A weighted typhoon similarity analysis method and system based on geometric shape and motion characteristics

Abstract

A weighted typhoon similarity analysis method and system based on geometric shape and motion characteristics are disclosed, comprising: obtaining the best path data set of a typhoon to be analyzed and historical typhoons, and geographical information of coastal landing boundaries; based on the path data set and the coastal landing boundaries, determining the predicted landing point of the typhoon to be analyzed and the historical typhoons; taking the predicted landing point as the reference, intercepting the path segment from the first preset time length before landing to the second preset time length after landing in each typhoon path as the key comparison path; calculating the spatial shape similarity between the typhoon to be analyzed and the historical typhoons by fusing geometric shape, cumulative distance and motion vector characteristics; based on the position information of the predicted landing point and the wind speed information at the time of landing, calculating the landing feature similarity; according to the preset weight proportion, weighting and summing the spatial shape similarity and the landing feature similarity to obtain the final typhoon similarity score. The application significantly improves the accuracy and scientificity of typhoon similarity evaluation.

Description

Technical Field

[0001] This invention relates to the technical field of flood control informatization and meteorological applications, and in particular to a weighted typhoon similarity analysis method and system based on geometric morphology and motion characteristics. Background Technology

[0002] Typhoon path similarity research serves as a bridge connecting historical experience with future predictions. By overcoming the technical bottlenecks of traditional methods and constructing a high-precision, multi-scenario similarity assessment system, we can significantly improve the efficiency of typhoon disaster response and provide crucial technical support for building "resilient cities." In the future, with the increasing precision of meteorological observation data and improved computing power, this field will develop further towards real-time and intelligent approaches. Currently, most studies focus only on the geometric shape of the path, lacking dynamic indicators and thus having limited applicability across different locations.

[0003] Currently, existing technologies typically extract static features such as the geometry, curvature, and azimuth angle of typhoon paths and combine them with minimum distance methods, Hausdorff distance, or dynamic time warping algorithms to quantify the similarity of path morphology. In addition, there are spatial analysis methods based on GIS (Geographic Information System) that retrieve similar typhoons by calculating the range of coastlines where they may make landfall.

[0004] However, existing technical solutions still have the following shortcomings in practical applications: Most current research and related algorithms tend to focus on static path morphology analysis, neglecting the dynamic attributes of typhoons at landfall (such as real-time wind speed and movement vector characteristics). These attributes are indispensable factors in assessing the actual impact of typhoons, resulting in weak reliability of matching results. Some algorithms only use path factors from typhoon information for similarity analysis, which is somewhat one-sided and can easily lead to biases in judging similar typhoons, making it difficult to build a high-precision similarity assessment system. Typhoon paths are usually long, and existing full-path analysis methods are easily affected by uncertainties during the typhoon's ocean phase. At the same time, the inconsistent release frequency of typhoon path points leads to uneven spatiotemporal distribution of path nodes in historical data, which can easily result in an overemphasis on the parts closer to land, thus affecting the accuracy of the analysis results.

[0005] Therefore, how to construct a similarity analysis scheme that can integrate spatial morphology and landfall dynamics and accurately focus on the core impact period of typhoons is a key technical problem that urgently needs to be solved in building "resilient cities" and improving the efficiency of meteorological disaster response. Summary of the Invention

[0006] To address the aforementioned technical problems in the existing technology, this invention proposes a weighted typhoon similarity analysis method and system based on geometric morphology and motion characteristics.

[0007] According to a first aspect of the present invention, a weighted typhoon similarity analysis method based on geometric morphology and motion characteristics is proposed, comprising: S1: Obtain the optimal path dataset of the typhoon to be analyzed and historical typhoons, as well as the geographical information of the coastal landfall boundary; S2: Based on the path dataset and the coastal landfall boundary, determine the expected landfall point of the typhoon to be analyzed and historical typhoons; S3: Based on the expected landfall point, extract the path segment from the first preset time before landfall to the second preset time after landfall in each typhoon path as the key comparison path; S4: Based on the key comparison path, calculate the spatial morphological similarity between the typhoon to be analyzed and historical typhoons by fusing geometric morphology, cumulative distance and motion vector features; S5: Calculate the similarity of landfall features based on the predicted landfall location information and wind speed information at landfall; and S6: Based on the preset weight ratio, the spatial morphological similarity and the landfall feature similarity are weighted and summed to obtain the final typhoon similarity score.

