Road meteorological data acquisition method and system based on meteorological bulletin image

Abstract

The invention discloses a highway meteorological data acquisition method and system based on meteorological bulletin images, and belongs to the technical field of meteorological data processing and geographic information systems. The method comprises the following steps: acquiring a meteorological bulletin image from the China Meteorological Bureau, and extracting effective meteorological data through preprocessing operations such as image boundary determination, scale-pixel conversion and data denoising; converting image pixel coordinates into latitude and longitude coordinates, and superposing the latitude and longitude coordinates with a road vector map to generate a road weather chart; identifying meteorological values based on a color threshold method, and extracting a longitude and latitude sequence of a target road section in combination with a region growing algorithm; and calculating continuous meteorological data by adopting a point-by-point interpolation method, and finally displaying meteorological distribution along the highway through a visualization technology. The problems that an existing meteorological data acquisition mode is high in cost, complex in operation and low in reliability are solved, low-cost and high-precision automatic extraction of the highway meteorological data is achieved, and real-time data support is provided for traffic management and disaster early warning. The system can be widely applied to the fields of highway meteorological monitoring, geological disaster early warning and the like.

Description

Technical Field

[0001] This invention relates to the field of meteorological data processing and geographic information system technology, specifically to a method and system for acquiring highway meteorological data based on meteorological bulletin images. Background Technology

[0002] Currently, meteorological data acquisition plays a crucial role in fields such as highway transportation and geological disaster monitoring. There are three main methods for acquiring meteorological data: meteorological bureau API interfaces, third-party meteorological data API interfaces, and self-built meteorological stations and meteorological data management platforms. While meteorological bureau API interfaces provide authoritative data, the calling process is cumbersome and commercially expensive, especially for large-scale or high-frequency data demands, resulting in a heavy economic burden. Third-party API interfaces simplify the calling process, but data quality varies, real-time performance and accuracy are difficult to guarantee, and the long-term cost accumulation issue is also prominent. Self-built meteorological stations and meteorological data management platforms can achieve customized monitoring, but they require significant hardware investment, are complex to maintain, and are limited by geographical conditions, making it difficult to cover a wide area, resulting in insufficient flexibility and universality of data acquisition.

[0003] Furthermore, traditional methods have shortcomings in integrating data visualization with geographic information. The fusion of meteorological data and highway geographic information often relies on multiple independent systems, resulting in excessively long and inefficient data processing chains. Moreover, the visualization results lack interactivity and fail to meet real-time decision-making needs. For example, geological disaster early warning requires the rapid integration of meteorological and geographic data, but current technologies struggle to achieve low-cost, high-timeliness integrated processing.

[0004] Therefore, there is an urgent need for a meteorological data extraction and visualization method that can balance low cost, high reliability, and ease of operation. By directly utilizing publicly available meteorological bulletin images and combining image processing and geographic information technology, the shortcomings of API interfaces and self-built systems can be effectively avoided, providing a more efficient and economical solution for highway meteorological monitoring. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a method and system for acquiring highway meteorological data based on meteorological bulletin images, which can acquire highway meteorological data conveniently and at low cost.

[0006] This invention is achieved through the following technical solution: A method for acquiring highway meteorological data based on meteorological bulletin images includes the following steps: Convert the pixel coordinates of the meteorological bulletin image of the target area into latitude and longitude coordinates to obtain the latitude and longitude data of the pixels in the meteorological bulletin image; Generate a highway map image based on the highway latitude and longitude data of the target area; By using image overlay technology and combining latitude and longitude data, meteorological bulletin images and highway map images are superimposed to generate highway meteorological maps; Based on the mapping relationship between color and meteorological data in meteorological bulletin images, highway meteorological data is obtained by combining highway latitude and longitude data from highway map images; Extract the regional image of the target highway segment from the highway meteorological map, and determine the latitude and longitude sequence of the target highway segment based on the boundary points of the regional image; Meteorological data for the target highway section are obtained by calculating the meteorological data of each latitude and longitude point on the latitude and longitude sequence of the highway meteorological data.

[0007] Preferably, the step of converting the pixel coordinates of the meteorological bulletin image of the target area into latitude and longitude coordinates includes: Based on the scale of the meteorological bulletin image and the pixel length corresponding to the scale, determine the actual physical distance per unit pixel in the meteorological bulletin image; Then, based on the latitude and longitude data of the two diagonal points on the meteorological bulletin image, and combined with the actual physical distance of each pixel in the meteorological bulletin image, the pixel coordinates of the meteorological bulletin image are converted into latitude and longitude coordinates.

