Typhoon disaster claim settlement prediction method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of data processing, and discloses a typhoon disaster claim settlement prediction method and device, computer equipment and a storage medium in the field of financial science and technology. The method comprises the following steps: acquiring a typhoon matching request, generating a matching space grid according to a precision index parameter, and performing interpolation analysis on historical typhoon data of each candidate historical typhoon based on the matching space grid to obtain candidate typhoon path points; determining real-time typhoon path points according to the real-time typhoon data of the typhoon to be analyzed Determining a candidate intensity path area according to the intensity index parameters and the candidate typhoon path points, and determining a real-time intensity path area according to the intensity index parameters and the real-time typhoon path points; performing similarity analysis on the real-time intensity path area and the candidate intensity path area, determining the area similarity of each candidate historical typhoon, and determining a target similar typhoon according to the area similarity; and determining a predicted claim amount according to the historical claim data of the target similar typhoon. According to the invention, the accuracy of claim settlement prediction is improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more particularly to a method, apparatus, computer equipment, and storage medium for predicting typhoon disaster claims in the field of financial technology. Background Technology

[0002] In the financial and insurance industry, daily operations revolve around natural disasters, unexpected risks, and property losses, reflecting the characteristics of risk management. Natural disasters (such as earthquakes, floods, and typhoons) are characterized by low frequency and high risk, posing a significant threat to individuals, businesses, and even national economies. Typhoon disaster claims require the integration of multiple data sources, relying not only on climate models and geographic information system data but also on historical disaster data.

[0003] Typhoon data from different periods exhibits certain correlations. Typhoon disaster claims processing requires similarity searches of historical typhoons to reference historical data and implement typhoon disaster loss assessment functions, ensuring the implementation of catastrophic insurance business. Currently, the industry commonly uses shortest path algorithms for typhoon similarity path analysis, measuring the similarity between two typhoon paths by calculating the distance difference between the intrinsic path point set and the critical path point. The shortest path algorithm's limitation is that it only considers path points, while a typhoon is actually a wind field, ignoring the influence of different wind field ranges and intensities. Therefore, existing typhoon path similarity analyses are not comprehensive enough, and the corresponding similarity analysis results are prone to bias, affecting the accuracy of typhoon disaster claims processing. Summary of the Invention

[0004] Therefore, it is necessary to provide a typhoon disaster claims prediction method, device, computer equipment, and storage medium to address the above-mentioned technical problems, so as to solve the problem that the existing typhoon path similarity analysis is not comprehensive enough and the analysis results are biased, resulting in inaccurate claims prediction.

[0005] A method for predicting typhoon disaster claims includes: Obtain a typhoon matching request, and determine the accuracy index parameters, intensity index parameters, the typhoon to be analyzed, and at least one candidate historical typhoon based on the typhoon matching request; A matching spatial grid is generated based on the accuracy index parameters. Interpolation analysis is performed on the historical typhoon data of each candidate historical typhoon based on the matching spatial grid to obtain the candidate typhoon path points of each candidate historical typhoon. The real-time typhoon path point of the typhoon to be analyzed is determined based on the real-time typhoon data of the typhoon to be analyzed. The candidate intensity path regions of each candidate historical typhoon are determined based on the intensity index parameters and the candidate typhoon path points, and the real-time intensity path region of the typhoon to be analyzed is determined based on the intensity index parameters and the real-time typhoon path points. A similarity analysis is performed on the real-time intensity path region of the typhoon to be analyzed and the candidate intensity path region of each candidate historical typhoon to determine the regional similarity of each candidate historical typhoon, and a target similar typhoon is determined from all the candidate historical typhoons based on the regional similarity. Obtain the historical claim amounts for typhoons similar to the target, and determine the predicted claim amount for the typhoon to be analyzed based on the historical claim data.

[0006] A typhoon disaster claims prediction device, comprising: The request acquisition module is used to acquire typhoon matching requests and determine accuracy index parameters, intensity index parameters, the typhoon to be analyzed, and at least one candidate historical typhoon based on the typhoon matching requests. The interpolation analysis module is used to generate a matching spatial grid based on the accuracy index parameters, and to perform interpolation analysis on the historical typhoon data of each candidate historical typhoon based on the matching spatial grid to obtain the candidate typhoon path points of each candidate historical typhoon. The real-time path point determination module is used to determine the real-time path point of the typhoon to be analyzed based on the real-time typhoon data of the typhoon to be analyzed. The path region determination module is used to determine the candidate intensity path region of each of the candidate historical typhoons based on the intensity index parameters and the candidate typhoon path points, and to determine the real-time intensity path region of the typhoon to be analyzed based on the intensity index parameters and the real-time typhoon path points. The similarity analysis module is used to perform similarity analysis on the real-time intensity path region of the typhoon to be analyzed and the candidate intensity path region of each candidate historical typhoon, determine the regional similarity of each candidate historical typhoon, and determine the target similar typhoon from all the candidate historical typhoons based on the regional similarity. The claims prediction module is used to obtain the historical claims amount of the target similar typhoons and determine the predicted claims amount of the typhoon to be analyzed based on the historical claims data.

[0007] A computer device includes a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor executes the computer-readable instructions to implement the above-described typhoon disaster compensation prediction method.

[0008] A computer-readable storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the typhoon disaster compensation prediction method described above.

