A method, device, program product and medium for monitoring convective clouds in synchronization

Abstract

A method, device, program product and medium for monitoring convective clouds in synchronization, relate to the technical field of meteorological monitoring. In the method, reflectivity mosaic data is obtained; the reflectivity mosaic data is respectively subjected to single-frame detection processing to obtain a set of convective cloud instances; the set of convective cloud instances is subjected to polygon overlap matching processing to obtain a first set of matched pairs, a first set of unmatched instances and a second set of unmatched instances; the first set of unmatched instances and the second set of unmatched instances are subjected to nearest neighbor matching processing to obtain a second set of matched pairs; the second set of matched pairs is subjected to multi-mapping conflict detection processing to obtain a third set of matched pairs; the first set of matched pairs and the third set of matched pairs are merged to obtain a target set of matching results; and based on the target set of matching results, time sequence state information of convective clouds in a monitored geographic region is updated. The accuracy of monitoring the time sequence state information of convective clouds is improved.

Description

Technical Field

[0001] This application relates to the field of meteorological monitoring technology, specifically to a method, equipment, program product, and medium for synchronous monitoring of convective clouds. Background Technology

[0002] In the field of meteorological monitoring and forecasting, it is crucial to use composite reflectivity (CR) mosaic data from weather radar to track the formation, dissipation, movement, merging, and splitting processes of severe convective clouds (such as thunderstorm and hail clouds) in real time and accurately. This directly affects the accuracy of nowcasting and warning of severe weather.

[0003] Currently, the mainstream convective cloud tracking methods in the industry are mainly divided into two categories, but each has its own insurmountable limitations: the region-overlapping method (such as the polygon overlap method). This method determines whether the convective cloud is the same target by calculating the overlapping area of ​​the polygon outline of the convective cloud at two consecutive time points (the previous frame t-1 and the current frame t). This method has a better tracking effect on large-scale convective clouds (with a large coverage area and relatively stable shape) because large-scale convective clouds usually maintain obvious shape overlap characteristics between adjacent time points.

[0004] Feature- or motion vector-based methods (such as optical flow and eigenvector methods) predict and match the location of convective clouds by extracting feature points (such as corners and centroids) or calculating pixel-level motion optical flow fields. This method is more effective for small-scale convective clouds (with small coverage areas and fast movement speeds) because small-scale convective clouds often have drastic shape changes between adjacent time points, with very small or no overlap, but their centroid positions and motion characteristics are relatively stable.

[0005] However, in real-world scenarios, large, medium, and small-scale convective clouds often coexist within the same monitoring area, and there is no absolutely clear boundary between their scales. Therefore, it is impossible to switch or combine these two methods by setting a simple size threshold. This results in low accuracy of existing technologies in monitoring the temporal state information of convective clouds in scenarios involving simultaneous monitoring. Summary of the Invention

[0006] This application provides a method, device, product, and medium for synchronous monitoring of convective clouds, which can improve the accuracy of monitoring the temporal status information of convective clouds.

[0007] The first aspect of this application provides a method for synchronous monitoring of convective clouds, specifically including: Acquire the first reflectance mosaic data at the current observation time and the second reflectance mosaic data at the previous observation time. The reflectance mosaic data is two-dimensional raster data representing the reflectance intensity distribution within the geographic area to be monitored. Single-frame detection processing is performed on the first reflectance mosaic data and the second reflectance mosaic data respectively to obtain the first convective cloud instance set and the second convective cloud instance set; For each convective cloud instance in the first convective cloud instance set and the second convective cloud instance set, polygon overlap matching processing is performed to obtain a first set of successfully matched pairs, a first set of unmatched instances that did not match successfully in the first convective cloud instance set, and a second set of unmatched instances that did not match successfully in the second convective cloud instance set. The first set of unmatched instances and the second set of unmatched instances are subjected to nearest neighbor matching to obtain a second set of matching pairs. Perform multi-mapping conflict detection processing on the second matching pair set to obtain a third matching pair set; merge the first matching pair set and the third matching pair set to obtain the target matching result set. Based on the target matching result set, update the temporal status information of convective clouds in the geographic area to be monitored.

[0008] By adopting the above technical solution, the first reflectance mosaic data at the current observation time and the second reflectance mosaic data at the previous observation time are first acquired, ensuring complete acquisition of the reflectance intensity distribution within the geographical area to be monitored. Single-frame detection processing is then performed on the first and second reflectance mosaic data to obtain a first set of convective cloud instances and a second set of convective cloud instances, achieving accurate identification and instantiation of convective clouds at the two time points. Furthermore, polygon overlap matching processing is performed on the first and second sets of convective cloud instances to obtain a first set of matched pairs, a first set of unmatched instances, and a second set of unmatched instances. Spatial relationships between convective clouds at adjacent time points were established. Nearest neighbor matching was performed on the first and second sets of unmatched instances to obtain a second set of matched pairs, improving the completeness of convective cloud matching. Multi-mapping conflict detection was applied to the second set of matched pairs to obtain a third set of matched pairs, eliminating conflicts and ambiguities in the matching process. The first and third sets of matched pairs were merged to obtain the target matching result set, achieving unified integration of the matching results. Finally, the temporal state information of convective clouds in the monitored geographical area was updated based on the target matching result set, completing the accurate tracking of convective cloud movement and evolution. By employing a progressive matching method from polygon overlap matching to nearest neighbor matching, combined with multi-mapping conflict detection, convective clouds at different scales can be accurately matched and tracked, thereby improving the monitoring accuracy of convective cloud temporal state information.

[0009] Optionally, the step of performing single-frame detection processing on the first reflectance mosaic data and the second reflectance mosaic data respectively to obtain the first convective cloud instance set and the second convective cloud instance set specifically includes: The first reflectance mosaic data and the second reflectance mosaic data are binarized based on a preset intensity threshold to obtain the corresponding binarized image; Connectivity analysis is performed on the binarized image to identify spatially connected pixel clusters; Each pixel cluster is identified as a convective cloud instance, and the feature information of each convective cloud instance is extracted to obtain the first set of convective cloud instances at the current observation time and the second set of convective cloud instances at the previous observation time. The feature information includes at least the centroid coordinates, the outline polygon, and the area.

