WO2022095249A1 - 一种噪音模拟方法、系统、终端以及存储介质 - Google Patents

一种噪音模拟方法、系统、终端以及存储介质 Download PDF

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WO2022095249A1
WO2022095249A1 PCT/CN2020/139125 CN2020139125W WO2022095249A1 WO 2022095249 A1 WO2022095249 A1 WO 2022095249A1 CN 2020139125 W CN2020139125 W CN 2020139125W WO 2022095249 A1 WO2022095249 A1 WO 2022095249A1
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noise
data
value
road
specific time
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车伟涛
陈劲松
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Shenzhen Institute of Advanced Technology of CAS
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  • the present application belongs to the technical field of noise processing, and particularly relates to a noise simulation method, system, terminal and storage medium.
  • the traditional acquisition of traffic noise data is usually based on long-term traffic flow statistics or using expensive and sparse monitoring station data to establish a noise database, combined with a traffic noise simulation model to simulate the noise distribution in the urban area, so as to obtain the urban area or a certain area.
  • the noise map provides a certain decision-making basis for urban overall planning, transportation development and planning, and noise pollution control measures.
  • the current noise simulation technology still has shortcomings such as high cost, excessive time-consuming and slow updating.
  • the present application provides a noise simulation method, system, terminal and storage medium, aiming to solve one of the above technical problems in the prior art at least to a certain extent.
  • a noise simulation method comprising the following steps:
  • the movement perception noise data includes the noise data collected at each collection time and the corresponding positioning data
  • performing spatial matching between the mobile perception noise data and the road network includes:
  • the technical solutions adopted in the embodiments of the present application further include: the calculation methods of the noise values of the various time scales are:
  • T represents different time scales
  • L T represents the estimated noise value of a specific time period T
  • N T represents the number of theoretical noise values in a specific time period, which can be calculated by dividing the time period by the sampling interval
  • n represents a specific time period.
  • L de represents the default value of the noise value, which is used to fill in empty values.
  • the dynamic region segmentation of the road segment according to the noise value of a specific time scale includes:
  • the technical solution adopted in the embodiment of the present application further includes: inputting the noise data set into a noise simulation model, and obtaining the noise simulation results of each area in the road section within the specific time period includes:
  • the noise simulation model combines the noise data set and road network data, building data and elevation data of the road section to obtain noise simulation results of each area in the road section within a specific time period.
  • the technical solution adopted in the embodiment of the present application further includes: the noise simulation model includes the RLS90 model, the CRTN model or the FHWA model.
  • the noise simulation results include hour-level noise simulation results or sky-level noise simulation results.
  • a noise simulation system comprising:
  • Spatial matching module used to spatially match the mobile perception noise data with the road network, and assign the mobile perception noise data to the corresponding road section; wherein, the mobile perception noise data includes the noise data collected at each collection time and Corresponding positioning data;
  • Noise value estimation module used for estimating the noise value of the road segment at various time scales by combining the motion perception data and the initial traffic flow of the corresponding road segment;
  • Road segment segmentation module used to perform dynamic area segmentation on the road segment according to the noise value of a specific time scale, and extract the noise data set of each area in the road segment within the specific time period;
  • Noise simulation module used to input the noise data set into a noise simulation model, and obtain the noise simulation results of each area in the road segment within the specific time period.
  • a terminal includes a processor and a memory coupled to the processor, wherein,
  • the memory stores program instructions for implementing the noise simulation method
  • the processor is configured to execute the program instructions stored in the memory to control noise simulation.
  • a storage medium storing program instructions executable by a processor, where the program instructions are used to execute the noise simulation method.
  • the beneficial effects of the embodiments of the present application are: the noise simulation method, system, terminal and storage medium of the embodiments of the present application fully utilize the mobile sensing technology to collect noise data, and simulate the noise to satisfy the noise simulation model. Input the noise data of the conditions, so as to complete the regional simulation of traffic noise at different time scales, which greatly improves the temporal and spatial resolution of urban traffic noise simulation, and effectively solves the problem of high noise simulation cost, slow update, and spatial resolution. Low deficiency.
  • FIG. 1 is a flowchart of a noise simulation method according to an embodiment of the present application.
  • FIG. 2 is a schematic diagram of road network space matching according to an embodiment of the present application.
  • FIG. 5 is a schematic structural diagram of a noise simulation system according to an embodiment of the present application.
  • FIG. 6 is a schematic structural diagram of a terminal according to an embodiment of the present application.
  • FIG. 7 is a schematic structural diagram of a storage medium according to an embodiment of the present application.
  • FIG. 1 is a flowchart of a noise simulation method according to an embodiment of the present application.
