A meteorological region identification method, device and computer equipment

Abstract

The application provides a meteorological region identification method and device and computer equipment, comprising: first, obtaining millimeter wave radar base data and laser scanning data of a target scanning region, and performing segmentation processing on a reflectivity factor two-dimensional image based on a reflectivity factor threshold to obtain a meteorological region; then, based on coordinate information and type information of each meteorological region, performing matching and merging processing to obtain each meteorological target block; next, performing second classification processing according to echo top height and echo bottom height of the meteorological target block to obtain a second classification result; finally, based on the laser scanning data, calculating average visibility of each type of meteorological target block in the second classification result, and performing third classification processing based on the average visibility to obtain a final classification result. The application can effectively give the spatial distribution of the meteorological region and perform automatic observation, thereby improving the accuracy of meteorological region detection.

Description

Technical Field

[0001] This application relates to the field of atmospheric science and technology, specifically to a meteorological region identification method, device, and computer equipment. Background Technology

[0002] Fog reduces ground visibility, often severely impacting human activities such as transportation and production. Therefore, strengthening the monitoring of fog formation and dissipation processes and conducting low-visibility weather monitoring and early warning systems are of great significance.

[0003] Currently, millimeter-wave radar, capable of acquiring the spatial distribution and internal structure characteristics of meteorological targets such as clouds, fog, and rain, can be more effectively applied to refined fog detection research. To observe the movement and spatial distribution of fog, millimeter-wave radar needs to be set to a low elevation angle PPI mode. Due to the lack of information such as echo top and bottom heights, it is difficult to intuitively distinguish non-significant precipitation meteorological targets such as fog, low clouds, drizzle, or clear-sky echoes on PPI images, increasing the difficulty of manual meteorological observation and reducing the efficiency of monitoring and early warning of low-visibility fog weather. While millimeter-wave radar is relatively mature in cloud observation, many studies have focused on automatic cloud echo classification techniques. However, the automatic identification and classification of different meteorological targets based on cloud and fog observation data, especially the automatic extraction and identification of low-visibility fog areas, remains a technology that millimeter-wave radar needs to explore and overcome in fog measurement applications. Summary of the Invention

[0004] This application provides a meteorological area identification method, device, and computer equipment, which improves the accuracy of meteorological area detection. The technical solution is as follows.

[0005] On the one hand, a meteorological region identification method is provided, the method comprising:

[0006] Acquire millimeter-wave radar base data and laser scanning data of the target scanning area; the millimeter-wave radar base data includes the reflectivity factor acquired by the millimeter-wave radar in PPI scanning mode and RHI scanning mode respectively;

[0007] For each scanning mode, the two-dimensional image of reflectance factor is segmented based on the reflectance factor threshold to obtain various types of meteorological regions and complete the preliminary classification.

[0008] Based on the coordinate and type information of each meteorological region, the meteorological regions are matched and merged to obtain the meteorological target blocks contained in the target scanning area.

[0009] For each meteorological target block, a second classification process is performed on the meteorological target block based on the echo top height and echo bottom height of the meteorological target block to obtain the second classification result;

[0010] Based on the laser scanning data, the average visibility of each type of meteorological target block in the second classification result is calculated, and based on the average visibility, a third classification process is performed on each type of meteorological target block to obtain the final classification result.

[0011] In another aspect, a meteorological area identification device is provided, the device comprising:

[0012] The scanning data acquisition module is used to acquire millimeter-wave radar base data and laser scanning data of the target scanning area; the millimeter-wave radar base data includes the reflectivity factor acquired by the millimeter-wave radar in PPI scanning mode and RHI scanning mode respectively;

[0013] The meteorological region acquisition module is used to segment the two-dimensional reflectance factor image for each scanning mode based on the reflectance factor threshold to obtain meteorological regions of various types and complete the preliminary classification.

[0014] The matching and merging processing module is used to match and merge the meteorological regions based on their coordinate and type information to obtain the meteorological target blocks contained in the target scanning area.

[0015] The second classification result acquisition module is used to perform a second classification process on each meteorological target block based on the echo top height and echo bottom height of the meteorological target block, so as to obtain the second classification result;

[0016] The final classification result acquisition module is used to calculate the average visibility of each type of meteorological target block in the second classification result based on the laser scanning data, and to perform a third classification process on each type of meteorological target block based on the average visibility to obtain the final classification result. In one possible implementation, the reflectivity factor threshold includes thresholds corresponding to each type of meteorological target; the various types of meteorological targets include at least one of fog droplets, cloud particles, and precipitation particles.

[0017] The meteorological region includes cloud areas, fog areas, and precipitation areas.

[0018] In one possible implementation, the two-dimensional images of each reflectivity factor include a PPI planar scan image and a RHI profile scan image.

