Large-scale noise map dynamic updating method and system and storage medium

Abstract

The invention discloses a large-scale noise map dynamic updating method and system and a storage medium, and belongs to the technical field of noise monitoring. The method comprises the following steps: S1, acquiring corresponding geographic model data according to monitoring ranges covered by different central stations, and sending the geographic model data to the corresponding central stations; s2, each central station obtains noise data of the associated micro stations; s3, generating noise map data; s4, each central station sends the noise map data to a server through a network; s5, the server automatically publishes the noise map data to spatial geographic information sharing software; and S6, the front end sends a request to the server, obtains the noise map data of all the central stations at the target moment and visually displays the noise map data. A monitoring network established by one central station and n micro stations is deployed in each area, real-time acquisition of noise data is realized, large-scale coverage of area ranges with different functions is realized, and timeliness, accuracy and diversity of the noise data are ensured.

Description

Technical Field

[0001] The present invention relates to the field of noise monitoring technology, and in particular to a large-scale noise map dynamic updating method, system and storage medium. Background Art

[0002] With the acceleration of urbanization, noise sources such as transportation and industry are increasing, and residents' complaints about noise interference are also increasing. Urban noise pollution is becoming increasingly serious. In order to meet this challenge, governments at all levels have actively promoted environmental protection policies, emphasized the importance of urban noise management, and strengthened the monitoring and control of environmental noise. Although many local governments have formulated noise control plans, scientific monitoring methods and accurate data support are still urgently needed for the long-term changes in regional noise or the effect of noise control, and how to effectively plan the construction of traffic trunk lines and industrial enterprises. The current shortcomings of noise maps include: 1. Low credibility. The number of regional noise monitoring points is limited, and the noise map obtained by the noise prediction model is not credible, which limits the application of noise maps. 2. Poor real-time performance. Most of the current noise maps use static display, that is, a one-time release map with a fixed time, which is not conducive to understanding the law of noise changes and affects the subsequent noise control effect. 3. Low scale (area range). Most of the noise maps that have been published show the noise distribution in some areas of a city. To display the noise map of the entire city, the conventional data collection hardware and server computing capacity required for noise data collection, geographic model data, noise prediction models, etc. are more expensive to build, which ultimately leads to the failure to truly realize large-scale noise maps. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide a large-scale noise map dynamic update method, system and storage medium.

[0004] The objective of the present invention is achieved through the following technical solutions: The first aspect of the present invention provides: a large-scale noise map dynamic update method, comprising the following steps: S1: The server loads the geographic model data of the target city, obtains the corresponding geographic model data according to the monitoring range covered by different central stations, and sends the data to the corresponding central station in the first file format; S2: Each central station obtains the noise data of the micro-stations with which it has established contact; S3: starting the noise prediction model of each central station to generate noise map data in a second file format; S4: Each central station sends the noise map data to the server through the network; S5: The server automatically publishes the noise map data to the spatial geographic information sharing software; S6: The front end sends a request to the server to obtain the noise map data of all central stations at the target time and displays it visually.

[0005] Preferably, the geographic model data is obtained from the WGS84 coordinate system or the CGS2000 coordinate system.

[0006] Preferably, the data layers of the geographic model data include noise prediction calculation area layers, building layers, bridge layers, road traffic layers, railway layers, urban rail transit layers, aircraft route layers, industrial enterprise layers, building construction layers, terrain layers, green forest layers, sound barrier layers, and monitoring point layers.

[0007] Preferably, the noise data includes minute Leq values, and the Leq values, daytime sound pressure levels Ld, and nighttime sound pressure levels Ln of each microstation are calculated based on the minute Leq values.

[0008] Preferably, the S3 further comprises the following steps: S31: running the geographic information processing module, loading geographic model data, and normalizing the geographic coordinates; S32: running a mesh segmentation module, using Delaunay triangulation to obtain a computational mesh in the noise prediction calculation area to generate noise prediction points; S33: Run the data update module to load the noise data of the monitoring point, the current air pressure, humidity, temperature, and wind speed; S34: running the noise source intensity processing module to update the sound pressure level value of the noise source; S35: running the noise prediction module to calculate the contribution of the noise source to the sound pressure level of the noise prediction point; S36: running the interpolation module to complete the calculation of the sound pressure level of the interpolation point with a preset interval accuracy according to the data interpolation of the noise prediction point; S37: Run the noise map data generation module to generate and store color noise map data using interpolation points.

[0009] Preferably, the S34 comprises the following steps: S341: Arrange the monitoring points in descending order according to the noise value; S342: Match the three-dimensional coordinates of the first monitoring point with the three-dimensional coordinates of the reference point of the noise source; S343: After the matching is successful, the source intensity sound pressure level of the matching noise source is calculated according to the noise value of the first monitoring point; S344: Calculate the impact of currently known noise sources on the sound pressure levels of the remaining monitoring points, and eliminate the contribution of currently known noise sources to the noise values ​​of the remaining monitoring points; S345: Arrange the remaining monitoring points in descending order according to the noise values; S346: Repeat steps S333-S335 until all monitoring points are matched.