[0008] In some specific embodiments, the method for determining the expected landfall point includes: converting the latitude and longitude coordinates in the path dataset into projected coordinates, and calculating the coordinates of the intersection point of the typhoon path fitting line and the coastal landfall boundary line as the expected landfall point.

[0009] In some specific embodiments, the first preset duration is 72 hours and the second preset duration is 48 hours.

[0010] In some specific embodiments, calculating spatial morphological similarity includes: using the Fraser distance algorithm to calculate the geometric morphological similarity score between two key alignment paths. The minimum cumulative distance score between two key alignment paths is calculated using a dynamic time warping algorithm. The cosine similarity algorithm is used to calculate the motion feature similarity score of the corresponding trajectory points in the two key comparison paths based on their moving speed and direction angle. .

[0011] In some specific embodiments, spatial morphological similarity is obtained by performing a second weighted fusion of geometric morphological similarity score, minimum cumulative distance score, and motion feature similarity score.

[0012] In some specific embodiments, calculating the similarity of landing features includes: normalizing the expected distance between landing points of the two paths using a distance-inverse weighted algorithm, and calculating the landing distance similarity. Calculate the absolute difference in wind speed between the two paths at the expected landfall point, and construct a wind speed similarity score based on the absolute difference. .

[0013] In some specific embodiments, the formula for calculating the final typhoon similarity score is as follows: ,in, , , , , For the corresponding preset weights, and satisfying .

[0014] According to a second aspect of the invention, a computer-readable storage medium is provided on which one or more computer programs are stored, which, when executed by a computer processor, implement the method described above.

[0015] According to a third aspect of the present invention, a weighted typhoon similarity analysis system based on geometric morphology and motion characteristics is proposed, comprising: The data acquisition module is configured to acquire the optimal path dataset of the typhoon to be analyzed and historical typhoons, as well as the geographical information of the coastal landfall boundary. The expected landfall point determination module is configured to determine the expected landfall point of the typhoon to be analyzed and historical typhoons based on the path dataset and the coastal landfall boundary. The critical path interception module is configured to use the expected landfall point as a benchmark to intercept the path segment from the first preset time before landfall to the second preset time after landfall in each typhoon path, as the critical comparison path. The spatial morphology similarity calculation module is configured to calculate the spatial morphology similarity between the typhoon to be analyzed and historical typhoons based on key comparison paths by fusing geometric morphology, cumulative distance and motion vector features. The landing feature similarity calculation module is configured to calculate landing feature similarity based on the predicted landing point's location information and the wind speed information at the time of landing; and The weighted scoring module is configured to perform a weighted summation of spatial morphological similarity and landfall feature similarity according to preset weight ratios to obtain the final typhoon similarity score.

[0016] In some specific embodiments, the predicted landfall point determination module is specifically used to convert the latitude and longitude coordinates in the path dataset into projected coordinates, calculate the coordinates of the intersection point of the typhoon path fitting line and the coastal landfall boundary line, and use it as the predicted landfall point.

[0017] In some specific embodiments, the critical path interception module has a first preset duration of 72 hours and a second preset duration of 48 hours.

[0018] In some specific embodiments, the spatial morphological similarity calculation module includes: calculating the geometric morphological similarity score between two key alignment paths using the Fraser distance algorithm. The minimum cumulative distance score between two key alignment paths is calculated using a dynamic time warping algorithm. The cosine similarity algorithm is used to calculate the motion feature similarity score of the corresponding trajectory points in the two key comparison paths based on their moving speed and direction angle. .

