CN-115952182-B - Calculation method for realizing rapid update of large-range dynamic noise map system

CN115952182BCN 115952182 BCN115952182 BCN 115952182BCN-115952182-B

Abstract

The invention relates to a calculation method for realizing rapid updating of a large-range dynamic noise map system, which comprises the following steps of simplifying a traditional road sound level mechanism model structure based on the existing day and night time period and urban road grade by utilizing data information of a system database, returning a sound source (day and night) sound level contribution quantity prediction model of each grade by utilizing a big data technology by adopting a mixed modeling method, constructing a road noise rapid calculation method based on the sound level contribution quantity prediction model, and realizing rapid updating of a dynamic noise map. Compared with the prior art, the method and the device have the advantages that the calculation accuracy is ensured, the calculation and update efficiency of the noise map system is greatly improved, the technical restriction of foreign core calculation software is broken, the localization of the noise map core calculation technology is realized, and the technical support is provided for the large-scale deep development of the noise map.

Inventors

HONG XIAODAN
XIA DAN
ZHU WENYING
ZHANG WEICHEN
CHU YIPING
YING LEDUN
LIU CHANGQING
SUN XIAOMING
WANG XIAONAN

Assignees

上海市环境科学研究院

Dates

Publication Date: 20260505
Application Date: 20221230

Claims (7)

1. The calculation method for realizing the rapid update of the large-range dynamic noise map system is characterized by comprising the following steps: S1, collecting and analyzing traffic data and noise contribution data of a road sound source in a system database, and constructing a noise map road sound source sound level contribution prediction model according to day and night time periods and different urban road grades; s2, regression is carried out on parameters of the sound level contribution prediction model of the road sound source of each level by adopting a multi-element nonlinear regression modeling method; S3, comparing the calculated values of the sound level contribution quantity prediction models of the road sound sources with different levels with the calculated values of the original core software based on a data driving method, and calculating the sound level contribution correction quantity of all the road sound sources of the noise map; s4, introducing the sound level contribution prediction model of the road sound source and the sound level contribution correction amount into a noise map system, and calculating the sound level contribution of each road sound source and road noise of a predicted point in the noise map system in real time; step S1 comprises the following sub-steps: S11, collecting traffic data, noise contribution data and longitude and latitude data of predicted points of all road sound sources in a system database, and constructing a road sound source traffic sample set and a noise contribution sample set: S12, analyzing the distribution characteristics of the traffic data and the noise contribution data, and classifying road sound source samples by combining the day and night time period of a noise map system and urban road class division; S13, constructing sound power level prediction models of road sound sources at all levels according to different urban road levels and day and night time periods by combining the road traffic sound source emission principle and the traffic data distribution characteristics of a noise map system; S14, constructing a noise map road sound source sound level contribution prediction model based on the constructed road sound source sound power level prediction model; In step S13, the sound power level prediction model of each level of road sound source R i is obtained by superimposing the sound power levels of the source of the large traffic flow and the small traffic flow, and the expression is as follows: Wherein, the Represents the index of the sound source of the road, Indicating the grade of the urban road, The time period of the day is indicated, Indicating a period of time at night, Representing an mth level road sound source The diurnal line source sound power level, A line source sound power level representing diurnal cart flow, A line source sound power level representing diurnal cart flow; Representing an mth level road sound source The sound power level of the night-time line source, A line source sound power level representing night large traffic, A line source sound power level representing night car flow; 、 respectively represents the sound power level of a single large vehicle and the sound power level of a single small vehicle of a daytime mth-level road sound source, 、 Respectively represents the line source sound power level of a single large vehicle and the line source sound power level of a single small vehicle of an mth-level road sound source at night, Is the average flow rate of the large vehicle in hours, Is the average flow rate of the trolley in an hour, Average speed of large car and small car The average speed of the trolley is the average speed of the trolley in hours; in step S14, the prediction model of the sound level contribution of the road sound source is a prediction model of the equivalent a sound level of the ith road sound source R i to the j-th predicted point P j in hours, and the expression is: Wherein, the The index of the predicted point is indicated, Representing an mth level road sound source For the predicted point Is a daytime sound level contribution of (1), Representing an mth level road sound source For the predicted point Night sound level contribution of (2); Sound level contribution correction Representing the i-th road sound source To the jth predicted point The sound level contribution correction amount includes a distance attenuation amount Building shielding attenuation Attenuation caused by other factors The expression is: 。
2. The method for quickly updating the large-range dynamic noise map system according to claim 1, wherein the system database is a database of the noise map system and comprises a road traffic flow and speed database, a core software calculation value database of a road sound source contribution amount and a prediction point longitude and latitude database.
3. The method according to claim 1, wherein in step S11, the road sound source noise contribution data represents a core software calculation value of the current road' S M sets of hour noise contributions to its predicted points.
4. A computing method for implementing a rapid update of a large-scale dynamic noise map system according to claim 1, characterized in that step S2 comprises the sub-steps of: S21, analyzing the distribution characteristics of the traffic data of the road sound sources of each level, and selecting the road sound sources R 1 ,…, R q in q levels of roads; s22, searching a predicted point P 1 ,…,P q corresponding to each sound source road R 1 , …, R q , and collecting a corresponding traffic sample set and a noise contribution sample set; S23, regression is carried out on parameters of the q-level road sound source sound level contribution prediction model constructed in the step S1 by adopting a multi-element nonlinear fitting method based on a MATLAB platform.
5. The method for rapidly updating a large-scale dynamic noise map system according to claim 4, wherein in step S23, the parameters of the road sound source sound level contribution prediction model include an mth-level road daytime single cart line sound source power level Single car line sound source power level in m-th road daytime Night single large vehicle line sound source power level of mth level road Night single car line sound source power level for mth level road Correction amount of sound level contribution of q-type road sound source to predicted point P 1 ,…,P q 。
6. A computing method for implementing a rapid update of a large-scale dynamic noise map system according to claim 1, characterized in that step S3 comprises the sub-steps of: s31 at Group sample set random selection Counting the corresponding relation between l predicted points and k road sound sources by using the acquired longitude and latitude data, and establishing one-to-one identification i-j i between the road sound sources and the predicted points of the noise map system; s32, calculating all road sound sources according to the one-to-one identification i-j i and the q-level road sound source sound level contribution quantity prediction model obtained by applying regression Sound level contribution of group traffic samples; s33 comparison Model calculation values of sound level contribution of group samples and calculation values of original core software, and differences between model calculation values of sound level contribution of k road sound sources and software calculation values are calculated Namely, the road sound source R i to the j i predicted point in the map Is added to the sound level contribution correction amount.
7. A computing method for implementing a rapid update of a large-scale dynamic noise map system according to claim 1, characterized in that step S4 comprises the sub-steps of: S41, connecting a q-level road sound source sound level contribution quantity prediction model into a noise map system background, and connecting one-to-one identification i-j i and a sound level contribution correction quantity between the road sound source and a predicted point Importing a system database; S42, automatically running a road sound source sound level contribution prediction model and sound level contribution correction calculation every hour to obtain an hour calculation value of each road sound source sound level contribution of the noise map; s43, based on the real-time calculated value of the sound level contribution of each road sound source, the road noise of all predicted points in the noise map system is calculated and updated in real time by utilizing the road traffic sound level superposition calculation formula.

