A Method, System and Storage Medium for Dynamically Updating Large-Scale Noise Maps

Abstract

The present invention discloses a method, system and storage medium for dynamically updating a large-scale noise map, belonging to the technical field of noise monitoring. The method includes: S1: According to the monitoring ranges covered by different central stations, obtain corresponding geographic model data and send it to the corresponding central stations; S2: Each central station obtains the noise data of the microsites that have established connections; S3: Generate noise map data; 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 moment and perform visual display. A monitoring network established by 1 central station and n microsites is deployed in each area to realize the real-time collection of noise data and large-scale coverage of different functional area ranges, ensuring the timeliness, accuracy and diversity of noise data.

Description

Technical Field

[0001] The present invention relates to the technical field of noise monitoring, and particularly to a method, system and storage medium for dynamically updating a large-scale noise map. Background Art

[0002] With the acceleration of the urbanization process, the number of noise sources such as transportation and industry is increasing continuously, and the complaints from residents about noise interference are also increasing accordingly. The problem of urban noise pollution is becoming increasingly serious. To address this challenge, governments at all levels are actively promoting environmental protection policies, emphasizing the importance of urban noise management, and strengthening the monitoring and control of environmental noise. Although many local governments have formulated noise control plans, for the long-term changes in regional noise or the noise control effect, and how to effectively plan the construction of transportation trunk lines and industrial enterprises, scientific monitoring means and accurate data support are still urgently needed. The current disadvantages of noise maps include: 1. Low credibility. The number of regional noise monitoring points is limited, and the credibility of the noise map obtained through the noise prediction model is not high, which restricts the application and promotion of the noise map. 2. Poor real-time performance. Most of the current noise maps use static display, that is, a one-time published map with a fixed time, which is not conducive to understanding the change law of noise and affects the subsequent noise control effect. 3. Low scale (area range). Most of the published noise maps show the noise distribution of some areas of a city. To display the noise map of the entire city, in terms of noise data collection, geographical model data, noise prediction model, etc., the construction costs of conventional data collection hardware and server computing facilities required are higher, and finally the large-scale noise map fails to be truly realized. Summary of the Invention

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

[0004] The purpose of the present invention is achieved through the following technical solutions: In the first aspect of the present invention, there is provided a method for dynamically updating a large-scale noise map, including the following steps:

[0005] S1: The server loads the geographical model data of the target city, obtains the corresponding geographical model data according to the monitoring range covered by different central stations, and sends it to the corresponding central station in the first file format;

[0006] S2: Each central station obtains the noise data of the microsites that have established connections;

[0007] S3: Start the noise prediction model of each central station to generate noise map data in the second file format;

[0008] S4: Each central station sends the noise map data to the server through the network;

[0009] S5: The server automatically publishes the noise map data to the spatial geographic information sharing software;

[0010] S6: The front end sends a request to the server to obtain the noise map data of all central stations at the target moment and perform visual display.

[0011] Preferably, the geographic model data is obtained from the WGS84 coordinate system or the CGS2000 coordinate system.

[0012] Preferably, the data layers of the geographic model data include a noise prediction calculation area layer, a building layer, a bridge layer, a road traffic layer, a railway layer, an urban rail transit layer, an aircraft route layer, an industrial enterprise layer, a construction layer, a terrain layer, a green forest layer, a sound barrier layer, and a monitoring point layer.

[0013] Preferably, the noise data includes the minute Leq value, and the Leq value, daytime sound pressure level Ld, and nighttime sound pressure level Ln of each micro-station are calculated through the minute Leq value.

[0014] Preferably, step S3 further includes the following steps:

[0015] S31: Run the geographic information processing module, load the geographic model data, and perform normalization processing on the geographic coordinates;

[0016] S32: Run the grid segmentation module, and use Delaunay triangulation in the noise prediction calculation area to obtain calculation grids and generate noise prediction points;

[0017] S33: Run the data update module, and load the noise data of the monitoring points, the air pressure, humidity, temperature, and wind speed at the current moment;

[0018] S34: Run the noise source strength processing module to update the sound pressure level value of the noise source;

[0019] S35: Run the noise prediction module to calculate the sound pressure level contribution of the noise source to the noise prediction point;

[0020] S36: Run the interpolation module to complete the calculation of the sound pressure level of the interpolation points with a precision of a preset interval according to the data of the noise prediction points;

[0021] S37: Run the noise map data generation module to generate and store the color noise map data using the interpolation points.

[0022] Preferably, step S34 includes the following steps:

[0023] S341: Arrange the monitoring points in descending order according to the noise value;

[0024] S342: Match the three-dimensional coordinates of the first monitoring point with the three-dimensional coordinates of the reference point of the noise source;

[0025] S343: After successful matching, estimate the source sound pressure level of the matched noise source based on the noise value of the first monitoring point;

[0026] S344: Calculate the influence of the currently known noise sources on the sound pressure levels of the remaining monitoring points, and at the same time eliminate the contribution of the currently known noise sources to the noise values of the remaining monitoring points;

[0027] S345: Arrange the remaining monitoring points in descending order according to the noise values;

[0028] S346: Repeat steps S333 - S335 until all monitoring points are successfully matched.