  • the noise simulation method of the embodiment of the present application includes the following steps:
  • the noise collection device includes a smartphone installed with a noise monitoring App or other portable noise monitoring device with wireless communication and positioning functions
  • the mobile perception noise data includes traffic noise data and corresponding positioning data collected at each collection time.
  • S2 Perform spatial matching between the mobile perception noise data and the road network, and assign the mobile perception noise data to the corresponding road section;
  • the spatial matching method of the road network is as follows: taking the road centerline as the axis and the road width as the radius, a cylindrical buffer zone is established; based on the analysis of the cylindrical buffer zone, the space between the mobile perception noise data and the cylindrical buffer zone is established. Include relationships, and delete invalid data (for example, data whose distance from the road centerline is greater than a set threshold); the spatial matching of road network is shown in Figure 2.
  • the noise value at the same position can be the value of a timestamp, or a group of noise values within a period of time.
  • Estimate the noise value The estimation formula is:
  • T represents different time scales (such as seconds, minutes, hours, days, months, etc.);
  • L T represents the estimated noise value in a specific time period T;
  • N T represents the number of theoretical noise values in a specific time period , which can be calculated by dividing the time period by the sampling interval;
  • n represents the actual number of effective noise values in a specific time period.
  • L de represents the default value of the noise value, which is used to fill in the null values.
  • the L de value can be obtained from an existing noise database.
  • S4 Segment the road segment dynamically according to the noise value of a specific time scale, and extract the noise data set of each area in the road segment within a specific time period;
  • the noise value is divided by selecting the minimum perceived difference value of 3db as a reasonable threshold value, and by comparing the noise value on the relevant time scale, the actual road sections in different time periods are divided into regions to form a dynamic based on the merging of adjacent virtual road sections. Region segmentation.
  • Specific algorithms include:
  • the second step perform the comparison of noise values, and repeatedly perform the merging of adjacent virtual partitions; first, on the basis of the temporary set A temp , select the next adjacent virtual partition P i+1 (i ⁇ n) and its associated The noise value V i+1 is used as the merging target; then calculate the difference between V i+ 1 and the V i value of the adjacent virtual partition Pi , and the maximum noise in V i+1 and A temp (maximum noise value) and the difference between the minimum noise value (minimum noise value) in V i+1 and A temp , and determine whether the above three difference calculation results are all smaller than the set Min_sup (minimum perceptible difference of noise value, the minimum perceptible difference of noise value), namely:
  • the road segment is dynamically segmented according to the noise value on a specific time scale, and the noise data set of each area in the road segment within a specific time period (such as a certain minute, hour, day, month, etc.) is extracted as noise. Simulated input data.
  • the noise simulation model includes but is not limited to RLS90 (shin für den an Straben) model, CRTN (Calculation of Road Traffic Noise) model or FHWA (Federal Highway Administration) model, etc.
  • the noise simulation model is based on the noise data set of a specific time period, combined with road network data, building data, elevation data, etc. to calculate the noise in each area, and can obtain hourly or day-level noise simulation results, as shown in Figure 3 and Figure 4.
  • Figure 3 is an hour-level noise simulation diagram
  • Figure 4 is a sky-level noise simulation diagram.
  • the noise simulation method of the embodiment of the present application makes full use of the mobile perception technology to collect noise data, and simulates the noise as noise data that meets the input conditions of the noise simulation model, so as to complete the regional simulation of traffic noise at different time scales. It greatly improves the temporal and spatial resolution of urban traffic noise simulation, effectively solving the problems of high noise simulation cost, slow update and low spatial resolution.
  • FIG. 5 is a structural diagram of a noise simulation system according to an embodiment of the present application.
  • the noise simulation system of the embodiment of the present application includes:
  • Data acquisition module used to acquire the mobile perception noise data collected by the noise acquisition device; the mobile perception noise data includes the traffic noise data collected at each collection time and the corresponding positioning data.
  • Spatial matching module It is used to spatially match the mobile perception noise data with the road network, and assign the mobile perception noise data to the corresponding road section; among them, since the positioning data in the mobile perception noise data will have a certain drift, it is necessary to
  • the mobile perception noise data is used for road network spatial matching, and the spatial relationship between the noise data and the nearest road can be established before it can be assigned to the corresponding road segment.
  • the spatial matching method of the road network is as follows: taking the road centerline as the axis and the road width as the radius, a cylindrical buffer zone is established; based on the analysis of the cylindrical buffer zone, the space between the mobile perception noise data and the cylindrical buffer zone is established. Include relationships, and delete invalid data (such as data whose distance from the road centerline is greater than a set threshold).