[0009] In the aforementioned typhoon disaster claims prediction method, device, computer equipment, and storage medium, the typhoon disaster claims prediction method obtains a typhoon matching request, determines accuracy index parameters, intensity index parameters, the typhoon to be analyzed, and at least one candidate historical typhoon based on the typhoon matching request; generates a matching spatial grid based on the accuracy index parameters, performs interpolation analysis on the historical typhoon data of each candidate historical typhoon based on the matching spatial grid, and obtains the candidate typhoon path points of each candidate historical typhoon; determines the real-time typhoon path point of the typhoon to be analyzed based on the real-time typhoon data of the typhoon to be analyzed; determines the candidate intensity path region of each candidate historical typhoon based on the intensity index parameters and the candidate typhoon path points, and determines the real-time intensity path region of the typhoon to be analyzed based on the intensity index parameters and the real-time typhoon path points; performs similarity analysis on the real-time intensity path region of the typhoon to be analyzed and the candidate intensity path region of each candidate historical typhoon, determines the regional similarity of each candidate historical typhoon, and determines the target similar typhoon from all candidate historical typhoons based on the regional similarity; obtains the historical claims amount of the target similar typhoon, and determines the predicted claims amount of the typhoon to be analyzed based on the historical claims data. This invention generates a matching spatial grid based on accuracy index parameters to facilitate interpolation analysis, thereby improving the accuracy of typhoon similarity measurement for candidate historical typhoons. Furthermore, in addition to considering typhoon path points, this invention also incorporates typhoon intensity attribute data based on intensity index parameters, ensuring the comprehensiveness of the similarity analysis, further reducing the deviation rate of the similarity analysis results, and improving the accuracy of typhoon disaster compensation prediction. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram of an application environment for the typhoon disaster claims prediction method in one embodiment of the present invention; Figure 2 This is a flowchart illustrating a typhoon disaster claims prediction method according to one embodiment of the present invention; Figure 3 This is a schematic diagram of the wind circle region of a typhoon path point in a typhoon disaster claims prediction method according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a typhoon disaster compensation prediction device according to an embodiment of the present invention; Figure 5 This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] The typhoon disaster claims prediction method provided in this embodiment can be applied to situations such as... Figure 1 In this application environment, the client communicates with the server. Clients include, but are not limited to, various personal computers, laptops, smartphones, and tablets. The server can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0014] The typhoon disaster claims prediction method in this embodiment can be applied to the financial insurance industry. When a typhoon disaster occurs, the insurance company's operations personnel obtain real-time typhoon data through a client, generate a typhoon matching request based on the real-time typhoon data, and send the typhoon matching request to the server. After receiving the typhoon matching request, the server selects the typhoon as the typhoon to be analyzed, searches the historical database for the most similar historical typhoon, and determines the predicted claim amount for the typhoon to be analyzed based on the historical claims data of the found historical typhoon, thus realizing the loss estimation function for typhoon disaster claims.

[0015] In one embodiment, such as Figure 2 As shown, a method for predicting typhoon disaster claims is provided, including the following steps S10-S60: S10. Obtain a typhoon matching request, and determine the accuracy index parameters, intensity index parameters, the typhoon to be analyzed, and at least one candidate historical typhoon based on the typhoon matching request.

[0016] Understandably, the server receives and parses typhoon matching requests to determine accuracy parameters, intensity parameters, the typhoon to be analyzed, and at least one candidate historical typhoon. A typhoon matching request is used to find the typhoon most similar to a specified real-time typhoon from potential historical typhoons according to specified parameter conditions. The typhoon to be analyzed refers to the real-time typhoon for which similarity needs to be found. Candidate historical typhoons are historical typhoons selected from the historical typhoon database for similarity comparison. Accuracy parameters are spatial dimension constraints used when comparing the similarity between the typhoon to be analyzed and candidate historical typhoons; for example, an accuracy parameter could be 0.1 latitude and longitude. Intensity parameters are wind intensity dimension constraints used when comparing the similarity between the typhoon to be analyzed and candidate historical typhoons; for example, an intensity parameter could be level 7. There can be one or more candidate historical typhoons. Specifically, the typhoon matching request may also include a requirement for the number of candidate historical typhoons, for example, five candidate historical typhoons.

[0017] S20. Generate a matching spatial grid based on the accuracy index parameters, and perform interpolation analysis on the historical typhoon data of each candidate historical typhoon based on the matching spatial grid to obtain the candidate typhoon path points of each candidate historical typhoon.

[0018] Understandably, the server determines the size of the smallest grid cell based on accuracy parameters and generates a matching spatial grid based on this size. The matching spatial grid refers to a unified grid reference background used when analyzing the path points of the typhoon to be analyzed and candidate historical typhoons. The server performs interpolation analysis on the historical typhoon data of each candidate historical typhoon based on the matching spatial grid to obtain the candidate typhoon path points. Interpolation analysis refers to the process of inserting new path points between the existing path points of candidate historical typhoons based on the matching spatial grid. Historical typhoon data records the characteristics of candidate historical typhoons during their movement. Candidate typhoon path points refer to all path points of candidate historical typhoons after interpolation analysis.

[0019] In one embodiment, step S20, namely determining the matching spatial grid based on the accuracy index parameters, includes: S201. Determine the target index type and target accuracy parameters based on the accuracy index parameters; S202. When the target indicator type is latitude and longitude type, generate a matching spatial grid in latitude and longitude values ​​according to the target accuracy parameters. S203. When the target index type is distance type, generate a matching spatial grid with distance values ​​in units based on the target accuracy parameters.

[0020] Understandably, the server determines the target indicator type and target precision parameters based on the precision indicator parameters. The target indicator type is the type of cell size metric used to characterize the matching spatial grid, such as latitude and longitude, distance, etc. The target precision parameters are the specific numerical values ​​used to characterize the cell size of the matching spatial grid, matching the target indicator type, such as latitude and longitude values, distance values, etc.

[0021] When the target indicator type is latitude and longitude, it indicates that the cell size unit of the matching spatial grid is latitude and longitude. The matching spatial grid is generated based on the target precision parameter, with the latitude and longitude value as the smallest cell. For example, when the precision indicator parameter is 0.1 latitude and longitude, the matching spatial grid consists of multiple cells with a latitude of 0.1 and a dimension of 0.1.

[0022] When the target metric type is distance type, it indicates that the cell size unit of the matching spatial grid is distance length, and a matching spatial grid is generated based on the target accuracy parameter, with the cell containing the distance value as the smallest. For example, when the accuracy parameter is 1 kilometer, the matching spatial grid consists of multiple 1-kilometer × 1-kilometer cells.

[0023] Based on the determination of the target index type and target accuracy parameters by the accuracy index parameters, this embodiment can realize on-demand customized mesh, generate matching spatial meshes of different granularities according to different accuracy requirements, and improve the flexibility of similar typhoon analysis.

[0024] In one embodiment, the candidate typhoon path points include historical path points and interpolated path points; step S20, namely, performing interpolation analysis on the historical typhoon data of each candidate historical typhoon based on the matching spatial grid to obtain the candidate typhoon path points of each candidate historical typhoon, includes: S204. For each of the candidate historical typhoons, determine the historical path point of the candidate historical typhoon based on the historical typhoon data of the candidate historical typhoon. S205. Generate the historical path of each candidate historical typhoon based on its historical path points. S206. The intersection point between the historical path line of each candidate historical typhoon and the grid line of the matching spatial grid is determined as the interpolation path point of the candidate historical typhoon. S207. Determine the candidate typhoon path point of each candidate historical typhoon by combining all the historical path points and interpolated path points of each candidate historical typhoon.