[0019] In one possible implementation, the meteorological area acquisition module is further configured to:

[0020] Obtain the reflectance factor matrices corresponding to the PPI planar scan image and the RHI profile scan image, respectively;

[0021] Based on the reflectivity factor threshold, the reflectivity factor matrix is ​​binarized and subjected to expansion and erosion processing to segment out various types of planar meteorological regions and various types of profile meteorological regions.

[0022] In one possible implementation, the matching and merging processing module is further configured to:

[0023] In the various types of planar meteorological regions and various types of profile meteorological regions, meteorological regions whose location information meets the splicing conditions and are of the same type are merged to obtain the various meteorological target blocks contained in the target scanning area.

[0024] In one possible implementation, the location information includes coordinate information, which indicates the coordinate value of the target observation point corresponding to the meteorological area in a three-dimensional coordinate system with the millimeter-wave radar as the origin.

[0025] In one possible implementation, the second classification result acquisition module is further configured to:

[0026] For each meteorological target block, a second classification process is performed based on its echo bottom height, echo top height, and area threshold to obtain a second classification result. The types of meteorological target blocks in the second classification result include fog target blocks, low-moisture cloud target blocks, high-moisture cloud target blocks, and precipitation target blocks. In one possible implementation, the final classification result acquisition module is further used for:

[0027] Based on the laser scanning data, the geographical location information of the observation range gate of the visibility lidar is obtained;

[0028] Based on the laser scanning data and the geographical location information of the observation distance gate, the average visibility of each type of meteorological target block in the second classification result is calculated;

[0029] Based on the average visibility, a third classification process is performed on the meteorological target blocks of each type to obtain the final classification result. The types of meteorological target blocks in the final classification result include low visibility fog target blocks, clear sky echo target blocks, low water content cloud target blocks, high water content cloud target blocks, and precipitation target blocks.

[0030] In another aspect, a computer device is provided, the computer device including a processor and a memory, the memory storing at least one instruction, the at least one instruction being loaded and executed by the processor to implement a meteorological area identification method as described above.

[0031] In another aspect, a computer-readable storage medium is provided, wherein at least one instruction is stored therein, the at least one instruction being loaded and executed by a processor to implement a meteorological area identification method as described above.

[0032] The technical solution provided in this application may include the following beneficial effects:

[0033] First, millimeter-wave radar base data and laser scanning data of the target scanning area are acquired. For the two-dimensional reflectivity factor image of each scanning mode, the reflectivity factor two-dimensional image is segmented based on the reflectivity factor threshold to obtain meteorological regions of various types, completing the preliminary classification. Then, based on the coordinate and type information of each meteorological region, the meteorological regions are matched and merged to obtain each meteorological target block corresponding to the target scanning area. Next, for each meteorological target block, a second classification process is performed based on the echo top height and echo bottom height of the meteorological target block to obtain the second classification result. Finally, based on the laser scanning data, the average visibility of each type of meteorological target block in the second classification result is calculated, and based on the average visibility, a third classification process is performed on each type of meteorological target block to obtain the final classification result. In the above scheme, the micro and macro features such as the reflectivity factor threshold of millimeter-wave radar and the echo top and bottom height of different meteorological targets, as well as the visibility features of visibility lidar, are used to automatically extract and classify meteorological areas. This can effectively identify different types of meteorological targets, obtain the spatial distribution characteristics of meteorological areas, and help to realize automated observation of low-visibility fog areas. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the specific embodiments of this application or 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 this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0035] Figure 1 This is a schematic diagram illustrating the structure of a meteorological area identification system according to an exemplary embodiment.

[0036] Figure 2 This is a flowchart illustrating a meteorological area identification method according to an exemplary embodiment.

[0037] Figure 3 This is a flowchart illustrating a meteorological area identification method according to an exemplary embodiment.

[0038] Figure 4 This is a flowchart illustrating a meteorological area identification method according to an exemplary embodiment.

[0039] Figure 5 This is a structural block diagram of a meteorological area identification device according to an exemplary embodiment.

[0040] Figure 6 A structural block diagram of a computer device illustrated in an exemplary embodiment of this application is shown. Detailed Implementation

[0041] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0042] It should be understood that in the description of the embodiments of this application, the term "correspondence" may indicate that there is a direct or indirect correspondence between the two, or that there is an association between the two, or that there is a relationship of instruction and being instructed, configuration and being configured, etc.

[0043] Figure 1 This is a schematic diagram illustrating the structure of a meteorological area identification system according to an exemplary embodiment. The meteorological area identification system includes a computer device 110 and a meteorological area scanning device 120. The meteorological area scanning device 120 may include millimeter-wave radar and visibility lidar.