[0010] By performing binarization segmentation based on a preset intensity threshold on the first and second reflectivity mosaic data, a binarized image is generated, clearly distinguishing convective cloud regions from non-convective cloud regions. Connectivity component analysis is then performed on the binarized image to identify spatially connected pixel clusters, achieving effective segmentation of continuous convective cloud regions. Each pixel cluster is identified as a convective cloud instance, and its centroid coordinates, contour polygon, and area are extracted to form a first set of convective cloud instances and a second set of convective cloud instances, completing the accurate localization and feature representation of convective clouds at two different time points. This processing method based on binarization segmentation and connected component analysis, combined with the extraction of multi-dimensional feature information such as centroid coordinates, contour polygons, and area, achieves accurate identification and complete feature characterization of convective cloud instances.

[0011] Optionally, the step of performing polygon overlap matching processing on each convective cloud instance in the first convective cloud instance set and the second convective cloud instance set to obtain a first set of successfully matched pairs, a first set of unmatched instances that did not match successfully in the first convective cloud instance set, and a second set of unmatched instances that did not match successfully in the second convective cloud instance set specifically includes: Calculate the overlap area between the outline polygons of any convective cloud instance in the first set of convective cloud instances and any convective cloud instance in the second set of convective cloud instances; Establish a matching relationship between any first convection cloud instance and all second convection cloud instances whose overlapping area is greater than a preset overlap threshold to obtain a first set of matching pairs. Other convection cloud instances in the first convection cloud instance set that do not contain the first matching pair set are determined as the first unmatched instance set; other convection cloud instances in the second convection cloud instance set that do not contain the first matching pair set are determined as the second unmatched instance set.

[0012] By employing the above technical solution, a quantified value of spatial overlap between convective cloud instances is obtained by calculating the overlap area between the outline polygons of any convective cloud instance in the first set of convective cloud instances and any convective cloud instance in the second set of convective cloud instances. A matching relationship is established between any first convective cloud instance and all second convective cloud instances whose overlap area is greater than a preset overlap threshold, resulting in a first set of matching pairs, ensuring the reliability of the matching results. Other convective cloud instances in the first set of convective cloud instances that do not contain the first set of matching pairs are defined as the first set of unmatched instances, and other convective cloud instances in the second set of convective cloud instances that do not contain the first set of matching pairs are defined as the second set of unmatched instances. This achieves complete classification and filtering of all convective cloud instances, providing an accurate set of objects to be matched for subsequent nearest neighbor matching processing. Because a polygon overlap area-based matching method is used, and a preset overlap threshold is set as the matching condition, the accuracy and reliability of convective cloud instance matching are improved.

[0013] Optionally, the step of performing nearest neighbor matching on the first set of unmatched instances and the second set of unmatched instances to obtain a second set of matching pairs specifically includes: The matching cost between each first unmatched instance in the first unmatched instance set and each second unmatched instance in the second unmatched instance set is calculated using a preset cost function. Candidate convective cloud instances that meet the preset maximum movement distance constraint are selected from the second set of unmatched instances, and the matching cost between each first unmatched instance and the candidate convective cloud instance is calculated respectively. Candidate convection cloud instances with the minimum matching cost value to each of the first unmatched instances are selected as matching objects to obtain a second set of matching pairs.

[0014] By adopting the above technical solution, the matching cost value between each unmatched instance in the first unmatched instance set and each unmatched instance in the second unmatched instance set is calculated using a preset cost function, establishing a quantitative standard for the similarity between convective cloud instances. Candidate convective cloud instances that meet the preset maximum movement distance constraint are selected from the second unmatched instance set, and the matching cost value between each first unmatched instance and the candidate convective cloud instance is calculated respectively, ensuring that the matching process conforms to the movement law of convective clouds. The candidate convective cloud instance with the minimum matching cost value with each first unmatched instance is selected as the matching object, resulting in the second matching pair set, achieving optimal matching of unmatched instances. By employing a nearest neighbor matching method based on a cost function and introducing a maximum movement distance constraint for candidate object selection, the matching accuracy of unmatched convective cloud instances is improved, while ensuring the physical rationality of the matching results.

[0015] Optionally, the cost function is calculated by weighting the Euclidean distance and area difference between the centroid coordinates of two convective cloud instances in the first unmatched instance set and the second unmatched instance set to obtain the matching cost.

[0016] By adopting the above technical solution, a quantitative evaluation standard for the similarity of convective cloud instances is established by calculating the matching cost value between each first unmatched instance in the first unmatched instance set and each second unmatched instance in the second unmatched instance set using a preset cost function. Candidate convective cloud instances that meet the preset maximum motion distance constraint are selected from the second unmatched instance set, and the candidate convective cloud instance with the minimum matching cost value is selected as the matching object, resulting in a second set of matching pairs. This ensures that the convective cloud matching results conform to actual motion patterns. Because the nearest neighbor matching method based on the cost function and maximum motion distance constraint is used, convective cloud instances with large motion amplitudes or significant morphological changes can be effectively matched, improving the applicability and accuracy of convective cloud matching.

[0017] Optionally, the step of performing multi-mapping conflict detection processing on the second set of matching pairs to obtain a third set of matching pairs specifically includes: In the second set of matching pairs, a convection cloud instance in the second set of unmatched instances that is simultaneously matched by multiple different convection cloud instances in the first set of convection cloud instances, or a convection cloud instance in the first set of convection cloud instances that is simultaneously matched by multiple different convection cloud instances in the second set of unmatched instances, is regarded as a multi-mapping conflict pair. All matching pairs in the set of matching pairs consist of one convection cloud instance in the first set of convection cloud instances and one convection cloud instance in the second set of unmatched instances. Among the various multi-mapping conflict pairs, the matching pair with the minimum matching cost value is retained to obtain the third matching pair set.