  • Noise value estimation module It is used to combine the mobile perception data with the initial traffic flow of the corresponding road section to estimate the noise value at different time scales; the noise value at the same location can be the value of a timestamp, or it can be A set of noise values within a period of time, based on which the noise values of different time scales such as seconds, minutes, hours, days, months, and years can be estimated: the estimation formula is:
  • T represents different time scales (such as seconds, minutes, hours, days, months, etc.);
  • L T represents the estimated noise value of a specific time period T;
  • N T represents the theoretical noise value in a given time period. The number can be calculated by dividing the time period by the sampling interval;
  • n represents the actual number of valid noise values in a given time period.
  • L de represents the default value of the noise value, which is used to fill in the null values.
  • the L de value can be obtained from an existing noise database.
  • Road segment segmentation module It is used to segment the road segment dynamically according to the noise value of a specific time scale, and extract the noise data set of each area in the road segment within a specific time period; among them, the minimum perceived difference value 3db is selected as a reasonable threshold pair.
  • the noise value is divided, and by comparing the noise value on the relevant time scale, the actual road segments in different time periods are divided into regions, and a dynamic region segmentation based on the merging of adjacent virtual road segments is formed.
  • Specific algorithms include:
  • the second step perform the comparison of noise values, and repeatedly perform the merging of adjacent virtual partitions; first, on the basis of the temporary set A temp , select the next adjacent virtual partition P i+1 (i ⁇ n) and its associated The noise value V i+1 is used as the merging target; then calculate the difference between V i+ 1 and the V i value of the adjacent virtual partition Pi , and the maximum noise in V i+1 and A temp (maximum noise value) and the difference between the minimum noise value (minimum noise value) in V i+1 and A temp , and determine whether the above three difference calculation results are all smaller than the set Min_sup (minimum perceptible difference of noise value, the minimum perceptible difference of noise value), namely:
  • the road segment is dynamically segmented according to the noise value on a specific time scale, and the noise data set of each area in the road segment within a specific time period (such as a certain minute, hour, day, month, etc.) is extracted, as Input data for noise simulation.
  • a specific time period such as a certain minute, hour, day, month, etc.
  • Noise simulation module It is used to input the noise data set into the noise simulation model.
  • the noise simulation model combines road network data, building data and elevation data to obtain the noise simulation results of each area in the road section within a specific time period.
  • FIG. 6 is a schematic structural diagram of a terminal according to an embodiment of the present application.
  • the terminal 50 includes a processor 51 and a memory 52 coupled to the processor 51 .
  • the memory 52 stores program instructions for implementing the above-described noise simulation method.
  • the processor 51 is used to execute program instructions stored in the memory 52 to control the noise simulation.
  • the processor 51 may also be referred to as a CPU (Central Processing Unit, central processing unit).
  • the processor 51 may be an integrated circuit chip with signal processing capability.
  • the processor 51 may also be a general purpose processor, digital signal processor (DSP), application specific integrated circuit (ASIC), off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component .
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • FPGA off-the-shelf programmable gate array
  • a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
  • FIG. 7 is a schematic structural diagram of a storage medium according to an embodiment of the present application.
  • the storage medium of this embodiment of the present application stores a program file 61 capable of implementing all the above methods, wherein the program file 61 may be stored in the above-mentioned storage medium in the form of a software product, and includes several instructions to make a computer device (which may It is a personal computer, a server, or a network device, etc.) or a processor that executes all or part of the steps of the methods of the various embodiments of the present invention.
  • a computer device which may It is a personal computer, a server, or a network device, etc.
  • a processor that executes all or part of the steps of the methods of the various embodiments of the present invention.
  • the aforementioned storage medium includes: U disk, mobile hard disk, Read-Only Memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes , or terminal devices such as computers, servers, mobile phones, and tablets.