[0044] Optionally, the millimeter-wave radar in the meteorological area scanning device 120 and the visibility lidar in the meteorological area scanning device 120 can be observed at the same site and scanned synchronously in time and space.

[0045] Optionally, the millimeter-wave radar uses PPI and RHI modes to scan the target scanning area, while the visibility lidar uses PPI mode. Each scan cycle of the millimeter-wave radar includes one PPI mode and several RHI modes, while each scan cycle of the visibility lidar uses only one PPI mode. The scan cycles of the millimeter-wave radar and the visibility lidar are consistent.

[0046] Optionally, the meteorological area scanning device 120 can communicate with the computer device 110 through a transmission network (such as a wireless communication network). The meteorological area scanning device 120 can upload the scanning data of the target scanning area to the computer device 110 through the wireless communication network, so that the computer device 110 can process the collected scanning data and identify the meteorological area based on the scanning data.

[0047] Optionally, the computer device 110 can also wirelessly connect to the meteorological area scanning device 120 via a wireless communication network. Optionally, the computer device 110 can be implemented as a server, which can be a server cluster composed of multiple physical servers or a distributed system. It can also be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platform computing services.

[0048] Optionally, the computer device 110 can also be a terminal device, such as a personal computer, a mobile terminal, or other terminal device with certain data processing capabilities.

[0049] Optionally, the meteorological area identification system may also include a management device for managing the system (such as managing the connection status between each module and the server), and the management device is connected to the server via a communication network. Optionally, the communication network may be a wired network or a wireless network.

[0050] Optionally, the aforementioned wireless or wired networks use standard communication technologies and / or protocols. The network is typically the Internet, but can also be any other network, including but not limited to any combination of local area networks (LANs), metropolitan area networks (MANs), wide area networks (WANs), mobile, wired or wireless networks, private networks, or virtual private networks (VPNs). In some embodiments, technologies and / or formats including Hypertext Markup Language (HTML), Extensible Markup Language (XML), etc., are used to represent data exchanged over the network. Furthermore, conventional encryption technologies such as Secure Sockets Layer (SSL), Transport Layer Security (TLS), VPNs, and Internet Protocol (IP) security can be used to encrypt all or some links. In other embodiments, customized and / or dedicated data communication technologies can be used to replace or supplement the aforementioned data communication technologies.

[0051] Figure 2 This is a flowchart illustrating a meteorological area identification method according to an exemplary embodiment. The method can be implemented by, for example... Figure 1 The computer device 110 in the meteorological area identification system shown executes the operation. For example... Figure 2 As shown, the meteorological area identification method may include the following steps:

[0052] S201. Acquire millimeter-wave radar base data and laser scanning data of the target scanning area; the millimeter-wave radar base data includes the reflectivity factor acquired by the millimeter-wave radar in PPI scanning mode and RHI scanning mode respectively.

[0053] In one possible implementation, when meteorological area identification of the target scanning area is required, the target scanning area is first observed at the same site and scanned in a time and space synchronously by millimeter-wave radar and visibility lidar, thereby obtaining millimeter-wave radar base data and lidar scanning data of the target scanning area. The millimeter-wave radar scans the target scanning area using PPI mode and RHI mode.

[0054] Furthermore, the target scanning area can be any meteorological target area to be identified.

[0055] S202. For the two-dimensional image of reflectance factor for each scanning mode, the two-dimensional image of reflectance factor is segmented based on the reflectance factor threshold to obtain the meteorological regions of each type and complete the preliminary classification.

[0056] Furthermore, the radar reflectivity factor value depends only on the cloud and raindrop spectra and is proportional to the sixth power of the particle diameter. Cloud particles and precipitation particles have different scale spectral distributions, concentrated at the smaller and larger particle size ends of the spectral distribution, respectively. Therefore, cloud particles and precipitation particles can be approximately distinguished by setting a radar reflectivity factor threshold. Similarly, apart from cloud height, the biggest difference between fog and clouds is their lower water content and smaller average droplet size. Since water content also has a certain positive nonlinear relationship with the radar reflectivity factor, it can also be distinguished by setting a radar reflectivity factor threshold. However, for dense fog, thin clouds, drizzle, etc., the radar reflectivity factors are relatively similar, making it difficult to classify them using a single threshold. Therefore, in step S202, setting the radar reflectivity factor only roughly distinguishes and classifies cloud particles, precipitation particles, and fog particles, and then a more accurate reclassification is performed by calculating the echo top height and echo bottom height in subsequent steps (corresponding to step S204).

[0057] Optionally, the meteorological region includes cloud areas, fog areas, and precipitation areas.

[0058] In other words, based on the millimeter-wave radar base data obtained from millimeter-wave radar scanning, two-dimensional images of each reflectivity factor are acquired. Then, for each two-dimensional image of reflectivity factor, based on the reflectivity factor threshold corresponding to each type of meteorological target, the two-dimensional image of each reflectivity factor is segmented and processed to obtain meteorological regions of each type.