[0029] Preferably, a central station connects to multiple micro-stations to establish a monitoring network within the target area.

[0030] Preferably, the first file format is shp file, the second file format is GeoTIFF file, and the spatial geographic information sharing software is GeoServer.

[0031] The second aspect of the present invention provides: A large-scale noise map dynamic update system for implementing any of the above large-scale noise map dynamic update methods, including:

[0032] Geographical model data acquisition module, used to load the geographical model data of the target city by the server, obtain the corresponding geographical model data according to the monitoring ranges covered by different central stations, and send it to the corresponding central stations in the first file format;

[0033] Noise data acquisition module, used to acquire the noise data of the micro-stations with which connections have been established by each central station;

[0034] Noise map data generation module, used to start the noise prediction models of each central station to generate noise map data in the second file format;

[0035] Data transmission module, used to send the noise map data to the server through the network by each central station;

[0036] Visualization module, used to automatically publish the noise map data to the spatial geographic information sharing software by the server; the front end sends a request to the server to obtain the noise map data of all central stations at the target moment and perform visual display.

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

[0038] The beneficial effects of the present invention are as follows:

[0039] 1) A large-scale noise data monitoring network divides the city into zones, and a monitoring network consisting of 1 central station and n micro-stations is deployed within 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.

[0040] 2) After the central station collects the real-time noise data of the connected micro-stations, it respectively obtains the minute Leq, hourly Leq, daytime sound pressure level Ld, and nighttime sound pressure level Ln data of each micro-station through statistical calculations and stores them, providing noise data of different statistical types for the noise prediction model calculation, and quickly processing and storing the noise data.

[0041] 3) The geographical model data includes elements such as buildings, bridges, road traffic, railways, urban rail transit, aircraft routes, industrial enterprises, construction sites, terrain, green forests, and sound barriers, which can comprehensively restore the geographical information of the city and ensure the accuracy of noise prediction.

[0042] 4) Distributed noise prediction model calculation. The prediction model includes a geographical information processing module, a grid segmentation module, a data update module, a noise source strength processing module, a noise prediction module, an interpolation module, and a noise map data generation module. After each central station embeds this model, a noise map of the corresponding coverage area is generated, achieving the effect of quickly and efficiently obtaining noise map data, reducing the model calculation pressure on the server, and lowering the hardware cost of the large-scale noise map system.

[0043] 5) After the GIS data is transmitted to the database of the server, it can be stored and queried through PostgreSQL. At the same time, it also supports the automatic publishing of GIS data to GeoServer for front-end display and use, enabling quick access to GIS data.

[0044] 6) Using the lightweight Leaflet front-end framework, the Leaflet framework enables developers to quickly build map applications with rich functions through simple and easy-to-use APIs. This framework supports basic functions such as layer control, zooming, and panning, and can be extended with more functions through plugins, such as markers, pop-up boxes, and animation effects.

[0045] 7) Use color coding and intuitive icons to convert complex noise data into easy-to-understand information in the form of a map. Introduce interactive elements to allow users to customize the view, select parameters such as time range, noise type, etc., and enhance user participation and the efficiency of information acquisition. Description of the Drawings

[0046] Figure 1 It is a flowchart of the dynamic update method for large-scale noise maps;

[0047] Figure 2 It is a flowchart for obtaining large-scale noise maps. Detailed Implementation Manner

[0048] Next, in combination with the embodiments, the technical solutions of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the scope of protection of the present invention.

[0049] Refer to Figure 1 - Figure 2 , the first aspect of the present invention provides: A method for dynamically updating large-scale noise maps, including the following steps:

[0050] S1: The server loads the geographical model data of the target city, obtains the corresponding geographical model data according to the monitoring ranges covered by different central stations, and sends it to the corresponding central stations in the first file format;

[0051] S2: Each central station obtains the noise data of the microsites that have established connections;

[0052] S3: Start the noise prediction model of each central station to generate noise map data in the second file format;

[0053] S4: Each central station sends the noise map data to the server through the network;

[0054] S5: The server automatically publishes the noise map data to the spatial geographic information sharing software;

[0055] S6: The front end sends a request to the server, obtains the noise map data of all central stations at the target moment and performs visual display.

[0056] In this embodiment, the monitoring network uses LoRA communication technology to realize the acquisition and transmission of noise data, and combines and optimizes multiple monitoring networks to realize large-scale noise maps at the city level.

[0057] In some embodiments, the geographical model data is obtained from the WGS84 coordinate system or the CGS2000 coordinate system.

[0058] In this embodiment, the geographic model data is an shp file based on WGS84, CGS2000 or other fixed coordinate systems, and the accuracy is not lower than 1:2000.