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Abstract

本申请涉及一种噪音模拟方法、系统、终端以及存储介质。包括：对移动感知噪音数据与路网进行空间匹配，将所述移动感知噪音数据赋予到对应的路段中；其中，所述移动感知噪音数据包括每个采集时间采集的噪音数据和对应的定位数据；结合所述移动感知数据与对应路段的初始交通流量估算所述路段在各个时间尺度的噪音值；根据特定时间尺度的噪音值对所述路段进行动态区域分割，并提取所述路段中各个区域在所述特定时间段内的噪音数据集；将所述噪音数据集输入噪音模拟模型，得到所述路段中各个区域在所述特定时间段内的噪音模拟结果。本申请提高了城市交通噪音模拟的时间分辨率和空间分辨率，有效解决了噪音模拟成本高、更新过慢、空间分辨率低的不足。

Description

一种噪音模拟方法、系统、终端以及存储介质 技术领域
本申请属于噪音处理技术领域，特别涉及一种噪音模拟方法、系统、终端以及存储介质。
背景技术
距世界卫生组织研究表示，噪音能导致精神不振、心情烦躁及工作能力下降等连锁式的生理及心理的问题之外，长远亦会提高患心血管病的风险。香港环保署曾与香港中文大学、澳洲格里菲斯大学、荷兰公共健康及环境国家研究所在2012年完成有关交通噪音对公众健康不良影响的研究，在10077个受访者之中，约8％认为道路噪音属“高度烦扰”类别(仅次于首位的装修工程：11％)。调查发现最多受访者认为道路噪音“于晚间高度烦扰”及“高度干扰睡眠”，报告推算每天有50万以上市民受到道路噪音的滋扰。过量的交通噪音已成为大城市最严重的噪音问题之一，滋扰大量市民的生活。
传统的交通噪音数据获取通常是通过长期的车流量统计数据或利用昂贵而稀疏的监测站数据建立噪音数据库，结合交通噪音模拟模型对城区内噪音分布进行模拟，从而得到城市范围或者某个区域内的噪音图，为城市总体规划、交通发展与规划、噪声污染控制措施提供了一定的决策依据。然而，目前的噪音模拟技术仍存在成本过高、耗时过多以及更新过慢等缺陷。
近年来逐渐兴起的移动感知技术成为解决这一问题的有效方式。例如通过手机App对环境噪音进行移动监测，但手机App大多是单机版，仅供查看，或者基于二维地图对监测结果进行简单的显示，并没有将这种移动感知的数据应用于噪音模拟。另外，由于移动感知所获数据的不完整性导致难以对数据进行整合以支持后期的噪音模拟和分析等应用，形成数据和模拟或分析等应用之间的鸿沟。
发明内容
本申请提供了一种噪音模拟方法、系统、终端以及存储介质，旨在至少在一定程度上解决现有技术中的上述技术问题之一。
为了解决上述问题，本申请提供了如下技术方案：
一种噪音模拟方法，包括以下步骤：
对移动感知噪音数据与路网进行空间匹配，将所述移动感知噪音数据赋予到对应的路段中；其中，所述移动感知噪音数据包括每个采集时间采集的噪音数据和对应的定位数据；
结合所述移动感知数据与对应路段的初始交通流量估算所述路段在各个时间尺度的噪音值；
根据特定时间尺度的噪音值对所述路段进行动态区域分割，并提取所述路段中各个区域在所述特定时间段内的噪音数据集；
将所述噪音数据集输入噪音模拟模型，得到所述路段中各个区域在所述特定时间段内的噪音模拟结果。
本申请实施例采取的技术方案还包括：所述对移动感知噪音数据与路网进行空间匹配包括：
以道路中心线为轴，以道路宽度为半径，建立圆柱形缓冲区；
对所述圆柱形缓冲区进行分析，建立所述移动感知噪音数据与所述圆柱形缓冲区之间的空间包含关系，并删除无效数据。
本申请实施例采取的技术方案还包括：所述各个时间尺度的噪音值计算方式为：
Figure PCTCN2020139125-appb-000001
上式中，T代表不同时间尺度，L T代表特定时间段T的估算噪音值；N T代表特定时间段内的理论噪音值个数，可以用时间段除以采样间隔来计算；n代表特定时间段内的实际有效噪音值个数；L i代表时间i的噪音值，i＝1，2，3，…，N T；L de代表噪音值的默认值，用于填补空值。
本申请实施例采取的技术方案还包括：所述根据特定时间尺度的噪音值对路段进行动态区域分割包括：
假设使用P＝[P 1，P 2，P 3，…，P n]表示所述路段中的一组虚拟分区，使用特定时间段内的噪音值构建所述路段的噪音集V＝[V 1，V 2，V 3，…，V n]，使用临时集合A temp表示所述路段的实际分区集A＝[A 1，A 2，A 3，…，A m]，由所述虚拟分区P中的第一个虚拟分区P 1及其关联的噪音值V 1将所所述临时集合A temp初始化为(P 1，V 1)；