[0008] Preferably, generating a highway map image based on the highway latitude and longitude data of the target area includes: Acquire the latitude and longitude data of the highways in the target area, construct a highway vector map based on the highway latitude and longitude data, and convert the highway vector map into a highway map image using visualization drawing techniques.

[0009] Preferably, the step of using image overlay technology and combining latitude and longitude data to overlay meteorological bulletin images with highway map images to generate highway meteorological maps includes: Make the meteorological bulletin image and the highway map image the same size and resolution, set the transparency of the meteorological bulletin image and the highway map image, and use latitude and longitude data to synthesize the meteorological bulletin image and the highway map image into a highway meteorological map.

[0010] Preferably, the step of obtaining highway meteorological data based on the mapping relationship between colors and meteorological data in meteorological bulletin images, combined with highway latitude and longitude data from highway map images, includes: A mapping relationship between the color of meteorological bulletin images and meteorological data is established. The color thresholding method is used to identify the pixel regions corresponding to meteorological values. Then, the highway latitude and longitude data of highway map images are combined to obtain highway meteorological data.

[0011] Preferably, the step of extracting the regional image of the target highway segment from the highway meteorological map and determining the latitude and longitude sequence of the target highway segment based on the boundary points of the regional image includes: A region growing algorithm is used, with the starting pixel of the target highway segment as the seed point, and the region image of the target highway segment is gradually grown according to a pre-set similarity criterion. Boundary point coordinates are obtained by extracting the boundary from the image of the region. These boundary point coordinates are then converted into latitude and longitude coordinates to obtain the latitude and longitude sequence of the target highway segment.

[0012] Preferably, the step of calculating the meteorological data of each latitude and longitude point on the latitude and longitude sequence of the target highway segment based on highway meteorological data to obtain the meteorological data of the target highway segment includes: Based on the point-by-point interpolation method, the meteorological data of the missing latitude and longitude coordinates in the latitude and longitude sequence of the target highway section are filled in to obtain the continuous meteorological data of the target highway section.

[0013] Preferably, the step of converting the pixel coordinates of the meteorological bulletin image of the target area into latitude and longitude coordinates includes: The meteorological bulletin images of the target area are preprocessed, including contour extraction, smoothing and noise reduction of the original meteorological bulletin images of the target area.

[0014] Preferably, after obtaining the meteorological data for the target highway section, the method further includes: A visualization image is constructed based on meteorological data of the target highway section.

[0015] A highway meteorological data acquisition system based on meteorological bulletin images includes: The latitude and longitude module is used to convert the pixel coordinates of the meteorological bulletin image of the target area into latitude and longitude coordinates, and to obtain the latitude and longitude data of the pixels in the meteorological bulletin image; The map module is used to generate a highway map image based on the highway latitude and longitude data of the target area; The synthesis module is used to overlay meteorological bulletin images with highway map images using image overlay technology and latitude and longitude data to generate highway meteorological maps; The meteorological module is used to obtain highway meteorological data based on the mapping relationship between colors and meteorological data in meteorological bulletin images, combined with the latitude and longitude data of highway map images. The highway segment module is used to extract the regional image of the target highway segment from the highway meteorological map and determine the latitude and longitude sequence of the target highway segment based on the boundary points of the regional image. The highway section meteorological module is used to calculate the meteorological data of each latitude and longitude point on the latitude and longitude sequence of the target highway section based on highway meteorological data, and obtain the meteorological data of the target highway section.