[0059] Optionally, the meteorological target may include fog droplets, cloud particles, and precipitation particles. Different meteorological targets correspond to different reflectivity factor thresholds, which can be obtained through pre-training based on historical meteorological data and prior knowledge.

[0060] S203. Based on the coordinate and type information of each meteorological region, perform matching and merging processing on each meteorological region to obtain the meteorological target blocks contained in the target scanning area.

[0061] In one possible implementation, after segmenting the two-dimensional image of each reflectance factor into corresponding meteorological regions of various types, the meteorological regions are matched and merged based on the coordinate information and type information of each meteorological region, thereby obtaining the merged meteorological regions, which form a three-dimensional meteorological target block.

[0062] After obtaining different types of meteorological regions, these regions can be merged. For example, computer equipment can identify regions with similar coordinates and the same type as matching regions, and then merge these matching meteorological regions to obtain the merged meteorological regions.

[0063] S204. For each meteorological target block, a second classification process is performed on the meteorological target block based on the echo top height and echo bottom height of the meteorological target block to obtain the second classification result.

[0064] In one possible implementation, after merging various meteorological regions into meteorological target blocks contained in the target scanning area, for each meteorological target block, the attribute information of the meteorological target block is calculated, and a second classification process is performed on the meteorological target block based on the attribute threshold to obtain the second classification result. The attribute information of the meteorological target block may include the echo top height, echo bottom height, and area of ​​the meteorological target block.

[0065] For example, when the echo top and bottom heights of a meteorological target block are identified, the vertical thickness of the target block and whether it is grounded can be determined. This allows for the differentiation between grounded fog echoes and ungrounded cloud echoes with low water content, which are difficult to distinguish using radar reflectivity factors, or between cloud echoes with high water content and precipitation echoes. At this point, computer equipment can perform secondary classification of the meteorological target block based on more information, resulting in a more accurate classification.

[0066] Optionally, the types of meteorological target blocks in the second classification result include fog target blocks, low water content cloud target blocks, high water content cloud target blocks, and precipitation target blocks.

[0067] S205. Based on the laser scanning data, calculate the average visibility of each type of meteorological target block in the second classification result, and based on the average visibility, perform a third classification process on each meteorological target block to obtain the final classification result.

[0068] In one possible implementation, the third classification process is also called classification verification. Since the visibility corresponding to different types of meteorological targets is also different, in order to further identify low-visibility fog areas and verify the classification results in this embodiment, the average visibility of each type of meteorological target block in the second classification result can be determined by laser scanning data, and the meteorological target blocks of each type can be reclassified by the visibility thresholds corresponding to different types of meteorological targets to obtain the final classification result.

[0069] Optionally, based on visibility characteristics, areas with weak reflectivity factors but high visibility can be further distinguished from fog echoes and classified as clear-sky echoes. The final classification results include meteorological target blocks of various types, such as low-visibility fog target blocks, clear-sky echo target blocks, low-moisture-content cloud target blocks, high-moisture-content cloud target blocks, and precipitation target blocks.

[0070] The final classification result obtained by the computer equipment at this time takes into account radar reflectivity factor, echo height, vertical thickness and visibility characteristics to classify and identify different meteorological targets, and extracts low visibility fog areas. Therefore, the meteorological target type indicated by the final classification result is more accurate.

[0071] In summary, the process involves first acquiring millimeter-wave radar base data and laser scanning data of the target scanning area. For each scanning mode, a two-dimensional image of the reflectivity factor is segmented based on a reflectivity factor threshold to obtain various types of meteorological regions, thus completing the initial classification. Next, based on the coordinate and type information of each meteorological region, matching and merging processes are performed to obtain the meteorological target blocks corresponding to the target scanning area. Then, for each meteorological target block, a second classification process is conducted based on its echo top and bottom heights to obtain the second classification results. Finally, based on the laser scanning data, the average visibility of each type of meteorological target block in the second classification results is calculated, and based on this average visibility, a third classification is performed to obtain the final classification results. In the above scheme, the reflectivity factor threshold, echo top height and echo bottom height and other micro and macro features of millimeter-wave radar for different meteorological targets, as well as the visibility features of visibility lidar, are used to automatically extract meteorological areas in order to distinguish different types of meteorological targets. This can effectively provide the spatial distribution of meteorological areas and enable automated observation.