选择相邻虚拟分区P i+1(i＜n)及其关联的噪音值V i+1作为合并目标，分别计算V i+1与所述相邻虚拟分区P i的V i值之间的差值、以及V i+1与A temp中的噪声最大值的差值和V i+1与A temp中的噪声最小值之间的差值，并判断所述三个差 值计算结果是否均小于设定的噪声值最小可感知差，如果是，则将所述相邻虚拟分区P i+1合并到所述临时集合A temp中；否则，使用(P i+1，V i+1)更新所述临时集合A temp；重复上述合并过程，直到完成所述路段所有虚拟分区的合并。
本申请实施例采取的技术方案还包括：所述将所述噪音数据集输入噪音模拟模型，得到所述路段中各个区域在所述特定时间段内的噪音模拟结果包括：
所述噪音模拟模型结合所述噪音数据集以及所述路段的路网数据、建筑数据和高程数据得到所述路段中各个区域在特定时间段内的噪音模拟结果。
本申请实施例采取的技术方案还包括：所述噪音模拟模型包括RLS90模型、CRTN模型或FHWA模型。
本申请实施例采取的技术方案还包括：所述噪音模拟结果包括小时级噪音模拟结果或天级噪音模拟结果。
本申请实施例采取的另一技术方案为：一种噪音模拟系统，包括：
空间匹配模块：用于对移动感知噪音数据与路网进行空间匹配，将所述移动感知噪音数据赋予到对应的路段中；其中，所述移动感知噪音数据包括每个采集时间采集的噪音数据和对应的定位数据；
噪音值估算模块：用于结合所述移动感知数据与对应路段的初始交通流量估算所述路段在各个时间尺度的噪音值；
路段分割模块：用于根据特定时间尺度的噪音值对所述路段进行动态区域分割，并提取所述路段中各个区域在所述特定时间段内的噪音数据集；
噪音模拟模块：用于将所述噪音数据集输入噪音模拟模型，得到所述路段中各个区域在所述特定时间段内的噪音模拟结果。
本申请实施例采取的又一技术方案为：一种终端，所述终端包括处理器、与所述处理器耦接的存储器，其中，
所述存储器存储有用于实现所述噪音模拟方法的程序指令；
所述处理器用于执行所述存储器存储的所述程序指令以控制噪音模拟。
本申请实施例采取的又一技术方案为：一种存储介质，存储有处理器可运行的程序指令，所述程序指令用于执行所述噪音模拟方法。
相对于现有技术，本申请实施例产生的有益效果在于：本申请实施例的噪音模拟方法、系统、终端及存储介质充分利用移动感知技术进行噪音数据采集，并将噪音模拟为满足噪音模拟模型输入条件的噪音数据，从而完成交通噪音在不同时间尺度的区域模拟，极大地提高了城市交通噪音模拟的时间分辨率和空间分辨率，有效解决了噪音模拟成本高、更新过慢、空间分辨率低的不足。
附图说明
图1是本申请实施例的噪音模拟方法的流程图；
图2为本申请实施例的路网空间匹配示意图；
图3为本申请实施例的小时级噪音模拟图；
图4为本申请实施例的天级噪音模拟图；
图5是本申请实施例的噪音模拟系统的结构示意图；
图6为本申请实施例的终端结构示意图；
图7为本申请实施例的存储介质的结构示意图。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本申请，并不用于限定本申请。
请参阅图1，是本申请实施例的噪音模拟方法的流程图。本申请实施例的噪音模拟方法包括以下步骤：
S1：获取噪音采集设备采集到的移动感知噪音数据；
本步骤中，噪音采集设备包括安装有噪音监测App的智能手机或其他具备无线通讯及定位功能的便携噪音监测设备，移动感知噪音数据包括每个采集时间采集的交通噪音数据和对应的定位数据。
S2：对移动感知噪音数据与路网进行空间匹配，将移动感知噪音数据赋予到对应的路段中；
本步骤中，由于移动感知噪音数据中的定位数据会产生一定的漂移，因此需要对移动感知噪音数据进行路网空间匹配，建立噪音数据与最邻近道路的空间关系，才能将其赋予到对应的路段中。路网空间匹配方式具体为：以道路中心线为轴，以道路宽度为半径，建立圆柱形缓冲区；基于该圆柱形缓冲区进行分析，建立移动感知噪音数据与圆柱形缓冲区之间的空间包含关系，并删除无效数据(例如与道路中心线的距离大于设定阈值的数据)；路网空间匹配具体如图2所示。
S3：将移动感知数据与对应路段的初始交通流量相结合，对不同时间尺度的噪音值进行估算；
本步骤中，同一位置的噪音值可以是某个时间戳的值，也可以是一段时间内的一组噪音值，基于此可以对秒、分、时、日、月、年等不同时间尺度的噪音值进行估算：估算公式为：
Figure PCTCN2020139125-appb-000002
式(1)中，T代表不同时间尺度(如秒、分、小时、天、月等)；L T代表特定时间段T的估算噪音值；N T代表特定时间段内的理论噪音值个数，可以用时间段除以采样间隔来计算；n代表特定时间段内的实际有效噪音值个数。L i代表时间i的噪音值，i＝1，2，3，…，N T。L de代表噪音值的默认值，用于填补空值。L de值可以从现有的噪音数据库中获取。