[0016] Compared with the prior art, the present invention has the following beneficial technical effects: This application provides a method for acquiring highway meteorological data based on meteorological bulletin images. Its core lies in combining image processing with geographic information technology to achieve low-cost, high-efficiency, and high-reliability meteorological data extraction and visualization. The method first converts the pixel coordinates of the target area's meteorological bulletin image into latitude and longitude coordinates, ensuring the geographic positioning accuracy of the data. Then, a highway map image is generated based on the highway's latitude and longitude data, providing a foundational layer for subsequent data fusion. Through image overlay technology, the meteorological bulletin image and the highway map image are accurately synthesized, visually displaying the meteorological distribution along the highway. Furthermore, by utilizing the mapping relationship between color and meteorological data, combined with the highway's latitude and longitude data, highway meteorological data is accurately extracted. Finally, through a region growing algorithm and point-by-point interpolation, continuous acquisition of meteorological data for the target highway section is achieved. This technical solution effectively avoids the drawbacks of traditional API interface calls (cumbersome and costly) and the large hardware investment and complex operation and maintenance of self-built meteorological stations. By directly utilizing publicly available meteorological bulletin images, it significantly reduces data acquisition costs while ensuring the real-time nature and reliability of the data. In addition, the method is simple to operate and easy to automate and scale up, providing strong data support and technical guarantee for fields such as highway meteorological monitoring and geological disaster early warning.

[0017] This application also proposes a highway meteorological data acquisition system based on meteorological bulletin images, an electronic device, and a computer storage medium, which possess all the advantages of the aforementioned highway meteorological data acquisition method based on meteorological bulletin images. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of the method for acquiring highway meteorological data based on meteorological bulletin images according to the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0021] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0022] A method for acquiring highway meteorological data based on meteorological bulletin images includes the following steps: Step 1: Convert the pixel coordinates of the meteorological bulletin image of the target area into latitude and longitude coordinates, and obtain the latitude and longitude data of the pixels in the meteorological bulletin image; This step establishes a mapping relationship between pixels and actual geographical locations using image scales and known geographic coordinates. First, the actual physical distance corresponding to each pixel is calculated using the scale information in the meteorological bulletin image. Then, combined with the latitude and longitude data of the image's diagonal points, the coordinates of any pixel are converted into latitude and longitude coordinates. Its purpose is to lay the foundation for accurate matching of subsequent meteorological data and highway geographic information, ensuring the spatial accuracy of data extraction and visualization, and avoiding information deviations caused by inconsistent coordinate systems.

[0023] Step 2: Generate a highway map image based on the highway latitude and longitude data of the target area; Geographic Information System (GIS) technology is used to transform the latitude and longitude data of highways into vector maps. First, the latitude and longitude coordinates of all highways within the target area are collected. A GeoDataFrame data structure is constructed using libraries such as GeoPandas. Then, tools such as Matplotlib are used to generate visualized highway vector map images. Its purpose is to transform abstract highway spatial data into an intuitive graphical representation, providing a base layer for subsequent overlay with meteorological images, and facilitating users to quickly identify highway directions and distribution characteristics.

[0024] Step 3: Using image overlay technology and combining latitude and longitude data, the meteorological bulletin image and the highway map image are overlaid to generate a highway meteorological map; Based on image registration technology, the resolution and size of two images are unified, and their spatial positions are aligned using latitude and longitude coordinates. Image fusion methods, such as transparency adjustment, are used to overlay meteorological data layers onto the highway vector map. Its purpose is to achieve seamless integration of meteorological information and highway geographic information, intuitively displaying the weather distribution along the highway. For example, color gradients can reflect weather conditions on different road sections, improving data readability and decision support efficiency.

[0025] Step 4: Based on the mapping relationship between colors and meteorological data in the meteorological bulletin image, and combined with the latitude and longitude data of the highway map image, obtain highway meteorological data; This method analyzes the correspondence between colors and meteorological values ​​in meteorological bulletin images, identifies pixel regions representing specific meteorological data, and then associates them with specific road sections using highway latitude and longitude data. Its purpose is to transform visual information in images into structured meteorological data, solving the data lag problem of traditional API interfaces while reducing data acquisition costs, making it particularly suitable for scenarios with high real-time requirements. Step 5: Extract the regional image of the target highway segment from the highway meteorological map, and determine the latitude and longitude sequence of the target highway segment based on the boundary points of the regional image; This algorithm uses a region growing mechanism to extract target road segments from highway weather maps. Starting with a user-specified starting point as the seed point, it expands the region boundaries based on color or texture similarity, ultimately converting the boundary pixel coordinates into a latitude and longitude sequence. Its purpose is to accurately locate the spatial extent of road segments of interest to the user, providing a spatial framework for subsequent meteorological data interpolation and ensuring the relevance and completeness of data extraction, such as focusing on road segments at high risk of geological disasters.

[0026] Step 6: Calculate the meteorological data of each latitude and longitude point on the latitude and longitude sequence of the target highway section based on the highway meteorological data to obtain the meteorological data of the set highway section.