[0019] In some specific embodiments, the landing feature similarity calculation module includes: normalizing the expected landing point distance between two paths using a distance-inverse weighted algorithm, and calculating the landing distance similarity. Calculate the absolute difference in wind speed between the two paths at the expected landfall point, and construct a wind speed similarity score based on the absolute difference. .

[0020] In some specific embodiments, the formula for calculating the final typhoon similarity score is as follows: ,in, , , , , For the corresponding preset weights, and satisfying .

[0021] Compared with the prior art, the present invention has at least the following technical effects: The method and system provided by this invention significantly improve the accuracy and scientific rigor of typhoon similarity assessment. By introducing motion vector features and dynamic physical indicators at landfall, it effectively overcomes the limitations of traditional algorithms that focus only on path geometry while ignoring dynamic attributes. In calculating spatial morphological similarity, Fraser distance, dynamic time warping, and velocity and direction angle features based on cosine similarity are integrated, enabling the model to not only identify path shape matches but also accurately capture differences in the dynamic characteristics of typhoons during their movement. Multi-dimensional coupling analysis ensures that the selected similar typhoons have a higher degree of fit in terms of actual disaster impact, providing a more rigorous quantitative basis for meteorological forecasting and historical case matching.

[0022] Furthermore, this invention employs an innovative critical path interception mechanism, focusing the analysis on the core period before and after typhoon landfall. Specifically, by intercepting the path segment from 72 hours before landfall to 48 hours after, it effectively eliminates redundant interference from path fluctuations during the open ocean phase on similarity calculations. Combined with the conversion from latitude and longitude coordinates to projected coordinates, this invention can determine the actual intersection point of the typhoon path and the coastline with extremely high accuracy, thereby obtaining more scientific geospatial indicators in the reverse weighted calculation of landfall distance. This approach, focusing on key spatiotemporal windows, not only significantly improves computational efficiency but also greatly enhances the algorithm's flood prevention reference value in different regional scenarios.

[0023] The five-factor weighted scoring system constructed in this invention achieves deep integration of five core dimensions: geometric shape, cumulative distance, motion characteristics, landing location, and landing intensity. By flexibly adjusting the weight ratio of each similarity factor, the system can perform refined evaluation according to the needs of different defense scenarios, ensuring the final similarity score is accurate and reliable. It can comprehensively reflect the correlation between historical typhoons and the typhoon to be analyzed. This solution not only breaks through the one-dimensional limitations of traditional technologies, but also provides key technical support for building resilient cities. Through scientific and intelligent collaborative reasoning, it significantly improves the timeliness of decision-making and quantitative analysis capabilities in coastal areas in response to typhoon disasters. Attached Figure Description

[0024] The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments and, together with the description, serve to explain the principles of the invention. Other embodiments and many anticipated advantages of the embodiments will be readily recognized as they become better understood through reference to the following detailed description. Other features, objects, and advantages of this application will become more apparent from reading the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart of a weighted typhoon similarity analysis method based on geometric shape and motion characteristics, according to an embodiment of this application. Figure 2 This is a flowchart of a weighted typhoon similarity analysis method based on geometric shape and motion characteristics, which is a specific embodiment of this application. Figure 3 This is a framework diagram of a weighted typhoon similarity analysis system based on geometric shape and motion characteristics, according to one embodiment of this application. Figure 4 This is a schematic diagram of the structure of a computer system used to implement the electronic device of the present application. Detailed Implementation

[0025] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0026] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0027] Figure 1 A flowchart illustrating a weighted typhoon similarity analysis method based on geometric morphology and motion characteristics, according to an embodiment of this application, is shown. Figure 1 As shown, the method includes the following steps: S1: Obtain the optimal path dataset of the typhoon to be analyzed and historical typhoons, as well as the geographic information of the coastal landfall boundaries. By obtaining a standardized optimal path dataset and accurate geographic boundary information, high-precision, multi-dimensional basic data support is provided for subsequent similarity analysis, ensuring the authority and consistency of the analysis results.