[0018] By employing the above technical solution, multiple mapping conflict pairs are identified in the second set of matched pairs. These pairs involve instances where the same convection cloud instance in the second set of unmatched instances is simultaneously matched by multiple different convection cloud instances in the first set of convection cloud instances, or vice versa. This clarifies the conflict situation during the matching process. From each set of multiple mapping conflict pairs, the matching pair with the minimum matching cost is retained to obtain the third set of matched pairs, ensuring that each convection cloud instance is associated with only one optimal matching object. Because a conflict elimination method based on the minimum matching cost is used, the many-to-many ambiguity in the convection cloud matching process is eliminated, improving the uniqueness and reliability of the convection cloud matching results.

[0019] Optionally, updating the temporal state information of convective clouds in the geographic area to be monitored based on the target matching result set specifically includes: For the one-to-one matching relationship in the target matching result set, the corresponding convective cloud is marked as a continuous state; For the many-to-one matching relationship in the target matching result set, the corresponding convection cloud is marked as merged; For a one-to-many matching relationship in the target matching result set, the corresponding convective cloud is marked as a split state; For convective cloud instances in the first set of convective cloud instances that are not matched in the target matching result set, they are marked as new; For convective cloud instances in the second set of convective cloud instances that are not matched in the target matching result set, they are marked as dissipated.

[0020] By classifying and labeling the matching relationships in the target matching result set, one-to-one matching relationships are marked as persistent, many-to-one matching relationships as merged, and one-to-many matching relationships as split. Furthermore, unmatched convective cloud instances in the first set of convective cloud instances are marked as newly formed, and unmatched convective cloud instances in the second set of convective cloud instances are marked as dissipated. This state labeling method based on matching relationship type enables accurate identification of each stage of the convective cloud lifecycle, comprehensively depicting the evolution of convective clouds over time.

[0021] In a second aspect, this application provides an electronic device for synchronous monitoring of convective clouds, the electronic device comprising: one or more processors and a memory; the memory being coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors calling the computer instructions to cause the electronic device for synchronous monitoring of convective clouds to perform the method described in the first aspect and any possible implementation thereof.

[0022] Thirdly, this application provides a computer program product containing instructions that, when run on an electronic device for synchronous monitoring of cloud flow, causes the electronic device to perform the method described in the first aspect and any possible implementation thereof.

[0023] Fourthly, this application provides a computer-readable storage medium including instructions that, when executed on a device for synchronous monitoring of cloud flow, cause the electronic device to perform the method described in the first aspect and any possible implementation thereof. Attached Figure Description

[0024] Figure 1This is a schematic diagram of the architecture of a convective cloud synchronous monitoring system provided in an embodiment of this application; Figure 2 This is a flowchart illustrating a method for synchronous monitoring of convective clouds provided in an embodiment of this application; Figure 3 This is a schematic diagram of the temporal state evolution of convective clouds provided in an embodiment of this application; Figure 4 This is an exemplary hardware structure diagram of an electronic device for synchronous monitoring of convective clouds provided in an embodiment of this application. Detailed Implementation

[0025] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification 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.

[0026] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0027] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0028] Figure 1 An exemplary system architecture for a convective cloud synchronous monitoring system is shown.

[0029] like Figure 1 As shown, the system architecture may include electronic device 11, network 12, and weather monitoring device 13. Network 12 serves as the medium for providing a communication link between electronic device 11 and weather monitoring device 13. Network 12 may include various connection types, such as wired, wireless communication links, or fiber optic cables.

[0030] Meteorological workers can use electronic device 11 to interact with weather monitoring device 13 via network 12 to receive or send reflectance mosaic data, etc. Various convective cloud monitoring applications can be installed on electronic device 11, such as convective cloud tracking applications and convective cloud state analysis applications.

[0031] Electronic device 11 is hardware and can be various electronic devices with a display screen, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0032] The weather monitoring device 13 can be a weather radar device that provides reflectivity mosaic data, used to collect reflectivity intensity distribution data in the geographic area to be monitored, and transmit the collected data to the electronic device 11 for processing.

[0033] The following detailed explanation uses the electronic device side as an example.

[0034] This embodiment provides a method for synchronous monitoring of convective clouds. Figure 2 This is a flowchart illustrating a method for synchronous monitoring of convective clouds provided in an embodiment of this application, as shown below. Figure 2 As shown, the method includes S101 to S106: S101: Obtain the first reflectance mosaic data at the current observation time and the second reflectance mosaic data at the previous observation time. The reflectance mosaic data is two-dimensional raster data representing the reflectance intensity distribution within the geographic area to be monitored.

[0035] In this embodiment, reflectivity mosaic data refers to digitized radar echo intensity information generated after scanning the atmospheric environment with weather radar equipment. It is used to represent the spatial distribution of the reflection intensity of radar electromagnetic waves by precipitation particles within a specific geographical area. The reflectivity mosaic data is stored in the form of a two-dimensional grid matrix. Each grid point in the matrix corresponds to a fixed geographical location on the ground. The value of each grid point represents the radar reflectivity intensity above the corresponding location; a higher intensity value indicates stronger precipitation intensity or more intense convective activity in the corresponding area.

[0036] Specifically, the electronic device first extracts the first reflectance mosaic data for the current observation time from the meteorological radar data storage system of the weather monitoring device. This data contains the reflectance intensity values ​​of all grid points in the geographic area to be monitored at the current time. Then, the system continues to obtain the second reflectance mosaic data for the previous observation time from the data storage module, ensuring that the two sets of data are consistent in terms of spatial resolution and geographic coverage. During the data acquisition process, the system verifies whether the time interval between the two times meets the preset observation cycle requirements, and checks the integrity and validity of the data. Through a timestamp matching mechanism, it ensures that the first and second reflectance mosaic data can accurately correspond to the correct observation time. Finally, the system loads the two sets of reflectance mosaic data into memory and completes the data format standardization processing, providing a standardized input data source for subsequent convective cloud detection and matching analysis.

[0037] S102: Perform single-frame detection processing on the first reflectivity mosaic data and the second reflectivity mosaic data respectively to obtain the first set of convective cloud instances and the second set of convective cloud instances.