[0027] Based on point-by-point interpolation, the system fills in the missing values ​​of the target road segment sequence with meteorological data from known latitude and longitude points, while also optimizing the data to ensure its spatiotemporal continuity. Its purpose is to generate a continuous and complete meteorological dataset along the highway, supporting refined analysis and providing high-resolution data support for traffic management or disaster early warning.

[0028] In some embodiments, after acquiring the meteorological bulletin image of the target area, the meteorological bulletin image of the target area is preprocessed to obtain a standardized meteorological bulletin image of the target area.

[0029] The preprocessing includes contour extraction, smoothing, and noise reduction of the meteorological bulletin images of the target area.

[0030] Contour extraction (such as Canny edge detection and contour detection algorithms) can accurately locate the effective region boundary of meteorological bulletin images, eliminate interference from irrelevant background information, and ensure that subsequent processing is only applied to the effective meteorological data area. Smoothing processing (such as Gaussian filtering) and noise reduction processing (such as wavelet transform) can effectively eliminate random noise and interference signals in the image, reduce errors in the data extraction process, and improve the accuracy and reliability of meteorological data, especially suitable for meteorological bulletin images with low quality or complex backgrounds.

[0031] The preprocessed standardized images have a uniform format and quality, providing stable and consistent input for subsequent operations such as pixel-to-latitude / longitude conversion, color mapping, and image overlay, reducing algorithm complexity and improving overall processing efficiency. Furthermore, the standardized preprocessing workflow can adapt to meteorological bulletin images from different sources or resolutions, enhancing the method's versatility and scalability, making it more robust in practical applications, such as handling diverse bulletin images issued by different regions or meteorological agencies.

[0032] In some embodiments, converting the pixel coordinates of a meteorological bulletin image of a target area into latitude and longitude coordinates includes: First, based on the scale of the meteorological bulletin image and the pixel length corresponding to the scale, determine the actual physical distance of a unit pixel in the meteorological bulletin image; Then, based on the latitude and longitude data of the two diagonal points on the meteorological bulletin image, and combined with the actual physical distance of each pixel in the meteorological bulletin image, the pixel coordinates of the meteorological bulletin image are converted into latitude and longitude coordinates.

[0033] This method establishes a precise spatial scale benchmark through scale pixel ratio conversion and, combined with a bilinear interpolation model of diagonal latitude and longitude, achieves sub-pixel-level geographic coordinate transformation accuracy. Compared to methods that solely rely on geographic reference points, this physical scale-based conversion method reduces errors caused by projection distortion, making it particularly suitable for meteorological image processing over large areas. Furthermore, this method requires only a scale and two diagonal points as basic parameters, resulting in a computational complexity of only 0 / 1, significantly outperforming complex transformation algorithms requiring multi-point registration while maintaining accuracy.

[0034] By decoupling the scale conversion and coordinate transformation steps, the method can adapt to meteorological bulletin images from different sources and with different projection methods. Scale detection can automatically correct scale distortions caused during image scanning or shooting, while the diagonal-point-based linear interpolation model has low equipment requirements, can be implemented without specialized GIS software, and fully meets the accuracy requirements of highway meteorological monitoring.

[0035] In some embodiments, step 2, generating a highway map image based on the highway latitude and longitude data of the target area, includes: First, it should be noted that the highway latitude and longitude data of the target area refers to the latitude and longitude coordinates of all highways in the target area. The target highway segment below refers to the highway queried by the user, that is, the target highway segment is one or more highways in the target area.

[0036] First, obtain the latitude and longitude data of the highways in the target area, and then construct a highway vector map based on the highway latitude and longitude data; Then, the highway vector map is converted into a highway map image using visualization drawing techniques.

[0037] By directly processing raw latitude and longitude data using geographic information libraries such as GeoPandas, topologically accurate vector maps are constructed (e.g., generating LineString geometric objects). Compared to rasterization, this method fully preserves the spatial topology and attribute information of the road network. The Matplotlib-based visualization transformation supports dynamic adjustment of style parameters such as line width and color. In some embodiments, image overlay technology is used in conjunction with latitude and longitude data to overlay meteorological bulletin images with highway map images to generate highway meteorological maps, including: Make the meteorological bulletin images the same size and resolution as the highway map images; Adjust the transparency of weather bulletin images and highway map images; A highway meteorological map is created by combining meteorological bulletin images and highway map images using latitude and longitude data.

