traffic noise map has multiple mapping results of the receiving points of the complete noise sub-map, then the equivalent sound level higher than the noise propagation threshold is selected and the sound energy is superimposed to obtain the target road traffic noise map.

[0013] To achieve the above objectives, the present invention also provides a device for rapid calculation of noise maps based on spatial autocorrelation, the device comprising: The initialization unit is used to determine the initial road traffic noise map and receiver point set of the target area based on the acquired road network data and building vector data; The sub-map construction unit is used to traverse each road segment in the initial road traffic noise map and determine the calculation boundary of the initial noise sub-map based on the preset noise propagation threshold and equivalent sound level. The sub-map cropping unit is used to filter receiving points from the initial road traffic noise map according to the calculation boundary to form a first noise sub-map. After calculating the receiving points in the first noise sub-map using a preset attenuation term, the first noise sub-map is cropped in combination with the noise propagation threshold to obtain a second noise sub-map. The calculation unit is used to divide the receiver points in the second noise sub-map into a prediction set and an interpolation set, calculate the building occlusion attenuation based on the receiver points in the prediction set to obtain the noise attenuation result, perform interpolation calculation on the noise attenuation result based on the receiver points in the interpolation set to obtain the interpolation result, and fuse and reconstruct the noise attenuation result and the interpolation result to obtain the complete noise sub-map. The mapping unit is used to fuse the complete noise sub-map corresponding to each road segment with the initial road traffic noise map based on the spatial mapping relationship to generate a target road traffic noise map.

[0014] To achieve the above objectives, the present invention also proposes a device for rapid calculation of noise maps based on spatial autocorrelation, comprising a processor, a memory, and a computer program stored in the memory, wherein the computer program is executed by the processor to implement the steps of a method for rapid calculation of noise maps based on spatial autocorrelation as described in the above embodiments.

[0015] To achieve the above objectives, the present invention also proposes a computer-readable storage medium storing a computer program that is executed by a processor to implement the steps of a method for fast calculation of a noise map based on spatial autocorrelation as described in the above embodiments.

[0016] To achieve the above objectives, the present invention also proposes a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of a method for fast calculation of a noise map based on spatial autocorrelation as described in the above embodiments.

[0017] Beneficial effects: The above scheme integrates road network and building vector data to generate an initial road traffic noise map and receiver point set, providing a standardized and structured computational framework for the entire calculation process. Based on this framework, the sub-map boundaries are determined, forming the first noise sub-map. A two-level filtering mechanism precisely focuses on the core computational area, thereby accurately eliminating a large number of edge receiver points whose noise contribution is negligible even without obstruction, ensuring that the most time-consuming computational resources are used only for the most necessary areas. By utilizing the spatial autocorrelation of noise attenuation calculation results caused by building obstruction during noise propagation in urban scenarios, the receiver points in the second noise sub-map are divided into a prediction set and an interpolation set. The prediction set is accurately calculated for building obstruction attenuation, while the interpolation set is estimated based on spatial interpolation methods. This reduces a large number of complex polygon intersection calculations and avoids complex calculations for all points, significantly improving the computational efficiency of noise maps in urban scenarios. At the same time, the fusion and reconstruction ensures the accuracy and completeness of the calculation results, effectively solving the problem of excessive computation time in urban scenarios using traditional methods. This provides an efficient and feasible solution for rapidly generating road traffic noise maps. It is particularly suitable for urban scenarios with dense buildings, reducing the computation time from exponential growth to linear or near-linear growth.

[0018] By clarifying the initial road traffic noise map and the method for determining the receiver point set, and by determining the map boundary through coordinate extreme values, the calculation area is ensured to cover all roads and buildings, providing a complete spatial framework. By standardizing the receiver point distribution through preset grid size, the receiver points are evenly covered in the target area. This not only facilitates management but also provides a regular grid basis for subsequent spatial interpolation calculations, which is beneficial for the application of interpolation algorithms and the guarantee of accuracy.

