Atmospheric Environment Simulation Diffusion Method and System Based on GIS and Virtual Reality

Abstract

This invention relates to the field of atmospheric environmental risk early warning technology, and in particular to an atmospheric environmental simulation diffusion method and system based on GIS and virtual reality. In simulating atmospheric environmental diffusion, this invention first acquires multi-source data for the simulation, and then processes the multi-source data. During the data processing step, when simulating atmospheric diffusion of pollution sources, a connection is established between the resolution of the geographic grid and the resolution of the pollution source data grid. This reduces information loss due to data mismatch during data fusion and improves the reliability of the atmospheric environmental diffusion simulation results.

Description

Technical Field

[0001] This invention relates to the field of atmospheric environmental risk early warning technology, and in particular to an atmospheric environmental simulation diffusion method and system based on GIS and virtual reality. Background Technology

[0002] Since the 1980s, with the rapid development of information technology, the popularization of the Internet, the construction of the information superhighway, and the introduction of the "Digital Earth" concept, a powerful wave of informatization has swept the globe. With the rapid development of industrialization and urbanization, air quality, as a crucial aspect of urbanization, has received increasing attention. Accurate simulation and prediction of the diffusion of air pollutants are of great significance for environmental monitoring, pollution control, and emergency response. Existing atmospheric environment simulation systems are mostly based on traditional numerical models, such as the Gaussian diffusion model, but these models have shortcomings in spatial visualization and interactivity. In recent years, GIS technology has made significant progress in spatial data management and analysis. However, existing studies, when combining GIS data and pollution data, only set the resolution of the GIS data and pollution source data separately, without considering the grid mismatch problem during simulation, which may affect the realism of the simulation results. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides an atmospheric environment simulation diffusion method and system based on GIS and virtual reality, which solves the problems existing in the prior art.

[0004] This invention provides an atmospheric environment simulation diffusion method based on GIS and virtual reality, comprising:

[0005] S1: Collect multi-source data, including GIS data, pollution source data, and meteorological data;

[0006] S2: Perform data processing operations on the multi-source data to obtain processed multi-source data;

[0007] The processing operations on the multi-source data include GIS data processing and pollution source data processing;

[0008] The GIS data processing involves converting the GIS data into a high-resolution geographic grid.

[0009] The pollution source data processing involves converting discrete air quality monitoring data into continuous air quality monitoring data.

[0010] Specifically:

[0011] The pollution source data is converted into a pollution source data grid.

[0012] The resolution of the pollution source data grid is determined based on the set grid resolution of the atmospheric environment diffusion simulation and the resolution of the geographic grid. Discrete air quality monitoring data is then converted into continuous air quality monitoring data based on the resolution of the pollution source data grid.

[0013] S3: Establish an atmospheric diffusion model;

[0014] S4: Input meteorological data and processed pollution source data into the atmospheric diffusion model to obtain pollutant diffusion results;

[0015] S5: The processed GIS data grid and pollutant diffusion results are dynamically displayed in the virtual reality scene tactical module.

[0016] Preferably, in step S2, the specific formula for determining the resolution of the pollution source data grid based on the set grid resolution of the atmospheric environment diffusion simulation and the resolution of the geographic grid is as follows:

[0017]

[0018]

[0019] In the formula, Let be the resolution value of the pollution source data grid, and m be the adjustment factor of the pollution source data grid. is the resolution value of the geographic grid, and k is the adjustment factor of the geographic grid; Set the grid resolution for atmospheric environment diffusion simulation.

[0020] Preferably, k=5.

[0021] Preferably, the GIS data includes topographic elevation, building distribution, and road network data, and the pollution source data is air quality monitoring data provided by regulatory agencies.

[0022] Preferably, the GIS data is converted into a high-resolution geographic grid using the inverse distance weighted difference (IDW) method.

[0023] Preferably, in step S3, the atmospheric diffusion model is a Gaussian model.

[0024] Preferably, in step S4, the meteorological data is measured meteorological data, including temperature, humidity, wind speed, wind direction, and air pressure.

[0025] According to another aspect of the present invention, an atmospheric environment simulation diffusion system based on GIS and virtual reality is provided. The system employs the aforementioned atmospheric environment simulation diffusion method based on GIS and virtual reality. The system includes:

[0026] A multi-source data acquisition module is used to acquire multi-source data, including GIS data, pollution source data, and meteorological data;

[0027] A multi-source data processing module is used to perform data processing operations on the multi-source data to obtain processed multi-source data.