[0038] By forcibly unifying the resolution of the two images (and performing strict spatial alignment based on latitude and longitude data), sub-pixel-level matching of meteorological elements and road alignments can be achieved. Alpha channel blending technology (adjustable transparency from 30% to 70%) is used for layer synthesis, preserving the continuous distribution characteristics of meteorological data without obscuring the details of the underlying road network. For example, in rainstorm warning scenarios, the spatial correspondence between waterlogged road sections and precipitation intensity can be accurately displayed.

[0039] In some embodiments, highway meteorological data is obtained by combining the latitude and longitude data of a highway map image with the mapping relationship between colors and meteorological data in a meteorological bulletin image, including: Establish a mapping relationship between the color of meteorological bulletin images and meteorological data. Use color thresholding methods (RGB or HSV space) to identify the pixel regions corresponding to meteorological values. Then combine the highway latitude and longitude data of highway map images to obtain highway meteorological data.

[0040] It should be noted that the highway meteorological data refers to the meteorological data along all highways within the target area.

[0041] In some embodiments, extracting a regional image of the target highway segment from a highway meteorological map, and determining the latitude and longitude sequence of the target highway segment based on the boundary points of the regional image, includes: A region growing algorithm is used, with the starting pixel of the target highway segment as the seed point, and the region image of the target highway segment is gradually grown according to a pre-set similarity criterion. Boundary extraction is performed on the image of the region to obtain the coordinates of the boundary points. The coordinates of the boundary points are then converted into latitude and longitude coordinates to obtain the latitude and longitude sequence of the target highway segment.

[0042] An improved region growing algorithm is used to accurately identify target road segments in complex backgrounds by setting a dynamic similarity threshold. Combined with the Douglas-Peucker algorithm to optimize boundary extraction, a high-precision latitude and longitude sequence is generated, while fully preserving the topological features of road curves.

[0043] In some embodiments, meteorological data of each latitude and longitude point on the latitude and longitude sequence of the target highway segment are calculated based on highway meteorological data to obtain the meteorological data of the target highway segment.

[0044] Based on the point-by-point interpolation method, the meteorological data of the missing latitude and longitude coordinates in the latitude and longitude sequence of the target highway section are filled in to obtain the continuous meteorological data of the target highway section.

[0045] Example 1 See Figure 1 A method for acquiring highway meteorological data based on meteorological bulletin images includes the following steps: S1. Use search technology to obtain meteorological bulletin images from the China Meteorological Administration website.

[0046] This project utilizes web scraping technology to retrieve meteorological bulletin image data from the China Meteorological Administration website. A program using the Scrapy library in Python is written to access the bulletin page, parse the HTML code, identify the image URLs, send requests to these URLs, download the meteorological bulletin images, and save them to a specified local folder.

[0047] S2. Extract the contours of the meteorological bulletin image to obtain the effective area map of the meteorological bulletin image.

[0048] First, the effective region boundary of the meteorological bulletin image is determined. The image is then converted to grayscale using the OpenCV library. Next, the Canny edge detection algorithm is applied to find the edge contours in the image. Then, the findContours function is used to extract the outer contours. The boundary coordinates of the effective region are determined based on the coordinate information of the contours so that subsequent operations can focus on the effective region, thereby improving processing efficiency and accuracy.

[0049] S3. Based on the scale of the meteorological bulletin image, perform image scale-pixel conversion on the effective area map of the meteorological bulletin image to determine the true physical distance per unit pixel in the effective area map.

[0050] Image scale bar to pixel conversion. Locate the scale bar section in the weather bulletin image, measure its pixel length in the image, and given the actual physical distance represented by the scale bar, substitute it into the formula:

[0051] The actual distance per unit pixel in the image is calculated, providing basic parameters for the subsequent conversion of pixel coordinates into actual geographic coordinates.

[0052] S4. Smooth and reduce noise in the effective area map of the meteorological bulletin image.

[0053] Noise removal is performed on the meteorological image data. First, Gaussian filtering is used to smooth the image to remove random noise interference and make the image clearer. On this basis, wavelet transform is further used to decompose and reconstruct the image at different scales to achieve multi-scale noise removal and improve the accuracy of meteorological data extraction.