[0072] Figure 3 This is a flowchart illustrating a meteorological area identification method according to an exemplary embodiment. The method can be implemented by, for example... Figure 1 The computer device 110 in the meteorological area identification system shown executes the operation. For example... Figure 3 As shown, the meteorological area identification method may include the following steps:

[0073] S301. Acquire millimeter-wave radar base data and laser scanning data of the target scanning area; the millimeter-wave radar base data includes the reflectivity factor acquired by the millimeter-wave radar in PPI scanning mode and RHI scanning mode respectively.

[0074] In one possible implementation, the reflectivity factors constitute a two-dimensional reflectivity factor image, which includes a PPI planar scan image and a RHI profile scan image.

[0075] For further details, please refer to the following: Figure 4 The flowchart illustrates a meteorological area identification method. The millimeter-wave radar uses PPI and RHI modes to scan the target area, while the visibility lidar uses PPI mode. Each scan cycle of the millimeter-wave radar includes one PPI mode and several RHI modes, while the visibility lidar uses only one PPI mode per scan cycle. The scan cycles of the millimeter-wave radar and the visibility lidar are consistent. The PPI mode scan of the millimeter-wave radar yields a two-dimensional planar reflectivity factor image (i.e., the aforementioned PPI planar scan image), and the RHI mode scan of the millimeter-wave radar yields a two-dimensional profile reflectivity factor image (i.e., the aforementioned RHI profile scan image).

[0076] Furthermore, each PPI mode of the millimeter-wave radar will obtain a two-dimensional planar reflectivity factor image (i.e., the aforementioned PPI planar scan image), and each RHI mode of the millimeter-wave radar will also obtain a two-dimensional profile reflectivity factor image (i.e., the aforementioned RHI profile scan image). Therefore, after one scan cycle, the millimeter-wave radar can scan one two-dimensional planar reflectivity factor image (i.e., the aforementioned PPI planar scan image) and several two-dimensional profile reflectivity factor images (i.e., the aforementioned RHI profile scan images).

[0077] Furthermore, this millimeter-wave radar uses PPI and RHI modes to scan the target area, solving the problem in existing technologies where millimeter-wave radar needs to be set to a low elevation angle PPI mode for observation. Due to the lack of information such as echo top height and echo bottom height, it is difficult to intuitively distinguish non-significant precipitation meteorological targets such as fog, low clouds, drizzle, or clear sky echoes on PPI images.

[0078] S302. For the two-dimensional image of reflectance factor for each scanning mode, segment the two-dimensional image of reflectance factor based on the reflectance factor threshold to obtain meteorological areas of various types and complete the preliminary classification.

[0079] In one possible implementation, the reflectivity factor threshold includes thresholds corresponding to various types of meteorological targets; the various types of meteorological targets include at least one of fog droplet particles, cloud particles, and precipitation particles.

[0080] This meteorological region includes cloud areas, fog areas, and precipitation areas.

[0081] In one possible implementation, the reflectance factor matrices corresponding to the PPI planar scan image and the RHI profile scan image are obtained respectively;

[0082] Based on the reflectivity factor threshold, the reflectivity factor matrix is ​​binarized and subjected to expansion and erosion processing to segment out various types of planar meteorological regions and various types of profile meteorological regions.

[0083] Furthermore, before conducting meteorological area identification, it is necessary to collect historical millimeter-wave radar echo data corresponding to the target scanning area, and combine it with other meteorological data and prior knowledge to manually classify the historical millimeter-wave radar echoes. Statistical values ​​are then used to distinguish between three types of meteorological targets: fog droplets, cloud particles, and precipitation particles, along with other characteristic parameter thresholds. Based on these reflectivity factor thresholds and other characteristic parameter thresholds, the two-dimensional reflectivity factor image is segmented to obtain various types of meteorological areas.

[0084] Furthermore, clutter preprocessing is performed on the millimeter-wave radar base data to obtain two-dimensional matrices of each reflectivity factor. A multi-threshold segmentation algorithm is then used to segment these matrices, such as... Figure 4 As shown, the reflectivity factor matrices corresponding to the planar and profile scan images within a single scan cycle are binarized using reflectivity factor thresholds for fog droplets, cloud particles, and precipitation particles, respectively. For example, points that meet the reflectivity factor threshold conditions are set to 1, and points that do not meet the conditions are set to 0. Expansion and erosion processing is performed on each reflectivity factor matrix to identify the regions of single targets such as clouds, fog, and precipitation. The identified meteorological regions are then marked to obtain the meteorological regions of each type.

[0085] S303. Based on the coordinate and type information of each meteorological region, perform matching and merging processing on each meteorological region to obtain the meteorological target blocks contained in the target scanning area.

[0086] In one possible implementation, meteorological areas of the same type that meet the splicing conditions in the various types of planar meteorological areas and various types of profile meteorological areas are merged to obtain various meteorological target blocks contained in the target scanning area.