[0025] Understandably, candidate typhoon path points include historical path points and interpolated path points. Historical path points refer to existing path points recorded in the historical typhoon database for candidate historical typhoons, while interpolated path points refer to newly generated path points when performing interpolation analysis on the path points of candidate historical typhoons. A historical path line is a line formed by connecting historical path points.

[0026] In one specific embodiment, such as Figure 3 As shown, the server first determines historical path points A, B, C, D, and E based on the historical typhoon data of the candidate historical typhoon. Next, it generates the historical path line of the candidate historical typhoon based on these historical path points, i.e., the line connecting A, B, C, D, and E sequentially. Then, the intersection points between the historical path line of the candidate historical typhoon and the grid lines of the matching spatial grid are determined as the interpolation path points of the candidate historical typhoon. For example, the line connecting C and D will intersect with 10 grid lines of the matching spatial grid, generating 10 interpolation path points. Finally, all historical path points and interpolation path points of the candidate historical typhoon are used to determine the candidate typhoon path points.

[0027] This embodiment generates interpolated path points by aligning discrete paths with a fixed spatial grid, reducing jumps in typhoon path data and enhancing the continuity and smoothness of path points. It can be seamlessly integrated into a gridded analysis framework. Furthermore, all path points for candidate historical typhoons are generated based on the same matching spatial grid, eliminating discrepancies in analysis results caused by differences in data resolution and improving the accuracy of similar typhoon analysis.

[0028] S30. Determine the real-time typhoon path point of the typhoon to be analyzed based on the real-time typhoon data of the typhoon to be analyzed.

[0029] Understandably, the server obtains real-time typhoon data for the typhoon to be analyzed and determines its real-time path points based on this data. Real-time typhoon data is data recorded in real-time to characterize the movement of the typhoon to be analyzed. Real-time typhoon path points refer to all path points of the typhoon to be analyzed, recorded in real-time at a specified frequency.

[0030] S40. Determine the candidate intensity path region of each of the candidate historical typhoons based on the intensity index parameters and the candidate typhoon path points, and determine the real-time intensity path region of the typhoon to be analyzed based on the intensity index parameters and the real-time typhoon path points.

[0031] Understandably, the server can determine the specified wind intensity for similarity comparison between the typhoon to be analyzed and candidate historical typhoons based on the intensity index parameters. Using each typhoon path point in both the typhoon to be analyzed and the candidate historical typhoons as the center and the specified wind intensity as the radius, a wind buffer area corresponding to each typhoon path point can be determined. The server defines the area covered by the wind buffers corresponding to all candidate typhoon path points within the same candidate historical typhoon as the candidate intensity path area for that candidate historical typhoon. Simultaneously, the server defines the area covered by the wind buffers corresponding to all real-time typhoon path points in the typhoon to be analyzed as the real-time intensity path area for the typhoon to be analyzed.

[0032] In one embodiment, the intensity index parameters include a first wind level intensity, a second wind level intensity, and a third wind level intensity, wherein the first wind level intensity is less than the second wind level intensity, and the second wind level intensity is less than the third wind level intensity; the candidate intensity path region includes a first intensity candidate region, a second intensity candidate region, and a third intensity candidate region, and the real-time intensity path region includes a first intensity real-time region, a second intensity real-time region, and a third intensity real-time region; step S40, namely determining the candidate intensity path region of each of the candidate historical typhoons based on the intensity index parameters and the candidate typhoon path points, and determining the real-time intensity path region of the typhoon to be analyzed based on the intensity index parameters and the real-time typhoon path points, includes: S401. For each of the candidate historical typhoons, the first candidate wind circle region of each candidate typhoon path point is determined based on the first wind intensity and historical typhoon data, and the set of the first candidate wind circle regions of all the candidate typhoon path points is determined as the first intensity candidate region of the candidate historical typhoon. S402. Determine the second candidate wind circle region for each candidate typhoon path point based on the second wind intensity and historical typhoon data, and determine the set of the second candidate wind circle regions for all candidate typhoon path points as the second intensity candidate region for the candidate historical typhoon. S403. Determine the third candidate wind circle region for each candidate typhoon path point based on the third wind intensity and historical typhoon data, and determine the set of the third candidate wind circle regions for all candidate typhoon path points as the third intensity candidate region for the candidate historical typhoon. S404. For the typhoon to be analyzed, the first real-time wind circle region of each of the real-time typhoon path points is determined according to the first wind intensity and real-time typhoon data, and the set of the first real-time wind circle regions of all the real-time typhoon path points is determined as the first intensity real-time region of the typhoon to be analyzed. S405. Determine the second real-time wind circle region of each of the real-time typhoon path points based on the second wind intensity and real-time typhoon data, and determine the set of the second real-time wind circle regions of all the real-time typhoon path points as the second intensity real-time region of the typhoon to be analyzed. S406. Determine the third real-time wind circle region for each of the real-time typhoon path points based on the third wind intensity and real-time typhoon data, and determine the set of the third real-time wind circle regions of all the real-time typhoon path points as the third intensity real-time region of the typhoon to be analyzed.

[0033] Understandably, there can be one or more intensity index parameters. In this embodiment, the intensity index parameters include three levels: a first wind level, a second wind level, and a third wind level, with the first wind level being less than the second wind level, and the second wind level being less than the third wind level. The magnitudes of the first, second, and third wind levels can be adjusted as needed. For example, the first wind level is level 7, the second wind level is level 10, and the third wind level is level 12. In this case, the candidate intensity path region includes the first intensity candidate region corresponding to the first wind level, the second intensity candidate region corresponding to the second wind level, and the third intensity candidate region corresponding to the third wind level. The real-time intensity path region includes the first real-time intensity region corresponding to the first wind level, the second real-time intensity region corresponding to the second wind level, and the third real-time intensity region corresponding to the third wind level.

[0034] For each candidate historical typhoon, the server determines the first candidate wind circle region for each candidate typhoon's path point based on the first wind strength and historical typhoon data. The set of all first candidate wind circle regions for all candidate typhoon path points is then defined as the first intensity candidate region for that candidate historical typhoon. The first candidate wind circle region is a circle generated in a matching spatial grid with the candidate typhoon path point as its center, based on the radius corresponding to the first wind strength. Each candidate typhoon path point corresponds to one first candidate wind circle region. The set of first candidate wind circle regions for all candidate typhoon path points within the same candidate historical typhoon is then defined as the first intensity candidate region for that candidate historical typhoon. The first intensity candidate region refers to the area covered by the wind circles corresponding to all candidate typhoon path points at the first wind strength.