[0028] The multi-source data processing includes GIS data processing and pollution source data processing; the pollution source data processing involves converting discrete air quality monitoring data into continuous air quality monitoring data; specifically, it involves determining the resolution of the pollution source data grid based on the resolution of the geographic grid; and determining the resolution of the pollution source data grid based on the set grid resolution of the atmospheric environment diffusion simulation and the resolution of the geographic grid.

[0029] The atmospheric diffusion model building module is used to build atmospheric diffusion models.

[0030] The pollutant diffusion calculation module is used to input meteorological data and processed pollution source data into the atmospheric diffusion model to obtain pollutant diffusion results;

[0031] The virtual reality display module is used to dynamically display the pre-processed GIS data grid and pollutant diffusion results in the virtual reality scene tactical module.

[0032] The embodiments of the present invention have the following technical effects:

[0033] In simulating atmospheric diffusion, this invention first acquires multi-source data for atmospheric diffusion simulation, and then processes the multi-source data. In the data processing step, when simulating atmospheric diffusion of pollution sources, a relationship is established between the resolution of the geographic grid and the resolution of the pollution source data grid. This reduces information loss caused by data mismatch during data fusion and improves the reliability of atmospheric diffusion simulation results. Attached Figure Description

[0034] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0035] Figure 1 This is a flowchart of an atmospheric environment simulation diffusion method based on GIS and virtual reality provided in an embodiment of the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0037] Appendix Figure 1 A flowchart of an atmospheric environment simulation diffusion method based on GIS and virtual reality is shown in the attached figure. Figure 1 As shown, an atmospheric environment simulation diffusion method based on GIS and virtual reality includes:

[0038] S1: Collect multi-source data, including GIS data, pollution source data, and meteorological data;

[0039] The GIS data includes topographic elevation, building distribution, road network, and other data. The acquisition process is as follows: high-resolution images of the target area are captured by a drone, and then GIS software is used to interpret the high-resolution images to extract geographic features, including topographic elevation, building distribution, road network, etc. The extracted geographic features are then labeled and assigned attribute values ​​to obtain the GIS data.

[0040] The pollution source data refers to air quality monitoring data provided by regulatory agencies.

[0041] S2: Perform data processing operations on the multi-source data to obtain processed multi-source data;

[0042] The multi-source data processing includes GIS data processing and pollution source data processing.

[0043] The GIS data processing specifically involves processing the GIS data to generate a high-resolution geographic grid;

[0044] In this embodiment, the inverse distance-weighted interpolation (IDW) method is used to convert the GIS data into a high-resolution geographic grid.

[0045] Specifically, the process of converting the GIS data into a high-resolution geographic grid using the inverse distance-weighted interpolation (IDW) method involves:

[0046] Determine the resolution and grid extent of the output geographic grid;

[0047] For example, the resolution of the geographic grid is 10m × 10m;

[0048] For each grid point (x0, y0), calculate its relationship with all known points (x0, y0). i ,y i The distance d(x0, x) between them i );

[0049] The calculation formula is as follows:

[0050]

[0051] Calculate the weight ω of each called number based on the distance. i ;

[0052] The calculation formula is as follows:

[0053]

[0054] In the formula, p is a power parameter, and in this embodiment p=2;

[0055] The values ​​of the grid points are calculated using the weighted average formula. ;

[0056]

[0057] In the formula, n is the total number of grid points. The values ​​of the known grid points;

[0058] Repeat the above process to produce a continuous interpolation network.

[0059] Meanwhile, the GIS data processing also includes data preprocessing operations on the GIS data, including: data cleaning, data format conversion, and coordinate system adjustment.

[0060] The data cleaning includes removing duplicate data, filling in missing values, and correcting erroneous data. Removing duplicate data involves deleting duplicate geographic features from the GIS data. Filling in missing values ​​involves using interpolation algorithms to adjust and supplement missing geographic features. Correcting erroneous data includes correcting coordinate errors or attribute errors in the GIS data.

[0061] The data format conversion involves converting the GIS data into a format supported by the GIS software. In this embodiment, it can be converted to Shapefile format, GeoJSON format, GeoTIFF format, etc.

[0062] The coordinate system one is to convert the GIS data to a unified coordinate system, for example, it can be converted to WGS84, UTM, etc.;

[0063] The pollution source data processing involves converting discrete air quality monitoring data into continuous air quality monitoring data.