[0038] Specifically, the electronic device performs the same single-frame detection processing flow on the first reflectivity mosaic data and the second reflectivity mosaic data respectively. The processing first compares the input reflectivity mosaic data with a preset intensity threshold pixel by pixel. Pixels with intensity values ​​exceeding the threshold are marked as convective cloud candidate regions, and pixels with intensity values ​​below the threshold are marked as background regions. After completing the binarization segmentation, the system performs a connected component analysis algorithm on the binarized image. The algorithm scans each pixel in the binarized image line by line starting from the top left corner. When an unvisited foreground pixel is encountered, the region growing process is started. All foreground pixels spatially adjacent to the current pixel are merged into the same connected region through an eight-neighbor search method. After the connected component analysis is completed, the system calculates the centroid coordinates, contour polygon, area and other geometric feature information for each connected region, and encapsulates each connected region and its corresponding feature information into a convective cloud instance object. Finally, the system organizes all convective cloud instance objects into a first convective cloud instance set and a second convective cloud instance set.

[0039] Based on the above embodiments, as an optional embodiment, the step of performing single-frame detection processing on the first reflectivity mosaic data and the second reflectivity mosaic data to obtain the first convective cloud instance set and the second convective cloud instance set may include steps S201 to S203: S201: Perform binarization segmentation on the first reflectance mosaic data and the second reflectance mosaic data based on a preset intensity threshold to obtain the corresponding binarized image.

[0040] Specifically, the electronic device first reads the reflectance value of each grid point in the first and second reflectance mosaic data. Then, it compares the reflectance value of each grid point with a preset intensity threshold. When the reflectance value of a grid point is greater than or equal to the preset intensity threshold, the electronic device sets the pixel value of the corresponding grid position to 1; when the reflectance value of a grid point is less than the preset intensity threshold, it sets the pixel value of the corresponding grid position to 0. By traversing all grid points, the binarization conversion process of the entire image is completed, ultimately generating a binary image containing only 0 and 1 pixel values. In the binary image, continuous areas with pixel values ​​of 1 represent spatial locations where convective clouds may exist, while areas with pixel values ​​of 0 represent background areas without convective cloud activity. The preset intensity threshold is usually set to 35 dBZ or 40 dBZ based on meteorological experience to identify the core areas of convective clouds with significant precipitation characteristics.

[0041] S202: Perform connected component analysis on the binarized image to identify spatially connected pixel clusters.

[0042] In this embodiment, connected component analysis refers to an image processing algorithm that finds all interconnected pixel regions in a binarized image and marks them as independent regions. A pixel cluster represents a set of pixels in a binarized image that have the same pixel value and are spatially adjacent to each other. Connected component analysis typically employs 8-connectivity or 4-connectivity neighborhood determination rules, which can organize scattered pixels into continuous regions with clear boundaries. Pixel clusters represent potential independent convective cloud cells, and each pixel cluster corresponds to a spatially relatively independent precipitation echo region.

[0043] Specifically, the electronic device scans each pixel row by row and column by column starting from the top left corner of the binarized image. When it encounters a pixel with a value of 1 that has not yet been marked, the electronic device initiates a connected component search algorithm. It searches for all pixels that are spatially adjacent to the current pixel and also have a pixel value of 1 using either depth-first search or breadth-first search. These interconnected pixels are marked as the same connected component and assigned a unique connected component identifier. The connected component search process continues to expand along the path with a pixel value of 1 until no more adjacent unmarked pixels can be found. After identifying a connected component, the remaining pixels are scanned and the above process is repeated. Finally, all regions with a pixel value of 1 in the binarized image are divided into several independent pixel clusters. Each pixel cluster contains a group of pixels that are continuously distributed in spatial location.

[0044] S203: Determine each pixel cluster as a convective cloud instance, and extract the feature information of each convective cloud instance to obtain the first convective cloud instance set at the current observation time and the second convective cloud instance set at the previous observation time. The feature information includes at least the centroid coordinates, the outline polygon, and the area.

[0045] In this embodiment, the feature information represents the key physical characteristics and spatial distribution of a convective cloud instance, including centroid coordinates, contour polygons, and area. Here, a convective cloud instance refers to a data structure object representing an independent convective cloud unit; the centroid coordinates represent the geometric center of the convective cloud instance in the geographic coordinate system; the contour polygon represents an ordered sequence of vertex coordinates describing the shape of the outer boundary of the convective cloud instance; and the area represents the size of the geographic area covered by the convective cloud instance.

[0046] Specifically, the electronic device iterates through each pixel cluster obtained from connected component analysis, creates a convective cloud instance object for each pixel cluster, and then calculates the average horizontal and vertical coordinates of all pixels in the pixel cluster as centroid coordinates. Next, it uses a contour extraction algorithm to trace the boundary of the pixel cluster point by point to obtain a complete boundary pixel sequence. The boundary pixel sequence is converted into polygon vertex coordinates in the geographic coordinate system and stored as a contour polygon. The total area of ​​the convective cloud instance is calculated by counting the number of pixels contained in the pixel cluster and multiplying it by the actual geographic area represented by a single pixel. The electronic device encapsulates the feature information such as centroid coordinates, contour polygons, and area into the convective cloud instance object. All convective cloud instances obtained from processing the first reflectivity mosaic data are organized into a first convective cloud instance set, and all convective cloud instances obtained from processing the second reflectivity mosaic data are organized into a second convective cloud instance set.

[0047] S103: Perform polygon overlap matching processing on each convection cloud instance in the first convection cloud instance set and the second convection cloud instance set to obtain a first set of successfully matched pairs, a first set of unmatched instances that did not match successfully in the first set of convection cloud instances, and a second set of unmatched instances that did not match successfully in the second set of convection cloud instances.