[0028] S2: Based on the path dataset and coastal landfall boundaries, determine the expected landfall points of the typhoon to be analyzed and historical typhoons.

[0029] In a specific embodiment, the method for determining the expected landfall point includes: converting the latitude and longitude coordinates in the path dataset into projected coordinates, and calculating the coordinates of the intersection point between the fitted typhoon path line and the coastal landfall boundary line, which is then used as the expected landfall point. By employing projected coordinate transformation and geometric intersection algorithms, the spatial intersection point between the typhoon and the coastline can be scientifically and objectively identified automatically, effectively avoiding the biases inherent in traditional manual determination of landfall locations and significantly improving the spatial accuracy of landfall location determination.

[0030] S3: Based on the expected landfall point, extract the path segment from the first preset time before landfall to the second preset time after landfall in each typhoon path as the key comparison path.

[0031] In a specific embodiment, the first preset duration is 72 hours, and the second preset duration is 48 hours. By accurately focusing on the core time domain window before and after the typhoon's landfall, interference information caused by the uncertainty of the path during the typhoon's ocean phase and later dissipation is effectively eliminated. This allows the similarity analysis to focus deeply on the critical period with the most significant disaster impact, greatly reducing redundant calculations and improving the reference value of early warnings.

[0032] S4: Based on the key comparison path, calculate the spatial morphological similarity between the typhoon to be analyzed and historical typhoons by fusing geometric morphology, cumulative distance and motion vector features.

[0033] In a specific embodiment, calculating spatial morphological similarity includes: using the Fraser distance algorithm to calculate the geometric morphological similarity score between two key alignment paths. The minimum cumulative distance score between two key alignment paths is calculated using a dynamic time warping algorithm. The cosine similarity algorithm is used to calculate the motion feature similarity score of the corresponding trajectory points in the two key comparison paths based on their moving speed and direction angle. Spatial morphological similarity is obtained by a weighted fusion of geometric morphological similarity scores, minimum cumulative distance scores, and motion feature similarity scores.

[0034] In calculating typhoon similarity, the Friesian distance algorithm is primarily used to measure the overall similarity in spatial geometry between two key alignment paths. Unlike the traditional Euclidean distance algorithm, this algorithm considers the order relationship between path points. In its implementation, the algorithm searches for typhoon paths... With historical path For all possible point-pair coupling methods, calculate the maximum distance between point pairs under each coupling. The Fréchet distance is obtained by finding an optimal coupling path from the start to the end of the path that minimizes the maximum distance between point pairs. Ultimately, this distance is converted into a score between 0 and 1 using an exponential function or linear mapping. The smaller the distance, the better. The higher the score, the closer the zigzag patterns of the two typhoon paths are.

[0035] The Dynamic Time Warping (RTW) algorithm is used to calculate the minimum cumulative distance between two paths, aiming to solve the point-to-point matching distortion problem caused by inconsistent typhoon movement speeds. The core logic of this algorithm is to find the optimal correspondence between points on the two paths by non-linearly scaling the time axis, thereby minimizing the sum of the cumulative Euclidean distances. In the calculation steps, the cost matrix is ​​first constructed. Each element in the matrix represents a path. The Points and Paths The The Euclidean distance between the points is then calculated using dynamic programming recursively, and obtained through the formula... Find the optimal path. The final value in the bottom right corner of the matrix is ​​the minimum cumulative distance under the optimal matching, which is then normalized to obtain the score. This indicator can effectively identify typhoon cases where, despite differences in their speed of movement, their overall trajectories are highly similar.

[0036] The cosine similarity algorithm is introduced to quantify the vector features of typhoon motion, namely the coordination of movement speed and direction angle between adjacent trajectory points. In the vector construction stage, the algorithm models the movement between every two adjacent trajectory points as a two-dimensional vector. ,in For movement speed, This refers to the direction angle of movement. Logically, the algorithm calculates the angle of movement for the typhoon being analyzed. The cosine of the angle between the motion vector at a given moment and the motion vector at the corresponding moment in historical typhoon data. A motion feature similarity score is obtained by averaging the cosine similarity of all corresponding vector points across the entire path segment. The higher the score, the more consistent the acceleration, deceleration, and turning trends of the two typhoons during their movement, reflecting the similarity of their dynamic characteristics.