[0054] S5. Based on the latitude and longitude information of the diagonal endpoints of the effective area of ​​the meteorological bulletin image, and combined with the actual physical distance of each pixel in the effective area of ​​the meteorological bulletin image, the pixel coordinates of the effective area of ​​the meteorological bulletin image are converted into latitude and longitude coordinates to achieve the geographic positioning of meteorological data.

[0055] Convert pixel coordinates to latitude and longitude coordinates. Obtain the latitude and longitude information of the top left and bottom right corners of the image, and combine this with the image size using the formula:

[0056]

[0057] S6. Obtain the latitude and longitude data of all highways in the meteorological bulletin image, and then construct a highway map image.

[0058] The GeoPandas library was used to obtain the latitude and longitude data of all highways in the meteorological bulletin images and convert them into GeoDataFrame format for geographic data processing and analysis. Then, the Matplotlib library was used to visualize the highway vector map, generating an intuitive highway map image, providing a basic map background for subsequent overlay and merging with the meteorological bulletin images.

[0059] S7. Overlay the meteorological bulletin image with the highway map image to obtain a highway meteorological map.

[0060] To combine meteorological data with highway information, it is necessary to overlay and merge highway map images with meteorological bulletin images.

[0061] First, the meteorological bulletin images and highway map images are preprocessed to ensure that their size and resolution are uniform, so as to ensure that the information in the superimposed images can correspond accurately.

[0062] Then, image overlay technology is used, such as setting the transparency of the meteorological bulletin image, and using latitude and longitude data to synthesize the meteorological bulletin image and the highway vector map image into a highway meteorological map. This highway meteorological map not only retains the geographical distribution information of the highway, but also displays the corresponding meteorological data, intuitively reflecting the meteorological conditions along the highway.

[0063] S8. Obtain meteorological data along the highway based on meteorological bulletin images.

[0064] Extracting meteorological data from the pixel colors of meteorological bulletin images. Analyzing the mapping relationship between colors and meteorological data in meteorological bulletin images, such as different colors representing different temperature ranges or precipitation levels. Using Python's OpenCV or Matplotlib libraries, the image data is converted to RGB or HSV color spaces. Based on a set color threshold range, the pixel regions in the image corresponding to the meteorological values ​​are identified. Combined with the latitude and longitude data of highway maps, meteorological data such as temperature, rainfall, and wind speed along all highways can be extracted.

[0065] S9. Obtain the latitude and longitude sequence of the target highway section based on the meteorological bulletin image.

[0066] A region growing algorithm is employed, using the starting pixel of the target highway segment as the seed point. Based on pre-defined similarity criteria, including pixel color and grayscale, the highway region is gradually grown. Then, the boundary of the target highway segment is extracted, obtaining a series of boundary point coordinates, which are converted into a latitude and longitude sequence to accurately represent the geographical location and direction of the target highway segment, providing spatial basis for subsequent acquisition of meteorological data for the highway area.

[0067] S10. Obtain the corresponding meteorological data based on the latitude and longitude sequence of the target highway section to obtain the meteorological data of the target highway section.

[0068] After obtaining the latitude and longitude sequence of the target highway segment, the corresponding meteorological data is acquired using this sequence. Based on point-by-point interpolation methods, such as nearest-neighbor interpolation, the meteorological data for each latitude and longitude point on the target highway segment is calculated according to the values ​​of surrounding known meteorological data points. Simultaneously, considering the timeliness and spatial continuity of the meteorological data, the interpolation calculation is optimized to ensure that the acquired meteorological data accurately reflects the actual meteorological conditions along the highway, providing reliable real-time meteorological data support for applications such as geological disaster monitoring.

[0069] S11. Construct a visualization image based on the highway section and the corresponding meteorological data.

[0070] Visualization libraries such as Matplotlib and Folium are used to visualize highway and meteorological data. Matplotlib can be used to create static charts, such as vector maps of highways and distribution maps of meteorological data; Folium can create interactive maps, displaying detailed meteorological data such as temperature and precipitation at different locations along the highway by adding markers, pop-ups, and other elements. It can also be combined with overlay and heatmap functions to intuitively present the spatial relationship and trends between highway and meteorological data, facilitating user viewing and analysis. This provides strong data support and intuitive visualization for decision-making in areas such as geological disaster monitoring and traffic management.