[0019] By pre-calculating the file to account for the effects of temperature and humidity on sound propagation, the calculation of noise propagation distance is made to better reflect actual environmental conditions. This allows for the accurate determination of the initial noise sub-map range based on the actual conditions of different road sections, thus improving the accuracy of noise prediction. Furthermore, the use of pre-generated calculation files for rapid querying, replacing complex real-time formula calculations, significantly accelerates the determination of the boundaries of the noise sub-map for each road section and improves the overall process efficiency.

[0020] By calculating preset attenuation terms such as geometric attenuation, atmospheric absorption attenuation, and finite length correction for road noise sources at the receiver points in the first noise sub-map, preliminary attenuation results are obtained. Based on these results and the noise propagation threshold, trimming is performed, effectively removing areas less affected by noise, further narrowing the calculation scope, reducing subsequent computational workload, and ensuring that the noise data in the retained areas is meaningful and representative, thus improving computational efficiency and the reliability of the results. By searching row by row and column by column to determine effective boundary points, the second noise sub-map is ensured to contain only receiver points with noise levels exceeding the threshold, accurately removing points with minimal contributions, avoiding a large amount of unnecessary fine-grained calculations, and further improving computational efficiency.

[0021] By performing precise building occlusion attenuation calculations on the prediction set and employing spatial interpolation methods on the interpolation set, the effect of "precise local calculation and rapid global estimation" is achieved, striking an optimal balance between computational efficiency and accuracy. This ensures the accuracy of the prediction set calculation while also considering the computational efficiency of the interpolation set through the interpolation method. It fully utilizes spatial autocorrelation, reducing computational load while maximizing the accuracy and integrity of the complete noise sub-map, further improving the performance and effectiveness of the entire calculation method. Furthermore, by merging the prediction set and interpolation set results according to the receiver point coordinates, the spatial structural integrity of the complete noise sub-map is ensured, facilitating accurate mapping and fusion with the overall map in the future.

[0022] Mapping based on the spatial correspondence of the top-left corner coordinates ensures that the coordinate system of the sub-map and the overall map is consistent, avoiding mapping errors. It can effectively integrate the complete noise sub-map information corresponding to each road segment, ensuring that the final generated target road traffic noise map comprehensively and accurately reflects the noise distribution in the target area, thus ensuring the spatial accuracy of the final noise map. Energy superposition is only performed on the equivalent sound level exceeding the threshold, which not only conforms to acoustic principles (ignoring weak contributions) but also reduces the amount of computation during data fusion, making the final result both accurate and efficient. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating a method for rapid calculation of noise maps based on spatial autocorrelation, provided in an embodiment of the present invention.

[0025] Figure 2This is a schematic diagram of the overall process for rapid calculation of noise maps according to an embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram illustrating the construction of a second noise sub-map according to an embodiment of the present invention.

[0027] Figure 4 This is a schematic diagram of a device for rapid calculation of noise maps based on spatial autocorrelation, provided in an embodiment of the present invention.

[0028] The realization of the invention's objective, its functional characteristics, and advantages will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to represent selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] The present invention will be described in detail below with reference to the embodiments.

[0031] Reference Figure 1 The diagram shown is a flowchart illustrating a method for rapid calculation of noise maps based on spatial autocorrelation, according to an embodiment of the present invention.

[0032] In this embodiment, the method includes: S11. Determine the initial road traffic noise map and receiver point set for the target area based on the acquired road network data and building vector data.

[0033] Furthermore, in step S11, determining the initial road traffic noise map and receiver point set of the target area based on the acquired road network data and building vector data includes: S11-1, Based on the coordinates of the road network data and the building vector data, obtain the maximum and minimum horizontal and vertical coordinates, and determine the boundary range of the initial road traffic noise map based on the maximum and minimum horizontal and vertical coordinates. S11-2, Determine the number of receiving points participating in the calculation in the initial road traffic noise map within the boundary range according to the preset grid size, and obtain the receiving point set.