[0037] S5: Calculate the similarity of landing features based on the expected location information of the landing point and the wind speed information at the time of landing.

[0038] In a specific embodiment, calculating the landing feature similarity includes: normalizing the expected landing point distance between the two paths using a distance-inverse weighted algorithm, and calculating the landing distance similarity. Calculate the absolute difference in wind speed between the two paths at the expected landfall point, and construct a wind speed similarity score based on the absolute difference. .

[0039] When calculating landing feature similarity, landing distance similarity is also considered. The determination of similarity scores primarily relies on the predicted landfall coordinates of the typhoon to be analyzed and historical typhoons obtained in step S2. By calculating the Euclidean distance between the two predicted landfall points in the projected coordinate system, the deviation in geographic spatial location between the two typhoons can be intuitively reflected. To transform this physical distance into a quantifiable similarity index, this embodiment employs a distance-inverse weighted algorithm to normalize the distance. Specifically, this algorithm maps the spatial distance to the interval between 0 and 1 using a preset transformation function (such as an inverse proportional function or an exponential decay function), so that typhoons that are closer in distance receive higher similarity scores. This approach not only eliminates the influence of physical units at different geographic scales but also highlights cases of typhoons with highly similar landfall locations through the logic of inverse weighting, providing accurate geographic spatial evidence for assessing the consistency of typhoon impacts on specific coastal areas.

[0040] Wind speed similarity The calculation focuses on the dynamic intensity characteristics of the typhoon at the moment of landfall, achieved by comparing the real-time wind speed data of the typhoon under analysis with historical typhoons at their respective predicted landfall points. The algorithm first structurally parses the core wind speed field in the path dataset and calculates the absolute difference in wind speed between the two typhoons at landfall. To construct a wind speed similarity score between 0 and 1, the system performs a normalization mapping based on this absolute difference. The core logic is that the smaller the absolute difference in wind speed, the closer the destructive intensity of the two typhoons, and the higher the corresponding wind speed similarity. The closer it gets to 1. By incorporating this dynamic intensity index, which reflects the energy level of typhoons, into the evaluation system, this scheme can effectively identify and distinguish typhoon cases that have "similar path patterns but vastly different landfall intensities," thereby significantly enhancing the scientific reference value of similarity analysis results in disaster level assessment and disaster prevention resource allocation.

[0041] S6: Based on the preset weight ratio, the spatial morphological similarity and the landfall feature similarity are weighted and summed to obtain the final typhoon similarity score.

[0042] In a specific embodiment, the formula for calculating the final typhoon similarity score is as follows: ,in, , , , , For the corresponding preset weights, and satisfying The five-factor linear weighted model constructed in this application possesses extremely high operational flexibility, allowing users to flexibly adjust the weight parameters according to different defense scenarios (such as emphasizing path matching or intensity matching). This comprehensive scoring system can reflect the correlation between typhoons in a comprehensive and multi-granular manner, providing a highly valuable quantitative score for typhoon prevention and disaster reduction decision-making in coastal cities.

[0043] Figure 2 A flowchart illustrating a specific embodiment of the weighted typhoon similarity analysis method based on geometric morphology and motion characteristics of this application is shown, as follows: Figure 2 As shown, the specific implementation steps of this method are as follows: Step 1: Data collection. Obtain the publicly available best track dataset from the Tropical Cyclone Data Center of the China Meteorological Administration, and perform structured parsing of the data. Its core fields are track time, intensity marker, wind speed, latitude and longitude, and central pressure. Draw a rough latitude and longitude information of the coastal landfall boundary.

[0044] Step 2: Based on the data from Step 1, calculate the intersection of the typhoon's path and the coastal landfall boundary by converting latitude and longitude to a projected coordinate system. This is the expected landfall point of the typhoon.