[0071] This method for acquiring highway meteorological data based on meteorological bulletin images obtains meteorological bulletin images from the China Meteorological Administration, and extracts highway meteorological data through operations such as image boundary determination, scale-to-pixel conversion, and data denoising. The data is then visualized, solving the problems of high cost, complex operation, or low reliability of existing meteorological data acquisition methods. It can provide convenient, reliable, and low-cost meteorological data for projects such as geological disaster monitoring.

[0072] Example 2 This application also provides a highway meteorological data acquisition system based on meteorological bulletin images, including: The latitude and longitude module is used to convert the pixel coordinates of the meteorological bulletin image of the target area into latitude and longitude coordinates, and to obtain the latitude and longitude data of the pixels in the meteorological bulletin image; The map module is used to generate a highway map image based on the highway latitude and longitude data of the target area; The synthesis module is used to overlay meteorological bulletin images with highway map images using image overlay technology and latitude and longitude data to generate highway meteorological maps; The meteorological module is used to obtain highway meteorological data based on the mapping relationship between colors and meteorological data in meteorological bulletin images, combined with the latitude and longitude data of highway map images. The highway segment module is used to extract the regional image of the target highway segment from the highway meteorological map and determine the latitude and longitude sequence of the target highway segment based on the boundary points of the regional image. The highway section meteorological module is used to calculate the meteorological data of each latitude and longitude point on the latitude and longitude sequence of the target highway section based on highway meteorological data, and obtain the meteorological data of the target highway section.

[0073] It should be noted that, in the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another device, or some features may be ignored or not executed. The modules described as separate components may or may not be physically separated. The components shown as modules may be one or more physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the modules can be selected to achieve the purpose of the solution in this embodiment according to actual needs.

[0074] Furthermore, in the various embodiments of the present invention, the modules can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The integrated unit described above can be implemented in hardware or as a software functional unit.

[0075] An electronic device provided in this application includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the method for acquiring highway meteorological data based on meteorological bulletin images as described in any of the above embodiments.

[0076] Another electronic device provided in this application embodiment may further include: an input port connected to a processor for transmitting multimodal data collected by an external acquisition device to the processor; a display unit connected to the processor for displaying the processor's processing results to the outside world; and a communication module connected to the processor for enabling communication between the electronic device and the outside world. The display unit may be a display panel, a laser scanning display, etc.; the communication method adopted by the communication module includes, but is not limited to, Mobile High Definition Link (HML), Universal Serial Bus (USB), High Definition Multimedia Interface (HDMI), and wireless connection (including Wi-Fi, Bluetooth, Bluetooth Low Energy, and IEEE 802.11s-based communication technology).

[0077] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the steps of the method for acquiring highway meteorological data based on meteorological bulletin images as described in any of the above embodiments.

[0078] For descriptions of relevant parts of the highway meteorological data acquisition system, electronic equipment, and computer-readable storage medium based on meteorological bulletin images provided in this application's embodiments, please refer to the detailed descriptions of the corresponding parts in the highway meteorological data acquisition method based on meteorological bulletin images provided in this application's embodiments; they will not be repeated here. Furthermore, parts of the technical solutions provided in this application that are consistent with the implementation principles of corresponding technical solutions in the prior art have not been described in detail to avoid excessive elaboration.

[0079] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for acquiring highway meteorological data based on meteorological bulletin images, characterized in that, Includes the following steps: Convert the pixel coordinates of the meteorological bulletin image of the target area into latitude and longitude coordinates to obtain the latitude and longitude data of the pixels in the meteorological bulletin image; Generate a highway map image based on the highway latitude and longitude data of the target area; By using image overlay technology and combining latitude and longitude data, meteorological bulletin images and highway map images are superimposed to generate highway meteorological maps; Based on the mapping relationship between color and meteorological data in meteorological bulletin images, highway meteorological data is obtained by combining highway latitude and longitude data from highway map images; Extract the regional image of the target highway segment from the highway meteorological map, and determine the latitude and longitude sequence of the target highway segment based on the boundary points of the regional image; Meteorological data for the target highway section are obtained by calculating the meteorological data of each latitude and longitude point on the latitude and longitude sequence of the highway meteorological data.

