Typhoon path prediction method and system based on comprehensive standard and computer equipment

Abstract

The invention relates to the technical field of typhoon analysis, and discloses a typhoon path prediction method and system based on a comprehensive standard and computer equipment, and the prediction method comprises the steps: dividing the whole year into at least two seasonal sections according to the weather characteristics of a predicted target, and carrying out the first-stage screening of historical typhoon data according to the seasonal sections; defining a geographic area by taking the prediction target as a center, giving a weight, and carrying out second-level screening; key factors are comprehensively considered based on distance, wind direction and wind speed similarity; then flexibly adjusting the weight of each factor according to typhoon characteristics by applying a dynamic weight distribution formula, and generating a comprehensive similarity score; and finally, selecting a historical typhoon path with the highest score, predicting a current typhoon moving track and an influence range, and accurately and efficiently providing a reliable basis for disaster prevention and reduction so as to prevent typhoon hazards.

Description

Technical Field

[0001] The present invention relates to the technical field of typhoon analysis, and particularly to a typhoon path prediction method, system and computer device based on comprehensive criteria. Background Art

[0002] A typhoon is a highly destructive meteorological disaster. Accurately predicting its path is crucial for ensuring people's lives and property safety and reducing economic losses. Existing typhoon path prediction methods have many limitations. On the one hand, some prediction methods only analyze based on a single criterion or a small number of factors, such as only considering the wind speed of the typhoon or simple geographical location relationships, and it is difficult to comprehensively and accurately reflect the complex characteristics and movement laws of typhoons. On the other hand, the screening and utilization of historical data are not refined enough, and the impacts of different seasons and different geographical regions on the typhoon path are not fully considered, resulting in a large deviation between the prediction results and the actual situation. For example, in some traditional prediction methods, there is no targeted analysis of the climate characteristics and geographical features of a specific area such as the prediction target, so that when predicting the typhoon path affecting the prediction target, the accuracy is greatly reduced. Therefore, it is necessary to improve the existing technology.

[0003] The above information is given as background information only to assist in understanding the present disclosure, and it is not determined or admitted whether any of the above content can be used as prior art relative to the present disclosure. Summary of the Invention

[0004] The present invention provides a typhoon path prediction method, system and computer device based on comprehensive criteria to solve the problems existing in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A typhoon path prediction method based on comprehensive criteria, comprising the following steps:

[0007] (1) Perform a first-level screening on historical typhoon data according to seasonal similarity. The seasonal similarity is divided according to the climate characteristics of the prediction target, and the whole year is divided into at least two seasonal sections;

[0008] (2) Perform a second-level screening on the historical typhoon data after the first-level screening. The geographical similarity defines at least one geographical area centered on the prediction target and assigns a regional weight;

[0009] (3) Based on distance similarity, wind direction similarity, and wind speed similarity, perform multi-factor similarity calculations on the historical typhoon data after the second-level screening; where: the distance similarity is calculated by the Haus-dorff distance method to calculate the Euclidean distance between the predicted typhoon and the set of key points on the historical typhoon path, and is normalized to a similarity score; the wind direction similarity is calculated by the key point angle difference method; the wind speed similarity is calculated by the relative error method;

[0010] (4) According to the dynamic weight distribution formula, dynamically allocate the distance, wind direction, and wind speed similarities to generate a comprehensive similarity score for the historical typhoon;

[0011] (5) Select at least one historical typhoon path with the highest comprehensive similarity score to predict the movement trajectory and influence range of the current typhoon.

[0012] Optionally, the seasonal similarity division in step (1) is specifically as follows:

[0013] Preset geographical area: Set in advance the geographical area used to screen historical typhoon data;

[0014] Determine the filtering condition: Clearly use whether the initial key point of the typhoon is within the preset geographical area as the filtering basis;

[0015] Perform data filtering: Based on the filtering condition, screen the historical typhoon data according to the following formula to filter out the data that meets the requirements from many historical typhoon data:

[0016]

[0017] where filter is the filtering function, and month_date is the seasonal section divided by month.

[0018] Optionally, the geographical similarity in step (2) is specifically as follows:

[0019] Define the rectangular area around the prediction target as the first similarity area, and its fixed weight is 1;

[0020] Allow manual addition of at least one extended geographical area and set the weight of the extended area ;

[0021] Satisfy 0 < < 1;

[0022] Through the formula for screening, where filter is the filtering function for judging whether the initial key point of the typhoon is located within the area inside.