[0045] Step 3: Extract the spatial morphological similarity path. Since the path of typhoon formation is very long, when conducting similarity analysis, it is necessary to capture the 72 hours before landfall and the 48 hours after landfall as the critical path. Based on the landfall point in Step 2, extract the critical path of the typhoon.

[0046] Step 4: Using the typhoon path from Step 3 as the critical path and the typhoon to be compared as the baseline, calculate the spatial morphological similarity between the two typhoon paths, and perform geometric morphological similarity calculation using the Fraser distance. The minimum cumulative distance is calculated through dynamic time warping. Cosine similarity is used to calculate the spatial morphological similarity of adjacent trajectory points by considering their moving speed and direction angle. .

[0047] Step 5: Using the predicted landfall of the typhoon in Step 2 as the landfall information, obtain the typhoon landfall information. Perform distance normalization using a reverse-weighted distance method to calculate the landfall distance similarity. Wind speed similarity is constructed by calculating the absolute difference in wind speed. .

[0048] Step 6: Combine steps 4 and 5 to construct the final typhoon similarity: ,in .

[0049] The method disclosed in this invention first obtains and parses the optimal path dataset, and accurately determines the expected landfall point of the typhoon using latitude and longitude projection coordinate transformation. Then, using this landfall point as a benchmark, the path segment from 72 hours before landfall to 48 hours after landfall is extracted as the core critical path for analysis, effectively focusing on the core impact period of the typhoon. In the similarity calculation stage, the system integrates Fraser distance, dynamic time warping, and velocity and direction angle indices based on the cosine similarity of motion vectors to quantify spatial similarity from both geometric shape and motion law dimensions. At the same time, the similarity of landfall features is further extracted through inverse distance weighting and absolute wind speed difference. Finally, a comprehensive scoring model is constructed based on the preset weights of various factors, thereby achieving high-precision, multi-scenario quantitative evaluation of typhoon similarity.

[0050] Figure 3 This paper illustrates a framework diagram of a weighted typhoon similarity analysis system based on geometric morphology and motion features, according to an embodiment of this application. Figure 3As shown, the system includes a data acquisition module 401, a predicted landing point determination module 402, a critical path interception module 403, a spatial morphology similarity calculation module 404, a landing feature similarity calculation module 405, and a weighted scoring module 406. The system comprises the following modules: a data acquisition module 401, configured to acquire the optimal path dataset of the typhoon to be analyzed and historical typhoons, as well as the geographical information of the coastal landfall boundary; an expected landfall point determination module 402, configured to determine the expected landfall point of the typhoon to be analyzed and historical typhoons based on the path dataset and the coastal landfall boundary; a critical path extraction module 403, configured to extract the path segment from the first preset time before landfall to the second preset time after landfall from each typhoon path as the critical comparison path; a spatial morphology similarity calculation module 404, configured to calculate the spatial morphology similarity between the typhoon to be analyzed and historical typhoons based on the critical comparison path by fusing geometric morphology, cumulative distance, and motion vector features; a landfall feature similarity calculation module 405, configured to calculate the landfall feature similarity based on the location information of the expected landfall point and the wind speed information at landfall; and a weighted scoring module 406, configured to perform a weighted summation of the spatial morphology similarity and the landfall feature similarity according to a preset weight ratio to obtain the final typhoon similarity score. The system can execute the specific steps of the aforementioned methods.

[0051] This application proposes a weighted typhoon similarity analysis method and system based on geometric morphology and motion characteristics. By organically integrating the optimal dataset of typhoon paths with coastal geographical boundary information, it achieves a comprehensive coupled evaluation from static spatial trajectory to dynamic motion characteristics. Using the predicted landfall point accurately identified through projected coordinate transformation as the anchor point, this application innovatively extracts path segments during key impact periods before and after landfall as the analysis object, effectively filtering out interference from redundant data in the open ocean phase. Furthermore, it constructs a scientifically rigorous comprehensive scoring model by integrating a five-factor quantitative scoring mechanism that incorporates Fraser distance, dynamic time warping, cosine similarity of motion vectors, inverse weighting of landfall distance, and absolute difference in wind speed. Compared to traditional methods that only focus on path geometry, this application significantly improves the quantitative analysis capability of dynamic attributes and motion vector characteristics of typhoons at landfall, ensuring a high degree of adaptability to actual disaster impacts from historically similar cases. This provides highly valuable quantitative decision-making support for flood and typhoon prevention and the construction of "resilient cities" in coastal cities.