[0064] Specifically, converting discrete air quality monitoring data into continuous air quality monitoring data involves:

[0065] The resolution of the pollution source data grid is determined based on the resolution of the geographic grid;

[0066] In fact, the above steps involved interpolating the GIS data using a denser grid. However, existing studies, when combining GIS data and pollution source data, only set the resolution separately for the GIS data and pollution source data, without considering the grid mismatch during simulation. This could affect the realism of the simulation results. Therefore, in this embodiment, when simulating the atmospheric diffusion of pollution sources, a correlation is established between the resolution of the geographic grid and the resolution of the pollution source data grid. Specifically:

[0067] The resolution of the pollution source data grid is determined based on the set grid resolution of the atmospheric environment diffusion simulation and the resolution of the geographic grid;

[0068] The specific formula is as follows:

[0069]

[0070]

[0071] In the formula, Let be the resolution value of the pollution source data grid, and m be the adjustment factor of the pollution source data grid. is the resolution value of the geographic grid, and k is the adjustment factor of the geographic grid; The set grid resolution for atmospheric environment diffusion simulation; in this embodiment, k=5; the set grid resolution for atmospheric environment diffusion simulation is a parameter preset during atmospheric environment diffusion simulation.

[0072] It is worth emphasizing that if the resolution of the geographic grid is 10m×10m, then the resolution value of the geographic grid is 10.

[0073] In this embodiment, the above process can reduce information loss caused by data mismatch during data fusion and improve the reliability of atmospheric environment simulation diffusion results.

[0074] S3: Establish an atmospheric diffusion model;

[0075] In this embodiment, an atmospheric diffusion model is established based on numerical simulation methods to simulate the spatial and temporal diffusion process of pollutants released from pollution sources.

[0076] The atmospheric diffusion model mentioned is a Gaussian model; the Gaussian model is a classic atmospheric diffusion model widely used to simulate the diffusion process of pollutants in the atmosphere. Based on the Gaussian distribution (normal distribution) assumption, it can quickly calculate the spatial and temporal concentration distribution of pollutants.

[0077] S4: Input meteorological data and processed pollution source data into the atmospheric diffusion model to obtain pollutant diffusion results;

[0078] The meteorological data mentioned above are measured meteorological data, including temperature, humidity, wind speed, wind direction, air pressure, etc. of the target area;

[0079] The pollutant diffusion results refer to the practical and spatial diffusion of pollutants under the meteorological conditions at that time.

[0080] S5: The processed GIS data grid and pollutant diffusion results are dynamically displayed in the virtual reality scene tactical module.

[0081] Example 2: This invention also provides an atmospheric environment simulation diffusion system based on GIS and virtual reality. The system employs an atmospheric environment simulation diffusion method based on GIS and virtual reality as described in Example 1. The system includes:

[0082] A multi-source data acquisition module is used to acquire multi-source data, including GIS data and pollution source data;

[0083] A multi-source data processing module is used to perform data processing operations on the multi-source data to obtain processed multi-source data.

[0084] The multi-source data processing includes GIS data processing and pollution source data processing; the pollution source data processing involves converting discrete air quality monitoring data into continuous air quality monitoring data; specifically, it involves determining the resolution of the pollution source data grid based on the resolution of the geographic grid; and determining the resolution of the pollution source data grid based on the set grid resolution of the atmospheric environment diffusion simulation and the resolution of the geographic grid.

[0085] The atmospheric diffusion model building module is used to build atmospheric diffusion models.

[0086] The pollutant diffusion calculation module is used to input meteorological data and processed pollution source data into the atmospheric diffusion model to obtain pollutant diffusion results;

[0087] The virtual reality display module is used to dynamically display the processed GIS data grid and pollutant diffusion results in a virtual reality scene tactical module.

[0088] Example 3: The present invention also provides an electronic device, including one or more processors and a memory.

[0089] A processor can be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and can control other components in an electronic device to perform desired functions.

[0090] The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and a processor may execute the program instructions to implement the atmospheric environment simulation diffusion method based on GIS and virtual reality described in any embodiment of this application above, and / or other desired functions. Various contents such as initial extrinsic parameters and thresholds may also be stored in the computer-readable storage medium.

[0091] In one example, the electronic device may also include input and output devices, which are interconnected via a bus system and / or other forms of connection (not shown). The input device may include, for example, a keyboard, a mouse, etc. The output device may output various information to the outside, including warning messages, braking force, etc. The output device may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0092] Of course, for simplicity, components such as buses and input / output interfaces have been omitted. In addition, depending on the specific application, the electronic device may include any other appropriate components.

[0093] In addition to the methods and devices described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the function of an atmospheric environment simulation diffusion method based on GIS and virtual reality provided in any embodiment of this application.

[0094] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0095] Furthermore, embodiments of this application may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to implement an atmospheric environment simulation diffusion method based on GIS and virtual reality provided in any embodiment of this application.