[0035] In one specific embodiment, such as Figure 3As shown, the candidate typhoon track points for historical typhoons include A, B, C, D, and E. When the first wind intensity is level 7, the radius corresponding to level 7 for each candidate typhoon track point is determined based on records in the historical typhoon data. The first candidate wind circle region at point B is a circle generated by matching the spatial grid with point B as the center and the radius corresponding to level 7 wind intensity at point B in the candidate historical typhoons.

[0036] Using the same method, the server can determine the second and third intensity candidate regions of candidate historical typhoons, as well as the first, second, and third intensity real-time regions of the typhoon to be analyzed.

[0037] This embodiment calculates the wind circle region by using typhoon path points and corresponding wind intensities, which can more realistically reflect the actual impact range of typhoons at different locations and intensities. Based on the correspondence between intensity and wind circle radius at historical typhoon path points, a dynamic wind circle is established, which helps to improve the accuracy of subsequent analysis of similar typhoons.

[0038] S50. Perform similarity analysis on the real-time intensity path region of the typhoon to be analyzed and the candidate intensity path region of each candidate historical typhoon to determine the regional similarity of each candidate historical typhoon, and determine the target similar typhoon from all the candidate historical typhoons based on the regional similarity.

[0039] Understandably, the server performs a similarity analysis between the real-time intensity path region of the typhoon to be analyzed and the candidate intensity path region of each candidate historical typhoon to determine the regional similarity of each candidate historical typhoon. Each candidate historical typhoon corresponds to a regional similarity score, which is a quantitative value used to characterize the degree of similarity between the typhoon to be analyzed and the candidate historical typhoon. The server sorts all candidate historical typhoons according to their regional similarity scores and identifies one or more candidate historical typhoons with the highest regional similarity scores as target similar typhoons. Target similar typhoons are those historical typhoons that are most similar to the typhoon to be analyzed, selected from all candidate historical typhoons. For example, the top N candidate historical typhoons may be identified as target similar typhoons, where N can be set and adjusted as needed; the default value for N is 3.

[0040] In one embodiment, step S50, namely, performing similarity analysis on the real-time intensity path region of the typhoon to be analyzed and the candidate intensity path region of each candidate historical typhoon to determine the regional similarity of each candidate historical typhoon, includes: S501. For each of the candidate historical typhoons, perform a similarity analysis on the real-time region of the first intensity of the typhoon to be analyzed and the candidate region of the first intensity of the candidate historical typhoon to determine the first intensity similarity of the candidate historical typhoon. S502. Perform similarity analysis on the real-time region of the second intensity of the typhoon to be analyzed and the candidate region of the second intensity of the candidate historical typhoon to determine the second intensity similarity of the candidate historical typhoon. S503. Perform a similarity analysis on the real-time region of the third intensity of the typhoon to be analyzed and the candidate region of the third intensity of the candidate historical typhoon to determine the similarity of the third intensity of the candidate historical typhoon. S504. The first intensity similarity, second intensity similarity and third intensity similarity of each candidate historical typhoon are weighted and calculated to obtain the regional similarity of each candidate historical typhoon.

[0041] Understandably, in one embodiment, for each candidate historical typhoon, the server performs a similarity analysis on the real-time intensity path region corresponding to the typhoon to be analyzed and the candidate intensity path region corresponding to the candidate historical typhoon at the same wind intensity. For example, the server performs a similarity analysis on the first intensity real-time region of the typhoon to be analyzed and the first intensity candidate region of the candidate historical typhoon to determine the first intensity similarity of the candidate historical typhoon. The first intensity similarity refers to the quantified value of the degree of similarity between the typhoon to be analyzed and the candidate historical typhoon at the first wind intensity. In the same way, the server can determine the first intensity similarity, second intensity similarity, and third intensity similarity of each candidate historical typhoon. The server performs a weighted calculation on the first intensity similarity, second intensity similarity, and third intensity similarity of each candidate historical typhoon to obtain the regional similarity of each candidate historical typhoon.

[0042] In another embodiment, for each candidate historical typhoon, the server can further perform similarity analysis on the real-time intensity path region corresponding to the typhoon to be analyzed at different wind intensities and the candidate intensity path region corresponding to the candidate historical typhoon, to determine the fourth, fifth, and sixth intensity similarity of the candidate historical typhoon. For example, the server performs similarity analysis on the first intensity real-time region of the typhoon to be analyzed and the second intensity candidate region of the candidate historical typhoon to determine the fourth intensity similarity of the candidate historical typhoon. The server performs similarity analysis on the first intensity real-time region of the typhoon to be analyzed and the third intensity candidate region of the candidate historical typhoon to determine the fifth intensity similarity of the candidate historical typhoon. The server performs similarity analysis on the second intensity real-time region of the typhoon to be analyzed and the third intensity candidate region of the candidate historical typhoon to determine the sixth intensity similarity of the candidate historical typhoon. The server performs weighted calculations on the first, second, third, fourth, fifth, and sixth intensity similarities of each candidate historical typhoon to obtain the regional similarity of each candidate historical typhoon.

[0043] This embodiment performs a similarity analysis on the real-time intensity path region of the typhoon under analysis and the candidate intensity path region of a candidate historical typhoon at the same wind intensity when searching for historical typhoons similar to the one being analyzed. Combining path point location and wind intensity, it filters out historical typhoons with similar paths and consistent intensity impact ranges, improving the accuracy of similar typhoon analysis. This embodiment can more accurately match historical typhoons with similar impact patterns, providing a more reliable reference for insurance forecasting.

[0044] In one embodiment, step S501, namely, for each candidate historical typhoon, performing a similarity analysis on the real-time region of the first intensity of the typhoon to be analyzed and the candidate region of the first intensity of the candidate historical typhoon to determine the first intensity similarity of the candidate historical typhoon, includes: S5011. For each of the candidate historical typhoons, determine the first number of overlapping grids based on the first intensity real-time region of the typhoon to be analyzed and the first intensity candidate region of the candidate historical typhoon. S5012. Obtain the total number of grids in the matching spatial grid, and determine the ratio of the first overlapping grid number to the total number of grids as the first intensity similarity of the candidate historical typhoon.