[0034] Reference Figure 2 As shown. In this embodiment, road segment physical information data is acquired based on a publicly available dataset. Traffic flow parameters are obtained through traffic checkpoint video data or on-site surveys, i.e., the coordinates of road network data and building vector data within the target area are obtained to determine the boundary range of the initial road traffic noise map (further, the maximum and minimum coordinates of the road segment and building polygons are obtained to determine the boundary of the initial road traffic noise map). The road network data includes road name, road grade, lane width, number of lanes, lane centerline coordinates, average traffic flow speed, traffic volume, and the proportion of large, medium, and small vehicle types. The building vector data refers to the white film data of buildings; each building is a building polygon. Corresponding data for the target area is obtained through a publicly available dataset, including fields such as building polygon corner coordinates, floor level, and floor height. Assume the road network data is P... road ={r1,r2,...,r n}, where each road segment r={(x1,y1),(x2,y2)}; building vector data is P building ={b1,b2,...,b m}, where each building polygon b={(x1,y1),(x2,y2)...,(x k ,y k The formulas for obtaining the maximum and minimum x-coordinates and y-coordinates of two data points are shown below: x max ,x min ,y min ,y max ={r1,r2,...,r n ,b1,b2....,b m}

[0035] The number of receivers, R, in the initial road traffic noise map is determined based on the preset grid size gap. The preset grid size, such as 1×1, 4×4, or 8×8, represents the distance between receivers. Each receiver represents the average equivalent sound level within the grid size, and also determines the number of receivers in the initial road traffic noise map that need to participate in the equivalent sound level calculation. The calculation formula is as follows: R = (x max - x min ) / gap x (y min - y max gap y .

[0036] S12, traverse each road segment in the initial road traffic noise map, and determine the calculation boundary of the initial noise sub-map based on the preset noise propagation threshold and equivalent sound level.

[0037] Furthermore, in step S12, determining the calculation boundary of the initial noise sub-map based on a preset noise propagation threshold and equivalent sound level includes: S12-1, Calculate the difference based on the noise propagation threshold and the equivalent sound level, and determine the noise propagation distance of the corresponding road segment by traversing the target calculation file based on the difference; wherein, the target calculation file is determined in the calculation file set based on the preset temperature and humidity, and the calculation file set is constructed based on the atmospheric absorption coefficient of different frequencies and the geometric attenuation formula; S12-2, Determine the calculation boundary of the initial noise sub-map based on the noise propagation distance and the center coordinates of the corresponding road segment.

[0038] In this embodiment, based on the atmospheric absorption attenuation coefficients and geometric attenuation formulas for different frequencies (GB / T 17247.1-2000, "Acoustics Outdoor Sound Propagation Attenuation Part 1: Calculation of Atmospheric Sound Absorption"), and combined with the center octave frequency of road traffic noise specified in the "Technical Guidelines for Environmental Impact Assessment of Highway Construction Projects" (HJ 1358-2024), a calculation file is constructed to calculate the distance corresponding to the increasing unit noise attenuation under each temperature and humidity. By traversing the total atmospheric absorption attenuation and geometric attenuation at different distances, the total attenuation and the corresponding distance where the current total attenuation is just greater than or equal to 1 dB(A) between the previous total attenuation are recorded. Figure 3 As shown. Then, the target calculation file corresponding to the above calculation file is determined according to the preset temperature and humidity. The difference is calculated according to the preset propagation attenuation threshold (30dB(A)) and the equivalent sound level of each road segment. The noise propagation distance of the road segment is determined by traversing the target calculation file according to the difference. The calculation boundary of the initial noise sub-map is determined by combining the center coordinates of the road segment.

[0039] S13, according to the calculation boundary, select receiving points from the initial road traffic noise map to form a first noise sub-map. After calculating the receiving points in the first noise sub-map using a preset attenuation term, and then cropping the first noise sub-map in conjunction with the noise propagation threshold, a second noise sub-map is obtained.