[0023] Optionally, the distance similarity in step (3) is calculated by the following formula:

[0024]

[0025]

[0026] Among them, A and B refer to the sets of key points of the predicted initial typhoon path and the historical typhoon path. d(a, b) is the Euclidean distance calculation function, and S refers to the similarity. is the Hausdorff distance between the predicted typhoon and the set of key points of the historical typhoon path. is the preset maximum normalized distance threshold.

[0027] The wind direction similarity is calculated by the following formula:

[0028]

[0029] Among them, n represents the number of intercepted time periods, h represents the subscript of the current time period. and are the wind direction angle values of the predicted typhoon and the historical typhoon in the time period h, respectively.

[0030] Optionally, when predicting the moving track of the current typhoon in step (5), the initial path used shall include at least 4 key points, the time interval between adjacent key points shall not exceed 3 hours, and the total time span shall not exceed 12 hours; each key point includes longitude and latitude, wind speed and wind direction data.

[0031] Optionally, the dynamic weight allocation satisfies:

[0032] The weight ratio formulas for wind speed and wind direction are as follows:

[0033]

[0034] Among them, w is the weight, S is the similarity, the subscript g refers to the geographical location, d refers to the distance, t refers to the wind direction, v refers to the wind speed, where 、 、 refer to the distance, wind direction, and wind speed similarity weights respectively, and .

[0035] The present invention also provides a typhoon path prediction system based on comprehensive criteria, including:

[0036] A data access module for obtaining the typhoon path data and the grid tower position data released by the meteorological station in real time;

[0037] A prediction calculation module, which deploys the typhoon path prediction method based on comprehensive criteria according to any one of claims 1-6, and outputs the typhoon path prediction result and the influence range level;

[0038] The two-dimensional visualization module superimposes and displays the following information on the predicted target two-dimensional digital map: real-time typhoon path, predicted path, and historical similar paths; the positions and risk levels of power grid poles and towers affected by the typhoon; and the dynamically changing areas within the typhoon's influence range.

[0039] The present invention also provides a computer device, including a memory and a processor, where the memory stores a computer program, and is characterized in that

[0040] when the processor executes the computer program, it implements the typhoon path prediction method based on comprehensive criteria as described in any one of the above.

[0041] Compared with the prior art, the typhoon path prediction method, system, and computer device based on comprehensive criteria provided by the present invention have the following beneficial effects:

[0042] The present invention performs two-level screening according to the climatic and geographical characteristics of the prediction target, making historical data more in line with the actual situation and effectively improving the prediction accuracy. At the same time, based on the calculation of distance, wind direction, and wind speed similarity, scientific methods such as the Haus-dorff distance method, the key point angle difference method, and the relative error method are used to consider key factors, laying a solid foundation for prediction. By dynamically allocating weights using formulas, it can be flexibly adjusted according to the typhoon characteristics, further improving the prediction accuracy. Finally, the path with the highest similarity is selected for prediction, providing a reliable basis for disaster prevention and mitigation, helping to take preventive measures in advance, and reducing the typhoon hazards.

[0043] The present invention has other characteristics and advantages, which will be obvious from the accompanying drawings incorporated herein and the subsequent specific embodiments, or will be described in detail in the accompanying drawings incorporated herein and the subsequent specific embodiments, and these accompanying drawings and specific embodiments are jointly used to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0045] Figure 1 is a flowchart of a typhoon path prediction method based on comprehensive criteria provided by Embodiment 1 of the present invention;

[0046] Figure 2 is an architecture diagram of a typhoon path prediction system based on comprehensive criteria provided by Embodiment 1 of the present invention;

[0047] Figure 3 It is a structural block diagram of a typhoon path prediction system based on comprehensive criteria provided by Embodiment 1 of the present invention.

[0048] Reference numerals: 10, data access module; 20, prediction calculation module; 30, two-dimensional visualization module. Specific embodiments

[0049] To describe in detail the possible application scenarios, technical principles, specific implementable solutions, achievable objectives and effects of the present application, etc., the following will be described in detail in combination with the specific embodiments listed and in conjunction with the attached drawings. The embodiments described herein are only used to more clearly illustrate the technical solutions of the present application, so they are only examples and cannot be used to limit the protection scope of the present application.

[0050] Referring to "embodiment" in this article means that the specific features, structures or characteristics described in combination with the embodiment can be included in at least one embodiment of the present application. The term "embodiment" appearing in various positions in the specification does not necessarily refer to the same embodiment, nor does it particularly limit its independence or relevance to other embodiments. In principle, in the present application, as long as there is no technical contradiction or conflict, the technical features mentioned in each embodiment can be combined in any way to form corresponding implementable technical solutions.