[0048] In this embodiment, polygon overlap matching refers to a method of establishing a matching relationship by calculating the degree of spatial overlap between the outline polygons of convective cloud instances at different times. The first set of matching pairs represents the combination of convective cloud instance pairs that successfully established a matching relationship between the current observation time and the previous observation time. The first set of unmatched instances represents the combination of convective cloud instances in the first set of convective cloud instances at the current observation time that failed to establish a matching relationship with the convective cloud instance at the previous time. The second set of unmatched instances represents the combination of convective cloud instances in the second set of convective cloud instances at the previous observation time that failed to establish a matching relationship with the convective cloud instance at the current time.

[0049] Specifically, the electronic device first iterates through each convective cloud instance in the first convective cloud instance set, and calculates the overlap area between the outline polygon of each convective cloud instance at the current moment and the outline polygons of all convective cloud instances in the second convective cloud instance set. When the overlap area exceeds a preset overlap threshold, the electronic device establishes the two convective cloud instances as a matching pair and adds them to the first matching pair set. After completing the calculation of the overlap area of ​​all convective cloud instances and the establishment of matching pairs, the electronic device collects the convective cloud instances in the first convective cloud instance set that did not participate in the establishment of any matching pairs to form the first unmatched instance set, and at the same time collects the convective cloud instances in the second convective cloud instance set that did not participate in the establishment of any matching pairs to form the second unmatched instance set.

[0050] Based on the above embodiments, as an optional embodiment, performing polygon overlap matching processing on each convective cloud instance in the first convective cloud instance set and the second convective cloud instance set to obtain a first set of successfully matched pairs, a first set of unmatched instances that did not match successfully in the first convective cloud instance set, and a second set of unmatched instances that did not match successfully in the second convective cloud instance set may include steps S301 to S303: S301: Calculate the overlap area between the outline polygons of any convective cloud instance in the first set of convective cloud instances and any convective cloud instance in the second set of convective cloud instances.

[0051] Specifically, the electronic device selects one convective cloud instance from the first set of convective cloud instances and one convective cloud instance from the second set of convective cloud instances as the calculation object. It obtains the vertex coordinate sequence of the outline polygons of each of the two convective cloud instances, uses a polygon intersection algorithm to perform geometric operations on the two outline polygons to find all intersecting boundary segments and vertices, calculates the new polygon region formed by the overlapping part of the two outline polygons through a polygon clipping algorithm, divides the overlapping polygon into several basic geometric figures according to triangulation or trapezoidal division, calculates the area of ​​each basic geometric figure, and sums them to obtain the total overlapping area. The electronic device repeats the above calculation process until the overlapping area calculation of all paired combinations of convective cloud instances in the first set of convective cloud instances and the second set of convective cloud instances is completed.

[0052] S302: Establish a matching relationship between any first convection cloud instance and all second convection cloud instances whose overlapping area is greater than a preset overlap threshold, to obtain a first matching pair set.

[0053] Specifically, the electronic device uses each convection cloud instance in the first set of convection cloud instances as a matching starting point. It compares the overlap area of ​​the current convection cloud instance with each convection cloud instance in the second set of convection cloud instances. When it finds that the overlap area between a second convection cloud instance and the current first convection cloud instance is greater than a preset overlap threshold, the electronic device immediately forms a matching pair between the two convection cloud instances and adds this matching pair to the first matching pair set. It continues to check the overlap area between the current first convection cloud instance and other convection cloud instances in the second set of convection cloud instances. When it finds that the overlap area between multiple second convection cloud instances and the same first convection cloud instance is greater than the preset overlap threshold, multiple matching pairs will be established and all will be added to the first matching pair set.

[0054] S303: Other convection cloud instances in the first convection cloud instance set that do not contain the first matching pair set are identified as the first unmatched instance set; other convection cloud instances in the second convection cloud instance set that do not contain the first matching pair set are identified as the second unmatched instance set.

[0055] Specifically, the electronic device first iterates through each convection cloud instance in the first set of convection cloud instances, checking whether the current convection cloud instance is part of the first convection cloud instance of any matching pair in the first set of matching pairs. When it finds that a certain first convection cloud instance does not appear in any matching pair in the first set of matching pairs, the electronic device adds this convection cloud instance to the first set of unmatched instances. At the same time, the electronic device iterates through each convection cloud instance in the second set of convection cloud instances, checking whether the current convection cloud instance is part of the second convection cloud instance of any matching pair in the first set of matching pairs. When it finds that a certain second convection cloud instance does not appear in any matching pair in the first set of matching pairs, it adds this convection cloud instance to the second set of unmatched instances.

[0056] S104: Perform nearest neighbor matching on the first unmatched instance set and the second unmatched instance set to obtain the second matching pair set.

[0057] In this embodiment, nearest neighbor matching refers to a process of finding the closest pair of convective cloud instances and establishing a matching relationship by calculating the spatial distance between different convective cloud instances. The second matching pair set represents the set of convective cloud instances paired in the first unmatched instance set and the second unmatched instance set that were successfully matched by nearest neighbor distance.

[0058] Specifically, the electronic device calculates the spatial distance between each convection cloud instance in the first unmatched instance set and all convection cloud instances in the second unmatched instance set. By comparing the distances, it finds the second unmatched convection cloud instance that is closest to the current first unmatched convection cloud instance. The electronic device forms a matching pair between these two closest convection cloud instances and adds them to the second matching pair set. To avoid duplicate matching, convection cloud instances that have already participated in the establishment of matching pairs will be removed from their respective unmatched instance sets. The distance calculation and nearest neighbor matching process is repeated for the remaining first unmatched convection cloud instances until the first unmatched instance set or the second unmatched instance set is empty, at which point the matching process stops.

[0059] Based on the above embodiments, as an optional embodiment, the step of performing nearest neighbor matching processing on each convection cloud instance in the first unmatched instance set and the second convection cloud instance set to obtain a second matching pair set may include steps S401 to S403: S401: Calculate the matching cost between each first unmatched instance in the first unmatched instance set and each second unmatched instance in the second unmatched instance set using a preset cost function.