[0045] Understandably, for each candidate historical typhoon, the server, based on the same matching spatial grid, determines the number of grids that overlap between the real-time region of the first intensity of the typhoon to be analyzed and the candidate region of the first intensity of the candidate historical typhoon. The first overlapping grid number refers to the number of grids that overlap between the real-time intensity path region of the typhoon to be analyzed and the candidate intensity path region of the candidate historical typhoon in the same matching spatial grid. The server obtains the total number of grids in the matching spatial grid and determines the first intensity similarity of the candidate historical typhoon by the ratio of the first overlapping grid number to the total number of grids. Alternatively, the server can obtain the total number of real-time region grids covered by the real-time region of the first intensity of the typhoon to be analyzed in the matching spatial grid and determine the first intensity similarity of the candidate historical typhoon by the ratio of the first overlapping grid number to the total number of real-time region grids.

[0046] In this embodiment, the number of overlapping grids is counted for the real-time intensity path region of the typhoon to be analyzed and the candidate intensity path region of a candidate historical typhoon under the same matching spatial grid. This can avoid the difference in analysis results caused by the difference in data resolution and improve the accuracy of similar typhoon analysis.

[0047] In one embodiment, step S5011, namely, weighting the first intensity similarity, second intensity similarity, and third intensity similarity of each candidate historical typhoon to obtain the regional similarity of each candidate historical typhoon, includes: S50111. Determine the first weighting coefficient corresponding to the first wind intensity, the second weighting coefficient corresponding to the second wind intensity, and the third weighting coefficient corresponding to the third wind intensity based on the intensity index parameters. S50112. Based on the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient, the first intensity similarity, the second intensity similarity, and the third intensity similarity of each candidate historical typhoon are weighted and calculated to obtain the regional similarity of each candidate historical typhoon.

[0048] Understandably, when the intensity index parameters include first-level wind intensity, second-level wind intensity, and third-level wind intensity, the server can determine the first weighting coefficient corresponding to the first-level wind intensity, the second weighting coefficient corresponding to the second-level wind intensity, and the third weighting coefficient corresponding to the third-level wind intensity based on the intensity index parameters. The first weighting coefficient is a quantified value of the importance of the regional similarity corresponding to the first-level wind intensity in similarity analysis. The second weighting coefficient is a quantified value of the importance of the regional similarity corresponding to the second-level wind intensity in similarity analysis. The third weighting coefficient is a quantified value of the importance of the regional similarity corresponding to the third-level wind intensity in similarity analysis. The sum of the first, second, and third weighting coefficients is 1. Typhoon similarity is influenced by multiple wind intensities (such as low wind speed, medium-high wind speed, and extreme wind speed). For example, a wind intensity of level 7 (low wind speed) has a relatively small impact on disaster similarity and is assigned a lower weighting coefficient (e.g., 0.2); a wind intensity of level 10 (medium to high wind speed) has a greater impact on disaster similarity and is assigned a higher weighting coefficient (e.g., 0.3); a wind intensity of level 12 (extreme wind speed) has a greater impact on disaster similarity and is assigned a higher weighting coefficient (e.g., 0.5).

[0049] In one specific embodiment, the server generates a weighted calculation formula based on a first weighting coefficient, a second weighting coefficient, and a third weighting coefficient. Using this weighted calculation formula, the server calculates the first, second, and third intensity similarity of each candidate historical typhoon to obtain the regional similarity of each candidate historical typhoon. The weighted calculation formula is specifically as follows: ,in, Indicates regional similarity. Indicates the strength index parameter, Indicates the intensity of the first wind level. Indicates the intensity of the second wind level. Indicates the intensity of the third wind level. This represents the weighting coefficients corresponding to different wind intensities. This indicates the intensity similarity corresponding to different wind intensities.

[0050] This embodiment takes into account the different degrees of impact of different wind intensities on disasters. By determining the weighting coefficients of different wind intensities, the contribution of each wind level to the overall risk is quantified and the overall similarity is calculated. The overall similarity, by weighting and integrating information from multiple wind levels, can more comprehensively reflect the disaster impact characteristics of typhoons and improve the comprehensiveness and accuracy of typhoon similarity analysis.

[0051] S60. Obtain the historical claim amount of the target similar typhoon, and determine the predicted claim amount of the typhoon to be analyzed based on the historical claim data.

[0052] Understandably, after identifying similar typhoons, the server retrieves the historical claim amounts for those similar typhoons and determines the predicted claim amount for the typhoon to be analyzed based on this historical claim data. Historical claim data refers to the specific data on disaster insurance claims for historical typhoons that have already occurred. The predicted claim amount refers to the expected amount of disaster insurance claims for typhoons that are currently occurring. For example, the server selects the Top N similar typhoons from the candidate historical typhoons, determines the claim weight coefficient for each similar typhoon based on regional similarity, and uses this claim weight coefficient to calculate a weighted average of the historical claim amounts for the N similar typhoons to obtain the predicted claim amount for the typhoon to be analyzed.

[0053] This embodiment obtains a typhoon matching request, and determines the accuracy index parameters, intensity index parameters, the typhoon to be analyzed, and at least one candidate historical typhoon based on the typhoon matching request; generates a matching spatial grid based on the accuracy index parameters, and performs interpolation analysis on the historical typhoon data of each candidate historical typhoon based on the matching spatial grid to obtain the candidate typhoon path points of each candidate historical typhoon; determines the real-time typhoon path point of the typhoon to be analyzed based on the real-time typhoon data of the typhoon to be analyzed; determines the candidate intensity path region of each candidate historical typhoon based on the intensity index parameters and the candidate typhoon path points, and determines the real-time intensity path region of the typhoon to be analyzed based on the intensity index parameters and the real-time typhoon path points; performs similarity analysis on the real-time intensity path region of the typhoon to be analyzed and the candidate intensity path region of each candidate historical typhoon to determine the regional similarity of each candidate historical typhoon, and determines the target similar typhoon from all candidate historical typhoons based on the regional similarity; obtains the historical claim amount of the target similar typhoon, and determines the predicted claim amount of the typhoon to be analyzed based on the historical claim data. This embodiment generates a matching spatial grid based on accuracy index parameters to facilitate interpolation analysis, thereby improving the accuracy of typhoon similarity measurement for candidate historical typhoons. Furthermore, in addition to considering typhoon path points, this embodiment also incorporates typhoon intensity attribute data based on intensity index parameters, ensuring the comprehensiveness of the similarity analysis, further reducing the deviation rate of the similarity analysis results, and improving the accuracy of typhoon disaster compensation prediction.