[0051] Unless otherwise defined, the meanings of the technical terms used in this article are the same as those generally understood by those skilled in the technical field to which the present application belongs; the use of the relevant terms in this article is only to describe specific embodiments and is not intended to limit the present application.

[0052] The power grid is one of the important infrastructures in modern society, with a large number of points and a wide area, and is easily affected by natural disasters and bad weather. In recent years, with the global warming, typhoon events with a large impact range and strong destructive power have occurred frequently, posing a great threat to the operation of the power grid and the safety of equipment. Hainan Island is surrounded by the sea on all sides and is invaded by typhoons every year. To ensure the safe and stable operation of the power grid, accurately and efficiently monitoring the typhoon path and its influence range is an important reference for guiding relevant units to formulate typhoon prevention measures, emergency plans, etc.

[0053] Existing typhoon path prediction algorithms use a single criterion, and their calculation results often select historical typhoon paths, with little reference significance. The present invention calculates the typhoon path by comprehensively using criteria and adopting the method of historical path weight ratio. Experiments show that the similarity rate of the typhoon path and its influence range calculated by this method is above 90%.

[0054] The movement path of a typhoon is affected by many factors, and it is difficult to accurately predict the movement path of a typhoon. However, experimental evidence has shown that the similar path prediction method is a scientific method. The typhoon prediction in China mainly adopts three similar criteria: seasonal similarity, geographical similarity, and movement direction and speed. The calculation methods mainly include the area index method, Hausdorff distance method, key point similarity method, etc. However, these criteria are relatively single, which will have a certain impact on the selection of similar typhoon paths.

[0055] Currently, the key processes for predicting typhoons in existing technical solutions are as follows:

[0056] Hausdorff distance

[0057] The Hausdorff distance is the maximum distance from the closest point in one set to another set. It is defined as:

[0058]

[0059] where a and b are points in sets A and B respectively, and d(a, b) is any metric between these points.

[0060] The key processes are as follows: (1) Analysis based on the Hausdorff distance: Obtain the real-time longitude and latitude positions of the typhoon to be predicted; obtain the longitude and latitude information of the positions of historical typhoons at 6 hours (with an interval of 2 hours) before and after the current time; use the Hausdorff distance calculation method to obtain the Euclidean distances of each historical typhoon data; use the historical typhoon data with the shortest distance as a reference to calculate the current typhoon path and range. (2) Analysis based on the key point similarity method: Determine the geographical space grid strength for typhoon analysis and calculation; generate a corresponding path process line graph based on the longitude and latitude data values of the typhoon path, and confirm the points intersecting with the grid as key points; determine the threshold of the angle range in the due north direction; determine whether the path point is a longitude grid key point or a latitude grid key point according to whether the due north angle of the key point on the moving path is greater than the angle range threshold; traverse and calculate the key path points on each line segment to obtain a set of key path points; calculate the differences in longitude and latitude between two typhoons respectively: The formula is: ; Cumulatively sum all the key point differences and divide by the number of points to obtain the similarity between the paths of two typhoons; finally, select the typhoon with the highest similarity as the typhoon path prediction model.

[0061] Deduce the disadvantages of the existing technology in the way of causal relationship reasoning as follows:

[0062] (2) The reference criteria are single

[0063] The Hausdorff distance method only considers the influence of distance and does not take into account conditions such as wind speed, wind direction, and season; while the key point similarity method considers the problem of wind direction but does not consider the problems of wind speed and distance, and the final result is accidental and the accuracy is not high.

[0064] (2) Poor calculation efficiency

[0065] Both the Hausdorff distance method and the key point similarity method adopt a traversal method. As the historical data increases year by year, the calculation efficiency will be significantly reduced.

[0066] (3) Poor utilization efficiency of historical data

[0067] Both the Hausdorff distance method and the key point similarity method adopt a traversal method to traverse all historical data. As time goes by, the data from a long time ago will not be of reference significance, and some special situations may cause the prediction result to deviate too much from the expectation.

[0068] Based on meteorology, the present invention comprehensively uses four different criteria of the same season, similar geography, similar movement direction, and similar movement speed to screen historical typhoons, and calculates the current typhoon movement speed and movement direction according to the screened historical typhoons and their weight ratios.