[0040] Further, in step S13, after calculating the preset attenuation term for the receiving points in the first noise sub-map, the first noise sub-map is cropped in conjunction with the noise propagation threshold to obtain the second noise sub-map, including: S13-1, Perform geometric attenuation, atmospheric absorption attenuation, and finite length correction of road noise sources on the receiving points in the first noise sub-map to obtain preliminary attenuation results; S13-2, based on the preliminary attenuation result and the noise propagation threshold, the first noise sub-map is cropped to obtain the second noise sub-map.

[0041] Furthermore, step S13-2 includes: S13-2-1, Search the receiving points of the first noise sub-map row by row and column by column toward the center, and determine the coordinates of the first receiving point whose equivalent sound level is greater than the propagation attenuation threshold as an effective boundary point during the search process; S13-2-2, the first noise sub-map is cropped according to the effective boundary points to obtain the second noise sub-map.

[0042] In this embodiment, by traversing all road segments in the initial road traffic noise map, an initial noise sub-map is constructed for each road segment. This sub-map considers geometric attenuation, atmospheric absorption attenuation, finite-length correction for road segment noise sources, equivalent sound level of the road segment, and a preset noise propagation threshold (30 dB(A)). The specific calculation formulas and parameters are obtained according to the standard "Acoustics Outdoor Sound Propagation Attenuation Part 1: Calculation of Atmospheric Sound Absorption" (GB / T 17247.1-2000). Specifically: Based on GB / T 17247.1-2000, the distance to reach the preset noise propagation threshold under different equivalent sound levels, considering only geometric attenuation and atmospheric absorption attenuation, is constructed. The calculation boundary of the initial noise sub-map is determined by combining the coordinates of the center point of the road segment. The receiving points in the initial road traffic noise map are filtered according to the calculation boundary, and the receiving points within the calculation boundary are selected to obtain the first noise sub-map corresponding to a road segment. The receiving points of the first noise sub-map are traversed, and the geometric attenuation value, atmospheric absorption attenuation value, and finite length correction value of the road segment noise source between each road segment and the receiving point are calculated to form the geometric attenuation matrix, atmospheric absorption attenuation matrix, road segment noise finite length correction matrix, and noise sub-map (considering the preliminary attenuation results of these three attenuation terms). Based on the preliminary attenuation results and the preset noise propagation threshold (30 dB(A)), the first noise sub-map is cropped to determine the size of the second noise sub-map for the final building obstruction attenuation calculation (determining the number of receiving points participating in the calculation). The process of cropping the first noise sub-map includes: searching for the receiving points in the first noise sub-map of each road segment calculated above from the first row, the last row, and the first or last row towards the middle to obtain the coordinates of the receiving points where the first equivalent sound level is greater than the propagation attenuation threshold (30dB(A)). The first noise sub-map is cropped using these boundary coordinates to further determine the boundary of the second noise sub-map for building occlusion attenuation calculation.

[0043] S14, the receiving points in the second noise sub-map are divided into a prediction set and an interpolation set. The building occlusion attenuation is calculated based on the receiving points in the prediction set to obtain the noise attenuation result. The noise attenuation result is interpolated based on the receiving points in the interpolation set to obtain the interpolation result. The noise attenuation result and the interpolation result are fused and reconstructed to obtain the complete noise sub-map.

[0044] Further, in step S14, the calculation of building occlusion attenuation based on the receiver points in the prediction set to obtain noise attenuation results, the interpolation calculation of the noise attenuation results based on the receiver points in the interpolation set to obtain interpolation results, and the fusion and reconstruction of the noise attenuation results and the interpolation results to obtain a complete noise sub-map, including: S14-1, Traverse the receiving points in the prediction set, calculate the building density attenuation and the occlusion attenuation of the front buildings, and combine the preliminary attenuation results to obtain the total attenuation value of each receiving point in the prediction set and the attenuation result as the noise attenuation result. S14-2, Traverse the receiving points in the interpolation set, and calculate using the spatial interpolation method based on the noise attenuation result to obtain the total attenuation and attenuation result of each receiving point in the interpolation set as the interpolation result; S14-3, Arrange the noise attenuation results and the interpolation results in sequence according to the row coordinates and column coordinates of the receiving points in the second noise sub-map to obtain the complete noise sub-map corresponding to each road segment.