[0087] Furthermore, one can first acquire meteorological regions of the same type, and then merge meteorological regions of the same type whose location information meets the splicing criteria to obtain individual meteorological target blocks. These splicing criteria can indicate meteorological regions with adjacent or overlapping areas.

[0088] In one possible implementation, the location information includes coordinate information indicating the coordinates (x, y, z) of the target observation point corresponding to the meteorological area in three-dimensional coordinates with the millimeter-wave radar as the origin.

[0089] Furthermore, after identifying the meteorological regions for single targets such as clouds, fog, and precipitation, the location and area of ​​each observation point within each meteorological region (including cloud areas, fog areas, and precipitation areas) are calculated. Based on this location and area information, meteorological regions of the same category with adjacent or overlapping areas are matched and merged, and areas with excessively small areas are removed, ultimately yielding individual meteorological target blocks. The matching and merging process is based on the principle that two meteorological regions are of the same category and have adjacent or overlapping areas. Simultaneously, areas with excessively small areas are removed during this matching and merging process.

[0090] S304. For each meteorological target block, a second classification process is performed on the meteorological target block based on the echo top height and echo bottom height of the meteorological target block to obtain the second classification result.

[0091] In one possible implementation, for each meteorological target block, a second classification process is performed on each meteorological target block based on the echo bottom height, echo top height, and area threshold of the meteorological target block to obtain a second classification result; the types of meteorological target blocks in the second classification result include fog target blocks, low water content cloud target blocks, high water content cloud target blocks, and precipitation target blocks.

[0092] Furthermore, such as Figure 4 As shown, the attribute information of each meteorological target block, namely echo bottom height and echo top height, is calculated. Then, based on preset attribute threshold categories, a second classification process is performed on the meteorological target area, further identifying fog, cloud, and precipitation areas as fog target blocks, low-moisture-content cloud target blocks, high-moisture-content cloud target blocks, and precipitation target blocks. The attribute threshold categories are the corresponding echo top height threshold and echo bottom height threshold for each type of meteorological target block.

[0093] S305. Based on the laser scanning data, obtain the geographical location information of the observation range gate of the visibility lidar, and based on the laser scanning data and the geographical location information of the observation range gate, calculate the average visibility of each type of meteorological target block in the second classification result.

[0094] Furthermore, laser scanning data in PPI mode of the visibility lidar is acquired, the geographical location information of each observation range gate is calculated, that is, the coordinate value of the visibility lidar range gate position on the coordinate system with the millimeter-wave radar as the origin, the visibility observation value falling on the classified meteorological target block is extracted, and the average visibility value is calculated.

[0095] S306. Based on the average visibility, a third classification process is performed on each type of meteorological target block to obtain the final classification result. The types of meteorological target blocks in the final classification result include low visibility fog target blocks, clear sky echo target blocks, low water content cloud target blocks, high water content cloud target blocks, and precipitation target blocks.

[0096] Furthermore, based on the average visibility information, the types of meteorological target blocks in the second classification results are corrected, further distinguishing between low visibility fog target blocks and clear sky echo target blocks, etc., to obtain the final classification results, namely low visibility fog target blocks, clear sky echo target blocks, low water content cloud target blocks, high water content cloud target blocks, and precipitation target blocks.

[0097] In summary, the process involves first acquiring millimeter-wave radar base data and laser scanning data of the target scanning area. For each scanning mode, a two-dimensional image of the reflectivity factor is segmented based on a reflectivity factor threshold to obtain various types of meteorological regions, thus completing the initial classification. Next, based on the coordinate and type information of each meteorological region, matching and merging processes are performed to obtain the meteorological target blocks corresponding to the target scanning area. Then, for each meteorological target block, a second classification process is conducted based on its echo top and bottom heights to obtain the second classification result. Finally, based on the laser scanning data, the average visibility of each type of meteorological target block in the second classification result is calculated, and based on this average visibility, a third classification process is performed to obtain the final classification result. In the above scheme, the reflectivity factor threshold, echo top height and echo bottom height and other micro and macro features of millimeter-wave radar for different meteorological targets, as well as the visibility features of visibility lidar, are used to automatically extract meteorological areas in order to distinguish different types of meteorological targets. This can effectively provide the spatial distribution of meteorological areas and enable automated observation.

[0098] Figure 5This is a structural block diagram illustrating a meteorological area identification device according to an exemplary embodiment. The meteorological area identification device includes:

[0099] The scanning data acquisition module 501 is used to acquire millimeter-wave radar base data and laser scanning data of the target scanning area; the millimeter-wave radar base data includes the reflectivity factor acquired by the millimeter-wave radar in PPI scanning mode and RHI scanning mode respectively;

[0100] The meteorological region acquisition module 502 is used to segment the two-dimensional image of reflectance factor for each scanning mode based on the reflectance factor threshold to obtain meteorological regions of various types and complete the preliminary classification.