[0010] Preferably, a central station is connected to multiple micro stations to establish a monitoring network within the target area.

[0011] Preferably, the first file format is a shp file, the second file format is a GeoTIFF file, and the spatial geographic information sharing software is GeoServer.

[0012] The second aspect of the present invention provides: a large-scale noise map dynamic update system, used to implement any of the above large-scale noise map dynamic update methods, comprising: A geographic model data acquisition module, used to use a server to load geographic model data of a target city, acquire corresponding geographic model data according to monitoring ranges covered by different central stations, and send the data to the corresponding central station in a first file format; A noise data acquisition module is used to use each central station to acquire the noise data of the micro stations with which the connection has been established; A noise map data generating module, used for starting the noise prediction model of each central station to generate noise map data in a second file format; A data transmission module, used to send noise map data to a server via the network through various central stations; The visualization module is used to automatically publish the noise map data to the spatial geographic information sharing software through the server; the front end sends a request to the server to obtain the noise map data of all central stations at the target time and display it visually.

[0013] The third aspect of the present invention provides: a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are loaded and executed by a processor, any of the above-mentioned large-scale noise map dynamic update methods is implemented.

[0014] The beneficial effects of the present invention are: 1) A large-scale noise data monitoring network divides the city into zones and deploys a monitoring network consisting of one central station and n micro stations in each zone to achieve real-time collection of noise data and large-scale coverage of different functional areas, ensuring the timeliness, accuracy and diversity of noise data.

[0015] 2) After the central station collects the real-time noise data of the connected micro-stations, it obtains and stores the minute Leq, hour Leq, daytime sound pressure level Ld, and nighttime sound pressure level Ln data of each micro-station through statistical calculations, and provides different statistical types of noise data for noise prediction model calculations, so as to quickly process and store noise data.

[0016] 3) The geographic model data includes elements such as buildings, bridges, road traffic, railways, urban rail transit, aircraft routes, industrial enterprises, construction, terrain, green forests, sound barriers, etc., which can fully restore the city’s geographic information and ensure the accuracy of noise prediction.

[0017] 4) Distributed noise prediction model calculation, the prediction model includes geographic information processing module, grid segmentation module, data update module, noise source intensity processing module, noise prediction module, interpolation module, and noise map data generation module. After each central station is embedded in the model, a noise map of the corresponding coverage area is generated to achieve the effect of quickly and efficiently obtaining noise map data, reduce the model calculation pressure of the server, and reduce the hardware cost of the large-scale noise map system.

[0018] 5) After the GIS data is transferred to the server database, it can be stored and queried through PostgreSQL. It also supports automatic publishing of GIS data to GeoServer for front-end display and enables fast access to GIS data.

[0019] 6) Use the lightweight Leaflet front-end framework, which enables developers to quickly build feature-rich map applications through a simple and easy-to-use API. The framework supports basic functions such as layer control, zooming, and panning, and can be extended with more functions such as markers, pop-up boxes, and animation effects through plug-ins.

[0020] 7) Use color coding and intuitive icons to transform complex noise data into easy-to-understand information in the form of maps. Introduce interactive elements to allow users to customize views and select parameters such as time range and noise type to improve user participation and efficiency of information acquisition. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Flowchart of the method for dynamically updating large-scale noise maps; Figure 2 Flowchart for obtaining large-scale noise maps. DETAILED DESCRIPTION

[0022] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0023] See also Figure 1-Figure 2 The first aspect of the present invention provides: a large-scale noise map dynamic update method, comprising the following steps: S1: The server loads the geographic model data of the target city, obtains the corresponding geographic model data according to the monitoring range covered by different central stations, and sends the data to the corresponding central station in the first file format; S2: Each central station obtains the noise data of the micro-stations with which it has established contact; S3: starting the noise prediction model of each central station to generate noise map data in a second file format; S4: Each central station sends the noise map data to the server through the network; S5: The server automatically publishes the noise map data to the spatial geographic information sharing software; S6: The front end sends a request to the server to obtain the noise map data of all central stations at the target time and displays it visually.

[0024] In this embodiment, the monitoring network uses LoRA communication technology to realize noise data collection and transmission, and combines and optimizes multiple monitoring networks to realize a large-scale noise map at the city level.

[0025] In some embodiments, the geographic model data is obtained from the WGS84 coordinate system or the CGS2000 coordinate system.

[0026] In this embodiment, the geographic model data is a shp file based on WGS84, CGS2000 or other fixed coordinate systems, and the accuracy is not less than 1:2000.