[0045] In this embodiment, the receiver points in the second noise sub-map are divided into a prediction set and an interpolation set. The prediction set performs accurate calculations of building occlusion attenuation according to standards (including calculations of occlusion attenuation of front-row buildings and calculations of occlusion attenuation due to building density). The interpolation set performs interpolation operations on the attenuation results based on the principles of function smoothness and continuity (this process can skip the complex process of accurately calculating building occlusion attenuation for a large portion of the receiver points, which utilizes the spatial correlation assumption—the closer objects are, the more similar their feature attributes are). By fusing and reconstructing the calculation results of the two datasets, a complete noise sub-map of a road segment is formed.

[0046] Specifically, by traversing the receiver points in the prediction set of the second noise sub-map corresponding to each road segment, the building density attenuation and occlusion attenuation during the process from each road segment to the receiver point are calculated according to the following formula. Combined with the preliminary attenuation results, the total attenuation value and attenuation result of each receiver point in the prediction set are obtained as the noise attenuation result.

[0047] A firstRow =10log 10 (1-p); A density =-0.1Bd b ; B=S buildings / S ground ; In the formula, A firstRow A represents the cumulative length of the projection of the foreground buildings onto the road segment within the propagation area formed by the road segment and the receiving point; density The ratio of the total building area within the propagation area formed by the road segment and the receiving point to the propagation area itself; p represents the percentage of the projected length of the preceding buildings onto the road segment, generally less than 90%; B represents the building density along the propagation path; d b Indicates the propagation distance, in meters (m); S buildings This represents the area of ​​buildings along the propagation path, in meters (m²). 2 S ground The area of ​​the propagation path, in meters (m²). 2 .

[0048] The receivers in the interpolation set of the second noise sub-map corresponding to each road segment are traversed. Based on the calculation results (noise attenuation results) of the corresponding prediction set, a spatial interpolation method (such as the Clough-Tocher piecewise cubic interpolation method) is used to calculate the total attenuation value and the attenuation result, which are then used as the interpolation results. The results of the corresponding prediction set and interpolation set obtained above are arranged in order according to the row and column coordinates of the receivers to obtain the complete noise sub-map for each road segment.

[0049] Furthermore, a uniform sampling algorithm is used to divide the receiver points in the second noise sub-map into a prediction set and an interpolation set. This is achieved by differentiating between odd and even columns to ensure the symmetry and uniformity of the spatial distribution. The division process involves symmetrically setting different starting sampling positions for odd and even columns, and then extracting receiver points from the second noise sub-map at fixed intervals to be assigned to the prediction set. The remaining receiver points are assigned to the interpolation set. This ensures that the prediction set samples are spatially uniformly distributed, laying a solid foundation for subsequent interpolation calculations based on spatial autocorrelation. The algorithm flow is as follows: enter: dataArray / / A two-dimensional data array to be sampled sampleGap / / Sampling interval Output: Sample Data is a list of sample point coordinates ([columns, rows]). Predicted point coordinates list ([columns, rows]) predictionData Algorithm flow: (1) / / Initialization: (2) roadHeight = number of rows in dataArray (3) roadWidth = number of columns in dataArray (4) Create two empty lists, sampleData and predictionData, to store the sampled data and prediction data respectively. (5) For a, from 0 to roadHeight-1 (traversing each row): (6) Initialize temp = 0 / / Used to control the sampling interval (7) For b, from 0 to roadWidth-1 (traversing each column): (8) If b == 0: / / If it is the first position of the current line (9) Add (b, a) to the sampleData list. (10) temp = sampleGap / / Set the sampling interval to (11) Otherwise: (12) If temp > 0: Add (b, a) to the predictionData list / / Add as a prediction data point (13) temp -= 1 / / Reduce the remaining sampling interval (14) Otherwise: (15) Add (b, a) to the sampleData list # Add as a sample data point (15) temp = sampleGap / / Reset the sampling interval to sampleGap (16) Returns: sampleData, predictionData S15, based on the spatial mapping relationship, the complete noise sub-map corresponding to each road segment is fused with the initial road traffic noise map to generate the target road traffic noise map.