[0060] Specifically, the electronic device iterates through each of the first unmatched instances in the first unmatched instance set, obtaining the centroid coordinates (x1, y1) and area of ​​the first unmatched instance. Then, iterate through each second convection cloud instance in the second unmatched instance set to obtain the corresponding centroid coordinates (x2, y2) and area. The electronic device calculates the Euclidean distance between the coordinates of two centroids. As a spatial distance component, the area difference is calculated. As an area component, where The absolute value of the spatial distance component and the area component are weighted and summed using preset weighting coefficients to obtain the comprehensive matching cost value. The electronic device stores the calculated matching cost value and the corresponding convective cloud instance pairing relationship in the matching cost matrix, and repeats the above calculation process until the matching cost value calculation of all possible pairing combinations of the first unmatched instance set and the second unmatched instance set is completed.

[0061] S402: Select candidate convective cloud instances that meet the preset maximum motion distance constraint from the second unmatched instance set, and calculate the matching cost between each first unmatched instance and the candidate convective cloud instance.

[0062] Specifically, the electronic device iterates through each unmatched instance in the first unmatched instance set, obtains the centroid coordinates of the unmatched instance as a reference position, and then iterates through each convective cloud instance in the second unmatched instance set, calculates the Euclidean distance between the centroid coordinates of the convective cloud instance and the centroid coordinates of the first unmatched instance, compares the calculated Euclidean distance with a preset maximum movement distance threshold, and marks the corresponding convective cloud instance as a candidate convective cloud instance and adds it to the candidate set when the Euclidean distance is less than or equal to the preset maximum movement distance threshold. The electronic device uses a preset cost function to calculate the matching cost between the current unmatched instance and all candidate convective cloud instances, and stores the first unmatched instance, candidate convective cloud instances and corresponding matching cost as a triple in the matching cost record. The above process is repeated until the candidate screening and cost calculation of all unmatched instances in the first unmatched instance set are completed.

[0063] S403: Select the candidate convection cloud instance with the minimum matching cost value with each of the first unmatched instances as the matching object to obtain the second matching pair set.

[0064] Specifically, the electronic device iterates through each unmatched instance in the first unmatched instance set, searches for the candidate convection cloud instance with the minimum matching cost value from all candidate convection cloud instances and their matching cost records corresponding to the unmatched instance, determines the candidate convection cloud instance as the matching object of the current unmatched instance, creates a matching pair structure containing the unmatched instance and the matching object, and adds it to the second matching pair set. After completing the matching object selection for a single unmatched instance, the electronic device continues to process the next unmatched instance, repeating the process of finding the minimum matching cost value and determining the matching object in the candidate convection cloud instances. After processing all unmatched instances in the first unmatched instance set, the electronic device obtains a complete second matching pair set containing all nearest neighbor matching results. Each matching pair in the second matching pair set represents a convection cloud instance pairing relationship established by the nearest neighbor matching algorithm.

[0065] S105: Perform multi-mapping conflict detection processing on the second matching pair set to obtain the third matching pair set; merge the first matching pair set and the third matching pair set to obtain the target matching result set.

[0066] Specifically, since the second set of matching pairs obtained through nearest neighbor matching may contain instances that match multiple targets, conflict resolution is required to ensure the uniqueness of the matches. The electronic device traverses the second set of matching pairs. If it finds that the same instance in the second set of unmatched instances is matched by multiple instances in the first set of unmatched instances (many-to-one conflict), or vice versa (one-to-many conflict), then among these conflicting matching relationships, only the matching pair with the minimum matching cost value is retained. After this conflict resolution process, a third set of matching pairs containing only one-to-one matching relationships is obtained. Subsequently, the electronic device merges the high-confidence first set of matching pairs obtained in the first stage (which may contain one-to-many and many-to-one relationships) with the supplementary third set of matching pairs obtained in the second stage (which contains only one-to-one relationships) to form the final target matching result set.

[0067] Based on the above embodiments, as an optional embodiment, the step of performing multi-mapping conflict detection processing on the second matching pair set to obtain the third matching pair set may include steps S501 to S502: S501: In the second set of matching pairs, a convection cloud instance in the second set of unmatched instances that is simultaneously matched by multiple different convection cloud instances in the first set of convection cloud instances, or a convection cloud instance in the first set of convection cloud instances that is simultaneously matched by multiple different convection cloud instances in the second set of unmatched instances, is regarded as a multi-mapping conflict pair. Each matching pair in the set of matching pairs consists of one convection cloud instance in the first set of convection cloud instances and one convection cloud instance in the second set of unmatched instances.

[0068] Specifically, when the electronic device detects multiple mapping conflict pairs in the second matching pair set, it first traverses all matching pairs in the second matching pair set and counts the frequency of each convection cloud instance in the matching pair. When it finds that a convection cloud instance in the second unmatched instance set appears as part of the second convection cloud instance in multiple matching pairs, the electronic device marks this convection cloud instance as a multiple mapping conflict pair. Similarly, when it finds that a convection cloud instance in the first convection cloud instance set appears as part of the first convection cloud instance in multiple matching pairs, it also marks this convection cloud instance as a multiple mapping conflict pair. These multiple mapping conflict pairs need further processing to resolve the one-to-many matching conflict problem.

[0069] S502: Among the multiple mapping conflict pairs, retain the matching pair with the minimum matching cost value to obtain the third matching pair set.

[0070] Specifically, the electronic device calculates the matching cost of the matching pairs involved in each multi-mapping conflict pair. The matching cost is usually measured by the spatial distance between convection cloud instances. The electronic device compares the matching cost of all matching pairs in the same multi-mapping conflict pair and selects the matching pair with the minimum matching cost as the retention object. At the same time, conflict matching pairs involving the same convection cloud instance are removed from the second matching pair set. This ensures that each convection cloud instance participates in the establishment of only one matching pair. Finally, all the retained matching pairs are combined into a third matching pair set.

[0071] It should be noted that the many-to-one or one-to-many matching relationships generated by polygon overlap matching in step S103 are directly recognized as valid matches representing the "merging" or "splitting" of convective clouds because they are based on significant physical overlap. The conflict detection in step S105 is specifically used to resolve non-physical matching ambiguities caused by feature similarity in step S104.