[0069] Combined with the above improved algorithm, the present invention calculates the current typhoon movement speed and movement direction based on historical typhoon data, then calculates the typhoon movement path and the impact on the power transmission lines in Hainan Island during the typhoon movement, achieves efficient early warning and early prevention, and finally displays the results on a two-dimensional digital platform.

[0070] Please refer to Figure 1 , the embodiment of the present invention provides a typhoon path prediction method based on comprehensive criteria. The purpose of the present invention is to provide a typhoon disaster survey and early warning system. Based on the traditional single-criterion prediction algorithm, by integrating the characteristics of each algorithm, including distance, wind speed, and wind direction, and combining characteristics such as season and geography, the typhoon path and wind speed for the next 18 hours are predicted in 6-hour segments; then, according to the prediction results, the impact of the typhoon on the power transmission equipment in Hainan Island is judged, and the prediction results and impact distribution are displayed on a two-dimensional digital platform.

[0071] Specifically, the prediction method includes the following steps:

[0072] (1) Perform the first-level screening on historical typhoon data according to seasonal similarity. The seasonal similarity is divided according to the climate characteristics of the prediction target, and the whole year is divided into at least two season segments;

[0073] (2) Perform the second-level screening on the historical typhoon data after the first-level screening. The geographical similarity defines at least one geographical area centered on the prediction target and assigns a regional weight;

[0074] (3) Based on distance similarity, wind direction similarity, and wind speed similarity, perform multi-factor similarity calculation on the historical typhoon data after the second-level screening; wherein: the distance similarity calculates the Euclidean distance between the predicted typhoon and the set of key points on the historical typhoon path through the Haus-dorff distance method and normalizes it into a similarity score; the wind direction similarity is calculated through the key point angle difference method; the wind speed similarity is calculated through the relative error method.

[0075] (4) According to the dynamic weight allocation formula, dynamically allocate the distance, wind direction, and wind speed similarities to generate a comprehensive similarity score for the historical typhoon.

[0076] (5) Select at least one historical typhoon path with the highest comprehensive similarity score to predict the movement trajectory and influence range of the current typhoon.

[0077] Exemplarily, taking Hainan Island as an example for typhoon prediction, the overall process of this solution can be described as follows:

[0078] (1) Determination of seasonal similarity

[0079] For the climate characteristics of Hainan Island, by default, the seasonal section is divided into two parts, the dry season from November to April and the rainy season from May to October; the division of seasonal similarity supports manual modification and can support up to 10 sections according to the climate characteristics of the current year.

[0080] (2) Confirmation of geographical similarity

[0081] The system default divides a rectangular area near Hainan Island into the first similarity area, and the weight level of this area is 1 and does not support modification; the system supports manual addition of similarity areas and setting of area weights, and the weight level shall not be higher than 1 and greater than 0.

[0082] (3) Select the initial prediction path and wind speed

[0083] For the prediction of the typhoon path, an initial path needs to be provided. To ensure the accuracy of the prediction results, this path needs to provide no less than 4 key points, and the path time period shall not exceed 12 hours. The key point information includes longitude, latitude, wind speed, and wind direction.

[0084] (4) Application of calculation results

[0085] According to the predicted typhoon path information, calculate the affected range and range impact level during the typhoon movement process through the formula; finally, return all results including the predicted path information and the affected range information to the front end for display by drawing in a two-dimensional scene.

[0086] Further, the seasonal similarity division in step (1) is specifically:

[0087] Predetermined geographical area: A geographical area that is set in advance for screening historical typhoon data;

[0088] Determine the filtering conditions: Clearly use whether the initial key points of the typhoon are within the predetermined geographical area as the filtering basis;

[0089] Execute data filtering: Based on the filtering conditions, screen the historical typhoon data according to the following formula, and filter out the data that meets the requirements from numerous historical typhoon data:

[0090]

[0091] where filter is the filtering function, and month_date is the seasonal section divided by month.

[0092] Furthermore, the geographical similarity described in step (2) is specifically:

[0093] Define the rectangular area around the prediction target as the first similar area, and its fixed weight is 1;

[0094] Allow manual addition of at least one extended geographical area and set the weight of the extended area ;

[0095] Satisfy 0 < < 1;

[0096] Filter through the formula where filter is the filtering function for judging whether the initial key points of the typhoon are located in the area inside.