[0050] Furthermore, in step S15, the process of fusing the complete noise sub-map corresponding to each road segment with the initial road traffic noise map based on spatial mapping relationships to generate a target road traffic noise map includes: S15-1, Based on the spatial correspondence between the upper left corner coordinates of the complete noise sub-map and the initial road traffic noise map, the receiving points in the complete noise sub-map are mapped to the initial road traffic noise map to obtain the mapping result; S15-2, if the receiving point in the initial road traffic noise map has multiple mapping results of the receiving points of the complete noise sub-map, then the equivalent sound level higher than the noise propagation threshold is selected and the sound energy is superimposed to obtain the target road traffic noise map.

[0051] In this embodiment, the complete noise sub-maps are fused with the initial road traffic noise map using coordinate mapping to generate the final target road traffic noise map. Specifically, based on the top-left corner coordinates of the complete noise sub-maps and the initial road traffic noise map, the receiver points in the complete noise sub-maps are mapped to the initial road traffic noise map. If the initial road traffic noise map contains receiver points mapped from multiple complete noise sub-maps, then equivalent sound levels higher than the noise propagation threshold (30dB(A)) are selected and their sound energy is superimposed to obtain the target road traffic noise map. The calculation formula is as follows: , Among them, Leq L is the equivalent sound level at the current receiving point. eqi The equivalent sound level at the current receiving point is the sound energy superposition required for the i-th complete noise sub-map.

[0052] Reference Figure 4 The diagram shown is a structural schematic of a noise map fast calculation device based on spatial autocorrelation provided in an embodiment of the present invention.

[0053] In this embodiment, the device 20 includes: Initialization unit 21 is used to determine the initial road traffic noise map and receiver point set of the target area based on the acquired road network data and building vector data; The sub-map construction unit 22 is used to traverse each road segment in the initial road traffic noise map and determine the calculation boundary of the initial noise sub-map based on the preset noise propagation threshold and equivalent sound level. The sub-map cropping unit 23 is used to filter receiving points from the initial road traffic noise map according to the calculation boundary to form a first noise sub-map, calculate the receiving points in the first noise sub-map by performing a preset attenuation term calculation, and crop the first noise sub-map in combination with the noise propagation threshold to obtain a second noise sub-map. The calculation unit 24 is used to divide the receiver points in the second noise sub-map into a prediction set and an interpolation set, calculate the building occlusion attenuation based on the receiver points in the prediction set to obtain the noise attenuation result, perform interpolation calculation on the noise attenuation result based on the receiver points in the interpolation set to obtain the interpolation result, and fuse and reconstruct the noise attenuation result and the interpolation result to obtain the complete noise sub-map. The mapping unit 25 is used to fuse the complete noise sub-map corresponding to each road segment with the initial road traffic noise map based on the spatial mapping relationship to generate a target road traffic noise map.

[0054] Each unit module of the device 20 can execute the corresponding steps in the above method embodiment, so the details of each unit module will not be elaborated here. Please refer to the description of the corresponding steps above for details.

[0055] This invention also provides a device for rapid calculation of noise maps based on spatial autocorrelation. This device includes the aforementioned apparatus for rapid calculation of noise maps based on spatial autocorrelation, wherein the apparatus for rapid calculation of noise maps based on spatial autocorrelation can employ… Figure 4 The structure of the embodiment, correspondingly, can be executed Figure 1 The technical solutions of the method embodiments shown are similar in implementation principle and technical effect. For details, please refer to the relevant records in the above embodiments, which will not be repeated here.

[0056] The device includes: a mobile phone, digital camera, or tablet computer, or other device with a camera function; or a device with an image processing function; or a device with an image display function. The device may include components such as a memory, processor, input unit, display unit, and power supply.

[0057] The memory can be used to store software programs and modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store the operating system, application programs required for at least one function (such as image playback function), etc.; the data storage area can store data created according to the use of the device. In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory can also include a memory controller to provide access to the memory for the processor and input units.