[0072] S106: Based on the target matching result set, update the temporal status information of convective clouds in the geographic area to be monitored.

[0073] Specifically, the electronic device traverses each matching pair in the target matching result set, obtains convective cloud instances from the first and second convective cloud instance sets in the matching pair, marks the successfully matched convective cloud instance pairs as continuously evolving convective clouds, updates the corresponding convective cloud's movement trajectory coordinate sequence, reflectivity intensity change history, and coverage area evolution record in the time-series state database, calculates the displacement distance and movement direction angle of the convective cloud's centroid coordinates between two observation times, calculates the convective cloud's movement speed based on the observation time interval, and stores it in the time-series state information. The electronic device scans convective cloud instances in the first convective cloud instance set that do not appear in any matching pair, marks these instances as newly generated convective cloud states, creates new convective cloud tracking records in the time-series state database, scans convective cloud instances in the second convective cloud instance set that do not appear in any matching pair, marks the corresponding time-series state records as convective cloud dissipation states, and records the observation time when dissipation occurred.

[0074] Based on the above embodiments, as an optional embodiment, the step of updating the temporal state information of convective clouds in the geographical area to be monitored based on the target matching result set may include the following steps S601 to S605: S601: For a one-to-one matching relationship in the target matching result set, mark the corresponding convective cloud as a persistent state; S602: For many-to-one matching relationships in the target matching result set, mark the corresponding convection clouds as merged. S603: For one-to-many matching relationships in the target matching result set, mark the corresponding convective cloud as a split state; S604: For convective cloud instances in the first set of convective cloud instances that are not matched in the target matching result set, mark them as new; S605: For convective cloud instances in the second set of convective cloud instances that are not matched in the target matching result set, mark them as dissipated.

[0075] Specifically, the electronic device analyzes the matching relationship type of each matching pair in the target matching result set. When a first convective cloud instance is found to be paired with a second convective cloud instance, the corresponding convective cloud is marked as a continuous state, indicating that the convective cloud remains continuously present between two observation times. When multiple first convective cloud instances are found to be paired with the same second convective cloud instance, the involved convective clouds are marked as a merged state, indicating that multiple convective cloud systems have merged into one convective cloud system. When a first convective cloud instance is found to be paired with multiple second convective cloud instances, the involved convective clouds are marked as a split state, indicating that a convective cloud system has decomposed into multiple independent convective cloud systems. The electronic device scans the convective cloud instances in the first convective cloud instance set that do not appear in the target matching result set and marks these instances as newly generated convective cloud systems at the current observation time. It also scans the convective cloud instances in the second convective cloud instance set that do not appear in the target matching result set and marks these instances as dissipated states, indicating convective cloud systems that existed at the previous observation time but have now disappeared.

[0076] For example, the target matching result set contains matching pairs (A1, B1), (A2, B2), (A3, B2), (A4, B3), and (A4, B4). The electronic device marks the convective clouds in matching pair (A1, B1) as persistent, the convective clouds involved in matching pairs (A2, B2) and (A3, B2) as merged, and the convective clouds involved in matching pairs (A4, B3) and (A4, B4) as split. When instance A5 in the first convective cloud instance set does not appear in the target matching result set, it is marked as newly emerging. When instance B5 in the second convective cloud instance set does not appear in the target matching result set, it is marked as dissipated.

[0077] To more intuitively illustrate the different temporal states of convective clouds identified in this application, please refer to [link / reference needed]. Figure 3 , Figure 3 This is a schematic diagram of the temporal state evolution of convective clouds provided in an embodiment of this application.

[0078] Figure 3This diagram schematically illustrates several typical evolutions of convective cloud instances that may occur in the monitored geographic area from the first observation time (e.g., t-1) to the second observation time (e.g., t). The left side of the diagram represents the first set of convective cloud instances identified at the first observation time, and the right side represents the second set of convective cloud instances identified at the second observation time.

[0079] Continuous state: The convective cloud instance A(t-1) at the first observation time evolves into the convective cloud instance A(t) at the second observation time. A stable one-to-one matching relationship is formed between them, indicating that the convective cloud instance maintains its individual continuity within the observation interval.

[0080] Merged State: Two independent convective cloud instances B(t-1) and C(t-1) at the first observation time merge to form a single convective cloud instance BC(t) at the second observation time. In the algorithm's identification, this is reflected as a many-to-one matching relationship, that is, multiple instances (B(t-1) and C(t-1)) in the first convective cloud instance set are simultaneously matched with the same instance (BC(t)) in the second convective cloud instance set, and are therefore identified as "merged state".

[0081] Split State: A single large convective cloud instance D(t-1) at the first observation time splits into two independent convective cloud instances D1(t) and D2(t) at the second observation time. In the algorithm's identification, this is reflected as a one-to-many matching relationship, that is, one instance (D(t-1)) in the first convective cloud instance set matches multiple instances (D1(t) and D2(t)) in the second convective cloud instance set, and is therefore identified as a "split state".

[0082] Newborn state: A convective cloud instance F(t) is identified in the image at the second observation time, but there is no corresponding entity in the image at the first observation time. It cannot find any matching object from the first set of convective cloud instances during the matching process, and is therefore marked as "newborn state".

[0083] Dissipation state: The convective cloud instance E(t-1) exists in the first convective cloud instance set, but after matching, it does not form a matching relationship with any instance in the second convective cloud instance set, indicating that it has dissipated at the second observation time, and is therefore marked as "dissipation state".

[0084] pass Figure 3 The examples clearly show that this application, based on the target matching result set formed after matching, accurately classifies the lifecycle events of each convective cloud instance according to the different matching relationship types (one-to-one, many-to-one, one-to-many, and no matching), and updates its time-series status information accordingly, thereby achieving accurate and robust convective cloud tracking.

[0085] The following describes an exemplary electronic device for synchronous monitoring of convective clouds provided in the embodiments of this application. Figure 4 This is an exemplary hardware structure diagram of an electronic device for synchronous monitoring of convective clouds provided in an embodiment of this application.