[0052] The following is for reference. Figure 4 It shows a schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application. Figure 4 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0053] like Figure 4As shown, the computer system includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 502 or programs loaded from storage section 508 into random access memory (RAM) 503. RAM 503 also stores various programs and data required for system operation. CPU 501, ROM 502, and RAM 503 are interconnected via bus 504. Input / output (I / O) interface 505 is also connected to bus 504.

[0054] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a liquid crystal display (LCD) and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card and a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 510 as needed so that computer programs read from it can be installed into storage section 508 as needed.

[0055] Specifically, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit (CPU) 501, it performs the functions defined in the methods of this application. It should be noted that the computer-readable storage medium of this application can be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable storage medium other than a computer-readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. Program code contained on a computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0056] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0057] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0058] The modules described in the embodiments of this application can be implemented in software or in hardware.

[0059] In another aspect, this application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to: acquire the optimal path dataset of the typhoon to be analyzed and historical typhoons, as well as the geographical information of the coastal landfall boundary; determine the expected landfall points of the typhoon to be analyzed and historical typhoons based on the path dataset and the coastal landfall boundary; extract a path segment from a first preset time before landfall to a second preset time after landfall from the expected landfall point as a key comparison path; calculate the spatial morphological similarity between the typhoon to be analyzed and historical typhoons by fusing geometric morphology, cumulative distance, and motion vector features according to the key comparison path; calculate the landfall feature similarity based on the location information of the expected landfall point and the wind speed information at landfall; and weight and sum the spatial morphological similarity and landfall feature similarity according to a preset weight ratio to obtain the final typhoon similarity score.

[0060] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A weighted typhoon similarity analysis method based on geometric morphology and motion characteristics, characterized in that, include: S1: Obtain the optimal path dataset of the typhoon to be analyzed and historical typhoons, as well as the geographical information of the coastal landfall boundary; S2: Based on the aforementioned path dataset and coastal landfall boundaries, determine the predicted landfall points of the typhoons to be analyzed and historical typhoons; S3: Based on the predicted landfall point, extract the path segment from the first preset time before landfall to the second preset time after landfall in each typhoon path as the key comparison path; S4: Based on the key comparison path, calculate the spatial morphological similarity between the typhoon to be analyzed and historical typhoons by fusing geometric shape, cumulative distance and motion vector features; S5: Calculate the similarity of landing features based on the location information of the expected landing point and the wind speed information at the time of landing; as well as S6: Based on the preset weight ratio, the spatial morphological similarity and the landfall feature similarity are weighted and summed to obtain the final typhoon similarity score.

2. The weighted typhoon similarity analysis method based on geometric morphology and motion characteristics according to claim 1, characterized in that, The method for determining the expected landfall point includes: converting the latitude and longitude coordinates in the path dataset into projected coordinates, and calculating the coordinates of the intersection point of the typhoon path fitting line and the coastal landfall boundary line as the expected landfall point.

3. The weighted typhoon similarity analysis method based on geometric morphology and motion characteristics according to claim 1, characterized in that, The first preset duration is 72 hours, and the second preset duration is 48 hours.

4. The weighted typhoon similarity analysis method based on geometric morphology and motion characteristics according to claim 1, characterized in that, Calculating the spatial morphological similarity includes: using the Fraser distance algorithm to calculate the geometric morphological similarity score between two key alignment paths. The minimum cumulative distance score between two key alignment paths is calculated using a dynamic time warping algorithm. The cosine similarity algorithm is used to calculate the motion feature similarity score of the corresponding trajectory points in the two key comparison paths based on their moving speed and direction angle. .