2. The method for acquiring highway meteorological data based on meteorological bulletin images according to claim 1, characterized in that, The process of converting the pixel coordinates of the meteorological bulletin image of the target area into latitude and longitude coordinates includes: Based on the scale of the meteorological bulletin image and the pixel length corresponding to the scale, determine the actual physical distance per unit pixel in the meteorological bulletin image; Then, based on the latitude and longitude data of the two diagonal points on the meteorological bulletin image, and combined with the actual physical distance of each pixel in the meteorological bulletin image, the pixel coordinates of the meteorological bulletin image are converted into latitude and longitude coordinates.

3. The method for acquiring highway meteorological data based on meteorological bulletin images according to claim 1, characterized in that, The process of generating a highway map image based on the highway latitude and longitude data of the target area includes: Acquire the latitude and longitude data of the highways in the target area, construct a highway vector map based on the highway latitude and longitude data, and convert the highway vector map into a highway map image using visualization drawing techniques.

4. The method for acquiring highway meteorological data based on meteorological bulletin images according to claim 1, characterized in that, The method of using image overlay technology and combining latitude and longitude data to overlay meteorological bulletin images with highway map images to generate highway meteorological maps includes: Make the meteorological bulletin image and the highway map image the same size and resolution, set the transparency of the meteorological bulletin image and the highway map image, and use latitude and longitude data to synthesize the meteorological bulletin image and the highway map image into a highway meteorological map.

5. The method for acquiring highway meteorological data based on meteorological bulletin images according to claim 1, characterized in that, The process of obtaining highway meteorological data based on the mapping relationship between colors and meteorological data in meteorological bulletin images, combined with highway latitude and longitude data from highway map images, includes: A mapping relationship between the color of meteorological bulletin images and meteorological data is established. The color thresholding method is used to identify the pixel regions corresponding to meteorological values. Then, the highway latitude and longitude data of highway map images are combined to obtain highway meteorological data.

6. The method for acquiring highway meteorological data based on meteorological bulletin images according to claim 1, characterized in that, The step of extracting the regional image of the target highway segment from the highway meteorological map and determining the latitude and longitude sequence of the target highway segment based on the boundary points of the regional image includes: A region growing algorithm is used, with the starting pixel of the target highway segment as the seed point, and the region image of the target highway segment is gradually grown according to a pre-set similarity criterion. Boundary extraction is performed on the image of the region to obtain the coordinates of the boundary points. The coordinates of the boundary points are then converted into latitude and longitude coordinates to obtain the latitude and longitude sequence of the target highway segment.

7. The method for acquiring highway meteorological data based on meteorological bulletin images according to claim 1, characterized in that, The process of calculating meteorological data for each latitude and longitude point on the target highway segment based on highway meteorological data, to obtain meteorological data for the target highway segment, includes: Based on the point-by-point interpolation method, the meteorological data of the missing latitude and longitude coordinates in the latitude and longitude sequence of the target highway section are filled in to obtain the continuous meteorological data of the target highway section.

8. The method for acquiring highway meteorological data based on meteorological bulletin images according to claim 1, characterized in that, The process of converting the pixel coordinates of the meteorological bulletin image of the target area into latitude and longitude coordinates includes: The meteorological bulletin images of the target area are preprocessed, including contour extraction, smoothing and noise reduction of the original meteorological bulletin images of the target area.

9. A method for acquiring highway meteorological data based on meteorological bulletin images according to claim 1, characterized in that, After obtaining the meteorological data for the target highway section, the process also includes: A visualization image is constructed based on meteorological data of the target highway section.

10. A highway meteorological data acquisition system based on meteorological bulletin images, characterized in that, include: The latitude and longitude module is used to convert the pixel coordinates of the meteorological bulletin image of the target area into latitude and longitude coordinates, and to obtain the latitude and longitude data of the pixels in the meteorological bulletin image; The map module is used to generate a highway map image based on the highway latitude and longitude data of the target area; The synthesis module is used to overlay meteorological bulletin images with highway map images using image overlay technology and latitude and longitude data to generate highway meteorological maps; The meteorological module is used to obtain highway meteorological data based on the mapping relationship between colors and meteorological data in meteorological bulletin images, combined with the latitude and longitude data of highway map images. The highway segment module is used to extract the regional image of the target highway segment from the highway meteorological map and determine the latitude and longitude sequence of the target highway segment based on the boundary points of the regional image. The highway section meteorological module is used to calculate the meteorological data of each latitude and longitude point on the latitude and longitude sequence of the target highway section based on highway meteorological data, and obtain the meteorological data of the target highway section.

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