[0086] In some embodiments, the electronic device for synchronous monitoring of convective clouds is a computer device or includes a computer device. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores data. The network interface of the computer device is used to communicate with other external terminals or servers via a network connection. In some embodiments, the network interface can be a wired network interface; in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, it implements the methods in the embodiments of this application.

[0087] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0088] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0089] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0090] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0091] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for synchronous monitoring of convective clouds, characterized in that, The method includes: Acquire the first reflectance mosaic data at the current observation time and the second reflectance mosaic data at the previous observation time. The reflectance mosaic data is two-dimensional raster data representing the reflectance intensity distribution within the geographic area to be monitored. Single-frame detection processing is performed on the first reflectance mosaic data and the second reflectance mosaic data respectively to obtain the first convective cloud instance set and the second convective cloud instance set; For each convective cloud instance in the first convective cloud instance set and the second convective cloud instance set, polygon overlap matching processing is performed to obtain a first set of successfully matched pairs, a first set of unmatched instances that did not match successfully in the first convective cloud instance set, and a second set of unmatched instances that did not match successfully in the second convective cloud instance set. The first set of unmatched instances and the second set of unmatched instances are subjected to nearest neighbor matching to obtain a second set of matching pairs. Perform multi-mapping conflict detection processing on the second matching pair set to obtain a third matching pair set; merge the first matching pair set and the third matching pair set to obtain the target matching result set. Based on the target matching result set, update the temporal status information of convective clouds in the geographic area to be monitored.

2. The method for synchronous monitoring of convective clouds according to claim 1, characterized in that, The step of performing single-frame detection processing on the first reflectance mosaic data and the second reflectance mosaic data respectively to obtain a first set of convective cloud instances and a second set of convective cloud instances specifically includes: The first reflectance mosaic data and the second reflectance mosaic data are binarized based on a preset intensity threshold to obtain the corresponding binarized image; Connectivity analysis is performed on the binarized image to identify spatially connected pixel clusters; Each pixel cluster is identified as a convective cloud instance, and the feature information of each convective cloud instance is extracted to obtain the first set of convective cloud instances at the current observation time and the second set of convective cloud instances at the previous observation time. The feature information includes at least the centroid coordinates, the outline polygon, and the area.

3. The method for synchronous monitoring of convective clouds according to claim 2, characterized in that, The step of performing polygon overlap matching on each convective cloud instance in the first convective cloud instance set and the second convective cloud instance set to obtain a first set of successfully matched pairs, a first set of unmatched instances that did not match in the first convective cloud instance set, and a second set of unmatched instances that did not match in the second convective cloud instance set specifically includes: Calculate the overlap area between the outline polygons of any convective cloud instance in the first set of convective cloud instances and any convective cloud instance in the second set of convective cloud instances; Establish a matching relationship between any first convection cloud instance and all second convection cloud instances whose overlapping area is greater than a preset overlap threshold, and obtain the first matching pair set; Other convection cloud instances in the first convection cloud instance set that do not contain the first matching pair set are determined as the first unmatched instance set; other convection cloud instances in the second convection cloud instance set that do not contain the first matching pair set are determined as the second unmatched instance set.

4. The method for synchronous monitoring of convective clouds according to claim 1, characterized in that, The step of performing nearest neighbor matching on the first set of unmatched instances and the second set of unmatched instances to obtain a second set of matching pairs specifically includes: The matching cost between each first unmatched instance in the first unmatched instance set and each second unmatched instance in the second unmatched instance set is calculated using a preset cost function. Candidate convective cloud instances that meet the preset maximum movement distance constraint are selected from the second set of unmatched instances, and the matching cost between each first unmatched instance and the candidate convective cloud instance is calculated respectively. Candidate convection cloud instances with the minimum matching cost value to each of the first unmatched instances are selected as matching objects to obtain a second set of matching pairs.

5. The method for synchronous monitoring of convective clouds according to claim 4, characterized in that, The cost function is calculated by weighting the Euclidean distance and area difference between the centroid coordinates of two convective cloud instances in the first and second unmatched instance sets to obtain the matching cost.

6. The method for synchronous monitoring of convective clouds according to claim 4, characterized in that, The process of performing multi-mapping conflict detection on the second set of matching pairs to obtain the third set of matching pairs specifically includes: In the second set of matching pairs, a convection cloud instance in the second set of unmatched instances that is simultaneously matched by multiple different convection cloud instances in the first set of convection cloud instances, or a convection cloud instance in the first set of convection cloud instances that is simultaneously matched by multiple different convection cloud instances in the second set of unmatched instances, is regarded as a multi-mapping conflict pair. All matching pairs in the set of matching pairs consist of one convection cloud instance in the first set of convection cloud instances and one convection cloud instance in the second set of unmatched instances. Among the various multi-mapping conflict pairs, the matching pair with the minimum matching cost value is retained to obtain the third matching pair set.

7. The method for synchronous monitoring of convective clouds according to claim 1, characterized in that, The step of updating the temporal state information of convective clouds in the geographic area to be monitored based on the target matching result set specifically includes: For the one-to-one matching relationship in the target matching result set, the corresponding convective cloud is marked as a continuous state; For the many-to-one matching relationship in the target matching result set, the corresponding convection cloud is marked as merged; For a one-to-many matching relationship in the target matching result set, the corresponding convective cloud is marked as a split state; For convective cloud instances in the first set of convective cloud instances that are not matched in the target matching result set, they are marked as new; For convective cloud instances in the second set of convective cloud instances that are not matched in the target matching result set, they are marked as dissipated.

8. An electronic device for synchronous monitoring of convective clouds, characterized in that, The electronic device includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the electronic device to perform the method as described in any one of claims 1-7.

9. A computer program product containing instructions, characterized in that, When the computer program product is run on an electronic device for synchronous monitoring of convective clouds, the electronic device performs the method as described in any one of claims 1-7.

10. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on an electronic device for synchronous monitoring of convective clouds, the electronic device performs the method as described in any one of claims 1-7.

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