[0058] The input unit can be used to receive input numerical, character, or image information, and to generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function control. Specifically, in addition to a camera, the input unit of this embodiment may also include a touch-sensitive surface (e.g., a touch screen) and other input devices.

[0059] The display unit can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces of the device. These graphical user interfaces can be composed of graphics, text, icons, video, and any combination thereof. The display unit may include a display panel, optionally configured as an LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), or other similar display panel. Furthermore, a touch-sensitive surface may cover the display panel. When the touch-sensitive surface detects a touch operation on or near it, it transmits the information to the processor to determine the type of touch event. Subsequently, the processor provides corresponding visual output on the display panel based on the type of touch event.

[0060] This invention also provides a computer-readable storage medium, which may be a computer-readable storage medium included in the memory described in the above embodiments; or it may be a standalone computer-readable storage medium not assembled into a device. The computer-readable storage medium stores at least one instruction, which is loaded and executed by a processor to implement... Figure 1The method for fast calculation of noise maps based on spatial autocorrelation is shown. The computer-readable storage medium can be a read-only memory, a hard disk, or an optical disk, etc.

[0061] This invention also provides a computer program product, including a computer program / instructions, which are loaded and executed by a processor to achieve [the desired result]. Figure 1 This paper presents a method for fast calculation of noise maps based on spatial autocorrelation.

[0062] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, device embodiments, and storage medium embodiments, since they are basically similar to the method embodiments, the descriptions are relatively simple, and relevant parts can be referred to the descriptions in the method embodiments.

[0063] Furthermore, in this document, 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.

[0064] The foregoing description illustrates and describes preferred embodiments of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the inventive concept by means of the foregoing teachings or techniques or knowledge in related fields. Any modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for fast computation of a noise map based on spatial autocorrelation, characterized in that, The method includes: The initial road traffic noise map and receiver set for the target area are determined based on the acquired road network data and building vector data. Traverse each road segment in the initial road traffic noise map and determine the calculation boundary of the initial noise sub-map based on the preset noise propagation threshold and equivalent sound level. Based on the calculation boundary, receiving points are selected from the initial road traffic noise map to form a first noise sub-map. After calculating the receiving points in the first noise sub-map using a preset attenuation term, the first noise sub-map is cropped in conjunction with the noise propagation threshold to obtain a second noise sub-map. The receiver points in the second noise sub-map are divided into a prediction set and an interpolation set. Building occlusion attenuation is calculated based on the receiver points in the prediction set to obtain a noise attenuation result. The noise attenuation result is interpolated based on the receiver points in the interpolation set to obtain an interpolation result. The noise attenuation result and the interpolation result are fused and reconstructed to obtain a complete noise sub-map. Based on spatial mapping relationships, the complete noise sub-map corresponding to each road segment is fused with the initial road traffic noise map to generate a target road traffic noise map.

2. The method according to claim 1, wherein, The process of determining the initial road traffic noise map and receiver point set for the target area based on the acquired road network data and building vector data includes: Based on the coordinates of the road network data and the building vector data, the maximum and minimum horizontal and vertical coordinates are obtained, and the boundary range of the initial road traffic noise map is determined based on the maximum and minimum horizontal and vertical coordinates. The number of receiving points participating in the calculation in the initial road traffic noise map is determined within the boundary range according to the preset grid size, and the receiving point set is obtained.

3. The method of claim 1, wherein, The calculation boundary for determining the initial noise sub-map based on a preset noise propagation threshold and equivalent sound level includes: The difference is calculated based on the noise propagation threshold and the equivalent sound level. The noise propagation distance of the corresponding road segment is determined by traversing the target calculation file based on the difference. The target calculation file is determined in the calculation file set based on the preset temperature and humidity. The calculation file set is constructed based on the atmospheric absorption coefficient of different frequencies and the geometric attenuation formula. The calculation boundary of the initial noise sub-map is determined based on the noise propagation distance and the center coordinates of the corresponding road segment.