[0054] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0055] In one embodiment, a typhoon disaster claims prediction device is provided, which corresponds one-to-one with the typhoon disaster claims prediction method described in the above embodiments. For example... Figure 4 As shown, the typhoon disaster claims prediction device includes a request acquisition module 10, an interpolation analysis module 20, a real-time path point determination module 30, a path area determination module 40, a similarity analysis module 50, and a claims prediction module 60. Detailed descriptions of each functional module are as follows: The request acquisition module 10 is used to acquire typhoon matching requests and determine accuracy index parameters, intensity index parameters, the typhoon to be analyzed, and at least one candidate historical typhoon based on the typhoon matching requests. The interpolation analysis module 20 is used to generate a matching spatial grid based on the accuracy index parameters, and to perform interpolation analysis on the historical typhoon data of each candidate historical typhoon based on the matching spatial grid to obtain the candidate typhoon path points of each candidate historical typhoon. The real-time path point determination module 30 is used to determine the real-time path point of the typhoon to be analyzed based on the real-time typhoon data of the typhoon to be analyzed. The path region determination module 40 is used to determine the candidate intensity path region of each of the candidate historical typhoons based on the intensity index parameters and the candidate typhoon path points, and to determine the real-time intensity path region of the typhoon to be analyzed based on the intensity index parameters and the real-time typhoon path points. The similarity analysis module 50 is used to perform similarity analysis on the real-time intensity path region of the typhoon to be analyzed and the candidate intensity path region of each candidate historical typhoon, determine the regional similarity of each candidate historical typhoon, and determine the target similar typhoon from all the candidate historical typhoons based on the regional similarity. The claims prediction module 60 is used to obtain the historical claims amount of the target similar typhoons and determine the predicted claims amount of the typhoon to be analyzed based on the historical claims data.

[0056] In one embodiment, the interpolation analysis module 20 includes: A precision index parameter parsing unit is used to determine the target index type and target precision parameters based on the precision index parameters. Latitude and longitude gridded units are used to generate a matching spatial grid in latitude and longitude values ​​based on the target accuracy parameters when the target index type is latitude and longitude type. Distance gridding unit, used to generate a matching spatial grid in distance units based on the target accuracy parameter when the target index type is distance type.

[0057] In one embodiment, the interpolation analysis module 20 further includes: The historical path point determination unit is used to determine the historical path point of each candidate historical typhoon based on the historical typhoon data of the candidate historical typhoon. The historical path determination unit is used to generate the historical path of each candidate historical typhoon based on its historical path points. An interpolation path point determination unit is used to determine the intersection point between the historical path line of each candidate historical typhoon and the grid line of the matching spatial grid as the interpolation path point of the candidate historical typhoon. The candidate typhoon path point determination unit is used to determine the candidate typhoon path point of each candidate historical typhoon by combining all the historical path points and interpolated path points of each candidate historical typhoon.

[0058] In one embodiment, the path region determination module 40 includes: The first intensity candidate region determination unit is used to determine the first candidate wind circle region of each candidate typhoon path point based on the first wind intensity and historical typhoon data for each candidate historical typhoon, and to determine the set of the first candidate wind circle regions of all candidate typhoon path points as the first intensity candidate region of the candidate historical typhoon. The second intensity candidate region determination unit is used to determine the second candidate wind circle region of each candidate typhoon path point based on the second wind intensity and historical typhoon data, and to determine the set of the second candidate wind circle regions of all candidate typhoon path points as the second intensity candidate region of the candidate historical typhoon. The third intensity candidate region determination unit is used to determine the third candidate wind circle region of each candidate typhoon path point based on the third wind intensity and historical typhoon data, and to determine the set of the third candidate wind circle regions of all candidate typhoon path points as the third intensity candidate region of the candidate historical typhoon. The first intensity real-time region determination unit is used to determine the first real-time wind circle region of each real-time typhoon path point based on the first wind level intensity and real-time typhoon data for the typhoon to be analyzed, and to determine the set of the first real-time wind circle regions of all the real-time typhoon path points as the first intensity real-time region of the typhoon to be analyzed. The second intensity real-time region determination unit is used to determine the second real-time wind circle region of each of the real-time typhoon path points based on the second wind intensity and real-time typhoon data, and to determine the set of the second real-time wind circle regions of all the real-time typhoon path points as the second intensity real-time region of the typhoon to be analyzed. The third intensity real-time region determination unit is used to determine the third real-time wind circle region of each of the real-time typhoon path points based on the third wind intensity and real-time typhoon data, and to determine the set of the third real-time wind circle regions of all the real-time typhoon path points as the third intensity real-time region of the typhoon to be analyzed.

[0059] In one embodiment, the similarity analysis module 50 includes: The first intensity similarity determination unit is used to perform similarity analysis on the real-time region of the first intensity of the typhoon to be analyzed and the candidate region of the first intensity of the candidate historical typhoon for each candidate historical typhoon, and determine the first intensity similarity of the candidate historical typhoon. The second intensity similarity determination unit is used to perform similarity analysis on the real-time region of the second intensity of the typhoon to be analyzed and the candidate region of the second intensity of the candidate historical typhoon, and determine the second intensity similarity of the candidate historical typhoon. The third intensity similarity determination unit is used to perform similarity analysis on the real-time region of the third intensity of the typhoon to be analyzed and the candidate region of the third intensity of the candidate historical typhoon, and determine the third intensity similarity of the candidate historical typhoon. The regional similarity determination unit is used to perform weighted calculations on the first intensity similarity, second intensity similarity and third intensity similarity of each candidate historical typhoon to obtain the regional similarity of each candidate historical typhoon.

[0060] In one embodiment, the similarity analysis module 50 includes: The overlapping grid number determination unit is used to determine the first overlapping grid number for each candidate historical typhoon based on the first intensity real-time region of the typhoon to be analyzed and the first intensity candidate region of the candidate historical typhoon. An intensity similarity determination unit is used to obtain the total number of grids in the matching spatial grid and determine the ratio of the first overlapping grid number to the total number of grids as the first intensity similarity of the candidate historical typhoon.