S4：根据特定时间尺度的噪音值对路段进行动态区域分割，并提取该路段中各个区域在特定时间段内的噪音数据集；
本步骤中，通过选择最小感知差值3db作为合理阈值对噪音值进行划分，通过比较相关时间尺度上的噪音值，对不同时间段的实际路段进行区域划分，形成基于相邻虚拟路段合并的动态区域分割。具体算法包括：
Figure PCTCN2020139125-appb-000003
Figure PCTCN2020139125-appb-000004
第一步：路段虚拟分区；假设一个路段中的一组虚拟分区用P＝[P 1，P 2，P 3，…，P n]表示，使用所需时间段内的噪音值构建该路段的噪音集，表示为V＝[V 1，V 2，V 3，…，V n]；临时集合A temp用于建立该路段的实际分区集，表示为A＝[A 1，A 2，A 3，…，A m]，由虚拟分区P中的第一个虚拟分区P 1及其关联的噪音值V 1将临时集合A temp初始化为(P 1，V 1)；
第二步：执行噪音值的比较，并重复执行相邻虚拟分区的合并；首先在临时集合A temp的基础上，选择下一个相邻的虚拟分区P i+1(i＜n)及其关联的噪音值V i+1作为合并目标；然后分别计算V i+1与相邻虚拟分区P i的V i值之间的差值、以及V i+1与A temp中的噪声最大值(maximum noise value)的差值和V i+1与A temp中的噪声最小值(minimum noise value)之间的差值，判断上述的三个差值计算结果是否均小于设定的Min_sup(minimum perceptible difference of noise value，噪声值最小可感知差)，即：|V i-V i+1|≤Min_sup、|maximum noise value in A temp-V i+1|≤Min_sup、|minimum noise value in A temp-V i+1|≤Min_sup均成立，如果均成立，则将下一个相邻的虚拟分区P i+1合并到临时集合A temp中；否则，使用(P i+1，V i+1)更新临时集合A temp。重复此过程，直到完成该路段所有虚拟分区的合并，得到该路段的动态区域分割结果。
通过上述算法，根据特定时间尺度上的噪音值对路段进行动态区域分割，提取到该路段中各个区域在特定时间段内(例如某分、时、天、月等)的噪音数据集，作为噪音模拟的输入数据。
S5：将噪音数据集输入噪音模拟模型，噪音模拟模型结合路网数据、建筑数据和高程数据等得到该路段中各个区域在特定时间段内的噪音模拟结果；
本步骤中，噪音模拟模型包括但不限于RLS90(Richtlinien für den 
Figure PCTCN2020139125-appb-000005
an Straben)模型、CRTN(Calculation of Road Traffic Noise)模型或FHWA(Federal Highway Administration)模型等。噪音模拟模型基于特定时间段的噪音数据集，并结合路网数据、建筑数据、高程数据等进行各个区域的噪音计算，可以得到小时级或天级的噪音模拟结果，具体如图3和图4所示，图3为小时级噪音模拟图，图4为天级噪音模拟图。
基于上述方案，本申请实施例的噪音模拟方法充分利用移动感知技术进行噪音数据采集，并将噪音模拟为满足噪音模拟模型输入条件的噪音数据，从而完成交通噪音在不同时间尺度的区域模拟，极大地提高了城市交通噪音模拟的时间分辨率和空间分辨率，有效解决了噪音模拟成本高、更新过慢、空间分辨率低的不足。
请参阅图5，是本申请实施例的噪音模拟系统的结构图。本申请实施例的噪音模拟系统包括：
数据获取模块：用于获取噪音采集设备采集到的移动感知噪音数据；移动感知噪音数据包括每个采集时间采集的交通噪音数据和对应的定位数据。
空间匹配模块：用于对移动感知噪音数据与路网进行空间匹配，将移动感知噪音数据赋予到对应的路段中；其中，由于移动感知噪音数据中的定位数据会产生一定的漂移，因此需要对移动感知噪音数据进行路网空间匹配，建立噪音数据与最邻近道路的空间关系，才能将其赋予到对应的路段中。路网空间匹配方式具体为：以道路中心线为轴，以道路宽度为半径，建立圆柱形缓冲区；基于该圆柱形缓冲区进行分析，建立移动感知噪音数据与圆柱形缓冲区之间的空间包含关系，并删除无效数据(例如与道路中心线的距离大于设定阈值的数据)。
噪音值估算模块：用于将移动感知数据与对应路段的初始交通流量相结合，对不同时间尺度的噪音值进行估算；其中，同一位置的噪音值可以是某个时间戳的值，也可以是一段时间内的一组噪音值，基于此可以对秒、分、时、日、月、年等不同时间尺度的噪音值进行估算：估算公式为：
Figure PCTCN2020139125-appb-000006