[0059] In some embodiments, the data layers of the geographic model data include a noise prediction calculation area layer, a building layer, a bridge layer, a road traffic layer, a railway layer, an urban rail transit layer, an aircraft route layer, an industrial enterprise layer, a construction layer, a terrain layer, a greening forest layer, a sound barrier layer, and a monitoring point layer.

[0060] In this embodiment, the geographical positions of the elements in the layer are given as Polygons in topological form. The noise prediction calculation area layer includes the position and height of the noise prediction calculation area; the building layer includes the position, height, sound attenuation amount, 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, road surface material, vehicle speed, traffic flow, and three-dimensional coordinates of the reference points in the section of the road; the railway layer includes the position, width, height, vehicle speed on the railway, number of carriages in the vehicle formation, and three-dimensional coordinates of the reference points in 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 source intensity; the construction layer includes the construction position; the terrain layer includes terrain areas at different altitudes, height, and sound reflection coefficient; the greening forest layer includes the position, height, and sound attenuation amount of the greening forest; the sound barrier layer includes the position, height, whether it is fully enclosed, sound attenuation amount, and sound reflection coefficient; the monitoring point layer includes the three-dimensional coordinate data of the monitoring points.

[0061] In some embodiments, the noise data includes the minute Leq value, and the Leq value, daytime sound pressure level Ld, and nighttime sound pressure level Ln of each micro-station are calculated through the minute Leq value.

[0062] In some embodiments, step S3 further includes the following steps:

[0063] S31: Run the geographic information processing module, load the geographic model data, and perform normalization processing on the geographic coordinates;

[0064] S32: Run the grid segmentation module, and use Delaunay triangulation in the noise prediction calculation area to obtain calculation grids and generate noise prediction points;

[0065] S33: Run the data update module, and load the noise data of the monitoring points, the air pressure, humidity, temperature, and wind speed at the current moment;

[0066] S34: running the noise source intensity processing module to update the sound pressure level value of the noise source;

[0067] S35: running the noise prediction module to calculate the contribution of the noise source to the sound pressure level of the noise prediction point;

[0068] 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;

[0069] S37: Run the noise map data generation module to generate and store color noise map data using interpolation points.

[0070] 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.

[0071] In some embodiments, the S34 comprises the following steps:

[0072] S341: Arrange the monitoring points in descending order according to the noise values;

[0073] S342: Match the three-dimensional coordinates of the first monitoring point with the three-dimensional coordinates of the reference point of the noise source;

[0074] 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;

[0075] 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;

[0076] S345: Arrange the remaining monitoring points in descending order according to the noise values;

[0077] S346: Repeat steps S333-S335 until all monitoring points are matched.

[0078] In some embodiments, a central station connects multiple micro stations to establish a monitoring network within the target area.

[0079] 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.

[0080] like Figure 2As shown in the figure, it is the process of obtaining the large-scale noise map. The GeoServer server sends the geographical model data of the coverage area of each central station to the corresponding central station. Each central station receives the noise data of all the microsites in the coverage network, and through the noise prediction model algorithm embedded in the central station, obtains the GIS data (GeoTIFF file) of the coverage area of the central station. The server regularly obtains the GIS data of each central station and automatically publishes it to the GeoServer for the front-end to request and call. Finally, the front-end page displays the noise map data of all central stations at the same time according to the time. In addition, considering the data traffic and quality between the microsites and the central stations, the frequency of generating the noise map data by the central station is 1 sheet / min.

[0081] The second aspect of the present invention provides: A large-scale noise map dynamic update system for implementing any of the above large-scale noise map dynamic update methods, including:

[0082] A geographical model data acquisition module, configured to use the server to load the geographical model data of the target city, obtain the corresponding geographical model data according to the monitoring ranges covered by different central stations, and send it to the corresponding central station in the first file format;

[0083] A noise data acquisition module, configured to use each central station to acquire the noise data of the microsites that have established connections;

[0084] A noise map data generation module, configured to start the noise prediction model of each central station to generate the noise map data in the second file format;

[0085] A data transmission module, configured to send the noise map data to the server through the network by each central station;

[0086] A visualization module, configured to automatically publish the noise map data to the spatial geographical 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 moment and perform visualization display.

[0087] The third aspect of the present invention provides: A computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are loaded and executed by a processor, any of the above large-scale noise map dynamic update methods is implemented.

[0088] The above are only the 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 regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be changed within the scope of the concept described herein through the above teachings or the technology or knowledge in related fields. As long as the changes and variations made by those skilled in the art do not depart from the spirit and scope of the present invention, they should all be within the protection scope of the appended claims of 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 display it visually; The S3 further comprises the following steps: S31: running the geographic information processing module, loading the 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: running a noise map data generation module, generating and storing color noise map data using interpolation points; The S34 comprises the following steps: S341: Arrange the monitoring points in descending order according to the noise values; 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.

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: A central station connects multiple micro stations to establish a monitoring network within the target area.

6. The large-scale noise map dynamic updating method according to any one of claims 1 to 5, 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.

7. 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 6, 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.

8. 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 6 is implemented.

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