[0061] In one embodiment, the similarity analysis module 50 includes: The weighting coefficient determination unit is used to determine, based on the intensity index parameters, a first weighting coefficient corresponding to the first wind intensity, a second weighting coefficient corresponding to the second wind intensity, and a third weighting coefficient corresponding to the third wind intensity; The weighted calculation unit is used to perform weighted calculations on the first intensity similarity, second intensity similarity, and third intensity similarity of each candidate historical typhoon based on the first weighted coefficient, the second weighted coefficient, and the third weighted coefficient, so as to obtain the regional similarity of each candidate historical typhoon.

[0062] Specific limitations regarding the typhoon disaster claims prediction device can be found in the limitations of the typhoon disaster claims prediction method described above, and will not be repeated here. Each module in the aforementioned typhoon disaster claims prediction device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0063] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes a readable storage medium and internal memory. The readable storage medium stores an operating system, computer-readable instructions, and a database. The internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The database stores data related to the typhoon disaster claims prediction method. The network interface communicates with external terminals via a network connection. When the computer-readable instructions are executed by the processor, a typhoon disaster claims prediction method is implemented. The readable storage medium provided in this embodiment includes both non-volatile and volatile readable storage media.

[0064] In one embodiment, a computer device is provided, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor performs the following steps when executing the computer-readable instructions: Obtain a typhoon matching request, and determine the accuracy index parameters, intensity index parameters, the typhoon to be analyzed, and at least one candidate historical typhoon based on the typhoon matching request; A matching spatial grid is generated based on the accuracy index parameters. Interpolation analysis is performed on the historical typhoon data of each candidate historical typhoon based on the matching spatial grid to obtain the candidate typhoon path points of each candidate historical typhoon. The real-time typhoon path point of the typhoon to be analyzed is determined based on the real-time typhoon data of the typhoon to be analyzed. The candidate intensity path regions of each candidate historical typhoon are determined based on the intensity index parameters and the candidate typhoon path points, and the real-time intensity path region of the typhoon to be analyzed is determined based on the intensity index parameters and the real-time typhoon path points. A similarity analysis is performed on the real-time intensity path region of the typhoon to be analyzed and the candidate intensity path region of each candidate historical typhoon to determine the regional similarity of each candidate historical typhoon, and a target similar typhoon is determined from all the candidate historical typhoons based on the regional similarity. Obtain the historical claim amounts for typhoons similar to the target, and determine the predicted claim amount for the typhoon to be analyzed based on the historical claim data.

[0065] In one embodiment, one or more computer-readable storage media storing computer-readable instructions are provided. The readable storage media provided in this embodiment include non-volatile readable storage media and volatile readable storage media. The readable storage media stores computer-readable instructions, which, when executed by one or more processors, perform the following steps: Obtain a typhoon matching request, and determine the accuracy index parameters, intensity index parameters, the typhoon to be analyzed, and at least one candidate historical typhoon based on the typhoon matching request; A matching spatial grid is generated based on the accuracy index parameters. Interpolation analysis is performed on the historical typhoon data of each candidate historical typhoon based on the matching spatial grid to obtain the candidate typhoon path points of each candidate historical typhoon. The real-time typhoon path point of the typhoon to be analyzed is determined based on the real-time typhoon data of the typhoon to be analyzed. The candidate intensity path regions of each candidate historical typhoon are determined based on the intensity index parameters and the candidate typhoon path points, and the real-time intensity path region of the typhoon to be analyzed is determined based on the intensity index parameters and the real-time typhoon path points. A similarity analysis is performed on the real-time intensity path region of the typhoon to be analyzed and the candidate intensity path region of each candidate historical typhoon to determine the regional similarity of each candidate historical typhoon, and a target similar typhoon is determined from all the candidate historical typhoons based on the regional similarity. Obtain the historical claim amounts for typhoons similar to the target, and determine the predicted claim amount for the typhoon to be analyzed based on the historical claim data.

[0066] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by instructing related hardware with computer-readable instructions. These computer-readable instructions can be stored in a non-volatile readable storage medium or a volatile readable storage medium. When executed, these computer-readable instructions can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0067] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0068] The software tools or components not belonging to this company that appear in the embodiments of this application are merely illustrative examples and do not represent actual use. The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for predicting typhoon disaster claims, characterized in that, include: Obtain a typhoon matching request, and determine the accuracy index parameters, intensity index parameters, the typhoon to be analyzed, and at least one candidate historical typhoon based on the typhoon matching request; A matching spatial grid is generated based on the accuracy index parameters. Interpolation analysis is performed on the historical typhoon data of each candidate historical typhoon based on the matching spatial grid to obtain the candidate typhoon path points of each candidate historical typhoon. The real-time typhoon path point of the typhoon to be analyzed is determined based on the real-time typhoon data of the typhoon to be analyzed. The candidate intensity path regions of each candidate historical typhoon are determined based on the intensity index parameters and the candidate typhoon path points, and the real-time intensity path region of the typhoon to be analyzed is determined based on the intensity index parameters and the real-time typhoon path points. A similarity analysis is performed on the real-time intensity path region of the typhoon to be analyzed and the candidate intensity path region of each candidate historical typhoon to determine the regional similarity of each candidate historical typhoon, and a target similar typhoon is determined from all the candidate historical typhoons based on the regional similarity. Obtain the historical claim amounts for typhoons similar to the target, and determine the predicted claim amount for the typhoon to be analyzed based on the historical claim data.

2. The typhoon disaster claims prediction method as described in claim 1, characterized in that, The step of determining the matching spatial grid based on the accuracy index parameters includes: The target index type and target accuracy parameters are determined based on the accuracy index parameters. When the target indicator type is latitude and longitude type, a matching spatial grid with latitude and longitude values ​​is generated according to the target accuracy parameters; When the target index type is distance type, a matching spatial grid with distance values ​​is generated based on the target accuracy parameter.

3. The typhoon disaster claims prediction method as described in claim 1, characterized in that, The candidate typhoon path points include historical path points and interpolated path points; The interpolation analysis of historical typhoon data for each candidate historical typhoon based on the matching spatial grid to obtain candidate typhoon path points for each candidate historical typhoon includes: For each of the candidate historical typhoons, the historical path points of the candidate historical typhoons are determined based on the historical typhoon data of the candidate historical typhoons. Generate the historical path of each candidate historical typhoon based on its historical path points. The intersection point between the historical path line of each candidate historical typhoon and the grid line of the matching spatial grid is determined as the interpolation path point of that candidate historical typhoon. The candidate typhoon path points for each candidate historical typhoon are determined by combining all the historical path points and interpolated path points of that candidate historical typhoon.