式(1)中，T代表不同时间尺度(如秒、分、小时、天、月等)；L T代表特定时间段T的估算噪音值；N T代表给定时间段内的理论噪音值个数，可以用时间段除以采样间隔来计算；n代表给定时间段内的实际有效噪音值个数。L i代表时间i的噪音值，i＝1，2，3，…，N T。L de代表噪音值的默认值，用于填补空值。L de值可以从现有的噪音数据库中获取。
路段分割模块：用于根据特定时间尺度的噪音值对路段进行动态区域分割，并提取该路段中各个区域在特定时间段内的噪音数据集；其中，通过选择最小感知差值3db作为合理阈值对噪音值进行划分，通过比较相关时间尺 度上的噪音值，对不同时间段的实际路段进行区域划分，形成基于相邻虚拟路段合并的动态区域分割。具体算法包括：
Figure PCTCN2020139125-appb-000007
第一步：路段虚拟分区；假设一个路段中的一组虚拟分区用P＝[P 1，P 2，P 3，…，P n]表示，使用所需时间段内的噪音值构建该路段的噪音集，表示为V＝[V 1，V 2，V 3，…，V n]；临时集合A temp用于建立该路段的实际分区集，表示为A＝[A 1，A 2，A 3，…，A m]，由虚拟分区P中的第一个虚拟分区P 1及其关联的噪音值V 1将临时集合A temp初始化为(P 1，V 1)；
第二步：执行噪音值的比较，并重复执行相邻虚拟分区的合并；首先在临时集合A temp的基础上，选择下一个相邻的虚拟分区P i+1(i＜n)及其关联的噪音值V i+1作为合并目标；然后分别计算V i+1与相邻虚拟分区P i的V i值之间的差值、以及V i+1与A temp中的噪声最大值(maximum noise value)的差值和V i+1与A temp中的噪声最小值(minimum noise value)之间的差值，判断上述的三个 差值计算结果是否均小于设定的Min_sup(minimum perceptible difference of noise value，噪声值最小可感知差)，即：|V i-V i+1|≤Min_sup、|maximum noise value in A temp-V i+1|≤Min_sup、|minimum noise value in A temp-V i+1|≤Min_sup均成立，如果均成立，则将下一个相邻的虚拟分区P i+1合并到临时集合A temp中；否则，使用(P i+1，V i+1)更新临时集合A temp。重复此过程，直到完成该路段所有虚拟分区的合并，得到该路段的动态区域分割结果。
通过上述算法，根据特定时间尺度上的噪音值对路段进行动态区域分割，并提取到该路段中各个区域在特定时间段内(例如某分、时、天、月等)的噪音数据集，作为噪音模拟的输入数据。
噪音模拟模块：用于将噪音数据集输入噪音模拟模型，噪音模拟模型结合路网数据、建筑数据和高程数据等得到该路段中各个区域在特定时间段内的噪音模拟结果。
请参阅图6，为本申请实施例的终端结构示意图。该终端50包括处理器51、与处理器51耦接的存储器52。
存储器52存储有用于实现上述噪音模拟方法的程序指令。
处理器51用于执行存储器52存储的程序指令以控制噪音模拟。
其中，处理器51还可以称为CPU(Central Processing Unit，中央处理单元)。处理器51可能是一种集成电路芯片，具有信号的处理能力。处理器51还可以是通用处理器、数字信号处理器(DSP)、专用集成电路(ASIC)、现成可编程门阵列(FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑 器件、分立硬件组件。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
请参阅图7，为本申请实施例的存储介质的结构示意图。本申请实施例的存储介质存储有能够实现上述所有方法的程序文件61，其中，该程序文件61可以以软件产品的形式存储在上述存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)或处理器(processor)执行本发明各个实施方式方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read－Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质，或者是计算机、服务器、手机、平板等终端设备。
对所公开的实施例的上述说明，使本领域专业技术人员能够实现或使用本申请。对这些实施例的多种修改对本领域的专业技术人员来说将是显而易见的，本申请中所定义的一般原理可以在不脱离本申请的精神或范围的情况下，在其它实施例中实现。因此，本申请将不会被限制于本申请所示的这些实施例，而是要符合与本申请所公开的原理和新颖特点相一致的最宽的范围。