Description

Calculation method for realizing rapid update of large-range dynamic noise map system Technical Field The invention relates to the technical field of urban environment noise control and management, in particular to a calculation method for realizing rapid updating of a large-range dynamic noise map system. Background With the rapid development of urban areas, the problem of environmental noise pollution is increasingly concerned by the public. The noise map shows the distribution of noise pollution in urban areas in a digital and graphical manner. In recent years, the urban area scale of China is continuously enlarged, the population density is continuously increased, and the traditional noise map can not meet the real-time supervision requirement of urban environment noise. Therefore, shanghai city environmental science institute initiated dynamic noise map management system, dynamically updating noise map is achieved by calculating noise of grid point in time, and sound level correction is achieved by assisting with on-site monitoring and road noise correction system. In a noise map of a small and medium range (below 20 square kilometers), the noise level hour updating and corresponding management functions are better realized. However, the core calculation of the dynamic noise map at the present stage is mostly dependent on the mature acoustic software abroad, and the research on the core calculation technology of the noise map at home is still in the starting stage, and no related achievement of independent property rights exists. The sealing property of foreign core software also restricts the deep development and the wide application of the noise map system. In fact, the current method for quickly updating the dynamic noise map in a large range cannot be realized by using mature acoustic computing software, mainly because the noise map has a certain requirement on the accuracy of computing grid points (generally not lower than 10m×10m), 20 square kilometers of computing grid quantity has 20 ten thousands, the time for completing one computation by adopting a high-efficiency server is about 45 minutes, and the time for processing data and images about 20 minutes before and after the time exceeds the minimum requirement of updating the map in hours, so the huge computing quantity of the grid points restricts the dynamic updating of the large-range noise map, and a computing method capable of quickly updating the large-range noise map is needed. Disclosure of Invention The invention aims to optimize the updating efficiency of the existing dynamic noise map system and realize the localization of the noise map core technology, the invention provides a calculation method for realizing rapid updating of a large-range dynamic noise map system based on the existing database of the noise map system by utilizing a big data driving method. The aim of the invention can be achieved by the following technical scheme: A calculation method for realizing rapid update of a large-range dynamic noise map system comprises the following steps: S1, collecting and analyzing traffic data and noise contribution data of a road sound source in a system database, and constructing a noise map road sound source sound level contribution prediction model according to day and night time periods and different urban road grades; S2, adopting a multi-element nonlinear regression modeling method to regress parameters of a sound source (day and night) sound level contribution prediction model of each level of road; S3, comparing the calculated values of the sound level contribution quantity prediction models of the road sound sources with different levels with the calculated values of the original core software based on a data driving method, and calculating the sound level contribution correction quantity of all the road sound sources of the noise map; and S4, introducing the sound level contribution quantity prediction model of the road sound source and the sound level contribution correction quantity into a noise map system, and calculating the sound level contribution quantity of each road sound source and the road noise of a predicted point in the noise map system in real time. Further, the system database is a database of a noise map system and comprises a road traffic flow and speed database, a core software calculation value database of a road sound source contribution quantity and a prediction point longitude and latitude database. Further, step S1 comprises the sub-steps of: S11, collecting traffic data, noise contribution data and longitude and latitude data of predicted points of all road sound sources in a system database, and constructing a road sound source traffic sample set and a noise contribution sample set: S12, analyzing the distribution characteristics of the traffic data and the noise contribution data, and classifying road sound source samples by combining the day and night time period of a noise map system and urban