4. The typhoon disaster claims prediction method as described in claim 1, characterized in that, The intensity index parameters include a first wind level intensity, a second wind level intensity, and a third wind level intensity, wherein the first wind level intensity is less than the second wind level intensity, and the second wind level intensity is less than the third wind level intensity; the candidate intensity path region includes a first intensity candidate region, a second intensity candidate region, and a third intensity candidate region, and the real-time intensity path region includes a first intensity real-time region, a second intensity real-time region, and a third intensity real-time region; The step of determining the candidate intensity path region of each of the candidate historical typhoons based on the intensity index parameters and the candidate typhoon path points, and determining the real-time intensity path region of the typhoon to be analyzed based on the intensity index parameters and the real-time typhoon path points, includes: For each of the candidate historical typhoons, the first candidate wind circle region of each candidate typhoon path point is determined based on the first wind intensity and historical typhoon data, and the set of the first candidate wind circle regions of all the candidate typhoon path points is determined as the first intensity candidate region of the candidate historical typhoon. Based on the second wind intensity and historical typhoon data, the second candidate wind circle region of each candidate typhoon path point is determined, and the set of the second candidate wind circle regions of all candidate typhoon path points is determined as the second intensity candidate region of the candidate historical typhoon. Based on the third wind intensity and historical typhoon data, the third candidate wind circle region of each candidate typhoon path point is determined, and the set of the third candidate wind circle regions of all candidate typhoon path points is determined as the third intensity candidate region of the candidate historical typhoon. For the typhoon to be analyzed, the first real-time wind circle region of each of the real-time typhoon path points is determined based on the first wind intensity and real-time typhoon data, and the set of the first real-time wind circle regions of all the real-time typhoon path points is determined as the first intensity real-time region of the typhoon to be analyzed. The second real-time wind circle region of each of the real-time typhoon path points is determined based on the second wind intensity and real-time typhoon data, and the set of the second real-time wind circle regions of all the real-time typhoon path points is determined as the second intensity real-time region of the typhoon to be analyzed. Based on the third wind intensity and real-time typhoon data, the third real-time wind circle region of each of the real-time typhoon path points is determined, and the set of the third real-time wind circle regions of all the real-time typhoon path points is determined as the third intensity real-time region of the typhoon to be analyzed.

5. The typhoon disaster claims prediction method as described in claim 4, characterized in that, The step of performing similarity analysis on the real-time intensity path region of the typhoon to be analyzed and the candidate intensity path region of each candidate historical typhoon to determine the regional similarity of each candidate historical typhoon includes: For each candidate historical typhoon, a similarity analysis is performed on the real-time region of the first intensity of the typhoon to be analyzed and the candidate region of the first intensity of the candidate historical typhoon to determine the first intensity similarity of the candidate historical typhoon. A similarity analysis is performed on the real-time region of the second intensity of the typhoon to be analyzed and the candidate region of the second intensity of the candidate historical typhoon to determine the second intensity similarity of the candidate historical typhoon. A similarity analysis is performed on the real-time region of the third intensity of the typhoon to be analyzed and the candidate region of the third intensity of the candidate historical typhoon to determine the similarity of the third intensity of the candidate historical typhoon. The first intensity similarity, second intensity similarity and third intensity similarity of each candidate historical typhoon are weighted and calculated to obtain the regional similarity of each candidate historical typhoon.

6. The typhoon disaster claims prediction method as described in claim 5, characterized in that, For each candidate historical typhoon, a similarity analysis is performed on the real-time region of the first intensity of the typhoon to be analyzed and the candidate region of the first intensity of the candidate historical typhoon to determine the first intensity similarity of the candidate historical typhoon, including: For each candidate historical typhoon, the first number of overlapping grids is determined based on the first intensity real-time region of the typhoon to be analyzed and the first intensity candidate region of the candidate historical typhoon. The total number of grids in the matching spatial grid is obtained, and the ratio of the first overlapping grid number to the total number of grids is determined as the first intensity similarity of the candidate historical typhoon.

7. The typhoon disaster claims prediction method as described in claim 5, characterized in that, The weighted calculation of the first intensity similarity, second intensity similarity, and third intensity similarity of each candidate historical typhoon to obtain the regional similarity of each candidate historical typhoon includes: Based on the intensity index parameters, determine the first weighting coefficient corresponding to the first wind intensity, the second weighting coefficient corresponding to the second wind intensity, and the third weighting coefficient corresponding to the third wind intensity; Based on the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient, the first intensity similarity, the second intensity similarity, and the third intensity similarity of each candidate historical typhoon are weighted and calculated to obtain the regional similarity of each candidate historical typhoon.

8. A typhoon disaster claims prediction device, characterized in that, include: The request acquisition module is used to acquire typhoon matching requests and determine accuracy index parameters, intensity index parameters, the typhoon to be analyzed, and at least one candidate historical typhoon based on the typhoon matching requests. The interpolation analysis module is used to generate a matching spatial grid based on the accuracy index parameters, and to perform interpolation analysis on the historical typhoon data of each candidate historical typhoon based on the matching spatial grid to obtain the candidate typhoon path points of each candidate historical typhoon. The real-time path point determination module is used to determine the real-time path point of the typhoon to be analyzed based on the real-time typhoon data of the typhoon to be analyzed. The path region determination module is used to determine the candidate intensity path region of each of the candidate historical typhoons based on the intensity index parameters and the candidate typhoon path points, and to determine the real-time intensity path region of the typhoon to be analyzed based on the intensity index parameters and the real-time typhoon path points. The similarity analysis module is used to perform similarity analysis on the real-time intensity path region of the typhoon to be analyzed and the candidate intensity path region of each candidate historical typhoon, determine the regional similarity of each candidate historical typhoon, and determine the target similar typhoon from all the candidate historical typhoons based on the regional similarity. The claims prediction module is used to obtain the historical claims amount of the target similar typhoons and determine the predicted claims amount of the typhoon to be analyzed based on the historical claims data.

9. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, characterized in that, When the processor executes the computer-readable instructions, it implements the typhoon disaster claims prediction method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-readable instructions, characterized in that, When the computer-readable instructions are executed by one or more processors, the one or more processors perform the typhoon disaster claims prediction method as described in any one of claims 1 to 7.

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