Claims (10)

  1. 一种噪音模拟方法，其特征在于，包括以下步骤：
    对移动感知噪音数据与路网进行空间匹配，将所述移动感知噪音数据赋予到对应的路段中；其中，所述移动感知噪音数据包括每个采集时间采集的噪音数据和对应的定位数据；
    结合所述移动感知数据与对应路段的初始交通流量估算所述路段在各个时间尺度的噪音值；
    根据特定时间尺度的噪音值对所述路段进行动态区域分割，并提取所述路段中各个区域在所述特定时间段内的噪音数据集；
    将所述噪音数据集输入噪音模拟模型，得到所述路段中各个区域在所述特定时间段内的噪音模拟结果。
  2. 根据权利要求1所述的噪音模拟方法，其特征在于，所述对移动感知噪音数据与路网进行空间匹配包括：
    以道路中心线为轴，以道路宽度为半径，建立圆柱形缓冲区；
    对所述圆柱形缓冲区进行分析，建立所述移动感知噪音数据与所述圆柱形缓冲区之间的空间包含关系，并删除无效数据。
  3. 根据权利要求1所述的噪音模拟方法，其特征在于，所述各个时间尺度的噪音值计算方式为：
    Figure PCTCN2020139125-appb-100001
    上式中，T代表不同时间尺度，L T代表特定时间段T的估算噪音值；N T代表特定时间段内的理论噪音值个数，可以用时间段除以采样间隔来计算；n代表特定时间段内的实际有效噪音值个数；L i代表时间i的噪音值，i＝1，2，3，…，N T；L de代表噪音值的默认值，用于填补空值。
  4. 根据权利要求3所述的噪音模拟方法，其特征在于，所述根据特定时间尺度的噪音值对路段进行动态区域分割包括：
    假设使用P＝[P 1，P 2，P 3，…，P n]表示所述路段中的一组虚拟分区，使用特定时间段内的噪音值构建所述路段的噪音集V＝[V 1，V 2，V 3，…，V n]，使用临时集合A temp表示所述路段的实际分区集A＝[A 1，A 2，A 3，…，A m]，由所述虚拟分区P中的第一个虚拟分区P 1及其关联的噪音值V 1将所所述临时集合A temp初始化为(P 1，V 1)；
    选择相邻虚拟分区P i+1(i＜n)及其关联的噪音值V i+1作为合并目标，分别计算V i+1与所述相邻虚拟分区P i的V i值之间的差值、以及V i+1与A temp中的噪声最大值的差值和V i+1与A temp中的噪声最小值之间的差值，并判断所述三个差值计算结果是否均小于设定的噪声值最小可感知差，如果是，则将所述相邻虚拟分区P i+1合并到所述临时集合A temp中；否则，使用(P i+1，V i+1)更新所述临时集合A temp；重复上述合并过程，直到完成所述路段所有虚拟分区的合并。
  5. 根据权利要求1－4任一项所述的噪音模拟方法，其特征在于，所述将所述噪音数据集输入噪音模拟模型，得到所述路段中各个区域在所述特定时间段内的噪音模拟结果包括：
    所述噪音模拟模型结合所述噪音数据集以及所述路段的路网数据、建筑数据和高程数据得到所述路段中各个区域在特定时间段内的噪音模拟结果。
  6. 根据权利要求5所述的噪音模拟方法，其特征在于，所述噪音模拟模型包括RLS90模型、CRTN模型或FHWA模型。
  7. 根据权利要求5所述的噪音模拟方法，其特征在于，所述噪音模拟结果包括小时级噪音模拟结果或天级噪音模拟结果。
  8. 一种噪音模拟系统，其特征在于，包括：
    空间匹配模块：用于对移动感知噪音数据与路网进行空间匹配，将所述移动感知噪音数据赋予到对应的路段中；其中，所述移动感知噪音数据包括每个采集时间采集的噪音数据和对应的定位数据；
    噪音值估算模块：用于结合所述移动感知数据与对应路段的初始交通流量估算所述路段在各个时间尺度的噪音值；
    路段分割模块：用于根据特定时间尺度的噪音值对所述路段进行动态区域分割，并提取所述路段中各个区域在所述特定时间段内的噪音数据集；
    噪音模拟模块：用于将所述噪音数据集输入噪音模拟模型，得到所述路段中各个区域在所述特定时间段内的噪音模拟结果。
  9. 一种终端，其特征在于，所述终端包括处理器、与所述处理器耦接的存储器，其中，
    所述存储器存储有用于实现权利要求1－7任一项所述的噪音模拟方法的程序指令；
    所述处理器用于执行所述存储器存储的所述程序指令以控制噪音模拟。
  10. 一种存储介质，其特征在于，存储有处理器可运行的程序指令，所述程序指令用于执行权利要求1至7任一项所述噪音模拟方法。
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