[0101] The matching and merging processing module 503 is used to match and merge each meteorological region based on the coordinate information and type information of each meteorological region, so as to obtain each meteorological target block contained in the target scanning area.

[0102] The second classification result acquisition module 504 is used to perform a second classification process on each meteorological target block based on the echo top height and echo bottom height of the meteorological target block, so as to obtain the second classification result;

[0103] The final classification result acquisition module 505 is used to calculate the average visibility of each type of meteorological target block in the second classification result based on the laser scanning data, and to perform a third classification process on each type of meteorological target block based on the average visibility to obtain the final classification result. In one possible implementation, the reflectivity factor threshold includes thresholds corresponding to each type of meteorological target; the various types of meteorological targets include at least one of fog droplets, cloud particles, and precipitation particles.

[0104] This meteorological region includes cloud areas, fog areas, and precipitation areas.

[0105] In one possible implementation, the two-dimensional images of each reflectivity factor include a PPI planar scan image and a RHI profile scan image.

[0106] In one possible implementation, the meteorological area acquisition module 502 is further configured to:

[0107] Obtain the reflectance factor matrices corresponding to the PPI planar scan image and the RHI profile scan image, respectively;

[0108] Based on the reflectivity factor threshold, the reflectivity factor matrix is ​​binarized and subjected to expansion and erosion processing to segment out various types of planar meteorological regions and various types of profile meteorological regions.

[0109] In one possible implementation, the matching and merging processing module 503 is further configured to:

[0110] In the various types of planar meteorological regions and various types of profile meteorological regions, meteorological regions whose location information meets the stitching conditions and are of the same type are merged to obtain the various meteorological target blocks contained in the target scanning area.

[0111] In one possible implementation, the location information includes coordinate information indicating the coordinates of the target observation point corresponding to the meteorological area in a three-dimensional coordinate system with the millimeter-wave radar as the origin.

[0112] In one possible implementation, the second classification result acquisition module 504 is further configured to:

[0113] For each meteorological target block, a second classification process is performed based on the echo bottom height, echo top height, and area threshold of the meteorological target block to obtain the second classification result. The types of meteorological target blocks in the second classification result include fog target blocks, low water content cloud target blocks, high water content cloud target blocks, and precipitation target blocks.

[0114] In one possible implementation, the final classification result 505 is also used for:

[0115] Based on the laser scanning data, the geographical location information of the observation range gate of the visibility lidar is obtained;

[0116] Based on the laser scanning data and the geographical location information of the observation distance gate, the average visibility of each type of meteorological target block in the second classification result is calculated;

[0117] Based on the average visibility, a third classification process is performed on each type of meteorological target block to obtain the final classification result. The types of meteorological target blocks in the final classification result include low visibility fog target blocks, clear sky echo target blocks, low water content cloud target blocks, high water content cloud target blocks, and precipitation target blocks.

[0118] In summary, the process involves first acquiring millimeter-wave radar base data and laser scanning data of the target scanning area. For each scanning mode, a two-dimensional image of the reflectivity factor is segmented based on a reflectivity factor threshold to obtain various types of meteorological regions, thus completing the initial classification. Next, based on the coordinate and type information of each meteorological region, matching and merging processes are performed to obtain the meteorological target blocks corresponding to the target scanning area. Then, for each meteorological target block, a second classification process is conducted based on its echo top and bottom heights to obtain the second classification result. Finally, based on the laser scanning data, the average visibility of each type of meteorological target block in the second classification result is calculated, and based on this average visibility, a third classification process is performed to obtain the final classification result. In the above scheme, the reflectivity factor threshold, echo top height and echo bottom height and other micro and macro features of millimeter-wave radar for different meteorological targets, as well as the visibility features of visibility lidar, are used to automatically extract meteorological areas in order to distinguish different types of meteorological targets. This can effectively provide the spatial distribution of meteorological areas and enable automated observation.

[0119] Please see Figure 6 This is a schematic diagram of a computer device provided according to an exemplary embodiment of the present application. The computer device includes a memory and a processor. The memory is used to store a computer program. When the computer program is executed by the processor, it implements the above-described meteorological area identification method.

[0120] The processor can be a central processing unit (CPU). It can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations thereof.

[0121] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the methods in the embodiments of this application. The processor executes various functional applications and data processing by running the non-transitory software programs, instructions, and modules stored in the memory, thereby implementing the methods in the above-described embodiments.

[0122] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor, etc. Furthermore, the memory may include high-speed random access memory and non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0123] In one exemplary embodiment, a computer-readable storage medium is also provided for storing at least one computer program, which is loaded and executed by a processor to implement all or part of the steps in the above-described method. For example, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, or optical data storage device, etc.