[0097] Furthermore, the distance similarity described in step (3) is calculated by the following formula:

[0098]

[0099]

[0100] where A and B refer to the set of key points of the predicted typhoon initial path and the historical typhoon path, d(a,b) is the Euclidean distance calculation function, S refers to the similarity, is the Hausdorff distance between the predicted typhoon and the set of key points of the historical typhoon path, is the preset maximum normalized distance threshold;

[0101] The wind direction similarity is calculated by the following formula:

[0102]

[0103] where n represents the number of intercepted time periods, and h represents the current time period subscript, and are the wind direction angle values ​​of the predicted typhoon and the historical typhoon in time period h respectively;

[0104] Furthermore, when predicting the current typhoon movement trajectory in step (5), the initial path used must contain at least 4 key points, the time interval between adjacent key points shall not exceed 3 hours, and the total time span shall not exceed 12 hours; each key point includes latitude and longitude, wind speed and wind direction data.

[0105] Furthermore, dynamic weight allocation satisfies:

[0106] The weight ratio formula of wind speed and wind direction is as follows:

[0107]

[0108] Where w is the weight, S is the similarity, subscript g refers to the geographical location, d refers to the distance, t refers to the wind direction, and v refers to the wind speed. , , Respectively refer to the similarity weights of distance, wind direction, and wind speed. .

[0109] The prediction method provided by the embodiment of the present invention has the following beneficial effects:

[0110] (1) Precise and accurate data screening: The first level of screening is performed by dividing the seasonal segments according to the climate characteristics of the forecast target (such as Hainan Island), and then defining the geographical areas around the forecast target and assigning weights for the second level of screening. This fine data screening method makes the data involved in the forecast highly consistent with the actual situation, effectively improving the accuracy of the forecast. The seasonal segment division supports manual modification, up to 10 segments, and the geographical areas also support manual addition and weight setting. It is highly flexible and can adapt to different climate and geographical conditions.

[0111] (2) Comprehensive consideration of multiple factors: Comprehensively consider the distance similarity, wind direction similarity and wind speed similarity, and use scientific methods such as Haus-dorff distance method, key point angle difference method, relative error method, etc. to calculate the similarity of each factor, comprehensively covering the key factors affecting the typhoon path, laying a solid data foundation for accurate prediction. At the same time, the dynamic weight allocation formula can flexibly adjust the weight of each factor according to the characteristics of different typhoons, further improving the prediction accuracy.

[0112] (3) Reasonable division of forecast time periods: The typhoon path and wind speed for the next 18 hours are predicted in 6-hour segments. Reasonable time division not only ensures the timeliness of the forecast, but also presents the dynamic changes of the typhoon in more detail, thus buying more time for taking preventive measures in advance.

[0113] (4) High application value: It can not only accurately predict the moving track and influence range of the current typhoon, but also judge the impact of the typhoon on the power transmission equipment in Hainan Island according to the prediction results, and display the prediction results and influence distribution on a two-dimensional digital platform, providing highly targeted and reliable decision-making basis for disaster prevention and mitigation work, helping the power department take protective measures for power transmission equipment in advance, minimizing the damage of typhoons to the power transmission system, ensuring the stable power supply, and reducing economic losses.

[0114] Please refer to Figure 2 and Figure 3 Based on the foregoing embodiments, this embodiment provides a typhoon path prediction system based on comprehensive criteria, including:

[0115] A data access module 10 for obtaining real-time typhoon path data and power grid pole position data released by the meteorological station;

[0116] A prediction calculation module 20 that deploys the typhoon path prediction method based on comprehensive criteria as described above and outputs the typhoon path prediction results and influence range levels;

[0117] A two-dimensional visualization module 30 that superimposes and displays the following information on the predicted target two-dimensional digital map: real-time typhoon path, predicted path, and historical similar paths; the positions and risk levels of power grid poles affected by the typhoon; the dynamic change area of the typhoon influence range.

[0118] By way of example, taking Hainan Island as an example, in this prediction system, the two-dimensional scene module displays the two-dimensional plane map of Hainan Island. When a typhoon occurs, users can input typhoon data for path prediction, display, and display of the typhoon influence range, and at the same time display the pole account data on the island and the affected account data.

[0119] Based on the foregoing embodiments, this embodiment provides a computer device, including a memory and a processor, the memory stores a computer program, and is characterized in that

[0120] When the processor executes the computer program, it implements the typhoon path prediction method based on comprehensive criteria as described above.