[0096] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0097] It should be noted that the terminology used in this invention is for describing specific embodiments only and is not intended to limit the scope of this application. As shown in this specification, unless the context clearly indicates otherwise, words such as "a," "an," "an," and / or "the" do not specifically refer to the singular and may include the plural. The terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element.

[0098] It should also be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Unless otherwise expressly specified and limited, the terms "installed," "connected," "linked," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components. For those skilled in the art, the specific meaning of the above terms in the present invention can be understood according to the specific circumstances.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method for simulating atmospheric environment diffusion based on GIS and virtual reality, characterized in that, include: S1: Collect multi-source data, including GIS data, pollution source data, and meteorological data; S2: Perform data processing operations on the multi-source data to obtain processed multi-source data; The processing operations on the multi-source data include GIS data processing and pollution source data processing; The GIS data processing involves: converting the GIS data into a high-resolution geographic grid; and using the inverse distance-weighted interpolation method to convert the GIS data into a high-resolution geographic grid. Specifically: Determine the resolution and grid extent of the output geographic grid; For each grid point (x0, y0), calculate its relationship with all known points (x0, y0). i ,y i The distance d(x0, x) between them i ); Calculate the weight ω of each known point based on the distance. i ; The values ​​of the grid points are calculated using the weighted average formula. ; Repeat the above process to generate a continuous interpolation network; The pollution source data processing involves converting discrete air quality monitoring data into continuous air quality monitoring data. Specifically: The pollution source data is converted into a pollution source data grid. The resolution of the pollution source data grid is determined based on the set grid resolution of the atmospheric environment diffusion simulation and the resolution of the geographic grid. Discrete air quality monitoring data is then converted into continuous air quality monitoring data based on the resolution of the pollution source data grid. In step S2, the specific formula for determining the resolution of the pollution source data grid based on the set grid resolution of the atmospheric environment diffusion simulation and the resolution of the geographic grid is as follows: In the formula, Let be the resolution value of the pollution source data grid, and m be the adjustment factor of the pollution source data grid. is the resolution value of the geographic grid, and k is the adjustment factor of the geographic grid; Set the grid resolution for atmospheric environment diffusion simulation; S3: Establish an atmospheric diffusion model; S4: Input meteorological data and processed pollution source data into the atmospheric diffusion model to obtain pollutant diffusion results; S5: The processed GIS data grid and pollutant diffusion results are dynamically displayed in the virtual reality scene tactical module.

2. The atmospheric environment simulation diffusion method based on GIS and virtual reality according to claim 1, characterized in that: The constant k=5 is related to the simulated diffusion range.

3. The atmospheric environment simulation diffusion method based on GIS and virtual reality according to claim 1, characterized in that: The GIS data includes topographic elevation, building distribution, and road network data, while the pollution source data is air quality monitoring data.

4. The atmospheric environment simulation diffusion method based on GIS and virtual reality according to claim 1, characterized in that: The GIS data is converted into a high-resolution geographic grid using the inverse distance weighted interpolation method.

5. The atmospheric environment simulation diffusion method based on GIS and virtual reality according to claim 1, characterized in that: In S3, the atmospheric diffusion model is a Gaussian model.

6. The atmospheric environment simulation diffusion method based on GIS and virtual reality according to claim 1, characterized in that: In S4, the meteorological data is measured meteorological data, including temperature, humidity, wind speed, wind direction, and air pressure.

7. An atmospheric environment simulation diffusion system based on GIS and virtual reality, characterized in that, The system employs the atmospheric environment simulation diffusion method based on GIS and virtual reality as described in any one of claims 1-6, and the system comprises: A multi-source data acquisition module is used to acquire multi-source data, including GIS data, pollution source data, and meteorological data; A multi-source data processing module is used to perform data processing operations on the multi-source data to obtain processed multi-source data. The multi-source data processing includes GIS data processing and pollution source data processing; the pollution source data processing involves converting discrete air quality monitoring data into continuous air quality monitoring data; specifically, it involves determining the resolution of the pollution source data grid based on the resolution of the geographic grid; and determining the resolution of the pollution source data grid based on the set grid resolution of the atmospheric environment diffusion simulation and the resolution of the geographic grid. The atmospheric diffusion model building module is used to build atmospheric diffusion models. The pollutant diffusion calculation module is used to input meteorological data and processed pollution source data into the atmospheric diffusion model to obtain pollutant diffusion results; The virtual reality display module is used to dynamically display the processed GIS data grid and pollutant diffusion results in a virtual reality scene tactical module.

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