[0124] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0125] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for identifying meteorological regions, characterized in that, The method includes: Acquire millimeter-wave radar base data and laser scanning data of the target scanning area; the millimeter-wave radar base data includes the reflectivity factor acquired by the millimeter-wave radar in PPI scanning mode and RHI scanning mode respectively; For each scanning mode, the two-dimensional image of reflectance factor is segmented based on the reflectance factor threshold to obtain various types of meteorological regions and complete the preliminary classification. Based on the coordinate and type information of each meteorological region, the meteorological regions are matched and merged to obtain the meteorological target blocks contained in the target scanning area. For each meteorological target block, a second classification process is performed on the meteorological target block based on the echo top height and echo bottom height of the meteorological target block to obtain the second classification result; Based on the laser scanning data, the average visibility of each type of meteorological target block in the second classification result is calculated, and based on the average visibility, a third classification process is performed on each type of meteorological target block to obtain the final classification result.

2. The method according to claim 1, characterized in that, The reflectance factor threshold includes thresholds corresponding to various types of meteorological targets; the various types of meteorological targets include at least one of fog droplet particles, cloud particles, and precipitation particles. The meteorological region includes cloud areas, fog areas, and precipitation areas.

3. The method according to claim 2, characterized in that, The two-dimensional image of reflectivity factor includes a PPI planar scan image and a RHI profile scan image.

4. The method according to claim 3, characterized in that, The two-dimensional reflectance factor image for each scanning mode is segmented based on a reflectance factor threshold to obtain various types of meteorological regions, completing preliminary classification, including: Obtain the reflectance factor matrices corresponding to the PPI planar scan image and the RHI profile scan image, respectively; Based on the reflectivity factor threshold, the reflectivity factor matrix is ​​binarized and subjected to expansion and erosion processing to segment out various types of planar meteorological regions and various types of profile meteorological regions.

5. The method according to claim 4, characterized in that, The process of matching and merging meteorological regions based on their coordinate and type information to obtain meteorological target blocks within the target scanning area includes: In the various types of planar meteorological regions and various types of profile meteorological regions, meteorological regions whose location information meets the splicing conditions and are of the same type are merged to obtain the various meteorological target blocks contained in the target scanning area.

6. The method according to claim 5, characterized in that, The location information includes coordinate information, which indicates the coordinate value of the target observation point corresponding to the meteorological area in three-dimensional coordinates with the millimeter-wave radar as the origin.

7. The method according to any one of claims 1-6, characterized in that, For each meteorological target block, a second classification process is performed based on the echo top height and echo bottom height of the meteorological target block to obtain the second classification result, including: For each meteorological target block, a second classification process is performed on the meteorological target block based on the echo bottom height, echo top height, and area threshold to obtain the second classification result; the types of meteorological target blocks in the second classification result include fog target blocks, low water content cloud target blocks, high water content cloud target blocks, and precipitation target blocks.

8. The method according to claim 7, characterized in that, Based on the laser scanning data, the average visibility of each type of meteorological target block in the second classification result is calculated, and based on the average visibility, a third classification process is performed on each type of meteorological target block to obtain the final classification result, including: Based on the laser scanning data, the geographical location information of the observation range gate of the visibility lidar is obtained; Based on the laser scanning data and the geographical location information of the observation distance gate, the average visibility of each type of meteorological target block in the second classification result is calculated; Based on the average visibility, a third classification process is performed on the meteorological target blocks of each type to obtain the final classification result. The types of meteorological target blocks in the final classification result include low visibility fog target blocks, clear sky echo target blocks, low water content cloud target blocks, high water content cloud target blocks, and precipitation target blocks.

9. A meteorological area identification device, characterized in that, The device includes: The scanning data acquisition module is used to acquire millimeter-wave radar base data and laser scanning data of the target scanning area; the millimeter-wave radar base data includes the reflectivity factor acquired by the millimeter-wave radar in PPI scanning mode and RHI scanning mode respectively; The meteorological region acquisition module is used to segment the two-dimensional reflectance factor image for each scanning mode based on the reflectance factor threshold to obtain meteorological regions of various types and complete the preliminary classification. The matching and merging processing module is used to match and merge the meteorological regions based on their coordinate and type information to obtain the meteorological target blocks contained in the target scanning area. The second classification result acquisition module is used to perform a second classification process on each meteorological target block based on the echo top height and echo bottom height of the meteorological target block, so as to obtain the second classification result; The final classification result acquisition module is used to calculate the average visibility of each type of meteorological target block in the second classification result based on the laser scanning data, and to perform a third classification process on each type of meteorological target block based on the average visibility to obtain the final classification result.

10. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one instruction, which is loaded and executed by the processor to implement a meteorological area identification method as described in any one of claims 1 to 8.

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