[0027] In some embodiments, the data layers of the geographic model data include noise prediction calculation area layers, building layers, bridge layers, road traffic layers, railway layers, urban rail transit layers, aircraft route layers, industrial enterprise layers, construction layers, terrain layers, green forest layers, sound barrier layers, and monitoring point layers.

[0028] In this embodiment, the geographical locations of the elements in the layer are given as Polygons in a topological form. The noise prediction and calculation area layer includes the position and height of the noise prediction and calculation area; the building layer includes the position, height, sound attenuation and sound reflection coefficient of the building; the bridge layer includes the position and height of the bridge; the road traffic layer includes the position, width, height, pavement material, vehicle speed on the road, traffic volume and three-dimensional coordinates of the reference points of the section; the railway layer includes the position, width, height, vehicle speed on the railway, vehicle marshaling section number and three-dimensional coordinates of the reference points of the section; the urban rail transit layer includes the position, width, height and vehicle length on the track; the aircraft route layer includes the position, height and aircraft length on the route; the industrial enterprise layer includes the position, daytime sound pressure level, nighttime sound pressure level and three-dimensional coordinates of the reference points of the industrial enterprise; the building construction layer includes the construction location; the terrain layer includes the terrain areas, heights and sound reflection coefficients at different altitudes; the green forest layer includes the position, height and sound attenuation of the green forest; the sound barrier layer includes the position, height, whether it is fully enclosed, sound attenuation and sound reflection coefficient of the sound barrier; the monitoring point layer includes the three-dimensional coordinate data of the monitoring point.

[0029] In some embodiments, the noise data includes minute Leq values, and the Leq values, daytime sound pressure levels Ld, and nighttime sound pressure levels Ln of each micro station are calculated based on the minute Leq values.

[0030] In some embodiments, the S3 further comprises the following steps: S31: running the geographic information processing module, loading geographic model data, and normalizing the geographic coordinates; S32: running a mesh segmentation module, using Delaunay triangulation to obtain a computational mesh in the noise prediction calculation area to generate noise prediction points; S33: Run the data update module to load the noise data of the monitoring point, the current air pressure, humidity, temperature, and wind speed; S34: running the noise source intensity processing module to update the sound pressure level value of the noise source; S35: running the noise prediction module to calculate the contribution of the noise source to the sound pressure level of the noise prediction point; S36: running the interpolation module to complete the calculation of the sound pressure level of the interpolation point with a preset interval accuracy according to the data interpolation of the noise prediction point; S37: Run the noise map data generation module to generate and store color noise map data using interpolation points.

[0031] In this embodiment, considering the geometric boundaries of buildings, roads, railways, and sound barriers, Delaunay triangulation is used to obtain the calculation grid, and the generated grid points are the noise prediction points.

[0032] In some embodiments, the S34 comprises the following steps: S341: Arrange the monitoring points in descending order according to the noise value; S342: Match the three-dimensional coordinates of the first monitoring point with the three-dimensional coordinates of the reference point of the noise source; S343: After the matching is successful, the source intensity sound pressure level of the matching noise source is calculated according to the noise value of the first monitoring point; S344: Calculate the impact of currently known noise sources on the sound pressure levels of the remaining monitoring points, and eliminate the contribution of currently known noise sources to the noise values ​​of the remaining monitoring points; S345: Arrange the remaining monitoring points in descending order according to the noise values; S346: Repeat steps S333-S335 until all monitoring points are matched.

[0033] In some embodiments, a central station connects multiple micro stations to establish a monitoring network within the target area.

[0034] In some embodiments, the first file format is a shp file, the second file format is a GeoTIFF file, and the spatial geographic information sharing software is GeoServer.

[0035] like Figure 2 The figure shows the process of obtaining a large-scale noise map. The GeoServer sends the geographic model data of the coverage area of ​​each central station to the corresponding central station. Each central station receives the noise data of all micro stations in the coverage network. After the noise prediction model algorithm embedded in the central station, the GIS data (GeoTIFF file) of the area covered by the central station is obtained. The server regularly obtains the GIS data of each central station and automatically publishes it to GeoServer for front-end request calls. Finally, the front-end page displays the noise map data of all central stations at the same time according to time. In addition, considering the data communication volume and quality between the micro station and the central station, the frequency of generating noise map data by the central station is 1 sheet / min.

[0036] The second aspect of the present invention provides: a large-scale noise map dynamic update system, used to implement any of the above large-scale noise map dynamic update methods, comprising: A geographic model data acquisition module, used to use a server to load geographic model data of a target city, acquire corresponding geographic model data according to monitoring ranges covered by different central stations, and send the data to the corresponding central station in a first file format; A noise data acquisition module is used to use each central station to acquire the noise data of the micro stations with which the connection has been established; A noise map data generating module, used for starting the noise prediction model of each central station to generate noise map data in a second file format; A data transmission module, used to send noise map data to a server via the network through various central stations; The visualization module is used to automatically publish the noise map data to the spatial geographic information sharing software through the server; the front end sends a request to the server to obtain the noise map data of all central stations at the target time and display it visually.