5. The weighted typhoon similarity analysis method based on geometric morphology and motion characteristics according to claim 4, characterized in that, The spatial morphological similarity is obtained by a weighted fusion of geometric morphological similarity score, minimum cumulative distance score, and motion feature similarity score.

6. The weighted typhoon similarity analysis method based on geometric morphology and motion characteristics according to claim 4, characterized in that, The calculation of landing feature similarity includes: normalizing the expected landing point distance between the two paths using a distance-inverse weighted algorithm, and calculating the landing distance similarity. Calculate the absolute difference in wind speed between the two paths at the expected landfall point, and construct a wind speed similarity based on the absolute difference. .

7. The weighted typhoon similarity analysis method based on geometric morphology and motion characteristics according to claim 6, characterized in that, The formula for calculating the final typhoon similarity score is as follows: ,in, , , , , For the corresponding preset weights, and satisfying .

8. A computer-readable storage medium having one or more computer programs stored thereon, characterized in that, When the one or more computer programs are executed by a computer processor, they perform the method according to any one of claims 1-7.

9. A weighted typhoon similarity analysis system based on geometric morphology and motion characteristics, characterized in that, include: The data acquisition module is configured to acquire the optimal path dataset of the typhoon to be analyzed and historical typhoons, as well as the geographical information of the coastal landfall boundary. The expected landfall point determination module is configured to determine the expected landfall point of the typhoon to be analyzed and historical typhoons based on the path dataset and the coastal landfall boundary. The critical path interception module is configured to use the expected landfall point as a reference to intercept the path segment from the first preset time before landfall to the second preset time after landfall in each typhoon path, as the critical comparison path. The spatial morphology similarity calculation module is configured to calculate the spatial morphology similarity between the typhoon to be analyzed and historical typhoons based on the key comparison path by fusing geometric morphology, cumulative distance and motion vector features. The landing feature similarity calculation module is configured to calculate the landing feature similarity based on the location information of the expected landing point and the wind speed information at the time of landing. as well as The weighted scoring module is configured to perform a weighted summation of the spatial morphological similarity and the landfall feature similarity according to a preset weight ratio to obtain the final typhoon similarity score.

10. A weighted typhoon similarity analysis system based on geometric morphology and motion characteristics according to claim 9, characterized in that, The predicted landfall point determination module is specifically used to convert the latitude and longitude coordinates in the path dataset into projected coordinates, calculate the coordinates of the intersection point of the typhoon path fitting line and the coastal landfall boundary line, and use it as the predicted landfall point.

11. A weighted typhoon similarity analysis system based on geometric morphology and motion characteristics according to claim 9, characterized in that, In the critical path interception module, the first preset duration is 72 hours, and the second preset duration is 48 hours.

12. The weighted typhoon similarity analysis system based on geometric morphology and motion characteristics according to claim 9, characterized in that, The spatial morphological similarity calculation module includes: calculating the geometric morphological similarity score between two key alignment paths using the Fraser distance algorithm. The minimum cumulative distance score between two key alignment paths is calculated using a dynamic time warping algorithm. The cosine similarity algorithm is used to calculate the motion feature similarity score of the corresponding trajectory points in the two key comparison paths based on their moving speed and direction angle. .

13. The weighted typhoon similarity analysis system based on geometric morphology and motion characteristics according to claim 12, characterized in that, The landing feature similarity calculation module includes: normalizing the expected landing point distance between two paths using a distance-inverse weighted algorithm, and calculating the landing distance similarity. Calculate the absolute difference in wind speed between the two paths at the expected landfall point, and construct a wind speed similarity based on the absolute difference. .

14. The weighted typhoon similarity analysis system based on geometric morphology and motion characteristics according to claim 13, characterized in that, The formula for calculating the final typhoon similarity score is as follows: ,in, , , , , For the corresponding preset weights, and satisfying .

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