[0121] Finally, it should be noted that although the above embodiments have been described in the text and drawings of the specification of this application, the patent protection scope of this application cannot be limited thereby. Any technical solutions generated by equivalent structure or equivalent process substitution or modification using the content recorded in the text and drawings of the specification of this application based on the essential concept of this application, as well as those directly or indirectly implementing the technical solutions of the above embodiments in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A typhoon path prediction method based on comprehensive standards, characterized in that: The following steps are involved: (1) Performing a first-level screening of historical typhoon data based on seasonal similarity, wherein the seasonal similarity division is based on the predicted target climate characteristics and divides the whole year into at least two seasonal segments; (2) Performing a second level of screening based on the historical typhoon data after the first level of screening, the geographical similarity defines at least one geographical area with the prediction target as the center, and assigns regional weights; (3) Based on distance similarity, wind direction similarity and wind speed similarity, multi-factor similarity calculation is performed on the historical typhoon data after the second-level screening; wherein: the distance similarity is calculated by the Haus-dorff distance method, the Euclidean distance between the predicted typhoon and the key point set of the historical typhoon path is calculated, and normalized into a similarity score; the wind direction similarity is calculated by the key point angle difference method; the wind speed similarity is calculated by the relative error method; (4) According to the dynamic weight allocation formula, the distance, wind direction, and wind speed similarities are dynamically allocated to generate a comprehensive similarity score for historical typhoons; (5) Select at least one historical typhoon path with the highest comprehensive similarity score to predict the movement trajectory and impact range of the current typhoon.

2. The typhoon path prediction method based on comprehensive standards according to claim 1, characterized in that: The seasonal similarity division in step (1) is specifically as follows: Preset geographical area: Preset the geographical area for filtering historical typhoon data in advance; Determine the filtering conditions: clearly define whether the initial key point of the typhoon is within the preset geographical area as the filtering basis; Perform data filtering: According to the filtering conditions, filter the historical typhoon data based on the following formula, and filter out the data that meets the requirements from a large number of historical typhoon data: ； Among them, filter is the filtering function, and month_date is the seasonal segment divided by month.

3. The typhoon path prediction method based on comprehensive standards according to claim 1, characterized in that: The geographical similarity in step (2) is specifically: Define the rectangular area around the predicted target as the first similar area, and its fixed weight is 1; Allows you to manually add at least one extended geographic region and set the weight of the extended region ; Satisfy 0< <1; By formula Filtering is performed, where filter is used to determine whether the initial key point of the typhoon is located in the area The filter function inside.

4. The typhoon path prediction method based on comprehensive standards according to claim 1, characterized in that: The distance similarity in step (3) is calculated using the following formula: ； ； Among them, A and B refer to the predicted typhoon initial path and the key point set of the historical typhoon path, d(a,b) is the Euclidean distance calculation function, and S refers to the similarity. is the Hausdorff distance between the predicted typhoon and the key point set of the historical typhoon path, is the preset maximum normalized distance threshold; The wind direction similarity is calculated by the following formula: ； Among them, n represents the number of interception periods, h represents the subscript of the current period, and are the wind direction angle values ​​of the predicted typhoon and the historical typhoon in time period h respectively.

5. The typhoon path prediction method based on comprehensive standards according to claim 1, characterized in that: When predicting the current typhoon movement trajectory in step (5), the initial path used must contain at least 4 key points, the time interval between adjacent key points shall not exceed 3 hours, and the total time span shall not exceed 12 hours; each key point includes latitude and longitude, wind speed and wind direction data.

6. The typhoon path prediction method based on comprehensive standards according to claim 1, characterized in that: Dynamic weight allocation satisfies: The weight ratio formula of wind speed and wind direction is as follows: ； Where w is the weight, S is the similarity, subscript g refers to the geographical location, d refers to the distance, t refers to the wind direction, and v refers to the wind speed. , , Respectively refer to the similarity weights of distance, wind direction, and wind speed. .

7. A typhoon path prediction system based on comprehensive standards, characterized in that: include: Data access module, used to obtain typhoon path data and power grid tower location data released by the meteorological station in real time; A prediction and calculation module, deploying the typhoon path prediction method based on comprehensive standards as described in any one of claims 1 to 6, and outputting the typhoon path prediction result and the impact range level; The two-dimensional visualization module overlays and displays the following information on the two-dimensional digital map of the predicted target: real-time typhoon path, predicted path and historical similar paths; the location and risk level of power grid towers affected by the typhoon; and the dynamically changing areas of the typhoon's impact range.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the typhoon path prediction method based on comprehensive standards as described in any one of claims 1 to 6 is implemented.

Images