[0037] The third aspect of the present invention provides: a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are loaded and executed by a processor, any of the above-mentioned large-scale noise map dynamic update methods is implemented.

[0038] The above is only a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art shall not deviate from the spirit and scope of the present invention, and shall be within the scope of protection of the claims attached to the present invention.

Claims

1. A large-scale noise map dynamic update method, characterized by: The following steps are involved: S1: The server loads the geographic model data of the target city, obtains the corresponding geographic model data according to the monitoring range covered by different central stations, and sends the data to the corresponding central station in the first file format; S2: Each central station obtains the noise data of the micro-stations with which it has established contact; S3: starting the noise prediction model of each central station to generate noise map data in a second file format; S4: Each central station sends the noise map data to the server through the network; S5: The server automatically publishes the noise map data to the spatial geographic information sharing software; S6: The front end sends a request to the server to obtain the noise map data of all central stations at the target time and displays it visually.

2. The large-scale noise map dynamic updating method according to claim 1 is characterized in that: The geographic model data is obtained from the WGS84 coordinate system or the CGS2000 coordinate system.

3. The large-scale noise map dynamic updating method according to claim 1 is characterized in that: The data layers of the geographic model data include noise prediction calculation area layer, building layer, bridge layer, road traffic layer, railway layer, urban rail transit layer, aircraft route layer, industrial enterprise layer, building construction layer, terrain layer, green forest layer, sound barrier layer, and monitoring point layer.

4. The large-scale noise map dynamic updating method according to claim 1 is characterized in that: The noise data includes minute Leq values, through which the Leq values ​​of each microstation, the daytime sound pressure level Ld, and the nighttime sound pressure level Ln are calculated.

5. The large-scale noise map dynamic updating method according to claim 1 is characterized in that: The S3 further comprises the following steps: S31: running the geographic information processing module, loading geographic model data, and normalizing the geographic coordinates; S32: running a mesh segmentation module, using Delaunay triangulation to obtain a computational mesh in the noise prediction calculation area to generate noise prediction points; S33: Run the data update module to load the noise data of the monitoring point, the current air pressure, humidity, temperature, and wind speed; S34: running the noise source intensity processing module to update the sound pressure level value of the noise source; S35: running the noise prediction module to calculate the contribution of the noise source to the sound pressure level of the noise prediction point; S36: running the interpolation module to complete the calculation of the sound pressure level of the interpolation point with a preset interval accuracy according to the data interpolation of the noise prediction point; S37: Run the noise map data generation module to generate and store color noise map data using interpolation points.

6. The large-scale noise map dynamic updating method according to claim 5 is characterized in that: The S34 comprises the following steps: S341: Arrange the monitoring points in descending order according to the noise value; S342: Match the three-dimensional coordinates of the first monitoring point with the three-dimensional coordinates of the reference point of the noise source; S343: After the matching is successful, the source intensity sound pressure level of the matching noise source is calculated according to the noise value of the first monitoring point; S344: Calculate the impact of currently known noise sources on the sound pressure levels of the remaining monitoring points, and eliminate the contribution of currently known noise sources to the noise values ​​of the remaining monitoring points; S345: Arrange the remaining monitoring points in descending order according to the noise values; S346: Repeat steps S333-S335 until all monitoring points are matched.

7. The large-scale noise map dynamic updating method according to claim 1 is characterized in that: A central station connects multiple micro stations to establish a monitoring network within the target area.

8. The large-scale noise map dynamic updating method according to any one of claims 1 to 7, characterized in that: The first file format is a shp file, the second file format is a GeoTIFF file, and the spatial geographic information sharing software is GeoServer.

9. A large-scale noise map dynamic update system, characterized by: A method for dynamically updating a large-scale noise map according to any one of claims 1 to 8, comprising: A geographic model data acquisition module, used to use a server to load geographic model data of a target city, acquire corresponding geographic model data according to monitoring ranges covered by different central stations, and send the data to the corresponding central station in a first file format; A noise data acquisition module is used to use each central station to acquire the noise data of the micro stations with which the connection has been established; A noise map data generating module, used for starting the noise prediction model of each central station to generate noise map data in a second file format; A data transmission module, used to send noise map data to a server via the network through various central stations; The visualization module is used to automatically publish the noise map data to the spatial geographic information sharing software through the server; the front end sends a request to the server to obtain the noise map data of all central stations at the target time and display it visually.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are loaded and executed by the processor, the large-scale noise map dynamic update method as described in any one of claims 1 to 8 is implemented.

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