4. The method of claim 1, wherein, After calculating a preset attenuation term for the receiver points in the first noise sub-map, the first noise sub-map is cropped based on the noise propagation threshold to obtain a second noise sub-map, including: The geometric attenuation, atmospheric absorption attenuation, and finite length correction of road noise sources are calculated for the receiving points in the first noise sub-map to obtain preliminary attenuation results; The first noise sub-map is cropped based on the preliminary attenuation result and the noise propagation threshold to obtain the second noise sub-map.

5. The method of claim 4, wherein, The step of cropping the first noise sub-map based on the preliminary attenuation result and the noise propagation threshold to obtain the second noise sub-map includes: The receiving points of the first noise sub-map are searched row by row and column by column toward the center. The coordinates of the first receiving point whose equivalent sound level is greater than the propagation attenuation threshold are determined as the effective boundary point. The first noise sub-map is cropped based on the effective boundary points to obtain the second noise sub-map.

6. The method of claim 1, wherein, The step of calculating building obstruction attenuation based on the receiver points in the prediction set to obtain noise attenuation results, and then performing interpolation calculations on the noise attenuation results based on the receiver points in the interpolation set to obtain interpolation results, includes: The receivers in the prediction set are traversed, the building density attenuation and the occlusion attenuation of the front buildings are calculated, and the total attenuation value and the attenuation result of each receiver in the prediction set are obtained as the noise attenuation result. The receiving points in the interpolation set are traversed, and spatial interpolation is used to calculate the total attenuation and attenuation result for each receiving point in the interpolation set based on the noise attenuation result.

7. The method of claim 1, wherein, The step of fusing and reconstructing the noise attenuation result and the interpolation result to obtain a complete noise sub-map includes: The noise attenuation results and the interpolation results are arranged sequentially according to the row and column coordinates of the receiving points in the second noise sub-map to obtain the complete noise sub-map corresponding to each road segment.

8. The method of claim 1, wherein, The process of fusing the complete noise sub-map corresponding to each road segment with the initial road traffic noise map based on spatial mapping relationships to generate a target road traffic noise map includes: Based on the spatial correspondence between the top left corner coordinates of the complete noise sub-map and the initial road traffic noise map, the receiving points in the complete noise sub-map are mapped to the initial road traffic noise map to obtain the mapping result; If the receiving point in the initial road traffic noise map has multiple mapping results of the receiving points of the complete noise sub-map, then the equivalent sound level higher than the noise propagation threshold is selected and the sound energy is superimposed to obtain the target road traffic noise map.

9. A device for fast computation of a noise map based on spatial autocorrelation, characterized in that, The device includes: The initialization unit is used to determine the initial road traffic noise map and receiver point set of the target area based on the acquired road network data and building vector data; The sub-map construction unit is used to traverse each road segment in the initial road traffic noise map and determine the calculation boundary of the initial noise sub-map based on the preset noise propagation threshold and equivalent sound level. The sub-map cropping unit is used to filter receiving points from the initial road traffic noise map according to the calculation boundary to form a first noise sub-map. After calculating the receiving points in the first noise sub-map using a preset attenuation term, the first noise sub-map is cropped in combination with the noise propagation threshold to obtain a second noise sub-map. The calculation unit is used to divide the receiver points in the second noise sub-map into a prediction set and an interpolation set, calculate the building occlusion attenuation based on the receiver points in the prediction set to obtain the noise attenuation result, perform interpolation calculation on the noise attenuation result based on the receiver points in the interpolation set to obtain the interpolation result, and fuse and reconstruct the noise attenuation result and the interpolation result to obtain the complete noise sub-map. The mapping unit is used to fuse the complete noise sub-map corresponding to each road segment with the initial road traffic noise map based on the spatial mapping relationship to generate a target road traffic noise map. 10.A device for fast computation of a noise map based on spatial autocorrelation, comprising: The system includes a processor, a memory, and a computer program stored in the memory, the computer program being executed by the processor to implement the steps of a method for fast calculation of a noise map based on spatial autocorrelation as described in any one of claims 1 to 8.

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